Reviewerly/exHarmony huggingface dataset:
Digital Forensics Investigation on Xiaomi Smart Router Using SNI ISO/IEC 27037:2014 and NIST SP 800-86 Framework

The factual conditions reinforced by research data show the increasing number of connected smart devices in a house.The interconnection of these smart devices is able to form a smart home ecosystem with capabilities in the form of file sharing services, multimedia services, access to the internet network, to surveillance applications.Today's smart home interconnection is supported by the use of smart routers.The existence of the threat of exploitation of security gaps in the smart home ecosystem requires mitigation steps after cyber attacks on the smart home ecosystem.The research proposes a digital forensic investigation on the Xiaomi Smart Router using NIST SP 800-86 and SNI ISO/IEC 27037:2014 in a smart home ecosystem.Based on the findings in this study, to obtain and secure potential digital evidence from the Xiaomi Smart Router using the live acquisition method through the Xiaomi Smart Router web interface and the Mi Wi-Fi application installed on an Android smartphone.Live acquisition via the Xiaomi Smart Router web interface using web scraping techniques to obtain and secure digital evidence in the form of smart router device logs.Meanwhile, the digital evidence found from the Mi Wi-Fi application is a SQLite database file.
Unveiling the Wireless Network Limitations in Federated Learning

With the advent of 5G and beyond (5GB) communications, decentralized Machine Learning models in various 5GB use cases have become critical. However, wireless network settings are often overlooked in federated learning although they are the crucial factors for edge devices to send their gradient updates. In this paper, the consequences of the background traffic on the performance of the federated learning are evaluated profoundly. Simulation results point out that the convergence is delayed by 60 communication rounds to reach 0.9 accuracy when the background network traffic is dense. Moreover, it is observed that the background traffic with packet interarrival time below 400 ms reduce the accuracy by half in the first 30 communication rounds.
Evaluating Annotated Dataset of Customer Reviews for Aspect Based Sentiment Analysis

Sentiment analysis of product reviews on e-commerce platforms aids in determining the preferences of customers. Aspect-based sentiment analysis (ABSA) assists in identifying the contributing aspects and their corresponding polarity, thereby allowing for a more detailed analysis of the customer’s inclination toward product aspects. This analysis helps in the transition from the traditional rating-based recommendation process to an improved aspect-based process. To automate ABSA, a labelled dataset is required to train a supervised machine learning model. As the availability of such dataset is limited due to the involvement of human efforts, an annotated dataset has been provided here for performing ABSA on customer reviews of mobile phones. The dataset comprising of product reviews of Apple-iPhone11 has been manually annotated with predefined aspect categories and aspect sentiments. The dataset’s accuracy has been validated using state-of-the-art machine learning techniques such as Naïve Bayes, Support Vector Machine, Logistic Regression, Random Forest, K-Nearest Neighbor and Multi Layer Perceptron, a sequential model built with Keras API. The MLP model built through Keras Sequential API for classifying review text into aspect categories produced the most accurate result with 67.45 percent accuracy. K- nearest neighbor performed the worst with only 49.92 percent accuracy. The Support Vector Machine had the highest accuracy for classifying review text into aspect sentiments with an accuracy of 79.46 percent. The model built with Keras API had the lowest 76.30 percent accuracy. The contribution is beneficial as a benchmark dataset for ABSA of mobile phone reviews.
An Improved Sentiment Extraction Model for Social Media Contents using spaCy Based Deep Neural Networks

Modern communication devices generate huge amount of data through the manifold usage of various social media applications. Among the entire generated data more than 40% are unstructured in nature. The industry also is reluctant to retain the data with the following characteristics: data containing asynchronous time stamp, replicated data, data which are broken while transmission and data that leads to misclassification. It is high time to drop-out the irrelevant data and considering the synchronous ones. In this article, a sentiment extraction model is proposed that governs the various social media contents. SpaCy is used as the preferred implementation language as it has many readily available libraries for the purpose of content classification. To avoid the over-fitting problem the actuators like “relu” and “sigmoid” are used. Even though many such classifiers are available for content classification, this article with the appropriate setting of Epoch counts, a categorical accuracy of 68% is obtained. The entire model is implemented in the TensorFlow based platform.
Analysis of Language Errors in the “Larise” Magazine Article at SMP Negeri 8 Surakarta

This study aims to analyze language errors and their interpretations at the level of syntax, morphology, and Indonesian Spelling System as well as to increase knowledge and insight about how to write properly and correctly according to the language rules that have been regulated in the KBBI and PUEBI. This research uses descriptive qualitative research method. The data in this study are in the form of words (not numbers) sourced from Larise magazine in an article entitled "Philosophy of Kidungan Jawa "Ana Kidung Rumeksa ing Wengi" published on Sunday, October 11, 2020. The data collection technique in this study is a note-taking technique, namely: by reading Larise magazine as a data source. The analysis used in this study is an interactive analysis which includes the steps of a) data collection, b) error identification, c) error explanation, d) error classification, and e) error evaluation. In this study, an analysis of errors in writing rules was carried out at the level of syntax, morphology, and accuracy in the use of Indonesian Spelling (EBI). Errors at the syntactic level are in the form of errors in the use of effective sentences, errors at the morphological level are affixation errors, and errors related to Indonesian spelling include errors in using punctuation marks, using capital letters, using standard words, using prepositions, and using particles
AN INTERACTIVE E-MODULE DEVELOMPENT TO INCREASE THE SELF-REGULATED LEARNING OF BASIC GRAPHIC DESIGN

This study aims to produce an interactive E-Module Image Processing Bitmap (Raster) Elementary Graphic Design class X Vocational High School Multimedia Skills Competency to improve student self-regulated learning. This research is a Research and Development (R&amp;D) using the ADDIE model which includes the stages of analyzing, design, development, implementation, and evaluation. The product trial design consists of preliminary trials, main field trials, and operational field trials by carrying out pre-experimental. Data collection instruments used were a needs analysis questionnaire, a scale of eligibility for e-modules by material and media experts, a scale of student self-regulated learning, and a student response scale. The data analysis technique used in the study is descriptive technique. The results showed that the interactive e-module was very feasible and effective enough to be used to improve the student self-regulated learning of basic graphic design of vocational students.
Besting the Black-Box: Barrier Zones for Adversarial Example Defense

Adversarial machine learning defenses have primarily been focused on mitigating static, white-box attacks. However, it remains an open question whether such defenses are robust under an adaptive black-box adversary. In this paper, we specifically focus on the black-box threat model and make the following contributions: First we develop an enhanced adaptive black-box attack which is experimentally shown to be <inline-formula> <tex-math notation="LaTeX">$\geq 30\%$ </tex-math></inline-formula> more effective than the original adaptive black-box attack proposed by Papernot <i>et al.</i> For our second contribution, we test 10 recent defenses using our new attack and propose our own black-box defense (barrier zones). We show that our defense based on barrier zones offers significant improvements in security over state-of-the-art defenses. This improvement includes greater than 85&#x0025; robust accuracy against black-box boundary attacks, transfer attacks and our new adaptive black-box attack, for the datasets we study. For completeness, we verify our claims through extensive experimentation with 10 other defenses using three adversarial models (14 different black-box attacks) on two datasets (CIFAR-10 and Fashion-MNIST).
Writing Style Author Embedding Evaluation

Learning authors representations from their textual productions is now widely used to solve multiple downstream tasks, such as classification, link prediction or user recommendation.Author embedding methods are often built on top of either Doc2Vec (Le and Mikolov, 2014) or the Transformer architecture (Devlin et al., 2019).Evaluating the quality of these embeddings and what they capture is a difficult task.Most articles use either classification accuracy or authorship attribution, which does not clearly measure the quality of the representation space, if it really captures what it has been built for.In this paper, we propose a novel evaluation framework of author embedding methods based on the writing style.It allows to quantify if the embedding space effectively captures a set of stylistic features, chosen to be the best proxy of an author writing style.This approach gives less importance to the topics conveyed by the documents.It turns out that recent models are mostly driven by the inner semantic of authors' production.They are outperformed by simple baselines, based on state-of-the-art pretrained sentence embedding models, on several linguistic axes.These baselines can grasp complex linguistic phenomena and writing style more efficiently, paving the way for designing new style-driven author embedding models.
The Contribution of Family Environment and Learning Motivation Toward Students 'Learning Outcomes in Online Learning

This study aims to measure the contribution of the family environment and learning motivation on students' learning outcomes in class XI ICT subjects. This type of research is quantitative through a correlational approach. The population of this research was all students of class XI which consisted of 4 classes with a total of 101 students. Sampling in this study was carried out by proportional random sampling with the Slovin formula of 81 respondents. The data collection instrument used a questionnaire using a Likert scale, data analysis techniques including simple regression and multiple regression. The results showed that (1) the family environment contributed to learning outcomes by 34.2%; (2) Motivation to learn contributes to learning outcomes by 33.6% and (3) Family environment and learning motivation together contribute to learning outcomes by 44.3%. Thus, the family environment and learning motivation contribute together to student learning outcomes in ICT subjects.
The Role of Non-Governmental Organizations of Horting in the Formation of High School Student’s Socially Successful Personality

The article is devoted to one of the topical problems of modern psychological and pedagogical theory and educational practice – formation of high school student’ socially successful personality in general secondary education institution. The tasks of the proposed work are to generalize data on the phenomenon of social success and activities of non-governmental organizations in education of the younger generation, as well as analysis of the results of the application of appropriate methods aimed at clarifying the role of non-governmental organizations of horting in the formation of high school student’ socially successful personality. In the process of research were used theoretical methods (analysis, comparison and generalization), empirical methods (interviewing, blitz survey, method of unfinished sentences). The author considers the phenomenon of social success and socio-psychological features of high school students and clarifies the essence of the concepts of «social success» and «high school students’ socially successful personality». The paper emphasizes the need for partnership relations of general secondary education institutions with non-governmental organizations, defines the role of non-governmental organizations in education of the younger generation. It is established that more than half of high school students of experimental institutions from time to time participate in the activities of a certain non-governmental organization, and outlines the defining characteristics of horting as a general system of self-improvement of the individual and found that the participation of students in non-governmental organizations of horting is greater than other non-governmental organizations of military-patriotic and physical culture and sports. The proposed study identifies the necessary qualities for the social success of the student’s personality, the development of which involves horting education, and highlights the features of the use of «success situations» in horting. Based on the results obtained, the researcher comes to conclusions about the important role of non-governmental organizations of horting in the formation of high school students’ socially successful personality.
Adoption of Virtual Reality in Construction Projects

In recent years, virtual reality has emerged in various fields such as manufacturing sectors, construction sectors etc. Virtual reality technology has a wide variety of applications in various fields. Several applications of VR in construction sector are design review and support, construction support, operations and management, safety training, stakeholder management. Recent researches had shown that VR can also be used widely in decision making process. Despite the various applications of VR in construction sectors, the adoption level of the VR technology is comparatively very low. This study aims to identify the factors that are preventing the adoption of virtual reality in construction projects. The factors are identified through a wide review of literature survey and analysed by means of questionnaire survey with statistical methods. The factors are ranked through the analysis and the most affecting factors are found out. The findings of this study show that requirements of specialized high processing equipment, high investment in VR, lack of awareness about the technology, lack of client’s interest, limited finance of the organizations, existence of champions using VR are the most preventing factors for the adoption of VR in construction projects. To mitigate these issues, several possible mitigation measures has been suggested in this study.
Suspended Affixation needs no morphological word: The suffix -(y)Ip

I re-evaluate the morphological word constraint for suspended affixation(SA) in Turkish (Kabak 2007). I argue that SA, as an operation, is not bound by the morphological word constraint. What is constrained is the exponent insertion and SA takes place before it. SA is a morphological ellipsis taking place in conjunctions. After suspension, conjunction peripheral affixes are interpreted for all the conjuncts. The morphological word constraint in Turkish is related to SA in verbals, and it defines the limit of suspendability for an affix. This limit is defined by Kabak to be the morphological word. What is left of the conjunct after suspension should be a morphological word, otherwise the derivation is ungrammatical. To show that this is not the case, I propose a different analysis for the suffix -(y)Ip and provide empirical points for its relation to SA.
Hashtags in Linguistic Anthropology: A COVID-19 Case Study

Hashtags in social media reflect the Coronavirus pandemic outbreak and the consequential shifts and swifts in people’s lifestyles. Several studies related to the pandemic have used hashtags from linguistic, economic, and sociological perspectives. However, the potentiality of hashtags in addressing the pandemic-related anthropological questions is still underexplored. This study verifies how hashtags are primarily anthropocentric and can act as a source for anthropological studies. We have collected COVID-related hashtags in Instagram under five different pandemic-related words like #newnormal, #oldnormal, #quarantine, #lockdown, #pandemic, #corona. Eighty-eight hashtags under the mentioned pandemic-related keywords and 11 neologisms are collected to analyse their linguistic patterns and anthropological implications. The hashtags are segmented into basetags and stemtags to find the lexical significance of individual word units in the pandemic lifestyle. The segmentation helped analyse the range of vocabulary in hashtags used to describe people’s lifestyles during the pandemic. Hashtags are user-friendly and are primarily used as an expressing and recording device. The archival and emotive quality added to hashtags’ searchability and accessibility make them a potential linguistic anthropological research source for the ’COVID-19 pandemic.
Face Mask Detection Using Deep Learning and Computer Vision

Abstract: COVID-19 pandemic has rapidly affected our day-to-day life the world trade and movements. Wearing a face mask is very essentials for protecting against virus. People also wear mask to cover themselves in order to reduce the spread of covid virus. The corona virus covid-19 pandemic is causing a global health crisis so the effective protection method is wearing a face mask in public area according to the world health organization (WHO). The covid-19 pandemic forced government across the world to impose lockdowns to prevent virus transmission report indicates that wearing face mask while at work clearly reduce the risk of transmission .we will use the dataset to build a covid-19 face mask detector with computer vision using python,opencv,tensorflow,keras library and deep learning. Our goal is to identify whether the person on image or live video stream is wearing mask or not wearing face mask this can help to society and whole organization to avoid the transfer of virus one person to antother.we used computer vision and deep learning modules to detect a with mask image and without mask image. Keywords: face detection, face recognition, CNN, SVM, opencv, python, tensorflow, keras.
MAPS, AND ONTOLOGIES BPM FOR MODELING AND ANALYSIS OF FLOW INFORMATION: laboratory journal

This research shows modeling based on mental and conceptual maps the workflow of a university journal and proposes a domain ontology.The study was carried out based on concepts such as multiplatform journalism, web journalism and the use of ICT.In methodological terms, it is a case study, combined with participant observation through semi-structured interviews.It was developed, as a part of the modeling, a mind map of the Campus Multiplataforma workflow.Then the process was improved to a concept map and finally the study arrived at a ontology.The results show the possibility of visually understanding the process of a newspaper, sharing or extrapolating it and applying it to other newsrooms, and also improving the workflow with the support of the process management map (BPM).
Footprint Extraction and Sports Training Action Recognition Based on Wireless Network Communication

The combination of scientific and technological achievements and sports has found new opportunities to change people’s sports habits. Sport training takes up an increasing proportion of people’s lives. In order to improve the efficiency of sports training and standardize the training actions of players, this article is based on wireless network communication and uses different types of recognition methods in the field of action recognition to build basic classifications. Iterative mutual training to improve generalization performance can reduce the cost of labeling and realize the complementary advantages of different recognition methods, thereby improving the recognition accuracy of human actions. Finally, the algorithm is used to recognize human movements. This method can effectively overcome the problem of differential degradation of base classifiers in the iterative process of collaborative training and further improve the accuracy of human action recognition. The experimental results prove that the motion recognition of wireless network communication proposed in this paper can effectively improve the accuracy of athletes’ movements, which is more than 20% higher than traditional methods, and, under the guidance of standardized movements, can reduce athletes’ sports injuries.
FPGA implementation of random number generator using LFSR and scrambling algorithm for lightweight cryptography

The IoT (Internet of Things) is a network of devices that are interconnected and are uniquely addressable, based on common communication protocols and links to perform certain tasks. The recent developments in the wireless communications have increased the need for the IoT-connected devices. The sensors and the sensor nodes used in these networks are low-resource devices, thus increasing the vulnerability and hence becoming a possible target for hackers. The development and deployment of lightweight protection schemes for such low resource devices have also increased. The random number generation or the key generation used in the encryption process is the most important element in protecting these resource-constrained devices, as the security of the entire data depends on the key used. In this paper a novel random number generation using LFSR (Linear Feedback Shift Register) and Scrambling Algorithm for lightweight encryption algorithms is proposed using which the keys for the encryption process can be generated, thus improving the security of data transmitted in the IoT environment. The randomness of the numbers generated by this Random number generator algorithm is tested using pertinent set of statistical tests. These statistical tests analyze the cryptographic properties of the sub keys generated by the key scheduling algorithm, such as confusion, diffusion, independence, and randomness. For the purpose of simulation, the code is written in Verilog and simulated using Xilinx Vivado and the implementation is carried out using Artix-7 FPGA family for analyzing the parameters like Area, power and timing.
List Point Marker Path Finding for Artificial Intelligence Movement in 3D Games

In making a path finding algorithm in a 3D game to determine the direction of the NPC agent towards the destination, the Djiksra algorithm, Depth First Search, Breadth First Search and so on, usually the shortest distance is directly proportional to the travel duration to the target point. In this study, a test will be made using a list marker point such as the Djiksra algorithm to get the shortest distance and fastest time to reach the destination, in making this algorithm the C# language is used and the Unity software is used. After experimenting with various list points in different places in two directions, it was found that the distance traveled is always directly proportional to duration. So the selection of the fastest or shortest path can be done with this list point marker algorithm.