Reviewerly/exHarmony huggingface dataset:
Improving face morph detection with the pairs training effect

Abstract It is becoming increasingly common for face morphs (weighted combinations of two people's photographs) to be submitted for inclusion in an official document, such as a passport. These images may sufficiently resemble both individuals that they can be used by either person in a ‘fraudulently obtained genuine’ document. Problematically, people are poor at detecting face morphs and there is limited evidence that this can be improved. Here, we tested whether the ‘pairs training effect’ (working in pairs, which we know improves unfamiliar face matching) can improve face morph detection. We found morph detection was more accurate when working in a pair. Further, the lower performer in the pair maintained this benefit when completing the task again individually. We conclude that the pairs training effect translates to face morph detection, and these findings have important implications for improving the detection of face morphs at the initial application stage.
Expanding the magic circle: Immersive storytelling that trains environmental perception

Scholarship on immersion in simulated environments often emphasizes cognitive immersion, or the suspension of disbelief that takes place in an illusionistic space that simulates reality, making the fact of mediation disappear in the experience. Marie-Laure Ryan writes that: “immersivity can be understood in two ways: in a properly VR sense, as the technology-induced experience of being surrounded by data, and in a narrative sense... as being imaginatively captivated by a storyworld” (230). Both of these definitions rest on the notion of cognitive immersion. Grounded in the field of post-dramatic multimedia performance, this paper will focus instead on immersive storytelling that activates the senses in a phenomenological experience. Rather than transporting the spectator into a fictional imaginary space, post-dramatic multimedia performance aims to make participants aware of their presence in the here and now (Klich and Scheer, 128). This paper will describe an immersive storytelling project that integrates virtual reality (VR) into live participatory performance events that take place outdoors. The paper is co-authored by an artist-researcher and two students who are working as research assistants on this project. We recount our creative research process in developing a pervasive game, which Montola defines as a “game that has one or more salient features that expand the contractual magic circle of play socially, spatially or temporally” (2005, 3). This game is played in a park and at key moments, inside VR environments that simulate that same park. The purpose of the game is to attune participants to the species in that particular environment.
A novel approach of privacy protection of mobile users while using location-based services applications

The necessity of protecting mobile users' privacy when using location-based services (LBS) is highlighted in recent study, and location cloaking is one of the solutions offered. Location cloaking makes an effort to safeguard users' location privacy by introducing uncertainty while finding out the users’ location-context. The existing location cloaking algorithms are cautious about quality of the query service metrics (QoS), which mainly include success rate, anonymity level and spatial resolution. However, performance metric that is time to generate the cloaked regions should be improved. This was particularly important when user density is high. This work provides an efficient location cloaking scheme where a clique based cloaked region is constructed via partitioning of user-graph using graph partitioning algorithm, thereby generates reasonably-sized cloaked regions in less time. Further, to optimize cloaked region generation, multi-depth partitioning algorithm called BLH (Balanced Location Hiding) is proposed. Through concurrent and optimal generations of cloaked regions the proposed location cloaking preserves users' location privacy to a higher extent while retaining both the QoS and performance criteria. Experimental findings demonstrate that the success rate of the proposed algorithms is significantly increased. Even with a higher user density, it is approximately 98% with quicker response time.
Proposed ethical framework for software requirements engineering

Abstract Requirements engineering is a fundamental process in software development phases. At the same time, it is a difficult phase and exposed many ethical violations. The main purpose is proposing an ethical framework for software requirements engineering that addresses the identified concerns. These concerns include problems associated with a knowledge gap, requirements identification, quality‐related concerns, unwillingness to give requirements, and practicing forbidden activities. These concerns are grouped into a category as the proposed framework components. Each of the categories encompasses more than one problem domain. The proposed framework suggests resolving mechanisms as collections of clauses for each of those concerns. An expert evaluation technique is used to validate the proposed framework. The experts are purposefully selected from software industries and institutions. Questionnaires and focus group discussions were used as data‐gathering tools for the validation of the proposed framework. The validity (face validity, content validity, and construct validity) and the reliability of the proposed framework were checked. The evaluation results show that the proposed framework has an acceptable range of validity and reliability. The proposed framework can be used as a guideline for software engineers to minimise the occurrence of those identified concerns during the requirements engineering process.
Analysis of Variables Affecting Transfer of Training of Ministry of Employment and Labor Learning Organization

이 연구는 고용노동부 학습조직의 현업적용도에 영향을 미치는 여러 변인을 탐색하고 상대적인 영향력을 분석하여 학습조직 참가자의 현업에 도움이 되도록 필요한 시사점을 제공하는데 그 목적이 있다. 이러한 연구목적을 달성하고자 선행연구 검토를 통해 학습조직의 현업적용도 영향 변인을 개인배경 특성, 학습자 특성(자기효능감), 교육프로그램 특성(전이설계, 직무관련성), 조직 특성(상사와 동료지원)으로 선별하고 분석을 진행하였다. 실증분석을 위해 고용노동부 학습조직 참가자 255명을 대상으로 기술통계 및 정규성검증, 상관관계분석, 위계적 회귀분석 등을 실시하여 연구결과를 다음과 같이 도출하였다. 첫째, 학습조직 참가자의 개인배경 특성에서는 근속연수가 2년 초과한 참가자가 2년 이하 참가자보다 현업적용도에 긍정적인 영향을 미치는 것으로 나타났다. 둘째, 학습자 특성의 자기효능감은 현업적용도에 긍정적인 영향을 미치는 것으로 나타났다. 셋째, 교육프로그램 특성의 전이설계와 직무관련성은 현업적용도에 긍정적인 영향을 미치는 것으로 나타났다. 넷째, 조직특성의 상사와 동료지원은 현업적용도에 긍정적인 영향을 미치는 것으로 확인되었다. 다섯째, 여러 변인 중 학습조직의 현업적용도에 가장 긍정적인 영향을 미치는 변인은 자기효능감으로 나타났다. 따라서 학습조직의 현업적용도를 높이기 위해서는 학습조직이 또 다른 업무가 되어 부담이 되는 것이 아니라, 학습조직을 통해 현업에 적용할 수 있는 자기효능감이 높은 참가자의 모집이 필요하겠다. 아울러 전이설계와 직무관련성이 높은 학습조직 활동과 상사와 동료의 지원이 현업에서의 적용을 높이는 변인임을 인식하고 개인의 노력과 조직에서의 학습하는 분위기 조성 등의 지원이 이루어져야 할 것이다.The purpose of this study is to explore the variables affecting the transfer of training of ministry of employment and labor learning organization to provide implications. The Survey data were collected from 255 participants in the Ministry of Employment and Labor's learning organization. For empirical analysis, descriptive statistics, regularity test, correlation analysis, hierarchical regression analysis were conducted. The results of this paper were as follows. First, in the background of participant, years of service which is more than 2 years had a positive effect on transfer of training. Second, self-efficacy of learner characteristic has a positive effect on transfer of training. Third, transfer of training and job relevance of educational program have positive effect on transfer of training. Fourth, supervisor and peer support have positive effects on transfer of training. Fifth, among the variables, the most positive variable affecting the transfer of training of learning organization was self-efficacy. Therefore, in order to increase the transfer of training of learning organization, it should not be a burden, but recruiting high self-efficacy participants, learning organization activities that are highly related to transfer design and job relevance, and support from supervisors and peers that can be applied to the field.
CINEMATIC DISCOURSE OF HISTORY IN THE CAMERA GLASS OF A HISTORIAN

The article is devoted to the interdisciplinary problem of using cinematic interpretations of the historical past in the professional activity of a historian, including its research and educational tasks.&#x0D; &#x0D; The purpose of the study is to create opportunities for a scientifically based definition of a historian’s professional “niche” when he turns to the cinematic discourse of the historical past.&#x0D; &#x0D; Materials and methods. To determine the methodological foundations of selected cognitive models, the principles of neo-positivism, narrative philosophy, methods of discursive analysis of texts, including such features of the language of cinema as imagery and pronounced symbolism, as well as the provisions of phenomenological epistemology, were used. In their unity, these cognitive theories of culture allowed the author of the article to present a verified version of the results obtained by analyzing interpretations of the creative intent of film directors found in the scientific literature.&#x0D; &#x0D; Study results. In the first part of the article, the main debatable aspects of the problem are considered in line with modern scientific trends, convincing of the relevance of the research undertaken. In its second part, the analysis of existing research practices in this field of knowledge is presented, the algorithms for reading historical information in feature films characteristic of each variant are identified and disclosed.&#x0D; &#x0D; Conclusions. The final part of the article contains as results the definition for each of the identified research models, conventionally called “descriptive-informational”, “imagological”, “source studies” and “memorial”. The classification is based on the criterion of goal-setting and the scientific apparatus associated with it – the methodological approach, methods, the object and the subject of research. The author of the article gives assessment of their importance in the interdisciplinary paradigm of scientific historical creativity, thereby determining possible prospects for the development of historians’ research practice in the field of cognition of the past based on cinematography works. It seems obvious that the practical significance of observations proposed by the article’s author about the possibilities of solving the problem posed by not only active, but also critically meaningful inclusion of the cinematic discourse of history in the sphere of research practice and in the construction of educational space.
Analysis Content Type and Emotion of the Presidential Election Users Tweets using Agglomerative Hierarchical Clustering

Over the past few years, social media has become essential for getting up-to-date information and interacting online. During presidential elections in Indonesia, Twitter has grown as a crucial platform for expressing opinions and sharing information. This study focuses on analyzing the content types and emotions of tweets related to Anies Baswedan, one of the presidential candidates. The results show a variety of discussions, including support, criticism, and discussion of policies for the 2024 presidential candidate. Clustering enables meaningful information extraction from vast Twitter data. Data were clustered using Agglomerative Hierarchical Clustering, which resulted in the identification of 10 clusters. With 4 clusters containing opinion content and 6 clusters containing information content. In addition, 6 clusters reflect excitement, 3 reflect expectations, and 1 reflect doubt. This research provides insights into the Twitter conversation around the 2024 presidential election, providing an understanding of content and emotions expressed by users.
Guiding the design and implementation of interactive public displays in educational settings

Abstract Interactive Public Displays (IPD) enable new ways of interaction as well as communication channels, extending online communities into physical places and supporting a culture of participation. While educational environments have seen how new digital technologies can enhance learning activities beyond the traditional classroom context, the use of IPDs is still an area insufficiently explored. This paper proposes a set of design goals for the implementation and deployment of engaging interactive public display applications in educational settings. Based on findings from a series of design workshops and two deployment studies in authentic settings, seven design goals were identified and defined. The design goals provide clear guidelines for the design of IPDs for schools by making design teams and stakeholders focus on factors fostering user adoption, social interactions and collaboration. The design goals also opened up paths for further explorations regarding display awareness, level of commitment in interactions, the displays’ integration into structured activities, and display management at the educational institutions.
Word2vec Architecture in Sentiment Classification of Fuel Price Increase Using CNN-BiLSTM Method

Fuel price increases have been a frequent problem in recent years, mainly due to unstable international price fluctuations. This research uses sentiment analysis to examine the increase in fuel prices and its impact on public sentiment. Sentiment analysis is a data processing method to obtain information about an issue by recognizing and extracting emotions or opinions from existing text. In this research, two architectures, Word2vec Continous Bag of Words (CBOW) and Skip-gram, were tested with different vector dimension sizes in each architecture using the CNN-BiLSTM hybrid deep learning method. The results showed that the CBOW model with 300 vector dimensions produced the best performance with 87% accuracy, 87% Recall, 89% Precision, and 88% F1-score on the tested Indonesian language dataset.
Anomaly and change point detection for time series with concept drift

Abstract Anomaly detection is one of the most important research contents in time series data analysis, which is widely used in many fields. In real world, the environment is usually dynamically changing, and the distribution of data changes over time, namely concept drift. The accuracy of static anomaly detection methods is bound to be reduced by concept drift. In addition, there is a sudden concept drift, which is manifested as a abrupt variation in a data point that changes the statistical properties of data. Such a point is called a change point, and it has very similar behavior to an anomaly. However, the existing methods cannot distinguish between anomaly and change point, so the existence of change point will affect the result of anomaly detection. In this paper, we propose an unsupervised method to simultaneously detect anomaly and change point for time series with concept drift. The method is based on the fluctuation features of data and converts the original data into the rate of change of data. It not only solves the concept drift, but also effectively detects and distinguishes anomalies and change points. Experiments on both public and synthetic datasets show that compared with the state-of-the-art anomaly detection methods, our method is superior to most of the existing works and significantly superior to existing methods for change point detection. It fully demonstrates the superiority of our method in detecting anomalies and change points simultaneously.
Certifying the quantum Fisher information from a given set of mean values: a semidefinite programming approach

We introduce a semidefinite programming algorithm to find the minimal quantum Fisher information compatible with an arbitrary dataset of mean values. This certification task allows one to quantify the resource content of a quantum system for metrology applications without complete knowledge of the quantum state. We implement the algorithm to study quantum spin ensembles. We first focus on Dicke states, where our findings challenge and complement previous results in the literature. We then investigate states generated during the one-axis twisting dynamics, where in particular we find that the metrological power of the so-called multi-headed cat states can be certified using simple collective spin observables, such as fourth-order moments for small systems, and parity measurements for arbitrary system sizes.
Search engine discourse analysis: how ‘shadowban’ affects policy

Amidst company denials, public interest in ‘shadowbans’ has grown. Without notifying the creator, ‘shadowbans’ limit the visibility of user-generated content. Shadowbans can be an effective content moderation technique, although they are subject to the same policy concerns. Existing research has focused on technologically demonstrating shadowbans, while other research has focused on people’s experiences, including how social media users use the word ‘shadowban’ to call for platform accountability. However, less research has focused on media discourses. Thus, I ask: what does ‘shadowban’ do in search engine results? Using Search Engine Discourse Analysis (SEDA) of 107 search engine results, I argue that when incorporated into media discourses returned via search engine, ‘shadowban’ maintains existing power dynamics through attributions of agency, strategically managing conflict, and producing markets, all of which have the potential to shape policy debates. Future discussions of ‘shadowbans’ should critically attend to power differentials in the public sphere. Moreover, if governance is legitimized through media, this article demonstrates how critical analysis of how media sources frame platform governance is an important line of inquiry.
Digital forensics approach for handling audio and video files

Digital forensics is the modern approach in scientific data analysis for data acquisition, analysis of data, and preservation of data contained in digital sources, which can be used as electronic evidence in cybercrime investigations. One of the essential digital forensics tasks is audio and video data analysis, also called multimedia forensics. Audio and video are digitalized sources of evidence that can be found at a crime scene, with the victim, or with the accused in audio tape from a mobile device or any CCTV footage. In cybercrime investigations, digital evidence is critical and plays a vital role in proving the crime before a court of law. As a result, audio and video forensics is the most important discipline of forensic research in the digital age. The present paper briefly discusses the basic audio-video forensics approach using simple tools.
MDS coding enabled proxy‐based Internet censorship circumvention system

Abstract In this letter, a novel maximum distance separable coding enabled proxy‐based Internet censorship circumvention system is proposed to handle severe security threats. By introducing redundancy encoding and multi‐proxy transmission, its anti‐censorship capability is significantly enhanced in terms of transmission reliability and data security. A generalized characterization method is constructed for the proposed system utilizing a six‐parameter tuple. Defining successful transmission probability and secure transmission probability as the evaluation metrics of system performances, their mathematical formulations are further derived theoretically. Extensive simulation results are provided to demonstrate the effectiveness and advantages of the proposed system.
Using a Graph Engine to Visualize the Reconnaissance Tactic of the MITRE ATT&amp;CK Framework from UWF-ZeekData22

There has been a great deal of research in the area of using graph engines and graph databases to model network traffic and network attacks, but the novelty of this research lies in visually or graphically representing the Reconnaissance Tactic (TA0043) of the MITRE ATT&amp;CK framework. Using the newly created dataset, UWF-Zeekdata22, based on the MITRE ATT&amp;CK framework, patterns involving network connectivity, connection duration, and data volume were found and loaded into a graph environment. Patterns were also found in the graphed data that matched the Reconnaissance as well as other tactics captured by UWF-Zeekdata22. The star motif was particularly useful in mapping the Reconnaissance Tactic. The results of this paper show that graph databases/graph engines can be essential tools for understanding network traffic and trying to detect network intrusions before they happen. Finally, an analysis of the runtime performance of the reduced dataset used to create the graph databases showed that the reduced datasets performed better than the full dataset.
Fuzzy coloured petri nets‐based method to analyse and verify the functionality of software

Abstract Some types of software systems, like event‐based and non‐deterministic ones, are usually specified as rules so that we can analyse the system behaviour by drawing inferences from firing the rules. However, when the fuzzy rules are used for the specification of non‐deterministic behaviour and they contain a large number of variables, they constitute a complex form that is difficult to understand and infer. A solution is to visualise the system specification with the capability of automatic rule inference. In this study, by representing a high‐level system specification, the authors visualise rule representation and firing using fuzzy coloured Petri‐nets . Already, several fuzzy Petri‐nets‐based methods have been presented, but they either do not support a large number of rules and variables or do not consider significant cases like (a) the weight of the premise's propositions in the occurrence of the rule conclusion, (b) the weight of conclusion's proposition, (c) threshold values for premise and conclusion's propositions of the rule, and (d) the certainty factor (CF) for the rule or the conclusion's proposition. By considering cases (a)–(d), a wider variety of fuzzy rules are supported. The authors applied their model to the analysis of attacks against a part of a real secure water treatment system. In another real experiment, the authors applied the model to the two scenarios from their previous work and analysed the results.