Reviewerly/exHarmony huggingface dataset:
Deep Neural Networks are Really Undefeatable for Human Conflicting and Non-conflicting Event Detection

Human activity analysis has been really vital nowadays for the notification of any threats to the system as well as human. This paper aims to compare conventional AI approaches with recent deep learning approach for the conflicting and Non-conflicting human activity detection. In this paper, we propose the system for the detection of Conflicting human activities namely; Kicking, Punching and Pushing. These systems are developed with AI conventional approaches and Deep Neural Networks. Classifying various activities only into two subgroups of activities based on their intrinsic aggression is really a challenging task. The proposed system effectively uses the well-known features namely contour and optical flow. System is tested and validated with the well-known dataset using traditional AI approaches specifically Hidden Markov Model and Support vector machine. We obtain very good accuracy with SVM with polynomial kernel. However, Convolutional Neural network is found outperforming compared to traditional AI approaches and achieves the peak accuracy.
Logic-Based Answer Set Creation for Question Answering System

Question-Answering system is a system to collect the possible answers in each question to suitable response. In the instance of a highly competitive era in living, education, including work. The popularity effects on life, causing competition in all matters. However, people are not equal due to their different abilities or opportunities. These are effects to career choices. This research, therefore, offers a framework that focuses the method of creating a logic-based answer using the logic to matches hard and soft skills. In order to give the most appropriate answer for both entrepreneurs and applicants in finding the most suitable thing for themselves.
Large-Scale Dialog Corpus Towards Automatic Mental Disease Diagnosis.

Recently, the number of people who are diagnosed as mental diseases is increasing. Efficient and objective diagnosis is important to start medical treatments in earlier stages. However, mental disease diagnosis is difficult to quantify criteria, because it is performed through conversations with patients, not by physical surveys. We aim to automate mental disease diagnosis in order to resolve these issues. We recorded conversations between psychologists and subjects to build our diagnosis speech corpus. Our subjects include healthy persons, people with mental diseases of depression, bipolar disorder, schizophrenia, anxiety and dementia. All of our subjects are diagnosed by doctors of psychiatry. Then we made accurate transcription manually, adding utterance time stamps, linguistic and non-linguistic annotations. Using our corpus, we performed feature analysis to find characteristics for each disease. We also tried automatic mental disease diagnosis by machine learning, while the number of sample data is few because we were still in our pilot study phase. We will increase the number of subjects in future.
Blockchain Based High Performance User Authentication in Electric Information Management System

User Authentication is essential in electric information management system where the operations should be controlled according to standard specifications. Traditionally the authentication is implemented by rule/rights definitions in the static database, which is difficult to manage and audit when there are a number of users and resources. Therefore, blockchain technology is introduced to facilitate user authentication considering its decentralized and security features. However, the performance of blockchain based authentication is relatively low compared with the centralized database. In this paper, we propose a high-performance user authentication framework for electric information management system based on multiple level structure and parallel computing. The proposed framework is implemented in a Provincial electric company of China State Grid. According to the experimental results, the proposed framework not only implements the blockchain based user authentication by also improves the system performance by 60% compared with the non-optimized system.
A Novel Approach to Kinect-Based Gesture Recognition for HCI Applications

With the advent of Kinect and other modern-day sensors, gesture recognition has emerged as one of the most promising research disciplines for developing innovative and efficient human-computer interaction platforms. In the present work, the authors aim to build an interactive system by combining the principles of pattern recognition along with the intelligent application of Kinect sensor. Involving Kinect sensor has served the purpose of collecting skeletal data, and after processing the same, the extracted relevant features have been fed to principal component analysis for dimensionality reduction phase. Finally, instead of using a single classifier for detection, in this chapter, an ensemble of k-nearest neighbor classifiers has been chosen since an ensemble algorithm is always likely to provide better results than a single classifier. To justify the efficacy of the designed framework it is implemented for interpretation of 20 distinct gestures, and in each case, it has generated better performances as compared to the other existing techniques.
Building A Prototype of a Pair of Weighing Shoes Based on Machine Learning Techniques

Changes in the human body weight may be caused by diseases, overeating, lack of exercise, or other health problems. In this paper, we built a prototype of a pair of weighing shoes to enable people to constantly monitor their weight changes so that they can detect and treat their health problems earlier. We placed several thin and flexible pressure sensors under the shoes and used machine learning techniques to learn the relationship between their values and the actual weight of the human body being measured. The trained model then was used to predict the weight of a human body. In our experiments, we varied the number of used pressure sensors to study its impact on the prediction error. We found that the prediction error of the trained model could be controlled within 5.8% when 8 or more pressure sensors were used for the current prototype.
Comparing simulation model for objective QoE video evaluation with real IPTV test scenario during appearance of packet losses

The aim of this paper is comparing a simulation model with real IPTV (Internet Protocol TeleVision) scenario in access network. In real IPTV scenarios, it is known that packet losses appear suddenly and might have an "explosive" character, especially in DSL (Digital Subscriber Line) case. In addition, these packet losses usually appear in groups and lead to huge degradation of the video service, which decreases customer's QoE (Quality of Experience) level. Hence, estimation of this degradation in access network is important and the paper explained one simulation model based on SSIM (Structural Similarity Index) analysis, which can be used as one perceptive video quality assessment by imitating a real environment with packet losses. To check this, we compared our simulation model with the real IPTV video distributed over DSL (Digital Subscriber Line) and exposed to different packet loss appearances.
Resource and Duration Evaluation of Quantum Secure Communication Protocols

The paper proposes resource and duration evaluations for quantum secure communication (QSC) protocols. In terms of both evaluations, two QSC protocols - two-step quantum secure direct communication (QSDC) protocol and quantum channel compression (QCC)-based QSDC protocol, are compared. It is verified that the QCC-QSDC protocol excels the two-step QSDC in terms of duration, and utilizes slightly less resources for its implementation.
Information Verification Enhancement Using Entailment Methods

Information verification is a hot topic, especially because of the fact that the rate of information generation is so high and increases every day, mainly in social networks like Twitter. This also causes social networks be invoked as a news agency for most of the people. Accordingly, information verification in social networks becomes more significant. Therefore, in this paper a method for information verification on Twitter is proposed. The proposed method for Tweet verification is going to employ textual entailment methods for enhancement of previous verification methods on Twitter. Aggregating the results of entailment methods in addition to the state-of-the-art methods, can enhance the outcomes of tweet verification. Also, as writing style of tweets is not perfect and formal enough for textual entailment, we used the language model to supplement tweets with more formal and proper texts for textual entailment. Although, singly utilizing of entailment methods for information verification may result in acceptable results, it is not possible to provide relevant and valid sources for all of the tweets, especially in early times by posting tweets. Therefore, we utilized other sources like as a User Conversational Tree (UCT) besides utilizing entailment methods for tweet information verification. The analysis of UCT is based on the pattern extraction from the UCT. Experimental results indicate that using entailment methods enhances tweet verification.
A Bangla Word Sense Disambiguation Technique using Minimum Edit Distance Algorithm and Cosine Distance

In Natural Language Processing, Morphology known as the most decisive part. It can be more difficult when there are several meanings for only one word. Ambiguous word is a word which has those several meanings. The human brain can easily identify these ambiguities but for machines, it is very complicated to detect. Word Sense Disambiguation(WSD) is such a technique that trains machines to detect ambiguities. Different types of research work have been published in different languages for this technique. But developing an optimized Bangla WSD system is still a great research challenge. To overcome this challenge we have to propose a new technique to detect ambiguous word in a sentence. A corpus containing 3860 sentences is built from different resources. We applied the Levenshtein distance algorithm to detect ambiguous word and Cosine Similarity to sense actual meaning in a given Bangla sentence. The accuracy of our method is 80.82%. The validity of our claims has been proved through comparisons with other well established methods.
Discovering Traffic Anomaly Propagation in Urban Space Using Enhanced Traffic Change Peaks

Discovering traffic anomaly propagation enables a thorough understanding of traffic anomalies and dynamics. Existing methods, such as Outlier-Tree, are not accurate to find out the trend of abnormal traffic for two reasons. First, they discover the propagation pattern based on the detected traffic anomalies. The imperfection of the detection method itself may introduce false anomalies and miss the real anomaly. Second, they develop a propagation tree of anomalies by searching continuous spatial and temporal outlier neighborhoods rather than considering from a global perspective, and thus cannot form a complete propagation tree if a spatial or temporal gap exists. In this paper, we propose a novel discovering traffic anomaly propagation method using the mesh data and enhanced traffic change peaks (en-TCP) to visualize the change of traffic anomalies (e.g., an area where vehicles are gathering or evacuating) and thus accurately capture traffic anomaly propagation. Inspired by image processing techniques, the GPS trajectory dataset in each time bin can be converted to one grid traffic image and be stored in the grid density matrix, in which the grid cell corresponds to the pixel and the density of grid cells corresponds to the Gray level ([Formula: see text]) of pixels. An enhanced adaptive filter is developed to generate traffic change graph sequences from grid traffic images in consecutive periods, and clustering en-TCP in a continuous period is to discover the propagation of traffic anomalies. The accuracy and effectiveness of the proposed method have been demonstrated using a real-world GPS trajectory dataset.
Implementation of Image Segmentation Techniques to Detect MRI Glioma Tumour: Mid-Level Image-Processing

Image identification to detect a tumour needs several stages of image processing along with identifying analysis. To get an accurate segmentation of the tumour contour and to identify brain tumour based on brain magnetic resonance imaging (MRI), a suitable techniques and stages of image processing are required to be applied. One technique of mid-level image processing became an objective this work. The objective of the study is to segment the boundary of tumour by applying the Modification of Region Fitting (MRF) method in term of data fitting. The performance of the Region Scalable Fitting (RSF) method and Modified Region Scalable Fitting (MRSF) is evaluated by comparing the number of iterations. As the result, the MRF method has successfully segmented the initial region of brain tumour images.
Password Guessing Using Deep Learning

Password is one of the authentication protocol, which is still the most widely used and is vulnerable to many different attacks. PassGAN was the first password guessing technique was introduced, which is based on generative adversarial networks. It essentially matched the performance of Melicher et al. There are already some papers regarding neural network being used to develop an intrusion detection system or similar defensive strategies. Database leaks and dumps happen almost every day, some make the news, and some goes by unnoticed. Most Websites uses some hash algorithm while storing their passwords. When cracking these hashed passwords, There are specific patterns used by the users when they are choosing their password. Neural networks can be used to figure out these patterns and use it generate a list for passwords which can be used as wordlists to get the plaintext passwords. In this paper, we are going to investigate how well PassGAN model will perform if trained on Russian based email database.
The role of educational reward in the process of learning

Unfortunately, since the first theory of scientifically grounded learning has emerged and up to present theories, there are two minuses that directly affect the status of the reward.A first drawback is that psychologists and researchers in the related fields have not yet reached an agreement regarding the role of reward in the process of learning.If some of them praise it up to the skies while others put a blame on it, one can only ascertain that the role of reward is so complex that it is not thoroughly understood, yet.The second matter is related to the first and it seems to be more serious.It refers to improper communication, deficient to the educational specialists and, why not, to the general public, of the major discoveries made by the researchers regarding the importance of reward for the proper course of the learning process.That is why, the aim of this article is not only to present the stage the researches on reward and their interpretation are in, but also to particularly present them in a form that is more accessible to all those involved in the educational process, in expectation that the information comprised herein will be useful and easier to apply.
Wide Separate 3D Convolution for Video Super Resolution

Video super-resolution (VSR) aims to recover realistic high-resolution (HR) frame from its corresponding center low-resolution (LR) frame and some neighbouring supporting frames. To utilize the extra temporal information of supporting LR frames, most of VSR methods highly rely on accurate motion estimation and compensation models to align LR frames. However, the motions between frames have no ground truth, and it is difficult to train motion estimation and compensation models. Inaccurate results will lead to artifacts and blurs, which also will damage the recovery of high-resolution frames. We propose an effective separate 3D Convolution Neural Network (CNN) with wide activation to overcome the drawback of utilizing motion estimation and compensation models. Separate 3D convolution is factorizing the 3D convolution into 2D convolution along spatial domain and 1D convolution along temporal domain, which can not only capture temporal and spatial information simultaneously but also reduce the computation complexity compared to 3D CNN.
The Cosine Number Transform: A Graph Signal Processing Approach

In this paper, we provide a matrix that diagonalizes the Laplacian of a path graph over a finite field. By analogy with the fact that the discrete cosine transform matrix diagonalizes the Laplacian of (real-valued) path graphs, we identify the provided matrix as that of the cosine number transform (CNT). Together with the fact that the Fourier number transform matrix diagonalizes any circulant matrix (the Laplacian of a ring graph, for example), this motivates us to introduce a preliminary definition of what should be the graph Fourier number transform (GFNT). We revisit the application of the CNT in image encryption and give examples of the GFNT.
Eye Fixation Assisted Detection of Video Salient Objects

With the increasing maturity of image saliency detection, more and more people are focusing their research on video saliency detection. Currently, video saliency detection can be divided into two forms, eye fixation detection and salient objects detection. In this article, we focus on exploring the relationship between them. Firstly, we propose a network called fixation assisted video salient object detection network (FAVSODNet), which uses the eye gaze information in videos to assist in detecting video salient objects. A fixation assisted module (FAM) is designed to connect FP task and SOD task deeply. Under the guidance of the eye fixation information, multiple salient objects in complex scene can be detected more correctly. Moreover, when the scene suddenly changes or a new person appears, it can better to detect the correct salient objects with the aid of fixation maps. In addition, we adopt an extended multi-scale feature extraction module (EMFEM) to extract rich object features. Thus, the neural network can aware the objects with variable scales in videos more comprehensively. Finally, the experimental results show that our method advances the state-of-art in video salient object detection.
Bio-Inspired Optimization of Rateless Codes for Reliable Intelligent Transportation Systems

The quality of service the users of intelligent transportation systems highly depends on the methods of information processing in telecommunication networks. In particular, forward error correction codes are an important element of many telecommunication technologies. In last decade a lot of studies have been carried out and attempted to improve the performance of different codes. The study indicates that rateless codes can be used to increase the reliability of data transmission in telecommunication networks of intelligent transportation systems. In the article bio-inspired optimization method of degree distributions of rateless codes is discussed. The main stages and features of this method are presented. The simulation results approve that proposed approach significantly improves error performance of rateless codes. Due to this, the effectiveness and robustness of network-based intelligent transportation systems are increased.
Data mining and image analysis using genetic programming

Genetic programming (GP) is an artificial intelligence technique that benefits from evolutionary computations allowing computers to solve problems automatically. In this paper, we present an optimized genetic-programming-based classifier that directly solves the multi-class classification problems in data mining and image analysis. A new fitness function is proposed for multiclass classification and brain tumor detection, which is validated by 10-fold cross validation. Instead of defining static thresholds as boundaries to differentiate between multiple labels, our work presents a method of classification where a GP system learns the relationships among experiential data and models them mathematically during the evolutionary process. We propose an optimized GP classifier based on a combination of pruning subtrees and a new fitness function. An orthogonal least squares algorithm is also applied in the training phase to create a robust GP classifier. Our approach has been assessed on six multiclass datasets and on a magnetic resonance imaging (MRI) brain image for tumor detection. The results of data classification for Iris, Wine, Glass, Pima, BUPA Liver and Balance Scale datasets are compared with existing algorithms. The high accuracy of brain tumor classification provided by our GP classifier confirms the strong ability of the developed technique for complicated classification problems. We compared our approach in terms of speed with previous GP algorithms as well. The analyzed results illustrate that the developed classifier produces a productive and rapid method for classification tasks that outperforms the previous methods for more challenging multiclass classification problems.
Image Counterpane

Image is the collection and representation of pixels. The details of the image involves with the overlapping of adjacent pixels. Image Stitching is a process of combining many individual images to get a detailed information. Eventually, a good resolution image with details emerges. Analysing the maps of the complete world can be the best example of image stitching process. In digitally sewing the pictures or data, there may occur stitching errors. This flaw can overcome with software emerging now along with the reference data available. Image stitching can also be called Image Counterpane. This image processing is widely gaining attention in various fields like computer vision, deterministic process based on the geological models, stochastic simulation of meandering rivers. In this paper, image counterpane algorithm, texture synthesis and texture transfer are presented.
Acceleration Command based Visual Servoing with Artificial Induced Time-Delay

The tracking of an object using visual feedback by controlling the camera motion is known as Visual Servoing. In general most of visual servoing techniques use camera velocity as control input. In this paper, camera acceleration is used for deriving the control law. The use of acceleration input can accomplish smoother operating trajectory. However, this controller requires the first order feature measurement which may not be available. In this paper, to circumvent the issue, a time-delayed approximation is used for the non-measurable system states. The controller parameters are obtained from the stability analysis of the closed-loop time-delay system using Lyapunov-Razumikhin approach. The implemented controller is able to track the desired feature smoothly. These results establish a complementary approach to the existing acceleration-based augmented controller design.