Reviewerly/exHarmony huggingface dataset:
Ambient Intelligence Changes the Office Environment

Technology proliferation is on a movement to outpace an 18th-century computing industry paradigm known as “Moore’s law.” This law establishes the rate of technological advancements. The premise of this edict is evident in our coupled workplace with the integration of an emerging technology known as Ambient Intelligence (Aml).The modernization of the traditional office is designed to be collaborative and environment-friendly. Modernization is primarily due to ambient intelligence. “Opportunities for process and business improvements will derive from a “real-world Web” of smart objects and ambient intelligence, and from consumer-oriented trends such as Web business platforms, aesthetic design, and mobile robots as they move into the business world” (Fenn and Smith, 2005, para. 1). It is safe to reason that ambient intelligence is on a trendy trajectory in many business-oriented workplaces, worksites and workspaces. The business culture is inconspicuously changing before our eyes. Architects and designers are seamlessly incorporating this trend into their respective end-to-end processes of constructing new or retrofitting existing office spaces.Its unnoticeably embedded adoption is in conference rooms, doorways, elevators, escalators, lighting, meeting rooms, phone displays, and walkways. As ambient technology naturally collides with the functional way an office professional interactively operates through a usual workday, its adaptation becomes seemingly smart and swift. The interesting facet of this technology is that one would not know it unless it was pointed out.Although there are equipment and devices that offer a singular approach of being convenient and hands-free, there exist common misconceptions and unassuming annoyances that are in place as inherent issues. Once the work environment impedes productivity or natural flow of movement, we realize something is different. These differences align to the surrounding tangible and intangible cues. The information presented in this chapter will disclose the underlying issues at a practical level.
Visual front-end for underwater scene change detection and environment monitoring by the autonomous drone

Autonomous underwater drone operation requires on-line analysis of signals coming from various sensors. In this paper we focus on design of the visual front-end of an underwater drone which is optimized for abrupt signal change detection for help in maneuvering and underwater object search operations. The proposed method relies on tensor space comparison with the chordal kernel function. This kernel measures a distance expressed as principal angles on Grassman manifolds of unfolded tensors. Although tested on color videos, the method can be scaled to accept more signal types in the input tensors. Experiments show promising results.
Temporal Event Detection Using Supervised Machine Learning Based Algorithm

Natural Language Processing is a way for computers to explore, analyze, comprehend, and derive significant sense from any language in a smart and useful way. By using NLP, knowledge can be organized and structured to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation. Various NLP applications require the identification of events from text documents. The time and event are closely associated with each other. The time dimension is often used to measure the quality and value of events and it has a strong influence in many domains like topic-detection and tracking, query log analysis. In this work, we present an annotation framework to extract temporal information and to specify the temporal relation between extracted events from news corpus by applying the combination of supervised machine learning technique and rule-based method according to the TimeML task std. Artificial neural network (ANN) is trained by the using the TimeBank and AQUAINT TimeML corpus to recognize the events and temporal expressions and for the temporal normalization, part heuristics rules have been used. The efficiency of the proposed work is measured in sense of precision and recall. The system outperformed to the best systems and it is likely that the technique used could be improved further by considering more aspects of the available information when relating the temporal information with events.
Performance Evaluation of Denoising Techniques Applied to Mammograms of Dense Breasts

Research indicates out that early diagnosis of the tumor may increase the chance of disease treatment and currently, the most effective method for early detection of illness is mammography. For this reason, using techniques that result in better quality images, that is, with as little noise as possible can aid in the visualization and detection of lesions and improve the accuracy of the diagnosis. For this work were used three filters being them: Wiener, Lee and Frost and to measure its efficiency were used the following parameters: Peak Signal to Noise-Ratio (PSNR), Signal to Noise-Ratio (SNR) and Structural SIMilarity (SSIM). It was verified that the Wiener filter presented better performance when compared to the others.
Uncertainty-aware situational understanding

Situational understanding is impossible without causal reasoning and reasoning under and about uncertainty, i.e. probabilistic reasoning and reasoning about the confidence in the uncertainty assessment. We therefore consider the case of subjective (uncertain) Bayesian networks. In previous work we notice that when observations are out of the ordinary, confidence decreases because the relevant training data, effective instantiations, to determine the probabilities for unobserved variables, on the basis of the observed variables, is significantly smaller than the size of the training data, the total number of instantiations. It is therefore of primary importance for the ultimate goal of situational understanding to be able to efficiently determine the reasoning paths that lead to low confidence whenever and wherever it occurs: this can guide specific data collection exercises to reduce such an uncertainty. We propose three methods to this end, and we evaluate them on the basis of a case-study developed in collaboration with professional intelligence analysts.
Analyses of Aspects on an Entrepreneurial Mindset

The article discusses the conceptual issues of formation of the competence approach to increase entrepreneurial skills in engineering and ICT studies, foreign experience of study competencies, the introduction of competence-based approach in the Russian universities, presented requirements of the labor market to an employee. The article considers teaching methods and types of schools for increasing efficiency of entrepreneurships education in formal and informal education.
Multi-valued deontic Halpern-Shoham logic for fuzzy deontic-temporal expressions

Temporal expressions describing the so-called Allen’s relations between intervals are broadly exploited in Artificial Intelligence and engineering, for example, in a system specification. They may be formally rendered in terms of Halpern-Shoham logic. If these expressions are combined with some ex pressions describing differents obligations or permissions, then we need a kind of a deontic Halpern-Shoham logic to represent the combinations. If also obligations are gradable and they introduce a kind of a fuzziness, then a new multi-valued (fuzzy) deontic logic should be expected. According to it – a multi-valued deontic Halpern-Shoham logic is proposed in the paper. Its combined formulae are interpreted in the interval-based fibred semantics, which consitutes a modification of the pioneering ideas of Gabbay’s fibred semantics.
Enhanced Feature Selection for Facial Expression Recognition Systems with Genetic Algorithms

Humans use voice and facial expressions to infer their state of emotions. Key expressions include being happy, angry, sad and neutral. The expressions accounts for a third of non-verbal communication. The study presents an efficient method to identify facial expressions in images based on artificial neural networks enhanced by genetic algorithms. We use Viola Jones for facial detections and PCA, a statistical method to reduce the dimensionality and extract the features with a local variant of local binary patterns called CS-LBP for static images which is a local algorithm that reduces feature set by comparing symmetrical pixels halving the feature set. The features are then optimally selected using genetic algorithm before classification using artificial neural networks. It is also crucial to note that with these emotions being natural reactions, recognition of feature selection and edge detection from the images can increase accuracy and reduce the error rate. This can be achieved by removing unimportant information from the facial images. The genetic algorithm (GA) chooses a subset of image features based on a reduced-dimensional dataset. The study proposes local binary pattern variant central symmetric local directional pattern (CS-LDP), central symmetric LBP (CS-LBP) and artificial neural networks aided by genetic algorithms for feature selection. The study used the Japanese Female Facial Expression, JAFFE database. The approach outperformed other traditional approaches and proved that with added feature selection and optimization the processing time is reduced and accuracy improved.
An Improved Image Annotation and Retrieval System Using Hybrid Based Semantic and Context Data Analysis

Due to the subjectivity nature of social tagging, noisy and incompletion of human generated tags, an automatic image annotation system is required to filter and expand the associated tags for efficient image retrieval in social photo web sites. This paper presents a Hybrid based semantic and context image annotation system for noise tag filtration and tag expansion. Experimental results shows that, the proposed system effectively identify and expand tags and enhance performance of image retrieval in social photo web sites.
On Persistence of Convergence of Kernel Density Estimates in Particle Filtering

A sufficient condition is provided for keeping the character of the filtering density in the filtering task. This is done for the Sobolev class of filtering densities. As a consequence, estimating the filtering density in particle filtering persists its convergence at any time of filtering. Specifying the condition complements previous results on using the kernel density estimates in particle filtering.
In-situ measurement of aspherics with sub-aperture deflectometry for precision optical manufacturing

The measurement of aspheric optics has attracted intensive attention in precision engineering, and efficient in-situ measurement technologies are required urgently. Phase measuring deflectometry is a powerful measuring method for complex specular surfaces. In this paper, an in-situ measurement method is developed based on the sub-aperture deflectometry. A complete measuring procedure is developed, including initial calibration, self-adaptive calibration, route planning, imaging acquisition, phase retrieval, gradient calculation, surface reconstruction and sub-aperture stitching. Several key points concerning the sub-aperture measurement are investigated, and effective solutions are proposed to balance the measuring accuracy and aperture, to overcome the height/slope ambiguity and to eliminate the stitching errors caused by point sampling and measuring errors. The measuring flexibility and stability can be greatly improved compared to the existing SCOTS measuring approach.
Applying software variability methods to design adaptive assistance robots

We believe that assistance and service robots are a major technological trend that could lead to a spread similar to smartphones today. Thus, it could be normal to have such a robot at home to support people during their daily life. The main challenge with today's developments in this segment is a lack of features and a lack of adaptivity. Most service robots are specialised systems (vacuum cleaners) or do not consider the user's needs and preferences when fulfilling their tasks. Our goal is to cope with these challenges and propose a solution by applying principles of software variability engineering to create an adaptive and accessible robot that can be extended and re-configured to fit the user's needs better. Therefore, we modified feature and variability modelling known from product lines and software ecosystems and developed an approach to configure the robot based on user requirements (needs and preferences).
Unintended Consequence: Pervasive Games and Public Art

Games such as Niantic’s Ingress and Pokémon GO have demonstrated that Pervasive Games can be highly engaging for their audience by creatively combining location-based data and augmented content. This research asks how Pervasive Games using Augmented Reality can contribute to a changing perception of Public Art through a playful exploration of place. By unintentionally discovering Art whilst playing games, new meanings are seen to emerge and are explored from new perspectives through the surprise of discovery and re-negotiation with art in public places. This chapter is a semiotic exploration, through analysis and interview of some of the signs and symbols of Public Art and particularly the ontological shifts arising from the re-contextualisation and revisitations of symbols in augmented frameworks. The analysis of semiotic change seeks to explore the shifting interpretations of Public Art afforded by playing games with augmented technologies. Pervasive Games such as Wayfinder Live, and even Pokémon GO and its precursors unravel an evolving set of rich discourses in which to investigate the recursive correlations of Art and Place. The outcomes of this analysis of Pervasive Games reveal the ontological implications not of intentional design, but of the unintended consequence of playing Games through Art.
1C:EDUCATION QUALITY ASSESSMENT. SCHOOL ANALYTICAL SYSTEM: NEW SOFTWARE FOR EDUCATION QUALITY MANAGEMENT

The article discusses conceptual approaches to develop a system for assessing the quality of education in accordance with the Federal State Education Standards. This task is relevant because of the requirements of regulatory documents defining the transition from education quality control methodology to education quality management methodology as a strategic direction in building an education quality assessment system from school to federal level. We consider faults of the traditional model of the school system for assessing the quality of education, the requirements for developing a modern system of quality assessment and their implementation in the author’s methodology. The need for an automated system for assessing the quality of education for the implementation of the presented model is noted. An example of the implementation of the model for assessing the quality of education in the new software product 1C:Education quality assessment. School is described, the features of implementation in this program of a three-level quality assessment system (student level, class level, school level) are discussed.
Accuracy of Convolution Neural Networks for Classifying Sentiments on Movie Reviews

Convolution Neural Networks (CNN) models have shown remarkable results for classifying text and sentiments. In this paper, we present our approach on the task of classifying movie reviews using the word embedding on a large-scale dataset provided by Stanford. We compare the accuracy of word-based CNN models for different kernel sizes with structured and unstructured sentences. We use the standard GLoVe for word embedding and use MXNet framework to perform our experiments. Our results suggest that word orderings in a sentence do not affect the classification accuracy of CNN models significantly. However, we also note that accuracy may depend on frequency of occurrences of words.
Evaluating the Effectiveness of Embeddings in Representing the Structure of Geospatial Ontologies

Nowadays word embeddings are used for many natural language processing (NLP) tasks thanks to their ability of capturing the semantic relations between words. Word embeddings have been mostly used to solve traditional NLP problems, such as question answering, textual entailment and sentiment analysis. This work proposes a new way of thinking about word embeddings that exploits them in order to represent geographical knowledge (e.g., geographical ontologies). We also propose metrics for evaluating the effectiveness of an embedding with respect to the ontological structure on which it is created both in an absolute way and with reference to its application within geolocation algorithms.
MiND: Mind Networked Device Architecture for Attention-Gated Ambient Assisted Living Systems

This paper proposes and evaluates an online architecture for network brain-computer interfaced smart home systems for enabling ambient assisted living. This paper uses a number of portable, low-cost, single-channel electroencephalograph systems to achieve Mind Networked Devices (MiND) for home-appliance control, to enable better accessibility for the infirm and people with mobility disability. We tested our approach on real-life hardware toward the control of devices such as lights, fans, and room-temperature conditioning systems, furthermore, we emulated the behavior of the networked hardware for a more substantial number of users on conceptualized scenarios, which will be encountered in implementing this architecture. The Network Simulator 2 simulation results of the large-scale implementation show promising results and give a precise idea about various essential features such as routing protocols, routing configuration, mode of transmission, attenuation losses, and delays incurred during the in-house operation of this architecture. The real-life evaluation of the system also supports the proposed MiND architecture.
Comparison of Bayesian Networks for Diabetes Prediction

A Bayesian network (BN) can be used to predict the prevalence of diabetes from the cause–effect relationship among risk factors. By applying a BN model, we can capture the interdependencies between direct and indirect risks hierarchically. In this study, we propose to investigate and compare the predictive performances of BN models with non-hierarchical (BNNH), and non-hierarchical and reduced variables (BNNHR) structures, hierarchical structure by expert judgment (BNHE), and hierarchical learning structure (BNHL) with type-2 diabetes. ROC curves, AUC, percentage error, and F1 score were applied to compare performances of those classification techniques. The results of the model comparison from both datasets (training and testing) obtained from the Thai National Health Examination Survey IV ensured that BNHE can predict the prevalence of diabetes most effectively with the highest AUC values of 0.7670 and 0.7760 from the training and the testing dataset, respectively.
Captioning of full motion video from unmanned aerial platforms

In this work, we aim to address the needs of human analysts to consume and exploit data given the proliferation of overhead imaging sensors. We have investigated automatic captioning methods capable of describing and summarizing scenes and activities by providing textual descriptions using natural language for overhead full motion video (FMV). We have integrated methods to provide three types of outputs: (1) summaries of short video clips; (2) semantic maps, where each pixel is labeled with a semantic category; and (3) dense object description to capture object attributes and activities. We show results obtained from VIRAT and Aeroscapes publicly available datasets.
A New Technique for Lossless Compression of Color Images Based on Hierarchical Prediction, Inversion and Context Adaptive Coding

This work introduces a new technique for lossless compression of color images. The technique is composed of first transforming an RGB image into luminance and chrominance domain (Y CuCv). Then, the luminance channel Y is compressed with a context-based, adaptive, lossless image coding technique (CALIC). After processing the chrominance channels with a hierarchical prediction technique that was introduced by Kim and Cho, Burrows-Wheeler Inversion Coder (BWIC) or JPEG 2000 is used to compress of the chrominance channels Cu and Cv. It is demonstrated that, on a wide variety of images, particularly on medical images, the technique achieves substantial compression gains over other well-known compression schemes such as CALIC, JPEG 2000, LOCO-I, BPG(HEVC), and the previously proposed hierarchical prediction and context adaptive coding technique LCIC.
Talent Management System at I.N. Ulianov Сhuvash State University

Involving the talented youth in the processes of innovation development is an important factor in the development of the society and modernization of the Russian economy. The national education system defines working with gifted children and young people as a key priority. The role of universities in youth talents training and management increases inasmuch the purpose of educational institutions is not only training of specialists with the highest professional qualification but creating the so-called intellectual elite of the country as well. The model of talent management system functioning at I.N. Ulianov Chuvash State University is described in the article. The article examines the complex multi-level system presented by interaction between elements «school – higher education provider – production». The basic forms of identifying and following up gifted schoolchildren and students and forms of supporting young lecturers are considered. A number of the University structural subdivisions such as Centre for working with the gifted youth, Center of Supplementary Education, Vocational Guidance Center, Innovation-implementation Center take part in working with talents by implementing a year-round complex of events. One of the key factors in functioning the talent management system is staffing. With a view to maintaining continuity, fostering young teachers’ motivation the University implements in-house target programs. One of the most effective forms of preserving and transferring the best practices of professional adaptation is tutorship. A multilevel approach makes it possible to create conditions for maximum revealing talents in students, professionals and to build the system of continuity within the system of working with young talents .
An Integrated Framework for RESTful Web Services Using Linked Open Data

RESTful web services have evolved based on REST architectural design and gained popularity because of their inherent simplicity and suitability features in comparison with SOAP-based web services. Moreover, linked open data (LOD) provides a uniform data model for RESTful web services which in turn avoids manual intervention of users to perform tasks such as, searching, selection, and integration. Researchers have worked on LOD based RESTful web services searching, selection and composition but focused on individual basis though they are interrelated tasks. This article presents an integrated framework and approach to automate the discovery, selection and composition of RESTful Web services using linked open data to provide an efficient composition solution. We work with RDF descriptions to express the state of linked data resources on which SPARQL queries would be applied for the extraction, filtering and integration of RESTful services. Use case scenarios of population information systems and healthcare recommendation systems are presented as a proof of concept with necessary results.