Reviewerly/exHarmony huggingface dataset:
Technological support to foster students’ artificial intelligence ethics: An augmented reality-based contextualized dilemma discussion approach

Research evidence has emphasized the potential of questioning-based dilemmas and has contextualized role-play simulation to foster students' artificial intelligence (AI) ethics. Therefore, this study combined the viewpoints to design a contextualized dilemma discussion approach in the context of developing primary school students' AI ethics. However, without case-specific and suitable guidelines, students might have difficulties participating in the learning tasks to play different roles in the AI ethical dilemma discussion. Accordingly, for this study, we developed an augmented reality (AR) learning system-based contextualized dilemma discussion approach to foster students' active engagement in learning AI ethics with dilemma discussion using visualized AR guidance and feedback. A quasi-experiment and lag sequential analysis were executed by recruiting 79 primary school students to examine the effects of the proposed approach. The experimental results showed that the proposed approach was better able to improve students' learning achievements of AI ethical awareness, ethical reasoning, and higher order thinking tendency than the mobile learning system-guided contextualized dilemma discussion approach. Lag sequential analysis findings identified that the proposed approach could promote students' AI ethical learning behavior patterns by some crucial guidelines, including (1) embedding the non-intrusive AR guidance and feedback in the contextualized dilemma discussion, (2) immersive role-playing scenarios, and (3) case-based visual discussion. Administrators could provide a supportive environment to promote the combination of AR guidance and the contextualized dilemma discussion approach to improve the effectiveness of learning AI ethics.
Trustworthy AI That Engages Humans as Partners in Teaching and Learning

We review some state-of-the-art approaches in the area of human-in-the-loop artificial intelligence (AI) applications, examine some promising human–AI collaboration use cases in adaptive instructional system settings, and also discuss IEEE artificial intelligence and learning technology standards efforts to advance this endeavor.
A Framework for Trustworthy AI in Credit Risk Management: Perspectives and Practices

We present a reference architecture for trustworthy artificial intelligence based on three key tenets (usable, reliable, and transparent) and analyze its applicability in managing the challenges involved in the credit risk model from a practitioner’s perspective. We propose a holistic approach covering relevant concerns in practical implementations.
On Neural Networks Redundancy and Diversity for Their Use in Safety-Critical Systems

An increasing number of critical functionalities integrated in embedded critical systems rely on deep learning (DL) technology. This article summarizes certain key aspects of DL’s intrinsic stochastic and training-data-dependent nature that are at odds with current domain-specific functional safety standards. We exemplify how redundancy and diversity of neural networks can help to reconcile DL technology and functional safety requirements.
One Ring to Rule Them All: An Open Source Smartring Platform for Finger Motion Analytics and Healthcare Applications

This paper presents OmniRing, an open-source smartring platform with IMU and PPG sensors for activity tracking and health analytics applications. Smartring platforms are on the rise because of comfortable wearing, with the market size expected to reach $92 million soon. Nevertheless, most existing platforms are either commercial and proprietary without details of software/hardware or use suboptimal PCB design resulting in bulky form factors, inconvenient for wearing in daily life. Towards bridging the gap, OmniRing presents an extensible design of a smartring with a miniature form factor, longer battery life, wireless communication, and water resistance so that users can wear it all the time. Towards this end, OmniRing exploits opportunities in SoC, and carefully integrates the sensing units with a microcontroller and BLE modules. The electronic components are integrated on both sides of a flexible PCB that is bent in the shape of a ring and enclosed in a flexible and waterproof case for smooth skin contact. The overall cost is under $25, with weight of 2.5g, and up to a week of battery life. Extensive usability surveys validate the comfort levels. To validate the sensing capabilities, we enable an application in 3D finger motion tracking. By extracting synthetic training data from public videos coupled with data augmentation to minimize the overhead of training data generation for a new platform, OmniRing designs a transformer-based model that exploits correlations across fingers and time to track 3D finger motion with an accuracy of 6.57mm. We also validate the use of PPG data from OmniRing for heart rate monitoring. We believe the platform can enable exciting applications in fitness tracking, metaverse, sports, and healthcare.
Efficient Secure Privacy Preserving Multi Keywords Rank Search over Encrypted Data in Cloud Computing

Verifiable multi keywords rank searchable encryption is an important technique to retrieve encrypted data in a cloud server and verify the validity of search results. The cloud server is malicious it may return incorrect search results or system fault. Thus, how to design verifiable secure multi keywords ranked search of outsourced encrypted data is an unsolved problem. Therefore, in this paper, we leverage the (T-SNE) techniques to reduce dimensionality and feathers transformation. Then adopt similar keywords clustering groups grouping strategy to construct an enhanced inverted index. Which links to coordinate matching techniques to improve accurate multi-keyword rank searches for practical user experience. Then Paillier Homorphic Encryption (P.H.E) and HMAC technique is adopted for search index encryption and keyword search verification to explore verifiable multi keywords rank search over encrypted data in the cloud server. The time cost of ranking score computation and search efficiency can be improved in two approaches, keywords grouping idea and no dummy keywords are added in query and documents vector. Thus, our scheme realizes verifiable multi keywords search exact ciphertext retrieval. We evaluated the performance of our proposed scheme using real-world datasets that ensure adequate security and privacy.
REACT: Streaming Video Analytics On The Edge With Asynchronous Cloud Support

Emerging Internet of Things (IoT) and mobile computing applications are expected to support latency-sensitive deep neural network (DNN) workloads. To realize this vision, the Internet is evolving towards an edge-computing architecture, where computing infrastructure is located closer to the end device to help achieve low latency. However, edge computing may have limited resources compared to cloud environments and thus, cannot run large DNN models that often have high accuracy. In this work, we develop REACT, a framework that leverages cloud resources to execute large DNN models with higher accuracy to improve the accuracy of models running on edge devices. To do so, we propose a novel edge-cloud fusion algorithm that fuses edge and cloud predictions, achieving low latency and high accuracy. We extensively evaluate our approach and show that our approach can significantly improve the accuracy compared to baseline approaches. We focus specifically on object detection in videos (applicable in many video analytics scenarios) and show that the fused edge-cloud predictions can outperform the accuracy of edge-only and cloud-only scenarios by as much as 50%. REACT shows that for Edge AI, the choice between offloading and on-device inference is not binary — redundant execution at cloud and edge locations complement each other when carefully employed.
Unsupervised Event Chain Mining from Multiple Documents

Massive and fast-evolving news articles keep emerging on the web. To effectively summarize and provide concise insights into real-world events, we propose a new event knowledge extraction task Event Chain Mining in this paper. Given multiple documents about a super event, it aims to mine a series of salient events in temporal order. For example, the event chain of super event Mexico Earthquake in 2017 is {earthquake hit Mexico, destroy houses, kill people, block roads}. This task can help readers capture the gist of texts quickly, thereby improving reading efficiency and deepening text comprehension. To address this task, we regard an event as a cluster of different mentions of similar meanings. In this way, we can identify the different expressions of events, enrich their semantic knowledge and replenish relation information among them. Taking events as the basic unit, we present a novel unsupervised framework, EMiner. Specifically, we extract event mentions from texts and merge them with similar meanings into a cluster as a single event. By jointly incorporating both content and commonsense, essential events are then selected and arranged chronologically to form an event chain. Meanwhile, we annotate a multi-document benchmark to build a comprehensive testbed for the proposed task. Extensive experiments are conducted to verify the effectiveness of EMiner in terms of both automatic and human evaluations.
A Blockchain-Based Privacy-Preserving Model for Consent and Transparency in Human-Centered Internet of Things

The inclusion of human-related aspects in the Internet of Things paradigm leads to the development of models and solutions that address several challenges of our society. The adoption of these novel approaches is expanding rapidly on the road to what is now termed Society 5.0. However, leaving aside all the potential benefits that come from the interaction with these novel systems, an increasing number of people are concerned with the amount of data these systems can collect and share with data requesters. Several legal frameworks call for the adoption of practices regarding data protection and pushing for data control by the data owners. Unfortunately, most human-centric IoT-based systems lack mechanisms for managing resources and data in the user domain. Moreover, these tasks are typically delegated to a central entity; this necessarily implies a relationship of trust and can lead to problems related to transparency. To cope with these issues in this paper we present a privacy-preserving model that leverages the intrinsic features of the blockchain technology for consent management and transparency in Human-Centered Internet of Things environments. To show the feasibility of our approach, the proposed model is implemented, deployed in a test environment, and assessed using realistic scenarios.
MSQ-BioBERT: Ambiguity Resolution to Enhance BioBERT Medical Question-Answering

Question answering (QA) is a task in the field of natural language processing (NLP) and information retrieval, which has pivotal applications in areas such as online reading comprehension and web search engines. Currently, Bidirectional Encoder Representations from Transformers (BERT) and its biomedical variation (BioBERT) achieve impressive results on the reading comprehension QA datasets and medical-related QA datasets, and so they are widely used for a variety of passage-based QA tasks. However, their performances rapidly deteriorate when encountering passage and context ambiguities. This issue is prevalent and unavoidable in many fields, notably the web-based medical field. In this paper, we introduced a novel approach called the Multiple Synonymous Questions BioBERT (MSQ-BioBERT), which integrates question augmentation, rather than the typical single question used by traditional BioBERT, to elevate BioBERT’s performance on medical QA tasks. In addition, we constructed an ambiguous medical dataset based on the information from Wikipedia web. Experiments with both this web-based constructed medical dataset and open biomedical datasets demonstrate the significant performance gains of the MSQ-BioBERT approach, showcasing a new method for addressing ambiguity in medical QA tasks.
Semi-decentralized Federated Ego Graph Learning for Recommendation

Collaborative filtering (CF) based recommender systems are typically trained based on personal interaction data (e.g., clicks and purchases) that could be naturally represented as ego graphs. However, most existing recommendation methods collect these ego graphs from all users to compose a global graph to obtain high-order collaborative information between users and items, and these centralized CF recommendation methods inevitably lead to a high risk of user privacy leakage. Although recently proposed federated recommendation systems can mitigate the privacy problem, they either restrict the on-device local training to an isolated ego graph or rely on an additional third-party server to access other ego graphs resulting in a cumbersome pipeline, which is hard to work in practice. In addition, existing federated recommendation systems require resource-limited devices to maintain the entire embedding tables resulting in high communication costs.
BiSR: Bidirectionally Optimized Super-Resolution for Mobile Video Streaming

The user experience of mobile web video streaming is often impacted by insufficient and dynamic network bandwidth. In this paper, we design Bidirectionally Optimized Super-Resolution (BiSR) to improve the quality of experience (QoE) for mobile web users under limited bandwidth. BiSR exploits a deep neural network (DNN)-based model to super-resolve key frames efficiently without changing the inter-frame spatial-temporal information. We then propose a downscaling DNN and a mobile-specific optimized lightweight super-resolution DNN to enhance the performance. Finally, a novel reinforcement learning-based adaptive bitrate (ABR) algorithm is proposed to verify the performance of BiSR on real network traces. Our evaluation, using a full system implementation, shows that BiSR saves 26% of bitrate compared to the traditional H.264 codec and improves the SSIM of video by 3.7% compared to the prior state-of-the-art. Overall, BiSR enhances the user-perceived quality of experience by up to 30.6%.
TIGER: Temporal Interaction Graph Embedding with Restarts

Temporal interaction graphs (TIGs), consisting of sequences of timestamped interaction events, are prevalent in fields like e-commerce and social networks. To better learn dynamic node embeddings that vary over time, researchers have proposed a series of temporal graph neural networks for TIGs. However, due to the entangled temporal and structural dependencies, existing methods have to process the sequence of events chronologically and consecutively to ensure node representations are up-to-date. This prevents existing models from parallelization and reduces their flexibility in industrial applications. To tackle the above challenge, in this paper, we propose TIGER, a TIG embedding model that can restart at any timestamp. We introduce a restarter module that generates surrogate representations acting as the warm initialization of node representations. By restarting from multiple timestamps simultaneously, we divide the sequence into multiple chunks and naturally enable the parallelization of the model. Moreover, in contrast to previous models that utilize a single memory unit, we introduce a dual memory module to better exploit neighborhood information and alleviate the staleness problem. Extensive experiments on four public datasets and one industrial dataset are conducted, and the results verify both the effectiveness and the efficiency of our work.
Practical Cryptographic Forensic Tools for Lightweight Internet of Things and Cold Storage Systems

Internet of Things (IoT) and Storage-as-a-Service (STaaS) continuum permit cost-effective maintenance of security-sensitive information collected by IoT devices over cloud systems. It is necessary to guarantee the security of sensitive data in IoT-STaaS applications. Especially, log entries trace critical events in computer systems and play a vital role in the trustworthiness of IoT-STaaS. An ideal log protection tool must be scalable and lightweight for vast quantities of resource-limited IoT devices while permitting efficient and public verification at STaaS. However, the existing cryptographic logging schemes either incur significant computation/signature overhead to the logger or extreme storage and verification costs to the cloud. There is a critical need for a cryptographic forensic log tool that respects the efficiency requirements of the IoT-STaaS continuum.
Popular, but hardly used: Has Google Analytics been to the detriment of Web Analytics?

Since 2005, Google has been offering a free version of Google Analytics, allowing website owners to access detailed user behavior data. However, while more and more features and tools have been added to the Google measurement suite since then, it is unclear if the free availability of these tools has really helped users to derive actionable insights for their websites. Earlier studies based on a small number of interviews have suggested that users tend to play with the tools as they lack data literacy, but a broader analysis has been missing by now. Our contribution is a large-scale study of Google Analytics implementations to examine what advanced features are used, allowing conclusions to be drawn about the webmasters' analysis capabilities. In addition, we detail how difficult it has become to conduct such a study due to the arrangements that website owners have to put in place to comply with the GDPR requirements, but also due to the possibility of obfuscation with the latest development of web analytics software.
A Self-Correcting Sequential Recommender

Sequential recommendations aim to capture users’ preferences from their historical interactions so as to predict the next item that they will interact with. Sequential recommendation methods usually assume that all items in a user’s historical interactions reflect her/his preferences and transition patterns between items. However, real-world interaction data is imperfect in that (i) users might erroneously click on items, i.e., so-called misclicks on irrelevant items, and (ii) users might miss items, i.e., unexposed relevant items due to inaccurate recommendations.
CitationSum: Citation-aware Graph Contrastive Learning for Scientific Paper Summarization

Citation graphs can be helpful in generating high-quality summaries of scientific papers, where references of a scientific paper and their correlations can provide additional knowledge for contextualising its background and main contributions. Despite the promising contributions of citation graphs, it is still challenging to incorporate them into summarization tasks. This is due to the difficulty of accurately identifying and leveraging relevant content in references for a source paper, as well as capturing their correlations of different intensities. Existing methods either ignore references or utilize only abstracts indiscriminately from them, failing to tackle the challenge mentioned above. To fill that gap, we propose a novel citation-aware scientific paper summarization framework based on the citation graph, able to accurately locate and incorporate the salient contents from references, as well as capture varying relevance between source papers and their references. Specifically, we first build a domain-specific dataset PubMedCite with about 192K biomedical scientific papers and a large citation graph preserving 917K citation relationships between them. It is characterized by preserving the salient contents extracted from full texts of references, and the weighted correlation between the salient contents of references and the source paper. Based on it, we design a self-supervised citation-aware summarization framework (CitationSum) with graph contrastive learning, which boosts the summarization generation by efficiently fusing the salient information in references with source paper contents under the guidance of their correlations. Experimental results show that our model outperforms the state-of-the-art methods, due to efficiently leveraging the information of references and citation correlations.
SINCERE: Sequential Interaction Networks representation learning on Co-Evolving RiEmannian manifolds

Sequential interaction networks (SIN) have been commonly adopted in many applications such as recommendation systems, search engines and social networks to describe the mutual influence between users and items/products. Efforts on representing SIN are mainly focused on capturing the dynamics of networks in Euclidean space, and recently plenty of work has extended to hyperbolic geometry for implicit hierarchical learning. Previous approaches which learn the embedding trajectories of users and items achieve promising results. However, there are still a range of fundamental issues remaining open. For example, is it appropriate to place user and item nodes in one identical space regardless of their inherent discrepancy? Instead of residing in a single fixed curvature space, how will the representation spaces evolve when new interaction occurs?
Node-wise Diffusion for Scalable Graph Learning

Graph Neural Networks (GNNs) have shown superior performance for semi-supervised learning of numerous web applications, such as classification on web services and pages, analysis of online social networks, and recommendation in e-commerce. The state of the art derives representations for all nodes in graphs following the same diffusion (message passing) model without discriminating their uniqueness. However, (i) labeled nodes involved in model training usually account for a small portion of graphs in the semi-supervised setting, and (ii) different nodes locate at different graph local contexts and it inevitably degrades the representation qualities if treating them undistinguishedly in diffusion.
AgrEvader: Poisoning Membership Inference against Byzantine-robust Federated Learning

The Poisoning Membership Inference Attack (PMIA) is a newly emerging privacy attack that poses a significant threat to federated learning (FL). An adversary conducts data poisoning (i.e., performing adversarial manipulations on training examples) to extract membership information by exploiting the changes in loss resulting from data poisoning. The PMIA significantly exacerbates the traditional poisoning attack that is primarily focused on model corruption. However, there has been a lack of a comprehensive systematic study that thoroughly investigates this topic. In this work, we conduct a benchmark evaluation to assess the performance of PMIA against the Byzantine-robust FL setting that is specifically designed to mitigate poisoning attacks. We find that all existing coordinate-wise averaging mechanisms fail to defend against the PMIA, while the detect-then-drop strategy was proven to be effective in most cases, implying that the poison injection is memorized and the poisonous effect rarely dissipates. Inspired by this observation, we propose AgrEvader, a PMIA that maximizes the adversarial impact on the victim samples while circumventing the detection by Byzantine-robust mechanisms. AgrEvader significantly outperforms existing PMIAs. For instance, AgrEvader achieved a high attack accuracy of between 72.78% (on CIFAR-10) to 97.80% (on Texas100), which is an average accuracy increase of 13.89% compared to the strongest PMIA reported in the literature. We evaluated AgrEvader on five datasets across different domains, against a comprehensive list of threat models, which included black-box, gray-box and white-box models for targeted and non-targeted scenarios. AgrEvader demonstrated consistent high accuracy across all settings tested. The code is available at: https://github.com/PrivSecML/AgrEvader.