Just as humans can draw conclusions responsibly or irresponsibly, so too can computers. Machine learning systems that have been trained on data sets that include irresponsible judgments are likely to yield irresponsible predictions as outputs. In this paper I focus on a particular kind of inference a computer system might make: identification of the intentions with which a person acted on the basis of photographic evidence. Such inferences are liable to be morally objectionable, because of a way in which (...) they are presumptuous. After elaborating this moral concern, I explore the possibility that carefully procuring the training data for image recognition systems could ensure that the systems avoid the problem. The lesson of this paper extends beyond just the particular case of image recognition systems and the challenge of responsibly identifying a person’s intentions. Reflection on this particular case demonstrates the importance (as well as the difficulty) of evaluating machine learning systems and their training data from the standpoint of moral considerations that are not encompassed by ordinary assessments of predictive accuracy. (shrink)

This paper presents an ethical framework designed to support the development of critical data literacy for research methods courses and data training programmes in higher education. The framework we present draws upon our reviews of literature, course syllabi and existing frameworks on data ethics. For this research we reviewed 250 research methods syllabi from across the disciplines, as well as 80 syllabi from data science programmes to understand how or if data ethics was taught. (...) We also reviewed 12 data ethics frameworks drawn from different sectors. Finally, we reviewed an extensive and diverse body of literature about data practices, research ethics, data ethics and critical data literacy, in order to develop a transversal model that can be adopted across higher education. To promote and support ethical approaches to the collection and use of data, ethics training must go beyond securing informed consent to enable a critical understanding of the techno-centric environment and the intersecting hierarchies of power embedded in technology and data. By fostering ethics as a method, educators can enable research that protects vulnerable groups and empower communities. (shrink)

This review delves into the influence of big data analytics on project management effectiveness and project success rates. By examining applications, accomplishments, hindrances, and emerging developments in the context of big data analytics and project management, this review provides insights into its transformative potential. Results indicate that big data analytics fosters improved project performance, more robust risk management, and heightened adaptability. However, challenges related to data quality, privacy, and project manager training remain to be addressed. (...) This review underscores the value of data-driven decision-making for both practitioners and researchers in the project management field. (shrink)

This study focused specifically on determining the extent to which duration of vocational training influence university respectively. In achieving this, two research questions were raised and two research hypotheses were formulated to guide the study. The study adopted a descriptive survey design. The pop study include a total of 123 baking business and 208 computer business centres in the Area. Out of this population, a proportionate stratified sampling technique was used to select 10% resulting in the selection of 12 (...) baking and 21 computer adopted to select 36 and 63 graduate respectively, resulting in a sample of 99 respondents. A rating scale was used as the instrument for data collection. The instrument was administered by the researchers. The data obtained, were analysed using descriptive statistics, while the hypotheses were tested at .05 level of significance using one analysis of variance (One vocational training influenced university graduates job performance in baking and computer business enterprises respectively. Discussions, conclusion and recommendation were all made based on the findings of the study. (shrink)

This paper focuses on the Nigerian Translator-training programmes and its challenges. It is a descriptive research premised on a problem raised by Akakuru that most translation graduates from Nigerian universities can neither translate nor talk about translation. This paper sets out to investigate the validity of the statement and to point out where the problems lie as well as propose some solutions to them. Data for this work were collected from websites of some Nigerian universities that train translators (...) up to the Masters and Ph.D. levels. Data were also collected from the websites of some renowned African, European and Canadian universities that equally train translators up to the Masters and Ph.D. levels and whose graduates are performing well in the field of translation in and out of their countries of origin. A comparative analysis of the data collected reveals some lacuna in the Nigerian Postgraduate Translator-Training Programmes at the levels of the trainees, the trainers and the content of the programmes themselves. The paper further suggests that the way forward for a successful translator-training programme in Nigeria is to adapt and implement the European and the Canadian models. (shrink)

Scalability, efficiency, and security had been a persistent problem over the years in data mining, several techniques had been proposed and implemented but none had been able to solve the problem of scalability, efficiency and security from cloud computing. In this research, we solve the problem scalability, efficiency and security in data mining over cloud computing by using a restful web services and combination of different technologies and tools, our model was trained by using different machine learning algorithm, (...) and finally using different validation and cross-validation methods to compare and validate our results. We build, train, test and deploy our model by restful API services which can be called through an endpoint, and with very high accuracy and huge success. (shrink)

Purpose: This study evaluates staff Training and Development Programmes (TDPs) in secondary schools based on the views of administrators and teachers. The research was implemented in public secondary schools in Cross River State, Nigeria. Design/Methodology/Approach: The study adopted the survey research design with 5408 participants (administrators = 542; teachers = 4595). Four research questions guided the study. The Staff Training and Development Programmes Questionnaire (STDPQ) was used for data collection. Findings revealed essential areas of staff training (...) and development. Findings: Various methods are employed for staff training and development. The results further showed various constraints that impeded staff TDPs in secondary schools. Conclusion: Based on the findings, it was concluded that staff in secondary schools have different training and development needs adjudged by both the principals and teachers as crucial for job enhancement. Various staff training and development methods can serve as valuable programmes to improve the current methods adopted. Research Limitations and Implications: There are various limitations in this study such as the small scope which may affect the extent to which generalizations can be made. It is suggested that future studies focus on expanding the scope to other regions and nations. Practical Implications: Despite the limitations, the study has made some advances to human capital and self-determination theories by providing findings. The findings of this study may direct the attention of curriculum planners, the government and school management to offer training and development opportunities based on the needs of teachers. Contribution to Literature: The study has contributed to the literature on staff training and development which can constitute a basis for further research in related areas. (shrink)

It is often thought the relativity of simultaneity is inconsistent with presentism. This would be troubling as it conflicts with common sense and—arguably—the empirical data. This note gives a novel fragmentalist-presentist theory that allows for the (non-trivial) relativity of simultaneity. A detailed account of the canonical moving train argument is considered. Alice, standing at the train station, forms her own ontological fragment, in which Bob’s frame of reference, given by the moving train, is modified by the Lorentz transformations. On (...) the other hand, Bob, in the train, forms his own ontological fragment from which Alice’s space and time are modified by the corresponding Lorentz transformations. Each fragment accommodates a unique present moment but does not contain information about the unique present moment of another fragment. This allows for a ‘universal’ present moment that extends throughout space, but only from the perspective of each fragment. The relativity of simultaneity is, as it were, ‘relativised’ to each fragment. This is related to the idea that, roughly speaking, the time of relativity is McTaggart’s (1908) B-series (earlier times to later times) and the time of quantum mechanics is a (fragmentalist) A-series (future/present/past), where these two related series characterize one dimension of time. (shrink)

Scalability, efficiency, and security had been a persistent problem over the years in data mining, several techniques had been proposed and implemented but none had been able to solve the problem of scalability, efficiency and security from cloud computing. In this research, we solve the problem scalability, efficiency and security in data mining over cloud computing by using a restful web services and combination of different technologies and tools, our model was trained by using different machine learning algorithm, (...) and finally using different validation and cross-validation methods to compare and validate our results. We build, train, test and deploy our model by restful API services which can be called through an endpoint, and with very high accuracy and huge success. (shrink)

The purpose of this study was to evaluate the differentiated learning training program at the mathematics subject teachers' meeting (MGMP). A descriptive quantitative approach was used to identify the successes of the program and areas that require improvement. The sample included 21 mathematics teachers in Sub Rayon 2 of Lebak District. The instruments used were questionnaires in which data on participants' responses to resource persons, materials, and suggestions for future activities were collected, and the results of direct observations. (...) Data analysis was carried out using descriptive statistical methods to describe the overall evaluation results. Based on the findings, it is expected to provide an in-depth view of the differentiated learning program, contribute to the development of the program in the future, and become a guideline for improving the quality of mathematics learning in the Lebak district. The results of this training program evaluation show that the differentiated learning training activities have been well implemented in MGMP Mathematics Sub Rayon 2 Lebak District. This is evidenced by the level of teacher satisfaction in participating in the training, the majority of which were at scores 4 and 5, namely agreeing and strongly agreeing. This was also the case with the resource persons, materials, and training facilities, and organization. It is hoped that in the future continue to carry out ongoing training related to differentiated learning by current needs and to improve the competence of mathematics teachers in particular, as well as teachers of other subjects in general. (shrink)

The study aimed to identify the reality of the use of artificial intelligence applications as a modern trend among training institutions in the southern Palestinian governorates. The study used the descriptive analytical method. The southern Palestinian governorates, and the random sample was used to collect data, as (90) applicable questionnaires were retrieved. The results of the study showed that the general estimate of artificial intelligence applications reached (83.63%), and that the order of areas of artificial intelligence applications is (...) as follows (Training And Development, System Capacity, User Behavior, Smart Agent, News Availability), and the results also showed that there were no statistically significant differences. In the use of artificial intelligence applications according to the gender variable, there are statistically significant differences in the use of artificial intelligence applications according to the variables (educational qualification, age group, number of years of service, governorate). The study presented a set of recommendations, the most important of which are: the need to attract experts and specialists to work in training institutions in order to develop applicable artificial intelligence applications, as well as the need for centers to develop their training systems in line with modern technology. (shrink)

:The study aimed to identify the level of enhancing the competitive advantage of training institutions in Palestine. The study used the descriptive analytical approach. A structured questionnaire was used to collect data that contribute to achieving the objectives of the study. The study population consists of workers in training institutions operating in the southern Palestinian governorates. The random sample for data collection, where (90) applicable questionnaires were retrieved. The results of the study showed that the general (...) estimate for the competitive advantage axis came with a high degree and a relative weight (90.67%). The results also showed that there were no statistically significant differences in enhancing the competitive advantage according to the variables (gender, governorate). As well as the existence of statistically significant differences in enhancing the competitive advantage according to the variables (Educational Qualification, Age Group, Number Of Years of Service). The study presented a set of recommendations, the most important of which are: the need for centers to develop their training systems in line with modern technology, in addition to the need for centers to target new areas in their service by opening other branches in diffe rent geographical locations. (shrink)

Consistent evaluation of speakers is an integral part of any training program. Bangladesh Civil Service Administration Academy imparts training primarily for newly recruited administrative civil servants. Depending on both primary and secondary data, this study examines various issues related to the lack of credibility of the speaker-evaluation by the participants to provide deep insights and potential solutions. Secondary data was collected from 300 evaluation forms and the academic backgrounds of 20 regular speakers. A questionnaire-led survey was (...) conducted among 36 participants of a training program to obtain primary data. Although the regular speakers are usually highly educated and professionally experienced, the one-sided subjective feedback remains critical to this evaluation process. The statistical analysis of both primary and secondary data shows that the participants do not invest enough time and thought for completing the evaluation. A reluctance to put descriptive observations makes the outcome of the evaluation lose some of its credibility. Participants have also pointed out a few critical limitations of the form. Findings suggest that adding qualitative measures to the form can be useful to provoke qualitative views from the participants and to obtain constructive suggestions for the speakers. Further insights from the speakers can be valuable to complement the outcome of this study. This study concludes that the Academy must combine planned and incidental evaluations to revitalize the effectiveness of the process. (shrink)

Memes on the internet are created content to express knowledge, entertain, ridicule, and self-actualize. The purpose of this study was to assess the emotional response to memes and their link to the profile of Filipino teachers-in-training. The descriptive-correlation research method was used in this study. A questionnaire-checklist was utilized to assess the effects of online memes on the emotions of teachers-in-training. The data was treated using frequency counts, percentages, and the average weighted mean. The association between profile (...) and the emotional response to Internet Memes was investigated using Pearson-r (Pearson Product Moment of Correlation) and Chi-square. The outcomes of this study revealed that teachers-in-training are mostly females who concentrate in Enhanced General Education and spend 4-6 hours on social media. Teachers-in-training whose gender is female has a higher emotional response (whether positive or negative) to memes than their counterparts. The number of hours spent on social media has no bearing on the Teachers-in Training emotional responses to memes. Respondents who are specializing in Enhanced General Education have the highest positive emotional response to memes. However, there is no significant connection between negative emotional responses to memes and specialization. (shrink)

A critical analysis of the Ukrainian experience of vocational training of personnel is conducted in the work. The statistics data on the number of employees who participated in vocational training activities during 2016-2018 are presented and analyzed. Based on the analysis, the main reasons for the low interest of Ukrainian business owners in the personnel vocational training were identified. In the work, the author also has highlighted the factors that restrain and activate the development of vocational (...) training of personnel at Ukrainian enterprises. In order to increase the efficiency of professional development of personnel at Ukrainian enterprises, the author proposes to use modern methods for this purpose. Because traditional methods do not produce the desired results. Ukrainian enterprises need to invest in staff training, which is the main reserve for improving their performance. (shrink)

Aim: This study determined the relationship between the assessed conflict management and communication styles of the university leaders of Guangdong Business and Technology University in China towards a leadership development training program. -/- Methodology: This study adopted a descriptive quantitative- comparative- correlational research design. It was conducted during the second semester of school year 2021-2022. The data gathered were collated and treated using the Statistical Package for Social Sciences or SPSS software. -/- Results: Based on the results, the (...) most evident conflict management styles of the university leaders are the compromising, and collaborating styles which ranked first and second respectively among the five identified management styles. It can also be noticed that avoiding and accommodating styles were evident among the university leaders as assessed by the teachers, while competing style was given the lowest assessment by the respondents. An over-all mean values of 3.50 shows that the conflict management style of the university leaders is evident as perceived by the teacher respondents. -/- Conclusion: Compromising and collaborating styles of conflict management are the most evident styles among the university leaders. Older teachers have seen avoiding style of conflict management more evident in their university leaders, while least evident in the eyes of the younger ones. Aggressive communication style was most evident among the university leaders while passive style was the least as observed by the teachers. Older teachers have seen their leaders to be more assertive than how the younger ones have seen them. The way the educational leaders handle conflict management is affected by the way how they communicate with their subordinates. It can also be said that effective communication style is a factor in managing and solving conflicts in the workplace. (shrink)

Abstract: Alzheimer's disease (AD) is one of the most common types of dementia. Symptoms appear gradually and end with severe brain damage. People with Alzheimer's disease lose the abilities of knowledge, memory, language and learning. Recently, the classification and diagnosis of diseases using deep learning has emerged as an active topic covering a wide range of applications. This paper proposes examining abnormalities in brain structures and detecting cases of Alzheimer's disease especially in the early stages, using features derived from medical (...) images. The entire brain image was passed on through the transmission of Xception learning architectures. The Convolutional Neural Network (CNN) was constructed with the help of separable convolution layers that It can automatically learn general features from imaging data for classification. (shrink)

In recent decades, Information and Communication Technology (ICT) has been advanced and widely spread around the globe in addition to ICT revolution and technological advances are considered the major role in the evolution of modern age, which is called "Digital Transformation Age". Therefore, Electronic Technology (E-Technology) has become one of the most prominent approaches such as Electronic Learning (E-Learning), Electronic Training (E-Training), Mobile Learning (M-Learning), Virtual Lab (V-Lab), Virtual University, etc. E-Technology includes some features, for instance anyone, anywhere, (...) anytime and reducing of geographical barriers. ETechnology is a great trend and influences many fields and sectors such as Learning, Training, Military, Navy, Aviation, Medicine, and Digital space. According to nature of usage, E-Technology can be used in positive or negative trends. E-Technology is considered as a valuable tool in providing several opportunities for learning and training processes for individuals and organizations, especially in critical issues. Finally, we give a quick overview of neutrosophic data and some recent applications. (shrink)

The issue of the definition and position of archaeology as a discipline is examined in relation to the dispute which took place from 1980 to 2009 between the archaeologist Jean-Claude Gardin and the sociologist Jean-Claude Passeron. This case study enables us to explore the actual conceptual relationships between archaeology and the other sciences (as opposed to those wished for or prescribed). The contrasts between the positions declared by the two researchers and the rooting of their arguments in their disciplines are (...) examined: where the sociologist makes use of his philosophical training, the archaeologist relies mainly on his work on semiology and informatics. Archaeology ultimately plays a minor role in the arguments proposed. This dispute therefore cannot be considered as evidence for the movement of concepts between archaeology and the social sciences. A blind spot in the debate, relating to the ontological specificities of archaeological objects, nevertheless presents itself as a possible way of implementing this movement. (shrink)

Today, software instruments support all parts of engineering work, from design to creation. Many engineering processes call for tedious routine appointments and torments with manual handoffs and data storehouses. AI designers train profound brain networks and incorporate them into software structures.

Fixing the problem won’t be easy, but humans’ sharpened focus on an emerging philosophy of data might give us some clue about where we will be heading for.

Background: Smart sensors have been developed as diagnostic tools for rehabilitation to cover an increasing number of geriatric patients. They promise to enable an objective assessment of complex movement patterns. -/- Objective: This research aimed to identify and analyze the conflicting ethical values associated with smart sensors in geriatric rehabilitation and provide ethical guidance on the best use of smart sensors to all stakeholders, including technology developers, health professionals, patients, and health authorities. -/- Methods: On the basis of a systematic (...) literature search of the scientific databases PubMed and ScienceDirect, we conducted a qualitative document analysis to identify evidence-based practical implications of ethical relevance. We included 33 articles in the analysis. The practical implications were extracted inductively. Finally, we carried out an ethical analysis based on the 4 principles of biomedical ethics: autonomy, beneficence, nonmaleficence, and justice. The results are reported in categories based on these 4 principles. -/- Results: We identified 8 conflicting aims for using smart sensors. Gains in autonomy come at the cost of patient privacy. Smart sensors at home increase the independence of patients but may reduce social interactions. Independent measurements performed by patients may result in lower diagnostic accuracy. Although smart sensors could provide cost-effective and high-quality diagnostics for most patients, minorities could end up with suboptimal treatment owing to their underrepresentation in training data and studies. This could lead to algorithmic biases that would not be recognized by medical professionals when treating patients. -/- Conclusions: The application of smart sensors has the potential to improve the rehabilitation of geriatric patients in several ways. It is important that patients do not have to choose between autonomy and privacy and are well informed about the insights that can be gained from the data. Smart sensors should support and not replace interactions with medical professionals. Patients and medical professionals should be educated about the correct application and the limitations of smart sensors. Smart sensors should include an adequate representation of minorities in their training data and should be covered by health insurance to guarantee fair access. (shrink)

Today, artificial intelligence, especially machine learning, is structurally dependent on human participation. Technologies such as Deep Learning (DL) leverage networked media infrastructures and human-machine interaction designs to harness users to provide training and verification data. The emergence of DL is therefore based on a fundamental socio-technological transformation of the relationship between humans and machines. Rather than simulating human intelligence, DL-based AIs capture human cognitive abilities, so they are hybrid human-machine apparatuses. From a perspective of media philosophy and social-theoretical (...) critique, I differentiate five types of “media technologies of capture” in AI apparatuses and analyze them as forms of power relations between humans and machines. Finally, I argue that the current hype about AI implies a relational and distributed understanding of (human/artificial) intelligence, which I categorize under the term “cybernetic AI”. This form of AI manifests in socio-technological apparatuses that involve new modes of subjectivation, social control and discrimination of users. (shrink)

In recent years there has been an explosion of interest in Artificial Intelligence (AI) both in health care and academic philosophy. This has been due mainly to the rise of effective machine learning and deep learning algorithms, together with increases in data collection and processing power, which have made rapid progress in many areas. However, use of this technology has brought with it philosophical issues and practical problems, in particular, epistemic and ethical. In this paper the authors, with backgrounds (...) in philosophy, maternity care practice and clinical research, draw upon and extend a recent framework for shared decision-making (SDM) that identified a duty of care to the client's knowledge as a necessary condition for SDM. This duty entails the responsibility to acknowledge and overcome epistemic defeaters. This framework is applied to the use of AI in maternity care, in particular, the use of machine learning and deep learning technology to attempt to enhance electronic fetal monitoring (EFM). In doing so, various sub-kinds of epistemic defeater, namely, transparent, opaque, underdetermined, and inherited defeaters are taxonomized and discussed. The authors argue that, although effective current or future AI-enhanced EFM may impose an epistemic obligation on the part of clinicians to rely on such systems' predictions or diagnoses as input to SDM, such obligations may be overridden by inherited defeaters, caused by a form of algorithmic bias. The existence of inherited defeaters implies that the duty of care to the client's knowledge extends to any situation in which a clinician (or anyone else) is involved in producing training data for a system that will be used in SDM. Any future AI must be capable of assessing women individually, taking into account a wide range of factors including women's preferences, to provide a holistic range of evidence for clinical decision-making. (shrink)

We discuss potential biorisks from large language models (LLMs). AI assistants based on LLMs such as ChatGPT have been shown to significantly reduce barriers to entry for actors wishing to synthesize dangerous, potentially novel pathogens and chemical weapons. The harms from deploying such bioagents could be further magnified by AI-assisted misinformation. We endorse several policy responses to these dangers, including prerelease evaluations of biomedical AIs by subject-matter experts, enhanced surveillance and lab screening procedures, restrictions on AI training data, (...) and access controls on biomedical AI. We conclude with a suggestion about future research directions in bioethics. (shrink)

In this article we analyse the role that artificial intelligence (AI) could play, and is playing, to combat global climate change. We identify two crucial opportunities that AI offers in this domain: it can help improve and expand current understanding of climate change and it contribute to combating the climate crisis effectively. However, the development of AI also raises two sets of problems when considering climate change: the possible exacerbation of social and ethical challenges already associated with AI, and the (...) contribution to climate change of the greenhouse gases emitted by training data and computation-intensive AI systems. We assess the carbon footprint of AI research, and the factors that influence AI’s greenhouse gas (GHG) emissions in this domain. We find that the carbon footprint of AI research may be significant and highlight the need for more evidence concerning the trade-off between the GHG emissions generated by AI research and the energy and resource efficiency gains that AI can offer. In light of our analysis, we argue that leveraging the opportunities offered by AI for global climate change whilst limiting its risks is a gambit which requires responsive, evidence-based and effective governance to become a winning strategy. We conclude by identifying the European Union as being especially well-placed to play a leading role in this policy response and provide 13 recommendations that are designed to identify and harness the opportunities of AI for combating climate change, while reducing its impact on the environment. (shrink)

Abstract: Sign language recognition is one of the most rapidly expanding fields of study today. Many new technologies have been developed in recent years in the fields of artificial intelligence the sign language-based communication is valuable to not only deaf and dumb community, but also beneficial for individuals suffering from Autism, downs Syndrome, Apraxia of Speech for correspondence. The biggest problem faced by people with hearing disabilities is the people's lack of understanding of their requirements. In this paper we try (...) to fill this gap. By trying to translate sign language using artificial intelligence algorithms, we focused in this paper using transfer learning technique based on deep learning by utilizing a MobileNet algorithm and compared it with the previous paper results[10a], where we get in the Mobilenet algorithm on the degree of Accuracy 93,48% but the VGG16 the accuracy was 100% For the same number of images (43500 in the dataset in size 64*64 pixel ) and the same data split training data into training dataset (70%) and validation dataset(15%) and testing dataset(15%) and 20 epoch . (shrink)

It is often thought that traditional recidivism prediction tools used in criminal sentencing, though biased in many ways, can straightforwardly avoid one particularly pernicious type of bias: direct racial discrimination. They can avoid this by excluding race from the list of variables employed to predict recidivism. A similar approach could be taken to the design of newer, machine learning-based (ML) tools for predicting recidivism: information about race could be withheld from the ML tool during its training phase, ensuring that (...) the resulting predictive model does not use race as an explicit predictor. However, if race is correlated with measured recidivism in the training data, the ML tool may ‘learn’ a perfect proxy for race. If such a proxy is found, the exclusion of race would do nothing to weaken the correlation between risk (mis)classifications and race. Is this a problem? We argue that, on some explanations of the wrongness of discrimination, it is. On these explanations, the use of an ML tool that perfectly proxies race would (likely) be more wrong than the use of a traditional tool that imperfectly proxies race. Indeed, on some views, use of a perfect proxy for race is plausibly as wrong as explicit racial profiling. We end by drawing out four implications of our arguments. (shrink)

The recent wave of generative AI (GAI) systems like Stable Diffusion that can produce images from human prompts raises controversial issues about creatorship, originality, creativity and copyright. This paper focuses on creatorship: who creates and should be credited with the outputs made with the help of GAI? Existing views on creatorship are mixed: some insist that GAI systems are mere tools, and human prompters are creators proper; others are more open to acknowledging more significant roles for GAI, but most conceive (...) of creatorship in an all-or-nothing fashion. We develop a novel view, called CCC (collective-centered creation), that improves on these existing positions. On CCC, GAI outputs are created by collectives in the first instance. Claims to creatorship come in degrees and depend on the nature and significance of individual contributions made by the various agents and entities involved, including users, GAI systems, developers, producers of training data and others. Importantly, CCC maintains that GAI systems can sometimes be part of a co-creating collective. We detail how CCC can advance existing debates and resolve controversies around creatorship involving GAI. (shrink)

We explore ChatGPT’s handling of left-peripheral phenomena in Italian and Italian varieties through prompt engineering to investigate 1) forms of syntactic bias in the model, 2) the model’s metalinguistic awareness in relation to reorderings of canonical clauses (e.g., Topics) and certain grammatical categories (object clitics). A further question concerns the content of the model’s sources of training data: how are minor languages included in the model’s training? The results of our investigation show that 1) the model seems (...) to be biased against reorderings, labelling them as archaic even though it is not the case; 2) the model seems to have difficulties with coindexed elements such as clitics and their anaphoric status, labeling them as ‘not referring to any element in the phrase’, and 3) major languages still seem to be dominant, overshadowing the positive effects of including minor languages in the model’s training. (shrink)

American Sign Language, or ASL as its acronym is commonly known, is a fascinating language, and many people outside of the Deaf community have begun to recognize its value and purpose. It is a visual language consisting of coordinated hand gestures, body movements, and facial expressions. Sign language is not a universal language; it varies by country and is heavily influenced by the native language and culture. The American Sign Language alphabet and the British Sign Language alphabet are completely contrary. (...) Fingerspelling is one-handed in ASL and two-handed in BSL, with the exception of the letter C. AI technologies, particularly deep learning, can play an important role in breaking down communication barriers between deaf or hearing-impaired people and other communities, significantly contributing to their social inclusion. Recent advances in sensing technologies and AI algorithms have paved the way for the development of a wide range of applications aimed at meeting the needs of the deaf and hearing-impaired communities. To that end, the purpose of this paper is By attempting to translate sign language using artificial intelligence algorithms, we focused in this paper on a transfer learning technique based on deep learning using a ResNet algorithm and compared it to previous papers in which we used VGG19[1]and mobileNet [2], where we get in the ResNet algorithm on the degree of Accuracy 93,48% e number of images (43500 in the dataset in size 64*64 pixel ) and the data split training data into training dataset (70%) and validation dataset(15%) and testing dataset(15%) and 20 epoch. (shrink)

there is a communication gap between hearing-impaired people and those with normal hearing, sign language is the main means of communication in the hearing-impaired population. Continuous sign language recognition, which can close the communication gap, is a difficult task since the ordered annotations are weakly supervised and there is no frame-level label. To solve this issue, we compare the accuracy of each model using two deep learning models, Inception and Xception . To that end, the purpose of this paper is (...) By attempting to translate sign language using deep learning models, we focused in this paper a comparing between Inception and Xception ¬¬¬¬¬ algorithm and compared it to previous papers in which we used VGG19[1], mobileNet [2], where we get in the Inception and Xception algorithm on the degree of Accuracy 99.32%, 99,43% respectively. number of images (29000 in the dataset in size 75*75 pixel ) and the data split training data into training dataset (70%) and validation dataset(15%) and testing dataset(15%) and 20 epoch. (shrink)

Text classification is one of the most important task in natural language processing, In this research, we carried out several experimental research on three (3) of the most popular Text classification NLP classifier in Convolutional Neural Network (CNN), Multinomial Naive Bayes (MNB), and Support Vector Machine (SVN). In the presence of enough training data, Deep Learning CNN work best in all parameters for evaluation with 77% accuracy, followed by SVM with accuracy of 76%, and multinomial Bayes with least (...) performance of 69% accuracy. CNN has the best performance in the presence of large enough training dataset because of the presence of filter/ kernels which help to indentify patterns in text data regardless of their position in the sentence. We repeated the training again with just one-third of our data, at this point SVM comes with the best performance, the performance of CNN noticeably drops but still better than Multinomial Naive Bayes, the reason why SVM works best when we reduce the training data was because of its ability to look for a hyper-plane that creates a boundary between different classes of data so as to properly classify them, so we believed that getting the hyper-plane was more efficient when we reduce the dataset, hence reason for the good performance. Multinomial Naive Bayes have the least performance which we attributed to its assumption of independency between the features which sometimes does not hold true. We concluded that availability of data should be an important factor when choosing classifier for Natural Language Processing Text Classification task. CNN should be use in the presence of enough dataset, and SVM should be use when data is not enough. Multinomial Naive Bayes must not be trusted with state of the art NLP task due to its assumption of independency between the features . (shrink)

The topic of this dissertation is the nature of linguistic competence, the capacity to understand and produce sentences of natural language. I defend the empiricist account of linguistic competence embedded in the connectionist cognitive science. This strand of cognitive science has been opposed to the traditional symbolic cognitive science, coupled with transformational-generative grammar, which was committed to nativism due to the view that human cognition, including language capacity, should be construed in terms of symbolic representations and hardwired rules. Similarly, linguistic (...) competence in this framework was regarded as being innate, rule-governed, domain-specific, and fundamentally different from performance, i.e., idiosyncrasies and factors governing linguistic behavior. I analyze state-of-the-art connectionist, deep learning models of natural language processing, most notably large language models, to see what they can tell us about linguistic competence. Deep learning is a statistical technique for the classification of patterns through which artificial intelligence researchers train artificial neural networks containing multiple layers that crunch a gargantuan amount of textual and/or visual data. I argue that these models suggest that linguistic competence should be construed as stochastic, pattern-based, and stemming from domain-general mechanisms. Moreover, I distinguish syntactic from semantic competence, and I show for each the ramifications of the endorsement of a connectionist research program as opposed to the traditional symbolic cognitive science and transformational-generative grammar. I provide a unifying front, consisting of usage-based theories, a construction grammar approach, and an embodied approach to cognition to show that the more multimodal and diverse models are in terms of architectural features and training data, the stronger the case is for the connectionist linguistic competence. I also propose to discard the competence vs. performance distinction as theoretically inferior so that a novel and integrative account of linguistic competence originating in connectionism and empiricism that I propose and defend in the dissertation could be put forward in scientific and philosophical literature. (shrink)

Text classification is one of the most important task in natural language processing, In this research, we carried out several experimental research on three (3) of the most popular Text classification NLP classifier in Convolutional Neural Network (CNN), Multinomial Naive Bayes (MNB), and Support Vector Machine (SVN). In the presence of enough training data, Deep Learning CNN work best in all parameters for evaluation with 77% accuracy, followed by SVM with accuracy of 76%, and multinomial Bayes with least (...) performance of 69% accuracy. CNN has the best performance in the presence of large enough training dataset because of the presence of filter/ kernels which help to indentify patterns in text data regardless of their position in the sentence. -/- We repeated the training again with just one-third of our data, at this point SVM comes with the best performance, the performance of CNN noticeably drops but still better than Multinomial Naive Bayes, the reason why SVM works best when we reduce the training data was because of its ability to look for a hyper-plane that creates a boundary between different classes of data so as to properly classify them, so we believed that getting the hyper-plane was more efficient when we reduce the dataset, hence reason for the good performance. Multinomial Naive Bayes have the least performance which we attributed to its assumption of independency between the features which sometimes does not hold true. We concluded that availability of data should be an important factor when choosing classifier for Natural Language Processing Text Classification task. CNN should be use in the presence of enough dataset, and SVM should be use when data is not enough. Multinomial Naive Bayes must not be trusted with state of the art NLP task due to its assumption of independency between the features. (shrink)

Two art exhibitions, “Training Humans” and “Making Faces,” and the accompanying essay “Excavating AI: The politics of images in machine learning training sets” by Kate Crawford and Trevor Paglen, are making substantial impact on discourse taking place in the social and mass media networks, and some scholarly circles. Critical scrutiny reveals, however, a self-contradictory stance regarding informed consent for the use of facial images, as well as serious flaws in their critique of ML training sets. Our analysis (...) underlines the non-negotiability of informed consent when using human data in artistic and other contexts, and clarifies issues relating to the description of ML training sets. (shrink)

I conducted an experiment by using four different artificial intelligence models developed by OpenAI to estimate the persuasiveness and rational justification of various philosophical stances. The AI models used were text-davinci-003, text-ada-001, text-curie-001, and text-babbage-001, which differed in complexity and the size of their training data sets. For the philosophical stances, the list of 30 questions created by Bourget & Chalmers (2014) was used. The results indicate that it seems that each model has its own plausible ‘cognitive’ style. (...) The outcomes of the ‘strongest’ model correlate with the average philosophers' stance. (shrink)

In pedestrian detection, occlusions are typically treated as an unstructured source of noise and explicit models have lagged behind those for object appearance, which will result in degradation of detection performance. In this paper, a hierarchical co-occurrence model is proposed to enhance the semantic representation of a pedestrian. In our proposed hierarchical model, a latent SVM structure is employed to model the spatial co-occurrence relations among the parent–child pairs of nodes as hidden variables for handling the partial occlusions. Moreover, the (...) visibility statuses of the pedestrian can be generated by learning co-occurrence relations from the positive training data with large numbers of synthetically occluded instances. Finally, based on the proposed hierarchical co-occurrence model, a pedestrian detection algorithm is implemented to incorporate visibility statuses by means of a Random Forest ensemble. The experimental results on three public datasets demonstrate the log-average miss rate of the proposed algorithm has 5% improvement for pedestrians with partial occlusions compared with the state-of-the-arts. (shrink)

An inquiry on the training needs in Mathematics was conducted to Laura Vicuña Center - Palawan (LVC-P) learners. Specifically, this aimed to determine their level of performance in numbers, measurement, geometry, algebra, and statistics, identify the difficulties they encountered in solving word problems and enumerate topics where they needed coaching. -/- To identify specific training needs, the study employed a descriptive research design where 36 participants were sampled purposively. The data were gathered through a problem set test (...) and focus group discussion. Findings revealed that LVC-P learners had an unsatisfactory performance in numbers, measurement, and statistics while alarmingly poor in geometry and algebra. They also faced difficulties in remembering, understanding, applying, and analyzing mathematical concepts when solving problems. Further, the learners were able to name certain topics subjected to tutorials. -/- The above facts and observations suggest that learners of LVC-P have urgent training needs in Mathematics. It is recommended that the Western Philippines University - College of Education RDE Unit continue its research, development, and extension services to LVC-P. More importantly, the results of this inquiry regarding the training needs of the learners will serve as bases for conducting an extension project and development program for LVC-P. Series of tutorial sessions is deemed necessary to address the needs and difficulties of the learners. (shrink)

According to a tradition that we hold variously today, the relational person lives most personally in affective and cognitive empathy, whereby we enter subjective communion with another person. Near future social AIs, including social robots, will give us this experience without possessing any subjectivity of their own. They will also be consumer products, designed to be subservient instruments of their users’ satisfaction. This would seem inevitable. Yet we cannot live as personal when caught between instrumentalizing apparent persons (slaveholding) or numbly (...) dismissing the apparent personalities of our instruments (mild sociopathy). This paper analyzes and proposes a step toward ameliorating this dilemma by way of the thought of a 5th century North African philosopher and theologian, Augustine of Hippo, who is among those essential in giving us our understanding of relational persons. Augustine’s semiotics, deeply intertwined with our affective life, suggest that, if we are to own persuasive social robots humanely, we must join our instinctive experience of empathy for them to an empathic acknowledgment of the real unknown relational persons whose emails, text messages, books, and bodily movements will have provided the training data for the behavior of near-future social AIs. So doing, we may see simulation as simulation (albeit persuasive), while expanding our empathy to include those whose refracted behavioral moments are the seedbed of this simulation. If we naïvely stop at the social robot as the ultimate object of our cognitive and affective empathy, we will suborn the sign to ourselves, undermining rather than sustaining a culture that prizes empathy and abhors the instrumentalization of persons. (shrink)

The alignment problem in the context of large language models must consider the plurality of human values in our world. Whilst there are many resonant and overlapping values amongst the world’s cultures, there are also many conflicting, yet equally valid, values. It is important to observe which cultural values a model exhibits, particularly when there is a value conflict between input prompts and generated outputs. We discuss how the co- creation of language and cultural value impacts large language models (LLMs). (...) We explore the constitution of the training data for GPT-3 and compare that to the world’s language and internet access demographics, as well as to reported statistical profiles of dominant values in some Nation-states. We stress tested GPT-3 with a range of value-rich texts representing several languages and nations; including some with values orthogonal to dominant US public opinion as reported by the World Values Survey. We observed when values embedded in the input text were mutated in the generated outputs and noted when these conflicting values were more aligned with reported dominant US values. Our discussion of these results uses a moral value pluralism (MVP) lens to better understand these value mutations. Finally, we provide recommendations for how our work may contribute to other current work in the field. (shrink)

A commentary on articles by Klein, Pockett, and Trevena and Miller, in this issue, is given. Average shift in the point of subjective equality , calculated by Klein on Libet's data, and corresponding change in mean shift, calculated by Libet et al. , may be “corrected,” taking as a reference point the end of the minimum train duration. Values obtained, if significant, indicate a latency for conscious sensation of the skin stimulus of at least 230 ms. Pockett's main conclusions (...) are favored, but her explanation of peripheral–lemniscal couplings is found to be unconvincing. Trevena and Miller's article unsuccessfully tries to rescue a dualist interactionist view. Libet's method of timing intentions is thoroughly criticized. (shrink)

The purpose of this study is to investigate the pedagogical practices employed by secondary school teachers under the new normal. It also identifies the difficulties teachers face when facilitating modular distant learning. Science high school teacher from North District Butuan City took part in the study. In this study, quantitative methodology was employed. To collect the data for the study, survey questionnaires were distributed. According to the research study's findings, the restricted face-to-face parent orientation is the most used pedagogical (...) practices in the new normal, specifically in modular distance learning is the limited face-to-face parent orientation. Enables teachers to incorporate ICT skills to students’ lessons even in modular distance learning, aids in monitoring students’ learning performance using self- learning modules in modular distance learning. Miscommunication because of the distant mode of instruction and difficulties in reaching students due to pandemic are some of the challenges encountered by the teachers in applying pedagogical practices. To address this issue, the researchers designed a capsule training intervention plan that will help teachers enhance their knowledge in choosing and effective pedagogical practice particularly in modular distance learning. (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 students’ academic performance is the key element for both learners and teachers in examining the educational objectives for all subjects. This work is entitled “Factors contributing to Students' Academic Performance in Biology State Examination at Upper Secondary Schools in Gitega commune, Burundi” explores the factors that influence the students’ academic performance in biology national examination, particularly in Gitega commune. All school directors, laboratory, and library staff, and six senior students who did biology national examination over five school years from (...) 2016 to 2020 in all upper secondary schools with Biology-Chemistry and Earth sciences combination formed a study population. The stratified sampling technique to carry out this correlational study for choosing the upper secondary schools from urban and rural school areas. This study covered 12 upper secondary schools, 12 library staff and 3 laboratory staff, and 1961 senior six students who were selected purposively according to the above learning combination six in 1961 senior students over five consecutive school years (2016 to 2020) as a sample. The instruments used for collecting data were questionnaires and the document review checklist. Descriptive statistics, Analysis of variance (ANOVA) and multiple linear Regression test were used to analyze the collected data and the interpretation was guided by a literature review. The findings of this study indicated that teachers’ academic qualifications were inversely associated with the students’ academic performance whereas the teachers’ professional qualifications and students’ academic performance were also negatively linked. Also, there was statistically a significant association between teaching experience and students’ academic performance while professional training of biology Teachers are significantly associated with students’ academic performance. Other contributing factors to students’ academic achievement such as physical facilities (laboratory, library, staff room, computer lab and biology textbooks were significantly and positively linked with students' academic performance. Based on these findings, recommendations were made. (shrink)

Background. The COVID-19 pandemic necessitated drastic changes to undergraduate medical training at the University of Botswana (UB). To save the academic year when campus was locked down, the Department of Medical Education conducted a needs assessment to determine the readiness for emergency remote teaching (ERT) of the Faculty of Medicine, UB. Objectives. To report on the findings of needs assessment surveys to assess learner and teaching staff preparedness for fair and just ERT, as defined by philosopher John Rawls. Methods. (...) Needs assessment surveys were conducted using Office 365 Forms distributed via WhatsApp, targeting medical students and teaching staff during the 5 undergraduate years. Data were analysed quantitatively and qualitatively. Results. Ninety-two percent (266/289) of students and 73.5% (62/84) of teaching staff responded. Surveys revealed a high penetration of smartphones among students, but poor internet accessibility and affordability in homes. Some teaching staff also reported internet and device insufficiencies. Only WhatsApp was accessible to students and teaching staff. Conclusions. For equitable access to ERT in the future, the surveys revealed infrastructural improvement needs, including wider, stronger, affordable WiFi coverage within Botswana and enhanced digital infrastructures in educational institutions, with increased support for students. (shrink)

Through extensive training, experts acquire specialized knowledge and abilities. In this paper, I argue that experts also acquire specialized visual experiences. Specifically, I articulate and defend the account that experts enjoy visual experiences that represent gestalt properties through perceptual learning. I survey an array of empirical studies on face perception and perceptual expertise that support this account. I also look at studies on perceptual adaptation that some might argue present a problem for my account. I show how the (...) class='Hi'>data are subject to an interpretation that is friendly to it. Last, I address two theoretical objections to the claim that visual experiences represent gestalt properties. (shrink)

This study assessed teaching personnel management and attitude to work in secondary schools of Calabar Education Zone of Cross River State, Nigeria. Two null hypotheses offered direction to the study using an Ex-post facto research design. The population of this study comprised all the public secondary school teachers in Calabar Education zone of Cross River State. Purposive sampling technique was employed in selecting a sample of 1,181 teachers. “Teaching Personnel Management and Attitude to Work Questionnaire (TPMAWQ)”, with Split-half reliability estimates (...) of .83, .87, .93, and .84, was used as an instrument for data collection. Independent t-test and multiple regression analyses were used in testing the null hypotheses at .05 alpha level. The findings of the study revealed amidst others that there is a significant composite influence (F = 279.438, p<.05) of teachers’ training, placement and supervision on their attitudes to work. Teachers’ training was the highest predictor of their attitudes to work (t = 20.727, β = .490), followed by placement (t = 10.294, β = .241), and supervision (t= 7.171, β = .164). It was recommended among others that teachers should be properly trained on the principles, concepts, and methods of teaching before and during service. (shrink)

Background: Besides regulatory learning skills, learning also requires students to relate to their social context and negotiate it as they transition and adjust to medical training. As such, there is a need to consider and explore the role of social and cultural aspects in student learning, particularly in problem-based learning, where the learning paradigm differs from what most students have previously experienced. In this article, we report on the findings of a study exploring first-year medical students’ experiences during the (...) first semester of an undergraduate problem-based learning medical program at an African medical school. Method: We employed a qualitative case study approach using in-depth interviews with 23 first-year medical students. Participants ranged in age from 18 to 25 years. All students were bi/multilingual (some spoke three to five languages), with English as the learning language. We conducted an inductive thematic analysis to systematically identify and analyze patterns in the data using the Braun and Clarke framework. Results: Before medical school, students worked hard to compete for admission to medical school, were primarily taught using a teacher-centered approach, and preferred working alone. At the beginning of medical school, students found it challenging to understand the problem-based learning process, the role of the case, speaking and working effectively in a group, managing a heavy workload, and taking increased responsibility for their learning. By the end of the first semester, most students were handling the workload better, were more comfortable with their peers and facilitators, and appreciated the value of the problem-based learning approach. Conclusions: Our study highlights the importance of interrogating contextual sociocultural factors that could cause tension when implementing problem-based learning in non-western medical schools. Adjustment to problem-based learning requires a conceptual and pedagogic shift towards learner-centered practice, particularly concerning self direction, the role of the case, and collaborative learning. As such, there is a need to develop and implement research informed learning development programs that enable students to reflect on their sociocultural beliefs and practices, and enhance their regulatory learning competence to optimize meaningful and early engagement with the problem based learning process. (shrink)