Reviewerly/exHarmony huggingface dataset:
m-OCKRA: An Efficient One-Class Classifier for Personal Risk Detection, Based on Weighted Selection of Attributes

Personal risk detection refers to the timely recognition of situations that may jeopardise a person's physical integrity, for example, during a fall or an accident. During this process, information obtained by monitoring vital signs and human activities is used to develop a mechanism capable of distinguishing between a person's normal behaviour and a risk-prone situation. Such a mechanism is meant to be fully implemented in mobile devices with limited resources in terms of memory, processing power, and battery life. OCKRA (One-Class K-means with Randomly-projected features Algorithm), a oneclass classification ensemble specially designed to detect anomalies in a person's behaviour patterns, has been reported in the literature as the best algorithm in terms of accuracy in the context of personal risk detection. Experiments were performed using the publicly available PRIDE (Personal RIsk DEtection) dataset. However, reported training execution times seem prohibitive for mobile implementation. Our contribution is based on two strategies to reduce the execution time during the training phase of a oneclass classification algorithm, aiming at its efficient implementation in mobile devices, and at the same time to maintain a good classification performance. The first strategy concerns the PRIDE dataset, for which we applied a filter-based approach to select its most relevant attributes. Eliminating attributes aids to identify sensors that can be turned off to avoid unnecessary data collection, thereby saving hardware resources. In the second strategy, we modified the internal structure of OCKRA based on the analysis of its design. Our proposed algorithm, called m-OCKRA, incorporates weighted attribute projection using filters to create data subsets for each classifier in the ensemble. Also, we reduced its computational complexity from O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) to O(n). Our results show that m-OCKRA outperforms the original OCKRA version since the gain in execution time for training is almost an order of magnitude, and according to the performed statistical tests, the new algorithm preserves a good and equivalent classification performance.
Limit Datalog

Motivated by applications in declarative data analysis, we study DatalogZ-an extension of Datalog with stratified negation and arithmetics over integers. Reasoning in this language is undecidable, so we present a fragment, called limit DatalogZ, that is powerful enough to naturally capture many important data analysis tasks. In limit DatalogZ, all intensional predicates with a numeric argument are limit predicates that keep only the maximal or minimal bounds on numeric values. Reasoning in limit DatalogZ is decidable if multiplication is used in a way that satisfies our linearity condition. Moreover, fact entailment in limit-linear DatalogZ is ΔEXP 2 -complete in combined and ΔP2 -complete in data complexity, and it drops to coNEXP and coNP, respectively, if only (semi-)positive programs are considered. We also propose an additional stability requirement, for which the complexity drops to EXP and P, matching the bounds for usual Datalog. Limit DatalogZ thus provides us with a unified logical framework for declarative data analysis and can be used as a basis for understanding the expressive power of the key data analysis constructs.
Face and Human Detection in Low Light for Surveillance Purposes

Surveillance based on computer vision is the need of the current era. Most of the existing surveillance cameras which are being used by the Security Services and Military Forces works on the sensor-based analysis of conditions, it activates a signal indicating the presence of some movable creature and cannot accurately differentiate between a hare, a deer or a human. The current surveillance methods show high accuracy in presence of daylight but fail miserably in low light conditions because these sensors equipped with normal cameras are not able to capture images with the same accuracy in such conditions. Hence to overcome this problem, we propose a network that is a combination of Human and Face Detection systems to distinguish between different entities and enhance the accuracy of the results. This model works well in extreme conditions such as low-light, blurred images, fog, haze or when most of the body or face is covered.
Implementation and Performance Analysis of a Video Streaming System using WEBRTC and Equal Cost Load Balancing

Real-time Internet based video communication applications are becoming widely adopted by internet users. However, the exponentially rising numbers of internet users is proving to be a real challenge to guarantee a good Quality of Service (QoS) for such applications. This paper investigates how video streaming quality is impacted by the increasing number of users using a video streaming application developed with Web Real Time Communications (WebRTC). The application considers both live video streaming from of an event captured by the initiator's webcam and live streaming of a video file from the initiator's device. Tests were performed using a Wi-Fi connection with different types of video sources. Moreover, equal cost load balancing was used to assess the impact on the video quality two servers are used at the initiator's site. Videos were recorded for five minutes on both the initiator and client sides using the application's record function and the Peak Signal to Noise Ratio (PSNR) was used to assess the quality of the videos. Results showed that with equal cost load balancing using two servers a maximum gain of up to 5.39 dB in PSNR could be obtained with live streaming from a webcam to ten clients.
Discovery of Action Rules for Continuously Valued Data

Action rule mining develops rules that describe which attributes should be changed in order to move an object from an undesired state to a desired state, with the understanding that some attributes cannot be changed. While such rules can be very useful for end-users, a limitation in prior work is the underlying assumption that the attributes of a dataset are discrete in nature. To address this limitation, we propose a method for generating action rules for objects described by continuously valued data. As part of the process, we developed a model for determining the effectiveness of the change, which permits more tailored recommendations for how to modify objects. Experimental results indicate that we can successfully create action rules for continuous valued data, and the use of automated tuning reduces the number of changes that must be performed to move an object from an undesired state to a desired state.
Learning to Propose Amendments: Identifying Patterns in the Right to Information Query Log

The Right to Information (RTI) Act 2005 provides Indian citizens with the platform to access information present in government offices. Every government office has the RTI query-log data containing citizens' interaction with the government. The objective of this work is to analyze the RTI query-log data to identify underlying latent patterns such that the identified latent patterns have the potential to act as feedback for amendments to laws. We, for the first time, collect RTI queries and reply-statistics from government educational institutions across India. Three latent patterns are quantified namely (i) transparency of institutions from RTI query-reply statistics (ii) factors that influence the transparency of institutions (iii) effectiveness of implementation of the RTI act. A two-dimensional institute-query-category matrix representation of the RTI data is proposed. The above three latent parameters are simultaneously quantified via maximum likelihood estimation of the data matrix using Graded Response Model. Through the extracted latent patterns we identify inconsistencies in the execution of the RTI act and indicate a feedback for potential amendment to the RTI act.
Research on Occlusion Relationship Recognition Based on Distance Measurement of Moving Objects in Video

Aiming at the problem of moving objects occluded in the field of automatic driving, in this paper, we propose a framework for occlusion relationship recognition based on distance measurement of the moving objects in video. The framework performs moving objects detection by YOLOv3 and occlusion recognition of moving objects by the information entropy. When the occlusion occurs, the framework will obtain the distance between the moving objects and the camera by binocular stereo vision distance measurement. Finally, the framework completes occlusion relationship recognition using a principle derived from this paper. The experimental results show that distance measurement of moving objects in video is effective and feasible for occlusion relationship recognition, which can provide some help for visual technology in the field of automatic driving.
Digitalization of the educational process

The authors analyze the problems of digitalization of education in Russia. Realizing that the widespread introduction of information and communication technologies in educational activities is dictated by the need to train new qualified personnel for the labor market in the digital economy, the authors at the same time draw attention to the possible negative consequences of this process. In their opinion, the use of digital technologies in the educational process, in addition to the undoubted advantages, which are the expansion of the resource of educational information, the ability to use educational content at a convenient time and in a convenient manner, the ability to build individual educational paths, improve educational system management tools, etc., can contribute and a decrease in the quality of education, since it changes the mechanisms of perception of information, without forcing a person to think creatively and reducing his communication skills.
Theoretical Bases of Motivational Readiness for Pedagogical Creativity

In this article, a theoretical analysis of bases of motivational readiness for creativity in pedagogues is presented. The levels and components of the motivational readiness have been studied.
Data Deduplication with Encrypted Big Data Management in Cloud Computing

Cloud storage service is an efficient solution for increasing storage needs of organizations and individuals. In order to ensure security and data privacy, users may encrypt their data before uploading to cloud. In such cases same or different users may up-load same data in encrypted form and it results in the existence of duplicated data. Data deduplication is a technique designed to identify and eliminate redundant data. When users are uploading data in encrypted form, then deduplication is a challenging problem. This project proposes a deduplication system in cloud storage that supports big data deduplication also. The sytem is based on symmetric encryption and re-encryption. The system aims at paragraph-level deduplication of text data files. A token will be generated for each paragraph in a file before uploading to cloud. When a new request for uploading is received, system checks the existence of duplicate files using these tokens. If the file is already exists in the cloud, then system rejects uploading, else system will perform deduplication using re-encryption algorithm.
Development of a System to Teach Users Gymnastics with Humanoid Robots

In order to solve the problem of caregiver shortages, we attempt to develop a system that can teach users gymnastics with humanoid robots. The system was developed with a depth camera and a humanoid robot. This paper presents the method we used to obtain the motion and posture data when a user imitates the motion performed by the robot, and the evaluation method used to determine whether the gymnastics were done correctly.
Cybersickness Evaluation While using Driving Simulator in a Head-Mounted Display Environment

This study aims to investigate the cybersickness severity in a driving simulator presented with two different displays: A head-mounted display and a three-monitor display. Twenty students participated in this study. They performed a set of driving task on a driving simulator. Half participants performed the task using the head-mounted display (HMD group), and the other half performed using the monitor display screen (MD group). Here, we found that using a head-mounted display to display the virtual environment of the driving simulator will cause cybersickness more severe than using the monitor display. Participants in the HMD group reported higher nausea, oculomotor, and disorientation problem after performing the driving simulation task. Of the three symptoms, the disorientation was reported to be the most severe symptom contribute to cybersickness occurrence when using head-mounted display.
Finer-grained image splicing localization method based on noise level estimation

Digital image forensics is one of the important technologies to protect image content security, which aims to reveal malicious tampering in digital images. In this work, we propose a finer-grained image splicing localization method based on noise level estimation. In the proposed method, we extract statistical feature from the DCT coefficients of image, use such feature to estimate the noise of kurtosis statistic and feature of principal component. On the other hand, we estimate local noise using Laplace operator. The forensics features are formed by combining the kurtosis statistics noise, local Laplace noise, and the principal component feature. Then we use fuzzy C-means clustering to recognize suspect image blocks, and use a method of region marking to realize the finer-grained partition for regions. The spliced regions are detected according to the area ratio of image regions. Experimental results show that the proposed method has higher detection accuracy and stronger robustness.
Effects of Mining Parameters on the Performance of the Sequence Pattern Variants Analyzing Method Applied to Electronic Medical Record Systems

Sequential pattern mining (SPM) is widely used for data mining and knowledge discovery in various application domains. Recently, we have proposed an analyzing method to evaluate the sequence pattern variant (SPV) that is the original sequence containing frequent patterns including variants. Such a study is meaningful for medical tasks such as improving the quality of a disease's treatment method. This paper aims to evaluate the effectiveness of the proposed analyzing method in more detail when it was applied to Electronic Medical Record Systems. Using a real dataset, it is observed that the analyzing method is successful in statistically discovering the meaningful indicators that are leading to the difference between comparative SPVs, such as complicated risk, severity risk of the disease, the length of stay in the hospital and the total medical cost. Moreover, it is observed that the length of stay and the medical cost can gain more benefit from increasing the significance level parameter used in comparing the SPVs.
An Intelligent Approach for Noise Elimination from Brain Image

In general, medical images corrupt due to many causes. The brain image is essential for better and faster diagnosis by the physicians. In this work acquired brain image is considered instead of additive noise. As an intelligent technique the novel fuzzy based morphology is applied to remove the impulsive noise that occurs at the time of acquisition. After preprocessing of the image in one stage the noise is detected using Laplacian and morphological operator-based. Further the noise from the boundaries are tried to remove using modified fuzzy-based morphological operators. The operation provides clear view. The connectivity among pixels using fuzzy morphology helps to remove noise from the pixels. The proposed method performs successfully in terms of PSNR and MSE and has been presented in the result section.
Latent Factor Models Fusing User &amp; Item Attributes

Data sparsity, cold-start, and suboptimal recommendation for local users or items have been recognized as the most crucial three challenges in the latent factor model (LFM) for recommender systems. This paper proposes an approach that integrates the User-Item attributes into the classical LFM named UILFM focusing on above challenges. First, for the problem of data sparsity and cold-start, we develop an online learning algorithm to update the weights of user or item attribute for identifying the importance of different attributes. By aggregating the users and items based on their similar attributes, we obtain the local neighbor group which makes it possible for recom- mender to estimate some missing ratings based on adjacent user's ratings towards items and adjacent item's ratings. By introducing the convex mixed-parameters, we combine the estimate ratings with the classical LFM to predict the missing entries of the high-dimensional and sparse (HiDS) matrix for further closing the true ratings and reducing matrix sparsity. Second, for the suboptimal recommendation problem, we propose a new matrix filling (for missing ratings) method based on positive and negative samples, in which when the sparsity of the HiDS matrix is reduced to a threshold, the classical LFM will dominate the filling procedure, instead, the prediction based on neighbors' ratings remains a domination role. This method elegantly solves the suboptimal recommendation problem that the ratings of partial users are extremely sparse and the number of ratings per user are unbalanced. The proposed algorithm is tested by the MovieLens dataset, the results show that it promotes the recommendation accuracy compared with the classical LFM algorithm and the dimensionality reduction approaches as well as the collaborative filtering (CF) algorithms.
Cloud-based Dynamic Tiled Display Adapting to Grouping by Distinction of Mobile Devices

Mobile devices such as mobile phones and tablets are daily devices as personal viewing of various streaming. They have potentials as a big virtual screen by a tiling display. It is important to keep video quality among tiled video displays in the case of cloud video streaming through the Internet. To solve this issue, we design and develop a mobile tiled display system which supports dynamic tiling adapting to the situation of mobile devices and network status. The proposed system has a dynamic grouping mechanism by devices properties and network status using attribute-based cryptography. Only authorized devices can join the group of the tiled display. Since the video resource is assumed to be on clouds, we introduce edge computing in the architecture of the system and it enables effective processing of video streaming for real-time adapting to variation of distinction of devices.
Investigation of Shallow Wavenet Vocoder with Laplacian Distribution Output

In this paper, an investigation of shallow architecture and Laplacian distribution output for WaveNet vocoder trained with limited training data is presented. The use of shallower WaveNet architecture is proposed to accommodate the possibility of more suitable use case with limited data and to reduce the computation time. In order to further improve the modeling of WaveNet vocoder, the use of Laplacian distribution output is proposed. Laplacian distribution is inherently a sparse distribution, with higher peak and fatter tail than the Gaussian, which might be more suitable for speech signal modeling. The experimental results demonstrate that: 1) the proposed shallow variant of WaveNet architecture gives comparable performance compared to the deep one with softmax output, while reducing the computation time by 73%; and 2) the use of Laplacian distribution output consistently improves the speech quality in various amounts of limited training data, reaching a value of 4.22 for the two highest mean opinion scores.
IoT-based smart mirror for health monitoring

The smart mirror is an IoT-based intelligent system to facilitate and enhance the user’s awareness of various physiological functions using biomedical functions in real time. The system comprises different biomedical sensors that collect physiological data. The system also provides an intelligent and adaptive user interface that provides a natural communication between the user and the system. Today many sensors are found within products such as mobile phones, smart watches, etc., which are seldom used by elderly people. This chapter introduces health monitoring through mirrors, which can be used by people irrespective of their ages. The information collected by the biomedical sensors present in the smart mirror will be sent to doctors, to monitor the health conditions of their patients remotely.
Leveraging on Semantic Textual Similarity for Developing a Portuguese Dialogue System

We describe an IR-based dialogue system that, in order to match user interactions with FAQs on a list, leverages on a model for computing the semantic similarity between two fragments of Portuguese text. It was mainly used for answering questions about the economic activity in Portugal and, when no FAQ has a higher score than a threshold, it may search for similar interactions in a corpus of movie subtitles and still tries to give a suitable response. Besides describing the underlying model and its integration, we assess it when answering variations of FAQs and report on an experiment to set the aforementioned threshold.
Impact of Social Media on Youth

Social Networking sites offer a large platform for discussion on burning problems that has been unnoted in today’s emerging world. Usage of social networking is among the most common activities of today’s children and adolescent. These sites offer today’s youth a medium for entertainment and communication, and these have grown expendably. Parents and teachers become unaware of the nature of mixer media sites, thus they do not know that not all of them are healthy environments for their children. The study is conducted to test the impact of social networking sites within the youths and various problems associated with it. This is often a survey sort analysis and here the information is collected through tests. The tests are related to favor kind of social media for youth to see the perspective of youth towards social networking sites. Negative use of social media is deteriorating the connection among the countries, how adversely and positively is the youth affected by the employment of these websites but also will help the youth to understand the effective utilization of these networks. The paper also emphasizes the users on various sites like Facebook, Twitter. This aim of this paper is to analyze the growing issues on youth due to the constant use of Social Network.
Clinical Knowledge Graph Embedding Representation Bridging the Gap between Electronic Health Records and Prediction Models

Learning knowledge embedding representation is an increasingly important technology. However, the choice of hyperparameters is seldom justified and usually relies on exhaustive search. Understanding the effect of hyperparameter combinations on embedding quality is crucial to avoid the inefficient process and enhance practicality of embedding representation along subsequent machine learning applications. This work focuses on translational embedding models for multi-relational categorized data in the clinical domain. We trained and evaluated models with different combinations of hyperparameters on two clinical datasets. We contrasted the results by comparing metric distributions and fitting a random forest regression model. Classifiers were trained to assess embedding representation quality. Finally, clustering was tested as a validation protocol. We observed consistent patterns of hyperparameter preference and identified those that achieved better results respectively. However, results show different patterns regarding link prediction, which is taken as strong evidence that traditional evaluation protocol used for open-domain data does not necessarily lead to the best embedding representation for categorized data.