Reviewerly/exHarmony huggingface dataset:
Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning

While existing federated learning approaches mostly require that clients have fully-labeled data to train on, in realistic settings, data obtained at the client-side often comes without any accompanying labels. Such deficiency of labels may result from either high labeling cost, or difficulty of annotation due to the requirement of expert knowledge. Thus the private data at each client may be either partly labeled, or completely unlabeled with labeled data being available only at the server, which leads us to a new practical federated learning problem, namely Federated Semi-Supervised Learning (FSSL). In this work, we study two essential scenarios of FSSL based on the location of the labeled data. The first scenario considers a conventional case where clients have both labeled and unlabeled data (labels-at-client), and the second scenario considers a more challenging case, where the labeled data is only available at the server (labels-at-server). We then propose a novel method to tackle the problems, which we refer to as Federated Matching (FedMatch). FedMatch improves upon naive combinations of federated learning and semi-supervised learning approaches with a new inter-client consistency loss and decomposition of the parameters for disjoint learning on labeled and unlabeled data. Through extensive experimental validation of our method in the two different scenarios, we show that our method outperforms both local semi-supervised learning and baselines which naively combine federated learning with semi-supervised learning.
Traffic Characterization of Instant Messaging Apps: A Campus-Level View

Over the past decade, Instant Messaging (IM) apps have become an extremely popular tool for billions of people to communicate online. In this paper, we use a combination of active and passive measurement techniques to study one week of IM app traffic on a large campus edge network. Despite the challenges of end-to-end encryption, user privacy, NAT, DHCP, and high traffic volumes, we identify the key characteristics of four popular IM apps: Facebook Messenger, Google Hangouts, Snapchat, and WeChat. The main observations from our study indicate a rich ecosystem of IM apps, many of which exhibit strong diurnal patterns, complex user interactions, and heavy-tailed distributions for connection durations and transfer sizes. Collectively, these four IM apps contribute about 650 GB of daily traffic volume on our campus network.
Modeling and filtering for a stochastic uncertain system in a complex scenario

This paper presents a new approach to filter signals for discrete-time physical problems with stochastic uncertain in the presence of random data transmission delays, out-of-order packets and correlated noise. To deal with the packet disorder, the system model synthesizing the transmission delays and out-of-order packets from the plant to the filter is established by utilizing signal reconstruction schemes based on the zero-order-holder and logic zero-order-holder. A robust finite horizon Kalman filter is proposed by augmenting the state-space model and minimizing the error covariance. To further improve the filtering performance, a linear estimation-based delay compensation strategy is proposed by employing the reorganized time-stamped measurements. Moreover, for solving the missing measurement problem whilst reducing the computational costs, an artificial delay compensation approach is established using an one-step prediction approach. Simulation results show the effectiveness of the proposed method.
Leveraging Multisource Heterogeneous Data for Financial Risk Prediction: A Novel Hybrid-Strategy-Based Self-Adaptive Method

Emerging phenomena of ubiquitous multisource data offer promising avenues for making breakthroughs in financial risk prediction. While most existing methods for financial risk prediction are based on a single information source, which may not adequately capture various complex factors that jointly influence financial risks, we propose a hybrid-strategy-based self-adaptive method to effectively leverage heterogeneous soft information drawn from a variety of sources. The method uses a proposed new feature- sparsity learning method to adaptively integrate multisource heterogeneous soft features with hard features and a proposed improved evidential reasoning rule to adaptively aggregate base classifier predictions, thereby alleviating both the declarative bias and the procedural bias of the learning process. Evaluation in two cases at the individual level (concerning borrowers at a P2P lending platform) and the company level (concerning listed companies in the Chinese stock market) showed that, compared with relying solely on hard features, effectively incorporating multisource heterogeneous soft features using our proposed method enabled earlier prediction of financial risks with desirable performance.
Telemedicine, Privacy, and Information Security in the Age of COVID-19

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The EU Response to Criminal Misuse of Cryptocurrencies: The Young, Already Outdated 5th Anti-Money Laundering Directive

Combatting criminal misuse of cryptocurrencies was at the core of the FATF agenda under the US Presidency, culminating in June 2019 with the thorough extension of international standards against money laundering over virtual assets’ markets. This echoed the first legislative measure regulating virtual currencies adopted by the EU a year before. Directive 2018/843, better known as the 5th Anti-Money Laundering Directive, fails however to address key technological breakthroughs and new business models, which continuously make the evergrowing and fast-paced crypto economy evolve. Against this background, the present contribution investigates shortfalls and challenges that lay ahead in the light of the new FATF Recommendations. It ultimately argues that the preventive anti-money laundering measures cannot dispense with the establishment of a cross-border integrated supervisory and enforcement system.
Many Hands Make Light Work: Using Essay Traits to Automatically Score Essays

Most research in the area of automatic essay grading (AEG) is geared towards scoring the essay holistically while there has also been little work done on scoring individual essay traits.In this paper, we describe a way to score essays using a multi-task learning (MTL) approach, where scoring the essay holistically is the primary task, and scoring the essay traits is the auxiliary task.We compare our results with a single-task learning (STL) approach, using both LSTMs and BiLSTMs.To find out which traits work best for different types of essays, we conduct ablation tests for each of the essay traits.We also report the runtime and number of training parameters for each system.We find that MTL-based BiLSTM system gives the best results for scoring the essay holistically, as well as performing well on scoring the essay traits.The MTL systems also give a speed-up of between 2.30 to 3.70 times the speed of the STL system, when it comes to scoring the essay and all the traits.
WaveTransform: Crafting Adversarial Examples via Input Decomposition

Frequency spectrum has played a significant role in learning unique and discriminating features for object recognition. Both low and high frequency information present in images have been extracted and learnt by a host of representation learning techniques, including deep learning. Inspired by this observation, we introduce a novel class of adversarial attacks, namely ‘WaveTransform’, that creates adversarial noise corresponding to low-frequency and high-frequency subbands, separately (or in combination). The frequency subbands are analyzed using wavelet decomposition; the subbands are corrupted and then used to construct an adversarial example. Experiments are performed using multiple databases and CNN models to establish the effectiveness of the proposed WaveTransform attack and analyze the importance of a particular frequency component. The robustness of the proposed attack is also evaluated through its transferability and resiliency against a recent adversarial defense algorithm. Experiments show that the proposed attack is effective against the defense algorithm and is also transferable across CNNs.
Attention Modulated Multiple Object Tracking with Motion Enhancement and Dual Correlation

The one-shot multiple object tracking (MOT) framework has drawn more and more attention in the MOT research community due to its advantage in inference speed. However, the tracking accuracy of current one-shot approaches could lead to an inferior performance compared with their two-stage counterparts. The reasons are two-fold: one is that motion information is often neglected due to the single-image input. The other is that detection and re-identification (ReID) are two different tasks with different focuses. Joining detection and re-identification at the training stage could lead to a suboptimal performance. To alleviate the above limitations, we propose a one-shot network named Motion and Correlation-Multiple Object Tracking (MAC-MOT). MAC-MOT introduces a motion enhance attention module (MEA) and a dual correlation attention module (DCA). MEA performs differences on adjacent feature maps which enhances the motion-related features while suppressing irrelevant information. The DCA module focuses on decoupling the detection task and re-identification task to strike a balance and reduce the competition between these two tasks. Moreover, symmetry is a core design idea in our proposed framework which is reflected in Siamese-based deep learning backbone networks, the input of dual stream images, as well as a dual correlation attention module. Our proposed approach is evaluated on the popular multiple object tracking benchmarks MOT16 and MOT17. We demonstrate that the proposed MAC-MOT can achieve a better performance than the baseline state of the arts (SOTAs).
Learning to Improve Image Compression Without Changing the Standard Decoder

In recent years we have witnessed an increasing interest in applying Deep Neural Networks (DNNs) to improve the rate-distortion performance in image compression. However, the existing approaches either train a post-processing DNN on the decoder side, or propose learning for image compression in an end-to-end manner. This way, the trained DNNs are required in the decoder, leading to the incompatibility to the standard image decoders (e.g., JPEG) in personal computers and mobiles. Therefore, we propose learning to improve the encoding performance with the standard decoder. In this paper, We work on JPEG as an example. Specifically, a frequency-domain pre-editing method is proposed to optimize the distribution of DCT coefficients, aiming at facilitating the JPEG compression. Moreover, we propose learning the JPEG quantization table jointly with the pre-editing network. Most importantly, we do not modify the JPEG decoder and therefore our approach is applicable when viewing images with the widely used standard JPEG decoder. The experiments validate that our approach successfully improves the rate-distortion performance of JPEG in terms of various quality metrics, such as PSNR, MS-SSIM and LPIPS. Visually, this translates to better overall color retention especially when strong compression is applied.
A Framework for Modelling Blockchain based Supply Chain Management System to ensure soundness of Smart Contract Workflow

We propose a smart contract workflow verification framework for blockchain-based supply chain management systems.The proposed framework introduces a Petri-Net-based formalism to model smart contract workflow in a supply-chain context.Smart contracts are deployed in the blockchain nodes and executed automatically if a predefined condition is met.As deployment and execution of smart contracts require payment, it is necessary to ensure smart contract logic before the deployment to avoid unnecessary execution costs.Multiple business rules should be followed in a predefined order and criteria to complete a complex supply chain process.Therefore, a set of smart contracts representing digital business rules should also be executed in a predefined order and criteria.To verify the soundness of smart contract-based blockchain systems, the modeling of smart contract execution is required.The proposed Petri-Net model for smart contracts ensures smart contract workflow correctness before execution in a blockchain-based system.We conduct multiple experiments to evaluate the performance of our proposed framework.
Efficient Document-level Event Extraction via Pseudo-Trigger-aware Pruned Complete Graph

Most previous studies of document-level event extraction mainly focus on building argument chains in an autoregressive way, which achieves a certain success but is inefficient in both training and inference. In contrast to the previous studies, we propose a fast and lightweight model named as PTPCG. In our model, we design a novel strategy for event argument combination together with a non-autoregressive decoding algorithm via pruned complete graphs, which are constructed under the guidance of the automatically selected pseudo triggers. Compared to the previous systems, our system achieves competitive results with 19.8% of parameters and much lower resource consumption, taking only 3.8% GPU hours for training and up to 8.5 times faster for inference. Besides, our model shows superior compatibility for the datasets with (or without) triggers and the pseudo triggers can be the supplements for annotated triggers to make further improvements. Codes are available at https://github.com/Spico197/DocEE .
Anatomy of Catastrophic Forgetting: Hidden Representations and Task Semantics

Catastrophic forgetting is a recurring challenge to developing versatile deep learning models. Despite its ubiquity, there is limited understanding of its connections to neural network (hidden) representations and task semantics. In this paper, we address this important knowledge gap. Through quantitative analysis of neural representations, we find that deeper layers are disproportionately responsible for forgetting, with sequential training resulting in an erasure of earlier task representational subspaces. Methods to mitigate forgetting stabilize these deeper layers, but show diversity on precise effects, with some increasing feature reuse while others store task representations orthogonally, preventing interference. These insights also enable the development of an analytic argument and empirical picture relating forgetting to task semantic similarity, where we find that maximal forgetting occurs for task sequences with intermediate similarity.
CGGAN: a context‐guided generative adversarial network for single image dehazing

Image haze removal is highly desired for the application of computer vision. This study proposes a novel context-guided generative adversarial network (CGGAN) for single image dehazing. Of which, a novel new encoder–decoder is employed as the generator. In addition, it consists of a feature-extraction net, a context-extraction net, and a fusion net in sequence. The feature-extraction net acts as an encoder, and is used for extracting haze features. The content-extraction net is a multi-scale parallel pyramid decoder and is used for extracting the deep features of the encoder and generating coarse dehazing image. The fusion net is a decoder and is used for obtaining the final haze-free image. In order to get better dehazing results, multi-scale information obtained during the decoding process of the context extraction decoder is used for guiding the fusion decoder. By introducing an extra coarse decoder to the original encoder–decoder, the CGGAN can make better use of the deep feature information extracted by the encoder. To ensure that the proposed CGGAN works effectively for different haze scenarios, different loss functions are employed for the two decoders. Experiments results show the advantage and the effectiveness of the proposed CGGAN, evidential improvements over existing state-of-the-art methods are obtained.
Lightweight End to end Pose-Robust face recognition system with Deep Residual Equivariant Mapping

In the face recognition field of study, pose-robustness and lightness of a model are few of the critical improvement factors of face recognition. However, these fields are still providing challenge for researchers. Even though pose variance is proven to drop the accuracy of deep learning-based models, pose-robustness is not studied often in lightweight face recognition models. Existing pose-robust models have heavier implementation costs compared to lightweight models. We propose a deep learning architecture that implements Deep Residual Equivariant Mapping (DREAM) to improve pose-robustness of a lightweight MobileFaceNets model as a solution to the underlying issue. In the proposed model, the DREAM block is stitched to the MobileFaceNets stem CNN architecture. The evaluation process compares the speed, file size, and accuracy on pose diverse datasets, such as the CFP and IJB-A dataset. The evaluation results of the proposed model show an accuracy improvement of 0.07% with verification speed difference of 0.17 ms. Both of the results show a better performance compared to the baseline naive model.
Hand Gesture Recognition Using IR-UWB Radar with ShuffleNet V2

In recent years, gesture recognition has developed rapidly in non-contact Human-Computer Interaction(HCI).This paper presents an efficient hand gesture recognition system for HCI based on Impulse-Radio Ultra-Wideband (IR-UWB) radar using ShuffleNet V2, which performed well on accuracy, speed and robustness. We convert time-domain radar signals to continuous Range-Doppler Map(RDM) by algorithm1. RDM images are easier to understand than waveform diagrams for Convolutional Neural Networks(CN-N). ShuffleNet V2 is a masterpiece of lightweight CNN and is used to analyze the patterns of different gesture RDM images to classify gestures in this paper. In order to ensure the robustness of the algorithm, we invited 7 participants to construct the gesture data set. The proposed hand gesture recognition system can classify 7 gestures with a promising accuracy of 98.52%.
Deep Learning-Based Mammogram Classification for Breast Cancer Diagnosis Using Multi-level Support Vector Machine

Breast cancer is one of the most threatening diseases among women worldwide because of its painful treatment process which provides a temporary cure but becomes fatal in most cases. It is a complex health problem which possesses a high mortality rate. But the good news is that it is completely curable if diagnosed during the early stage. Breast cancer is caused due to the uncontrolled multiplication of abnormal breast cells that forms a tumor in the breast area. The tumor could be either benign (non-cancerous) or malignant (cancerous) one. The malignant cells may spread from the original tumor area to neighboring lymph nodes or to other distant parts of the body, in which case it is called metastasis. The symptoms of breast cancer may include pain in the breast, swelling of the breast, thickening of the breast tissues, dimpling of the breast or as a lump in the breast or the armpit. Many types of research have been made to predict breast cancer accurately. This paper reviews and compares three different models using the SVM (Support Vector Machine) method that is tried and tested to detect the presence of breast cancer with greater accuracy. Later in this study, a novel system that employs Artificial Neural Network and Multi-Level Support Vector Machine model is employed for the detection of breast cancer. The proposed system employs the mini-MIAS database. The accuracy of the intelligent system has proved to be better than the other approaches that have been studied.
A Novel Authentication and Key Agreement Scheme for Countering MITM and Impersonation Attack in Medical Facilities

Authentication isused to enfold the privacy of the patient to implement security onto the communication between patients and service providers. Several types of research have proposed support for anonymity for contextual privacy in medical systems that are still vulnerable to impersonation attack and Man-in-the-middle attack. By using powerful technology that is used in medical facilities, it can help in building an advanced system. However, the same powerful tools can also be used by the attackers to gain personal profits and to cause chaos. The proposed countermeasure that is to be taken to prevent this kind of attacks is by implementing mutual authentication between users, their devices/mobile devices, and the system’s cloud server, and also a key agreement scheme together with the help of Elliptic Curve Cryptography (ECC). A novel authentication scheme which consists of two phases, a signature generation, and authentication process. The ECC implementation is to ensure that the keys are thoroughly secured and is not copy- able, together with a Key generation scheme that shields the system against impersonation attacks. The usage of Elliptic Curve Digital Signature Algorithm (ECDSA), in a signature generation, on the other hand, provides users more secure way to hide the user private key and bring additional security layer before proceeding to authentication phase due to the existence of extra elements of domain parameters. Authentication is still considered as a crucial component in maintaining the security of any critical facilities that require the CIA tried and non-repudiation as a need to maintain their data. It does not only apply to medical centers, but any organizations that possess valuable data that is needed to be protected also requires strong authentication protocols. Thus, the trend for the need of novel authentication protocols will keep on rising as technology gets fancier and fancier.
Analyzing the Impact of Soft Errors in Deep Neural Networks on GPUsfrom Instruction Level

­ Deep Neural Networks (DNNs) used in safety­critical systems cannot compromise their performance due to reliability issues. In particular, soft errors are the worst. Selective software­based protection solutions are among the best techniques to improve the reliability of DNNs efficiently. However, their most significant challenge is precisely hardening portions of the DNN model to avoid performance degradation. In this work, we propose a comprehensive methodology to analyze the reliability of object detection and classification algorithms run on GPUs from the lowest (instruction) evaluation level. The ultimate goal is to avoid the performance penalty of full instruction duplication by confidently identifying the vulnerable instructions. For this purpose, we propose a technique, Instruction Vulnerability Factor (IVF). By applying our methodology on ResNet and YOLO models, we demonstrate that both models’ most vulnerable instructions can be precisely determined. Moreover, we show that YOLO is more sensitive to the changes caused by soft errors than ResNet. Also, ResNet depends on the input image in its reliability, while YOLO tends to be independent.