Reviewerly/exHarmony huggingface dataset:
Monitoring and Sharing of Music Teaching Environment Resources Using Big Data Technology

The teaching materials for different music courses in schools are getting more and more plentiful as education continues to become more and more digitised. This paper develops a resource monitoring and recommendation algorithm for the music teaching environment based on big data analysis technology, achieves the unified management and parallel retrieval of large music teaching resources, and completes the construction and sharing of music teaching resources with the goal of promoting online music teaching courses and increasing the efficiency of instruction. According to the experimental findings, the algorithm's recall and precision may both approach 95.84% and 95.19%, respectively. This demonstrates the benefits of this strategy. With the ability to analyse and retrieve large teaching resources and data quickly and accurately, this model can offer an advantageous solution for the storage and sharing of existing and future massive teaching resources. In order to effectively integrate music curriculum materials into our lessons, music teachers need to have a thorough awareness of not just the knowledge found in textbooks but also the adaptable use of music resources both within and outside of the classroom.
A Blockchain-Based Personal Health Record System for Emergency Situation

A personal health record (PHR) system stores personal health-related information, which can assist physicians in quickly forming appropriate treatment plans in emergency situations. Because a PHR contains lots of sensitive information, the patients are only willing to share their records with authorized doctors with their permission. There are three main challenging issues: (1) it is costly to store and maintain the growing PHRs data; (2) the existing PHR systems still face the privacy leakage risk during data transmission and access control processes; and (3) the response speed cannot meet the need in an emergency situation, especially when the patients are unconscious. In this paper, based on the permissioned blockchain Hyperledger Fabric, we propose a PHR management system preserving patients’ privacy and also supporting emergency access. In the system, we use reencryption technology and the anonymous identity mapping mechanism to protect patient privacy and use smart contracts to define access control strategies in an emergency situation. Furthermore, we use a quick response code and bloom filter to optimize system performance. The security analysis and experimental results show that our proposed framework guarantees the privacy of patient data from multiple aspects while improving the efficiency with which doctors can obtain PHR information.
Forensic Analysis of IoT File Systems for Linux-Compatible Platforms

Due to recent developments in IT technology, various IoT devices have been developed for use in various environments, such as card smart TVs, and smartphones Communication between IoT devices has become possible. Various IoT devices are found in homes and in daily life, and IoT technologies are being combined with vehicles, power, and wearables, amongst others. Although the usage of IoT devices has increased, the level of security technology applied to IoT devices is still insufficient. There is sensitive information stored inside IoT devices, such as personal information and usage history, so if security accidents happen, such as data leakage, it can be very damaging for users. Since research on data storage and acquisition in IoT devices is very important, in this paper we conducted a security analysis, from a forensic perspective, on IoT platform file systems used in various environments. The analysis was conducted on two mechanical platforms: Tizen (VDFS) and Linux (JFFS2 and UBIFS). Through file system metadata analysis, file system type, size, list of files and folders, deleted file information were obtained so that we could analyze file system structure with the obtained information. We also used the obtained information to check the recoverability of deleted data to investigate the recovery plan. In this study, we explain the characteristics of platforms used in various environments, and the characteristics of data stored in each platform. By analyzing the security issues of data stored during platform communications, we aimed to help in solving the problems affecting devices. In addition, we explain the analysis method for file system forensics so that it can be referred to in other platform forensics.
Multi-Party Quantum Secret Sharing Based on GHZ State

In this paper, we propose an efficient multi-party quantum secret sharing scheme based on GHZ entangled state. The participants in this scheme are divided into two groups, and share secrets as a group. There is no need to exchange any measurement information between the two groups, reducing the security problems caused by the communication process. Each participant holds one particle from each GHZ state; it can be found that the particles of each GHZ state are related after measuring them, and the eavesdropping detection can detect external attacks based on this characteristic. Furthermore, since the participants within the two groups encode the measured particles, they can recover the same secrets. Security analysis shows that the protocol can resist the intercept-and-resend attack and entanglement measurement attack, and the simulation results show that the probability of an external attacker being detected is proportional to the amount of information he can obtain. Compared with the existing protocols, this proposed protocol is more secure, has less quantum resources and is more practical.
XOR-Based (n, n) Visual Cryptography Schemes for Grayscale or Color Images with Meaningful Shares

XOR-based Visual Cryptography Scheme (XOR-based VCS) is a method of secret image sharing. The principle of XOR-based VCS is to encrypt a secret image into several encrypted images, called shares. No information about the secret can be obtained from any of the shares, and after applying the logical XOR operation to stack these shares, the original secret image can be recovered. In this paper, we present a new XOR-based VCS for grayscale or a color secret image. This scheme encrypts the secret grayscale (or color) image into n meaningful grayscale (or color) shares, which can import n difference cover images. After stacking n shares using the XOR operation, the original secret image can be completely restored. Both the theoretical proof and experimental results show that our method is accurate and efficient. To the best of our knowledge, ours is the only scheme that currently provides this functionality for grayscale and color secret images.
The Effect of Economic Freedom on Quality of Life: Exploring the Role of Political Risk Factors in Africa

This article examines economic freedom’s impact on quality of life conditional on the political risk factors in Africa over the period 1985–2016, using the Generalised Method of Moments (GMM) estimation technique. The results show that economic freedom has a significant positive effect on the quality of life. However, political risk fundamentals, namely civil liberties, political rights and conflict, cause economic freedom to deteriorate the quality of life in African. These results support North’s (1990) argument that political institutions play a cardinal role in Africa’s economic outcomes and well-being. Therefore, governments in Africa must improve on the political factors to enhance economic freedom’s impact on quality of life. Moreover, policies that lead to an increase in aid and economic growth will improve the quality of life in Africa. JEL: C23, I31, P25
A Blockchain-Based Secure Multi-Party Computation Scheme with Multi-Key Fully Homomorphic Proxy Re-Encryption

At present, secure multi-party computing is an effective solution for organizations and institutions that want to derive greater value and benefit from the collaborative computing of their data. Most current secure multi-party computing solutions use encryption schemes that are not resistant to quantum attacks, which is a security risk in today’s quickly growing quantum computing, and, when obtaining results, the result querier needs to collect the private keys of multiple data owners to jointly decrypt them, or there needs to be an interaction between the data owner and the querier during the decryption process. Based on the NTRU cryptosystem, which is resistant to quantum computing attacks and has a simple and easy-to-implement structure, and combined with multi-key fully homomorphic encryption (MKFHE) and proxy re-encryption, this paper proposes a secure multi-party computing scheme based on NTRU-type multi-key fully homomorphic proxy re-encryption in the blockchain environment, using the blockchain as trusted storage and a trusted execution environment to provide data security for multi-party computing. The scheme meets the requirements of being verifiable, conspiracy-proof, individually decryptable by the querier, and resistant to quantum attacks.
Role of Union Digital Centre (UDC) to Empower Rural People: A Case Study on Pabna Sadar Upazila, Bangladesh

Union Digital Centre (UDC) is a union based information service center and the purpose of which is to ensure information services at the doorsteps of grassroots people. The main purpose of this paper is to explore the role of UDC to empower the rural community as well as to find out the overall challenges of e-service delivery through UDC to the local citizens. Both the primary and the secondary data have been used to achieve the objectives of this study. The overall findings indicate that UDCs have the potential to empower the rural community but the study reveals that the poor section is very low in taking services from UDCs and the marginalized sections remain far behind from the empowerment goal as they lack engagement with the facilities provided through the UDCs. The paper also observed that, in spite of having some drawbacks, the beneficiaries have begun to realize the importance of UDCs which has emerged as a very effective strategy to empower those rural people. Since there does exist some challenges associated with e-services, it should be overcome as soon as possible. This paper also puts forward some recommendations in order to overcome the existing problem.
Hybrid Particle Swarm and Whale Optimization Algorithm for Multi-Visit and Multi-Period Dynamic Workforce Scheduling and Routing Problems

This paper focuses on the dynamic workforce scheduling and routing problem for the maintenance work of harvesters in a sugarcane harvesting operation. Technician teams categorized as mechanical, hydraulic, and electrical teams are assumed to have different skills at different levels to perform services. The jobs are skill-constrained and have time windows. During a working day, a repair request from a sugarcane harvester may arrive, and as time passes, the harvester’s position may shift to other sugarcane fields. We formulated this problem as a multi-visit and multi-period dynamic workforce scheduling and routing problem (MMDWSRP) and our study is the first to address the workforce scheduling and routing problem (WSRP). A mixed-integer programming formulation and a hybrid particle swarm and whale optimization algorithm (HPSWOA) were firstly developed to solve the problem, with the objective of minimizing the total cost, including technician labor cost, penalty for late service, overtime, travel, and subcontracting costs. The HPSWOA was developed for route planning and maintenance work for each mechanical harvester to be provided by technician teams. The proposed algorithm (HPSWOA) was validated against Lingo computational software using numerical experiments in respect of static problems. It was also tested against the current practice, the traditional whale optimization algorithm (WOA), and traditional particle swarm optimization (PSO) in respect of dynamic problems. The computational results show that the HPSWOA yielded a solution with significantly better quality. The HPSWO was also tested against the traditional genetic algorithm (GA), bat algorithm (BA), WOA, and PSO to solve the well-known CEC 2017 benchmark functions. The computational results show that the HPSWOA achieved more superior performance in most cases compared to the GA, BA, WOA, and PSO algorithms.
Hyperspectral image denoising and destriping based on sparse representation, graph Laplacian regularization and stripe low-rank property

Abstract During the acquisition of a hyperspectral image (HSI), it is easily corrupted by many kinds of noises, which limits the subsequent applications. For decades, numerous HSI denoising methods have been proposed. However, these methods rarely consider the stripe noise as an independent component, thus cannot effectively remove the stripe noise. In this paper, we propose a mixed noise removal algorithm to destripe an HSI by taking advantage of the low-rank property of stripe noise. In the meantime, sparse representation and graph Laplacian regularization are utilized to remove Gaussian and sparse noise. Roughly speaking, the sparse representation helps achieve the approximation of the original image. A graph Laplacian regularization term can ensure the non-local spatial similarity of an HSI. Separate constraints on the sparse coefficient matrix and stripe noise components can help remove different types of noises. Experimental results on both simulated and real data demonstrate the effectiveness of the proposed method for HSI restoration.
The Pandora's box of social, process, and people debts in software engineering

Abstract In software engineering, technical debt (TD) has been widely investigated, but debt regarding social issues, people, and processes has not been explored as much. It should be noted here that we use nontechnical debt (NTD) as an umbrella term to cover social, process, and people debts . Although the number of studies on NTD in software is increasing, the majority of them are descriptive rather than rigorous, and there is no systematic development of cumulative knowledge. As a result, identifying the fundamental causes of NTD and the associated mitigation techniques in software engineering is challenging. Therefore, this study investigates the scientific evidence regarding NTD till date by conducting a systematic mapping review of software engineering research between January 2000 and October 2021. The search strategy resulted in 175 studies, 17 of which were identified as unique and relevant primary papers. The primary studies show that NTD and TD are inextricably linked. In addition, this study also captured a plethora of causes and mitigation strategies for managing NTD and thus makes four important contributions: (i) highlighting state‐of‐the‐art NTD research; (ii) identification of the reported causes and mitigation strategies in the primary papers; and (iii) determination of opportunities for future NTD research.
Quantum Tomography of Two-Qutrit Werner States

In this article, we introduce a framework for two-qutrit Werner states tomography with Gaussian noise. The measurement scheme is based on the symmetric, informationally complete positive operator-valued measure. To make the framework realistic, we impose the Gaussian noise on the measured states numbers. Through numerical simulation, we successfully reconstructed the two-qutrit Werner states in various experimental scenarios and analyzed the optimal scenario from four aspects: fidelity, purity, entanglement, and coherence.
SoftMatch: Comparing Scanpaths Using Combinatorial Spatio-Temporal Sequences with Fractal Curves

Recent studies matching eye gaze patterns with those of others contain research that is heavily reliant on string editing methods borrowed from early work in bioinformatics. Previous studies have shown string editing methods to be susceptible to false negative results when matching mutated genes or unordered regions of interest in scanpaths. Even as new methods have emerged for matching amino acids using novel combinatorial techniques, scanpath matching is still limited by a traditional collinear approach. This approach reduces the ability to discriminate between free viewing scanpaths of two people looking at the same stimulus due to the heavy weight placed on linearity. To overcome this limitation, we here introduce a new method called SoftMatch to compare pairs of scanpaths. SoftMatch diverges from traditional scanpath matching in two different ways: firstly, by preserving locality using fractal curves to reduce dimensionality from 2D Cartesian (x,y) coordinates into 1D (h) Hilbert distances, and secondly by taking a combinatorial approach to fixation matching using discrete Fréchet distance measurements between segments of scanpath fixation sequences. These matching "sequences of fixations over time" are a loose acronym for SoftMatch. Results indicate high degrees of statistical and substantive significance when scoring matches between scanpaths made during free-form viewing of unfamiliar stimuli. Applications of this method can be used to better understand bottom up perceptual processes extending to scanpath outlier detection, expertise analysis, pathological screening, and salience prediction.
Transformer Based Binocular Disparity Prediction with Occlusion Predict and Novel Full Connection Layers

The depth estimation algorithm based on the convolutional neural network has many limitations and defects by constructing matching cost volume to calculate the disparity: using a limited disparity range, the authentic disparity beyond the predetermined range can not be acquired; Besides, the matching process lacks constraints on occlusion and matching uniqueness; Also, as a local feature extractor, a convolutional neural network lacks the ability of global context information perception. Aiming at the problems in the matching method of constructing matching cost volume, we propose a disparity prediction algorithm based on Transformer, which specifically comprises the Swin-SPP module for feature extraction based on Swin Transformer, Transformer disparity matching network based on self-attention and cross-attention mechanism, and occlusion prediction sub-network. In addition, we propose a double skip connection fully connected layer to solve the problems of gradient vanishing and explosion during the training process for the Transformer model, thus further enhancing inference accuracy. The proposed model in this paper achieved an EPE (Absolute error) of 0.57 and 0.61, and a 3PE (Percentage error greater than 3 px) of 1.74% and 1.56% on KITTI 2012 and KITTI 2015 datasets, respectively, with an inference time of 0.46 s and parameters as low as only 2.6 M, showing great advantages compared with other algorithms in various evaluation metrics.
Understanding the information landscape in agricultural communities in rural Bangladesh

Abstract Knowledge sharing in rural agricultural communities is vital to the success of farmers and sustaining high yields. A range of actors in the knowledge landscape participate in knowledge sharing, and with this, a variety of complexities are introduced. In this paper, we report on a set of field visits, interviews and focus groups in various settings to understand this complex nature of the knowledge landscape. Our study was set within multiple locations within 20 miles North‐East of Dhaka, the capital city of Bangladesh. Our findings highlight the high level of interconnectedness of different actors in the agricultural communities and the complexities involved in establishing trust of information. We report on the importance of fostering successful relationships within the communities and the growing strains of climate change.
Learning fair representations by separating the relevance of potential information

Representation learning has recently been used to remove sensitive information from data and improve the fairness of machine learning algorithms in social applications. However, previous works that used neural networks are opaque and poorly interpretable, as it is difficult to intuitively determine the independence between representations and sensitive information. The internal correlation among data features has not been fully discussed, and it may be the key to improving the interpretability of neural networks. A novel fair representation algorithm referred to as FRC is proposed from this conjecture. It indicates how representations independent of multiple sensitive attributes can be learned by applying specific correlation constraints on representation dimensions. Specifically, dimensions of the representation and sensitive attributes are treated as statistical variables. The representation variables are divided into two parts related to and unrelated to the sensitive variables by adjusting their absolute correlation coefficient with sensitive variables. The potential impact of sensitive information on representations is concentrated in the related part. The unrelated part of the representation can be used in downstream tasks to yield fair results. FRC takes the correlation between dimensions as the key to solving the problem of fair representation. Empirical results show that our representations enhance the ability of neural networks to show fairness and achieve better fairness-accuracy tradeoffs than state-of-the-art works.
Ranked-based mechanism-assisted Biogeography optimization: Application of global optimization problems

The resolution of an optimization issue is a topic studied and debated by academics from a wide variety of areas continuously. Without an optimal solution, a lot of time and resources are likely to be wasted, and the issue will remain unsolved in the worst-case scenario. Due to these concerns, researchers are increasingly resorting to the development of robust meta-heuristic optimization techniques to improve current methods while creating new population-based approaches capable of exploring the required feature space. Therefore, biogeography-based optimization (BBO) is proposed in this paper, which is impacted by species' movement and emigration across islands in quest of better favorable environments. The BBO is indeed a population-based optimization approach used to solve complicated optimization issues. However, due to the nature of its operators, this technique might become stuck in sub-optimal solutions, which slows down convergence and increases the amount of computing time needed to find optimal solutions. In order to address these issues, this research proposes a BBO version that incorporates a ranked-based strategy (RBS), an exponential dynamic Brownian random differential (EBD) mechanism, and a successful adaptive random differential mutation (SarDM) mechanism to find a more effective solution in the feasible region. RBBO is the name given to the suggested approach. It has been evaluated for addressing four standard engineering problems with restrictions and 23 benchmark functions (seven unimodal, eight multimodal, and ten composite functions). The experimental findings and evaluations show that the suggested technique outperforms the conventional BBO's efficiency and accuracy. In the case of engineering problems, for instance, the four-reservoir problem, the proposed RBBO can provide superior results in terms of the average objective function (308.71) and standard deviation (0.16) over 30 different runs compared with the other optimization methods. We expect the community to use the suggested BBO-based technique to solve more challenging tasks.