Reviewerly/exHarmony huggingface dataset:
Factors Affecting Code Security in South African Organization

Cybersecurity has become an area that most researchers and practitioners invest their resources in. Yet, cyber related threats remain rampant. This is partly due to vulnerabilities in the systems, specifically in the code, that can be exploited by cyber attackers. Given that cyber related attacks have been more prominent on the African continent, this study seeks to identify and understand the factors affecting code security in South African organizations. The study is informed by a qualitative enquiry. Interviews were used to collect data from nine IT knowledge workers responsible for the development and testing of organizational systems. The findings show that code security should be understood from the context of (1) resource availability, specifically IT professionals with both practical expertise and experience, cyber specific training and adoption and continuous use of security tools; (2) confidence and reliance on IT infrastructure; and (3) work-life balance practice that encourages collaboration between employees.
CGXplain: Rule-Based Deep Neural Network Explanations Using Dual Linear Programs

Rule-based surrogate models are an effective and interpretable way to approximate a Deep Neural Network’s (DNN) decision boundaries, allowing humans to easily understand deep learning models. Current state-of-the-art decompositional methods, which are those that consider the DNN’s latent space to extract more exact rule sets, manage to derive rule sets at high accuracy. However, they a) do not guarantee that the surrogate model has learned from the same variables as the DNN (alignment), b) only allow optimising for a single objective, such as accuracy, which can result in excessively large rule sets (complexity), and c) use decision tree algorithms as intermediate models, which can result in different explanations for the same DNN (stability). This paper introduces Column Generation eXplainer to address these limitations – a decompositional method using dual linear programming to extract rules from the hidden representations of the DNN. This approach allows optimising for any number of objectives and empowers users to tweak the explanation model to their needs. We evaluate our results on a wide variety of tasks and show that CGX meets all three criteria, by having exact reproducibility of the explanation model that guarantees stability and reduces the rule set size by >80% (complexity) at improved accuracy and fidelity across tasks (alignment).
A Digital Human System with Realistic Facial Expressions for Friendly Human-Machine Interaction

Digital human technology can be applied to many areas, especially at the interaction of intelligent machines, digital 3D, and human-machine interaction. This paper develops a digital human system that enhances friendly interaction between humans and machines through synchronized and realistic facial expressions, based on tracking data from a human user. The details of this paper can be summarized in three parts. First, customize and build a 3D model for the digital human. Second, create a parameterized 3D model for different facial animations. Third, track the user for synchronizing the facial expressions of the customized digital human. The experimental results and analysis in this paper demonstrate the effectiveness and advantages of the developed system, which can create realistic and natural synthesized facial expressions. The system can be further generalized to other intelligent computing areas, such as smart medical care, autonomous vehicles, and companion robots.
Use of Artificial Intelligence in the Digital Marketing Strategy of Latvian Companies

As technologies advance and the interest of digital marketers and researchers in artificial intelligence increases, the ways to develop a digital marketing strategy with the tools that can improve both marketing operations and interactions with the consumers are being sought. The objective of the research is to investigate, to assess and to analyse the possibilities of using artificial intelligence in digital marketing strategy of Latvian companies, based on theoretical knowledge about the use of artificial intelligence in digital marketing strategy, the results of survey of companies’ representatives and expert interviews. Research methods: In order to achieve the objective of the research, a descriptive analysis, expert interviews and a survey of representatives of Latvian companies have been carried out. Results: Artificial intelligence provides companies with several advantages that the companies can use to improve their marketing activities or to interact with the consumers; however, in Latvia, companies use artificial intelligence little, and it is difficult for a large number of companies to say whether they will implement artificial intelligence in their digital marketing strategy in the near future.
ACADEMIC ACHIEVEMENT AND ACADEMIC FRAUDULENT BEHAVIOR: EFFECTS OF CAMPUS CULTURE, INFORMATION TECHNOLOGY IMPLEMENTATION, AND ACADEMIC ACHIEVEMENT

Academic fraud is an act committed by students to get maximum results by committing bad and prohibited acts such as cheating, discussing with friends during exams or other ways. The problems formulated in this study are: 1) does campus culture influence fraudulent academic behavior?; Does the use of information technology influence academic fraud behavior?; and 3) does academic achievement influence academic fraud behavior? This type of quantitative research is an ex post facto model with 118 students at Ivet University Semarang as subjects. Data collection techniques used documentation and questionnaires with instruments that met the requirements of validity and reliability. The data analysis technique was used with the help of SPSS data processing, with the results that: 1) based on descriptive statistical analysis, the academic fraud variable was high, while campus culture was relatively high, while the use of information technology was included in the high criteria and the academic achievement score was relatively high; 2) based on hypothesis testing only campus culture variables have a positive and significant effect on academic cheating, while the variables of information technology use and academic achievement are found to have no significant effect on academic cheating.
Scholarly Stockholm Syndrome in full gallop

I was a bit shaken to read this short article, Submit It Again! Learning From Rejected Manuscripts (Campbell et al. 2022), recently posted on Mastodon by open-access legend Peter Suber. For example: Journals may reject manuscripts because the paper is not in the scope of the journal, because they recently published a similar article, because the formatting of the article is incorrect, or because the paper is not noteworthy. In addition, editors may reject a paper expecting authors to make...
Penguatan Literasi Digital melalui Pelatihan Microsoft PowerPoint di SMK Muhammadiyah

Digital literacy is one of the abilities that students must have in this digitalization era. However, the facts on the ground show that digital literacy in Vocational High Schools (SMK) has not been properly realized. From the survey results, it was found that students at Muhammadiyah Kartasura Vocational School and Muhammadiyah 4 Surakarta Vocational School were less proficient in using Microsoft Powerpoint. The purpose of carrying out this service is to increase digital literacy at SMK Muhammadiyah Kartasura and SMK Muhammadiyah 4 Surakarta. The stages carried out in the service include Pre-Test, mentoring, and Post-Test. The form of community service is in the form of mentoring and training with a participatory approach. Meanwhile, the implementation of activities is divided into several stages, namely licensing, mentoring, and evaluation. The mentoring method is by direct practice regarding the features of Microsoft PowerPoint directly and providing experience to students. The results of the service show that students are proficient in digital literacy. Previously, students were not familiar with the various features of Microsoft PowerPoint. However, after this activity, students became proficient in using the features contained in Microsoft PowerPoint. This community service has reached the target as evidenced by increasing students' skills in understanding Microsoft PowerPoint, gaining experience through practices that can produce product outputs in the form of PowerPoint and increasing digital literacy for students at SMK Muhammadiyah 4 Surakarta and SMK Muhammadiyah Kartasura. Microsoft PowerPoint digital literacy skills have increased from before and after the dedication as evidenced by the increase in pre-test and post-test results. It can be concluded that this activity can improve students' digital literacy skills. This community service activity has the hope that students who already have initial skills regarding making Microsoft PowerPoint from the results of community service can continue to practice. In addition, the school can facilitate by continuing to assist in strengthening continuous digital literacy.
THE APPLICATION OF SUPERVISION BY VISITATION IN IMPROVING THE ABILITY OF TEACHERS IN THE CLASSROOM LEARNING PROCESS AT UPT SMP NEGERI 11 GRESIK

This study aims to improve the ability of teachers during learning process in the classroom through the implementation of supervision, particularly by visitation. The research focus in the classroom is the ability of teachers in conducting the learning process. Teacher's ability in identifying learners' is around 15 teachers or 60 %. Other focuses cover the ability of teachers to motivate students to learn actively and the ability to conduct classroom management, the ability of teachers to identify students' behavior and develop students' potential and overcome the students' shortcomings, as well as the ability of teachers to observe to student weaknesses. This research was conducted in UPT SMP Negeri 25 Gresik for a month. The results showed that the supervision process by visitation is very effective in improving the ability of teachers in learning management
Role of Digitalization of Banking Operation Issue for Military Organisations: An Analytical Study

Digitalization plays an immensely significant role in the operations of military institutions. The utilization of electronic fund transfers and online banking platforms empowers military personnel to efficiently receive their salaries and allowances, even when stationed in remote and inaccessible locations. This not only contributes to their financial stability but also boosts morale within the ranks. Digitalization plays a critical role in expense management and budgeting. Military institutions can optimize their processes by embracing digital platforms, thereby automating expense tracking, and eliminating manual procedures that are susceptible to errors.
Conflict Resolution Technique for Managers: An Analytical Study

Managers play a crucial role in creating a productive and harmonious work environment by possessing effective conflict-resolution skills. Conflict is an inevitable occurrence in any workplace, and this analytical study delves into various approaches that managers can employ to successfully address and resolve conflicts. The objective of this study is to provide managers with practical insights on applying these strategies, enabling them to effectively manage conflicts. The analysis emphasizes the significance of open communication, active listening, empathy, and seeking mutually beneficial solutions when resolving conflicts. It stresses the importance of selecting the appropriate approach depending on the specific situation and individuals involved. Furthermore, the study explores different methods for handling conflicts related to tasks and highlights how effectively managing such conflicts contributes to team innovation. By utilizing adaptable conflict management strategies, employees can enhance their collaborative efforts as a team. To broaden our understanding of conflict resolution, future research should investigate the implications of this analysis across various domains, facilitating the generalization of the findings. This will contribute to a more comprehensive comprehension of techniques for resolving conflicts effectively.
The Relationship Between Interpersonal Communication And Social Intelligence In Online Learning On Social Knowledge Learning Outcomes (Ips) Elementary School Students

There are no packages and require more costs in the learning process. Researchers are interested in researching the relationship between Interpersonal Communication and Social Intelligence in Online Learning on Social Science Learning Outcomes (Ips) of Bancar Elementary School Students. The research method that researchers use is quantitative. Collecting data using interviews, observation and distribution of questionnaires to fifth grade students. The results of this study are based on the attached tcount value of 8.814 and a significance of 0.000. Because tcount &gt; ttable (8.814&gt;2.048) or significance &lt;0.05 (0.000&lt;0.05) then Ha is accepted and ho is rejected meaning that there is a significant relationship between interpersonal communication in online learning and student learning outcomes in social studies subjects at Bancar. In the attachment the tcount value is 0.323 and a significance of 0.750. Because tcount &lt; ttable (0.323&gt;2.048) or significance &lt;0.05 (0.750&gt;0.05) then Ho is rejected and ha is accepted meaning that there is no significant relationship between social intelligence in online learning and student learning outcomes in social studies subjects at Bancar.
Pengembangan dan Pemanfaatan Media Pembelajaran Fun Indonesian Folktales Pada Pembelajaran Bahasa Inggris Siswa Kelas X SMA Negeri 1 Negara

The objective of this research was to create a Fun Indonesian Folktales learning medium to be utilized to teach English for the Grade X students at the Senior High School of 1 Negara. The Research and Development (RnD) method was used in this research, according to (Borg and Gall 2003). The researchers modified and simplified it into 9 adjusted cycles. The interviews and questionnaires were used to collect data, which were then analyzed qualitatively and quantitatively. Three tryouts were conducted three times during the product development process: initial tryout, field tryouts, and field implementation tryout. VR expert, pedagogy expert, material expert, and an English translation were all involved in the initial tryout. Five students participated in the field tryout. Seventeen students participated in the field implementation tryout. The experts and students were allowed to try the product and respond to the provided questionnaire. According to the final data analysis, the attractiveness of the interactive media was 93.5%, the level of practicality was 94.8%, and the student learning motivation was 94.3%. As a result, the product being developed is feasible and has shown the potential to be used as a new medium in the English subjects.
Proximal Symmetric Non-negative Latent Factor Analysis: A Novel Approach to Highly-Accurate Representation of Undirected Weighted Networks

An undirected weighted network (UWN) is constantly encountered in various big data-related applications. A UWN’s topological information can be expressed by a Symmetric, High-dimensional and Incomplete (SHDI) matrix, upon which the representation learning task is essential for knowledge acquisition. However, existing models mostly fail in modeling its intrinsic symmetry or low-data density, resulting in the model’s weak representation learning ability to its numerical features. For addressing this vital issue, this study presents a Proximal Symmetric Non-negative Latent-factor-analysis (PSNL) model with three-fold ideas: a) building a proximal term-incorporated, symmetry-aware, and data density-oriented learning objective subjected to the non-negativity constraints for ensuring its high representation learning ability; b) designing an Alternating Direction Method of Multipliers (ADMM)-based learning scheme for solving the learning objective on the premise of fast convergence; c) implementing self-adaptation of the model’s multiple hyper-parameters via the Tree-structured of Parzen Estimators (TPE) algorithm, thus enabling its high scalability. Empirical studies on four UWNs from real applications demonstrate that the proposed PSNL model achieves higher representation accuracy than state-of-the-art models do, as well as promising computational efficiency.
InterFormer: Human Interaction Understanding with Deformed Transformer

Human interaction understanding (HIU) is a crucial and challenging problem, which consists of two subtasks: individual action recognition and pairwise interactive recognition. Previous methods do not fully capture the temporal and spatial correlations when understanding human interactions. To alleviate the problem, we decouple HIU into complementary parts for exploring comprehensive correlations among individuals. Especially, we design a multi-branch network, named InterFormer, to jointly model these interactive relations, which contains two parallel encoders to generate spatial and temporal features separately, and Spatial-Temporal Transformers (STTransformer) to exploit spatial and temporal contextual information in a cross-manner. Extensive experiments are conducted on two benchmarks, and the proposed InterFormer achieves state-of-the-art performance on these datasets.
Information Extraction System for Invoices and Receipts

Rapid growth in the digitization of documents, such as paper-based invoices or receipts, has alleviated the demand for methods to process information accurately and efficiently. However, it has become impractical for humans to extract the data manually, as it is labor-intensive and time-consuming. Digital documents contain various components such as tables, key-value pairs and figures. Existing optical character recognition (OCR) methods can recognize texts, but it is challenging to extract the key-value pairs in unformatted digital invoices or receipts. Hence, developing an information extraction system with intelligent algorithms would be beneficial, as it can increase the workflow efficiency for knowledge discovery and data recognition. In this paper, a pipeline of the information extraction system is proposed with intelligent computing and deep learning approaches for classifying key-value pairs first, followed by linking the key-value pairs. Two key-value pairing rules are developed in the proposed pipeline. Various experiments with intelligent algorithms are conducted to evaluate the performance of the pipeline of information extraction system.
A Survey on Multimodal Named Entity Recognition

Multimodal Named Entity Recognition (MNER) is a task of identifying entities with specific semantic types from natural language text and using image information to improve the accuracy and robustness of entity recognition. Named entity recognition is an important application of multimodal learning and a fundamental problem in the field of natural language processing. This article reviews existing multimodal named entity recognition techniques for social media. We first introduce commonly used datasets for MNER. Then, we classify existing MNER techniques into five categories: pre-trained models, single-modal representation, multimodal representation, multimodal fusion and main models. Next, we investigate the most representative methods applied in MNER. Finally, we present the challenges faced by MNER and discuss the future directions of this field.
HSRG-WSD: A Novel Unsupervised Chinese Word Sense Disambiguation Method Based on Heterogeneous Sememe-Relation Graph

Word sense disambiguation (WSD) plays a crucial role in natural language processing. Unsupervised WSD approaches based on knowledge bases like HowNet offer improved applicability compared to supervised learning, but existing research tends to oversimplify disambiguation and neglect hierarchical sememe relationships which lead to the inability to accurately differentiate between senses with the same combination of sememes radicals. This paper pre-sent an unsupervised Chinese word sense disambiguation method based on a Heterogeneous Sememe-Relation Graph (HSRG) that leverages sememe hierarchical relationships to uncover the intrinsic connections between sememes. Additionally, we incorporate cross-word sememe relationships and semantic dependency relationships, establishing both indirect and direct contextual associations while mitigating the influence of syntactic structures. This integration enhances the representation of ambiguous words and improves disambiguation outcomes. Furthermore, our study innovatively combines the principles of graph contrastive learning with node selection algorithms, employing heterogeneous graph neural networks to effectively represent graph models and facilitate unsupervised selection of accurate sense vertices in HSRG. The proposed model is evaluated on the HowNet-based Chinese WSD dataset, demonstrating superior performance over competitive baselines.
Expert Knowledge-Driven Clothing Matching Recommendation System

Internet shopping has become a major consumer channel, but the lack of professional knowledge in clothing matching and the inability to try on clothes often lead to blind consumption and high clothing return rates. Based on the experience of senior clothing experts, a digital clothing tagging system was established, the weight of clothing matching was quantified, and customer image parameters were designed to create a clothing recommendation system for dress matching based on a knowledge graph. According to the system testing results, it can be concluded that the system can meet the needs of at least 2000 users simultaneously, and the recommended response time should not exceed 1.5 s.
Semi-supervised Semantic Segmentation Algorithm for Video Frame Corruption

To address the problems of lack of labeled data and inaccurate segmentation in semantic segmentation of corrupted frame in surveillance video, a semi-supervised semantic segmentation method based on pseudo label filter with weak-strong perturbation and horizontal continuity enhancement module are proposed. The weak-strong perturbation-based pseudo label filter method performs selective re-training via prioritizing reliable unlabeled images based on holistic image-level stability. Concretely, weak-strong perturbations are applied on unlabeled images, and the discrepancy of their predictions serves as a measurement for stability of pseudo label. In addition, the horizontal continuity enhancement module is designed to make the model learn clearer inter-class boundaries of corrupted frame data. To validate the proposed method, the corrupted frame data from the security surveillance system are collected to produce a dataset for validation experiments. The experimental results show that our work outperforms other semi-supervised methods in terms of mean intersection over union (MIoU), demonstrating the effectiveness of the proposed method and the horizontal continuity enhancement module on the semantic segmentation task for corrupted frames.