Reviewerly/exHarmony huggingface dataset:
Context and Context Management

The user and user requirements are the main important concerns to design a pervasive computing system. The context of a user or device is another essential element in designing a context-aware pervasive computing system. This chapter is focused on what is the context, the variety of contexts, and the impact of that context information in developing a context-aware computing system. This chapter has also discussed several context-aware computing modelling as a part of the state of the art. On the line of existing models, this chapter has proposed 8C—a new context-aware computing model. The chapter also describes how RFID and sensors are used to sense context and shares the context information to further layers of context-aware computing system architecture. This chapter concludes with the context, its interpretations, storage and other important aspects.
Visual Servo Tracking of a Wheeled Mobile Robot Utilizing Both Feature and Depth Information

This paper presents a novel visual servo tracking method for a wheeled mobile robot based on target features and depth of scenery. The robot is equipped with an RGBD sensor to provide both color and depth information of the scene. The proposed method comprises two phases. The first phase utilizes feature matching to ensure that the target moves into the field of vision such that the robot may enter the second phase. The second phase implements visual servo aiming at object tacking accuracy. The depth information is employed in the proposed approach, which reduces the computational complexity required as in conventional approaches based on feature extraction. In addition, the second phase has built-in mechanism for detecting and exiting local minimum when the robot speed converges. The method is also capable of sequentially tracking multiple objects. Experimental results demonstrate the feasibility and effectiveness of the proposed two-phase method.
Applied and problem-oriented aspects and approaches of optimizing the motivation of professional activity

The object of the work is the motivation of professional activity, its enhancement and stimulation. The subject are techniques and methods of psychological, educational, and other value-orienting activities aimed at the formation and provision of professional-relevant motivation. The purpose of the research is to establish and define the basis of professional motivation, consideration of applied and problem-oriented aspects and approaches to optimize problem solving, increase professional motivation. The research methodology represents a complex of general scientific and private scientific methods of scientific cognition: dialectic, historical legal, comparative legal, system analysis, formal logical, sociological, etc. motivation of professional activity. The use of the system analysis method contributed to determine the place of motivation among other factors determining the content of professional activity.&#x0D; The scientific novelty of the research is expressed in the obtained new knowledge as a result of the research by the author of the traditional doctrinal approach to the consideration of the main institutional and functional foundations of the existing system of professional motivation. The formulated main provisions of the work allow defining reasonable approaches to studying and analyzing the practice of organizing and ensuring the motivation of professional activity, form an idea of the place of motivation as one of the key elements of the system of preparation for professional activity at the present stage of existence and improve the system of personnel training, theory and practice of personnel work .
Face Photo-Sketch Synthesis Based on Conditional Adversarial Networks

Face Photo-Sketch synthesis is designed to generate an image contains rich personal information and details facial. It is widely used in law enforcement and life areas. Although some existing methods have achieved remarkable results, however, due to the gap between the synthetic and real image distribution, the synthetic image does not achieve the expected effect. To solve this problem, In this paper, we proposed conditional generation adversarial networks (CGAN) based on arts drawing steps constrain. We divide the process into two stages. In the first stage, we take the original face photos which concat noise z as input, generating stage 1 low resolution synthesize sketches. In the second stage take the stage 1 results and original face photo as inputs, yielding stage 2 high resolution synthesize sketches, which can express more natural textures and details. To add realism, we train our network using an adversarial loss. Experiments have shown that, Compared with previous methods, our results generate visually comfortable face sketches. And express more natural textures and details.
Forecasting Psychological Symptoms in Mild Traumatic Brain Injury: A Nonparametric Approach

Background: Nowadays, forecasting models based on nonparametric models have been developed in many branches of science such as mathematics and economics. However, the relatively complicated structure of these models has made them less practical in medical sciences. Objectives: In this study, we investigated the application of a nonparametric regression model to predict the psychological symptoms appearing six months after mild traumatic brain injury. We also made a comparison between the performance of nonparametric regression and artificial neural network models in predicting the psychological symptoms. Methods: In a six-month period during 2015 - 2017, information of 100 mild traumatic patients was included in a prospective cohort study. The data were then divided randomly into two groups of training (n = 50) and testing (n = 50) for 100 times. In the training group, the focus was on 100 artificial neural network and nonparametric regression models. However, in the testing group, a comparison was made between the values obtained using the two final models. To compare the models, the ROC curve and the accuracy rate were finally applied. Results: According to the obtained results, the nonparametric regression model showed an accuracy rate of 91.25% while the neural network model had an accuracy rate of 85.34%. In the experimental set for both neural network and nonparametric regression models, the area under the ROC curve appeared to be 81.51% and 85.73 %, respectively. Conclusions: The nonparametric regression model appeared to be more powerful than the neural network model in predicting psychological symptoms.
A quantum-behaved particle swarm optimization algorithm with extended elitist breeding

A quantum-behaved particle swarm optimization with extended elitist breeding (EEB-QPSO) is proposed in this paper. In the proposed EEB-QPSO algorithm, the superior solutions found in the whole evolution process so far are regarded as the elitists and stored in an extra memory pool, and followed by elitist breeding strategy with some operators performed on extended elitists to improve the global search ability of the algorithm. Experiments testing on the unimodal and multimodal benchmark functions showed that the proposed EEB-QPSO is better than the original QPSO and EB-QPSO in terms of global search capability and convergence speed.
Book title: pragmatics, poetics and evolution

We consider history of the book title and how its role in the book and its place in the book edition changed. It also addresses the problem of studying the title of a book in Russian and foreign practices from the point of view of its poetics and functions, both in close connection with the text of the work, and with the help of the “distant reading” method. It is also being reviewed some current trends in book naming in modern publishing practices, such as the extensive use of citation and allusion techniques, the desire to reduce the title to a few characters, as well as the use of the letters of the Latin alphabet. The problems of the connection between the title and the genre of the novel and the structure of the title are also described. Separately, the question of the responsibility of the editor when working with the title of the translated work is raised. With concrete examples the problems of choosing the title of a book in the process of editorial preparation were analyzed,as well as some errors that the editor may make, dwelling on one or another variant of the title. We use the analytical-synthetic method of research. At the end of the work there are the main conclusions from the study.
Portable Crack Measurement Vision System Based on Parallel Laser

Crack measurement technology has important application requirements in highway bridges protection, cultural relic protection, and industrial production etc. But many target cannot be directly touched and measured. And most of the existing measuring tools are big. A portable crack width measurement system based on parallel laser is proposed in this paper. A parallel laser is designed as the measurement datum, and the intelligent cellphone is used as computing platform. Comparing with the traditional vision measurement method, it does not need the camera's internal parameters and the measuring distance, and it does not communicate with the cellphone except for take photos. So it can be used with any smartphone conveniently. The experiment shows that this system can be used as high accuracy portable non-contact measurement tool.
Organization of Secure Telecommunication Networks in Groups of Mobile Robots Based on Chaos and Blockchain Technologies

Currently, the topic of information exchange in groups of small-sized robots is very popular, the reason for this is the popularization of the use of mobile robots, and therefore the question of the crypto-security of the communication channels of the team becomes. The article discusses the existing approaches to the implementation of information exchange and provides a brief analysis of the features of information exchange, depending on the type of management strategy. The proposed method of organizing the exchange of protected information in a decentralized group of small-sized robots. The proposed method is based on the use of data encryption on the basis of chaotic models for the transmitted information to members of the coalition and the use of the blockchain technology, for entering the obtained information into a distributed database.
Comparing the Interpretation of Emotion in the Context of Human Experts and Artificial Intelligence

This paper explores the ability of artificial intelligence (AI) to detect or interpret emotions from information provided in the form of text. The study utilizes surveys (profiles) of 10 participants receiving palliative care. The profiles are analyzed manually by human experts and separately by IntenCheck, an AI system, to identify emotions displayed by each profile. The findings of each entity is then compared. This research is preliminary in nature and is the groundwork for forthcoming use of this technology. In the future, this work will incorporate a predictive model once a reliable form of emotion-identifying AI is achieved. The predictive model will assess overall positive or negative emotion of text, and subsequently, compare the success of treatment and livelihood of patients. After comparing the overall emotion with sustainability of numerous people, the AI will expectantly be able to analyze and predict the success of treatment and the likelihood of achieving preferred outcomes for patients based on their personal profiles.
Grouping does not help you to guide conjunction visual search

The knowledge of target features can be used to guide attention in many conjunction searches in a top-down manner. For example, in search for a red vertical line among blue vertical and red horizontal lines, observers can guide attention toward all red items and all vertical items. Items with both features would gain greater activation. It could be that attention is guided to the group of red items and the group of vertical items with items neatly divided into those with a target feature and those without. Alternatively, attention might be guided to any reddish and relatively vertical items, with no grouping. We tested whether clear, categorical groups were useful in guided search. Observers searched for color-orientation (Experiment 1) or length-orientation (Experiment 2) conjunction targets. Distractors could form two segmentable groups (e.g blue steep and red flat) or distractors could be “non-segmentable” varying from red to blue and steep to flat discouraging grouping and increasing overall heterogeneity. We found that, when the target was present, the searches were quite efficient in Experiment 1 (~9–14 ms/item) and more efficient in Experiment 2 (~0–6 ms/item). Target-present slopes were not affected by “segmentability” manipulations. However, target-absent slopes were less efficient if one of the dimensions was “non-segmentable” (especially in length-orientation conjunctions). In Experiment 3, we demonstrated that search in “non-segmentable” conjunction sets search no less and could be even more efficient than search in “non-segmentable” feature search. Our results suggest that attention is directly guided by the overlap between top-down activation signals corresponding to target features. The guidance mechanism bypasses grouping and segmentation cues that are very important in other tasks like scene parsing and object recognition.
Slovene Multi-word Units: Identification, Categorization, and Representation

In this paper, we present the results of a manual annotation of a Slovene training corpus with multi-word units (MWUs) relevant for inclusion in a lexicon of Slovene MWUs. We analyze the annotations in terms of (a) the frequency with which a string has been identified as a MWU, (b) the degree to which the annotators agree on the category of the identified MWU, and (c) the degree to which the annotators agree on the range of the MWU in terms of its lexicalized elements. The results of the analysis will be useful in different stages of the compilation of a Slovene MWU lexicon. The list of dictionary-relevant MWUs obtained in the annotation task will be used to enrich the lexicon and to train models for the automatic identification of MWUs in running text. The findings will also help revise the criteria for the identification and categorization of dictionary-relevant MWUs in relation to free phrases, as well as more clearly define the distinction between the lexicalized elements of MWUs and the more or less stable elements of their textual environment, which will be useful when determining the canonical forms of MWUs in the lexicon on one hand and their relation to their variable elements and syntactic conversions on the other.
Texture Image Categorization in Wavelet Domain via Naive Bayes Classifier Based on Laplace and Generalized Gaussian Distribution

In this paper, we have investigated recently proposed feature extraction technique for texture image representation. In the introduced method, features are extracted via bounded Laplace mixture model (BLMM) in wavelet domain. Due to nature of wavelet coefficients that can be modeled accurately with Laplace distribution, it is proposed to apply classifiers based on this distribution, which leads us to introduce Naive Bayes classifier with Laplace distribution for image categorization. The proposed approach is validated through experiments on different texture image datasets and it has shown very good results as compared to the model based on Gaussian distribution. The generalized Gaussian distribution is a generalization of both Laplace and Gaussian distributions, thus we have introduced also Naive Bayes classifier with generalized Gaussian distribution to achieve better performance as compared to the above two models. The proposed approach is also validated through extensive experiments and it is observed that by taking into account the nature of data, proposed models have very good performance. Classification results are presented by different performance metrics to ensure the effectiveness of proposed algorithms in texture image classification.
Ensembles of Active Adaptive Incremental Classifiers

An important and ubiquitous feature of the data stream generating process is its nonstationarity. Therefore, the models trained on such data streams have to be adaptive in order to react correctly on appearing concept drift. There is a number of concept drift detection methods that can be combined with a learning method to create active adaptive learner. However, none of the approaches was reported to be unambiguously the best. Within the presented work two ensemble approaches combining the adaptive base learners are applied in order to achieve higher classification quality. The analysis shows diversity of the utilised base adaptive learners justifying application of the proposed solution. The quality of results confirms that creating the ensemble of drift detectors can improve the classification quality.
Leveraging Boids to Implement an Intelligent Swarming Robotic System

The proposed project explores the utility of Boids as a basis for building an intelligent robotic platform, capable of exhibiting complex flocking and exploration behaviors. Ideally, this platform would retain sufficient flexibility, such that it may be trivially retooled for a variety of applications. The environment for our proposed platform will also include a simulator and user interface, to allow for easy and rapid testing of new algorithms and coefficients. The interface would also provide functionality that is necessary to view simulated units, as well as to change their parameters.
Direction of CAPTCHA

This chapter introduces the main challenges and novelties realized in the recent years for the CAPTCHA test. Hence, it focuses on the new directions of the test, by answering to the question: “What are the main characteristics of the new generation CAPTCHA tests?”. Accordingly, we will show some important features of the modern CAPTCHA. They answer to the need of designing more “user-friendly” puzzles which could be easier to be solved, and at the same time more powerful systems making harder the attacks made by the bots. These requirements are mainly accomplished by adding interactivity, animation, motion, image visualization and fun inside the CAPTCHA. In this chapter, we extend the description of some CAPTCHA types given in Chap. 6 and describe other CAPTCHA types.
Connectivity FIngerprints for the Visual Brain and Behavior

Since the advent of neuroimaging, there has been a considerable interest in mapping the visual system. This has led to exquisite maps of visual selectivity, but mapping is a purely descriptive approach to understanding the visual brain. In order to uncover mechanistic explanations, rather than descriptive maps of the visual system, neuroimagers will need to incorporate neural connectivity into their analyses. After all, connectivity is the principal constraint on the domain of information that a brain region can process, and thus should be highly predictive of neural selectivity. We previously showed that connectivity fingerprints can define a region so well that they can be used to predict the location of visual regions even in the absence of functional localizers, using only an individual’s connectivity patterns (Osher et al. 2015; Osher et al. 2018). Here we present a suite of analytic tools that will allow researchers to derive the connectivity fingerprints that best define any specific visual region, offering answers to questions such as “what connectivity pattern does a region need to be highly selective to faces?”Our software suite is applicable to DWI as well as functional connectivity data, any set of brain regions as seeds and targets, any fMRI task, and any number of individuals. We also demonstrate how connectivity fingerprints can be used to predict behavioral data, in addition to neural activation in each subject. We present a few specific results from the application of connectivity fingerprints to predict neural activation in individual subjects and behavioral variation in various mental tasks
Missing Data in Collaborative Data Mining

Incomplete data in the data mining process is a common case. In collaborative data mining, treating this case can bring better performance and results, so the study of missing data from the perspective of the collaborative data mining approach is addressed in this article. Completing missing data for research sources have been done in two ways: using the average from other sources or adding an operator from the RapidMiner application to the processes. Both approaches have generated good results and can be considered as viable alternatives in collaborative data mining processes.
Real-Time Rate Distortion Optimized and Adaptive Low Complexity Algorithms for Video Streaming

Mobile cyberphysical systems provide a platform where communication, computing and control are integrated together. With the increase in the usage of Internet of Things (IoT) devices, it is important to understand the complex interaction between these three subsystems. This paper aims to address this challenge in the context of video delivery over an autonomous aerial vehicle (AAV) network. This network suffers from limited bandwidth and delay constraints of real-time video streaming under time-varying wireless channel conditions. Additionally, it experiences limitations in power consumption and computational complexity due to the embedded system platform running the video transmission protocols. Hence, we design a low complexity real-time video transmission protocols that are suitable for such platforms. In this paper, we provide a comparison between different transmission protocols we developed with respect to temporal and spatial video distortion metrics.
Principled and Pragmatic Specification of Programming Languages

Programmers from the imperative tradition often have little experience of using inductive definitions and inference, and that may explain why executable SOS specifications have not become a standard feature of mainstream language development toolkits. We wish to ‘de-mystify’ SOS for such programmers, allowing precise and principled specifications to be given for even small industrial DSL’s. eSOS (elided Structural Operational Semantics) is a compact tool for specifying executable formal semantics. It is designed to be a translation target for enriched SOS specification languages. The simplicity of eSOS and its reference Java implementation allow programmers to follow the details of an execution trace, and to step through rules using a conventional debugging framework, allowing them to understand and use SOS-based specifications to construct usable language interpreters.
Functional Model to Online Information Seeking

This chapter focuses on developing a Functional Model of Online Information Seeking Behavior of Academicians to the effective seeking of an online platform. The unavailability of standard, uniform, and multilingual supportive model is the major reason to prepare this proposal. The proposed model will help in an enhancement of the utilization of the databases provided by government-funded agencies. The main aim of this chapter is to explore the online information seeking behavior and provide a functional model to effective seeking approaches for the academicians.
Applications of IVIFSs

Some applications of the IVIFSs will be discussed. In the period 1989–2000, as fas as the author knows, there were only 7 publications related to IVIFSs of other authors–Bustince and Burillo [61–66] and Hong [139]. In the new centure, more than 200 papers over IVIFSs were published. The biggest part of them are related to some IVIFS-applications.