Reviewerly/exHarmony huggingface dataset:
llhsc: A DeviceTree Syntax and Semantic Checker

Feature models are a commonly used technique for modeling software variability and representing possible configurations of a system. This paper presents the llhsc tool, which is designed to generate and check device trees for custom hardware based on the constraints specified in a feature model. The form of these constraints is a set of logical formulas, which enables product line validation using off-the-shelf satisfiability solvers. For checking purposes, a new set of constraints is defined, which includes the specffication of both syntax and semantic correctness of a delta-oriented software product line for DeviceTree bindings. This approach provides a more general and flexible solution for configuring static-partitioning hypervisors, but can also be used in systems without virtualization support, enabling the tool to be used in various contexts without sacrificing its generality. Through an empirical running example, we demonstrate the effectiveness of our approach, providing evidence that supports the construction of customized configurations for systems running static-partitioning hypervisors.
DQN-Based Dynamic Query Optimization Method of Data Cross-Chain Based on Multi-channel Relay Chain

With the rapid development of consortium blockchain technology, the interaction between consortium blockchains has attracted more and more attention. However, most of the traditional cross-chain technologies focus on value cross-chain. There is still a lot of research space for data cross-chain mechanism. Therefore, this paper proposes a DQN-based dynamic query optimization method of data cross-chain based on multi-channel relay chain (mRDC-DQ). In order to improve the efficiency of information interaction, this method constructs a multi-channel relay alliance chain architecture to realize parallel processing, and proposes a relay alliance chain priority query mechanism, which uses the relay alliance chain as a buffer for high-frequency query data. And a query filter is set in the block header of the relay block to achieve efficient search. Reinforcement learning is combined with the cross-chain mechanism for the first time, and the DQN model is used to dynamically select the optimal parameters according to the real-time state of the cross-chain system, so that the resources are fully utilized and the best cross-chain query efficiency of data is achieved. Experiments show that the proposed scheme has higher information query and interaction efficiency than the existing cross-chain schemes.
A Neural Network Anomaly Detection Model Based on Archimedes Optimization Algorithm for Intelligent Operation and Maintenance

The operation and maintenance data abnormal detection technology is currently hampered by difficult detection procedures and low accuracy. The primary purpose of this paper is to investigate and address the problem of low accuracy in the abnormal detection of operation and maintenance data by combining artificial neural network models with deep learning technology. We choose long and short term memory (LSTM) and Encoder-Decoder LSTM model as the object for further optimization because they perform better in data anomaly identification than other neural network models in many measures. Also, by altering model parameters like loss rate, learning rate, training epoch, and batch size, artificial neural network model efficiency can be increased. Therefore, we propose an artificial neural network model that combines the recently developed Archimedes optimization algorithm (AOA). By using the AOA algorithm to optimize the model parameters, we can make the LSTM and Encoder-Decoder LSTM models reach a better state. The optimized model performs better than the non-optimized model in several measures of anomaly detection, with accuracy and precision showing the most pronounced improvement. In addition, the recall rate, classification effect, and error between the predicted value and real value all show significant improvements in the optimized model. By applying the optimized model, the anomaly detection accuracy of the ordinary computer room operation and maintenance data can achieve good effects, and provide the possibility for the model to be widely used in the actual intelligent operation and maintenance data anomaly detection.
A Secure and Privacy-Preserving Student Credential Verification System Using Blockchain Technology

Advancements in digital technologies have made the storage, sharing, and verification of educational credentials extremely important for entities such as students, universities, institutions, and companies. Digital credentials play an important role in students’ lives as a lifelong learning passport. The educational field is experiencing numerous issues such as academic record forgery, record misuse, credential data tampering, time-consuming verification procedures, and issues related to ownership and control. Modern-day technology, Blockchain, is an appropriate alternative to resolve these issues and increase trust among entities. In this research, we intend to propose a Blockchain-based educational digital credential issuance, and verification model that addresses these issues in the education system using Ethereum Blockchain and smart contracts. The method we propose offers a way to demonstrate the correctness of specific credential attributes without revealing other attributes, thereby leading to ownership, minimal disclosure, and control. We offer an interface for storing massively encrypted academic records in a decentralized file system like Interplanetary File System (IPFS). Furthermore, Ethereum provides tamper-resistant chains to maintain the integrity of digital credentials. Finally, in comparison with the time it requires to issue credentials, our model safely accelerates the verification process by about 8%.
Using the Technique of Interaction Design (IxD) and Augmented Reality (AR) as Assistive Technology for Students with Disabilities

This paper considers the implement of Kids Can Read Application to assist kids with learning disabilities to spell and read correctly. The researchers applied the advantage of Quick Response (QR) code technology into the interaction design. The Kids Can Read will install on smartphone or tablet and will be using with word card with QR code integrated, kids will scan the QR code, and the spelling instruction will appear with sound of each word. Kids will be able to open the application to reviewing the word afterward without the re-scan the QR code. The interface design of the Kids Can Read is easy to use and kids friendly. Most of the kids in this study prefer to use Kids Can Read to help them learn and practice of word reading and spelling. As a result, when the application was conducted into practice in schools, the sample in this study had a statistically significant increase in spelling ability at 0.05. This is in consistent with the hypothesis that the ability to read word spelling of the Grade 3 students with learning disability in reading is higher after using the Kids Can Read. Hence, we anticipate that all of students with learning disabilities who study in Grade 3 in the country will have chance to use the application by the end of next year.
A Fine Line Between Irony and Sincerity: Identifying Bias in Transformer Models for Irony Detection

In this paper we investigate potential bias in fine-tuned transformer models for irony detection.Bias is defined in this research as spurious associations between word n-grams and class labels that can cause the system to rely too much on superficial cues and miss the essence of the irony.For this purpose, we looked for correlations between class labels and words that are prone to trigger irony, such as positive adjectives, intensifiers and topical nouns.Additionally, we investigate our irony model's predictions before and after manipulating the data set through irony trigger replacements.We further support these insights with stateof-the-art explainability techniques (Layer Integrated Gradients, Discretized Integrated Gradients and Layer-wise Relevance Propagation).Both approaches confirm the hypothesis that transformer models generally encode correlations between positive sentiments and ironic texts, with even higher correlations between vividly expressed sentiment and irony.Based on these insights, we implemented a number of modification strategies to enhance the robustness of our irony classifier.
Painsight: An Extendable Opinion Mining Framework for Detecting Pain Points Based on Online Customer Reviews

As the e-commerce market continues to expand and online transactions proliferate, customer reviews have emerged as a critical element in shaping the purchasing decisions of prospective buyers.Previous studies have endeavored to identify key aspects of customer reviews through the development of sentiment analysis models and topic models.However, extracting specific dissatisfaction factors remains a challenging task.In this study, we delineate the pain point detection problem and propose Painsight, an unsupervised framework for automatically extracting distinct dissatisfaction factors from customer reviews without relying on ground truth labels.Painsight employs pre-trained language models to construct sentiment analysis and topic models, leveraging attribution scores derived from model gradients to extract dissatisfaction factors.Upon application of the proposed methodology to customer review data spanning five product categories, we successfully identified and categorized dissatisfaction factors within each group, as well as isolated factors for each type.Notably, Painsight outperformed benchmark methods, achieving substantial performance enhancements and exceptional results in human evaluations.
Response Generation in Longitudinal Dialogues: Which Knowledge Representation Helps?

Longitudinal Dialogues (LD) are the most challenging type of conversation for humanmachine dialogue systems.LDs include the recollections of events, personal thoughts, and emotions specific to each individual in a sparse sequence of dialogue sessions.Dialogue systems designed for LDs should uniquely interact with the users over multiple sessions and long periods of time (e.g.weeks), and engage them in personal dialogues to elaborate on their feelings, thoughts, and real-life events.In this paper, we study the task of response generation in LDs.We evaluate whether general-purpose Pre-trained Language Models (PLM) are appropriate for this purpose.We fine-tune two PLMs, GePpeTto (GPT-2) and iT5, using a dataset of LDs.We experiment with different representations of the personal knowledge extracted from LDs for grounded response generation, including the graph representation of the mentioned events and participants.We evaluate the performance of the models via automatic metrics and the contribution of the knowledge via the Integrated Gradients technique.We categorize the natural language generation errors via human evaluations of contextualization, appropriateness and engagement of the user.
Emotion and Sentiment Guided Paraphrasing

Paraphrase generation, a.k.a.paraphrasing, is a common and important task in natural language processing.Emotional paraphrasing, which changes the emotion embodied in a piece of text while preserving its meaning, has many potential applications, including moderating online dialogues and preventing cyberbullying.We introduce a new task of fine-grained emotional paraphrasing along emotion gradients, that is, altering the emotional intensities of the paraphrases in fine-grained settings following smooth variations in affective dimensions while preserving the meaning of the original text.We reconstruct several widely used paraphrasing datasets by augmenting the input and target texts with their fine-grained emotion labels.Then, we propose a framework for emotion and sentiment guided paraphrasing by leveraging pre-trained language models for conditioned text generation.Extensive evaluation of the fine-tuned models suggests that including fine-grained emotion labels in the paraphrase task significantly improves the likelihood of obtaining high-quality paraphrases that reflect the desired emotions while achieving consistently better scores in paraphrase metrics such as BLEU, ROUGE, and METEOR.
A Dataset for Explainable Sentiment Analysis in the German Automotive Industry

While deep learning models have greatly improved the performance of many tasks related to sentiment analysis and classification, they are often criticized for being untrustworthy due to their black-box nature.As a result, numerous explainability techniques have been proposed to better understand the model predictions and to improve the deep learning models.In this work, we introduce InfoBarometer, the first benchmark for examining interpretable methods related to sentiment analysis in the German automotive sector based on online news.Each news article in our dataset is annotated with respect to the overall sentiment (i.e., positive, negative and neutral), the target of the sentiment (focusing on innovation-related topics such as e.g.electromobility) and the rationales, i.e., textual explanations for the sentiment label that can be leveraged during both training and evaluation.For this research, we compare different state-of-the-art approaches to perform sentiment analysis and observe that even models that perform very well in classification do not score high on explainability metrics like model plausibility and faithfulness.We calculated the polarity scores for the best method BERT and got a macro F1-score of 73.8.Moreover, we evaluated different interpretability algorithms (LIME, SHAP, Integrated Gradients, Saliency) based on explicitly marked rationales by human annotators quantitatively and qualitatively.Our experiments demonstrate that the textual explanations often do not agree with human interpretations, and rarely help to justify the models decision.However, global features provide useful insights to help uncover spurious features in the model and biases within the dataset.We intend to make our dataset public for other researchers.
Elements of Psychological in Maruthakkali Subject

Sangam literature is the literature that brings to the world the incomparable culture of the Tamils. Kalithogai book is highlighted by the educator’s in the ettuthogai of sanam literature. It is praised by the educators and the learned. This article explores only the psychological components of the maruthakali songs in the ainthinai. The science of the behaviour of an organism is called psychology. In the maruthakali songs the psychological component of correcting the attempt to alleviate the anger of the thalaivi and the anger of the concubine, which appears to be due to sexual intercourse, is revealed. The division between the thalaivan of the harlot and the thalaivan lies to cover up the division to the thalaivi has subtly articulated the psychology. Moreover, the emotions about the behaviour that arise in the thalaivi indicate the elements of the reaction expressed in psychology. Thalaivan who went to the harlot behaved like an infallible man, but the thalaivan looks straight at the opposite person and does not fell the news, but bows his head and avoids the sight. Such act refers to the psychology of renunciation. When a thozhi expresses his hatred for the thalaivan to baana, the migration takes place. This phenomenon works when it carries its misery into the sound of a cuckoo, which reduces stress and relieves distress. He also sees his face turning the blame on himself, saying that the thalaivan is not wrong. All the above messages are psychology expressed through the actions of the Mandars. The purpose of this article is to state such psychology.
DESAIN APLIKASI ABSENSI SANTRI PONDOK ABDUL DHOHIR MENGGUNAKAN PENGENALAN WAJAH BERBASIS WEB

The web-based face recognition application will provide convenience for students to LDII organizations or Islamic boarding school, it is possible for the students themselves to give their best in carrying out their activities. Attendance is crucial to any organization. Where participation is one of the key factors that may encourage and support any action that will take place. Operating the attendance system manually at the moment, namely utilizing manual paper. This might lead to a number of errors.and unhygienic times during the pandemic, example is the inefficiency of utilizing paper, forgetting to fill in the absences, missing paper used and using pens alternately causing the spread of the COVID-19 virus. By using Face Recognition, students only show the face to be scanned using a webcam provided by the boarding school teacher. The strategy utilized in this study is rapid application development using modeling using the Unified Modeling Language (UML). The Django web framework's tools were then used to create this application, Postgresql and Python programming. This application was built with the aim of making it easier for boarding school teacher and students.
Legal Protection of Cryptocurrency Users Against Cybercrime Attacks

The medium of exchange can be any object that can be accepted by everyone in society in the process of exchanging goods and services. Long before knowing money, humans had made transactions using barter practices, that is, the exchange of goods and/or services for the desired goods and/or services. In the preparation of this research, a normative juridical approach where approach is carried out based on the main legal material by examining theories, concepts, legal principles, and laws and regulations related to this research. Cryptocurrency assets don't just impact people who mine or trade crypto. It turns out that anonymous platforms that run crypto are also increasingly associated with cybercrime. A recent study from Interisle Consulting Group revealed that phishing attempts related to cryptocurrencies grew 257 percent compared to last year (compared to a 61 percent increase in phishing attacks overall), especially for attacks on wallets and exchanges. The rapid development of information and communication technology makes the journey of the development of crime in the virtual and digital world (cybercrime) sophisticated and complex.
Multi-objective Reinforcement Learning – Concept, Approaches and Applications

Real-world decision-making tasks are generally complicated and require trade-offs between multiple, even conflicting, objectives. As the advent and great development of advanced information technology, it has evolved into using reinforcement learning (RL) algorithms to tackle the multi-objective decision making (MODM) problems. In this paper, we will first identify the basic concepts and factors when modelling the MODM tasks with reinforcement learning, and then review the traditional RL, such as Sarsa, Q-Learning, Policy Gradients, Actor-Critic, Monte-Carlo learning, and modern deep RL algorithms applied in this process. Furthermore, the specific practical scenarios described in MODM problems will be summarized through analyzing some typical articles. Finally, the future trends of multi-objective reinforcement learning will be discussed.
Identification of creativity in collaborative conversations based on the polyphonic model

The paper presents a theoretical approach and a set of experiments that operationalize it for the identification of creative moments in conversations. State-of-the-art artificial intelligence technology is used for the operationalization: natural language processing, machine learning, and deep neural networks The approach is based on the polyphonic model introduced by Trausan-Matu, which starts from Mikhail Bakhtin's analogy of discourse building in texts with polyphonic music. The divergent and convergent steps of creativity are related to the inter-animation of voices through dissonances and consonances in polyphonic, contrapuntal music.
Combining Lightweight Encryption Methods to Secure IoT Networks

The rapid development of the underlying infrastructure of the internet has permitted the widespread acceptance of a number of emerging technologies that have far-reaching ramifications for current society. As a result of the expansion of the Internet of Things, several new networking protocols and upgrades have been created and implemented. On top of that, there is a significant shortage of financing for the development of information technology applications, as well as critical security problems that jeopardise private data. As if that weren’t enough, there are also substantial weaknesses in the data’s privacy protection. In spite of the enormous research that has been done, there has not been a single security solution for the IoT network that has been proved to be entirely effective. This is due to the fact that the network of things connected to the Internet of Things has a unique mix of low energy usage, rare materials, sufficient computational power, and considerable operational expenses. As a consequence of this, we consider the modification of the protocol to be a viable and comprehensive strategy for defending Internet of Things devices from cyberattacks. The inclusion of IoT layer-specific security solutions into enhanced code is one of the many ways in which algorithmic enhancements contribute to the accomplishment of this objective. In this study, we propose improving and integrating the DTLS Procedure with the earwigging mechanism, also then testing the protocol on representative models o
LETRAMENTO E EMANCIPAÇÃO DIGITAL CIDADÃ: Uma revisão de Literatura

EMANCIPATION: cartographies and traces in the constitution of Hybrid and Multimodal Living Spaces", within the scope of the Education, Development and Technologies research line of the UNISINOS Graduate Program in Education.The thesis is guided by the following problem: how does the development of gamified pedagogical practices contribute to literacy and citizen digital emancipation in basic education, in a hybrid and multimodal context?The research is qualitative, exploratory and descriptive.As a methodology for its development, the cartographic research-intervention method proposed by Kastrup (2008) was used; Passos, Kastrup and Escóssia (2009); among others.The purpose of this Thesis cut is to review the literature on Digital Literacy and Digital Emancipation, and consummate citizen in the perspective addressed in the Thesis: Digital Literacy and Emancipation and Citizen.It can be concluded that today one cannot think about textbooks without highlighting the presence and the "Doing".and citizen.
Quantum gates with oscillating exchange interaction

Abstract Two-qubit gates between spin qubits are often performed using a rectangular or an adiabatic exchange interaction pulse resulting in a CZ gate. An oscillating exchange pulse not only performs a CZ gate, but also enables the iSWAP gate, which offers more flexibility to perform quantum algorithms. We provide a detailed description for two-qubit gates using resonant and off-resonant exchange pulses, give conditions for performing the respective gates, and compare their performance to the state-of-the-art static counterpart. We find that for relatively low charge noise the gates still perform reliably and can outperform the conventional CZ gate.