Reviewerly/exHarmony huggingface dataset:
Geodesic Disparity Compensation for Inter-View Prediction in VR180

The VR180 format is gaining considerable traction among the various promising immersive multimedia formats that will arguably dominate future multimedia consumption applications. VR180 enables stereo viewing of a hemisphere about the user. The increased field of view and the stereo setting result in extensive volumes of data that strongly motivate the pursuit of novel efficient compression tools tailored to this format. This paper's focus is on the critical inter-view prediction module that exploits correlations between camera views. Existing approaches mainly consist of projection to a plane where traditional multi-view coders are applied, and disparity compensation employs simple block translation in the plane. However, warping due to the projection renders such compensation highly suboptimal. The proposed approach circumvents this shortcoming by performing geodesic disparity compensation on the sphere. It leverages the observation that, as an observer moves from one view point to the other, all points on surrounding objects are perceived to move along respective geodesics on the sphere, which all intersect at the two points where the axis connecting the two view points pierces the sphere. Thus, the proposed method performs inter-view prediction on the sphere by moving pixels along their predefined respective geodesics, and accurately captures the perceived deformations. Experimental results show significant bitrate savings and evidence the efficacy of the proposed approach.
Captcha Recognition using convolutional neural networks with low structural complexity

Abstract CAPTCHAs are automated tests designed to distinguish between humans and computers. They could be easily solved by humans, but they become challenging for machines to solve, therefore preventing programs from abusing online services and occupying internet resources. Using Convolutional Neural Networks, the CAPTCHA tests could be solved automatically at high efficiency. Current approaches of high accuracy CAPTCHA recognition can be structurally complicated. As a result, our team explored a different approach to solve CAPTCHAs using a Convolutional Neural Network that is more efficient in terms of structural complexity and run time, with image processing being a possibility to enhance the accuracy. We also tested our networks on CAPTCHA datasets with character adhesion and background noise.
Opening area and circularity of the lips during whistling

In recent years, instruction books on human whistling have been published. According to these books, when whistling pitch increases, lip opening is squeezed. Therefore, in this study, the opening area and circularity of the lips during the whistling process were measured. We conducted an experiment in which the lips of two professional whistlers were photographed. The subjects whistled twice at specified five pitches. The lip openings were extracted from the photographs, and the area and circularity were measured using an in-house software. The results show that during whistling, the lip opening area tended to decrease with the increase in frequency.
Learn to Bind and Grow Neural Structures

Task-incremental learning involves the challenging problem of learning new tasks continually, without forgetting past knowledge. Many approaches address the problem by expanding the structure of a shared neural network as tasks arrive, but struggle to grow optimally, without losing past knowledge. We present a new framework, Learn to Bind and Grow, which learns a neural architecture for a new task incrementally, either by binding with layers of a similar task or by expanding layers which are more likely to conflict between tasks. Central to our approach is a novel, interpretable, parameterization of the shared, multi-task architecture space, which then enables computing globally optimal architectures using Bayesian optimization. Experiments on continual learning benchmarks show that our framework performs comparably with earlier expansion based approaches and is able to flexibly compute multiple optimal solutions with performance-size trade-offs.
Part of Speech Tagging Using Hidden Markov Models

Abstract In this paper, we present a wide range of models based on less adaptive and adaptive approaches for a PoS tagging system. These parameters for the adaptive approach are based on the n-gram of the Hidden Markov Model, evaluated for bigram and trigram, and based on three different types of decoding method, in this case forward, backward, and bidirectional. We used the Brown Corpus for the training and the testing phase. The bidirectional trigram model almost reaches state of the art accuracy but is disadvantaged by the decoding speed time while the backward trigram reaches almost the same results with a way better decoding speed time. By these results, we can conclude that the decoding procedure it’s way better when it evaluates the sentence from the last word to the first word and although the backward trigram model is very good, we still recommend the bidirectional trigram model when we want good precision on real data.
The integration of information systems in a medical facility to improve the efficiency of decision-making

This paper describes a project for integrating a medical and diagnostic information system in a medical facility. At the moment, making effective and timely decisions in diagnosis and treatment is most important. Integration of systems has significantly improved the efficiency of decision-making by updating data. The unified information space allowed to increase the objectivity of diagnostics and increase the reliability of the information system. A critical factor in making a decision is the stage at which the disease is detected, as well as a description of what types of medical care are important for analysis. In addition, the need to synchronize on the basis of the detailed information for operational planning and routing of patients. Integration of solutions will increase the speed of passing examinations and the speed of making a final diagnosis. Thanks to the implemented solution, it became possible to increase the speed and improve the quality of services provided. The ability to synchronize activity planning based on effective diagnostics is essential.
Blockchain-Based Outsourced Privacy-Preserving Support Vector Machine Classification on IoT Data

With the enormous amount of IoT data generated, stored and exchanged, users are currently resorting to service providers to perform data mining classification tasks. Empirically, the IoT data used for training a classifier is moved to cloud servers running by third parties. Data integrity and the protection against data breaches is, therefore, becoming crucial. In this paper, we introduce BChainSVM: a blockchainized privacy-preserving support vector machine (SVM) classification between mutually distrustful IoT data owners and the cloud provider. With BChainSVM, we aim to bridge the gap between the theory of privacy-preserving classification in data mining and its practice. We first present the system model, design goals and construction details. Our security analysis indicates that BChainSVM achieves accurate and private classification, data correctness and data integrity. Furthermore, our performance evaluation shows that BChainSVM is feasible and efficient in terms of communication and computation costs.
Review Based Recommendations with Human-like Reasons

Recommendation Systems are widely deployed for all kinds of services across various websites to enhance user experience. However, existing systems do not make efficient use of text data associated with products and users, available as reviews and blogs to relate them better. Many recent works have tried to improve the accuracy of rating prediction. However, very few works have attempted to justify the reason for a particular recommendation. Explaining the recommendation would help in gaining the trust of the user, and lend the system human-like credibility. In this paper, we propose a model that can recommend movies and generate a reasoning text to help the user understand why a film was recommended to them. We use three parallel neural networks with an enhanced BERT Embedding for Aspect Based Sentiment Analysis (ABSA) to predict rating. The Seq2Seq transformer model is used to generate the reasoning text.
The SIGMA Framework for Managing Spatiotemporal Data Acquired by Various Cameras and Sensors

This paper describes the design of an application framework for advanced use of spatiotemporal imagery data, and the application developed with our framework. Our framework provides a mechanism to share the data captured from a wide variety of sensors in the real world and makes it easy for developers to implement applications which use those data. However, the captured data is scattered across time and space; thus, we designed the unified data management policy for handling a variety of sensor data as 4D data. We tried developing a few prototype applications with our framework and considered the prototype.
Design and implementation of Multi-kernel manager

Azalea is a multi-kernel to provide scalability of performance as the number of cores increases in the manycore environment. It consists of a full kernel for processing IO and unikernels for doing the actual computation. According to Azalea's characteristics, it is necessary to operate multiple unikernels that require a different number of cores and size of memory. So, we present a method for effectively allocating and deallocating system resources to unikernels and managing unikernels.
A Virtual Social Promotion Chatbot with Persuasion and Rhetorical Coordination

We build a chatbot that delivers content in the form of virtual dialogues automatically produced from documents. Given an initial query, this chatbot finds documents, extracts topics from them, organizes these topics in clusters, receives from the user clarification on which cluster is most relevant, and provides the content for this cluster. This content can be provided in the form of a virtual dialogue so that the answers are derived from the found documents by splitting it, and questions are automatically generated for these answers. Virtual dialogues as search results turn out to be more effective means of information access in comparison with original document chunks provided by a conventional chatbot. To support the natural flow of a conversation in a chatbot, the rhetorical structure of each message had to be analyzed. We classify a pair of paragraphs of text as appropriate for one to follow another, or inappropriate, based on communicative discourse considerations. We then describe a chatbot performing advertising and social promotion (CASP) to assist in the automation of managing friends and other social network contacts. This agent employs a domain-independent natural language relevance technique that filters web-mining results to support a conversation with friends. This technique relies on learning parse trees and parses thickets (sets of parse trees) of paragraphs of text such as Facebook postings. We evaluate CASP in a number of domains, acting on behalf of its human host. Although some Facebook friends did not like CASP postings and even unfriended the host, overall social promotion results are positive as long as relevance, style and rhetorical appropriateness are properly maintained. Finally, we propose a way to improve a discourse parsing by a refinement of default rhetorical relations, based on an analysis of Abstract Meaning Representation (AMR). A number of AMR semantic relations such as Contrast can be used to detect a specific rhetorical relation.
Software System for Physical Activity Monitoring: Smart Watch Case

In this paper, system for monitoring physical activity is presented. The system under development comprises hardware (smart watch and smartphone) and software parts (set of libraries and developed application). The target platform for running software is Apple smartphones that receive information about physical activity of the user from the watch device. The developed application relies in its work on the set of libraries that have been selected for development. The program for monitoring is a hybrid application that runs from the browser view. The paper provides full description of the software architecture with all used components and interconnections between them. The main function of the developed software is monitoring of user's physical activities. It allows collecting data about trainings, schedule trainings, and summarize aggregated results about the trainings. Detailed information about each training session is also provided as well. The application has been tested with three different training types (walking, running, and cycling) and has shown that it can be employed for monitoring of training process. It also can be extended further to provide graphical information about physical activity.
HTTP Adaptive Streaming

In this contribution, we present selected novel approaches and results of our research work in the ATHENA Christian Doppler Laboratory (Adaptive Streaming over HTTP and Emerging Networked Multimedia Services), a major research project at our department jointly funded by public sources and industry. By putting this work also into the context of related ongoing research activities, we aim at working out where HTTP Adaptive Streaming is currently heading.
Research of Micro-expression Recognition Model based on Feature Unit

Micro-expression which is the transient expression will be disclosed when people try to hide some kind of real inner emotions. Micro-expression changes so fast that few people detect its existence. As an effective behavioral clue, it is of great significance to understand the change of peoples true feelings. Based on this, image feature extraction in machine learning has made remarkable progress in the past two years. In order to improve the accuracy and practicability of micro-expression recognition, this paper deeply analyzed the practical micro-expression recognition method. Based on landmark detection. We construct a quantitative model and established a machine learning model of micro-expression judgment based on the comprehensive changes of eyes, mouth and eyebrows. This paper combine the feature unit and model application scene for judgement of micro-expression. This method reflect the design idea that the changes of face locality contributed to the overall micro-expression judgment, and helped to complete the high autonomous construction of the micro expression judgment model.
NFC Convenient Application: A Case of Personal Health Record

With the popularization of Near Field Communication (NFC), many mobile phones have built-in NFC functions, such as tag quick access, data transmission, identification, and micropayment, which makes personal health record (PHR) person-centered. It provides personal health-related information, customized preventive health information, and practices the concept of preventive care. However, due to the inconvenience of data input and concern of personal data leakage, the penetration rate of PHR has not been effectively increased. This paper uses NFC to provide PHR conveniently with a real-time input and authorization mechanism such as NFC tag writing, NFC tag input, quick start of NFC tag authorization, and a mobile phone NFC peer-to-peer authorization. Through the usage of NFC, the issue of inconvenient data input and data leakage of PHR can be solved. The result helps increase the utilization of PHR to practice the preventive care concept.
A few more showers then drying out for the start of the week.

Showers will start to move into the region overnight with lows running warmer than normal, landing in the low 40s.  We'll wake up to showers Sunday and they'll hang with us through mid-day.  Highs will push above average near 50 degrees.
A Study on Minimum Spectral Error Analysis of Speech

Conventional source-filter vocoders, such as WORLD, can quickly synthesize speech. However, the quality of synthetic speech is degraded due to speech parameters extraction errors. Therefore, this paper proposes minimum spectral error analysis, a speech analysis method that extracts speech parameters using Analysis-by-Synthesis (A-b-S), to improve the quality of speech synthesized by WORLD. We update speech parameters to minimize the error between the amplitude spectra of natural and synthetic speech. We developed the calculation process of the amplitude spectrum of synthetic speech from speech parameters to perform this analysis. A preliminary experiment shows that we have successfully constructed the calculation process.
Concept-Based Learning of Complainants’ Behavior

In this chapter, we apply concept learning techniques to solve a number of problems in the customer relationship management (CRM) domain. We present a concept learning technique for common scenarios of interaction between conflicting human agents. Customer complaints are classified as valid (requiring some kind of compensation) or invalid (requiring reassuring and calming down) the customer. Scenarios are represented by directed graphs with labeled vertices (for communicative actions) and arcs (for temporal and causal relationships between these actions and their parameters). The classification of a scenario is computed by comparing a partial matching of its graph with graphs of positive and negative examples. We illustrate machine learning of graph structures using the Nearest Neighbor approach and then proceed to JSM-based concept learning, which minimizes the number of false negatives and takes advantage of a more accurate way of matching sequences of communicative actions. Scenario representation and comparative analysis techniques developed herein are applied to the classification of textual customer complaints as a CRM component. In order to estimate complaint validity, we take advantage of the observation (Galitsky and Kuznetsov 2008) that analyzing the structure of communicative actions without context information is frequently sufficient to judge how humans explain their behavior. Therefore, because human attitudes are domain-independent, the proposed concept learning technique is a good compliment to a wide range of CRM technologies where a formal treatment of inter-human interactions such as customer complaints is required in a decision-support mode.
A C-IFGSM Based Adversarial Approach for Deep Learning Based Intrusion Detection

With the rapid development of machine learning algorithms, the security problem has gradually emerged. Most existing algorithms may be attacked by adversarial examples. An adversarial example is a slightly modified input sample that can lead to a false result of machine learning algorithms. This poses a potential security threat for many machine learning-based applications. Especially in the domain of intrusion detection, the intrusion adversarial examples may result in malicious attacks on intrusion detection classifiers. To our knowledge, all previous work only apply the adversarial examples generation methods in the field of image classification, which is not suitable for network traffic datasets. Aiming at generating more similar intrusion adversarial examples, this paper explores a Constraint-Iteration Fast Gradient Sign Method (C-IFGSM) that can adapt to complex network traffic datasets with multiple types of features and multiple relationship among features. Experiments show that the C-IFGSM based adversarial approach can achieve good performance on intrusion adversarial examples.
Recommender System for Food Startup

Abstract Automatic suggesting models have been available already for item buying, films, and amusements. Demand for one such system for food delivery startup is picking up now. Main aim of this research work is to come up with design of one such recommender system for food delivery startup makes it to sustain in the business on long run through better understanding as well as retention of potential customers automatically. Zomato reviews data are taken for analyzing using some selected algorithms of machine learning. From them the best predictor model is picked up for building the final proposed engine. Underlying methodology goes like this, it first knows and learns about customers using past and near past data (training data) and using that knowledge it tries to draw results of current data. Accuracy of this result is then observed and pruned, if not up to considered bench mark, by employing bias-variance tradeoff as well as considering more reliable characteristics. Once classifier design with required level of accuracy is obtained and it can then be developed. The focus of the research is to design a system that facilitates automatic recommendation for food startup to know and retain its good restaurants aggregation as well as potential users.
Trends of Modernization of professional training in the system of special education

The conceptual foundations of the theory and practice of the professional formation of the personality in the system of special education are substantiated. The essence of the given training is considered in various ways, on the basis of which its qualitative and classification characteristics, levels and systemic and structural components are determined; the content, organizational and pedagogical conditions and criteria for assessing the levels of formation of these components aredefined.The following components of professional training are defined: professional and motivational, cognitive and competence, operational and active, productive and reflexive. It is indicated that the given structural components are interrelated, interdependent and complementary to each other. Theoretical paradigm is scientifically substantiated, the structural-functional, organizational-didactic model of the system of professional training in the field of special education is developed and tested and the dynamics of the formation of its components according to the results of the implementation of this system is highlighted. Particular attention is paid to finding and developing of the optimal set of methods of its providing, developing programs and definition of developed program’s efficiency. The practical results of scientists’ work in defining spheres are analyzed. The results of the experimental study of the components of the professional readiness to work on a specialty are highlighted. The prospects for further research in the context of improving professional training in the field of special education are outlined.
Optical Flow Estimation Between Images of Different Resolutions via Variational Method

Traditional optical flow estimation methods mostly focus on images of the same resolution. However, there are some situations requiring optical flow between images of different resolutions, where the traditional approaches suffer from the inequality of spectrum aliasing level. In this paper, we propose a method estimating the flow fields between a clear image and a highly undersampled one. The proposed method simultaneously describes the motion and integral relationship between the images via an integral form image under the assumption of brightness and gradient consistency as well as motion smoothness. We also derive the numerical solution briefly, through which we can solve the equations easily via linearizations. Experimental results on Middlebury and MPI-Sintel datasets demonstrate that our proposed method outperforms traditional methods preprocessing images of different resolutions to be the same size, offering more accurate results.