Reviewerly/exHarmony huggingface dataset:
Modeling Inter Round Attack of Online Debaters for Winner Prediction

In a debate, two debaters with opposite stances put forward arguments to fight for their viewpoints. Debaters organize their arguments to support their proposition and attack opponents’ points. The common purpose of debating is to persuade the opponents and the audiences to agree with the mentioned propositions. Previous works have investigated the issue of identifying which debater is more persuasive. However, modeling the interaction of arguments between rounds is rarely discussed. In this paper, we focus on assessing the overall performance of debaters in a multi-round debate on online forums. To predict the winner in a multi-round debate, we propose a novel neural model that is aimed at capturing the interaction of arguments by exploiting raw text, structure information, argumentative discourse units (ADUs), and the relations among ADUs. Experimental results show that our model achieves competitive performance compared with the existing models, and is capable of extracting essential argument relations during a multi-round debate by leveraging argumentative structure and attention mechanism.
Quantifying Bias in a Face Verification System

Machine learning models perform face verification (FV) for a variety of highly consequential applications, such as biometric authentication, face identification, and surveillance. Many state-of-the-art FV systems suffer from unequal performance across demographic groups, which is commonly overlooked by evaluation measures that do not assess population-specific performance. Deployed systems with bias may result in serious harm against individuals or groups who experience underperformance. We explore several fairness definitions and metrics, attempting to quantify bias in Google&rsquo;s FaceNet model. In addition to statistical fairness metrics, we analyze clustered face embeddings produced by the FV model. We link well-clustered embeddings (well-defined, dense clusters) for a demographic group to biased model performance against that group. We present the intuition that FV systems underperform on protected demographic groups because they are less sensitive to differences between features within those groups, as evidenced by clustered embeddings. We show how this performance discrepancy results from a combination of representation and aggregation bias.
Aesthetic image captioning on the FAE-Captions dataset

At present, most of the research on image aesthetics focuses on scoring pictures. We propose Aesthetic Assessment of Images, which means the dense aesthetic captioning. The image captioning model uses many photos for www.flickr.com as a training set, including lots of captions about every photo meantime. Then we use LDA for aesthetic topics and active learning to screen sentence. The remaining sentence dataset, we called FAE-Captions, is a professional dataset on the topic of image aesthetics. Put the dataset into a CNN-LSTM model for predicting and generating answers. In order to let neural network connected with aesthetics, we propose an aesthetic loss function for many aesthetics topics additionally.
Generation of a Dynamical Logic Gate From Unstable Dissipative Systems of Type 1

The obtainment of a dynamical logic gate (DLG), which is a device capable of implementing several logic functions using the same model, has been one of the goals of the scientific community. Dynamical systems, specifically those that display chaotic behavior, have been widely used to emulate different logic gates which are the basis of general-purpose computing. In this study, we present a methodology based on unstable dissipative systems of type 1 (UDS-1), a kind of dynamical system capable of generating multi-scrolls and multi-stability. Using these two features, we codify inputs, subsequently, we get the adequate output, developing in this way a dynamical (reconfigurable) logic gate that performs any of the sixteen possible logic functions of two inputs. A highlight of the proposed methodology is that the selection of the desired logic gate is realized just by varying a couple of parameters.
Is this Question Real? Dataset Collection on Perceived Intentions and Implicit Attack Detection

The proliferation of social media and online communication platforms has made social interactions more accessible, leading to a significant expansion of research into language use with a particular focus on toxic behavior and hate speech. Few studies, however, have focused on the tacit information that may imply a negative intention and the perspective that impacts the interpretation of such intention. Conversation is a joint activity that relies on coordination between what one party expresses and how the other party construes what has been expressed. Thus, how a message is perceived becomes equally important regardless of whether the sent message includes any form of explicit attack or offense. This study focuses on identifying the implicit attacks and negative intentions in text-based conversation from the reader’s point of view. We focus on questions in conversations and investigate the underlying perceived intention. We introduce our dataset that includes questions, intention polarity, and type of attacks. We conduct a meta-analysis on the data to demonstrate how a question may be used as a means of attack and how different perspectives can lead to multiple interpretations. We also report benchmark results of several models for detecting instances of tacit attacks in questions with the aim of avoiding latent or manifest conflict in conversations.
Disc Diffusion Antibody Sensitivity Testing Using Image Processing and Machine Learning

The aim in many Image Processing applications is to extract important features from image data, from which a description, interpretation, or understanding of the scene can be provided by the machine. Image processing can be defined as, the processing or altering an existing image in a desired manner. This system allows the user to take hard copy of the image using printer routines and allows the user to store screen image into the disk file using file format (bmp, jpg.gif). Image processing in its general form pertains to the alteration and analysis of pictorial information. We find instances of image processing occurring all the time in our daily lives probably the most powerful image processing system is the human brain together with the eye. The system receives, enhances, and stores images at enormous rates of speed. Machine Learning is a booming technology because it benefits every type of business across every industry. The applications are limitless. From healthcare to financial services, transportation to cyber security, and marketing to government, machine learning can help every type of business adapt and move forward in an agile manner. Machine learning allows businesses to collect insights quickly and efficiently, speeding the time to business value. That’s why machine learning is important for every organization. Machine learning also takes the guesswork out of decisions A computer has become an essential commodity in every hospital. Everything is computerized today. from open heart surgeries to X-rays to various clinical tests. Everything is carried out efficiently and effectively by the computer. There are many advantages of computers that make it a must have by every medical professional. Thus, we have using computer for getting fast and accurate result for calculating area of inhibition and suggesting medicine by the results. By using this type of technique manual error can be reduce and accurate result can be fetched.
How Attacker Knowledge Affects Privacy Risks

Governments and businesses routinely disclose large amounts of private data on individuals, for data analytics. However, despite attempts by data controllers to anonymise data, attackers frequently deanonymise disclosed data by matching it with their prior knowledge. When is a chosen anonymisation method adequate? For this, a data controller must consider attackers befitting their scenario; how does attacker knowledge affect disclosure risk? We present a multi-dimensional conceptual framework for assessing privacy risks given prior knowledge about data. The framework defines three dimensions: distinctness (of input records), informedness (of attacker), and granularity (of anonymisation program output). We model three well-known types of disclosure risk: identity disclosure, attribute disclosure, and quantitative attribute disclosure. We demonstrate how to apply this framework in a health record privacy scenario: We analyse how informing the attacker with COVID-19 infection rates affects privacy risks. We perform this analysis using Privug, a method that uses probabilistic programming to do standard statistical analysis with Bayesian Inference.
Information Processing by Selective Machines

The goal of this paper is to develop the novel automaton model of learning processes called a selective machine and to study the properties of these machines. The model is based on the analysis of the process of language acquisition by people, although it correctly reflects how learning occurs in nature when animals, birds and even fish learn. A selective machine is an abstract automaton that has processors that can belong to different classes of conventional abstract automata. This creates various classes of selective machines. It is proved that in a general case, a selective machine can have higher learning abilities than any of its processors. This shows how synergy emerges in the technological sphere and explains why computer networks are able to outperform separate computers.
Chapter 2: The role of big data, AI and blockchain technology in digital public governance

Artificial intelligence and blockchain technologies allow organisations to leverage big data and to gain insights in order to make sound public policy decisions. Public sector organisations need to learn about these technologies and develop the necessary skills and competencies to help their organisations stay competitive. One distinguishing characteristic of these emerging digital technologies is that they are not only tools for public sector organisations to enhance existing capabilities in public policy processes, but their technological features demand a complete revisit of the existing administrative systems and processes. This chapter focuses on the implications of these technologies for public sector organisations. Specifically, the chapter elucidates on how these technologies are currently employed in the public sector, what new features and capabilities can be unlocked through their conjoint use, and what opportunities and challenges are associated with these technologies. Furthermore, the chapter presents the specific governance requirements associated with these technologies in the transformation towards digital public governance.
The Impact of using a Contract-Driven, Test-Interceptor based Software Development Approach

Contract Driven Development formalizes functional requirements within component contracts. The process aims to produce higher quality software, reduce quality assurance costs and improve reusability. However, the perceived complexity and cost of requirements formalization has limited the adoption of this approach in industry. In this article, we consider the extent to which the overheads of requirements formalization can be netted off against the reduced quality assurance costs arising from being able to autogenerate functional test interceptors from component contracts. Test-interceptors are used during testing to verify that component contracts are satisfied. In particular, we investigate the impact of contract-driven development on both the quality attributes of the software development process and the quality of the software produced by the process. Empirical data obtained from an actual software project using contract-driven development with test interceptor generation is compared to that obtained from similar projects that used a traditional software development process with informal requirements and manually written functional tests.
Capturing Diverse and Precise Reactions to a Comment with User-Generated Labels

Simple up/downvotes, arguably the most widely used reaction design across social media platforms, allow users to efficiently express their opinions and quickly evaluate others’ opinions from aggregated votes. However, such design forces users to project their diverse opinions onto dichotomized reactions and provides limited information to readers on why a comment was up/downvoted. We explore user-generated labels (UGLs) as an alternative reaction design to capture the rich context of user reactions to comments. We conducted a between-subjects study with 218 participants to understand how people use and are influenced by UGLs compared to up/downvotes. Specifically, we examine how UGLs affect users’ ability to express and perceive diverse opinions. Participants generated 234 unique labels on diverse aspects of a comment. Leaving more reactions than participants in the up/downvotes condition, participants reported that the ability to express their opinions improved with UGLs. UGLs also enabled participants to better understand the multifacetedness of public evaluation of a comment.
Framework and Evaluation Methodology for Autonomous Drone Racing

In recent years, autonomous drone races have become increasingly popular in the aerial robotics research community due to challenges in perception, localization, navigation, and control at high speeds, pushing forward the state-of-the-art every year. However, autonomous racing drones are still far from reaching human pilot performance, and a lot of research has to be done to accomplish that. In this work, a complete architecture system and an evaluation method for autonomous drone racing research are proposed, based on the Aerostack 5.0 open-source framework. To evaluate the performance of the whole system and of each algorithm used separately, this framework is validated not only with simulated flights, but also through real flights in an indoor drone race circuit using different configurations.
Research on Dance Movement Recognition Based on Multi-Source Information

A huge number of scientific research institutions and scholars are now researching this topic in depth, with promising results. Meanwhile, research development in dance visual frequency movement detection is rather modest due to the high complexity of dance movement and the challenges of human body self-shielding in dance performance. Aiming at the problem of the combination of motion recognition and dance video, the feature extraction, representation, and motion recognition methods based on dance video are emphatically studied. This paper studies an effective feature extraction method according to the characteristics of dance movements. Firstly, each dance movement video in the data set is separated into equal sections, and the edge characteristics of all video pictures in each segment are gathered into one image, from which the direction gradient histogram features are extracted. Secondly, a group of directional gradient histogram feature vectors is used to represent the local appearance information and shape features of the video dance moves. In view of the existing problem of heterogeneous feature fusion, this paper chooses the multi-core learning method to fuse the three kinds of features for dance movement recognition. Finally, the effectiveness of the proposed dance movement detection algorithm is tested using the Dance DB data set from the University of Cyprus and the Folk Dance data set from my laboratory. Experimental results show that the proposed algorithm can maintain a certain recognition rate for relatively complex dance movements and can still ensure a certain accuracy when the background and target are easily confused. This also confirms the efficacy of the movement recognition system used in this paper for recognizing dance movements.
A Method of Key Posture Detection and Motion Recognition in Sports Based on Deep Learning

Moving target recognition and analysis is an important research direction in the field of computer vision, which is widely used in our life, such as intelligent robot, video surveillance, medical education, sports competition, and national defense security. By analyzing the video of weightlifting, this paper extracts the key postures of athletes’ training, so as to assist coaches to train athletes more professionally. Based on DL (Deep Learning), a key pose extraction method of sports video (RoI_KP for short) based on classified learning of regions of interest is proposed. By fine-tuning CNN (Convolutional Neural Network), a network model suitable for video classification of weightlifting in the region of interest is obtained. Finally, according to the classification results, the selection strategy of classification results is designed to extract key poses. According to the characteristics of different modal information, different DNN (deep neural network) is adopted, and various depth networks are combined to mine the multi-modal spatio-temporal depth features of human movements in video. Experimental results show that the method proposed in this paper is very competitive.
Massive Text Normalization via an Efficient Randomized Algorithm

Current popular machine learning techniques in natural language processing and data mining rely heavily on high-quality text sources. Nevertheless, real-world text datasets contain a significant amount of spelling errors and improperly punctuated variants where the performance of these models would quickly deteriorate. Moreover, existing text normalization methods are prohibitively expensive to execute over web-scale datasets, can hardly process noisy texts from social networks, or require annotations to learn the corrections in a supervised manner. In this paper, we present Flan (Fast LSH Algorithm for Text Normalization), a scalable randomized algorithm to clean and canonicalize massive text data. Our approach suggests corrections based on the morphology of the words, where lexically similar words are considered the same with high probability. We efficiently handle the pairwise word-to-word comparisons via locality sensitive hashing (LSH). We also propose a novel stabilization process to address the issue of hash collisions between dissimilar words, which is a consequence of the randomized nature of LSH and is exacerbated by the massive scale of real-world datasets. Compared with existing approaches, our method is more efficient, both asymptotically and in empirical evaluations, does not rely on feature engineering, and does not require any annotation. Our experimental results on real-world datasets demonstrate the efficiency and efficacy of Flan. Based on recent advances in densified Minhash, our approach requires much less computational time compared to baseline text normalization techniques on large-scale Twitter and Reddit datasets. In a human evaluation of the quality of the normalization, Flan achieves 5% and 14% improvement against the baselines over the Reddit and Twitter datasets, respectively. Our method also improves performance on Twitter sentiment classification applications and the perturbed GLUE benchmark datasets, where we introduce random errors into the text.
Construction and Analysis of Computer Model in Sports Education Effect Evaluation Based on Grey System Theory

College physical education is the most common course type in college students’ education. It undertakes the important responsibility of improving students’ physical quality. In order to make effective use of teaching curriculum, the evaluation of curriculum reform is the key of research. Based on this, this paper puts forward the evaluation of physical education teaching effect based on grey system theory. This paper expounds the research progress of teaching effect evaluation and then analyzes the grey system theory. In the evaluation, the key of AHP is to control the traditional fuzzy analysis method, which can be optimized by using fuzzy analysis method. At the same time, the grey system theory is used for comprehensive analysis to establish sports teaching scoring index effect. Through the analysis of physical education teaching examples, the effectiveness of grey system theory in evaluating teaching is confirmed. The application of grey system theory in teaching effect evaluation is more intuitive, which can make full use of the existing whitening information and reduce human errors. In addition, it can comprehensively consider the impact of each sample data on the final result to the greatest extent, which is closer to the actual evaluation result than the theoretical fuzzy evaluation.
Resource-Efficient Training for Large Graph Convolutional Networks with Label-Centric Cumulative Sampling

Graph Convolutional Networks (GCNs) are popular for learning representation of graph data and have a wide range of applications in social networks, recommendation systems, etc. However, training GCN models for large networks is resource intensive and time consuming, which hinders them from real deployment. The existing GCN training methods intended to optimize the sampling of mini-batches for stochastic gradient descent to accelerate training process, which did not reduce the problem size and had limited reduction in computation complexity. In this paper, we argue that a GCN can be trained with a sampled subgraph to produce approximate node representations, which inspires us a novel perspective to accelerate GCN training via network sampling. To this end, we propose a label-centric cumulative sampling (LCS) framework for training GCNs for large graphs. The proposed method constructs a subgraph cumulatively based on probabilistic sampling, and trains the GCN model iteratively to generate approximate node representations. The optimality of LCS is theoretically guaranteed to minimize the bias during node aggregation procedure in GCN training. Extensive experiments based on four real-world network datasets show that the LCS framework accelerates the training for the state-of-the-art GCN models up to 17x without causing noteworthy model accuracy drop.
Evaluation Method of English Learning Engagement Based on Wireless Communication Network and Big Data

The existing English learning engagement assessment methods still have some deficiencies, which cannot predict students’ interest in English learning, and the teaching effect is not good enough, resulting in the improvement of students’ performance which is not obvious. Therefore, an English learning engagement assessment method based on wireless communication network and big data is proposed. Firstly, build a mobile wireless communication network model, analyze the metaphor processing and evaluation methods adopted by English learners according to big data, and realize the acquisition and interpretation of metaphor meaning through three steps: the establishment of metaphor knowledge base, the analysis and description of metaphor language, and the classification and recognition of metaphor; build an evaluation framework based on wireless communication network and big data, propose a Java application platform for English learning engagement evaluation, and realize the English learning engagement evaluation method. The experimental research shows that the evaluation method has good stability, short evaluation time, and high efficiency and has practical application effect.