Reviewerly/exHarmony huggingface dataset:
Primary school students’ experiences of immersive virtual reality use in the classroom

The purpose of the present study was to examine primary school students' learning experiences with immersive virtual reality (I-VR). Traditional education practices are failing to inspire new cohorts of young people who have grown up with digital culture based on active participation. Given this development, the pedagogic use of I-VR systems represents a promising way of fostering students' learning engagement. This study involved engaging three groups of 10–12-year-old students with an I-VR system as part of an environmental studies project. Data were collected from the students before, during, and after the project via surveys that included structured and open-ended items. Through qualitative content analysis, we analysed the students' experiences, comprehension of virtual reality, assessment of learning impact, and desire for educational I-VRs. In general, we aimed to contribute to more learner-driven development for the technology. The students' experiences were primarily positive and revealed various actualised physical, cognitive, and emotional affordances. Their comprehension of virtual reality was influenced by the devices used, mental immersion, and aspects of the current project. Furthermore, I-VR was experienced as having influenced learning motivation, methods, and content. Finally, the students imagined various I-VRs for learning, most of which included time travel to imaginary spaces.
FEGNet: A feature enhancement and guided network for infrared object detection in underground mines

Object detection plays an important role in underground intelligent vehicles and intelligent transportation systems. Due to the uneven light in underground mining scenarios, infrared cameras are one of the typical onboard sensors for environmental perception. Although object detection has been studied for decades, it still confronts the challenge of detecting infrared objects in underground mines. The contributing factors include weak and small objects in infrared images and similar environments in mining scenarios. In this paper, a Feature Enhancement and Guided Network (FEGNet) is proposed to address these problems. Based on the characteristics of infrared images, the feature enhancement module (FEM) preserves the image details from global and local perspectives to improve the discrimination of weak and small objects. To tackle the problem of overfitting caused by similar environments, a receptive-field-guided (RFG) backbone is proposed to learn multi-scale context and spatial information. The experimental results on the underground mining (UM) dataset demonstrate that the mAP of the proposed FEGNet achieves 86.1%, which is 4.6% higher than the state-of-the-art CNN-based network YOLOv7.
Hybrid gated recurrent unit and convolutional neural network-based deep learning mechanism for efficient shilling attack detection in social networks

The degree of openness of the socially aware recommendation systems and the possibility of the attackers injecting vast numbers of fake profiles biases the prediction of the system. Most classical shilling attack discovery mechanisms rely on artificially derived features, generally determined from user-generated data. Moreover, these baseline approaches cannot comprehensively capture the fine-grained relation between users and objects, thereby lagging in detection accuracy. This work introduces a Hybrid deep learning mechanism to address the Shilling Attack discovery with improved accuracy. The proposed mechanism highly enhances the detection accuracy by determining significant feature vectors managed by recurrent gated units and other high dimensional attribute vectors attained by using CNN. The GRU component is incorporated for the dimensionality transition process for existing weight classification data to learn the inherent significant features. The CNN mechanism is mainly used for analyzing the conditions of spatial-temporal data and transforming the data into a dormant feature vector. The experimental analysis of our proposed method is conducted using the datasets of Amazon, Netflix, and Movielens to demonstrate its predominance by enhancing accuracy, precision, and F-score.
Reading Health and the Reading Health Index

This paper introduces Reading Health as a concept to help chart the collective reading habits of the individuals that make up a society—at any aggregate level. Now that the digital medium is transforming reading practices, and particularly the position of paper books and longform reading in society, the authors suggest a thorough examination of prevalent assumptions about reading and a fresh formulation of explicit reading aims for the future. The concept of Reading Health (RH) is paired with a corresponding Reading Health Index (RHI), an instrument to benchmark and keep track of changes in a society’s Reading Health over time.
Graph Neural Networks with Motisf-aware for Tenuous Subgraph Finding

Tenuous subgraph finding aims to detect a subgraph with few social interactions and weak relationships among nodes. Despite significant efforts made on this task, they are mostly carried out in view of graph-structured data. These methods depend on calculating the shortest path and need to enumerate all the paths between nodes, which suffer the combinatorial explosion. Moreover, they all lack the integration of neighborhood information. To this end, we propose a novel model named Graph Neural Network with Motif-aware for tenuous subgraph finding (GNNM), a neighborhood aggregation-based GNN framework that can capture the latent relationship between nodes. We design a GNN module to project nodes into a low-dimensional vector combining the higher-order correlation within nodes based on a motif-aware module. Then we design greedy algorithms in vector space to obtain a tenuous subgraph whose size is greater than a specified constraint. Particularly, considering that existing evaluation indicators cannot capture the latent friendship between nodes, we introduce a novel Potential Friend concept to measure the tenuity of a graph from a new perspective. Experimental results on the real-world and synthetic datasets demonstrate that our proposed method GNNM outperforms existing algorithms in efficiency and subgraph quality.
Random image frequency aggregation dropout in image classification for deep convolutional neural networks

Modern deep neural networks are core technologies used in various fields of artificial intelligence. However, they require a sufficient dataset to leverage their high representation power and full potential. As a result, data augmentation is an essential method for fully utilizing these networks’ capabilities. This study proposes a novel data augmentation method called random image frequency aggregation dropout, (RIFAD). RIFAD consists of two sub-algorithms: Fourier spectrum analysis (FSA) and frequency aggregation dropout (FAD). In FSA, the angular distribution is extracted by analyzing the Fourier spectrum of the input image. In FAD, frequency aggregation is deleted after randomly selecting an angle from the angle distribution. Empirically, we demonstrated that our method significantly improves prior state-of-the-art data augmentation using various convolutional neural network (CNN) architectures for image classification. We achieved superior Top-1 errors of 5.14%, 24.65%, and 21.45% on CIFAR-10, CIFAR-100, and STL-10 with Wide ResNet-40-2, respectively. We also demonstrated that the CNN with RIFAD had the best accuracy of 0.8961, precision of 0.9034, recall of 0.9046, f1-score of 0.8942, and area under the curve (AUC) of 0.9858 on a chest X-ray dataset.
Home Activity Recognition by Sounds of Daily Life Using Improved Feature Extraction Method

In recent years, with the aging of society, many kinds of research have been actively conducted to recognize human activity in a home to watch over the elderly. Multiple sensors for activity recognition are used. However, we need to consider privacy when using these sensors. One of the candidates of the sensors that keep privacy is a sound sensor. MFCC (Mel-Frequency Cepstral Coefficient) is widely used as a feature extraction algorithm for voice recognition. However, it is not suitable to apply conventional MFCC to activity recognition by sounds of daily life. We denote “sounds of daily life” as “life sounds” simply in this paper. The reason is that conventional MFCC does not extract well several features of life sounds that appear at high frequencies. This paper proposes the improved MFCC and reports the evaluation results of activity recognition by machine learning SVM (Support Vector Machine) using features extracted by improved MFCC.
An Efficient Combined Bit-Width Reducing Method for Ising Models

Annealing machines such as quantum annealing machines and semiconductor-based annealing machines have been attracting attention as an efficient computing alternative for solving combinatorial optimization problems. They solve original combinatorial optimization problems by transforming them into a data structure called an Ising model. At that time, the bit-widths of the coefficients of the Ising model have to be kept within the range that an annealing machine can deal with. However, by reducing the Ising-model bit-widths, its minimum energy state, or ground state, may become different from that of the original one, and hence the targeted combinatorial optimization problem cannot be well solved. This paper proposes an effective method for reducing Ising model's bit-widths. The proposed method is composed of two processes: First, given an Ising model with large coefficient bit-widths, the shift method is applied to reduce its bit-widths roughly. Second, the spin-adding method is applied to further reduce its bit-widths to those that annealing machines can deal with. Without adding too many extra spins, we efficiently reduce the coefficient bit-widths of the original Ising model. Furthermore, the ground state before and after reducing the coefficient bit-widths is not much changed in most of the practical cases. Experimental evaluations demonstrate the effectiveness of the proposed method, compared to existing methods.
In the Name of Dark Web Justice: A Crime Script Analysis of Hacking Services and the Underground Justice System

The Dark Web serves as a key venue for peddling illegal goods and services, from stolen possessions and drugs to illicit activities. The concurrent increase in Tor network usage and the development of cryptocurrency has led to the creation of major black market sites. One of the most popular illicit services offered on the Dark Web is hacking, which includes website/social media account hacking, Denial-of-service attacks, and custom malware. At the same time, the Dark Web community has formed its own justice system leveraging the layer of anonymity that exists between underground community users and the cybercrime ecosystem. Existing studies predominantly focus on the major drug market operating on the Dark Web, firearm sales, sexual exploitation, and money laundering. To address the gaps in the current research as well as the relative nascency of an underground justice system to monitor hacking services on the Dark Web, this study attempts to broadly capture the dynamic nature of hacking services, which requires continuous research to identify new trends and develop effective responses. The study aims to examine the characteristics and the operations of the hacking service market and the underground justice system on the Dark Web via an in-depth examination of Dark Web forums with a crime script analysis vis-à-vis thematic analysis. The study defines the crime script as that which includes pretrial, mid-trial, finalization, and exit stages. This research sheds light on Dark Web justice courts’ procedures and the courts’ implications for shaping the future of the Dark Web.
An e-Coin Based Construction for Unlinkable Priced Oblivious Transfer

Abstract A ‘1-out-of-n’ oblivious transfer (OT) protocol involves two participants: a sender, who provides a set of items as input, and a receiver. The protocol guarantees that the receiver gets exactly one of the items of the input set, while the sender is unable to determine which. Priced OT (POT) protocols further allow each item of the input set to be assigned a price in such a way that, after a proper execution of the protocol, the receiver gets the requested item if and only if the corresponding sum of money has been paid. In this paper, we present a construction which takes a ‘1-out-of-n’ OT protocol and transform it into a POT one. Moreover, the resulting system is unlinkable in the sense that the sender is unable to determine whether two executions of the protocol were run by the same receiver or not. When compared to existing unlinkable POT protocols, our construction offers a lower conceptual complexity as it does not involve the use of zero-knowledge proofs.
Policy-Based Chameleon Hash with Black-Box Traceability for Redactable Blockchain in IoT

Blockchain has become an integral part of various IoT applications, and it has been successful in boosting performance in various aspects. Applying blockchain as a trust solution for Internet-of-Things is a viable approach. The immutability of blockchain is essential to prevent anyone from manipulating registered IoT data transactions for illegitimate benefits. However, the increasing abuse of blockchain storage negatively impacts the development of IoT blockchain and potential stakeholders are discouraged from joining the IoT data sharing as the IoT data recorded on the blockchain contains private information. Hence, it is crucial to find ways to redact data stored on the IoT blockchain, which is also supported by relevant laws and regulations. Policy-based chameleon hash is useful primitive for blockchain rewriting, allowing the modifier to rewrite the transaction if they possess enough rewriting privileges that satisfy the access policy. However, this approach lacks traceability, which can be exploited by malicious modifiers to grant unauthorized user modification privileges for personal gain. To overcome this deficiency, we introduce a new design of policy-based chameleon hash with black-box traceability (PCHT), which enables the authority to identify the set of producers responsible for generating the pirate decoder. Specifically, PCHT is constructed by practical attribute-based encryption with black-box traceability (ABET) and collision-resistant chameleon hash with ephemeral trapdoor (CHET). After modeling PCHT, we present its concrete instantiation and rigorous security proofs. Finally, a PCHT-based redactable transaction scheme for IoT blockchain is given. Compared to the state-of-the-art mutable blockchain solutions, our scheme provides fine-grained blockchain rewriting and black-box traceability. The evaluation results demonstrate that our scheme is efficient and practical while still ensuring that no computational overhead is placed on IoT devices with limited computing resources.
Deterministic entanglement distribution on series-parallel quantum networks

The performance of distributing entanglement between two distant nodes in a large-scale quantum network (QN) of partially entangled bipartite pure states is generally benchmarked against the classical entanglement percolation (CEP) scheme. Improvements beyond CEP were only achieved by nonscalable strategies for restricted QN topologies. This paper explores and amplifies a new and more effective mapping of a QN, referred to as concurrence percolation theory (ConPT), that suggests using deterministic rather than probabilistic protocols for scalably improving on CEP across arbitrary QN topologies. More precisely, we implement ConPT via a deterministic entanglement transmission (DET) scheme that is fully analogous to resistor network analysis, with the corresponding series and parallel rules represented by deterministic entanglement swapping and concentration protocols, respectively. The main contribution of this paper is to establish a powerful mathematical framework, which is applicable to arbitrary d-dimensional information carriers (qudits), that provides different natural optimality metrics in terms of generalized k-concurrences (a family of fundamental entanglement measures) for different QN topology. In particular, we conclude that the introduced DET scheme (a) is optimal over the well-known nested repeater protocol for distilling entanglement from partially entangled qubits and (b) leads to higher success probabilities of obtaining a maximally entangled state than using CEP. The implementation of the DET scheme is experimentally feasible as tested on IBM's quantum computation platform.
Contribution of Smart Class Platform on Grade Seven Students’ Achievement and Interest in Mathematics in Nyagatare District, Rwanda

This study desalt with contribution of Smart Class platform on grade seven students’ achievement and interest in mathematics, using the experimental research design. The target population was 8620 grade seven students from 92 public secondary schools of Nyagatare District in Rwanda. Two schools were purposively selected as they fulfilled conditions of having students who can use computer assisted technologies very well. From the two sampled schools, 174 grade seven students were selected to participate in the study whereby 87 constituted the control and 87 constituted the experimental group. During the intervention, the control group was taught using conventional approaches while the experimental group was taught using technology. Data was analyzed using paired sample t-test. The study established that the use of technology in teaching significantly enhanced the mathematics achievement and interest of grade seven pupils in the experimental group who were taught using technology during the intervention period. Therefore, the use of technology enhanced both the interest and the achievement in mathematics. Based on the conclusions, the study recommends that teachers should use technology along with conventional ways in teaching mathematics so as to enhance mathematics interest and achievement in Secondary Schools.
A convolutional neural network for face mask detection in IoT-based smart healthcare systems

The new coronavirus that produced the pandemic known as COVID-19 has been going across the world for a while. Nearly every area of development has been impacted by COVID-19. There is an urgent need for improvement in the healthcare system. However, this contagious illness can be controlled by appropriately donning a facial mask. If people keep a strong social distance and wear face masks, COVID-19 can be controlled. A method for detecting these violations is proposed in this paper. These infractions include failing to wear a facemask and failing to maintain social distancing. To train a deep learning architecture, a dataset compiled from several sources is used. To compute the distance between two people in a particular area and also predicts the people wearing and not wearing the mask, The proposed system makes use of YOLOv3 architecture and computer vision. The goal of this research is to provide valuable tool for reducing the transmission of this contagious disease in various environments, including streets and supermarkets. The proposed system is evaluated using the COCO dataset. It is evident from the experimental analysis that the proposed system performs well in predicting the people wearing the mask because it has acquired an accuracy of 99.2% and an F1-score of 0.99.
The Information Quality of Youtube Videos on Amputee Rehabilitation

Aim: People with disabilities use YouTube as a tool to educate themselves about the rehabilitation process.The aim of the present study was to examine the reliability, quality, and content of YouTube videos for amputee rehabilitation. Methods:In the present cross-sectional study, videos related to amputee rehabilitation in the last three years were included.Journal of American Medical Association (JAMA) benchmarks, the modified Discern tool, and the Global Quality Scale (GQS) were used.The name, length, source, date of upload, likes or dislikes, and number of views of videos were noted.Results: Seventy videos were included.Five videos (7.1%) were about the upper extremity, forty-five (64.3%) were about the lower extremity, and twenty (28.6%) were about both upper and lower extremity amputations.Regarding the number of likes and dislikes, total/daily views, and duration of videos, they were not statistically significant.There was a significant difference between the two uploaded profiles (medical, n=55, and non-medical, n=15) (p>0.05).However, medical professionals had considerably higher GQS, JAMA, and mDISCERN (p=0.020,p=0.006, and 0.008).Journal of American Medical Association, GQS, and mDISCERN showed positive correlations with likes, dislikes, length, and views (p<0.05). Conclusion:The quality of amputee rehabilitation videos was found to be moderate.There is a need for up-to-date videos prepared by preventive health professionals against possible complications, patient education, prosthetics, stump care, and pain.
Refractory times for excitable dual-state quantum dot laser neurons

Excitable photonic systems show promise for ultrafast analog computation, several orders of magnitude faster than biological neurons. Optically injected quantum dot lasers display several excitable mechanisms with dual-state quantum lasers recently emerging as true all-or-none excitable artificial neurons. For use in applications, deterministic triggering is necessary and this has previously been demonstrated in the literature. In this work we analyze the crucially important refractory time for this dual-state system, which defines the minimum time between distinct pulses in any train.
VLSI implementation of lightweight cryptography technique for FPGA-IOT application

The use of cryptography in algorithms is seen to be safe and effective. The secret key distribution is still regarded as a crucial problem despite the fact that it is similar to other symmetric encryption schemes. One single block of data (128 bits) must be encrypted or decrypted, and this requires more computer work that uses more power. The Internet is being extended to link almost everything on the world via the Internet of Things (IoT). In this article, a 256-bit key, lightweight cryptography-based data security technique is implemented for an IOT application. Instead of the traditional 2-D S-box and the prior 1-D S-box, a novel one-dimensional substitution Box (S-box) is suggested. The results of the simulations demonstrate that the suggested lightweight cryptography performs better than the traditional methods in terms of latency, throughput, transmission time, and efficiency rate.