Reviewerly/exHarmony huggingface dataset:
An Individualized Face Pairing Model for AgeInvariant Face Recognition

Among other factors affecting face recognition and verification, the aging of individuals is a particularly challenging one.Unlike other factors such as pose, expression, and illumination, aging is uncontrollable, personalized, and takes place throughout human life.Thus, while the effects of factors such as head pose, illumination, and facial expression on face recognition can be minimized by using images from controlled environments, the effect of aging cannot be so controlled.This work exploits the personalized nature of aging to reduce the effect of aging on face recognition so that an individual can be correctly recognized across his/her different age-separated face images.To achieve this, an individualized face pairing method was developed in this work to pair faces against entire sets of faces grouped by individuals then, similarity score vectors are obtained for both matching and non-matching image-individual pairs, and the vectors are then used for age-invariant face recognition.This model has the advantage of being able to capture all possible face matchings (intra-class and inter-class) within a face dataset without having to compute all possible image-to-image pairs.This reduces the computational demand of the model without compromising the impact of the ageing factor on the identity of the human face.The developed model was evaluated on the publicly available FG-NET dataset, two subsets of the CACD dataset, and a locally obtained FAGE dataset using leave-one-person (LOPO) cross-validation.The model achieved recognition accuracies of 97.01%, 99.89%, 99.92%, and 99.53% respectively.The developed model can be used to improve face recognition models by making them robust to agevariations in individuals in the dataset.
Peran Strategis Teknologi Informasi dan Komunikasi di STIT Al-Ittihadiyah Labuhan Batu Utara

Higher education such as universities, colleges and other high schools education management is applied differently from secondary or primary education institutions because it is more complex. Educational institutions must carry out educational programs as well as possible so that the quality of education is increasingly advanced. In order to carry out education as well as possible, it is necessary to have good management or institutional management. The development of information and communication technology has given many roles in the world of education. Not only applied in direct learning, but in the implementation of management of educational institutions is also needed so that it can support the course of learning and lectures. Many universities have used information technology to support their activities. In general, computer technology is used to support administrative processes, such as academic administration, finance, and personnel. The emergence of problems like the one above and the way universities deal with them shows that universities still view the implementation of information technology as something that is "separate" from the business processes they carry out. Whereas information technology should be an enabler for various efforts to improve quality, effectiveness, efficiency, and accountability in tertiary institutions.
Mathematics student personas for the design of technology-enhanced learning environments

To benefit from the quickly expanding range of new possibilities of technology-enhanced education, school systems, schools, and teachers need to adapt quickly. Conversely, the needs of students and teachers in a technology-enhanced classroom require technology developers to provide and improve suitable technologies. In this paper, we aim to show how to make the professional knowledge of mathematics teachers accessible to developers of technologies and also to teacher trainers and trainees by the use of student personas, i.e., portraits of archetypical students with particular characteristics and needs. We have collected qualitative data from pre-service and in-service mathematics teachers in Austrian academic upper secondary schools about the characteristics and needs of their students. We have analysed these data using a grounded theory approach to derive demands of students on technology-enhanced learning environments (TELEs). We have identified and presented five personas, each with specific demands on TELEs, to represent this target group. By introducing this approach that combines techniques from mathematics education research and from user experience research, we are able to represent user groups of a mathematics technology-enhanced learning environment in a more relatable way than was previously possible. These relatable representations of students in Austrian academic upper secondary schools could be of particular importance for developers of technology-enhanced learning environments for teaching and learning mathematics. The methodology presented in this paper is adaptable to other contexts.
The Worthwhileness of Immersive Journalism—Taking on an Audience Perspective

A growing number of studies in journalism research are concerned with the effects of immersive journalism (IJ) on audience perceptions and behaviors. This interest in IJ is logical, because IJ has the potential to become an impactful innovation for the industry. However, we have largely neglected the question of whether audiences want this form of emotional journalism. This study fills this gap and investigates whether people consider IJ worth their while. Using a factorial survey design, we presented a sample of 2000 German citizens with descriptions of an immersive production about protests in Belarus, in which we manipulate the use of inclusive technology (VR vs. AR vs. video), immersive narratives (first person vs. third person), agency (choice of perspective vs. no choice of perspective and control of location vs. no control), and emotionality (positive vs. negative vs. neutral tone). The analyzes reveal that an immersive narrative perspective, control and emotionality do not predict worthwhileness perceptions. However, productions that present people with inclusion and technological agency render this production more worthwhile in the eye of the individual user.
Estimating and Evaluating the Uncertainty of Rating Predictions and Top-n Recommendations in Recommender Systems

Uncertainty is a characteristic of every data-driven application, including recommender systems. The quantification of uncertainty can be key to increasing user trust in recommendations or choosing which recommendations should be accompanied by an explanation; uncertainty estimates can be used to accomplish recommender tasks such as active learning and co-training. Many uncertainty estimators are available, but to date, the literature has lacked a comprehensive survey and a detailed comparison. In this article, we fulfill these needs. We review the existing methods for uncertainty estimation and metrics for evaluating uncertainty estimates, while also proposing some estimation methods and evaluation metrics of our own. Using two datasets, we compare the methods using the evaluation metrics that we describe, and we discuss their strengths and potential issues. The goal of this work is to provide a foundation to the field of uncertainty estimation in recommender systems, on which further research can be built.
Thwarting Instant Messaging Phishing Attacks: The Role of Self-Efficacy and the Mediating Effect of Attitude towards Online Sharing of Personal Information

Context: The cause of cybercrime phishing threats in Malaysia is a lack of knowledge and awareness of phishing. Objective: The effects of self-efficacy (the ability to gain anti-phishing knowledge) and protection motivation (attitude toward sharing personal information online) on the risk of instant messaging phishing attacks (phishing susceptibility) are investigated in this study. The protection motivation theory (PMT) was tested in the context of attitudes toward sharing personal information online with a view to improving interventions to reduce the risk of phishing victimisation. Methods: Data were collected using non-probability purposive sampling. An online survey of 328 Malaysian active instant messaging users was collected and analysed in SmartPLS version 4.0.8.6 using partial least squares structural equation modelling. Results: The results showed that a person’s cognitive factor (either high or low self-efficacy) affected their chance of being a victim of instant message phishing. A higher level of self-efficacy and a negative attitude towards sharing personal information online were significant predictors of phishing susceptibility. A negative attitude towards sharing personal information online mediated the relationship between high levels of self-efficacy and phishing susceptibility. A higher level of self-efficacy led to the formation of negative attitudes among internet users. Attitudes toward the sharing of personal information online are critical because they allow phishing attempts to exist and succeed. Conclusions: The findings give government agencies more information on how to organise anti-phishing campaigns and awareness programmes; awareness and education can improve one’s ability to acquire anti-phishing knowledge (self-efficacy).
GPU-accelerated Faster Mean Shift with euclidean distance metrics

Handling clustering problems are important in data statistics, pattern recognition and image processing. The mean-shift algorithm, a common unsupervised algorithms, is widely used to solve clustering problems. However, the mean-shift algorithm is restricted by its huge computational resource cost. In previous research[10], we proposed a novel GPU-accelerated Faster Mean-shift algorithm, which greatly speed up the cosine-embedding clustering problem. In this study, we extend and improve the previous algorithm to handle Euclidean distance metrics. Different from conventional GPU-based mean-shift algorithms, our algorithm adopts novel Seed Selection & Early Stopping approaches, which greatly increase computing speed and reduce GPU memory consumption. In the simulation testing, when processing a 200K points clustering problem, our algorithm achieved around 3 times speedup compared to the state-of-the-art GPU-based mean-shift algorithms with optimized GPU memory consumption. Moreover, in this study, we implemented a plug-and-play model for faster mean-shift algorithm, which can be easily deployed. (Plug-and-play model is available: https://github.com/masqm/Faster-Mean-Shift-Euc)
Research on the Technology of Computer Network Security Protection

The continuous deepening of the number and importance of sensitive and key info stored in computer network system has brought severe challenges to the computer cyber security protection tech. It is urgent to upgrade the cyber security protection, so that it can serve and match the healthy development of computer network. Based on this, this paper first analyses the concept and connotation of computer cyber security, then studies the main content of computer cyber security protection tech, and finally gives the computer cyber security protection measures.
Researchers’ perceptions, patterns, motives, and challenges in self-archiving as a function of the discipline

The green open access (OA) model, which offers the most economical approach to comply with open access policies, can increase researchers’ audience and scientific outputs impact by delivering wider and easier access. This study examined researchers’ perceptions from STEM (science, technology, engineering, math) and SSH (social sciences, art and humanities) disciplines in order to reveal the types, patterns, motives, and challenges underlying their articles’ self-archiving in the green route to open-access (repositories and institutional repositories) and ASNs (academic social networks). Interviews were conducted with 20 Israeli academic researchers. Half were from STEM and half from SSH disciplines. Interviews were mapped using a bottom-up thematic analysis and follow-up quantitative comparisons. According to the findings, STEM researchers self-archived pre/post-print versions of their articles to subject-based repositories as a part of their discipline norm resulting from their funding grant requirements and as a way to receive recognition and claim priority. SSH researchers post a link to the printed-published article at the publisher’s website in ASNs, and their goal is greater visibility. In addition, findings indicate a lack of awareness, mostly by SSH researchers, regarding copyright issues and OA repositories. The green OA model provides opportunities for researchers to self-archive their work. However, there are differences between the disciplines regarding where, when, why, and how to self-archive, and what is considered a legitimate mode of green OA. This indicates an urgent need to raise SSH researchers’ awareness of the existence of open subject-based repositories and of the terms of self-archiving from publishers.
Pragmatic functions of formulaic expressions in Cebuano

Natural conversations contain expressions that can be deciphered through their contextual use. These expressions are often flexible, allowing them to function beyond their literal meaning. Thus, pragmatics is functional in explaining their significance in speakers' utterances, mainly when expressing affirmation and disapproval. This study investigates the pragmatic functions of agreeing and disagreeing formulaic expressions in the Cebuano language, namely o/oo, lagi, bitaw, mao, wala, and dili. The framework of Tanangkingsing (2009) was employed to analyze and describe the functions of agreeing and disagreeing formulaic expressions in the Cebuano language used in a radio conversation. The study revealed that agreeing formulaic expressions are primarily used to convey agreement to the statements of the other participants in the conversation and affirm their own statements. On the other hand, disagreeing formulaic expressions are primarily used to convey contradiction to the perceived wrong information given by the other speakers in a conversation, opposing views or counterarguments to the opinions stated in a conversation, and expressions to serve as negation. Agreeing and disagreeing formulaic expressions in the Cebuano language have various functions depending on the context and the speaker's purpose.
A Conic Radon-based Convolutional Neural Network for Image Recognition

This article presents a new approach for image recognition that proposes to combine Conical Radon Transform (CRT) and Convolutional Neural Networks (CNN). In order to evaluate the performance of this approach for pattern recognition task, we have built a Radon descriptor enhancing features extracted by linear, circular and parabolic RT. The main idea consists in exploring the use of Conic Radon transform to define a robust image descriptor. Specifically, the Radon transformation is initially applied on the image. Afterwards, the extracted features are combined with image and then entered as an input into the convolutional layers. Experimental evaluation demonstrates that our descriptor which joins together extraction of features of different shapes and the convolutional neural networks achieves satisfactory results for describing images on public available datasets such as, ETH80, and FLAVIA. Our proposed approach recognizes objects with an accuracy of 96 % when tested on the ETH80 dataset. It also has yielded competitive accuracy than state-of-the-art methods when tested on the FLAVIA dataset with accuracy of 98 %. We also carried out experiments on traffic signs dataset GTSBR. We investigate in this work the use of simple CNN models to focus on the utility of our descriptor. We propose a new lightweight network for traffic signs that does not require a large number of parameters. The objective of this work is to achieve optimal results in terms of accuracy and to reduce network parameters. This approach could be adopted in real time applications. It classified traffic signs with high accuracy of 99%.
Artificial Neural Networks and its Applications: A Review

Neural networks have seen an explosion of interest over the last few years and are being successfully applied across an extraordinary range of problem domains, in areas as diverse as finance, medicine, engineering, geology, physics and biology. The excitement stems from the fact that these networks are attempts to model the capabilities of the human brain. From a statistical perspective neural networks are interesting because of their potential use in prediction and classification problems. Artificial neural networks (ANNs) are non-linear data driven self adaptive approach as opposed to the traditional model based methods. They are powerful tools for modelling, especially when the underlying data relationship is unknown. ANNs can identify and learn correlated patterns between input data sets and corresponding target values. After training, ANNs can be used to predict the outcome of new independent input data. ANNs imitate the learning process of the human brain and can process problems involving non-linear and complex data even if the data are imprecise and noisy. Thus they are ideally suited for the modeling of agricultural data which are known to be complex and often non-linear. A very important feature of these networks is their adaptive nature, where “learning by example” replaces “programming” in solving problems. This feature makes such computational models very appealing in application domains where one has little or incomplete understanding of the problem to be solved but where training data is readily available
A Categorical Normalization Proof for the Modal Lambda-Calculus

We investigate a simply typed modal $\lambda$-calculus, $\lambda^{\to\square}$, due to Pfenning, Wong and Davies, where we define a well-typed term with respect to a context stack that captures the possible world semantics in a syntactic way. It provides logical foundation for multi-staged meta-programming. Our main contribution in this paper is a normalization by evaluation (NbE) algorithm for $\lambda^{\to\square}$ which we prove sound and complete. The NbE algorithm is a moderate extension to the standard presheaf model of simply typed $\lambda$-calculus. However, central to the model construction and the NbE algorithm is the observation of Kripke-style substitutions on context stacks which brings together two previously separate concepts, structural modal transformations on context stacks and substitutions for individual assumptions. Moreover, Kripke-style substitutions allow us to give a formulation for contextual types, which can represent open code in a meta-programming setting. Our work lays the foundation for extending the logical foundation by Pfenning, Wong, and Davies towards building a practical, dependently typed foundation for meta-programming.
Event-Triggered Distributed Bias-Compensated Pseudolinear Information Filter for Bearings-Only Tracking Under Measurement Uncertainty

This article deals with the problem of multisensor bearings-only tracking (BOT) under measurement uncertainty. In order to effectively track the target while reducing the communication times and keeping the estimation accuracy, a novel distributed bias-compensated pseudolinear information filter with event-triggered comm- unication mechanism and hybrid-consensus-based fusion strategy is proposed. Each sensor transmits the local information to its neighbors only when it is considered to be valuable for the fusion of its neighboring sensors based on the normalized innovation. Besides, the weight of the transmitted information, which represents the importance degree of the local estimation result, is also taken into account. The stability of the proposed algorithm is proved. Simulation results verify the effectiveness and robustness of the proposed algorithm.
Multipartite quantum cryptography based on the violation of Svetlichny’s inequality

Multipartite cryptography is useful for some particular missions. In this paper, we present a quantum key distribution scheme in which three separated observers can securely share a set of keys by using a sequence of 3-particle GHZ states. We prove that the violation of Svetlichny’s inequality can be utilized to test for eavesdropping, and even when the eavesdropper can completely control the outcomes of two participants’ measurements, our scheme still ensures the security of the keys distribution. This scheme can be easily extended to the case of N-party keys distribution, and the violation of N-partite Svetlichny’s inequality guarantees the security of the generalized scheme. Since the GHZ state has maximum entanglement, its perfect monogamy guarantee the device-independent security of our protocol. However, quantum entanglement is a vulnerable resource which is often decayed during transmission, so we need here to derive the secret-key rate of our protocol under the condition of using quantum states with non-maximal entanglement. We then calculate the extractable secret-key rate of the three-party key distribution protocol for the Werner state in the device-independent scenario. We find that the value of the extractable secret-key rate monotonously approaches 1 as the value of the visibility of the Werner state increases, and it reaches its maximum value 1 when the Werner state becomes the GHZ state.
Blockchain: Applications, Challenges, and Opportunities in Consumer Electronics

One of the most beneficial and original solutions for cybersecurity is blockchain. Although the technology has only been on the market for a short time, it has already effectively replaced economic transaction systems in a number of businesses in its early stages and is capable of modernizing various business models in numerous consumer electronic applications. It is critical for planners and decision makers to thoroughly evaluate the suitability of Blockchain for their particular use cases and commercial applications, even as it promises a secure distributed framework for sharing, exchanging, and integrating information across users and third parties. Blockchain should only be deployed if it is suitable, offers security, and provides better opportunities to drive revenue and save costs. In this paper, you will get an overview of this technology that can be used to achieve security in consumer electronic applications. We also discuss the difficulties and potential benefits of using blockchain technologies in consumer electronics applications.
Quality methods in Virtual and Augmented Reality: A systematic literature review

Abstract To develop a uniform approach for the implementation of training in quality methods with the help of innovative assistance systems, the current state of research is determined in the context of this work. For this purpose, a systematic literature search (SLR) is used to determine the state of research with a focus on Augmented and Virtual Reality glasses, since these are particularly immersive technologies. Based on the databases Scopus and Web of Science, an extended SLR was used for the research. First research projects could be identified, but standardized models for training concepts of these technologies are still at the beginning and there is a need for further research, especially about the training of quality methods.
Controllable Radiance Fields for Dynamic Face Synthesis

Recent work on 3D-aware image synthesis has achieved compelling results using advances in neural rendering. However, 3D-aware synthesis of face dynamics hasn't received much attention. Here, we study how to explicitly control generative model synthesis of face dynamics exhibiting non-rigid motion (e.g., facial expression change), while simultaneously ensuring 3D-awareness. For this we propose a Controllable Radiance Field (CoRF): 1) Motion control is achieved by embedding motion features within the layered latent motion space of a style-based generator; 2) To ensure consistency of background, motion features and subject-specific attributes such as lighting, texture, shapes, albedo, and identity, a face parsing net, a head regressor and an identity encoder are incorporated. On head image/video data we show that CoRFs are 3D-aware while enabling editing of identity, viewing directions, and motion.