Reviewerly/exHarmony huggingface dataset:
Improving Uncertainty in Chain of Custody for Image Forensics Investigation Applications

Cybercrime investigations rely heavily on digital evidence to establish links between suspects and the criminal conduct they are allegedly involved in. As a result, digital evidence must be protected since it is complex, volatile, and susceptible to alteration. In the digital evidence method, the chain of custody (CoC) is essential. As a result of the CoC, it is possible to establish that the evidence was never tampered with. Due to the inherent uncertainty of digital evidence, the trustworthiness of the CoC cannot be judged at this time. It is the duty of forensic examiners to challenge this inclination and publicly admit the inherent ambiguity in whatever evidence they use to make their decisions. This article suggests a new paradigm for maintaining the integrity of digital evidence in order to overcome these challenges. To handle the uncertainty generated by error-prone technologies while dealing with CoC documents, the new paradigm used a fuzzy hash inside the blockchain data structure. Traditional hashing methods are only able to tell whether two inputs are precisely the same or not because they are sensitive to even the smallest input changes. Using fuzzy hash functions, we can figure out how dissimilar two images are by comparing their similarities. As an example of how this paradigm may be applied to computer systems and make digital investigations more successful, we utilize image forensics as the focus of an in-depth look at how it works.
A Novel UAV Path Planning Algorithm Based on Double-Dynamic Biogeography-Based Learning Particle Swarm Optimization

Particle swarm optimization (PSO), one of the classical path planning algorithms, has been considered for unmanned aerial vehicle (UAV) path planning more frequently in recent years. A large amount of studies on UAV path planning based on modified PSO have been reported. However, most UAV path planning algorithms still optimize only one kind terrain problem which is mountain terrain. At the same time, many modified PSO algorithms also have some problems, such as insufficient convergence and unsatisfactory efficiency. In this paper, six kinds of terrain functions of UAV path planning are proposed to simulate real-world application. The terrain functions contain city, village without houses, village with houses, mountainous area without houses, mountainous area with houses, and mountainous area with a huge building. Inspired by CLPSO and BLPSO, we proposed a new double-dynamic biogeography-based learning particle swarm optimization (DDBLPSO) algorithm to solve these problems. The double-dynamic biogeography-based learning strategy replacing the traditional learning mechanism from the personal and global best particles is used to select the learning particles. In this strategy, each particle will learn from the better one of two selected particles which are not worse than itself. However, one random component of particle will replaced by corresponding component of other particle if all components of the particle only learn from itself. In this way, particles sufficiently learn from better objects and maintain the ability of jumping out of local optimality. The superiority of our algorithm is verified with four relevant algorithms, a PSO variant, and a BBO variant on the benchmark suite of CEC2015. Real-world application demonstrates that the algorithm we proposed outperforms four relevant algorithms, a PSO variant, and a BBO variant both in small-scale problems and large-scale problems. This paper shows a good application of our novel algorithm.
Computing with Injection-Locked Spintronic Diodes

Spintronic diodes (STDs) are emerging as a technology for the realization of high-performance microwave detectors. The key advantages of such devices are their high sensitivity, capability to work at low input power, and compactness. In this work, we show a possible use of STDs for neuromorphic computing to expand the realm of their functionalities for the implementation of analog multiplication, which is a key operation in convolutional neural networks (CNNs). In particular, we introduce the concept of degree of rectification (DOR) in injection-locked STDs. Micromagnetic simulations are used to design and identify the working range of the STDs for the implementation of the DOR. Previous experimental data confirm the applicability of the proposed solution, which is tested in image processing and in a CNN that recognizes handwritten digits.
Gamification in the Informal Learning Space of Higher Education (in the Context of the Digital Transformation of Education)

The article explores the way gamification transforms the informal learning space of the higher education institutions inspired by information technologies and the all-consuming digitalization of human society. An overview is presented of the existing ideas about gamification in the context of the digital transformation of higher education. It is established that the effective use of gamification as a learning technique in the context of digital transformation of higher education contributes to the implementation of its core principles. We have attempted to analyze the factors that gave rise to gamification as well as to examine the ways to introduce gamification to the informal learning experience. The components of this technology are singled out along with the principles of its functioning. In the process, we reveal the role of gamification and opportunities that open up for the organization of educational activities in the informal space of higher education. The given work identifies peculiarities and advantages of this digital form of education as well as risks associated with its introduction in the informal learning space of higher education. The authors elaborate on the specifics of the development of informal learning space in the context of global gamification, as well as the technological and semantic modernization of higher education. It is established that the main purpose of gamification is to boost the internal motivation of higher education students to study and to reveal their creative and professional abilities.
Investigation of performance of fuzzy logic controllers optimized with the hybrid genetic-gravitational search algorithm for PMSM speed control

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A Genetic Algorithm-Based Approach for Fluctuating QoS Aware Selection of IoT Services

In recent years, the Internet of Things (IoT) has evolved at an exceptional speed, which enables to interconnect a very large number of heterogeneous, distributed, and mobile devices.This number will exceed 70 billion by 2025 according to Statista.a Therefore, with this huge amount of connected objects, the fulfillment of complex IoT applications, which usually requires a combination of several IoT objects, remains a real challenge.Besides, several requirements of Quality of Service (QoS) must be fulfilled, which makes the problem of selecting the appropriate IoT services NP-hard.In the literature, two main techniques for QoS-driven service selection are proposed: global selection characterized by a poor performance in dynamic and distributed huge environments and local selection which considers pre-defined local QoS constraints.Mainly, the existing works consider static QoS.However, in real life scenarios, QoS of IoT services can be fluctuating.To enhance the reliability of IoT applications, it is of paramount importance to consider the fluctuation dimension.In this context, we propose a QoS fluctuation-aware selection approach of IoT services.To do so, we propose a near-to optimal distributed approach that relies on decomposing the global QoS into distinct local constraints that serve as upper/lower bounds for selection while enhancing the reliability of the resulting composition by considering the QoS fluctuation of the candidate IoT services.The approach, we propose is based on a multi-objective evolutionary algorithm (MOEA) to solve the global QoS decomposition problem.Then a local selection using the obtained local constraints is performed in a parallel and distributed way.The performance of the proposed approach is evaluated and validated via experiment series.a https://www.statista.com/statistics/
Dual-Chain Blockchain in Agricultural E-Commerce Information Traceability Considering the Viniar Algorithm

In this paper, we consider the Vennia algorithm to conduct in-depth research and analysis on the traceability of dual-chain blockchain agricultural products' E-commerce information. This paper adds a collaborative verification module to the traceability system and carries out a detailed design of information storage, traceability consensus algorithm, and smart contract for agricultural products according to the characteristics of the agricultural products supply chain, among which the collaborative verification module adopts dynamic data storage technology; the ConsiderVinia consensus algorithm is improved by introducing the way of integral penalty mechanism to ensure the block data validity. After a comparative study of the features and differences of the three major blockchain technology platforms, this paper selects the super ledger to implement the agricultural traceability system based on blockchain technology, introduces the partitioning and credit mechanism into the ConsiderVinia algorithm, and elaborates the improvement process of the algorithm. The improved algorithm reduces the malicious behavior of nodes and maintains the system security through a credit mechanism while maintaining the consistency of blockchain. In the event of a transaction dispute, the third-party platform will determine the party at fault based on the transaction records and other evidence and make corresponding punishments and compensations. The experiment proves that the algorithm proposed in this paper can reduce the amount of network data transmission in the process of node consensus, which is better than the ConsiderVinia algorithm in terms of both throughput and latency, improves the consensus efficiency, and alleviates the communication bottleneck caused by the increase of users in blockchain applications, and the solution of applying the blockchain technology to the agricultural products traceability system is practical and feasible. The blockchain-based agricultural products information traceability system solves the problems of information asymmetry, difficult sharing, easy tampering, and storage centralization in the traditional IoT-based agricultural products traceability system and truly realizes the credible and reliable traceability of the whole chain of agricultural products information. The research content and results of this paper have certain theoretical and practical values.
The psychological reassurance effect of mobile tracing apps in Covid-19 Era

As part of their public health policies, most countries have launched mobile tracing applications (apps) to reduce the spread of the COVID-19 virus and reassure their citizens. To the best of our knowledge, no study has explored the importance of ‘well-being’ and ‘trust in the future’ in the context of digital contact-tracing apps. This is an important gap, especially given the importance of citizens' acceptance of a mobile tracing app and its role in reassuring citizens. Therefore, we study the French government's tracing app—StopCovid—as experienced by a sample of 832 participants from France. The results establish strong links between perceived value and trust in government, well-being, and trust in the future, which are considered the key features of the reassurance effect in a pandemic context. In addition, a multigroup analysis (MGA) allows us to compare the effect of several moderators on the overall model, such as the users versus nonusers of tracking apps or infected versus noninfected with COVID-19. The study provides practical implications by highlighting how governments should deploy mobile tracing apps to contribute to public health and reassure their citizens during the pandemic.
Smart Contract-based Gamification Scheme for College in Higher Education

The Financial Scheme that plays an important role in helping students is the Gamification Scholarship Scheme. The Gamification scholarship scheme helps human life in the world of education. Gamification is an approach that uses elements in games or video games with the aim of motivating students in the learning process and maximizing feelings of enjoyment and engagement in the learning process. To encourage higher education, the government has done various things. However, there are several things that make it difficult for students to use it, such as distributing scholarships to students belonging to underprivileged families. The Gamification scholarship scheme is one of the most financially instrumental schemes in helping students, but it has not been verified which results in a lack of transparency between students and their respective Education Offices, then there is still a lack of tracking of application forms in this study, we propose a Smart Contract which addresses the lack of Scheme Scholarships for Colleges and Universities outlined above. It has four entities: Board of Education, Students, College and Bank. This Blockchain based smart contract has been developed to provide a user-friendly and transparent and constant environment between students and the education board. Blockchain platform has developed many applications but none can overcome the government scholarship scheme.
Cybercrime and Artificial Intelligence. An overview of the work of international organizations on criminal justice and the international applicable instruments

Abstract The purpose of this paper is to assess whether current international instruments to counter cybercrime may apply in the context of Artificial Intelligence (AI) technologies and to provide a short analysis of the ongoing policy initiatives of international organizations that would have a relevant impact in the law-making process in the field of cybercrime in the near future. This paper discusses the implications that AI policy making would bring to the administration of the criminal justice system to specifically counter cybercrimes. Current trends and uses of AI systems and applications to commit harmful and illegal conduct are analysed including deep fakes. The paper finalizes with a conclusion that offers an alternative to create effective policy responses to counter cybercrime committed through AI systems.
AFD-StackGAN: Automatic Mask Generation Network for Face De-Occlusion Using StackGAN

To address the problem of automatically detecting and removing the mask without user interaction, we present a GAN-based automatic approach for face de-occlusion, called Automatic Mask Generation Network for Face De-occlusion Using Stacked Generative Adversarial Networks (AFD-StackGAN). In this approach, we decompose the problem into two primary stages (i.e., Stage-I Network and Stage-II Network) and employ a separate GAN in both stages. Stage-I Network (Binary Mask Generation Network) automatically creates a binary mask for the masked region in the input images (occluded images). Then, Stage-II Network (Face De-occlusion Network) removes the mask object and synthesizes the damaged region with fine details while retaining the restored face’s appearance and structural consistency. Furthermore, we create a paired synthetic face-occluded dataset using the publicly available CelebA face images to train the proposed model. AFD-StackGAN is evaluated using real-world test images gathered from the Internet. Our extensive experimental results confirm the robustness and efficiency of the proposed model in removing complex mask objects from facial images compared to the previous image manipulation approaches. Additionally, we provide ablation studies for performance comparison between the user-defined mask and auto-defined mask and demonstrate the benefits of refiner networks in the generation process.
Quantum state preparation by adiabatic evolution with custom gates

Quantum state preparation by adiabatic evolution is currently rendered ineffective by the long implementation times of the underlying quantum circuits, comparable to the decoherence time of present and near-term quantum devices. These implementation times can be significantly reduced by realizing the evolution with a minimal number of customized gates. Employing a realistic model of a two-qubit processor, we carried out classical device-level simulations of the adiabatic evolution of a two-spin system implemented with customized two-qubit gates. These device-level simulations were compared with (experimental) ones solving the same problem on IBMQ systems. When used to emulate the IBMQ quantum circuit, our device-level simulations reached state fidelities ranging from 65% to 85%, similar to the actual performance of a diverse set of IBMQ devices.When we reduced the implementation time by using a minimal number of customized gates, however, the loss of fidelity was reduced by at least a factor of four, allowing us to accurately extract the energy of the target state. This improvement is enough to render adiabatic evolution useful for quantum state preparation for small systems or as a preconditioner for other state preparation methods.
A New Item-Based Collaborative Filtering Algorithm to Improve the Accuracy of Prediction in Sparse Data

Abstract In memory-based collaborative filtering (CF) algorithms, the similarity and prediction method have a significant impact on the recommendation results. Most of the existing recommendation techniques have improved different similarity measures to alleviate inaccurate similarity results in sparse data, however, ignored the impact of sparse data on prediction results. To enhance the adaptability to sparse data, we propose a new item-based CF algorithm, which consists of the item similarity measure based vague sets and item-based prediction method with the new neighbor selection strategy. First, in the stage of similarity calculation, the Kullback–Leibler (KL) divergence based on vague sets is proposed from the perspective of user preference probability to measure item similarity. Following this, the impact of rating quantity is further considered to improve the accuracy of similarity results. Next, in the prediction stage, we relax the limit of depending on explicitly ratings and integrate more rating information to adjust prediction results. Experimental results on benchmark data sets show that, compared with other representative algorithms, our algorithm has better prediction and recommendation quality, and effectively alleviates the data sparseness problem.
Opportunities for Using Smartphones in the Digital Era to Facilitate Students in Learning Sociology in High Schools

The development of information and communication technology in the digital era encourages the use of smartphones in various fields of life, such as the field of education. This condition opens up opportunities for the use of smartphones in teaching and learning activities. This study was conducted to identify opportunities for using smartphones to make it easier for students to learn, especially in understanding sociology learning materials in high school. This type of research is descriptive qualitative research. The research subjects were social studies students of class X SMA with a total of 149 students. Data collection methods in this study were surveys and interviews using questionnaires and interview guidelines as research instruments. Data analysis was carried out in four stages, namely (1) data collection, (2) data reduction, (3) data presentation, and (4) drawing conclusions. The results of this study indicate that there is a great opportunity for the use of smartphones in the digital era in the learning process, especially sociology in high school. The opportunity is the implementation of game-based mobile learning or Mobile Game-Based Learning (MGBL). It is known that the use of smartphones by students is most dominantly used to play games with a percentage of 32.90%. Thus, Mobile Game-Based Learning (MGBL) as a learning medium is an opportunity to use smartphones to make it easier for students to learn sociology because it encourages students to be more active, creative, and innovative in the learning process. The conclusion of this study is that Mobile Game-Based Learning (MGBL) is a great opportunity for using smartphones in the digital era to make it easier for students to learn sociology in high school.
Identifying the influence of transfer learning method in developing an end-to-end automatic speech recognition system with a low data level

Ensuring the best quality and performance of modern speech technologies, today, is possible based on the widespread use of machine learning methods. The idea of this project is to study and implement an end-to-end system of automatic speech recognition using machine learning methods, as well as to develop new mathematical models and algorithms for solving the problem of automatic speech recognition for agglutinative (Turkic) languages. Many research papers have shown that deep learning methods make it easier to train automatic speech recognition systems that use an end-to-end approach. This method can also train an automatic speech recognition system directly, that is, without manual work with raw signals. Despite the good recognition quality, this model has some drawbacks. These disadvantages are based on the need for a large amount of data for training. This is a serious problem for low-data languages, especially Turkic languages such as Kazakh and Azerbaijani. To solve this problem, various methods are needed to apply. Some methods are used for end-to-end speech recognition of languages belonging to the group of languages of the same family (agglutinative languages). Method for low-resource languages is transfer learning, and for large resources – multi-task learning. To increase efficiency and quickly solve the problem associated with a limited resource, transfer learning was used for the end-to-end model. The transfer learning method helped to fit a model trained on the Kazakh dataset to the Azerbaijani dataset. Thereby, two language corpora were trained simultaneously. Conducted experiments with two corpora show that transfer learning can reduce the symbol error rate, phoneme error rate (PER), by 14.23 % compared to baseline models (DNN+HMM, WaveNet, and CNC+LM). Therefore, the realized model with the transfer method can be used to recognize other low-resource languages.
A hybrid improved BRO algorithm and its application in inverse kinematics of 7R 6DOF robot

To advance the calculation performance of the battle royale optimization algorithm (BRO), a hybrid improved BRO algorithm (HBC) is proposed in this paper. The level mechanism of the chicken swarm optimization algorithm (CSO) is integrated into the BRO algorithm to divide all into elite players and ordinary players, and the level relationship of different players is established. Then, an elite player update method of random exploring and directional update in a small range is proposed to improve the development ability. The update method of ordinary players improved, the update mechanism of elite random guidance is introduced to make full use of the excellent location information in the population. The performance verification experiment of the HBC algorithm is carried out on 20 benchmark functions and a practical project. Comparing with several other algorithms, the computational performance of the HBC algorithm is the best. Furthermore, the HBC algorithm is applied to solve the inverse kinematics of the 7R 6DOF robot. The experimental results show that the HBC algorithm effectively improves the average convergence accuracy and reduces the running time, compared with the BRO algorithm. This fully shows that the HBC algorithm is more competitive in stability, calculation accuracy, and speed.