Reviewerly/exHarmony huggingface dataset:
CBRec: a book recommendation system for children using the matrix factorisation and content-based filtering approaches

Promoting good reading habits among children is essential, given the enormous influence of reading on students' development as learners and members of the society. Unfortunately, very few (children) websites or online applications recommend books to children, even though they can play a significant role in encouraging children to read. Given that a few popular book websites suggest books to children based on the popularity of books or rankings on books, they are not customised/personalised for each individual user and likely recommend books that users do not want or like. We have integrated the matrix factorisation approach and the content-based approach, in addition to predicting the grade levels of books, to recommend books for children. Recent research works have demonstrated that a hybrid approach, which combines different filtering approaches, is more effective in making recommendations. Conducted empirical study has verified the effectiveness of our proposed children book recommendation system.
The individual and integrated impact of Blockchain and IoT on sustainable supply chains:a systematic review

Industry 4.0 technologies are transforming the conventional supply chain (SC) functioning and positively impacting sustainability. Blockchain and the Internet of Things (IoT) are two dimensions of industry 4.0 that play a crucial role in sustainable supply chain management (SSCM) and reverse logistics. This review paper focuses on investigating the impact of these technologies on SSCM separately and with their combination on forward and reverse SCs. A total of 197 papers were extracted from the Scopus database till 2020 and finally, 174 articles relevant to our paper were shortlisted and analysed after an in-depth review. The effects of these technologies on sustainability are studied separately by different researchers earlier, but their integration can lead to various insights and disclose several opportunities. The paper portrays insights on the development of Blockchain and IoT to know the current trends, industry-wide applications, challenges, and future research opportunities on SC sustainability.
Privacy-Preserving Aggregation in Federated Learning: A Survey

Over the recent years, with the increasing adoption of Federated Learning (FL) algorithms and growing concerns over personal data privacy, Privacy-Preserving Federated Learning (PPFL) has attracted tremendous attention from both academia and industry. Practical PPFL typically allows multiple participants to individually train their machine learning models, which are then aggregated to construct a global model in a privacy-preserving manner. As such, Privacy-Preserving Aggregation (PPAgg) as the key protocol in PPFL has received substantial research interest. This survey aims to fill the gap between a large number of studies on PPFL, where PPAgg is adopted to provide a privacy guarantee, and the lack of a comprehensive survey on the PPAgg protocols applied in FL systems. This survey reviews the PPAgg protocols proposed to address privacy and security issues in FL systems. The focus is placed on the construction of PPAgg protocols with an extensive analysis of the advantages and disadvantages of these selected PPAgg protocols and solutions. Additionally, we discuss the open-source FL frameworks that support PPAgg. Finally, we highlight significant challenges and future research directions for applying PPAgg to FL systems and the combination of PPAgg with other technologies for further security improvement.
TV and video game streaming with a quantum receiver: A study on a Rydberg atom-based receiver's bandwidth and reception clarity

We demonstrate the ability to receive live color analog television and video game signals with the use of the Rydberg atom receiver. The typical signal expected for traditional 480i National Television Standards Committee format video signals requires a bandwidth of over 3 MHz. We determine the beam sizes, powers, and detection method required for the Rydberg atoms to receive this type of signal. The beam size affects the average time the atoms remain in the interaction volume, which is inversely proportional to the bandwidth of the receiver. We find that small beam diameters (less than 100 μm) lead to much faster responses and allow for color reception. We demonstrate the effect of the beam size on bandwidth by receiving a live 480i video stream with the Rydberg atom receiver. The best video reception was achieved with a beam width of 85 μm full-width at half-max.
Impact of Artificial Intelligence-Based Chatbots on Customer Engagement and Business Growth

For a variety of reasons, artificial intelligent devices are becoming increasingly vital for enterprises. One of the major characteristics that makes AI enhanced services ideal for use in organisations is their ability to complete jobs faster and more accurately than humans. Almost all firms are utilising AI-based Chatbots on social media and messaging applications such as Whats App, Facebook, and others to engage with their enormous consumer base and provide real-time help. But AI is capable of much more. For many individuals, having a discussion with a bot that sounds like a human is a new disruption, and it serves as a tool to engage and entice customers to the point where they leave the website. But AI do more than just that. Having a conversation with a bot which feels like a human talking is a new disruption for a lot of people and it works as a tool to engage these customers and attract them in such a way that they leave the website after making the purchase. And as far as disruption is concerned from guiding the customer how to order pizza to describing through complex sales processes, AI Chatbots have been able to help both B2C and B2B interactions. This chapter provides an insight at how the AI Chatbots influences user interactions, how brands are using Chatbots for marketing and customer service, and why customers are attracted to interact with augmented agents such as Chatbots.
A Systematic Survey on Deep Generative Models for Graph Generation

Graphs are important data representations for describing objects and their relationships, which appear in a wide diversity of real-world scenarios. As one of a critical problem in this area, graph generation considers learning the distributions of given graphs and generating more novel graphs. Owing to their wide range of applications, generative models for graphs, which have a rich history, however, are traditionally hand-crafted and only capable of modeling a few statistical properties of graphs. Recent advances in deep generative models for graph generation is an important step towards improving the fidelity of generated graphs and paves the way for new kinds of applications. This article provides an extensive overview of the literature in the field of deep generative models for graph generation. Firstly, the formal definition of deep generative models for the graph generation and the preliminary knowledge are provided. Secondly, taxonomies of deep generative models for both unconditional and conditional graph generation are proposed respectively; the existing works of each are compared and analyzed. After that, an overview of the evaluation metrics in this specific domain is provided. Finally, the applications that deep graph generation enables are summarized and five promising future research directions are highlighted.
A descriptive human visual cognitive strategy using graph neural network for facial expression recognition

Abstract In the period of rapid development on the new information technologies, computer vision has become the most common application of artificial intelligence, which is represented by deep learning in the current society. As the most direct and effective application of computer vision, facial expression recognition (FER) has become a hot topic and used in many studies and domains. However, the existing FER methods focus on deep learning to generate increasingly complex attention structures, so they are unable to consider the connotative relationship between different parts of facial expressions. Moreover, the human expression recognition method based on complex deep learning network has serious interpretability issues. Therefore, in this paper, a novel Graph Neural Network (GNN) model is proposed to consider the systematic process of FER in human visual perception. Firstly, a region division mechanism is proposed, which divides the face region into six parts to unify the selection of key facial features. On this basis, in order to better consider the connotative relationship between different parts of facial expression, a human visual cognition strategy is proposed, which uses the divided six regions to learn facial expression features, and evenly selects the key features with high reliability as graph nodes. In combination with the human regional cooperative recognition process, the connotative relationship (such as relative position and similar structure) between graph nodes is extracted, so as to construct the GNN model. Finally, the effect of FER is obtained by the modeled GNN model. The experimental results compared with other related algorithms show that the model not only has stronger characterization and generalization ability, but also has better robustness compared with state-of-the-art methods.
Effective delineation of rare metal-bearing granites from remote sensing data using machine learning methods: A case study from the Umm Naggat Area, Central Eastern Desert, Egypt

Albitized granite (ABG) is considered as one of the most significant hosts of rare metals (RMs). Consequently, adequate recognition of ABG through proper lithological discrimination highly increases the targeting of rare metal resources. In order to delineate outcrops of ABG from satellite data, our study integrates eight image enhancement techniques, including optimum index factor, false color composites, band rationing, relative band depth, independent component analysis, principal component analysis, decorrelation stretch, minimum noise fraction transform, and spectral indices ratios, for the interpretation of ASTER and Sentinel-2 (S2) datasets. This integrated approach allows the effective discrimination of AGB outcrops in the Umm Naggat area, Central Eastern Desert, Egypt. The interpretation maps derived from these integrated image processing techniques were systematically verified in the field and formed the base for the feature selection process (i.e., training and testing data delineation) of different lithologies supported by the support vector machine algorithm (SVM). In order to produce a high‐quality lithological interpretation map, SVM was applied to Sentinel-2, ASTER, and combined ASTER-S2 datasets. The fused ASTER-S2 classification properly delineates ABG, as verified by our field investigations and confirmed by previous geological maps. Furthermore, comprehensive structural analysis (lineaments extraction and their density map) and hydrothermal alteration detection were performed to check the spatial association between the distribution of ABG, higher density zones, and highly altered areas, that in turn, could shed light on new potentially mineralized zones and proposed exploration targets. Our study reveals new ABG occurrences mainly situated in the southern and southwestern parts of the study area, and it confirms the location of known mineralized zones in the northern part of the Umm Naggat region. The distribution of ABG and its spatial correlation with alteration and high structural density zones suggest that rare‐metal mineralization is mostly structurally controlled (NW, NNW, NNE, and N-S), demonstrating the higher possibility of metasomatic enrichment of rare-metals within the study area. Our study provides an updated geological map of the study area based on the SVM‐supported interpretation of ASTER-S2 data. Importantly, the results reveal a high exploration potential for rare‐metal mineralization at Umm Naggat and defining new anomalies for follow‐up work by geochemical soil surveys.
On the Adaptive Security of the Threshold BLS Signature Scheme

Threshold signatures are a crucial tool for many distributed protocols. As shown by Cachin, Kursawe, and Shoup (PODC '00), schemes with unique signatures are of particular importance, as they allow to implement distributed coin flipping very efficiently and without any timing assumptions. This makes them an ideal building block for (inherently randomized) asynchronous consensus protocols. The threshold-BLS signature of Boldyreva (PKC '03) is both unique and very compact, but unfortunately lacks a security proof against adaptive adversaries. Thus, current consensus protocols either rely on less efficient alternatives or are not adaptively secure. In this work, we revisit the security of the threshold BLS signature by showing the following results, assuming t adaptive corruptions: - We give a modular security proof that follows a two-step approach: 1) We introduce a new security notion for distributed key generation protocols (DKG). We show that it is satisfied by several protocols that previously only had a static security proof. 2) Assuming any DKG protocol with this property, we then prove unforgeability of the threshold BLS scheme. Our reductions are tight and can be used to substantiate real-world parameter choices. - To justify our use of strong assumptions such as the algebraic group model (AGM) and the hardness of one-more-discrete logarithm (OMDL), we prove an impossibility result: Even in the AGM, a strong interactive assumption is required in order to prove the scheme secure.
Assessing Cyber Risk in Cyber-Physical Systems Using the ATT&amp;CK Framework

Autonomous transport is receiving increasing attention, with research and development activities already providing prototype implementations. In this article we focus on Autonomous Passenger Ships (APS) , which are being considered as a solution for passenger transport across urban waterways. The ambition of the authors has been to examine the safety and security implications of such a Cyber Physical System (CPS) , particularly focusing on threats that endanger the passengers and the operational environment of the APS. Accordingly, the article presents a new risk assessment approach based on a Failure Modes Effects and Criticality Analysis (FMECA) that is enriched with selected semantics and components of the MITRE ATT&amp;CK framework, in order to utilize the encoded common knowledge and facilitate the expression of attacks. Then, the proposed approach is demonstrated through conducting a risk assessment for a communication architecture tailored to the requirements of APSs that were proposed in earlier work. Moreover, we propose a group of graph theory-based metrics for estimating the impact of the identified risks. The use of this method has resulted in the identification of risks and their corresponding countermeasures, in addition to identifying risks with limited existing mitigation mechanisms. The benefits of the proposed approach are the comprehensive, atomic, and descriptive nature of the identified threats, which reduce the need for expert judgment, and the granular impact estimation metrics that reduce the impact of bias. All these features are provided in a semi-automated approach to reduce the required effort and collectively are argued to enrich the design-level risk assessment processes with an updatable industry threat model standard, namely ATT&amp;CK.
Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion?

Automatic speech recognition (ASR) is a key technology in many services and applications. This typically requires user devices to send their speech data to the cloud for ASR decoding. As the speech signal carries a lot of information about the speaker, this raises serious privacy concerns. As a solution, an encoder may reside on each user device which performs local computations to anonymize the representation. In this paper, we focus on the protection of speaker identity and study the extent to which users can be recognized based on the encoded representation of their speech as obtained by a deep encoder-decoder architecture trained for ASR. Through speaker identification and verification experiments on the Librispeech corpus with open and closed sets of speakers, we show that the representations obtained from a standard architecture still carry a lot of information about speaker identity. We then propose to use adversarial training to learn representations that perform well in ASR while hiding speaker identity. Our results demonstrate that adversarial training dramatically reduces the closed-set classification accuracy, but this does not translate into increased open-set verification error hence into increased protection of the speaker identity in practice. We suggest several possible reasons behind this negative result.
ProposalCLIP: Unsupervised Open-Category Object Proposal Generation via Exploiting CLIP Cues

Object proposal generation is an important and fundamental task in computer vision. In this paper, we propose ProposalCLIP, a method towards unsupervised open-category object proposal generation. Unlike previous works which require a large number of bounding box annotations and/or can only generate proposals for limited object categories, our ProposalCLIP is able to predict proposals for a large variety of object categories without annotations, by exploiting CLIP (contrastive language-image pre-training) cues. Firstly, we analyze CLIP for unsupervised open-category proposal generation and design an objectness score based on our empirical analysis on proposal selection. Secondly, a graph-based merging module is proposed to solve the limitations of CLIP cues and merge fragmented proposals. Finally, we present a proposal regression module that extracts pseudo labels based on CLIP cues and trains a lightweight network to further refine proposals. Extensive experiments on PASCAL VOC, COCO and Visual Genome datasets show that our ProposalCLIP can better generate proposals than previous state-of-the-art methods. Our ProposalCLIP also shows benefits for downstream tasks, such as unsupervised object detection.
Lifelong Graph Learning

Graph neural networks (GNN) are powerful models for many graph-structured tasks. Existing models often assume that the complete structure of the graph is available during training. In practice, however, graph-structured data is usually formed in a streaming fashion so that learning a graph continuously is often necessary. In this paper, we bridge GNN and lifelong learning by converting a continual graph learning problem to a regular graph learning problem so GNN can inherit the lifelong learning techniques developed for convolutional neural networks (CNN). We propose a new topology, the feature graph, which takes features as new nodes and turns nodes into independent graphs. This successfully converts the original problem of node classification to graph classification. In the experiments, we demonstrate the efficiency and effectiveness of feature graph networks (FGN) by continuously learning a sequence of classical graph datasets. We also show that FGN achieves superior performance in two applications, i.e., lifelong human action recognition with wearable devices and feature matching. To the best of our knowledge, FGN is the first method to bridge graph learning and lifelong learning via a novel graph topology. Source code is available at https://github.com/wang-chen/LGL.
Semi-supervised 3D Object Detection with Proficient Teachers

Dominated point cloud-based 3D object detectors in autonomous driving scenarios rely heavily on the huge amount of accurately labeled samples, however, 3D annotation in the point cloud is extremely tedious, expensive and time-consuming. To reduce the dependence on large supervision, semi-supervised learning (SSL) based approaches have been proposed. The Pseudo-Labeling methodology is commonly used for SSL frameworks, however, the low-quality predictions from the teacher model have seriously limited its performance. In this work, we propose a new Pseudo-Labeling framework for semi-supervised 3D object detection, by enhancing the teacher model to a proficient one with several necessary designs. First, to improve the recall of pseudo labels, a Spatial-temporal Ensemble (STE) module is proposed to generate sufficient seed boxes. Second, to improve the precision of recalled boxes, a Clustering-based Box Voting (CBV) module is designed to get aggregated votes from the clustered seed boxes. This also eliminates the necessity of sophisticated thresholds to select pseudo labels. Furthermore, to reduce the negative influence of wrongly pseudo-labeled samples during the training, a soft supervision signal is proposed by considering Box-wise Contrastive Learning (BCL). The effectiveness of our model is verified on both ONCE and Waymo datasets. For example, on ONCE, our approach significantly improves the baseline by 9.51 mAP. Moreover, with half annotations, our model outperforms the oracle model with full annotations on Waymo.
A network surveillance approach using machine learning based control charts

Network surveillance, i.e., the detection of anomalous behaviour in communications in a network, has become an important issue in recent years. In this field, techniques of statistical process monitoring, especially control charts, have been frequently applied to monitor the probability of existing connections between the members (nodes) of such a network. As another approach, monitoring the number of communications between the nodes is also an important way to identify network anomalies. Previous works have focused on conventional statistical control charts, but there are no machine learning approaches, although the performance of machine learning based control charts has been shown to be excellent in several other applications. This paper aims to develop machine learning based control charts for network surveillance problems in the case of monitoring the number of communications between the nodes. The results of extensive Monte Carlo simulations reveal the superiority of the proposed method over conventional competitors.
Marine Video Kit: A New Marine Video Dataset for Content-Based Analysis and Retrieval

Effective analysis of unusual domain specific video collections represents an important practical problem, where state-of-the-art general purpose models still face limitations. Hence, it is desirable to design benchmark datasets that challenge novel powerful models for specific domains with additional constraints. It is important to remember that domain specific data may be noisier (e.g., endoscopic or underwater videos) and often require more experienced users for effective search. In this paper, we focus on single-shot videos taken from moving cameras in underwater environments, which constitute a nontrivial challenge for research purposes. The first shard of a new Marine Video Kit dataset is presented to serve for video retrieval and other computer vision challenges. Our dataset is used in a special session during Video Browser Showdown 2023. In addition to basic meta-data statistics, we present several insights based on low-level features as well as semantic annotations of selected keyframes. The analysis also contains experiments showing limitations of respected general purpose models for retrieval. Our dataset and code are publicly available at https://hkust-vgd.github.io/marinevideokit .