Reviewerly/exHarmony huggingface dataset:
Unsupervised Discovery of Disentangled Interpretable Directions for Layer-Wise GAN

Many studies have shown that generative adversarial networks (GANs) can discover semantics at various levels of abstraction, yet GANs do not provide an intuitive way to show how they understand and control semantics. In order to identify interpretable directions in GAN’s latent space, both supervised and unsupervised approaches have been proposed. But the supervised methods can only find the directions consistent with the supervised conditions. However, many current unsupervised methods are hampered by varying degrees of semantic property disentanglement. This paper proposes an unsupervised method with a layer-wise design. The model embeds subspace in each generator layer to capture the disentangled interpretable semantics in GAN. And the research also introduces a latent mapping network to map the inputs to an intermediate latent space with rich disentangled semantics. Additionally, the paper applies an Orthogonal Jacobian regularization to the model to impose constraints on the overall input, further enhancing disentanglement. Experiments demonstrate the method’s applicability in the human face, anime face, and scene datasets and its efficacy in finding interpretable directions. Compared with existing unsupervised methods in both qualitative and quantitative aspects, this study proposed method achieves excellent improvement in the disentanglement effect.
Data Visualization

Visualization via graphics like charts, graphs, and images is an effective and efficient way to interpret and understand data and help spot valuable information such as patterns, trends, and anomalies [1]. The reason is that, unlike tables and written text, graphs are primarily visual in nature, and approximately 70% of our sense receptors are dedicated to vision [2]. Moreover, our eyes are drawn to patterns and colors, can easily differentiate red from blue and a circle from a square, and can quickly see trends and outliers [3].
Research on the cultivation of innovative thinking and innovation ability based on IEC interdisciplinary talents

Contemporary college students are the first source of social progress and development.They must adapt to the development of economic times and have strong innovative thinking and innovation ability.Based on the current situation of college students' innovation and entrepreneurship in China, this paper puts forward the methods to improve the innovative thinking ability and enhance the innovative quality of college students in China, strengthens the practical teaching link, integrates the entrepreneurial practice education activities into the whole teaching process of IEC talent training in colleges and universities, establishes the cultivation goal and scale, adopts the diversified talent training mode, constructs the complete education theory system suitable for the professional quality training of IEC compound talents, and creates an atmosphere more suitable for cultivating the innovative thinking ability and innovation ability of IEC talents.
Internet Gambling through Text Mining Analysis

The current study aimed to identify the rising phenomenon of online gambling in news data using Big Kinds, and to check the connection structure of important words.This study used the text-mining method to analyze text data collected from the Naver News portal to identify important patternsof Internet gambling.The web-crawling technique was adopted for data collection from January 1, 2020 to December 31, 2021.Since the coronavirus began to spread across Korea in 2020, a two-year time window was established.All news articles within this timeframe containing the keyword "internet gambling" were identified.A total of 1,494 articles were included.The text mining analysis showed that there have been many cases of arrests due to crimes, mainly online gambling, or crimes being committed after the perpetrator became debt-riddendue to online gambling.It can be seen that those who gamble online are mostly linked to crimes.Journalists have brought these topics up in their provocative articles, but the fact that there are no positive articles about online gambling shows that people should be careful about online gambling.Moreover, many articles state that the number of people who enjoyed online gambling has increased significantly as the time spent using the Internet at home increasedrapidly due to COVID-19 restrictions.Online gamblers are increasingly having a negative impact on society; however.the government has not formulated any clear measures against this issue.The online gambling industry is expanding, leading to a negative impact on society, and the government needs to take an urgent action.
Inductive Matrix Completion Based on Graph Attention

We propose an inductive matrix completion model based on graph attention (IGAT-MC) for the rating prediction recommendation task. IGAT-MC is inductive through enclosing subgraph extraction and node labeling, which makes it can be generalized to predict user/item ratings that do not appear during the training process. More importantly, we use the graph attention mechanism to improve the rating prediction performance of IGAT-MC, and focus on more important user/item interactions for message passing. Based on the MovieLens-100K, the effectiveness of IGAT-MC is verified based on the evaluation of RMSE. By comparing with several mainstream methods, IGAT-MC surpasses some of the mainstream methods and shows competitive performance.
Towards Sentiment and Emotion aided Intent Detection

Intent detection is one of the crucial Natural Language Understanding(NLU) tasks studied extensively. Misclassification of intents impacts the overall performance of the conversational systems as natural language understanding is the first mean of interaction between a user and a virtual agent. The traditional approach of intent detection is limited only to textual features of user utterances and overlooks other semantic features. The sentiment and emotional state of the speaker are two such semantic features that have essential impacts on intent detection, as they implicitly express user intention conveyed through the user’s message. Depending on the context, these features can help the dialogue agent respond to the same intent with varying degrees of sentiment and emotion. Thus, investigating the role of sentiment and emotion on intent detection is a matter of great interest. The current work investigates the impact of utilizing sentiment and emotion information on intent detection tasks and proposes emotion and sentiment aided intent detection models. We also investigate the impact of sentiment and emotion using three different multitasking frameworks and present a joint model that utilizes the co-relation information across these tasks to correctly identify all these NLU aspects (intent, sentiment, and emotion). The obtained experimental results by the proposed models outperform several baselines and state-of-the-art intent detection models on multiple datasets by a significant margin of 1.8% - 3%, demonstrating the significant role of sentiment and emotion features in intent detection <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Analysis of human resource development in the context of commercialization of university innovations

The purpose of the study is to offer the author's approach to the integrated use of methods of analysis and evaluation of human resources development to find effective solutions for the implementation of the process of commercialization of university innovations. The scientific novelty lies in the establishment of interrelations of methods for analyzing the state of human resources of the university and the development of theoretical provisions for their assessment based on the integrated use of existing and developed methods. As a result, the debatable nature of the possibilities of the processes of measuring the parameters of human resources development and obtaining objective data of practical importance is revealed; factors influencing the innovative development of human resources are identified; methods for assessing the readiness of human resources for the transfer and commercialization of knowledge in the conditions of a modern university are determined.
Admissible Heuristics for Multi-Objective Planning

Planning problems of practical relevance commonly include multiple objectives that are difficult to weight a priori. Several heuristic search algorithms computing the Pareto front of non-dominated solutions have been proposed to handle these multi-objective (MO) planning problems. However, the design of informative admissible heuristics to guide these algorithms has not received the same level of attention. The standard practice is to use the so-called ideal point combination, which applies a single-objective heuristic to each objective independently, without capturing any of the trade-offs between them. This paper fills this gap: we extend several classes of classical planning heuristics to the multi-objective case, in such a way as to reflect the tradeoffs underlying the various objectives. We find that MO abstraction heuristics achieve overall the best performance, but that not every MO generalisation pays off.
ELECTRA-KG: A Transformer-Knowledge Graph Recommender System

Knowledge graphs (KGs) are becoming popular in recommender systems in recent times because of the wealth of side information they provide. Many researchers rely on KGs to help resolve the issues of cold start, diversity, and explainability in recommendations. However, the existing approaches usually ignore entity descriptions, which are essential in providing content information for entities in KGs. In this work, we propose a contextual language model for KG completion known as ELECTRA-KG (Efficiently Learning an Encoder that Classifies Token Replacements Accurately). We formulate the recommendation task as a KG link prediction task where we have an incomplete knowledge graph and we use state-of-the-art approaches to complete it. We do this by identifying missing facts among entities from our test data. To evaluate and validate our method, we perform a couple of experiments. First, we run experiments to demonstrate how well our model compares to state-of-the-art KG embedding models. Second, we run further experiments with our model on the tag recommendation task and compare our results to existing baselines. Our results show that our model outperforms the existing baselines on the tag recommendation task.
Teaching Reform of Course and Certificate Integration under the 1+x Certificate System— Taking Industrial Robots as an Example

With the development of market economy, the demand for vocational talents is increasing, which promotes the great development of vocational education in China.Both the number of vocational colleges and the number of students are increasing than before, but at this stage, the teaching mode cannot meet the needs of market talents.Many students cannot get the salary they want after graduation, which seriously hits the learning enthusiasm of vocational education students.With the implementation of the 1+X certificate system, vocational education reform has entered a new level, and curriculum integration is also an important part of the reform.By formulating certificate standards for industrial robot majors and combing the curriculum content in combination with the path of curriculum integration, it is conducive to enhancing students' competitive advantage in employment and ensuring the sustainable development of higher vocational colleges.
Preparing Lifelong Learners for a Diversifying Economy Through Micro-Credentials and Laddering at Athabasca University

Learners need relevant and transformative skills to adapt to a world of increasing change and complexity. It is important to provide diverse opportunities to support lifelong learning. In response to the Alberta 2030: Building Skills for Jobs report and Alberta’s Recovery Plan to the Covid-19 pandemic, PowerED™ by Athabasca University, Ethically Aligned AI, and Athabasca University’s Faculty of Science and Technology developed three online, on-demand micro-credentials. The three micro-credentials, Ethics and Artificial Intelligence, Innovative and Diversified Energy Resources, and Energy Efficiency in Architecture Engineering (AEC) and Construction Industry, were funded by the Government of Alberta to provide job-ready skills in priority areas. Athabasca University’s PowerED unit is designing and developing these three micro-credentials in partnership with Athabasca University faculty and subject matter experts. PowerED™ is Athabasca University’s award-winning continuing education unit that provides an on-demand approach to the online learning experience which includes a mix of multi-media (videos, podcasts) interactive tools, case studies, gamification, competency assessment, downloadable materials, and AI simulations for immediate assessment. The micro-credentials are being designed to be flexible and can be accessed from any device that connects to the internet. Each micro-credential is made up of a set of modules and learners can combine different micro-credentials to develop specific competencies to focus on specific skill development requirements. Modules are being designed so that in the future, individual modules can be re-packaged into unique micro-credential offerings. In completing these micro-credentials, learners will be able to obtain relevant skills in key areas of employment. These micro-credentials will ladder into the BSc programs at Athabasca University, creating additional opportunities to continue learning in a flexible and accessible way. To facilitate this, we are developing a micro-credential framework at the institutional level that will also align with future frameworks in Alberta and Canada.
Utilization Of Openkm In The Context Of Iterative Process Automation

This study aims to implement a management management system in a vocational high school in Pandeglang. The literature used as the basis for research is knowledge management, personal knowledge management. The research approach used is an action research approach, because the researcher wants to show changes before and before implementing the action. The results of this study indicate the benefits of using OpenKM in schools that are the object of research.
The Role of Absorptive Capacity in the Blockchain Enabled Traceability Alignment: An Empirical Investigation

The food supply chain argues as one of the most complex and challenge systems and plays a major role in human societies for thousand years, but still faces some fundamental challenges on the data storage, forward and access. Current research indicates that blockchain is a promising technology for handling these challenges, as various nodes (actors) are connected through the network with efficiency, feasibility, and trust. However, as a novel technology, many mysteries are still waiting for discover urgently, such as information sharing, dynamic context, and emerging network scale. This study adopted absorptive capacity, as the theoretical lens, investigates a blockchain network in a longitudinal perspective, which contributes to the IS literature by providing an account of the blockchain network from the knowledge processing perspective by considering the interplay among the actors and uncover the dynamics of absorptive capacities in a loose connected network.
Human Embryology teaching mediated through a virtual environment room

Difficulties linked to the way of how we understand and present Human Embryology contents are aggravated by traditionalist characteristics still identified in the educational field. The way this science is taught must be based on interactive didactic resources, enhancing the best visualization of its phenomena. In this context, guided by the David Ausubel's Meaningful Learning Theory, the Human Embryology Virtual Environment Room was developed strengthening this content's teaching-learning process through a virtual game resource that dynamically represents Human Embryonic Development main phases. This resource was submitted to appreciation by Basic Education expert teachers, validating it as an educational product, assessing didactic, methodological and other issue related aspects through an electronic validation form. The quantitative results analysis and interpretation was carried out using Likert's Scale Median Ranking calculation; qualitative data was analyzed under Lefèvre &amp; Lefèvre's Discourse of Collective Subject precepts. The results demonstrate that the educational product has been validated by expert teachers and it is possible to infer that it does constitute a potentially significant product for Human Embryology teaching.
A Search-Enhanced Path Mining and Ranking Method for Cross-lingual Knowledge Base Question Answering

Knowledge Base Question Answering (KBQA) is a popular research direction in knowledge graphs, and most of KBQA work focuses on natural language parsing. In order to enhance the application of cross-lingual knowledge, the 16th China Conference on Knowledge Graph and Semantic Computing (CCKS2022) has released a cross-lingual knowledge base question answering (CKBQA) task. This paper presents the submission of our team (HW-TSC) to the CKBQA task, in which we propose a search-enhanced path mining and ranking method. The method divides the process of CKBQA into four parts: question classification, principal entity extraction, search-enhanced candidate path mining and candidate path ranking. Finally, both the preliminary and final evaluation results prove the effectiveness of our method.
Text Recognition for Nepalese Manuscripts in Pracalit Script

This dataset is a model for handwritten text recognition (HTR) of Sanskrit and Newar Nepalese manuscripts in Pracalit script. This paper introduces the state of the field in Newar literature, Newar manuscripts, and HTR engines. It explains our methodology for developing the requisite ground truth consisting of manuscript images and corresponding transcriptions, training our model with a PyLAia engine, and this model’s limitations. This dataset shared on Zenodo can be used by anyone working with manuscripts in Pracalit script, which will benefit the fields of Indology and Newar studies, as well as historical and linguistic analysis.
Evaluating the Veracity of Software Bug Reports using Entropy-based Measures

The wide usage of open source software (OSS) results in an increase of bug data forming an integral part of the extensive data ecosystem. This bug report data needs to be analyzed for bug fixing and prediction of various important attributes like bug severity, priority, fix time, assignees, etc. The increased volume of bug data and different bug reporters from different geographical locations make veracity an important concern. We assume that the bug reports (i.e., different bug attributes) reported in software bug repositories are trustworthy during the bug triaging process. In reality, the bug report data are not trustworthy regarding various aspects like integrity, authenticity, and trusted origin as the bugs are reported by users who may or may not have proper knowledge of the software. In this paper, we proposed entropy-based models for veracity estimation of different bug attributes.
Biorthogonality Collection of Finite System of Functions in Multiresolution Analysis on L<sup>2</sup>(K)

This paper consists of using the scaling function and wavelet theory concepts from the definition of (MRA) on <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{L}^{2}(\mathrm{K})$</tex> where K is working as local field by utilizing these in way of finite system of function forms the orthonormal (basis) system. Moreover, such system of function can also be written in terms of integral periodic, orthonormal basis over <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{D}$</tex> . The finite system of function (wavelet) and the function belongs to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{L}^{2}(\mathrm{K})$</tex> and have the same norm in it. The biorthogonal system of function concept has been discussed. The finite system of functions has been considered whereas it is been showed it makes the orthonormal basis. Results show that considered finite state of functions make the orthogonal basis. It is also shown that the Fourier transform of such system functions system could be written as using the function which satisfy the integral periodi property and such system of functions also form biorthogonal system.
M-LEARNING - A MULTI AGENT APPROACH

In this paper we have developed a framework for learning by mobile based on JADE and JESS as software components.Such a software integration makes it possible to exhibit ACL communication properties from JADE and rule-based reasoning capability from JESS.Software agents are implemented following the guidelines of the theoretical concept of Zone of Proximal Development (ZPD), as a means of supporting and promoting opportunities for effective learning.
Deep Physical Neural Networks based on Ultrafast Nonlinear Optics

We experimentally demonstrate multilayer neural networks using ultrafast nonlinear optics, to perform audio and image classification. The proposed framework for constructing and training neural networks is general and applicable to other complex non-linear systems.
TRENDS IN THE INTRODUCTION OF ARTIFICIAL INTELLIGENCE AND ROBOTIZATION OF PROCESSES IN LABOR RELATIONS

TRENDS IN THE INTRODUCTION OF ARTIFICIAL INTELLIGENCE AND ROBOTIZATION OF PROCESSES IN LABOR RELATIONS Ilya Evdokimov student, Moscow University for the Humanities, Russia, Moscow &nbsp; ТЕНДЕНЦИИ ВНЕДРЕНИЯ ИСКУС
Use of Artificial Intelligence in Journalism by Pakistani and Foreign Journalists

Artificial intelligence is a technology that has become prominent in our daily lives. It’s an the innovative technology of the digital age that has changed the pattern of many business sectors and the media sector is not apart from it. This study identifies the use of AI by both Pakistani and foreign journalists as well as the threats and benefits of AI. To achieve the objectives of this study in-depth interviews were conducted. Diffusion of Innovation is taken as theoretical support. “Use of AI enhances the working skills of journalists” and “AI plays a vital role in changing traditional journalism to modern journals” were testable statements. It was found that AI is being used by Pakistani and foreign journalists to enhance the quality of work but international media is one step ahead then Pakistani media. But every organization nationally and internationally has not adopted the technology yet and it will take more time to use this technology
Web-Based Souvenir Ordering Information System for Pasuruan

Dry Bakpia is a typical food from Pasuruan city made from green beans. One place that produces bakpia in a simple and conventional way is a new bakpia shop. In a survey conducted by the new bakpia shop, they still use the manual system, at this time the new bakpia shop is experiencing difficulties in selling production.The Covid-19 pandemic has an impact on all sectors, including business actors or MSMEs. For this reason, a final project or thesis entitled "Sales Information System By - By Pasuruan is made", so that it can still serve the sale of souvenirs via online. The system is made using the waterfall method, the programming language used is PHP with SQL database. The results obtained in the form of a web-based sales system.