Reviewerly/exHarmony huggingface dataset:
Robust entanglement by continuous dynamical decoupling of the J-coupling interaction

We propose a $\sigma_z\otimes \sigma_z$ laser-free entangling gate which uses the intrinsic J-coupling of ions in a static magnetic gradient. Dephasing of the interaction is suppressed by means of continuous dynamical decoupling using pairs of microwave fields. The gate is virtually insensitive to common amplitude noise of the microwave fields and enables high fidelities despite qubit frequency fluctuations, while the J-coupling interaction's inherent robustness to motional decoherence is retained. Errors far below the fault-tolerant threshold can be achieved at high initial temperatures, negating the requirement of sideband cooling below the Doppler temperature. By adjusting the powers of the continuous microwave fields, the J-coupling interaction can be tuned and can be used to implement parallel entangling gates within an ion chain.
A Probabilistic Approach for Complete Coverage Path Planning with low-cost Systems

Domestic robots, such as vacuum cleaners or lawn mowers, are mostly based on a low-cost design to make them affordable for the consumer. This often results in such robots being equipped with only simple sensors, such as in-/outside area detectors for lawn mowers. Intelligent navigation and planning strategies, however, usually require additional sensors like LiDAR sensors, cameras or time of flight sensors. Thus, there is a lack of intelligent approaches for the complete coverage of the workspace under consideration of only minimal sensing capabilities.In this work, we propose a probabilistic planning method for low-cost robots with limited sensing capabilities to completely cover an enclosed environment. Our planning approach thereby utilizes Monte Carlo Localization for estimating coverage probabilities based on the particle distribution. These coverage probabilities are stored in a grid map on the basis of which an intelligent path planning approach determines the next locations to be visited. We demonstrate our approach in different simulation scenarios for a realistic autonomous lawn mower with only in-/outside area detection capabilities. As comparison benchmark we use the common random walk mowing pattern.
COVID-19 Contact Tracing and Detection-Based on Blockchain Technology

The fight against the COVID-19 pandemic still involves many struggles and challenges. The greatest challenge that most governments are currently facing is the lack of a precise, accurate, and automated mechanism for detecting and tracking new COVID-19 cases. In response to this challenge, this study proposes the first blockchain-based system, called the COVID-19 contact tracing system (CCTS), to verify, track, and detect new cases of COVID-19. The proposed system consists of four integrated components: an infection verifier subsystem, a mass surveillance subsystem, a P2P mobile application, and a blockchain platform for managing all transactions between the three subsystem models. To investigate the performance of the proposed system, CCTS has been simulated and tested against a created dataset consisting of 300 confirmed cases and 2539 contacts. Based on the metrics of the confusion matrix (i.e., recall, precision, accuracy, and F1 Score), the detection evaluation results proved that the proposed blockchain-based system achieved an average of accuracy of 75.79% and a false discovery rate (FDR) of 0.004 in recognizing persons in contact with COVID-19 patients within two different areas of infection covered by GPS. Moreover, the simulation results also demonstrated the success of the proposed system in performing self-estimation of infection probabilities and sending and receiving infection alerts in P2P communications in crowds of people by users. The infection probability results have been calculated using the binomial distribution function technique. This result can be considered unique compared with other similar systems in the literature. The new system could support governments, health authorities, and citizens in making critical decisions regarding infection detection, prediction, tracking, and avoiding the COVID-19 outbreak. Moreover, the functionality of the proposed CCTS can be adapted to work against any other similar pandemics in the future.
Techniques for Image Segmentation Based on Edge Detection

Image segmentation is a foundation in image processing, and edge detection directly affects image segmentation quality. However, there is no perfect and universal technique for image segmentation, and each method has its own feature. To compare the techniques, this paper conducted experiments of some edge detection fundamental techniques for image segmentation, which can be utilized to analyze the advantages and applicable scenes of various techniques. In this paper, experiments of edge detection based on methods such as ‘Sobel’, ‘Prewitt’, ‘Roberts’, ‘Canny’ method, and line hough transform are conducted using MATLAB. At the same time, this paper implements different thresholds on the Canny operator and compares the influence. As a result, we get different outputs according to these experiments. Canny performs better than the other three methods, and the low and high thresholds can influence the accuracy of the edge. Meanwhile, the hough transform can detect the line edge.
On Handling Class Imbalance in Continual Learning based Network Intrusion Detection Systems

Modern-day cyber threats are growing more rapidly than ever before. To effectively defend against them, Anomaly-based Network intrusion detection systems (A-NIDS) must evolve continuously. Traditional machine learning techniques are ineffective in handling sequentially evolving tasks, and Neural Networks (NNs) in particular suffer from Catastrophic Forgetting (CF) of old tasks when trained on new ones. Continual Learning (CL) strategies help to mitigate CF by imposing constraints while training NNs on sequentially evolving data like network traffic. However, applying the CL framework in the design of A-NIDS is not straightforward due to the heavy Class Imbalance (CI) in the network traffic datasets. As a result, the performance of the system is very sensitive to the task execution order. In this work, we propose a CL based A-NIDS by applying sample replay with Class Balancing Reservoir Sampling (CBRS) to mitigate CI in a Class Incremental Setting (CIS). Using the CICIDS-2017 dataset, experiments are conducted by permuting the majority class across the different task execution orders using the proposed CL based A-NIDS. We find that using auxiliary memory with context-aware sample replacing strategies, CF can be reduced to a greater extent, as opposed to data augmentation techniques which may alter the original data distribution and increase training time (with oversampling methods).
Double Head Predictor based Few-Shot Object Detection for Aerial Imagery

Many applications based on aerial imagery rely on ac-curate object detection, which requires a high number of annotated training data. However, the number of annotated training data is often limited. In this paper, we propose a novel few-shot detection method for aerial imagery that aims at detecting objects of unseen classes with only a few annotated examples. For this purpose, we extend the Two-Stage Fine-Tuning Approach (TFA), which achieves state-of-the-art results on common benchmark datasets. We pro-pose a novel annotation sampling and pre-processing strategy to yield a better exploitation of base class annotations and a more stable training. We further apply a modified fine-tuning scheme to reduce the number of missed detections. To prevent loss of knowledge learned during the base training, we introduce a novel double head predictor, yielding the best trade-off in detection accuracy between the novel and base classes. Our proposed Double Head Few-Shot Detection (DH-FSDet) method outperforms state-of-the-art baselines on publicly available aerial imagery datasets. Finally, ablation experiments are performed in or-der to get better insight how few-shot detection in aerial imagery is affected by the selection of base and novel classes. We provide the source code at https://github.com/Jonas-Meier/FrustratinglySimpleFsDet.
Enhance Text-to-Text Transfer Transformer with Generated Questions for Thai Question Answering

Question Answering (QA) is a natural language processing task that enables the machine to understand a given context and answer a given question. There are several QA research trials containing high resources of the English language. However, Thai is one of the languages that have low availability of labeled corpora in QA studies. According to previous studies, while the English QA models could achieve more than 90% of F1 scores, Thai QA models could obtain only 70% in our baseline. In this study, we aim to improve the performance of Thai QA models by generating more question-answer pairs with Multilingual Text-to-Text Transfer Transformer (mT5) along with data preprocessing methods for Thai. With this method, the question-answer pairs can synthesize more than 100 thousand pairs from provided Thai Wikipedia articles. Utilizing our synthesized data, many fine-tuning strategies were investigated to achieve the highest model performance. Furthermore, we have presented that the syllable-level F1 is a more suitable evaluation measure than Exact Match (EM) and the word-level F1 for Thai QA corpora. The experiment was conducted on two Thai QA corpora: Thai Wiki QA and iApp Wiki QA. The results show that our augmented model is the winner on both datasets compared to other modern transformer models: Roberta and mT5.
“Implementation of Blockchain Technologies in Smart Cities, Opportunities and Challenges”

A blockchain can be thought of as the shared distributed ledger type of technology that stores the information of every transaction in its network. The blockchain has emerged with vast diversity of applications in the economical and non-economical areas. Blockchain technology has the potential to provide a robust span of solutions to the issues faced in the implementation of smart cities. As such, it displays the potential to create smart types of contracts more securely, by eliminating the need for centralized authority. A blockchain can be envisioned as a secure decentralized database that stores information utilizing a peer-to-peer type of network. The blockchain can be seen as a type of special stack, where blocks could be placed or stacked on top of each other. Subsequent blocks composing the blockchain have to be linked to each other by cryptographic type of hash. In contemporary times an increasing interest in the concept of blockchain technology has been observed. This secondary research utilizes detailed literature review of multifaceted sources of information, such as peer-reviewed and quality academic journal articles from renowned databases. With the introduction of blockchain, numerous fields like banking, finance, healthcare, and supply chain shall experience positive effects. The sustainability of smart cities can be further enhanced and ensured with the application of blockchain technology. One major aspect wherein blockchain can play an essential role is real estate and smart cities. Blockchain and Smart Cities concepts are fated to influence the future of our planet in numerous ways. Incorporating blockchain into the expansion of Smart Cities will make it possible to have a cross-cutting platform that connects the cities’ different services, adding greater transparency and security to all services and processes.
An Anchor-Free Method for Arbitrary-Oriented Ship Detection in SAR Images

Deep learning have been playing an increasing important role in ship detection in synthetic aperture radar (SAR) image because of its high accuracy, fast speed and no need of manual design features. It is difficult to set appropriate anchors when using anchor-based method because of multi-resolution SAR images and multi-scale ships, thus resulting in degradation of detection performance in operational applications. Recently, a lot of anchor-free architecture have been put forward, among which most of them apply horizontal bounding boxes (HBBs) to encode ship location, leading to inaccurate location description and decreased detection performance especially for arbitrary-oriented ships in complex scenes. Therefore, an anchor-free detector for arbitrary-oriented ship detection in SAR images, called R-FCOS is proposed in this paper. Specifically, based on the FCOS baseline method, ship aspect angle and rotate regression branch are introduced to regress oriented bounding boxes (OBBs) for arbitrary-oriented ships detection. Besides, to alleviate the effects of feature misalignment problem, ratio-ness branch is proposed to predict the aspect ratio of ship, which is used to filter and refine the OBBs in the post-processing step to improve detection accuracy. Finally, we carry out experiments on the SSDD+ data set to verify the effectiveness of our method. It turns out that our method obtains mAP 90.65&#x0025; under an IoU threshold of 0.5.
Machine Learning Methods for Local Motion Planning: A Study of End-to-End vs. Parameter Learning

While decades of research efforts have been devoted to developing classical autonomous navigation systems to move robots from one point to another in a collision-free manner, machine learning approaches to navigation have been recently proposed to learn navigation behaviors from data. Two representative paradigms are end-to-end learning (directly from perception to motion) and parameter learning (from perception to parameters used by a classical underlying planner). These two types of methods are believed to have complementary pros and cons: parameter learning is expected to be robust to different scenarios, have provable guarantees, and exhibit explainable behaviors; end-to-end learning does not require extensive engineering and has the potential to outperform approaches that rely on classical systems. However, these beliefs have not been verified through real-world experiments in a comprehensive way. In this paper, we report on an extensive study to compare end-to-end and parameter learning for local motion planners in a large suite of simulated and physical experiments. In particular, we test the performance of end-to-end motion policies, which directly compute raw motor commands, and parameter policies, which compute parameters to be used by classical planners, with different inputs (e.g., raw sensor data, costmaps), and provide an analysis of the results.
Deep Learning Channel Estimation Based on Edge Intelligence for NR-V2I

The fifth generation New Radio vehicle-to-everything (5G NR-V2X) has higher requirements for delay and reliability, which brings challenges to the physical layer signal processing. Mobile edge computing (MEC) can provide larger capacity data storage and more efficient computation through localization of on-board unit (OBU) and next generation Node B (gNB) services. This paper combines the channel estimation of New Radio vehicle-to-infrastructure (NR-V2I) communication baseband signal processing with MEC and designs an intelligent channel estimation framework. In the MEC server, this paper proposes a channel estimation algorithm based on deep learning. This algorithm uses a one-dimensional convolutional neural network (1D CNN) to complete frequency-domain interpolation and conditional recurrent unit (CRU) for time-domain state prediction. Additional velocity coding vector and multipath coding vector track changes in the environment, and accurately train channel data in different mobile environments. System simulation and analysis show that the proposed algorithm improves the channel estimation accuracy, reduces the bit error rate, and enhances the robustness compared with the representative channel estimation algorithms for NR-V2I.
Cognitive Based Authentication Protocol for Distributed Data and Web Technologies

The objective of the verification process, besides guaranteeing security, is also to be effective and robust. This means that the login should take as little time as possible, and each time allow for a successful authentication of the authorised account. In recent years, however, online users have been experiencing more and more issues with recalling their own passwords on the spot. According to research done in 2017 by LastPass on its employees, the number of personal accounts assigned to one business user currently exceeds 191 profiles and keeps growing. Remembering these many passwords, especially to applications which are not used every week, seems to be impossible without storing them either on paper, in a password manager, or saved in a file somewhere on a PC. In this article a new verification model using a Google Street View image as well as the user's personal experience and knowledge will be presented. The purpose of this scheme is to assure secure verification by creating longer passwords as well as delivering a 'password reminder' already embedded into the login scheme.
Camera Adversaria

In this paper we introduce Camera Adversaria; a mobile app designed to disrupt the automatic surveillance of personal photographs by technology companies. The app leverages the brittleness of deep neural networks with respect to high-frequency signals, adding generative adversarial perturbations to users' photographs. These perturbations confound image classification systems but are virtually imperceptible to human viewers. Camera Adversaria builds on methods developed by machine learning researchers as well as a growing body of work, primarily from art and design, which transgresses contemporary surveillance systems. We map the design space of responses to surveillance and identify an under-explored region where our project is situated. Finally we show that the language typically used in the adversarial perturbation literature serves to affirm corporate surveillance practices and malign resistance. This raises significant questions about the function of the research community in countenancing systems of surveillance.
An adaptive defect detection method for LNG storage tank insulation layer based on visual saliency

As the core equipment of the industrial chain, the LNG storage tank is developing in the direction of large-scale and increasing attention has been paid to its economy and safety. The defects of the insulation layer not only affect the insulation performance of the tank but also easily induce safety accidents. To automatically detect these defects, considering the large temperature difference between the defect area and other areas, an adaptive detection method based on infrared thermal imaging technology and a saliency detection algorithm is proposed. Firstly, the median filtering and the Contrast Limited Adaptive Histogram (CLAHE) are introduced to reduce noise and smooth the image. Then, the Adaptive Simple Linear Iterative Clustering algorithm (A-SLIC) based on the entropy of the image is purposed for super-pixel segmentation. After super-pixel segmentation, the saliency map is obtained by manifold ranking, and the salient areas that contain defects of the insulation layer will be segmented automatically. The proposed method is compared with other traditional algorithms on the MSRA1000 dataset and the infrared thermal imaging dataset of LNG storage tanks. The results of the experiments illustrate the effectiveness of the method proposed.
MSGCN: Multi-Subgraph Based Heterogeneous Graph Convolution Network Embedding

Heterogeneous graph embedding has become a hot topic in network embedding in recent years and has been widely used in lots of practical scenarios. However, most of the existing heterogeneous graph embedding methods cannot make full use of all the auxiliary information. So we proposed a new method called Multi-Subgraph based Graph Convolution Network (MSGCN), which uses topology information, semantic information, and node feature information to learn node embedding vector. In MSGCN, the graph is firstly decomposed into multiple subgraphs according to the type of edges. Then convolution operation is adopted for each subgraph to obtain the node representations of each subgraph. Finally, the node representations are obtained by aggregating the representation vectors of nodes in each subgraph. Furthermore, we discussed the application of MSGCN with respect to a transductive learning task and inductive learning task, respectively. A node sampling method for inductive learning tasks to obtain representations of new nodes is proposed. This sampling method uses the attention mechanism to find important nodes and then assigns different weights to different nodes during aggregation. We conducted an experiment on three datasets. The experimental results indicate that our MSGCN outperforms the state-of-the-art methods in multi-class node classification tasks.
Object Detection: Harmful Weapons Detection using YOLOv4

Closed-circuit television (CCTV) is essential in the security industry by providing surveillance, monitoring activities, recording incidents, and storing evidence. Research and developments have been performed to ameliorate its application to meet the ever-changing security landscape. This paper presents a revolutionary method to enhance the application of CCTVs in Malaysia. The purpose of this study is to develop an Artificial Intelligence (AI) based weapons detection that helps people in identifying violent crimes that are currently happening. This study focuses on detecting harmful weapons such as handguns and knives using the custom trained object detection model that has been trained using the YOLOv4 Darknet framework. Two sets of training have been done to test the effectiveness of this system. The first training was done on a single class custom object detection model while the second was done on a multiple class custom object detection model. Based on the results obtained, the single class object detection only managed to achieve 66.67% to 77.78% accuracy on average whilst the multiple class object detection managed to achieve up to 100% accuracy on most of its input images.
Counteracting social engineering attacks

Social engineers are very successful at exploiting human weaknesses, gathering information, communicating with people and creating creative storylines attacking people's psychological needs and weaknesses. And that is why people, who are among the weakest links in the information security chain, remain susceptible to social engineering attacks.
Maxmin Data Range Heuristic-Based Initial Centroid Method of Partitional Clustering for Big Data Mining

The centroid-based clustering algorithm depends on the number of clusters, initial centroid, distance measures, and statistical approach of central tendencies. The initial centroid initialization algorithm defines convergence speed, computing efficiency, execution time, scalability, memory utilization, and performance issues for big data clustering. Nowadays various researchers have proposed the cluster initialization techniques, where some initialization techniques reduce the number of iterations with the lowest cluster quality, and some initialization techniques increase the cluster quality with high iterations. For these reasons, this study proposed the initial centroid initialization based Maxmin Data Range Heuristic (MDRH) method for K-Means (KM) clustering that reduces the execution times, iterations, and improves quality for big data clustering. The proposed MDRH method has compared against the classical KM and KM++ algorithms with four real datasets. The MDRH method has achieved better effectiveness and efficiency over RS, DB, CH, SC, IS, and CT quantitative measurements.