Reviewerly/exHarmony huggingface dataset:
ASC-Net: Adversarial-Based Selective Network for Unsupervised Anomaly Segmentation

We introduce a neural network framework, utilizing adversarial learning to partition an image into two cuts, with one cut falling into a reference distribution provided by the user. This concept tackles the task of unsupervised anomaly segmentation, which has attracted increasing attention in recent years due to their broad applications in tasks with unlabelled data. This Adversarial-based Selective Cutting network (ASC-Net) bridges the two domains of cluster-based deep learning methods and adversarial-based anomaly/novelty detection algorithms. We evaluate this unsupervised learning model on BraTS brain tumor segmentation, LiTS liver lesion segmentation, and MS-SEG2015 segmentation tasks. Compared to existing methods like the AnoGAN family, our model demonstrates tremendous performance gains in unsupervised anomaly segmentation tasks. Although there is still room to further improve performance compared to supervised learning algorithms, the promising experimental results shed light on building an unsupervised learning algorithm using user-defined knowledge.
Geometric compression of invariant manifolds in neural networks

Abstract We study how neural networks compress uninformative input space in models where data lie in d dimensions, but the labels of which only vary within a linear manifold of dimension d ∥ &lt; d . We show that for a one-hidden-layer network initialized with infinitesimal weights (i.e. in the feature learning regime) trained with gradient descent, the first layer of weights evolves to become nearly insensitive to the d ⊥ = d − d ∥ uninformative directions. These are effectively compressed by a factor <?CDATA $\lambda \sim \sqrt{p}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>λ</mml:mi> <mml:mo>∼</mml:mo> <mml:msqrt> <mml:mrow> <mml:mi>p</mml:mi> </mml:mrow> </mml:msqrt> </mml:math> , where p is the size of the training set. We quantify the benefit of such a compression on the test error ϵ . For large initialization of the weights (the lazy training regime), no compression occurs and for regular boundaries separating labels we find that ϵ ∼ p − β , with β Lazy = d /(3 d − 2). Compression improves the learning curves so that β Feature = (2 d − 1)/(3 d − 2) if d ∥ = 1 and β Feature = ( d + d ⊥ /2)/(3 d − 2) if d ∥ &gt; 1. We test these predictions for a stripe model where boundaries are parallel interfaces ( d ∥ = 1) as well as for a cylindrical boundary ( d ∥ = 2). Next, we show that compression shapes the neural tangent kernel (NTK) evolution in time, so that its top eigenvectors become more informative and display a larger projection on the labels. Consequently, kernel learning with the frozen NTK at the end of training outperforms the initial NTK. We confirm these predictions both for a one-hidden-layer fully connected network trained on the stripe model and for a 16-layer convolutional neural network trained on the Modified National Institute of Standards and Technology database (MNIST), for which we also find β Feature &gt; β Lazy . The great similarities found in these two cases support the idea that compression is central to the training of MNIST, and puts forward kernel principal component analysis on the evolving NTK as a useful diagnostic of compression in deep networks.
Families of SNARK-Friendly 2-Chains of Elliptic Curves

At CANS’20, El Housni and Guillevic introduced a new 2-chain of pairing-friendly elliptic curves for recursive zero-knowledge Succinct Non-interactive ARguments of Knowledge (zk-SNARKs) made of the former BLS12-377 curve (a Barreto–Lynn–Scott curve over a 377-bit prime field) and the new BW6-761 curve (a Brezing–Weng curve of embedding degree 6 over a 761-bit prime field). First we generalise the curve construction, the pairing formulas ( $$e :\mathbb G_1 \times \mathbb G_2 \rightarrow \mathbb G_T$$ ) and the group operations to any BW6 curve defined on top of any BLS12 curve, forming a family of 2-chain pairing-friendly curves. Second, we investigate other possible 2-chain families made on top of the BLS12 and BLS24 curves. We compare BW6 to Cocks–Pinch curves of higher embedding degrees 8 and 12 (CP8, CP12) at the 128-bit security level. We derive formulas for efficient optimal ate and optimal Tate pairings on our new CP curves. We show that for both BLS12 and BLS24, the BW6 construction always gives the fastest pairing and curve arithmetic compared to Cocks-Pinch curves. Finally, we suggest a short list of curves suitable for Groth16 and KZG-based universal SNARKs and present an optimized implementation of these curves. Based on Groth16 and PlonK (a KZG-based SNARK) implementations in the gnark ecosystem, we obtain that the BLS12-377/BW6-761 pair is optimized for the former while the BLS24-315/BW6-672 pair is optimized for the latter.
COVID-19: A Pandemic of Anti-Asian Cyberhate

Hateful online attacks targeting individuals of Asian descent have increased dramatically in the wake of the coronavirus pandemic. This study examines daily trends in inflammatory keywords associated with anti-Asian cyberhate using data collected from Twitter between January 1, 2020 and February 28, 2021. Additionally, we explore the sentiment of tweets and track the evolution of discourse on Twitter regarding Asians and the coronavirus. Our results indicate that the usage of inflammatory keywords associated with anti-Asian hate on Twitter rose dramatically at the outset of the outbreak, and then spiked at various points over the next fourteen months. Notably, offline events and rhetoric appear to have affected the rate of hateful tweets about the pandemic targeting Asians. Additionally, we find that discourse on Twitter around the words China, Chinese, Asians, and virus has become increasingly negative and stigmatizing during the course of the coronavirus outbreak.
Human Pose Estimation for Training Assistance

Human pose estimation is an important field of Computer Vision that aims to predict poses of individuals from videos and images. It has been used in many different areas including human-computer interaction, motion analysis, surveillance, action prediction, action correction, augmented reality, virtual reality, and healthcare. This review is focused on the most significant contributions in human pose estimation for training assistance. Executing movements correctly is crucial in all kinds of physical activities, both to increase performance and reduce risk of injury. Human pose estimation is poised to help athletes better analyse the quality of their movements. The systematic review study was conducted in five databases including articles from January 2011 to March 2021. The initial search resulted in 129 articles, of which 8 were selected after applying the filtering criteria. Moreover this study presents the challenges related to pose estimation, which pose estimation methods have been used in recent years, in which specific activities the selected articles have focused on, and a taxonomy of human pose estimation methods.
Explanation-Aware Experience Replay in Rule-Dense Environments

Human environments are often regulated by explicit and complex rulesets. Integrating Reinforcement Learning (RL) agents into such environments motivates the development of learning mechanisms that perform well in rule-dense and exception-ridden environments such as autonomous driving on regulated roads. In this letter, we propose a method for organising experience by means of partitioning the experience buffer into clusters labelled on a per-explanation basis. We present discrete and continuous navigation environments compatible with modular rulesets and 9 learning tasks. For environments with explainable rulesets, we convert rule-based explanations into case-based explanations by allocating state-transitions into clusters labelled with explanations. This allows us to sample experiences in a curricular and task-oriented manner, focusing on the rarity, importance, and meaning of events. We label this concept Explanation-Awareness (XA). We perform XA experience replay (XAER) with intra and inter-cluster prioritisation, and introduce XA-compatible versions of DQN, TD3, and SAC. Performance is consistently superior with XA versions of those algorithms, compared to traditional Prioritised Experience Replay baselines, indicating that explanation engineering can be used in lieu of reward engineering for environments with explainable features.
Model-Assisted Estimation Through Random Forests in Finite Population Sampling

In surveys, the interest lies in estimating finite population parameters such as population totals and means. In most surveys, some auxiliary information is available at the estimation stage. This information may be incorporated in the estimation procedures to increase their precision. In this article, we use random forests (RFs) to estimate the functional relationship between the survey variable and the auxiliary variables. In recent years, RFs have become attractive as National Statistical Offices have now access to a variety of data sources, potentially exhibiting a large number of observations on a large number of variables. We establish the theoretical properties of model-assisted procedures based on RFs and derive corresponding variance estimators. A model-calibration procedure for handling multiple survey variables is also discussed. The results of a simulation study suggest that the proposed point and estimation procedures perform well in terms of bias, efficiency and coverage of normal-based confidence intervals, in a wide variety of settings. Finally, we apply the proposed methods using data on radio audiences collected by Médiamétrie, a French audience company. Supplementary materials for this article are available online.
Analysis of Return and Risk of Cryptocurrency Bitcoin Asset as Investment Instrument

This study aims to explore the potential use of the cryptocurrency bitcoin as an investment instrument in Indonesia. The return obtained from bitcoin cryptocurrency is compared to other investment instruments, namely stock returns, gold and the rupiah exchange rate. The research period was carried out based on research data from 2011 to 2020. This study employee compares means test (t test) and analysis of variance (F test) on rate of return of bitcoin investment. The bitcoin return compare to the rate of return form the others investments instruments namely exchange rate, gold and stock. The study collected 120 data of each investments instruments: bitcoin, exchange rate, gold and stock from various of sources during 2011–2020. Then, we calculate the return and risk of individual investment instruments. The results showed that the bitcoin currency had the highest rate of return 18% with a standard deviation of 61% compared to exchange rate, gold and stock returns. While the rate of return for the others investment instruments showed less than 0.5% with standard deviation less than 5%. The rate of return bitcoin has significance difference compare to the rate of return of exchange rate, gold and stock. The study contribute for the investors who would like to invest on bitcoin. The investors should understand the characteristic of bitcoin in term of rate of returns and also the risk. This study also contributes to government of Indonesia on crypto currency development. The Indonesia government should adopt and regulate on crypto currency in the future to secure the investor and economic growth.
Quality-Aware Memory Network for Interactive Volumetric Image Segmentation

Despite recent progress of automatic medical image segmentation techniques, fully automatic results usually fail to meet the clinical use and typically require further refinement. In this work, we propose a quality-aware memory network for interactive segmentation of 3D medical images. Provided by user guidance on an arbitrary slice, an interaction network is firstly employed to obtain an initial 2D segmentation. The quality-aware memory network subsequently propagates the initial segmentation estimation bidirectionally over the entire volume. Subsequent refinement based on additional user guidance on other slices can be incorporated in the same manner. To further facilitate interactive segmentation, a quality assessment module is introduced to suggest the next slice to segment based on the current segmentation quality of each slice. The proposed network has two appealing characteristics: 1) The memory-augmented network offers the ability to quickly encode past segmentation information, which will be retrieved for the segmentation of other slices; 2) The quality assessment module enables the model to directly estimate the qualities of segmentation predictions, which allows an active learning paradigm where users preferentially label the lowest-quality slice for multi-round refinement. The proposed network leads to a robust interactive segmentation engine, which can generalize well to various types of user annotations (e.g., scribbles, boxes). Experimental results on various medical datasets demonstrate the superiority of our approach in comparison with existing techniques.
Spatially-Aware Autoencoders for Detecting Contextual Anomalies in Geo-Distributed Data

The huge amount of data generated by sensor networks enables many potential analyses. However, one important limiting factor for the analyses of sensor data is the possible presence of anomalies, which may affect the validity of any conclusion we could draw. This aspect motivates the adoption of a preliminary anomaly detection method. Existing methods usually do not consider the spatial nature of data generated by sensor networks. Properly modeling the spatial nature of the data, by explicitly considering spatial autocorrelation phenomena, has the potential to highlight the degree of agreement or disagreement of multiple sensor measurements located in different geographical positions. The intuition is that one could improve anomaly detection performance by considering the spatial context. In this paper, we propose a spatially-aware anomaly detection method based on a stacked auto-encoder architecture. Specifically, the proposed architecture includes a specific encoding stage that models the spatial autocorrelation in data observed at different locations. Finally, a distance-based approach leverages the embedding features returned by the auto-encoder to identify possible anomalies. Our experimental evaluation on real-world geo-distributed data collected from renewable energy plants shows the effectiveness of the proposed method, also when compared to state-of-the-art anomaly detection methods.
Self-feature Learning

Deep learning-based models have achieved unprecedented performance in single image super-resolution (SISR). However, existing deep learning-based models usually require high computational complexity to generate high-quality images, which limits their applications in edge devices, e.g., mobile phones. To address this issue, we propose a dynamic, channel-agnostic filtering method in this paper. The proposed method not only adaptively generates convolutional kernels based on the local information of each position, but also can significantly reduce the cost of computing the inter-channel redundancy. Based on this, we further propose a simple, yet effective, deep lightweight model for SISR. Experiment results show that our proposed model outperforms other state-of-the-art deep lightweight SISR models, leading to the best trade-off between the performance and the number of model parameters.
A vehicle re-identification framework based on the improved multi-branch feature fusion network

Vehicle re-identification (re-id) aims to solve the problems of matching and identifying the same vehicle under the scenes across multiple surveillance cameras. For public security and intelligent transportation system (ITS), it is extremely important to locate the target vehicle quickly and accurately in the massive vehicle database. However, re-id of the target vehicle is very challenging due to many factors, such as the orientation variations, illumination changes, occlusion, low resolution, rapid vehicle movement, and amounts of similar vehicle models. In order to resolve the difficulties and enhance the accuracy for vehicle re-id, in this work, we propose an improved multi-branch network in which global-local feature fusion, channel attention mechanism and weighted local feature are comprehensively combined. Firstly, the fusion of global and local features is adopted to obtain more information of the vehicle and enhance the learning ability of the model; Secondly, the channel attention module in the feature extraction branch is embedded to extract the personalized features of the targeting vehicle; Finally, the background and noise information on feature extraction is controlled by weighted local feature. The results of comprehensive experiments on the mainstream evaluation datasets including VeRi-776, VRIC, and VehicleID indicate that our method can effectively improve the accuracy of vehicle re-identification and is superior to the state-of-the-art methods.
Grey Wolf Optimization and Crow Search Algorithm for Resource Allocation Scheme in Cloud Computing

The computing resources are supplied by cloud computing on basis of cloud user requirements demand.Using virtualization and distributed computing, the resource allocation model is constructed to highlight the cloud services scalability.Nevertheless, a complex problem is created by the user, to manage the demand in the on-demand resource allocation model.Hence, a novel optimization approach is developed called Grey Wolf Optimization and Crow Search Algorithm (GWO-CSA) to resolve the problem in the resource allocation model.On the basis of the availability of the resources, the tasks are executed with the aid of the virtualization concept, minimize the response time.In a distributed manner, to the virtual machine, the tasks are allocated, to balance workload in the cloud.The proposed optimization method is exploited to attain effectual resource allocation.Finally, the developed method performance showed that it attains the utmost resource consumption, utmost memory consumption, and utmost CPU utilization, least skewness.
A General Destriping Framework for Remote Sensing Images Using Flatness Constraint

Removing stripe noise, i.e., destriping, from remote sensing images is an essential task in terms of visual quality and subsequent processing. Most existing destriping methods are designed by combining a particular image regularization with a stripe noise characterization that cooperates with the regularization, which precludes us to examine and activate different regularizations to adapt to various target images. To resolve this, two requirements need to be considered: a general framework that can handle a variety of image regularizations in destriping, and a strong stripe noise characterization that can consistently capture the nature of stripe noise, regardless of the choice of image regularization. To this end, this paper proposes a general destriping framework using a newly-introduced stripe noise characterization, named flatness constraint, where we can handle various regularization functions in a unified manner. Specifically, we formulate the destriping problem as a nonsmooth convex optimization problem involving a general form of image regularization and the flatness constraint. The constraint mathematically models that the intensity of each stripe is constant along one direction, resulting in a strong characterization of stripe noise. For solving the optimization problem, we also develop an efficient algorithm based on a diagonally preconditioned primal-dual splitting algorithm (DP-PDS), which can automatically adjust the stepsizes. The effectiveness of our framework is demonstrated through destriping experiments, where we comprehensively compare combinations of a variety of image regularizations and stripe noise characterizations using hyperspectral images (HSI) and infrared (IR) videos.
Experimental accreditation of outputs of noisy quantum computers

We provide and experimentally demonstrate an accreditation protocol that upper bounds the variation distance between noisy and noiseless probability distributions of the outputs of arbitrary quantum computations. We accredit the outputs of 24 quantum circuits executed on programmable superconducting hardware, ranging from depth-9 circuits on 10 qubits to depth-21 circuits on 4 qubits. Our protocol requires implementing the ``target'' quantum circuit along with a number of random Clifford circuits and subsequently postprocessing the outputs of these Clifford circuits. Importantly, the number of Clifford circuits is chosen to obtain the bound with the desired confidence and accuracy and is independent of the size and nature of the target circuit. We thus demonstrate a practical and scalable method of ascertaining the correctness of the outputs of arbitrary-sized noisy quantum computers---the ultimate arbiter of the utility of the computer itself.
Data Mining for Cyberbullying and Harassment Detection in Arabic Texts

Broadly cyberbullying is viewed as a severe social danger that influences many individuals around the globe, particularly young people and teenagers. The Arabic world has embraced technology and continues using it in different ways to communicate inside social media platforms. However, the Arabic text has drawbacks for its complexity, challenges, and scarcity of its resources. This paper investigates several questions related to the content of how to protect an Arabic text from cyberbullying/harassment through the information posted on Twitter. To answer this question, we collected the Arab corpus covering the topics with specific words, which will explain in detail. We devised experiments in which we investigated several learning approaches. Our results suggest that deep learning models like LSTM achieve better performance compared to other traditional cyberbullying classifiers with an accuracy of 72%.
Aprendizaje de la lectoescritura en época de pandemia

This review article entitled "Literacy Learning in Pandemic Times", determines the factors that influence and arise at the educational level due to the confinement that has generated difficulties in the learning of literacy. The objective is to synthesize bibliographic information on the different literacy techniques that were developed during the pandemic period. From this perspective, the study is oriented to a documentary bibliographic research with a qualitative analysis, in which information was collected from different bibliographic sources. Contributions from various authors are analyzed, with several perspectives on techniques and importance to determine not only the factors that affect this process, but also the ideal strategies for strengthening literacy that involve institutional, family and student environments. It is concluded that reading and writing in times of pandemic brought with it a series of challenges due to the fact that education moved towards virtuality with all the demands and new limitations imposed by physical distance for the development of a learning process that in itself requires accompaniment; the family becomes the nucleus of education, it learned to use reading methods, stimulated the development of social, cognitive and affective skills necessary for the effective development of reading and writing.