Reviewerly/exHarmony huggingface dataset:
Causal Disentanglement for Semantic-Aware Intent Learning in Recommendation

Traditional recommendation models trained on observational interaction data have generated large impacts in a wide range of applications, it faces bias problems that cover users’ true intent and thus deteriorate the recommendation effectiveness. Existing methods track this problem as eliminating bias for the robust recommendation, e.g., by re-weighting training samples or learning disentangled representations. The disentangled representation methods as the state-of-the-art eliminate bias by revealing cause-effect of the bias generation. However, how to design the semantic-aware and unbiased representations for users’ true intents is largely unexplored. To bridge the gap, we are the first to propose an unbiased and semantic-aware disentanglement learning called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CaDSI</i> ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ca</b> usal <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> isentanglement for <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> emantics-Aware <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</b> ntent Learning) from a causal perspective. Particularly, CaDSI explicitly models the causal relations underlying recommendation task, and thus produces semantic-aware representations via disentangling users’ true intents aware of specific item context. Moreover, the causal intervention mechanism is designed to eliminate confounding bias stemming from context information, which further aligns the semantic-aware representation with users’ true intent. Extensive experiments and case studies both validate the robustness and interpretability of our proposed model.
FEDIC: Federated Learning on Non-IID and Long-Tailed Data via Calibrated Distillation

Federated learning provides a privacy guarantee for generating good deep learning models on distributed clients with different kinds of data. Nevertheless, dealing with non-IID data is one of the most challenging problems for federated learning. Researchers have proposed a variety of methods to eliminate the negative influence of non-IIDness. However, they only focus on the non-IID data provided that the universal class distribution is balanced. In many real-world applications, the universal class distribution is long-tailed, which causes the model seriously biased. Therefore, this paper studies the joint problem of non-IID and long-tailed data in federated learning and proposes a corresponding solution called Federated Ensemble Distillation with Imbalance Calibration (FEDIC). To deal with non-IID data, FEDIC uses model ensemble to take advantage of the diversity of models trained on non-IID data. Then, a new distillation method with logit adjustment and calibration gating network is proposed to solve the long-tail problem effectively. We evaluate FEDIC on CIFAR-10-LT, CIFAR-100-LT, and ImageNet-LT with a highly non-IID experimental setting, in comparison with the state-of-the-art methods of federated learning and long-tail learning. Our code is available at https://github.com/shangxinyi/FEDIC.
Immersive technology-enabled digital transformation in transportation fields: A literature overview

• Explore immersive technologies and applications of DT for transportation. • Comprehensive literature review of DT for transportation following PSS lifecycle. • Design a novel evaluation matrix for measuring levels of immersive experiences. • Propose a framework of immersive technology-enabled digital transformation. Immersive technology is rapidly emerging as a powerful tool for enhancing the digital transformation in transportation to deal with the complexity, high cost, and uncertainty in the dynamic traffic environment. In this study, we investigate the innovations induced by immersive technologies for transportation fields. The literature (153 articles) published over the past five years were collected and analyzed systematically, pertaining to the lifecycle model of the product–service system, i.e., design, development, evaluation, and applications. The review indicated that immersive systems and scenarios have been developed and applied in various transportation areas, such as air traffic management, autonomous vehicles, railways, highways, and vehicle maintenance. Additionally, the review indicated that immersive technology brings significant benefits to the transportation field and induces significant changes in the interactions between humans and the physical world. These changes are expected to induce digital transformation in several aspects, e.g., digital testbeds for theoretical models and algorithms, immersive and safe digital environments for user studies, and digital training. Based on the review, a novel framework and conceptual lifecycle model of developing immersive technology-enabled digital transformation in transportation is proposed, a novel evaluation matrix for measuring the immersive experience level is established. The results provide essential references for practitioners to apply immersive technology and guidance regarding potential future research directions.
Adaptive Short-Temporal Induced Aware Fusion Network for Predicting Attention Regions Like a Driver

Driver attention prediction can solve the problem of ‘Where should the driver pay attention?’, Most previous methods are designed to predict regional attention with redundant regions. Furthermore, popular spatial-temporal feature extraction networks such as ConvLSTM and 3D-CNN are difficult to achieve real-time. To overcome these difficulties, we propose an Adaptive Short-temporal Induced Aware Fusion Network (ASIAF-Net) for region-level and object-level driver attention prediction. <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</xref> In ASIAF-Net, we design an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Attention Related Spatial Feature Encoder</i> (AF-Encoder) and an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Induced Aware Fusion Network</i> (IAF-Net) as the main network; with an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Association Analysis Cell</i> (AAC), the AF-Encoder makes it possible to effectively capture the relationship information of different objects. Considering most vital visual cues from moving objects, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Self-adaptive Short-temporal Feature Extraction Module</i> (SSFE-Module) to obtain inter-frame motion features. In IAF-Net, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Multi-scale Driver Attention Region Prediction Branch</i> is designed to predict the regional attention, and an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Object Saliency Estimation Branch</i> is proposed to fuse the perception results and the regional attention map to estimate the object-level attention. Experiments show that the proposed ASIAF-Net can predict driver’s attention on regions and objects more robustly and precisely than state-of-the-art methods on three datasets, and that it achieves real-time on our ADAS platform.
ReMCA: A Reconfigurable Multi-Core Architecture for Full RNS Variant of BFV Homomorphic Evaluation

Fully homomorphic encryption (FHE) allows arbitrary computation on encrypted data and thus has potential in privacy-preserving computing. However, efficiency is still the bottleneck. In this paper we present an area-efficient and highly unified reconfigurable multi-core architecture (named ReMCA) for full Residue Number System (RNS) variant of Fan-Vercauteren variant of Brakerski’s scheme (RNS-BFV), which employs a variable number of reconfigurable processing elements (PEs) and RNS channels. The PE unit can be flexibly configured as NTT, INTT or modular multiplier, thereby avoiding the need of other extra computational units. To reduce the computational complexity, ReMCA merges the pre/post-processing into NTT/INTT and unifies the read/write structure of NTT and INTT. Also, a conflict-free memory access pattern that doesn’t need separate bit-reversal operation is proposed to optimize the memory access. Furthermore, targeting different computational requirements, a unified hardware architecture mapping model and data memory organization model are introduced, and all the computing units that RNS-BFV involved are optimized and mapped on ReMCA. ReMCA is evaluated on a Xilinx Virtex-7 FPGA platform. Running at 250MHz, it can perform 2260 homomorphic multiplication per second. When normalized to the same parameter set, the throughput and Area-Time-Products (ATPs) of ReMCA achieve <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.45\times \sim 5.51\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.58\times \sim 5.12\times $ </tex-math></inline-formula> improvements.
Detection and classification of sunspots via deep convolutional neural network

Sunspots are known to be the most prominent feature of the solar photosphere. Solar activities play a vital role in Space weather which greatly affects the Earth's environment. The appearance of sunspots determines the solar activities and being observed from early eighteenth century. In this work, we have implemented a deep learning model which automatically detects sunspots from MDI and HMI image datasets. Proposed model uses Alexnet based deep convolutional networks to generate promising deep hierarchical features and proposed deep learning approach achieved excellent classification accuracies. Also, model has shown the improved result with MDI data set which is equal to 99.71%, 100%, 100%, and 100 for accuracy, precision, recall, and F-score respectively. This is to construct and build robust and reliable event recognition system to monitor solar activities which are crucial to understanding space weather and for physicists it is an aid for their research.
An Efficient Adaboost Algorithm with the Multiple Thresholds Classification

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DocR-BERT: Document-Level R-BERT for Chemical-Induced Disease Relation Extraction via Gaussian Probability Distribution

Chemical-induced disease (CID) relation extraction from biomedical articles plays an important role in disease treatment and drug development. Existing methods are insufficient for capturing complete document level semantic information due to ignoring semantic information of entities in different sentences. In this work, we proposed an effective document-level relation extraction model to automatically extract intra-/inter-sentential CID relations from articles. Firstly, our model employed BERT to generate contextual semantic representations of the title, abstract and shortest dependency paths (SDPs). Secondly, to enhance the semantic representation of the whole document, cross attention with self-attention (named cross2self-attention) between abstract, title and SDPs was proposed to learn the mutual semantic information. Thirdly, to distinguish the importance of the target entity in different sentences, the Gaussian probability distribution was utilized to compute the weights of the co-occurrence sentence and its adjacent entity sentences. More complete semantic information of the target entity is collected from all entities occurring in the document via our presented document-level R-BERT (DocR-BERT). Finally, the related representations were concatenated and fed into the softmax function to extract CIDs. We evaluated the model on the CDR corpus provided by BioCreative V. The proposed model without external resources is superior in performance as compared with other state-of-the-art models (our model achieves 53.5%, 70%, and 63.7% of the F1-score on inter-/intra-sentential and overall CDR dataset). The experimental results indicate that cross2self-attention, the Gaussian probability distribution and DocR-BERT can effectively improve the CID extraction performance. Furthermore, the mutual semantic information learned by the cross self-attention from abstract towards title can significantly influence the extraction performance of document-level biomedical relation extraction tasks.
Consideration of a robust watermarking algorithm for color image using improved QR decomposition

In order to protect the digital image copyright, it is necessary to design a robust watermarking algorithm. To achieve this purpose, a novel color image watermarking scheme based on an improved QR decomposition is proposed in this paper. The proposed method gives a new algorithm to find elements of Q and R matrices instead of using the Gram–Schmidt algorithm for QR factorization. First, the R matrix is performed by solving a set of linear equations where diagonal elements of R are checked and modified if they are zero or negative. After that, the Q matrix is computed based on the R matrix. In addition, a novel formula is proposed to improve the extracting time where the first element R(1, 1) of the R matrix is found instead of computing QR decomposition as the previous proposals. Experimental results show that the proposed method outperforms other considered methods in this paper in terms of the quality of the watermarked images. Furthermore, the execution time is significantly improved, and the extracted watermark is more robust against almost tested attacks.
Optimizing Data Center Energy Efficiency via Event-Driven Deep Reinforcement Learning

To reduce the skyrocketing energy consumption of data centers, the prevailing approaches adopt the time-driven manner to control IT and cooling subsystems. These methods suffer from highly dynamic system states, complex action spaces and the risk of instability caused by frequent and unnecessary control operations. To tackle these problems, we propose a novel event-driven control paradigm and an optimization algorithm, under the deep reinforcement learning (DRL) framework. The principle is to make decisions based on certain critical events (e.g., overheating), rather than fixed periodic control. Specifically, we design an event-driven optimization framework to trigger control operations. Then, we present several models to describe IT and cooling subsystems, and mathematically define events to capture four types of prior factors that impact system performance. Furthermore, we develop an event-driven DRL (E-DRL) optimization algorithm to dispatch jobs and regulate cooling facilities for energy efficiency. Using two different types of real workload traces, we conduct extensive experiments to demonstrate that: 1) E-DRL reduces the number of regulating decisions by 70% <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 95% while achieving a comparable or even better energy efficiency in comparison with the state-of-the-art algorithm; and 2) E-DRL can adapt the control frequency to the changing operational conditions and diverse workloads.
Unambiguous Text Localization, Retrieval, and Recognition for Cluttered Scenes

Text instance as one category of self-described objects provides valuable information for understanding and describing cluttered scenes. The rich and precise high-level semantics embodied in the text could drastically benefit the understanding of the world around us. While most recent visual phrase grounding approaches focus on general objects, this paper explores extracting designated texts and predicting unambiguous scene text information, i.e., to accurately localize and recognize a specific targeted text instance in a cluttered image from natural language descriptions (referring expressions). To address this issue, first a novel recurrent dense text localization network (DTLN) is proposed to sequentially decode the intermediate convolutional representations of a cluttered scene image into a set of distinct text instance detections. Our approach avoids repeated text detections at multiple scales by recurrently memorizing previous detections, and effectively tackles crowded text instances in close proximity. Second, we propose a context reasoning text retrieval (CRTR) model, which jointly encodes text instances and their context information through a recurrent network, and ranks localized text bounding boxes by a scoring function of context compatibility. Third, a recurrent text recognition module is introduced to extend the applicability of aforementioned DTLN and CRTR models, via text verification or transcription. Quantitative evaluations on standard scene text extraction benchmarks and a newly collected scene text retrieval dataset demonstrate the effectiveness and advantages of our models for the joint scene text localization, retrieval, and recognition task.
Constrained nonlinear optimization method for accurate calibration of a bi-telecentric camera in a three-dimensional microtopography system

Telecentric cameras are widely used in the field of microscopic imaging because of their constant magnification and tiny distortion in the depth of field. Camera calibration has always been a key step in the field of computer vision. Usually, the precise parameters of the telecentric camera are obtained by nonlinear optimization; however, the randomness of the optimization algorithm without proper constraints will cause the results to be inconsistent with reality. Existing studies paid little attention to this issue; therefore, we show a reliable optimization approach for the bi-telecentric camera in a structured illumination three-dimensional microtopography measurement system. In this method, the distortion-free camera parameters are solved through a closed-form solution. Then a nonlinear optimization algorithm with constraining the world coordinates of the precise calibration target is proposed to refine the global parameters, leading to the calibration results being more accurate and authentic. The real experiments are conducted to verify the feasibility of the proposed method. The comparative experiments with the exiting approach are then carried out, manifesting that the proposed method enjoys advantages in terms of both reprojection error and operating efficiency. Additionally, the average offset of the world coordinates on the calibration target derived from the proposed method verifies its effectiveness and reasonability.
FedTor: An Anonymous Framework of Federated Learning in Internet of Things

With a large number of devices and a wealth of user data sets, the Internet of Things (IoT) has become a great host for federated learning (FL). At the same time, the massive amount of user data in IoT results in desperate demand for privacy preserving. The onion router (Tor) is a promising method to solve the privacy issue in IoT-based FL by user anonymity. However, IoT devices’ resource is too limited to execute the cryptographic operations in Tor. Moreover, network traffics in Tor can be easily controlled by malicious routers with a fake high self-reported bandwidth. In this article, taking advantage of the Tor, we will introduce an anonymous FL framework in IoT called FedTor. To decrease the cryptographic cost in conventional Tor, we propose a lightweight shared key generation scheme for resource-limited IoT devices. Furthermore, we use the difference between the self-reported bandwidth and the bandwidth observed from others to measure the reputation of onion routers. A reputation-based router selection (RBRS) scheme is then brought up to defend traffic control from malicious routers. We conducted extensive simulations to compare FedTor with related works. The results show that the RBRS scheme can decrease the malicious rate of onion routers and the lightweight shared key has a cost advantage over other schemes.
Employing Mozilla Hubs as an Alternative Tool for Student Outreach: A Design Challenge Use Case

Due to the 2020 COVID-19 pandemic, online virtual gathering platforms have risen at all levels as alternatives to traditional videoconferencing. The implementation of physical distancing and limited capacity implemented to reduce the spread of COVID-19 changed how academic activities are conducted. For example, student outreach presents students with experiential learning opportunities and teamwork on campus. While video conferencing tools have thrived over the pandemic, these lack immersion and presence, lead to fatigue, and lack engagement. In this paper, we present the development and hosting of a Design Challenge employing the open-source virtual reality (VR) platform, Hubs by Mozilla. Usability perceptions from five out of ten participants were gathered and analyzed employing a simplified version of the System Usability Scale questionnaire. The process of developing the Mozilla Hubs environment allowed us to identify technical issues associated with performance and audio quality.
Irrationality, Extortion, or Trusted Third-parties: Why it is Impossible to Buy and Sell Physical Goods Securely on the Blockchain

Suppose that Alice plans to buy a physical good from Bob over a programmable Blockchain. Alice does not trust Bob, so she is not willing to pay before the good is delivered off-chain. Similarly, Bob does not trust Alice, so he is not willing to deliver the good before getting paid on-chain. Moreover, they are not inclined to use the services of a trusted third-party. Traditionally, such scenarios are handled by game-theoretic escrow smart contracts, such as BitHalo. In this work, we first show that the common method for this problem suffers from a major flaw which can be exploited by Bob in order to extort Alice. We also show that, unlike the case of auctions, this flaw cannot be addressed by a commitment-scheme-based approach. We then provide a much more general result: assuming that the two sides are rational actors and the smart contract language is Turing-complete, there is no escrow smart contract that can facilitate this exchange without either relying on third parties or enabling at least one side to extort the other.**A preprint of this work appeared in [1].
TDNet: transformer-based network for point cloud denoising

This study proposes a novel, to the best of our knowledge, transformer-based end-to-end network (TDNet) for point cloud denoising based on encoder-decoder architecture. The encoder is based on the structure of a transformer in natural language processing (NLP). Even though points and sentences are different types of data, the NLP transformer can be improved to be suitable for a point cloud because the point can be regarded as a word. The improved model facilitates point cloud feature extraction and transformation of the input point cloud into the underlying high-dimensional space, which can characterize the semantic relevance between points. Subsequently, the decoder learns the latent manifold of each sampled point from the high-dimensional features obtained by the encoder, finally achieving a clean point cloud. An adaptive sampling approach is introduced during denoising to select points closer to the clean point cloud to reconstruct the surface. This is based on the view that a 3D object is essentially a 2D manifold. Extensive experiments demonstrate that the proposed network is superior in terms of quantitative and qualitative results for synthetic data sets and real-world terracotta warrior fragments.