Reviewerly/exHarmony huggingface dataset:
Developing IoT Artifacts in a MAS Platform

The Internet of Things (IoT) is a growing computational paradigm where all kinds of everyday objects are interconnected, forming a vast cyberphysical environment at the edge between the virtual and the real world. Since the emergence of the IoT, Multi-Agent Systems (MAS) technology has been successfully applied in this area, proving itself to be an appropriate paradigm for developing distributed, intelligent systems containing sets of IoT devices. However, this technology still lacks effective mechanisms to integrate the enormous diversity of existing IoT devices systematically. In this context, this paper introduces the concept of the IoT artifact as a new interface abstraction for the development of MAS based on IoT devices. The IoT artifact strictly conforms to the Agents and Artifacts (A&amp;A) meta-model, and it also adopts the programming model of the SPADE multi-agent platform, providing both a consistent theoretical framework and a practical model for real-world applications.
How Good Are We at Detecting a Phishing Attack? Investigating the Evolving Phishing Attack Email and Why It Continues to Successfully Deceive Society

Phishing attacks are on the increase. The fact that our ways of living, studying and working have drastically changed as a result of the COVID pandemic (i.e., almost everything being done online) has created many new cyber security concerns. In particular, with the move to remote working, the number of phishing emails threatening employees has increased. The 2020 Phishing Attack Landscape Report (Greathorn: 2020 Phishing attack landscape. https://info.greathorn.com/report-2020-phishing-attack-landscape/, 2020) highlights a sharp increase in the frequency of attempted phishing attacks. In this paper, we are interested in how the phishing email attack has evolved to this very threatening state. In detail, we explore the current phishing attack characteristics especially the growing challenges that have emerged as a result of the COVID-19 pandemic. The paper documents a study that presented test participants with five different categories of emails (including phishing and non phishing) . The findings from the study show that participants, generally, found it difficult to detect modern phishing email attacks. Saying that, participants were alert to the spelling mistakes of the older phishing email attacks, sensitive information being requested from them and any slight change to what they were normally used to from an email. Moreover, we have found that people were not confident, worried and often dissatisfied with the current technologies available to protect them against phishing emails. In terms of trust, these feelings alerted us to the increasing severity of the phishing attack situation and just how vulnerable society has become/ still is.
Detecting racism and xenophobia using deep learning models on Twitter data: CNN, LSTM and BERT

With the growth that social networks have experienced in recent years, it is entirely impossible to moderate content manually. Thanks to the different existing techniques in natural language processing, it is possible to generate predictive models that automatically classify texts into different categories. However, a weakness has been detected concerning the language used to train such models. This work aimed to develop a predictive model based on BERT, capable of detecting racist and xenophobic messages in tweets written in Spanish. A comparison was made with different Deep Learning models. A total of five predictive models were developed, two based on BERT and three using other deep learning techniques, CNN, LSTM and a model combining CNN + LSTM techniques. After exhaustively analyzing the results obtained by the different models, it was found that the one that got the best metrics was BETO, a BERT-based model trained only with texts written in Spanish. The results of our study show that the BETO model achieves a precision of 85.22% compared to the 82.00% precision of the mBERT model. The rest of the models obtained between 79.34% and 80.48% precision. On this basis, it has been possible to justify the vital importance of developing native transfer learning models for solving Natural Language Processing (NLP) problems in Spanish. Our main contribution is the achievement of promising results in the field of racism and hate speech in Spanish by applying different deep learning techniques.
Attack detection and mitigation scheme through novel authentication model enabled optimized neural network in smart healthcare

The Internet of Things (IoT) have become an important part of human in day-to-day life as it permits accesses and manages data flawlessly, the security of data in cloud storage is of great concern in healthcare applications. This paper proposes a secure authentication to protect the sensible data related to healthcare through IoT network. Initially, the Electrocardiography (ECG) signal from the patients are stored in the cloud in encrypted form, where the proposed modified Elliptic-curve Diffie–Hellman (ECDH) encryption is applied to ensure secure access to the stored data to be used for the analysis of arrhythmia. The obtained data for the arrhythmia diagnosis is subjected to classify the attack using the neural network (NN). The weights of the NN are tuned using the proposed hybrid tempest brain optimization algorithm, which integrates the characteristic features of collaborative search agents and the hybrid search agents. The proposed method obtained the values of 95%, 7150, and 111 of detection accuracy, number of genuine users, and information loss of the respectively, which shows the superiority of the proposed method in attack detection and mitigation.
Discriminating healthy from tumor tissue in breast lumpectomy specimens using deep learning-based hyperspectral imaging

Achieving an adequate resection margin during breast-conserving surgery remains challenging due to the lack of intraoperative feedback. Here, we evaluated the use of hyperspectral imaging to discriminate healthy tissue from tumor tissue in lumpectomy specimens. We first used a dataset obtained on tissue slices to develop and evaluate three convolutional neural networks. Second, we fine-tuned the networks with lumpectomy data to predict the tissue percentages of the lumpectomy resection surface. A MCC of 0.92 was achieved on the tissue slices and an RMSE of 9% on the lumpectomy resection surface. This shows the potential of hyperspectral imaging to classify the resection margins of lumpectomy specimens.
Robust and optimum color image watermarking method based on a combination of DWT and DCT

A hybrid and robust digital image watermarking is suggested by using advantages of both frequency domain and wavelet domain. In this method, the most suitable color component of the host image and the most efficient wavelet sub-band are chosen, concluding from the comprehensive experiments. In this paper, three parallel stages are used to embed and extract the watermark message. After transforming color component of the host image into the wavelet domain, some preprocessing with Discrete Cosines Transformers are applied, and then the watermark message is embedded into the best range by using a secret key. By detecting the rank of constructed detection matrices and voting among three output bits, the watermark bit is extracted. Simulation results demonstrate that the proposed method, besides the high robustness against Gaussian noise-based attacks especially AWGN, has performed well in imperceptibility and embedding capacity factors.
Stance-level Sarcasm Detection with BERT and Stance-centered Graph Attention Networks

Computational Linguistics (CL) associated with the Internet of Multimedia Things (IoMT)-enabled multimedia computing applications brings several research challenges, such as real-time speech understanding, deep fake video detection, emotion recognition, home automation, and so on. Due to the emergence of machine translation, CL solutions have increased tremendously for different natural language processing (NLP) applications. Nowadays, NLP-enabled IoMT is essential for its success. Sarcasm detection, a recently emerging artificial intelligence (AI) and NLP task, aims at discovering sarcastic, ironic, and metaphoric information implied in texts that are generated in the IoMT. It has drawn much attention from the AI and IoMT research community. The advance of sarcasm detection and NLP techniques will provide a cost-effective, intelligent way to work together with machine devices and high-level human-to-device interactions. However, existing sarcasm detection approaches neglect the hidden stance behind texts, thus insufficient to exploit the full potential of the task. Indeed, the stance, i.e., whether the author of a text is in favor of, against, or neutral toward the proposition or target talked in the text, largely determines the text’s actual sarcasm orientation. To fill the gap, in this research, we propose a new task: stance-level sarcasm detection (SLSD), where the goal is to uncover the author’s latent stance and based on it to identify the sarcasm polarity expressed in the text. We then propose an integral framework, which consists of Bidirectional Encoder Representations from Transformers (BERT) and a novel stance-centered graph attention networks (SCGAT). Specifically, BERT is used to capture the sentence representation, and SCGAT is designed to capture the stance information on specific target. Extensive experiments are conducted on a Chinese sarcasm sentiment dataset we created and the SemEval-2018 Task 3 English sarcasm dataset. The experimental results prove the effectiveness of the SCGAT framework over state-of-the-art baselines by a large margin.
The Development of Digital Competencies and Digital Literacy in the 21st Century: A Survey of Studies

The development of digital competencies and digital literacy in the 21st century is one of the most urgent in the theory and practice of modern education. The need for an understanding of the analysis of scientific knowledge on this issue is confirmed by the high demand for digital competencies of specialists in the labor market. The paper organizes and presents publication trends in the field of human digital abilities, with an emphasis on in-depth study of topics in higher education. Reviews and analytical work were carried out using the Web of Science, Google Scholar, RSCI (eLibrary). The analysis was based on the 120 most cited publications for the period from 2000 to 2020 and this article provides a final overview of 55 articles. The results are presented in visual-graphic form. In particular, the concepts related to digital abilities used in scientific everyday life are presented in the form of a pyramid of concepts: from the basic concept of ‘digital skills’ to the broadest meaning of the concept of ‘digital literacy.’ The word cloud reflects the most frequently used words in scientific works describing human abilities in the digital sphere. The article presents a graphical visualization of the factors influencing the formation and development of digital literacy and digital competencies of university students. The results obtained will be of interest to researchers of digital literacy and digital competencies, teachers, and university leaders in the implementation of educational programs.
A new evaluation method for English MOOC teaching quality based on AHP

In order to overcome the problems of traditional English MOOC teaching quality evaluation methods, such as accuracy and low evaluation efficiency, this paper proposes a new method of English MOOC teaching quality evaluation based on AHP. This method selects evaluation indexes through factor analysis, establishes evaluation index sets, and performs normalisation processing. This paper sets up a comment set, uses AHP to calculate the index weight, calculates the membership degree according to the weight calculation result, constructs the membership degree matrix, and finally conducts a comprehensive evaluation of the English MOOC teaching quality. The experimental results show that compared with traditional evaluation methods, the proposed method has higher accuracy of teaching quality evaluation, the highest evaluation accuracy is 97.8%, and the detection efficiency of the proposed method is higher, the shortest evaluation time is only 0.9 min. It shows that the proposed method has higher practical application performance.
Multigoal Visual Navigation With Collision Avoidance via Deep Reinforcement Learning

Learning to map the images acquired by a moving agent equipped with a camera sensor to motion commands for multigoal navigation is challenging. Most existing approaches are still struggling against collision avoidance, faster convergence, and generalization. In this article, a novel actor&#x2013;critic architecture is presented to learn the optimal navigation policy. We introduce single-step reward observation and collision penalty to reshape the reinforcement learning (RL) reward function. The collision perception can be obtained by the reshaped reward function and treated as measurement information from the visual observation to avoid obstacles. Besides, expert trajectories are used to generate subgoals. A subgoal reward shaping is then proposed to accelerate policy learning with the expert knowledge of subgoals. In order to generate human-aware navigation policies, an observation-action consistency (OAC) model is introduced to ensure that the agent reaches the subgoals in turn, and moves toward the target. The whole training process is performed on a self-supervised RL approach, accompanied by an expert supervision signal. This method balances the exploration and exploitation, helping the proposed model to generalize to unseen goals. The training experiments on AI2-THOR show better performance and faster convergence speed, compared with the existing approaches. For the generalization capacity to unseen goals, the proposed method achieves the state-of-the-art success rate, with at least a 30&#x0025; improvement of average episode collision.
Attribute Alignment: Controlling Text Generation from Pre-trained Language Models

Large language models benefit from training with a large amount of unlabeled text, which gives them increasingly fluent and diverse generation capabilities.However, using these models for text generation that takes into account target attributes, such as sentiment polarity or specific topics, remains a challenge.We propose a simple and flexible method for controlling text generation by aligning disentangled attribute representations.In contrast to recent efforts on training a discriminator to perturb the token level distribution for an attribute, we use the same data to learn an alignment function to guide the pre-trained, non-controlled language model to generate texts with the target attribute without changing the original language model parameters.We evaluate our method on sentiment-and topiccontrolled generation, and show large performance gains over previous methods while retaining fluency and diversity.
Dependence structure between bitcoin price and its influence factors

Bitcoin is a decentralised digital currency which attracts growing interest over recent years. Much research from different subjects emerged as bitcoin is a multidisciplinary product. Among all these studies, the interpretation of the drastic fluctuation of bitcoin price attracts a great attention. Many influence factors of bitcoin price were found. However, seldom research reveals the dependence structure between price and its influence factors. By selecting ten interpretable influence factors from the bitcoin network and using copula theory, we find that the bitcoin price has different correlation structures with its influence factors. These findings provide new insights into the behaviour of miners, users, and coins in the bitcoin system, thus leading to meaningful implications for policymakers, investors and risk managers dealing with bitcoin and other cryptocurrencies.
Do foreign investment and economic freedom matter for behavioral entrepreneurship? Comparing opportunity versus necessity entrepreneurs

This paper provides new evidence on how outward and inward foreign direct investment (FDI) and economic freedom influence opportunity and necessity entrepreneurship in emerging markets. Studying 38 emerging economies, the empirical results show that both types of FDI exert opposite spillover effects on opportunity and necessity entrepreneurship and that greater economic freedom increases inward FDI and reduces outward FDI, thereby enhancing entrepreneurial activities. Additional analysis shows that the influence of FDI on entrepreneurship development depends on the dimensions of economic freedom. In particular, better economic freedom allows FDI to increase opportunity-driven entrepreneurship and decreases necessity-driven entrepreneurship. Policy and practical implications are also discussed.
A Scoping Review of Ethics Across SIGCHI

Ethical deliberation has proved a consistent feature of Human-Computer Interaction (HCI) since its earliest years, spanning the respectful involvement of research participants to design choices impacting fairness, freedom and welfare. Despite growing discussions, applied knowledge and practical approaches for navigating complex moral dilemmas remain challenging to grasp. Motivated by the need for a structured overview, this paper contributes a scoping review of ethics as discussed across 129 full-length SIGCHI papers containing the search term 'ethic*' in their title, abstract or authors' keywords over the last ten years. Findings show increasing prioritisation of the topic, particularly within Artificial Intelligence. Value-Sensitive and Critical Design appear as the most frequently applied orientations, and participatory approaches are more prevalent than those without end-user input. Engaging with a spectrum from personal to societal concerns, the SIGCHI literature thus echos calls for critical perspectives on user-centred processes and the need to establish more sustainable responsibility structures.
Real-time adaptive data filtering with multiple sensors for indoor monitoring

This demonstration proposes a scheme that suppresses the data size of a 3D-image sensing network by adaptively filtering low-importance points, such as the points of floors and ceilings when the task is to track pedestrians. The adaptive filter can be dynamically changed to further reduce the amount of data if it is difficult for packets to reach the edge computer. We evaluate the proposed scheme through experiments and demonstrate that it performs better than benchmark schemes in terms of prompt arrival of the data.
VQAMix: Conditional Triplet Mixup for Medical Visual Question Answering

Medical visual question answering (VQA) aims to correctly answer a clinical question related to a given medical image. Nevertheless, owing to the expensive manual annotations of medical data, the lack of labeled data limits the development of medical VQA. In this paper, we propose a simple yet effective data augmentation method, VQAMix, to mitigate the data limitation problem. Specifically, VQAMix generates more labeled training samples by linearly combining a pair of VQA samples, which can be easily embedded into any visual-language model to boost performance. However, mixing two VQA samples would construct new connections between images and questions from different samples, which will cause the answers for those new fabricated image-question pairs to be missing or meaningless. To solve the missing answer problem, we first develop the Learning with Missing Labels (LML) strategy, which roughly excludes the missing answers. To alleviate the meaningless answer issue, we design the Learning with Conditional-mixed Labels (LCL) strategy, which further utilizes language-type prior to forcing the mixed pairs to have reasonable answers that belong to the same category. Experimental results on the VQA-RAD and PathVQA benchmarks show that our proposed method significantly improves the performance of the baseline by about 7% and 5% on the averaging result of two backbones, respectively. More importantly, VQAMix could improve confidence calibration and model interpretability, which is significant for medical VQA models in practical applications. All code and models are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/haifangong/VQAMix</uri> .
The Augmented Museum: A Multimodal, Game-Based, Augmented Reality Narrative for Cultural Heritage

As digitization has transformed media and Augmented Reality (AR) is evolving from a research area to a commodity, museums are creating interactive AR experiences to digitally enhance their collection and increase audience engagement. Head-worn AR experiences, though, face interaction challenges as they are often employed in busy spaces and are in need of intuitive multimodal interfaces for users on the move. This paper presents an innovative, work-in-progress, multimodal AR experience integrating non-obtrusive dialogue, music, and sound as well as gesture and gaze-based interaction, while a user is wearing a head-worn AR display. Users are motivated to explore and interact with digital cultural artefacts superimposed onto the real-world museum setting and physical artefacts, while moving around in a museum setting. We initially analyze interactive AR experiences to identify specific user requirements related to head-worn AR experiences. We deploy these requirements for the design of interactive, multimodal AR in a museum setting.
Pruned RNN-T for fast, memory-eﬀicient ASR training

The RNN-Transducer (RNN-T) framework for speech recognition has been growing in popularity, particularly for deployed real-time ASR systems, because it combines high accuracy with naturally streaming recognition. One of the drawbacks of RNN-T is that its loss function is relatively slow to compute, and can use a lot of memory. Excessive GPU memory usage can make it impractical to use RNN-T loss in cases where the vocabulary size is large: for example, for Chinese character-based ASR. We introduce a method for faster and more memory-efficient RNN-T loss computation. We first obtain pruning bounds for the RNN-T recursion using a simple joiner network that is linear in the encoder and decoder embeddings; we can evaluate this without using much memory. We then use those pruning bounds to evaluate the full, non-linear joiner network.
SAIL: Self-Augmented Graph Contrastive Learning

This paper studies learning node representations with graph neural networks (GNNs) for unsupervised scenario. Specifically, we derive a theoretical analysis and provide an empirical demonstration about the non-steady performance of GNNs over different graph datasets, when the supervision signals are not appropriately defined. The performance of GNNs depends on both the node feature smoothness and the locality of graph structure. To smooth the discrepancy of node proximity measured by graph topology and node feature, we proposed SAIL - a novel self-augmented graph contrastive learning framework, with two complementary self-distilling regularization modules, i.e., intra- and inter-graph knowledge distillation. We demonstrate the competitive performance of SAIL on a variety of graph applications. Even with a single GNN layer, SAIL has consistently competitive or even better performance on various benchmark datasets, comparing with state-of-the-art baselines.