Reviewerly/exHarmony huggingface dataset:
SWP:A Sliding Window Prompt for Emotion Recognition in Conversation

Emotion Recognition in Conversation(ERC), also referred to as sentiment mining in dialogues, aims at analyzing the speaker’s state and recognizing their emotions during conversation. The study of emotion recognition has attracted increasing interest in recent years due to its wide range of applications, such as customer service analysis, medical consulting, and intelligent robot conversations. However, the weak correlation between emotion and semantics has posed several challenges to emotion recognition in dialogues. Even for semantically similar utterances, their emotions may differ greatly because of contextual or speaker differences. To address this, we propose a new method for recognizing emotions in dialogues called adjustable sliding chat window context modeling, which uses prompt learning templates to focus on the speaker’s emotional changes. Moreover, we introduce curriculum learning technique in the training stage to alleviate the impact of extreme samples on the training process. We achieved state-of-the-art performance on three widely used public datasets. To demonstrate the effectiveness of our proposed curriculum learning strategy and adversarial training, we conducted an ablation study.
Safe Reinforcement Learning in a Simulated Robotic Arm

Reinforcement learning (RL) agents need to explore their environments in order to learn optimal policies. In many environments and tasks, safety is of critical importance. The widespread use of simulators offers a number of advantages, including safe exploration which will be inevitable in cases when RL systems need to be trained directly in the physical environment (e.g. in human-robot interaction). The popular Safety Gym library offers three mobile agent types that can learn goal-directed tasks while considering various safety constraints. In this paper, we extend the applicability of safe RL algorithms by creating a customized environment with Panda robotic arm where Safety Gym algorithms can be tested. We performed pilot experiments with the popular PPO algorithm comparing the baseline with the constrained version and show that the constrained version is able to learn the equally good policy while better complying with safety constraints and taking longer training time as expected.
A Classification Performance Evaluation Measure Considering Data Separability

Machine learning and deep learning classification models are data-driven, and the model and the data jointly determine their classification performance. It is biased to evaluate the model’s performance only based on the classifier accuracy while ignoring the data separability. Sometimes, the model exhibits excellent accuracy, which might be attributed to its testing on highly separable data. Most of the current studies on data separability measures are defined based on the distance between sample points, but this has been demonstrated to fail in several circumstances. In this paper, we propose a new separability measure–the rate of separability (RS), which is based on the data coding rate. We validate its effectiveness as a supplement to the separability measure by comparing it to four other distance-based measures on synthetic dataset. Then, we discover the positive correlation between the proposed measure and recognition accuracy in a multi-task scenario constructed from a real dataset. Finally, we discuss the methods for evaluating the classification performance of machine learning and deep learning models considering data separability.
Dynamics of Intellectual Confidence in Problem Solving

Background. The problem of criteria for evaluating solution success is one of the most important for psychology of creative thinking. Evaluation of the solution by the subject during the process of thinking in the form of «confidence / doubt» can be criteria and basis for subject's experiencing potential success or failure, which leads to continuation or stop in solving. The objective. The study was to research the dynamics of intellectual confidence / doubt at different stages of problem-solving process. Sample. The study involved 66 university students, specialists and non- specialists in relation to the content of the tasks. Methods. The subjects solved 8 problematic tasks of scientific and artistic content with hints and estimate interest and confidence at different stages of solving with subjective scales. The following methods were used for intellectual characteristics: "Raven's Progressive Matrices", "Complex Analogies". Results. 6 types of dynamics of intellectual confidence were identified, which correlated in various ways with the success of the solution. Successful solution of problematic scientific situations for specialists is associated with confidence at every stage, for non-specialists it is associated with doubts at the stages of understanding the problem and the formulation of solution. For problematic situations of artistic content solved by non-specialist, the following types of dynamics are associated with successful solution: «doubt — doubt — confidence», «confidence at all stages»; the latter was also observed with an unsuccessful solution. For this reason 2 types of confidence were identified: (1) reasonable, based on hints, leading to a successful solution, and (2) non- reasonable, without relying on a hint, not leading to a successful solution. Conclusion. The procedural criteria to evaluate solution are the intellectual emotions of confidence or doubt that regulate the process of thinking. Depending on the stage of solution and the content of the task, intellectual confidence changes in different ways for specialists and non-specialists, performing the function of a preliminary assessment and regulation of solving process.
History of Technology Education in Romania

The chapter presents a synthesis of the evolution of technical education in Romania. The first attempts from the beginning of the nineteenth century to found an engineering education are presented, starting with the School of Surveying and Civil Engineers in Moldova (1813) and Wallachia (1818). Later these schools evolved into a French form of engineering schools. Thus, in 1851, the School of Bridges and Roads was founded in Bucharest with a curriculum similar to that of engineering schools in France. This can be considered the first technical university in Romania. The evolution of this school is presented in detail, the transformations undergone in the second half of the nineteenth century due to significant political (establishment of the Kingdom of Romania) and economic (influence of the first industrial revolution on the Romanian economy) transformations. The establishment of the universities of Iasi (1860) and Bucharest (1864) also had a great influence on engineering education, especially in the fields of chemical and electrotechnical engineering. The political changes after the First World War (the union of Transylvania with Romania) radically transformed the Romanian technical education. The National School of Bridges and Roads in Bucharest was transformed into the Polytechnic School of Bucharest (1920), the Politehnica din Timisoara was founded (1920) and the ‘Gheorghe Asachi’ Polytechnic School was founded (1937). This period of higher technical education in Romania can be regarded as a stage of growth, when the level reached could bear comparison to technologically developed countries. After the end of the Second World War the Romanian educational system is changing from the ground up, using the Soviet system as a model. As regards the higher technical education, in 1948, there were three polytechnics in Romania (in Bucharest, Timișoara, and Iași) which trained engineers in all relevant fields. These three universities are developing strongly, expanding the number of faculties covering all fields of engineering. At the same time, new technical universities were established in Cluj Napoca, Galati and Brasov, as well as some specialized technical universities (Technical Military Academy, Technical University of Construction, Institute of Oil and Gas, Coal Institute). At the same time, new universities were founded (Craiova, Sibiu, Constanta, Suceava, Resita, Bacau, Pitesti, Petrosani, Ploiesti) where technical faculties were also included. A new stage in the development of technical education began after the change of political regime in 1989, when state or private universities were established or re-established in the vast majority of large cities in Romania, which also included faculties with a technical profile. An overview of these universities, which also include technical faculties, is presented at the end of the chapter.
Memory Network-Based Interpreter of User Preferences in Content-Aware Recommender Systems

This article introduces a novel architecture for two objectives recommendation and interpretability in a unified model. We leverage textual content as a source of interpretability in content-aware recommender systems. The goal is to characterize user preferences with a set of human-understandable attributes, each is described by a single word, enabling comprehension of user interests behind item adoptions. This is achieved via a dedicated architecture, which is interpretable by design, involving two components for recommendation and interpretation. In particular, we seek an interpreter , which accepts holistic user’s representation from a recommender to output a set of activated attributes describing user preferences. Besides encoding interpretability properties such as fidelity, conciseness and diversity, the proposed memory network-based interpreter enables the generalization of user representation by discovering relevant attributes that go beyond her adopted items’ textual content. We design experiments involving both human- and functionally-grounded evaluations of interpretability. Results on four real-world datasets show that our proposed model not only discovers highly relevant attributes for interpreting user preferences, but also enjoys comparable or better recommendation accuracy than a series of baselines.
Deep learning-based object detection for smart solid waste management system

Currently in Ethiopia, pollution and environmental damage brought on by waste increased along with industrialization, urbanization, and global population levels. Waste sorting, which is still done improperly from the household level to the final disposal site, is a prevalent issue. Real-time and accurate waste detection in image and video data is a crucial and difficult task in the intelligent waste management system. Accurately locating and classifying these wastes is challenging, particularly when there are various types of waste present. So, a single-stage YOLOv4-waste deep neural network model is proposed. In this study, a deep learning algorithm for object detection using YOLOv4 and YOLOv4-tiny is trained and evaluated. A total of 3529 waste images are divided into 7 classes, which include, cardboard, glass, metal, organic, paper, plastic, and trash. Each model uses three various inputs throughout the testing phase, including input images, videos, and webcams. Experiments with hyper-parameters on subdivision values and mosaic data augmentation were also done in the YOLOv4-tiny model. The outcome demonstrates that YOLOv4 performs better than YOLOv4-tiny for object detection specifically for waste detection. The outcome shows that YOLOv4 performs better than YOLOv4-tiny for object detection, even if YOLOv4-tiny’s scores are higher in terms of computing speed. The best results from the YOLOv4 model reach mAP 91.25%, precision 0.91, recall 0.88, F1-score 0.89, and Average IoU 81.55%, while the best YOLOv4-tiny results are mAP 82.02%, precision 0.75, recall 0.76, F1-score 0.75, and Average IoU 63.59%. This research also proves that the models with smaller subdivision values and using a mosaic have optimal performance.
Enabling Graph Neural Networks for Semi-Supervised Risk Prediction in Online Credit Loan Services

Graph neural networks (GNNs) are playing exciting roles in the application scenarios where features are hidden in information associations. Fraud prediction of online credit loan services (OCLSs) is such a typical scenario. But it has another rather critical challenge, i.e., the scarcity of data labels. Fortunately, GNNs can also cope with this problem due to their good ability of semi-supervised learning by mining structure and feature information within graphs. Nevertheless, the gain of internal information is often too limited to help GNNs handle the extreme deficiency of labels with high performance beyond the basic requirement of fraud prediction in OCLSs. Therefore, adding labels from the experts, such as manually adding labels through rules, has become a logical practice. However, the existing rule engines for OCLSs have the confliction problem among continuously accumulated rules. To address this issue, we propose a Snorkel-based Semi-Supervised GNN (S3GNN). Under S3GNN, we specially design an upgraded version of the rule engines, called Graph-Oriented Snorkel (GOS), a graph-specific extension of Snorkel, a widely-used weakly supervised learning framework, to design rules by subject matter experts (SMEs) and resolve confliction. In particular, in the graph of anti-fraud scenario, each node pair may have multiple different types of edges, so we propose the Multiple Edge-Types Based Attention mechanism. In general, for the heterogeneous information and multiple relations in the graph, we first obtain the embedding of applicant nodes by aggregating the representation of attribute nodes, and then use the attention mechanism to aggregate neighbor nodes on multiple meta-paths to get ultimate applicant node embedding. We conduct experiments over the real-life data of a large financial platform. The results demonstrate that S3GNN can outperform the state-of-the-art methods, including the method of pilot platform.
FUKG: answering flexible queries over knowledge graphs

Purpose There is an increasing interest in the use of knowledge graphs to represent real-world knowledge and a common need to manage imprecise knowledge in many real-world applications. This paper aims to study approaches to solve flexible queries over knowledge graphs. Design/methodology/approach By introducing fuzzy logic in the query answering process, the authors are able to obtain a novel algorithm to solve flexible queries over knowledge graphs. This approach is implemented in the FUzzy Knowledge Graphs system, a software tool with an intuitive user-graphical interface. Findings This approach makes it possible to reuse semantic web standards (RDF, SPARQL and OWL 2) and builds a fuzzy layer on top of them. The application to a use case shows that the system can aggregate information in different ways by selecting different fusion operators and adapting to different user needs. Originality/value This approach is more general than similar previous works in the literature and provides a specific way to represent the flexible restrictions (using fuzzy OWL 2 datatypes).
Meditating in a neurofeedback virtual reality: effects on sense of presence, meditation depth and brain oscillations

ABSTRACTWith the advent of consumer-grade electroencephalography (EEG) and virtual reality (VR) devices, the use of human cognitive processes directly as means for user-adapted interaction in immersive virtual environments has become increasingly relevant. In this study (N = 43), we investigate electroencephalography-based neurofeedback interaction in virtual reality (VR). Particularly, we investigate this phenomenon in the context of meditation which enables the studying of cognitive processes (attention, sense of presence, meditation depth) when using an immersive interface that is adaptive based on neural responses. A prototype virtual reality environment was built that employs head-mounted display (HMD), and the neurofeedback functionality guided the user to increase the neural indices of meditation-related concentration and relaxation. The observed findings provide evidence for the effectiveness of neurofeedback functionality and VR in evoking desired types of neural activation and subjective experiences.KEYWORDS: EEGimmersionmeditationneurofeedbackpresencevirtual reality Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by Academy of Finland: [Grant Number 305576]; The Finnish Work Environment Fund: [Grant Number 114169].
A privacy-preserving attribute-based framework for IoT identity lifecycle management

The Internet of Things (IoT) has brought a new era of interconnected devices and seamless data exchange. As the IoT ecosystem continues to expand, there is an increasing need for effective identity management mechanisms, specifically for authorization processes and access control. The pervasiveness of such devices demands that desirable solutions tackle not only security properties but also privacy aspects like granular control over which identity data is shared in authentication/authorization processes, covering aspects like bootstrapping, enrolment, and service provision. In this context, it is natural to turn to privacy-enhancing technologies, like (privacy-preserving) Attribute-Based Credentials (p-ABC), for achieving both high security and privacy guarantees. Nonetheless, these technical tools need to be accompanied by a comprehensive approach that deals with the particularities of IoT scenarios and covers the full lifetime of the device. In this work, we propose the use of a p-ABC scheme with support for distributed issuance (dp-ABC) as a keystone for privacy-preserving attribute-based authentication and authorization in IoT scenarios. We integrate said cryptographic scheme with W3C’s Verifiable Credentials standard, evaluating its impact to gauge its feasibility. The integration facilitates adoption and, particularly, allows the solution to transparently coexist with simpler techniques in heterogeneous scenarios that demand them. Moreover, we define and analyse a generic and comprehensive framework for identity management that identifies challenges throughout the device’s lifetime to achieve IoT privacy-preserving identity management following self-sovereign principles. We show how the various aspects identified in the framework are tackled in a concrete instantiation as part of the H2020 project ERATOSTHENES.
Co-Curricular Activity Risk Management (CoARM) Development and Validation: A Fuzzy-Delphi Method

Co-curricular activities encompassing uniform units, clubs, and associations are compulsory courses in all educational institutions in Malaysia. The implementation of co-curricular activities requires neat preparation in order to reduce exposure to the risk of loss, injury, and accidents during the activities. Therefore, this study aimed to produce an instrument for effective co-curricular activity risk management (also known as (CoARM) to guide administrators, lecturers, and students, in conducting safe co-curricular activities, especially at Malaysian teacher education institute (TEIM). This study used the Fuzzy-Delphi method involving 30 experts in the field of co-curriculum from public universities and TEIM. The questionnaire was constructed based on the analysis of field expert interviews and a literature review. It was then sent for expert agreement on the elements, sub-elements, and content of CoARM at TEIM. The findings distinguish three (3) elements and fifteen (15) sub-elements of CoARM when planning and implementing co-curricular activities. The first element was the human element involving trainers, participants, support staff, service providers, and external agencies. The second element, the equipment element, involved anchorage, accommodation, facilities for special activity equipment, and safety equipment. The third element was the environment element with the sub-elements of weather, location, activity routes, security threats, and rescue routes. The CoARM instrument is expected to provide new knowledge as well as to serve as a guide in the field to increase the public’s confidence in organising co-curricular activities at Malaysian educational institutions and as a reference source in the future construction of CoARM modules.
An Error Analysis on Writing in Simple Present Tense

This paper aims to know the type of errors that happened in writing daily activities of fifth grade of MI Al Khairiyah Kupang Teba Bandar Lampung. This research used qualitative design. This study was focused on analyzing the type of errors that's produce by students in simple present tense that have been collected from 20 students of fifth grade of MI Al khairiyah kupang teba, Bandar Lampung. The data can be found by written test. The researcher gives for students to write daily activities. This test should be completed by the subjects in order to find out the errors in their writing. The results of this research, the researcher found some type of errors such as omission, addition, misordering and misformation. misordering ( 89 errors = 36.6%), followed by addition (87 errors = 35.8%), misformation (40 errors = 16.45%), and omission (27 errors = 11.2%).
D{S}^2L{C}^3Net : A Decision Support System for Lung Colon Cancer Classification using Fusion of Deep Neural Networks and Normal Distribution based Gray Wolf Optimization

Lung and Colorectal (LC) cancer is life-threatening and rapidly developing cancers. According to World Health Organization (WHO), approximately 4.14 million lung and colorectal cancer cases were newly diagnosed, with 2.7 million fatalities. An International Agency for Research on Cancer (IARC) reported that there will be more than 3 million additional instances of colorectal cancer worldwide between 2020 and 2040. Early diagnosis of LC cancer is very helpful for treatment and can save the precious human life. The conventional diagnosis methods are expensive and time consuming. In this work, we present an accurate and efficient model for the classification of Lung and Colorectal (LC) cancer. We utilize two well-known pre-trained deep learning models, ResNet50 and EfficientNetB0, and fine-tuned the both models based on the addition and removal of layers. After the fine-tuning, manual hit and trail based hyperparameters are initialized. Later on, the deep transfer learning was performed and obtained the trained models. Two different feature vectors have been extracted from both models and fused using a priority based serial approach. To further improve the performance of extracted features, Normal Distribution based Gray Wolf Optimization algorithm is employed and obtained the best features that given as input to five classifiers. The output of these five classifiers is then utilized by soft voting technique to generate the final prediction. Experimental results show that the proposed architecture achieved an overall 98.73% accuracy on LC25000 dataset. Furthermore, prediction time was reduced by 19.14%. Comparison with the state-of-the-art techniques shows that the proposed technique obtained the improved performance results.