Reviewerly/exHarmony huggingface dataset:
Reducibility of quartic polynomials and its relation to Galois groups

This paper deals with special classes of quartic polynomials and properties pertaining to their Galois groups and reducibility over certain fields. The existence of quartic polynomials irreducible over Q but reducible over every prime field is first proven, after which criteria are established for the Galois group of polynomials with this property. By constructing classes of V4-generic polynomials and comparing them with criteria put forth in previous studies for determining polynomials with this property, it can be shown that a polynomial of the biquadratic form x4 + ax2 + b has this property if and only if it can be written as x^4 - 2(u + v)x^2 + (u -v)^2 with u, v Q such that none of u, v, or uv can be expressed as ratio of two squares, and 2(u+v),(uv)2 Z . The general form for biquadratic polynomials irreducible over Q and reducible modulo every integer n is found to have a general form similar to this one.
AI-based End-to-End and AI-Enhanced Video Coding

A standards body for AI-based data coding Moving Picture, Audio, and Data Coding by Artificial Intelligence is the Standards Developing Organisation whose mission is Data Coding by Artificial Intelligence. Founded in September 2020, it has developed and published 12 technical documents on audio enhancement, human-machine conversation, execution of AI applications, company performance prediction, neural network watermarking, and metaverse model. The talk on [MPAI](https://mpai.community) will briefly describe the mission, the organisation, and the scope of activities. AI-Enhanced video coding Video internet traffic has grown rapidly, leading to increased strain on infrastructure, slower speeds, and higher costs. Video compression can alleviate this problem; deep learning-based coding tools have shown great potential to reduce bitrate and improve quality. MPAI is working to standardize these tools. In particular, MPAI-EVC is working to improve an existing video codec by 25% in terms of encoding gain by exploiting deep learning. MPAI - end-to-end Video (EEV) Project and Activities Recently, neural network for video coding has been an active research topic, receiving attention from both academic society and industry entity. The end-to-end video coding (EEV) of MPAI exploits AI-based data coding technologies in an end-to-end fashion, namely fully neural network based video codec. MPAI-EEV is at the level of Functional Requirements. The essential use cases and related reference model are under construction. This talk elaborates recent advances in EEV and reports the model design, which outperforms the latest H.266/VVC standard in terms of MS-SSIM.
AllyChat: Developing a VR Conversational AI Agent Using Few-Shot Learning to Support Individuals with Intellectual Disabilities

Virtual Reality (VR) environments have been used for training, education, and entertainment due to the interactive and embodied experiences the technology provides. Studies have shown that VR can be used to help support individuals with intellectual disabilities in various aspects of their lives. Likewise, conversational agents such as chatbots can be used to bolster competence training and well-being management for this user population. This paper addresses the need for inclusive job interview practice for individuals with intellectual disabilities by discussing the development of a VR application, AllyChat. A two-part development phase is presented with pilot testing included for each phase. First, a conversational AI chatbot is tested and with positive feedback iterated upon to develop an immersive mock job interview experience in VR. A second pilot study is conducted with university students to test the functionality of a high-fidelity prototype. Future work will include improvement upon the developed VR application and further testing.
Development of a Framework for Road Test Score Prediction and Significant Keywords Extraction based on Vehicle Specification Data

Specification considered in designing vehicles plays an important role in determining the driving satisfaction along with the performance of a new vehicle. Therefore, the layout and customer evaluations of existing vehicles should be analyzed in the initial architecture design phase and reflected in the package concept validation. In this study, we propose a framework to predict satisfaction and extract keywords for new vehicles using specifications and driving evaluation data of existing vehicles. The proposed framework consists of three modules: predicting item-level impact on evaluation scores of Consumer Report, predicting satisfaction for input specifications based on similar vehicles
Trends and challenges of Arabic Chatbots: Literature review

A conversational system is a natural language processing task that has recently attracted increasing attention with the advancements in Large Language Models (LLMs) and Language Models for Dialogue Applications (LaMDA). However, Conversational Artificial Intelligence (AI) research has mainly been carried out in English. Despite the growing popularity of Arabic as one of the most widely used languages on the Internet, only a few studies have concentrated on Arabic conversational dialogue systems thus far. In this study, we conduct a comprehensive qualitative analysis of the key research works in this domain, examining the limitations and strengths of existing approaches. We start with chatbot history and classification. Then, we examine approaches that leverage Arabic chatbots Rule-based/Retrieval-based and Deep learning-based. In particular, we survey the evolution of Generative Conversational AI with the evolution of deep-learning techniques. Next, we look at the different metrics used to assess conversational systems. Finally, we outline language Challenges for building Generative Arabic Conversational AI.
DA-DAN: A Dual Adversarial Domain Adaption Network for Unsupervised Non-overlapping Cross-domain Recommendation

Unsupervised Non-overlapping Cross-domain Recommendation (UNCR) is the task that recommends source domain items to the target domain users, which is more challenging as the users are non-overlapped, and its learning process is unsupervised. Unsupervised Non-overlapping Cross-domain Recommendation UNCR is still unsolved due to the following: (1) Previous studies need extra auxiliary information to learn transferable features when aligning two domains, which is unrealistic and hard to obtain due to privacy concerns. (2) Since the adoption of the shared network, existing works cannot well eliminate the domain-specific features in the common feature space, which may incorporate domain noise and harm the cross-domain recommendation. In this work, we propose a domain adaption-based method, namely DA-DAN, to address the above challenges. Specifically, to let DA-DAN be free of auxiliary information, we learn users’ preferences by only exploring their sequential patterns, and propose an improved self-attention layer to model them. To well eliminate the domain-specific features from the common feature space, we resort to a dual generative adversarial network with a multi-target adversarial loss, where two generators and discriminators are leveraged to model each domain separately. Experimental results on three real-world datasets demonstrate the advantage of DA-DAN compared with the state-of-the-art recommendation baselines. Moreover, our source codes have been publicly released. 1
Digital printing image generation method based on style transfer

Digital printing has been widely used in textile printing production. In the process of designing digital printing patterns, an image generative model is needed to assist in obtaining more diversified patterns. However, the current model involves large storage space and high computing cost, which affects the promotion of digital printing customized production. To solve the problem, this article proposes a digital printing image generation method based on style transfer. Firstly, a style transfer method based on exact feature distribution matching is constructed to realize the accurate matching from image content to style features. And a balanced loss function is used to enhance the universality of the proposed method. Furthermore, knowledge distillation is introduced to compress the method proposed to reduce the hardware requirements when processing high-resolution digital printing images. Finally, a segmented training strategy is proposed to solve the performance degradation caused by model compression. The experimental results show that when processing images with a resolution of 3000 × 3000, the storage capacity of the model is only 2.68 MB and only 0.20 TFLOPs is required. The maximum processing resolution is more than 8K. The pattern obtained by this model is of high quality and can meet the needs of digital printing production.
The M-P of cyber security

Network SecurityVol. 2023, No. 8 The FirewallThe M-P of cyber securityKaren RenaudKaren RenaudUniversity of StrathclydeSearch for more papers by this authorKaren RenaudPublished Online:26 Aug 2023https://doi.org/10.12968/S1353-4858(23)70035-4AboutSectionsView articleView Full TextView EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article References 1. Human Factors in Diving Conference, home page. Accessed Aug 2023. www.hf-in-diving-conference.com. Google Scholar2. Lock, Gareth. ‘You need more than mistakes to learn’. The Human Diver, 2 Jul 2023. Accessed Aug 2023. www.thehumandiver.com/blog/you-need-more-than-mistakes-to-learn. Google Scholar3. Geiger, Eric. ‘Navy SEALs and 4 reasons trust is greater than performance’. Eric Geiger, 11 Feb 2020. https://ericgeiger.com/2020/02/navy-seals-and-4-reasons-trust-is-greater-than-performance/. Google Scholar FiguresReferencesRelatedDetails 1 August 2023Volume 2023Issue 8ISSN (print): 1353-4858ISSN (online): 1872-9371 Metrics History Published online 26 August 2023 Published in print 1 August 2023 Information© MA Business Ltd.
MIND THE GAP: THE OUTCOME MAP AS A BRIDGE FROM SYSTEMIC SENSEMAKING TO PSS DESIGN IN A CASE STUDY ABOUT CHILDREN WITH INCARCERATED PARENTS

According to an estimate by Children of Prisoners Europe, there are 2.1 million children in Europe with a parent in detention. Despite this alarming number, children of prisoners remain invisible to the general public. Children of Prisoners Europe describe a child with an incarcerated parent as a double victim. Beyond the loss of a parent, these children face stigmatization, trauma and stress. The effects of parental detention impact the emotional well-being, physical well-being and personal development of the child. Children with detained parents are innocent, but carry the sentence too, they are three times more likely of mental health problems, and five times more likely to end up in prison than other children. The impact of these children on society is underestimated. Scientific research shows that a good relationship between the child and the incarcerated parent results in less recidivism. So by supporting and guiding children with imprisoned parents, the number of prisoners will decrease in the long run, the mental health will increase, and the amount of recidivism will decrease because both children and parents are taken into account. However, designing for children in precarious social situations requires a specific approach. Unheard and unseen in a world made by adults for adults, they are reliant on their environment. The whole system of actors and actants around them should be included and addressed. On the one hand, this paper reports on a design research that applies a systemic design approach to tackle social issues, providing insight in the complexity of the context. On the other hand, after mapping the context, the major challenge lies in translating the output towards a design solution, in order to shape a product-service system that empowers children with imprisoned parents. Thus, obtaining autonomy for children with incarcerated parents based on the levers of trauma, attachment and resilience. The paper focuses on the transition from systemic sensemaking to the design of a product-service system by employing an outcome map, allowing the designer to advance from the analysis to the possibility space. The process applied in this research is about supporting children with incarcerated parents as a case study. Qualitative research was applied to understand the context of children with imprisoned parents in the analysis phase. The data collection methods supporting this research are literature research, observations, in-depth interviews with psychologists and prison staff, cultural probes, brainstorming sessions, focus groups and user tests. This paper contributes to the design field by evaluating outcome mapping as a possible bridge between the analyses and the idea generation.
INVESTIGATING THE PERIMETER OF INNOVATION IN DESIGN EDUCATION THROUGH MAPPING OF EXPERIMENTAL PRACTICES

The term "innovative didactic" often describes the notion of a cultural and epistemological shift that can transform teaching and learning practices.Notably, the instructional design innovation implies a shift from traditional ex-cathedra teachings towards more active student engagement in the learning process.Recently, the pandemic and digital transformation have been among the main factors that have propelled the conversation about this topic.Design has always explored innovation in its didactics.By its very nature, design-based learning provides a teaching environment that places students at the heart of the learning process.There may be examples of these creative teaching approaches in the design literature.Still, no significant research exists on how design schools manage and monitor the teaching innovation process.Hence, this paper describes a funded research project "Innovation in Design Teaching", which aims to investigate the boundaries of innovation in design education in a situated context such as the School of Design in Politecnico di Milano.The study comprehends two research activities: the first is a survey delivered to every lecturer at the school in an effort to map the experimental approaches in the courses during the past decade, while the second one comprehends two sets of interviews.This paper summarizes the findings of the second research activity.The research contributes to understanding how experimenting happens in design schools and how this might contribute to didactic innovation.Hence, it provides a series of protocols that might be used in other contexts to expand the scope of the research.
Online classroom – Bibliometric analysis (2004-2021)

Advances in information and communication technology (ICT) have profoundly transformed organizational processes, educational institutions have not been left out of these changes either. Moreover in the beginning of 2020 the Covid-19 pandemic came to extend the use of ICT tools in education through the educational design of emergency remote teaching (ERE). It was sought to understand the evolution of teaching in academic production, for this a bibliometric study was carried out between 2004 and 2021 on the subject "online teaching" comparing the Scopus base and Web of Science (WoS) that according to Marín-Velásquez and Arrojas-Tocuyo (2021) are worldwide recognized and accepted bases of academic search. In this way, the evolution of publications, the areas, the authors, the most cited documents, as well as the key words that appear the most, are highlighted in this work. We noticed as main findings the dominance of the number of Scopus publications and while Scopus is dominated by the area of computing and WoS is led by the area of education.
Relation with Free Objects for Action Recognition

Relevant objects are widely used for aiding human action recognition in still images. Such objects are founded by a dedicated and pre-trained object detector in all previous methods. Such methods have two drawbacks. First, training an object detector requires intensive data annotation. This is costly and sometimes unaffordable in practice. Second, the relation between objects and humans are not fully taken into account in training. This work proposes a systematic approach to address the two problems. We propose two novel network modules. The first is an object extraction module that automatically finds relevant objects for action recognition, without requiring annotations. Thus, it is free . The second is a human-object relation module that models the pairwise relation between humans and objects, and enhances their features. Both modules are trained in the action recognition network, end-to-end. Comprehensive experiments and ablation studies on three datasets for action recognition in still images demonstrate the effectiveness of the proposed approach. Our method yields state-of-the-art results. Specifically, on the HICO dataset, it achieves 44.9% mAP, which is 12% relative improvement over the previous best result. In addition, this work makes an observational contribution that it is no longer necessary to rely on a pre-trained object detector for this task. Relevant objects can be found via end-to-end learning with only action labels. This is encouraging for action recognition in the wild. Models and code will be released.
How Gaze Visualization Facilitates Initiation of Informal Communication in 3D Virtual Spaces

This study explores how gaze visualization in virtual spaces facilitates the initiation of informal communication. Three styles of gaze cue visualization (arrow, bubbles, and miniature avatar) with two types of gaze behavior (one-sided gaze and joint gaze) were evaluated. 96 participants used either a non-visualized gaze cue or one of the three visualized gaze cues. The results showed that all visualized gaze cues facilitated the initiation of informal communication more effectively than the non-visualized gaze cue. For one-sided gaze, overall, bubbles had more positive effects on the gaze receiver's behaviors and experiences than the other two visualized gaze cues, although the only statistically significant difference was in the verbal reaction rates. For joint gaze, all three visualized gaze cues had positive effects on the receiver's behaviors and experiences. The design implications of the gaze visualization and the confederate-based evaluation method contribute to research on informal communication and social virtual reality.
Learning in Cooperative Multiagent Systems Using Cognitive and Machine Models

Developing effective multi-agent systems (MASs) is critical for many applications requiring collaboration and coordination with humans. Despite the rapid advance of multi-agent deep reinforcement learning (MADRL) in cooperative MASs, one of the major challenges that remain is the simultaneous learning and interaction of independent agents in dynamic environments in the presence of stochastic rewards. State-of-the-art MADRL models struggle to perform well in Coordinated Multi-agent Object Transportation Problems (CMOTPs) wherein agents must coordinate with each other and learn from stochastic rewards. In contrast, humans often learn rapidly to adapt to non-stationary environments that require coordination among people. In this article, motivated by the demonstrated ability of cognitive models based on Instance-based Learning Theory (IBLT) to capture human decisions in many dynamic decision-making tasks, we propose three variants of multi-agent IBL models (MAIBLs). The idea of these MAIBL algorithms is to combine the cognitive mechanisms of IBLT and the techniques of MADRL models to deal with coordination MASs in stochastic environments from the perspective of independent learners. We demonstrate that the MAIBL models exhibit faster learning and achieve better coordination in a dynamic CMOTP task with various settings of stochastic rewards compared to current MADRL models. We discuss the benefits of integrating cognitive insights into MADRL models.
EDUCATIONAL CONCEPT “LOGIC AND CRITICAL THINKING”: METHODOLOGICAL SPECIFICS OF THE ANALYSIS OF SYSTEMS WITH REFLECTION

The study of the functioning of critical thinking involves a methodological understanding of the refl exive nature of activity. Learning as a system with refl ection directs to the implementation of refl ection analysis as an integral component of the development of the educational concept «Logic and critical thinking». The subject of the study is the phenomenon of a system with refl ection, directed to the description of the present behavior and the use of the received description to guide its activities. The specifi c nature of refl ecting systems is revealed based on the model of studying systems of this kind in the theory of social relays by M. A. Rozov. The refl exive nature of the description of activity is justifi ed from an empirical and theoretical position. The possibilities of a refl exive and over-refl exive approach to explication of the mechanisms of behavior of a system with refl ection are considered. It is concluded that the implementation of the principle of over-refl exive description makes it possible to establish the actual content of the refl exive system, taking into account the system’s awareness of its own refl exive construction.
TRADITIONAL VALUES AS THE BASIS OF EDUCATION IN A MODERN RAPIDLY CHANGING SOCIETY

The article discusses ways to implement Decree No. 809 “On the approval of the Fundamentals of State Policy for the Preservation and Strengthening of Traditional Russian Spiritual and Moral Values”, determined by the essence of values as such. The article substantiates that the preservation and strengthening of traditional values is possible only through the organization of targeted education. At the same time, the problem of identifying the essence of traditional values and selecting those that could become the basis for determining the goals and content of education in modern Russian society is raised and in a certain way solved.
Prediction nullity of graph using data mining

Nullity computation is widely used to determine the stability of a chemical molecule.Mainly, a molecule is presented as a graph, and the graph nullity value clarifies the strength of the molecule.Some formulas for specific graphs help us compute the nullity value, but it is challenging to remember the formula of each particular graph.However, another formula for calculating the nullity value is based on the graph rank.Nevertheless, processing time would be increased by the growth of the number of vertices of graphs.This paper suggests machine learning methods for computing the nullity value of a given graph.We leveraged random graph generation methods to collect many graph instances.Then, the experimental results on the collected dataset offer accuracy of 97.0878% for binary classification and 94.56% for value prediction.
Machine Learning-Based Phishing Detection in Heterogeneous Information Network

Phishing is a kind of social engineering technique that lures a person into sharing sensitive details such as social security numbers, pass codes and pins. There are a variety of phishing detection techniques in the emerging world of technology. List-based techniques or the site content-based techniques could not cope up with the uninterrupted development of phishing methodology. Hence, in this paper, an enhanced technique is proposed which constructs a heterogeneous information network (HIN) using the hyperlinks from a webpage and extracts features from the HIN which in turn is used by a classifier to classify the webpage as phishing or legal. Experiments are carried out using a dataset published by Phishpedia to validate the proposed method. The performance of the proposed method was compared among different classification algorithms in terms of different quality metrics, namely recall, F1-score, precision and accuracy.