Reviewerly/exHarmony huggingface dataset:
A Global Stablecoin: Revolutionary Reserve Asset or Reinventing the Wheel?

This article examines the geopolitics, legal nuances and monetary policy which inform Carney's proposal. Part I sets out the application of block-chain technology in the context of payment systems, and discusses the geopolitics of Libra and the shift in focus to Central Bank Digital Currencies ('CBDCs'). It suggests that state-backed cryptocurrencies are more likely than their private sector counterparts to fit the role of true digital currencies, at least on a domestic level.Part II explains the legal distinctions between the cryptocurrency models proposed by Facebook and others, and CBDCs. The former fulfils the main functions of money by granting a claim to the holder, whereas the latter does so as an 'object' of monetary policy. A Global Stablecoin ('GSC') such as the SHC does not necessarily fall into either camp, but its success as a global reserve currency may hinge upon the fine details of its legal construction. In Part III, one conception of the SHC, based on the 'claims model' is set out. Whilst the use of block-chain technology is undoubtedly novel, there are significant similarities between this version of the SHC and previously rejected reforms to the 'Special Drawing Rights' (SDR) allocated by the International Monetary Fund ('IMF'). It is argued that the extent of the similarities indicates that the claims model is likely to face the same hurdles which stymied the campaign to develop the SDRs into a global reserve currency.This begs the question, however, of why Carney would propose the SHC if the odds in monetary policy were already so firmly stacked against it. Part IV considers the 'objects model' as applied to a SHC. It tentatively concludes that the features of the objects model which distinguish it from the claims model greatly improve its prospects of success. Specifically, the generation of network effect value, as a substitute for the convertibility value of SDRs, may be the decisive contribution of block-chain technology to a truly international currency. The article concludes that a claims-based SHC would, to a great extent, be an attempt to reinvent SDRs in a digital age, and that the advantages of block-chain technology will be insufficient to surmount well-established US monetary policy. The objects model, whilst more ambitious in some respects, constitutes a radically novel proposal which may, for that reason amongst others, gain global traction.
A NOVEL APPROACH FOR HUMAN INTENTION RECOGNITION BASED ON HALL EFFECT SENSORS AND PERMANENT MAGNETS

Human intention recognition is important for any interaction between the user and the exoskeleton. This study proposes a novel approach, based on a contactless sensory system, using linear Hall effect sensors to recognize human intentions. This contactless sensory system consists of four Hall effect sensors mounted on the exoskeleton, whilst a ring-shaped permanent magnet with diametrical magnetization consisting of two semi-rings is worn on the user's forearm. The model of the magnetic field created by the permanent magnet is also developed. Based on the developed magnetic field model and by interpreting the signals from the Hall effect sensory system received while the user's elbow and forearm move, the intention identification algorithm is derived. A lightweight elbow and forearm assistive exoskeleton is developed. The proposed approach for human intention recognition is used to assist in controlling the exoskeleton, following the wearer's intended motions. By implementing this contactless sensory system, wearers can use the exoskeleton easily and can move their forearm comfortably, while the human intention motion is recognized and used to control the exoskeleton. Moreover, achieved signals are unaffected by skin perspiration and muscle fatigue. As the sensory system is mounted on the exoskeleton, there is only indirect contact between the user's body and the sensors, leading to improved comfort. Finally, the system does not require expert knowledge to place the sensors on the body of the user. This approach can be extended to detect human intentions for the control of exoskeletons with more degrees of freedom.
Stretching Disciplinary Boundaries in Language Resource Development and Use: a Linguistic Data Consortium Position Paper

Given the persistent gap between demand and supply, the impetus to reuse language resources is great. Researchers benefit from building upon the work of others including reusing data, tools and methodology. Such reuse should always consider the original intent of the language resource and how that impacts potential reanalysis. When the reuse crosses disciplinary boundaries, the re-user also needs to consider how research standards that differ between social science and humanities on the one hand and human language technologies on the other might lead to differences in unspoken assumptions. Data centers that aim to support multiple research communities have a responsibility to build bridges across disciplinary divides by sharing data in all directions, encouraging re-use and re-sharing and engaging directly in research that improves methodologies.
Anti-forensics: the image asymmetry key and single layer perceptron for digital data security

Abstract Accessing or theft of public network information is very vulnerable to data transmission on public networks, this has an impact on the loss of those who have the information. In the digital forensic world, securing data is a technique for anti-forensic that aims to keep data safe. One way to secure information when sending is done is to encrypt the data and information. Encryption techniques play an important role in cryptography to hide text or messages into an image as a public key. The use of images as key objects is very effective for data security and the information cannot be read or unknown by unauthorized persons. In this case the asymmetric key cryptographic technique uses images and the single layer perceptron, asymmetric cryptography through the encryption and description process can produce data and information security keys that are difficult to guess. The use of keys is done by extracting image features using the GLCM (grey level co-occurrence matrix) to produce random symbols perfectly through the training process using a single layer perceptron encryption asymmetry key and description that can be accounted for integrity.
Cyber Attacks, Spillovers and Contagion in the Cryptocurrency Markets

This paper examines mean and volatility spillovers between three major cryptocurrencies (Bitcoin, Litecoin and Ethereum) and the role played by cyber attacks. Specifically, trivariate GARCH-BEKK models are estimated which include suitably defined dummies corresponding to different types, targets and number per day of cyber attacks. Significant dynamic linkages (interdependence) among the three cryptocurrencies under investigation are found in most cases when cyber attacks are taken into account, Bitcoin appearing to be the dominant one. Further, Wald tests for parameter shifts during episodes of turbulence resulting from cyber attacks provide evidence that the latter affect the transmission mechanism between cryptocurrency returns and volatilities (contagion). More precisely, cyber attacks appear to strengthen cross-market linkages, thereby reducing portfolio diversification opportunities for cryptocurrency investors. Finally, the conditional correlation analysis confirms the previous findings.
Entropy-Based Algorithms for Signal Processing

Entropy, the key factor of information theory, is one of the most important research areas in computer science [...].
VR the World: Investigating the Effectiveness of Virtual Reality for Destination Marketing through Presence, Emotion, and Intention

VR’s unprecedented ability to virtually transport the user is purported to be its biggest strength. Yet, despite early postulations about VR’s benefits as a destination marketing; substantial, theory-based VR research in tourism remains in infancy. The objective of this study is to empirically investigate the effectiveness of VR as a destination marketing tool, theoretically underpinned by the concept of presence and its influence on emotions and intentions. A within-subjects experiment is utilized to compare the effectiveness of VR, videos and pictures for cruise ship marketing. The results suggest VR could be more effective as a marketing tool and also provide insights into the impact of key presence determinants. Practical implications and avenues for future research are also discussed.
Extreme Multi-label Classification from Aggregated Labels

Extreme multi-label classification (XMC) is the problem of finding the relevant labels for an input, from a very large universe of possible labels. We consider XMC in the setting where labels are available only for groups of samples - but not for individual ones. Current XMC approaches are not built for such multi-instance multi-label (MIML) training data, and MIML approaches do not scale to XMC sizes. We develop a new and scalable algorithm to impute individual-sample labels from the group labels; this can be paired with any existing XMC method to solve the aggregated label problem. We characterize the statistical properties of our algorithm under mild assumptions, and provide a new end-to-end framework for MIML as an extension. Experiments on both aggregated label XMC and MIML tasks show the advantages over existing approaches.
Conveying Complexity in Contemporary Academic Library Service Models

This article will provide both practical and critical insights into contemporary library service practices using the UBC Okanagan service model redesign as a case study. In 2018 the service desk at UBC Okanagan Library was redesigned into a service zone with a fundamental goal of increasing the prominence of complex library services. By improving the visibility of research support within a newly conceptualized service zone, we addressed inclusivity through design and staffing practices while facilitating campus engagement through programming. This article offers a contribution to the ongoing discussion of consolidated service models and challenges the profession to continue experimenting with service model design and delivery in order to support diverse library patrons in an increasingly neoliberal university environment.
Research on Acoustic Model of Multi-Task Learning for Speech Recognition

Abstract Applying the idea of multi-task learning, this paper proposes a speech recognition acoustic model structure, which can share information between multiple tasks to improve the performance of each task. Applying this structure to the acoustic model modeling of resource-poor languages has achieved good recognition results.
Decomposable Pauli diagonal maps and tensor squares of qubit maps

It is a well-known result due to Størmer [Acta Math. 110, 233–278 (1963)] that every positive qubit map is decomposable into a sum of a completely positive map and a completely copositive map. Here, we generalize this result to tensor squares of qubit maps. Specifically, we show that any positive tensor product of a qubit map with itself is decomposable. This solves a recent conjecture by Filippov and Magadov [J. Phys. A: Math. Theor. 50(5), 055301 (2017)]. We contrast this result with examples of non-decomposable positive maps arising as the tensor product of two distinct qubit maps or as the tensor square of a decomposable map from a qubit to a ququart. To show our main result, we reduce the problem to Pauli diagonal maps. We then characterize the cone of decomposable ququart Pauli diagonal maps by determining all 252 extremal rays of ququart Pauli diagonal maps that are both completely positive and completely copositive. These extremal rays split into three disjoint orbits under a natural symmetry group, and two of these orbits contain only entanglement breaking maps. Finally, we develop a general combinatorial method to determine the extremal rays of Pauli diagonal maps that are both completely positive and completely copositive between multi-qubit systems using the ordered spectra of their Choi matrices. Classifying these extremal rays beyond ququarts is left as an open problem.
Developing a Book Recommendation Model Based on Reading Level Using Book Information and Book Review Data

This study presented a book recommendation model based on reading level which can be applied to the publishing industry. Based on the converged technologies of IT industry and AI industry, book level was calculated based on text difficulty analysis, and the reviews were analyzed using NLP techniques and artificial neural networks based on clusters based on customers’ characteristics. The derived book level was set as a pre-score and the expected level of book experience of target customers derived through a fully connected neural network was set as a post-score. From those scores, we derived a model of book recommendation as a rank considering customers’ reading level. From a survey conducted to verify this mdoel, the model had a statistical significance of correlation with survey results. In addition, this study presented a reading roadmap and business model considering reading level as a way to apply this model developed in this study.
Block Chain Technology: The Future of Tourism

Digitalization in every field has created a remarkable development in one way or another. The changes due to digitalization is evident across the industries and one such recent technological development happened across the world is that of “Blockchain”; which have generated a significant excitement across the industries. Most of the industries have adopted the blockchain technology for its ease of use. Tourism industry is considered to be one of the promising industries which doesn’t have a retirement. The impact of digitalization has reflected in tourism sector too; but the question is; “Whether the tourism industry copes up with the emerging technologies?”. Sadly, the answer is a big NO. Gradually, the tourism industry is planning to introduce Blockchain Technology into its operations to create a revolution. The introduction of Blockchain Technology into Crypto Currencies reaped huge success in the past. The same level or a little higher level of success is expected from this technology; but, the aversion and dilemma towards the new technologies makes the tourism sector to wait for its adoption. The future milestones of the tourism industry will definitely witness Blockchain technology. The tourism industry gets benefitted from Block chain technology through many ways, including secure payment, identification of the customers, management of the baggage, customer rewards management and the ratings of business. This paper aims to study the development of blockchain technology in the tourism sector, its applications and its challenges, and its future perspectives on the tourism industry.
CYBERBULLYING DETECTION IN ROMAN URDULANGUAGE USING LEXICON BASED APPROACH

Nowadays, online social networks (OSNs) have become integral part of our daily life and onlineusers of social media are massively growing. The increasing use of OSNs by users leads to large amount of usercommunication data. This study focuses on OSNs users who communicate in Roman Urdu (Urdu languagewritten in English alphabets). Pakistan alone has over 44 million OSNs users who communicate in Roman Urdu.In this paper, we addressed the issue of cyberbullying behavior on Twitter platform, where users use RomanUrdu as medium of their communication. To the best of our knowledge, this is the first study addressingcyberbullying behavior in Roman Urdu. To address this issue, we developed supervised machine learningmethod and proposed a lexicon-based model with set of features derived from Twitter. An evaluation modelshows that the developed model attained results with area under receiver operating characteristics curve (AUC)of 0.986 and f-measure of 0.984. These results indicate that the proposed lexicon-based method gives feasiblesolution for detecting cyberbullying behavior in Roman Urdu in OSNs. Finally, we compared results achievedwith our proposed lexicon-based method and the results obtained from other well-known models. Thecomparison results show the significance of our proposed model.
The Variable Amplitude Coefficient Fireworks Algorithm with Uniform Local Search Operator

Fireworks Algorithm (FWA) is a relatively novel swarm-based metaheuristic algorithm for global optimization. To solve the low-efficient local searching problem and convergence of the FWA, this paper presents a Variable Amplitude Coefficient Fireworks Algorithm with Uniform Local Search Operator (namely VACUFWA). Firstly, the explosive amplitude is used to adjust improving the convergence speed dynamically. Secondly, Uniform Local Search (ULS) enhances exploitation capability of the FWA. Finally, the ULS and Variable Amplitude Coefficient operator are used in the VACUFWA. The comprehensive experiment carried out on 13 benchmark functions. Its results indicate that the performance of VACUFWA is significantly improved compared with the FWA, Differential Evolution, and Particle Swarm Optimization.
Secure Hybrid Crypto-system AES/RSA on FPGA for Data Communication

With the development of information technologies, our environment is surrounded by digital data that transit via networks. When data are important, they become vulnerable to external attacks which can be avoided by using cryptography which provides confidentiality, integrity and availability required to secure digital data transactions such as e- commerce, mobile telephony and Internet. This paper deals with securing data transmitted over a network composed by a server and several clients, where a security platform has been integrated into the server and embedded on an FPGA circuit. The protection of data transfer between clients is provided by hybrid cryptography combining symmetric and asymmetric cryptographies. The security of client-server communication is ensured by the AES protocol and the Diffie-Hellman key exchange protocol. To offer a good management of keys and their sharing, a dedicated system for generating keys is designed to fit with public key infrastructures. This system is a part of the server and has been implemented using JAVA language and executed on a computer. This communication system provides a Graphical User Interface (GUI) offering to clients ease and flexibility in transferring their data.
Transforming Diagrams’ Semantics to Text for Visually Impaired

Using models and diagrams is a very useful and effective tool for representing information and systems in a graphical form to communicate and understand them better. On the other hand, graphical representations bring extra cognitive load and the process for understanding the diagrams is long and tedious in most cases for the visually impaired. To solve this problem, semantics of the diagrams should be converted to a different format that is both human and machine readable as well as communicable for the visually impaired. Most existing diagramming tools are not easily usable for the visually impaired as a tool for creating and using diagrams. In this paper, we propose an online system for defining specific diagrams and converting their semantics to text which can have a speech output for the visually impaired. We present analysis and design of this online system as well as a proof of concept prototype implementation. The prototype system provides create, save, load and transform features and tested with participants to recreate the diagrams using the automatically generated text output. Our case study showed that the results are very promising and the proposed solution can provide a way to correctly and accurately represent the information in diagrams textually.
Secure Reversible Information Hiding in Image Based on Loseless Compression

Information hiding mainly studies how to hide certain confidential information in certain public information and then transmit the confidential information through the public channel. However, embedding additional information in the original image results in permanent distortion of the original image, that is, the original image cannot be fully recovered after the information is embedded in. In order to solve this problem, based on lossless compression, we put forward a reversible information hiding technology in cipher text domain. Given an 8-bit gray image as the carrier, the value of each pixel is divided into eight bit planes. On the higher bit pixel plane, especially the highest one, the gradient of bit value change is very small, i.e., there appears a lot of continuous '0's or continuous '1's strings. Therefore, we can compress the data losslessly on that plane by using running length coding. The experiment results show that the method has fast running speed, high compression ratio and strong generality. Moreover, the proposed technique can be extended to reversible information hiding of video frames.
On the social and technical challenges of Web search autosuggestion moderation

Past research shows that users benefit from systems that support them in their writing and exploration tasks. The autosuggestion feature of Web search engines is an example of such a system: It helps users in formulating their queries by offering a list of suggestions as they type. Autosuggestions are typically generated by machine learning (ML) systems trained on a corpus of search logs and document representations. Such automated methods can become prone to issues that result in problematic suggestions that are biased, racist, sexist or in other ways inappropriate. While current search engines have become increasingly proficient at suppressing such problematic suggestions, there are still persistent issues that remain. In this paper, we reflect on past efforts and on why certain issues still linger by covering explored solutions along a prototypical pipeline for identifying, detecting, and addressing problematic autosuggestions. To showcase their complexity, we discuss several dimensions of problematic suggestions, difficult issues along the pipeline, and why our discussion applies to the increasing number of applications beyond web search that implement similar textual suggestion features. By outlining persistent social and technical challenges in moderating web search suggestions, we provide a renewed call for action.
Human-Computer Interaction Considerations When Developing Cyber Ranges

Cyber-attacks are continuing to rise globally. It is therefore vital for organisations to develop the necessary skills to secure their assets and to protect critical national infrastructure. In this short paper, we outline human-computer interaction elements which should be considered when developing a cybersecurity training platform, in an eﬀort to maintain levels of user engagement. We provide an overview of existing training platforms before covering specialist cyber ranges. Aspects of human-computer interaction are explored with regards to their relevance in the context of cyber ranges. We conclude with design suggestions when developing a cyber range platform.
Mineral waters classification using fuzzy linear discriminant analysis

Fuzzy linear discriminant analysis is efficiently applied for the characterization and classification of some Romanian and German mineral waters according to their mineral composition.The samples were successfully classified according to the degrees of membership and canonical scores.A correct classification rate of 88% was obtained when the samples were divided into four groups corresponding to origin and nature of samples.The proposed methodology based on the fuzzy sets theory may be considered as a promising tool with future applications in analytical chemistry and other related fields.