Reviewerly/exHarmony huggingface dataset:
Development of Aqidah Akhlak Learning Media "Board Game Based on Education Fun on the Theme of Commendable Morals (E-Fun A2M)" for High School Students

Monotonous learning patterns lead to ineffective learning behavior which impacts on learning outcomes. Lack of teacher creativity and mastery in processing learning will make students reluctant to take part in learning. The use of learning media as a support for learning is not optimal. The purpose of this study was to develop Board Game Learning Media based on Commendable Morals Education Fun (E-Fun A2M) in Akidah Akhlak High School subjects. This research is research development or Research and Development (R&amp;D) with the Thiagarajan, Semmel and Semmel models by testing the validity of A2M's E-Fun learning media. This is intended to determine the practicality and effectiveness. The specialty of this research is research and development based on the four-D development model (Define, design, develop, disseminate). However, due to the limitations of researchers, this development only reached the develop stage. The research subjects were six students of class X. The instruments used were validation sheets, teacher and student response questionnaires, and student learning outcomes tests. The survey results show that the relevance score of E-Fun A2M learning media is in the very valid category with a value of 92.5%. This shows that the E-Fun A2M learning media can be used by students as a learning medium for Aqidah Akhlak
A novel hybrid cryptographic framework for secure data storage in cloud computing: Integrating AES-OTP and RSA with adaptive key management and Time-Limited access control

The rapid proliferation of cloud computing has necessitated advanced security mechanisms to safeguard sensitive data stored in remote servers. However, this paradigm shift has raised several security concerns, particularly safeguarding private information stored on faraway cloud servers. This study proposes a ground-breaking hybrid cryptographic framework for the secure data storage requirements of cloud computing. The framework incorporates time-limited access control, adaptive key management, and two strong encryption methods: RSA and Advanced Encryption Standard - One Time Password (AES-OTP). AES-OTP and RSA provide symmetric and asymmetric encryption levels to improve data confidentiality and integrity. With the introduction of an intelligent framework for key creation, distribution, and rotation through the adaptive key management component, the security of cryptographic operations is gradually increased. Additionally, time-limited access control helps to protect data privacy by imposing rigorous temporal constraints on data access and reducing security flaws. The effectiveness of the suggested framework is confirmed by thorough performance assessments, which show astonishing accuracy, precision, recall, and F1-score values of 99.12%, 98.78%, 98.11%, and 98.56%. This demonstrates its outstanding skills in protecting private information from unauthorized access and guaranteeing its secrecy in cloud storage settings.
A depth iterative illumination estimation network for low-light image enhancement based on retinex theory

Existing low-light image enhancement techniques face challenges in achieving high visual quality and computational efficiency, as well as in effectively removing noise and adjusting illumination in extremely dark scenes. To address these problems, in this paper, we propose an illumination enhancement network based on Retinex theory for fast and accurate brightening of images in low-illumination scenes. Two learning-based networks are carefully constructed: decomposition network and enhancement network. The decomposition network is responsible for decomposing the low-light input image into the initial reflectance and illumination map. The enhanced network includes two sub-modules: the illumination enhancement module and the reflection denoising module, which are used for efficient brightness enhancement and accurate reflectance. Specially, we have established a cascaded iterative lighting learning process and utilized weight sharing to conduct accurate illumination estimation. Additionally, unsupervised training losses are defined to improve the generalization ability of the model. The proposed illumination enhancement framework enables noise suppression and detail preservation of the final decomposition results. To establish the efficacy and superiority of the model, on the widely applicable LOL dataset, our approach achieves a significant 9.16% increase in PSNR compared to the classical Retinex-Net, and a remarkable enhancement of 19.26% compared to the latest SCI method.
Multi-modal interaction with transformers: bridging robots and human with natural language

Abstract The language-guided visual robotic grasping task focuses on enabling robots to grasp objects based on human language instructions. However, real-world human-robot collaboration tasks often involve situations with ambiguous language instructions and complex scenarios. These challenges arise in the understanding of linguistic queries, discrimination of key concepts in visual and language information, and generation of executable grasping configurations for the robot’s end-effector. To overcome these challenges, we propose a novel multi-modal transformer-based framework in this study, which assists robots in localizing spatial interactions of objects using text queries and visual sensing. This framework facilitates object grasping in accordance with human instructions. Our developed framework consists of two main components. First, a visual-linguistic transformer encoder is employed to model multi-modal interactions for objects referred to in the text. Second, the framework performs joint spatial localization and grasping. Extensive ablation studies have been conducted on multiple datasets to evaluate the advantages of each component in our model. Additionally, physical experiments have been performed with natural language-driven human-robot interactions on a physical robot to validate the practicality of our approach.
Hate Speech on Twitter: Vox in the Catalan Parliamentary Elections

The Spanish far-right Vox party ran for the first time as a candidate for the Catalan parliamentary elections in February 2021. This paper analyses the party's political discourse on Twitter during the Catalan parliamentary election campaign in order to assess whether the degree of dialectical confrontation of their messages can exceed the limits of democratic debate. The results reveal how this far-right party's inflammatory messages, which seek to insult its political opponents, ultimately foster an undemocratic environment that turns Vox's political rivals into its enemy, driving social polarisation and a discourse of hatred towards individuals defending other ideologies or parties. Ultimately, it is intended to add that political-ideological nuance for future research that surpasses traditional hate speech —such as those based on criteria such as race or sex— and the suitability of Twitter for the proliferation of the latter.
FedCSS: Joint Client-and-Sample Selection for Hard Sample-Aware Noise-Robust Federated Learning

Federated Learning (FL) enables a large number of data owners (a.k.a. FL clients) to jointly train a machine learning model without disclosing private local data. The importance of local data samples to the FL model vary widely. This is exacerbated by the presence of noisy data, which exhibit large losses similar to important (hard) samples. Currently, there lacks an FL approach that can effectively distinguish hard samples (which are beneficial) from noisy samples (which are harmful). To bridge this gap, we propose the Federated Client and Sample Selection (FedCSS) approach. It is a bilevel optimization approach for FL client-and-sample selection to achieve hard sample-aware noise-robust learning in a privacy preserving manner. It performs meta-learning based online approximation to iteratively update global FL models, select the most positively influential samples and deal with training data noise. Theoretical analysis shows that it is guaranteed to converge in an efficient manner. Experimental comparison against six state-of-the-art baselines on five real-world datasets in the presence of data noise and heterogeneity shows that it achieves up to 26.4% higher test accuracy, while saving communication and computation costs by at least 41.5% and 1.2%, respectively.
Punctuation Prediction for the Arabic language

Determining the context of speech in the Arabic language helps greatly in clarifying the meanings of words and thus accurately determining the content of the sentence. Punctuation marks implicitly define an important part of this context. In this paper, We discuss the characteristics of the Arabic language and the problems associated with missing punctuation. We achieve good accuracy in punctuation prediction compared to other languages. In this work, we focus on five main punctuation marks: Commas, interrogation marks, exclamation marks, and periods. The interrogation and exclamation marks outperform (achieve high accuracy 94%) compared to the rest of the punctuation marks prediction. And we provide a new corpus for Arabic punctuation that will be the first corpus in this field.
Recommender Systems for Social Networks: A Short Review

Since the 1990s, recommendation systems have been the subject of numerous studies. A recommender system is a software tool that assists users in the choice-making process by recommending items similar to their preferences and interests. However, in the age of the internet and social networking platforms, it is becoming more and more complex to provide the appropriate items for users due to the information overload problem. Hence, the introduction of a novel type of RS called "social recommender systems", an approach that employs social data to generate precise recommendations and surpass the classic recommender system's limitations. This article provides a brief overview of social recommender systems and their various methods.
Descriptive and Coherent Paragraph Generation for Image Paragraph Captioning Using Vision Transformer and Post-processing

The task of visual paragraph generation involves generating a descriptive and coherent paragraph based on an input image. The current state-of-the-art approaches use Regions of Interest (RoI) identification to generate paragraphs. The proposed approach eliminates the need for RoI identification. A transformer-based encoder-decoder model is used for paragraph generation. A post-processing step is introduced to enhance the semantic relevance of the generated paragraphs. This is achieved by incorporating the image-text similarity scores and related-classes similarity scores. The results of our studies demonstrate that the proposed model generates paragraphs with improved coherence and a higher Flesch reading ease score.
Speech-Gesture GAN: Gesture Generation for Robots and Embodied Agents

Embodied agents, in the form of virtual agents or social robots, are rapidly becoming more widespread. In human-human interactions, humans use nonverbal behaviours to convey their attitudes, feelings, and intentions. Therefore, this capability is also required for embodied agents in order to enhance the quality and effectiveness of their interactions with humans. In this paper, we propose a novel framework that can generate sequences of joint angles from the speech text and speech audio utterances. Based on a conditional Generative Adversarial Network (GAN), our proposed neural network model learns the relationships between the co-speech gestures and both semantic and acoustic features from the speech input. In order to train our neural network model, we employ a public dataset containing co-speech gestures with corresponding speech audio utterances, which were captured from a single male native English speaker. The results from both objective and subjective evaluations demonstrate the efficacy of our gesture-generation framework for Robots and Embodied Agents.
XNL-CNN: An Improved Version Of The NL-CNN Model, For Running With TPU Accelerators And Large Image Datasets

This paper introduces XNL-CNN, an extended version of the previously introduced, compact NL-CNN model. While the NL-CNN was designed with typical Tiny-ML constraints in order to make possible integration of various AI image recognition solutions into low power, low complexity computing platforms, the same stands for its extended version discussed herein. In addition, we consider additional layers to cope with larger image sizes, as found in most actual relevant datasets (e.g. the Cars196 or Flowers104). Moreover, since working with large image sizes requires intensive computational power, TPU accelerators readily available on Kaggle platform were used for training and evaluation of our model. In comparison to other, widely known resources-constrained architectures, such as EfficientNet or MobileNet our model offers the following advantages: i) a larger set of hyper-parameters allowing the user to do better designs (maximal accuracy for minimal resources), adapted for a wide variety of datasets; ii) has a low number of layer primitives, thus making easier their specific design for deployment on various TinyML or EdgeAI platforms, including FPGAS.
Effectiveness of Generative Artificial Intelligence for Scientific Content Analysis

Generative artificial intelligence (GenAI) in general, and large language models (LLMs) in particular, are highly fashionable.As they have the ability to generate coherent output based on prompts in natural language, they are promoted as tools to free knowledge workers from tedious tasks such as content writing, customer support and routine computer code generation.Unsurprisingly, their application is also attractive to professionals in the research domain, where mundane and laborious tasks, such as literature screening, are commonplace.We evaluate Vertex AI 'text-bison', a foundational LLM model, in a real-world academic scenario by replicating parts of a popular systematic review in the information management domain.By comparing the results of a zero-shot LLM-based approach with those of the original study, we gather evidence on the suitability of state-of-theart general-purpose LLMs for the analysis of scientific content.We show that the LLM-based approach delivers good scientific content analysis performance for a general classification problem (ACC = 0.9), acceptable performance for a domain-specific classification problem (ACC = 0.8) and borderline performance for a text comprehension problem (ACC ≈ 0.69).We conclude that some content analysis tasks with moderate accuracy requirements may be supported by current LLMs.As the technology will evolve rapidly in the foreseeable future, studies on large corpora, where some inaccuracies are tolerable, or workflows that prepare large data sets for human processing, may increasingly benefit from the capabilities of GenAI.
Robot Causal Discovery Aided by Human Interaction

Causality is relatively unexplored in robotics even if it is highly relevant, in several respects. In this paper, we study how a robot’s causal understanding can be improved by allowing the robot to ask humans causal questions. We propose a general algorithm for selecting direct causal effects to ask about, given a partial causal representation (using partially directed acyclic graphs, PDAGs) obtained from observational data. We propose three versions of the algorithm inspired by different causal discovery techniques, such as constraint-based, score-based, and interventions. We evaluate the versions in a simulation study and our results show that asking causal questions improves the causal representation over all simulated scenarios. Further, the results show that asking causal questions based on PDAGs discovered from data provides a significant improvement compared to asking questions at random, and the version inspired by score-based techniques performs particularly well over all simulated experiments.
Spatio-Temporal Avoidance of Predicted Occupancy in Human-Robot Collaboration

This paper addresses human-robot collaboration (HRC) challenges of integrating predictions of human activity to provide a proactive-n-reactive response capability for the robot. Prior works that consider current or predicted human poses as static obstacles are too nearsighted or too conservative in planning, potentially causing delayed robot paths. Alternatively, time-varying prediction of human poses would enable robot paths that avoid anticipated human poses, synchronized dynamically in time and space. Herein, a proactive path planning method, denoted STAP, is presented that uses spatiotemporal human occupancy maps to find robot trajectories that anticipate human movements, allowing robot passage without stopping. In addition, STAP anticipates delays from robot speed restrictions required by ISO/TS 15066 speed and separation monitoring (SSM). STAP also proposes a sampling-based planning algorithm based on RRT* to solve the spatio-temporal motion planning problem and find paths of minimum expected duration. Experimental results show STAP generates paths of shorter duration and greater average robot-human separation distance throughout tasks. Additionally, STAP more accurately estimates robot trajectory durations in HRC, which are useful in arriving at proactive-n-reactive robot sequencing.
Automatic Interaction and Activity Recognition from Videos of Human Manual Demonstrations with Application to Anomaly Detection

This paper presents a new method to describe spatio-temporal relations between objects and hands, to recognize both interactions and activities within video demonstrations of manual tasks. The approach exploits Scene Graphs to extract key interaction features from image sequences while simultaneously encoding motion patterns and context. Additionally, the method introduces event-based automatic video segmentation and clustering, which allow for the grouping of similar events and detect if a monitored activity is executed correctly. The effectiveness of the approach was demonstrated in two multi-subject experiments, showing the ability to recognize and cluster hand-object and object-object interactions without prior knowledge of the activity, as well as matching the same activity performed by different subjects.
Successful Swarms: Operator Situational Awareness with Modelling and Verification at Runtime

Robot swarms, through redundancy, offer faulttolerant distributed sensing and actuation, but can lack complex mission-level decision making.Pairing a human operator with the swarm can improve decision making but only if the operator maintains situational awareness-knowledge of the current state of the swarm-as well as being able to anticipate future states.We show how formal methods, in the form of probabilistic models, executed and verified at runtime alongside the system can aid situational awareness by providing valuable insight into both current and future situations.Two models, for determining task and mission success probabilities, are given, and we show that statistical model checking allows timely approximate predictions that take no more than 1s while staying within 2% of the exact solution.We highlight and implement approaches to display this information to an operator, and show how models can be used to try what-if scenarios before decisions are made.
CLIPGraphs: Multimodal Graph Networks to Infer Object-Room Affinities

This paper introduces a novel method for determining the best room to place an object in, for embodied scene rearrangement. While state-of-the-art approaches rely on large language models (LLMs) or reinforcement learned (RL) policies for this task, our approach, CLIPGraphs, efficiently combines commonsense domain knowledge, data-driven methods, and recent advances in multimodal learning. Specifically, it (a) encodes a knowledge graph of prior human preferences about the room location of different objects in home environments, (b) incorporates vision-language features to support multimodal queries based on images or text, and (c) uses a graph network to learn object-room affinities based on embeddings of the prior knowledge and the vision-language features. We demonstrate that our approach provides better estimates of the most appropriate location of objects from a benchmark set of object categories in comparison with state-of-the-art baselines. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Supplementary material and code: https://clipgraphs.github.io
Making an Android Robot Head Talk

We present two approaches to animate an android robot head according to audio speech input, both are adopted from recent machine learning based works in computer graphics animation. More concrete we implemented a viseme-based and a mesh-based approach on our robot. After a subjective comparison we conduct a speech-reading study to evaluate our preferred, the mesh-based, approach. The results show that on average the intelligibility is not increased by the visual cues provided through the robot head in comparison to noisy audio alone. This underlines the importance of carefully designing and controlling the facial co-speech movements of talking android heads.
Does It Affect You? Social and Learning Implications of Using Cognitive-Affective State Recognition for Proactive Human-Robot Tutoring

Robotic technology has proven to be advantageous for student learning and social development in educational settings. However, in order to enhance their effectiveness and provide a more human-like tutoring experience, robots must be capable of adapting to the user and exhibiting proactivity. By acting proactively, these intelligent robotic tutors can anticipate potential obstacles and take preventative measures to avoid negative outcomes. However, determining when and how to behave proactively remains an open question. This study investigates how a robotic tutor can utilize a student’s cognitive-affective states to trigger proactive tutoring dialogue and improve the learning experience. Specifically, we observed a concept learning task scenario where a robotic assistant proactively assisted the user when negative states, such as frustration and confusion, were detected. In an empirical study involving 40 undergraduate and doctoral students, we evaluated whether the initiation of proactive behavior after the detection of signs of confusion and frustration improves the student’s concentration and trust in the robot. We also examined which level of proactive dialogue is most effective for promoting concentration and trust. The results indicate that high levels of proactive behavior can harm trust, especially when triggered during negative cognitive-affective states. However, this behavior does contribute to keeping the student focused on the task when triggered during these states. Based on our findings, we discuss potential future steps for improving the proactive assistance of robotic tutoring systems.