Reviewerly/exHarmony huggingface dataset:
Cryptoliquidity: the blockchain and monetary stability

Purpose The development of blockchain and cryptocurrency may alleviate the economic strain associated with recession. Economic recessions tend to be aggregate-demand driven, meaning that they are caused by fluctuations in the supply of or demand for money. Holding monetary policy as solution assumes that stability must arise from outside of the economic system. Under a policy regime that allows innovations in blockchain to develop, blockchain technology may promote a money supply that is responsive to changes in demand to hold money. The purpose of this paper is to suggest that cryptocurrencies present an opportunity to profitably implement rules that promote macroeconomic stability. In particular, cryptocurrency that is asset-backed may provide a means for cheaply attaining liquidity during a crisis. Design/methodology/approach The role of cryptocurrency in promoting macroeconomic equilibrium is approached through the lens of monetary theory. Moves away from macroeconomic equilibrium necessitate either a change in the average price of money or a change in the quantity of money, or a change in portfolio demand for money. Cryptocurrency promotes an increase, however this requires the alignment of policy regulating the use of cryptocurrency, reduction in taxes placed on the use of cryptocurrency and cryptocurrency protocol. Findings Cryptocurrency is unlikely to become legal tender, but it may alleviate macroeconomic fluctuations as a near money that provides liquidity and whose supply is sensitive to changes in demand to hold money and money-like substitutes. This role might be inhibited if policy stifles the development of cryptocurrencies and blockchain technology. Research limitations/implications New financial innovations like cryptocurrencies can be analyzed applying the equation of exchange in light of the mechanics of money creation under conditions of disequilibrium. Monetary disequilibrium may be promoted by policy that causes bottlenecks in financial markets. Originality/value Theory of monetary disequilibrium has broad implications for the development and regulation of financial markets. This theory has not been applied to the development of cryptocurrency markets.
Digital image correlation utilization in measuring displacement and strain during plastic film blowing process: A feasibility study

This research work explores on Digital Image Correlation (DIC) technique as a potential tool for measuring displacement and strain in film blowing extrusion process. A feasibility study on the surface patterning technique, validation of the DIC analysis and selection of DIC parameters were investigated. Understanding on the effects of these factors on the analysis of DIC in film blowing process is crucial as the film moves vertically at a relative speed while being stretched and deformed. Results showed that ink application was the best practice for surface pattern creation. The DIC was validated as a reliable method as the results obtained with the ink procedure were comparable to the conventional grid method. Subset size of 11 pixels and 21 pixels were preferred for small and large pattern, respectively. Regardless of pattern size, subset space of 1 pixel and strain radius of 5 pixels was chosen for yielding accurate result without any distortion in spatial resolution.
AgriBot: Agriculture-Specific Question Answer System

India is an agro-based economy and proper information about agricultural practices is the key to optimal agricultural growth and output. In order to answer the queries of the farmer, we have build an agricultural chatbot based on the dataset from Kisan Call Center. This system is robust enough to answer queries related to weather, market rates, plant protection and government schemes. This system is available 24*7, can be accessed through any electronic device and the information is delivered with the ease of understanding. The system is based on a sentence embedding model which gives an accuracy of 56%. After eliminating synonyms and incorporating entity extraction, the accuracy jumps to 86%. With such a system, farmers can progress towards easier information about farming related practices and hence a better agricultural output. The job of the Call Center workforce would be made easier and the hard work of various such workers can be redirected to a better goal.
Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptions

Automated rationale generation is an approach for real-time explanation generation whereby a computational model learns to translate an autonomous agent's internal state and action data representations into natural language. Training on human explanation data can enable agents to learn to generate human-like explanations for their behavior. In this paper, using the context of an agent that plays Frogger, we describe (a) how to collect a corpus of explanations, (b) how to train a neural rationale generator to produce different styles of rationales, and (c) how people perceive these rationales. We conducted two user studies. The first study establishes the plausibility of each type of generated rationale and situates their user perceptions along the dimensions of confidence, humanlike-ness, adequate justification, and understandability. The second study further explores user preferences between the generated rationales with regard to confidence in the autonomous agent, communicating failure and unexpected behavior. Overall, we find alignment between the intended differences in features of the generated rationales and the perceived differences by users. Moreover, context permitting, participants preferred detailed rationales to form a stable mental model of the agent's behavior.
The risk analysis of Bitcoin and major currencies: value at risk approach

Purpose This study aims to compare investors of major conventional currencies and Bitcoin (BTC) investors by using the value at risk (VaR) method common risk measure. Design/methodology/approach The paper used a risk analysis named as VaR. The analysis has various computations that Historical Simulation and Monte Carlo Simulation methods were used for this paper. Findings Findings of the analysis are assessed in two different aspects of singular currency risk and portfolios built. First, BTC is found to be significantly risky with respect to the major currencies; and it is six times riskier than the singular most risky currency. Second, in terms of inclusion of BTC into a portfolio, which equally weights all currencies, it elevates overall portfolio risk by 98 per cent. Practical implications In spite of the remarkable risk level, it could be considered that investors are desirous of making an investment on BTC could mitigate their overall exposed risk relatively by building a portfolio. Originality/value The paper questions the risk level of Bitcoin, which is a digital currency. BTC, a matter of debate in the contemporary period, is seen as a digital currency free from control or supervision of a regulatory board. With the comparison of major currencies and BTC shows that how could be risky of a financial instrument without regulations. However, there is some advice for investors who would like to invest digital currencies despite the risk level in this study.
Security analysis and enhancement of a cryptosystem based on phase truncation and a designed amplitude modulator

In this paper, the security of a cryptosystem based on phase truncation and a designed amplitude modulator (AM) is evaluated. In the cryptosystem, an undercover AM used as an additional key is added to modulate the amplitude information of the spectrum in the Fourier plane. Compared to the conventional phase-truncated Fourier transform (PTFT)-based cryptosystem, the security of the cryptosystem is improved by increasing the number of unknown keys. However, it is found that the designed AM is irrelative to the plaintext, and one of the parameters in the designed AM contributes less to the security enhancement of the cryptosystem due to low key sensitivity. Based on the analysis, a special attack containing two iterative processes is proposed to crack the cryptosystem, in which the known-plaintext-attack-based iterative process I with a specific normalization operator is used to retrieve the designed AM and the amplitude-phase-retrieval-technique-based iterative process II is used to retrieve the corresponding plaintext from the arbitrarily given ciphertext with the help of the retrieved AM. In addition, an inherent drawback widely existing in PTFT-based cryptosystems is reported for the first time: most information of the original image could be retrieved using two correct phase keys (or only the first phase key) generated in the encryption process, even without the corresponding ciphertext in PTFT-based cryptosystems. To address this issue, a security-enhanced cryptosystem is proposed in this paper. Numerical simulation is carried out to demonstrate the effectiveness and feasibility of the proposed attack and cryptosystem.
An Approach for Multimodal Medical Image Retrieval using Latent Dirichlet Allocation

Modern medical practices are increasingly dependent on Medical Imaging for clinical analysis and diagnoses of patient illnesses. A significant challenge when dealing with the extensively available medical data is that it often consists of heterogeneous modalities. Existing works in the field of Content based medical image retrieval (CBMIR) have several limitations as they focus mainly on visual or textual features for retrieval. Given the unique manifold of medical data, we seek to leverage both the visual and textual modalities to improve the image retrieval. We propose a Latent Dirichlet Allocation (LDA) based technique for encoding the visual features and show that these features effectively model the medical images. We explore early fusion and late fusion techniques to combine these visual features with the textual features. The proposed late fusion technique achieved a higher mAP than the state-of-the-art on the ImageCLEF 2009 dataset, underscoring its suitability for effective multimodal medical image retrieval.
PPQAR: Parallel PSO for quantitative association rule mining

Mining quantitative association rules is one of the most important tasks in data mining and exists in many real-world problems. Many researches have proved that particle swarm optimization(PSO) algorithm is suitable for quantitative association rule mining (ARM) and there are many successful cases in different fields. However, the method becomes inefficient even unavailable on huge datasets. This paper proposes a parallel PSO for quantitative association rule mining(PPQAR). The parallel algorithm designs two methods, particle-oriented and data-oriented parallelization, to fit different application scenarios. Experiments were conducted to evaluate these two methods. Results show that particle-oriented parallelization has a higher speedup, and data-oriented method is more general on large datasets.
Continuous-time quantum walks on dynamic graphs

Continuous-time quantum walks (CTQWs) on static graphs provide efficient methods for search and sampling as well as a model for universal quantum computation. We consider an extension of CTQWs to the case of dynamic graphs, in which an ordered sequence of graphs governs free evolution of the quantum walk. We then consider how perfect state transfer during the quantum walk can be used to design dynamic graphs that implement a universal set of quantum logic gates. We give explicit examples for a complete logical basis, and we validate implementations using numerical simulations for quantum teleportation and addition circuits. Finally, we discuss the potential for realizing CTQWs on dynamic graphs using actively controlled quantum optical waveguides.
Quantum Language Processing

We present a representation for linguistic structure that we call a Fock-space representation, which allows us to embed problems in language processing into small quantum devices. We further develop a formalism for understanding both classical as well as quantum linguistic problems and phrase them both as a Harmony optimization problem that can be solved on a quantum computer which we show is related to classifying vectors using quantum Boltzmann machines. We further provide a new training method for learning quantum Harmony operators that describe a language. This also provides a new algorithm for training quantum Boltzmann machines that requires no approximations and works in the presence of hidden units. We additionally show that quantum language processing is BQP-complete, meaning that it is polynomially equivalent to the circuit model of quantum computing which implies that quantum language models are richer than classical models unless BPP=BQP. It also implies that, under certain circumstances, quantum Boltzmann machines are more expressive than classical Boltzmann machines. Finally, we examine the performance of our approach numerically for the case of classical harmonic grammars and find that the method is capable of rapidly parsing even non-trivial grammars. This suggests that the work may have value as a quantum inspired algorithm beyond its initial motivation as a new quantum algorithm.
Ontology for Enterprise Interactions: Extended and Virtual Enterprises

The interaction concept has been given much importance in computer science and information systems. The interactions happen at different levels and in different situations. Interactions involve actors that act in re-action to one other action. The enterprise, as an actor, needs to implement interactions. Indeed, the knowledge emerging from interactions is greater than the sum of the involved actor’s knowledge. Traditionally, interactions are implemented on a case-by-case basis without managed view, which yields costly integration architectures, because they do not consider the semantic aspect of the interactions. Ontology is the solution for the semantic problem, which provides a smooth integration. This paper aims at building Ontology for enterprise interactions, specifically Extended Enterprise (EE) and Virtual Enterprise (VE), whereby an EE is a kind of collaboration between loosely coupled enterprises that combine their economic output to provide product/service offerings. An EV is a temporary relationship between distributed enterprises, competitors, and partners which access each other market.
Maximum advantage of quantum illumination

The discrimination of quantum states is at the heart of several quantum information protocols. Here, a specific quantum channel discrimination protocol, known as quantum illumination, is shown to be optimal when one uses a Bell state; the simplified theoretical tools used by the authors might find broader applicability.
Predicting quality of experience for online video service provisioning

The expansion of the online video content continues in every area of the modern connected world and the need for measuring and predicting the Quality of Experience (QoE) for online video systems has never been this important. This paper has designed and developed a machine learning based methodology to derive QoE for online video systems. For this purpose, a platform has been developed where video content is unicasted to users so that objective video metrics are collected into a database. At the end of each video session, users are queried with a subjective survey about their experience. Both quantitative statistics and qualitative user survey information are used as training data to a variety of machine learning techniques including Artificial Neural Network (ANN), K-nearest Neighbours Algorithm (KNN) and Support Vector Machine (SVM) with a collection of cross-validation strategies. This methodology can efficiently answer the problem of predicting user experience for any online video service provider, while overcoming the problematic interpretation of subjective consumer experience in terms of quantitative system capacity metrics.
Illustration Design Teaching Mode Based on Virtual Wall Painting Technology

With rapid development of 3D virtual scene, virtual reality (VR) technology starts to be applied in teaching practice. Because of its conception and innovation, VR technology has certain advantage in art creation learning and teaching. In traditional art course teaching, theoretical knowledge is more than design practice, and the depth of design cases is insufficient. Thus, students lack driving force of art creation. On this basis, wall painting technology and VR technology were combined and applied in the course of Illustration Design in this paper. According to the course requirement of Illustration Design – many design cases, Illustration Design teaching mode based on virtual wall painting technology was constructed. The study result showed that, students more approved the new teaching mode, their interest improved greatly and teaching efficiency boosted significantly. The teaching mode can make up for defects of traditional art design teaching mode and contributes to cultivating students’ art creation ability.
A New Approach for Scheduling Tasks and/or Jobs in Big Data Cluster

400 hundred million tweets are sent each day, 4.75 billion multimedia content is shared every day on Facebook, and an estimated of hundreds of hours of video are uploaded to YouTube every minute. Moreover, also IOT devices (RFID and WIFI wearable devices) are generating a huge number of data per seconds. During the last two years, the amount of data that has been created is about 90% of the whole data created so far. All these facts require clusters of computers with high specs in order to treat them. Knowing that the prices of computers are continuously dropping from year to another, almost all companies have started their Big Data projects. The return on investment (ROI) of such a project is beneficial to companies in terms of business. Since the advent of Big Data, a lot improvement is been done in order to optimize the usage of the resources (especially RAM) and to reduce the required amount of time needed for running Big Data projects. Still, effort needs to be done for the scheduler for efficiently scheduling the tasks inside the DataNodes of the Big Data Cluster. In this paper, we propose a new approach for scheduling tasks and/or jobs in Big Data Cluster in which mainly focus on optimizing the assignment of tasks to the data nodes by the NameNode. Prominent obtained results proved that our task scheduler outperforms the traditional task scheduler: FIFO Scheduler and Capacity Scheduler available in Big Data open source distributions such as Cloudera [19] and HortonWorks [20].
“I am I”: Self-constructed transgender identities in internet-mediated forum communication

Abstract This article analyses identity constructions and representations of self-identifying transgender individuals on a web-based forum. Although the forum is aimed towards all transgender users, the primary user-group are transfeminine users (intending on) undergoing medico-surgical interventions to align their physiology and identity. The data for this analysis are initial text posts from the forum board used for introductions (i.e. new users of the forum introducing themselves). The article assumes that introductions are the context in which one asserts key identity features; hence, this board is the most pertinent for analysing identity construction. In this article, I use a combination of corpus linguistics and Critical Discourse Studies tools to analyse the use of pronouns and gender-indexical nouns in identity constructions and the representation of social categorisations . This article is an attempt to demonstrate that transgender is not a collective homogeneous identity, and that gender-sex incongruence may not be a salient identity feature for some forum-users. I also examine the ideologies (re)produced in the local forum-communication discourse, and the evaluation of hegemonic practices within transgender discourse and wider gender discourse to further demonstrate the heterogeneity of transgender identity.
Imagining and tinkering with assistive robotics in care for the disabled

Abstract The media and political-managerial levels focus on the opportunities to re-perform the Scandinavian welfare states through digitization. Especially in Denmark, this trend is prominent. Welfare technology is a Scandinavian notion used to point at assistive technologies intending to support the elderly, the disabled and care providers. Feeding assistive robotics (FAR) is a welfare technology relevant to citizens with no or low function in their arms. Despite national dissemination strategies, it proves difficult to recruit suitable users. There have been many promises for the potential of assistive robotics including more cost-efficient healthcare delivery, engaged patients and connected care providers. However, the realities of enacting assistive robotics, whether as patients or care providers, can be complicated in ways often unanticipated by government agencies and technology developers. This study discusses governmental agencies’ and technology developers’ visions with regard to what robotics may do and argues that these visions intertwine with affected stakeholders’ organizing of theirworlds. On this founding, the article discusses the resulting tinkering during implementation. The study exemplifies and demonstrates how ethnography can be used as an important method in Human Robot Interaction (HRI) research. The Actor Network Theory idea of ‘follow the actor’ inspired the study that took place as multi-sited ethnography at different locations in Denmark and Sweden. Based on desk research, observation of meals and interviews the study examines sociotechnical imaginaries and their practical and ethical implications.Human and FAR interaction demands engagement, sustained patience and understanding of the citizen’s particular body, identity and situation. The article contributes to the HRI literature by providing detailed empirical analysis based on an ethnographic studywhere political strategies, technology developers’ assumptions and affected stakeholders’ everyday hassles are in focus at the same time.
Semantic Representation With Heterogeneous Information Network Using Matrix Factorization for Clustering in the Internet of Things

Emerging communications technologies, such as IPv6 and 5G, will enable massive numbers of devices to connect to the Internet of Things. With the scale of networking equipment increasing, how to effectively use extensive IoT data is an increasingly urgent issue. The interaction relationships between IoT devices based on various application requirements have not received enough attention in traditional data mining methods. However, IoT devices require a large amount of information to interact in practical applications every moment, which produces a variety of semantic relationship. Different from the traditional methods, this paper employs graph structure to represent the semantic relations of IoT data, which exploits fine-grained semantic information more efficiently. Our contributions are as follows: 1) we propose the IoT data representation framework by converting semantic relations into graph structure, 2) the framework can leverage different meta-paths in the graph to measure the similarity among entities in the IoT from different perspectives, and 3) we conduct a cluster experiment based on regularization improved matrix factorization for different application scenarios that consider the semantic similarity. We demonstrate our method using a real-world dataset, and the experimental results show the practicability and effectiveness of our proposed approach. This paper presents a new research angle to analyze semantic data in the IoT.