Reviewerly/exHarmony huggingface dataset:
Neural Group Testing to Accelerate Deep Learning

Recent advances in deep learning have made the use of large, deep neural networks with tens of millions of parameters. The sheer size of these networks imposes a challenging computational burden during inference. Existing work focuses primarily on accelerating each forward pass of a neural network. Inspired by the group testing strategy for efficient disease testing, we propose neural group testing, which accelerates by testing a group of samples in one forward pass. Groups of samples that test negative are ruled out. If a group tests positive, samples in that group are then retested adaptively. A key challenge of neural group testing is to modify a deep neural network so that it could test multiple samples in one forward pass. We propose three designs to achieve this without introducing any new parameters and evaluate their performances. We applied neural group testing in an image moderation task to detect rare but inappropriate images. We found that neural group testing can group up to 16 images in one forward pass and reduce the overall computation cost by over 73% while improving detection performance. Our code is available at https://github.com/Weixin-Liang/NeuralGroupTesting/
Online Reinforcement Learning of Optimal Threshold Policies for Markov Decision Processes

To overcome the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">curses of dimensionality and modeling</i> of dynamic programming methods to solve Markov decision process problems, reinforcement learning (RL) methods are adopted in practice. Contrary to traditional RL algorithms, which do not consider the structural properties of the optimal policy, we propose a structure-aware learning algorithm to exploit the ordered multithreshold structure of the optimal policy, if any. We prove the asymptotic convergence of the proposed algorithm to the optimal policy. Due to the reduction in the policy space, the proposed algorithm provides remarkable improvements in storage and computational complexities over classical RL algorithms. Simulation results establish that the proposed algorithm converges faster than other RL algorithms.
On known constructions of APN and AB functions and their relation to each other

This work is dedicated to APN and AB functions which are optimal against differential and linear cryptanlysis when used as Sboxes in block ciphers.They also have numerous applications in other branches of mathematics and information theory such as coding theory, sequence design, combinatorics, algebra and projective geometry.In this paper we give an overview of known constructions of APN and AB functions, in particular, those leading to infinite classes of these functions.Among them, the bivariate construction method, the idea first introduced in 2011 by the third author of the present paper, turned out to be one of the most fruitful.It has been known since 2011 that one of the families derived from the bivariate construction contains the infinite families derived by Dillon's hexanomial method.Whether the former family is larger than the ones it contains has stayed an open problem which we solve in this paper.Further we consider the general bivariate construction from 2013 by the third author and study its relation to the recently found infinite families of bivariate APN functions.2020 Mathematics
Decentralized Blockchain Based System for Secure Data Storage in Cloud

Cloud computing is one of the leading options to store massive amount of data. The metaphor of the cloud was borrowed from old telecoms network schematics, in which the public telephone network was often represented as a cloud to denote that the just did not a matter it was just a cloud of stuff. However, cost and risk will increase inthe organization which are using cloud platforms. Hence this paper combines Cloud with blockchain technology to produce a secured file storage system. Blockchain is considered by many to be the safest technology. Here, a single file is splited and stored in blocks using the file manipulation and Advanced Encryption Standard (AES) algorithms. A high level, contract oriented language, Solidity is used for writing smart contracts. The system architecture which explains the cloud and blockchain processes is constructed. This system also ensures that it is free from attacks such as Brute force. Finally, the blockchain network in the cloud platform is implemented.
Detecting Faces With Face Masks

This paper deals with the evaluation of several methods for face detection when the face is covered by a mask. The methods evaluated are Haar cascade and Histogram of Oriented Gradients as feature-based approaches, Multitask Cascade Convolutional Neural Network, Max Margin Object Detection and TinyFace as convolutional neural network based approaches. Various types of face masks are considered: disposal face mask, burka, balaclava, ski helmet with ski goggles, hockey helmet with protective grill, costumes, and others. The TinyFace method achieves the best accuracy result, but also requires much more computational power than other approaches. Therefore, this paper describes an experiment to see if the accuracy of some of the remaining methods can be improved by retraining their models with new image data containing faces with various face masks.
Attention-Driven Graph Convolution Network for Remote Sensing Image Retrieval

Graph convolution networks (GCNs) are useful in remote sensing (RS) image retrieval. It is found to be effective because, in a graph representation, the relative geometrical interactions between different regions (or segments) are appropriately captured, along with their region-wise features in their region adjacency graphs. Also, the attention mechanism has often been applied to the nodes to highlight the essential features in each node. In this regard, a significant amount of high-frequency information is missed since each image segment is effectively summarized within a single node. To account for this and increase the learning capacity, we propose to attend over the edge/adjacency matrix to highlight the interactions among meaningful regions that contribute to supervised learning from images. We exploit this novel edge attention mechanism together with node attention to highlight essential image context by allowing more importance to the meaningful neighboring regions that highlight a relevant node. We implement the proposed context-attended GCN framework for image retrieval on the benchmarked UC-Merced and the PatternNet datasets. We observe a notable improvement in the results compared to the state of the art.
Hybrid market-based resources allocation in Mobile Edge Computing systems under stochastic information

In order to deal with the problem of user diversity in Mobile Edge Computing (MEC) resource trading market, in this paper, we propose a hybrid market-based resource transaction mechanism consisting of futures market and spot market. Two different types of users have been taken into consideration. One is registered users and another is unregistered users. In futures market, registered users pay a registration fee to the agent and use the reserved resources according to the contract signed exclusively. We design optimal contracts for the registered users by adjusting the registration fee in order to maximize the servers’ utility. In spot market, unregistered users compete with one another to purchase the resources on demand. We model the trading process as a multi-seller and multi-buyer market, and propose auction algorithms to match the asking price from servers and the bidding price from unregistered users by assigning computation resources to the users. The agent acts as the auctioneer to host the auction, and the unregistered users bid on computation resources based on the estimated valuation. We study the optimal solution under both complete and incomplete information scenarios, depending on whether the agent can observe the users’ private information. Simulation results demonstrate the existences of the asking price and registration fee for the servers to maximize utility.
Look Back Again

Nowadays, it is a trend that using a parallel-decoupled encoder-decoder (PDED) framework in scene text recognition for its flexibility and efficiency. However, due to the inconsistent information content between queries and keys in the parallel positional attention module (PPAM) used in this kind of framework(queries: position information, keys: context and position information), visual misalignment tends to appear when confronting hard samples(e.g., blurred texts, irregular texts, or low-quality images). To tackle this issue, in this paper, we propose a dual parallel attention network (DPAN), in which a newly designed parallel context attention module (PCAM) is cascaded with the original PPAM, using linguistic contextual information to compensate for the information inconsistency between queries and keys. Specifically, in PCAM, we take the visual features from PPAM as inputs and present a bidirectional language model to enhance them with linguistic contexts to produce queries. In this way, we make the information content of the queries and keys consistent in PCAM, which helps to generate more precise visual glimpses to improve the entire PDED framework's accuracy and robustness. Experimental results verify the effectiveness of the proposed PCAM, showing the necessity of keeping the information consistency between queries and keys in the attention mechanism. On six benchmarks, including regular text and irregular text, the performance of DPAN surpasses the existing leading methods by large margins, achieving new state-of-the-art performance. The code is available on \urlhttps://github.com/Jackandrome/DPAN.
High-Performance Concatenation Decoding of Reed–Solomon Codes With SPC Codes

A novel single parity check-multiplicity assignment decoding algorithm based on voltage magnitude (VM_SPC-MA) is proposed, which is applied to the concatenated scheme of single parity check (SPC) inner code and Reed-Solomon (RS) outer code, following the Consultative Committee for Space Data Systems (CCSDS) standard. The algorithm determines whether the SPC code is in error by SPC, then obtains the reliability information of the inner code bits for error correction based on the characteristics of the received bit-level voltage, and decodes the outer code based on the reliability information of the inner codewords and the channel information. The decoding performance is greatly improved by connecting the inner and outer codes through the multiplicity assignment (MA) module, which makes full use of the channel information. Compared with the low-complexity Chase decoding based on the hard-decision decoding (HDD-LCC) and SPC_Kaneko-RS_Chase decoding, simulation results show that the SPC-RS concatenated decoding scheme based on VM_SPC-MA algorithm can provide up to 1.78 and 1.05 dB of coding gain when the bit error rate is (BER) = 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> . Besides, the hardware design of the SPC-MA module is provided and applied to the serial LCC decoder based on syndrome calculation-polynomial selection (PS)-Chien search and Forney algorithm (SPCF). The implementation results in ASIC show that the area efficiency of the complete concatenated decoder increases by 27.38% compared to the unified syndrome computation (USC)-based LCC decoder in a 0.13- μm process.
TML: A Triple-Wise Multi-Task Learning Framework for Distracted Driver Recognition

We propose a multi-task learning framework for improving the performance of vision-based deep-learning approaches for driver distraction recognition. The most popular tool so far for solving this task is convolutional neural networks (CNNs) that have proven to be strongly biased toward local features. Such bias causes CNNs to neglect global structural information, adversely affecting the robustness of the distracted driver recognition task. To solve this problem, we generate positive and negative samples of each given input, and construct a triplet of images (i.e., raw image, positive sample, and negative sample). The positive sample is generated by applying structure-aware illumination to the human body region of each given input. The negative sample is generated by randomly shuffling the local regions of each given input. The networks are then trained with the triplets using a multi-task learning strategy to force the networks to explore global information by multiple tasks: (a) recognizing the raw input and positive sample as the given ground truth; (b) recognizing the negative sample as an extra “meaningless” label; (c) pulling closer the distance between the features obtained from the raw input and positive sample while pushing away the distance between the features obtained from the raw input and negative sample. By doing so, the model can be trained so that it neglects the background information and pays more attention to the global structual information of the scene. The proposed approach reaches state-of-the-art performance on the AUC Distracted Driver Dataset and performs better than state-of-the-art studies on the Drive and Act Dataset. With raw images as input, we have achieved an accuracy of 96.0% for the AUC distracted driver dataset and 66.8% for the Drive and Act Dataset. Our approach does not introduce extra overhead during the testing procedure (i.e., utilization procedure), which is helpful for real-life applications. Moreover, better accuracy can be achieved by fusing the predictions respectively obtained with the raw input and positive sample. As a result, we have achieved an accuracy of 96.3% for the AUC distracted driver Dataset and 66.9% for the Drive and Act Dataset. The class activation map (CAM) of our proposed method is subjectively more reasonable, which would enhance the reliability and explainability of the model.
Crafting, connecting, and commoning in everyday maker projects

Makerspaces have spread to schools, museums, and libraries around the world. These are spaces that make technology construction more accessible and afford practices that situate makers as everyday designers. In this paper, the connection between makerspaces and makers' everyday lives is examined. For this study, thirteen makers were interviewed about the process of their everyday maker projects, and the data material was analyzed for practices, reflections on agency, and relations to places like the makerspace, the home, and beyond. Three distinct practices have been discovered and are described. Crafting is an individual, recreative, skillful, and immersive practice that connects to an individual workspace aside from the makerspace. Connecting is a creative, object-oriented, and interventionist practice that connects to everyday life situations open for inventions. Commoning is a social and communal practice that connects to the makerspace community. The makers' practices inform a discussion of the agency the makerspace enables, and the spatial practices that both enable and constrain actions. Also, because makerspaces are places that intend to make technology construction accessible and inclusive, diversity in maker communities, barriers to inclusion, and strategies to overcome these are discussed.
No Interface, No Problem: Gesture Recognition on Physical Objects Using Radar Sensing

Physical objects are usually not designed with interaction capabilities to control digital content. Nevertheless, they provide an untapped source for interactions since every object could be used to control our digital lives. We call this the missing interface problem: Instead of embedding computational capacity into objects, we can simply detect users’ gestures on them. However, gesture detection on such unmodified objects has to date been limited in the spatial resolution and detection fidelity. To address this gap, we conducted research on micro-gesture detection on physical objects based on Google Soli’s radar sensor. We introduced two novel deep learning architectures to process range Doppler images, namely a three-dimensional convolutional neural network (Conv3D) and a spectrogram-based ConvNet. The results show that our architectures enable robust on-object gesture detection, achieving an accuracy of approximately 94% for a five-gesture set, surpassing previous state-of-the-art performance results by up to 39%. We also showed that the decibel (dB) Doppler range setting has a significant effect on system performance, as accuracy can vary up to 20% across the dB range. As a result, we provide guidelines on how to best calibrate the radar sensor.
Remote vocational learning opportunities—A comparative eye‐tracking investigation of educational 2D videos versus 360° videos for car mechanics

Abstract This study utilises a novel approach to investigate the effectiveness of different learning modalities by combining video‐based learning with eye‐tracking. An excerpt taken from a vocational education instruction for car mechanics was videotaped using two different cameras: a standard 2D video camera and a professional 360° camera. The video recorded with the 2D camera was presented on a tablet, with a fixed angle, whereas the video recorded with the 360° camera was presented as non‐interactive 3DoF virtual reality (nVR) environment using a head‐mounted display. In both conditions, participants’ fixation patterns were recorded and analysed in conjunction with a set of standardised questionnaires. Participants ( N = 48) were randomly assigned to either the 2D‐video group or the nVR group, with 23 participants in the 2D‐video and 25 participants in the nVR group. The task of the participants in both groups was to watch the educational video while wearing an eye‐tracker and then complete a standardised test on the presented content. The eye‐tracking data indicated that participants in the nVR group showed longer total fixation durations on the instructor, but not other areas of interest, compared to the 2D video group. The standardised test indicated no differences in learning outcome between the groups. Implications from the current study as well as limitations and a outlook for further research will be discussed. Practitioner notes What is already known about this topic Virtual reality (VR) technology is getting more commonplace in educational settings. It is unclear whether VR‐based learning holds clear benefits over more traditional approaches such as hands‐on‐training or video‐based learning. What this paper adds This paper adds a comparison of two common video‐based teaching techniques and compares them in respect to learning outcome and learner's attention. The novelty of the current paper is the addition of eye‐tracking in both tablet‐based and HMD‐based learning scenarios to investigate learners' visual attention. Describes potential attention benefits of 360° videos compared to 2D videos despite no differences in learning outcome directly. Implications for practice and/or policy Results highlight the potential of placing more focus on social factors in 360° video material. Future 360° videos and VR environments can add additional focus on instructors or other socially relevant aspects to foster student learning and engagement.
BlockTrail: A Service for Secure and Transparent Blockchain-Driven Audit Trails

Audit trails are critical components in enterprise business applications, typically used for storing, tracking, and auditing data. Entities in the audit trail applications have weak trust boundaries, which expose them to various security risks and attacks. To harden the security and develop secure by design applications, blockchain technology has been recently introduced in the audit trails. Blockchains take a consensus-driven clean slate approach to equip audit trails with secure and transparent data processing, without a trusted intermediary. On a downside, blockchains significantly increase the space-time complexity of the audit trails, leading to high storage costs and low transaction throughput. In this article, we introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> BlockTrail <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> , a novel blockchain architecture that fragments the legacy blockchain systems into layers of codependent hierarchies, thereby reducing the space-time complexity and increasing the throughput. BlockTrail is prototyped on the “practical Byzantine fault tolerance” protocol with a custom-built blockchain. Experiments with BlockTrail show that compared to the conventional schemes, BlockTrail is secure and efficient, with low storage footprint.
Robust Single-Image Super-Resolution via CNNs and TV-TV Minimization

Single-image super-resolution is the process of increasing the resolution of an image, obtaining a high-resolution (HR) image from a low-resolution (LR) one. By leveraging large training datasets, convolutional neural networks (CNNs) currently achieve the state-of-the-art performance in this task. Yet, during testing/deployment, they fail to enforce consistency between the HR and LR images: if we downsample the output HR image, it never matches its LR input. Based on this observation, we propose to post-process the CNN outputs with an optimization problem that we call TV-TV minimization, which enforces consistency. As our extensive experiments show, such post-processing not only improves the quality of the images, in terms of PSNR and SSIM, but also makes the super-resolution task robust to operator mismatch, i.e., when the true downsampling operator is different from the one used to create the training dataset.
Three Perceptual Tools for Seeing and Understanding Visualized Data

The visual system evolved and develops to process the scenes, faces, and objects of the natural world, but people adapt this powerful system to process data within an artificial world of visualizations. To extract patterns in data from these artificial displays, viewers appear to use at least three perceptual tools, including a tool that extracts global statistics, one that extracts shapes within the data, and one that produces sentence-like comparisons. A better understanding of the power, limits, and deployment of these tools would lead to better guidelines for designing effective data displays.
A Novel Proof-of-Reputation Consensus for Storage Allocation in Edge Blockchain Systems

Edge computing guides the collaborative work of widely distributed nodes with different sensing, storage, and computing resources. For example, sensor nodes collect data and then store it in storage nodes so that computing nodes can access the data when needed. In this paper, we focus on the quality of service (QoS) in storage allocation in edge networks. We design a reputation mechanism for nodes in edge networks, which enables interactive nodes to evaluate the quality of service for reference. Each node publicly broadcasts a personal reputation list to evaluate all other nodes, and each node can calculate the global reputation of all nodes by aggregating personal reputations. We then propose a storage allocation algorithm that stores data to appropriate locations. The algorithm considers fairness, efficiency, and reliability which is derived from reputations. We build a novel Proof-of-Reputation (PoR) blockchain to support consensus on the reputation mechanism and storage allocation. The PoR blockchain ensures safety performance, saves computing resources, and avoids centralization. Extensive simulation results show our proposed algorithm is fair, efficient, and reliable. The results also show that in the presence of attackers, the success rate of honest nodes accessing data can reach 99.9%.