Reviewerly/exHarmony huggingface dataset:
Acute Teaching Method of College Physical Skills Based on Mobile Intelligent Terminal

Mobile technology can make lessons of physical skills for the students to prepare them more vibrant. Its multicultural, consolidated, and communicative characteristics contribute to the improving teaching performance, excitement of learners, reformation of learning, and a basic idea of teaching mode. The challenge facing college physical education professionals is emotionally labile students, repetition of their education, and the lack of appropriate technology, resources, school violence, and behavioral issues. Because of the high levels of bullying experienced by students with learning and attention difficulties, misbehavior and absence are more likely to occur. The school environment and negative emotions can both aggravate academic difficulties. The proposed methodology involves evaluating student’s physical training based on big data analysis (ESPT-BDA) to compensate voluntary sport behavior by neglecting its unwanted attitude or values and solving the problem of uniform learning to meet the student’s requirements. Repetition helps hone a talent by putting it to use repeatedly. The interval at which a skill is performed is another critical component of repetition. Using spaced repetition as a learning strategy involves gradually increasing the time between repetitions of the same piece of knowledge. The regulating intelligent control framework is used to compute an effective method of directing the insufficient and improper system to achieve a specific objective in an uncertain context. A convolution neural network is implemented to automatically detect vital physical activity characteristics without a need for human monitoring. Various experiment’s findings demonstrate the effect of different sequences up to 96.51% properly classified test data and improve evaluation of intelligent control framework, intelligent practicing intensity control 94.3%.
Attend to Your Review: A Deep Neural Network to Extract Aspects from Peer Reviews

Peer-review process is fraught with issues like bias, inconsistencies, arbitrariness, non-committal weak rejects, etc. However, it is anticipated that the peer reviews provide constructive feedback to the authors against some aspects of the paper such as Motivation/Impact, Soundness/Correctness, Novelty, Substance, etc. A good review is expected to evaluate a paper under the lens of these aspects. An automated system to extract these implicit aspects from the reviews would help determine the quality/goodness of the peer review. In this work, we propose a deep neural architecture to extract the aspects of the paper on which the reviewer commented in their review. Our automatic aspect-extraction model based on BERT and neural attention mechanism achieves superior performance over the standard baselines. We make our codes, analyses and other matrials available at https://github.com/cruxieu17/aspect-extraction-peer-reviews .
Similarity-based domain adaptation network

Domain adaptation utilizes labeled source domains to solve classification problems in the unlabeled target domain. Previous domain adaptation methods consider global domain adaptation while neglecting class-wise information, thus leading to poor transfer performance. In recent years, many researchers studied class-wise domain adaptation, the focus of which is to accurately align the distribution of different domains. However, these methods cannot distinguish samples with different similarities during domain adaptation, which results in the inaccurate matching of different domains. Therefore, this paper proposes a Similarity-Based Adaptation Network (SBAN), which optimizes Similarity-Based Domain Discrepancy (SBDD) that models similarity-based intra-domain and inter-domain discrepancies, and proposes an alternating update strategy to train the SBAN. Specifically, we assign different weights to samples with different similarities, and minimize the similarity-based inter-domain discrepancy to make similar samples dominate the distribution alignment across domains while alleviating the effect of dissimilar samples, and minimize the similarity-based intra-domain discrepancy to align dissimilar samples with similar samples within the same domain to learn more discriminative representations. Extensive experiments on four widely used benchmark datasets show that SBAN performs better than several latest domain adaptation methods.
Self-Parameterized Chaotic Map: A Hardware-efficient Scheme Providing Wide Chaotic Range

We present a general method called “self-parameterization” for designing one-dimensional (1-D) chaotic maps that provide wider chaotic regions than existing 1-D maps. A wide chaotic range is a desirable property as it strengthens the security feature by enlarging the design space in many hardware-security applications, including reconfigurable logic and encryption. The proposed self-parameterized scheme reduces the hardware cost by involving only one chaotic map that modulates its own control parameter at every iteration by passing the map's previous output through a simple linear transformation. The widening of chaotic range after adding self-parameterization is demonstrated on three classical map functions: logistic, tent, and sine. Two hardware-efficient self-parameterized schemes are presented: one for field-programmable gate array (FPGA) implementation and the other one for integrated circuit (IC) implementation. The chaotic performance of the proposed scheme is evaluated with bifurcation plots and three established chaotic entropy metrics including, Lyapunov exponent, correlation coefficient, and correlation dimension.
A self-adaptive batch request aggregation pattern for improving resource management, response time and costs in microservice and serverless environments

Addressing increased demand in cloud environments is typically achieved through auto-scaling the use of further resources allocated. However this comes at the trade-off of increased back-end resource stress as well as runtime costs. In this paper a request batch aggregation and management pattern is proposed and implemented, especially for cases of computational load that needs a heavyweight environment setup (such as performance prediction and AI services). The pattern acts as a preprocessing layer and accumulates requests, withholding their forwarding to the backend based on the conditions of execution (frequency of requests), in order to regulate back-end resource stress without adding extra resources. Different controlling logics are tried out for batch request release, ranging from simpler ones (batch size static regulation, time out interval) to more elaborate ones (ANN model predicting the desired batch size) for adapting the pattern configuration during runtime. Results indicate severe reduction of back-end stress through significantly reducing needed containers (therefore costs as well), enhanced response time as well as avoidance of system breakdown under heavy load. The pattern can be applied in both microservices and serverless environments, especially in Edge cases where resources are constrained.
Blockchain-Guided Dynamic Best-Relay Selection for Trustworthy Vehicular Communication

Considering the highly dynamic nature of the vehicular environment and the delay-sensitive wireless medium, enabling secure and reliable vehicle-to-vehicle communication becomes a very challenging task. In this paper, we propose a novel system design that uses blockchain technology to establish a high-level trust-management successful trustworthy cooperative vehicular wireless communication. The proposed system is a cross-layer approach that optimizes the best-relay selection process allowing only trustworthy relays to participate in data transmission. In this work, blockchain stores real-time information about relaying, transmitting, and receiving vehicles. The information includes channel characteristics and participation quality. The information is vetted and verified by mining vehicles. The result guides the relay selection process to block untrustworthy vehicles from participation. The entire system is comprehensively modeled and mathematically analyzed. Simulations using throughput and Bit Error Rate (BER) as evaluation metrics demonstrated the effectiveness and efficacy of the presented approach in enabling reliable wireless communication in presence of maliciously behaving vehicles. On average, the overall system throughput rate increased by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3bps$ </tex-math></inline-formula> , the BER was reduced by almost by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2dB$ </tex-math></inline-formula> , and the percentage of false messages decreased by 90% considering high Signal to Noise Ratio (SNR).
Video Compression based on Jointly Learned Down-Sampling and Super-Resolution Networks

With the blooming of deep learning technology in computer vision, the integration of deep learning and the traditional video coding has made significant improvements, especially applying the super-resolution neural network as the post-processing module in the down-sampling-based video compression framework. However, the pre-processing module lacks back-propagated gradients for jointly considering down-sampling and up-sampling due to the non-differentiability of the traditional video codec. In this paper, we propose an end- to-end down-sampling-based video compression framework applying convolutional neural networks both as down-sampling and upsampling. We use a virtual codec neural network to approximate the actual video codec so that the gradient can be effectively back-propagated for joint training. Experimental results show the superiority of our proposed framework compared with the predefined down-sampling-based video compression and various methods of joint training.
VLSI Architecture of S-Box With High Area Efficiency Based on Composite Field Arithmetic

This work aims at optimizing the hardware implementation of the SubBytes and inverse SubBytes operations in the advanced encryption standard (AES). To this, the composite field arithmetic (CFA) is employed to optimize all building blocks in S-box (and inverse S-box) of SubBytes (and inverse SubBytes) transformation. A joint design of S-box and inverse S-box is also proposed to further enhance the area efficiency. Specifically, the area of multiplier in the Galois composite field, GF <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$((2^{2})^{2})$ </tex-math></inline-formula> , is reduced. The squaring and multiplication with constant <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> in GF <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$((2^{2})^{2})$ </tex-math></inline-formula> are combined and optimized as well. Moreover, the multiplicative inversion in GF <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$((2^{2})^{2})$ </tex-math></inline-formula> is manually optimized. Furthermore, the S-box and inverse S-box are combined and optimized using the pre_processing and post_processing modules. To increase the throughput, a balanced and pipelined architecture is derived. Using the proposed architecture, a throughput of 5.79 Gbps for the S-box can be achieved on Virtex-6 XC6VLX240T and 10% better than the conventional work. According to the ASIC implementation result, the proposed design can still achieve the highest area efficiency and approximately 30% better than conventional works using TSMC 90nm process.
Automatic Identification of Teachers in Social Media using Positive Unlabeled Learning

With the emergence of online social media platforms, there has been a surge of teachers/educators turning to these platforms for professional purposes, e.g., supplementing their students’ educational needs. Consequently, teachers in social media have been the subject of many educational studies. Despite the progress in this line of research, one of the major obstacles is the limited number of teachers being investigated. Current studies usually suffice to at most a few hundreds of surveyed teachers while there are thousands of other teachers online. To better understand teachers in online social media and enable modern machine learning approaches to process teacher-related data, we need to identify more teachers. Thus, this paper proposes a framework to automatically identify teachers on Pinterest– an image-based social media platform popular among teachers. We formulate the teacher identification problem as a positive unlabeled learning task where positive samples are a small set of surveyed teachers, and unlabeled samples are their connected users on Pinterest. We perform extensive experiments on a real dataset of teachers on Pinterest and show the effectiveness of our framework. We believe the proposed framework can potentially improve the quality of many research endeavors concerned with studying teachers in social media.
Infrastructural state capacity in the digital age: What drives the performance of <scp>COVID</scp>‐19 tracing apps?

Abstract In a context where states increasingly complement their physical presence with digital means, the classic idea of infrastructural state power needs reconsideration. The COVID‐19 pandemic represents an unparalleled opportunity to that end, given the uniformity of one type of digital infrastructural intervention—the offer of contact tracing apps‐ and its highly varying performance across geographies. Against this backdrop, the article seeks to expand traditional discussions on state infrastructural power by testing a series of propositions on the explanatory factors behind tracing apps' emergence and adoption rates. The findings stemming from a pool of 150 countries show no straightforward connection between traditional and digital forms of infrastructural power. They also point at the primacy of governmental trust over financial, institutional, and technological factors—including privacy standards—when explaining high adoption, suggesting that the relational aspect of infrastructural power acquires a proportionally greater weight in the digital era.
Ultimate precision of joint parameter estimation under noisy Gaussian environment

The major problem of multiparameter quantum estimation theory is to find an ultimate measurement scheme to go beyond the standard quantum limits that each quasi-classical estimation measurement is limited by. Although, in some specifics quantum protocols without environmental noise, the ultimate sensitivity of a multiparameter quantum estimation can beat the standard quantum limit. However, the presence of noise imposes limitations on the enhancement of precision due to the inevitable existence of environmental fluctuations. Here, we address the motivation behind the usage of Gaussian quantum resources and their advantages in reaching the standard quantum limits under realistic noise. In this context, our work aims to explore the ultimate limits of precision for the simultaneous estimation of a pair of parameters that characterize the displacement channel acting on Gaussian probes and subjected to open dynamics. More precisely, we focus on a general two-mode mixed squeezed displaced thermal state, after reducing it to various Gaussian probes states, like; a two-mode pure squeezed vacuum, two-mode pure displaced vacuum, two-mode mixed displaced thermal, two-mode mixed squeezed thermal. To study the ultimate estimation precision, we evaluate the upper and bottom bound of HCRB in various cases. We find that when the entangled states, two-mode pure squeezed vacuum and two-mode mixed squeezed thermal, are employed as probes states, the upper and bottom bound of HCRB beats the standard quantum limit in the presence of a noisy environment.
Generating adversarial examples via enhancing latent spatial features of benign traffic and preserving malicious functions

Well-crafted adversarial examples can easily deceive neural network models into producing misclassified results while contributing to evaluating and improving the performance and robustness of the classification model. However, most adversarial examples generation methods still have the following drawbacks: (1) the original samples ignore the distribution regularity of benign samples and directly add noise, so the generated adversarial examples have significant differences in latent spatial distribution with benign samples, which makes them difficult to escape detection; (2) the discriminant features of the adversarial examples are directly modified, which causes their malicious patterns to change or malicious functions to be unattainable. In this paper, a novel malicious traffic adversarial examples generation method, NIDSFM, is proposed. Through NIDSFM, the feature space of the traffic samples is reconstructed to avoid interference with the malicious functions of the generated adversarial examples by isolating the discriminant features. By using the ability of the flow-based model to represent the latent space distribution, the distribution of adversarial examples is modeled around the benign samples, then fine-tuned based on generative adversarial networks (GAN) with additional latent spatial noise so that the distribution of generated adversarial examples is similar to benign samples. Extensive experiments were conducted on multiple datasets (NSL-KDD, UNSW-NB15, CIC-DDoS2019) and compared with various adversarial examples generation methods. The experimental results show that the proposed method leads to a significant reduction in the detection rate of multiple NIDSs and is competitive in escaping NIDS detection.
Deep Video Prior for Video Consistency and Propagation

Applying an image processing algorithm independently to each video frame often leads to temporal inconsistency in the resulting video. To address this issue, we present a novel and general approach for blind video temporal consistency. Our method is only trained on a pair of original and processed videos directly instead of a large dataset. Unlike most previous methods that enforce temporal consistency with optical flow, we show that temporal consistency can be achieved by training a convolutional network on a video with Deep Video Prior (DVP). Moreover, a carefully designed iteratively reweighted training strategy is proposed to address the challenging multimodal inconsistency problem. We demonstrate the effectiveness of our approach on 7 computer vision tasks on videos. Extensive quantitative and perceptual experiments show that our approach obtains superior performance than state-of-the-art methods on blind video temporal consistency. We further extend DVP to video propagation and demonstrate its effectiveness in propagating three different types of information (color, artistic style, and object segmentation). A progressive propagation strategy with pseudo labels is also proposed to enhance DVP's performance on video propagation. Our source codes are publicly available at https://github.com/ChenyangLEI/deep-video-prior.
Fast Supersingular Isogeny Diffie–Hellman and Key Encapsulation Using a Customized Pipelined Montgomery Multiplier

We present a pipelined Montgomery multiplier tailored for SIKE primes. The latency of this multiplier is far shorter than that of the previous work while its frequency competes with the highest-rated ones. The implementation results on a Virtex-7 FPGA show that this multiplier improves the time, the area-time product (AT), and the throughput of computing modular multiplication by at least 2.30, 1.60, and 1.36 times over SIKE primes respectively. We have also developed a CPU-like architecture to perform SIDH and SIKE using several instances of our modular multiplier. Using four multipliers on a Virtex-7 FPGA, the encapsulation and the decapsulation of SIKE can be performed at least 1.45 times faster while improving the AT by at least 1.35 times over all SIKE primes. We have also evaluated our implementation on two other FPGAs. The implementation on Artix-7 improves the time and the AT of performing these two steps of SIKE by at least 1.90 and 1.80 times, respectively. On Kintex UltraScale+, these improvement factors are 2.05 and 2.08, respectively. On this device, these two steps take 3.11, 3.52, 4.66, and 6.59 milliseconds on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p_{434}$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p_{503}$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p_{610}$ </tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p_{751}$ </tex-math></inline-formula> , respectively.
LwTool: A data processing toolkit for building a real-time pressure mapping smart textile software system

Pressure mapping smart textile is a new type of sensing modality that transforms the pressure distribution over surfaces into digital "image" and "video", that has rich application scenarios in Human Activity Recognition (HAR), because all human activities are linked with force change over certain surfaces. To speed up its application exploration, we propose a toolkit named LwTool for the data processing, including: (a) a feature library, including 1830 ready-to-use temporal and spatial features, (b) a hierarchical feature selection framework that automatically picks out the best features for a new application from the feature library. As real-time processing capability is important for instant user feedback, we emphasize not only on good recognition result but also on reducing time cost when selecting features. Our library and algorithms are validated on Smart-Toy and Smart-Bedsheet applications, an 89.7% accuracy for Smart-Toy and an 83.8% accuracy for Smart-Bedsheet can be achieved (10-fold cross-validation) using our feature library. Adopting the feature selection algorithm, the processing speed is increased by more than 3 times while maintaining high accuracy for both two applications. We believe our method could be a general and powerful toolkit in building real-time recognition software systems for pressure mapping smart textile.
Speech Recognition Based On Transformer Neural Networks

This article is devoted to the creation of a speech recognition system in the Uzbek language based on the analysis of existing approaches to speech recognition and the selection of the preferred approach, which provides information on the End2End approach and its effective use. In addition, several manifestations of transformer neural networks have been analyzed. Based on the results of the analysis, high quality and efficiency were used in speech recognition in Uzbek. The results obtained showed that transformer-based models were superior to other widely used models in terms of training time and accuracy.