Reviewerly/exHarmony huggingface dataset:
KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam. One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.
The Seven Layers of Complexity of Recommender Systems for Children in Educational Contexts

Recommender systems (RS) in their majority focus on an average target user: adults. We argue that for non-traditional populations in specific contexts, the task is not as straightforward–we must look beyond existing recommendation algorithms, premises for interface design, and standard evaluation metrics and frameworks. We explore the complexity of RS in an educational context for which young children are the target audience. The aim of this position paper is to spell out, label, and organize the specific layers of complexity observed in this context.
Issues with Common Assumptions about the Camera Pipeline and Their Impact in HDR Imaging from Multiple Exposures

Multiple-exposure approaches for high dynamic range (HDR) image generation share a set of building assumptions: that color channels are independent and that the camera response function (CRF) remains constant while changing the exposure.The first contribution of this paper is to highlight how these assumptions, which were correct for film photography, do not hold in general for digital cameras.As a consequence, results of multiexposure HDR methods are less accurate, and when tonemapped they often present problems like hue shifts and color artifacts.The second contribution is to propose a method to stabilize the CRF while coupling all color channels, which can be applied to both static and dynamic scenes, and yield artifact-free results that are more accurate than those obtained with state-of-the-art methods according to several image metrics.
Graph-Based Algorithm For Mobile Robot Navigation In A Known Environment

Autonomous navigation of mobile robots is a vast area of research in the field of robotics. Previous studies have proposed various approaches and algorithms in order to solve this problem. One of the important aspects of navigation is path planning which helps the robot to avoid obstacles and further to navigate it in the environments. There are different types of path planning. The robot should choose the shortest path or should cover the entire area of the environment. Coverage path planning algorithms does the latter. This paper completely focuses on these algorithms and proposes a new algorithm, a modified depth search so that, given a map, the robot can navigate in an environment, say a maze, without colliding into obstacles and plan its path in the maze.
Learning Sparsity and Quantization Jointly and Automatically for Neural Network Compression via Constrained Optimization.

Deep Neural Networks (DNNs) are applied in a wide range of usecases. There is an increased demand for deploying DNNs on devices that do not have abundant resources such as memory and computation units. Recently, network compression through a variety of techniques such as pruning and quantization have been proposed to reduce the resource requirement. A key parameter that all existing compression techniques are sensitive to is the compression ratio (e.g., pruning sparsity, quantization bitwidth) of each layer. Traditional solutions treat the compression ratios of each layer as hyper-parameters, and tune them using human heuristic. Recent researchers start using black-box hyper-parameter optimizations, but they will introduce new hyper-parameters and have efficiency issue. In this paper, we propose a framework to jointly prune and quantize the DNNs automatically according to a target model size without using any hyper-parameters to manually set the compression ratio for each layer. In the experiments, we show that our framework can compress the weights data of ResNet-50 to be 836$\times$ smaller without accuracy loss on CIFAR-10, and compress AlexNet to be 205$\times$ smaller without accuracy loss on ImageNet classification.
Big Data Deployment for an Efficient Resource Prerequisite Job

Integration of cloud and big data very difficult and challenging task and to find the number of resources to complete their job is very difficult and challenging. So, virtualization is implemented it involves 3 phases map reduce, shuffle phase and reduce phase. Many researchers have been done already they have applied Heterogeneousmap reduce application and they use least-work-left policy technique to distributed server system. In this paper we have discussed about virtualization is used for hadoop jobs for effective data processing and to find the processing time of job and balance partition algorithm is used. The main objective is to implement virtualization in our local machines.
AccompCare: A Model for Accompaniment of People with Disabilities in an Intelligent Assistive City

To increase the quality of life and independence of people with disabilities and enderly (PDE), there must be solutions that provide accessibility in urban environments. AccompCare is a model for tracking and assisting PDE in their displacement, using mobile and ubiquitous computing to provide accessibility services. Accompcare's main contribution is the strategy of disponibilization of help. This strategy initially seeks collaborators who are close to the PDE and, not finding them, directs the request to a remote call center. The call center can guide the user through the resources of the mobile device. In addition, AccompCare contemplates an urban scenario, allowing the delimitation of several service areas. The model was implemented and evaluated by five wheelchair users through scenarios. The results showed a good acceptance by the users, considering their perceptions of usability and utility of the system.
Smart Contracts Implementation, Applications, Benefits, and Limitations

The world today has realized the vast technological evolution that has greatly shaped the production and management functions of business enterprises.Traditional contracts can take weeks or even months to initiate, and there have been numerous instances of breaches and lack of trust for contracts in both the private and public sector.A smart contract can be defined as a self-executing contract that utilizes blockchain technology to digitally enforce, verify, or facilitate the performance or negotiation of a contract.Owing to the security and decentralized system exhibited by blockchain technology, smart contracts can foster transaction credibility between contracting parties without the necessity of third parties as exhibited in traditional contracts.Any business organization that aims at achieving greater heights in management and production dimensions must consider utilizing robust technologies that are aimed at bolstering its competitive edge.Owing to the newness of smart contracts, characterized by very few studies on the same, this research reviews how smart contracts through blockchain technology can be implemented in an organization to enhance performance and outlines the applications, benefits, and limitations associated with such contracts.
Automatic User Domain Classification Based on Support Vector Machine (SVM)

Finding domain of a research paper and a researcher is a crucial task and would be highly appreciable in order to provide personalized search results to the user. An automatic user domain classification technique based on SVM has been proposed in this paper in order to determine the domain of a user based on her publications. In this technique, for a given user, his specific area of domain is determined by classifying the keywords from his publication works. It consists of two phases: keyword extraction and domain classification. In keyword extraction phase, the list of publications corresponding to a user mail id is retrieved by using publish or perish tool. From each of the published papers, the keywords are extracted. In domain classification, SVM classifier is applied to determine the domain of the user. This is performed by training standard keywords from each domain into the SVM classifier. If a user belongs to more than one domain, then the primary domain with more publications will be considered.
Generating Questions Based on Semi-Automated and End-to-End Neural Network

With the emergence of large-scale knowledge base, how to use triple information to generate natural questions is a key technology in question answering systems. The traditional way of generating questions require a lot of manual intervention and produce lots of noise. To solve these problems, we propose a joint model based on semi-automated model and End-to-End neural network to automatically generate questions. The semi-automated model can generate question templates and real questions combining the knowledge base and center graph. The End-to-End neural network directly sends the knowledge base and real questions to BiLSTM network. Meanwhile, the attention mechanism is utilized in the decoding layer, which makes the triples and generated questions more relevant. Finally, the experimental results on SimpleQuestions demonstrate the effectiveness of the proposed approach.
Evaluation of 3D Pointing Accuracy in the Fovea and Periphery in Immersive Head-Mounted Display Environments

The coupling between perception and action has seldom been explored in sophisticated motor behaviour such as 3D pointing. In this study, we investigated how 3D pointing accuracy, measured by a depth estimation task, could be affected by the target appearing in different visual eccentricities. Specifically, we manipulated the visual eccentricity of the target and its depth in virtual reality. Participants wore a head-mounted-display with an integrated eye-tracker and docked a cursor into a target. We adopted a within-participants factorial design with three variables. The first variable is Eccentricity: the location of the target on one of five horizontal eccentricities (left far periphery, left near periphery, foveal, right near periphery and right far periphery). The second variable is Depth at three levels and the third variable is Feedback Loop with two levels: open/closed. Eccentricity is refactored into Motion Correspondence between the starting location of the cursor and the target location with four levels: periphery to fovea, fovea to periphery, periphery to periphery, fovea to fovea. The results showed that the pointing accuracy is modulated mainly by the target locations rather than the initial locations of the effector (hand). Visible feedback during pointing improved performance.
Long-Horizon Active SLAM system for multi-agent coordinated exploration

Exploring efficiently an unknown environment with several autonomous agents is a challenging task. In this work we propose an multi-agent Active SLAM method that is able to evaluate a long planning horizon of actions and perform exploration while maintaining estimation uncertainties bounded. Candidate actions are generated using a variant of the Rapidly exploring Random Tree approach (RRT*) followed by a joint entropy minimization to select a path. Entropy estimation is performed in two stages, a short horizon evaluation is carried using exhaustive filter updates while entropy in long horizons is approximated considering reductions on predicted loop closures between robot trajectories. We pursue a fully decentralized exploration approach to cope with typical uncertainties in multiagent coordination. We performed simulations for decentralized exploration planning, which is both dynamically adapting to new situations as well as concerning long horizon plans.
Special Issue on Machine Learning in Scientometrics

Scientometrics is a domain that performs a quantitative and qualitative assessment of research and scientific progress. The field has earned popularity in last few years owing to the need to measure research outputs at individual, institutional and geographical levels. As a result of this need, different parameters are brought-up and various databases like Scopus, Web of Science and Google scholar are built for computation of these parameters. The data generated and stored as a result of proliferation of research papers and other scientific activities is vast. Analysis of the data cannot be performed without the intervention of sophisticated tools and techniques. Consequently, the use of Machine leaning algorithms for carrying out tasks like classification, regression, clustering and associations on these databases becomes imminent. The indicators to mark research performance use citation information in a well-defined way. Citations have become a key component in evaluating performance for authors, articles and journals. To evaluate the role of Machine Learning in Scientometrics, ML techniques can help in predicting citation count, can provide useful insights on computing new bibliometric indexes and also, in finding associations among them. The usage of powerful statistical tools like multiple linear regression, convex/ concave optimization and gradient ascent/descent algorithms can be explored in scientometric and bibliographic analysis. Read more...
Review of The Application of Deep Learning in Fault Diagnosis

In recent years, deep learning has shown its unique potentials and advantages in feature extraction and model fitting. Many scholars have applied deep learning to the field of fault diagnosis, and have achieved many results. In this paper, several typical methods based on deep learning have been introduced first, which can be employed to realize the fault diagnosis for industrial system. And then, this paper analyzes the characteristics and limitations of the fault detection model based on deep learning, and points out the importance of multi-diagnostic method fusion for the development of current intelligent fault diagnosis. Finally, the main functions and problems of in-depth learning in fault diagnosis are summarized, and the future research directions are prospected.
Counter Forensics: A New PRNU Based Method for Image Source Anonymization

Digital Image Forensics deals with the authenticity or originality and/or the detection of image forgery. Even though there are different forensic techniques for the analysis of digital images, hackers are advancing their methods to manipulate images. There arises the need of counter forensic techniques to resist the robust attacks. The Photo Response Non Uniformity (PRNU) of the source camera can be considered as the unique identity of the device, just like the fingerprint of a human being. This paper introduces a new counter forensic method to anonymize the identity of the image from attackers. The PRNU (fingerprint) of the image is removed and the variance of the PRNU of another camera is introduced to anonymize the identity. Also the image quality of the forged image is analyzed using Image Quality Metrics (IQM) and PSNR (Peak Signal to Noise Ratio) values. The results show that even after source anonymization, the image has better visual quality with average Mean Absolute Error (MAE) and Mean Square Error (MSE) values of 0.1278 and 0.1111 respectively. The noise residual of the forged image is correlated with the PRNU of the original acquisition device, and verified that they are uncorrelated as the values ranging from -0.0025 to 0.0117. This indicates that the identity of the image is forged or the forged image has little traces of the original acquisition device. The method was tested with images of the same scene captured by five different cameras.
Survey on Soft Computing Methods for Accident Condition and Severity Predictions

Roadways are a boon for both the agricultural and industrial sectors and are referred to as lifelines for any nation. The ever-increasing volume of transport and motor vehicles has literally choked these road networks resulting in traffic jams and road accidents. Road accidents are a combined result of the negligence of the vehicle driver, weather conditions, road condition and lighting conditions which results in a huge loss of life and property every year. Hence, there is a very strong need to develop an automated system to predict the severity of accidents and the possible reasons behind it. So, that preventive actions can be taken to avert these accidents in the future. This paper deals with the description of the prediction model and compares the model with the other soft computing methods to find out the most efficient method to predict the accident conditions and severity. SVM is found to have good average accuracy value among the available methods.
P1-491: REAL-TIME VIDEO DETECTION OF FALLS IN DEMENTIA MANAGED CARE: A SIGNIFICANT REDUCTION OF TIME UNTIL ASSISTANCE AND TIME ON THE GROUND IN FALLERS THANKS TO SAFELYYOU TECHNOLOGY

Falls of individuals with dementia are frequent, dangerous and costly. Early detection and access to the history of a fall is crucial for efficient post-fall care. Real-time video detection and capture of falls thanks to artificial intelligence and secure transmission (SafelyYou technology; published in JMIR 2017, presented at AAIC 2018) offers high potential to support conventional care management in fallers. The objective of the study was to assess whether introduction of SafelyYou technology in memory care facilities would modify post-fall patterns of care provided to individuals with dementia. An observational study (November 2017-August 2018) was carried out in six memory care facilities (California). Falls were video-captured (24/24, 7/7) thanks to wall-mounted cameras deployed in 66 out of 199 (33.2%) private bedrooms of consenting residents and families. Falls were video captured and provided to memory care facilities during a 10-months period; real-time detection and notification of falls was provided to facility staff thanks to a customized web-based application after a period of six months. Post-fall outcomes included automated measures of time until assistance (TUA) and of time spent on the ground (TOG). A total of 436 falls were captured in 66 fallers (mean age 87y; min=65-max=104), including 12/66 single and 54/66 (82%) multifallers, resulting in an average of 6.6 falls per patient over ten months (SD=8.6; min=1-max=49). No difference in number of falls was found according to gender. Grading of falls’ severity showed: 51.1% minor; 34.2% behavioral slipping with no impact-no recovery position; 8.1% moderate-severe; 6.6% near fall with independent recovery. Falls in private spaces (bedrooms) were predominant (82.3% including all moderate-severe falls) and happen at night (maximal time slot 4-8am) and in the evening (8-12pm). Post-fall TUA dropped (p=0.004) from a mean 35.3 minutes (SD=48.3) during the 2-months baseline to a mean 12.6 minutes (34.2). A parallel reduction was observed (p=0.003) with TOG dropping from a mean 43.2minutes (SD=48) at baseline to a mean 19.1minutes (34.2). The present study highlights the potential of real-time detection and video review of falls to lower post-fall morbidity and complement existing conventional care in memory care facilities.
The Role of Smartphones for Online Language Use in the Context of Polish and Croatian Students of Different Disciplines

Abstract Easy and cheap access to the Internet and a wide array of new technologies, such as smartphones, have multiplied opportunities for online informal learning of English (OILE). Yet, despite sizeable research, few studies have examined the issue of OILE in the context of university students of different disciplines. The aim of this research study was to examine the role of online language use through smartphones among students of various disciplines and its possible effects on enhancement of their foreign language skills. The study assumed both qualitative and quantitative methodology encompassing three research instruments: an online survey, the self-assessment language competence grid of the Common European Framework of Reference for Languages (CEFRL; Council of Europe, 2001) and a semi-structured interview. Data were collected with 377 students from two universities: University of Białystok, Poland and University of Osijek, Croatia. The research outcomes indicate that both Croatian and Polish students of different disciplines use their smartphones chiefly for receptive activities with infrequent use for language learning purposes and the predominant use of the mother tongue or English for the listed online activities through smartphones do not significantly affect the self-assessed level of their communicative competence in English. However, it can be stated that the choice of the preponderant language for online use through smartphones, especially for study-related issues, is conditioned by the students’ discipline of study.
Parallelizing Neural Network Learning to Build Safe Trained Model

Deep learning has produced a wide range of application in recent past. Deep learning techniques can help us solve complex problems like regression, clustering, classification for variety of data like unstructured, structured, and semi-structured input data. One of the most challenging task with deep learning in current era is to make it execute faster. Also if we host this model on cloud then security of trained model is a concern. The most common strategy that can be used is by solving the gradient descent in parallel on systems by either making use of model parallelism or data parallelism and to apply homomorphic encryption to build a safe trained model. In this paper, we demonstrate how basic parallelism concepts can be used to improve the performance of neural network training. We also demonstrate how homomorphic encryption techniques can help us provide security to trained model. Our experimental analysis uses MNIST dataset for handwritten character recognition as data for neural network learning problem. Experimental results indicate that the performance improvement in parallel version of neural network learning is achieved that provides a safe trained model.
A Robust Bayesian Probabilistic Matrix Factorization Model for Collaborative Filtering Recommender Systems Based on User Anomaly Rating Behavior Detection

Collaborative filtering recommender systems are vulnerable to shilling attacks in which malicious users may inject biased profiles to promote or demote a particular item being recommended.To tackle this problem, many robust collaborative recommendation methods have been presented.Unfortunately, the robustness of most methods is improved at the expense of prediction accuracy.In this paper, we construct a robust Bayesian probabilistic matrix factorization model for collaborative filtering recommender systems by incorporating the detection of user anomaly rating behaviors.We first detect the anomaly rating behaviors of users by the modified K-means algorithm and target item identification method to generate an indicator matrix of attack users.Then we incorporate the indicator matrix of attack users to construct a robust Bayesian probabilistic matrix factorization model and based on which a robust collaborative recommendation algorithm is devised.The experimental results on the MovieLens and Netflix datasets show that our model can significantly improve the robustness and recommendation accuracy compared with three baseline methods.