Reviewerly/exHarmony huggingface dataset:
Cybersecurity and Cybercrimes

Cybersecurity law is not simply the application of legal systems to the protection of private data; it also includes using our legal systems to address criminal activity that is conducted using networked technologies—otherwise known as cybercrime. This chapter will outline the types of cybersecurity laws needed to address issues such as interpersonal privacy, criminal copyright infringement, data breaches, network attacks, and other computer-related activities of a criminally malicious nature.
The Teaching Reform and Research of C Programming Course Based on BOPPPS Model and Rain Classroom

C programming is an introductory programming course. How to improve students' high-level "cognitive" ability is the key and difficult point of this course. To optimize the effect of students' participatory learning, while using BOPPPS model to design classroom teaching, this paper combines the online teaching tools of rain classroom and MOOC resources of Chinese universities. Teaching practice shows that the combination of various elements can improve students' initiative and creativity, ensure their participation in class and the learning depth, and improve the teaching efficiency.
Multiple GAN Inversion for Exemplar-based Image-to-Image Translation

Existing state-of-the-art techniques in exemplar-based image-to-image translation hold several critical concerns. Existing methods related to exemplar-based image-to-image translation are impossible to translate on an image tuple input (source, target) that is not aligned. Additionally, we can confirm that the existing method exhibits limited generalization ability to unseen images. In order to overcome this limitation, we propose Multiple GAN Inversion for Exemplar-based Image-to-Image Translation. Our novel Multiple GAN Inversion avoids human intervention by using a self-deciding algorithm to choose the number of layers using Fréchet Inception Distance(FID), which selects more plausible image reconstruction results among multiple hypotheses without any training or supervision. Experimental results have in fact, shown the advantage of the proposed method compared to existing state-of-the-art exemplar-based image-to-image translation methods.
Ship tracking based on the fusion of Kalman filter and particle filter

Abstract Aiming at the problem of low filtering accuracy and even divergence caused by model mismatch when using extended Kalman filter in ship GPS navigation and positioning state estimation, a positioning ship state estimation algorithm based on the fusion of improved unscented Kalman filter and particle filter is proposed. Compared with the traditional particle filtering algorithm, the algorithm has two improvements: first, the algorithm uses untraced Kalman as the main framework, and uses the optimal estimation of particle updating state by particle algorithm; Secondly, in the resampling process, a resampling algorithm based on weight optimization is proposed to increase the diversity of particles. The simulation results show that not only the particle degradation degree of the particle filter is reduced, but also the particle tracking accuracy is improved.
DBRG: Description-Based Non-Quality Requirements Generator

Requirements quality checking is a key process in requirements engineering. For complex and large scale systems, it is recommended to use automated requirements quality checking tools because of the size and complexity of requirements. However, such tools are typically evaluated on a small set of manually curated requirements. This limitation affects the comprehensiveness and reliability of the evaluation and leaves several possible quality issues undetected. In this paper, we de-scribe a novel quality-checking-oriented synthesised requirements generator. We provide an input description language so that several quality checking issues and scenarios can be defined. The generator utilises an input dictionary of nouns and verb frames, and generates requirements sentences complying to a user-defined description of a quality affected requirement.
Visual notations for viewing RDF constraints with UnSHACLed

The quality of knowledge graphs can be assessed by a validation against specified constraints, typically use-case specific and modeled by human users in a manual fashion. Visualizations can improve the modeling process as they are specifically designed for human information processing, possibly leading to more accurate constraints, and in turn higher quality knowledge graphs. However, it is currently unknown how such visualizations support users when viewing RDF constraints as no scientific evidence for the visualizations’ effectiveness is provided. Furthermore, some of the existing tools are likely suboptimal, as they lack support for edit operations or common constraints types. To establish a baseline, we have defined visual notations to represent RDF constraints and implemented them in UnSHACLed, a tool that is independent of a concrete RDF constraint language. In this paper, we (i) present two visual notations that support all SHACL core constraints, built upon the commonly used visualizations VOWL and UML, (ii) analyze both notations based on cognitive effective design principles, (iii) perform a comparative user study between both visual notations, and (iv) present our open source tool UnSHACLed incorporating our efforts. Users were presented RDF constraints in both visual notations and had to answer questions based on visualization task taxonomies. Although no statistical significant difference in mean error rates was observed, all study participants preferred ShapeVOWL in a self assessment to answer RDF constraint-related questions. Furthermore, ShapeVOWL adheres to more cognitive effective design principles according to our performed comparison. Study participants argued that the increased visual features of ShapeVOWL made it easier to spot constraints, but a list of constraints – as in ShapeUML – is easier to read. However, also that more deviations from the strict UML specification and introduction of more visual features can improve ShapeUML. From these findings we conclude that ShapeVOWL has a higher potential to represent RDF constraints more effective compared to ShapeUML. But also that the clear and efficient text encoding of ShapeUML can be improved with visual features. A one-size-fits-all approach to RDF constraint visualization and editing will be insufficient. Therefore, to support different audiences and use cases, user interfaces of RDF constraint editors need to support different visual notations.
Double Action Mechanism for Vehicle Edge Computing Resource Allocation and Pricing

With the advent of the 5G era, the internet of vehicles industry is booming. In order to solve the problem of explosive growth of data. This paper studies the resource allocation and price strategy of edge computing in the internet of vehicles based on the double auction mechanism. We propose Double Action of Multi-type Multi-resource Based on Breakeven (DAMMB) and Double Action of Multi-type Multi-resource Based on Dynamic Pricing (DAMMDP) algorithms, DAMMB and DAMMDP can significantly improve the utility and effective matching of internet of vehicles systems.
Flexi-Compression: A Flexible Model Compression Method for Autonomous Driving

Benefiting from the rapid development of convolutional neural networks, computer vision-based autonomous driving technologies are gradually being deployed in vehicles. However, these neural networks typically have a large number of parameters and extremely high computational cost, making them difficult to deploy in autonomous vehicles with limited storage and computational power. In this paper, we propose an innovative model compression approach to compress convolutional neural networks in autonomous driving algorithms, which we call Flexi-Compression. Flexi-Compression first modifies the model structure by replacing the traditional convolutional layers with our proposed Flexi-CP module, thus reducing the computation of the convolutional layers. Then, we leverage knowledge distillation to enable the compressed model to quickly acquire the knowledge of the original model. In addition, we use a Flexi-Batch Normalization layer to prune the model and finally further reduce the model size by model quantization. We compress the VGG-16 network using our proposed model compression algorithm, which is a commonly used backbone network in autonomous driving algorithms. On the CIFAR-10 dataset, our compression method can reduce the parameters of the VGG-16 network by 86% and the computation by 87% with 4% loss of accuracy. To verify the effectiveness of our compression algorithm in real-world applications, we also compress an autonomous driving algorithm and achieve excellent performance.
Vehicle detection using YOLO and mobility tracking during COVID-19 pandemic lockdowns

Data-driven approaches to traffic monitoring have important applications in tracking vehicle mobility in the COVID-19 pandemic lockdowns. We report preliminary results of a pipeline that uses the You Only Look Once (YOLOv3) and the Simple Online and Realtime Tracking (SORT) algorithms to count and classify vehicles in traffic videos. We correlate vehicle counts in Katipunan Avenue, Metro Manila and Google COVID-19 Community Mobility Reports from May to August 2020 and we show that vehicle detection data may be considered for monitoring community response to changes in COVID-19 lockdown stringency levels.
Blockchain-Based IoT Architecture for Software-Defined Networking

The most popular research advances in emerging technologies are software-defined networking (SDN) and blockchain technology (BCT). These systems have been used in the Internet of things (IoT) environment for various purposes, such as security, reliability, interoperability, privacy, and so on. In this respect, such strategies can be used to efficiently include cloud storage with protection, confidentiality, and privacy. Privacy and power aspects play a significant role in the transferring of information through IoT due to the power and processing of restricted interconnected systems, that is, processing and storage resources. Whether fraudulent or unintentional, there are possible real-world effects of data intrusion in an IoT system. In this chapter, we discuss the potential to combine blockchain technology and software-defined networking (SDN) to alleviate some of the problems. Particularly, using cluster architecture with a modern routing protocol, we suggest a stable and energy-saving blockchain-enabled SDN architecture for IoT. The experimental results suggest that the cluster structure-based routing and packet-oriented routing have better latency, power, and rugged performance than the above routing protocols. The cluster structure-based routing protocol has improved performance than other routing protocols with respect to performance, power savings, and robustness. The cluster structure-based routing protocol has better sensitivity to packet loss. The cluster structure-based routing protocol has improved performance in the presence of foundering. The performance of the suggested architecture is justified based on the observation of simulation results with the choice of applicable parameters.
Blockchain-Based IoT Architecture

It represents a new technology of the Internet of Things (IoT) that both the virtual and physical are objects enabled to communicate and connect through each other, and the new quality of life produces improved digitized services. The IoT organization offers some benefits, still, current centralized architecture (CCA) introduces various problems such as privacy, safety, data integrity, transparency, and single point of failure. It is an obstacle in the way of IoT applications and challenges future developments. Moving IoT toward distributed ledger technologies is moving IoT to the correct choice to resolve these problems. The popular and common type of distributed ledger technologies is the blockchain. Integrating the IoT with blockchain innovation can bring endless advantages. They depend on centralized models that present another assortment of specialized impediments from all around the world. It is another form of engineering for refereeing jobs and authorizations in the IoT. It is completely new engineering conveying admittance to the IoT of the control framework dependent on blockchain innovation. The architecture is sponsored through proof of idea usage and assessed in practical IoT situations.
Estimation and inference in adaptive learning models with slowly decreasing gains

An asymptotic theory for estimation and inference in adaptive learning models with strong mixing regressors and martingale difference innovations is developed. The maintained polynomial gain specification provides a unified framework which permits slow convergence of agents' beliefs and contains recursive least squares as a prominent special case. Reminiscent of the classical literature on co‐integration, an asymptotic equivalence between two approaches to estimation of long‐run equilibrium and short‐run dynamics is established. Notwithstanding potential threats to inference arising from non‐standard convergence rates and a singular variance–covariance matrix, hypotheses about single, as well as joint restrictions remain testable. Monte Carlo evidence confirms the accuracy of the asymptotic theory in finite samples.
Named Entity Recognition over FBNER: A New Facebook Dataset in Turkish

In this paper, we introduce a new Named Entity Recognition (NER) dataset of Facebook messages written in the Turkish language. We also employ a Conditional Random Fields based NER system to discover named entities from Facebook messages. Our system achieves an F1 score of 0.713 when training and test sets include Facebook posts. We also obtained an F1 score of 0.599 when the training set is from the news domain. A strength of this research is that it is one of the first studies in this field that focuses on NER over Turkish Facebook messages. This is because performing NER on user-generated content turns into a very challenging task since such informal contents are often noisy texts that have arammatical and spelling errors.
Automatic Rain Detection System Based on Digital Images of CCTV Cameras Using Convolutional Neural Network Method

Abstract The Meteorology, Climatology and Geophysics Agency (BMKG) has a duty to provide weather information including rainfall. BMKG has several types of rainfall gauges, but these are not evenly distributed across regions. The solution to increase the density of rainfall observations is to use existing sources to obtain weather information. This research uses Closed Circuit Television (CCTV) that is spread across the Jakarta area to produce information on rainy conditions. The method used is the Convolutional Neural Network (CNN). The image from CCTV will be used for the training and testing process, so as to get the best accuracy model. The results of this model will be used for rain detection on CCTV digital images. The rain detection process is carried out automatically and in real time. The results of the rain detection process will be displayed on the map according to the location where the CCTV was installed. This research has succeeded in making a CNN model for rain detection with a training accuracy of 98.8% and a testing accuracy of 96.4%, as well as evaluating the BMKG observation data, so it has an evaluation accuracy of 96.7%.
A Provable Softmax Reputation-Based Protocol for Permissioned Blockchains

We consider a hierarchical structure of a permissioned blockchain with three types of participant: providers, collectors, and governors. Providers forward transactions to collectors; collectors upload received transactions to governors after verifying and labeling them; and governors validate a portion of the labeled transactions they receive, pack valid transactions into a block, and append the block to the ledger. This model has various fields of application including data collection from the Internet-of-Things and second-hand markets. Our main contribution is to propose a reputation-based protocol to help governors evaluate the reliability of collectors. Specifically, given a transaction, each governor runs a softmax-based function to calculate a probability for each collector that sent and labeled this transaction. The probabilities, calculated using collectors’ reputations as inputs, represent the likelihood of the lead governor selecting the labeled transaction from collectors to consider for further validation. After the lead governor verifies a transaction, all collectors’ reputations are updated in line with the agreement of their labeling and the validity of the transaction as found by the lead governor. We show, both theoretically and empirically, that our protocol can significantly reduce governors’ verification workloads while maintaining firm liveness and high incentives.
Secure Accountable Dynamic Storage Integrity Verification

Integrity verification of cloud data is of great importance for secure and effective cloud storage since attackers can change the data even though it is encrypted. Traditional integrity verification schemes only let the client know the integrity status of the remote data. When the data is corrupted, the system cannot hold the server accountable. Besides, almost all existing schemes assume that the users are credible. Instead, especially in a dynamic operation environment, users can deny their behaviors, and let the server bear the penalty of data loss. To address the issues above, we propose an accountable dynamic storage integrity verification (ADS-IV) scheme which provides means to detect or eliminate misbehavior of all participants. In the meanwhile, we modify the Invertible Bloom Filter (IBF) to recover the corrupted data and use the Mahalanobis distance to calculate the degree of damage. We prove that our scheme is secure under Computational Diffie-Hellman (CDH) assumption and Discrete Logarithm (DL) assumption and that the audit process is privacy-preserving. The experimental results demonstrate that the computational complexity of the audit is constant; the storage overhead is $O(\sqrt n )$, which is only 1/400 of the size of the original data; and the whole communication overhead is O(1).As a result, the proposed scheme is not only suitable for large-scale cloud data storage systems, but also for systems with sensitive data, such as banking systems, medical systems, and so on.
Intercept Graph: An Interactive Radial Visualization for Comparison of State Changes

State change comparison of multiple data items is often necessary in multiple application domains, such as medical science, financial engineering, sociology, biological science, etc. Slope graphs and grouped bar charts have been widely used to show a “before-and-after” story of different data states and indicate their changes. However, they visualize state changes as either slope or difference of bars, which has been proved less effective for quantitative comparison. Also, both visual designs suffer from visual clutter issues with an increasing number of data items. In this paper, we propose Intercept Graph, a novel visual design to facilitate effective interactive comparison of state changes. Specifically, a radial design is proposed to visualize the starting and ending states of each data item and the line segment length explicitly encodes the “state change By interactively adjusting the radius of the inner circular axis, Intercept Graph can smoothly filter the large state changes and magnify the difference between similar state changes, mitigating the visual clutter issues and enhancing the effective comparison of state changes. We conducted a case study through comparing Intercept Graph with slope graphs and grouped bar charts on real datasets to demonstrate the effectiveness of Intercept Graph.
Tailored Design Thinking Approach - A Shortcut for Agile Teams

Context: Agile practices as well as UX methods are nowadays well-known and often adopted to develop complex software and products more efficiently and effectively. However, in the so called VUCA environment, which many companies are confronted with, the sole use of UX research is not sufficient to find the best solutions for customers. The implementation of Design Thinking can support this process. But many companies and their product owners don't know how much resources they should spend for conducting Design Thinking. Objective: This paper aims at suggesting a supportive tool, the "Discovery Effort Worthiness (DEW) Index", for product owners and agile teams to determine a suitable amount of effort that should be spent for Design Thinking activities. Method: A case study was conducted for the development of the DEW index. Design Thinking was introduced into the regular development cycle of an industry Scrum team. With the support of UX and Design Thinking experts, a formula was developed to determine the appropriate effort for Design Thinking. Results: The developed "Discovery Effort Worthiness Index" provides an easy-to-use tool for companies and their product owners to determine how much effort they should spend on Design Thinking methods to discover and validate requirements. A company can map the corresponding Design Thinking methods to the results of the DEW Index calculation, and product owners can select the appropriate measures from this mapping. Therefore, they can optimize the effort spent for discovery and validation.
CDGAN: Cyclic Discriminative Generative Adversarial Networks for image-to-image transformation

Generative Adversarial Networks (GANs) have facilitated a new direction to tackle the image-to-image transformation problem. Different GANs use generator and discriminator networks with different losses in the objective function. Still there is a gap to fill in terms of both the quality of the generated images and close to the ground truth images. In this work, we introduce a new Image-to-Image Transformation network named Cyclic Discriminative Generative Adversarial Networks (CDGAN) that fills the above mentioned gaps. The proposed CDGAN generates high quality and more realistic images by incorporating the additional discriminator networks for cycled images in addition to the original architecture of the CycleGAN. The proposed CDGAN is tested over three image-to-image transformation datasets. The quantitative and qualitative results are analyzed and compared with the state-of-the-art methods. The proposed CDGAN method outperforms the state-of-the-art methods when compared over the three baseline Image-to-Image transformation datasets. The code is available at https://github.com/KishanKancharagunta/CDGAN.
An Improved Image Segmentation Method of BiSeNetV2 Network

The task of image semantic segmentation is to annotate and segment the semantic information of different types of objects in the image, and predict the category and location information of objects. The difficulty lies in obtaining enough semantic information while retaining enough space information. In order to solve this problem, this paper proposes an improved BiSeNetV2 network. The main idea is to add DenseASPP module to detail branch to obtain larger receptive field, and add efficient channel attention (ECA) module to detail and semantic branch to optimize the feature graph extracted in each stage. so as to further improve the network acquisition. Experimental results show that the proposed algorithm improves the MIoU index by 1.62% on cityscapes dataset, and achieves better performance than BiSeNetV2 network.