Reviewerly/exHarmony huggingface dataset:
Modulation Recognition of 5G Signals Based on AlexNet Convolutional Neural Network

Abstract Aiming at the problem that signal modulation recognition under a non-cooperative condition requires substantial a priori information of the signal and a complex artificial selection of the features, this paper proposes a modulation recognition method for the 5th-generation (5G) signal modulation based on the AlexNet convolutional neural network. For the five commonly used 5G signals (3GPP R15 protocol recommendations) of π/2-BPSK, QPSK, 16QAM, 64QAM, and 256QAM, the constellation is selected as input feature of the AlexNet network to construct the recognition classification algorithm. The simulation results show that the average recognition accuracy of the five commonly used 5G signals is up to 90% under a 15 dB signal-to-noise ratio (SNR), an improved performance compared with that of the existing recognition algorithms based on signal scatter plots.
Performance Analysis of AES, RSA and Hashing Algorithm Using Web Technology

Lot of research and development has been done in recent years to stop unauthorized access but it is not possible to design security flawless environment. For an organization, the IT manager assesses the security needs and policies and designs the system so that unauthorized access can be stopped. Encryption of data is a viable solution so that information security can be maintained. For encryption of data, various algorithms are designed so that security in information can be identified. In this paper, we describe the basic characteristics of encryption algorithm via web technology. The time versus message length performance is also analyzed for RSA, AES and Hashing Algorithm considering the parameter like cpu time, jitter, etc. Encryption and decryption are also performed for different file sizes in local environment.
Bandwidth Constrained Holographic Telepresence with 3D Model Reconstruction

With the emergence of virtual reality (VR) like holographic telepresence, it is envisaged that the current networks may be overwhelmed due to the higher bandwidth demand to support high-resolution videos. On the other hand, Camera Blending Field (CBF) has been proposed to restore the occluded regions of cameras to enhance the userâ€™s Quality of Experience (QoE) in VR. In this paper, we explore the source selection to maximize the userâ€™s QoE under the network capacity and 3D reconstruction constraints. We first formulate a new optimization problem, named Source Selection for Real-time Telepresence with Occlusion (SSRTO), with the objective of QoE maximization. Then, we prove the NP-hardness and propose a new algorithm, Maximum Quality of Complete Model (MQCM), to maximize the userâ€™s QoE by examining the proximity of cameras on the built Angle Directed Graph (ADG) and Neighbor Directed Graph (NDG) for reconstructing the complete 3D model. Simulation results show that MQCM can effectively improve the userâ€™s QoE by more than 100% compared with the baseline schemes.
Identifying Patient Groups based on Frequent Patterns of Patient Samples

Grouping patients meaningfully can give insights about the different types of patients, their needs, and the priorities.Finding groups that are meaningful is however very challenging as background knowledge is often required to determine what a useful grouping is.In this paper we propose an approach that is able to find groups of patients based on a small sample of positive examples given by a domain expert.Because of that, the approach relies on very limited efforts by the domain experts.The approach groups based on the activities and diagnostic/billing codes within health pathways of patients.To define such a grouping based on the sample of patients efficiently, frequent patterns of activities are discovered and used to measure the similarity between the care pathways of other patients to the patients in the sample group.This approach results in an insightful definition of the group.The proposed approach is evaluated using several datasets obtained from a large university medical center.The evaluation shows F1-scores of around 0.7 for grouping kidney injury and around 0.6 for diabetes.
MLEU

This paper presents the method for tackling the challenge of fully automatically image colorization. We improve U-net by fusion multi-level feature from the pre-trained ImageNet to enhance the model under the small datasets. Furthermore, we reduce the unbalance colors by the enhancement distribution over quantized colors based on the smoothness of the prior distribution. The experiments in the DIV2K dataset show that our results are very encouraging. Our method improves PSNR as well as colorizes the images under complex textures.
Semi-blind image deblurring by a proximal alternating minimization method with convergence guarantees

The semi-blind image deblurring problem aims to simultaneously estimate the clean image and the point spread function (PSF), which results in a (jointly) nonconvex optimization problem. In this paper, we develop an efficient algorithm to tackle the corresponding minimization problem based on the framework of the proximal alternating minimization (PAM). We also establish the convergence of the proposed algorithm under a mild assumption. Numerical experiments demonstrate our approach could obtain a more robust performance than the related state-of-the-art semi-blind image deblurring method.
Mining algorithm for weighted FP-tree frequent item sets based on two-dimensional table

Abstract FP-growth algorithm is a classic algorithm of mining frequent item sets, but there exist certain disadvantages for mining the weighted frequent item sets. Based on the weighted downward closure property of the weighted model, this paper proposed that the weighted support was recorded in the two-dimensional table. This method has saved the process to search for the first conditional pattern base by traversing the weighted FP-tree, improved the efficiency of the weighted frequent item sets generated.
Soft Dropout Method in Training of Contextual Neural Networks

Various regularization techniques were developed to prevent many adverse effects that may appear during the training of contextual and non-contextual neural networks. The problems include e.g.: overfitting, vanishing of the gradient and too high increase in weight values. A commonly used solution that limits many of those is the dropout. The goal of this paper is to propose and analyze a new type of dropout - Soft Dropout. Unlike traditional dropout regularization, in Soft Dropout neurons are excluded only partially, what is regulated by additional, continuous muting factor. This change can help to generate classification models with lower overfitting. The paper present results suggesting that Soft Dropout can help to generate classification models with lower overfitting than standard dropout technique. Experiments are performed for selected benchmark and real-life datasets with MLP and Contextual Neural Networks.
Action Recognition in Sports Video Considering Location Information

The purpose of this study is to develop a tactics analysis system using image recognition for rugby. With the Rugby World Cup in 2019 and the Tokyo Olympics in 2020, demand for sports video analysis is increasing. Rugby has more complicated play such as dense play than other sports, and the ball is hidden between players, making it difficult to track. By developing a high-precision analysis technology for rugby with few research cases, we thought that it could be used for other sports and industrial fields other than sports. In this research, we propose a method that adds spatial information to time-series information as a new feature. Using the coordinates obtained by projectively transforming the match video onto the bird’s-eye view image, play classification was performed using the player position, the ball position, and the dense area position as feature amounts. Also, in order to further improve the detection accuracy of the boundaries between plays, attention was paid to the positional relationship of each player on the field.
Spatiotemporal Saliency Based Multi-stream Networks for Action Recognition

Human action recognition is a challenging research topic since videos often contain clutter backgrounds, which impairs the performance of human action recognition. In this paper, we propose a novel spatiotemporal saliency based multi-stream ResNet for human action recognition, which combines three different streams: a spatial stream with RGB frames as input, a temporal stream with optical flow frames as input, and a spatiotemporal saliency stream with spatiotemporal saliency maps as input. The spatiotemporal saliency stream is responsible for capturing the spatiotemporal object foreground information from spatiotemporal saliency maps which are generated by a geodesic distance based video segmentation method. Such architecture can reduce the background interference in videos and provide the spatiotemporal object foreground information for human action recognition. Experimental results on UCF101 and HMDB51 datasets demonstrate that the complementary spatiotemporal information can further improve the performance of action recognition, and our proposed method obtains the competitive performance compared with the state-of-the-art methods.
A Novel Approach for Collective Anomaly Detection in Internet of Things

Internet of Things (IoT) is a network which connects several devices with the internet to have quick and real-time information transmission, attaining intelligent management. IoT is still in its beginning so it faces a lot of challenges. One huge challenge related to data management is the detection of collective anomalies. A collective anomaly is a set of points which differ as a whole from the rest of the data points. However, the individual points are not considered anomalies. Detection of collective anomalies is of huge importance as collective anomalies usually denote the occurrence of events or certain incidents that should be detected as soon as possible before they cause damage to the surrounding environment. Detection of collective anomalies is considered a complicated process as it involves more than one point rather than only one point as in point anomaly detection process. As a result, in this paper, we propose a novel approach of collective anomaly detection using a variable sized window in IoT. Two techniques were used to detect the collective anomalies, namely: K-Means clustering and isolation forests. The proposed approach was applied on a machine temperature dataset of size around 22,000 and the experiments showed promising results.
A Matlab Implementation of the Emerging HTJ2K Standard

The High Throughput JPEG 2000 (HTJ2K) is the new part of JPEG 2000 family of standards and it is currently at FDIS status within ISO/IEC JTC 1/SC 29/WG 1 (JPEG). In this paper, an implementation with MATLAB is introduced. The MATLAB implementation is not only a good prototype of an actual product but an option for easy understanding of the new block-coding algorithm adopted in the HTJ2K standard. The experimental results demonstrate the encoding/decoding speed of the HTJ2K is significantly faster than that of the conventional JPEG 2000 Part 1.
Detection of Depression Related Posts in Tweets Using Classification Methods – A Comparative Analysis

A 15-step pre-processing procedure is proposed to improve the accuracy of sentiment mining of depression related posts in the tweets. Perhaps, for the first time, converting emoticons in the depression related tweets into text form is proposed during the pre-processing stage. In this paper, Term Frequency-Inverse Document Frequency with n-grams is used for feature extraction. Sentiment analysis conducted on a dataset consisting of 1.6 million depression related tweets using the proposed pre-processing module with feature extraction using Term Frequency-Inverse Document Frequency with n-grams and Logistic Regression (LR) for classification resulted in 81% of accuracy in detecting depression related tweets.
Network Traffic Classification by Packet Length Signature Extraction

Encrypted network traffic classification has been a topic of research for many decades now, where the goal is to provide traffic visibility and enhance network management. Recent research work mainly talks about statistical feature based network traffic classification as traditional methods can no longer be used due to its inaccuracy or overhead. In this regard, we propose a packet length signature extraction based approach to classify different classes of traffic such as Audio streaming, Video streaming, Browsing, Chat, P2P etc. In order to achieve this, we have come up with novel feature set to train a J48 decision tree classifier for identifying the classes of network traffic and we also discuss the interpretability of the model.
Performance Analysis of Speaker Identification using Gaussian Mixture Model and Support Vector Machine

This paper presents a comparative analysis of text-independent speaker identification techniques with Gaussian Mixture Model (GMM) and Support Vector Machine (SVM) separately. As feature vector, it uses Mel-frequency cepstral coefficients (MFCC), its delta derivatives (ΔMFCC) and double delta derivatives (ΔΔMFCC). The aim is to test the accuracy of the proposed models for different sizes of MFCC feature vector on fine-tuning GMM and SVM models. The proposed experimental setup with a frame overlap of 75% and MFCC feature size of 20MFCC + 20-ΔMFCC + 20-ΔΔMFCC coefficients perform better on SVM than GMM. On prepared data set, the utmost accuracy of the model built using SVM is 100% whereas that of GMM is 95.74%. The data set is prepared using voice samples of native speakers taken from Voxforge website and personal survey. The diversity in the speech corpus shows that the method performs equally well irrespective of language, gender, age, and region.
Image Based Deep Learning Model for Movie Trailer Genre Classification

A deep learning based movie genre classification model, integrating the convolutional neural network (CNN) with the long-short term memory network (LSTM), was presented in this study. In the image process, a series of key-frame features was obtained by using the CNN so that the LSTM can be applied to learn the dynamic features of the key-frames for classification. In the experiment, well-known traditional movie features as well as the deep learning models used in other researches were used for comparisons to verify the performance of the proposed deep learning based movie genre classification model.
An application to Generate, Correct and Grade Multiple-choice Tests

When having hundreds or thousands of multiple-choice exams to correct and grade, time optimization, minimized delays on grades delivery, a transparent correction process and agility during the period of grade complaints are important issues. Our paper describes the design, implementation and results of SICOA, a web-based application for the automatic correction and grading of multiple-choice tests to tackle these challenges. The automatic correction is based on the optical recognition of the marks done by candidates on exams. This web-based software generates fully customized answer sheets that are printed and distributed to candidates. The applicant's personal information is encrypted and protected with a QR Code printed on exams. Answer sheets are scanned into the system and its marks are compared against previously defined exam variants with the correct answers. Digitization avoids the loss of exams, creates a digital archive of exams thus facilitating their review when analyzing the complaints of candidates or for future research work. A roll-based access control matrix is used. Operations performed in the automatic correction system can be audited.
Competencias mediáticas de estudiantes de bachillerato en Ecuador frente al currículo escolar

This article is based on the diagnosis made of the competence levels of the Baccalaureate students in Ecuador in relation to the media. A qualitative study of content analysis in the baccalaureate curriculum following the dimensions of the concept of media competence of Ferrés and Piscitelli was carried out. The study made it possible to determine that 8% of the total skills contemplated in the baccalaureate contribute to the development of media skills, while 92% do not. This proves that the percentage is low compared to the total of skills worked, limiting with this the ability of students to perceive, analyze, discriminate and enjoy media communication.
Crowdsensing Based Prompt Emergency Discovery: A Sequential Detection Approach

The growth in the number of smart devices has mobilized the rise of CrowdSensing (CS) as an enabler of smart cities, where state-of-the-art technologies are utilized to improve citizens' quality of life. CS is a novel sensing paradigm that leverages data collected from smart devices to support a wide range of services. Particularly, smart emergency management systems are attracting increasing attention due to their potential to save lives, as they accelerate the delivery of emergency services including detection, mitigation and recovery. In this paper, we study the problem of the detection of an abnormal change in a monitored sensory variable, where the change is suggestive of an emergency situation. Specifically, we formulate our problem as a sequential change-point detection problem, where the underlying distribution of the variable changes at an unknown time. Our aim is to detect the change-point with minimal delay, subject to certain performance constraints. We utilize Shiryaev's optimal solution in two variants of the problem depending on the mobility behaviour of the participants, and conduct simulation experiments to show the performance of our schemes.
Comparison of Algorithms for Clustering of Partial Discharge Signals under DC Voltage

The following topics are dealt with: partial discharge measurement; transformer oil; power generation economics; ageing; power transformer insulation; power transmission lines; power engineering computing; photovoltaic power systems; power grids; power transformers.
An Improved Deep Mutual-Attention Learning Model for Person Re-Identification

Person re-identification is the task of matching pedestrian images across a network of non-overlapping camera views. It poses aggregated challenges resulted from random human pose, clutter from the background, illumination variations, and other factors. There has been a vast number of studies in recent years with promising success. However, key challenges have not been adequately addressed and continue to result in sub-optimal performance. Attention-based person re-identification gains more popularity in identifying discriminatory features from person images. Its potential in terms of extracting features common to a pair of person images across the feature extraction pipeline has not been be fully exploited. In this paper, we propose a novel attention-based Siamese network driven by a mutual-attention module decomposed into spatial and channel components. The proposed mutual-attention module not only leads feature extraction to the discriminative part of individual images, but also fuses mutual features symmetrically across pairs of person images to get informative regions common to both input images. Our model simultaneously learns feature embedding for discriminative cues and the similarity measure. The proposed model is optimized with multi-task loss, namely classification and verification loss. It is further optimized by a learnable mutual-attention module to facilitate an efficient and adaptive learning. The proposed model is thoroughly evaluated on extensively used large-scale datasets, Market-1501 and Duke-MTMC-ReID. Our experimental results show competitive results with the state-of-the-art works and the effectiveness of the mutual-attention module.
Exploiting flaws in Windbg: how to escape or fool debuggers from existing flaws

In order to perform their goals without being detected, Malware should have a battle of wits with the analyzer. Such a way, they use a large variety of stealth methods to perform their missions. These methods allow to slow or block analysis. Most of the time, these tricks are often operating system or CPU oriented (dll injection, exception handler or API abuse). In addition, they are although focused on the most used analyst tools. These attacks, allow, among other things, to display erroneous information on the analysis tools or to silently detect it so that the malware can change its behavior in case of analysis. Depending of the degree of error of the analyzing tools used, it could become partially or totally ineffective. More than just flowed malware analysts, it is a great drawback in order to find bugs in regular software. In this article, we show how to exploits errors inside debuggers and mainly inside one of the most use: Windbg. This list of errors impacting this Microsoft's tool mainly concerns few flaws in the disassembly engine or in the debug procedure. Some are present in the debugger from years... More directly, we show different ways to block or disturb the normal behaviour of Windbg. Thus, even if these errors are not always critical, they can negatively impact the use of software by any user. For instance, we describe a new way to know if the current process is running under the control of Windbg. This is exactly what malware author are looking for to detect analysis. Due to the complexity of architecture such as x64 and x86, it is hard to design and develop a complete disassembling tool. In fact, no disassembling tool is perfect and most of those we tested have at least one of the flaws which are shown in this article. Among the different flaws, we have int 3 misinterpretation, wrong jump interpretation, partial instruction prefix handling and unsupported instruction. Moreover, nothing prevents these tools to have other kind of errors. Thus, in order to analyze software efficiently, it is necessary to improve the analyzer tools. In this way, we offer different solution to correct the bug we encounter on the different tools.
A Comparative Study of Task Scheduling Algorithm in Cloud Computing

Nowadays, cloud computing plays an important role in both academia and industry. However, due to high demands from the clients and limited resources, it is required to transfer a few workloads to other data centers. This process helps to complete the applications submitted to the data centers even through more flexible and cheaper resources. As there are many tasks lined up at the server side and the resources are heterogeneous in nature, scheduling the tasks in multi-cloud environment is always a challenge. The loads should be processed in such a way that all the resources are used very efficiently. In this work, we have performed a comparative study on different task scheduling algorithms proposed in the literature in cloud computing.