Reviewerly/exHarmony huggingface dataset:
Review on Various Machine Learning and Deep Learning Techniques for Prediction and Classification of Quotidian Datasets

Machine learning can be defined as the ability of a computer to learn and solve a problem without being explicitly coded. The efficiency of the program increases with experience through the task specified. In traditional programming, the program and the input are specified to get the output, but in the case of machine learning, the targets and predictors are provided to the algorithm make the process trained. This chapter focuses on various machine learning techniques and their performance with commonly used datasets. A supervised learning algorithm consists of a target variable that is to be predicted from a given set of predictors. Using these established targets is a function that plots targets to a given set of predictors. The training process allows the system to train the unknown data and continues until the model achieves a desired level of accuracy on the training data. The supervised methods can be usually categorized as classification and regression. This chapter discourses some of the popular supervised machine learning algorithms and their performances using quotidian datasets. This chapter also discusses some of the non-linear regression techniques and some insights on deep learning with respect to object recognition.
Integrating Physics in Disaster Risk Reduction (DRR) through YouTube Videos

This paper is focused on the integration of physics in Disaster Risk Reduction (DRR) using a Web 2.0 tool, YouTube and its effect on the students' understanding of the physics of DRR. There were 96 senior high school students from the General Academic Strand (GAS) who were taking Disaster Risk Reduction (DRR) Management subject in one semester of School Year 2019-2020 in one of the public school institution in Bacoor City, Cavite. One of the required outputs in the subject is to develop a community preparedness plan to lessen and avoid the effect of different disasters. However, the student participants did not have any physics subject in the entire school year yet had physics classes when they were in Grade 7 to Grade 11. YouTube videos were used to help the students revisit and connect those physics lessons to their DRR lessons. The YT videos were selected by the researchers and was checked and validated by the DRR teacher. The video links were given to the students at the start of the discussion of the specific DRR lesson and formative tests were given at the end of each lesson. Results showed that there was an increasing mean formative test scores and that the DRR teacher observed that students became more interested in the lessons. It was noted that students became more active in class and some were able connect physics concepts in their DRR lessons as they explain and give examples. Students' responses to the questionnaire also revealed that YT videos helped the students understand the physics concepts of DRR and that it taught them to be an independent learner. Further, specific cases and/or situations that limit the students from recommending the use of YT videos in their other subjects was elucidated.
Using Topic Modelling to Improve Prediction of Financial Report Commentary Classes

We consider the task of predicting the class of commentaries associated with financial discrepancies between actual and estimated sales data. Such analysis of the financial data is helpful in meeting targets and assessing the overall performance of the company. While generating a commentary and its associated class is the task of an analyst, these manual operations might be erroneous and as a result, might lead to a diminished performance for the employed prediction model due to wrong class labels. Accordingly, we propose using topic modelling, namely Latent Dirichlet Allocation (LDA), for automated extraction of the classes of the commentaries. In addition, we use feature selection strategies to improve the accuracy of the prediction models. Our analysis with various time series classification methods points to improved performance due to LDA and feature selection.
Grading Evaluation of Typhoon Fault Impact in Electric Distribution Network Based on Data Mining

A grading evaluation method for typhoon faults impact on distribution network lines is proposed in this paper based on data mining. Firstly, based on the online monitoring of big data resources in the distribution network, the typhoon fault impact evaluation index system is constructed. Then, the data mining method such as principal component analysis and singlelevel analysis is used to obtain the weight of evaluation index. Finally, the typhoon fault impact comprehensive score of each line is calculated and the evaluation level is divided. The data of typhoon fault line of distribution network in a certain province of China is selected to analyze the practicality and effectiveness of the proposed algorithm. Using the rating result, we can accurately locate the weak link of distribution network line typhoon fault. It is of great significance to the precise operation and distribution network line upgrade of power supply company.
Exploring Lawful Hacking as a Possible Answer to the 'Going Dark' Debate

The debate on government access to encrypted data, popularly known as the “going dark” debate, has intensified over the years. On the one hand, law enforcement authorities have been pushing for mandatory exceptional access mechanisms on encryption systems in order to enable criminal investigations of both data in transit and at rest. On the other hand, both technical and industry experts argue that this solution compromises the security of encrypted systems and, thus, the privacy of their users. Some claim that other means of investigation could provide the information authorities seek without weakening encryption, with lawful hacking being one of the most suggested alternatives. “Lawful hacking,” also known as “government hacking,” consists in the deployment, by investigative authorities, of tools that allow for the intrusion into computer systems, enabling access to its contents. Although this form of investigation seems to be essential in an increasingly connected society, it is important to understand security and privacy risks of different lawful hacking regulatory approaches. Considering that some countries are already enacting legal frameworks related to it, I aim to highlight the issues that should be properly addressed in order to position lawful hacking as one of the viable answers to the “going dark” debate.
Multiclass Multiple Object Tracking

Efficient and accurate object detection, classification and tracking have played a very important role in the advancement of computer vision systems. With the rise of deep learning techniques, the accuracy of visioning systems has increased a lot. This proposed work targets to incorporate state-of-the-art technology with the aim of achieving very high accuracy for real-time multiclass multiple object tracking with occlusion handling. A real-time multiclass multiple object tracker (MOT) is proposed using state-of-the-art object detection framework and deep convolutional neural networks. The proposed model is developed using YOLOv3 architecture for object detection and for real-time tracking, and a longer period of occlusion handling simple online and real-time tracking (SORT) is used as a base algorithm. The velocity and acceleration of objects along with their appearance information are used to predict the object location upon occlusion. Later detections are compared with MOT dataset detections and the YOLO bounding boxes, and based on their IOU matrix, normalized detections are provided.
A Flow Correlation Scheme Based on Perceptual Hash and Time-Frequency Feature

Flow correlation can identify attackers who use anonymous networks or stepping stones. The current flow correlation scheme based on watermark can effectively trace the network traffic. But it is difficult to balance robustness and invisibility. This paper presents an innovative flow correlation scheme that guarantees invisibility. First, the scheme generates a two-dimensional feature matrix by segmenting the network flow. Then, features of frequency and time are extracted from the matrix and mapped into perceptual hash sequences. Finally, by comparing the hash sequence similarity to correlate the network flow, the scheme reduces the complexity of the correlation while ensuring the accuracy of the flow correlation. Experimental results show that our scheme is robust to jitter, packet insertion and loss.
Infinite Swapping Algorithm for Training Restricted Boltzmann Machines

Given the important role latent variable models play, for example in statistical learning, there is currently a growing need for efficient Monte Carlo methods for conducting inference on the latent variables given data. Recently, Desjardins et al. (JMLR Workshop and Conference Proceedings: AISTATS 2010, pp. 145–152, 2010 [3]) explored the use of the parallel tempering algorithm for training restricted Boltzmann machines, showing considerable improvement over the previous state-of-the-art. In this paper we continue their efforts by comparing previous methods, including parallel tempering, with the infinite swapping algorithm, an MCMC method first conceived when attempting to optimise performance of parallel tempering (Dupuis et al. in J. Chem. Phys. 137, 2012 [7]), for the training task. We implement a Gibbs-sampling version of infinite swapping and evaluate its performance on a number of test cases, concluding that the algorithm enjoys better mixing properties than both persistent contrastive divergence and parallel tempering for complex energy landscapes associated with restricted Boltzmann machines.
An Intelligent Approach for CRC Models Based Agile Software Requirement Engineering Using SBVR

In requirement engineering (RE) for agile software development, the Class-Responsibility-Collaborator (CRC) models are used as important brainstorming tool. However, manual generation of such CRC models by analyzing the requirements is a difficult and time-consuming task due to ambiguity and informal nature of natural languages-based software requirements. This paper introduces an improved requirement engineering technique based on CRC models that can help in specifying and analyzing software requirements in a better and faster way and curtailing difficulties associated with the traditional RE analysis technique. The proposed technique employs Semantics of Business Vocabulary and Rules (SBVR) to capture and specify software requirements in a controlled natural language. The SBVR representation is processed to extract object-oriented information and map the extracted information to CRC models in both textual and visual form. The proposed approach is implemented as an Eclipse plugin prototype SBVR2CRC as a proof of concept and the results of the experiments validate the effectiveness of the presented approach. Results show that such automated approach not only saves certain time and effort but also assists in generation of better CRC models and simplifies the CRC models based agile software development.
Image Saliency Detection Based on Low-Level Features and Boundary Prior

In this paper, a saliency detection algorithm based on image low-level features and boundary prior is proposed. Firstly, the structure of image patch is constructed. Based on the image patch, the saliency value of patch contrast based on the low-level features is calculated by using image color and texture, and the contrast saliency image is obtained. Second, by using the image boundary prior condition, considering the color difference and spatial connectivity between image patch and boundary area, the saliency value of patch contrast based on the boundary prior is calculated, and the prior saliency image is obtained. Then, the contrast saliency image and the prior saliency image are fused to highlight the salient object and enhance the saliency image, and obtain higher quality saliency image and the precision. Finally, Experiments on international open datasets show that the algorithm is in line with human visual perception and has higher precision and better recall rate compared with the existing mature methods.
Accuracy Improvement of Human Tracking in Aerial Images Using Error Correction Based on Color Information

The authors are trying to construct a real-time vital sensing system during exercise where humans wearing sensor nodes move quickly and their density becomes sometimes higher. In this case, existing multi-hop networking using RSSI or GPS to gather vital signs exercisers may not work appropriately. To solve this problem, the authors are proposing image-assisted routing (shortly IAR) that estimates the locations of sensor nodes by image processing. This paper proposes a tracking scheme with error correction based on color information, which is indispensable for IAR. Experimental results using actual images taken from a UAV showed that the proposed scheme achieved accurate tracking using only simple operations without sophisticated state estimation and computationally exhaustive deep learning: MT reached 100% by the proposed scheme.
Domain-Specific Linguistic Support for Students Studying Translation in Oil Industry

The paper considers the current stage of our research and work on the UNITECH-project open at www.unitechbase.com, which is being developed by our group to provide teaching and learning within academic curricula in Professional Translation and Foreign Language for Special Purposes. Our UNITECH-platform integrates online courses with domain-specific translation topics and academic texts, special terminology knowledgebase, presented as descriptions of visual objects and technical tools, domain ontologies and multilingual glossaries. This platform is constantly developing, it is verified and used within our partner network of Russian universities. This paper presents a new course in Oil Industry and two new linguistic resources we have recently developed to support this course. These are the Student Thesaurus of Petroleum industry and Student Ontology of Oil and Gas production. We also discuss our approach to select terminology units of the oil thesaurus and domain ontology. In developing the structure of these linguistic tools we have analyzed about 40,000 term units and their relations for the English and Russian languages.
A Method of Workpiece Coherent Line Detection Based on Progressive Probabilistic Hough Transform

The detection and extraction of workpiece lines is the basis and key in the industrial production practice. In order to solve the discontinuity and disconnection problem during the line detection and extraction of a workpiece, we propose a coherent line detection method, which is Improved Progressive Probabilistic Hough Transform line detection(PPHT). Improved PPHT first performs edge detection combine with original PPHT algorithm to find lines of workpiece object. After discarding noise lines, this method divide the detected lines into several groups by finding collinear candidates. Then we calculate the supporting pixels of every group with foreground image, and apply Least Square Regression to achieve final line results. In this paper, we performed an experiment on thirty images of three different rectangular workpieces.The results indicate that, comparing to the PPHT, Improved PPHT decreased the relative error rate of the linear detection accuracy p by 62.06% on average, and the relative error rate of the recall rate R has decreased by 43.6% on average, thereby significantly mitigate the discontinuity existing in PPHT.
Analyzing the Applicability of Intelligent Data Mining Techniques for Texture Classification

Textures represent the irregularities and spatial arrangements of pixels in a surface. Textures are significant in representing any real-world object, and hence, the classification of various textures has significant application in today’s world especially with respect to medical, satellite images and visual inspection. Real-world textures are complex and more difficult to represent. We introduce the data mining techniques used in the image processing domain especially for classifying textures. Enormous amount of data is generated everyday. Data is mined for identifying knowledge by uncovering the hidden patterns in the data which are helpful in classification, prediction and decision-making. This paper explores the potential of using data mining techniques in texture classification domain. In this work, we also analyze the literature to find various data mining techniques applied for texture classification in different applications. Our objective is to present some recent developments in applying data mining to texture classification system.
Human Action Analysis Using Virtual Grounding Point and Motion History

In this paper, we propose an approach to human action analysis for home care monitoring system in the aspect of image processing and life science technologies. We introduce a new concept of a virtual grounding point representing the position of a target person as an innovated feature for action analysis. Specifically, in developing action analysis, the background subtraction is firstly conducted by applying the Mixture of Gaussian and low rank subspace learning. After that, the graph cut is embedded to enhance the foregrounds in order to detect both of moving and motionless object. Secondly, the virtual grounding point is calculated by using the centroid of silhouette image. Finally, motion of the person is estimated by using timed motion history image in order to improve the accuracy of action analysis. A series of the experiments are conducted to confirm the effectiveness of the proposed method.
Single View 3D Face Reconstruction

3D face reconstruction from a single 2D image is a fundamental computer vision problem of extraordinary difficulty that dates back to the 1980s. Briefly, it is the task of recovering the three-dimensional geometry of a human face from a single RGB image. While the problem of automatically estimating the 3D structure of a generic scene from RGB images can be regarded as a general task, the particular morphology and non-rigid nature of human faces make it a challenging problem for which dedicated approaches are still currently studied. This chapter aims at providing an overview of the problem, its evolutions, the current state of the art, and future trends.
Montenegrin Dictionary Based Brute Force Attack

Cyber-attacks and cyber-crimes are growing quickly and users are becoming unsafe on the internet. Cyber threats will put users increasingly at risk. For that reason statistical analysis have been done in order to determine how strong passwords are in the area of Montenegro, and to determine if there is a way to crack them by using brute force attack. Brute force attack or attack by “force” is based on cracking the password with all possible combinations, including cracking Wi-Fi routers, social media account, e-mails, hashes, zip or pdf files protected by password, internet protocols (telnet, ftp, http, https, smb.) etc. We will explain brute force attack for cracking passwords based on statistical analysis data in Montenegro. The first reason we will be doing this is to improve security, password strength, and the conscience of the people in Montenegro. The second reason is to help the people in law enforcement who deal with digital forensics, to find digital evidence in cyber incidents in the shortest amount of time.
LibGuides: BrowZine Library: Installation

Use BrowZine, on your desktop or mobile device, to easily find, read, and monitor thousands of scholarly journals! How to install BrowZine on your desktop or mobile device.
Sharpening Method using TV Regularization for Black-out Images

We propose an algorithm that can obtain clear and sharp images without losing the characteristics for blackout images. It utilizes Total Variation regularization, and uses gamma correction for cartoon components and Pulse Enhancement Filter for texture components. The experimental results show that it can obtain clear and sharp images. Our proposed method can be used for life science technology such as security camera and face authentication system.
Pixel Wise Image Fusion by using Quantization Table for Data Security

Secure data transmission is preceded by use of steganography which enhances the media in such a way so that it cannot be decrypted unethically or viewed by a third party. At present, media such as images are mainly used to hide data while transmission is taking place. In this paper, we have used this method of encryption in a modified manner, where the main focus is to encrypt image data with higher PSNR value, because the peak signal-to-noise ratio (PSNR) and payload value determines the effectiveness, security and difficulty level of decryption. The Structural Similarity Index (SSIM) tells the similarity value that shows the need of filtration and noise removal. Initially the number of pixels in a cover photo is decreased which is done by dividing the pixels based on the quantization record. To increase the efficiency of the procedure we used the quantization value as the key value which is unique for encryption and decryption. Pixel intensity and positional values of the pixels which is defined by the respective graphical coordinate values is used to generate the quantization key. After recognizing the position and positional ranges of each pixel the coordinates are divided in 16 sub parts and based on that their quantization values are implemented from proposed quantization table. In this methodology, the cipher data cannot be decrypted without knowing the encryption algorithm and key value for encryption of original data.
LBVis: Interactive Dynamic Load Balancing Visualization for Parallel Particle Tracing

We propose an interactive visual analytical approach to exploring and diagnosing the dynamic load balance (data and task partition) process of parallel particle tracing in flow visualization. To understand the complex nature of the parallel processes, it is necessary to integrate the information of the behaviors and patterns of the computing processes, data changes and movements, task status and exchanges, and gain the insight of the relationships among them. In our proposed approach, the data and task behaviors are visualized through a graph with a fine-designed layout, in which node glyphs are dedicated to showing the status of processes and the links represent the data or task transfer between different computation rounds and processes. User interactions are supported to facilitate the exploration of performance analysis. We provide a case study to demonstrate that the proposed approach enables users to identify the bottlenecks during this process, and thus help optimize the related algorithms.
On alternative quantization for doubly weighted approximation and integration over unbounded domains

It is known that for a ϱ-weighted Lq approximation of single variable functions defined on a finite or infinite interval, whose rth derivatives are in a ψ-weighted Lp space, the minimal error of approximations that use n samples of f is proportional to ‖ω1∕α‖L1α‖f(r)ψ‖Lpn−r+(1∕p−1∕q)+, where ω=ϱ∕ψ and α=r−1∕p+1∕q, provided that ‖ω1∕α‖L1<+∞. Moreover, the optimal sample points are determined by quantiles of ω1∕α. In this paper, we show how the error of the best approximation changes when the sample points are determined by a quantizer κ other than ω. Our results can be applied in situations when an alternative quantizer has to be used because ω is not known exactly or is too complicated to handle computationally. The results for q=1 are also applicable to ϱ-weighted integration over finite and infinite intervals.