Reviewerly/exHarmony huggingface dataset:
Influence of Brand Credibility, Satisfaction and Quality on Brand Equity in Non-Conventional Health Industry

Consumer perceptions of quality, satisfaction, and brand credibility are critical ingredients for developing healthy brands with high value in a competitive market. However, few authors have looked into the brand credibility`s intervening role in the effect of quality and satisfaction on brand equity in the non-conventional health industry. Hence, this paper sought to evaluate the mediating role of brand credibility in the effect of quality and satisfaction on brand equity in the non-conventional health industry. A covariance-based structural equation model was the analytical tool employed to evaluate the hypotheses stated in this paper. Data were gathered from 265 customers using a systematic sampling technique. The research confirmed that brand credibility contributes partially to the impact of quality on brand equity and completely to satisfaction on equity in the non-conventional health industry. Accordingly, this paper contributes to expanding the current brand management literature by demonstrating the brand credibility`s intervening role in the path between satisfaction, quality, and equity, particularly in the non-conventional health industry. This paper also adds to the brand manager`s knowledge of how to build and harness credibility, quality, and satisfaction to increase brand equity in the non-conventional health industry.
The Application of Artificial Intelligence for the Identification of Relevant Forensic Information Among Video Surveillance System Data

The purpose of the paper is to demonstrate the applicability of artificial intelligence methods for the identification of relevant forensic information from records of video surveillance systems and the opportunity to classify this information by the level of forensic relevance from potentially significant to less important events. Methodology: The paper deals with common problems related to the work of a criminal investigator, provides their analysis, and offers solutions based on modern AI developments. The solutions are justified by the new capabilities of mathematical simulation and numerical methods and specially designed programs. Findings: The paper offers a comprehensive solution for the identification of relevant forensic information, which allows achieving high efficiency against conventional approaches as well as reducing man-caused errors. It has been remarked that the most usable form of an efficient video review tool is a comprehensive solution involving the following processing stages: (1) Reducing non-informative events to a minimum and using video frame space in an efficient way; (2) Classification of objects and search by required criteria; (3) Identification and re-identification of people between all sources of video streams. This three-stage solution will help the investigator to focus only on the events that are relevant to the investigation. Besides, there will be new opportunities against conventional methods. The paper discusses some constraints that may serve as a guide for future research.
Text Emotion Classification Using Stacked CNNs and LSTMs

In this world and age social media has become popular among the masses and has become an indispensable part of the lives of many. Social media has become a important source of information as it is on them that the users describe their feelings unabashedly. This can help get genuine reactions on many political issues, films, reforms, places, products etc. This helps not only know what’s trending but the majority reaction towards what’s trending. To make sense of this surge of information generated about products or events and have a sense of the reaction by the public it is required to know about the emotion that is widespread. This leads us to the problem of capturing emotions from text which is addressed by training deep learning models to learn this capability.
Auto-COP: Adaptation generation in Context-oriented Programming using Reinforcement Learning options

Self-adaptive software systems continuously adapt in response to internal and external changes in their execution environment, captured as contexts. The Context-oriented Programming (COP) paradigm posits a technique for the development of self-adaptive systems, capturing their main characteristics with specialized programming language constructs. In COP, adaptations are specified as independent modules that are composed in and out of the base system as contexts are activated and deactivated in response to sensed circumstances from the surrounding environment. However, the definition of adaptations, their contexts and associated specialized behavior, need to be specified at design time. In complex cyber physical systems this is intractable, if not impossible, due to new unpredicted operating conditions arising. In this paper, we propose Auto-COP, a new technique to enable generation of adaptations at run time. Auto-COP uses Reinforcement Learning (RL) options to build action sequences, based on the previous instances of the system execution (for example, atomic system actions enacted by human operators). Options are further explored in interaction with the environment, and the most suitable options for each context are used to generate the adaptations, exploiting COP abstractions. To validate Auto-COP, we present two case studies exhibiting different system characteristics and application domains: a driving assistant and a robot delivery system. We present examples of Auto-COP to illustrate the types of circumstances (contexts) requiring adaptation at run time, and the corresponding generated adaptations for each context. We confirm that the generated adaptations exhibit correct system behavior measured by domain-specific performance metrics (e.g., conformance to specified speed limit), while reducing the number of required execution/actuation steps by a factor of two showing that the adaptations are regularly selected by the running system as adaptive behavior is more appropriate than the execution of atomic actions. Therefore, we demonstrate that Auto-COP is able to increase system adaptivity by enabling run-time generation of new adaptations for conditions detected at run time, while retaining the modularity offered by COP languages, and reducing the upfront specification required by system developers.
Digital socialization of students by means of educational media

The article is devoted to the problem of determining the factors of the effectiveness of digitalization of education, which is motivated by the unprecedented rapid transition of educational organizations to e-learning using distance educational technologies not only in Russia but also in most countries of the world. One of these factors is the level of digital socialization of all the participants of the educational process. Digital socialization is considered to be a feature of a new form of socialization of a modern person. The novelty of the research is in the determination of a set of key characteristics of educational media content that forms and develops digital competence, as well as increases the level of digital socialization of all the participants of the educational process. The study identifies the main ideas of an educational media project that has the following characteristics: it is “cognitive” and “congruent” (aimed at forming the information and media competence); it is “conative”, “constructive”, “conformal”, and “comic” (aimed at forming the communication competence); it is “creative” and “constructive” (aimed at forming the technical competence); finally, it is “conformal” (aimed at forming the consumer competence). Distance educational projects, which in practice acquire the identified characteristics, expand the functionality of educational media and their ability to influence children’s educational results.
Analyzing How a Talk Show Host Performs Follow-Up Questions for Developing an Interview Agent

This paper aims to reveal how human experts delve into the topics of a conversation and to apply such strategies in developing an interview agent. The purpose of recent studies on chat-oriented dialogue systems has centered around how to respond in conversation to a wide variety of topics. However, in interviews, it is also important to delve into particular topics to deepen the conversation. Since interviewers in talk shows, such as talk show hosts, are considered to be proficient at such skills, in this paper we analyze how a talk show host interviews her guests in a TV program and thus reveal the strategies used to delve into specific topics. In particular, we focus on follow-up questions and analyze the kinds of follow-up questions used. We also develop a prediction model that judges whether the next utterance should be a follow-up question and then evaluate the model’s performance.
Design and Development of Scene Recognition and Classification Model Based on Human Pre-attentive Visual Attention

Abstract Recent works on scene classification still utilize the advantages of generic feature of Convolutional Neural Network while applying object-ontology technique that generates limited amount of object regions. Human can successfully recognize and classify scene effortlessly within short period of time. By utilizing this idea, we present a novel approach of scene classification model that built based on human pre-attentive visual attention. We firstly utilize saliency model to generate a set of high-quality regions that potentially contain salient objects. Then we apply a pre-trained Convolutional Neural Network model on these regions to extract deep features. Extracted features of every region are then concatenated to a final features vector and feed into one-vs-all linear Support Vector Machines. We evaluate our model on MIT Indoor 67 dataset. The result proved that saliency model used in this work is capable to generate high-quality informative salient regions that lead to good classification output. Our model achieves a better average accuracy rate than a standard approach that classifies as one whole image.
Towards a Robust Knowledge Graph-Enabled Machine Learning Service Description Framework

Although machine learning (ML) is widely expected to become a key enabler of innovative applications in a number of important domains, building, deploying and managing robust ML pipelines for diverse domains are very challenging as they require expertise in both the application domain as well as the ML field. Recently, machine learning as a service (MLAAS) is being explored as a paradigm to address these challenges and to democratize artificial intelligence (AI). MLAAS envisions an ecosystem with powerful mechanisms for publishing, searching/discovering, composing and deploying ML models. This paper argues that a semantic-rich and flexible ML service description is indispensable for realizing such an ecosystem. Towards this end, we outline a unique approach that leverages knowledge graphs (KGs) for ML service description. A requirements study highlights the ML services aspects that need to be provisioned in the description framework. This paper presents a novel five-dimensional KG-enabled ML service description framework, which incorporates ML task description, Input-Output (I-O) description, ML-model description, dataset and training description and performance characteristics description. In designing this ML service description framework, we introduce several conceptual structures such as functional specifications with semantically-extended types and compound knowledge graphs for representing ML model architectures.
A mobile based communication solution for monitoring remote agricultural locations

Abstract In this paper a method for the effective transmission of data, from a Wireless Sensor Network (WSN) in a remote agricultural location with limited connectivity, is proposed; along with a meaningful visualisation of the data at the user end to help in decision making and control of the irrigation system. The paper encompasses three main segments which are data compression, checking for network availability, and user interface. First data compression is implemented to reduce the amount of transmitted data, due to limited connectivity in the area, which is translated in low data rate and high cost of sending the data. Four different techniques are compared: Huffman encoding, Lempel-Ziv-Welsh (LZW), differential encoding, and Run Length Encoding (RLE) to carry out data compression, where Huffman encoding showed the best results when combined with differential encoding. The second phase is the handling of poor network conditions in the agricultural plant by switching between two different communication modes: Mobile data and Short Message Service (SMS). Lastly, the user monitoring centre which is a mobile application that allows the user to monitor and control the agricultural plant remotely. It includes mapping and visualization of temperature and soil moisture data and a control system to control the irrigation system. The solution presents a low-cost alternative for start-up agricultural projects in third world countries, where network instability is an issue and off-the-shelf expensive solutions are not a viable option.
Numerical Simulation of the Denture Prosthesis Integrity Under Typical Chewing Loads

The chapter considers the problems of stress analysis for the removable prosthesis bases of the upper and lower jaws under typical chewing loads. The prosthesis structural integrity is studied. In this modeling, a physically justified boundary conditions were developed and applied. The influence of various technological features of the prosthesis’s geometry on its structural integrity is studied. The critical load values and its locations are identified for different types of chewing loads. It was shown that taking into account the compliance and expression of the torus area are very important for correct stress distribution calculation for the prosthesis of the upper jaw. Also, it is shown that the presence of torus area leads to additional longitudinal displacements of the back part of the prosthesis basis that leads to higher value of the tensile stresses at the front part of the structure. For the case of the lower jaw prosthesis’s basis, it is of great importance to take into account the non-uniform distribution of stiffness of the mucosa along the alveolar ridge. It is shown that for both models (upper and lower jaw), the maximum stress occurs under the chewing loads applied at the blocks of incisors and canines. In this case, maximum stresses, as a rule, are localized in the vicinity of technological notches or teeth-basis connection. It is shown that for all the studied configurations, the maximum stresses do not exceed the ultimate tensile strength (UTS) of the corresponding material, however, they can exceed their fatigue limits. Thus, the most likely mechanism of the prosthesis basis failure under chewing loads is fatigue of material. In this chapter, we introduce the models of the upper and lower jaws that takes into account the physico-mechanical features of soft tissues.
Identifying High-Level Concept Clones in Software Programs Using Method’s Descriptive Documentation

Software clones are code fragments with similar or nearly similar functionality or structures. These clones are introduced in a project either accidentally or deliberately during software development or maintenance process. The presence of clones poses a significant threat to the maintenance of software systems and is on the top of the list of code smell types. Clones can be simple (fine-grained) or high-level (coarse-grained), depending on the chosen granularity of code for the clone detection. Simple clones are generally viewed at the lines/statements level, whereas high-level clones have granularity as a block, method, class, or file. High-level clones are said to be composed of multiple simple clones. This study aims to detect high-level conceptual code clones (having granularity as java methods) in java-based projects, which is extendable to the projects developed in other languages as well. Conceptual code clones are the ones implementing a similar higher-level abstraction such as an Abstract Data Type (ADT) list. Based on the assumption that “similar documentation implies similar methods”, the proposed mechanism uses “documentation” associated with methods to identify method-level concept clones. As complete documentation does not contribute to the method’s semantics, we extracted only the description part of the method’s documentation, which led to two benefits: increased efficiency and reduced text corpus size. Further, we used Latent Semantic Indexing (LSI) with different combinations of weight and similarity measures to identify similar descriptions in the text corpus. To show the efficacy of the proposed approach, we validated it using three java open source systems of sufficient length. The findings suggest that the proposed mechanism can detect methods implementing similar high-level concepts with improved recall values.
Range of motion measurement of Articulatio cubiti based on Hough transformation

Range of Motion (ROM) is one of the movement parameters for evaluating physical rehabilitation. Articulatio cubiti or elbow is one of the most important organs in the human body that is most commonly injured in some accidents happens. A physical test is needed using ROM measurement to see the severity of the injury and the effectiveness of rehabilitation. Therefore, accurate and precise ROM measurements are needed to decrease error value while diagnosing injury severity and can represent the actual condition of the joints, so the effectiveness of rehabilitation can be evaluated properly. So far, ROM measurement is still using manual devices, named goniometer. However, this device has several disadvantages: it requires human intervention, requires high clinical experience from a therapist or doctor, and goniometer cannot be used for ROM measurement in certain joints. Therefore, we need a device to do ROM measurement automatically, precise, and flexible. In this research, ROM measurements were taken on Articulatio cubiti dextra using Hough transformation method based on kinect sensors and Python programming language. There are three variations of the distance between human subject and kinect sensor; 140 cm (first distance), 220 cm (second distance), and 300 cm (third distance), and there are 10 variations of ROM values from 4° to 120°. The obtained results showed that the best ROM measurement at a first distance with a linearity of 99.59%, a sensitivity of 97.38%, an accuracy of 96.64%, and a relative standard deviation of 1.65%, followed by ROM measurement result at the second distance with a linearity of 99.46%, a sensitivity of 92.28%, an accuracy of 92.51%, and a relative standard deviation of 5.92%, and the lowest ROM measurement results are at a third distance with a linearity of 99.27%, a sensitivity of 91.68%, an accuracy of 90.25%, and a relative standard deviation of 7.28%.
Algorithms in Action: Reassembling Contact Tracing and Risk Assessment during the COVID-19 Pandemic in China

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Quantifying the ratio-plot for the geometric distribution

The geometric distribution is one of the most widely used count distributions. Novel goodness of fit tests for this distribution are suggested taking advantage of a characterization of that distribution in terms of a differential equation involving its probability generating function. Several ways of looking at the characterization allow us to derive six test statistics. The connection between some of these test statistics and the ratio-plot device is stated. The asymptotic null distributions of these test statistics are derived. However, they depend on the unknown parameter of the geometric law. A suitable parametric bootstrap is used to estimate consistently each null distribution. Moreover, the almost sure limits of the test statistics under alternatives are obtained. The finite sample performance of the bootstrap approximation is assessed via simulation. The powers of the new tests are numerically compared with that of some existing ones, exhibiting competitive behaviour. Some real-life data set applications are included.
Application of Information Technology and Internet of Things in Intelligent Construction of Stadiums and Gyms

Application of the information technology and Internet of Things in the intelligent construction of stadium and gym buildings is studied in this paper. In the process of modern intelligent building construction, where automated building design will play a key role and also the process of integrating the state-of-the-art technologies into the modern construction activities will be essential. In the proposed method, the 2 aspects of novelties are reflected. First, R&D has been incorporated to design and develop the products according to demand. The lack of modeling methods for R&D and the traditional mode design with the inefficiency of the document-based design mode, where the intelligent model is used to enhance the traditional method. Secondly, the cloud computing environment has a large number of storage and computing nodes, which can realize the rapid calculation for massive data. The novelty of cloud computing techniques is used to assist the intelligent construction of stadium and gym buildings. The performance of the proposed technique is evaluated and the application scenarios are discussed.
A Largish Sum-Of-Squares Implies Circuit Hardness and Derandomization

For a polynomial f, we study the sum of squares representation (SOS), i.e. f = ∑_{i ∈ [s]} c_i f_i² , where c_i are field elements and the f_i’s are polynomials. The size of the representation is the number of monomials that appear across the f_i’s. Its minimum is the support-sum S(f) of f.For simplicity of exposition, we consider univariate f. A trivial lower bound for the support-sum of, a full-support univariate polynomial, f of degree d is S(f) ≥ d^{0.5}. We show that the existence of an explicit polynomial f with support-sum just slightly larger than the trivial bound, that is, S(f) ≥ d^{0.5+e(d)}, for a sub-constant function e(d) > ω(√{log log d/log d}), implies that VP ≠ VNP. The latter is a major open problem in algebraic complexity. A further consequence is that blackbox-PIT is in SUBEXP. Note that a random polynomial fulfills the condition, as there we have S(f) = Θ(d).We also consider the sum-of-cubes representation (SOC) of polynomials. In a similar way, we show that here, an explicit hard polynomial even implies that blackbox-PIT is in P.