Reviewerly/exHarmony huggingface dataset:
Reconfigurable and hardware efficient adaptive quantization model-based accelerator for binarized neural network

Binarized neural networks (BNNs) architecture play a vital role in the development of deep learning accelerator for memory-constrained IoT devices. However, the cost-efficiency of the domain-specific accelerators still requires a simplified structure to enhance the computing performance of BNNs. We propose and present a reconfigurable BNN accelerator to improve the computing speed through channel amplitude and an adaptive spatial amplitude model. To reduce the redundant operations in the binarization of matrix multiplication, we introduce the channel amplitude model for BNN convolution functions. The adaptive spatial amplitude is implemented with the help of a matrix multiplication layer with 3 × 3 convolutions to get spatial information and increases the computation speed. For hardware implementation, the channel amplitude conversion is deployed through XNOR-popcount convolutions of each layer. The performance results of our proposed accelerator provide 2–10 × line buffer efficiency, 1.6–3.7 × processing frequency enhancement, and 2.35 × processing element reduction compared with existing baselines using VGG-16 based FPGA implementation.
Multi-label out-of-distribution detection via exploiting sparsity and co-occurrence of labels

Although Out-Of-Distribution (OOD) detection has been extensively studied, the OOD detection under multi-label settings, which is closer to the real world, is still in its infancy. The pioneer work ignores some unique properties of multi-label images, such as the sparsity and co-occurrence of labels. Here, we empirically observe that these properties readily distinguish OOD and in-distribution data. Motivated by this observation, we propose a novel multi-label OOD detection approach named Sparse Label Co-occurrence Scoring (SLCS) to exploit the sparsity and co-occurrence information of labels. SLCS follows conventions and deems the logits outputted by the penultimate layer of the trained multi-label image classification model as the prediction confidences of a sample to categories in the training label set. A logit sparse filtering process is employed to filter out the low-confidence logits for avoiding the interference of low-confidence predictions while preserving the high-confidence logits to obtain the label sparsity. Then, the label co-occurrence pairs are counted for each sample based on its predicted categories and the label co-occurrence matrix constructed on the training set. Finally, the preserved logits are weighted by the label co-occurrence information and accumulated to produce the OOD detection score for each sample. Extensive experimental results on three well-known multi-label image datasets demonstrate the discriminating power of SLCS, which achieves greatly improved performances compared with the only multi-label OOD detection approach — JointEnergry and the state-of-the-art single-label OOD detection approaches. The performance improvements of SLCS over JointEnergy in FPR95 are 12.85%, 12.41%, and 9.50% on MS-COCO, VOC 2012, and NUS-WIDE datasets respectively.
Measuring node decentralisation in blockchain peer to peer networks

New blockchain platforms are launching at a high cadence, each fighting for attention, adoption, and infrastructure resources. Several studies have measured the peer-to-peer (P2P) network decentralisation of Bitcoin and Ethereum (i.e., two of the largest used platforms). However, with the increasing demand for blockchain infrastructure, it is important to study node decentralisation across multiple blockchain networks, especially those containing a small number of nodes. In this paper, we propose NodeMaps, a data processing framework to capture, analyse, and visualise data from several popular P2P blockchain platforms, such as Cosmos, Stellar, Bitcoin, and Lightning Network. We compare and contrast the geographic distribution, the hosting provider diversity, and the software client variance in each of these platforms. Through our comparative analysis of node data, we found that Bitcoin and its Lightning Network Layer 2 protocol are widely decentralised P2P blockchain platforms, with the largest geographical reach and a high proportion of nodes operating on The Onion Router (TOR) privacy-focused network. Cosmos and Stellar blockchains have reduced node participation, with nodes predominantly operating in large cloud providers or well-known data centres.
Jitter-based Adaptive True Random Number Generation Circuits for FPGAs in the Cloud

In this article, we present and evaluate a true random number generator (TRNG) design that is compatible with the restrictions imposed by cloud-based Field Programmable Gate Array (FPGA) providers such as Amazon Web Services (AWS) EC2 F1. Because cloud FPGA providers disallow the ring oscillator circuits that conventionally generate TRNG entropy, our design is oscillator-free and uses clock jitter as its entropy source. The clock jitter is harvested with a time-to-digital converter (TDC) and a controllable delay line that is continuously tuned to compensate for process, voltage, and temperature variations. After describing the design, we present and validate a stochastic model that conservatively quantifies its worst-case entropy. We deploy and model the design in the cloud on 60 EC2 F1 FPGA instances to ensure sufficient randomness is captured. TRNG entropy is further validated using NIST test suites, and experiments are performed to understand how the TRNG responds to on-die power attacks that disturb the FPGA supply voltage in the vicinity of the TRNG. After introducing and validating our basic TRNG design, we introduce and validate a new variant that uses four instances of a linkable sampling module to increase the entropy per sample and improve throughput. The new variant improves throughput by 250% at a modest 17% increase in CLB count.
LiDAR Super-Resolution Based on Segmentation and Geometric Analysis

In this work, we propose a new LiDAR super-resolution method for indoor and outdoor scenes in urban environments without training, converting to images and auxiliary sensors. To generate high-resolution (high-res) point clouds from the low-resolution (low-res) measurement of a sparse LiDAR, we deconstruct the whole problem into three sequential modules. First, to avoid feature interference, the raw data are segmented into the ground and non-ground points. Then, these two types of points are reorganized into a more regular representation of the coordinates. Finally, the high-res point clouds are generated via a new geometric method. We present abundant synthetic and real data based on testing and evaluation of the proposed method. Qualitative and quantitative comparisons show that our method is effective and robust.
Weakly Semi-supervised phenotyping using Electronic Health records

Electronic Health Record (EHR) based phenotyping is a crucial yet challenging problem in the biomedical field. Though clinicians typically determine patient-level diagnoses via manual chart review, the sheer volume and heterogeneity of EHR data renders such tasks challenging, time-consuming, and prohibitively expensive, thus leading to a scarcity of clinical annotations in EHRs. Weakly supervised learning algorithms have been successfully applied to various EHR phenotyping problems, due to their ability to leverage information from large quantities of unlabeled samples to better inform predictions based on a far smaller number of patients. However, most weakly supervised methods are subject to the challenge to choose the right cutoff value to generate an optimal classifier. Furthermore, since they only utilize the most informative features (i.e., main ICD and NLP counts) they may fail for episodic phenotypes that cannot be consistently detected via ICD and NLP data. In this paper, we propose a label-efficient, weakly semi-supervised deep learning algorithm for EHR phenotyping (WSS-DL), which overcomes the limitations above.WSS-DL classifies patient-level disease status through a series of learning stages: 1) generating silver standard labels, 2) deriving enhanced-silver-standard labels by fitting a weakly supervised deep learning model to data with silver standard labels as outcomes and high dimensional EHR features as input, and 3) obtaining the final prediction score and classifier by fitting a supervised learning model to data with a minimal number of gold standard labels as the outcome, and the enhanced-silver-standard labels and a minimal set of most informative EHR features as input. To assess the generalizability of WSS-DL across different phenotypes and medical institutions, we apply WSS-DL to classify a total of 17 diseases, including both acute and chronic conditions, using EHR data from three healthcare systems. Additionally, we determine the minimum quantity of training labels required by WSS-DL to outperform existing supervised and semi-supervised phenotyping methods.The proposed method, in combining the strengths of deep learning and weakly semi-supervised learning, successfully leverages the crucial phenotyping information contained in EHR features from unlabeled samples. Indeed, the deep learning model's ability to handle high-dimensional EHR features allows it to generate strong phenotype status predictions from silver standard labels. These predictions, in turn, provide highly effective features in the final logistic regression stage, leading to high phenotyping accuracy in notably small subsets of labeled data (e.g. n = 40 labeled samples).Our method's high performance in EHR datasets with very small numbers of labels indicates its potential value in aiding doctors to diagnose rare diseases as well as conditions susceptible to misdiagnosis.
Bitcoin: jumps, convenience yields, and option prices

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Verifiable quantum secret sharing with multi access structures

In this paper, we propose a verifiable quantum secret sharing scheme. We share multi classical secrets by using the qudit state among the participants based on the different access structures. Moreover, it is possible to check the honesty of participants in the recovery phase. Analyses show that the proposed scheme is secure against internal and external eavesdroppers.
Frequency-driven channel attention-augmented full-scale temporal modeling network for skeleton-based action recognition

The skeleton-based human action recognition has become a popular research focus due to its promising applications. The current methods that model skeletons as spatial–temporal graphs have solidly advanced the state-of-the-art performance. However, there are still two problems to be addressed: (1) Although many existing methods employ multi-scale temporal modeling modules, they are still insufficient to fully capture both short-term and long-term temporal clues. (2) Channel attention modules are often employed for this task to improve recognition accuracy. However, they all use the same strategies to aggregate information from spatial and temporal dimensions, ignoring the fact that the semantic information in these two dimensions is quite different, thus leading to suboptimal network performance. In response to the above problems, we propose a frequency-driven channel attention-augmented full-scale temporal modeling network (FF-TMN) that incorporates two novel modules: (1) An effective and efficient full-scale temporal modeling module (FTMM) encompassing up to three multi-scale modeling strategies is proposed to equip the network with full-scale temporal modeling capabilities. (2) We propose a frequency-driven channel attention module (FCAM) tailored for the HAR task, which is the first module to generate channel descriptors with two different aggregation strategies: global average pooling for the spatial dimension and discrete cosine transform for the temporal dimension. Exhaustive experiments on three challenging large-scale datasets demonstrate that our FF-TMN achieves state-of-the-art performance. The code will be released on https://github.com/desertfex/FF-TMN to facilitate communication.
Performance Evaluation of Low-Latency Live Streaming of MPEG-DASH UHD video over Commercial 5G NSA/SA Network

5G Standalone (SA) is the goal of the 5G evolution, which aims to provide higher throughput and lower latency than the existing LTE network. One of the main applications of 5G is the real-time distribution of Ultra High-Definition (UHD) content with a resolution of 4K or 8K. In Q2/2021, Advanced Info Service (AIS), the biggest operator in Thailand, launched 5G SA, providing both 5G SA/NSA service nationwide in addition to the existing LTE network. While many parts of the world are still in process of rolling out the first phase of 5G in Non-Standalone (NSA) mode, 5G SA in Thailand already covers more than 76% of the population. In this paper, UHD video will be a real-time live streaming via MPEG-DASH over different mobile network technologies with minimal buffer size to provide the lowest latency. Then, performance such as the number of dropped segments, MAC throughput, and latency are evaluated in various situations such as stationary, moving in the urban area, moving at high speed, and also an ideal condition with maximum SINR. It has been found that 5G SA can deliver more than 95% of the UHD video segment successfully within the required time window in all situations, while 5G NSA produced mixed results depending on the condition of the LTE network. The result also reveals that the LTE network failed to deliver more than 20 % of the video segment within the deadline, which shows that 5G SA is absolutely necessary for low-latency UHD video streaming and 5G NSA may not be good enough for such task as it relies on the legacy control signal.
Dynamic Backdoors with Global Average Pooling

Outsourced training and machine learning as a service have resulted in novel attack vectors like backdoor attacks. Such attacks embed a secret functionality in a neural network activated when the trigger is added to its input. In most works in the literature, the trigger is static, both in terms of location and pattern. The effectiveness of various detection mechanisms depends on this property. It was recently shown that countermeasures in image classification, like Neural Cleanse and ABS, could be bypassed with dynamic triggers that are effective regardless of their pattern and location. Still, such backdoors are demanding as they require a large percentage of poisoned training data. In this work, we are the first to show that dynamic backdoor attacks could happen due to a global average pooling layer without increasing the percentage of the poisoned training data. Nevertheless, our experiments in sound classification, text sentiment analysis, and image classification show this to be very difficult in practice.
A Survey and Guideline on Privacy Enhancing Technologies for Collaborative Machine Learning

As machine learning and artificial intelligence (ML/AI) are becoming more popular and advanced, there is a wish to turn sensitive data into valuable information via ML/AI techniques revealing only data that is allowed by concerned parties or without revealing any information about the data to third parties.Collaborative ML approaches like federated learning (FL) help tackle these needs and concerns, bringing a way to use sensitive data without disclosing critically sensitive features of that data.In this paper, we provide a detailed analysis of state of the art for collaborative ML approaches from a privacy perspective.A detailed threat model and security and privacy considerations are given for each collaborative method.We deeply analyze Privacy Enhancing Technologies (PETs), covering secure multi-party computation (SMPC), homomorphic encryption (HE), differential privacy (DP), and confidential computing (CC) in the context of collaborative ML.We introduce a guideline on the selection of the privacy preserving technologies for collaborative ML and privacy practitioners.This study constitutes the first survey to provide an indepth focus on collaborative ML requirements and constraints for privacy solutions while also providing guidelines on the selection of PETs.
Bug Severity Prediction Algorithm Using Topic-Based Feature Selection and CNN-LSTM Algorithm

Increasing software usage has gradually increased the occurrence of bugs. When writing a bug report, the severity of the bug can be freely selected, so the subjective judgment of the author is involved. In subjective judgment, a severity error may occur depending on the background knowledge between the user and the developer. To resolve this problem, in this paper, the severity was predicted using the feature selection algorithm of the severity of each topic. We utilize the dataset in Eclipse and Mozilla open source projects. First, we classify bug reports by topic-based severity, and extract features from the severity of each topic. The severity was predicted by learning the characteristics from the CNN-LSTM algorithm, and the F-measure was 90.62% and 93.22% of Mozilla. To evaluate the effectiveness of the proposed model, we compared the baselines including DeepSeverity and EWD-Multinomial studies with Eclipse and Mozilla open source projects and showed that the proposed model is more efficient.
StRegA: Unsupervised anomaly detection in brain MRIs using a compact context-encoding variational autoencoder

Expert interpretation of anatomical images of the human brain is the central part of neuro-radiology. Several machine learning-based techniques have been proposed to assist in the analysis process. However, the ML models typically need to be trained to perform a specific task, e.g., brain tumour segmentation or classification. Not only do the corresponding training data require laborious manual annotations, but a wide variety of abnormalities can be present in a human brain MRI - even more than one simultaneously, which renders representation of all possible anomalies very challenging. Hence, a possible solution is an unsupervised anomaly detection (UAD) system that can learn a data distribution from an unlabelled dataset of healthy subjects and then be applied to detect out of distribution samples. Such a technique can then be used to detect anomalies - lesions or abnormalities, for example, brain tumours, without explicitly training the model for that specific pathology. Several Variational Autoencoder (VAE) based techniques have been proposed in the past for this task. Even though they perform very well on controlled artificially simulated anomalies, many of them perform poorly while detecting anomalies in clinical data. This research proposes a compact version of the "context-encoding" VAE (ceVAE) model, combined with pre and post-processing steps, creating a UAD pipeline (StRegA), which is more robust on clinical data, and shows its applicability in detecting anomalies such as tumours in brain MRIs. The proposed pipeline achieved a Dice score of 0.642$\pm$0.101 while detecting tumours in T2w images of the BraTS dataset and 0.859$\pm$0.112 while detecting artificially induced anomalies, while the best performing baseline achieved 0.522$\pm$0.135 and 0.783$\pm$0.111, respectively.
Breast Tumor Tissue Segmentation with Area-Based Annotation Using Convolutional Neural Network

In this paper, we propose a novel approach to segment tumor and normal regions in human breast tissues. Cancer is the second most common cause of death in our society; every eighth woman will be diagnosed with breast cancer in her life. Histological diagnosis is key in the process where oncotherapy is administered. Due to the time-consuming analysis and the lack of specialists alike, obtaining a timely diagnosis is often a difficult process in healthcare institutions, so there is an urgent need for improvement in diagnostics. To reduce costs and speed up the process, an automated algorithm could aid routine diagnostics. We propose an area-based annotation approach generalized by a new rule template to accurately solve high-resolution biological segmentation tasks in a time-efficient way. These algorithm and implementation rules provide an alternative solution for pathologists to make decisions as accurate as manually. This research is based on an individual database from Semmelweis University, containing 291 high-resolution, bright field microscopy breast tumor tissue images. A total of 70% of the 128 × 128-pixel resolution images (206,174 patches) were used for training a convolutional neural network to learn the features of normal and tumor tissue samples. The evaluation of the small regions results in high-resolution histopathological image segmentation; the optimal parameters were calculated on the validation dataset (29 images, 10%), considering the accuracy and time factor as well. The algorithm was tested on the test dataset (61 images, 20%), reaching a 99.10% f1 score on pixel level evaluation within 3 min on average. Besides the quantitative analyses, the system's accuracy was measured qualitatively by a histopathologist, who confirmed that the algorithm was also accurate in regions not annotated before.
Spatio-Temporal Sentiment Mining of COVID-19 Arabic Social Media

Since the recent outbreak of COVID-19, many scientists have started working on distinct challenges related to mining the available large datasets from social media as an effective asset to understand people’s responses to the pandemic. This study presents a comprehensive social data mining approach to provide in-depth insights related to the COVID-19 pandemic and applied to the Arabic language. We first developed a technique to infer geospatial information from non-geotagged Arabic tweets. Secondly, a sentiment analysis mechanism at various levels of spatial granularities and separate topic scales is introduced. We applied sentiment-based classifications at various location resolutions (regions/countries) and separate topic abstraction levels (subtopics and main topics). In addition, a correlation-based analysis of Arabic tweets and the official health providers’ data will be presented. Moreover, we implemented several mechanisms of topic-based analysis using occurrence-based and statistical correlation approaches. Finally, we conducted a set of experiments and visualized our results based on a combined geo-social dataset, official health records, and lockdown data worldwide. Our results show that the total percentage of location-enabled tweets has increased from 2% to 46% (about 2.5M tweets). A positive correlation between top topics (lockdown and vaccine) and the COVID-19 new cases has also been recorded, while negative feelings of Arab Twitter users were generally raised during this pandemic, on topics related to lockdown, closure, and law enforcement.