Reviewerly/exHarmony huggingface dataset:
Space Discretization-Based Optimal Trajectory Planning for Automated Vehicles in Narrow Corridor Scenes

The narrow corridor is a common working scene for automated vehicles, where it is pretty challenging to plan a safe, feasible, and smooth trajectory due to the narrow passable area constraints. This paper presents a space discretization-based optimal trajectory planning method for automated vehicles in a narrow corridor scene with the consideration of travel time minimization and boundary collision avoidance. In this method, we first design a mathematically-described driving corridor model. Then, we build a space discretization-based trajectory optimization model in which the objective function is travel efficiency, and the vehicle-kinematics constraints, collision avoidance constraints, and several other constraints are proposed to ensure the feasibility and comfortability of the planned trajectory. Finally, the proposed method is verified with both simulations and field tests. The experimental results demonstrate the trajectory planned by the proposed method is smoother and more computationally efficient compared with the baseline methods while significantly reducing the tracking error indicating the proposed method has huge application potential in trajectory planning in the narrow corridor scenario for automated vehicles.
Analysis of Industrial Revolution 4.0 Technology-Based Learning in Higher Education

This study aimed to obtain an overview of the implementation of industrial revolution 4.0 technology-based learning management to improve university learning management, particularly in Bengkulu. The research method used was descriptive qualitative. According to the study's findings, Dehasen University of Bengkulu, Muhammadiyah University of Bengkulu, and Hazairin University of Bengkulu all have access to ICT network infrastructure. As a result, 84% of students and members of the academic community can easily find ICT tools for learning, 83% can easily learn how to use ICT tools for learning on campus, 87% can understand when and how to use ICT tools to achieve specific learning objectives, and 73% have s. This ease of using and accessing SIAKAD, e-learning, and an electronic library
Utilization of Hand Puppets from Used Socks and Let's Read Application as Learning Media to Improve Students’ Story-Telling Skills

This research aims at describing the utilization of hand puppets made from used socks adn Let’s Read Application as a storytelling medium in improving the speaking skills of the students at Faculty of Teacher Training and Education (FKIP) Al Washliyah Univeristy (Univa), Medan. This study also aims to 1) produce the benefits of hand puppets from used socks and the Let's Read application as learning media, 2) test the feasibility of the learning media, and 3) test the effectiveness of the media. This study uses the Research and Development (RD) ADDIE model. The results show that the feasibility test result conducted by the material validator got an average of 87.5% and media experts got an average of 96.67% with very decent criteria. The limited-scale effectiveness tests of hand puppets made from used socks as a learning medium for the students of FKIP Univa Medan in the 2020-2021 academic year result in an average pre-test score of 41 and an average post-test score of 78. The Hand puppets and Let's Read application are effective to be used in the speaking skills course with t-count of 3,791 and t-table of 2,021. The N-gain calculation data is 0.43. It means that hand puppets made from used socks and the Let's Read application are feasible and effective to be used as learning media for storytelling in the speaking skills course. Students make recordings of storytelling using the hand puppets to display on YouTube. The media can be developed as one of the Indonesian language learning media so that students' understanding and Indonesian language skills increase.
Convolutional Neural Networks (CNN)

Deep learning is one of the main technologies of machine learning. With deep learning, this chapter is talking about algorithms capable of mimicking the actions of the human brain through artificial neural networks. Compared to other algorithmic structures, neural networks have great advantages: first, their structure based on the stacking of non-linear functions gives them an enormous capacity. In this chapter, the authors will encompass the different models of the convolutional neural network that exist in the literature, their parameters, their architectures, their layers, their advantages, and their disadvantages. The objective is to help researchers to have a summary on deep learning.
Privacy Preserving in the Medical Sector

A number of fundamental rights are inseparable from human personality. These are in particular the right to the name, the right to missive letters, the right to physical integrity, the moral right of the author and, in the field which interests us, the right to the image, the right to honor, or the “right to secrecy”. These rights even have constitutional value. The Constitutional Council considers that they proceed from “respect for individual freedom.” In particular, laws that violate the search of vehicles, tax searches, and the publication of certain elements of tax declarations, have been declared unconstitutional. In this chapter the authors will talk about the different techniques that can be used for the preservation of privacy. This chapter will illustrate an overview of the different fundamental techniques for the preservation of privacy in medical sector such as: differential privacy, secure multiparty computation protocol, data anonymization, deidentification, fingerprint privacy, and others.
A Hybrid Classification Algorithm and Its Application on Four Real-World Data Sets

The aim of this chapter is to propose a hybrid classification algorithm based on particle swarm optimization (PSO) to enhance the generalization performance of the adaptive boosting (AdaBoost) algorithm. AdaBoost enhances any given machine learning algorithm performance by producing some weak classifiers which requires more time and memory and may not give the best classification accuracy. For this purpose, PSO is proposed as a post optimization procedure for the resulted weak classifiers and removes the redundant classifiers. The experiments were conducted on the basis of ionosphere data set, thoracic surgery data set, blood transfusion service center data set (btsc) and Statlog (Australian credit approval) data set. The experimental results show that a given boosted classifier with post optimization based on PSO improves the classification accuracy for all used data. Also, the experiments show that the proposed algorithm outperforms other techniques with best generalization.
Advanced Bioinspiration Methods for Optimisation Problems

Over the past decade, solving complex optimization problems with metaheuristics has received considerable attention among practitioners and researchers. As a result, many metaheuristic algorithms have been developed in recent years, and a large majority of these algorithms are often inspired by nature. Today, bio-inspired methods are becoming more and more popular. This popularity and success stems mainly from the fact that these algorithms were developed by mimicking nature's most efficient processes, including biological systems and physical and chemical processes. In this chapter, we will talk about metaheuristics and bio-inspired algorithms in general. From their basic concepts to their applications.
Secure and Compact Elliptic Curve Scalar Multiplication with Optimized Inversion

Abstract Elliptic curve cryptography (ECC) is a typical public key cryptography technique that can ensure equivalent security with considerably smaller key sizes than Rivest-Shamir-Adleman (RSA). Hence, various implementations based on ECC are recommended for block chain and Internet of Things (IoT) devices. Because elliptic curve scalar multiplication (ECSM) is a fundamental computation in ECC, enhancing the security and efficiency of ECSM is important. ECSM specifies a scalar multiplication algorithm and elliptic curve addition formulae. Elliptic curve addition formulae on affine coordinates are compact from a memory cost perspective but weak against side channel attacks. Elliptic curve complete addition (CA) formulae can achieve secure ECSMs but are inefficient. A newly proposed secure ECSM, which uses a right-to-left (RL) scalar multiplication algorithm and (extended) affine coordinates, takes advantage of elliptic curve addition formulae on affine coordinates. However, it can only scan the input scalars from right to left. We propose new ECSMs, which can scan the input scalars from left to right (LR) based on (extended) affine coordinates. We also prove that our LR ECSMs satisfy secure generality without requiring exceptional computations. We enhance the efficiency of both LR and RL ECSMs with optimized inversion. Our LR ECSM with a memory of 12 field elements reduces that of the Montgomery ladder and Joye’s LR with CA formulae by 36.84% and that of 2-ary RL with (extended) affine coordinates by 14.29%, respectively. Our compact ECSMs are fit for applications on IoT devices and block chain, with a critical memory requirement.
Automated rolling shutter calibration with an LED panel

Cameras with rolling shutters (RSs) dominate consumer markets but are subject to distortions when capturing motion. Many methods have been proposed to mitigate RS distortions for applications such as vision-aided odometry and three-dimensional (3D) reconstruction. They usually need known line delay d between successive image rows. To calibrate d, several methods have been proposed that often involve complex procedures. This Letter proposes an easy RS calibration method by using an off-the-shelf light-emitting diode (LED) panel, using the fact that the RS causes the blinking LED columns to appear slanted in images by a static camera. The calibration starts with extracting the LED lights and then rectifies the images to remove the lens distortion and misalignment between the camera and the LED panel. Next, blocks of slanted bright LEDs are recognized and their inclination leads to the line delay estimate. Our method needs not to move the camera, adjust the ambient light, or calibrate camera intrinsic parameters beforehand, and it can usually estimate the line delay given two LED panel images in one second. Extensive tests with industrial cameras and consumer cameras of wide-angle and fish-eye lenses validate its competitive accuracy relative to the established methods.
A Parallel Framework for Efficiently Updating Graph Properties in Large Dynamic Networks

Graph queries on large networks leverage the stored graph properties to provide faster results. Since real-world graphs are mostly dynamic, i.e., the graph topology changes over time, the corresponding graph attributes also change over time. In certain situations, recompiling or updating earlier properties is necessary to maintain the accuracy of a response to a graph query. Here, we first propose a generic framework for developing parallel algorithms to update graph properties on large dynamic networks. We use our framework to develop algorithms for updating Single Source Shortest Path (SSSP) and Vertex Color. Then we propose applications of the developed algorithms in Unmanned Aerial Vehicle (UAV) based delivery systems under time-varying dynamics. Finally, we implement our SSSP and vertex color update algorithms for Nvidia GPU architecture and show empirically that the developed algorithms can update properties in large dynamic networks faster than the state-of-the-art techniques.
Characteristics of Deep Learning Workloads in Industry, Academic Institutions and National Laboratories

Over the past two decades, deep learning techniques have emerged as an immensely powerful technology, with break-through in computer vision, speech to text technologies, natural language processing and many more such fields. As neural networks grow in size and capacity, they require higher compute power and larger datasets to converge to a model with higher accuracy. In this paper, we present a data-centric approach to study the system level requirements (GPU utilization, CPU utilization, I/O) of deep learning training workloads, and uncover a few insights that help us understand the nature of deep learning training workloads. We analyze three datasets found in industry, academia and national laboratories to understand the requirements and properties of deep learning training workloads in different settings.
Invited Paper: Hierarchical Activity Recognition with Smartwatch IMU

Advances in the area of ubiquitous computing have paved the way for a plethora of mobile applications operating in smart devices that aim to assist various aspects of everyday life. In the context of human-environment interaction in smart homes, remote monitoring of older adults, or general improvement of daily habits, the estimation of human activities in real-time and in the appropriate context is becoming of increasing importance. At the same time, the ever-increasing capabilities of smartwatches and their premium wrist-attached placement makes them an exciting tool for solving the above problem even for tasks of high manual dexterity.
An effective software cross-project fault prediction model for quality improvement

As a quality assurance activity, cross-project fault prediction (CPFP) involves building a model for predicting the faults in a specific software project (aka target project) facing the shortage of within-project training data, leveraging cross-projects data. However, the quality of training data decides the success of a CPFP model. Existing CPFP approaches mainly focused on instance selection with minimal attention to feature selection. Further, the validation of their models has been performed through non-effort-based performance measures (NEPMs) only, considering the availability of unlimited inspection resources, which is impractical. Addressing these problems, we propose a Hybrid Training Data Selection (HTDS) approach combining feature selection along with instance selection for building a proficient and pragmatic CPFP model by validating its effectiveness in terms of effort-based performance measures (EPMs) along with the NEPMs to confirm its practical applicability. The empirical results on 62 datasets manifested the potency of the proposed approach over all the compared approaches in terms of NEPMs and EPMs collectively. Thus, our proposed HTDS approach results in a more productive and practical CPFP model that can empower practitioners to produce quality software at a lesser cost.
A Data-Centric Approach for Analyzing Large-Scale Deep Learning Applications

Over the past two decades, deep learning techniques have emerged as an immensely powerful technology, with break-through in computer vision, speech to text technologies, natural language processing and many more such fields. As neural networks grow in size and capacity, they require higher compute power and larger datasets to converge to a model with higher accuracy. In this poster, we present a data-centric approach to studying the system level requirements (GPU utilization, CPU utilization, I/O) of deep learning training workloads, and uncover a few insights that help us understand the nature of deep learning training workloads. We analyse three datasets found in industry, academia and national laboratories to understand the requirements and properties of deep learning training workloads.
Invited Paper: Fault-tolerant and Expressive Cross-Chain Swaps

Cross-chain swaps enable exchange of different assets that reside on different blockchains. Several protocols have been proposed for atomic cross-chain swaps. However, those protocols are not fault-tolerant, in the sense that if any party deviates, no asset transfer can happen. In this paper, we propose two alternative protocols for structuring composable and robust cross-chain swaps. Participants can propose multiple swaps simultaneously and then complete a subset of those swaps according to their needs. Their needs are expressed as predicates which capture acceptable payoff of each participant. Our proposed protocols are thus more expressive due to the introduction of predicates. The proposed protocols are fault-tolerant since, even if some participants deviate, those predicates can still be satisfied, and conforming parties can complete an acceptable set of swaps.
Greedy Algorithms for Finding Entanglement Swap Paths in Quantum Networks

The entanglement swap primitive facilitates the establishment of shared entanglement between non-adjacent nodes in a quantum network. This shared entanglement can subsequently be used for executing quantum communication protocols. The fundamental problem in quantum networks is to determine a path for entanglement swapping in response to demands for entanglement sharing between pairs of nodes. We investigate variants of this problem in this work.
An encrypted traffic classification method based on contrastive learning

Network traffic classification has become an important part of network management, which is conducive to realizing intelligent network operation and maintenance, improving network quality of service (QoS), and ensuring network security. With the rapid development of various applications and protocols, more and more encrypted traffic appears in the network. Due to the loss of semantic information after traffic encryption, poor content intelligibility, and difficulty in feature extraction, traditional detection methods are no longer applicable. Existing solutions mainly rely on the powerful feature self-learning ability of end-to-end deep neural networks to identify encrypted traffic. However, such methods are overly dependent on data size, and it has been experimentally proven that it is often difficult to achieve satisfactory results when validating across datasets. In order to solve this problem, this paper proposes an encrypted traffic identification method based on contrastive learning. First, the clustering method is used to expand the labeled data set. When the encrypted traffic features are difficult to extract, it is only necessary to learn the feature space to achieve discrimination.more suitable for encrypted traffic identification. When validating across datasets, only fine-tuning is required on a small amount of labeled data to achieve good recognition results. Compared with the end-to-end learning method, there is an improvement of about 5%.
Privacy Protection Technology in Supply Chain-oriented Blockchain

The supply chain refers to the process that starts from the production of parts, goes through transportation, storage and other processes, and finally forms products and sells them. The supply chain generally consists of suppliers, manufacturers, sales companies, consumers and other elements. With the increase in the scale of the supply chain and the increase in transactions, it becomes difficult to track the transportation process of goods, and the transaction reliability is low, which leads to problems such as low logistics and transportation efficiency, difficulty in market supervision, and difficulty in confirming the ownership of goods. Therefore, we need a means to informatize the supply chain. Blockchain consists of blocks containing information connected in chronological order. Compared with traditional networks, blockchain has two core characteristics: one is the immutability of data, and the other is decentralization. Its structure naturally fit the characteristics and development trend of the supply chain, so we can consider using blockchain technology to solve the problems in the supply chain, so as to realize the innovative development of the supply chain.
Post quantum identity authentication mechanism in blockchain

The blockchain technology has developed rapidly in recent years and has been widely used in all walks of life. However, most of the authentication systems adopted by the current blockchain technology are public key infrastructure based on large integer decomposition or discrete logarithm difficulties, and these cryptosystems are not secure in the quantum environment. Therefore, this paper considers an identity based post quantum authentication system applicable to the blockchain, which provides anti quantum protection and eliminates the dependence on public key certificates. Under the control of the supervision node, the authentication system has the key revocation function.
Optimization Tracking Algorithm Based on Extended Target Gaussian Mixture PHD Filter

Under low signal-to-noise ratio (SNR) target tracking, poor target information and high clutter limit the tracking effect. Extended targets potentially generate more than one measurement per time step. Multiple extended targets tracking is therefore can be used to improve tracking performance with low SNR, due to the expanded data than point targets tracking. Based on the classical probability hypothesis density (PHD) filter, the extended target PHD (ET- PHD) filter is proposed to track multiple extended targets. The main contribution of this paper is the improvement of the classical extended target Gaussian-mixture probability hypothesis density (ET-GM-PHD) filter. A method based on the ET-GM-PHD filter is proposed for decreasing false alarms and improving measurement set partition performance under low SNR cases. The optimized method is shown a better tracking performance in estimation accuracy of the targets number and targets state in comparison with a point PHD filter.
Detecting and Punishing Mute Nodes in Shard-Based Permissionless Blockchains

As a key technology to solve the problem of trust in application systems, blockchain has attracted more and more attention in recent years. Many researches focus on applying voting-based consensus in permissionless blockchain to improve the throughput. However, in these methods, mute nodes may get the reward without participating in consensus, which may impact the availability of the system. We propose an observation exchanging protocol based on double-chain architecture to detect mute nodes. Nodes will vote on whether other nodes have sent protocol messages, and agree on an observation matrix which is generated by merging the observation of all nodes through consensus. We also propose an incentive mechanism and adopt a reputation system based on the matrix to punish mute nodes. Block reward is divided into three parts and is distributed according to the observation matrix. Security analysis shows that the observation exchanging protocol ensures mute nodes can be detected, and the incentive mechanism ensures mute nodes and the nodes adopt selfish strategies can be punished. Finally, we implement a prototype to evaluate our observation exchanging protocol and incentive mechanism. Experiment shows that the observation exchanging protocol merely has small influence on consensus delay.
AN RDN-based image super-resolution method using Meta-learning

In this paper, we propose a residual dense networks (RDN)-based image super-resolution method using Meta-learning. Specifically, deep extraction of global features is performed on the external dataset through an RDN, meta-learning to obtain an initial parameter definitely for internal learning, so we can utilize both external and internal data. Our method achieves good results with only one gradient update. And it can be appropriate for image super-resolution under the action of different blur kernels, with a wider application range and high flexibility.