Reviewerly/exHarmony huggingface dataset:
The use of XR technology in criminological research: a scoping review

Abstract Objectives Criminology has begun to use virtual reality (VR) as a tool to understand criminal behaviour. There have been many advances in VR that open up the possibility of novel research designs, in addition to the inclusion of augmented reality (AR) as a potential research tool. A review of the efficacy of the use of AR and VR (together known as XR) within criminology is now required to guide the further development of this field. Methods We employed a scoping review, which after screening, incorporated 41 papers for review. Results We uncovered three important characteristics for the papers: research topics for which XR is employed, the prominence of VR research over AR and XR and methodological rigour. VR is mostly employed for creating scenarios, in particular in research on memory and aggression. Studies vary greatly in their use of interaction within VR scenes, description of technical specifications and sample sizes. Conclusions Despite the identification of some methodological limitations in the existing literature, VR has found its place in criminological research. With technological advancement, AR may also find its place in the future, allowing for the ongoing development of research applications. Creating comprehensive guidelines on how to use XR technology in criminology remains necessary.
An efficient deepfake video detection using robust deep learning

<h2>Abstract</h2> The creation and manipulation of synthetic images have evolved rapidly, creating serious concerns about their effects on society. Although there have been various attempts to identify deep fake videos, these approaches are not universal. Identifying these misleading deepfakes is the first step in preventing them from following on social media sites. We introduce a unique deep-learning technique to identify fraudulent clips. Most deepfake identifiers currently focus on identifying face exchange, lip synchronous, expression modification, puppeteers, and other factors. However, exploring a consistent basis for all forms of fake video and images in real-time forensics is challenging. We propose a hybrid technique that takes input from videos of successive targeted frames, then feeds these to the ResNet-Swish-BiLSTM, an optimized convolutional BiLSTM-based residual network for training and classification. This proposed method helps identify artifacts in deepfake images that do not seem real. To assess the robustness of our proposed model, we used the open deepfake detection challenge dataset (DFDC) and Face Forensics deepfake collections (FF++.) We achieved 96.23% accuracy when using the FF++ digital record. In contrast, we attained 78.33% accuracy using the aggregated records from FF++ and DFDC. We performed extensive experiments and believe that our proposed method provides more significant results than existing techniques.
Online teaching intention among distance education course tutors: Modelling the effects of human resource factors and moderating role of gender

This study examined the effect of human resource factors and the moderating role of gender on online teaching intention among course tutors in distance education. A quantitative approach and survey design were adopted for the study. Data were gathered with a self-administered questionnaire from 539-course tutors (teachers on the distance mode) in a university of distance education programmes. Data were analysed with the Partial Least Squares-Structural Equation Modelling (PLS-SEM) technique. The study found that remuneration and work relations were the two most important human resource factors that have significant predictive relationships with online use intention. Regarding the moderating role of gender, the results revealed that remuneration was relevant to both male and female facilitators. In addition, while work relations were found to be very important to male facilitators, retrenchment was considered to be important among female facilitators. It was therefore recommended that managers of distance education institutions pay attention to gender-biased human resource factors such as remuneration, work relations, and retrenchment as they embark on the use of online learning technologies for distance education delivery.
Exceptional Set Estimate Through Brascamp–Lieb Inequality

Abstract Fix integers $1\le k&amp;lt;n$, and numbers $a,s$ satisfying $0&amp;lt;s&amp;lt;\min \{k,a\}$. The problem of exceptional set estimate is to determine $$\begin{align*} &amp; T(a,s):=\sup_{A\subset \mathbb{R}^{n}\ \textrm{dim}A=a}\textrm{dim}(\{ V\in G(k,n): \textrm{dim}(\pi_{V}(A))&amp;lt;s \}). \end{align*}$$In this paper, we prove a new upper bound for $T(a,s)$ by using Brascamp–Lieb inequality. As one of the corollaries, we obtain the estimate $$\begin{align*} &amp; T(a,\frac{k}{n}a)\le k(n-k)-\min\{k,n-k\}, \end{align*}$$which improves a previous result $T(a,\frac{k}{n}a)\le k(n-k)-1$ of He. By constructing examples, we can determine the explicit value of $T(a,s)$ for certain $(a,s)$: When $k\le \frac{n}{2}$, $\beta \in (0,1]$ and $\gamma \in (\beta ,\frac{k}{n}(1+\beta )]$, we have $$\begin{align*} &amp;T(1+\beta,\gamma)=k(n-k)-k.\end{align*}$$ When $k\ge \frac{n}{2}$, $\beta \in (0,1]$ and $\gamma \in (\beta , (1-\frac{k}{n})+\frac{k}{n}\beta ]$, we have $$\begin{align*} &amp;T(n-1+\beta,k-1+\gamma)=k(n-k)-(n-k).\end{align*}$$
Emphasizing Writing Acts: The Exclamation Point in German as a Lexical Operator for Verum

Abstract This paper is concerned with the interpretation of the exclamation point in German; for example, Es hat geschneit! ‘It has been snowing!’. I argue that the exclamation point contributes a lexical operator for verum at the layer of writing acts. Specifically, it introduces the writer’s wish to ensure the recognition of the writing act in its scope. This lexicon-based proposal builds on a lexicon-based analysis of verum focus. However, while verum focus is related to the propositional layer, the exclamation point is argued to relate to the act layer. The following advantages are defended: (i) The proposal accounts for both the functional kinship between exclamation points and verum focus and their distributional differences. On the one hand, both means introduce affirmative emphasis by a bouletic attitude; on the other hand, the exclamation point has a much wider distribution than verum focus, as it is not bound to contexts that provide a controversy between a proposition and its negation. (ii) The difference in semantic scope can be traced back to a structural difference. While verum focus is integrated into its host clause, exclamation points occupy a peripheral structural position, which is typical of act modifiers. (iii) The lexicon-based approach to the interpretation of exclamation points has noteworthy broader implications. It provides an independent cross-modal piece of evidence in favor of a lexicon-based analysis of verum focus instead of a focus-based one. Furthermore, it is amenable to a formal compositional implementation. This implementation comprises first steps toward a formal model of writing acts in terms of Commitment Space Semantics and advances the general hypothesis that the graphematic form licenses a systematic mapping from form to meaning in its own right.
Testing and refinement of elemental proxies in tropical lakes from the Indian subcontinent

Abstract The conventional approach of using modern values of lake hydrochemistry and sediment composition as key parameters for identifying proxies for environmental reconstruction is complicated by regional climate, catchment geology, lake morphometry and anthropogenic input. In contrast to modern studies from lakes in the north‐temperate regions, reliable proxy indicators for past environmental change from tropical lake systems are still lacking. In this study, we compare new surface geochemical and mineralogical data from the Manasbal Lake, Kashmir (India), with other lakes situated in climatically and geomorphologically distinct regimes with varying degrees of pollution (Tso Moriri in north‐western Himalaya and Lonar in central India). We characterise external (e.g., geology and weathering), internal (e.g., lake water mixing and evaporation) factors and human activities influencing sediment composition and spatial distribution of proxies in each of these lakes to evaluate the usefulness of some of the commonly used proxies, as well as ascertain conditions where they might be applicable. Our results show that (i) morphometric parameters (such as lake size, water depth and catchment vs. lake area) variably influence the spatial distribution of sediments and proxies on the lake bottom highlighting the limitations of using single cores for long‐term palaeoclimate reconstruction; (ii) hydrochemical proxies (such as Na + /Na + +Ca 2+ and Mg/Ca) can delineate endogenic (Lonar and Tso Moriri lakes) versus allogenic (Manasbal Lake) influence on modern sediment characteristics; (iii) the compilation of heavy metal data (e.g., Fe, Zn, Mn, Cr and Ni) from all three lake basins with varying degrees of pollution highlights the limitations of using them to characterise weathering conditions in these basins; and (iv) human impact must be precluded before using the most promising endogenic minerals (calcite, aragonite, gypsum and pyrite) as palaeoenvironmental/palaeoclimate indicators.
Breast Cancer Classification from Histopathological Images using Future Search Optimization Algorithm and Deep Learning

In medical imaging, precise recognition of Breast Cancer (BC) is a challenge due to the complications of breast tissues. Histopathological detection is still considered the standard in BC detection. Still, the dramatic increase in workload and the complexity of histopathological image (HPI) make this task labor-intensive and dependent on the pathologist, making the advance of automated and precise HPI analysis techniques needed. Due to the automated feature extraction capability, Deep Learning (DL) methods have been effectively used in different sectors, particularly in the medical imaging sector. This study develops the future search algorithm with a DL-based breast cancer detection and classification (FSADL-BCDC) method. The FSADL-BCDC technique examines HPIs to detect and classify BC. To achieve this, the FSADL-BCDC technique implements Wiener Filtering (WF)-based preprocessing to eliminate the noise in the images. Additionally, the FSADL-BCDC uses the ResNeXt method for feature extraction with a Future Search Algorithm (FSA)-based tuning procedure. For BCDC, the FSADL-BCDC technique employs a Hybrid Convolutional Neural Network along with the Long Short-Term Memory (HCNN-LSTM) approach. Finally, the Sunflower Optimization (SFO) approach adjusts the hyperparameter values of the HCNN-LSTM. The outcomes of the FSADL-BCDC are inspected on a standard medical image dataset. Extensive relational studies highlighted the improved performance of the FSADL-BCDC approach in comparison with known methods by exhibiting an output of 96.94% and 98.69% under diverse datasets.
Governance, debt service, information technology and access to electricity in Africa

Abstract The study investigates the role of governance (i.e., ‘voice and accountability,’ political stability/no violence, regulatory quality, government effectiveness, corruption‐control and the rule of law) in the incidence of short‐term debt services on infrastructure development in the perspective of telecommunication infrastructure and access to electricity. The focus of the study is on 52 African countries for the period 2002–2021. The generalised method of moments is employed as estimation strategy and the following findings are established. Debt service has a negative unconditional effect on access to electricity and telecommunication infrastructure. Governance dynamics moderate the negative effect of debt service on infrastructure dynamics. Effective moderation is from regulatory quality and corruption‐control for access to electricity and from government effectiveness, regulatory quality, corruption‐control and rule of law, for telecommunication infrastructure. Policy implications are discussed.
ADAM: a robotic companion for enhanced quality of life in aging populations

One of the major problems of today's society is the rapid aging of its population. Life expectancy is increasing, but the quality of life is not. Faced with the growing number of people who require cognitive or physical assistance, new technological tools are emerging to help them. In this article, we present the ADAM robot, a new robot designed for domestic physical assistance. It mainly consists of a mobile base, two arms with grippers and vision systems. All this allows the performance of physical tasks that require navigation and manipulation of the environment. Among ADAM's features are its modularity, its adaptability to indoor environments and its versatility to function as an experimental platform and for service applications. In addition, it is designed to work respecting the user's personal space and is collaborative, so it can learn from experiences taught by them. We present the design of the robot as well as examples of use in domestic environments both alone and in collaboration with other domestic platforms, demonstrating its potential.
A stochastic variance reduced gradient method with adaptive step for stochastic optimization

Abstract In this paper, we propose a stochastic variance reduction gradient method with adaptive step size, referred to as the SVRG‐New BB method, to solve the convex stochastic optimization problem. The method could be roughly viewed as a hybrid of the SVRG algorithm and a new BB step mechanism. Under the condition that the objective function is strongly convex, we provide the linear convergence proof of this algorithm. Numerical experiment results show that the performance of the SVRG‐New BB algorithm can surpass other existing algorithms if parameters in the algorithm are properly chosen.
Challenges and solutions in the development of blockchain applications: Extraction from SLR and empirical study

Abstract Data protection is the most prior and valuable concern as stealing personal/organizational data by cyber criminals may arise issues ranging from privacy disclosure to system hacking. Blockchain is considered a top leading technology with the influence to reshape future industries, but like other revolutionary technologies, blockchain has its merits and demerits as well. However, due to its nascent stage, blockchain technology poses numerous challenges that hinder the effective development of blockchain. To uncover this potential hindrance and group the relevant barriers, a total of 12 challenges that may hinder the progress of blockchain application development have been identified from a sample of 52 primary studies. Based on the results of our research, the six challenges are considered critical an account for more than 30% occurrence. The critical challenges are “Lack of Proper Development Tools and Technology,” “Security Precaution Measures,” “Lack of Governance and Standards,” “Lack of Professional Expertise with Essential Skills and Knowledge,” “Lack of Organizational Support,” and “Interoperability Integration.” Using a comprehensive systematic literature review (SLR) and a questionnaire survey, a list of 65 solutions/practices has been identified to address the challenges that were identified. These solutions/practices will help blockchain developers to address the identified challenges and develop benign blockchain application in future. The results of our questionnaire survey largely align with the findings of the SLR. However, there are variations in the ranking of the challenges between the two datasets. The finding of this paper is to provide insights that can assist in streamlining and optimizing the development process of blockchain applications with greater ease and efficiency.
Two‐Server Oblivious Transfer for Quantum Messages

Abstract Oblivious transfer is considered as a cryptographic primitive task for quantum information processing over a quantum network. It is an essential building block for secure multiparty computation. It is known that one‐server oblivious transfer is impossible. When the task is the transmission of classical messages, protocols for two‐server oblivious transfer exist, i.e., existing protocols work under the assumption that two servers do not communicate with each other. However, when the task is the transmission of a quantum state, no existing method works even under the above two‐server assumption. Two‐server oblivious transfer protocols for quantum messages are proposed for the first time.
The effectiveness of VR environment on primary and secondary school students’ learning performance in science courses

VR technology is being widely used in education due to it's a sense of immersion, real-time interaction, and ability to stimulate imagination. However, there is a lack of research comparing students at different stages. This study used a quasi-experimental design involving 73 fourth-grade and 86 eighth-grade students in two schools in northwest China minority gathering area. One class in each grade level used Virtual Reality (VR) headsets device to teach science content, while the other used interactive whiteboards. The study aimed to examine the differences in students' learning performance, problem-solving ability, self-efficacy, and technology acceptance. The findings showed that VR environment: (a) enhanced the learning performance of primary school students but did not have a significant impact on secondary students; (b) significantly improved problem-solving ability for both primary and secondary school students; (c) significantly promoted self-efficacy for both primary and secondary students; and (d) had a significant influence on technology acceptance for both primary and secondary students. Therefore, we recommended that teachers could integrate VR technology into science labs to enhance secondary students' problem-solving ability and self-efficacy, and improve primary students' learning performance, problem-solving ability, and self-efficacy.
Pelatihan MIT App Inventor sebagai Upaya Meningkatkan Kemampuan Berpikir Logis Siswa SMAN 4 Parepare

This community service activity aims to improve the logical thinking abilities of SMAN 4 Parepare students by teaching the application of mathematical logic in compiling computer instructions based on MIT App Inventor. The method for implementing this service was problem-based learning training by taking the example of the traditional game of rock-paper-scissors as a learning case. Next, create a rock-paper-scissors game program using MIT App Inventor as a student learning experience in applying mathematical logic concepts. The evaluation instrument employed was a questionnaire to collect pre-test and post-test results, which were then analyzed using descriptive statistics and paired sample t-tests. The results of this service showed an increase in the average participant score on the post-test results, where the average pre-test score was 7.24 out of 15, while the average post-test score was 9.17 out of 15.
Velocity-aware spatial-temporal attention LSTM model for inverse dynamic model learning of manipulators

Introduction An accurate inverse dynamics model of manipulators can be effectively learned using neural networks. However, further research is required to investigate the impact of spatiotemporal variations in manipulator motion sequences on network learning. In this work, the Velocity Aware Spatial-Temporal Attention Residual LSTM neural network (VA-STA-ResLSTM) is proposed to learn a more accurate inverse dynamics model, which uses a velocity-aware spatial-temporal attention mechanism to extract dynamic spatiotemporal features selectively from the motion sequence of the serial manipulator. Methods The multi-layer perception (MLP) attention mechanism is adopted to capture the correlation between joint position and velocity in the motion sequence, and the state correlation between hidden units in the LSTM network to reduce the weight of invalid features. A velocity-aware state fusion approach of LSTM network hidden units' states is proposed, which utilizes variation in joint velocity to adapt to the temporal characteristics of the manipulator dynamic motion, improving the generalization and accuracy of the neural network. Results Comparative experiments have been conducted on two open datasets and a self-built dataset. Specifically, the proposed method achieved an average accuracy improvement of 61.88% and 43.93% on the two different open datasets and 71.13% on the self-built dataset compared to the LSTM network. These results demonstrate a significant advancement in accuracy for the proposed method. Discussion Compared with the state-of-the-art inverse dynamics model learning methods of manipulators, the modeling accuracy of the proposed method in this paper is higher by an average of 10%. Finally, by visualizing attention weights to explain the training procedure, it was found that dynamic modeling only relies on partial features, which is meaningful for future optimization of inverse dynamic model learning methods.
A terminological frame-network model of the conceptual and semantic organization of notions of computational linguistics

The research object is the conceptual and semantic organization of the terminological apparatus, the research subject is the logical and conceptual structure of notions denoted by terms of computational linguistics. The study aims to identify the features of the systemic organization of notions of computational linguistics based on the use of a terminological frame-network model. The paper briefly analyzes the use of the frame approach to modeling the structure of terminological systems by linguists. A terminological frame-network model of the subject area “Computational linguistics” is proposed. The study is illustrated with fragments of the terminological network of frames of the subject area “Synonymy of terms of computational linguistics” presented in the form of diagrams. The work is novel in that it provides an original definition of a terminological network of frames and is the first to formulate the rules for its construction. The research findings showed the possibility of using a terminological frame-network model to analyze the logical and conceptual structure of the subject area “Computational linguistics” and the organization of semantic term-formation processes and lexicosemantic relations in the terminology under study.
Enhancing teachers’ instruction on how to study: an exploration of the effectiveness of learning strategies for particular secondary school subjects

Teachers’ instructions on learning strategies play a key role on how students study. However, how the effectiveness of learning strategies can be matched to different types of knowledge in a diversity of subject content has remained unexplored. In the present study, we related the effectiveness of learning strategies to different types of knowledge through an umbrella review. Furthermore, using focus-groups interviews, we explored the relation between school subjects and these knowledge types. We concluded that the effects of particular learning strategies vary across different learning objectives of the subjects and we offered suggestions for the instruction of effective learning strategies.