Reviewerly/exHarmony huggingface dataset:
Optimizing Uplink Bandwidth Utilization for Crowdsourced Livecast

Driven by the prevalence of video generation devices and the development of network infrastructures, there has been an explosive growth of Crowdsourced Video Livecast (CVL) services in the past few years. Significant efforts have been made to provide high quality CVL services with limited bandwidth availability. However, most of the existing works focused on optimizing downlink bandwidth for video distribution rather than uplink bandwidth for video uploading. For example, uploaders (i.e., broadcasters) in Twitch can arbitrarily set their upload rates, which may lead to a significant waste of upload bandwidth with the increasing number of uploaders. In this paper, we propose an effective low-complexity algorithm called Bubal to optimize upload bandwidth allocation among massive uploaders. Our objective is to optimize the utility of video uploading from the perspective of CVL platform operators by considering both viewers Quality-of-Experience (QoE) and upload bandwidth cost. To guarantee the effectiveness and fairness of bandwidth allocation, we adopt the optimization framework of Nash Bargaining Solution (NBS), which can determine the optimal bandwidth budget, upload bitrate and datacenter selection for each uploader jointly. Finally, we conduct extensive trace-driven simulations to evaluate our proposed algorithm and the results show that our algorithm achieves much higher utility than alternative strategies in various conditions.
AGS: Attribution Guided Sharpening as a Defense Against Adversarial Attacks

Even though deep learning has allowed for significant advances in the last decade, it is still vulnerable to adversarial attacks - inputs that, despite looking similar to clean data, can force neural networks to make incorrect predictions. Moreover, deep learning models usually act as a black box or an oracle that does not provide any explanations behind its outputs. In this paper, we propose Attribution Guided Sharpening (AGS), a defense against adversarial attacks that incorporates explainability techniques as a means to make neural networks robust. AGS uses the saliency maps generated on a non-robust model to guide Choi and Hall’s sharpening method to denoise input images before passing them to a classifier. We show that AGS can outperform previous defenses on three benchmark datasets: MNIST, CIFAR-10 and CIFAR-100, and achieve state-of-the-art performance against AutoAttack.
Educational Concept and Application of Problem-Based Learning Using a Virtual Learning Factory

The COVID-19 pandemic affected the common ways of teaching students, and new formats for lectures and exams are required. The paper presents a new lecture concept that combines problem-based learning and a virtual learning factory. The virtual learning factory offers a digital production system which offers several opportunities to collect and analyze production data. The students can use the data to solve their selected problems. The new lecture concept was applied and evaluated in the lecture “simulation of energy systems.” The paper concludes with a discussion of the students’ evaluation results and lecturers´ experiences.
Litstudy: A Python Package for Literature Reviews

Researchers are often faced with exploring a new research domain, which can be challenging due to the overwhelming number of relevant publications. Broad questions, such as what are the novel research avenues in this domain, are difficult to answer. Therefore, we present litstudy, a Python package that allows answering such questions using simple scripts or Jupyter notebooks.The package enables selecting scientific publications and studying their metadata using visualizations, network analysis, and natural language processing. The software was previously used in a publication about the landscape of Exascale computing, and we envision great potential for reuse.
Localisation of Persons by Means of Impulse-Radar Sensors: Basic Methods

This chapter is devoted to the basic methods for preprocessing of the measurement data from the impulse-radar sensors; in particular: the methods for extraction of a useful signal from those measurement data, the methods for estimation of some parameters of the extracted impulse-radar signal, the methods for smoothing of the one-dimensional trajectories of the distance between a monitored person and the corresponding impulse-radar sensors, and the methods for conversion of two distance trajectories into the two-dimensional trajectory of the movement of the monitored person. All these methods, commonly interpreted as non-probabilistic, may be considered as reference methods because they have been most frequently used by the research community.
THE IMPORTANCE OF INCREMENTAL KNOWLEDGE EXPANSION (IKE) FROM A CENTRAL AND EASTERN MAINE PERSPECTIVE

The bedrock of central and eastern Maine has not changed appreciably in hundreds of millions of years. Yet our knowledge and understanding of the lithology, structure, age, stratigraphy, intrusive relationships, petrology, and tectonics have changed dramatically since the time of Jackson (1837). On the occasion of Prof. Ludman’s retirement from teaching it is appropriate to reflect on the process of changing knowledge with his first 56 field seasons as an example, from the perspective of colleagues, students, and the Maine Geological Survey (MGS). Most knowledge that geologists share comes from textbooks or from “milestone” papers in which authors synthesize information to present the latest answer. As field geologists should know, the word milestone comes from the Latin milles, which is the distance of a thousand paces. This is where fundamental new knowledge comes from, one pace at a time. While Allan has published his share of milestone papers on central Maine stratigraphy (1976), Calais-NB area stratigraphy (1978), suture-hunting (1981), terrane accretion (1986), St. Croix Belt (1987), local polymetamorphism (1989), Acadian orogeny (1993), Norumbega structure (1998, 2002, 2004), Norumbega Special Paper (1999), paleogeography (2017), detrital zircon provenance (2018), palynology (2020), and Miramichi volcanics (2021), we contend his most lasting contributions have been through what we here call the IKE system. Since his first field season in 1966, Allan has filed maps and reports with the MGS on a regular basis. He has organized two NEIGC’s and prepared 15 field guides. He has published maps in the Skowhegan area, Lincoln 1° and Fredericton 2° sheets, an area that now rivals the size of Connecticut. Each outcrop, each field season incrementally expands the body of knowledge. From this foundation of fact-based knowledge, Allan has supported the work of the MGS through brittle fracture maps to address seismic hazards for the US DOE, the 1985 State map compilation to support NRC analysis of potential nuclear waste sites, study of the Kellyland dam hazard, and consulting on wind farm installation. Students have seen him learn new technologies (GPS, CARIS, lidar), challenge common wisdom (Where are the subduction zones? Once one or always two?) and invite collaborators from other disciplines to address field problems.
Identification of Face Mask Detection Using Convolutional Neural Networks

This paper delineates the algorithms to decipher the persons who are wearing mask. Sensing faces with sealings is a thought-provoking task due to the nonappearance of huge datasets of masked faces, thereby we have gathered a colossal of datasets from various resources and arranged them in a structured form. Preprocessing methods were applied on the dataset for reducing time and space complexity of an algorithm. The minimized dataset is used for training, utilizing this dataset we have developed different models using Convolution Neural Networks (CNN). The results have been comprehended from this model and an impeccable result of 91% accuracy is obtained using convolution neural networks. The construction of Covid-19 face mask detector here today can possibly be used to help ensure your safety and the security of others. This detection system can also be widely used in travel scenes, with mobile phone and surveillance images.
A Smart IoT Based Agro-Management System Using Distributed Ledger and Circular Supply Chain Methodology

To enhance the entire prospective future of renewing, recycling and reusing we propose a solution. The solution we have been working on consists of an IoT Circular Supply Chain Management and Blockchain based approach to conventional grocery shopping. Our distinctive approach would also enhance hyper-local stores/individual cultivators by helping them get better business and visibility. With this vision forming the base of the project we hope to bring in automation for the future, where smart cities are an industry standard. The IoT device developed during this project has helped us in understanding a lot about how conventional agriculture works and how technology can leverage these principles to raise a better yield using the same resources. To achieve the sustainable circular supply chain methodology. Our innovative approach brings in a drastic change for the packaging sector. The learnings from this proposed solution can be used for a wide range of problems faced in many industries. With various stakeholders involved in this solution, it assures that all parts of this solution work hand-in-hand and will lead to a better and connected tomorrow.
Source Code Summarization &amp; Categorization of Python and R Codes Using Deep Learning Technique

Source code summarization is the methodology of generating the description or comments from the code snippet. Source code summary gives the understanding about the functionality performed by the source code. Code summary is not only useful for maintenance of the software system but also to search the relevant code. In this paper, we have proposed a deep learning architecture to summarize the source code and to classify the category of source code as Python or R. To classify the source code category, we have used K Nearest Neighbor (KNN) algorithm. Proposed approach uses both encoder and decoder as Long Short Term Memory (LSTM) networks with additive attention architecture to generate the summary. The proposed summarization model has been evaluated on the standard evaluation criteria’s i.e., Bilingual Evaluation Understudy (BLEU) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE-L) which shows that model can produce good results.
“Sign in to Speech” Smart Hand Glove for Especially Abled

More than 9 Crore people on the earth are facing the issue of dumbness. The communication between a dumb and normal person i.e. man or woman poses to be a crucial downside in comparison to verbal exchange among blind and historically visible human beings. The human who is facing the blindness can talk smoothly through normal human or regional language while the person facing the issue of dumbness have their own manual-visible language known as Sign or gesture using by the figures and hand language. Language is likewise a non-verbal Gesture that`s located amongst deaf groups at durations the planet. The languages have not been given a regular beginning and thence difficult to interpret. This challenge enables deaf and dumb people to which they speak with the not unusual place human beings easily. Sign languages are utilized by mute human beings as a medium of verbal exchange. Sign languages are used to deliver the mind with symbols, items, etc. They additionally deliver an aggregate of phrases and symbols (i.e. gestures). The gesture is a non-verbal shape of verbal exchange. Sign language reputation structures are used to transform signal language into textual content or speech to permit verbal exchange with those who do now no longer recognize those gestures. Usually, the focal point of those structures is to apprehend hand configurations along with position, orientation, and actions A the person facing the issue of dumbness can communicate with the normal people with the help of the device or gloves which can translate the hand or fingers gestures to sensible speech with the help of the converse used in the device. A gesture in accomplice diploma extraordinarily language is likewise a sure motion of the arms with a selected type created out of them. A posture alternatively is likewise a static form of the hand to cause an emblem. The main aim of these device is to translate the hand or figure movements of the deaf and dumb people to the language which can be understand by the normal people such as textual content and sensible or understandable voice. The interpreter uses a Gloves for the capturing the hand movements which consist of flex sensors, tool sensors which can be compared with the predefined hand movement’s data saved as the input signed language.
COVID-19 News: An Analysis of Netizen’s Scathing Criticism on Facebook

Communicating using social media such as Facebook to discuss social issues, politics or economy is increasingly significant among today’s society. This is because those aspects play an important role in daily life. This includes issues pertaining to the transmission of coronavirus (COVID-19) and its outbreaks and the death rate which causes panic among Malaysians. These concerns contribute to the increase in virtual communication on all social media platforms, be it news or information, which saw negative comments uncontrollably left on social media sites. Malaysiakini and Sinar Harian received a lot of reactions and responses from Facebook users on COVID-19 issues. Indirectly, the social media sites of the two organisations have become a communication platform between the people and the government. Researchers aim to the forms of language used by Facebook users and how they evaluate and perceive the comments section. A total of 400 comments were selected and categorised according to the framework proposed by Culpepper and Entman. The findings of the study show that netizens in both social media sites use more language in the form of scathing or complaints while the reactions highlighted lead to. This shows that the government failed to address this issue thus giving netizens space to express and use rude language on their Facebook comments. This is a reflection of the dissipating values of being tact and polite in the Malaysian society. This study also shows that the reaction of netizens who tend to make their own interpretations are caused by individual hatred sentiments, political influence, cybertroopers or merely reading the title. Keywords: Netizens, impolite, Facebook, COVID-19, framing.
Binary and Multiclass Classifications Using a Deep Fusion Network

This paper describes a method for providing a unified model from multiple classification-based datasets. Dilated convolutional neural networks are used for applications that require high accuracy, speed, and real-time processing. This paper proposes a classifier that is accurate for both binary classification and multiclass classification. The model employs a number of techniques, including batch normalization, ReLU activation function, dilation blocks, and learning rate optimizations, and has provided us with a model with reasonable accuracy for classification-based datasets. The proposed model has been trained on four different classification-based datasets by including both binary classification and multiclass classification. Further, the proposed model is tested on malaria dataset, WBC dataset, driver drowsiness detection dataset, and handwritten symbol classification dataset. The experimental results provide high accuracy and elasticity.
An Architecture for Autonomous Proactive and Polymorphic Optimization of Cloud Applications

Existing autonomous Cloud application management platforms continuously monitor the load and the environment of the application react when optimization is needed. This paper introduces the concepts of polymorphic architecture optimization and proactive adaptation of Cloud applications, which is a significant improvement of the standard reactive optimization. Polymorphic architecture optimization considers the change of the technical form of the component while proactive adaptation uses the predicted future workload and context. Based on this, we propose an architecture for a Cross-Cloud application management platform that supports complex optimization of Cloud applications in terms of architecture, resources, and available offers.
A Semantic Matching Approach Addressing Multidimensional Representations for Web Service Discovery

In recent years, discovering appropriate web services has become increasingly difficult as the number of services has grown rapidly. With the goal of improving discovery performance through accurate text matching, this study developed a service discovery method that constructs a neural matching network based on multidimensional service representations. Specifically, we performed data processing and adopted three methods called term frequency-inverse document frequency, Word2Vec, and ELMo to generate multidimensional representations for capturing the word frequency, static context features, and dynamic context features of each keyword. Based on these features, we calculated the cosine similarity of pairs of keywords to construct a multidimensional similarity matrix. We then implemented convolution, pooling, and optimization operations to construct a neural matching network that has a direct impact on the accuracy of service discovery. Finally, for a given query, target services are retrieved by ranking candidate services according to the scores predicted by the matching network. The proposed method was evaluated through multiple comparisons and the experimental results demonstrate the effectiveness of optimal web service retrieval.
Telugu Text Classification Using Supervised Machine Learning Algorithm

We live in a world where knowledge is extremely valuable, and the amount of information available in text documents has grown to the point, where it is difficult to find those that are important to us. As a result, language-based classification of text documents is important. Telugu is one of the morphologically rich Dravidian languages. Since there are many Telugu documents available on the Internet, it is important to organize the data by automatically assigning a collection of documents into predefined labels based on their content using modern techniques. On the basis of the Telugu corpus, we proposed Telugu text document classification using a variety of machine learning algorithms and feature extraction techniques. We gathered 1990 documents from an online newspaper, divided into three categories: cinema (467), sports (839), and politics (684). In this paper, we used the N-gram feature extraction method to apply the naive Bayes (NB) classifier and the one-hot encoding vectorization method to apply multinomial naive Bayes (MNB), support vector machine(SVM), and logistic regression (LR). We used 1990 documents to extract uni-gram and bi-gram features and 120 unseen documents to test a naive Bayes classifier for the n-gram approach, and we got 99% accuracy in uni-gram and 97% accuracy in bi-gram. We used 1375 documents (70%) for training and 597 documents (30%) for testing to construct a one-hot encoding vector (based on the size of the vocabulary). For classification, we used the multinomial naive Bayes, support vector machine, and logistic regression algorithms. MNB provides 98% accuracy, SVM provides 99% accuracy, and logistic regression provides 98% accuracy.
Counterfeit Product Detection System Using One-Time QR code

Counterfeit goods have become particularly crucial issue in the product manufacturing industry in recent years. This phenomenon has an impact on company sales and profits. This problem affects a variety of industries, including pharmaceuticals, electronics, jewellery, and cosmetics. We also recognize that in today's fast changing digital environment, smartphones have become both common and necessary. Various smartphone applications have been developed, allowing us to increase our productivity while also saving time. In recent years, several digital technologies have been introduced, such as QR codes, barcodes, OTP verification, call verification, and so on for detecting falsified products. However, due to reasons such as complicated functionality, easy cheating, and poor user experience, they were unable to implement these ideas on a large scale. A one-time QR code-based solution is used to ensure the identification of real products across the supply chain and at the consumer end, avoiding product counterfeiting. By using this system, consumers can easily differentiate between genuine and tampered or counterfeit product without any registration. The proposed system also helps the organization get data of the customers based on the region where their product is being sold. The given approach is quite scalable, fool-proof and cost effective as it uses centralized database, QR code generator and scanner.
Forms of Igneous Bodies

Building upon the award-winning second edition, this comprehensive textbook provides a fundamental understanding of the formative processes of igneous and metamorphic rocks. Encouraging a deeper comprehension of the subject by explaining the petrologic principles, and assuming knowledge of only introductory college-level courses in physics, chemistry, and calculus, it lucidly outlines mathematical derivations fully and at an elementary level, making this the ideal resource for intermediate and advanced courses in igneous and metamorphic petrology. With over 500 illustrations, many in color, this revised edition contains valuable new material and strengthened pedagogy, including boxed mathematical derivations allowing for a more accessible explanation of concepts, and more qualitative end-of-chapter questions to encourage discussion. With a new introductory chapter outlining the “bigger picture,” this fully updated resource will guide students to an even greater mastery of petrology.
Enhancing the Prediction of Breast Cancer Using Machine Learning and Deep Learning Techniques

With the surge of breast cancer, researchers have proposed many predicting methods and techniques. Currently, mammograms and analyzing the biopsy images are the two traditional methods used to detect the breast cancer. In this paper, the objective is to create a model that can classify or predict whether breast cancer is benign or malignant. Typically, a pathologist will take several days to analyze a biopsy, while the model can analyze thousands of biopsies in few seconds. For the numerical data, various machine learning classifications with supervised learning algorithms such as random forest (RF), K-nearest neighbor (KNN), Naïve Bayes, support vector machines (SVM), and decision trees (DT) are used. Then, deep learning—convolutional neural network is used to analyze the biopsy images from a dataset of images. An accurate result from the prediction are determined for saving the lives of people.