Reviewerly/exHarmony huggingface dataset:
A Real-Time Collision Avoidance Strategy in Dynamic Airspace Based on Dynamic Artificial Potential Field Algorithm

The key to the integration of unmanned aircrafts in the national airspace is to prevent them from colliding with the other traffics in the airspace. However, it is a great challenge to generate a safe, stable and robust collision-free path for unmanned aircraft system (UAS), a.k.a. unmanned aircraft vehicle (UAV), in real time, if the airspace is highly dynamics and heterogenous (i.e. thronged with aircrafts with different motion states). Based on the dynamic artificial potential field (DAPF) algorithm, this paper provides advisories on how to generate a real-time reactive collision-free path for unmanned aircraft vehicles flying in a dynamic airspace, aiming to ensure the flight safety and minimize the impact on surrounding traffic. Firstly, the safety distance was defined as a variable threshold, which scaled adaptively according to the relative motion states of the surrounding obstacles and the performance of the own UAV. Moreover, the forces of the potential field were improved, such that their magnitudes could be adjusted automatically according to the threat levels of the surrounding obstacles. The threat level of an obstacle depends on the relative position, speed and flight trend between the UAV and the obstacle. In addition, the repulsive force along the relative position of the traditional artificial potential field (APF) was retained, and a steering force was added to change the flight direction of the UAV, aiming to speed up the collision avoidance. Furthermore, the attractive force was modified to help the UAV return to the planned path quickly and stably. After that, the capacity were determined to ensure the feasible and practical path planning for the UAV. Finally, the proposed UAV path-planning method was proved effective, safe, stable and adaptive through simulations.
SE-Enc: A Secure and Efficient Encoding Scheme Using Elliptic Curve Cryptography

Many applications use asymmetric cryptography to secure communications between two parties. One of the main issues with asymmetric cryptography is the need for vast amounts of computation and storage. While this may be true, elliptic curve cryptography (ECC) is an approach to asymmetric cryptography used widely in low computation devices due to its effectiveness in generating small keys with a strong encryption mechanism. The ECC decreases power consumption and increases device performance, thereby making it suitable for a wide range of devices, ranging from sensors to the Internet of things (IoT) devices. It is necessary for the ECC to have a strong implementation to ensure secure communications, especially when encoding a message to an elliptic curve. It is equally important for the ECC to secure the mapping of the message to the curve used in the encryption. This work objective is to propose a trusted and proofed scheme that offers authenticated encryption (AE) for both encoding and mapping a message to the curve. In addition, this paper provides analytical results related to the security requirements of the proposed scheme against several encryption techniques. Additionally, a comparison is undertaken between the SE-Enc and other state-of-the-art encryption schemes to evaluate the performance of each scheme.
AI-GAN: Asynchronous interactive generative adversarial network for single image rain removal

Single image rain removal plays an important role in numerous multimedia applications. Existing algorithms usually tackle the deraining problem by the way of signal removal, which lead to over-smoothness and generate unexpected artifacts in de-rained images. This paper addresses the deraining problem from a completely different perspective of feature-wise disentanglement, and introduces the interactions and constraints between two disentangled latent spaces. Specifically, we propose an Asynchronous Interactive Generative Adversarial Network (AI-GAN) to progressively disentangle the rainy image into background and rain spaces in feature level through a two-branch structure. Each branch employs a two-stage synthesis strategy and interacts asynchronously by exchanging feed-forward information and sharing feedback gradients, achieving complementary adversarial optimization. This ‘adversarial’ is not only the ‘adversarial’ between the generator and the discriminator, but also means that the two generators are entangled, and interact with each other in the optimization process. Extensive experimental results demonstrate that AI-GAN outperforms state-of-the-art deraining methods and benefits various typical multimedia applications such as Image/Video Coding, Action Recognition, and Person Re-identification.
On the Stochastic Analysis of a Quantum Entanglement Switch

We study a quantum entanglement switch that serves k users in a star topology. We model variants of the system using continuous-time Markov chains (CTMCs) and obtain expressions for switch capacity and the expected number of qubits stored in memory at the switch. Using CTMCs allows us to obtain a number of analytic results for systems in which the links are homogeneous or heterogeneous and for switches that have infinite or finite buffer sizes. In addition, we can easily model the effects of decoherence of quantum states using this technique. From numerical observations, we discover that decoherence has little effect on capacity and expected number of stored qubits for homogeneous systems. For heterogeneous systems, especially those operating close to stability constraints, buffer size and decoherence can significantly affect performance. We also learn that, in general, increasing the buffer size from one to two qubits per link is advantageous to most systems, while increasing the buffer size further yields diminishing returns.
Quantum unary approach to option pricing

The authors present a quantum algorithm for the pricing of options, a financial derivative, which works in the unary representation of the value of the underlying asset. Compared to a previous binary algorithm, the present one is designed to be simple in terms of logic operations and in terms of connectivity requirements, while still giving sufficiently accurate results with less than 100 qubits.
On Integrating Design Thinking for Human-Centered Requirements Engineering

We elaborate on the possibilities and needs to integrate design thinking into requirements engineering, drawing from our research and project experiences. We suggest three approaches for tailoring and integrating design thinking and requirements engineering with complementary synergies and point at open challenges for research and practice.
Radio Frequency Fingerprint Identification Based on Denoising Autoencoders

Radio Frequency Fingerprinting (RFF) is one of the promising passive authentication approaches for improving the security of the Internet of Things (IoT). However, with the proliferation of low-power IoT devices, it becomes imperative to improve the identification accuracy at low SNR scenarios. To address this problem, this paper proposes a general Denoising AutoEncoder (DAE)-based model for deep learning RFF techniques. Besides, a partially stacking method is designed to appropriately combine the semi-steady and steady-state RFFs of ZigBee devices. The proposed Partially Stacking-based Convolutional DAE (PSC-DAE) aims at reconstructing a high-SNR signal as well as device identification. Experimental results demonstrate that compared to Convolutional Neural Network (CNN), PSCDAE can improve the identification accuracy by 14% to 23.5% at low SNRs (from -10 dB to 5 dB) under Additive White Gaussian Noise (AWGN) corrupted channels. Even at SNR = 10 dB, the identification accuracy is as high as 97.5%.
Data Stream Classification by Dynamic Incremental Semi-Supervised Fuzzy Clustering

A data stream classification method called DISSFCM (Dynamic Incremental Semi-Supervised FCM) is presented, which is based on an incremental semi-supervised fuzzy clustering algorithm. The method assumes that partially labeled data belonging to different classes are continuously available during time in form of chunks. Each chunk is processed by semi-supervised fuzzy clustering leading to a cluster-based classification model. The proposed DISSFCM is capable of dynamically adapting the number of clusters to data streams, by splitting low-quality clusters so as to improve classification quality. Experimental results on both synthetic and real-world data show the effectiveness of the proposed method in data stream classification.
How to Fully Exploit The Abilities of Aerial Image Detectors

Detecting objects in aerial images usually faces two major challenges: (1) detecting difficult targets (e.g., small objects, objects that are interfered by the background, or various orientation of the objects, etc.); (2) the imbalance problem inherent in object detection (e.g., imbalanced quantity in different categories, imbalanced sampling method, or imbalanced loss between classification and localization, etc.). Due to these challenges, detectors are often unable to perform the most effective training and testing. In this paper, we propose a simple but effective framework to address these concerns. First, we propose an adaptive cropping method based on a Difficult Region Estimation Network (DREN) to enhance the detection of the difficult targets, which allows the detector to fully exploit its performance during the testing phase. Second, we use the well-trained DREN to generate more diverse and representative training images, which is effective in enhancing the training set. Besides, in order to alleviate the impact of imbalance during training, we add a balance module in which the IoU balanced sampling method and balanced L1 loss are adopted. Finally, we evaluate our method on two aerial image datasets. Without bells and whistles, our framework achieves 8.0 points and 3.3 points higher Average Precision (AP) than the corresponding baselines on VisDrone and UAVDT, respectively.
Reversible Data Hiding in JPEG Images Based on Negative Influence Models

Reversible data hiding (RDH) can be used to imperceptibly embed data into images in a reversible manner. Many RDH schemes have been developed for uncompressed images. However, JPEG compressed images are more widely used in our daily lives. The existing RDH techniques for JPEG images may cause significant distortion or a large increase in the file size of marked images. In this paper, a novel RDH scheme for JPEG images is proposed. First, the negative influence models of data embedding, including image visual distortion model and file size change model, are mathematically established. Then, a negative index for each frequency is defined as the weighted sum of the normalized average image visual distortion and the normalized average file size change per 1-bit hidden data, and the frequencies with small negative indices will be used for data embedding with a high priority. The weighting factor can be adjusted according to the user's preference for less image distortion or smaller file size. Lastly, secret data is embedded into non-zero quantized AC coefficients of the selected frequencies in ascending order of zero-run length. Extensive experiments conducted on typical images and a well-known image database show that the presented negative influence models are effective, and the proposed RDH scheme based on the models can achieve low image distortion and small increase in the file size of marked images.
Ensemble Discrete Wavelet Transform and Gray-Level Co-Occurrence Matrix for Microcalcification Cluster Classification in Digital Mammography

The presence of clusters of microcalcifications is a primary sign of breast cancer. Their identification is still difficult today for radiologists, and the wrong evaluations involve unnecessary biopsies. In this paper, an automatic tool for characterizing and discriminating clusters of microcalcifications into benign/malignant in digital mammograms is proposed. A set of 104 digital mammograms including microcalcification clusters was randomly extracted from a public available database and manually labeled by our radiologists, obtaining 96 abnormal ROIs. For each so-identified ROI, a multi-scale image decomposition based on the Haar wavelet transform was performed. On the decomposition, a textural features extraction step was carried out both on each sub-image and on the corresponding gray-level co-occurrence matrix. Then, a random forest classifier was employed for classifying microcalcification clusters into benign and malignant. The study found that the most discriminant features extracted from the ROIs decomposition by Haar transform were variance and relative smoothness, whereas as regards the textural features calculated on the GLCMs corresponding to the Haar-decomposed ROI, it emerged that the relationship between the pixels of the sub-image in the diagonal direction had high discriminating power for the classification of microcalcification clusters into benign and malignant. The proposed method was evaluated in cross-validation and performed highly in the prediction of the benign/malignant ROIs, with a mean AUC value of 97.39 ± 0.01 % .
Study of Blockchain Based Decentralized Consensus Algorithms

Blockchain is the backbone technology behind crypto-currency and Bitcoin. By concept, Blockchain is a distributed database where transactions are recorded in an incorruptible and non-modifiable manner. Currently, Blockchain technology is envisioned as a powerful framework for open-access networks, decentralized information processing and sharing systems, etc. This review is motivated due to the lack of an extensive survey on the existing decentralized consensus mechanisms in Blockchain technology. So in this paper, an in-depth review of the distributed consensus mechanisms has been presented. In addition to this, a comparative analysis of the consensus protocols based on the type of Blockchain is also demonstrated.
Joint Distribution Estimation and Naïve Bayes Classification Under Local Differential Privacy

Naïve Bayes classifier (NBC) is a fundamental and widely-used data mining tool. To respond to the growing privacy concern, several privacy-preserving NBC schemes have been proposed recently. Generally, the existing schemes mainly exploit two different techniques: one is secure multi-party computation (SMCP); and the other is (local) differential privacy (DP/LDP). However, the approaches with SMCP would employ some encryption algorithms, which results in heavy computation and communication costs and impedes the usage of the approaches in practice. The existing methods with DP/LDP also have some deficiencies. For instance, a trusted data curator is required or the class labels of individuals are revealed. To make up the drawbacks of previous schemes, in this article, we develop a novel scheme to train a Naïve Bayes classifier with privacy guarantee in the local setting. We first design a method (JESS) for joint distribution estimation under LDP. Then we apply JESS to calculate the conditional probability, the key of the Naïve Bayes classification and train a Naïve Bayes classifier. Additionally, we leverage extensive experiments to evaluate the effectiveness of our schemes designed for joint distribution and Naïve Bayes classification. Compared with the existing schemes, ours can achieve higher accuracy.
Physical Workload Tracking Using Human Activity Recognition with Wearable Devices

In this work, authors address workload computation combining human activity recognition and heart rate measurements to establish a scalable framework for health at work and fitness-related applications. The proposed architecture consists of two wearable sensors: one for motion, and another for heart rate. The system employs machine learning algorithms to determine the activity performed by a user, and takes a concept from ergonomics, the Frimat's score, to compute the corresponding physical workload from measured heart rate values providing in addition a qualitative description of the workload. A random forest activity classifier is trained and validated with data from nine subjects, achieving an accuracy of 97.5%. Then, tests with 20 subjects show the reliability of the activity classifier, which keeps an accuracy up to 92% during real-time testing. Additionally, a single-subject twenty-day physical workload tracking case study evinces the system capabilities to detect body adaptation to a custom exercise routine. The proposed system enables remote and multi-user workload monitoring, which facilitates the job for experts in ergonomics and workplace health.
Secure Remote Multi-Factor Authentication Scheme Based on Chaotic Map Zero-Knowledge Proof for Crowdsourcing Internet of Things

Recently, application scenario of crowdsourcing IoT has covered to e-healthcare service, smart home, smart city, internet of vehicles due to the proliferation of smart devices such as smart mobile devices, smart wearable device, smart medical devices and smart furniture, etc. Patient's data collected by the smart devices send to the various remote medical servers. A group of medical professionals remote access patient data stored at the medical server database. Smart home users want to remote real-time access information of smart devices at home. All these operations need via wireless remote communication, which is suffering from various kinds of threat and attacks. Hence, there are a large number of multi-factor remote authentication and key agreement schemes designed for the application of crowdsourcing IoT. However, in most existing related multi-factor schemes, all factors for identity authentication only act as a parameter for encrypting the local secret key. In this paper, we propose a new secure remote multi-factor authentication scheme that includes three factors: 1) user identity; 2) password; and 3) user biometrics, which are authenticated by the remote server, act as a part of the secret key and participate in the key agreement process. We choose the chaotic map since it has a smaller key size and lower computational overhead, and then achieve remote multi-factor authentication and key agreement by artfully employ it to zero-knowledge technology and the fuzzy extractor technology. Our scheme is more secure and robust since the user revealing nothing sensitive information, and the adversary cannot impersonate any user even if he gets the server's master key. We have done security proof for our proposed scheme using the Random-Or-Real(ROR) model, Burrows-Abadi-Needham (BAN) logic, and ProVerif 2.00 to show that the presented scheme is secure. Also, we give an additional security analysis for other various attacks. Finally, according to the test and simulation result, the proposed scheme is very suitable for the power-constrained smart devices, and in the next generation 5G communication environment, its applicability and usability will be greatly enhanced.
Information security assessment in public administration

The aim of the article is to characterise and assess information security management in units of public administration and to define recommended solutions facilitating an increase in the level of information security. The article is considered a theoretical-empirical research paper. The aim of theoretical research is to explain the basic terms related to information security management and to define conditions for the implementation of Information Security Management System (ISMS). Within the scope of theoretical considerations, source literature, legislation and reports are being referred to. In the years 2016-2019, empirical research has been conducted, which aim was to assess the efficiency of information security management in public administration offices. The evaluation of results of surveys was accompanied by an analysis of statistical relations between the researched variables, which enabled to define effects of European Union regulations on the delivery of information security in public administration. Results of the empirical data show that in the years 2016-2017, in public administration offices, certain problem areas in the aspect of information security management were present, which include, among others: lack of ISMS organisation, incomplete or outdated ISMS documentation, lack of regular risk analysis, lack of reviews, audits or controls, limited use of physical and technological protection measures, lack of training or professional development. In the years 2018-2019, European Union solutions, i.e. the GDPR Regulation and the NIS Directive, have affected the increase in the security level of information in public administration and have a significantly limited occurrence of identified irregularities. Results of the research enable to assume that the delivery of information security in public administration requires a systemic approach arising from the need for permanent improvement.
Survey of Attack Graph Analysis Methods from the Perspective of Data and Knowledge Processing

Attack graph can simulate the possible paths used by attackers to invade the network. By using the attack graph, the administrator can evaluate the security of the network and analyze and predict the behavior of the attacker. Although there are many research studies on attack graph, there is no systematic survey for the related analysis methods. This paper firstly introduces the basic concepts, generation methods, and computing tasks of the attack graph, and then, several kinds of analysis methods of attack graph, namely, graph-based method, Bayesian network-based method, Markov model-based method, cost optimization method, and uncertainty analysis method, are described in detail. Finally, comparative study of the methods and future work are provided. We believe that this work would help the research community to understand the attack graph analysis method systematically.
Unsupervised Domain Adaptation via Importance Sampling

Unsupervised domain adaptation aims to generalize a model from the label-rich source domain to the unlabeled target domain. Existing works mainly focus on aligning the global distribution statistics between source and target domains. However, they neglect distractions from the unexpected noisy samples in domain distribution estimation, leading to domain misalignment or even negative transfer. In this paper, we present an importance sampling method for domain adaptation (ISDA), to measure sample contributions according to their “informative” levels. In particular, informative samples, as well as outliers, can be effectively modeled using feature-norm and prediction entropy of the network. The importance of information is further formulated as the importance sampling losses in features and label spaces. In this way, the proposed model mitigates the noisy outliers while enhancing the important samples during domain alignment. In addition, our model is easy to implement yet effective, and it does not introduce any extra parameters. Extensive experiments on several benchmark datasets show that our method outperforms state-of-the-art methods under both the standard and partial domain adaptation settings.