Reviewerly/exHarmony huggingface dataset:
Area-Time Efficient Hardware Implementation of Modular Multiplication for Elliptic Curve Cryptography

In this paper, an area-time efficient hardware implementation of modular multiplication over five National Institute of Standard and Technology (NIST)-recommended prime fields is proposed for lightweight elliptic curve cryptography (ECC). A modified radix-2 interleaved algorithm is proposed to reduce the time complexity of conventional interleaved modular multiplication. The proposed multiplication algorithm is designed in hardware and separately implemented on Xilinx Virtex-7, Virtex-6, Virtex-5, and Virtex-4 field-programmable gate array (FPGA) platforms. On the Virtex-7 FPGA, the proposed design occupies only 1151, 1409, 1491, 2355, and 2496 look up tables (LUTs) and performs single modular multiplication in 0.93 μs, 1.18 μs, 1.45 μs, 2.80 μs, and 4.69 μs with maximum clock frequencies of 207.1 MHz, 190.7 MHz, 177.3 MHz, 137.6 MHz, and 111.2 MHz over five NIST prime fields of size 192, 224, 256, 384, and 521 bits, respectively. The hardware implementations on the Virtex-6, Virtex-5, and Virtex-4 FPGAs also show that the proposed design is highly efficient in terms of hardware resource utilization and area-delay product compared with other designs for modular multiplication.
Retweeting Covid-19 disability issues: Risks, support and outrage

The Covid-19 pandemic has greatly uneven impacts on sectors of society. People with disabilities are particularly vulnerable to it and so it is important to understand both the disability perspective and the role of social media. This information may help to reduce the risk from the disease. In response, this article uses thematic analysis to investigate 59 disability-related tweets from March 10 to April 4, 2020 that were retweeted at least 500 times, with a quarter of a million retweets altogether. This approach generates quick insights into widely resonating disability-related issues. The results suggest the value of Twitter for disseminating information about the risk, offers or requests for support, the ability of many people with disabilities to adjust to the changes well, and information about individuals with the disease. In addition, there was outrage at suggestions that the disease was less serious because young people without disabilities were relatively low risk, and that people with disabilities might be denied equal access to medical treatment. As one tweet pointed out, people in less vulnerable categories should not be told on Twitter or elsewhere that the disease is less relevant to them because their actions can impact others through social spreading.
Automatic Anomaly Detection on In-Production Manufacturing Machines Using Statistical Learning Methods

Anomaly detection is becoming increasingly important to enhance reliability and resiliency in the Industry 4.0 framework. In this work, we investigate different methods for anomaly detection on in-production manufacturing machines taking into account their variability, both in operation and in wear conditions. We demonstrate how the nature of the available data, featuring any anomaly or not, is of importance for the algorithmic choice, discussing both statistical machine learning methods and control charts. We finally develop methods for automatic anomaly detection, which obtain a recall close to one on our data. Our developed methods are designed not to rely on a continuous recalibration and hand-tuning by the machine user, thereby allowing their deployment in an in-production environment robustly and efficiently.
CNN-Based Quality Assurance for Automatic Segmentation of Breast Cancer in Radiotherapy

Purpose: More and more automatic segmentation tools are being introduced in routine clinical practice. However, physicians need to spend a considerable amount of time in examining the generated contours slice by slice. This greatly reduces the benefit of the tool's automaticity. In order to overcome this shortcoming, we developed an automatic quality assurance (QA) method for automatic segmentation using convolutional neural networks (CNNs). Materials and Methods: The study cohort comprised 680 patients with early-stage breast cancer who received whole breast radiation. The overall architecture of the automatic QA method for deep learning-based segmentation included the following two main parts: a segmentation CNN model and a QA network that was established based on ResNet-101. The inputs were from computed tomography, segmentation probability maps, and uncertainty maps. Two kinds of Dice similarity coefficient (DSC) outputs were tested. One predicted the DSC quality level of each slice ([0.95, 1] for "good," [0.8, 0.95] for "medium," and [0, 0.8] for "bad" quality), and the other predicted the DSC value of each slice directly. The performances of the method to predict the quality levels were evaluated with quantitative metrics: balanced accuracy, F score, and the area under the receiving operator characteristic curve (AUC). The mean absolute error (MAE) was used to evaluate the DSC value outputs. Results: The proposed methods involved two types of output, both of which achieved promising accuracy in terms of predicting the quality level. For the good, medium, and bad quality level prediction, the balanced accuracy was 0.97, 0.94, and 0.89, respectively; the F score was 0.98, 0.91, and 0.81, respectively; and the AUC was 0.96, 0.93, and 0.88, respectively. For the DSC value prediction, the MAE was 0.06 ± 0.19. The prediction time was approximately 2 s per patient. Conclusions: Our method could predict the segmentation quality automatically. It can provide useful information for physicians regarding further verification and revision of automatic contours. The integration of our method into current automatic segmentation pipelines can improve the efficiency of radiotherapy contouring.
Usability analysis of on-line educational courses on the platform “Higher school mathematics teacher”

The article addresses the issue of implementing the usability principles of educational internet resources. The paper debates the latest researches on the question concerning the search for the factors that influence the results of online education. The analysis, which we carried out, allowed us to focus on such known six criteria of usability design as Information Quality, System Navigation, System Learnability, Visual Design, Instructional Assessment, and System Interactivity and suggest the existence of the seventh criterion named Responsiveness. The research considers the principles of usability implementation following the example of the open platform of online education “Higher School Mathematics Teacher”. The answers given by 203 respondents during the survey allowed defining the direction of implementing the usability criteria on the platform. We were eager to know the opinion of teachers and students, who became the first users of the platform. The article discusses the criteria implementation while developing online courses on the platform. There was ground to conclude that when designing on-line platform courses, all seven usability subcategories are important.
Microwave photon detection by an Al Josephson junction

An aluminium Josephson junction (JJ), with a critical current suppressed by a factor of three compared with the maximal value calculated from the gap, is experimentally investigated for application as a threshold detector for microwave photons. We present the preliminary results of measurements of the lifetime of the superconducting state and the probability of switching by a 9 GHz external signal. We found an anomalously large lifetime, not described by the Kramers' theory for the escape time over a barrier under the influence of fluctuations. We explain it by the phase diffusion regime, which is evident from the temperature dependence of the switching current histograms. Therefore, phase diffusion allows for a significant improvement of the noise immunity of a device, radically decreasing the dark count rate, but it will also decrease the single-photon sensitivity of the considered threshold detector. Quantization of the switching probability tilt as a function of the signal attenuation for various bias currents through the JJ is observed, which resembles the differentiation between N and N + 1 photon absorption.
Adaptive core fusion-based density peak clustering for complex data with arbitrary shapes and densities

A challenging issue of clustering in real-word application is to detect clusters with arbitrary shapes and densities in complex data. Many conventional clustering algorithms are capable of detecting non-spherical clusters, but their performance is limited when processing data with complex shapes and multiple density peaks in a cluster without knowing the number of clusters. This paper proposes an adaptive core fusion-based density peak clustering (CFDPC) for detecting clusters in any shape and density adaptively. An initial clustering based on automatic finding of density peaks is proposed first. An adaptive searching approach is then proposed to find core points, and a within-cluster similarity-based core fusion strategy is proposed to obtain the final clustering results. The CFDPC where the number of clusters arises intuitively is simple and efficient. The performance of CFDPC is successfully verified in clustering several benchmark complex datasets with diverse shapes and densities.
A novel non-iterative correction method for short-term photovoltaic power forecasting

Short-term photovoltaic (PV) power forecasting is of great significance for the real-time dispatching of power systems, but the accuracy of short-term forecasting of PV power is not satisfactory. Mastering the distribution characteristics of forecasting error and correcting the forecasting results are effective ways to improve the short-term forecasting accuracy. In this paper, the error distribution characteristics of short-term prediction results of PV power are studied, and then a non-iterative correction method for PV power short-term forecasting is proposed. The statistical result show that the error distribution is different in different seasons, the power forecasted error is strongly similar to the irradiance error distribution in numerical weather prediction (NWP). Therefore, this paper calculates the short-term forecasting results through seasonal models and uses non-iterative method to correct forecasting results, which can effective avoids the influence of accumulation errors. Compared with other methods, the root mean square error (RMSE) of this method is reduced by about 4.5%, and the mean absolute error (MAE) is reduced by about 2.6%, it shows the method can effectively improve the short-term forecasting accuracy of PV power.
MLS3RDUH: Deep Unsupervised Hashing via Manifold based Local Semantic Similarity Structure Reconstructing

Most of the unsupervised hashing methods usually map images into semantic similarity-preserving hash codes by constructing local semantic similarity structure as guiding information, i.e., treating each point similar to its k nearest neighbours. However, for an image, some of its k nearest neighbours may be dissimilar to it, i.e., they are noisy datapoints which will damage the retrieval performance. Thus, to tackle this problem, in this paper, we propose a novel deep unsupervised hashing method, called MLS3RDUH, which can reduce the noisy datapoints to further enhance retrieval performance. Specifically, the proposed method first defines a novel similarity matrix by utilising the intrinsic manifold structure in feature space and the cosine similarity of datapoints to reconstruct the local semantic similarity structure. Then a novel log-cosh hashing loss function is used to optimize the hashing network to generate compact hash codes by incorporating the defined similarity as guiding information. Extensive experiments on three public datasets show that the proposed method outperforms the state-of-the-art baselines.
A Tale of a Probe and a Parser

Measuring what linguistic information is encoded in neural models of language has become popular in NLP.Researchers approach this enterprise by training "probes"supervised models designed to extract linguistic structure from another model's output.One such probe is the structural probe (Hewitt and Manning, 2019), designed to quantify the extent to which syntactic information is encoded in contextualised word representations.The structural probe has a novel design, unattested in the parsing literature, the precise benefit of which is not immediately obvious.To explore whether syntactic probes would do better to make use of existing techniques, we compare the structural probe to a more traditional parser with an identical lightweight parameterisation.The parser outperforms structural probe on UUAS in seven of nine analysed languages, often by a substantial amount (e.g. by 11.1 points in English).Under a second less common metric, however, there is the opposite trend-the structural probe outperforms the parser.This begs the question: which metric should we prefer?
Text Style Transfer via Learning Style Instance Supported Latent Space

Text style transfer pursues altering the style of a sentence while remaining its main content unchanged. Due to the lack of parallel corpora, most recent work focuses on unsupervised methods and has achieved noticeable progress. Nonetheless, the intractability of completely disentangling content from style for text leads to a contradiction of content preservation and style transfer accuracy. To address this problem, we propose a style instance supported method, StyIns. Instead of representing styles with embeddings or latent variables learned from single sentences, our model leverages the generative flow technique to extract underlying stylistic properties from multiple instances of each style, which form a more discriminative and expressive latent style space. By combining such a space with the attention-based structure, our model can better maintain the content and simultaneously achieve high transfer accuracy. Furthermore, the proposed method can be flexibly extended to semi-supervised learning so as to utilize available limited paired data. Experiments on three transfer tasks, sentiment modification, formality rephrasing, and poeticness generation, show that StyIns obtains a better balance between content and style, outperforming several recent baselines.
Lightweight multi-scale residual networks with attention for image super-resolution

In recent years, constructing various deep convolutional neural networks (CNNs) for single-image super-resolution (SISR) tasks has made significant progress. Despite their high performance, numerous CNNs are limited in practical applications, owing to the requirement of heavy computation. This paper proposes a lightweight network for SISR, known as attention-based multi-scale residual network (AMSRN). In detail, a residual atrous spatial pyramid pooling (ASPP) block as well as a spatial and channel-wise attention residual (SCAR) block is stacked alternately to support the main framework of the entire network. The residual ASPP block utilizes parallel dilated convolutions of different dilation rates to achieve the purpose of capturing multi-scale features. The SCAR block adds the channel attention (CA) and spatial attention (SA) mechanisms based on a double-layer convolution residual block. In addition, group convolution is introduced in the SCAR block to further reduce the parameters while preventing over-fitting. Moreover, a multi-scale feature attention module is designed to provide instructive multi-scale attention information for shallow features. Particularly, we propose a novel upscale module, which adopts dual paths to upscale the features by jointly using sub-pixel convolution and nearest interpolation layers, instead of using deconvolution layer or sub-pixel convolution layer alone. The experimental results demonstrate that our method achieves comparable performance to the state-of-the-art methods, both quantitatively and qualitatively.
Probing Linguistic Systematicity

Recently, there has been much interest in the question of whether deep natural language understanding models exhibit systematicitygeneralizing such that units like words make consistent contributions to the meaning of the sentences in which they appear.There is accumulating evidence that neural models often generalize non-systematically.We examined the notion of systematicity from a linguistic perspective, defining a set of probes and a set of metrics to measure systematic behaviour.We also identified ways in which network architectures can generalize non-systematically, and discuss why such forms of generalization may be unsatisfying.As a case study, we performed a series of experiments in the setting of natural language inference (NLI), demonstrating that some NLU systems achieve high overall performance despite being non-systematic.
Fractional Skipping: Towards Finer-Grained Dynamic CNN Inference

While increasingly deep networks are still in general desired for achieving state-of-the-art performance, for many specific inputs a simpler network might already suffice. Existing works exploited this observation by learning to skip convolutional layers in an input-dependent manner. However, we argue their binary decision scheme, i.e., either fully executing or completely bypassing one layer for a specific input, can be enhanced by introducing finer-grained, “softer” decisions. We therefore propose a Dynamic Fractional Skipping (DFS) framework. The core idea of DFS is to hypothesize layer-wise quantization (to different bitwidths) as intermediate “soft” choices to be made between fully utilizing and skipping a layer. For each input, DFS dynamically assigns a bitwidth to both weights and activations of each layer, where fully executing and skipping could be viewed as two “extremes” (i.e., full bitwidth and zero bitwidth). In this way, DFS can “fractionally” exploit a layer's expressive power during input-adaptive inference, enabling finer-grained accuracy-computational cost trade-offs. It presents a unified view to link input-adaptive layer skipping and input-adaptive hybrid quantization. Extensive experimental results demonstrate the superior tradeoff between computational cost and model expressive power (accuracy) achieved by DFS. More visualizations also indicate a smooth and consistent transition in the DFS behaviors, especially the learned choices between layer skipping and different quantizations when the total computational budgets vary, validating our hypothesis that layer quantization could be viewed as intermediate variants of layer skipping. Our source code and supplementary material are available at https://github.com/Torment123/DFS.
MFRNet: A New CNN Architecture for Post-Processing and In-loop Filtering

In this paper, we propose a novel convolutional neural network (CNN) architecture, MFRNet, for post-processing (PP) and in-loop filtering (ILF) in the context of video compression. This network consists of four multi-level feature review residual dense blocks (MFRBs), which are connected using a cascading structure. Each MFRB extracts features from multiple convolutional layers using dense connections and a multi-level residual learning structure. In order to further improve information flow between these blocks, each of them also reuses high dimensional features from the previous MFRB. This network has been integrated into PP and ILF coding modules for both HEVC (HM 16.20) and VVC (VTM 7.0), and fully evaluated under the JVET Common Test Conditions using the Random Access configuration. The experimental results show significant and consistent coding gains over both anchor codecs (HEVC HM and VVC VTM) and also over other existing CNN-based PP/ILF approaches based on Bjøntegaard Delta measurements using both PSNR and VMAF for quality assessment. When MFRNet is integrated into HM 16.20, gains up to 16.0% (BD-rate VMAF) are demonstrated for ILF, and up to 21.0% (BD-rate VMAF) for PP. The respective gains for VTM 7.0 are up to 5.1% for ILF and up to 7.1% for PP.
SmartExchange: Trading Higher-cost Memory Storage/Access for Lower-cost Computation

We present SmartExchange, an algorithm-hardware co-design framework to trade higher-cost memory storage/access for lower-cost computation, for energy-efficient inference of deep neural networks (DNNs). We develop a novel algorithm to enforce a specially favorable DNN weight structure, where each layerwise weight matrix can be stored as the product of a small basis matrix and a large sparse coefficient matrix whose non-zero elements are all power-of-2. To our best knowledge, this algorithm is the first formulation that integrates three mainstream model compression ideas: sparsification or pruning, decomposition, and quantization, into one unified framework. The resulting sparse and readily-quantized DNN thus enjoys greatly reduced energy consumption in data movement as well as weight storage. On top of that, we further design a dedicated accelerator to fully utilize the SmartExchange-enforced weights to improve both energy efficiency and latency performance. Extensive experiments show that 1) on the algorithm level, SmartExchange outperforms stateof-the-art compression techniques, including merely sparsification or pruning, decomposition, and quantization, in various ablation studies based on nine models and four datasets; and 2) on the hardware level, SmartExchange can boost the energy efficiency by up to 6.7× and reduce the latency by up to 19.2× over four state-of-the-art DNN accelerators, when benchmarked on seven DNN models (including four standard DNNs, two compact DNN models, and one segmentation model) and three datasets.
Autonomics: In search of a foundation for next-generation autonomous systems

The potential benefits of autonomous systems are obvious. However, there are still major issues to be dealt with before developing such systems becomes a commonplace engineering practice, with accepted and trustworthy deliverables. We argue that a solid, evolving, publicly available, community-controlled foundation for developing next-generation autonomous systems is a must, and term the desired foundation "autonomics." We focus on three main challenges: 1) how to specify autonomous system behavior in the face of unpredictability; 2) how to carry out faithful analysis of system behavior with respect to rich environments that include humans, physical artifacts, and other systems; and 3) how to build such systems by combining executable modeling techniques from software engineering with artificial intelligence and machine learning.
Energy efficiency optimization of an integrated heat pipe cooling system in data center based on genetic algorithm

The energy consumption of data centers has become an increasing concern in the whole world. Actually, the cooling system consumes the most energy among the auxiliary facilities. An integrated heat pipe cooling system has been proposed to reduce the energy consumption of cooling system by using natural cooling sources in our previous work. However, it still has huge energy-saving potential in the operating process. In this paper, the heat transfer model and energy consumption model of the integrated heat pipe cooling system was established to describe the relationship between operating parameters and energy efficiency. Based on genetic algorithm, energy efficiency optimization operation strategy was calculated at different load rates and outdoor ambient temperature. An experiment was carried out to verify the accuracy of the optimization algorithm. Compared with the previous work, the energy efficiency ratio of the integrated heat pipe cooling system can be improved by 2–3 times in natural cooling model and integrated cooling mode after energy efficiency optimization. The switching rules of the system’s operating mode was studied, and analyzed the energy-saving effects of application in different climate cities.