Reviewerly/exHarmony huggingface dataset:
Multi-layer Adaptive Sampling for Per-Flow Spread Measurement

Per-flow spread measurement in high-speed networks, which aims to estimate the number of distinct elements of each flow, plays an important role in many practical applications. Most existing solutions adopt compact data structures (i.e., sketches) to share memory units among flows so that they can fit in limited on-chip memory, resulting in low estimation accuracy for small flows. Unlike sketch-based solutions, non-duplicate sampling measures per-flow spreads by sampling each distinct element with the same sampling probability. However, it ignores that, compared to small flows, large flows only need lower sampling probabilities to achieve the same relative estimation error, wasting significant on-chip memory for large flows. This paper presents multi-layer adaptive sampling to complement the prior work by assigning lower probabilities to larger flows. The proposed framework employs a multi-layer model to sample distinct elements, ensuring that most small flows will stay in lower layers and large flows will get to higher layers. Besides, higher layers are designed with smaller overall probabilities to ensure that larger flows have lower sampling probabilities. Experimental results based on real Internet traces show that, compared to the state-of-the-art method, our solution can reduce up to $$86\%$$ average relative errors for per-flow spread estimation and reduce the FPRs and FNRs of flow misclassification by around one to two magnitudes.
SIITR: A Semantic Infused Intelligent Approach for Tag Recommendation

In the present-day time, Tag recommendation is of utmost importance as it heads into annotations and labeling of several entities or elements, be it images or data over the World Wide Web. Thereby, in recent times of Semantic Web, tagging is one of the crucial aspects to incorporate meanings to the entities or elements on the World Wide Web. As a result, socially relevant tag recommendation is of extreme value. This paper puts forth a Semantic infused intelligent approach is proposed to recommend tags for socially relevant images using Jaccard and Cosine similarity and SVM to classify the documents. The above experiments have been conducted for Heritage CHIC dataset and New Image Corpus and the accuracy percentage of 94.48% is achieved.
Efficiency of Students’ Independent Work: The Problem and Ways to Improve

The relevance of the study is due to the importance of the problem of educating professionals and specialists who can think critically and independently becomes in modern society. The amount of information and knowledge increase that a student should learn. In higher education institutions, there is an objective need to pay more attention to the independent work of students to contribute to their effective and effective professional development. To preserve and improve the effectiveness of learning, educational institutions should strive to find new ways to use the educational process to organize independent work of students. In this regard, the author’s article is aimed at determining the essence and features of independent work of students, ways to implement it and principles for improving performance in higher educational institutions. The leading methods of research of this problem are methods of analysis, synthesis, deduction, generalization and comparison, which helped to determine the main essence of independent work of students and the problems of this component of learning. The article presents various approaches to the study and interpretation of the process of independent work of students; analyzed the work of teachers, researchers who deal with the problem of motivation and improving the effectiveness of this type of activity; focused on the difficulties and features of its organization; proposed various methods and methods to optimize this process; described the aspects of successful implementation of this stage of training; diagnosed the problems that the student faces during the performance of independent work; determined the levels of this activity (reproductive (training), reconstructive and creative search); demonstrated the latest information technology approaches to the organization of independent educational activities of students; diagnosed the effectiveness of independent work in the learning process based on the assimilation of educational material, monitoring of educational and cognitive activity of students; recommendations and requirements for improving the effectiveness of independent work are revealed. The materials of the article are of practical and theoretical value for students, as they contribute to the development of their initiative and professional independence. In addition, the information may be relevant for future teachers, scientists, and teachers who will be able to adjust tasks according to the individual characteristics of students and systematically monitor the educational process
Spectrum-Based Fault Localization Aided by Program Slicing

The International Conference on Software Maintenance and Evolution is the premier international forum for researchers and practitioners from academia, industry, and government to present, discuss, and debate the most recent ideas, experiences, and challenges in software maintenance and evolution.
ASP-CSP: Grouping Algorithm for Decision Support System of Community Service Program

Forming a working group that is not customized to the interests and abilities of each student of the Community Service Program (CSP) can affect student activities. Universitas Sebelas Maret (UNS) has a system that can form student groups and locations, but can't consider the student's interest and ability factors and location conditions in choosing a theme. Therefore, this work developed a decision support system that can help to give the choice of CSP themes that according to the ability of the students and the needs of the location, and can help in forming some alternatives of the group. The system implements the ASP-CSP algorithm which is combination of AHP-SAW methods and the permutation model. AHP is used for determining the weight of criteria, SAW is used for determining the rank of alternative, and the permutation model is used to form a group of students and locations. The result of this work showed that the ASP-CSP algorithm was successfully applied to a varied testing scenario, some of which were grouping 10 locations for the overall theme with each theme forming 1 location group resulting in average distance (total average distance of all combinations) 0.013. Grouping of 22 students for the overall theme with each theme form a varied student group resulting minimum average distance 0.027.
An Approach for Composing Multiple Control Loops Hierarchically

To ensure self-adaptation, the Manager Subsystem must include Control Loop (CL). However, create and perform a single CL able to achieve multi-attributes self-adaptation is difficult. So, the system must include many CLs to ensure separation of concerns. The major challenge is that how multiple CL entities can interact with each other to coordinate the system management? On the other hand, how concerns can separate among different CLs? In this paper, we propose HCLs (Hierarchical Control Loops), a pattern for the manager sub-system, with benefits from the leveraging of the hierarchical and dynamic component model, Fractal. The proposed approach allows managing the complexity of self-adaptation by separating concerns using Fractal component model in the form of an hierarchy of MAPE loops. Furthermore, we distinguish three types of adaptations as following: Local Adaptation, Regional Adaptation, and Superior Adaptation in order to achieve multi-level adapting.
Conducting Effective Digital Forensics Investigations in the COVID-19 Era

The COVID-19 pandemic has impacted many lives around the world and many business processes including the digital forensics investigation processes and how investigations should be conducted from here on out. As early as 1984 a general computer forensics investigation process was developed and continues to evolve with the need of the everchanging environment. The environment has changed once more due to the COVID-19 pandemic that was first detected in December 2019 and is with us up to date. This report reviews existing digital forensics processes throughout the years and adapts new steps to be followed in digital forensics investigations to accommodate the effects of the pandemic. We propose a Pandemic Digital Forensics Model (PDFM) based on the general digital forensics investigation processes that can be utilised in the COVID-19 era or in any other pandemic crisis that may occur.
Selling Political Data: How Political Ad Tech Firms’ Discourses Legitimate Microtargeting

In spite of mounting concerns about the use of microtargeting in politics and attendant regulatory pressure, spending on digital advertising and ad tech has significantly increased over the past decade. In this article, I explore how political ad tech firms pursue continuity for the high stakes business of political microtargeting. Specifically, I present findings from a discourse analysis of the websites of 34 political ad tech firms who have developed microtargeting tools. Applying van Leeuwen’s framework for legitimation in discourse to this analysis, I find that the firms legitimate microtargeting through four key discourses: Rationalization (“Microtargeting is the Right Approach”), mythopoesis (“Microtargeting is How you Win”), moral evaluation (“Microtargeting is the Democratic Thing to Do”), and Authorization (“Everybody’s Doing It”). I argue that these discourses offer insight for understanding and contextualizing ongoing discussions about the future of microtargeting in politics.
Screw Propelled Autonomous Amphibian Waste Collector

Waste collection has become one of the most challenging tasks in highly populated countries. In places like pond and surrounding&#x0027;s, people gather and put light weight waste regularly. Waste collection in both water and land requires unusual manual effort. However, by using robot with special design it become very easy. In this paper, a novel design of screw propelled amphibian robot is proposed to manage waste collection in both land and water. The robot can work autonomously in wide variety of terrains which includes grass, road, marsh, water, etc. The mechanical design of the robot was developed in computer Aided Design (CAD) and prototype was designed and tested in various working environments. Mobile Application was developed for manual control.
Research on Street Space Quality Combined with Attention Multi-Task Deep Learning

In the context of advocating human-oriented urbanism, in order to more accurately evaluate the urban scene. This paper uses the sentiment evaluation of millions of street view images in the Place Pulse 2.0 data set to design a multi-task deep learning network (Mtl-attn-vgg) fused with attention to analyze the urban spatial quality attributes. Multi-task learning is used to simultaneously learn different attributes and the attention mechanism to focus on the characteristics of the object to improve the effect of the convolutional layer on the attribute feature extraction, combined with deep relative attribute learning, use the structural sparsity of the feature matrix to compare the attribute features In addition, classification loss and ranking loss are introduced to constrain the parameters of the deep learning network. Finally, six attributes of street view are beautiful, boring, depressed, lively, safe, and rich. The results show that the Mtl-attn-vgg model improves the feature extraction effect and improves the accuracy of attribute sorting to 78.48%, which is more conducive to urban designers and planners to understand the attribute characteristics of the street scene environment.
Usefulness of Rubric in Assessing Writing Skills Using Clustering

Teaching-Learning process is incomplete without assessment part. The results of the assessment must represent various learning styles and weak areas of learners. Rubric is one popular assessment tool and is used for assessing subjective assessments such as assignments, essays, projects etc. In this paper, its power is exhibited in extracting useful observations about student learning in assessing writing skills. For this purpose, Fuzzy C-means Clustering technique is used to group the student written essays based on the ratings of two raters on six different traits according to the rubric given. The data is taken from one of the Kaggle datasets. The cluster results indicate that if good students pay a little more attention to their writing conventions, they can also come under the list of top performers. This type of revelation benefits those students who are weak in one or two performance areas and can improve quickly by being more attentive towards these.
SPACE: Sparsity Propagation Based DCNN Training Accelerator on Edge

On-edge learning enables edge devices to continually adapt to the new data of AI applications. However, much more computing capacity and memory space are needed to achieve the batch backward propagation oriented training, in which the system power budget is limited by the edge circumstance. As the operands are propagated during the training, useless zero values are inevitably propagated, which will cause unnecessary waste of memory accesses and computations. This paper conducted a thorough analysis of the origin of sparsity in all three phases of the training based on sparse propagation and gives three insights about the absolute sparsity and the nonabsolute sparsity found for efficient deployment of the training process. An efficient training accelerator named SPACE which can not only reduce memory footprint but also delete a massive amount of computations by exploiting the nonabsolute sparsity and the absolute sparsity is proposed. SPACE can improve performance and energy efficiency by a factor of 3.2x and 2.8x, respectively, compared with dense training architecture.
The development of android-based mathematics learning application

Abstract The purpose of this research was to produce an android based mathematics learning application. This research was motivated by the Indonesian government’s policy during the covid-19 pandemic. The government asked people to do all their activity from home. The government’s policy changed all the system including the learning activity system. Based on that, it was necessary to develop a learning application, which would enable students to learn from home. In this research, the learning application that would be developed was an Android based mathematics learning application. The research method used for this purpose was the development research developed by Dick and Carey. The quality standard used for the Product developed in this research were validity, practical, and effective, which was checked based on expert validation and trial results.
Methods for recovering deleted data from the Realm database: Case study on Minitalk and Xabber

Mobile devices are essential in digital forensics, and their artifacts are frequently used as critical evidence in court. People use various applications (apps) on mobile devices, and the app data are managed and stored in the form of a database. Users can arbitrarily delete data from the database; such capability acts as an anti-forensic method. Recovering deleted data is important for the acquisition of additional data, and can help to overcome this limitation. In this work, we focused on Realm, a database optimized for mobile devices. We analyzed the structure of the Realm database and various deletion functions. We devised a method for recovering deleted Realm database data on the basis of the data structure and unallocated area. The proposed method was verified using proof of concept code, and the average recovery rate was determined for the MiniTalk and Xabber apps. • Analyzing the data structure of Realm DB and identifying the operations and features of four types of deletion functions. • Suggesting three types of methods for recovering deleted data based on database structure and type. Recovering non-overwritten nodes Recovering deleted DB tables on the basis of root node structure (table unit recovery with normal root node) Recovering deleted DB columns on the basis of leaf node structure disconnected from the root node (column unit recovery) Recovering partially overwritten nodes Recovering deleted DB tables on the basis of partially overwritten root node structure (table unit recovery with damaged root node) Recovering deleted DB field on the basis of data type from partially overwritten the leaf node (field unit recovery) •Implementing the PoC code for data recovery experiments in the MiniTalk and Xabber apps, and presenting the rate of deleted data recovery.
Analysis of the level of stress in teachers

This research aims to know the stress level of teachers of one of the Santa Catarina Technological University majors and, if it is high, take the appropriate measures to try to reduce it to a safer proportion for teachers, and thus prevent teachers from getting sick or incapacitated very often. It is known to all that if the teacher does not fulfill his obligations, the one directly affected is the student, who will have a poor academic performance. To measure the level of stress, a questionnaire consisting of 23 questions will be applied to the teachers of the cited school, the options that will have to answer those questions are 4: nothing at all, a little, moderately and strongly, with values of 1, 2, 3 and 4 respectively; The answers selected by the teachers will be added and compared with a low, medium and high scale. Subsequently, a relationship of the level of stress against teacher performance will be established through a statistical treatment, where the correlation of these variables will be established by the method of the linear correlation coefficient r of Pearson. Likewise, the equation of the regression line by least squares will also be established with its corresponding graph for analysis.
Cloud Security in Reliable Blockchain Technology

The Blockchain is a release and dispersed ledger. Latest communications and information can be added on to a blockchain but precedent information cannot be obliterated. The Blockchain operation connecting two or more parties is confirmable and everlasting verification of information. In present system A BaaS platform which grant blockchain tune-up over cloud computing system exploitation and method supervising, smart convention investigation with analysis. This research work nearby consistency of BaaS communications. Looking for more exhaustive and adaptable assessment technique for BaaS communications. Transaction through blockchain technology is more secure and reliable, and it collaborate with decentralized cloud computing will get more reliability. The proposed investigation exertion can affect merkle tree in the deliberated algorithm on smart convention presentation optimization and involuntary refurbish. In this system merkle tree allows competent and protected authentication of huge data structures. Our proposed system include a trusted authority or a cloud provider to become a distribute service provider. Each dealer sends their IoT data with communication integrity, authentication code to the cloud server. Every consumer gives to the proceedings they are concerned in on the cloud. Every supplier becomes authenticated data generator on the cloud. Like this, examination contributors otherwise the users know how to optimize applicable apparatus or obtain equivalent defensive procedures according to the evaluation results. The smart contracts deployments and function calls within that block get executed on the node that mines the block cloud based IoT ecosystem proposed by many companies. All IoT devices communicate to the cloud and get global state info from the cloud. Blockchain technology integrated with cloud avoids cyber attack on cloud.
Research on Design of High-Performance Graph Computing System Based on CPU Heterogeneous Architecture

High performance computing is also known as supercomputing, mainly refers to more data in a short time to help the computer system to complete more computing tasks. Cloud computing, as a parallel distributed computing application concept proposed by the current technological innovation, will use the network to connect a large number of computing resources in practical application, thus forming a computing resource pool with stronger computing capacity and parallel processing capacity, so as to bring more superior computing services to users. From the perspective of overall development, cloud computing, as a new processing mode proposed to achieve high performance computing, is very novel in both application scope and promotion form, which plays a positive role in the future research of computer technology. Therefore, based on the understanding of high performance computing research significance and the central processing unit (CPU) implementation on the basis of the advantages of high performance computing, start from a computer unified device architecture (CUDA) for fast image processing system, in-depth studies are proving CPU heterogeneous architecture as the core of high performance computing system has a certain application advantages.
Cloud Computing Transformation Considering Operational Efficiency

Companies that have their own IT Center built and operated an on-premise system. But recently, cloud computing is considered to secure system reliability and reduce management costs. IT resources can be efficiently utilized and paid for used. Therefore, companies were deploying and migrating their intranet services to cloud servers. However, it is also true that cloud migration makes system complexity increase, and system reliability is compromised by the increase of management points. Cloud computing for each service unit was considered as a way to meet an efficient utilization of IT resources without compromising existing system reliability when providing new services. Cloud system enables real-time orchestration, and has excellent security. As a hybrid method that implemented cloud computing while maintaining the existing infrastructure, it was possible to operate an uninterrupted system as a result of system construction, and security was also enhanced. Cloud computing used to as a service unit, that both operational efficiency and functionality can be satisfied.
Impulse Noise Restoration Using Combined Fuzzy Logic and Adaptive Trimmed Median Filter

This work focuses on the development of algorithm for the removal of impulse noise from digital images in spatial domain in low density as well as high density of noises. Various issues related to the noisy problem are studied for grayscale as well as color images and suitable filtering methods are suggested. In this paper, fuzzy logic is used to detect the noisy pixels in an impulse noise corrupted image and an adaptive trimmed median filtering technique is proposed to remove the detected noisy pixel from gray and color images. When some of the pixels in a considered window of an image are noisy pixels, then median value of the noise-free pixels in that window replaces the processing detected noisy pixel. However, when all the pixels in a selected window are all noisy pixels, then in such cases the possible solution is to replace the processing pixel by the mean and standard deviation values of the elements in the selected window. The combined fuzzy logic and adaptive trimmed median filter approach is also used to preserve the edges and fine details of the images. To assess the performance of the proposed method, several standard grayscale and color test images are used in the experiments which have distinctly different features. The efficacy of the various filtering systems is evaluated both qualitatively and quantitatively in terms of PSNR and MSE for grayscale as well as color images.