Reviewerly/exHarmony huggingface dataset:
Sign Language Detection using Action Recognition

Sign Language Detection has become crucial and effective for humans and research in this area is in progress and is one of the applications of Computer Vision. Earlier works included detection using static signs with the help of a simple deep learning-based Convolutional Neural Network. This proposal is based on continuous detection of image frames in real-time using action detection so as to detect the action performed by the user. The model uses LSTM neural network model after identifying keypoints using mediapipe holistic which includes face, pose and hand features. The proposed work is done by collecting key value points for training and testing, pre-processing the data, and creating labels and features. It saves the weights and evaluates the model using confusion matrix accuracy.
A Hybrid Approach Towards Automated Essay Evaluation based on Bert and Feature Engineering

Educational institutions often assess a student's critical thinking and communication skills based on essay responses. Manual Evaluation is time-consuming, and there may be wide variations when multiple evaluators rate batches of essays. In the last few years, the automated grading of essay scripts has emerged as a new area of research. Most studies essentially focus on visible attributes such as length, vocabulary, sentiment or spelling. The use of neural networks requires the conversion of text into some vector representations. However solely using handcrafted attributes or text encodings implies primarily operating on word granularity. On the other hand, Transformers can handle dependencies between words of the text. In this paper, we propose a hybrid model that can capture the interaction of words in the essay using the BERT self-attention transformer, along with handcrafted syntactical features. While previous studies have built individual models for every essay topic, our model has been incrementally trained on multiple essay topics to test its generalizability. The validation of the model uses quadratic weighted kappa to compare human-rated scores and model scores.
GM-Attack: Improving the Transferability of Adversarial Attacks

In the real world, blackbox attacks seem to be widely existed due to the lack of detailed information of models to be attacked. Hence, it is desirable to obtain adversarial examples with high transferability which will facilitate practical adversarial attacks. Instead of adopting traditional input transformation approaches, we propose a mechanism to derive masked images through removing some regions from the initial input images. In this manuscript, the removed regions are spatially uniformly distributed squares. For comparison, several transferable attack methods are adopted as the baselines. Eventually, extensive empirical evaluations are conducted on the standard ImageNet dataset to validate the effectiveness of GM-Attack. As indicated, our GM-Attack can craft more transferable adversarial examples compared with other input transformation methods and attack success rate on Inc-v4 has been improved by 6.5% over state-of-the-art methods.
Nereus: Anonymous and Secure Ride-Hailing Service Based on Private Smart Contracts

Security and privacy issues have become a major hindrance to the broad adoption of Ride-Hailing Services (RHSs). In this article, we introduce a new collusion attack initiated by the Ride-Hailing Service Provider (RHSP) and a driver that could easily link the real riders and their anonymous requests (credentials). Besides this attack, existing work requires heavy computations to execute user matching, and it is challenging for riders to verify matching results. Meanwhile, a malicious driver may cancel an assigned ride order due to its short distance. To address these issues, we present a RHS system named Nereus to support collusion resistance, efficiency, verifiability, and accountability. First, we integrate a smart contract into a Software Guard Extensions (SGX) enclave to establish a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">private smart contract</i> for collusion resistance. We use a Bloom filter to achieve efficient matching. Second, we leverage privacy-preserving range query and Merkle proofs to make matching results verifiable. Meanwhile, we adopt short group signatures to provide anonymous authentication and deposit commitments to hold the runaway driver accountable. We formally state and prove the security and privacy of Nereus. We build a prototype based on Ethereum and SGX to conduct extensive performance analysis in regard to gas costs, computational costs, and communication overhead. Experimental results show that Nereus significantly improves over existing schemes in terms of computational costs.
CorefDRE: Coref-Aware Document-Level Relation Extraction

Document-level Relation Extraction (Doc-level RE) aims to extract relations among entities from a document, which requires reasoning over multiple sentences. The pronouns are ubiquitous in the document, which can provide reasoning clues for Doc-level RE. However, previous works do not take the pronouns into account. In this paper, we propose Coref-aware Doc-level RE based on Graph Inference Network (CorefDRE) to infer relations. CorefDRE first dynamically constructs the heterogeneous Mention-Pronoun Affinity Graph (MPAG) by integrating coreference information of pronouns. Then, Entity Graph (EG) is aggregated from MPAG through the weight of mention-pronoun pairs, calculated by the noise suppression mechanism, and GCN. Finally, we infer relations between entities based the normalized EG. Moreover, We introduce the noise suppression mechanism via calculating affinity between pronouns and corresponding mentions to filter the noise caused by pronouns. Experimental results significantly outperform baselines by nearly 1.7–2.0 in F1 on three public datasets, DocRED, DialogRE, and MPDD. We further conduct ablation experiments to demonstrate the effectiveness of the proposed MPAG structure and the noise suppression mechanism.
Towards Better Dependency Management: A First Look at Dependency Smells in Python Projects

Managing cross-project dependencies is tricky in modern software development. A primary way to manage dependencies is using dependency configuration files, which brings convenience to the entire software ecosystem, including developers, maintainers, and users. However, developers may introduce dependency smells if dependency configuration files are not well written and maintained. Dependency smells are recurring violations of dependency management in dependency configuration files and can potentially lead to severe consequences. This paper provides an in-depth look at three dependency smells, namely, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Missing Dependency</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Bloated Dependency</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Version Constraint Inconsistency</i> in Python projects. First, we implement a tool called <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Py</u> thon <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> ross-project <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</u> ependency- PyCD to accurately extract dependency information from configuration files. The evaluation result on 212 Python projects shows that PyCD outperforms state-of-the-art tools. Then, we make an empirical study for three dependency smells in 132 Python projects to investigate the pervasiveness, causes, and evolution. The results show that: 1) dependency smells are prevalent in Python projects and exist inconsistently in different projects; 2) dependency smells are introduced into Python projects for different reasons, mainly due to the problems of synchronous update and collaborative development; and 3) dependency smells can be removed with different patterns according to different dependency smells. Furthermore, we report and get responses for 40 harmful dependency smell instances, 34 of which have been responded that these dependency smells do exist in the projects, and 10 instances are fixed or under process. The feedback from developers indicates that dependency smells can have a negative impact on project maintenance. Our study highlights that these dependency smells deserve the attention of developers.
ER-Chat: A Text-to-Text Open-Domain Dialogue Framework for Emotion Regulation

Emotions are essential for constructing social relationships between humans and interactive systems. Although emotional and empathetic dialogue generation methods have been proposed for dialogue systems, appropriate dialogue involves not only mirroring emotions and always being empathetic but also complex factors such as context. This paper proposes Emotion Regulation Chat (ER-Chat) as an end-to-end dialogue framework for emotion regulation. Emotion regulation is concerned with actions to approach appropriate emotional states. Learning appropriate emotion and intent when responding on the basis of the context of the dialogue enables the generation of more human-like dialogue. We conducted automatic and human evaluations to demonstrate the superiority of ER-Chat over the baseline system. The results show that inclusion of emotion and intent prediction mechanisms enable generation of dialogues with greater fluency, diversity, emotion awareness, and emotion appropriateness, which are greatly preferred by humans.
AOH: Online Multiple Object Tracking With Adaptive Occlusion Handling

Multiple object tracking has improved drastically in recent years due to one-shot tracking methods. These methods design joint-detection-and-tracking structures to achieve real-time tracking performance and introduce more powerful detectors to deal with missed objects. However, most of them perform poorly in crowded scenes because of frequent occlusions. Several previous works have attempted to alleviate the occlusion issue, but they hardly involve the essence of the problem. In this letter, we suppose that occlusion is closely related to crowd density, so the degree of occlusion can be estimated. Therefore, we propose a Potential Object Mining strategy to adaptively obtain occluded objects for reducing broken trajectories, which re-weights detections based on the predicted density map. Additionally, for the strategy, Dense Estimator is designed to predict the density of each region in an image by employing a Transformer-based structure. Combining them together forms our Adaptive Occlusion Handling (AOH) tracking framework. Extensive experiments on MOTChallenge benchmarks (MOT17 and MOT20) demonstrate that our AOH achieves the state-of-the-art performance, especially on the heavily occluded MOT20.
Enhancing Knowledge Graph Generation with Ontology Reshaping – Bosch Case

In the context of Industry 4.0 [1] and Internet of Things (IoT) [2], modern manufacturing and production [3, 4] lines are equipped with software systems and sensors that constantly collect and send a high volume of data.
O’FAIRe: Ontology FAIRness Evaluator in the AgroPortal Semantic Resource Repository

O'FAIRe, the Ontology FAIRness Evaluator, is a methodology to automatically assess to which level a semantic resource or ontology respects the FAIR Principles. This paper describes the online tool implementing O'FAIRe within the AgroPortal ontology repository, through 61 questions/tests, among 80% are based on the ontology metadata description. For a specific ontology or a group of semantic resources, O'FAIRe web service outputs both global and detailed scores (normalized) against the 15 FAIR Principles. O'FAIRe results are visualized and explained with new specific user-friendly interfaces (such as the FAIRness wheel) in order to help AgroPortal users improve the FAIRness of their resources. O'FAIRe is currently implemented in three different public ontology repositories as they offer the required metadata descriptions. In the future, we will deploy the service in other OntoPortal repositories.
Privacy-preserving decentralized price coordination for EV charging stations

Price competition among electric vehicle (EV) charging stations is as fierce as the competition among gas stations. Nash equilibrium (NE) is a solution concept that can characterize a competition's efficient and stable state. However, calculation of the equilibrium is often time-consuming and requires complete information on the charging stations. Rapidly changing charging stations often hinder reaching equilibrium. In this study, we analyze price competition with service capacity constraints and use an ordinal potential game framework to investigate the structure of the competition. By constructing the ordinal potential function, the equilibrium characterization is converted to identifying the solution through a single-objective optimization. We further propose a decentralized algorithm to enable effective price coordination to achieve equilibrium with maximized social welfare. To preserve the privacy of charging stations from internal collusion and external attacks, an advanced secure multi-party computation technology known as the Paillier Cryptosystem is customized for our proposed decentralized algorithm. Numerical studies based on field data suggest the significance of our framework.
Detection method of obstacles in the dangerous area of electric locomotive driving based on MSE-YOLOv4-Tiny

Abstract Aiming at the problems of error warning, low detection efficiency and inability to meet the requirements of lightweight deployment in current track obstacle detection algorithms based on computer vision, a detection method of obstacles in the dangerous area of electric locomotive driving based on improved YOLOv4-Tiny (MSE-YOLOv4-Tiny) was proposed. An obstacle image dataset was constructed to provide a testing environment for various target detection algorithms. The method of perspective transformation, sliding window and least square cubic polynomial was used to fit the track line. By finding the area where the track was located and extending a certain distance to the outside of the track, the dangerous area of the electric locomotive running was obtained. A three-scale detection structure was formed by increasing the shallow detection scale in the detection layer, so as to improve the detection accuracy of the network for smaller targets, such as stones. An improved SKNet (ECA_SKNet) attention mechanism module was added to the output ends of the three scales of the backbone network, and the weight was reassigned to realize feature reconstruction, thus further improving the detection accuracy of the target. By adding the Spatial Pyramid Pooling module, the local and global features of the image were fused to improve the accuracy of localization and detection accuracy of the network. A comparative experiment was carried out on the dataset constructed in this paper. The experimental results show that the problem of false warnings caused by taking the target in the safe area as an obstacle can be effectively solved by dividing the danger area of electric locomotive driving. Compared with the original YOLOv4-Tiny algorithm, the MSE-YOLOv4-Tiny algorithm has a 3.80% increase in mean average precision while maintaining a higher detection speed and a smaller model memory. It has better detection performance and can be used for autonomous driving electric locomotive obstacle detection to provide technical support.
An Authentication Protocol for the Medical Internet of Things

The progress in biomedical sensors, Internet of Things technologies, big data, cloud computing, and artificial intelligence is leading the development of e-health medical systems, offering a range of new and innovative services. One such service is remote patient monitoring, where medical professionals are able to collect and examine a patient’s medical data remotely. Of course, in these systems, security and privacy are of utmost importance and we need to verify the identities of system users before granting them access to sensitive patient-related data. To this end, several authentication protocols have been recently designed specifically for e-health systems. We survey several of these protocols and report on flaws and shortcomings we discovered. Moreover, we propose an authentication protocol that enables a medical professional and the network of sensors used by a patient to authenticate each other and share a cryptographic key to be used for security in a communication session. The protocol also enables the dynamic assignment of patients to doctors in order to control access to patients’ data. We perform a security analysis of the protocol both formally, using the ProVerif protocol analysis tool, and informally, demonstrating its security features. We show that our protocol achieves mutual authentication, secret key establishment, forward secrecy, and anonymity. In terms of performance, the protocol is computationally lightweight, as it relies on symmetric key cryptography. This is demonstrated by comparing the computational cost of our protocol (in terms of execution time) with that of other similar protocols.
An Efficient Data Sharing Scheme for Privacy Protection Based on Blockchain and Edge Intelligence in 6G-VANET

With the substantial increase in the number of smart cars, vehicular ad hoc network (VANET), where data can be shared between vehicles to enrich existing vehicle services and improve driving safety, is gaining more and more attention, thus creating a more efficient intelligent transportation system. Moreover, the in-depth research and development of 6G and AI technology further strengthen the interconnection of various entities in VANET and can realize edge intelligence, which fundamentally enhances the efficiency of data sharing. However, reliable transmission and secure storage of data have always been a great challenge in data sharing. Although some schemes store shared data in the blockchain, most of the consensus mechanisms they use employ full nodes to verify signature information and timestamps, which cannot effectively judge the reliability of the shared data itself. Some other schemes use scoring mechanisms to evaluate data uploaded by vehicles, but these methods can be affected by network hardware failures and cannot effectively detect duplicate data. In addition, participants’ privacy may also be disclosed in the process of data sharing, such as participants’ location and identity information. Therefore, to address the above problems, this paper proposes a data sharing scheme in 6G-VANET, which can not only ensure the reliability and security of shared data but also protect the privacy of participants. Firstly, a consortium chain is adopted to realize the secure storage of shared data in 6G-VANET, which meets the requirements of tamper-proof and traceability of data. Secondly, a voting consensus mechanism is designed in combination with smart contract to ensure the reliability of data. Thirdly, the trained word2vec natural language processing model is deployed to edge nodes to realize edge intelligence, effectively eliminate the duplicate shared data, and enhance storage efficiency. Finally, a participant privacy protection mechanism is designed using the Private Set Intersection (PSI) protocol, and a secure and efficient data sharing scheme is finally realized. The effectiveness of the proposed scheme is demonstrated by security analysis and experimental evaluation. The experimental results show that the time and space overhead of blockchain can meet the practical requirements, and the proposed PSI protocol of large-scale vehicles can be completed in a short time.
Teaching Inclusive Design Skills with the CIDER Assumption Elicitation Technique

Technology should be accessible and inclusive, so designers should learn to consider the needs of different users. Toward this end, we created the theoretically-grounded CIDER assumption elicitation technique, an educational analytical design evaluation method to teach inclusive design skills. CIDER ( Critique , Imagine , Design , Expand , Repeat ) helps designers recognize and respond to bias using the critical lens of assumptions about users . Through an 11-week mixed-method case study in an interaction design course with 40 undergraduate students and follow-up interviews, we found that activities based on the CIDER technique may have helped students identify increasingly many types of design bias over time and reflect on their unconscious biases about users. The activities also had lasting impacts, encouraging some students to adopt more inclusive approaches in subsequent design work. We discuss the implications of these findings, namely that educational techniques like CIDER can help designers learn to create equitable technology designs.
Sentence subjectivity analysis of a political and ideological debate dataset using LSTM and BiLSTM with attention and GRU models

Subjectivity analysis is one of the key tasks in the field of natural language processing. Used to annotate data as subjective or objective, subjectivity analysis can be implemented on its own or as a precursor to other NLP applications such as sentiment analysis, emotion analysis, consumer review analysis, political opinion analysis, document summarization, and question answering systems. The main objective of this article is to test and compare six deep learning methods for subjectivity classification, including Long Short-Term Memory Networks (LSTM), Gated Recurrent Units (GRU), bidirectional GRU, bidirectional LSTM, LSTM with attention, and bidirectional LSTM with attention. We introduced a combination method for subjectivity annotation using lexicon-based and syntactic pattern-based methods. We evaluated the performance of GloVe versus one-hot encoding. We also reformatted, preprocessed, and annotated a political and ideological debate dataset for use in subjectivity analysis. Our research compares favorably with the performance of existing research on subjectivity analysis, achieving very high accuracy and evaluation metrics. LSTM with attention performed the best out of all the methods we tested with an accuracy of 97.39%.
Joint-Aware Action Recognition for Ambient Assisted Living

As the aged population is rapidly increased, the need for efficient and low-cost ambient systems becomes vital. The effectiveness of such systems lies upon the accurate and fast motion analysis in order to predict the elderly's action and develop systems to act in need. To achieve that, the precise estimation of the entire human body pose is often exploited, providing the required motion-related information. Yet, the exploitation of the entire human pose can present several limitations. The paper at hand exploits state-of-the-art data-driven classifiers and compares their efficiency in action recognition based on a specific set of joints or coordinates, i.e., the x, y and z-axis. The above rests upon the notion that each action in real life can be effectively perceived by observing only a specific set of joints. Considering that, we aim to investigate the capacity of such a joint analysis and its ability to deliver an enhanced pose-based action recognition system. To that end, we correlate specific joints with each action, indicating the joints that contribute the most. We evaluate our findings on two different senior subjects using two different classifiers, viz., support vector machine (SVM) and convolutional neural network (CNN), showing that the above strategy can improve recognition rates.