Reviewerly/exHarmony huggingface dataset:
Around-Body Interaction: Interacting While on the Go

We provide a concise overview on how the skills and dexterity of our upper and lower limbs, acquired and trained in interacting with the physical world, can be transferred to the interaction with HMD. We present the vision of around-body interaction, in which we use the space around our body, defined by the reach of our limbs, for fast, accurate, and enjoyable interactions. In the remainder of this article, we first introduce a design space for such around-body interactions and, second, present two examples of such interaction techniques, which use the degrees of freedom of the lower limbs for on-the go interactions. Finally, we conclude with future research challenges.
ELEKTRONİK TİCARETTE “DOMAIN” ADI ve “WEB-HOSTING” ŞİRKETİ SEÇİMİ

The Internet is becoming the foundation for new business models, new business processes, and new ways of distributing knowledge. Companies can use the Internet and networking technology to conduct more of their work electronically, seamlessly linking factories, offıces, and sales forces around the globe. 1-Commerce companies should carefully select their domain names, and register it. The selection of the web-hosting company is also important for being successful in e-commerce.
A methodology to realize energy-related use cases for a cloud-based smart energy management

The aim of the related research project eCloud is to enable small and medium sized enterprises (SMEs) to implement flexible energy management without in-depth energy knowledge and with little distraction from day-to-day business, which is prepared for current and future challenges in the field of energy use. The overall result is a validated prototype for a plug and automate capable (i.e. without implementation effort) operational energy management, which can be successively set up in SMEs based on a cloud platform. Through its gradual and modular implementation, energy management meets the individual needs of each company and contributes to energy system transformation and climate protection by reducing energy costs and greenhouse gas emissions by up to 25%. In total, three expansion stages are available with the levels of monitoring, load management and grid usage, which consist of various Software as a Service (SaaS) modules from the cloud that can be retrieved as required. Thus, the user only needs a minimal hardware intervention in his production and saves a complex IT infrastructure. The methodology developed has been successfully applied by two user companies so far. This proves the effectiveness of the method.
Proposal of an approach of Selection and Impact Study of CMMS use on Maintenance functioning, a case study

Computerized Maintenance Management System (CMMS) is widely considered as a powerful tool for organizations to improve the efficiency of their maintenance function. Nevertheless, the selection of the appropriate CMMS is not a trivial task. Furthermore, how proving the impact of the selected software on the functioning of the maintenance structure. The aim of this work is twofold: on the one hand, define reusable criteria for selecting a CMMS from a range of tools available online. On the other hand, propose a reusable approach to study and analyze the impact of the use of a CMMS on the functioning of a maintenance structure. The proposed approach is illustrated throughout a case study within an oil company.
PredictRV: A Prediction Based Strategy for Negotiations with Dynamically Changing Reservation Value

Negotiation is an important component of the interaction process among humans. With increasing automation, autonomous agents are expected to take over a lot of this interaction process. Much of automated negotiation literature focuses on agents having a static and known reservation value. In situations involving dynamic environments e.g., an agent negotiating on behalf of a human regarding a meeting, agents can have a reservation value (RV) that is a function of time. This leads to a different set of challenges that may need additional reasoning about the concession behavior. In this paper, we build upon Negotiation algorithms such as ONAC (Optimal Non-Adaptive Concession) and Time-Dependent Techniques such as Boulware which work on settings where the reservation value of the agent is fixed and known. Although these algorithms can encode dynamic RV, their concession behavior and hence the properties they were expected to display would be different from when the RV is static, even though the underlying negotiation algorithm remains the same. We, therefore, propose to use one of Counter, Bayesian Learning with Regression Analysis or LSTM model on top of each algorithm to develop the PredictRV strategy and show that PredictRV indeed performs better on two different metrics tested on two different domains on a variety of parameter settings.
OpinionRank: Trustworthy Website Detection Using Three Valued Subjective Logic

For a web search engine, it is critical to design a mechanism to promote trustworthy websites and eliminate spam ones in the searching results. In this paper, we propose the OpinionRank algorithm to compute the trustworthiness of a website and identify trustworthy ones with high trust values. OpinionRank is essentially a breadth-first-search based algorithm that starts from an existing set of trustworthy websites, also called seeds. Because seeds play a vital role in OpinionRank, we put forward a novel seed selection scheme, named HarMean PageRank algorithm. HarMean combines the results of two seed selection algorithms, i.e. High PageRank and Inverse PageRank, to rank websites based on their trustworthiness. After trustworthy seeds are chosen, OpinionRank iteratively computes the trustworthiness of every website, leveraging trust propagation and trust combination. Using the public dataset WEBSPAM-UK2006, we validate OpinionRank and HarMean PageRank, analyze the impact of seed selection, and evaluate the convergence speed of OpinionRank. Experimental results indicate that OpinionRank can detect more trustworthy websites with fewer seeds, when compared to three state-of-the-art solutions, TrustRank, GoodRank, and Enhanced OpinionWalk algorithms.
Two-Step Anomaly Detection for Time Series Data

We propose a new two-step algorithm for anomaly detection in time series data. The first step uses a simple data forecasting model which is fast with high recall but potentially low precision, whereas the second step uses a complex data forecasting model which is slower but more accurate. The first model initially labels each data value. When an anomaly is reported by the first model, the second model relabels that data value, verifying or correcting the initial label. This verification step reduces the false positive rate of the first step. This scheme provides accurate identification of anomalous data values as well as fast rejection of non-anomalous data values. Our experimental results demonstrate higher accuracy compared to the individual component models, albeit with more processing time.
Quantum disentangling operator and squeezed vacuum state’s noise of a mesoscopic two-loop LC circuit with mutual inductance

Quantum disentanglement refers to the transformation of entangled quantum system into disentangled system via some physical processes. In this paper, we search for quantum disentangling operator for the mesoscopic two-loop [Formula: see text] circuit with mutual inductance [Formula: see text]. It is this mutual inductance that causes quantum entanglement. By virtue of the method of integration within ordered product (IWOP) of operators, we find the disentangling operator and deduce the energy level (characteristic frequency). The quantum noise expression of squeezed vacuum state is also derived based on which we see that the large number of quantum entanglement engendered by the mutual inductance is, the more quantum noise produces in the mesoscopic circuit.
Laying the Foundation for Design System Ontology

There is a growing need for more client applications for different platforms while maintaining a consistent appearance. Managing this usually requires a lot of tedious labour work. In this paper, we explored what should be included in the design system based on the real-world needs, how to represent and formalise it using semantic web technologies to achieve evolvability and interoperability, and how to convert it into code automatically leveraging the Normalised System theory. Our solution is already a foundation for the ontology representing the design system and working prototype of the code generator using the ontology.
Development of the point scoring system of medical parameters for HIV severity monitoring and modeling in numerical terms

Abstract The article considers the possibility of using a point scoring system of medical parameters for HIV severity monitoring and modeling in numerical terms. There were investigated the features of the use of medical parameters in the form of a point scoring system as well as the possibility of applying a numerical model for determining the HIV severity when tuberculosis is joined. To solve the problem, it was developed a step-by-step algorithm for applying the point scoring system. Conclusions were drawn. The possibility to predict in numerical terms the outcome of the disease (in particular the AIDS progression) using the number of systematically collected medical parameters in HIV carriers is of great interest. The information system development allows to determine the most important properties of the process and to separate its non-essential characteristics. Often the information system and modeling allow us to formulate new hypotheses and gain new knowledge about the object, those which were not available during the research.
Blockchain and Management Accounting Systems Based on Information Sharing

Companies need to share information on the costs and timing of the operations they carry out together with other companies to coordinate and synchronize the activities of each of them. Despite this need, companies avoid sharing information on their operations and activities for fear that they can be modified/changed against them and that competitors can use them to take advantage of them. This paper aims to highlight, from a logical-theoretical point of view, how the inviolability of the blockchain cryptography system can help overcome the resistance of companies to sharing information.
Inspection and feature specification of the fancy yarns using diffraction limited incoherent imaging

Chenille yarn features (pile yarn length, twist, hairiness, defect length, coefficient of diameter variation) were determined by diffraction limited incoherent imaging (DLIM) allowing to simulate sensor plane irradiance. The predicted image irradiance used in determining these features were obtained by deconvolving the captured image irradiance with incoherent point spread function designated by rectangular aperture. In twist values obtained by proposed DLIM, mean absolute relative error (|RE|mean) was determined as 0.822% by comparing Uster Twist Tester results. The DLIM in determining diameter provided better results with 0.8194% |RE|mean, compared to that 2.044% of optoelectronic method. The determination of hairiness fibers exceeding the ordinary lengths of pile yarns using Fourier domain high pass filtering (HPF) whose size is designated by the cut-off frequencies is proposed. These cut-off frequencies were determined in response to mean diameters obtained by image processing. The results of hairiness indices of HPF were compared with image processing results.
Egocentric Sonification of Continuous Spatial Data in Situated Analytics

The combination of increasingly ubiquitous spatial data and Augmented Reality (AR) facilitates interactive, spatially embedded reasoning for users who are physically present in a data environment. Many techniques from visual and immersive analytics carry over into this ”Situated Analytics” context. However, continuous, wide-area datasets like air quality records are difficult to display visually without obscuring a large part of the real environment. Established egocentric techniques usually represent a detailed view of the near field at the expense of the larger data context. We present an approach towards a situated sonification that can restore the surrounding data context.
The Results of an Empirical Study of High School Lecturers’ Concept of Semiotic Competence

The semiotic transprofessional competence of a modern person becomes more popular in the coherent arsenal of cognitive and communicative competencies of a digital society representative. Semiotic means “sign”, therefore, the information society suggests a new format for the subject, who knows how to understand, create, transfer, change signs and sign systems that carry information. To prepare a successful representative of the community, a teacher with appropriate abilities is needed. The teacher, fi rst of all, is formed in the process of vocational training at the university, therefore, the university teacher is obliged to possess the corresponding knowledge, skills and competencies. This article aims at identifying of the university teacher’s representations about semiotic competence. The objectives are: a) to determine the diff erence between semiotic competence and informational competency, with which the former is often identifi ed; b) justifi cation of the semiotic competence phenomenon importance for the education system and for the 21st century’s teachers training system; c) the development of measuring tools to identify the representations of the university educator, who works with future teachers, about semiotic competence; d) interpretation of the results of empirical research in the designated problem fi eld of educational semiotics.The main research methods are: comparative content analysis of scientifi c literature on the problem of the informational and semiotic competencies interaction; Google questionnaire; scaling; qualitative methods for processing an empirical data array. The novelty of the article material lies in the theoretical justifi cation and presentation of the approach to determining the relationship of semiotic and informational competencies; clarifi cation of the concept of «semiotic dominant» on three diff erent grounds for students — future teachers — in accordance with their training fi eld; interpretation of processing an empirical data array results and capturing specifi c facts of the current state in the representations of university teachers, working with students in “Pedagogical Education” fi eld, about semiotic competence (in a sense, revealing the willingness of teachers to respond to the challenges of the information society). The results of the article are: justifi cation for the inclusion of information competence in the semiotic one; development of measurement procedures to identify the representations of university teachers, who works with future teachers, about semiotic competence in the form of a questionnaire of 27 questions; obtaining and capturing specifi c facts about the state of information and semiotic skills of higher school teachers.
Photovoltaic Power Forecasting Based on SVM Optimized by Improved ABC

Abstract In order to improve the accuracy of photovoltaic power output prediction, a photovoltaic power prediction method based on similar days and improved artificial bee colony support vector machine is proposed. Firstly, through calculating the Euclidean distance of history day and measured day meteorological factors to determine similar days. Secondly, select historical data of photovoltaic power output, temperature, humidity and daily radiation on the slope of similar days and temperature, humidity and daily radiation on the slope of test date as input variables of support vector machine. And we adopt the improved artificial bees colony to optimize kernel function parameters and the penalty factor of support vector machine. Finally get the output in each period of photovoltaic power prediction. The experimental results showed that the proposed method can effectively improve the prediction accuracy of photovoltaic power.
SImBa: An Augmented Reality approach for creating Smart Immersive Bays in Software Delivery Environments

Software project workspaces are typically large office bays which have seen very little use of ambient technology as such, and potentially need to be considered for advances that we have started seeing in other industries, which are becoming intelligent and augmented through approaches like immersive visualizations. In this paper, we present our novel approach SImBa, which combines disparate data sources from the project based on a model of relationships between different project entities, and weaves together different immersive visualizations which can be dynamically positioned in the software project workspace/bay. These visualizations are contextual 3D overlays which can be continuously updated, are highly flexible and modifiable. Our approach thus converts software project bays, which are typically bereft of providing any technical assistance to the project teams, into smart collaborative workspaces by use of immersive technologies. A video demonstration is available at https://youtu.be/W1CnrrtOJ4c.
Training Job Interview Online Simulator for Hearing-Impaired People

This article describes an online training course for adult hearing-impaired individuals who use hearing aids or cochlear implants. This training course developed in HTML5 and JavaScript consists of a set of training activities to be solved online. The originality of this development is the recreation of a virtual scenario where a hearing-impaired person faces a job interview situation. This simulation strategy with multimedia features is based on a series of videos that recreate an office scenario where an interviewer asks questions that the interviewee must answer by choosing one of the options presented after every interviewer's question. The simulator prototype and the other activities make up a website developed in HTML5 and the PHP programming language. The user requires identification to have access to the site which has the ability to store in a MySQL relational base, the data of each registered person including the educational activities that he is carrying out each time he enters the training. The design of the proposal includes a pilot experience for the evaluation of the tool by a group of users by means of online surveys and personal interviews. According to the results, the optimization of the tool is foreseen for its subsequent implementation.
Object Classification as Key for Algorithmic Language Processing of Metonymy and Metaphor : A Sensory Language Retrieval Approach+

This paper proposes a Sensory Language Retrieval (SLR) algorithm and implicitly initiates a new approach. The algorithm utilizes the relationship between meaning and sensory functions by assigning human sensory states to elements of spoken language. The algorithm is based on the approach accepted in cognitive linguistics stating that there is a causal relationship between body and speech. The novelty of the algorithm is that the total sensory embeddedness of cognitive functions can be evaluated by the algorithm and this is also true for the cognitive functions considered to be impersonal. In contrast to semantic and statistical approaches, the algorithm analyzes objects and functions in language based on universal sensory functions and their associated data, such as universal human functions and sensory data. The proposed algorithm assigns a new codomain to language signals whose data is capable of defining meaning and sensory patterns that have so far been difficult to identify, such as metaphorical and metonymic functions of the language.
Latent multi-view semi-supervised classification by using graph learning

Multi-view learning is a hot research direction in the field of machine learning and pattern recognition, which is attracting more and more attention recently. In the real world, the available data commonly include a small number of labeled samples and a large number of unlabeled samples. In this paper, we propose a latent multi-view semi-supervised classification method by using graph learning. This work recovers a latent intact representation to utilize the complementary information of the multi-view data. In addition, an adaptive graph learning technique is adopted to explore the local structure of this latent intact representation. To fully use this latent intact representation to discover the label information of the unlabeled data, we consider to unify the procedures of computing the latent intact representation and the labels of unlabeled data as a whole. An alternating optimization algorithm is designed to effectively solve the optimization of the proposed method. Extensive experimental results demonstrate the effectiveness of our proposed method.
Learning key steps to attack deep reinforcement learning agents

Deep reinforcement learning agents are vulnerable to adversarial attacks. In particular, recent studies have shown that attacking a few key steps can effectively decrease the agent's cumulative reward. However, all existing attacking methods define those key steps with human-designed heuristics, and it is not clear how more effective key steps can be identified. This paper introduces a novel reinforcement learning framework that learns key steps through interacting with the agent. The proposed framework does not require any human heuristics nor knowledge, and can be flexibly coupled with any white-box or black-box adversarial attack scenarios. Experiments on benchmark Atari games across different scenarios demonstrate that the proposed framework is superior to existing methods for identifying effective key steps. The results highlight the weakness of RL agents even under budgeted attacks.
Deconstructing Andrei Tarkovsky’s Magic Realism

Rejecting the common practice of his time, Andrei Tarkovsky challenged the composer Edward Artemiev to create unprecedented sound design for his films in the place of conventional musical soundtracks. He also followed this path in his later films produced without Artemiev. This article analyzes the audio-visual sequences of Tarkovsky's films from the 1970s and the 1980s, exploring transitions from real mode to magic mode, as well as the role of sound design in these cinematic shifts. The new technology and the novel electronic sound that influenced creative processes are at the heart of this analysis. Unique media, such as the ANS and Synthi 100 synthesizers that became available for Artemiev, were key factors that motivated his original sound work. They allowed him to create a sound space capable of absorbing divergent sources (natural sounds, noises, acoustic instruments, and musical quotations), and triggering off transformation of the initial sound sources into sounds of ambiguous nature. This analysis recreates the process of sound-making from its conceptual origins to its final representations, interpreted through comments by Edward Artemiev and Owe Svensson (a Swedish sound-mixer, who worked with Tarkovsky on his last film, The Sacrifice).