Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence (AI) Application in Government Department Hybrid Work Environment: A Holistic Study and Policy Implications (IMRAD)

This study examines the integration of Artificial Intelligence (AI) in government departments, with a focus on hybrid work environments that combine remote and on-site operations. It explores the application of Artificial Intelligence (AI) in hybrid work setups within government departments, adhering to PRISMA guidelines for systematic review. The research indicates a significant positive relationship between AI implementation and organizational efficiency. AI technologies have been found to streamline operations, automate tasks, and enhance decision-making processes. However, the study also identifies challenges in AI integration, categorized into organizational readiness, technological complexities, and environmental threats. To ensure the continuity of vital government services, the study recommends several strategies. Investment in infrastructure and training is crucial for effective AI adoption. Standardized data collection methods and quality control mechanisms are essential for the reliability of AI systems. Ethical guidelines and regulatory frameworks need to be established to address issues related to data privacy and security. Stakeholder engagement, involving government bodies, technology providers, and the public, is vital for a comprehensive AI adoption process. Pilot testing should precede full-scale implementation to evaluate the technology's impact and identify any gaps or challenges. Continuous monitoring post-implementation is necessary to assess the ongoing impact of AI on service delivery and make timely adjustments.
Verifying Neural Networks by Approximating Convex Hulls

The increasing prevalence of neural networks necessitates their verification in order to ensure security. Verifying neural networks is a challenge due to the use of non-linear activation functions. This work concentrates on approximating the convex hull of activation functions. An approach is proposed to construct a convex polytope to over-approximate the ReLU hull (the convex hull of the ReLU function) when considering multi-variables. The key idea is to construct new faces based on the known faces and vertices by uniqueness of the ReLU hull. Our approach has been incorporated into the state-of-the-art PRIMA framework, which takes into account multi-neuron constraints. The experimental evaluation demonstrates that our method is more efficient and precise than existing ReLU hull exact/approximate approaches, and it makes a significant contribution to the verification of neural networks. Our concept can be applied to other non-linear functions in neural networks, and this could be explored further in future research.
Bit leakage-resistant quantum private magnitude comparison

Lots of ready quantum private comparison (QPC) protocols usually only judge whether two users’ data are equal, whereas the quantum private magnitude comparison (QPMC) protocol can compare the magnitude of two numbers, whose results are greater than, equal and less than. It is by announcing a comparison result of two bits in two data that the existent QPMC protocols determine the size relationships of the whole data. Although the announcement leaks no information to outside attackers, yet it alerts two participants to know each other’s value of a certain bit. This is termed as bit leakage in this paper. In order to remedy this shortage, the paper proposes a novel QPMC protocol using a type of Bell state. The protocol is thoroughly analyzed, correct and secure. Also, it proves free of bit leakage. Additionally, because it is easier to implement because of the usage of a type of Bell state, it counts as a better alternative for QPMC.
Formalizing Robustness Against Character-Level Perturbations for Neural Network Language Models

The remarkable success of neural networks has led to a growing demand for robustness verification and guarantee. However, the discrete nature of text data processed by language models presents challenges in measuring robustness, impeding verification efforts. To address this challenge, this work focuses on formalizing robustness specification against character-level perturbations for neural network language models. We introduce a key principle of three metrics, namely probability distribution, density, and diversity, for generalizing neural network language model perturbations and meanwhile, formulate the robustness specification against character-level perturbed text inputs. Based on the specification, we propose a novel approach to augment existing text datasets with specified perturbations, aiming to guide the robustness training of language models. Experimental results demonstrate that the training with our generated text datasets can enhance the overall robustness of the language model. Our contributions advance the field of neural network verification and provide a promising approach for handling robustness challenges in neural network language models.
Branch and Bound for Sigmoid-Like Neural Network Verification

The robustness of deep neural networks has received extensive attention and is considered to need guarantees by formal verification. For ReLU neural network verification, there are abundant studies and various techniques. However, verifying sigmoid-like neural networks still relies on linear approximation, which inevitably introduces errors and leads to imprecise results. To reduce error and get better results, we present a branch and bound framework for sigmoid-like neural network verification in this paper. In this framework, we design a neuron splitting method and a branching strategy. The splitting method can split neurons with non-linear sigmoid-like activation functions, and the branching strategy reduces the size of the branch and bound tree, which improves the verification performance. We implement our verification framework as SigBaB and evaluate its performance on open source benchmarks. Experiment results show that our method can produce more precise verification results than other state-of-the-art methods and our branching strategy shows superior performance compared to other strategies.
Sign language in immersive virtual reality: design, development, and evaluation of a virtual reality learning environment prototype

Non-Verbal Cues (NVCs) add to communication effectiveness among individuals in both real and virtual world. Thus, NVCs transference between the two receives increased attention from both the industry and research community. Their efforts lead to sophisticated technological solutions which allow high fidelity NVCs to be transferred from real individuals to virtual characters/avatars, in real-time, adding to the communication effectiveness of both standalone Immersive Virtual Reality (IVR) applications and Social Virtual Reality (SVR) platforms. That combination triggered the development of Virtual Reality Learning Environments (VRLEs), aiming at assisting students towards learning sign language and communicating through it in both virtual and real world. Still, technological limitations affect the quality of such learning and communication experience. This work presents the design, development, and evaluation of an SVR compatible system for sign language learning and communication in virtual worlds. The system considers lessons learned from previous studies and was used to create a VRLE mimicking learning and examination processes students of sign language follow in real life. The results of our study show that despite the appreciation of technological solutions efficiency towards NVCs transference, there are still several issues to be investigated to increase the effectiveness of communication through sign language in virtual worlds.
Towards Safe Autonomous Driving: Decision Making with Observation-Robust Reinforcement Learning

Abstract Most real-world situations involve unavoidable measurement noises or perception errors which result in unsafe decision making or even casualty in autonomous driving. To address these issues and further improve safety, automated driving is required to be capable of handling perception uncertainties. Here, this paper presents an observation-robust reinforcement learning against observational uncertainties to realize safe decision making for autonomous vehicles. Specifically, an adversarial agent is trained online to generate optimal adversarial attacks on observations, which attempts to amplify the average variation distance on perturbed policies. In addition, an observation-robust actor-critic approach is developed to enable the agent to learn the optimal policies and ensure that the changes of the policies perturbed by optimal adversarial attacks remain within a certain bound. Lastly, the safe decision making scheme is evaluated on a lane change task under complex highway traffic scenarios. The results show that the developed approach can ensure autonomous driving performance, as well as the policy robustness against adversarial attacks on observations.
A target tracking method based on adaptive occlusion judgment and model updating strategy

Target tracking is an important research in the field of computer vision. Despite the rapid development of technology, difficulties still remain in balancing the overall performance for target occlusion, motion blur, etc. To address the above issue, we propose an improved kernel correlation filter tracking algorithm with adaptive occlusion judgement and model updating strategy (called Aojmus) to achieve robust target tracking. Firstly, the algorithm fuses color-naming (CN) and histogram of gradients (HOG) features as a feature extraction scheme and introduces a scale filter to estimate the target scale, which reduces tracking error caused by the variations of target features and scales. Secondly, the Aojmus introduces four evaluation indicators and a double thresholding mechanism to determine whether the target is occluded and the degree of occlusion respectively. The four evaluation results are weighted and fused to a final value. Finally, the updating strategy of the model is adaptively adjusted based on the weighted fusion value and the result of the scale estimation. Experimental evaluations on the OTB-2015 dataset are conducted to compare the performance of the Aojmus algorithm with four other comparable algorithms in terms of tracking precision, success rate, and speed. The experimental results show that the proposed Aojmus algorithm outperforms all the algorithms compared in terms of tracking precision. The Aojmus also exhibits excellent performance on attributes such as target occlusion and motion blur in terms of success rate. In addition, the processing speed reaches 74.85 fps, which also demonstrates good real-time performance.
Chaos Meets Cryptography: Developing an S-Box Design with the Rössler Attractor

The volume of data transmitted over networks has significantly increased in recent years. For that reason, safeguarding the privacy, authenticity, and confidentiality of specific data is imperative, necessitating a type of encryption; symmetric encryption, known for its computational efficiency, is ideal for securing extensive datasets. A principal component within symmetric key algorithms is the substitution box (S-box), which creates confusion between plaintext and ciphertext, enhancing the security of the process. This paper proposes a fashion method to create chaotic S-boxes using the Rössler attractor as a chaotic process and the Rijndael S-box as a permutation box. The proposed S-boxes are evaluated with bijectivity, non-linearity (NL), strict avalanche criterion (SAC), bit independence criterion (BIC), linear approximation probability (LAP), and differential uniformity (DU). The analyses show that the proposed method helps generate a high-resistance S-box to well-known attacks and high efficiency, executing in short computational time.
A Bi-Radial Model for Lens Distortion Correction of Low-Cost UAV Cameras

Recently developed cameras in the low-cost sector exhibit lens distortion patterns that cannot be handled well with established models of radial lens distortion. This study presents an approach that divides the image sensor and distortion modeling into two concentric zones for the application of an extended radial lens distortion model. The mathematical model is explained in detail and it was validated on image data from a DJI Mavic Pro UAV camera. First, the special distortion pattern of the camera was examined by decomposing and analyzing the residuals. Then, a novel bi-radial model was introduced to describe the pattern. Eventually, the new model was integrated in a bundle adjustment software package. Practical tests revealed that the residuals of the bundle adjustment could be reduced by 63% with respect to the standard Brown model. On the basis of external reference measurements, an overall reduction in the residual errors of 40% was shown.
Privacy-Preserving Cooperative GNSS Positioning

<h3>Abstract</h3> The issue of user privacy in the context of collaborative positioning is addressed in this work, wherein information is passed between and processed by multiple cooperative agents, with the goal of achieving high levels of positioning accuracy. In particular, this paper discusses three privacy-preserving schemes in the context of differential global navigation satellite system (GNSS)-based and GNSS-based cooperative positioning methods. The discussed architectures provide the same positioning results, while yielding different levels of privacy to the cooperative users. These architectures also involve increased complexity as privacy grows and as non-encrypted, encrypted, and homomorphically encrypted solutions are implemented. The latter scheme is the most computationally demanding; however, it provides the highest level of privacy by employing homomorphic encryption, whereby addition and multiplication operations may be performed on encrypted data to produce encrypted outputs, without revealing information about the collaborative agent’s location. The proposed privacy-preserving cooperative position schemes are shown to provide the same results as their non-privacy-preserving counterparts, while providing privacy guarantees. Based on this analysis, some of the proposed solutions can be considered for real-time applications, while homomorphic encryption is a valid solution for latency-tolerant applications. Advances in computing power will increase their overall usability in the near future.
A Study on Double-Headed Entities and Relations Prediction Framework for Joint Triple Extraction

Relational triple extraction, a fundamental procedure in natural language processing knowledge graph construction, assumes a crucial and irreplaceable role in the domain of academic research related to information extraction. In this paper, we propose a Double-Headed Entities and Relations Prediction (DERP) framework, which divides the entity recognition process into two stages: head entity recognition and tail entity recognition, using the obtained head and tail entities as inputs. By utilizing the corresponding relation and the corresponding entity, the DERP framework further incorporates a triple prediction module to improve the accuracy and completeness of the joint relation triple extraction. We conducted experiments on two English datasets, NYT and WebNLG, and two Chinese datasets, DuIE2.0 and CMeIE-V2, and compared the English dataset experimental results with those derived from ten baseline models. The experimental results demonstrate the effectiveness of our proposed DERP framework for triple extraction.
Training an LSTM-based Seq2Seq Model on a Moroccan Biscript Lexicon

In today’s increasingly multilingual world, where diverse scripts coexist, training natural language processing (NLP) models on biscript lexicons holds immense value. This approach allows for a deeper understanding and more accurate analysis of text data by encompassing multiple writing systems. It enhances language processing tasks like translation and sentiment analysis by capturing nuances and contextual cues specific to each script. Additionally, it promotes cross-cultural communication, inclusivity, and collaborative knowledge sharing. By training models on biscript lexicons, we can achieve more accurate translation, language generation, and language understanding across different scripts. This paper aims to evaluate the performance of a Sequence-to-Sequence (Seq2Seq) model based on Long Short-Term Memory (LSTM) architecture. The evaluation focuses on a biscript lexicon within the context of the Moroccan language. While Seq2Seq models have shown promise in various natural language processing tasks, their application to biscript lexicons remains relatively unexplored. Our approach involves constructing a Moroccan biscript lexicon where each entry is represented in both Arabizi and Arabic writing scripts. Subsequently, we will train our LSTM-based Seq2Seq model using various optimization techniques to enhance its effectiveness and accuracy.
Towards Explainable Linguistic Summaries

As more AI solutions are implemented in every aspect of our lives, the need for Explainable Artificial Intelligence (XAI) rises.Explanations can have different forms, such as a number (or an equation), a figure, or a text.This paper investigates textual explanations to effectively communicate the reason for a decision made by an AI system.In previous works, linguistic summaries, as an example of a textual explanation, have already been tested and shown to have explanatory potential.In this paper, we explore this topic further and present a roadmap for linguistic summaries to become a proper XAI tool as Explainable Linguistic Summaries (XLSs).We discuss the challenges that an XLS has to overcome.We outline possible solutions and state their consequences.We consider different protoforms, the definition of the membership function, heuristics for the selection of XLS and personalizing the explanations as well as options to gain more insights into the explanations.
Similarity Function-Assisted Dynamic Fuzzy Rule Interpolation: An Improved Approach

Fuzzy rule interpolation (FRI) enables fuzzy inference systems to derive consequences when the observations are not covered by a system's sparse rule base. However, in conventional FRI systems, interpolated fuzzy rules are discarded once outcomes are obtained, despite they may contain valuable information about the problem space. Recent work has revealed the potential to improve the knowledge scope covered by a sparse rule base by reusing the interpolated rules. Particularly, dynamic fuzzy interpolation based on rule assessment (RAD-FRI) can reinforce the sparse rule base by adding high-quality interpolated rules into the rule base. It works via exploiting the similarity between interpolated rules and those given in the spare rule base. This paper further develops such research by improving the similarity function employed within RAD-FRI, through considering the location of the rules in a sparse rule base while filtering the interpolated rules that are not used in the subsequent inference processes. Comparative experimental outcomes on benchmark datasets demonstrate that the performance of the resulting FRI systems can be dynamically strengthened, with improved inference accuracy over that attainable by the popular existing FRI systems.
Time Series Classification Based on Fuzzy Cognitive Maps and Multi-Class Decomposition with Ensembling

This paper presents a time series classification method which utilizes ensemble learning. The multi-class classification is reduced to several binary classification tasks. Base classifiers incorporate a fuzzy cognitive map. Each of them learns independently to correctly associate time series with specific categories. Predicted memberships are later aggregated into the final class assignment. We compare four different decomposition methods implementing “one-vs-one” and “one-vs-all” approaches. We test these methods on real-world datasets and compare them with state-of-the-art solutions.
An Initial Step Towards Stable Explanations for Multivariate Time Series Classifiers with LIME

LIME, or ‘Local Interpretability Model-agnostic Explanations’ is a well-known post-hoc explanation technique for the interpretation of black-box models. While very useful, recent studies show that LIME suffers from stability problems: explanations provided for the same process can be different, making it difficult to trust their reliability. This paper investigates the stability of LIME when explaining multivariate time series classification problems. We demonstrate that due to the temporal dependency in time series data, the traditional artificial neighbour generation methods used in LIME have a higher risk of creating out-of-distribution inputs. We disucss how this behavior is one of the reasons resulting in unstable explanations. In addition, LIME weights neighbours based on user-defined hyperparameters which are problem-dependent and hard to tune, and we show how unsuitable hyperparameters can contribute to the generation of unstable explanations. As a preliminary step towards addressing these issues, we propose to employ a generative approach with an adaptive weighting method in the LIME framework. Specifically, we adopt a generative model based on variational autoencoder to create within-distribution neighbours, reducing the out-of-distribution problem, while the adaptive weight method eliminates the need for user-defined hyperparameters. Experiments on real-world datasets demonstrate the effectiveness of the proposed method in providing more stable explanations using the LIME framework.
A Reasoning System for Time and Vagueness

Temporal reasoning can be formalised by various systems of temporal logic, which has been broadly applied in formal verification via model checking and in other computer science fields. Temporal logic usually builds upon classical logic; however temporal propositions are not necessarily crisply true or false as Arthur Prior, a founder of temporal logic, used an intermediate truth value in his discussion on the so-called master argument. In this paper we develop fuzzy temporal logic, namely a temporal logical system that allows for intermediate truth values. Specifically, we develop temporal logic on the basis of Lukasiewicz logic (one of the most traditional fuzzy logical systems), and prove its fundamental properties such as completeness, the finite model property and decidability. We finally analyse Prior's discussion on the master argument by applying fuzzy temporal logic developed in this paper.
Evaluating Simple Exercises with a Fuzzy System Based on Human Skeleton Poses

Exercise is important for elderly people to maintain their physical ability. However, evaluating the quality of exercise requires human judgment, which is subjective and time-consuming. In this paper, we propose a method that uses a fuzzy system to evaluate a simple exercise (overhead arm clap) based on human skeleton poses extracted from video sequences. Exercise is important for elderly people to maintain their physical ability. However, evaluating the quality of exercise requires human judgment, which is subjective and time-consuming. In this paper, we propose a method that uses a fuzzy system to evaluate a simple exercise (overhead arm clap) based on human skeleton poses extracted from video sequences. Our method consists of four steps: (1) converting the video into a sequence of human skeleton poses using the MediaPipe tool, (2) extracting the essential poses from the sequence using a fixed incremental adaptive resonance theory, (3) generating the fuzzy membership functions based on the pose similarity and the completeness of the exercise, and (4) performing rule inferencing and defuzzification to obtain an exercise score. We evaluate our method on four types of videos: (1) correct exercise, (2) incorrect pose, (3) incomplete sequence, and (4) different exercise (squat). Our results show that our method can distinguish these different types of videos and provide a reasonable exercise score. We conclude that our method is a promising technique for evaluating simple exercises using human skeleton poses.