Reviewerly/exHarmony huggingface dataset:
FAIRifying a scholarly publishing service: Methodology based on the OpenEdition’s internal FAIR audit

The FAIR principles (Findability, Accessibility, Interoperability, and Reusability) constitute a guide whose aim is to improve the management of digital scholarly resources. Nevertheless, the literature regarding data services other than data repositories is still scarce.OpenEdition is a digital infrastructure for open scholarly communication in the Social Sciences and Humanities (SSH) that carried out an internal full review to assess the degree of FAIRness of its activities. The objective of this paper is to present the methodology employed by OpenEdition’s team and the recommendations for the FAIRification of a publishing system, and hence, the elements for the FAIR Publishing Toolkit. The FAIR review was conducted in three main phases: preparation, assessment, and result phase, which listed the recommendations for the FAIR principles implementation. The preparation phase gathered the available information to define the perimeter of the FAIR review. It comprised two steps: the landscape study and the exam of actual use cases. The assessment phase contextualized the FAIR principles according to the scholarly publishing context, defined the datasets to be analyzed, carried outa FAIR maturity review per dataset, and analyzed the state of the art of some important FAIR-related elements. The result phase produced the recommendations, organized as priorities and extended objectives. The priority recommendations regard persistent identifiers and licensing policies. The extended objectives focus on authors' information management, controlled vocabularies, machine-actionability, and Digital Management Plans.
Digit-Number Speech-Recognition using Spectrogram-Based Convolutional Neural Network

Natural Language Processing has been a developing topics lately. With the emergence of AI, Natural Language Processing has been developed better. In this paper, the author wants to design a digit-number speech-recognition system using Deep Learning. Speech spectrograms has been chosen for feature extraction method, and Convolution Neural Network based classifier has been chosen for classification. AlexNet and GoogleNet has been chosen as the speech spectrogram classifier. The resulting system has 72% classifying accuracy for AlexNet and 66% for GoogleNet, however, the system overfits the training data. In future works, the overfitting must be handled.
Robust Feature Graph for Point Cloud Denoising

Point cloud is an important and commonly used signal representation for volume objects or scenes in the real world. Due to the imperfect acquisition of the point cloud, there is nonnegligible noise in the point cloud. Most literatures that use graph signal processing (GSP) for point cloud denoising (PCD) generally construct k-NN graph to represent the point cloud. However, the graph constructed based on this scheme can not compactly represent the underlying structure of a noisy point cloud. In this paper, we propose a feature graph that can effectively and naturally represent the structure of the point cloud. To construct the feature graph, a feature sampling method is exploited to obtain the feature points. Then, patches are built based on the feature points. After that, the feature graph is constructed by connecting all the points in the patches. Finally, we apply the feature graph to the PCD problem and exploit graph Laplacian regularization (GLR) as smoothing prior information for denoising. Experimental results show that our proposed PCD method not only outperforms the existing PCD methods in objective evaluation metrics, but also performs better in processing the inner and edge structure of the point cloud.
Mixed Reality Workplace Training Systems for Smart Factories: Challenges and Future Directions

In recent years, mixed reality applications for training unskilled workers have gained a lot of attention in Industry 4.0 and smart factories. One application domain with a high potential of acceptance through management and employees are manual assembly tasks, since just in time production and many variants makes it quite difficult for assembly workers to build high quality products. Additionally, labor shortages and high turnover have made training of new employees especially important. But what are the challenges for such workplace training systems and how can they support users to learn new work steps in an independent and effective way? For our analysis, we have built prototypes for three different assembly use cases. In addition, all prototypes were analyzed by user studies to gain deeper insights from the assembly worker perspective. Our analysis of these workplace training systems has revealed new challenges. For instance, these systems should consider didactic concepts to enable and verify both learning and retention of acquired skills. In addition, providing feedback and reflection mechanisms is essential for long-term user motivation. Last but not least, we should enable the integration of training and guidance, so that new employees can work completely independent of other co-workers form the very first minute.
Integrated QKD and QRNG Photonic Technologies

Quantum key distribution (QKD) and quantum random number generation (QRNG) will play a key role in the future of data communication security. QKD can be used by trusted parties to share a secret key, leading to long-term secure communications. QRNG allows the production of fully unpredictable and certifiable random numbers, which can be used in numerous applications, including cryptography and large stochastic simulations. Both QKD and QRNG can be implemented using commercially available photonic components, which are mostly bulky and assembled into a discrete module. Although this approach can benefit from the superior performance and maturity of each individual component, it does not guarantee the large-scale deployment and commercial viability of the technology. A preferable alternative relies on integrating some or all functionalities in a single photonic integrated circuit (PIC) fabricated at the wafer scale, in this way reducing size, weight, complexity, cost, and power consumption. Integrated photonics can thus dramatically improve the performance and reliability of QKD systems and QRNG devices. Here, we review recent literature on designs, related materials, and fabrication processes, which is representative of state-of-the-art PIC-based QKD and QRNG. We conclude the paper by pointing out the challenges that need to be addressed in the future for reaching a wide-scale deployment of these integrated technologies.
Phase-Change Nanophotonic Circuits With Crossbar Electrodes and Integrated Microheaters

Phase-change photonics integrated circuits (PICs) have most recently attained considerable attention due to their broad bandwidth, superfast processing speed, and multiplexing function. However, their poor scalability adversely limited current PICs into single cell or simple architectures. To overcome this issue, for the first time we modelled the write and readout performances of Ge2Sb2Te5-based nanophotonic architecture with integrated indium-Tin-oxide (ITO) heater and crossbar platinum (Pt) electrodes. The updated architecture, when compared with other reported prototypes, exhibited better scalability and smaller energy consumptions. Its ability to achieve random access and multilevel functions at system level was also demonstrated by simulations, implying its bright prospect for in-memory computing and neuromorphic computing applications.
Deep Learning Aided Low Complex Sphere Decoding for MIMO Detection

In this paper, we propose a deep learning based sphere decoding (SD) scheme to reduce the detection complexity for the multiple-input multiple-output (MIMO) communication systems. Specifically, we first design the sparsely connected deep neural network (SC-DNN) to find a moderate radius for the SD algorithm. Then, we develop the SC-SD algorithm to reduce the computational complexity by deciding the detection order from the output of the SC-DNN, the zero-forcing (ZF) detector, and the transmit power. We further reduce the complexity of the SC-SD by defining partial layers without searching. For multi-stream MIMO, where a large number of parameters in neural networks should be trained, we propose a partitioned training procedure to achieve a reasonable computational complexity. Simulation results demonstrate that the SC-SD almost achieves the performance of the maximum likelihood (ML) in MIMO system but is much faster than the classic SD algorithm.
An end-to-end deep generative approach with meta-learning optimization for zero-shot object classification

Zero-shot object classification aims to recognize the object of unseen classes whose supervised data are unavailable in the training stage. Recent zero-shot learning (ZSL) methods usually propose to generate new supervised data for unseen classes by designing various deep generative networks. In this paper, we propose an end-to-end deep generative ZSL approach that trains the data generation module and object classification module jointly, rather than separately as in the majority of existing generation-based ZSL methods. Due to the ZSL assumption that unseen data are unavailable in the training stage, the distribution of generated unseen data will shift to the distribution of seen data, and subsequently causes the projection domain shift problem. Therefore, we further design a novel meta-learning optimization model to improve the proposed generation-based ZSL approach, where the parameters initialization and the parameters update algorithm are meta-learned to assist model convergence. We evaluate the proposed approach on five standard ZSL datasets. The average accuracy increased by the proposed jointly training strategy is 2.7% and 23.0% for the standard ZSL task and generalized ZSL task respectively, and the meta-learning optimization further improves the accuracy by 5.0% and 2.1% on two ZSL tasks respectively. Experimental results demonstrate that the proposed approach has significant superiority in various ZSL tasks.
Analysis of the effect of additives on the fermentation quality of whole-plant corn silage based on machine learning

Recently, many related researchers have discovered that a certain amount of propionic acid can be added to the silage mixture of barley straw and wheat rye, and the content of more than 4% can effectively improve the fermentation quality and effectiveness of the silage mixture. Studies have shown that the yield of whole corn crops is affected by many factors, and the addition of cellulase and other additives can effectively increase its yield. Temperature and precipitation are the main factors affecting the total amount of microorganisms and their different types. Some microorganisms, such as mold, are harmful microorganisms in silage, which can cause the aerobic deterioration of the whole corn silage. In addition, temperature and humidity will also significantly affect the fermentation of silage, and have a greater impact on the aerobic stability of some microorganisms. A large number of research materials have also proved this point. If the study area is suitable for the growth and reproduction of molds, the problem of aerobic corruption will become more prominent. How to reduce aerobic corruption has become an important issue facing the industry.In order to better control the fermentation quality of silage, the concept of machine learning was introduced in this paper. By building relevant models, the influences of additives on the fermentation quality of corn silage were analyzed, so as to improve the quality of silage and provide some help to solve the problems in the industry.
A cross-process Spectre attack via cache on RISC-V processor with trusted execution environment

The trust execution environment (TEE) provides a safe region, also known as a secret enclave, for executing private programs that need protection. This work proposed a cross-process exploitation scheme for conducting the cache side-channel attack, Spectre, on RISC-V processors with a trust execution environment. Practical experiments are provided to verify the protected enclave's security on RISC-V processors with the TEE. In these experiments, the attacker and victim do not share the same address space as in known implementations but are executed in separate processes. The experimental results show that initial leakage information from the cache memory can be recorded. To the best of our knowledge, no prior research has been conducted on the Spectre attack against RISC-V's TEE. This implementation will be a critical component for extending further cache side-channel experiments on the security of RISC-V processors.
Algorithm for orthogonal matrix nearness and its application to feature representation

Various learning problems can be represented as certain canonical forms of orthogonal matrix nearness problems under the unitarily invariant norm. Since varying unitarily invariant norms favor different structured learning of input data, it is crucial to construct a unified scheme to learn encouraging distributions via any unitarily invariant norm. In this paper, we find that the orthogonal matrix nearness problem can be generalized to a common architecture of the unitarily invariant norm minimization, thus further exploit iterative and closed-form solutions. Firstly, we start with several special circumstances of orthogonal matrix nearness problems, where the unitarily invariant norm resorts to Frobenius norm. Secondly, a general scheme for orthogonal matrix nearness problems under any unitarily invariant norm is addressed and the corresponding iterative algorithm is proposed. Thirdly, feature representation problems are formulated as certain forms of orthogonal matrix nearness, indicating a joint framework for learning low-dimensional features with closed-form solutions. Finally, comprehensive experiments on real-world data sets demonstrate the effectiveness of the proposed method against compared state-of-the-art feature representation approaches. In particular, the proposed method gains about 15% improvements of clustering accuracy on BASEHOCK, CNAE and RCV1 data sets than benchmarks, and achieves at least 3% improvements on other data sets.
A causal-based symbolic reasoning framework for uncertain knowledge graphs

Recently, reasoning methodologies for uncertain knowledge graphs have been extensively proposed. However, symbolic reasoning for uncertain knowledge graphs has rarely been studied. There are multiple paths between the subject and object entities, which makes it a challenge to deduce the confidence of triples base on symbolic reasoning. In this paper, we develop a causal-based symbolic reasoning framework UKGCSR, which aims to infer object entity and triple confidence through multi-hop reasoning and causal inference. The multi-hop reasoning module establishes the reasoning process as a Markov decision process, excavates paths and reliability between entities through pathfinding. Then, the causal inference module constructs a causal diagram and generates counterfactuals. It evaluates each path's contribution to the triple, so as to calculate the confidence of prediction facts. Our model provides the interpretability in reasoning process and shows relative high-performance in experimental results.
Close to open—Factors that hinder and promote open science in ecology research and education

The Open Science (OS) movement is rapidly gaining traction among policy-makers, research funders, scientific journals and individual scientists. Despite these tendencies, the pace of implementing OS throughout the scientific process and across the scientific community remains slow. Thus, a better understanding of the conditions that affect OS engagement, and in particular, of how practitioners learn, use, conduct and share research openly can guide those seeking to implement OS more broadly. We surveyed participants at an OS workshop hosted by the Living Norway Ecological Data Network in 2020 to learn how they perceived OS and its importance in their research, supervision and teaching. Further, we wanted to know what OS practices they had encountered in their education and what they saw as hindering or helping their engagement with OS. The survey contained scaled-response and open-ended questions, allowing for a mixed-methods approach. We obtained survey responses from 60 out of 128 workshop participants (47%). Responses indicated that usage and sharing of open data and code, as well as open access publication, were the most frequent OS practices. Only a minority of respondents reported having encountered OS in their formal education. A majority also viewed OS as less important in their teaching than in their research and supervisory roles. The respondents' suggestions for what would facilitate greater OS engagement in the future included knowledge, guidelines, and resources, but also social and structural support. These are aspects that could be strengthened by promoting explicit implementation of OS practices in higher education and by nurturing a more inclusive and equitable OS culture. We argue that incorporating OS in teaching and learning of science can yield substantial benefits to the research community, student learning, and ultimately, to the wider societal objectives of science and higher education.
Cybercrime in the Republic of Serbia: Prevalence, Situation and Perspectives

The year 2022 was marked by anonymous reports of bombings in schools and other important institutions across Serbia, spread via computers. These events have triggered a public debate on whether the authorities responsible for combating cybercrime can adequately respond to such attacks. As the number of internet users increases, so does the number of potential victims of cybercrime and the obligation of states to protect citizens. Recent events have shown the vulnerability of society to high-tech crime and the risks it poses. The paper analyses the normative and institutional framework for combating cybercrime and data from the Ministry of Internal Affairs on cybercrime. The paper aims to suggest possible changes, based on the analysis of existing cybercrime legislation and the analysis of the institutional framework here and in some European countries, to ensure more effective protection against cybercrime. The paper uses both a normative and a comparative method to present the measures taken in other countries. Descriptive analysis was used to analyze the data collected. The conclusion is that despite a good normative framework, the fight against cybercrime is not adequate, mainly because of the far too small number of staff in special departments and sections of the prosecutor's office and the police compared to the number of crimes, which is increasing year by year. The danger is also that many people are not educated about the dangers of using the internet. Therefore, more attention should be paid shortly to educating citizens of all ages about the dangers and the types of cybercrime.
Fractional Artificial Bee Chicken Swarm Optimization technique for <scp>QoS</scp> aware virtual machine placement in cloud

Summary Cloud computing provides different constructive services in order to share huge scale information, computing resources, storage resources, and offer research knowledge. Cloud Service Providers (CSPs) afforded its services to cloud customers, usually in structure of Virtual Machines (VMs). In this paper, Fractional Artificial Bee Chicken Swarm Optimization (Fractional ABCSO) is introduced for VM placement in the cloud. The Fractional ABCSO is obtained by integrating the Fractional concept (FC), Chicken Swarm Optimization (CSO), and Artificial Bee Colony (ABC). Here, the cloud simulation is performed by means of VM and physical machine (PM). At first, VM placement is carried out using different system factors, such as Central Processing Unit (CPU), Million Instructions per Second (MIPS), bandwidth, migration cost, memory, frequency, power, along with Quality of Service (QoS). The developed Fractional ABCSO algorithm outperformed other existing techniques with regard to load, migration cost, and power consumption of 0.1614, 0.0535, and 0.0408.
Content-, system-, and hardware-related effects on the experience of flow in VR gaming

The aim of this research was to conceptualize and measure the flow experience of Virtual Reality (VR) gaming as well as to develop and empirically test a research model that explains flow experience and subsequent usage behavior. This model highlights the effects of content-, system-, and hardware-related factors on flow, as well as the relevance of interaction effects. Based on a large survey of 1,784 German VR users, the structural equation model showed that gaming motives, perceived content quality, content range, system quality and barriers to VR usage significantly influence the experience of flow in VR gaming and thus behavioral intention and usage. Age, gamer identification, and VR technology experience had interaction effects with several of these relationships. This study contributes a multidimensional conception of flow and a better understanding of its antecedents and consequences. It yields practical implications by highlighting the need not only to differentiate between self-identified gamers and non-gamers but also to consider previous VR experience and the impact of real-world circumstances in the development and marketing of VR games.
Secured information sharing for supply chain management based on blockchain technology and optimal key generation process

Summary Supply Chain Management (SCM) is one of the biggest important focal areas in most corporate environments. However, some difficulties are presented in the structure like high cost, privacy issues and distortions. Thus, new secured information sharing for SCM is proposed in this work and deals with the introduction of privacy preservation of data transfers among supply chains. Initially, a supply chain is constructed and the data transfer among the diverse levels takes place via blockchain technology. In the blockchain, security being a crucial factor, a new privacy preservation method is introduced. The initial information is converted to a sanitization procedure prior to transmission via blockchain technology, in which the information is turned into sanitized data. Similarly, the sanitized data is restored on the receiving end to obtain the original data. More importantly, key generation has a significant role in both processes, and it must be ideally chosen. The key is optimally chosen for this study using a new optimization algorithm called Fitness‐based Self‐adaptive Sea lion Optimization (FSA‐SLnO), which is a better variant of the conventional Sealion Optimization technique (SLnO). In terms of security, the proposed protected information sharing in SCM using block chain technology is evaluated by comparing it with traditional approaches.
Predicting machine behavior from Google cluster workload traces

Summary Data centers today host a number of computational resources to support the increasing demand for computation and storage. Understanding how these physical and virtual machines transition between different states of operation (referred to as machine lifecycle) enables more efficient data center operation management. Furthermore, it helps data center operators define policies on how new computational resources can be added or existing infrastructure decommissioned. Using Google cluster trace data set version 3 collected from approximately 96 k machines, we analyze machine failure and changes in machine lifecycle over time. We observed that there is a 13% chance of another machine failure under the same network switch within 1 min of the previous machine failure. A Markov chain‐based model is proposed, that can predict machine states at any given time. Using the model and estimated probabilities, we predicted the machine state over a span of several days with a high probability. Using the predicted machine state, we reconstructed the active machines trend and compared this with the trend reported in the data set, observing an error of 1.76%.