Reviewerly/exHarmony huggingface dataset:
UIV-TSP: A Blockchain-Enabled Antileakage Sharing Protection Scheme for Undisclosed IIoT Vulnerabilities

With the large-scale deployment of industrial Internet of things (IIoT) devices in 5/6G environments, the number of vulnerabilities threatening IIoT security is growing dramatically, including a mass of undisclosed IIoT vulnerabilities that lack mitigation measures. Coordination vulnerability disclosure (CVD) is one of the most popular vulnerabilities sharing solutions, in which security workers (SWs) can develop undisclosed vulnerability patches together. However, CVD assumes that SWs are all honest and thus offering chances for dishonest SWs to internally leak undisclosed IIoT vulnerabilities. To combat such internal threats, we propose an undisclosed IIoT vulnerabilities sharing protection (UIV-TSP) scheme against internal leakage. In this paper, a dynamic token is an implicit access credential for an SW to acquire an undisclosed vulnerability message, which is only held by the system and constantly updated with the SW access. The latest updated token can be stealthily sneaked into the acquired information as the traceability token to prevent internal leakage. To quickly distinguish dishonest SWs, the feedforward neural network (FNN) is adopted to evaluate the trust value of SWs. Meanwhile, we design a blockchain-assisted continuous logs storage method to achieve the tamper-proofing of dynamic token and the transparency of undisclosed IIoT vulnerabilities sharing. The simulation results indicate that our proposed scheme is resilient to suppress dishonest SWs and protect the IIoT undisclosed vulnerabilities effectively.
A Deep Ranking Weighted Multihashing Recommender System for Item Recommendation

Collaborative filtering (CF) techniques are used in recommender systems to provide users with specialised recommendations on social websites and in e-commerce. But they suffer from sparsity and cold start problems (CSP) and fail to interpret why they recommend a new item. A novel deep ranking weighted multihash recommender (DRWMR) system is designed to suppress sparsity and CSP. The proposed DRWMR system contains two stages: the neighbours' formation and recommendation phases. Initially, the data is fed to the deep convolutional neural network (CNN). The significant features are extracted from CNN. The CNN contains an additional layer; the hash code is generated by minimising pairwise ranking loss and classification loss. Therefore, a weight is assigned to different hash tables and hash bits for a recommendation. Then, the similarity between users is obtained based on the weighted hammering distance; the similarity between users helps to form the neighbourhood for the active user. Finally, the rating for unknown items can be obtained by taking the weighted average rating of the neighbourhood, and a list of the top n items can be produced. The effectiveness and accuracy of the proposed DRWMR system are tested on the MovieLens 100 K dataset and compared with the existing methods. Based on the evaluation results, the proposed DRWMR system gives precision (0.16), the root mean squared error (RMSE) of 0.73 and the recall (0.08), the mean absolute error (MAE) of 0.57, and the F - 1 measure (0.101).
Transformer-based Dynamic Fusion Clustering Network

Clustering is an advanced task in machine learning. Numerous studies have improved clustering performance by integrating deep learning into clustering technology. However, some limitations have still existed in the current deep clustering research: (1) lack of a dynamic fusion mechanism to help multiple deep learning networks jointly train node information (2) data structure embedding methods are not mature enough, and as the number of layers in the deep learning network deepens, the ability to learn data representation decreases, resulting in low performance. In contrast to these clustering methods, we propose a Transformer-based Dynamic Fusion Clustering Network (TDCN), a novel deep clustering network mainly with Transformer architecture that can successfully address the current issues and improve the clustering performance. Specifically, a new dynamic attention mechanism is used to fuse the feature of Transformer and autoencoder (AE) networks in TDCN. In order to obtain data structural information, a new transformation operation G is designed. The transformation operation G varies according to the characteristics of the source data, helping to represent the data structure. In addition, TDCN stacks multi-layer multi-scale heterogeneous networks to learn node representation and further integrates information at different scales through specific modules, so as to facilitate efficient extraction of information. The whole deep clustering network is trained by a dual self-supervision mechanism. Experiments indicate that our model can achieve comparable or even better performance than the state-of-the-art methods on five datasets.
Identifying the Digital Camera from Natural Images Using Residual Noise and the Jensen–Shannon Divergence

Regarding the problem of digital camera identification, many methods have been proposed, and for several of them, their effectiveness has been verified on the basis of disputed flat images. However, in real cases the disputed images are natural images, rather than flat images. In that case, several of the already proposed methods are not effective. Hence, in this paper, a method is proposed for the digital camera identification from natural images based on the statistical comparison between the residual noise in the natural disputed images and the fingerprint defined for the eligible digital cameras. In the reported case studies, the HDR database provided by the Communications and Signal Processing Laboratory of University of Florence is used to select a set of eligible digital cameras, and from this image database, for each digital camera, a set of disputed flat images, a set of disputed natural images, and a set of flat reference images were selected. Thus, the fingerprint of each digital camera was calculated from the probability density function (PDF) of the photo-response nonuniformity (PRNU) extracted from its reference images. Therefore, in order to identify the source digital camera of a natural disputed image, the Jensen–Shannon divergence (JSD) was implemented to statistically compare the PRNU-based fingerprint of each eligible source camera against the noise residual of that disputed image. The proposed method has a similar effectiveness to methods based on the peak-to-correlation energy or the Kullback–Leibler divergence when the disputed images are flat images and the PRNU is considered, but it is significantly more effective than those methods when the disputed images are natural images.
Leveraging Digital Infrastructure for Pandemic Governance: Preparation, Praxis, and Paradox

Many cities around the world have adopted digital technologies to combat COVID-19, but they have experienced varying degrees of success. This research developed an analytical framework involving the components of “preparation”, “praxis”, and “paradox” regarding a city’s leveraging of digital infrastructure for pandemic governance. Then, the case of Hangzhou is employed to illustrate this framework. This research finds that (1) the application of digital technologies should be a systematic arrangement based on institutional and technological preparations, (2) innovative governance instruments supported by digital infrastructure can be developed to respond to a variety of practical scenarios, and (3) adopting a paradox lens aids in mitigating potential negative socio-economic consequences. This research suggests that leveraging digital infrastructure for pandemic governance requires a coherent system that has the potential to aid cities around the world in better managing public health crises going forward.
Benchmark of Sampling Based Motion Planners in Bin Picking Manipulation Task

There is a wide variety of motion planning techniques for robots; however, there are few tools to test and compare the performance of motion planners in various environments. This work proposes a test layout for a set of industrial robots to measure their performance in manipulation in a constrained environment. The developed work uses ROS and the benchmarking tool Robowflex. The comparison allowed to obtain the performance results of multiple motion planners in a shorter time than in previous works. These results facilitate the correct choice of a motion planner, which is critical in current industrial environments. The conducted benchmark provides a valuable tool for potential comparisons of new motion strategies to proven and established methods.
Quantum Key Distribution Post-processing: A Heterogeneous Computing Perspective

Recent experimental demonstrations of quantum key distribution (QKD) have caught worldwide attention for their ability to transmit encryption keys. Current commercial QKD typically uses discrete optical components, resulting in systems which are typically sized to fit within telecommunications racks. Widespread deployment of QKD systems will require low-SWaP (i.e., Size, Weight, and Power) QKD systems, thus Photonic Integration is seen as a key enabler of widespread adoption. QKD post-processing systems play a significant role in the distillation and high-speed generation of secure keys for discrete variable (DV) or continuous variable (CV) QKD protocols. Interest in using heterogeneous computing techniques for QKD post-processing is growing, owing to the emergence of domain-specific hardware accelerators for efficient quantum information processing applications. We hence discuss in a tutorial manner the principles and techniques of QKD post-processing. Characteristics of representative heterogeneous platforms for QKD post-processing implementations are compared, along with a review of the state-of-the-art QKD post-processing accelerators.
Implementation and Evaluation of Deep Neural Networks in Commercially Available Processing in Memory Hardware

Deep Neural Networks (DNNs) are often associated with a large number of data-parallel computations. Therefore, data-centric computing paradigms, such as Processing in Memory (PIM), are being widely explored for DNN acceleration applications. A recent PIM architecture, developed and commercialized by the UPMEM company, has demonstrated impressive performance boost over traditional CPU-based systems for a wide range of data-parallel applications. However, the application domain of DNN acceleration is yet to be explored on this PIM platform. In this work, we present successful implementations of DNNs on the UPMEM PIM system. We explore multiple operation mapping schemes with different optimization goals and accelerate two CNN algorithms using these schemes. Based on the data achieved from the physical implementation of the DNNs on the UPMEM system, we compare the performance of our DNN implementation with several other recently proposed PIM architecture.
Anomaly Warning: Learning and Memorizing Future Semantic Patterns for Unsupervised Ex-ante Potential Anomaly Prediction

Existing video anomaly detection methods typically utilize reconstruction or prediction error to detect anomalies in the current frame. However, these methods cannot predict ex-ante potential anomalies in future frames, which is imperative in real scenes. Inspired by the ex-ante prediction ability of humans, we propose an unsupervised Ex-ante Potential Anomaly Prediction Network (EPAP-Net), which learns to build a semantic pool to memorize the normal semantic patterns of future frames for indirect anomaly prediction. At the training time, the memorized patterns are encouraged to be discriminated through our Semantic Pool Building Module (SPBM) with the novel padding and updating strategies. Moreover, we present a novel Semantic Similarity Loss (SSLoss) at the feature level to maximize the semantic consistency of memorized items and corresponding future frames. Specially, to enhance the value of our work, we design a Multiple Frames Prediction module (MFP) to achieve anomaly prediction in future multiple frames. At the test time, we utilize the trained semantic pool instead of ground truth to evaluate the anomalies of future frames. Besides, to obtain better feature representations for our task, we introduce a novel Channel-selected Shift Encoder (CSE), which shifts channels along the temporal dimension between the input frames to capture motion information without generating redundant features. Experimental results demonstrate that the proposed EPAP-Net can effectively predict the potential anomalies in future frames and exhibit superior or competitive performance on video anomaly detection.
A Practical Man-in-the-Middle Attack on Deep Learning Edge Device by Sparse Light Strip Injection into Camera Data Lane

The vulnerability of deep neural networks (DNNs) has been exposed by adversarial examples. Although the adversarial perturbations can be made visually imperceptible or photorealistic on any image, they have to be added offline on pre-captured static input in order to accomplish the malicious goal. As opposed to subtle distortion, real-time misclassification on streaming images can be realized by manipulating the objects in physical world. Recently, object-contactless physical attacks, as exemplified by a translucent sticker affixed to the lens of a camera, show that a sensor-enabled edge computing platform can be an alluring target of adversarial attack. Nevertheless, success rates of reported camera-based patch attacks are not high enough to overshadow other forms of evasion attacks even when they are performed under the white-box scenario. In this paper, we present a practical and robust fault injection approach cooperated with a hardware-friendly sparse strip pattern to deceive the deployed DNN device on real-time streaming images. The strip perturbation is generated in a line-offset form by an optimization algorithm. It can be injected into camera data lane between the image sensor and the endpoint node stealthily without disturbing the data traffic through an interface bridge implemented by a tiny off-the-shelf FPGA device. We demonstrate our attack on the Raspberry Pi 4 platform with the Pi camera v2 and the Intel NCS2 inference stick. By evaluating 280 physically captured images from ten objects in 28 viewing angles, we show that the proposed attack on four ImageNet models including ResNet50, MobileNet-v2, Inception-v3 and EfficientNet-B0 can achieve 89.2% ∼ 96.1% success rates.
Hardware Oriented Strip-wise Optimization (HOSO) Framework for Efficient Deep Neural Network

Machine learning methods combined with other computer science technologies have exhibited great potential and unique opportunities to enhance computer vision tasks, offering an effective alternative and a viable method to image processing studies. However, current state-of-the-art object detection methods are either accuracy-oriented within a large model or speed-oriented within a lightweight model. Although some techniques are proposed to compress and accelerate neural networks, most algorithms mainly aim at achieving optimal goals on accuracy and model size based on evaluation in software and lead to a lack of consideration of hardware platforms.To tackle this challenge, in this work, we propose an Hard-ware Oriented Strip-wise Optimization (HOSO) framework, an efficient method that can consider target hardware platforms to compress the machine learning model while retaining high accuracy. Compared with the existing pruning methods, the proposed HOSO framework focuses on Hardware-oriented optimization based on strips instead of the filter or weight. In addition, the proposed HOSO framework converts the standard convolution into a strip-wise convolution. Thus, a novel hardware implementation is proposed. Experimental results show that our proposed HOSO framework and its hardware implementation can compress the model into a smaller size while retaining high accuracy.
Towards NoSQL databases: Experiences from actual projects

The rapid growth of the global network has led to the development of many digital services and social networks. The connection among people, devices, and systems has grown exponentially, leading to explosive growth in data. Data grows at a fast rate, and it is diverse and unstructured, which makes it difficult to store and process traditional relational databases. This has led to the generation of non-relational, distributed NoSQL databases that are easy to scale horizontally, so it is suitable for flexibly developing modern applications from very small scale to large multi-user applications. NoSQL databases are increasingly used in big data and real-time, or web applications. The recent trend has been a big shift towards NoSQL databases, but there hasn't been much research on this database and how it works. This study synthesizes and evaluates NoSQL after a period of practical use and provides necessary guidelines for developers when considering switching to this database.
Recognition of Shoulder Exercise Activity Based on EfficientNet Using Smartwatch Inertial Sensors

Recognition of human activity is an important research topic due to its potential applications in areas, such as the medical industry and other related fields. Sensor-based human activity recognition (HAR) employing deep learning (DL) techniques has grown in popularity as a result of its immense efficiency in detecting complicated tasks and its low cost in comparison to more traditional machine learning (ML) techniques. More recently, convolutional neural network (CNN) approaches have also been used with sensor-based HAR, and the results have shown promise for improvement. In this study, we applied the CNN-based architecture known as EfficientNet to perform sensor-based HAR. The goal of this research was to apply the EfficientNet architecture to the classification of complicated human activities, such as shoulder workout activities. To evaluate the recognition performance, we used a benchmark HAR dataset called SPARS9x, which collected sensor data from six sports activities. According to the results of the experiments, the EfficientNet-B3 had the best performance overall on the benchmark dataset receiving an F1-score of 98.87%.
Bayesian based Re-parameterization for DNN Model Pruning

Filter pruning, as an effective strategy to obtain efficient compact structures from over-parametric deep neural networks(DNN), has attracted a lot of attention. Previous pruning methods select channels for pruning by developing different criteria, yet little attention has been devoted to whether these criteria can represent correlations between channels. Meanwhile, most existing methods generally ignore the parameters being pruned and only perform additional training on the retained network to reduce accuracy loss. In this paper, we present a novel perspective of re-parametric pruning by Bayesian estimation. First, we estimate the probability distribution of different channels based on Bayesian estimation and indicate the importance of the channels by the discrepancy in the distribution before and after channel pruning. Second, to minimize the variation in distribution after pruning, we re-parameterize the pruned network based on the probability distribution to pursue optimal pruning. We evaluate our approach on popular datasets with some typical network architectures, and comprehensive experimental results validate that this method illustrates better performance compared to the state-of-the-art approaches.
Counterexample Contrastive Learning for Spurious Correlation Elimination

Biased dataset will lead models to learn bias features highly correlated to labels, which will deteriorate the performance especially when the test data deviates from the training distribution. Most existing solutions resort to introducing additional data to explicitly balance the dataset, e.g., counterfactually generating augmented data. In this paper, we argue that there actually exist valuable samples within the original dataset which are potential to assist model circumvent spurious correlations. We call those observed samples with inconsistent bias-task correspondences with the majority samples as counterexample. By analyzing when and how counterexamples assist in circumventing spurious correlations, we propose Counterexample Contrastive Learning (CounterCL) to exploit the limited observed counterexample to regulate feature representation. Specifically, CounterCL manages to pull counterexamples close to the samples with the different bias features in the same class and at the same time push them away from the samples with the same bias features in the different classes. Quantitative and qualitative experiments validate the effectiveness and demonstrate the compatibility to other debiasing solutions.
Fixed-time Adaptive Neural Control for Robot Manipulators with Input Saturation and Disturbance

A fixed-time adaptive neural network control scheme is designed for an unknown model manipulator system with input saturation and external environment disturbance, so that the system convergence time can be parameterized and not affected by the initial state of the system. The compensation control item is introduced to compensate for external disturbance. The scheme can ensure that the input torque always does not exceed the actuator saturation value and the transient and steady state performance will not significantly degrade. Furthermore, the Incremental Broad Neural Network (IBNN) is used for approximating unknown models with flexible adjustability and high computational efficiency, so it can be applied to scenarios with different control precision requirements. Simulation results verify the effectiveness of the scheme in the above aspects.
Real-Time Instance Segmentation of Pedestrians using Transfer Learning

Real-time instance segmentation of pedestrians presents a critical core task within an automated driving pipeline. Recent research focuses on existing real-world datasets to train their instance segmentation networks. However, due to the limited size of real-world datasets, they tend to either overfit or lack accuracy. Therefore, these networks remain useless for real-world applications. Hence, we introduce a transfer learning strategy by combining a large-scale synthetic dataset and a real-world dataset for instance segmentation of pedestrians. We showcase our approach on three state-of-the-art real-time instance segmentation methods: (1) YOLACT++, (2) SipMask, and (3) BlendMask. Finally, we provide a quantitative and qualitative evaluation of our introduced approach on two publicly available urban street scenes datasets, i.e. the real-world Cityscapes dataset and the synthetic Synscapes dataset.
LSTM-based Beam Tracking Using Computer Vision

Recently, a growing number of vehicular applications have emerged to provide an entirely new experience in our daily life. These applications are accelerating the need for the well-organized vehicle-to-everything (V2X) communications. However, due to the high variance in the V2X network, especially in the V2V network, acquiring the accurate beam direction is challenging. Thus, designing a techinque to acquire the beam direction in V2V networks is necessary. An aim of this paper is to propose a long short-term memory (LSTM)-based beam tracking framework assisted by CV technique. In the proposed technique, since the LSTM uses the image taken from a vehicle to identify the beam direction, the feedback process of beam tracking is removed, reducing the latency and energy consumption greatly. In addition, from the sequence of exact beam directions, LSTM can learn the amount of adjustment required for the beam direction, resulting in a maximization of the beamforming gain.