Reviewerly/exHarmony huggingface dataset:
Malignancy detection on mammograms by integrating modified convolutional neural network classifier and texture features

Abstract Breast cancer is detected by identifying malignancy on breast tissue. Emerging technologies in medical image processing are used to interpret histopathology images. For analyzing medical imaging and pathological data, modified deep neural networks are being used. Automatic detection of malignancy is usually achieved in deep learning by capturing features from a convolutional neural network (CNN) and then categorizing them using a fully connected network. A framework to automatically diagnose malignancy using an ensemble approach, including CNN and extraction of image texture features, is implemented in this research. In the CNN phase, the nine‐layer modified CNN is used to classify images. Texture features are derived and their dimension is minimized using maximum variance unfolding to enhance the efficiency of classification in the extraction‐based phase. The results of each phase were then merged to obtain the final decision. The testing specificity and accuracy of our ensemble method on MIAS repository are 98.9% and 99%, respectively and for the DDSM repository are 98.3% and 98.1%. The ensemble approach increases the measurement metrics compared to each phase separately, as per the experimental findings.
Semi-Supervised Temporal Action Proposal Generation via Exploiting 2-D Proposal Map

Temporal action proposal generation aims to generate temporal video segments containing human actions in untrimmed videos, which is always a preliminary for such video understanding tasks as action localization and temporally description grounding, <i>etc</i>. Fully-supervised solutions, though proven to be effective, suffer much from heavy data annotation overhead. To address this problem, this paper focuses on a rarely investigated yet practical problem of semi-supervised learning for temporal action proposal generation. Firstly, we propose a <i>Proposal Map oriented Mean-Teacher</i> (PM-MT) model, which can use both labeled and unlabeled data for end-to-end model training. Secondly, a <i>Suppression-and-Re-Generation</i> (SRG) strategy is designed to generate high-quality pseudo labels for unlabeled data, which are then used to finetune the model. Extensive experiments demonstrate the effectiveness of our proposed method, by achieving the state-of-the-art results on two public benchmark datatsets on the task of semi-supervised action proposal generation and outperforming fully-supervised learning methods with only a portion of labeled data.
Anti-Jamming Radiometric Automatic Tracking System

The paper studies an anti-jamming system for automatic tracking of single vehicles. Tracking is implemented by vehicles radiation in the millimeter wavelength range. Authors synthesized tracking using statistically optimal algorithm for joint filtering of continuous and discrete Markov sequences.
Capsule Boundary Network With 3D Convolutional Dynamic Routing for Temporal Action Detection

Temporal action detection is a challenging task in video understanding, due to the complexity of the background and rich action content impacting high-quality temporal proposals generation in untrimmed videos. Capsule networks can avoid some limitations of the invariance caused by pooling and inability from convolutional neural networks, which can better understand the temporal relations for temporal action detection. However, because of the extremely computationally expensive procedure, capsule network is difficult to be applied to the task of temporal action detection. To address this issue, this paper proposes a novel U-shaped capsule network framework with a k-Nearest Neighbor (k-NN) mechanism of 3D convolutional dynamic routing, which we named U-BlockConvCaps. Furthermore, we build a Capsules Boundary Network (CapsBoundNet) based on U-BlockConvCaps for dense temporal action proposal generation. Specifically, the first module is one 1D convolutional layer for fusing the two-stream with RGB and optical flow video features. The sampling module further processes the fused features to generate the 2D start-end action proposal feature maps. Then, the multi-scale U-Block convolutional capsule module with 3D convolutional dynamic routing is used to process the proposal feature map. Finally, the feature maps generated from the CapsBoundNet are used to predict starting, ending, action classification, and action regression score maps, which help to capture the boundary and intersection over union features. Our work innovatively improves the dynamic routing algorithm of capsule networks and extends the use of capsule networks to the temporal action detection task for the first time in the literature. The experimental results on benchmarks THUMOS14 show that the performance of CapsBoundNet is obviously beyond the state-of-the-art methods, e.g., the mAP@tIoU = 0.3, 0.4, 0.5 on THUMOS14 are improved from 63.6% to 70.0%, 57.8% to 63.1%, 51.3% to 52.9%, respectively. We also got competitive results on the action detection dataset of ActivityNet1.3.
Reproducibility Enhancement by Optimized Power Analysis Attacks in Vulnerability Assessment of IoT Transducers

An improvement in the metrological repeatability and reproducibility of the vulnerability assessment for Internet-of-Things transducers is proposed. In particular, the analysis and the reduction of repeatability and reproducibility uncertainty concern electronic power measurement techniques for the robustness assessment of a smart transducer against a specific hardware attack. The power attack metrological optimization also contributes in a more detailed description of the standard procedure for the vulnerability assessment. The attack is conducted by means of a market-leading and professional tool on a transducer protected by Advanced Encryption Standard (AES). The optimum attack configuration increases the number of disclosed AES key bytes at the least cost in terms of time and resources. Optimization is achieved through a well-settled statistical experimental design, and the analysis of variance (ANOVA) highlights the significance of the considered attack parameters: resampling frequency, number of power traces, and weight of a fast bidirectional filter. Four hundred traces resulted sufficient to discover the AES secret key in each of the experimented configurations with a resampling frequency of 500 kSa/s and a weight of the filter of 500. The optimum parameter configuration exhibited a null 1- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma $ </tex-math></inline-formula> repeatability. The 56% 1- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma $ </tex-math></inline-formula> reproducibility due to the variability of the attack parameters became negligible by adopting the optimum configuration.
A validation frame for deterministic solar irradiance forecasts

A novel validation frame for deterministic solar irradiance forecasts is presented. It bases on the perception that a perfect forecast of a random variable must be indistinguishable from its observation. In mathematical terms: The random variables observation and forecast must be exchangeable. This implies that they have the same probability distribution and that the pertaining copula is symmetric. The validation frame tallies the equality of the probability distributions of observed and forecast solar irradiance, the symmetry of the pertaining copula, and the forecasting skill with well-defined measures, namely the Kolmogorov-Smirnov test statistic, copula asymmetry, and Spearman's ρ. The three measures are scale-invariant under strictly increasing transformations of observation and forecast. Thus, the frame is particularly suited for benchmarking solar irradiance forecasts from different locations, different calendar days, and over different periods. The frame is demonstrated on 24-h-ahead solar irradiance forecasts from two sites with different climates. Yet, it can be applied to any forecast of a continuous random variable. Additional theoretical insights connect the method to present-day practice, such as the Murphy-Winkler framework and the skill score.
Efficient and Privacy-Preserving Speaker Recognition for Cybertwin-Driven 6G

With the introduction of cybertwin, a new approach to represent human or things in the cyberspace, it is foreseeable that vehicles will be able to offer more and more services in the future. Naturally, considering the safety of drivers, speaker recognition will be widely used in vehicle scenarios. Speaker recognition technologies are experiencing increasing popularity due to the unique and indissoluble link between individuals and their voices. However, the coming cybertwin-driven 6G brings speaker recognition technologies unprecedented challenges, especially in preventing the disclosure of voiceprint. To address these challenges, an efficient and privacy-preserving speaker recognition scheme for cybertwin-driven 6G, referred to as NEATEN, is proposed in this article. With NEATEN, the speaker identity can be recognized at multiple security levels without leaking the voiceprint data. More concretely, based on the random projection data perturbation, voiceprint perturbation algorithms in two phases and the corresponding ciphertext-based similarity computation algorithm are proposed. By using these algorithms, our efficient and accurate speaker recognition scheme can be achieved. Orthogonal to the previous works of biometric identification based on the Euclidean distance, NEATEN makes progress on the non-Euclidean distance, such as cosine distance and complicated distance. Detailed analysis shows that NEATEN can resist various known security threats. Experiments conducted on TIMIT and Voxceleb data sets have demonstrated that NEATEN is highly accurate and efficient, and can be flexibly deployed in a real cybertwin-driven 6G vehicle environment.
Integer-arithmetic-only Certified Robustness for Quantized Neural Networks

Adversarial data examples have drawn significant attention from the machine learning and security communities. A line of work on tackling adversarial examples is certified robustness via randomized smoothing that can provide a theoretical robustness guarantee. However, such a mechanism usually uses floating-point arithmetic for calculations in inference and requires large memory footprints and daunting computational costs. These defensive models cannot run efficiently on edge devices nor be deployed on integer-only logical units such as Turing Tensor Cores or integer-only ARM processors. To overcome these challenges, we propose an integer randomized smoothing approach with quantization to convert any classifier into a new smoothed classifier, which uses integer-only arithmetic for certified robustness against adversarial perturbations. We prove a tight robustness guarantee under ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> -norm for the proposed approach. We show our approach can obtain a comparable accuracy and 4× ∼ 5× speedup over floating-point arithmetic certified robust methods on general-purpose CPUs and mobile devices on two distinct datasets (CIFAR-10 and Caltech-101).
Learning-based extraction of first-order logic representations of API directives

Developers often rely on API documentation to learn API directives, i.e., constraints and guidelines related to API usage. Failing to follow API directives may cause defects or improper implementations. Since there are no industry-wide standards on how to document API directives, they take many forms and are often hard to understand by developers or challenging to parse with tools.
Beyond VQA: Generating Multi-word Answers and Rationales to Visual Questions

Visual Question Answering is a multi-modal task that aims to measure high-level visual understanding. Contemporary VQA models are restrictive in the sense that answers are obtained via classification over a limited vocabulary (in the case of open-ended VQA), or via classification over a set of multiple-choice-type answers. In this work, we present a completely generative formulation where a multi-word answer is generated for a visual query. To take this a step forward, we introduce a new task: ViQAR (Visual Question Answering and Reasoning), wherein a model must generate the complete answer and a rationale that seeks to justify the generated answer We propose an end-to-end architecture to solve this task and describe how to evaluate it. We show that our model generates strong answers and rationales through qualitative and quantitative evaluation, as well as through a human Turing Test.
Measuring associational thinking through word embeddings

Abstract The development of a model to quantify semantic similarity and relatedness between words has been the major focus of many studies in various fields, e.g. psychology, linguistics, and natural language processing. Unlike the measures proposed by most previous research, this article is aimed at estimating automatically the strength of associative words that can be semantically related or not. We demonstrate that the performance of the model depends not only on the combination of independently constructed word embeddings (namely, corpus- and network-based embeddings) but also on the way these word vectors interact. The research concludes that the weighted average of the cosine-similarity coefficients derived from independent word embeddings in a double vector space tends to yield high correlations with human judgements. Moreover, we demonstrate that evaluating word associations through a measure that relies on not only the rank ordering of word pairs but also the strength of associations can reveal some findings that go unnoticed by traditional measures such as Spearman’s and Pearson’s correlation coefficients.
A Systematic Analysis of Covert Channels in the Network Time Protocol

Covert channels in network protocols are a technique aiming to hide the very existence of secret communication in computer networks. In this work we present a systematic in-depth analysis of covert channels by modification for the Network Time Protocol (NTP). Our analysis results in the identification of 49 covert channels, by applying a covert channel pattern-based taxonomy. The summary and comparison based on nine selected key attributes show that NTP is a plausible carrier for covert channels. The analysis results are evaluated in regards to common behavior of NTP implementations in six major operating systems. Two channels are selected and implemented to be evaluated in network test-beds. By hiding encrypted high entropy data in a high entropy field of NTP we show in our first assessment that practically undetectable channels can be implemented in NTP, motivating the required further research. In our evaluation, we analyze 40,000 NTP server responses from public NTP server providers. We discuss the general approach of the research community that detection of covert channels is the more promising countermeasure, compared to active suppression of covert channels. Therefore, normalization approaches and a secure network environment are introduced.
Building a bot for automatic expert retrieval on discord

It is common for software practitioners to look for experts on online chat platforms, such as Discord. However, finding them is a complex activity that requires a deep knowledge of the open source community. As a consequence, newcomers and casual participants may not be able to adequately find experts willing to discuss a particular topic.
Real-time Abnormal Human Activity Detection Using 1DCNN-LSTM for 3D Skeleton Data

Over the last few years, together with the advent of deep learning and the accessibility of depth sensors, home care supervision has risen in demand. This is becoming even more evident with the ongoing COVID-19 epidemic, which has put vulnerable people like the elderly and handicapped at even a more significant health risk and created a need for online and distance-based alternatives for human interaction. As such, a lightweight method for real-time Abnormal Human Activity Detection is proposed. It uses a rolling window approach when processing streams of activities and pinpointing exactly when an abnormality occurs. A combination of an One-Dimensional Convolutional neural network (CNN) and a Long Short-Term Memory (LSTM) network is used, both overlapping in their application but having different advantages and disadvantages. The method achieves a precision of 96.6% and an accuracy of 91% on the TST Fall Dataset v2.
Efficient objects tracking from an unmanned aerial vehicle

object tracking is one of the most sophisticated and least researched tasks in computer vision, especially with respect to unmanned aerial vehicles. Primarily it caused by several challenges such as high distance to the tracking objects, variety in object sizes, camera motion, etc. This paper demonstrates the implementation of a tracking pipeline using the YOLOv4-based architecture network defined as YOLOv4eff with the doubled long-short time memory (LSTM) network for road-objects tracking. Our optimization method within YOLOv4eff addresses higher accuracy by improving the network architecture, using modified CSP techniques, Swish activation function, etc. Our object tracker used convolutional network as feature map extractor network of differential sequential frames, YOLOv4eff as detector and doubled long-short time memory (LSTM) network as tracking locations predictor. Our extensive experimental results on self-collected dataset at the height of 10-30 meters and performance comparison with other state-of-the-art tracking methods show that our LYOLOv4eff is more accurate and effectively applicable to objects tracking from a drone.
Self-Supervised Bodymap-to-Appearance Co-Attention for Partial Person Re-Identification

Person re-identification (re-ID) aims at recognizing the same person across distinct camera views. Although a series of works have been proposed to tackle re-ID, most existing methods are based on the assumption that full body detection is available. In practice, detection of pedestrians may not be perfect due to partial occlusion or background clutter, which would result in the challenging task of partial re-ID. To tackle this problem, we propose a novel deep learning framework which jointly performs image rescaling and bodymap-to-appearance co-attention, followed by image matching for re-ID purposes. The image rescaler presented in our framework learns to produce distortion-free images in a self-supervised manner, which allows the subsequent image matching based on the associated image regions via body part attention. Our quantitative and qualitative results on two benchmark partial re-ID datasets confirm the effectiveness of our approach and its superiority over state-of-the-art partial re-ID approaches.
A lightweight ISE for ChaCha on RISC-V

ChaCha is a high-throughput stream cipher designed with the aim of ensuring high-security margins while achieving high performance on software platforms. RISC-V, an emerging, free, and open Instruction Set Architecture (ISA) is being developed with many instruction set extensions (ISE). ISEs are a native concept in RISC-V to support a relatively small RISC-V ISA to suit different use-cases including cryptographic acceleration via either standard or custom ISEs. This paper proposes a lightweight ISE to support ChaCha on RISC-V architectures. This approach targets embedded computing systems such as IoT edge devices that don’t support a vector engine. The proposed ISE is designed to accelerate the computation of the ChaCha block function and align with the RISC-V design principles. We show that our proposed ISEs help to improve the efficiency of the ChaCha block function. The ISE-assisted implementation of ChaCha encryption speeds up at least 5.4× and 3.4× compared to the OpenSSL baseline and ISA-based optimised implementation, respectively. For encrypting short messages, the ISE-assisted implementation gains a comparative performance compared to the implementations using very high area overhead vector extensions.
Hyperspectral Image Denoising With Log-Based Robust PCA

It is a challenging task to remove heavy and mixed types of noise from Hyperspectral images (HSIs). In this paper, we propose a novel nonconvex approach to RPCA for HSI denoising, which adopts the log-determinant rank approximation and a novel $\ell_{2,\text{l}\text{o}\text{g}}$ norm, to restrict the low-rank or column-wise sparse properties for the component matrices, respectively. For the $\ell_{2,\text{l}\text{o}\text{g}}$-regularized shrinkage problem, we develop an efficient, closed-form solution, which is named $\ell_{2,\text{l}\text{o}\text{g}}$-shrinkage operator, which can be generally used in other problems. Extensive experiments on both simulated and real HSIs demonstrate the effectiveness of the proposed method in denoising HSIs.
An Energy-Efficient GAN Accelerator With On-Chip Training for Domain-Specific Optimization

Generative adversarial networks (GANs) consist of multiple deep neural networks cooperating and competing with each other. Due to their complex architectures and large feature map sizes, training GANs requires a huge amount of computations. Moreover, instance normalization (IN) layers in GANs dramatically increase the external memory access (EMA). However, retraining GANs with user-specific data is critical on mobile devices because the pre-trained model outputs distorted images under user-specific conditions. This article proposes a GAN training accelerator to enable energy-efficient domain-specific optimization of GAN with user’s local data. Selective layer retraining (SELRET) picks out layers that are effective in enhancing the quality of the retrained model. Without image quality degradation, the SELRET reduces the required computation by 69%. Moreover, reordering layers for instance normalization (ROLIN) is proposed to reduce the EMA of intermediate data. Through the implementation of the proposed architecture, which splits and reorders the IN layers, 38.7% and 32.2% of overall EMA reduction are achieved in the forward propagation (FP) stage and the error propagation (EP) stage, respectively. The proposed processor is fabricated in a 65-nm CMOS process, showing 0.38-TFLOPS/W energy efficiency. The chip can retrain a face modification GAN with a custom dataset of 256 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 256 images over 100 epochs under 30 s while only consuming 274 mW. Compared to the previous FPGA implementation, this work improved the retraining performance and energy efficiency by 2 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> and 39 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> , respectively. As a result, the proposed accelerator enables GAN’s domain-specific optimization on a mobile platform.