Reviewerly/exHarmony huggingface dataset:
Supporting Argumentation Dialogues in Group Decision Support Systems: An Approach Based on Dynamic Clustering

Group decision support systems (GDSSs) have been widely studied over the recent decades. The Web-based group decision support systems appeared to support the group decision-making process by creating the conditions for it to be effective, allowing the management and participation in the process to be carried out from any place and at any time. In GDSS, argumentation is ideal, since it makes it easier to use justifications and explanations in interactions between decision-makers so they can sustain their opinions. Aspect-based sentiment analysis (ABSA) intends to classify opinions at the aspect level and identify the elements of an opinion. Intelligent reports for GDSS provide decision makers with accurate information about each decision-making round. Applying ABSA techniques to group decision making context results in the automatic identification of alternatives and criteria, for instance. This automatic identification is essential to reduce the time decision makers take to step themselves up on group decision support systems and to offer them various insights and knowledge on the discussion they are participating in. In this work, we propose and implement a methodology that uses an unsupervised technique and clustering to group arguments on topics around a specific alternative, for example, or a discussion comparing two alternatives. We experimented with several combinations of word embedding, dimensionality reduction techniques, and different clustering algorithms to achieve the best approach. The best method consisted of applying the KMeans++ clustering technique, using SBERT as a word embedder with UMAP dimensionality reduction. These experiments achieved a silhouette score of 0.63 with eight clusters on the baseball dataset, which wielded good cluster results based on their manual review and word clouds. We obtained a silhouette score of 0.59 with 16 clusters on the car brand dataset, which we used as an approach validation dataset. With the results of this work, intelligent reports for GDSS become even more helpful, since they can dynamically organize the conversations taking place by grouping them on the arguments used.
A Road Segmentation Model Based on Mixture of the Convolutional Neural Network and the Transformer Network

Convolutional neural networks (CNN) based on U-shaped structures and skip connections play a pivotal role in various image segmentation tasks.Recently, Transformer starts to lead new trends in the image segmentation task.Transformer layer can construct the relationship between all pixels, and the two parties can complement each other well.On the basis of these characteristics, we try to combine Transformer pipeline and convolutional neural network pipeline to gain the advantages of both.The image is put into the U-shaped encoder-decoder architecture based on empirical combination of self-attention and convolution, in which skip connections are utilized for localglobal semantic feature learning.At the same time, the image is also put into the convolutional neural network architecture.The final segmentation result will be formed by Mix block which combines both.The mixture model of the convolutional neural network and the Transformer network for road segmentation (MCTNet) can achieve effective segmentation results on KITTI dataset and Unstructured Road Scene (URS) dataset built by ourselves.Codes, self-built datasets and trainable models will be available on https://github.com/xflxfl1992/MCTNet.
Analyzing Ethereum Smart Contract Vulnerabilities at Scale Based on營nter-Contract Dependency

Smart contracts running on public blockchains are permissionless and decentralized, attracting both developers and malicious participants.Ethereum, the world's largest decentralized application platform on which more than 40 million smart contracts are running, is frequently challenged by smart contract vulnerabilities.What's worse, since the homogeneity of a wide range of smart contracts and the increase in inter-contract dependencies, a vulnerability in a certain smart contract could affect a large number of other contracts in Ethereum.However, little is known about how vulnerable contracts affect other on-chain contracts and which contracts can be affected.Thus, we first present the contract dependency graph (CDG) to perform a vulnerability analysis for Ethereum smart contracts, where CDG characterizes inter-contract dependencies formed by DELEGATECALL-type internal transaction in Ethereum.Then, three generic definitions of security violations against CDG are given for finding respective potential victim contracts affected by different types of vulnerable contracts.Further, we construct the CDG with 195,247 smart contracts active in the latest blocks of the Ethereum and verify the above security violations against CDG by detecting three representative known vulnerabilities.Compared to previous large-scale vulnerability analysis, our analysis scheme marks potential victim contracts that can be affected by different types of vulnerable contracts, and identify their possible risks based on the type of security violation actually occurring.The analysis results show that the proportion of potential victim contracts reaches 14.7%, far more than that of corresponding vulnerable contracts (less than 0.02%) in CDG.
Anotação de Entidades Mencionadas na área do Gaming

This paper aims to analyse the effects of including gaming entities in the performance of the NER system, for the English language and in a machine translation industrial context of customer support content. To identify and classify gaming entities (by the Named Entity Recognition (NER) model), three new categories were created and added to the already used annotation typology: GAME NAME, GAME FEATURE and GAME CURRENCY. A set of reference annotations (gold standard) was also developed, allowing not only the training of the NER system but also the evaluation of its performance and accuracy in a more objective way, namely by counting the number of entities that the system identifies and categorises correctly. In the scope of this work, 6618 sentences from 7 gaming clients were manually annotated, constituting the gold standard which was then used to train and evaluate the NER system. The objective of the experiments was to assess whether the existing NER system improved its performance when trained with the gold standard created specifically for the gaming domain and if it could handle the new gaming categories added to the typology by identifying and categorizing them correctly. The results of both experiments were auspicious and positive, demonstrating the relevance of greater investment in domain-specific entity recognition, namely in the context of customer service text processing.
Protest Before and During a Pandemic

Liberal democracies have struggled recently with protecting freedom of speech and assembly during the COVID-19 pandemic. This is an old, general problem in new, specific guise. In Australia, the Supreme Court of New South Wales has been exercising a statutory jurisdiction to ‘authorise’ or ‘prohibit’ proposed public assemblies for 40 years. This article offers the first sustained analysis of the Court’s jurisprudence. After describing the operation of the statutory permit scheme and systematising the case law, this article critiques the Court’s jurisprudence from the perspective of free speech and freedom of assembly. It then argues that there is a puzzle at the heart of the legislative scheme: the conferral of a wide discretion the exercise of which produces a narrow legal order. This puzzle suggests that the legal effect of an authorising or prohibiting order does not exhaust its broader social significance.
Attacking Websites: Detecting and Preventing HTTP Request Smuggling Attacks

Until the development of HTTP request smuggling in 2005, individual HTTP requests were considered as independent entities and could not be split or merged. This is a security problem caused by inconsistent content length interpretation approach between web servers, or the web server is not fully implemented in accordance with the RFC standard. It is especially dangerous for web services with complex web architectures. It can route the victims to receive malicious responses, amplify the impact of certain low-threat vulnerabilities, steal user credentials, or bypass network devices’ defenses. However, since its concept and implementation are quite difficult to overcome, it is often ignored by many network administrators, making users who browse such websites vulnerable to the HTTP request smuggling attacks. This paper proposes a general solution to deal with various HTTP request smuggling attacks. A reverse proxy implemented by Flask validates and cleans dubious HTTP requests from the client side and ensures that the original requests comply with RFC standards. Therefore, the website administrators no longer need to configure complicated network settings or customize some open-source project codes to resist or minimize the risk of the HTTP request smuggling attacks. A series of experiments demonstrate that this method is effective and practical.
LNGAT: local neighborhood graph attention network

Graph attention network (GAT) is a promising framework to perform aggregation and massage passing on graphs. GATs attention layer extracts the attention coefficients mainly using node features of the current node. But, we believe that attention can be obtained directly from the structural information of the graph. We argue that local graph structures play a dominant role for calculating good attention coefficients and propose a structure-based graph attention layer called local neighborhood graph attention layer (LNGAL). Different from GAT, LNGAL obtains attention only by local graph structural information and abandons the dependence on the features of nodes, which calculates the attention coefficients directly from theoretical formulas, instead of training with labeled data. In LNGAL, the first-order neighborhood of the current node provides a primary contribution to the calculation of attention coefficients, while the second-order neighborhood plays a fine-tune role. Furthermore, we introduce the proposed LNGAL to a multi-scale architecture and design a novel network called local neighborhood graph attention network (LNGAT). In the experimental section, we show that LNGAT network outperforms several recently proposed graph convolutional network-like models and achieves state-of-the-art performance on six open graph datasets.
Eye Fitting Straight Lines in the Modern Era

How do statistical regression results compare to intuitive, visually fitted results? Fitting lines by eye through a set of points has been explored since the 20th century. Common methods of fitting trends by eye involve maneuvering a string, black thread, or ruler until the fit is suitable, then drawing the line through the set of points. In 2015, the New York Times introduced an interactive feature, called “You Draw It,” where readers were asked to input their own assumptions about various metrics and compare how these assumptions relate to reality. In this article, we validate “You Draw It” as a method for graphical testing, comparing results to the less technological method used in Mosteller et al. and extending that study with formal statistical analysis methods. Results were consistent with those found in the previous study; when shown points following a linear trend, participants tended to fit the slope of the principal axis over the slope of the least-squares regression line. This trend was most prominent when shown data simulated with larger variances. This study reinforces the differences between intuitive visual model fitting and statistical model fitting, providing information about human perception as it relates to the use of statistical graphics. Supplementary materials for this article are available online.
Counting points on diagonal equations over Galois rings GR(p2,2n)

Let R=GR(p2,2n) be a Galois ring and Fq a finite field, where q=p2n. In this paper, we study the number of solutions of equations of the form a1x1d1+⋯+asxsds=b with xi∈GR(p2,2ti), where ai,b∈R∖{0} and ti|n for all i=1,...,s, and employ results on quadratic forms over finite fields to give an explicit formula for the number of solutions of diagonal equations with restricted solution sets satisfying certain natural restrictions on the exponents. As a consequence, we present conditions for the existence of solutions.
Breast Cancer Detection Using Machine Learning

Through studies and statistics, it has been found that these days, breast cancer is the most common cancer leading to frequent deaths among women. Early screening and subsequent treatment can raise the chances of survival. Through this paper, we aim to demonstrate the ability to detect breast cancer cases using MRI scan data by analyzing the given data with machine learning algorithms. Using machine learning, we hope to ease the process of cancer detection in the hospitals so the patient can be afforded the right treatment as soon as possible before the situation can become critical. It also opens the door toward new possibilities in cancer detection for other different types of cancers as well as other diseases by use of machine learning in medical science, where detection using conventional means is usually laborious and time-taking.
Representing the Structure of a Debate

Abstract In this article I aim to use the 1948 Russell-Copleston debate to highlight some recent problems I have experienced teaching argument analysis in my philosophy courses. First, I will use argument diagramming to represent the arguments in the debate while reflecting on the use of this approach use to teach argument analysis skills. Then, I will discuss the tools and methods scholars have proposed to represent debates, rather than just individual arguments. Finally, I will argue that there is not, but needs to be, a good way to represent argumentative debates in a way that neither obscures the essential details of the exchange nor becomes too unwieldy to extract a sense of the overall debate.
Fusion-Based Celebrity Profiling Using Deep Learning

Celebrity profiling predicts sub-profiles such as gender, occupation, birth year, and fame from the celebrity tweets. This task is introduced in 2019 PAN challenge. Most of the researchers used stylistic features and content-based features and predicted the sub-profiles. The accuracies of the existing model are not satisfactory. The stylistic features learn the writing style of the celebrity, and the embedding vector learns context of the words. In this work, we propose a fusion-based deep learning technique which takes embedding’s vector and stylistic features. The proposed model is implemented to predict sub-profiles such as fame, gender, and occupation. The accuracies of the proposed model are improved over the existing models.
On the Studies and Analyzes of Facial Detection and Recognition Using Machine Learning Algorithms

This paper compares practical machine learning-based algorithms of detection and recognition such as Haar cascade classifier and local binary pattern histogram (LBPH) method against GoogleNet, which uses convolutional neural network (CNN) architecture, using transfer learning. From the comparative analyzes and studies, it was elucidated that LBPH and Haar cascade are computationally efficient, but CNN has more accuracy despite its longer computational time.
Current Trends in Methodology for Software Development Process

Software development processes are heart core to finish a new project successfully. A wide range of software development methodologies is available from literature. This study analyzes the available methodologies for software methodology and finds out the most suitable methodology for a project. A survey-based research is carried out among developers and managers with varying experience in their field. For this study, agile and waterfall methodologies are focused as these are the popular methodologies to find our results. The results from our survey show that there is no “perfect” method to for a project as one needs to consider factors like resources, duration of project, etc., to select methodology to follow. It infers that waterfall-based process is popular among small projects in which requirements are well defined, whereas agile methodology is preferred with all kinds of projects as it provided better flexibility and continuous feedbacks, and delivery was provided to deliver a project with higher satisfaction rate.
Classification of High-Dimensionality Data Using Machine Learning Techniques

In the digitization world, a large volume of information is being produced across a few areas such as medical services, creation, Web, and associations. Machine learning techniques are utilized to reveal designs among the train of this information for decision-making. Not all the features in the datasets produced are significant for preparing the machine learning (ML) algorithms. A few features may be not important and some probably won’t influence the result of the forecast. Disregarding or eliminating these immaterial or less significant features reduces the weight on ML algorithms. In this work, principal component analysis (PCA) is researched on different most popular ML algorithms, Naïve Bayes, support vector machine (SVM) classifier and KNN classifier, using freely accessible MINIST dataset. Experimentation results demonstrate that ML algorithms with PCA produce better outcomes when dimensionality of the datasets is high.
Image-space navigators

This chapter explores existing solutions from the literature that utilize MR-based navigators operating in the image domain to provide estimates of motion. Some general considerations are discussed, along with the pros and cons of an image-based approach.
Secured Messaging Using Image and Video Steganography

Protection and security of data is an increasing concern among individuals and organizations. Secure data transmission has a vital application in today’s world. Security has become a priority for every user who uses the Internet. Sensitive data transmission is always a risky task. In-order to address this concern, image and video steganography is quite useful in hiding the secret data inside of an image or a video which makes it harder for the outside world to notice the transmission. Steganography focuses more on high security and capacity. Steganography masks the sensitive data inside any cover media like images, videos, etc. The hidden information will not be perceived by the human eye as the cover looks the same as the encoded one. Thus, only the target user will be able to read the message hidden. This application proposes a secure way for transmitting messages using algorithms like least significant bits, K-least significant bits and key-frame selection for image and video steganography.
A Hybrid Model of Latent Semantic Analysis with Graph-Based Text Summarization on Telugu Text

In this paper, we are proposing a hybrid model of latent semantic analysis with graph-based xtractive text summarization on Telugu text. Latent semantic analysis (LSA) is an unsupervised method for extracting and representing the contextual-usage meaning of words by statistical computations applied to a corpus of text. Text rank algorithm is one of the graph-based ranking algorithm which is based on the similarity scores of the sentences. This hybrid method has been implemented on Eenadu Telugu e-news data. The ROUGE-1 measures are used to evaluate the summaries of proposed model and human-generated summaries in this extractive text summarization. The proposed LSA with Text rank method has a F1-score of 0.97 as against the F1-score of 0.50 for LSA and 0.49 of Text rank methods. The hybrid model yields better performance compared with the individual algorithms of latent semantic analysis and Text rank results.
Machine learning

Following the surge of deep learning in the computer vision community, a growing number of works in recent years have successfully applied learning-based methods to the problem of motion correction in MR. These techniques can be broadly grouped into image-based and raw-data-based approaches, both of which will be described in detail in this chapter. The theoretical foundations underlying neural network training and inference are described. Finally, practical challenges such as the question of confidence are discussed, and a brief outlook on potential future developments in this fast-moving field is given.
People Count from Surveillance Video Using Convolution Neural Net

People counting is used to count the quantity of people in the picture. People counting is not an easy task if it is done manually by our hand because we can lost count in the middle of doing this laborious task, especially when dealing with object that intersects with each other or dense crowd. This project automates the counting process by building a machine learning system that can convert a video into frames, then the model will output number of objects in a particular frame. We built the model using convolutional neural network (CNN) technique. The system that we built is capable of counting pedestrians in a mall. The frames/images are generated from CCTV that is placed somewhere in the mall. From those frames/images, the system will output how many pedestrians at that particular place in the mall. VGG16 is used to excerpt the topographies of the image and structural similarity index (SSIM) for measuring the similarity among the given images. Then, use the similarity measure as a loss function, named Euclidean and local pattern consistency loss. The experimental results show the predicted number of people and exact number of people in the image with 90% of accuracy using convolution neural net.
Secure Cloud Auditability for Virtual Machines by Adaptive Characterization Using Machine Learning Methods

Cloud computing and cloud-based data centres are becoming more popular solutions for hosting applications as the need for better performance and availability grows. Several more studies, which are discussed later in this paper, have shown that a data centre's infrastructure must be audited in order to be secure. Third-party auditors will be hired by the data centres to verify the infrastructure, or more specifically, the virtual machines, during the auditing process. VMs, programmes, and data may be accessed by these auditors. A drawback of this auditing procedure is that the auditors may explicitly access any areas of the VMs, where the VMs’ highly sensitive data is stored. It is difficult to allow access depending on the requests since this might increase the overall time required to conduct the audits. As a result, recent research calls for selective and adaptive auditing. Parallel researchers have proven a variety of ways to protect an auditing system. Although these results are more sophisticated in terms of both time and accuracy, they have been criticized for this. A unique prediction approach for analysing the features of the virtual machine applications and demand characteristics from auditors is proposed in this work, and eventually, the access to the virtual machine is granted using a predictive regression model. For making cloud-based application development safer and quicker, the suggested technique shows a 50% reduction in time complexity compared to existing parallel research results.