Reviewerly/exHarmony huggingface dataset:
Dynamics-aware novelty search with behavior repulsion

Searching solutions for the task with sparse or deceptive rewards is a fundamental problem in Evolutionary Algorithms (EA) and Reinforcement Learning (RL). Existing methods in RL have been proposed to enhance the exploration by encouraging agents to obtain novel states. However, solely seeking a single local optimal solution could be insufficient for the tasks with the deceptive local optima. Novelty-Search (NS) and Quality-Diversity (QD) have shown promising results for finding diverse solutions with different behavioral characteristics. However, manually defining the task-specific behavior description limits these methods to low-dimensional tasks. This paper presents Dynamics-aware Novelty Search with Behavior Repulsion (DANSBR), a hybrid algorithm that evolves high-performing solutions by introducing a generalized novelty measurement and a bidirectional gradient-based mutation operator based on the Quality-Diversity paradigm. The novelty of a single solution is defined as the prediction error of an approximate dynamic model in the task-agnostic behavior space. The mutation operator drives the solution to behave differently or obtain better performance in a sample-eficient manner. As a result of better exploration, our approach outperforms several baselines on high-dimensional continuous control tasks with sparse rewards. Empirical results also demonstrate that DANSBR improves the performance on the task with deceptive rewards.
ENSEMBLE LEARNING ALGORITHMS

Artificial intelligence is a method that is increasingly becoming widespread in all areas of life and enables machines to imitate human behavior. Machine learning is a subset of artificial intelligence techniques that use statistical methods to enable machines to evolve with experience. As a result of the advancement of technology and developments in the world of science, the interest and need for machine learning is increasing day by day. Human beings use machine learning techniques in their daily life without realizing it. In this study, ensemble learning algorithms, one of the machine learning techniques, are mentioned. The methods used in this study are Bagging and Adaboost algorithms which are from Ensemble Learning Algorithms. The main purpose of this study is to find the best performing classifier with the Classification and Regression Trees (CART) basic classifier on three different data sets taken from the UCI machine learning database and then to obtain the ensemble learning algorithms that can make this performance better and more determined using two different ensemble learning algorithms. For this purpose, the performance measures of the single basic classifier and the ensemble learning algorithms were compared
Libertas: Backward Private Dynamic Searchable Symmetric Encryption Supporting Wildcards

When outsourcing data, Searchable Symmetric Encryption (SSE) allows clients to query the server for their encrypted files without compromising data confidentiality. Several attacks against searchable encryption schemes have been proposed that leverage information leakage the schemes emit when operating. Schemes should achieve Forward and Backward Privacy to mitigate these types of attacks. Despite the variance of query types across SSE schemes, most forward and backward private schemes only support exact keyword search. In this research, we extend backward privacy notions and their underlying leakage functions to the Wildcard Search domain. Additionally, we present $$\mathsf {Libertas}$$ ; a construction that provides backward privacy to any wildcard supporting SSE scheme. If the scheme is forward private, this property is inherited. We prove security in the established $$\mathcal {L}$$ -adaptive security model with respect to a leakage function $$\mathcal {L}$$ . We show that the performance overhead scales linearly with the number of deletions.
Application of Kohonen Neural Network in Sports Cluster

People’s physical fitness is directly linked to the national physique of a country. It is an important analysis indicator of the country’s comprehensive national strength and economic level. Moreover, students’ physical and mental health is in a stage of rapid development. Their physical health is not only directly related to their study and life at this stage but it will also have a profound impact on the physical level, health status, and work ability of adults. Starting from the cluster analysis of sports, this article explores the communication effects of sports, communication strategies, and the relationship between sports and the development of users’ healthy habits by defining concepts and types and combining quantitative and qualitative analysis. Study the relationship and then analyze whether sports applications bring people the effect of promoting healthy behavior. This article retrieved documents about the application of Kohonen neural network in sports cluster analysis in domestic literature databases such as Weipu, Wanfang data, and CNKI. A total of 144 documents were retrieved from the database, and the retrieved documents were collected for sports activities. The study of class analysis can avoid human subjective factors and obtain clustering results quickly and objectively, thus providing an ideal clustering method for comprehensive evaluation of sports. The experiment proves the cluster analysis of the impact of sports on people’s physical fitness, the heterogeneity test results are 0% (boys) and 3% (girls), it is believed that there is no statistical heterogeneity in the physical fitness of middle school boys in each study, and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>P</mi> <mo>&lt;</mo> <mn>0.001</mn> </math> , indicating that the influence of sports on people’s physical fitness is significantly different between the experimental group and the control group. This shows that the application of Kohonen neural network clustering analysis method has great practical value for comprehensively evaluating people’s physical functions and physical fitness. It is an objective, reasonable, effective, and rapid quantitative evaluation method.
Insights from Incorporating Quantum Computing into Drug Design Workflows

Abstract While many quantum computing (QC) methods promise theoretical advantages over classical counterparts, quantum hardware remains limited. Exploiting near-term QC in computer-aided drug design (CADD) thus requires judicious partitioning between classical and quantum calculations. We present HypaCADD, a hybrid classical-quantum workflow for finding ligands binding to proteins, while accounting for genetic mutations. We explicitly identify modules of our drug design workflow currently amenable to replacement by QC: non-intuitively, we identify the mutation-impact predictor as the best candidate. HypaCADD thus combines classical docking and molecular dynamics with quantum machine learning (QML) to infer the impact of mutations. We present a case study with the SARS-CoV-2 protease and associated mutants. We map a classical machine-learning module onto QC, using a neural network constructed from qubit-rotation gates. We have implemented this in simulation and on two commercial quantum computers. We find that the QML models can perform on par with, if not better than, classical baselines. In summary, HypaCADD offers a successful strategy for leveraging QC for CADD.
CARETS: A Consistency And Robustness Evaluative Test Suite for VQA

We introduce CARETS, a systematic test suite to measure consistency and robustness of modern VQA models through a series of six fine-grained capability tests.In contrast to existing VQA test sets, CARETS features balanced question generation to create pairs of instances to test models, with each pair focusing on a specific capability such as rephrasing, logical symmetry or image obfuscation.We evaluate six modern VQA systems on CARETS and identify several actionable weaknesses in model comprehension, especially with concepts such as negation, disjunction, or hypernym invariance.Interestingly, even the most sophisticated models are sensitive to aspects such as swapping the order of terms in a conjunction or changing the number of answer choices mentioned in the question.We release CARETS to be used as an extensible tool for evaluating multi-modal model robustness. 1
A Taxonomy of Empathetic Questions in Social Dialogs

Effective question-asking is a crucial component of a successful conversational chatbot.It could help the bots manifest empathy and render the interaction more engaging by demonstrating attention to the speaker's emotions.However, current dialog generation approaches do not model this subtle emotion regulation technique due to the lack of a taxonomy of questions and their purpose in social chitchat.To address this gap, we have developed an empathetic question taxonomy (EQT), with special attention paid to questions' ability to capture communicative acts and their emotionregulation intents.We further design a crowdsourcing task to annotate a large subset of the EmpatheticDialogues dataset with the established labels.We use the crowd-annotated data to develop automatic labeling tools and produce labels for the whole dataset.Finally, we employ information visualization techniques to summarize co-occurrences of question acts and intents and their role in regulating interlocutor's emotion.These results reveal important question-asking strategies in social dialogs.The EQT classification scheme can facilitate computational analysis of questions in datasets.More importantly, it can inform future efforts in empathetic question generation using neural or hybrid methods. 1
Enveloping a day – Persona visual mapping

The following article describes a Persona Method through Visual Clues (PMVC) in a learning setting performed in the course of Interface Design I, degree of Design, School of Arts, University of Evora, Portugal, in the academic year of 2021/22. The study follows a descriptive case methodology. The purposed PMVC concerns the project’s early stage and reveals how a mosaic assemblage of visual clues allows an itinerary on cohesive information acquisition in guiding the design project. The proposed PMVC allowed students to valorize life contexts when projecting a persona, enabling a strong focus on consumers and work contexts. Although working in fictionalized settings, the PMVC triggers the reasoning on the complexity of the contexts, deploying information that will deliver more reality into Personas. Keywords: Visual methods, Communication Design, Persona Method, Design Literacy
Word-level Perturbation Considering Word Length and Compositional Subwords

We present two simple modifications for wordlevel perturbation: Word Replacement considering Length (WR-L) and Compositional Word Replacement (CWR).In conventional word replacement, a word in an input is replaced with a word sampled from the entire vocabulary, regardless of the length and context of the target word.WR-L considers the length of a target word by sampling words from the Poisson distribution.CWR considers the compositional candidates by restricting the source of sampling to related words that appear in subword regularization.Experimental results showed that the combination of WR-L and CWR improved the performance of text classification and machine translation.
Analysis of Data Augmentation for Grammatical Error Correction Based on Various Rules

ニューラル文法誤り訂正では，データ拡張によって学習データの不足を補う手法が活発に研究されている．本研究では，既存のデータ拡張手法が，より良いデータ拡張を行い性能向上を目指す上で重要な要素として，(1) 誤りの多様性が訂正性能に寄与すること，(2) 特定の種類の誤り生成がその種類の誤り訂正性能に寄与すること，(3) データ拡張に用いるコーパスの大きさが訂正性能に寄与することの 3 点が仮定されている．本研究では，これらの仮定の妥当性を検証するため，多様な文法カテゴリでの誤り生成規則を組み合わせる手法を提案し，生成する誤りの種類を変えて誤り訂正モデルを学習することで，比較検証を行う．結果として，仮定 (1) (2) は正しいが，一方で，仮定 (3) においては，コーパスの規模ではなく，パラメータの更新回数と誤りの生成回数が影響することが明らかになった．さらに，提案手法は，学習者コーパスを用いない教師なし設定でも高い性能のモデルを学習でき，学習者コーパスを用いた場合でも，既存の手法と同程度に高性能なモデルを学習できることが明らかになった．折り返し翻訳・逆翻訳によるデータ拡張手法との比較を通じて，また，ルールによる誤り生成とこれらの手法を用いたモデルでは，訂正において得意な誤り種類が異なることが判明した．
Online teaching in Serbia and the Republic of Srpska at the COVID-19 outbreak: Teachers' experiences

The purpose of this pilot study is to determine the nature, management and quality of experimental forms of online teaching based on the teachers' three-month teaching experience gained in the academic year 2019/20, and to identify the major social, economic and psychological factors that might be related to the way the online teaching as alternate modality was realized in all educational levels in Serbia and the Republic of Srpska at the COVID-19 outbreak. The conducted research involving 283 language and literature teachers was done by using the quantitative and qualitative method, and the survey questionnaire consisting of one open-ended and ten closed-ended questions was the measuring instrument. The responses are analyzed and classified into several categories according to their common denominator. The results highlighted the main challenges the teachers faced while teaching online, pointed to the key limitations of relying on face-to-face teaching as the only accessible delivery mode and provided the relevant insights regarding the directions for further investigations in online teaching in the field of humanities.
Data-Driven User Profiling and Personalization in Tiimo: Towards Characterizing Time Management Behaviors of Neurodivergent Users of a Scheduling Application

Deficits with time management and other cognitive functions can stem from multiple causes and be found across different diagnostic conditions. At the same time, cognitive function can differ within diagnostic classes, which calls for adaptable and personalized assistance. A great deal of literature on cognitive assistive technology (CAT) focus on diagnostic populations rather than cognitive impairments across different conditions. This study reports the initial steps towards a data-driven approach to map out the characteristics and behavior of users of a time management app, Tiimo, originally targeting children with ADHD. Based on results from a questionnaire and analysis of user activity data, findings indicate a tendency of attracting a more heterogeneous user population compared to the originally intended target group, thus supporting the need for a more complex and data-driven ‘design for all’ approach to CAT rather than delimitations based on diagnostic groups. Preliminary findings from the analysis of activity data across user groups and diagnoses show that users generally schedule fewer than five daily activities and most often in the morning, suggesting a potential emphasis on support particularly during morning routines. However, the analysis also highlights the need for more data points to enable assessment of progress, motivation, and effectiveness of the technology. Next steps include a more detailed analysis of user activity that takes different types of behavior and other relevant factors into account by applying NLP to further develop data-driven approaches to user profiling and personalization in time management apps for neurodivergent users.
Automated Digital Presentation Control using Hand Gesture Technique

In today’s digital world Presentation using a slideshow is an effective and attractive way that helps speakers to convey information and convince the audience. There are ways to control slides with devices like mouse, keyboard, or laser pointer, etc. The drawback is one should have previous knowledge about the devices in order to manage them. Gesture recognition has acquired importance a couple of years prior and are utilized to control applications like media players, robot control, gaming. The hand gesture recognition system builds the use of gloves, markers and so on However, the utilization of such gloves or markers expands the expense of the system. In this proposed system, Artificial intelligence-based hand gesture detection methodology is proposed. Users will be able to change the slides of the presentation in both forward and backward directions by just doing hand gestures. Use of hand gestures cause connection simple, helpful, and doesn’t need any additional gadget. The suggested method is to help speakers for a productive presentation with natural improved communication with the computer. Specifically, the proposed system is more viable than utilizing a laser pointer since the hand is more apparent and thus can better grab the attention of the audience.
Incremental Intent Detection for Medical Domain with Contrast Replay Networks

Conventional approaches to medical intent detection require fixed pre-defined intent categories.However, due to the incessant emergence of new medical intents in the real world, such requirement is not practical.Considering that it is computationally expensive to store and re-train the whole data every time new data and intents come in, we propose to incrementally learn emerged intents while avoiding catastrophically forgetting old intents.We first formulate incremental learning for medical intent detection.Then, we employ a memorybased method to handle incremental learning.We further propose to enhance the method with contrastive replay networks, which use multilevel distillation and contrastive objective to address training data imbalance and medical rare words respectively.Experiments show that the proposed method outperforms the state-of-the-art model by 5.7% and 9.1% of accuracy on two benchmarks respectively.
Polite Task-oriented Dialog Agents: To Generate or to Rewrite?

For task-oriented dialog agents, the tone of voice mediates user-agent interactions, playing a central role in the flow of a conversation.Distinct from domain-agnostic politeness constructs, in specific domains such as online stores, booking platforms, and others, agents need to be capable of adopting highly specific vocabulary, with significant impact on lexical and grammatical aspects of utterances.Then, the challenge is on improving utterances' politeness while preserving the actual content, an utterly central requirement to achieve the task goal.In this paper, we conduct a novel assessment of politeness strategies for task-oriented dialog agents under a transfer learning scenario.We extend existing generative and rewriting politeness approaches, towards overcoming domain-shifting issues, and enabling the transfer of politeness patterns to a novel domain.Both automatic and human evaluation is conducted on customer-store interactions, over the fashion domain, from which contribute with insightful and experimentally supported lessons regarding the improvement of politeness in task-specific dialog agents.
Introduction to Blockchain Technology

Blockchain is a modern technology that brings trust and reliability to various business operations. This is because it has concrete characteristics such as decentralized, immutable ledger, and cryptographic solutions. Moreover, various critical business operations need security and privacy solutions and they rely on blockchain technology to provide a feasible solution to alleviate security risks. However, it is challenging to decide which type of blockchain one should adopt to encounter security threats. A blockchain learner needs to know the prerequisites and collaborative technologies involved in implementing the blockchain network. Taking care of the aforementioned objectives, this chapter convey the fundamentals of blockchain technology, where it briefly discusses the blockchain and its architecture, along with its needs and features. Additionally, this chapter has given comprehensive details on the implementation platforms used to develop blockchain-based solutions.
Intelligent Medical Chatbot System For Women’s Healthcare

Even today talking about women health is big taboo in India. The society is unbiased about women especially about their health issues hence they face disparity in the society. Women feel hesitant to talk about or speak about their health issues or problems openly. Thus, the goal of this chatbot is to help women to find information and remedial solutions about their health. Query is processed by the bot and response will be displayed on web application. This chatbot will provide helpful information instantly. It will also provide prediction about the disease that the women might be suffering.So this bot will help to make Right decision and give right advice to women on 24/7 basis. It will act as an helping hand for working women to keep check on their health in their busy routine. At the same time this will also help women in rural areas who are apprehensive to talk about their health issues publicly.
Unsupervised Chinese Word Segmentation with BERT Oriented Probing and Transformation

Word Segmentation is a fundamental step for understanding many languages.Previous neural approaches for unsupervised Chinese Word Segmentation (CWS) only exploit shallow semantic information, which can miss important context.Large scale Pre-trained language models (PLM) have achieved great success in many areas.In this paper, we propose to take advantage of the deep semantic information embedded in PLM (e.g., BERT) with a self-training manner, which iteratively probes and transforms the semantic information in PLM into explicit word segmentation ability.Extensive experiment results show that our proposed approach achieves a state-of-the-art F1 score on two CWS benchmark datasets.The proposed method can also help understand low resource languages and protect language diversity.
Arabic Cyberbullying Detection from Imbalanced Dataset Using Machine Learning

In recent years, the number of online social networks users is dramatically increased. Cyberbullying is a serious threat in online social networks especially toward children and teenagers. Victims are harassed by perpetrators even with no physical attendance. Cyberbullying causes psychological damage to victims resulting in anxiety, depression, and even suicide. Cyberbullying is language and culture sensitive. Many prior works in cyberbullying detection was conducted in English and only few papers studied Arabic cyberbullying detection. In this research, Arabic cyberbullying detection using five machine learning techniques were studied using real Arabic messages from Twitter and YouTube. Many performance techniques were used to evaluate the robustness of the model. Naïve Bayes (NB) achieves the highest Receiver Operating Characteristic (ROC) curve (AUC) of 89% while Support Vector Machine (SVM) and Logistic Regression (LR) both achieve 88%.
The influence of inherent interactivity on information recall in knowledge visualization design

The inherent interactivity in knowledge visualization design is considered to be able to help users better understand and recall information content. However, little is known about whether and how such interactivity would affect information recall. Therefore, this study designs an experiment for subjects with a single factor at two levels (high and low intrinsic interaction) to explore the influencing mechanism between inherent interactivity and information recall, while measuring the influence of such intermediary variables as cognitive involvement, perceived active control and cognitive load on this mechanism. The results show that participants in low inherent interactivity can recall more information than in high inherent interactivity. In the analysis of mediating variables, the relationship between inherent interactivity and information recall is significantly affected by the mediating variables, and the perceived active control delivers a significant positive impact on this relationship.