Reviewerly/exHarmony huggingface dataset:
Edge Computing-Based Self-Organized Device Network for Awareness Activities of Daily Living in the Home

Activities of daily living (ADL) are important indicators for awareness of brain health in the elderly, and hospitals use ADL as a standard test for diagnosing chronic brain diseases such as dementia. However, since it is difficult to judge real-life ADL in hospitals, doctors typically predict ADL ability through interviews with patients or accompanying caregivers. Recently, many studies have attempted to diagnose accurate brain health by collecting and analyzing the real-life ADL of patients in their living environments. However, most of these were conducted by constructing and implementing expensive smart homes with the concept of centralized computing, and ADL data were collected from simple data about patients’ home appliance usage and the surrounding environment. Despite the high cost of building a smart home, the collected ADL data are inadequate for predicting accurate brain health. In this study, we developed and used three types of portable devices (wearable, tag, and stationary) that can be easily installed and operated in typical existing houses. We propose a self-organized device network structure based on edge computing that can perform user perception, location perception, and behavioral perception simultaneously. This approach enables us to collect user activity data, analyze ADL in real-time to determine if the user’s behavior was successful or abnormal, and record the physical ability of the user to move between fixed spaces. The characteristics of this proposed system enable us to distinguish patients from other family members and provide real-time notifications after a forgetful or mistaken action. We implemented devices that constitute the edge network of the smart home scenario and evaluated the performance of this system to verify its usefulness.
Analyzing Performance of Deep Learning Techniques for Web Navigation Prediction

The weblog is dynamic and its size is growing exponentially with time in terms of navigation sessions. These stored sessions are used for Web Navigation Prediction (WNP). Each user had varied behavior on the web so is their navigated sessions. With a variety of large dynamic sessions, the task of navigation prediction is becoming challenging. There is a need for an effective method to handle large sessions with multiple labels for predicting user desired information. This paper analyses the performance of Deep Learning techniques like Multi-Layer Perceptron and Long-Short Term Memory based on parameters like number of hidden units, number of layers, activation function, optimization function, learning rate, and batch size. The networks were trained on six experimental parameter setups to form 216 models. The performances of these models are evaluated on two real datasets: BMS and CTI. It has been observed that Long-Short Term Memory performs best on most of the setups.
CTR Prediction Models Considering the Dynamics of User Interest

Click-through rate (CTR) prediction is one of the key areas in industrial bidding advertising. Recently, to improve prediction performance, researchers have proposed the interest-based deep models that learn the user’s latent interest from historical click behaviors. However, the interest-based deep models lack the ability to trace the dynamics of interest from both the positive and negative samples, which leading to prediction errors. In particular, user’s interest in candidate advertisements could be positive (i.e. interested) or negative (i.e. uninterested), and changing over time. To solve this problem, we propose a deep-based dynamic interest perception network (DIPN) that can trace both positive interest and negative interest. The proposed DIPN model introduces three new parts to the interest-based deep model: a gated recurrent unit (GRU) learns the implied interest vector from historical click sequences; an attention mechanism improves the expressiveness of interest vector with the weight vector; an interest degree feature scales the weight vector by the d-Softmax function to improve the expressiveness of interest vector. We evaluate the effectiveness of the DIPN model by conducting extensive comparative experiments using real datasets. The experimental results demonstrate that compared with state-of-the-art models, DIPN achieves the highest prediction performance.
Motion Planning With Success Judgement Model Based on Learning From Demonstration

A technique named Learning from Demonstration allows robots to learn actions in a human living environment from the demonstrations directly. In a learning method from demonstrations directly, however, teaching actions cannot be reused between situations with different restrictions. In this study, we propose a method for training a success judgment model based on Learning from Demonstration and use this as a differentiable loss function of tasks. By formulating the constraints of the action in a manner in mathematical optimization and combining these constraints with the learned success judgment model into a loss function, an action generation model can be trained by the gradient method. This system was verified with the action of scooping up a pancake.
CDNB: CAVIAR-Dragonfly Optimization with Naive Bayes for the Sentiment and Affect Analysis in Social Media

With the advent of the new information technologies, the growth of online reviews regarding an organization or a company or any other sector has been playing a vital role in improving the sector plans and decisions. The vast significance of the online reviews that determine the sentiment polarity is the hectic challenge of the current scenario. Sentiment classification is a process of classifying the text according to the sentimental polarities of opinions, which has positive or negative. Thus, this article concentrates on presenting a novel method, named CAVIAR-Dragonfly optimization with Extended Naive Bayes (CDNB), for performing sentiment classification and affective state classification. At first, the BITS review from Twitter is subjected to preprocessing, which includes stop word removal and stemming. Then, the next step is the feature extraction, in which all the reviews are converted to a feature vector. After that, all the individual feature vectors are collected to form the feature matrix, which is applied to the proposed C-Dragonfly optimization algorithm, to perform the sentiment classification and affective state classification. The performance of the proposed method is analyzed using the Twitter Sentiment Analysis Training Corpus Data Set based on true positive rate (TPR), true negative rate (TNR), and accuracy. From the analysis, it can be shown that the proposed method yields the maximum TPR, TNR, and accuracy of 89.0934%, 72.3064%, and 79.3591% for sentiment classification and 84.2122%, 66.2187%, and 76.6249% for the sentiment affective state classification.
Virtual Grounding Point Concept for Detecting Abnormal and Normal Events in Home Care Monitoring Systems

In this paper, an innovative home care video monitoring system for detecting abnormal and normal events is proposed by introducing a virtual grounding point (VGP) concept. To be specific, the proposed system is composed of four main image processing components: (1) visual object detection, (2) feature extraction, (3) abnormal and normal event analysis, and (4) the decision-making process. In the object detection component, background subtraction is first achieved using a specific mixture of Gaussians (MoG) to model the foreground in the form of a low-rank matrix factorization. Then, a theory of graph cut is applied to refine the foreground. In the feature extraction component, the position and posture of the detected person is estimated by using a combination of the virtual grounding point, along with its related centroid, area, and aspect ratios. In analyzing the abnormal and normal events, the moving averages (MA) for the extracted features are calculated. After that, a new curve analysis is computed, specifically using the modified difference (MD). The local maximum (lmax), local minimum (lmin), and half width value (vhw) are determined on the observed curve of the modified difference. In the decision-making component, the support vector machine (SVM) method is applied to detect abnormal and normal events. In addition, a new concept called period detection (PD) is proposed to robustly detect the abnormal events. The experimental results were obtained using the Le2i fall detection dataset to confirm the reliability of the proposed method, and that it achieved a high detection rate.
Pedagogical aproaches to the formation of musical literacy of students in the sytem of higher education

This article is based on the need for collaboration between the “Acting skill” and “Vocal” disciplines in the education of future musical theater actors, and provides evidence that this collaboration of these two skills will have a positive effect. It will be avaiable for music teachers and the researchers in the field of entering the music pedagogy. The article provides experience based on the pedagogical advice for educators. Music plays an important role in art education. There are many students who are doing research in this area today and there are many teachers who teach them. The higher education system is the most important part of the learning process and the teaching profession needs to have a great deal of experience and skills. In summary, the two aforementioned disciplines must be interdependent. It is advisable to teach both disciplines in groups and individually three times a week in educational institutions. If today's practice reaches its strength tomorrow, it will be easier to learn another new topic. It is not enough to have a superficial acquaintance with the basic rules of practise in the development of music a through and profound study of this subject. As folk culters grow so does the folk art. This gives the composers greater access to the richness of their songs. It has been teaching high school students to improve the effectiveness of music lesson. At the birth of performances based on a musical drama, the composer writes music for that particular performance. Musical solutions to stage events are composed by the composer. After that the musical part of the performance is prepared. They work with the conductor, the concertmaster, the choirmaster, and the orchestra. Work on the partition will continue with the role-playing musicians.
Object Retrieval in Images using SIFT and R-CNN

Image-based object retrieval has numerous applications in the field of machine vision to inquire from an appropriate image or video sequence for a given query object. The object retrieval task is conventionally carried out by a set of handcrafted algorithms, which provides image depictions in the fashion of visual characteristics. During the last decade, an extensive change has been practiced to describe visual content from handcrafted characteristics to the application of machine learning approaches and to the real layout of the image descriptors. The extensive movement is based on Convolutional Neural Networks (CNN) which is popularly known as Deep Learning. This proposed work deals with a combination of both conventional and machine learning approaches to retrieve an image object from a given dataset. This is done by a series of activities such as feature extraction and storage of training images, query image selection and feature extraction. Similarity matching between database and query image features. Final retrieval is based on the objectness score.
English to Urdu: Optimizing Sequence Learning in Neural Machine Translation

Neural machine translation is a new approach for machine translation, which translates source language sentences into the target language. Source to target words mapping is the key task of attention mechanism in neural machine translation. The number of attention-based seq2seq encoder-decoder models have been proposed in the literature. But these models have a nonlinearity problem with attention in English to Urdu translation. A seq2seq encoder-decoder model named Convolutional English to Urdu Translation (CEUT) has been proposed in this paper. The model reduces nonlinearity in attention word mapping and achieves 29.94 BLEU score in English to Urdu translation.
Proposal of Fibonacci Heap in the Dijkstra Algorithm for Low-power Ad-hoc Mobile Transmissions

Mobile devices have become indispensable to communication over the last decade. In a hypothetical scenario in which conventional forms of connection such as antennas and satellites are not available, other forms of information propagation must be used. Ad-hoc broadcasts are strategies for maintaining communication between devices in these situations, however, they require high transmission power. This article proposes the use of priority queues, such as the Fibonacci heap and the binary heap in the Dijkstra algorithm, in order to reduce its computational cost in the search for the smallest network route. From the limitation of the signal strength to reach the nearest device, our results obtained lower transmission power compared to the standard device settings. Simulations show that a fibonacci heap has higher performance than binary heap in networks with higher number of connections. This way, the implementation of the fibonacci heap brings improvements in the computational cost of the algorithm. In addition, we show that the calculation of the smallest route is directly connected to the choice of the path with the lowest transmission power.
External flows and inclusive human development in Sub-Saharan Africa

The study assesses how external flows influence inclusive human development in Sub-Saharan Africa. The findings reveal that remittances and FDI increase inclusive development whereas foreign aid has the opposite effect. There are three other strands of findings. First, remittances are negatively associated with: 1) middle income countries compared to low income countries where the effect is not significant; 2) French civil law countries compared to English common law countries where the effect is positive; 3) resource-rich countries compared to their resource-poor counterparts where the effect is positive. Second, foreign aid is more negatively linked to low income, French civil law, Islam-dominated, un-landlocked, resource-rich and politically-unstable countries. Third, FDI is positively associated with: 1) low income, French civil law and landlocked countries compared to respectively middle income, English common law and un-landlocked countries where the effect is insignificant; 2) politically-stable countries compared to their politically-unstable counterparts where the effect is negative.
Quantization with Perception for Performance Improvement in HEVC for HDR Content

Human eye always reveals a non –linear understanding, for the disturbances caused by the lossy image and video coding. This is mainly because of the masking capability of the human eye to conceal the attributes such as contrast, luminous, spatial and temporal frequencies. To have a distortion less and efficient video encoding for the high dynamic video range content by eluding the invisible messages in the video that causes disturbances the paper puts forth the quantization with perception utilizing the luminous masking. The methodology utilized, computes the tone mapping to scale every frames in the HDR and later quantizes on unit basis with perception tuning. For this purpose the mechanism put forth incorporates the reference model of the HEVC with the extension range of the HEVC. The proposed model was validated by evaluating the reduction incurred in each rate of bit compared to the HDR range extension. The results acquired proved to have an enhancement in terms of the savings endured in the bit rate compared to the High efficient video coding that relied on the high dynamic range visible difference predictor-II
Use Of Big Book Media Towards Interest In Reading Elementary School Students

This research is motivated by the low interest of students, especially in reading interests. The learning media uses big book media. This study aims to determine the use of big book media on the reading interest of elementary school students in grade II in Pulokalapa II Elementary School because when observing in Pulocalapa II Elementary School, not all students have a high interest in reading and a lack of interesting learning media. This research is descriptive qualitative research. The research subjects were all grade II students of Pulocalapa II Elementary School, totaling 27 students consisting of 15 female students and 12 male students. Data collection methods used were observation, questionnaire/questionnaire, interview, and documentation. Research shows that the use of big book media is said to be successfully applied in the lower classes to foster interest in reading. From the results of the research that has been done, it can be concluded that the use of big book media can foster interest in reading elementary school students in grade II of SDN Pulokalapa II in the 2018/2019 school year.
Improving Japanese English pronunciation with speech recognition and feed-back system

For Japanese people, communicating with English speakers from abroad has become more common because of internationalization, and there are many people who want to improve their English-speaking skills. However, there are few environments where we can speak English outside of the classroom, so Japanese students rarely have a chance to study English pronunciation. Even if students do have a chance to take an English pronunciation class, teachers do not have enough time to individually teach each student pronunciation in a big class. Because of that, computers and smartphones may be one good type of tool to solve this problem. In this research, we develop a web-based application to help Japanese learners with their English pronunciation.
Prediction of web services reliability based on decision tree classification method

With the development of the service-oriented computing (SOC), web service has an important and popular solution for the design of the application system to various enterprises. Nowadays, the numerous web services are prov... | Find, read and cite all the research you need on Tech Science Press
Digital educational environment structure: Regulatory legal and methodological aspects

Active introduction of information technologies and the digitalization of various areas of society inevitably entail the need to modernize the education system. Informatization processes are being introduced into the educational environment and are gradually changing its structure. At the same time, the issue of its substantive component is becoming increasingly relevant. The article presents analysis of scientific publications and legal documents relating to the concept, components and methodological foundations of the formation of a digital educational environment, its integration into the digital space and its relationship with the nondigital educational environment. Based on the study, the main contradictions in its definition and structure, as well as the direction for the application of efforts to resolve them, are identified.
Pseudo-Bayesian Learning via Direct Loss Minimization with Applications to Sparse Gaussian Process Models.

We propose that approximate Bayesian algorithms should optimize a new criterion, directly derived from the loss, to calculate their approximate posterior which we refer to as pseudo-posterior. Unlike standard variational inference which optimizes a lower bound on the log marginal likelihood, the new algorithms can be analyzed to provide loss guarantees on the predictions with the pseudo-posterior. Our criterion can be used to derive new sparse Gaussian process algorithms that have error guarantees applicable to various likelihoods.
Detecting Spam Review through Spammer’s Behavior Analysis

Online reviews about the purchase of a product or services provided have become the main source of user opinions. To gain profit or fame usually spam reviews are written to promote or demote some target products or services. This practice is known as review spamming. In the last few years, different methods have been suggested to solve the problem of review spamming but there is still a need to introduce new spam review detection method to improve accuracy results. In this work, researchers have studied six different spammer behavioral features and analyzed the proposed spam review detection method using weight method. An experimental evaluation was conducted on a benchmark dataset and achieved 84.5% accuracy.
Attribute-based Access Control of Data Sharing Based on Hyperledger Blockchain

While public cloud service provides scholars a means to collaborate and share studies, depositing their data on a centralized semi-trusted cloud server managed by third party, raises security and privacy issues; secure decentralized storage can overcome this risky problem. In this paper, we propose an architecture model providing more fine-grained access control over the data stored in the cloud. Our proposed system is implemented by combining Hyperledger blockchain technology and Attribute-based Encryption (ABE) scheme to achieve this fine-grained access control of the shared files in decentralized environment. The implementation of the ABE prevents unauthorized users from accessing data and allows data owners to manage and control their data by encrypting shared data under an access control policy associated with a set of attributes. The Hyperledger blockchain assures the confidentiality and integrity of all stored files including key generation, policy assignment, and access request service. Our system prototype was implemented using chaincodes and tested on the Hyperledger Composer blockchain platform.
A Path Tracking Strategy for Car Like Robots with Sensor Unpredictability and Measurement Errors

This work is inspired by motion control of cleaning robots, operating in certain endogenous environments, and performing various tasks like door cleaning, wall sanitizing, etc. The base platform's motion for these robots is generally similar to the motion of four-wheel cars. Most of the cleaning and maintenance tasks require detection, path planning, and control. The motion controller's job is to ensure the robot follows the desired path or a set of points, pre-decided by the path planner. This control loop generally requires some feedback from the on-board sensors, and odometry modules, to compute the necessary velocity inputs for the wheels. As the sensors and odometry modules are prone to environmental noise, dead-reckoning errors, and calibration errors, the control input may not provide satisfactory performance in a closed-loop. This paper develops a robust-observer based sliding mode controller to fulfill the motion control task in the presence of incomplete state measurements and sensor inaccuracies. A robust intrinsic observer design is proposed to estimate the input matrix, which is used for dynamic feedback linearization. The resulting uncertain dynamics are then stabilized through a sliding mode controller. The proposed robust-observer based sliding mode technique assures asymptotic trajectory tracking in the presence of measurement uncertainties. Lyapunov based stability analysis is used to guarantee the convergence of the closed-loop system, and the proposed strategy is successfully validated through numerical simulations.