Reviewerly/exHarmony huggingface dataset:
UDBNET: Unsupervised Document Binarization Network via Adversarial Game

Degraded document image binarization is one of the most challenging tasks in the domain of document image analysis. In this paper, we present a novel approach towards document image binarization by introducing three-player minmax adversarial game. We train the network in an unsupervised setup by assuming that we do not have any paired-training data. In our approach, an Adversarial Texture Augmentation Network (ATANet) first superimposes the texture of a degraded reference image over a clean image. Later, the clean image along with its generated degraded version constitute the pseudo paired-data which is used to train the Unsupervised Document Binarization Network (UDBNet). Following this approach, we have enlarged the document binarization datasets as it generates multiple images having same content feature but different textual feature. These generated noisy images are then fed into the UDBNet to get back the clean version. The joint discriminator which is the third-player of our three-player min-max adversarial game tries to couple both the ATANet and UDBNet. The three-player min-max adversarial game stops, when the distributions modelled by the ATANet and the UDBNet align to the same joint distribution over time. Thus, the joint discriminator enforces the UDBNet to perform better on real degraded image. The experimental results indicate the superior performance of the proposed model over existing state-of-the-art algorithm on widely used DIBCO datasets. The source code of the proposed system is publicly available at https://github.com/VIROBO-15/UDBNET.
A Novel Feature Selection Method Based on Salp Swarm Algorithm

Biomedical and clinical data usually contain some redundant and irrelevant features, which may lead to misleading and over-fitting problems in the process of modeling algorithms. In order to effectively remove irrelevant or redundant features, the use of feature selection methods can reduce the number of features, improve the accuracy of the model, and reduce the running time. In recent years, Wrapper-based feature selection algorithms have received widespread attention because they can obtain better accuracy. This paper uses a wrapper feature selection algorithm FS_SSA based on Salp swarm. In the FS_SSA algorithm, the position of the follower salps is updated by the relative position of the Salp. The followers gradually moves to the leading Salp. The gradual movement of the follower salps can make the Salp Swarm Algorithm not easy to fall into a local optimal state. The two behaviors of exploration and development in subset searches are balanced, and the search process of feature subsets is prevented from falling into the local optimum. Experimental results based on public medical data sets show that the FS_SSA has better classification performance than other methods.
A Two Phases Self-healing Framework for Service-oriented Systems

Service-Oriented Architectures (SOA) enable the automatic creation of business applications from independently developed and deployed Web services. As Web services are inherently a priori unknown, how to deliver reliable Web services compositions is a significant and challenging problem. Services involved in an SOA often do not operate under a single processing environment and need to communicate using different protocols over a network. Under such conditions, designing a fault management system that is both efficient and extensible is a challenging task. In this article, we propose SFSS, a self-healing framework for SOA fault management. SFSS is predicting, identifying, and solving faults in SOAs. In SFSS, we identified a set of high-level exception handling strategies based on the QoS performances of different component services and the preferences articled by the service consumers. Multiple recovery plans are generated and evaluated according to the performance of the selected component services, and then we execute the best recovery plan. We assess the overall user dependence (i.e., the service is independent of other services) using the generated plan and the available invocation information of the component services. Due to the experiment results, the given technique enhances the service selection quality by choosing the services that have the highest score and betters the overall system performance. The experiment results indicate the applicability of SFSS and show improved performance in comparison to similar approaches.
Path Planning Based on Artificial Potential Field and Fuzzy Control

An improved artificial potential field method is proposed aiming at the problems of local minimization and trajectory oscillation of traditional artificial potential field method in complex environment. When the mobile robot falls into the local minimum position, set the virtual target by analyzing the angle between the robot and obstacles to guide the robot escape from the dead zone. A fuzzy controller is designed to improve the problem of trajectory oscillation, which outputs the environment danger factor to adjust the step size of robot and enhance the trajectory smoothness of robot in complex environment. Simulation results show that the proposed method can achieve good collision avoidance effect in the environment with liner or U-shaped obstacle and a large number of discrete obstacles, and effectively improve the trajectory oscillation in complex environment.
Trident: A Hybrid Correlation-Collision GPU Cache Timing Attack for AES Key Recovery

Given the parallel processing capabilities of Graphics Processing Units (GPUs), many applications are exploiting GPUs and cryptographic systems have also begun to leverage GPUs to accelerate encryption/decryption. Recent work has identified how microarchitectural side-channel attacks can be carried out on AES (Advanced Encryption Standard) by exploiting the SIMT characteristics and memory coalescing of GPUs. In this work, we first show that previously proposed correlation-based side-channel attacks are not feasible on modern GPUs that support narrower data-cache accesses via a sectored-cache microarchitecture-resulting in memory accesses from different levels of the memory hierarchy. In comparison, we identify how negative timing correlation can occur in modern GPUs when data is fetched from different levels of the cache hierarchy. We then propose Trident - a hybrid cache-collision timing attack on GPUs that can fully recover all AES key bytes on modern GPUs. Cache collisions in GPUs present challenges due to the large number of threads and the number of samples required. To address these challenges, Trident consists of three different components - negative timing correlation, cache-collision attack, and chosen plaintext attack. We leverage the negative timing correlation to recover earlier key bytes of AES while exploiting cache-collision attacks for the latter AES key bytes. To enable GPU cache collision attacks, we exploit memory coalescing to control the number of memory accesses through chosen-plaintext attacks to significantly reduce the number of timing samples needed. Our proposed Trident attack results in over 10× reduction in the number of samples needed to recover the key bytes compared with prior work, while still being successful in full AES key recovery in modern GPUs. We also propose TridentShield - a latency-based countermeasure to the Trident attack that minimizes throughput degradation in GPUs.
Text Mining for Cybercrime in Registrations of the Dutch Police

Aim: Surveys mention substantial rates of cybercrime victimization. However, little is known about the number of police registrations that refer to cybercrime. The aim of this study was to estimate the number of police registrations referring to cybercrime in the Netherlands based on text fields within the police registration system. We focused on cyber-dependent crime (hacking, ransomware and DDoS attacks), as well as on cyber-enabled crime (online threats, stalking, libel, identity fraud and buying and selling fraud). Method: A random sample of Dutch police registrations from 2016 (n = 100.000) was selected, to estimate the number of cybercrime referrals. A machine-learning classifier was developed using text, in order to classify police registrations as referring to a type of cybercrime. Results: In 2016, between 0.10% and 0.62% of all registrations refer to cyber-dependent crime and between 3.33% and 7.41% were related to cyber-enabled crime. These estimates fall in between the rate of police-reported victimization of cybercrime and the number of cybercrimes based on the police’s uniform crime registration. Conclusion: Estimates of the rate of police-registered cybercrime based on textual fields of the police registrations were found to be relatively low but in absolute numbers substantial.
Non-Autoregressive vs Autoregressive Neural Networks for System Identification

The application of neural networks to non-linear dynamic system identification tasks has a long history, which consists mostly of autoregressive approaches. Autoregression, the usage of the model outputs of previous time steps, is a method of transferring a system state between time steps, which is not necessary for modeling dynamic systems with modern neural network structures, such as gated recurrent units (GRUs) and Temporal Convolutional Networks (TCNs). We compare the accuracy and execution performance of autoregressive and non-autoregressive implementations of a GRU and TCN on the simulation task of three publicly available system identification benchmarks. Our results show, that the non-autoregressive neural networks are significantly faster and at least as accurate as their autoregressive counterparts. Comparisons with other state-of-the-art black-box system identification methods show, that our implementation of the non-autoregressive GRU is the best performing neural network-based system identification method, and in the benchmarks without extrapolation, the best performing black-box method.
Explain2Attack: Text Adversarial Attacks via Cross-Domain Interpretability

Training robust deep learning models for downstream tasks is a critical challenge. Research has shown that down-stream models can be easily fooled with adversarial inputs that look like the training data, but slightly perturbed, in a way imperceptible to humans. Understanding the behavior of natural language models under these attacks is crucial to better defend these models against such attacks. In the black-box attack setting, where no access to model parameters is available, the attacker can only query the output information from the targeted model to craft a successful attack. Current black-box state-of-the-art models are costly in both computational complexity and number of queries needed to craft successful adversarial examples. For real world scenarios, the number of queries is critical, where less queries are desired to avoid suspicion towards an attacking agent. In this paper, we propose Explain2Attack, a black-box adversarial attack on text classification task. Instead of searching for important words to be perturbed by querying the target model, Explain2Attack employs an interpretable substitute model from a similar domain to learn word importance scores. We show that our framework either achieves or out-performs attack rates of the state-of-the-art models, yet with lower queries cost and higher efficiency.
Self-tracking in Parkinson's The Lived Efforts of Self-management

People living with Parkinson's disease engage in self-tracking as part of their health self-management. Whilst health technologies designed for this group have primarily focused on improving the clinical assessments of the disease, less attention has been given to how people with Parkinson's use technology to track and manage their disease in their everyday experience. We report on a qualitative study in which we systematically analysed posts from an online health community (OHC) comprising people with Parkinson's (PwP). Our findings show that PwP track a diversity of information and use a wide range of digital and non-digital tools, informed by temporal and structured practices. Using an existing framework of sensemaking for chronic disease self-management, we also identify new ways in which PwP engage in sensemaking, alongside a set of new challenges that are particular to the character of this chronic disease. We relate our findings to technologies for self-tracking offering design implications.
A New Lightweight <i>In Situ</i> Adversarial Sample Detector for Edge Deep Neural Network

The flourishing of Internet of Things (IoT) has rekindled on-premise computing to allow data to be analyzed closer to the source.To support edge Artificial Intelligence (AI), hardware accelerators, open-source AI model compilers and commercially available toolkits have evolved to facilitate the development and deployment of applications that use AI at its core.This paradigm shift in deep learning computations does not, however, reduce the vulnerability of deep neural networks (DNN) against adversarial attacks but introduces a difficult catch-up.This is because existing methodologies rely mainly on offline analysis to detect adversarial inputs, assuming that the deep learning model is implemented on a 32-bit floatingpoint graphical processing unit (GPU) instance.In this paper, we propose a new hardware-oriented approach for in-situ detection of adversarial inputs feeding through a spatial DNN accelerator architecture or a third-party DNN Intellectual Property (IP) implemented on the edge.Our method exploits controlled glitch injection into the clock signal of the DNN accelerator to maximize the information gain for the discrimination of adversarial and benign inputs.A light gradient boosting machine (lightGBM) is constructed by analyzing the prediction probability of unmutated and mutated models and the label change inconsistency between the adversarial and benign samples in the training dataset.With negligibly small hardware overhead, the glitch injection circuit and the trained lightGBM detector can be easily implemented alongside with the deep learning model on a Xilinx ZU9EG chip.The effectiveness of the proposed detector is validated against four state-of-the-art adversarial attacks on two different types and scales of DNN models, VGG16 and ResNet50, for a thousand-class visual object recognition application.The results show a significant increase in true positive rate and a substantial reduction in false positive rate on the Fast Gradient Sign Method (FGSM), Iterative-FGSM (I-FGSM), C&W and universal perturbation attacks compared with modern softwareoriented adversarial sample detection methods.
Preemptive Prediction-Based Automated Cyberattack Framework Modeling

As the development of technology accelerates, the Fourth Industrial Revolution, which combines various technologies and provides them as one service, has been in the spotlight, and services using big data, Artificial Intelligence (AI) and Internet of Things (IoT) are becoming more intelligent and helpful to users. As these services are used in various fields, attacks by attackers also occur in various areas and ways. However, cyberattacks by attackers may vary depending on the attacking pattern of the attacker, and the same vulnerability can be attacked from different perspectives. Therefore, in this study, by constructing a cyberattack framework based on preemptive prediction, we can collect vulnerability information based on big data existing on the network and increase the accuracy by applying machine learning to the mapping of keywords frequently mentioned in attack strategies. We propose an attack strategy prediction framework.
A Fast Line Segment Detector Using Approximate Computing

The Line Segment Detector (LSD) algorithm is an underlying step of many image processing systems. Hence, its performance has a significant on the upper layers using the detected line segment for various purposed. In this paper, we propose a fast LSD algorithm. This method approximates several floating point operations, including the logarithmic Gamma function, by a series of lookup table searches. Due to the simplicity of such approximation (lookup table search) compared to the naïve implementation (calculation-based), this method is considerably faster. The proposed method has implications on reduction of the necessary efforts to implement and enhancement of the performance of the LSD hardware accelerators. Our experiments show that the proposed method reduces the run-time of the algorithm by 13% on average, with no considerable quality loss in the detection results. This improvement further propagates through other image processing algorithms using LSD.
Real-time face recognition: A survey

Face recognition in real-time has always been challenging due various reasons such as poses, illumination, occlusions. In this letter, we propose a method that solve these issues and provide better accuracy over existing state-of-art technology. In particular, we consider the process of face recognition as a 3-part process: detection, feature extraction and selection, recognition. By providing this technology we further improve the existing technology by increasing its accuracy and scope to a new level. This method to ensemble the 3 components we achieve remarkable real-time performance.
A Chinese named entity recognition method combined with relative position information

Named entity recognition is one of the important tasks of natural language processing, which can help people to select entity information from massive text data. Researchers try to use different methods and improve the recognition effect from different perspectives, including machine learning and deep learning methods, and have made great progress in English datasets. However, in Chinese named entity recognition, it is difficult to recognize entity class because of the complexity of semantic environment and the variety of word formation grammar. Therefore, in order to solve this problem, this paper proposes to use the multi-head attention mechanism of relative position, using the difference of relative position encoding between characters of different positions, to extract the feature of full sentence information, so as to make up for the lack of attention of Lattice-LSTM model to the feature information of full sentence, resulting in the weak ability to recognize complex sentences. Experiments on Chinese Weibo dataset, resume dataset, OntoNotes 4.0 dataset and MSRA dataset verify the model in terms of statement complexity and data volume respectively, and the recognition effect is improved. Finally, we find out a better combination of super parameters, which are further improved on the four datasets.
Cybercrime Issues in Smart Cities Networks and Prevention Using Ethical Hacking

Today, the need for security and data protection has increased because of the increase in Internet use. In today’s era, all industries have digitally moved their data to cloud platforms that bring new data protection issues and challenges especially in IoT and Smart cities networks. Internet of Things (IoT) is a growing field in today’s world that offers reliable and consistent communication via wireless and wired connections and generate a huge amount of data. Therefore, it is essential to ensure the security and reliability of generated data. IoT systems and networks should have strong security mechanism to protect users’ private data and processed information. Internet development and usability have brought numerous challenges in term of online frauds, hacking, and phishing activities, spamming and many others. According to Cybersecurity Ventures survey, cybercrime damages could cost the world $6 trillion per annum by 2021. This information shows growing number of Internet frauds, the finances losses and cybercrime in the coming era for every industry. Without adequate awareness and comprehensive knowledge, it has become difficult to defend against such practices. Ethical Hacking allows users and businesses to scrutinize their systems and networks vulnerabilities, take proper measures to protect their network and systems against unlawful and malicious attacks. It also strengthens network and systems by identifying common vulnerabilities, scrutinize, and taking proper security measures. Kali Linux Operating System (OS) is known as the most sophisticated penetration testing tool to perform Ethical Hacking. In this chapter, we addressed latest information regarding IoT and Smart City networks worldwide in terms of financial and data losses. We have also discussed the Ethical Hacking terminologies along with various kinds of social engineering and phishing attacks could occur on IoT and smart cities networks. We have performed several social engineering experiments using Kali Tools to demonstrate identification of common mistakes in web-based applications and smart networks for the apprentices. In the end, we have proposed some appropriate solutions to strengthen against hackers.
Embedding Based Personalized New Paper Recommendation

It is meaningful for researchers to find the interested and high quality new papers. We propose the Joint Text and Influence Embedding recommendation model (JTIE) to consider both the paper quality and the content correlation. We train a paper embedding based on its core elements: contents, authors and publication venues. The quality of a new paper is evaluated based on the author authority and the venue reputation. The citation relationships between papers are considered asymmetric such that they can reflect the user's consideration on the intrinsic influence of a paper. We learn user interests by one's historical references or a set of query keywords. Finally, papers are recommended according to the relatedness between user interests and paper embeddings. We perform experiments against three real-world datasets. The results show that our model outperforms baseline methods on both the personalized recommendation and the query keywords based retrieval.
Phase-Matching Quantum Key Distribution with Discrete Phase Randomization

The twin-field quantum key distribution (TF-QKD) protocol and its variations have been proposed to overcome the linear Pirandola-Laurenza-Ottaviani-Banchi (PLOB) bound. One variation called phase-matching QKD (PM-QKD) protocol employs discrete phase randomization and the phase post-compensation technique to improve the key rate quadratically. However, the discrete phase randomization opens a loophole to threaten the actual security. In this paper, we first introduce the unambiguous state discrimination (USD) measurement and the photon-number-splitting (PNS) attack against PM-QKD with imperfect phase randomization. Then, we prove the rigorous security of decoy state PM-QKD with discrete phase randomization. Simulation results show that, considering the intrinsic bit error rate and sifting factor, there is an optimal discrete phase randomization value to guarantee security and performance. Furthermore, as the number of discrete phase randomization increases, the key rate of adopting vacuum and one decoy state approaches infinite decoy states, the key rate between discrete phase randomization and continuous phase randomization is almost the same.