Reviewerly/exHarmony huggingface dataset:
A Moving Target Tracking Algorithm Based on Motion State Estimation and Convolutional Neural Network

Moving target tracking is widely used in different fields. In practical applications, it faces the complex situations such as mutual occlusions among targets and rapid movement of targets, and needs to solve the problems of tracking accuracy and real time for the algorithms. To address the problems, the paper takes the moving targets of ships, yachts, and aircraft carriers at sea as the research objects, studies the maritime moving target tracking models under big data and small sample data environments, presents a tracking algorithm combining motion state estimation and convolutional neural network (CNN). Integrating Edge Boxes with CNN to realize multi-target detection at sea, the recall rate and accuracy of target detection are improved under the condition of higher detection efficiency. The target recommendation areas are generated based on the motion state estimation, and continuous information between video frames is used to improve estimation accuracy and effectively deal with occlusions among targets through efficient prediction of target states. The training model is utilized to detect and track the targets in the recommended areas. Compared with the tracking method of Fast R-CNN, as well as the tracking method based on histogram of oriented gradient (HOG) and support vector machine (SVM), the experiment results show the proposed algorithm has higher tracking accuracy and better real time even in the scenes of mutual occlusions among targets and rapid movement of targets.
StrategicReading

Mobile devices are becoming an important platform for reading. However, existing research on mobile reading primarily focuses on low-level metrics such as speed and comprehension. For complex reading tasks involving information seeking and context switching, researchers still rely on verbal reports via think-aloud. We present StrategicReading, an intelligent reading system running on unmodified smartphones, to understand high-level strategic reading behaviors on mobile devices. StrategicReading leverages multimodal behavior sensing and takes advantage of signals from camera-based gaze sensing, kinematic scrolling patterns, and cross-page behavior changes. Through a 40-participant study, we found that gaze patterns, muscle stiffness signals, and reading paths captured by StrategicReading can infer both users' reading strategies and reading performance with high accuracy.
Service Recommendation Method for Integrated Energy Service Companies

With the change of energy structure, the development of renewable energy industry and the transformation of power grid companies, the concept of comprehensive energy services has been paid more attention in recent years. State Grid Corporation of China has established state grid integrated energy service group company to carry out the business of integrated energy services. This paper first analyzes the current situation of integrated energy services, then proposes a set of integrated energy services categories, and finally proposes an integrated energy services recommendation method. The effectiveness of the proposed method is demonstrated by the case study.
Further clarification on Mantin’s Digraph Repetition Bias in RC4

In this paper we provide a theoretical argument towards an unsolved question related to Mantin’s “Digraph Repetition Bias” (Eurocrypt 2005) that is observed in the key-stream of RC4. The open question, that depends on the observation that arrival of four consecutive same bytes (of the form AAAA) in RC4 key-stream is slightly negatively biased, was posed by Bricout et al (Des. Codes Cryptogr. 86:743–770, 2018) in 2016. Moreover, for the first time, we consider the “Reverse Digraph Repetition Bias” and show that there is significant negative bias in arrival of ABBA (A, B two distinct bytes) in RC4 key-stream.
“A tale of two virtual communities”: A preliminary analysis of discourse in two online programming support communities

Abstract Software programming is increasingly becoming a collaborative and community driven effort, with online discussions becoming vital resources for learning and knowledge sharing. This study explores differences in the discourse patterns in two popular online programming communities to provide preliminary insights for the question of how virtual learning communities should be designed and structured. A content analysis of a random sample of 15 discussion threads from each of r/Askprogramming (236 contributions) and Stack Overflow (SO; 224 contributions) was used to explore the observed interaction patterns. Differences between sites emerge in the scope of topics and the nature of responses the community provides. While Stack Overflow is more task‐specific, r/Askprogramming supports a greater sense of bonding and camaraderie among community members in addition to task‐specific discussions. These findings suggest key normative structures that regulate the nature of discourse in these communities which may in turn have design implications for such online learning initiatives.
Web 2.0 Technology in Libraries

Progress in information communication technology (ICT) has become the backbone for every branch of knowledge in academic arena and library, and information centers are not apart from it. Social networking is playing a significant role in serving to the library users. The chapter examines libraries and their importance in accepting the Web 2.0 phenomenon of social networking sites like Facebook Twitter, YouTube, and a number of social networking tools to enable libraries to engage with students in the virtual environment to promote library event and services, teaching and learning, and humanizing the concept of library and librarians. This chapter also focuses on the importance of social networking sites, which improve the professional relationships within the library profession and across the boundaries of particular national library education systems across the world, which is increasingly sharing information that ensures libraries are the head of changes in demands and needs of their users.
CAPITALISM - A MAJOR DRIVER OF MODERN-DAY TECHNOLOGICAL INNOVATIONS AND INVENTIONS: A SOCIOLOGICAL ANALYSIS

The last one hundred years have been years characterised by great scientific advancement and progress in technological innovations, international trade and multinational corporations. There is no civilization in recorded human history that has ever attended the height and successes we have been able to make in this age and time. Some scholars have alluded to this age as the age of machines, this is because of the amount of household, office, transport and telecommunication gadgets that surrounds us every day. These achievements have come with a great price both to the ecosystem and interpersonal relationships. The big question has always been are these technological innovations driven by human needs, national pride and ego, human curiosity or the capitalist quest to invent for profit maximisation? The study by way of discursive analysis tried to show that most of modern inventions and technological advancement are driven by the quest for wealth leading to various social vices, creating environmental hazard and unequal society. The study therefore tries in recommending that technological gadgets and tools should not be produced solely because of the financial benefits that accompany the producer, but they should rather be aimed at the basic needs with basic and efficient concerns.
Professional and Personal Reflexion of a Teacher-Musician: An Operational Component

The article deals with the specificity of professional reflection of the future music teacher in the light of the development of the ideas by E. B. Abdullin. The accumulation of one’s own experience of entering the world of music, the art of teaching, its comprehension and analysis allow a thoughtful student to look at the process of musical education through the eyes of students, their parents, that is, to take the reflective position of a teacher-researcher. The search for ways and means of solving professionally significant tasks requires a music teacher to think reflectively, understand the actions of his students and colleagues, and penetrate their feelings and thoughts. The importance of reflection in solving problem situations, pedagogical tasks, in mastering critical thinking by a future music teacher is substantiated. The conceptual foundations for professional reflection development of a future teacher-musician have been worked out, the structure of reflective analysis and the conditions for the effectiveness of its development by students have been revealed. The techniques and methods of preparing future bachelors and masters of music education for professional reflection in the process of studying at a university, during school practice, in various types of independent, research work of students are considered. The authors have adapted special reflexive techniques (artistic-pedagogical tasks, search games, storytelling, reflective life story, musical reflection, reflective analysis, interviews, pedagogical essays, creative portfolio). The article analyzes the experience of Dutch specialists, the author’s methods of foreign researchers (ALACT – model, “Wall”, “Arrows”, “Visiting card”, a system of reflective questions, etc.). Adapted for use by students in music lessons in the course of pedagogical practice, they become effective means for introspection, self-assessment of the activities of future bachelors / masters of music education. Based on the material of autobiographies and interviews of great musicians-performers, actual problems of music education, ways of spiritually reflexive entry into the world of art and artistic creation are analyzed.
Axisymmetric extended target tracking using Gaussian process

In extended target tracking scenarios, the size of each target is much larger than a single sensor resolution cell, which results in multiple measurements per scan. For many aerial targets, their shapes tend to be axisymmetric and in most cases the directions of motion nearly coincide with the axes of symmetry. In this paper, an extended Kalman filter with Gaussian process measurement model is used to simultaneously estimate the kinematic and extent states of such target and based on some assumptions about the target extent, a new form of the covariance function for Gaussian process is proposed. The effectiveness of the proposed method is demonstrated in several simulation experiments by comparing with the state-of-the-art algorithms, simulation results show that the proposed method has a significant improvement in estimation.
Markov Model of Quantized Speech Signal

Currently, automatic speech recognition systems are widely used. When developing such systems, significant attention is paid to choice of parameterization method of speech signals. The paper presents options for representing fragments of speech signals by simple and high-order Markov chains. Special cases of the implementation of these models are also presented. The results show that when using the methods of Markov parameterization in problems of automatic recognition of speech commands, it is advisable to use models of high order Markov chains. The considered models of speech signals can significantly reduce the requirements on the computing resources of automatic speech recognition systems.
Dynamic Local Ball Tracking in Middle Size League Robot Soccer ERSOW based on Kaiman Filter

The Robot Soccer uses the vision system to look for the ball continuously. The quality of vision object detection is the main factor that considered by the robot. Beside the quality, the performance of the detection process also affects the robot performance. The object detection is the heaviest process in entire ERSOW's robot process. In this paper, we addressed the ways optimizing the vision object detection process that enhanced by the tracking method using Kalman Filter. The Kalman filter is also widely used for robotic purposes. The object has been equipped with a local ROI around them to limit the scanning on the entire frame when detection method is running. The local ROI will reduce the computation process and keeping the process in the sufficient resources that processor can handle. The Kalman filter will predicted the object position and the direction of the object by considered the previous position and the times was taken. The Kalman filter will lock the object and will follow the object without using detection feature anymore. From the results of tests conducted, the predicted value in several position has showed promising result. The average error on x-axis is 1.425 pixels and in y-axis 1.7226 pixels. This system can also reduce the average computation time from 31.67 Ms into 20.4 Ms. This research is expected to overcome the overwhelmed of the ERSOW's computation and increased the performance of the robot
Abnormality Detection Using Image Quality Estimation Technique Through Border Surveillance For Wireless Transmission

Presently abnormality or anomaly detection from video streams of surveillance system has drawn quite a significant research interest. Modern surveillance system uses pervasive CCTV for recording surveillance video, so recording or capturing of video is not a problem. The main difficulties lie in the availability of human resources to observe and analyze the video clips for long hours. They are not only very exhaustive but are mostly quite costly. Automatically finding of abnormal or risky behavior significantly decreases the overhead to label and annotate the surveillance videos of huge number of hours. In the proposed technique abnormal changes in a surveillance frame is detected and achieved through image quality estimation by using Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM). The proposed anomaly detection is done automatically at the remote place of surveillance where uncorrelated (less similar) frames of the video represents anomalies or abnormality. False alarms are avoided by sending the reference and abnormal frame the control room using 1-D DWT and 2-D DWT at the remote surveillance place. The results reported are quite encouraging and the model will suitably work in any wireless communication domain.
Toward Improving Secret Sharing Scheme Based on Encryption in Cloud Computing Environment

Flexibility, acceptance of hire of Cloud Computing, control facilities originates a number of challenges. In precise, safety has been widely conveyed to create the key issues which stop movement to the cloud. Shamir’s Secret Sharing Scheme splits the secret message into n shares with threshold k and distribution of shares to n participants and each participant has unique share of secret message. For reconstructing the secret message, minimum k fragments are required. Threshold scheme which requires only k shares to re-construct the secret message is implemented, as collection of shares from all the n participants to reconstruct the secret message may be unfeasible. AES encryption turns out to be totally inappropriate if a password is used as an alternative of providing the key. The attacker finds file, downloads Ax crypt, and brute force/dictionary attacks it until it’s cracked. An additional security can provided to user password which of varying length by splitting the password into n shares. Advance Encryption Standard (AES) procedure is implemented to encipher the produced shares. AES with 256-bit key is applied to encode the share for every member. Covering arrangement alternative to Boolean masking established on Shamir’s Secret Sharing arrangement is suggested.
Prediction of Sales Using Stacking Classifier

This paper aims to explore approaches to machine learning for predictive analysis of sales. The ensemble learning technique known as stacking is considered to enhance the performance of the sales forecasting predictive model. A stacking methodology was studied to build a single model regression ensemble. The findings indicate that we can improve the performance of predictive sales forecasting models using stacking techniques. The concept is that it is useful to merge all these findings into one with various predictive models with different sets of features.
Impact of Functional Package Manager Over a Non-Traditional High Performance Computing System

Nowadays, practitioners in both industrial and academic circles are using embedded systems that interact with each other to tackle high performance computing workloads, but with individually less capable devices. Smart cities, autonomous machines and agriculture applications are some examples of these systems working in different fields. These systems run several applications that we want to handle in a simple way. There are several approaches for application management and deployment: containers, virtualization and package manager are some examples. In order to monitor the performance of these systems, we evaluated the impact of the package manager method in an embedded systems test bench. We presented an experiment using an 8 NVIDIA Jetson TX1 cluster with Nix package manager and Jacobi Benchmark, showing that this method has a negligible impact performance on execution time against its native version.
On the Effect of Noise on Software Engineers’ Performance: Results from Two Replicated Experiments

Noise, defined as an unwanted sound, is one of the most common factors people have to deal with when performing their daily working tasks. Researchers have marginally investigated the effect of noise on software engineers' performance. In this paper, we present the results of two replicated experiments whose main goal was to increase the body of knowledge, by confirming or not the results of the baseline experiments, on the effect of noise while comprehending functional requirements specifications and fixing faults in source code. The results of the replicated experiments suggest that: (i) noise does not significantly affect the comprehension of functional requirements specifications and (ii) noise significantly and negatively affects fixing faults if this task lasts 30 minutes, while it does not have a significant impact if the task lasts 60 minutes. The results of the replications confirm to a large extent those of the baseline experiments and allow us to postulate, as done for the baseline experiments, that fixing faults is more vulnerable to noise than comprehending the specifications of functional requirements.
Learning Flow Scheduling

Datacenter applications have different resource requirements from network and developing flow scheduling heuristics for every workload is practically infeasible. In this paper, we show that deep reinforcement learning (RL) can be used to efficiently learn flow scheduling policies for different workloads without manual feature engineering. Specifically, we present LFS, which learns to optimize a high-level performance objective, e.g., maximize the number of flow admissions while meeting the deadlines. The LFS scheduler is trained through deep RL to learn a scheduling policy on continuous online flow arrivals. The evaluation results show that the trained LFS scheduler admits 1.05× more flows than the greedy flow scheduling heuristics under varying network load.
LB-CGM: Latent Based Conditional Generative Model with Reliable Distribution Prediction

Randomness exists either due to the inherent noise of the problem or lack of important input features, which could lead to multimodality of the data distribution. Therefore, in more and more scenarios, it is required not only to predict a single point-value, but also the distribution of the prediction. However, well-studied prediction models usually focus on point prediction that minimizes the mean squared error or the mean absolute error. These approaches could miss important knowledge when their outputs are applied to the downstream decision process. In this paper, we combine the advantages of both GANs (Generative Adversarial Nets) and VAEs (Variational Auto-Encoders), and introduce a latent-based conditional generative model (LB-CGM) to handle the distribution regression problems. The VAE framework is adopted, and the adversarial network is applied to estimate the validity of the generated sample. Besides, the latent-based reconstruction loss is introduced to mitigate mode collapse, in which the direct pairwise comparison between the original and generated samples ensures the correctness and completeness of the generated mode pattern. In this work, we explore a path for the generative model to be used in probabilistic prediction problems. This method can produce conditional prediction distribution close to the actual distribution and is verified on both the synthetic dataset and benchmark dataset.
Complexwavelet Transform Cwt Based Video Magnification For 3d Facial Video Identification

Magnifying micro changes in motion and brightness of videos that are unnoticeable by the human visual system have recently been an interesting area to explore. In this paper, we explore this technique in 3D facial video identification, we utilize this technique to identify 3D objects from 2D images. We present a Complex Wavelet Transform CWT, 2D-Dual CWT based technique, to calculate any changes between subsequent video frames of CWT sub-bands at different spatial locations. In this technique, a gradient based method is proposed to determine the orientation of each CWT sub band in addition to the Radon Transform, RT, that is utilized to detect any periodic motion in the video frames. We conducted many simulation results to show that the proposed hybrid technique provides promising results when compared with the existing literature in micro magnification of videos, like steerable pyramids STR or the Riesz Transform based one RT-STR. The proposed technique has been employed to make a distinction between 3D facial object videos and 2D face image videos, as will be demonstrated in our simulation results.
Arabic Sexist Comments Detection in Youtube: A Context-Aware Opinion Analysis Approach

In this chapter, we present an approach to automatize the assessment of attitudes toward violence against women and women’s rights, by analyzing Youtube comments, written in Arabic. More specifically, we propose a context-aware approach to opinion analysis in comments by taking into account the polarity of videos to which comments are associated. We build a training set and use it to train a classifier that predicts videos’ polarity. The accuracy and precision of the produced video classifier are \(98\%\) and \(94\%\), respectively.
Parameter Estimation in Non-Linear State-Space Models by Automatic Differentiation of Non-Linear Kalman Filters

In this article, we propose automatic differentiation based methods for parameter estimation in non-linear state-space models. We use extended Kalman filter and cubature Kalman filters for approximating the negative log-likelihood (i.e., the energy function) of the parameter posterior distribution and compute the gradients and Hessians of this function by using automatic differentiation of the filter recursions. The proposed approach enables computing MAP estimates and forming Laplace approximations for the parameter posterior without a need for implementing complicated derivative recursions or manual computation of Jacobians. The methods are demonstrated in parameter estimation problems on a pendulum model and coordinated turn model.