Reviewerly/exHarmony huggingface dataset:
A Secure Quantum Protocol for Anonymous One-Vote Veto Voting

Voting is one of the most prominent components of democracy. The one-vote veto, which requires the voting result to be only “yes” or “no”, is a particularly noteworthy type of voting with widespread application. When obtaining a “no” result, no useful information about the number of “no” votes and who votes “no” is disclosed. In this paper, we introduce a protocol for anonymous one-vote veto utilizing qubits and local Pauli operations Z and X, building simple processes for easy implementation under the current technology. For better elaboration, we give two examples: all voters cast “yes” votes and only one voter casts a "no." Then, the corresponding experiment tests are conducted on the simulated IBM quantum computer to verify their feasibility.We also show that the proposed protocol has the desirable properties of privacy, fairness, verifiability and robustness. Furthermore, we analyze the proposed protocol’s security against cheating from eavesdroppers, a semi-honest server and malicious voters. This work is the first attempt to illustrate how qubits can be useful for building a secure anonymous one-vote veto strategy.
Ensuring Quality and Status: Peer Review Practices in Kriterium, A Portal for Quality-Marked Monographs and Edited Volumes in Swedish SSH

Although established forms of peer review are often criticized for being slow, secretive, and even unfair, they are repeatedly mentioned by academics as the most important indicator of quality in scholarly publishing. In many countries, the peer review of books is a less codified practice than that of journal articles or conference papers, and the processes and actors involved are far from uniform. In Sweden, the review process of books has seldom been formalized. However, more formal peer review of books has been identified as a response to the increasing importance placed on streamlined peer-reviewed publishing of journal articles in English, which has been described as a direct challenge to more pluralistic publication patterns found particularly in the humanities. In this study, we focus on a novel approach to book review, Kriterium, where an independent portal maintained by academic institutions oversees the reviewing of academic books. The portal administers peer reviews, providing a mark of quality through a process which involves reviewers, an academic coordinator, and an editorial board. The paper studies how this process functions in practice by exploring materials concerning 24 scholarly books reviewed within Kriterium. Our analysis specifically targets tensions identified in the process of reviewing books with a focus on three main themes, namely the intended audience, the edited volume, and the novel role of the academic coordinator. Moreover, we find that the two main aims of the portal-quality enhancement (making research better) and certification (displaying that research is of high quality)-are recurrent in deliberations made in the peer review process. Consequently, we argue that reviewing procedures and criteria of quality are negotiated within a broader discussion where more traditional forms of publishing are challenged by new standards and evaluation practices.
Recovering and Simulating Pedestrians in the Wild

Sensor simulation is a key component for testing the performance of self-driving vehicles and for data augmentation to better train perception systems. Typical approaches rely on artists to create both 3D assets and their animations to generate a new scenario. This, however, does not scale. In contrast, we propose to recover the shape and motion of pedestrians from sensor readings captured in the wild by a self-driving car driving around. Towards this goal, we formulate the problem as energy minimization in a deep structured model that exploits human shape priors, reprojection consistency with 2D poses extracted from images, and a ray-caster that encourages the reconstructed mesh to agree with the LiDAR readings. Importantly, we do not require any ground-truth 3D scans or 3D pose annotations. We then incorporate the reconstructed pedestrian assets bank in a realistic LiDAR simulation system by performing motion retargeting, and show that the simulated LiDAR data can be used to significantly reduce the amount of annotated real-world data required for visual perception tasks.
Applying three machine learning algorithms to three breast cancer diagnosis datasets

Breast cancer is the second most common cause of mortality in women in the United States [10]. An estimated 268,600 women were diagnosed with breast cancer in 2019, and 41,760 died from the disease...
A Systematic Review of Cloud Computing Adoption by Organisations

Cloud computing represents a paradigm shift in information technology outsourcing.The technology renders numerous benefits such as flexibility, cost reduction and scalability.Also, it presents organisations with the opportunity to conform to environmental management system.Several studies have applied different theories to investigate the factors that affect cloud computing adoption by organisations.However, the results are inconclusive.Therefore, this study systematically reviews factors influencing adoption the adoption of cloud computing by organisations.A total of 174 papers were reviewed from different databases such as Science direct, Emerald and IEEE Xplore.Consequently, 37 of these articles have been considered to come up with the most salient factors based on their frequency.The findings indicated that technology readiness, top management support, relative advantage, competitive pressure, compatibility, complexity and data security are the significant factors that affect cloud computing adoption in organisations.This review consolidates literature and can benefit decision makers in organisations who intend to adopt cloud computing technology.Furthermore, it has laid a foundation for a future part of this work which is conceptualising a model for cloud computing adoption in the upstream oil and gas sector.
Semi-deterministic and Contrastive Variational Graph Autoencoder for Recommendation

Variational AutoEncoder (VAE) is a popular deep generative framework with a solid theoretical basis. There are many research efforts on improving VAE. Among the existing works, a recently proposed deterministic Regularized AutoEncoder (RAE) provides a new scheme for generative modeling. RAE fixes the variance of the inferred Gaussian approximate posterior distribution as a hyperparameter, and substitutes the stochastic encoder by injecting noise into the input of a deterministic decoder. However, the deterministic RAE has three limitations: 1) RAE needs to fit the variance; 2) RAE requires ex-post density estimation to ensure sample quality; 3) RAE employs an additional gradient regularization to ensure training smoothness. Thus, it raises an interesting research question: Can we maintain the flexibility of variational inference while simplifying VAE, and at the same time ensuring a smooth training process to obtain good generative performance? Based on the above motivation, in this paper, we propose a novel Semi-deterministic and Contrastive Variational Graph autoencoder (SCVG) for item recommendation. The core design of SCVG is to learn the variance of the approximate Gaussian posterior distribution in a semi-deterministic manner by aggregating inferred mean vectors from other connected nodes via graph convolution operation. We analyze the expressive power of SCVG for the Weisfeiler-Lehman graph isomorphism test, and we deduce the simplified form of the evidence lower bound of SCVG. Besides, we introduce an efficient contrastive regularization instead of gradient regularization. We empirically show that the contrastive regularization makes learned user/item latent representation more personalized and helps to smooth the training process. We conduct extensive experiments on three real-world datasets to show the superiority of our model over state-of-the-art methods for the item recommendation task. Codes are available at https://github.com/syxkason/SCVG.
Tensor Product-Based Model Transformation Technique Applied to Servo Systems Modeling

This paper presents the design and validation of a Tensor Product (TP)-based model of a family of nonlinear servo systems using an appropriate technique. Two parameters of the first principles state-space model of the servo system are optimally tuned using a metaheuristic Grey Wolf Optimizer algorithm in terms of several runs that lead to the parameter intervals. The derivation of the TP model starts with the linear parameter varying model of the servo system, which is next transformed to the strictly speaking TP model, inserted in a series connection with the servo system nonlinearity. The behaviors of the servo system, the TP model and the first principles model are tested in a different scenario to the parameter identification one, and the outputs are measured. The experimental results on a servo system laboratory equipment show that the TP model derived for this system ensures good performance in terms of small relative modeling errors.
Impact of COVID19 on the Islamic cryptocurrencies

Cryptocurrency is a new form of digital asset based on a network distributed across a large number of computers. The main objective of this work is to investigate the impact of COVID-19 on the Islamic cryptocurrency markets returns. In this methodology, we identify a group of Islamic cryptocurrencies consisting of the X8X, Hellogold and OneGram. This paper uses the Newey-West standard errors regression to estimate the effect of the COVID-19 pandemic on the Islamic cryptocurrencies returns. The empirical results show that COVID-19 total deaths have a negative effect on respectively the X8X cryptocurrency, HelloGold, and OneGram cryptocurrencies. In the same way, the COVID-19 total confirmed cases growth has a negative effect on respectively the X8X cryptocurrency, and OneGram cryptocurrencies. This study contributes to the literature by identifying the impact of COVID-19 on the Islamic cryptocurrency markets.
A 3D head pointer: a manipulation method that enables the spatial position and posture for supernumerary robotic limbs

This paper introduces a novel interface ‘3D head pointer’ for the operation of a wearable robotic arm in 3D space. The developed system is intended to assist its user in the execution of routine tasks while operating a robotic arm. Previous studies have demonstrated the difficulty a user faces in simultaneously controlling a robotic arm and their own hands. The proposed method combines a head-based pointing device and voice recognition to manipulate the position and orientation as well as to switch between these two modes. In a virtual reality environment, the position instructions of the proposed system and its usefulness were evaluated by measuring the accuracy of the instructions and the time required using a fully immersive head-mounted display (HMD). In addition, the entire system, including posture instructions with two switching methods (voice recognition and head gestures), was evaluated using an optical transparent HMD. The obtained results displayed an accuracy of 1.25 cm and 3.56° with the 20-s time span necessary for communicating an instruction. These results demonstrate that voice recognition is a more effective switching method than head gestures.
Improving the Robustness of DCT-Based Handwritten Document Image Watermarking Against JPEG-Compression

Digital image watermarking is a powerful tool to secure digital images. An important step in these schemes is the quantification used to insert the watermark. In this paper, to improve the robustness of the watermark against JPEG-compression, different quantification variants are analyzed and optimized for a handwritten document image watermarking scheme. The results demonstrate that the embedded watermarks can be almost fully extracted from the JPEG-compressed images with high compression ratios.
The technoethics of contemporary intelligence practice

This chapter focuses on the ethical challenges posed by the still rapidly developing sphere of digital intelligence. States invest in intelligence and data collection to provide a sound basis for their decisions, especially regarding the security, safety and health of their citizens. Digitization is the conversion of any form of information into numerical form. The example of the COVID-19 pandemic illustrates this well. Modern digital surveillance, including the use of apps on mobile devices and monitoring of digital personal information, has provided health authorities in nations such as South Korea, Singapore and China the public health benefit of powerful tools for assisting in testing and tracking programmes limiting the spread of the virus. The principle of right intention does not rule out deception in the course of an intelligence operation, such as inserting into the digital code of malware clues that attempt to encourage a false-flag attribution to a third country.
An Improved Attention Layer assisted Recurrent Convolutional Neural Network Model for Abstractive Text Summarization

In the last few years text summarization has gained widespread attention across industries, especially in media and publications, research, business intelligence etc where it helps exploiting large documents to generate a new one with summarized inferences without losing the aspects. However, majority of the conventional approaches either employs extractive summarization or the abstractive summarization for single-document settings. On contrary, above stated application environments demand abstractive summarization in multiple document settings. Though, amongst the major efforts developed so far the attention based neural network methods have performed potentially; however their efficacy under multiple-documents setting and aspect-sensitive summarization has remained confined. Considering it as motive, in this research a novel and robust Improved Attention Layer assisted Recurrent Convolutional Neural Network (IA-RCNN) model is developed for Abstractive Text Summarization in multiple document settings. Unlike conventional efforts we have employed state-of-art techniques such as Sequence-to-Sequence (S2S) paradigm where the inclusion of RCNN, which is modified as Recurrent Neural Network (RNN) encoder technique for text summarization. Our proposed abstractive text summarization model encompasses semantic feature extraction, dependency parsing, semantic role labeling, semantic information etc where it exploits the structural, syntactic, and semantic information of the input text data to generate the summary. Unlike conventional Attention based Summarization, in our proposed model at first performs Clustering and Sentence Merging, which is followed by Transition-based Abstract Meaning Representation (TAMR) parsing, whose output is encoded by means of an improved Tree-LSTM RCNN model, which eventually generates single summarized sentence as output.  The overall proposed model is tested with multiple text documents where simulation results affirm satisfactory performance for real-time applications.
Crop Planning and Production Process Ontology (C3PO), a new model to assist diversified crop production

Crop planning and production process ontology (C3P0) is a representation of agricultural knowledge for diversified crop production. It is composed of eight modules to take into account all the elements involved in crop production as well as the complexity for representing farming practices and constraints. Especially, the crop management module is sliced in three different layers to capture the generic information about crop itineraries, the planned production process and the tasks eventually really achieved been by various agents in the plots. To implement this ontology, we designed a new pipeline, presented here, integrating various existing tools to ease the ontology documentation, population and maintenance tasks.
Positive education in college students: the effects of the “self-knowledge and soul care” workshop on the emotions and character strengths

Objective: most healthy and unhealthy behaviors are acquired or consolidated during youth, thus a good investment in the future of any nation, should be to promote the development of young people, incorporating them into projects and programs that aspire to increase their subjective wellbeing and personal health. The Psycho-Educational Intervention (PEI) presented here has been shown to have effects on the health and wellbeing of students as well as on their academic performance. However, its effects on strength of character, emotional balance and emotional intelligence are unknown. Materials and methods: in this paper it was shows the effects of this PEI on positive education in a group of 18 students through three questionnaires: VIA-240, PANAS-20 and PIEMO. To estimate the effects of the PEI, a comparison was made between the scores obtained on the three questionnaires before and after the PEI sessions. Results: the results show that not only did character strengths, positive affect and emotional intelligence improve with PEI, but that also the character strengths and the emotional bearings arranged in a network topography changed with intervention. Also, there were some changes in the most connected nodes of the network. Conclusions: these results show that PEI improved the previous reported variables, they also show the way in which the balance of the positive and negative affects, the development of emotional intelligence and the enhancement of character strengths give access to the three pillars of positive psychology.
A Lightweight Two-Layer Blockchain Mechanism for Reliable Crossing-Domain Communication in Smart Cities

The smart city is an emerging notion that is leveraging the Internet of Things (IoT) technique to achieve more comfortable, smart and controllable cities. The communications crossing domains between smart cities is indispensable to enhance collaborations. However, crossing-domain communications are more vulnerable since there are in different domains. Moreover, there are huge different devices with different computation capabilities, from sensors to the cloud servers. In this paper, we propose a lightweight two-layer blockchain mechanism for reliable crossing-domain communication in smart cities. Our mechanism provides a reliable communication mechanism for data sharing and communication between smart cities. We defined a two-layer blockchain structure for the communications inner and between smart cities to achieve reliable communications. We present a new block structure for the lightweight IoT devices. Moreover, we present a reputation-based multi-weight consensus protocol in order to achieve efficient communication while resistant to the nodes collusion attack for the proposed blockchain system. We also conduct a secure analysis to demonstrate the security of the proposed scheme. Finally, performance evaluation shows that our scheme is efficient and practical.
Bit-Error Aware Lossless Image Compression with 2D-Layer-Block Coding

With IoT development, it becomes more popular that image data is transmitted via wireless communication systems. If bit errors occur during transmission, the recovered image will become useless. To solve this problem, a bit-error aware lossless image compression based on bi-level coding is proposed for gray image compression. But bi-level coding has not considered the inherent statistical correlation in 2D context region. To resolve this shortage, a novel variable-size 2D-block extraction and encoding method with built-in bi-level coding for color image is developed to decrease the entropy of information and improve the compression ratio. A lossless color transformation from RGB to the YCrCb color space is used for the decorrelation of color components. Particularly, the layer-extraction method is proposed to keep the Laplacian distribution of the data in 2D blocks which is suitable for bi-level coding. In addition, optimization of 2D-block start bits is used to improve the performance. To evaluate the performance of our proposed method, many experiments including the comparison with state-of-the-art methods, the effects with different color space, etc. are conducted. The comparison experiments under a bit-error environment show that the average compression rate of our method is better than bi-level, Jpeg2000, WebP, FLIF, and L3C (deep learning method) with hamming code. Also, our method achieves the same image quality with the bi-level method. Other experiments illustrate the positive effect of built-in bi-level encoding and encoding with zero-mean values, which can maintain high image quality. At last, the results of the decrease of entropy and the procedure of our method are given and discussed.
User Perception Analysis of Online Learning Platform “Zenius” During the Coronavirus Pandemic Using Text Mining Techniques

Availability of access to online learning platforms is expected to support online learning activities amid the COVID-19 pandemic. There are several issues experienced by students in online learning activities. These issues are related to Internet access, learning content, and learning evaluation. Online learning platforms are indispensable to be able to provide good learning resources that are easily accessible to students. This study aims to explore data reviews on Google Play Store to perceive the priority of service improvements that need to be carried out by online learning platform providers. Topic modeling and sentiment analysis are applied to extract useful information based on user sentiment towards the topics discussed in the application review. The result of topic analysis shows that topic trends in user reviews are about Live Class, Tryout, Subject Matter, User Account, Tutorial Video, and Free Learning Access. Meanwhile, the result of NRS assessment identified aspects that need to be a priority for improving online learning platform services. These aspects are in the Tryout and User Account sections. On the other hand, the aspect of Free Learning Access received the highest NRS. Users are helped by having free access to all learning content during learning from home activities.
An Attention-Based Generative Adversarial Network for Producing Illustrative Sketches

An illustrative sketch style expresses important shapes and regions of objects and scenes with salient lines and dark tones, while abstracting less important shapes and regions as vacant spaces. We present a framework that produces illustrative sketch styles from various photographs. Our framework is designed using a generative adversarial network (GAN), which comprised four modules: a style extraction module, a generator module, a discriminator module and RCCL module. We devise two key ideas to effectively extract illustrative sketch styles from sample artworks and to apply them to input photographs. The first idea is using an attention map that extracts the required style features from important shapes and regions of sample illustrative sketch styles. This attention map is used in the generator module of our framework for the effective production of illustrative sketch styles. The second idea is using a relaxed cycle consistency loss that evaluates the quality of the produced illustrative sketch styles by comparing images that are reconstructed from the produced illustrative sketch styles and the input photographs. This relaxed cycle consistency loss focuses on the comparison of important shapes and regions for an effective evaluation of the quality of the produced illustrative sketch styles. Our GAN-based framework with an attention map and a relaxed cycle consistency loss effectively produces illustrative sketch styles on various target photographs, including portraits, landscapes, and still lifes. We demonstrate the effectiveness of our framework through a human study, ablation study, and Frechet Inception Distance evaluation.