Reviewerly/exHarmony huggingface dataset:
STFT: Spatial and temporal feature fusion for transformer tracker

Abstract Siamese‐based trackers have demonstrated robust performance in object tracking, while Transformers have achieved widespread success in object detection. Currently, many researchers use a hybrid structure of convolutional neural networks and Transformers to design the backbone network of trackers, aiming to improve performance. However, this approach often underutilises the global feature extraction capability of Transformers. The authors propose a novel Transformer‐based tracker that fuses spatial and temporal features. The tracker consists of a multilayer spatial feature fusion network (MSFFN), a temporal feature fusion network (TFFN), and a prediction head. The MSFFN includes two phases: feature extraction and feature fusion, and both phases are constructed with a Transformer. Compared with the hybrid structure of “CNNs + Transformer,” the proposed method enhances the continuity of feature extraction and the ability of information interaction between features, enabling comprehensive feature extraction. Moreover, to consider the temporal dimension, the authors propose a TFFN for updating the template image. The network utilises the Transformer to fuse the tracking results of multiple frames with the initial frame, allowing the template image to continuously incorporate more information and maintain the accuracy of target features. Extensive experiments show that the tracker STFT achieves state‐of‐the‐art results on multiple benchmarks (OTB100, VOT2018, LaSOT, GOT‐10K, and UAV123). Especially, the tracker STFT achieves remarkable area under the curve score of 0.652 and 0.706 on the LaSOT and OTB100 benchmark respectively.
Framing responsive architecture with soft robots—the exploratory practice of soft pneumatic robotic architectural system

Abstract Inspired by intelligent skin, this research constructs a soft pneumatic robotic architectural system based on the interaction between inflatable material agents and the human body. It aims to provide flexibly changeable scene modes for dynamic inhabiting through responsive spatial performance. From the idea of the Fun Palace and the structure and performance spatial methodology to the proposal of responsive/soft architecture and relevant examples such as Furl and Robot Soft, the literature review provides the foundation and direction for the establishment of research experiments. The research methodology is based on structure and performance, oriented to explore soft material agents, link input information and output modules to realize the manifestation of human-space interaction and spatial performance and assemble the basic configuration of the system. As a result, a synergistic architectural interaction system like a media center that can provide a variety of adaptive space scenes is built, which can realize real-time perception and responsive control of the interactive process, and at the same time provide richness of scene performance beyond established architectural configurations, confirming the possibility of creating a behaviorally responsive architecture by integrating modularly morphing spaces, AI-sensing systems, and interaction technologies. This study explored responsive human settlements by expanding soft pneumatic robots in architecture. It can expand to fields such as machine cognition at the input level, further realize the real-time feedback system at the output level, and can evolve with the information interconnection of urban landscapes and residential communities in terms of spatial interaction. The work gives a convincing vision of architectural intelligence through soft robotics and is expected to continue to develop and bring more inspiring innovations with its integrated methods and connotations.
Brazilian Integrated Cross-platform Security Assessment Framework: Context of Cybersecurity Methodology

Context is the circumstances set surrounding an event and encompasses all information about the analysis target, being a prerequisite for any assessment. The existing security assessment methodologies reinforce the importance of a context survey with quality. Its incorrect mapping can lead to false security feelings, and there is a lack of detailed methods for a proper context survey available in the market and the literature. In order to fill this gap, this paper presents the context of cybersecurity methodology (CoCs), which maps as much information as possible concerning the object under evaluation. This methodology will contribute to systems, products, services, processes, and organizations’ cyber protection improvement, circumventing attempted attacks, and ensuring business continuity. The future work is CoCs validation in case studies and the addition of a computational tool to support the methodology application, facilitating the execution by the security specialist and reducing the chance of errors. This method is part of the integrated cross-platform security assessment framework (ICpSAF), within the scope of the Brazilian TecSEG project.
Blockchain Technology in the Supply Chain: The Benefits and Challenges

The purpose of this research is to explain the current trend and recognize the benefits and challenges of blockchain technology in the supply chain. Blockchain technology enables organizations in the supply chain to become decentralized operations that are more secure and transparent than traditional forms. Blockchain technology has many benefits that can help organizations; however, we consider the most important one: People focusing on and discussing how, when, who, and where to use it. In terms of the current trend in the use of blockchain technology in the supply chain, the findings show the statistics on the increase in articles in recent years, publications by country, and top authors regarding this particular topic. Furthermore, content analysis of 35 articles related to the benefits of blockchain technology in the supply chain was conducted, revealing such benefits as traceability, transparency, security, cost reduction, trust, and information sharing. It also emerged that the advantages of blockchain utilization are being reaped across several industrial sectors. According to our research findings, when combining blockchain technology with IoT, RFID, or other digital tools, this will increase the efficiency of such technology working in the supply chain.
On fuzzification and optimization problems of clustering indices

Results of clustering are qualitatively evaluated by quantities called clustering indices. While many clustering indices are proposed, in B. Desgraupes [Clustering Indices (2016)], Desgraupes reviewed 27 indices, most of which are applicable only to crisp clustering and only one of which is applicable to fuzzy clustering. In the previous article [D. R. Sope and M. Fujio, On a fuzzification of clustering indices, in Proc. Fuzzy System Symposium of the Japan Intelligent Information Fuzzy Society, Vol. 35 (2002), pp. 443–448], the authors gazed on analytic 3 indices among them and modified them to fit fuzzy clustering by regarding the membership degrees as distribution functions of objects over clusters. In this paper, this method called fuzzification, is applied to all the indices of Desgraupes. This investigation also includes optimization of indices. A significant benefit of fuzzy clustering is that the membership degrees allow the optimization problems to be treated as continuous, whereas the ones in the crisp case are discrete, making it generally easier to solve. After giving a precise description of fuzzification which is briefly given in D. R. Sope and M. Fujio [On a fuzzification of clustering indices, in Proc. Fuzzy System Symposium of the Japan Intelligent Information Fuzzy Society, Vol. 35 (2002), pp. 443–448], the authors fuzzify all of 27 indices of Desgraupes and then solve the fuzzy clustering problems having 13 analytic indices among them as the objective functions by the gradient method.
Mobile Game of the Generals 3D: An Original Filipino Board Game Going Mobile

Board games are used for entertainment and pastime for people. Game of the Generals also known as “Salpakan,” is one of these games that are widely played during the 1990s and is slowly being forgotten because of the developing culture of converting everything into a mobile application including board games. This chapter aims to develop a mobile-based Game of the Generals 3D (GOG 3D). Specifically, the chapter aimed to create a mobile application based on the Filipino board game called "Game of the Generals;" Design the overall look of the game that resembles the board game in 3D with two players playing on their mobile phones connected through a hotspot and artificial intelligence to serve as the arbiter; and conduct alpha testing using test cases and evaluate the application using ISO/IEC 25010 game evaluation instrument. The GOG 3D is compatible with the android Operating System (OS) and can be played using a hotspot even without an internet connection. The Iterative model was used as the framework for the development of the application. GOG 3D was designed using adobe photoshop and developed using the Unity game engine and Blender.
Web Application to Improve the Competencies in the Area of Mathematics of 4th Grade

The objective of this research was to determine how a Web Application influences the improvement of mathematics competencies of 4th-grade students of an educational institution. For this purpose, the ability of the students to solve problems with respect to the competencies in the area of mathematics was studied. In this type of applied research with a quasi-experimental design with a quantitative approach was used. For data collection, the survey technique was used with the questionnaire instrument, which was validated by expert judgment and analyzed by means of the Kr-20 reliability analysis, showing a favorable acceptance with a coefficient of 0.802. To measure the proposed indicators, a sample of 30 students was used, obtained from a population of a similar size. On the other hand, in the analysis of the data, use was made of the SPSS Statistics 28 statistical program, in which descriptive and inferential statistics were applied, as well as the Wilcoxon nonparametric hypothesis test. The results obtained showed that the use of the Web Application has a positive influence on the improvement of competencies in the area of mathematics, given that the significance obtained in its dimensions was less than 0.001, which is less than the theoretical significance α = 0.05, thus rejecting the null hypothesis and accepting the alternative hypotheses proposed.
Digital Platform for Teachers’ Professional Development

An essential challenge is to provide an effective digital support tool for the processes of career advising, training, and teaching staff employment in the educational system. One of Kazan Federal University's major development goals is the building of a complex of evidence-based technologies and platform solutions to boost human potential in the face of emerging socioeconomic difficulties. A laboratory “Research-oriented teacher education” was established within the framework of this project, with one of its tasks being to develop an effective tool for career guidance, training, and employment of teaching staff. This platform might be dubbed the Digital Platform for Teachers’ Professional Development. The platform's goal is to automate the processes of support for a unified system of activities in educational institutions of various levels for the selection, career guidance, training, employment, and support for teaching staff career growth, as well as the creation of a single database of personnel reserve. The article gives an overview of the fundamental ideas behind the digital platform that supports teachers’ professional careers and provides a quick study of some existing digital tools for hiring and job searching in the education sector.
Toward a Framework to Improve Employees’ Compliance with Cybersecurity Policy in Organizations

Digital transformation has influenced organizations’ operations significantly. However, non-compliance with cybersecurity policy (CSP) is a growing concern for organizations. Technology alone cannot protect organizational cyber assets such as computer systems, networks, and data. Human aspects should be considered when designing and implementing a CSP. The lack of effective CSP training and awareness programs (CSPTAP) is attributable to employees’ non-compliance with the CSP. This paper aims to develop a framework to enhance employees’ compliance with the CSP by implementing effective CSPTAP. Drawing from the present literature and reflecting on the existing behavior change wheel (BCW) framework, and capability, opportunity, motivation, and behavior (COM-B) model, the cybersecurity policy compliance (CSPC) framework is developed. The CSPC framework comprises the following key concepts: learning existing policies, conducting employees’ gap analysis, reviewing existing policies/developing new policies, provision of relevant content and delivery mode, and periodic auditing. These key concepts are essential elements in cybersecurity policy compliance. The model indicates that the implementation of the essential elements will substantially influence employees’ compliance with CSP. Moreover, when organizations consider these key elements, cybersecurity policy training and awareness, can positively enhance employees’ CSP compliance. The proposed development of CSPTAP provides a firm base for future empirical work including action research.
Auto-Insurance Fraud Detection Using Machine Learning Classification Models

This work explored six machine learning algorithms: Extreme Gradient Boosting (XGBoost), Logistic Regression, Random Forest, Decision tree, Support Vector Machine (SVM), and Naïve Bayes to determine the best algorithm for detecting insurance fraud. The following were used to evaluate the six models: Confusion matrix, Accuracy, Precision, Recall, and F1-measure. The result showed that Random Forest outperformed the others in terms of accuracy. Extreme Gradient Boosting (Xgboost) had the highest precision and F1-measure scores, while the Decision Tree had the highest Recall score. Although two methods (Analysis of Variance (ANOVA) and Random Forest Classifier) were compared to determine the best feature selection, the significant features were selected using the Random Forest classifier because of the many benefits of using this method. The results of this study will be beneficial to insurance companies, stakeholders and policyholders.
Anomalous-Aggressive Event Detection Techniques

Human behavior refers to the way humans interact with their surroundings. It can be classified as either being normal or anomalous behavior. Anomalous events are behaviors that are deviant against all societal norms. One event makes another event happen, where the cause-effect relationship in behavior is the law of science. Automatically detecting anomalous events has become an important research area. It plays a significant role in video surveillance security, especially in public places also human–computer interaction. Recently, researchers have oscillated between traditional learning, deep learning, and hybrid-based models. The traditional learning-based model depends on extracting the best features to contribute to the result. The deep learning-based model deals directly with data and skips the manual step of feature extraction, offering an end-to-end problem solution. The hybrid-based model combines the benefits of both models to improve the detection result. This paper presents an inclusive review of traditional learning, deep learning, and hybrid-based models for anomalous event detection. It also hands out discussions on state-of-the-art approaches used.
Kompetensi Literasi Media Guru PAI di SMAN Kota Bahagia Kabupaten Aceh Selatan

Digital media literacy is an important key in dealing with various information technology phenomena that exist today. This study aims to find out how the media literacy competence of PAI teachers are, what are the media literacy competencies of PAI teachers, and the obstacles faced by PAI teachers related to media literacy competence at SMA Kota Bahagia, South Aceh. The research method used in this thesis is a descriptive method with a quantitative approach, the object of research is the principal and PAI teacher at SMAN Kota Bahagia, South Aceh. Data collection techniques that the author uses in this study are observation, interviews, and documentation. The researcher is the main research tool with the help of observation and interview guides. The data analysis techniques used are data collection, data reduction, data visualization, verification, and conclusion validation. The results of the study indicate that the teacher's lack of understanding of the media in using it as a tool in teaching, because there are still many teachers with a more mature age in teaching so that the media is not too important unilaterally, and also obstacles in the tools / media that exist in schools such as focus and others. in teaching, don't forget the problem of networking in teaching and the uneven ownership of cellphones not all students have cellphones to support the learning process.
Content Moderator Startle Response: A Qualitative Study

Commercial content moderators review user-generated content (UGC) to ensure the posts meet platform policies, guidelines, community standards, and regional laws. While the majority of posted UGC is deemed acceptable, a large amount of content remains, which is classified as non-compliant and may include gore, violence, suicide, child sexual abuse material (CSAM), and pornography, to name a few. Because of this, content moderators have a greater prevalence of their nervous system activating a startle response, which can impact emotional, psychological, and physiological processes. Prior research on content moderators has failed to explore moderators’ initial reactions to content from the start of employment through tenure as the subjection to material and habituation increases. This study takes an in-depth look at moderators’ experiences from recruiting, through training, and production to better understand the content moderators’ startle response and factors that enable startle habituation. The current study sample consisted of 78 total respondents—38 content moderators in the Philippines employed by TaskUs Inc. and 40 in Estonia employed by Sutherland Global Services. Employee tenure ranged from 0 to 6 months. Succeeding our analysis, transparency, understanding, and preparedness were major themes identified as the critical factors found within both companies when exploring the activation of the startle response and facilitation of habituation following content exposure. These themes were prevalent in the employment life-cycle's recruiting, training, and production phases.
How to train your pre-trained GAN models

Abstract Generative Adversarial Networks (GAN) show excellent performance in various problems of computer vision, computer graphics, and machine learning, but require large amounts of data and huge computational resources. There is also the issue of unstable training. If the generator and discriminator diverge during the training process, the GAN is subsequently difficult to converge. In order to tackle these problems, various transfer learning methods have been introduced; however, mode collapse, which is a form of overfitting, often arises. Moreover, there were limitations in learning the distribution of the training data. In this paper, we provide a comprehensive review of the latest transfer learning methods as a solution to the problem, propose the most effective method of fixing some layers of the generator and discriminator, and discuss future prospects. The model to be used for the experiment is StyleGAN, and the performance evaluation uses Fréchet Inception Distance (FID), coverage, and density. Results of the experiment revealed that the proposed method did not overfit. The model was able to learn the distribution of the training data relatively well compared to the previously proposed methods. Moreover, it outperformed existing methods at the Stanford Cars, Stanford Dogs, Oxford Flower, Caltech-256, CUB-200–2011, and Insect-30 datasets.
Determining the L2 academic writing development stage: a corpus-based research on doctoral dissertations

Abstract This corpus-based research investigates features representing different stages of development in Pakistani academic writing (AW) to determine the stage of Pakistani AW in physical and social sciences. Corpus for this research comprises texts from 80 doctoral dissertations and is analyzed through AntConc after tagging with Multidimensional Analysis Tagger (MAT) and TagAnt. Results show Pakistani AW in both disciplines frequently comprising nouns as pre-modifiers that represent Stage-3. These are lower level features used by the writers developing towards higher levels. Therefore, Pakistani advanced level AW in physical and social sciences is positioned at Stage-3. This suggests that Pakistani writers in these disciplines are operating below the expected developmental level specifically Stage-5. These results are found to oppose the general hypothesis i.e. L2 (English as a second language) academic writers frequently use phrasal features at the advanced level. Consequently, Pakistani AW is concluded to be below the required level of language development.
Framing your concerns right: an analysis of air passengers' complaints during two time periods

Purpose Consumers are increasingly choosing social media over other channels and mechanisms for grievance redressal. However, not all social media grievances elicit a response from businesses. Hence, in this research the authors aim to explore the effect of the complainant's social characteristics and the complaint's social and content characteristics on the likelihood of receiving a response to a grievance from the business on social media. Design/methodology/approach The authors build a conceptual model and then empirically test it to explore the effect of the complainant's characteristics and the complaint's characteristics on the likelihood of response from a business on social media. The authors use data of consumer grievances received by an Indian airline operator on Twitter during two time periods – the first corresponding to lockdown during Covid-19 pandemic, and the second corresponding to the resumption of business as usual following these lockdowns. The authors use logistic regression and the hazard rate model to model the likelihood of response and the response delay, respectively, for social media customer grievances. Findings Complainants with high social influence are not more likely to get a response for their grievances on social media. While tagging other individuals and business accounts in a social media complaint has negative effect on the likelihood of business response in both the time periods, the effect of tagging regulatory bodies on the likelihood of response was negative only in the Covid-19 lockdown period. The readability and valence of a complaint were found to positively affect the likelihood of response to a social media grievance. However, the effect of valence was significant only in lockdown period. Originality/value This research offers insights on what elicits responses from a service provider to consumers' grievances on social media platforms. The extant literature is a plenty on how firms should be engaging consumers on online media and how online communities should be built, but scanty on grievance redressal on social media. This research is, therefore, likely to be useful to service providers who are inclined to improve their grievance handling mechanisms, as well as, to regulatory authorities and ombudsmen.