Reviewerly/exHarmony huggingface dataset:
A Study on the Optimization of Blockchain Hashing Algorithm Based on PRCA

Blockchain is widely used in encrypted currency, Internet of Things (IoT), supply chain finance, data sharing, and other fields. However, there are security problems in blockchains to varying degrees. As an important component of blockchain, hash function has relatively low computational efficiency. Therefore, this paper proposes a new scheme to optimize the blockchain hashing algorithm based on PRCA (Proactive Reconfigurable Computing Architecture). In order to improve the calculation performance of hashing function, the paper realizes the pipeline hashing algorithm and optimizes the efficiency of communication facilities and network data transmission by combining blockchains with mimic computers. Meanwhile, to ensure the security of data information, this paper chooses lightweight hashing algorithm to do multiple hashing and transforms the hash algorithm structure as well. The experimental results show that the scheme given in the paper not only improves the security of blockchains but also improves the efficiency of data processing.
High throughput and area‐efficient FPGA implementation of AES for high‐traffic applications

This study presents a high throughput field-programmable gate array (FPGA) implementation of advanced encryption standard-128 (AES-128). AES is a well-known symmetric key encryption algorithm with high security against different attacks that are widely used in different applications. The main goal of this study is to design a high throughput and FPGA efficiency (FPGA-Eff) cryptosystem for high-traffic applications. To achieve high throughput, loop-unrolling, inner and outer pipelining techniques are employed. In AES, substitution bytes (Sub-Bytes) is one of the costly functions that occupy a large number of resources and has a large delay. To reduce the area of Sub-Bytes, new-affine-transformation, which is the combination of inverse isomorphic and affine transformation, is proposed and employed. Besides that, AES has been modified according to the proposed architecture. For the first nine rounds, Shift-Rows and Sub-Bytes have been exchanged, and Shift-Rows is merged with Add-Round-Key. To make an equal latency between stages, Mix-Columns is divided into two different stages. AES is implemented in counter mode on Xilinx Virtex-5 using VHDL. The proposed implementation achieves a throughput of 79.7 Gbps, FPGA-Eff of 13.3 Mbps/slice, and frequency of 622.4 MHz. Compared to the state-of-the-art work, the proposed design has improved data throughput by 8.02% and FPGA-Eff by 22.63%.
Deep Multi-Level Wavelet-CNN Denoiser Prior for Restoring Blurred Image With Cauchy Noise

Cauchy noise, as a typical non-Gaussian noise, appears frequently in many important fields, such as radar, medical, and biomedical imaging. In this letter, we focus on image recovery under Cauchy noise. Instead of the celebrated total variation or low-rank prior, we adopt a novel deep-learning-based image denoiser prior to effectively remove Cauchy noise with blur. To preserve more detailed texture and better balance between the receptive field size and the computational cost, we apply the multi-level wavelet convolutional neural network (MWCNN) to train this denoiser. We use the forward-backward splitting (FBS) method to handle the proposed model, which can be implemented efficiently without introducing auxiliary variables. Moreover, the multi-noise-levels strategy is employed to train a series of denoisers to restore the image corrupted by Cauchy noise and blur. Numerical experiments demonstrate clearly that our method has better performance than the existing image restoration methods for removing Cauchy noise in terms of the quantitative index and visual quality.
SCOUTER: Slot Attention-based Classifier for Explainable Image Recognition

Explainable artificial intelligence has been gaining attention in the past few years. However, most existing methods are based on gradients or intermediate features, which are not directly involved in the decision-making process of the classifier. In this paper, we propose a slot attention-based classifier called SCOUTER for transparent yet accurate classification. Two major differences from other attention-based methods include: (a) SCOUTER’s explanation is involved in the final confidence for each category, offering more intuitive interpretation, and (b) all the categories have their corresponding positive or negative explanation, which tells "why the image is of a certain category" or "why the image is not of a certain category." We design a new loss tailored for SCOUTER that controls the model’s behavior to switch between positive and negative explanations, as well as the size of explanatory regions. Experimental results show that SCOUTER can give better visual explanations in terms of various metrics while keeping good accuracy on small and medium-sized datasets. Code is available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
A Hybrid Board Game to Engage Students in Reviewing Organic Acids and Bases Concepts

We present a bilingual hybrid board game (English and Portuguese) as an innovative educational method to aid students in understanding concepts related to organic acids and bases. The proposed game promotes interaction between students in an enjoyable activity to review and learn the content, requiring minimal supervision from the professor.
Overview of PAN 2020: Authorship Verification, Celebrity Profiling, Profiling Fake News Spreaders on Twitter, and Style Change Detection

We briefly report on the four shared tasks organized as part of the PAN 2020 evaluation lab on digital text forensics and authorship analysis. Each tasks is introduced, motivated, and the results obtained are presented. Altogether, the four tasks attracted 230 registrations, yielding 83 successful submissions. This, and the fact that we continue to invite the submissions of software rather than its run output using the TIRA experimentation platform, marks for a good start into the second decade of PAN evaluations labs.
Vision‐based vehicle detection for road traffic congestion classification

Summary Due to the increasing number of vehicles in circulation in different urban cities, several automatic traffic monitoring systems have been developed. In particular, traffic monitoring systems using roadside cameras are becoming extensively deployed, as they offer imperative technological advantages compared with other traffic monitoring systems. Vehicle detection and traffic congestion classification are two main steps for video‐based traffic congestion detection systems; the associated methods have a deep impact on the performance of the whole system. In this paper, we investigate four selected vehicle detection methods namely Gaussian Mixture Model (GMM), GMM‐Kalman filter, Optical Flow, and ACF object detector in two contexts: urban and highway. Three traffic congestion classification methods are also studied. The comparative study of the different methods allows us to choose the most appropriate ones to be integrated in the framework proposed to solve the traffic issues in the bridge of Bizerte.
Exploiting the User Social Context to Address Neighborhood Bias in Collaborative Filtering Music Recommender Systems

Recent research in the field of recommender systems focuses on the incorporation of social information into collaborative filtering methods to improve the reliability of recommendations. Social networks enclose valuable data regarding user behavior and connections that can be exploited in this area to infer knowledge about user preferences and social influence. The fact that streaming music platforms have some social functionalities also allows this type of information to be used for music recommendation. In this work, we take advantage of the friendship structure to address a type of recommendation bias derived from the way collaborative filtering methods compute the neighborhood. These methods restrict the rating predictions for a user to the items that have been rated by their nearest neighbors while leaving out other items that might be of his/her interest. This problem is different from the popularity bias caused by the power-law distribution of the item rating frequency (long-tail), well-known in the music domain, although both shortcomings can be related. Our proposal is based on extending and diversifying the neighborhood by capturing trust and homophily effects between users through social structure metrics. The results show an increase in potentially recommendable items while reducing recommendation error rates.
E-commerce payment model using blockchain

Abstract The current e-commerce payment systems for credit or check cards require a payment gateway (PG). This incurs PG fees, which in turn increases the cost of engaging in e-commerce. This paper proposes a simple payment model that uses basic cryptocurrency features, such as public key, private key, and digital signature, to eliminate the need for transaction intermediaries such as public key certificate and PG. This model can process e-commerce payments without registering additional public key certificate, public key, or private key. The use of a digital signature guarantees the integrity and nonrepudiation of electronic payments, besides eliminating the fees for intermediary services such as PG, thereby reducing the overall cost of operating e-commerce services. This proposal is crucial as it is the first attempt to apply blockchain technology to e-commerce payment services. In addition, our model is important because it not only supports the evolution of e-commerce payment technology but also enhances the competitive advantage of using e-commerce.
Accelerated transformation: designing global online higher education

Studying higher education in 2020 has confirmed the difficulty of foresight analysis and need for courageous redesign of education fundamentals. This paper discusses the shift made by Tsinghua University into full online provision and considers broader implications for higher education. It articulates the emergence of global online higher education, and the capabilities underpinning this transformation. Drawing from the study of contemporary developments in China and the world, the paper concludes that now is the time for bold education experiments, informed by major useful university design.
Deep CNN, Body Pose, and Body-Object Interaction Features for Drivers’ Activity Monitoring

Automatic recognition and prediction of in-vehicle human activities has a significant impact on the next generation of driver assistance and intelligent autonomous vehicles. In this article, we present a novel single image driver action recognition algorithm inspired by human perception that often focuses selectively on parts of the images to acquire information at specific places which are distinct to a given task. Unlike existing approaches, we argue that human activity is a combination of pose and semantic contextual cues. In detail, we model this by considering the configuration of body joints, their interaction with objects being represented as a pairwise relation to capture the structural information. Our body-pose and body-object interaction representation is built to be semantically rich and meaningful, which is highly discriminative even though it is coupled with a basic linear SVM classifier. We also propose a Multi-stream Deep Fusion Network (MDFN) for combining high-level semantics with CNN features. Our experimental results demonstrate that the proposed approach significantly improves the drivers&#x2019; action recognition accuracy on two exacting datasets.
Extractive Text Summarization Models for Urdu Language

In the recent few years, a lot of advancement has been made in Urdu linguistics. There are many portals and news websites that are generating a huge amount of data every day. However, there is still no publicly available dataset nor any framework available for automatic Urdu extractive summary generation. In an automatic extractive summary generation, the sentences with the highest weights are given importance to be included in the summary. The sentence weight is computed by the sum of the weights of the words in the sentence. There are two famous approaches to compute the weight of the words in the English language: local weights (LW) approach and global weights (GW) approach. The sensitivity of the weights depends on the contents of the text, the one word may have different weights in a different article, known as LW based approach. Whereas, in the case of GW, the weights of the words are computed from the independent dataset, which implies the weights of all words remain the same in different articles. In the proposed framework, LW and GW based approaches are modeled for the Urdu language. The sentence weight method and the weighted term-frequency method are LW based approaches that compute the weights of the sentences by the sum of important words and the sum of frequencies of the important words, respectively. Whereas, vector space model (VSM) is GW based approach, that computes the weight of the words from the independent dataset, and then remain the same for all types of the text; GW is widely used in the English language for various applications such as information retrieval and text classification. The extractive summaries are generated by LW and GW based approaches and evaluated with ground-truth summaries that are obtained by the experts. The VSM is used as a baseline framework for sentence weighting. Experiments show that LW based approaches are better for extractive summary generation. The F-score of the sentence weight method and the weighted term-frequency method are 80% and 76%, respectively. The VSM achieved only 62% accuracy on the same dataset. Both, the datasets with ground-truth, and the code are made publicly available for the researchers.
Explanations for human-on-the-loop

Many self-adaptive systems benefit from human involvement and oversight, where a human operator can provide expertise not available to the system and can detect problems that the system is unaware of. One way of achieving this is by placing the human operator on the loop - i.e., providing supervisory oversight and intervening in the case of questionable adaptation decisions. To make such interaction effective, explanation is sometimes helpful to allow the human to understand why the system is making certain decisions and calibrate confidence from the human perspective. However, explanations come with costs in terms of delayed actions and the possibility that a human may make a bad judgement. Hence, it is not always obvious whether explanations will improve overall utility and, if so, what kinds of explanation to provide to the operator. In this work, we define a formal framework for reasoning about explanations of adaptive system behaviors and the conditions under which they are warranted. Specifically, we characterize explanations in terms of explanation content, effect, and cost. We then present a dynamic adaptation approach that leverages a probabilistic reasoning technique to determine when the explanation should be used in order to improve overall system utility.
Multi-Domain Level Generation and Blending with Sketches via Example-Driven BSP and Variational Autoencoders

Procedural content generation via machine learning (PCGML) has demonstrated its usefulness as a content and game creation approach, and has been shown to be able to support human creativity. An important facet of creativity is combinational creativity or the recombination, adaptation, and reuse of ideas and concepts between and across domains. In this paper, we present a PCGML approach for level generation that is able to recombine, adapt, and reuse structural patterns from several domains to approximate unseen domains. We extend prior work involving example-driven Binary Space Partitioning for recombining and reusing patterns in multiple domains, and incorporate Variational Autoencoders (VAEs) for generating unseen structures. We evaluate our approach by blending across 7 domains and subsets of those domains. We show that our approach is able to blend domains together while retaining structural components. Additionally, by using different groups of training domains our approach is able to generate both 1) levels that reproduce and capture features of a target domain, and 2) levels that have vastly different properties from the input domain.
Proof of Adjourn (PoAj): A Novel Approach to Mitigate Blockchain Attacks

The blockchain is a distributed ledger technology that is growing in importance since inception. Besides cryptocurrencies, it has also crossed its boundary inspiring various organizations, enterprises, or business establishments to adopt this technology benefiting from the most innovative security features. The decentralized and immutable aspects have been the key points that endorse blockchain as one of the most secure technologies at the present time. However, in recent times such features seemed to be faded due to new attacking techniques. One of the biggest challenges remains within the consensus protocol itself, which is an essential component to bring all network participants to an agreed state. Cryptocurrencies adopt suitable consensus protocols based on their mining requirement, and Proof of Work (PoW) is the consensus protocol that is being predominated in major cryptocurrencies. Recent consensus protocol-based attacks, such as the 51% attack, Selfish Mining, Miner Bribe Attack, Zero Confirmation Attack, and One Confirmation Attack have been demonstrated feasible. To overcome these attacks, we propose Proof of Adjourn (PoAj), a novel consensus protocol that provides strong protection regardless of attackers hashing capability. After analyzing the 5 major attacks, and current protection techniques indicating the causes of their failure, we compared the PoAj against the most widely used PoW, showing that PoAj is not only able to mitigate the 5 attacks but also attacks relying on having a large amount of hashing power. In addition, the proposed PoAj showed to be an effective approach to mitigate the processing time issue of large-sized transactions. PoAj is not tailored to any particular attack; therefore, it is effective against malicious powerful players. The proposed approach provides a strong barrier not only to current and known attacks but also to future unknown attacks based on different strategies that rely on controlling the majority of the hashing power.
How Avatars Influence User Behavior

The Proteus Effect suggests that users of a virtual environment adapt their behavior to the characteristics of their respective avatars. The effect was introduced by Yee & Bailenson in 2007. Since then, a number of studies and experiments regarding the Proteus Effect have been conducted. Based on a review and comparison of their findings and conclusions about the theoretical framework of the effect and its explanatory approaches such as self-perception theory and priming, we are classifying these studies with regard to self-similarity, wishful identification and embodied presence. This allows for revealing parallels to the processes of self-identification, as these components represent first-order dimensions of the user-avatar bond. The results show that self-similarity can enhance the effect, as it can lead to a higher personal relevance of the avatar and thus facilitates mental rapprochement between user and avatar. Desirable characteristics of the avatar are integrated into the self-concept, whereas undesirable characteristics can be a barrier to the occurrence of the effect. Embodiment is particularly important with regard to self-perception theory and can represent a threshold for self-perception from the perspective of the avatar.
Benchmarking vision kernels and neural network inference accelerators on embedded platforms

Developing efficient embedded vision applications requires exploring various algorithmic optimization trade-offs and a broad spectrum of hardware architecture choices. This makes navigating the solution space and finding the design points with optimal performance trade-offs a challenge for developers. To help provide a fair baseline comparison, we conducted comprehensive benchmarks of accuracy, run-time, and energy efficiency of a wide range of vision kernels and neural networks on multiple embedded platforms: ARM57 CPU, Nvidia Jetson TX2 GPU and Xilinx ZCU102 FPGA. Each platform utilizes their optimized libraries for vision kernels (OpenCV, VisionWorks and xfOpenCV) and neural networks (OpenCV DNN, TensorRT and Xilinx DPU). For vision kernels, our results show that the GPU achieves an energy/frame reduction ratio of 1.1–3.2× compared to the others for simple kernels. However, for more complicated kernels and complete vision pipelines, the FPGA outperforms the others with energy/frame reduction ratios of 1.2–22.3×. For neural networks [Inception-v2 and ResNet-50, ResNet-18, Mobilenet-v2 and SqueezeNet], it shows that the FPGA achieves a speed up of [2.5, 2.1, 2.6, 2.9 and 2.5]× and an EDP reduction ratio of [1.5, 1.1, 1.4, 2.4 and 1.7]× compared to the GPU FP16 implementations, respectively.
A Survey on Contactless Smart Cards and Payment System

In recent years, contactless transactions have risen rapidly. It includes NFC, MST, contactless cards, and many other payment methods. These payment methods have certain security issues, and attackers are in a regular search for the exploits to break its security. These security issues require proper analysis to secure user data from attackers. This article will discuss the contactless smart cards and payment systems in detail including the techniques used for securing user data and different possible attacks on the technology used for communication. The article also presents some countermeasures to prevent the attack and issues with those countermeasures. In addition, the article includes some future research issues and suggestions to overcome the security issues in contactless payment system.
SCNIFFER: Low-Cost, Automated, Efficient Electromagnetic Side-Channel Sniffing

Electromagnetic (EM) side-channel analysis (SCA) is a prominent tool to break mathematically-secure cryptographic engines, especially on resource-constrained devices.Presently, to perform EM SCA on an embedded device, the entire chip is manually scanned and the MTD (Minimum Traces to Disclosure) analysis is performed at each point on the chip to reveal the secret key of the encryption algorithm.However, an automated end-to-end framework for EM leakage localization, trace acquisition, and attack has been missing.This work proposes SCNIFFER: a low-cost, automated EM Side Channel leakage SNIFFing platform to perform efficient end-to-end Side-Channel attacks.Using a leakage measure such as Test Vector Leakage Assessment (TVLA), or the signal to noise ratio (SNR), we propose a greedy gradient-search heuristic that converges to one of the points of highest EM leakage on the chip (dimension: N × N ) within O(N ) iterations, and then perform Correlational EM Analysis (CEMA) at that point.This reduces the CEMA attack time by ∼ N times compared to an exhaustive MTD analysis, and by >20× compared to choosing an attack location at random.We demonstrate SCNIFFER using a low-cost custombuilt 3-D scanner with an H-field probe (<$500) compared to >$50, 000 commercial EM scanners, and a variety of microcontrollers as the devices under attack.The SCNIFFER framework is evaluated for several cryptographic algorithms (AES-128, DES, RSA) running on both an 8-bit Atmega microcontroller and a 32-bit ARM microcontroller to find a point of high leakage and then perform a CEMA at that point.