Reviewerly/exHarmony huggingface dataset:
Multitask Fine Tuning on Pretrained Language Model for Retrieval-Based Question Answering in Automotive Domain

Retrieval-based question answering in the automotive domain requires a model to comprehend and articulate relevant domain knowledge, accurately understand user intent, and effectively match the required information. Typically, these systems employ an encoder–retriever architecture. However, existing encoders, which rely on pretrained language models, suffer from limited specialization, insufficient awareness of domain knowledge, and biases in user intent understanding. To overcome these limitations, this paper constructs a Chinese corpus specifically tailored for the automotive domain, comprising question–answer pairs, document collections, and multitask annotated data. Subsequently, a pretraining–multitask fine-tuning framework based on masked language models is introduced to integrate domain knowledge as well as enhance semantic representations, thereby yielding benefits for downstream applications. To evaluate system performance, an evaluation dataset is created using ChatGPT, and a novel retrieval task evaluation metric called mean linear window rank (MLWR) is proposed. Experimental results demonstrate that the proposed system (based on BERTbase), achieves accuracies of 77.5% and 84.75% for Hit@1 and Hit@3, respectively, in the automotive domain retrieval-based question-answering task. Additionally, the MLWR reaches 87.71%. Compared to a system utilizing a general encoder, the proposed multitask fine-tuning strategy shows improvements of 12.5%, 12.5%, and 28.16% for Hit@1, Hit@3, and MLWR, respectively. Furthermore, when compared to the best single-task fine-tuning strategy, the enhancements amount to 0.5%, 1.25%, and 0.95% for Hit@1, Hit@3, and MLWR, respectively.
An Action Research: Improving Eleventh Grade Students’ Ability in Comprehending Report Text Through Skimming and Scanning Technique at SMAN 2 Tebo

Purpose of the study: The aim of this research is to improve reading comprehension of eleventh grade studentsof SMAN 2 Tebo on report text through Skimming and Scanning technique.&#x0D; Methodology: This study employed classroom action research design. In the study, the researcher collaborated with the English teacher who taught the target class. The subject of the research was eleventh grade students of IPA 1 class in SMAN 2 Tebo that consisted of 32 students.&#x0D; Main Findings: The result of the three cycles show that the use skimming and scanning technique was effective to improve the students’ reading comprehension on report text. Based on the reading test scores the students’ scores kept improving in every test. In cycle I test the mean was 67.50, in cycle II test the mean was 76.87 and in cycle III test the mean was 85.78.&#x0D; Novelty/Originality of this study: The improvement of students’ activeness and participation in the class were also seen from observation sheet and field note in implementing skimming and scanning when reading the texts in the classroom. The result of the research showed that Skimming and Scanning significantly improved students’ achievement in reading report text, particularly for eleventh grade students of IPA 1 class in SMAN 2 Tebo.
Fostering Trustworthiness of Federated Learning Ecosystem through Realistic Scenarios

The present study thoroughly evaluates the most common blocking challenges faced by the federated learning (FL) ecosystem and analyzes existing state-of-the-art solutions. A system adaptation pipeline is designed to enable the integration of different AI-based tools in the FL system, while FL training is conducted under realistic conditions using a distributed hardware infrastructure. The suggested pipeline and FL system’s robustness are tested against challenges related to tool deployment, data heterogeneity, and privacy attacks for multiple tasks and data types. A representative set of AI-based tools and related datasets have been selected to cover several validation cases and distributed to each edge device to closely reflect real-world scenarios. The study presents significant outcomes of the experiments and analyzes the models’ performance under different realistic FL conditions, while highlighting potential limitations and issues that occurred during the FL process.
A Computational Approach to Hand Pose Recognition in Early Modern Paintings

Hands represent an important aspect of pictorial narration but have rarely been addressed as an object of study in art history and digital humanities. Although hand gestures play a significant role in conveying emotions, narratives, and cultural symbolism in the context of visual art, a comprehensive terminology for the classification of depicted hand poses is still lacking. In this article, we present the process of creating a new annotated dataset of pictorial hand poses. The dataset is based on a collection of European early modern paintings, from which hands are extracted using human pose estimation (HPE) methods. The hand images are then manually annotated based on art historical categorization schemes. From this categorization, we introduce a new classification task and perform a series of experiments using different types of features, including our newly introduced 2D hand keypoint features, as well as existing neural network-based features. This classification task represents a new and complex challenge due to the subtle and contextually dependent differences between depicted hands. The presented computational approach to hand pose recognition in paintings represents an initial attempt to tackle this challenge, which could potentially advance the use of HPE methods on paintings, as well as foster new research on the understanding of hand gestures in art.
PrivBCS: a privacy-preserving and efficient crowdsourcing system with fine-grained worker selection based on blockchain

Crowdsourcing takes advantage of human intelligence to solve complex problems that computers cannot handle. People actively participate in computational tasks for rewards, especially those that are relatively simple for humans but challenging for computers. However, most traditional crowdsourcing systems must rely on a central organisation to handle the work associated with crowdsourcing. This may lead to privacy and security issues such as information leakage, single point of attack and unfair judgement. Meanwhile, the high service fees of existing central crowdsourcing platforms will reduce people's motivation to participate in crowdsourcing tasks. Consequently, PrivBCS is proposed in this paper, a blockchain-based decentralised crowdsourcing system that does not rely on third-party agencies and requires only low transaction fees. In this paper, PrivBCS protects the confidentiality of crowdsourcing data and the identity privacy of participants by cryptographic means, such as Paillier, El-Gamal and CPABE. It selects the most suitable worker to perform tasks by setting access control policies on task data and matching workers' attributes while ensuring the anonymity of workers' identity. Considering that the data volume of crowdsourcing tasks may be large and there is a certain upper limit to the block capacity of the blockchain, an on-chain and off-chain collaborative storage scheme is proposed, which can increase the throughput of the blockchain system and improve its scalability to a certain extent. In PrivBCS, we implement the transaction process of crowdsourcing tasks through smart contracts. The experimental results and security analysis in this paper demonstrate the effectiveness, efficiency, and feasibility of PrivBCS.
Factors influencing acceptance and trust of chatbots in juvenile offenders’ risk assessment training

Research has identified simulation-based training with chatbots and virtual avatars as an effective educational strategy in some domains, such as medicine and mental health disciplines. Several studies on interactive systems have also suggested that user experience is decisive for adoption. As interest increases, it becomes important to examine the factors influencing user acceptance and trust in simulation-based training systems, and to validate applicability to specific learning tasks. The aim of this research is twofold: (1) to examine the perceived acceptance and trust in a risk assessment training chatbot developed to help students assess risk and needs of juvenile offenders, and (2) to examine the factors influencing students' perceptions of acceptance and trust.Participants were 112 criminology students in an undergraduate course in a Canadian university. Participants were directed to use a custom-designed chatbot with a virtual 3D avatar for juvenile offenders' risk assessment training, to complete online questionnaires and a risk assessment exercise.Results show satisfactory levels of acceptance and trust in the chatbot. Concerning acceptance, more than half appeared to be satisfied or very satisfied with the chatbot, while most participants appeared to be neutral or satisfied with the benevolence and credibility of the chatbot.Results suggest that acceptance and trust do not only depend on the design of the chatbot software, but also on the characteristics of the user, and most prominently on self-efficacy, state anxiety, learning styles and neuroticism personality traits. As trust and acceptance play a vital role in determining technology success, these results are encouraging.
LOD4Culture: Easy exploration of cultural heritage linked open data

LOD4Culture is a web application that exploits Cultural Heritage Linked Open Data for tourism and education purposes. Since target users are not fluid on Semantic Web technologies, the user interface is designed to hide the intricacies of RDF or SPARQL. An interactive map is provided for exploring world-wide Cultural Heritage sites that can be filtered by type and that uses cluster markers to adapt the view to different zoom levels. LOD4Culture also includes a Cultural Heritage entity browser that builds comprehensive visualizations of sites, artists, and artworks. All data exchanges are facilitated through the use of a generator of REST APIs over Linked Open Data that translates API calls into SPARQL queries across multiple sources, including Wikidata and DBpedia. Since March 2022, more than 1.7K users have employed LOD4Culture. The application has been mentioned many times in social media and has been featured in the DBpedia Newsletter, in the list of Wikidata tools for visualizing data, and in the open data applications list of datos.gob.es.
Research on AGV task path planning based on improved A* algorithm

In recent years, automatic guided vehicles (AGVs) have developed rapidly and been widely applied in intelligent transportation, cargo assembly, military testing, and other fields. One of the key issues in these applications is path planning. Global path planning results based on known environmental information are used as the ideal path for AGVs combined with local path planning to achieve safe and fast arrival at the destination. The global planning method planning results as the ideal path should meet the requirements of as few turns as possible, short planning time, and continuous path curvature. We propose a global path-planning method based on an improved A * algorithm. And the robustness of the algorithm is verified by simulation experiments in typical multi obstacles and indoor scenarios. To improve the efficiency of pathfinding time, we increase the heuristic information weight of the target location and avoided the invalid cost calculation of the obstacle areas in the dynamic programming process. Then, the optimality of the number of turns in the path is ensured based on the turning node backtracking optimization method. Since the final global path needs to satisfy the AGV kinematic constraints and the curvature continuity condition, we adopt a curve smoothing scheme and select the optimal result that meets the constraints. Simulation results show that the improved algorithm proposed in this paper outperforms the traditional method and can help AGVs improve the efficiency of task execution by efficiently planning a path with low complexity and smoothness. Additionally, this scheme provides a new solution for global path planning of unmanned vehicles.
Systematically Improving the Efficiency of Grid-Based Coverage Path Planning Methodologies in Real-World UAVs’ Operations

This work focuses on the efficiency improvement of grid-based Coverage Path Planning (CPP) methodologies in real-world applications with UAVs. While several sophisticated approaches are met in literature, grid-based methods are not commonly used in real-life operations. This happens mostly due to the error that is introduced during the region’s representation on the grid, a step mandatory for such methods, that can have a great negative impact on their overall coverage efficiency. A previous work on UAVs’ coverage operations for remote sensing, has introduced a novel optimization procedure for finding the optimal relative placement between the region of interest and the grid, improving the coverage and resource utilization efficiency of the generated trajectories, but still, incorporating flaws that can affect certain aspects of the method’s effectiveness. This work goes one step forward and introduces a CPP method, that provides three different ad-hoc coverage modes: the Geo-fenced Coverage Mode, the Better Coverage Mode and the Complete Coverage Mode, each incorporating features suitable for specific types of vehicles and real-world applications. For the design of the coverage trajectories, user-defined percentages of overlap (sidelap and frontlap) are taken into consideration, so that the collected data will be appropriate for applications like orthomosaicing and 3D mapping. The newly introduced modes are evaluated through simulations, using 20 publicly available benchmark regions as testbed, demonstrating their stenghts and weaknesses in terms of coverage and efficiency. The proposed method with its ad-hoc modes can handle even the most complex-shaped, concave regions with obstacles, ensuring complete coverage, no-sharp-turns, non-overlapping trajectories and strict geo-fencing. The achieved results demonstrate that the common issues encountered in grid-based methods can be overcome by considering the appropriate parameters, so that such methods can provide robust solutions in the CPP domain.
Detecting Fake Audio of Arabic Speakers Using Self-Supervised Deep Learning

One of the most significant discussions in forensics is Audio Deepfake, where AI-generated tools are used to clone audio content of people's voices.Although it was intended to improve people's lives, attackers utilized it maliciously, compromising the public's safety.Thus, Machine Learning (ML) and Deep Learning (DL) methods have been developed to detect imitated or synthetically faked voices.However, the developed methods suffered from massive training data or excessive pre-processing.To the author's best knowledge, Arabic speech has not yet been explored with synthetic fake audio, and it is very limited to the challenged fakeness, which is imitation.This paper proposed a new Audio Deepfake detection method called Arabic-AD based on self-supervised learning techniques to detect both synthetic and imitated voices.Additionally, it contributed to the literature by creating the first synthetic dataset of a single speaker who perfectly speaks Modern Standard Arabic (MSA).Besides, the accent was also considered by collecting Arabic recordings from non-Arabic speakers to evaluate the robustness of Arabic-AD.Three extensive experiments were conducted to measure the proposed method and compare it to well-known benchmarks in literature.As a result, Arabic-AD outperformed other state-of-the-arts methods with the lowest EER rate (0.027%), and high detection accuracy (97%) while avoiding the need for excessive training.
Context-enhanced concept disambiguation in Wikification

Wikification is a method to automatically enrich a text with links to Wikipedia as a knowledge base. One step in Wikification is detecting ambiguous mentions, and one other step is disambiguating those mentions. In this paper, we worked on the mention disambiguation problem. Some state-of-the-art disambiguation approaches have divided long input document text into non-overlapping windows. Later, for each ambiguous mention, they pick the most similar sense to the chosen meaning of the key-entity (a word that helps disambiguation other words of the text). Partitioning the input into disjoint windows means that the most appropriate key-entity to disambiguate a given mention may be in an adjacent window. The disjoint windows negatively affect the accuracy of these methods. This work presents CACW (Context-Aware Concept Wikifier), a knowledge-based approach to produce the correct meaning for ambiguous mentions in the document. CACW incorporates two algorithms; the first uses co-occurring mentions in consecutive windows to augment the available contextual information to find the correct sense. The second algorithm ranks senses based on their context relevancy. We also define a new metric for disambiguation to measure the coherence of the whole text document. Comparing our approach with state-of-the-art methods shows the effectiveness of our method in terms of text coherence in the English Wikification task. We observed between 10-20 percent improvement in the F1 measure compared to the state-of-the-art techniques.
Soft Decision Decoding with Cyclic Information Set and the Decoder Architecture for Cyclic Codes

The soft decision decoding algorithm for cyclic codes, especially the maximum likelihood (ML) decoding algorithm, can obtain significant performance superior to that of algebraic decoding, but the complexity is much higher. To deal with this problem, an improved soft decision decoding algorithm based on a cyclic information set and its efficient implementation architecture are proposed. This algorithm employs the property of the cyclic codes to generate a series of cyclic information sequences by circularly shifting, constructing the cyclic information set. Then, a limited number of candidate information sequences are efficiently generated using an iterative computation method, and the candidate codewords are generated using the very concise encoding method of the cyclic codes. Furthermore, the efficient hardware architecture based on systolic arrays is also proposed to generate candidate information sequences and to select the optimal candidate codewords. An emulation platform is constructed to verify the error correction performance and to determine the optimal decoder parameters. Emulation results indicate that, with appropriate parameter selection, the proposed decoding algorithm can achieve a bit error rate approaching the ML performance while maintaining low complexity.
Improving the Accuracy of Coal-Rock Dynamic Hazard Anomaly Detection Using a Dynamic Threshold and a Depth Auto-Coding Gaussian Hybrid Model

A coal-rock dynamic disaster is a rapid instability and failure process with dynamic effects and huge disastrous consequences that occurs in coal-rock mass under mining disturbance. Disasters lead to catastrophic consequences, such as mine collapse, equipment damage, and casualties. Early detection can prevent the occurrence of disasters. However, due to the low accuracy of anomaly detection, disasters still occur frequently. In order to improve the accuracy of anomaly detection for coal-rock dynamic disasters, this paper proposes an anomaly detection method based on a dynamic threshold and a deep self-encoded Gaussian mixture model. First, pre-mining data were used as the initial threshold, and the subsequent continuously arriving flow data were used to dynamically update the threshold to solve the impact of artificially setting the threshold on the detection accuracy. Second, feature dimensionality reduction and reorganization of the data were carried out, and low-dimensional feature representation and feature reconstruction error modeling were used to solve the difficulty of extracting features from high-dimensional data in real time. Finally, through the end-to-end optimization calculation of the energy probability distribution between different categories for anomaly detection, the problem that key abnormal information may be lost due to dimensionality reduction was solved and accurate detection of monitoring data was realized. Experimental results showed that this method has better performance than other methods.
Application of Neural Networks to Power Analysis

The purpose of this work is to research the possibility of a side-channel attack, more precisely power consumption attack (recovering the encryption key according to the board’s power consumption schedule) on the AES-128 algorithm implemented in hardware. Basically, various methods can be used to make an attack, including SPA (Simple Power Consumption Attack) and DPA (Differential Power Consumption Attack). SPA methods involve a simple visual analysis of energy consumption graphs, while DPA involves the use of statistical methods to recover the encryption key. One way to make side-channel attacks more effective is to implement machine learning methods for the described purposes.
Lightweight 2D Map Construction of Vehicle Environments Using a Semi-Supervised Depth Estimation Approach

This paper addresses the problem of constructing a real-time 2D map for driving scenes from a single monocular RGB image. We presented a method based on three neural networks (depth estimation, 3D object detection, and semantic segmentation). We proposed a depth estimation neural network architecture that is fast and accurate in comparison with the state-of-the-art models. We designed our model to work in real time on light devices (such as an NVIDIA Jetson Nano and smartphones). The model is based on an encoder–decoder architecture with complex loss functions, i.e., normal loss, VNL, gradient loss (dx, dy), and mean absolute error. Our results show competitive results in comparison with the state-of-the-art methods, as our method is 30 times faster and smaller.
Vision Transformer-Based Photovoltaic Prediction Model

Sensing the cloud movement information has always been a difficult problem in photovoltaic (PV) prediction. The information used by current PV prediction methods makes it challenging to accurately perceive cloud movements. The obstruction of the sun by clouds will lead to a significant decrease in actual PV power generation. The PV prediction network model cannot respond in time, resulting in a significant decrease in prediction accuracy. In order to overcome this problem, this paper develops a visual transformer model for PV prediction, in which the target PV sensor information and the surrounding PV sensor auxiliary information are used as input data. By using the auxiliary information of the surrounding PV sensors and the spatial location information, our model can sense the movement of the cloud in advance. The experimental results confirm the effectiveness and superiority of our model.