Reviewerly/exHarmony huggingface dataset:
Self-critiquing models for assisting human evaluators

We fine-tune large language models to write natural language critiques (natural language critical comments) using behavioral cloning. On a topic-based summarization task, critiques written by our models help humans find flaws in summaries that they would have otherwise missed. Our models help find naturally occurring flaws in both model and human written summaries, and intentional flaws in summaries written by humans to be deliberately misleading. We study scaling properties of critiquing with both topic-based summarization and synthetic tasks. Larger models write more helpful critiques, and on most tasks, are better at self-critiquing, despite having harder-to-critique outputs. Larger models can also integrate their own self-critiques as feedback, refining their own summaries into better ones. Finally, we motivate and introduce a framework for comparing critiquing ability to generation and discrimination ability. Our measurements suggest that even large models may still have relevant knowledge they cannot or do not articulate as critiques. These results are a proof of concept for using AI-assisted human feedback to scale the supervision of machine learning systems to tasks that are difficult for humans to evaluate directly. We release our training datasets, as well as samples from our critique assistance experiments.
Context Knowledge-Aware Recognition of Composite Intents in Task-Oriented Human-Bot Conversations

Task-oriented dialogue systems employ third-party APIs to serve end-users via natural language interactions. while existing advances in Natural Language Processing (NLP) and Machine Learning (ML) techniques have produced promising and useful results to recognize user intents, the synthesis of API calls to support a broad range of potentially complex user intents is still largely a manual and costly process. In this paper, we propose a new approach to recognize and realize complex user intents. Our approach relies on a new rule-based technique that leverages both (i) natural language features extracted using existing NLP and ML techniques and (ii) contextual knowledge to capture the different classes of complex intents. We devise a context knowledge service to capture the requisite contextual knowledge.
Dreamscape Bricks VR: An Experimental Virtual Reality Tool for Architectural Design

In this paper, we propose a VR design tool framework called DREAMSCAPE, which adopts a direct manipulation approach focusing on embody, experience, and manipulation activities in design. The framework defines a VR design process using intuitive controls without being limited by the preconceptions of conventional CAD systems. To establish and demonstrate the framework, we designed and developed a VR design tool called Dreamscape Bricks VR in Unreal Engine 4, using LEGO bricks as base components in a high-fidelity interactive design environment. We conducted user tests and administered questionnaires assessing usability, performance, and comfort. Results showed that the user experience of the tool is positive. The developed tool is expected to establish the abstract framework and provide insights into the future of VR design tools with implications on design education
Resistance, social reproduction and emerging commitments for collaborative design from the margins

The paper introduces two design research cases tracing how the concepts and experiences of resistance and social reproduction can help design research become a more assertive part of the coding that interprets, exposes and disputes social reality, particularly in Latin America.Through reflections on (1) a design intervention contributing to an Indigenous popular education initiative in the Ecuadorian Amazon and (2) a series of ongoing public space design interventions taking place in colonias populares of Mexico City, we propose four interrelated commitments (visibility, sustainment, tensions and collectivity) to orient ourselves in understanding what it actually takes to build knowledge and create resources together.
Mutant in the mirror: Queer becomings with AI

This paper contributes to scholarly discourse on design and AI by using queerness as a theoretical grounding to explore potentialities for design to interface with and imagine artificial intelligence (AI) differently.The paper does so by reporting on an autotheoretical experiment in which I pose the questions: What if we understood AI as queer, a kind of mutant, in a state of becoming; a dynamic, relational, nonbinary gender variant?How then might AI show up in and act on the world (with us humans) differently?The experiment uses a Generative Adversarial Network (GAN) to unsettle how AI is understood today, and to allow for new AI propositions to take root.The work provides a glimpse into forms of design refusal that might illuminate designers to cultural computability and self-determination when designing with AI systems.
Self-Communicating Deep Reinforcement Learning Agents Develop External Number Representations

Symbolic numbers are a remarkable product of human cultural development. The developmental process involved the creation and progressive refinement of material representational tools, such as notched tallies, knotted strings, and counting boards. In this paper, we introduce a computational framework that allows the investigation of how material representations might support number processing in a deep reinforcement learning scenario. In this framework, agents can use an external, discrete state to communicate information to solve a simple numerical estimation task. We find that different perceptual and processing constraints result in different emergent representations, whose specific characteristics can facilitate the learning and communication of numbers.
Pilot study of real-time Emotional Recognition technology for Secondary school students

The large variety of students in a class makes the teaching task complex, making it difficult for the teacher to personalise learning to each student. Since students should be at the centre of the educational process, it is necessary to know them better, so this study aims to explore the possibilities of using a camera for emotion recognition (ER) with a view to the potential use of this information to improve the teaching-learning process. To accomplish the aim it is previously necessary to develop and apply code capable of detecting faces, ER and transfer this data into a database for further analysis, which consists of establishing the first approximations to the relationship between students' emotions and other conditions (subject, time of day, academic performance). By monitoring the emotional state of students, if used properly, can improve educational processes, such as the teacher's decision-making in the classroom, as well as optimise attention to students, adjusting their methodology or focusing on a specific student.
HaGRID - HAnd Gesture Recognition Image Dataset

This paper introduces an enormous dataset, HaGRID (HAnd Gesture Recognition Image Dataset), to build a hand gesture recognition (HGR) system concentrating on interaction with devices to manage them. That is why all 18 chosen gestures are endowed with the semiotic function and can be interpreted as a specific action. Although the gestures are static, they were picked up, especially for the ability to design several dynamic gestures. It allows the trained model to recognize not only static gestures such as "like" and "stop" but also "swipes" and "drag and drop" dynamic gestures. The HaGRID contains 554,800 images and bounding box annotations with gesture labels to solve hand detection and gesture classification tasks. The low variability in context and subjects of other datasets was the reason for creating the dataset without such limitations. Utilizing crowdsourcing platforms allowed us to collect samples recorded by 37,583 subjects in at least as many scenes with subject-to-camera distances from 0.5 to 4 meters in various natural light conditions. The influence of the diversity characteristics was assessed in ablation study experiments. Also, we demonstrate the HaGRID ability to be used for pretraining models in HGR tasks. The HaGRID and pretrained models are publicly available.
A Search-Based Testing Approach for Deep Reinforcement Learning Agents

Deep Reinforcement Learning (DRL) algorithms have been increasingly employed during the last decade to solve various decision-making problems such as autonomous driving and robotics. However, these algorithms have faced great challenges when deployed in safety-critical environments since they often exhibit erroneous behaviors that can lead to potentially critical errors. One way to assess the safety of DRL agents is to test them to detect possible faults leading to critical failures during their execution. This raises the question of how we can efficiently test DRL policies to ensure their correctness and adherence to safety requirements. Most existing works on testing DRL agents use adversarial attacks that perturb states or actions of the agent. However, such attacks often lead to unrealistic states of the environment. Their main goal is to test the robustness of DRL agents rather than testing the compliance of agents' policies with respect to requirements. Due to the huge state space of DRL environments, the high cost of test execution, and the black-box nature of DRL algorithms, the exhaustive testing of DRL agents is impossible. In this paper, we propose a Search-based Testing Approach of Reinforcement Learning Agents (STARLA) to test the policy of a DRL agent by effectively searching for failing executions of the agent within a limited testing budget. We use machine learning models and a dedicated genetic algorithm to narrow the search towards faulty episodes. We apply STARLA on Deep-Q-Learning agents which are widely used as benchmarks and show that it significantly outperforms Random Testing by detecting more faults related to the agent's policy. We also investigate how to extract rules that characterize faulty episodes of the DRL agent using our search results. Such rules can be used to understand the conditions under which the agent fails and thus assess its deployment risks.
Adversarial Patch Attacks and Defences in Vision-Based Tasks: A Survey

&lt;p&gt;Adversarial attacks in deep learning models, especially for safety-critical systems, are gaining more and more attention in recent years, due to the lack of trust in the security and robustness of AI models. Yet the more primitive adversarial attacks might be physically infeasible or require some resources that are hard to access like the training data, which motivated the emergence of patch attacks. In this survey, we provide a comprehensive overview to cover existing techniques of adversarial patch attacks, aiming to help interested researchers quickly catch up with the progress in this field. We also discuss existing techniques for developing detection and defences against adversarial patches, aiming to help the community better understand this field and its applications in the real world.&lt;/p&gt;
Characteristics of Harmful Text: Towards Rigorous Benchmarking of Language Models

Large language models produce human-like text that drive a growing number of applications. However, recent literature and, increasingly, real world observations, have demonstrated that these models can generate language that is toxic, biased, untruthful or otherwise harmful. Though work to evaluate language model harms is under way, translating foresight about which harms may arise into rigorous benchmarks is not straightforward. To facilitate this translation, we outline six ways of characterizing harmful text which merit explicit consideration when designing new benchmarks. We then use these characteristics as a lens to identify trends and gaps in existing benchmarks. Finally, we apply them in a case study of the Perspective API, a toxicity classifier that is widely used in harm benchmarks. Our characteristics provide one piece of the bridge that translates between foresight and effective evaluation.
The Mediating Effect of Knowledge Integration on the Relationship between Capability and Innovative Behaviour

One of the main success drivers of the Fourth Industrial Revolution is the integration of information technology and industry, which encourages innovation through collaboration between industry and academia. As a result, fostering innovative behaviour among undergraduates is essential for success in higher education throughout this revolution. The purpose of this study is to examine the direct effects of capability (leadership, technological literacy, interaction, knowledge sharing, and collaboration) on innovative behaviour and the indirect effect of knowledge integration's mediating role. Data from 3,230 undergraduates was collected and analysed. The provided hypotheses were validated using structural equation modelling, which demonstrated that capability significantly influenced the success of adopting innovative behaviour. Furthermore, we observed that knowledge sharing was the most powerful predictor. We also discovered that the intervening knowledge integration had an indirect effect, indicating the presence of a relationship between capability and innovative behaviour through this variable. When the direct and indirect effects were evaluated, the direct effect was substantially stronger than the indirect effect. The study's practical implications should focus strongly on promoting innovative behaviour and knowledge integration in the process of establishing appropriate educational programmes for universities. Furthermore, universities should develop strategies to encourage undergraduate students to share their knowledge and abilities with others since this improves performance and increases awareness of new prospects for innovation. In future research, we should include more educational institutions in our study, such as high schools, technical schools, and graduate students. Doi: 10.28991/ESJ-2022-SIED-07 Full Text: PDF
Visual Predictive Control Strategy for Mobile Manipulators

This work aims at designing a visual predictive control (VPC) scheme for a mobile manipulator. It consists in combining image-based visual servoing with model predictive control to benefit from the advantages of both control structures. Two challenges are addressed in this paper: the choice of the visual features and the closed-loop stability. The first ones rely on image moments to improve the end effector positioning precision. The second one is tackled through a terminal constraint coupled with suitable input constraints to reduce the computational burden. Simulation results using ROS and Gazebo validate the proposed approach.
The Effect of Marketing, Product, Process, and Organizational Innovations on the Marketing Performance

Innovation plays a vital role in increasing a company’s performance, market profits, and sales growth. The types of innovation within a company also vary; there are at least four known types of innovation – marketing innovation, product or service innovation, process innovation, and organizational innovation. The purpose of this study was to examine the role of innovation in a company’s growth. The samples used were 127 customers of a company. Primary data were collected using a Likert scale and secondary data through interviews. The technique used for data analysis was Structural Equation Modeling (SEM) which is operated using the AMOS application program. The hypotheses of this research were: H1: Marketing innovation influences innovation performance; H2: Product or service innovation influences innovation performance; H3: Process innovation influences innovation performance; H4: Organizational innovation influences innovation performance; and H5: Performance innovation has an influence on marketing performance. The results of this study indicated that marketing innovation, product innovation, organizational innovation, and innovation performance have a positive and significant effect on marketing performance.&#x0D; Keywords: marketing innovation, product innovation, process innovation, organizational innovation, innovation performance, marketing performance
A Research on Different Classroom Questioning Strategies Between Chinese and Foreign EFL Teachers

The application of questioning as a teaching tool has always been considered as a main part of the teaching process. Based on the viewpoint of several researchers, a survey was conducted on the application of different questioning strategies by Chinese and foreign EFL teachers in a middle school, in order to determine their effects on students’ language learning output and propose measures to optimize Chinese teachers’ questioning strategies for improving students’ learning efficiency. This research provides preliminary results based on the data collected from the video recordings of class sessions, a questionnaire, and an interview. According to the analysis, the questioning strategies differ between Chinese and foreign EFL teachers in many aspects, such as in information seeking, offering, modification, adjustment, feedback, and so forth.
Grammatical Errors on Descriptive Academic Writing

Generally, errors committed by EFL students in learning English at any level of study. This research focuses on university students who are taking Academic Writing course in the third semester. This research is aimed at identifying the types of errors and the dominant one committed by students in descriptive academic writing exercises. The method of the research was qualitative approach. The instrument that the researchers used in this research was through essay writing. There were 24 students were taken as subjects of the research. To analyse the data, the researchers used Error Analysis Method (EAM) based on Surface Structure Taxonomy and percentage error analysis. The findings showed that many students committed errors involving grammatical items in line with omission, addition, misinformation and misordering and the dominant was misordering error. The researchers assume that there were some factors that affects students’ errors such as lack of knowledge and competence in English grammar. In conclusion, error analysis method is such a suitable way to identify students’ errors using simple present tense in descriptive academic writing exercises.&#x0D;
Crowdsourcing Relative Rankings of Multi-Word Expressions: Experts versus Non-Experts

In this study we investigate to which degree experts and non-experts agree on questions of linguistic complexity in a crowdsourcing experiment. We ask non-experts (second language learners of Swedish) and two groups of experts (teachers of Swedish as a second/foreign language and CEFR experts) to rank multi-word expressions in a crowdsourcing experiment. We nd that the resulting rankings by all the three tested groups correlate to a very high degree, which suggests that judgments produced in a comparative setting are not inuenced by professional insights into Swedish as a second language.
SENTIMENT ATTRIBUTION ANALYSIS WITH HIERARCHICAL CLASSIFICATION AND AUTOMATIC ASPECT CATEGORIZATION ON ONLINE USER REVIEWS

Due to COVID-19 pandemic, most physical business transactions were pushed online. Online reviews became an excellent source for sentiment analysis to determine a customer's sentiment about a business. This insight is valuable asset for businesses, especially for tourism sector, to be harnessed for business intelligence and craft new marketing strategies. However, traditional sentiment analysis with flat classification and manual aspect categorization technique imposes challenges with non-opinionated reviews and outdated pre-defined aspect categories which limits businesses to filter relevant opinionated reviews and learn new aspects from reviews itself for aspect-based sentiment analysis. Therefore, this paper proposes sentiment attribution analysis with hierarchical classification and automatic aspect categorization to improve the social listening for diligent marketing and recommend potential business optimization to revive the business from surviving to thriving after this pandemic. Hierarchical classification is proposed using hybrid approach. While automatic aspect categorization is constructed with semantic similarity clustering and applied enhanced topic modelling on opinionated reviews. Experimental results on two real-world datasets from two different industries, Airline and Hotel, shows that the sentiment analysis with hierarchical classification outperforms the classification accuracy with a good F1-score compared to baseline papers. Automatic aspect categorization was found to be able to unhide the sentiment of the aspects which was not recognized in manual aspect categorization. Although it is accepted that the effectiveness of aspect-based sentiment analysis on flat classification and manual aspect categorization, none have assessed the effectiveness while using hierarchical classification with a hybrid approach and automatic aspect categorization.
The Use of Augmented Reality for Teaching Kazakhstani Students Physics Lessons

Augmented reality (AR) practices and technologies have the potential to redefine aspects of education, including laboratory work. AR tools provide an easy and cost-effective solution for helping students overcome the constraints imposed by online education. The article demonstrates the use of an AR application in Physics lessons at the Lyceum School No. 85 in Nur-Sultan city, Kazakhstan. The AR application includes an interactive 3D model of circuits and circuit components that can be viewed, manipulated, and explored. The AR application helps students learn about circuits through direct interaction with the circuit components without having to rely on expensive, time-consuming and dangerous experiments and equipment. The authors rely on the assumption that the use of interactive 3D models improves student learning outcomes and evaluate student attitudes towards the adoption of AR technology in classrooms. Students from the 8th grade are divided into four experimental groups based on their academic performance. The results show the effectiveness of the AR application in improving retention and understanding of physics concepts over traditional instruction methods. The experiment is not intended as a prescription for augmentation but shows that AR tools can be an efficient alternative to the risk-prone, costly laboratory environment and that such tools can be employed to enhance learning.
Influence of High-Tech Society on the Development of Modern Educational System

The authors consider such issues as (1) forming a valid educational system in a high-tech world; (2) forming the principles upon which it should be built; (3) considering the primary subjects of interaction with high-tech educational system; (4) imposing the effective methods, tools, development technologies, and self-development techniques of subjects in the context of the educational system; (5) defining what is possible to entrust to machines in a digital educational system, in comparison with humans; (6) evaluating the digital skills education efficiency; (7) defining methods that provide the efficiency of educational influence. The primary method of the research is the analysis and generalization of the national and foreign scholarly works in terms of what are the features of the evolution of the educational system in a modern high-tech society. The authors conclude that the key tendency in the development of educational technologies consists in the need to expand the existing formats of educational courses for the development of digital skills with technologies that use mobile learning and artificial intelligence programs.