Reviewerly/exHarmony huggingface dataset:
Deepfake Video Detection via Predictive Representation Learning

Increasingly advanced deepfake approaches have made the detection of deepfake videos very challenging. We observe that the general deepfake videos often exhibit appearance-level temporal inconsistencies in some facial components between frames, resulting in discriminative spatiotemporal latent patterns among semantic-level feature maps. Inspired by this finding, we propose a predictive representative learning approach termed Latent Pattern Sensing to capture these semantic change characteristics for deepfake video detection. The approach cascades a Convolution Neural Network-based encoder, a ConvGRU-based aggregator, and a single-layer binary classifier. The encoder and aggregator are pretrained in a self-supervised manner to form the representative spatiotemporal context features. Then, the classifier is trained to classify the context features, distinguishing fake videos from real ones. Finally, we propose a selective self-distillation fine-tuning method to further improve the robustness and performance of the detector. In this manner, the extracted features can simultaneously describe the latent patterns of videos across frames spatially and temporally in a unified way, leading to an effective and robust deepfake video detector. Extensive experiments and comprehensive analysis prove the effectiveness of our approach, e.g., achieving a very highest Area Under Curve (AUC) score of 99.94% on FaceForensics++ benchmark and surpassing 12 states of the art at least 7.90%@AUC and 8.69%@AUC on challenging DFDC and Celeb-DF(v2) benchmarks, respectively.
Leveraging transferability and improved beam search in textual adversarial attacks

Adversarial attacks in NLP are difficult to ward off because of the discrete and highly abstract nature of human languages. Prior works utilize different word replacement strategies to generate semantic-preserving adversarial texts. These query-based methods, however, have limited exploration of the search space. To fully explore the search space, an improved beam search with multiple random perturbing positions is used. Besides, we use the transferable vulnerability from surrogate models to choose vulnerable candidate words for target models. We empirically show that beam search with multiple random attacking positions works better than the commonly used greedy search with word importance ranking. Extensive experiments on three popular datasets demonstrate that our method can outperform three advanced attacking methods under black-box settings. We provide ablation studies to clearly show the effectiveness of our improved beam search which can achieve a higher success rate than the greedy approach under the same query budget.
A hybrid prototype selection-based deep learning approach for anomaly detection in industrial machines

Anomaly detection in time series is an important task to many applications, e.g, the maintenance policies of rotating machines within industries strongly rely on time series monitoring. Rotating machines are vital elements within industries. Therefore, maintenance policies on these critical elements concern the quality of products and safety issues. Condition-based maintenance is an example of those policies. In this context, we propose a novel method to train a deep learning-based feature extractor for the anomaly detection problem on rotating machinery. It consists of using a prototype selection algorithm to improve the training process of a randomly initialized feature extractor. We perform this process iteratively using data belonging to one probability distribution, i.e., the normal class. We carried the prototype selection out with the Nearest Neighbors algorithm, and the feature extractor was a Convolutional Neural Network. We validate the method on three datasets of spectrograms related to gearbox and compressors faults and achieved promising results. We obtained detection rates in anomalous data close to 100%, and the anomaly detectors classified normal instances with accuracy values superior to 95%. Those results were competitive concerning other deep learning-based anomaly detectors in the literature, with the advantage of being an integrated solution.
Conveying Intangible Cultural Heritage in Museums with Interactive Storytelling and Projection Mapping: The Case of the Mastic Villages

Spatial Augmented Reality (SAR), as implemented with projection mapping, is part of mixed-reality technology with numerous applications in the cultural domain. In museums, interactive projection mapping has been exploited to superimpose virtual content on exhibited artefacts, offering users various hybrid ways to interact with the artefacts’ physical and digital content. For this reason, it has been widely used in the context of architectural heritage to promote culture and raise awareness about historical buildings or landscapes by visualizing significant elements they convey. This paper presents the design, development, and iterative user evaluation of an interactive projection mapping installation for the Mastic Museum on Chios island in Greece that promotes UNESCO’s intangible cultural heritage. The installation affords tangible interaction to activate the video projections presented in a storytelling manner on a 3D-printed scale model of a representative historic settlement exhibited inside the museum. The concept of this installation aims to connect the tangible and intangible cultural heritage of mastic and the related villages with narration and vivid illustrations. Three evaluation phases took place during the development at the lab and the museum, informing UX, learning, and design considerations.
Evaluation of Machine Learning Models for Clinical Prediction Problems*

1 Departments of Pediatrics (Critical Care) and Preventive Medicine (Health & Biomedical Informatics), Northwestern University Feinberg School of Medicine and Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 2 Department of Pediatrics, Sections of Informatics and Data Science and Critical Care, University of Colorado School of Medicine and Children’s Hospital Colorado, Aurora, CO *See also p. 344. Supported, in part, by grant R01HD105939 from the Eunice Kennedy Shriver National Institute for Child Health and Human Development (to Drs. Sanchez-Pinto and Bennett). Drs. Sanchez-Pinto and Bennett helped in conception, design, and drafting the article. Dr. Bennett’s institution received funding from the National Institute of Child Health and Human Development, the National Center for Advancing Translational Sciences, and the National Heart, Lung, and Blood Institute; he received support for article research from the National Institutes of Health. Dr. Sanchez-Pinto has disclosed that he does not have any potential conflicts of interest.
A Secure and Privacy-Preserving Medical Data Sharing via Consortium Blockchain

Medical data sharing is of great significance in promoting smart medicine. However, the heterogeneity of information systems used by various medical institutions makes sharing difficult. In addition, since medical data involves a great deal of sensitive information, sharing it could easily lead to the leakage of personal privacy. Blockchain, gained popularity as a distributed ledger technology, has great potential to connect heterogeneous systems and provides authenticity and integrity guarantees for medical data sharing. Focusing on the issues of medical data sharing and privacy protection, we propose a medical data sharing scheme based on consortium blockchain. To achieve access control, attribute-based access control technique is implemented, where patients preset attribute-specific access policies for their medical records, and record requesters are described by a set of attributes. For patients, we devise a hybrid storage mode to write access policies of medical records on the consortium blockchain network and store encrypted medical records off-chain. Leveraging blockchain and smart contracts, access privilege control and access history tracking can be realized. To enhance the key management, a tree of medical records is constructed for each patient, and by simply keeping the medical record trees, patients can recover their encryption keys at any time. Furthermore, we carry out an extensive analysis to show the high security and efficiency of our proposed scheme. Finally, we build a Quorum consortium blockchain on the Tencent Cloud and deploy smart contracts on the chain to simulate transactions in our scheme. The experiment results indicate the proposed scheme achieves good feasibility.
Multi-Level Alignment Network for Cross-Domain Ship Detection

Ship detection is an important research topic in the field of remote sensing. Compared with optical detection methods, Synthetic Aperture Radar (SAR) ship detection can penetrate clouds to detect hidden ships in all-day and all-weather. Currently, the state-of-the-art methods exploit convolutional neural networks to train ship detectors, which require a considerable labeled dataset. However, it is difficult to label the SAR images because of expensive labor and well-trained experts. To address the above limitations, this paper explores a cross-domain ship detection task, which adapts the detector from labeled optical images to unlabeled SAR images. There is a significant visual difference between SAR images and optical images. To achieve cross-domain detection, the multi-level alignment network, which includes image-level, convolution-level, and instance-level, is proposed to reduce the large domain shift. First, image-level alignment exploits generative adversarial networks to generate SAR images from the optical images. Then, the generated SAR images and the real SAR images are used to train the detector. To further minimize domain distribution shift, the detector integrates convolution-level alignment and instance-level alignment. Convolution-level alignment trains the domain classifier on each activation of the convolutional features, which minimizes the domain distance to learn domain-invariant features. Instance-level alignment reduces domain distribution shift on the features extracted from the region proposals. The entire multi-level alignment network is trained end-to-end and its effectiveness is proved on multiple cross-domain ship detection datasets.
Deep learning in breast imaging

Millions of breast imaging exams are performed each year in an effort to reduce the morbidity and mortality of breast cancer. Breast imaging exams are performed for cancer screening, diagnostic work-up of suspicious findings, evaluating extent of disease in recently diagnosed breast cancer patients, and determining treatment response. Yet, the interpretation of breast imaging can be subjective, tedious, time-consuming, and prone to human error. Retrospective and small reader studies suggest that deep learning (DL) has great potential to perform medical imaging tasks at or above human-level performance, and may be used to automate aspects of the breast cancer screening process, improve cancer detection rates, decrease unnecessary callbacks and biopsies, optimize patient risk assessment, and open up new possibilities for disease prognostication. Prospective trials are urgently needed to validate these proposed tools, paving the way for real-world clinical use. New regulatory frameworks must also be developed to address the unique ethical, medicolegal, and quality control issues that DL algorithms present. In this article, we review the basics of DL, describe recent DL breast imaging applications including cancer detection and risk prediction, and discuss the challenges and future directions of artificial intelligence-based systems in the field of breast cancer.
TrashBox: Trash Detection and Classification using Quantum Transfer Learning

The problem of effective disposal of the trash generated by people has rightfully attracted major interest from various sections of society in recent times. Recently, deep learning solutions have been proposed to design automated mechanisms to segregate waste. However, most datasets used for this purpose are not adequate. In this paper, we introduce a new dataset, TrashBox, containing 17,785 images across seven different classes, including medical and e-waste classes which are not included in any other existing dataset. To the best of our knowledge, TrashBox is the most comprehensive dataset in this field. We also experiment with transfer learning based models trained on TrashBox to evaluate its generalizability, and achieved a remarkable accuracy of 98.47%. Furthermore, a novel deep learning framework leveraging quantum transfer learning was also explored. Experimental evaluation on benchmark datasets has shown very promising results. Further, parallelization was incorporated, which helped optimize the time taken to train the models, recording a 10.84% improvement in the performance and 27.4% decline in training time.
Fully automatic MRI brain tumor segmentation using efficient spatial attention convolutional networks with composite loss

Automatically segmenting tumors from brain magnetic resonance imaging scans is crucial for diagnosis and planning treatment. However, brain tumors are highly diverse in location, contrast, size, and shape, making automatic segmentation extremely challenging. Recent techniques for segmenting brain tumors are mostly built using convolutional neural networks (CNNs). However, most of these existing techniques are inefficient, having slow inference speed and high parameter count. To reduce the diagnostic time, we present an accurate and efficient CNN model having fast inference speed and low parameter count for fully automatic brain tumor segmentation. Our novel CNN, the efficient spatial attention network (ESA-Net), is an improved variant of the popular U-Net. ESA-Net was built using our proposed efficient spatial attention (ESA) blocks containing depthwise separable convolution layers and a lightweight spatial attention module. The ESA blocks significantly improve both efficiency and segmentation accuracy. We also proposed a new composite loss function by combining Dice, focal, and Hausdorff distance (HD) losses to significantly improve the segmentation accuracy by tackling extreme class imbalance and directly optimizing the Dice score and HD. The effectiveness of the proposed network and loss function was evaluated by performing extensive experiments on the BraTS 2021 benchmark dataset. ESA-Net significantly outperformed U-Net in segmentation accuracy while having four times faster inference speed and eight times fewer parameters. In addition, the composite loss outperformed other loss functions. The proposed model achieved significantly better segmentation accuracy than other efficient models while having faster inference speed and fewer parameters. Moreover, it obtained competitive segmentation accuracy against state-of-the-art models. The proposed system segments a patient’s brain in 2.7 s using a GPU and has 157 times faster inference speed and 177 times fewer parameters than other state-of-the-art systems.
ReGNN: A Redundancy-Eliminated Graph Neural Networks Accelerator

Graph neural networks (GNNs), which extend conventional deep learning technologies to process graph-structured data, have shown its powerful graph representation learning ability. Existing typical GNNs utilize neighborhood message passing mechanism based on neural networks that updates target vertex representations by aggregating feature messages from neighboring source vertices. To accelerate the computations of GNNs, some customized accelerators, which follow the neighborhood aggregation computation pattern for each vertex, have been proposed. Through analysis, we observe that a naive implementation of the neighborhood aggregation results in redundant computations and communications.In this paper, we propose a novel redundancy-eliminated GNN accelerator, shortly termed as ReGNN. ReGNN is supported by an algorithm and architecture co-design. We first propose a dynamic redundancy-eliminated neighborhood message passing algorithm for GNNs. Then a novel architecture is designed to support the proposed algorithm and transform the redundancy elimination into performance improvement. ReGNN is also a configurable pipelined architecture that can be configured to support different GNN variants. In terms of the same computations, ReGNN provides the same accuracy as traditional GNNs. To the best of our knowledge, ReGNN is the first accelerator that can eliminate computation redundancy in GNNs. Our proposed ReGNN system gains an average of 9.1&#x00D7; speedup and 8.9&#x00D7; energy efficiency over state-of-the-art GNN accelerators.
AFS: Accurate, Fast, and Scalable Error-Decoding for Fault-Tolerant Quantum Computers

Quantum computers promise computational advantages for many important problems across various application domains. Unfortunately, physical quantum devices are highly susceptible to errors that limit us from running most of these quantum applications. Quantum Error Correction (QEC) codes are required to implement Fault-Tolerant Quantum Computers (FTQC) on which computations can be performed without encountering errors. Error decoding is a critical component of quantum error correction and is responsible for transforming a set of qubit measurements generated by the QEC code, called the syndrome, into error locations and error types. For the feasibility of implementation, error decoders must not only identify errors with high accuracy, but also be fast and scalable to a large number of qubits. Unfortunately, most of the prior works on error decoding have focused primarily only on the accuracy and have relied on software implementations that are too slow to be of practical use. Furthermore, these studies only look at designing a single decoder and do not analyze the challenges involved in scaling the storage and bandwidth requirements when performing error correction in large systems with thousands of qubits.In this paper, we present AFS, an accurate, fast, and scalable decoder architecture that is designed to operate in the context of systems with hundreds of logical qubits. We present the hardware implementation of AFS, which is based on the Union Find decoding algorithm and employs a three-stage pipelined design. AFS provides orders of magnitude higher accuracy compared to recent SFQ-based hardware decoders (logical error rate of 6&#x00D7;10<sup>&#x2212;10</sup> for physical error rate of 10<sup>&#x2212;3</sup>) and low decoding latency (42ns on average), while being robust to measurement errors introduced while extracting syndromes during the QEC cycles. We also reduce the amount of decoding hardware required to perform QEC simultaneously on all the logical qubits by co-designing the micro-architecture across multiple decoding units. Our proposed Conjoined-Decoder Architecture (CDA) reduces the storage overhead by 70% (10MB to 2.8MB). Finally, we reduce the bandwidth overheads required to transmit syndromes from the qubits to the decoders by exploiting the sparsity in the syndromes and compressing the data. Our proposed Syndrome Compression reduces the bandwidth requirement by 30x, on an average.
TCN enhanced novel malicious traffic detection for IoT devices

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Privacy-Preserving Multi-Source Domain Adaptation for Medical Data

Great progress has been made in diagnosing medical diseases based on deep learning. Large-scale medical data are expected to improve deep learning performance further. It is almost impossible for a single institution to collect so much data due to the time-consuming and costly collection and labeling of medical data. Many studies have turned attention to data sharing among multiple medical institutions. However, due to different data acquiring and processing procedures, multiple institutions' medical data is characterized by distribution heterogeneity. Besides, the protection of patient privacy in medical data sharing has also been a common concern. To simultaneously address the problems of heterogeneous data distribution and privacy protection, we propose a novel multi-source source free domain adaptation. When aligning distributed heterogeneous data, our method only require to transfer the pre-trained source models rather than the direct source domain data, thus protecting patients' privacy. In addition, it has the advantages of being efficient and less costly in network resources. The proposed method is evaluated on the multi-site fMRI database Autism Brain Imaging Data Exchange (ABIDE) and yields an average accuracy of 69.37%. We also analyzed its effectiveness on network resource-saving and conducted additional experiments on Camelyon17 to validate the generalization.
Security Analysis of DeFi: Vulnerabilities, Attacks and Advances

Decentralized finance (DeFi) in Ethereum is a financial ecosystem built on the blockchain that has locked over 200 billion USD until April 2022. All transaction information is transparent and open when transacting through the DeFi protocol, which has led to a series of attacks. Several studies have attempted to optimize it from both economic and technical perspectives. However, few works analyze the vulnerabilities and optimizations of the entire DeFi system. In this paper, we first systematically analyze vulnerabilities related to DeFi in Ethereum at several levels, then we investigate real-world attacks. Finally, we summarize the achievements of DeFi optimization and provide some future directions.
SpotSpam: Intention Analysis–driven SMS Spam Detection Using BERT Embeddings

Short Message Service (SMS) is one of the widely used mobile applications for global communication for personal and business purposes. Its widespread use for customer interaction, business updates, and reminders has made it a billion-dollar industry in “Text Marketing.” Along with valid SMS, a tsunami of spam messages also pop up that serve various purposes for the sender and the majority of them are fraudulent. Filtering spam SMS in an accurate manner is a crucial and challenging task that will benefit human lives both mentally and economically. Some of the challenges in the filtering of spam SMS include less number of characters, texts in informal languages, lack of public SMS spam corpus, and so on. Focusing solely on the textual features of the SMS is a major handicap of the existing methods, as it lacks in dynamically adapting to the increasing number of new keywords and jargon. In this article, we develop an intention-based approach of SMS spam filtering that efficiently handles dynamic keywords by focusing on the semantics of the words. We capture both semantic and textual features of the short-text messages based on 13 pre-defined intention labels. Moreover, the contextual embeddings of the texts are generated using various pre-trained NLP (Natural Language Processing) models. Finally, intention scores are computed for the pre-defined labels and a bunch of supervised learning classifiers are employed for filtering as spam or ham. Our approaches are evaluated on the SMS Spam Collection [ 24 ] benchmark dataset, and extensive experimentation shows interesting results. Our model did remarkably well with an accuracy of 98.07%, Precision and Recall of ∼ 0.97, which is better than few of the existing state-of-the-art alternatives. Though the accuracy of our approach is not the best among other existing approaches, the model is highly stable due to its emphasis on extracting the contextual features from the text through intention labels.