Reviewerly/exHarmony huggingface dataset:
RLM: a new mask countermeasure to resist HO-CPA for GIFT

As a further development of the side channel technique, the security of crypto-chips has received extensive attention from researchers. A mask scheme based on a random number generator is usually used to protect crypto-chips, but it will result in extra memory and time consumption. How to balance the security of crypto-chips and the memory consumption has always been the research focus. In this paper, we use a random number generator to design a random loop mask scheme (RLM) for GIFT algorithm. The binary value of a random number is used to set the position and value of masks. In addition, the rotating S-box of the RSM scheme is combined with the small generation mask of a fixed mask scheme to make the RLM more secure. The experimental results show that the RLM scheme protects the intermediate values from being leaked. Compared to other common schemes, our RLM scheme could resist not only correlation power analysis (CPA) attacks but also higher-order correlation power analysis (HO-CP A) attacks. In addition, the RAM consumption was 49% less than that of the global mask scheme.
HotDAG: Hybrid Consensus via Sharding in the Permissionless Model

A major design to improve scalability and performance of blockchain is sharding, which maintains a distributed ledger by running classical Byzantine Fault Tolerance (BFT) protocols through several relatively small committees. However, there are several drawbacks with the existing sharding protocols. First, the sharding mechanism which ensures that each committee is strongly bias-resistant either weakens the decentralization or reduces the performance of the protocol. Second, BFT protocols are either unresponsive or take quadratic communication complexities under a byzantine leader. Third, they cannot defend against transaction censorship attacks. Finally, nodes do not have enough motivation to follow the protocol and selfish nodes can obtain more rewards through collusive behaviors. A recent study proposes HotStuff – a BFT protocol that achieves linear view-change and optimistic responsiveness. In this paper, we present HotDAG, a hybrid consensus protocol based on HotStuff via sharding in the permissionless model. By employing the parallel Nakamoto consensus protocol, we present a decentralized and bias-resistant sharding mechanism. HotDAG has a linear communication complexity on transaction confirmation by introducing a scalable BFT protocol and an inter-committee consensus mechanism based on blockDAG. By achieving an unpredictable leader rotation, HotDAG prevents the censorship attacks. At the same time, HotDAG provides an incentive mechanism that is compatible with the scalable BFT protocol to encourage nodes to actively participate in the protocol. Finally, we formally prove the security and analyze the performance of HotDAG.
Usability of Virtual Reality Vocational Skills Training System for Students with Intellectual Disabilities

Virtual reality has been applied to education widely since technology developed quickly. In order to apply the “Virtual Reality Vocational Skills Training System” to vocational high school students with intellectual disabilities, this study simplify the operation of the original system and develop an easy-to-use version to meet the learning needs of students with intellectual disabilities. Therefore, the purpose of this study is to test the usability of the easy-to-use version through the questionnaire, and to compare the operating efficiency between the easy-to-use version and the original one. Eight students with intellectual disabilities participated in the study. The results indicated that most students expressed that the easy-to-use version had good usability, and reduced the operation time and the number of wrong actions, as well as enhanced the accuracy. Overall, this designed “Virtual Reality Vocational Skills Training System” can be applied to the training of vocational skills for students with intellectual disabilities.
Using digital badges in business information literacy instruction: a case study

Digital badges are an increasingly popular way to display learning achievements, both in higher education and in professional environments. Digital badges can be used to help deliver information literacy instruction in a scalable and sustainable way and can increase the effectiveness of reaching a large audience of students. This case study discusses the work of a solo business librarian who uses digital badges to reach first year business students in a foundational level course.
Convex Subspace Clustering by Adaptive Block Diagonal Representation

Subspace clustering is a class of extensively studied clustering methods where the spectral-type approaches are its important subclass. Its key first step is to desire learning a representation coefficient matrix with block diagonal structure. To realize this step, many methods were successively proposed by imposing different structure priors on the coefficient matrix. These impositions can be roughly divided into two categories, i.e., indirect and direct. The former introduces the priors such as sparsity and low rankness to indirectly or implicitly learn the block diagonal structure. However, the desired block diagonalty cannot necessarily be guaranteed for noisy data. While the latter directly or explicitly imposes the block diagonal structure prior such as block diagonal representation (BDR) to ensure so-desired block diagonalty even if the data is noisy but at the expense of losing the convexity that the former's objective possesses. For compensating their respective shortcomings, in this article, we follow the direct line to propose adaptive BDR (ABDR) which explicitly pursues block diagonalty without sacrificing the convexity of the indirect one. Specifically, inspired by Convex BiClustering, ABDR coercively fuses both columns and rows of the coefficient matrix via a specially designed convex regularizer, thus naturally enjoying their merits and adaptively obtaining the number of blocks. Finally, experimental results on synthetic and real benchmarks demonstrate the superiority of ABDR to the state-of-the-arts (SOTAs).
Birth and Development of Data Librarianship, Nascita e sviluppo della biblioteconomia dei dati

Data librarianship and the role of the data librarian are an established reality in many countries, even though at different levels. Particularly, academic librarians have been involved in research data management for a long time and this role is acquiring precise features. In Italy, the data librarian is a figure still to be built and defined. The aim of the article is to offer a first systematic exploration in the fields of data librarianship and the role of the data librarian, both in their practical (what activities) and methodological (how activities are performed) features. The hope is to encourage the beginning of a necessary reflection on these topics.
Data-driven Choices in Neural Part-of-Speech Tagging for Latin

Textual data in ancient and historical languages such as Latin is increasingly available in machine readable forms, yet computational tools to analyze and process this data are still lacking. We describe our system for part-of-speech tagging in Latin, an entry in the EvaLatin 2020 shared task. Based on a detailed analysis of the training data, we make targeted preprocessing decisions and design our model. We leverage existing large unlabelled resources to pre-train representations at both the grapheme and word level, which serve as the inputs to our LSTM-based models. We perform an extensive cross-validated hyperparameter search, achieving an accuracy score of up to 93 on in-domain texts. We publicly release all our code and trained models in the hope that our system will be of use to social scientists and digital humanists alike. The insights we draw from our inital analysis can also inform future NLP work modeling syntactic information in Latin.
A Method of Predicting Crime of Theft Based on Bagging Ensemble Feature Selection

The crime is a complex social problem, and it is caused by many factors such as population factors, political factors, economic factors, social factors, cultural factors, and ecological factors. But how and to what extent the factors impact crime, is determined by industry experts according to their experience most of the time. It is probably to bring the risk of subjective experience, and it is difficult to figure out the effect of the objective factors from the quantitative point of view. Another problem is that the feature dimension is too high when choosing multi-factors indicators by experience, and it results in inefficient operation because of insignificant factors doped. This paper is devoted to solving the above two problems, and proposing a bagging method based on the principle of “good and different”. Heterogeneous learners are used to construct an integrated learning device to identify the impact of the occurrence factors, then improve the efficiency and accuracy of crime prediction with less dimension of factors. The experimental results show that the EFV_Bagging algorithm proposed in this paper has better generalization ability and stability, and the prediction accuracy on the test sample also has a better performance than other algorithms. In addition, the algorithm does not need a priori knowledge to manually set the selected feature subset dimension, which has obvious advantages in the application field of criminal data analysis and forecasting.
Research on private cloud platform for virtual resource adaptation

In order to meet the current needs of enterprises and institutions in data center construction, this paper designs and implements the deployment of private cloud platform based on KVM and open stack on the infrastructure layer of private cloud platform. The private cloud platform designed in this paper provides users with virtual resource adaptive services and cloud storage services through cloud middleware. It designs and implements resource monitoring, resource prediction, virtual resource adaptive, user management and cloud storage on the cloud middleware layer, so as to meet the application needs of current enterprises and institutions. Among them, virtual resource adaptation is the core function of private cloud platform. This paper focuses on the design process of virtual resource adaptation module, and gives the implementation method and process of each middleware function module.
Quantum circuits of CNOT gates

We study in detail the algebraic structures underlying quantum circuits generated by CNOT gates. Our results allow us to propose polynomial-time heuristics to reduce the number of gates used in a given CNOT circuit and we also give algorithms to optimize this type of circuits in some particular cases. Finally we show how to create some usefull entangled states when a CNOT circuit acts on a fully factorized state.
The Promise and Challenges of Quantum Computing

This brief overview of quantum computing gives an intuitive, albeit incomplete view of why it shows promise over any known classical approach to computing. The difficulty of making a qubit which allows us to protect and control quantum information will be illustrated in an overview of existing quantum technologies, including ions, superconducting qubits, spins and defects in solids. Finally, I will review a few of the problems which may benefit most from quantum computing and why many in industry are excited by the prospects.
Antecedents of Blockchain Technology Application among Malaysian Warehouse Industry

The purpose of this research is to ascertain the most influential factor that affects the blockchain technology adoption intentions in Malaysian warehouse operations. The unified theory of acceptance and use of technology 2 acts as the underpinning theory and from a comprehensive literature review, five factors have identified to be incorporated in the conceptual framework development. Numerous studies covering the use of a wide range of technologies in the warehouse industry can be found in recent literature. However, it should be noted that, for studies relating to the intentions and application of blockchain technology in the Malaysian warehouse industry, this research would appear to be a pioneering work. Given the countless advantages of blockchain technology adoption, it is crucial that the Malaysian warehouse industry would seriously consider its application as standard business practices. This paper bridges the theoretical gap that will help to identify the opportunities for blockchain technology applications.
Multi-source Information Data Fusion Method under Complex Battlefield Situation

Abstract Under complex battlefield situation, the situation information is instantaneous and changeable. The uncertain information always causes difficulty in acquisition information and miscalculation, and lead to low efficiency and poor accuracy for aircraft situation awareness. In order to solve the problem, in this paper it proposes a deep-learning based multi-sensor situation awareness data fusion method. With data acquitted from multiple sensors in multi-band and multi-angle, it realizes multi-sensor information data fusion by comparing and analysing of two AI data fusion methods that one is based on classical evidence theory and the other is based on deep learning. The simulation results indicate that the deep-learning based data fusion method presents higher efficiency in dealing with the environment information fusion with large evidence conflict.
Annotation of Quantification: The Current State of ISO 24617-12

This paper discusses the current state of developing an ISO standard annotation scheme for quantification phenomena in natural language, as part of the ISO Semantic Annotation Framework (ISO 24617). A proposed approach that combines ideas from the theory of generalised quantifiers and from neo-Davidsonian event semantics was adopted by the ISO organisation in 2019 as a starting point for developing such an annotation scheme. * This scheme consists of (1) a conceptual ‘metamodel’ that visualises the types of entities, functions and relations that go into annotations of quantification; (2) an abstract syntax which defines ‘annotation structures’ as triples and other set-theoretic constructs; (3) an XML-based representation of annotation structures (‘concrete syntax’); and (4) a compositional semantics of annotation structures. The latter three components together define the interpreted markup language QuantML. The focus in this paper is on the structuring of the semantic information needed to characterise quantification in natural language and the representation of these structures in QuantML.
Minimum Perturbation Cost Modulation for Side-Informed Steganography

A new rule for modulating costs in side-informed steganography is proposed. The modulation factors of costs are determined by the minimum perturbation of the precover to quantize to the desired stego value. This new rule is contrasted with the established way of weighting costs by the difference between the rounding errors to the cover and stego values. Experiments are used to demonstrate that the new rule improves security in ternary side-informed UNIWARD in JPEG domain. The new rule arises naturally as the correct cost modulation for JPEG side-informed steganography with the “trunc” quantizer used in many portable digital imaging devices.
Pre-Design Stage Cost Estimation for Cloud Services

Cloud computing is being increasingly employed to build systems. Rapid cost estimation for such systems is necessary to start new business. Here, the "Pay-Per-Use" billing model has been established, and providers offer various strategic pricing. There are many cost structures, so the users need to consider many pricing options when estimating system costs. Although companies often want to estimate the cost of their systems, it is difficult to obtain a comprehensive estimation that takes into account all of the components. Previously, researchers have implemented simulation-based and analytical approaches to solve this problem. In this study, we developed a cost-estimation method that employs directed acyclic graph (DAG)-based representation and matrix operations. Our method emphasizes simplicity through the use of a systematic procedure that can estimate the costs of new services at the pre-design stage.
The Cross-border C2B Development Study Based on the Information Cost Theory

With the rapid growth of Chinese economy and constant improvement of internet popularity, Chinese electronic commerce (for short e-commerce) has seen a remarkable performance. From the initial B2B mode to network retailing mode as well as the eye-catching C2B mode, the concern is focused on the status, problem and suggestion on the e-commerce mode, but there is short of corresponding theoretical study among the e-commerce modes. For this reason, the path, theoretical mechanism and future development direction of cross-border e-commerce mode are the emphases in this paper.
CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models

Pretrained language models, especially masked language models (MLMs) have seen success across many NLP tasks. However, there is ample evidence that they use the cultural biases that are undoubtedly present in the corpora they are trained on, implicitly creating harm with biased representations. To measure some forms of social bias in language models against protected demographic groups in the US, we introduce the Crowdsourced Stereotype Pairs benchmark (CrowS-Pairs). CrowS-Pairs has 1508 examples that cover stereotypes dealing with nine types of bias, like race, religion, and age. In CrowS-Pairs a model is presented with two sentences: one that is more stereotyping and another that is less stereotyping. The data focuses on stereotypes about historically disadvantaged groups and contrasts them with advantaged groups. We find that all three of the widely-used MLMs we evaluate substantially favor sentences that express stereotypes in every category in CrowS-Pairs. As work on building less biased models advances, this dataset can be used as a benchmark to evaluate progress.
Quantum Private Information Retrieval from MDS-coded and Colluding Servers

In the classical private information retrieval (PIR) setup, a user wants to retrieve a file from a database or a distributed storage system (DSS) without revealing the file identity to the servers holding the data. In the quantum PIR (QPIR) setting, a user privately retrieves a classical file by downloading quantum systems from the servers. The QPIR problem has been treated by Song et al. in the case of replicated servers, both without collusion and with all but one servers colluding. In this paper, the QPIR setting is extended to account for maximum distance separable (MDS) coded servers. The proposed protocol works for any [n, k]-MDS code and t-collusion with t = n - k. Similarly to the previous cases, the rates achieved are better than those known or conjectured in the classical counterparts.
Sentiment Analysis Using Long Short-Term Memory Model in Deep Learning

Sentiment recognition through deep learning technique is a challenge in artificial intelligence and affective computing as it involves the discovery of all associated emotions expressed in a specific manner. Recommended system involves a study of method based on user emotional state using deep learning technique. In addition with intrusion detection system on dataset used for text recognition, sentiment prediction can be done using modified version of recurrent neural network, i.e., long short-term memory neural network. We applied LSTM architecture to a RNN and trained the model using Amazon web service dataset in Microsoft Azure notebooks Jupyter. Through the performance test, we confirm that the deep learning approach, i.e., LSTM, is effective for sentiment recognition.
Deciphering adoption and adaption of ICTs in humanitarian services: the southern African context

Globally, the rate and frequency at which both natural and man-made disasters have been occurring are on the increase. The southern African region has its own fair share of disasters such as floods, droughts, wars and health issues, hence a need to establish structures for emergency preparedness. With the adoption of information communication technologies (ICTs), humanitarian services, for interventions during emergencies such as disaster preparedness, responses and awareness would be enhanced. This chapter discusses the nature of humanitarian issues affecting the region and how best adoption and adaption of ICTs have and/or can close the gaps of these issues, thereby maximising the efficiency of the humanitarian sector in mitigation, management and recovery efforts. The actor-actant-network theory was used as a theoretical framework for the study. The framework managed to outline how actors, their institutions and actants (technology) interact through their networks in disaster mitigation. The study revealed the ICTs that are used in the southern African region to mitigate, manage and recover from floods, droughts, health and conflict humanitarian disasters. The chapter further explored the challenges faced by the southern African regional countries such as funding, infrastructure, emerging technologies and computer literacy, among others that are affecting the adoption and adaption of ICTs in the humanitarian sector. Recommendations for adopting and adapting ICTs for the region by the policy makers are also discussed.
Assessment of Solar Power Potential Mapping in Telangana State Using GIS

Solar energy replacing conservative non-renewable energy is being witnessed in often around the world. Solar energy has a massive prospective in a humid country like India. Most parts of the country get around 300 sunshiny days in a year with 8 h of daily sunlight. Presently, one of the most interesting problems is how to mend the effectiveness of generating solar energy. Before installing solar panels, evaluating where solar panels should be positioned can considerably benefit panel performance. The present study is aimed at carrying out site selection analysis for setting up of solar panel using geographic information system (GIS). Telangana is a state which ranks fourth in terms of capacity to harness and utilize solar energy. The project is aimed at mapping the areas with high solar energy potential at both macro- and micro-levels. The solar irradiation data (GHI and DNI), land use data and digital elevation model (DEM) have been used in GIS environment while retaining land use criteria and topography to omit unsuitable sites for harnessing solar energy. The study carried out concludes total suitable area of 11,520.60 km2 at macro-analysis for economical and effective harnessing of solar power.
Global electronic thesis and dissertation repositories – collection diversity and management issues

This article discovers the collection diversity of electronic thesis and dissertation (ETD) repositories based on key parameters such as regional distribution, subject classification, language diversity, etc. and identifies the critical management issues of the ETD repositories related to collection management, software management, content management and metadata policies. The ETD repositories were identified in the Directory of Open Access Repositories (OpenDOAR). The required data were manually collected from the OpenDOAR and websites of repositories to achieve the prescribed objectives of the study. The data were later tabulated, analysed and interpreted using simple arithmetic techniques. The study was limited to the ETD repositories available in the OpenDOAR, and findings cannot be generalized across repositories and directories. It provides insights about ETD repositories worldwide, highlights their critical management issues and suggests mechanisms for their sustainable growth and development. This article is purely based on research and its findings are valid for scholars, faculty members, institutions – as well as administrators and managers of the ETD repositories.