Reviewerly/exHarmony huggingface dataset:
Introduction to the special section on recent advances on blockchain infrastructures and applications

This paper proposes a novel user-centric and privacy-preserving credential scheme over the blockchain. The proposed protocol allows users to access services without revealing sensitive attributes. This new paradigm is based on an efficient short signature, which uses pairing and self-blindable credentials that are verifiable on the blockchain. Our scheme achieves the advanced features of anonymity, unlinkability, and untraceability of users. Moreover, confidentiality of users’ attributes and unforgeability of their credentials are met. We provide security proofs and a real-world use-case where the protocol can be applied. To empirically assess the performance of our solution, the cryptographic components and communications between the various involved actors are implemented using GO and Java. In addition, an implementation for an online trading use case based on Hyperledger Fabric is provided. Finally, we prove the efficiency of our work by presenting some experimental results and exhibiting comparisons with known traditional credentials schemes.
LRERNet: Low Resolution Expression Recognition Network

Abstract Facial expression recognition (FER) is a meaningful but challenging research direction. An important reason is that the image resolution used for FER is usually low. At present, there is no specially designed model for the challenge of low resolution FER. We propose a low resolution expression recognition network with front and back end structure using specially designed dilated convolution groups. In addition, for the need of pension agency, we establish a dataset for studying FER of the old in Asian countries and we named it AOPFE. We evaluate our method on three standard datasets (RAF, SFEW and CK+) and AOPFE. In our experiments, the method achieved good results in these datasets especially on RAF dataset.
Question Generation Through Transfer Learning

An automatic question generation (QG) system aims to produce questions from a text, such as a sentence or a paragraph. Traditional approaches are mainly based on heuristic, hand-crafted rules to transduce a declarative sentence to a related interrogative sentence. However, creating such a set of rules requires deep linguistic knowledge and most of these rules are language-specific. Although a data-driven approach reduces the participation of linguistic experts, to get sufficient labeled data for QG model training is still a difficult task. In this paper, we applied a neural sequence-to-sequence pointer-generator network with various transfer learning strategies to capture the underlying information of making a question, on a target domain with rare training pairs. Our experiment demonstrates the viability of domain adaptation in QG task. We also show the possibility that transfer learning is helpful in a semi-supervised approach when the amount of training pairs in the target QG dataset is not large enough.
A High Precision Rotating Line Detection Method for the Rotation Angle Measurement Based on Machine Vision

Abstract The accuracy of the rotation angle measurement method based on machine vision with the line as the reference target is limited by line detection accuracy. The Line Segment Detector (LSD) method is a high precision and effective method, but there are detected false lines and discontinuous lines when the LSD method is actually used to detect the rotating lines in the images. In this paper, we adjust three parts of the LSD method: image scaling, gradient magnitude threshold, and aligned points density threshold to detect lines accurately. The high precision detection of the rotating line is obtained by our adjusted method. Besides, the rotation angle measurement accuracy with the lines detected by our adjusted LSD reaches the measurement error level of arcseconds, which proves its effective performance.
Detecting deepfakes using crowd consensus

Deepfake videos are becoming more and more believable, outpacing fake detection methods. As a research community, we must embrace a wider variety of detection tactics to keep up and quell the spread of misinformation.
Implementation of Multi Spin-Thread Architecture to Fully-Connected Annealing Processing AI Chips

Artificial intelligence (AI) processing on edge computing in the Internet of Things (IoT) society requires our focus on the Ising model to enable advanced information processing with low-power consumption, small area, and thorough implementation in LSI chips. The configuration is such that the fully-connected interaction layer (memory) and the spin and calculation layers of the Ising model are separated and directly connected in parallel. A stochastic operation is performed in the Ising model because annealing is used to obtain a solution. However, an optimal solution cannot always be obtained using one calculation. Therefore, we devised and implemented the concept of a multi spin-thread by taking advantage of the fact that multiple spin sets and calculation layers are possible. In the multi spin-thread, multiple threads can be calculated simultaneously with spin required for calculation being one thread. Therefore, the state of the other spin-threads can be adjusted to the state of the spin-thread with the smallest Hamiltonian, and a state with a lower Hamiltonian can be searched. This improves the accuracy of the solution. A 28-nm CMOS LSI (AI chip) with 512 spins and eight spin-threads was tested. In a 22-city traveling salesman problem (TSP), the average path length could be reduced by 19% and the dispersion reduced by 70% on average, and the number of cuts in the 512-node max-cut problem could be increased by 1.6% on average and the dispersion reduced by 84%. The time required to obtain the solution was 128 ms.
Global-Best Leading Artificial Bee Colony Algorithms

Artificial bee colony (ABC) has been a competitive population-based optimization algorithm in recent years. However, it still faces a challenge that shows slow convergence speed. To address this concerning issue, this chapter proposes two global-best leading algorithms, GLABC-pso and GLABC-de, to accelerate their convergence speed and make a precise search on the condition of guaranteeing their global search abilities. In our algorithms, GLABC-pso and GLABC-DE gets the merits of the Particle Swarm Optimization (PSO) and the Differential Evolution (DE) in the employed bee phase, respectively. Furthermore, both two algorithms utilize the same global-best leading strategy in the onlooker bee phase. To evaluate their performances, a set of benchmark functions are employed in this chapter. Experimental results demonstrate that our methods outperform the state-of-the-art algorithms in terms of solution accuracy and stability.
Feature Analysis and Target Detection of High-resolution Remote Sensing Image of Coal Mining Subsidence Area

Abstract Aiming at the phenomenon of regional ground collapse caused by mining, the site information is obtained through field investigation, and the features of typical collapse targets are described. The drone remote sensing images are used to extract and analyze the color features and shape features of typical collapse targets. Combine the Hough transform, shadow and Harris corner detection methods to achieve the target detection of collapsed pits and trough collapsed areas. The combined method uses Hough transform to detect the edge shape curve of the collapsed pits and trough collapsed areas, and use shadow detection method to extract its internal areas, and Harris corner detection method is used to locate local feature points in the image. Experimental analysis results show that the method has good application effect on target detection in collapsed pits and trough collapsed areas.
A Technical Survey on Approaches for Detecting Rogue Access Points

With the popularity of wireless local area networks (WLANs) and access points (APs) which play an integral part in the wireless infrastructure coordinating wireless users and connecting them to the wired side of networks to the Internet become increasingly vulnerable and are exposed to malicious attacks. This paper focuses on rogue access points (RAPs) common attacks. The attack principle of different types of RAPs and corresponding RAPs detection methods are presented. Besides, the disadvantage and strength of these RAPs detection methods are also compared in this survey. Finally, some possible issues and future research trends are introduced.
A Manager’s Guide to Free Speech and Social Media in the Public Workplace: An Analysis of the Lower Courts’ Recent Application of Pickering

Public organizations are experiencing a burgeoning of workplace challenges involving employee use of social media. Comments, images, or videos ranging from racist remarks, to calls to violence, simple criticism of one’s organization, to full on whistle blowing significantly challenge public organizations’ policies for addressing speech that creates discord in the workplace. With the blurring of lines between personal and professional lives, these challenges create uncertainty for public organizations regarding how to maintain the efficient operation of the workplace, deal with the social and political fallout of such instances, and manage organizational liability. This article performs content analysis on 33 federal lower court opinions involving speech/social media workplace issues. The study analyzes the manner in which the lower courts apply free speech precedent on contemporary workplace speech cases. The findings suggest that patterns emerge from the opinions providing key insights for public managers regarding how to better manage these complex issues.
SIP: Boosting Up Graph Computing by Separating the Irregular Property Data

Graph analytics is an important class of applications and is one of the cornerstone of big-data workloads. Unfortunately, due to poor data locality in most graph applications, conventional general-purpose computer architectures are unable to perform the best of their processing abilities. The main source of poor locality comes from accessing vertex properties. Upper-level caches cannot hold data blocks long enough due to their limited capacity and the long reuse distance of vertex properties. Moreover, accesses to properties can evict other useful data with good locality, which causes more conflicting misses. In this work, a small cache is added exclusively for the properties to solve this problem. We further enhance this structure with prefetchers to increase the hit rate of properties and improve performance of system. Experimental results show that compared to two state-of-the-art prefetcher and accelerator for graph computing, our proposed architecture achieves 1.13x-2.54x and 1.04x-1.27x performance improvements. In the meanwhile, the energy consumptions can be saved by 6.41%-13.43% and 34.67%-43.92% respectively.
Impact of Process Chambers Exhaust on Wafer Defectivity in Wet Clean tools

The published paper will discuss the impact of exhaust pressure and velocity on particle performance in wet clean process chambers. Correlations are drawn from chamber exhaust pressure, exhaust velocity, and exhaust duct condition to explain the observed degradation in particle performance. Based on the observations, key solutions including periodic preventative maintenance on exhaust duct lines, chamber wipe downs, exhaust rebalancing are recommended to improve chamber stability and particle performance.
On the Use of Cultural Enhancement Strategies to Improve the NEAT Algorithm

Knowledge transmitted between generations by non-genetic means can be understood as culture. The capacity of individuals from certain species to teach and learn plays a fundamental role in directing the evolutionary process. The Neuroevolution of Augmenting Topologies (NEAT) framework enables evolving neural structures to iteratively solve a given learning problem. However, the NEAT approach does not consider cultural aspects in its formulation. In such a context, the aim of this paper is to propose and evaluate ways of enhancing the NEAT framework with additional learning approaches. The parameters involved in the analysis comprise the Backpropagation and the Extreme Learning Machine (ELM) learning algorithms, the individuals to be taught, the moment when culture manifests in the system, and the nature of the lessons to be learned. Empirical results on sequential learning tasks indicate that cultural enhancements, as well as some of the proposed variations, accelerate the neuroevolution convergence.
Fuzzy Features for Quality Estimation of Activity Instances in a Dataset

Activity recognition in smart homes is a challenging problem that attracted a lot of attention in the past decades. Most approaches nowadays rely on data-driven methods from artificial intelligence, especially from the field of supervised machine learning. Therefore, those approaches heavily depend on the quality of the datasets they exploit. In this paper, we propose a generalizable method based upon fuzzy logic to estimate and diagnostic the quality of each activity instance of an existing dataset. We then apply it to a labeled dataset of the CASAS laboratory and analyze the results.
Novel Multi-objecitve Evolutionary Algorithm for Color Filter Arrays Design

Most digital cameras use a single sensor covered with a Color Filter Array (CFA) in order to reduce the size, complexity and cost. Now, more and more researchers have paid attentions to the representation of CFA since its crucial role in the process of reconstructing the full color image. The representation of CFA in frequency domain records all the frequency information of image mosaicked with the CFA, thus provides a theoretical approach to design the CFA. However, almost all the existing CFA design methods in frequency domain have limitations. In this paper, we propose a new automatic CFA design method in frequency domain. We choose the optimal frequency structures which satisfy all the design principles through a novel coded multi-objective evolutionary algorithm (MOEA) at first. Then we convert the parameters optimization of the given frequency structures into a constraint single optimization problem, and we solve it by converting it into a multi-objective optimization problem without constraints which is hard to handle. So a MOEA with novel selection and crossover-mutate strategies is proposed to solve the model. The experimental results show that the proposed CFA design method has more advantage when compared with the other existing method.
Front Moving Object Behavior Prediction System Exploiting Deep Learning Technology for ADAS Applications

This paper proposes a front pedestrian crossing and vehicle cut-in prediction system based on 3D convolution behavior prediction network. The proposed design improves the original 3D convolution network (C3D) to make behavior recognition network have the ability of object localization, which is important to detect multiple moving object behaviors. The proposed system is implemented on the embedded system in real-time, which achieves 20 frames per second when it is deployed on NVIDIA Jetson AGX Xavier and possesses over 92.8% accuracy for pedestrian crossing and 94.3% accuracy for vehicle cut-in behavior detection.
Comparison of Contrast Enhancement Methods for Underwater Target Sonar Images

The contrast enhancement of underwater target sonar images plays a key role in the smart perception of underwater targets and underwater smart services. In general, the contrast of sonar images is low, which is not conducive to the subsequent sonar image segmentation and target recognition. In the paper, the four selected methods for sonar image contrast enhancement are gray scale transformation, histogram equalization, unsharp masking and discrete wavelet transform. The four methods are contrasted in terms of contrast and signal to noise ratio of image.
Security Analysis and Optimization of BB84 QKD System Post-Processing

Abstract Considering the rapid development of quantum technology, it has become inevitable to use this technology to develop an ultimately secured key exchange system. Quantum key distribution (QKD) technology has become more sophisticated, is expected to provide secure key distribution mechanism for the actual information security systems. The unique advantage of quantum cryptography is precisely its ability to guarantee absolute security. Previous works have demonstrated unconditional security of the ideal BB84 QKD system. However, on a typical QKD system, many practical situations often cannot satisfy the requirements of the ideal condition. This problem causes some security issues for practical QKD system. Therefore, it is necessary to analyze the security of QKD system under practical conditions, and identify the security implications on the practical physical systems and equipment, in order to find a method that guarantees the security of QKD system. The security of QKD system based on BB84 involves many aspects; this paper mainly focuses on how to resolve security issues that may exist, especially in the BB84 protocol post-processing. We introduce ways to tackle these issues and make vital key information leakage very small. Their impact on the security of entire QKD system is analyzed, and the interaction and security optimization between the various parts is researched. Finally, the thesis briefly introduces five sub-modules of post-processing of QKD system: the base comparison, error estimation, information correction, data check and privacy amplification; the existent security factors of each module is given, for error estimation and information correction is provided the secure model with those issues. Thus, the security model of post-processing is further optimized.
Attention-aware invertible hashing network with skip connections

In recent years, Convolutional Neural Networks (CNNs) have shown promising performance on image hashing retrieval. However, due to the information-discarded nature of CNN, some meaningful information can not be further extracted into a deep level and embedded into hash codes. To solve the problem, this study attempts to design an invertible CNN feature extractor to fully maintain input information meanwhile having well generalization ability. Specifically, we propose a novel Attention-Aware Invertible Hashing Network with Skip Connection (AIHN-SC) for image retrieval. Represented by an invertible feature, the hash code can be learned and generated from image characteristics preserving all input information. For achieving favourable generalization ability in our invertible architecture, we present a novel spatial attention mechanism to highlight regions involving semantic information. In addition, we introduce two kinds of skip connection, i.e. hierarchical and residual connections, which aim to provide richer knowledges for hash code learning and ease our training process. Extensive experiments on benchmark datasets demonstrate the effectiveness of our proposed AIHN-SC and show the significant performance in image retrieval against the state-of-the-arts.
Design and Implementation of a Course Answering System Based on Error Correction and Trie Tree Language Model

Abstract With the rapid growth of network information, the accuracy of input text affects the retrieval results, text proofreading technology emerges as the times require in order to avoid the situation of “wrong answers” caused by wrong questions when searching for information. In the course question answering system, we also need to consider the query speed and efficiency. In order to avoid the trouble of manual proofreading, this paper proposes a system which can automatically correct wrong questions in the professional field of curriculum. Firstly, used the edit distance method for fuzzy matching of error strings; then, used trie tree language model to store data to improve query efficiency. Finally, compared the proofreading effect under different text similarity thresholds, and selected the best value for the experiment. After experimental analysis and comparison, the best result is selected when the text similarity is 0.5, the accuracy rate is 77.91%, the recall rate is 67%, and the F value is 72.04%. Experiments show that the system designed in this paper can effectively correct the wrong text in the field of computer.
An Order Sampling Processing-in-Memory Architecture for Approximate Graph Pattern Mining

There have been increasing interests in graph pattern mining due to the booming of data volume in various domains. Conventional graph mining implementations which calculate the exact count of patterns usually suffer from huge amounts of intermediate data and low performance on large-scale graphs. With the observation that the exact pattern counts are not required in many real-world graph pattern mining problems, previous works (e.g., ASAP) proposed an approximate graph pattern mining algorithm and improved the performance of graph pattern mining by up to two orders of magnitudes. The crucial sampling operation in the ASAP algorithm exposes high parallelism and complex edge searching. Moreover, the performance of ASAP is closely related the sampling order. However, previous works failed to tackle these problems in the design. Thus, we propose a novel Processing-in-Memory (PIM) architecture for parallel approximate graph pattern mining problems. We introduce dictionaries on the logic layer of PIM devices for edge indexing. We also explore the design space of sampling orders and give the optimal sampling strategy. The comprehensive experimental results show that, our design achieves up to 97 times performance improvement against ASAP system.
Deployment of Object Detection Enhanced with Multi-label Multi-classification on Edge Device

This paper presents a method that enables detecting multiple mutually exclusive features of each class using the same convolutional neural network (CNN). This method proposes the design of a Multi-Label Multi-Classification (MLMC) deep learning model on resource-constrained edge devices. The proposed technique is also extended to utilizing the same CNN for classifying heterogeneous datasets. The MLMC model is used in conjunction with object detection techniques that leverage on a cropping system. The combined model used in conjunction with Intel's Neural Compute Stick (NCS) achieves the detection of an object of interest at the rate of 10.51FPS out-performing conventional object detection speed on edge devices.
Weaving a Faster Tor: A Multi-Threaded Relay Architecture for Improved Throughput

The Tor anonymity network has millions of daily users and thousands of volunteer-run relays. Increasing the number of Tor users will enhance the privacy of not just new users, but also existing users by increasing their anonymity sets. However, growing the network further has several research and deployment challenges. One such challenge is supporting the increase in bandwidth required by additional users joining the network. While adding more Tor relays to the network would increase the total available bandwidth, it requires network architecture changes to reduce the impact of Tor’s growing directory documents. In order to increase the total available network bandwidth without needing to grow Tor’s directory documents, this work provides a multi-threaded relay architecture designed to improve the throughput of individual multi-core relays with available network capacity. We built an implementation of a subset of this new design on top of the standard Tor code base to demonstrate the potential throughput improvements of this architecture on both high- and low-performance hardware.