Reviewerly/exHarmony huggingface dataset:
FlashFlow: A Secure Speed Test for Tor

The Tor network uses a measurement system called TorFlow to estimate its relays' forwarding capacity and to balance traffic among them. This system has been shown to be vulnerable to adversarial manipulation, and inaccuracies even in benign circumstances have long been observed. To solve the issues with security and accuracy, we present FlashFlow, a system to measure the capacity of Tor relays. Our analysis shows that FlashFlow limits a malicious relay to obtaining a capacity estimate at most 1.33 times its true capacity. Through realistic Internet experiments, we find that FlashFlow measures relay capacity with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\geq {89\%}$</tex> accuracy 95 % of the time. Through simulation, we find that FlashFlow can measure the entire Tor network in less than 5 hours using 3 measurers with 1 Gbit/s of bandwidth each. Performance simulations using FlashFlow for load balancing shows that, compared to TorFlow, network weight error decreases by 86 %, while the median of 50 KiB, 1 MiB, and 5 MiB transfer times decreases by 15 %, 29 %, and 37 %, respectively. Moreover, FlashFlow yields more consistent client performance: the median rate of transfer timeouts decreases by 100 %, while the standard deviation of 50 KiB, 1 MiB, and 5 MiB transfer times decreases by 55%, 61 %, and 41 %, respectively. We also find that the performance improvements increase relative to TorFlow as the total client-traffic load increases, demonstrating that FlashFlow is better suited to supporting network growth.
TEDS-Net: Enforcing Diffeomorphisms in Spatial Transformers to Guarantee Topology Preservation in Segmentations

Accurate topology is key when performing meaningful anatomical segmentations, however, it is often overlooked in traditional deep learning methods. In this work we propose TEDS-Net: a novel segmentation method that guarantees accurate topology. Our method is built upon a continuous diffeomorphic framework, which enforces topology preservation. However, in practice, diffeomorphic fields are represented using a finite number of parameters and sampled using methods such as linear interpolation, violating the theoretical guarantees. We therefore introduce additional modifications to more strictly enforce it. Our network learns how to warp a binary prior, with the desired topological characteristics, to complete the segmentation task. We tested our method on myocardium segmentation from an open-source 2D heart dataset. TEDS-Net preserved topology in 100\(\%\) of the cases, compared to 90\(\%\) from the U-Net, without sacrificing on Hausdorff Distance or Dice performance. Code will be made available at: www.github.com/mwyburd/TEDS-Net.
An Empirical Study on Using Multi-Labels for Issues in GitHub

On GitHub, one of the most successful services for software project hosting, labels have been used to represent various information and decisions about reported issues.However, previous studies on labels were limited to simple statistics or label recommendations for issue types.In this paper, we aim to provide a better understanding of labels and their usage in software development.We particularly focus on using multiple and custom labels on issues.To analyze label usage, we collected software project data and label usage information from GitHub.We then quantitatively investigated the performance of projects with multi-label features and qualitatively investigated the categories of multi-labels, and the usage of multilabels based on these categories.Our analysis results show that multi-labels are common in the majority of software projects and that projects using multi-label features manage their issues more effectively.In addition, our analysis results reveal different types of information represented by labels, which are related to features, development, and issues.This study finds several facts that can be used for studies on issue management and thus that help develop labeling techniques to mitigate the burden of issue management.
Hardware-software hybrid implementation of non-deterministic ECC over Curve-25519 for resource constrained devices

Internet of Things (IoT) consists of various physical devices over the internet to communicate and exchange data with other devices and systems. By and large, IoT is dominated by resource constrained handheld devices. The major challenge to these devices is providing physical device security and network security. Elliptic Curve Cryptography (ECC) is an optimal solution for providing security in key exchange and digital signature. It requires very less memory, can perform faster field operations, and provides better resistance to side-channel-attacks, compared to other cryptography mechanisms. In this paper, we propose two different architectures- high-performance and lightweight. Both designs are hardware implementation of ECC over Curve25519, based on Montgomery ladder multiplication. Our high-performance architecture uses a modular hybrid adder and two stage pipelined Karatsuba multiplier. The lightweight architecture consists of Karatsuba multiplier and Brent Kung adder. We have designed a Physically Unclonable Function (PUF) to generate a true random number which is utilized to produce non-deterministic output from the design. An optimized LZ77 software CODEC is developed to achieve higher unicity-distance and throughput. Both designs are implemented on Xilinx Zynq 7000 FPGA. The result shows that our high-performance architecture can execute variable-base scalar point multiplication operation in 20201 cycles at 192 MHz frequency. The proposed architecture achieved 26.57% improvement in execution time than previous work. The lightweight design needs 693771 clock cycles, occupies 443 slices to perform one point multiplication. Our lightweight architecture reduces execution time and DSP_countXTime by 50.4% compared to recent implementations. Finally, various techniques are embedded in the proposed designs to countermeasure various side-channel attacks.
A CNN-based policy for optimizing continuous action control by learning state sequences

Continuous action control is widespread in real-world applications. It controls an agent to take action in continuous space for transiting from one state to another until achieving the desired goal. The optimization of continuous action control is an important issue, which aims to find the optimal policy for the agent to achieve the desired goal with the lowest consumption in continuous action space. A useful tool for this issue is reinforcement learning where an optimal policy is learned for the agent by maximizing the cumulative reward of the state transitions. When updating the policy at each state, most existing reinforcement learning methods consider only the one-step transition of this state. However, for each state in continuous action control, the recognizable information is usually hidden in the sequence of its previous states, thus these methods cannot learn the policy effectively enough for continuous action control. In this paper, we propose a new policy, called convolutional deterministic policy, to solve this problem. Enlightened from the convolutional neural networks used in natural language processing, our convolutional deterministic policy uses convolutional neural networks to learn the recognizable information in the state sequences. Then for each collected state, we update the convolutional deterministic policy by not only the recognizable information in the one-step transition of this state but also the recognizable information in the sequence of its previous states. As a result, our convolutional deterministic policy can make the agent take better action. Based on an effective reinforcement learning method, TD3, the implementation of our convolutional deterministic policy is in CTD3. The theoretical analysis and the experiment illustrate that our CTD3 can learn the policy not only better than but also faster than the existing RL methods for continuous action control. The source code can be downloaded from https://github.com/grcai.
Deep Multi-task Augmented Feature Learning via Hierarchical Graph Neural Network

Deep multi-task learning attracts much attention in recent years as it achieves good performance in many applications. Feature learning is important to deep multi-task learning for sharing common information among tasks. In this paper, we propose a Hierarchical Graph Neural Network (HGNN) to learn augmented features for deep multi-task learning. The HGNN consists of two-level graph neural networks. In the low level, an intra-task graph neural network is responsible of learning a powerful representation for each data point in a task by aggregating its neighbors. Based on the learned representation, a task embedding can be generated for each task in a similar way to max pooling. In the second level, an inter-task graph neural network updates task embeddings of all the tasks based on the attention mechanism to model task relations. Then the task embedding of one task is used to augment the feature representation of data points in this task. Moreover, for classification tasks, an inter-class graph neural network is introduced to conduct similar operations on a finer granularity, i.e., the class level, to generate class embeddings for each class in all the tasks using class embeddings to augment the feature representation. The proposed feature augmentation strategy can be used in many deep multi-task learning models. Experiments on real-world datasets show the significant performance improvement when using this strategy.
Reliability-Ensured Efficient Scheduling With Replication in Cloud Environment

Server consolidation in a virtualized cloud environment improves resource utilization by hosting many virtual machines (VMs) on a single machine. The host server failure induces a serious risk of the interruption of services provided through VMs hosted on the failed server. Ensuring high reliability and availability from the unpredicted host server failures is a challenging task, and this assurance guarantee is the quality of service to the users. Executing latency-sensitive workloads within a time frame, multireplica strategy-based reliability assurance is used in general. In this article, we propose two reliability-ensured scheduling approaches by strategically allocating the jobs to machines to satisfy their reliability and deadline requirements. The proposed approaches “Replication with equal failure rate” and “Efficient reliability replication method” are used to schedule the jobs to machines with equal and different failure rates, respectively, while satisfying both the deadline and reliability requirements of the jobs. Both the scheduling approaches dynamically select the machines for the jobs to minimize the number of replicas based on job characteristics and failure rate of machines. Also, we adopt an overlapping mechanism to further minimize the resource utilization. The extensive simulation results on randomly generated and real-world data at different scales show that the proposed approaches perform better than other state-of-the-art approaches.
PARTS: Unsupervised segmentation with slots, attention and independence maximization

From an early age, humans perceive the visual world as composed of coherent objects with distinctive properties such as shape, size, and color. There is great interest in building models that are able to learn similar structure, ideally in an unsupervised manner. Learning such structure from complex 3D scenes that include clutter, occlusions, interactions, and camera motion is still an open challenge. We present a model that is able to segment visual scenes from complex 3D environments into distinct objects, learn disentangled representations of individual objects, and form consistent and coherent predictions of future frames, in a fully unsupervised manner. Our model (named PARTS) builds on recent approaches that utilize iterative amortized inference and transition dynamics for deep generative models. We achieve dramatic improvements in performance by introducing several novel contributions. We introduce a recurrent slot-attention like encoder which allows for top-down influence during inference. We argue that when inferring scene structure from image sequences it is better to use a fixed prior which is shared across the sequence rather than an auto-regressive prior as often used in prior work. We demonstrate our model’s success on three different video datasets (the popular benchmark CLEVRER; a simulated 3D Playroom environment; and a real-world Robotics Arm dataset). Finally, we analyze the contributions of the various model components and the representations learned by the model.
A Thresholded Gabor-CNN Based Writer Identification System for Indic Scripts

Writer identification is the procedure of identifying individuals from handwriting.Writer identification is a common interest in biometric authentication and verification systems, and numerous studies are available for English, Chinese, Arabic, and Persian specific handwriting.This paper introduces a supervised offline Indic script writer identification system that can identify individuals using less than a single page of handwriting.A lightweight Convolutional Neural Network (CNN) architecture fused with non-trainable Gabor filters is used as an identification model that can recognize writers from scarce data.For the experiment, we used BanglaWriting dataset, which is openly available for Bengali writing and writer recognition.Further, we added Devanagari and Telugu datasets for evaluation.The overall evaluation shows that the proposed thresholded Gabor-based CNN architecture performs superior to numerous deep CNN architectures for Indic writer recognition.
Technical Composition and Creation of Interactive Installation Art Works under the Background of Artificial Intelligence

Interactive installation art is a kind of art that uses specific software and computer hardware as a platform, a platform for interaction between humans and machines or different people through computer hardware. It is an interactive art that uses material installations in nature as a medium. Traditional interactive installation art is not safe and convenient, in order to solve the shortcomings of traditional interactive installation art. This article introduces artificial intelligence technology by studying the overview, development, and application of artificial intelligence. The encryption algorithm for artificial intelligence data protection and the BP neural network prediction model under artificial intelligence are also introduced to ensure the safety of interactive installation art works. The part also introduces the creation tools and creation process of interactive installation art works. Finally, in the analysis part, a questionnaire analysis of the World Expo is carried out. The results of this article show that the art of connecting inserts is the most complete and open design era. Advances in science and technology, the development of digital art, and the needs of human life have led to the development of interconnected input technologies. In addition, in the survey of people’s satisfaction with artificial intelligence, we can conclude that 89% of people think that the security of artificial intelligence technology is very high. Yes, 92% of people think that artificial intelligence technology has a fast computing speed, 86% of people think that artificial intelligence technology is low in cost.
Hyperspectral Image Denoising via Framelet Transformation Based Three-Modal Tensor Nuclear Norm

During the acquisition process, hyperspectral images (HSIs) are inevitably contaminated by mixed noise, which seriously affects the image quality. To improve the image quality, HSI denoising is a critical preprocessing step. In HSI denoising tasks, the method based on low-rank prior has achieved satisfying results. Among numerous denoising methods, the tensor nuclear norm (TNN), based on the tensor singular value decomposition (t-SVD), is employed to describe the low-rank prior approximately. Its calculation can be sped up by the fast Fourier transform (FFT). However, TNN is computed by the Fourier transform, which lacks the function of locating frequency. Besides, it only describes the low-rankness of the spectral correlations and ignores the spatial dimensions’ information. In this paper, to overcome the above deficiencies, we use the basis redundancy of the framelet and the low-rank characteristics of HSI in three modes. We propose the framelet-based tensor fibered rank as a new representation of the tensor rank, and the framelet-based three-modal tensor nuclear norm (F-3MTNN) as its convex relaxation. Meanwhile, the F-3MTNN is the new regularization of the denoising model. It can explore the low-rank characteristics of HSI along three modes that are more flexible and comprehensive. Moreover, we design an efficient algorithm via the alternating direction method of multipliers (ADMM). Finally, the numerical results of several experiments have shown the superior denoising performance of the proposed F-3MTNN model.
Automated Market-Making for Fiat Currencies

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Hand-Key: Leveraging Multiple Hand Biometrics for Attack-Resilient User Authentication Using COTS RFID

Biometrics have been widely used in user authentications. However, existing outer-body biometrics (e.g., fingerprint), collecting from body surface, are vulnerable to spoofing attacks. Although inner-body biometrics, such as the electrocardiogram, are hard to be forged, their complex acquisition methods and instability lead to unsatisfactory user experience. Therefore, achieving good user-friendliness and high security simultaneously in biometric-based authentication is challenging. In this paper, we propose Hand-Key, an attack-resilient and user-friendly user authentication system to address the above challenge. Hand-Key utilizes a low-cost radio frequency identification (RFID) tag array to simultaneously collect the inner-body composition and outer-body geometric features of human hand to identify users. Users are merely required to hold their hands in a ‘handshaking’ pose between a reader's antenna and a tag array during authentication. To further enhance the security, we tactfully leverage the inherent randomness of the anti-collision scheme in RFID systems to make Hand-Key immune against replay attacks. We built a prototype of Hand-Key and conducted extensive experiments with 30 volunteers. The results show that Hand-Key achieves an authentication success rate of 99%+.
Feature-Rich Classifiers for Recognizing Textual Entailment in Indonesian

Recognizing Textual Entailment (RTE) is a Natural Language Processing task to determine whether a sentence (text) semantically entails another sentence (hypothesis). In this paper, we extracted and learned 35 features from a pair of text and hypothesis in Indonesian. The ablation study was conducted to analyze features contribution to RTE model. The experiments shown that, using Support Vector Machine (SVM) and Logistic Regression, the token-based features contribute positively to improve the model performance. The best model in our experiment is SVM that scored F1-Score of 79.65%. Despite sacrificing 5-points accuracy to the state-of-the-art BERT model, SVM classifier is 31 hours more efficient in terms of training time.
Social Distancing and Face Mask Detection using Deep Learning Models: A Survey

Over the recent past, the raging pandemic due to COVID-19 is making the headlines, bringing about a global crisis with an inevitable spread. The use of a face mask and maintaining physical distancing is a precautionary measure as suggested by the WHO. The individuals infected with COVID-19 suffer respiratory problems accompanied by shortness of breath. The surroundings of the concerned individuals can be contaminated by their droplets carrying the virus. It is mandatory to wear a mask and follow physical distancing, yet many citizens violate the regulations. In such scenarios, frequent checks for face masks in public places and imposing fines are common. As object detection has unfolded to be an approachable biometric process, it has been widely applied in surveillance, security, autonomous driving, etc. With the rapid development of deep learning models, object detectors are highly suitable to develop social distancing and face mask detectors to administer the crowd via CCTV and surveillance cameras. The paper surveys various deep learning networks to develop such detectors. In this survey, the existing object detection models used for surveillance and people detection are analyzed. The one-stage and two-stage detectors along with their applications and performance are outlined in a comprehensive manner. Deep Learning models such as AdaBoost, Voila-Jones, variants of CNN including ResNet, VGG-16, single-shot detectors MobileNet, and versions of YOLO are discussed and compared.
Out of Distribution Detection for Medical Images

Neural network architectures behave in unpredictable ways when testing on inputs which do not resemble their training data. It is valuable to detect any out-of-distribution (OOD) inputs to make any overseers aware of the limitations of the model's output. To address this need, a large number of methods for detecting OOD inputs have been proposed and tested on small datasets such as CIFAR10, SVHN, or LSUN. The purpose of this study is to determine the effectiveness of different methods for OOD detection on the domain of medical images. We investigate three common OOD detection methods (Maximum Softmax Probability, Confidence Branch, and Outlier Exposure) and report their effectiveness on widely used medical image datasets. We find that OOD detection metrics are volatile and can have large changes in performance in a short amount of training steps. Moreover, we also observe that OOD detection is sensitive to the choice of hyperparameters. Our code is reproducible at this link (https://github.com/oliverzhang42/ood_medical_images).
SRNHARB: A deep light-weight image super resolution network using hybrid activation residual blocks

The quality of the images in all image-based applications and specially in computer vision applications is very crucial. Hence, design of a light-weight high-performance image super resolution scheme that enhances the quality of the acquired images is crucial for satisfactory functioning of such applications. Design of most of image super resolution schemes ignore extracting and processing of the negative-valued features of the images. In this paper, a novel light-weight residual block, which efficiently extracts and processes both the positive and negative-valued features, is proposed. This new residual block is capable of producing a richer set of features in order to improve the super resolution performance of the network using a set of such blocks. The network using the new residual blocks is shown to yield a performance superior to those of the existing light-weight super resolution networks using other types of residual blocks.
Machine Learning Approaches for Classifying the Distribution of Covid-19 Sentiments

Previously, rapid disease detection and prevention was difficult.This is because disease modeling and prediction was dependent on a manually obtained dataset that includes use of survey.With the increased use of social media platforms like Twitter, Facebook, Instagram, etc., data mining and sen-How to cite this paper:
A quantum generative adversarial network for distributions

Abstract Recent advances in Quantum Computing have shown that, despite the absence of a fault-tolerant quantum computer so far, quantum techniques are providing exponential advantage over their classical counterparts. We develop a fully connected Quantum Generative Adversarial network and show how it can be applied in Mathematical Finance, with a particular focus on volatility modelling.