Reviewerly/exHarmony huggingface dataset:
An Artificial Intelligent Approach to User-Friendly Multi-flexible Bed Cum Wheelchair Using Internet of Things

The main theme of current research paper represents the design and fabrication of “Form Irresolute Couch,” which means “structure was changing bed” by using the Internet of Things with support artificial intelligence. The proposed designed device to move flexible and user friendly as per patient and hospital requirements, it is far difference from traditional bed in existing hospitality areas. When user wants to change any mode and move on to another place, easily operate by remote or switch mode. Intelligent automated monitoring system will predict based on previous data with patient reports and guide future precautions, and if any problems arises in patient body, then intelligent system automatically sends message alerts to existing relatives, doctors and healthcare centers along with caretakers. Here in this couch, the new operating mechanism is introduced by using Rack and Pinion and mechanical linkages. In this couch, three divisions are made in which the last parts are used to move from its position. While operating the mechanism, the part on head position would tend to move upward fixed at one end, the third part which is at leg position would tend to move downward. It is also fixed at one end, so that, during the working process, people on the bed has no need to shift from bed to chair. The bed itself would tend to turn from bed to chair. The unique design is more serviceable for lame/patients.
Form location and extraction based on deep learning

Abstract To obtain users’ information on form images with different types and resolutions, a form location and extraction method based on deep learning is proposed in this paper. We adopt two classification models to better the detection accuracy: model A is applied to detect the smallest rectangular area containing the main information; model B recognizes users’ names, phone numbers and addresses. Especially, the proposed scheme with the certain anchor sizes and positive sample selecting method achieves 82.44% accuracy on the challenging dataset from reality.
Clustering and Scheduling Tasks for Higher Utilization of Cloud Resources

Cloud developers, managers, and purveyors work in the manner to provide adequate services to clients with monitoring cloud infrastructural systems designed from part of the network, software, and high configuration hardware. It is difficult to maintain and manage such large-scale infrastructure and providing effective services to users in realtime. The main objective of scheduling is to surge the utilization of resources without affecting the services. Cloud computing provides computing resources (i.e., hardware and software) that delivered as services over a network to end-users. This paper inspects types of scheduling and classifications of tasks with quality of service (QoS) by estimating virtual machines. The article suggests a new scheduling policy for proper task scheduling compared to the existing policies. Results are presented based on the experimental work performed on cloud-based simulation software.
An Approach to Automated Spam Detection Using Deep Neural Network and Machine Learning Classifiers

This paper presents a deep neural network model for performing spam detection. Unlike conventional machine learning models like naïve Bayes, support vector machines, a deep neural network is immune to various fluctuating environments. This paper also proposes the application of CountVectorizer in order to perform feature extraction on the text-based data. In order to increase the accuracy score of the proposed model, hyperparameter tuning has also been done. This paper also compares the accuracy of the proposed deep neural network to various machine learning classifiers like logistic regression, support vector machine, k-nearest neighbor, Bayes, etc. Experimental results of this paper show that the proposed deep neural network model is able to outclass all other machine learning models in terms of achieved accuracy score, and naïve Bayes classifier is the most efficient model with respect to its computation cost.
Stability Evaluation by Digital Image Correlation of a Masonry Vault Prototype Under Loading

The new attention to masonry structures puts into evidence their continuous potential to be innovative architectural and engineering solutions. In the present paper, the form finding process for a pierced vault is realized. The geometry solutions were analyzed by global stability analysis. In addition, a 3D printing with FDM procedure realized the formworks for building a physical model adopted in the laboratory experiments. Finally, 3D-DIC was performed to recognize the structural behavior during the loading process on the model. DIC measurements were found in good agreement with the numerical results.
Designing self-monitoring data for chronic care

When a self-monitoring tool is developed and implemented in chronic care nurses' work, it changes the way nurses accomplish their work, creating new requirements. This article is based on a design ethnographic study that helps us understand the implications of these changes.
The 2018-2019 Measles Epidemic: Using the Law as a Public Health Response

Measles rates increased dramatically during the 2018-2019 season, both in the US and globally. This phenomenon is reflective of a general decline in world-wide vaccination. In the US, the outbreaks targeted the Ultra-Orthodox Jewish communities, as it did in Israel. This article explores various reasons for this sudden surge and evaluates various legal responses, demonstrating the power of the law in quashing epidemics and reaffirming the constitutionality of mandating vaccination, even in the face of objections such as violation of religious freedom, autonomy and liberty.
SPIRITUALITY IN THE EDUCATIONAL ENVIRONMENT: PROBLEMS OF IMPLEMENTATION AND REALIZATION

The article deals with the problems of implementation and realization of spirituality in the educational space. Particular attention is paid to the importance of recognizing the spiritual component of education, marking the difference between religious and spiritual issues.
A Privacy Enhancing Scheme for Mobile Devices Based Secure Multi-party Computation System

Mobile devices, such as smart phones, have recently become the typical computing platforms for many users. Consequently, in practice more and more multi-party computation systems are deployed on users’ mobile devices, resulting in various applications such as mobile outsourcing computing and mobile cooperative computing. However, as the mobile platforms may have inherent flaws, the connection of mobile devices and multi-party computation systems usually arouse new security risks. We point out that an application in one party’s mobile device can be a powerful privileged attacker to the multi-party computation system. Previous studies have mainly focused on avoiding the privacy leaks of one or several malicious parties or eavesdroppers on the Internet. This paper presents a privacy enhancing scheme for a kind of secure multi-party computation systems. The scheme can resist the privileged attackers from the party’s mobile device. Our scheme transforms the original computation process and puts the critical calculation process into trusted execution environment. We provide three components to build a privacy-enhanced multi-party computation system with our scheme. Our scheme is implemented to an actual secure multi-party computation system to demonstrate its validity and acceptable performance overhead.
Research of Human Appearance Level Quantification Algorithm Based on BP Neural Network

Abstract BP neural network has strong mistake tolerance and unsupervised speaker adaptation, so it has a wide range of applications in the field of pattern recognition. In this paper, an appearance level quantification algorithm based on BP neural network to extract face geometric features is proposed. We use 30 feature distances corresponding to facial features as the quantitative factor to calculate the appearance level and predict the appearance scores of the given face images.
Compromising Strategies for Agents in Multiple Interdependent Issues Negotiation

This paper presents a compromising strategy based on constraint relaxation for automated negotiating agents in the nonlinear utility domain. Automated negotiating agents have been studied widely and are one of the key technologies for a future society in which multiple heterogeneous agents act collaboratively and competitively in order to help humans perform daily activities. A pressing issue is that most of the proposed negotiating agents utilize an ad-hoc compromising process, in which they basically just adjust/reduce a threshold to forcibly accept their opponents' offers. Because the threshold is just reduced and the agent just accepts the offer since the value is more than the threshold, it is very difficult to show how and what the agent conceded even after an agreement has been reached. To address this issue, we describe an explainable concession process using a constraint relaxation process. In this process, an agent changes its belief by relaxing constraints, i.e., removing constraints, so that it can accept it is the opponent's offer. We also propose three types of compromising strategies. Experimental results demonstrate that these strategies are efficient.
A Cost-Effective Data Node Management Scheme for Hadoop Clusters in Cloud Environment

MapReduce framework in Hadoop is used to analyze the large set of data in a distributed storage system. MapReduce jobs are designate to the task node to perform the map-reduce operation based upon the scheduler. Each node has slots (virtual core) to process a task using the map and reduce operation. Map tasks done separately prior to the Reduce task. The different execution order of jobs and different slot configuration in the clusters affect the CPU performance significantly. In this paper, we have stated effective DataNode assignment techniques for resource allocation in the Hadoop MapReduce job. We performed various operations on Amazon EC2 and physical machine to demonstrate that our proposed technique helps to choose optimized node selection for assignment of DataNodes in the Hadoop cluster. This significantly scales down the cost of the node and increases the job execution performance in the Hadoop cluster.
Personalizing large-scale text classification by modeling individual differences

Large-scale text classification is used to organize and subsequently, analyze textual information into a variety of topics effectively. However, most of existing large-scale text classification models tend to draw similar classification results without accounting for the differences in individual perceptions, as may be discernable through the text semantics based on distinct human characteristics. In this paper, we propose a personalized large-scale text classification model, which factors in these individual differences when classifying data.
Research on Flying Catkins Detection and Removal in Target Video

In order to solve the practical application problem of the automatic target-scoring system based on computer vision under dynamic interference conditions such as flying catkins, this paper improves the traditional detection and removal methods of frame difference method and mean value method and proposes an effective flying catkins detection and removal algorithm based on time domain and brightness characteristics by fully studying the characteristics of flying catkins in target video and existing rain and snow removal algorithm. Experiments show that this method can effectively solve the problem of missed detection caused by the multiple moving states of flying catkins and realize the rapid flying catkins removal of the target video with good robustness and timeliness.
The application of power quality signal de-noising based on the improved wavelet threshold function

Abstract The contradiction between de-noising and preserving the local features of the original signal simultaneously cannot be solved with the traditional wavelet threshold function (TF), and thus an improved combination of soft and hard threshold functions (TFs) of power quality signal de-noising is proposed in this paper. The improved wavelet TF is utilized to overcome the problem of “Pseudo-Gibbs phenomenon”, the deviation of wavelet coefficients, caused by the traditional wavelet TF, which fills a gap for the deficiency of wavelet soft and hard threshold de-noising, and with stronger adaptability. Finally, the de-noising tests of three typical signals of power quality are simulated based on MATLAB R2016a. Compared with the traditional wavelet TFs, the results obtained based on the proposed improved wavelet TF can guarantee better effectiveness and feasibility.
Emotion Extrication and Analysis on Videos

Video contents are growing at higher rate and facial analysis and emotion detection might be significant for the content providers to publish. This study focuses on extricating the overall emotions conveyed in the video. It is done at two phases: learning phase and the validation phase. The learning phase includes the learning of emotions from human faces using convolutional neural networks and the validation phase includes feature engineering on videos and extrication of emotions conveyed in the video by applying a well learned system. Extensive experiments were conducted on videos and the performance is measured using standard evaluation metrics.
Neural Metric Matrix Factorization

Abstract Nowadays, matrix factorization(MF) has been widely adopted in industry and research as a classical collaborative filtering (CF) algorithm. Unfortunately, the dot product adopted by matrix factorization is against the triangle inequality, which is one of the main reasons why this model is opposed. To address the issue, we propose a recommendation algorithm combining metric learning and mf in the paper. It transforms users’ preferences into distances, using Euclidean distance which satisfies triangular inequalities instead of traditional dot products, then directly decomposes the distance matrix into latent factor matrices of users and items. A multi-layer feedforward neural network is adopted for learning the model. Extensive experiments on two real-world datasets show that the proposed model is obviously superior to some advanced models based on matrix factorization.
Dimensionality Reduction Gauss-Hermite Quadrature Filter Algorithm

In this paper, a dimensionality reduction Gauss-Hermite quadrature filter (DRGHQF) is proposed. Compared with the conventional GHQF algorithm and with the Rao-Blackwellised particle filter (RBPF), the proposed DRGHQF performs better in terms of computational cost and accuracy. The simulation results indicate that the DRGHQF can significantly reduce the computational cost without incurring a loss in accuracy.
K-NN Classification for Large Dataset in Hadoop Framework

In the big data domain, the traditional algorithms approach fails in the classification for the large data set as they are memory dependent and suitable for small and medium size data sets and our proposed model attempts to apply the existing classification for the large data set. The proposed model shows percentage of reduction of time for optimal k-number of nearest neighbors against the varying number of Datanodes in Hadoop framework.
Observations on the Performance of PQ KEMs

This note discusses two aspects of the performance of Round-2 KEM candidates: (a) the impact of Simultaneous MultiThreading (SMT); (b) the balance between encapsulation and decapsulation. –Software performance can sometimes be improved by parallelization of tasks. In some cases this can be achieved by simultaneous execution on logical CPUs (also known as SMT). Since such a technology opens the door to possible security vulnerabilities, its overall benefit needs careful evaluation. We evaluate the hyper-threaded performance of some of the Round-2 KEM candidates proposed to the NIST Post Quantum Cryptography project. –The common assumption is: that slow decapsulation is performed on a (strong) server side and the weaker client platforms execute the (faster) encapsulation. We argue that this is not necessarily the case in TLS 1.3, which is now suggested as the next generation of secure communication protocols and discuss the implications.
Mining Solutions for Extended Snippet Reuse

The introduction of question–answering services, such as Stack Overflow, has given rise to a new problem-solving paradigm in software development. Using these services, developers can post their programming questions online and get useful solutions by the community. In this chapter we propose a methodology that allows searching for solutions in Stack Overflow, using the main elements of a question post, including its title, tags, body, and source code snippets. We design a similarity scheme for these elements that can be used for finding similar question posts. Text elements are compared using Information Retrieval metrics, while snippet similarity is computed by first converting snippets into sequences using a representation that extracts structural information. The results of the evaluation of our methodology indicate that it can be effective for recommending similar question posts, and thus can be used to search for solutions without fully forming a question.
Vehicle Counting at Road Intersections Using Video Data and Zone-Based Method with Additional Contour

Abstract The problem of counting vehicles and determining the directions of their movement at an intersection is considered. A new zone-based method for counting vehicles which uses an additional inner contour is proposed. The presence of an additional inner contour helps to partially expands zones of interest and increases our chances of catching a vehicle in time. YOLO and CSRT tracker are used for detection and tracking of objects. Full flowchart of the working process including detection, tracking and counting, is given. The algorithm for inner contour constructing is proposed and it is shown that this contour presence reduces the error in counting vehicles.
Hybrid Encryption Technique using Cyclic Bit Shift and RC4

Data security is an important aspect when data is sent over public networks such as the internet. Securing data can use cryptographic techniques. In symmetric cryptographic techniques, there are permutation and stream techniques, where each technique has its own weaknesses and strengths. This research combines two techniques into a hybrid technique to get stronger encryption. The proposed permutation technique is cyclic bit shift, while the proposed stream technique is RC4. The results of the combination of two methods are then performed performance tests using the avalanche effect (AE), bit error ratio (BER), the time required for decryption encryption and character error rate (CER). Based on the results of testing the proposed method is superior compared to the method that has been previously proposed.
Fully Automatic Pitching Location Analysis System Using High-Speed Cameras

In this paper, we propose a ball image saving framework for an automatic baseball pitching location analysis system using high-speed cameras. In the existing system, it is difficult to manually select the frames in which the ball is included from the experimental pitching videos. The proposed automatic framework extracts ball candidate regions by differencing three consecutive frames from the acquired sequence in the buffer. The ball candidate regions are identified as a ball by comparing circularity with a pre-defined threshold value. If the ball exists among the ball candidate regions, the current sequence is saved to storage. Since the system utilizes high-speed cameras, we apply a ball detection method that has low computational cost. Experiments on many images captured by the proposed system demonstrate that the proposed framework and ball detection method can automate the pitching location analysis system.
Deep Neural Networks for Korean Fonts Generation

Korean fonts consist of 11,172 letters, requiring tremendous time and effort to design a new font. This cause a lack of diversity in Korean fonts. Deep neural networks have recently brought significant improvements in various fields of computer vision. Herein, we investigate deep neural networks as a means of generating a new set of Korean fonts. Three deep neural networks have been utilized to transfer the style of one font to the other. These networks are trained with a pair of 2,350 letters and used to generate the remaining 8,822 letters of one font in a style of the other font. The results are quantitatively and qualitatively evaluated, suggesting that the deep neural networks could facilitate a cost-effective and rapid design of Korean fonts.
The Crisis and Aftermath-II: Society and Economics

This chapter mentions some other groundbreaking innovations and technological improvements that came along after the crisis. The recent attempts at creating digital currencies, famously known as cryptocurrencies, and their corresponding platform blockchain were some of these. How and why did they emerge? What do their inventions imply for our future? And why are they important? Sharing economy and memberships (as opposed to ownerships) are other examples of profound changes. Increasing use of shared goods, services like Uber and Airbnb, and the unstoppable trend of membership services like e-newspapers, e-libraries, and music streaming points to one conclusion: the end of the idea of ownership. On the governmental side, we have been observing an increasing inclination toward authoritarian states. Liberalism seems to have failed. People have been losing their trust in economic regimes. My argument in this chapter is that without the 2008 crisis, there would have never been these developments and innovations.
Addition of Solid Recovered Fuel (SRF) to the Bio-drying Process and the Effects of Variation in Air Discharge on Temperature Parameters and Urban Waste Water Content

Abstract Bio-drying is a technology used to reduce water content in waste using microorganisms that naturally increase the temperature in the decomposition process. With this process, the water content can drop more within a month. Bio-drying produces a product in the form of Solid Recovered Fuel (SRF) which is produced from partially degraded waste. To obtain a waste that is not fully stabilized and maintains a high biomass content, degradation of organic compounds is carried out partially. During the bio drying process, temperature affects the degradation process. Temperature affects the bio drying, which will also affect the bio drying product that is indicated by the value of water content. Therefore, in this study, the change of process parameters will be explained, which is in the form of temperature and water content, that is caused by the difference in the air discharge entering the reactor (0, 2, 4, and 6 l/m) with the initial water content of 60%-65%. After 30 days, the optimum airflow is 4 l/m with a decrease in water content of 58.29%; on the last day of the bio drying process (30th day).
Juiced and Ready to Predict Private Information in Deep Cooperative Reinforcement Learning

In human-robot collaboration settings, each agent often has access to private information (PI) that is unavailable to others. Examples include task preferences, objectives, and beliefs. Here, we focus on the human-robot dyadic scenarios where the human has private information, but is unable to directly convey it to the robot. We present Q-Network with Private Information and Cooperation (Q-PICo), a method for training robots that can interactively assist humans with PI. In contrast to existing approaches, we explicitly model PI prediction, leading to a more interpretable network architecture. We also contribute Juiced, an environment inspired by the popular video gameOvercooked, to test Q-PICo and other related methods for human-robot collaboration. Our initial experiments in Juiced show that the agents trained with Q-PICo can accurately predict PI and exhibit collaborative behavior.