The attendance is taken in every organization. Traditional approach for attendance is, professor calls student name & record attendance. For each lecture this is wastage of time. To avoid these losses, we are about to use automatic process which is based on image processing. In this project approach, we are using face detection & face recognition system. The first phase is pre-processing where the face detection is processed through the step image (...) processing. It includes the face detection and face recognition process. Second phase is feature extraction. Step by step execution of these techniques (Image Processing) helps to achieve the final output. The working of this project is to detect and recognize the face and mark the attendance for the corresponding face in the database. Input of this project is face detection and recognition and output is to mark the attendance. Our project is being presented as a solution for the Automatic Attendance Marking System. It is designed to be reliable and low power. The Automatic face detection and recognition proposed to attendance marking in database acts as the solution for the automatic attendance marking system.. (shrink)

Study shows that mask-wearing is a critical factor in stopping the COVID-19 transmission. By the time of this article, most states have mandated face masking in public space. Therefore, real-time face mask detection becomes an essential application to prevent the spread of the pandemic. This study will present a face mask detection system that can detect and monitor mask-wearing from camera feeds and alert when there is a violation. The face mask detection (...) algorithm uses a haar cascade classifier to find facial features from the camera feed and then utilizes it to detect the mask-wearing status. With the increasing number of cases all over the world, a system to replace humans to check masks on the faces of people is greatly needed. This system satisfies that need. This system can be employed in public places like transport stations and malls. It will be of great help in companies and huge establishments where there will be a lot of workers. Alongside this, we have used basic concepts of transfer learning in neural networks to finally output the presence or absence of a face mask in an image or a video stream. Experimental results show that our model performs well on the test data with 100 percent and 99 percent precision and recall, respectively. We are making a savvy framework that will identify whether the specific user has worn the mask or not and further tell the accuracy level of the detection. Our framework will be python and AI-based which will be safe and quick for implementation. This system will be of great help there because it is easy to obtain and store the data of the employees working in that Company and will very easily find the people who are not wearing the mask and a will be sent to that respective person to take Precautions not wearing a mask. Potential delays are analyzed, and efforts are made to reduce them to achieve real-time detection. The experiment result shows that the presented system achieves a real-time 45fps 720p Video output, with a face mask detection response of 0.15s and the accuracy of detection 99%. (shrink)

Background: The castor bean is a large grassy or semi-wooden shrub or small tree. Any part of the castor plant parts can suffering from a disease that weakens the ability to grow and eliminates its production. Therefore, in this paper will identify the pests and diseases present in castor culture and detect the symptoms in each disease. Also images is showing the symptom form in this disease. Objectives: The main objective of this expert system is to obtain appropriate diagnosis (...) of the disease. Methods: In this paper, the expert system is designed for the ability of agricultural engineers to detect and diagnose disease of castor like as: seeding blight, alternaria blight, cercospora leaf spot, powdery mildew and wilt. This system presents the disease symptoms, survival and spread, favorable conditions and image for each disease. Clips and Delphi expert system languages are used for designing and implementing the proposed expert system. Results: The expert system in the diagnosis of castor diseases was assessed by farmers and agricultural engineers and they were satisfied and accepted with its quality of performance. Conclusions: The expert system is easy for farmers and people have experience in the plant of castor to detect and diagnosis the symptoms that may face this plant from several disease. (shrink)

Facial Expression conveys non-verbal cues, which plays an important role in interpersonal relations. The Cognitive Emotion AI system is the process of identifying the emotional state of a person. The main aim of our study is to develop a robust system which can detect as well as recognize human emotion from live feed. There are some emotions which are universal to all human beings like angry, sad, happy, surprise, fear, disgust and neutral. The methodology of this system (...) is based on two stages- facial detection is done by extraction of Haar Cascade features of a face using Viola Jones algorithm and then the emotion is verified and recognized using Artificial Intelligence Techniques. The system will take image or frame as an input and by providing the image to the model the model will perform the preprocessing and feature selection after that it will be predict the emotional state. (shrink)

The corona virus COVID-19 pandemic is causing a global health crisis so the effective protection methods is wearing a face mask in public areas according to the World Health Organization (WHO). The COVID-19 pandemic forced governments across the world to impose lockdowns to prevent virus transmissions. Reports indicate that wearing facemasks while at work clearly reduces the risk of transmission. An efficient and economic approach of using AI to create a safe environment in a manufacturing setup. A hybrid model (...) using deep and classical machine learning for face mask detection will be presented. A face mask detection dataset consists of with mask and without mask images, we are going to use OpenCV to do real-time face detection from a live stream via our webcam. We will use the dataset to build a COVID-19 face mask detector with computer vision using Python, OpenCV, and Tensor Flow and Keras. Our goal is to identify whether the person on image/video stream is wearing a face mask or not with the help of computer vision and deep learning. (shrink)

Today’s everyone normal life can include a normal rate of playing computer games or video games; but what about an excessive or compulsive use of video games that impact on our life? Our kids, who usually spend a lot of time in playing video games will likely have a trouble in paying attention to their school lessons. In this paper, we introduce an expert system to help users in getting the correct diagnosis of the health problem of video game (...) addictions that range from (Musculoskeletal issues, Vision problems and Obesity). Moreover, this expert system provides information about the problem and tell us how we can solve it. SL5 Object expert system language was used to design and implement the expert system. (shrink)

The coronavirus disease 2019 (COVID-19) pandemic has motivated medical ethicists and several task forces to revisit or issue new guidelines on allocating scarce medical resources. Such guidelines are relevant for the allocation of scarce therapeutics and vaccines and for allocation of ICU beds, ventilators, and other life-sustaining treatments or potentially scarce interventions. Principles underlying these guidelines, like saving the most lives, mitigating disparities, reciprocity to those who assume additional risk (eg, essential workers and clinical trial participants), and equal access may (...) compete with one another. We propose the use of a “categorized priority system” (also known as a “reserve system”) as an improvement over existing allocation methods, particularly because it may be able to achieve disparity mitigation better than other methods. (shrink)

Memory Detection Tests (MDTs) are a general class of psychophysiological tests that can be used to determine whether someone remembers a particular fact or datum. The P300 MDT is a type of MDT that relies on a presumed correlation between the presence of a detectable neural signal (the P300 “brainwave”) in a test subject, and the recognition of those facts in the subject’s mind. As such, the P300 MDT belongs to a class of brain-based forensic technologies which have proved (...) popular and controversial in recent years. With such tests increasingly being proffered for use in the courtroom — to either support or call into question testimony — it would behoove the legal system to have some systematic framework for ensuring that they are used responsibly. In this paper, I defend one such framework for ensuring that this is the case: the legitimacy enhancing test. According to this test, it is appropriate to make use of technologies such as the P300 MDT whenever doing so would (probably) enhance the legitimacy of the trial. I argue that this test addresses tensions between scientific and legal norms of evidence, and exhibits a number of additional virtues including unification, simplicity and flexibility. Although the test is defended by considering the example of the P300 MDT, its significance is much broader than that. If it has the virtues I claim for it, it should provide a general framework for the responsible use of technologies, and the responsible innovation of social epistemic systems. (shrink)

Abstract: Sarcasm is commonly used in news and detecting sarcasm in headline news is challenging for humans and thus for computers. The media regularly seem to engage sarcasm in their news headline to get the attention of people. However, people find it tough to detect the sarcasm in the headline news, hence receiving a mistaken idea about that specific news and additionally spreading it to their friends, colleagues, etc. Consequently, an intelligent system that is able to distinguish between can (...) sarcasm none sarcasm automatically is very important. The aim of the study is to build a sarcasm model that detect headline news using machine and deep learning and attempt to understand how a computer learns the patterns of sarcasm. The dataset used in this study was collected from Kaggle depository. We examined 21 algorithms of machine learning and one deep learning algorithm for detecting sarcasm in headline news. The evaluation metric used in this study are Accuracy, F1-measure, Recall, Precision, and Time needed for training and evaluation. The deep learning model achieved accuracy (95.27%), recall (96.62%), precision (94.15%), F1-score (95.37%) and time needed to train the mode (400 seconds), with loss of around 0.3398. However, the algorithm of machine learning that achieved the highest F1-Score is Passive Aggressive Classifier. It was the top classier for sarcasm detection among the machine learning algorithms with accuracy (95.50%), recall (96.09 %), precision (94.30%), F1-score (95.19%) and time needed to train the mode (0.31 seconds). (shrink)

Abstract: Sarcasm is commonly used in news and detecting sarcasm in headline news is challenging for humans and thus for computers. The media regularly seem to engage sarcasm in their news headline to get the attention of people. However, people find it tough to detect the sarcasm in the headline news, hence receiving a mistaken idea about that specific news and additionally spreading it to their friends, colleagues, etc. Consequently, an intelligent system that is able to distinguish between can (...) sarcasm none sarcasm automatically is very important. The aim of the study is to build a sarcasm model that detect headline news using machine and deep learning and attempt to understand how a computer learns the patterns of sarcasm. The dataset used in this study was collected from Kaggle depository. We examined 21 algorithms of machine learning and one deep learning algorithm for detecting sarcasm in headline news. The evaluation metric used in this study are Accuracy, F1-measure, Recall, Precision, and Time needed for training and evaluation. The deep learning model achieved accuracy (95.27%), recall (96.62%), precision (94.15%), F1-score (95.37%) and time needed to train the mode (400 seconds), with loss of around 0.3398. However, the algorithm of machine learning that achieved the highest F1-Score is Passive Aggressive Classifier. It was the top classier for sarcasm detection among the machine learning algorithms with accuracy (95.50%), recall (96.09 %), precision (94.30%), F1-score (95.19%) and time needed to train the mode (0.31 seconds). (shrink)

At the beginning of 2020, the world was experiencing the Covid-19 pandemic, and Indonesia was no exception. The occurrence of this affects the learning system in Indonesia, the learning system that was originally face-to-face was forced to online form, in this case the teachers are required to provide a creative, efficient and optimal learning system for students. So the purpose of this study is to find out the difference in the average learning result of elementary (...) school students during the pandemic. The method used in this study is quantitative with a posttest-only control group design. The population in this study were grade 4 elementary school students in Majalengka district, Indonesia. There were 64 samples and was taken by purposive sampling. The results of this study are that there are differences in the average student learning results where students who study with the hybrid learning system are higher than the face-to-face learning system. The hybrid learning system is very reliable in the 4.0 era as well as learning during the Covid-19 pandemic. However, for the record, it is necessary to look at the facilities and infrastructure considering that this system relies on technology, it is necessary to understand and be able to control the learning media for both teachers and students so that learning outcomes can be optimal and minimize the occurrence of obstacles. The present study revealed the implementation of 21st century learning. (shrink)

Museums lose their conceptual complexity and polysemy under conditions of war, forced confrontation, and struggles for survival, which may lead to a loss of diversity in the long run. Parametric General Systems analysis allows us to consider a museum as a system and to explore substratum, structural, and conceptual types of simplicity and complexity. Such qualitative analysis makes it possible to move the discussion from the ideological and value sphere to the field of rational and science-based justification. This justification, (...) in turn, illustrates why it is important to maintain complex interpretations in Ukrainian museums during the current war. (shrink)

Abstract: Cheating behaviors have been construed as a continuing and somewhat vexing issue for academic institutions as they increasingly conduct educational processes online and impose metrics on instructional evaluation. Research, development, and implementation initiatives on cheating detection have gained new dimensions in the advent of artificial intelligence (AI) applications; they have also engendered special challenges in terms of their social, ethical, and cultural implications. An assortment of commercial cheating–detection systems have been injected into educational contexts with little input (...) on the part of relevant stakeholders. This paper expands several specific cases of how systems for the detection of cheating have recently been implemented in higher education institutions in the US and UK. It investigates how such vehicles as wearable technologies, eye scanning, and keystroke capturing are being used to collect the data used for anti-cheating initiatives, often involving systems that have not gone through rigorous testing and evaluation for their validity and potential educational impacts. The paper discusses accountability- and policy-related issues concerning the outsourcing of cheating detection in institutional settings in the light of these emerging technological practices as well as student resistance against the systems involved. The cheating-detection practices can place students in a disempowered, asymmetrical position that is often at substantial variance with their cultural backgrounds. (shrink)

Abstract: Cheating behaviors have been construed as a continuing and somewhat vexing issue for academic institutions as they increasingly conduct educational processes online and impose metrics on instructional evaluation. Research, development, and implementation initiatives on cheating detection have gained new dimensions in the advent of artificial intelligence (AI) applications; they have also engendered special challenges in terms of their social, ethical, and cultural implications. An assortment of commercial cheating–detection systems have been injected into educational contexts with little input (...) on the part of relevant stakeholders. This paper expands several specific cases of how systems for the detection of cheating have recently been implemented in higher education institutions in the US and UK. It investigates how such vehicles as wearable technologies, eye scanning, and keystroke capturing are being used to collect the data used for anti-cheating initiatives, often involving systems that have not gone through rigorous testing and evaluation for their validity and potential educational impacts. The paper discusses accountability- and policy-related issues concerning the outsourcing of cheating detection in institutional settings in the light of these emerging technological practices as well as student resistance against the systems involved. The practices can place students in a disempowered position. (shrink)

Plagiarism detection is the process of finding similarities on electronic based documents. Recently, this process is highly required because of the large number of available documents on the internet and the ability to copy and paste the text of relevant documents with simply Control+C and Control+V commands. The proposed solution is to investigate and develop an easy, fast, and multi-language support plagiarism detector with the easy of one click to detect the document plagiarism. This process will be done with (...) the support of intelligent system that can learn, change and adapt to the input document and make a cross-fast search for the content on the local repository and the online repository and link the content of the file with the matching content everywhere found. Furthermore, the supported document type that we will use is word, text and in some cases, the pdf files –where is the text can be extracting from them- and this made possible by using the DLL file from Word application that Microsoft provided on OS. The using of DLL will let us to not constrain on how to get the text from files; and will help us to apply the file on our Delphi project and walk throw our methodology and read the file word by word to grantee the best working scenarios for the calculation. In the result, this process will help in the uprising the documents quality and enhance the writer experience related to his work and will save the copyrights for the official writer of the documents by providing a new alternative tool for plagiarism detection problem for easy and fast use to the concerned Institutions for free. (shrink)

The recent emergence of machine-manipulated media raises an important societal question: How can we know whether a video that we watch is real or fake? In two online studies with 15,016 participants, we present authentic videos and deepfakes and ask participants to identify which is which. We compare the performance of ordinary human observers with the leading computer vision deepfake detection model and find them similarly accurate, while making different kinds of mistakes. Together, participants with access to the model’s (...) prediction are more accurate than either alone, but inaccurate model predictions often decrease participants’ accuracy. To probe the relative strengths and weaknesses of humans and machines as detectors of deepfakes, we examine human and machine performance across video-level features, and we evaluate the impact of preregistered randomized interventions on deepfake detection. We find that manipulations designed to disrupt visual processing of faces hinder human participants’ performance while mostly not affecting the model’s performance, suggesting a role for specialized cognitive capacities in explaining human deepfake detection performance. -/- . (shrink)

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines or software that work and reacts like humans, some of the computer activities with artificial intelligence are designed to include speech, recognition, learning, planning and problem solving. Deep learning is a collection of algorithms used in machine learning, it is part of a broad family of methods used for machine learning that are based on learning representations of data. Deep learning is used as a (...) technique to produce brain tumor detection and classification models using Magnetic Resonance Imaging (MRI) imaging for rapid and easy detection and identification of brain tumor. In this thesis, some ways and mechanisms will be reviewed to use deep learning techniques to produce a model for brain tumor detection. The goal is to find a good and effective way to detect brain tumor based on MRI to help the brain doctor in making decisions easily, accurately and rapidly. A recent report by the World Health Organization in February 2018 showed that the death rate from brain cancer or central nervous system (CNS) is the highest in the Asian continent. It is important to detect cancer early so that many of these lives can be saved. The model has been designed and implemented, including a dataset which consist of 10,000 images for brain tumor detection through the use of Deep learning algorithms based on neural networks. For testing, we have used our model, Inception, VGG16, MobileNet and ResNet models. The f-score accuracy we got for each model was as follows: Our model was 98.28, VGG16 was 99.86%, ResNet50 was 98.14%, MobileNet was 88,98%, and InceptionV3 was 99.88%. (shrink)

It is crucial to actively detect the risks of transactions in a financial company to improve customer experience and minimize financial loss. In this study, we compare different machine learning algorithms to effectively and efficiently predict the legitimacy of financial transactions. The algorithms used in this study were: MLP Repressor, Random Forest Classifier, Complement NB, MLP Classifier, Gaussian NB, Bernoulli NB, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Bagging Classifier, Decision Tree Classifier and Deep Learning. The dataset (...) was collected from Kaggle depository. It consists of 6362620 rows and 10 columns. The best classifier with unbalanced dataset was the Random Forest Classifier. The Accuracy 99.97%, precession 99.96%, Recall 99.97% and the F1-score 99.96%. However, the best classifier with balanced dataset was the Bagging Classifier. The Accuracy 99.96%, precession 99.95%, Recall 99.98% and the F1-score 99.96%. (shrink)

What do we see when we look at someone's expression of fear? I argue that one of the things that we see is fear itself. I support this view by developing a theory of affect perception. The theory involves two claims. One is that expressions are patterns of facial changes that carry information about affects. The other is that the visual system extracts and processes such information. In particular, I argue that the visual system functions to detect the (...) affects of others when they are expressed in the face. I develop my theory by drawing on empirical data from psychology and brain science. Finally, I outline a theory of the semantics of affect perception. (shrink)

A fundamental assumption of theories of decision-making is that we detect mismatches between intention and outcome, adjust our behavior in the face of error, and adapt to changing circumstances. Is this always the case? We investigated the relation between intention, choice, and introspection. Participants made choices between presented face pairs on the basis of attractiveness, while we covertly manipulated the relationship between choice and outcome that they experienced. Participants failed to notice conspicuous mismatches between their intended choice and (...) the outcome they were presented with, while nevertheless offering introspectively derived reasons for why they chose the way they did. We call this effect choice blindness. (shrink)

Functional robustness refers to a system’s ability to maintain a function in the face of perturbations to the causal structures that support performance of that function. Modularity, a crucial element of standard methods of causal inference and difference-making accounts of causation, refers to the independent manipulability of causal relationships within a system. Functional robustness appears to be at odds with modularity. If a function is maintained despite manipulation of some causal structure that supports that function, then the (...) relationship between that structure and function fails to be manipulable independent of other causal relationships within the system. Contrary to this line of reasoning, I argue that functional robustness often attends feedback control, rather than failures of modularity. Feedback control poses its own challenges to causal explanation and inference, but those challenges do not undermine modularity—and indeed, modularity is crucial to grappling with them. (shrink)

Academic integrity is an essential pillar of any educational system. It is defined as acting in a manner consistent with the values and accepted standards of ethical practices in teaching, learning, and scholarship (Fishman, 2015). Contract cheating, or ghostwriting, is currently one of the most severe violations of academic integrity. It involves students engaging a third party, usually an online essay writing service, to complete their academic works on their behalf (Draper et al., 2021). Some of these services offer (...) pre-written essays, whereas others offer bespoke custom-written essays. According to the academic literature, the advent of the internet and digital technologies underlay this rapid deterioration of academic integrity (Ison, 2020; Lancaster & Clarke, 2014). Different learning environments, such as face-to-face (i.e., a learning environment involving the physical presence of both instructor and students) and online web-based (i.e., teaching mode that takes place partially or entirely over the internet), have been shown to affect academic integrity in different ways (Eshet et al., 2021). While contract cheating is common in both conventional face-to-face (F2F) and online settings, it is more likely to take place in the latter (Lancaster & Clarke, 2014; Slade et al., 2018). There are several possible explanations for why online students engage in contract cheating more often than F2F students. In particular, this includes the problem issue of psychological distance, which adversely affects interpersonal relationships; and the problem issue of moral distancing, because the internet can obscure the line between academically honest and dishonest behavior (Sharma & Maleyeff, 2003). These issues were further exacerbated by the sudden shift from F2F to emergency remote teaching (ERT) in response to the COVID-19 pandemic (Ahsan et al., 2021; Bjelobaba, 2021). Indeed, contract cheating had become a significant COVID-19 side effect for higher education institutions. In contrast to the well-planned F2F or online learning courses, ERTbased courses are not originally designed to be delivered virtually (Fatonia et al., 2020). The chaos brought by the abrupt campus closure and related unexpected transition to ERT provided both the opportunity and the incentive for contract cheating (Hill et al., 2021). Through social media, students have very quickly become fully aware of the possibilities of a wide variety of options to carry out Plagiarism (Bautista et al., 2022). There has been an increase in ways to try to bypass text-matching or text-reuse (i.e., plagiarism) detection systems, for example using micro-spaces, white ink, punctuation, and typos (Abdelhamid et al., 2022). Even though ghostwriters, especially commercial ones, claim their essays are original and therefore cannot be detected using text-matching software, third-party assignments may still contain recycled text (Aitken et al., 2017; Newton & Lang, 2015). Text matching detection software could use these breaches to identify outsourced academic work (Lancaster & Clarke, 2016; Wang & Xu, 2021). This study compared about four thousand term papers written in languages other than Hebrew. (shrink)

Though there are many challenges to Ekman’s thesis that there are basic emotions with universal corresponding facial expressions, our main criticism revolves around the extent to which grounding situations alter how people read faces. To that end, we recruit testifying experimental studies that show identical faces expressing varying emotions when contextualized differently. Rather than dismissing these as illusions, we start with the position—generally favored by embodied thinkers—that situations are primary: they are where specifiable and hence knowable properties first show up. (...) We further argue that situationally inflected emotional expressions are informationally meaningful. We reject the idea that reading expressions is primarily about ascertaining internal mental states, arguing instead that people are registering overall situations when looking at faces. However, if mind is understood as a situated phenomenon that extends into active ecological frames, then one can still argue that mindreading is going on. Although we do not claim isolated things like cliffs or cars have agency, we speculate networked systems with cliffs, people, cars, bears, etc., collectively function with intentionality, more so if advancing a robust situated mind thesis, contra figures like Dennett who argue that people over-impute mind to things. Our position has practical implications insofar as it casts doubts on recent attempts to develop AI systems that extract emotional intent out of facial expressions since many of these systems are grounded on Ekman’s basic view. (shrink)

Artificial intelligence (AI), deep learning, machine learning and neural networks represent extremely exciting and powerful machine learning-based techniques used to solve many real-world problems. Artificial intelligence is the branch of computer sciences that emphasizes the development of intelligent machines, thinking and working like humans. For example, recognition, problem-solving, learning, visual perception, decision-making and planning. Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Deep learning (...) is a technique used to generate face detection and recognize it for real or fake by using profile images and determine the differences between them. In this study, we used deep learning techniques to generate models for Real and Fake face detection. The goal is determining a suitable way to detect real and fake faces. The model was designed and implemented, including both Dataset of images: Real and Fake faces detection through the use of Deep learning algorithms based on neural networks. We have trained dataset which consists of 9,000 images for total in 150 epochs, and got the ResNet50 model to be the best model of network architectures used with 100% training accuracy, 99.18% validation accuracy, training loss 0.0003, validation loss 0.0265, and testing accuracy 99%. (shrink)

Symmetry in biological and physical systems is a product of self-organization driven by evolutionary processes, or mechanical systems under constraints. Symmetry-based feature extraction or representation by neural networks may unravel the most informative contents in large image databases. Despite significant achievements of artificial intelligence in recognition and classification of regular patterns, the problem of uncertainty remains a major challenge in ambiguous data. In this study, we present an artificial neural network that detects symmetry uncertainty states in human observers. To this (...) end, we exploit a neural network metric in the output of a biologically inspired Self- Organizing Map Quantization Error (SOM-QE). Shape pairs with perfect geometry mirror symmetry but a non-homogenous appearance, caused by local variations in hue, saturation, or lightness within and/or across the shapes in a given pair produce, as shown here, a longer choice response time (RT) for “yes” responses relative to symmetry. These data are consistently mirrored by the variations in the SOM-QE from unsupervised neural network analysis of the same stimulus images. The neural network metric is thus capable of detecting and scaling human symmetry uncertainty in response to patterns. Such capacity is tightly linked to the metric’s proven selectivity to local contrast and color variations in large and highly complex image data. (shrink)

Since the transparency approach to introspection need not posit a dedicated mechanism specialized for detecting one’s own mental states, its economy is often viewed as a major advantage by both proponents and opponents. But sometimes economy comes at the cost of relying on controversial views of the natures of mental states. Perceptual experience is a case in point. For example, Alex Byrne’s account relies on the view that experience constitutively involves belief, and Matthew Boyle’s account relies on the view that (...) experience constitutively involves a form of implicit consciousness of experience. In this paper, I develop a transparency account of our access to our experiences that retains the benefit of economy while avoiding the cost of relying on controversial views of the nature of experience. I start by discussing Byrne’s and Boyle’s accounts. I focus on the problems that lead them to hold the controversial views they do, and raise some challenges to their solutions. I then develop my own account. I argue that when you attend to an object, your experience gives you justification to believe that you see the object (under some descriptions). I close by highlighting some advantages of my account. I argue that it (1) can solve the problems facing Byrne and Boyle without relying on controversial views of the nature of experience; (2) respects the idea that there is something special about our access to our minds; and (3) can be generalized to other cases of introspection. (shrink)

In this paper, we developed an Artificial Neural Network (ANN) for detect the absence or presence of lung cancer in human body. Symptoms were used to diagnose the lung cancer, these symptoms such as Yellow fingers, Anxiety, Chronic Disease, Fatigue, Allergy, Wheezing, Coughing, Shortness of Breath, Swallowing Difficulty and Chest pain. They were used and other information about the person as input variables for our ANN. Our ANN established, trained, and validated using data set, which its title is “survey lung (...) cancer”. Model evaluation showed that the ANN model is able to detect the absence or presence of lung cancer with 96.67 % accuracy. (shrink)

The COVID-19 pandemic that began in 2020 has significantly impacted businesses, regardless of size or industry. The hybrid and remote working models have moved all meetings with potential and existing suppliers to an online environment. This also applies to small- and medium-sized enterprises (SMEs), which have had to adapt themselves to the new situation and implement the solutions necessary to survive on the market. On the other hand, clients have become more aware of the environment and its changes. Customers are (...) trying to be more eco-friendly, by choosing and moving towards Green IT. Thus, this needs to be considered. The acquisition of management information systems (MIS) in the pandemic era is based only on virtual meetings. The main goals of this paper were the identification of the changes in the negotiations caused by the COVID-19 pandemic, the transformation of this process into virtual environment, discussion of the possibility of using Green IT in addition to Management Information Systems, and the changes caused by the pandemic. The article was prepared based on the results of qualitative research using the case study method. The comparative analysis includes purposely selected cloud-based Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) systems’ acquisition processes, presented from the clients’ perspective. The research was conducted in 2021, based on the authors’ practical experience, and presents four cases. This research illustrates the negotiations concerning an acquisition transaction pre-pandemic and during the pandemic. Finally, the conclusions and main differences caused by the pandemic in the acquisition transaction process of management information systems (MIS) are presented. (shrink)

This sentence G ↔ ¬(F ⊢ G) and its negation G ↔ ~(F ⊢ ¬G) are shown to meet the conventional definition of incompleteness: Incomplete(T) ↔ ∃φ ((T ⊬ φ) ∧ (T ⊬ ¬φ)). They meet conventional definition of incompleteness because neither the sentence nor its negation is provable in F (or any other formal system). -- .

Web page phishing is a type of phishing attack that targets websites. In a web page phishing attack, the attacker creates a fake website that looks like a legitimate website, such as a bank or credit card company website. The attacker then sends a fraudulent message to the victim, which contains a link to the fake website. When the victim clicks on the link, they are taken to the fake website and tricked into entering their personal information.Web page phishing attacks (...) are a serious threat to online security. They can be very effective, as they often look very convincing. To protect yourself from web page phishing attacks, you should be suspicious of any emails or messages that ask for your personal information. Do not click on links in emails or messages from unknown senders. If you are unsure whether a website is legitimate, do not enter your personal information. Therefore, in this study, we present a novel approach to detect whether a web page is phishing or legitimate using a neural network model. (shrink)

Abstract: This research paper presents the development and evaluation of a neural network-based model for predicting fire alarms in smoke detection systems. Using a dataset from Kaggle containing 15 features and 3487 samples, we trained and validated a neural network with a three-layer architecture. The model achieved an accuracy of 100% and an average error of 0.0000003. Additionally, we identified the most influential features in predicting fire alarms.

Saṃvega is a morally motivating state of shock that -- according to Buddhaghosa -- should be evoked by meditating on death. What kind of mental state it is exactly, and how it is morally motivating is unclear, however. This article presents a theory of saṃvega -- what it is and how it works -- based on recent insights in psychology. According to dual process theories there are two kinds of mental processes organized in two" systems" : the experiential, automatic (...) class='Hi'>system 1, and the rational, controlled system 2. In normal circumstances, system 1 does not believe in its own mortality. Saṃvega occurs when system 1 suddenly realizes that the "subjective self" will inevitably die (while system 2 is already disposed to affirm the subject's mortality). This results in a state of shock that is morally motivating under certain conditions. Saṃvega increases mortality salience and produces insight in suffering, and in combination with a strengthened sense of loving-kindness or empathic concern both mortality salience and insight in suffering produce moral motivation. (shrink)

As a response to climate change, geoengineering with solar radiation management has the potential to result in unjust harm. Potentially, this injustice could be ameliorated by providing compensation to victims of SRM. However, establishing a just SRM compensation system faces severe challenges. First, there is scientific uncertainty in detecting particular harmful impacts and causally attributing them to SRM. Second, there is ethical uncertainty regarding what principles should be used to determine responsibility and eligibility for compensation, as well as determining (...) how much compensation ought to be paid. Significant challenges loom for crafting a just SRM compensation system. (shrink)

Dans son plus récent ouvrage, Figures du communisme, l’économiste et philosophe Fréderic Lordon s’attaque au problème brûlant de la double crise, économique et environnementale, actuelle. Lordon désigne le coupable : le capitalisme, qui détruit la planète par son exploitation sans vergogne de la nature, et qui détruit la vie par son exploitation des êtres humains. Face à ce système mortifère, une sortie du capitalisme s’impose. Sortir du capitalisme, certes, mais pour le remplacer par quoi ? Lordon nous présente ici (...) son alternative. Contre le capitalisme destructeur de monde, Fréderic Lordon veut la figure du communisme. En s’inspirant des travaux de Bernard Friot, Lordon nous expose son idéal de société communiste : salaire à vie, garantie économique générale, abolition du marché et de la finance, réduction du travail et accès généralisé à la culture et à l’éducation. Il s’en prend également aux obstacles de la révolution communiste, qu’il s’agisse de la bourgeoisie, des grandes institutions financières, des banques, des multinationales, des médias, de l’État, de son armée et de sa police, mais aussi de ceux que Fréderic Lordon appelle « les scouts », les Piketty et consorts, qui espèrent sauver l’humanité et la planète tout en sauvant le capitalisme. Une critique sans concession d’une actualité brûlante. (shrink)

Paul Feyerabend argued that theories can be faced with experimental anomalies whose refuting character can only be recognized by developing alternatives to the theory. The alternate theory must explain the experimental results without contrivance and it must also be supported by independent evidence. I show that the situation described by Feyerabend arises again and again in experiments or observations that test the postulates in the standard cosmological model relating to dark matter. The alternate theory is Milgrom’s modified dynamics. I discuss (...) three examples: the failure to detect dark-matter particles in laboratory experiments; the lack of evidence for dark-matter sub-haloes and the dwarf galaxies that are postulated to inhabit them; and the failure to confirm the predicted orbital decay of Milky Way satellite galaxies and other systems due to dynamical friction against the dark matter. In each case, Feyerabend’s criterion directs us to interpret the experimental or observational results as an indirect refutation of the standard cosmological model in favor of Milgrom’s theory. (shrink)

Covid-19 is a serious illness. Deaths are rising steadily, and health systems are under strain. While most countries are following international health recommendations to wear face masks during the covid-19 pandemic the situation has been complicated in Sweden. Officials in Halmstad municipality, Sweden, forced a teacher to remove their mask and prohibited the use of masks and all forms of medical face masks in their schools. The municipality said there was no scientific evidence that wearing masks offered protection, (...) citing the Swedish Public Health Agency. At the time, agency guidance stated that there were "great risks" that masks would be used incorrectly. This guidance has since been removed. To someone unfamiliar with the Swedish response to the covid-19 pandemic, this mask ban might sound appalling. Especially since growing evidence indicates that face masks possibly do help reduce the spread of covid-19, especially in situations where maintaining distance is impossible. This essay is asking the ethical question if policy makers should apply the precautionary principle and encourage people to wear face masks on the grounds that lives potentially could be save. (shrink)

The idea that the immune system distinguishes between self and non-self was one of the central assumptions of immunology in the second half of 20 th century. This idea influenced experimental design and data interpretation. However, in the face of new evidence there is a need for a new conceptual framework in immunology.

It is an important feature of creatures like us that our various motivations compete for control over our behaviour, including mental behaviour such as imagining and attending. In large part, this competition is adjudicated by the stimulation of affect — the intrinsically pleasant or unpleasant aspects of experience. In this paper I argue that the motivation-affect system controls a sub-type of attention called 'alerting attention' to bring various goals and stimuli to consciousness and thereby prioritize those contents for action. (...) This view allows me to flesh out the global workspace theory of consciousness, as well as some of the phenomenal characteristics of conscious experience. (shrink)

Ensemble perception—the encoding of objects by their group properties—is known to be resistant to outlier noise. However, this resistance is somewhat paradoxical: how can the visual system determine which stimuli are outliers without already having derived statistical properties of the ensemble? A simple solution would be that ensemble perception is not a simple, one-step process; instead, outliers are detected through iterative computations that identify items with high deviance from the mean and reduce their weight in the representation over time. (...) Here we tested this hypothesis. In Experiment 1, we found evidence that outliers are discounted from mean orientation judgments, extending previous results from ensemble face perception. In Experiment 2, we tested the timing of outlier rejection by having participants perform speeded judgments of sets with or without outliers. We observed significant increases in reaction time (RT) when outliers were present, but a decrease compared to no-outlier sets of matched range suggesting that range alone did not drive RTs. In Experiment 3 we tested the timing by which outlier noise reduces over time. We presented sets for variable exposure durations and found that noise decreases linearly over time. Altogether these results suggest that ensemble representations are optimized through iterative computations aimed at reducing noise. The finding that ensemble perception is an iterative process provides a useful framework for understanding contextual effects on ensemble perception. (shrink)

The paper describes the design of an intelligent tutoring system for teaching Introduction to Computer Science-a compulsory curriculum in Al-Azhar University of Gaza to students who attend the university. The basic idea of this system is a systematic introduction into computer science. The system presents topics with examples. The system is dynamically checks student's individual progress. An initial evaluation study was done to investigate the effect of using the intelligent tutoring system on the performance of (...) students enrolled in computer science curriculum at Al-Azhar University, Gaza. The results showed a positive impact on the evaluators. (shrink)

This essay presents the conditional probability of co-contribution account of the individuation of cognitive systems (CPC) and argues that CPC provides an attractive basis for a theory of the cognitive self. The argument proceeds in a largely indirect way, by emphasizing empirical challenges faced by an approach that relies entirely on predictive processing (PP) mechanisms to ground a theory of the cognitive self. Given the challenges faced by PP-based approaches, we should prefer a theory of the cognitive self of the (...) sort CPC offers, one that accommodates variety in the kinds of mechanism that, when integrated, constitute a cognitive system (and thus the cognitive self), to a theory according to which the cognitive self is composed of essentially one kind of thing, for instance, prediction-error minimization mechanisms. The final section focuses on one of the central functions of the cognitive self: to engage in conscious reasoning. It is argued that the phenomenon of conscious, deliberate reasoning poses an apparently insoluble problem for a PP-based view, one that seems to rest on a structural limitation of predictive-processing models. In a nutshell, conscious reasoning is a single-stream phenomenon, but, in order for PP to apply, two streams of activity must be involved, a prediction stream and an input stream. Thus, with regard to the question of the nature of the self, PP-based views must yield to an alternative approach, regardless of whether proponents of the predictive processing, as a comprehensive theory of cognition, can handle the various empirical challenges canvassed in the preceding section. (shrink)

The purpose of cultural competence education for medical professionals is to ensure respectful care and reduce health disparities. Yet as Berger and Miller (2021) show, the cultural competence framework is dated, confused, and self-defeating. They argue that the framework ignores the primary driver of health disparities—systemic racism—and is apt to exacerbate rather than mitigate bias and ethnocentrism. They propose replacing cultural competence with a framework that attends to two social aspects of structural inequality: health and social policy, and institutional-system (...) activity; and two psychological aspects of structural inequality: the clinical encounter, and the epistemic. -/- We agree with the structural approach. To that end, we think it would be fruitful to include attention to physical contributors to structural inequality, namely the material artifacts used in medicine. Devices, tools, and technologies can materialize biases, perpetuate oppression, and contribute to health disparities. Granted, not everything that interests philosophers can be squeezed into medical education. Nevertheless, there are compelling reasons for including the study of material artifacts in education designed to reduce health disparities. First, devices and tools often carry forward biases from the past, and keep biases hidden from plain sight. Second, by studying these artifacts, future clinicians can begin to see themselves as part of a larger sociotechnical system. Finally, as medicine becomes increasingly tech-laden, it’s important for clinicians to see how material artifacts (including algorithms) connect individuals to structures. This will help to undermine oversimplified narratives according to which objective tools and technologies can correct for the bias and subjectivity of flawed human beings. (shrink)

Happiness, like other basic emotions, has visual properties that create the conditions for happiness to be perceived in others. This is to say that happiness is perceivable. Its visual properties are to be identified with those facial expressions that are characteristic of happiness. Yet saying that something is perceivable does not suffice for us to conclude that it is perceived. We therefore need to show that happiness is perceived. Empirical evidence suggests that the visual system functions to perceive happiness (...) as well as other basic emotions. Experiences that can be had simply by virtue of how the perceptual system functions are known as observations. I will thus argue that visual experiences in which we become aware of others’ happiness are observations. This approach will provide the necessary conceptual framework to show that we have perceptual knowledge of others’ happiness. (shrink)

Scientists depend on complex computational systems that are often ineliminably opaque, to the detriment of our ability to give scientific explanations and detect artifacts. Some philosophers have s...

After years in distance learning, grade school teachers are now facing the threat of declining reading comprehension among elementary pupils in post-pandemic settings. Teachers observed a spike in the number of non-readers and frustrated readers in intermediate grade levels. Teachers expressed concerns about the state of the education system in the aftermath of the pandemic. The purpose of this exploratory study was to establish an understanding of the challenges and mechanisms of macro- and micro-management strategies of grade school institutions. (...) There were six teachers and six reading coordinators who participated in the study. The findings suggested that while reading comprehension was a problem before the pandemic, teachers realized a surge in the number of non-readers after opening back to face-to-face classes. This was an unanticipated phenomenon that called the academic institutions to respond immediately. Significant mechanisms of reading comprehension programs were profiling, initial detection, policy-based initiatives, and institution-based design. Collaborative and collective responsibility represented macro-management practices, while initiative and responsiveness reflected micromanagement practices. These practices and strategies provided conceptual and phenomenological underpinnings relevant to early-education reading comprehension. (shrink)

It is a matter of fact that Europe is facing more and more crucial challenges regarding health and social care due to the demographic change and the current economic context. The recent COVID-19 pandemic has stressed this situation even further, thus highlighting the need for taking action. Active and Assisted Living technologies come as a viable approach to help facing these challenges, thanks to the high potential they have in enabling remote care and support. Broadly speaking, AAL can be referred (...) to as the use of innovative and advanced Information and Communication Technologies to create supportive, inclusive and empowering applications and environments that enable older, impaired or frail people to live independently and stay active longer in society. AAL capitalizes on the growing pervasiveness and effectiveness of sensing and computing facilities to supply the persons in need with smart assistance, by responding to their necessities of autonomy, independence, comfort, security and safety. The application scenarios addressed by AAL are complex, due to the inherent heterogeneity of the end-user population, their living arrangements, and their physical conditions or impairment. Despite aiming at diverse goals, AAL systems should share some common characteristics. They are designed to provide support in daily life in an invisible, unobtrusive and user-friendly manner. Moreover, they are conceived to be intelligent, to be able to learn and adapt to the requirements and requests of the assisted people, and to synchronise with their specific needs. Nevertheless, to ensure the uptake of AAL in society, potential users must be willing to use AAL applications and to integrate them in their daily environments and lives. In this respect, video- and audio-based AAL applications have several advantages, in terms of unobtrusiveness and information richness. Indeed, cameras and microphones are far less obtrusive with respect to the hindrance other wearable sensors may cause to one’s activities. In addition, a single camera placed in a room can record most of the activities performed in the room, thus replacing many other non-visual sensors. Currently, video-based applications are effective in recognising and monitoring the activities, the movements, and the overall conditions of the assisted individuals as well as to assess their vital parameters. Similarly, audio sensors have the potential to become one of the most important modalities for interaction with AAL systems, as they can have a large range of sensing, do not require physical presence at a particular location and are physically intangible. Moreover, relevant information about individuals’ activities and health status can derive from processing audio signals. Nevertheless, as the other side of the coin, cameras and microphones are often perceived as the most intrusive technologies from the viewpoint of the privacy of the monitored individuals. This is due to the richness of the information these technologies convey and the intimate setting where they may be deployed. Solutions able to ensure privacy preservation by context and by design, as well as to ensure high legal and ethical standards are in high demand. After the review of the current state of play and the discussion in GoodBrother, we may claim that the first solutions in this direction are starting to appear in the literature. A multidisciplinary debate among experts and stakeholders is paving the way towards AAL ensuring ergonomics, usability, acceptance and privacy preservation. The DIANA, PAAL, and VisuAAL projects are examples of this fresh approach. This report provides the reader with a review of the most recent advances in audio- and video-based monitoring technologies for AAL. It has been drafted as a collective effort of WG3 to supply an introduction to AAL, its evolution over time and its main functional and technological underpinnings. In this respect, the report contributes to the field with the outline of a new generation of ethical-aware AAL technologies and a proposal for a novel comprehensive taxonomy of AAL systems and applications. Moreover, the report allows non-technical readers to gather an overview of the main components of an AAL system and how these function and interact with the end-users. The report illustrates the state of the art of the most successful AAL applications and functions based on audio and video data, namely lifelogging and self-monitoring, remote monitoring of vital signs, emotional state recognition, food intake monitoring, activity and behaviour recognition, activity and personal assistance, gesture recognition, fall detection and prevention, mobility assessment and frailty recognition, and cognitive and motor rehabilitation. For these application scenarios, the report illustrates the state of play in terms of scientific advances, available products and research project. The open challenges are also highlighted. The report ends with an overview of the challenges, the hindrances and the opportunities posed by the uptake in real world settings of AAL technologies. In this respect, the report illustrates the current procedural and technological approaches to cope with acceptability, usability and trust in the AAL technology, by surveying strategies and approaches to co-design, to privacy preservation in video and audio data, to transparency and explainability in data processing, and to data transmission and communication. User acceptance and ethical considerations are also debated. Finally, the potentials coming from the silver economy are overviewed. (shrink)

This abstract is a synopsis of the paper "An Expert System for Diagnosing Whooping Cough Using CLIPS." The bacterium Bordetella pertussis causes whooping cough, a highly infectious respiratory ailment with several phases of symptoms. An accurate and timely diagnosis is critical for effective treatment and the avoidance of future transmission. The construction of an expert system for detecting whooping cough using the CLIPS (C Language Integrated Production System) architecture is highlighted in this abstract. The expert system (...) provides standardized and systematic evaluation, which reduces the chance of misdiagnosis and improves patient outcomes. Maintenance and upgrades are required to keep the system current with growing medical knowledge. More study in this area has the potential to advance expert systems in identifying and managing a variety of medical disorders. (shrink)

I believe that the sort of disagreements we encounter in philosophy—disagreements that often take the form that I have elsewhere called system- atic peer disagreements—make it unreasonable to think that there is any knowledge, or even justified belief, when the disagreements themselves are systematic. I readily acknowledge that this skeptical view is quite controversial; I suspect many are unconvinced. However, I will not be defending it here. Rather, I will be exploring a worry, or set of worries, that arise (...) on the assumption that this view is correct. For if it is unreasonable to think that there is justified belief in contexts of systematic philosophical disagreements, by what right do we continue to advance philosophical claims in such contexts? Indeed, by what right do we believe the philosophical claims we advance? And if we don’t believe them, why do we advance them in the first place? An inability to respond to these worries would leave us with the dis- tinct impression that the practice or activity of philosophy is quite suspect: what sort of practice or activity would have us believe unreasonably, assert unwarrantedly, and perhaps exhibit insincerity to boot? (shrink)

In the classical Turing test, participants are challenged to tell whether they are interacting with another human being or with a machine. The way the interaction takes place is not direct, but a distant conversation through computer screen messages. Basic forms of interaction are face-to-face and embodied, context-dependent and based on the detection of reciprocal sensorimotor contingencies. Our idea is that interaction detection requires the integration of proprioceptive and interoceptive patterns with sensorimotor patterns, within quite short (...) time lapses, so that they appear as mutually contingent, as reciprocal. In other words, the experience of interaction takes place when sensorimotor patterns are contingent upon one’s own movements, and vice versa. I react to your movement, you react to mine. When I notice both components, I come to experience an interaction. Therefore, we designed a “minimal” Turing test to investigate how much information is required to detect these reciprocal sensorimotor contingencies. Using a new version of the perceptual crossing paradigm, we tested whether participants resorted to interaction detection to tell apart human from machine agents in repeated encounters with these agents. In two studies, we presented participants with movements of a human agent, either online or offline, and movements of a computerized oscillatory agent in three different blocks. In each block, either auditory or audiovisual feedback was provided along each trial. Analysis of participants’ explicit responses and of the implicit information subsumed in the dynamics of their series will reveal evidence that participants use the reciprocal sensorimotor contingencies within short time windows. For a machine to pass this minimal Turing test, it should be able to generate this sort of reciprocal contingencies. (shrink)

Literature frequently describes how ineffective implementation of instructional policy frameworks can make distance learning a lonely and unrewarding academic pursuit, characterized by high student drop-out rates, high failure rates and academic exclusion. In trying to mitigate this catastrophe, academic departments in distance learning institutions utilize learning management systems (LMSs) to stimulate students’ learning experiences. In keeping with techno-progressivism, the researchers (and authors of this paper) turned to extant documentary policy and literature to review – qualitatively – how the University of (...) South Africa’s (Unisa’s) Open Distance Learning Policy (ODLP) promotes academic inclusion and learning agency as cornerstones of student success and social justice, especially in an unequal society like South Africa. The findings revealed the following: 1) Unisa’s ODLP position has the impetus to influence the deployment of the LMS to promote academic inclusion; 2) Practical means of promoting inclusion were mirrored in the provision of manual or electronic learning material, computer laboratories and free internet connectivity in regional centres across all nine provinces of South Africa, as well as in the provision of laptops for National Student Financial Aid Scheme (NSFAS)-funded students, 30 gigabytes of free monthly mobile data to all students, and assistive technologies for students with disabilities; 3) Practical utilization of an LMS to foster self-regulated learning occurred through problem-based individual activities supplemented by asynchronous demonstrative learning material (e.g. audio, video), while collaborative learning agency was enabled through e-tutoring, which afforded students the opportunity to interact with e-tutors and their peers about the learning content; 4) Drawbacks in the use of the LMS stemmed from the insufficiency of assistive learning technologies that are required for students with disabilities to participate fully in online learning, and a corpus of students’ irregular attendance at and participation in e-tutoring discussions and their projection of a negative attitude towards the e-tutors. -/- . (shrink)