Reviewerly/exHarmony huggingface dataset:
<scp>UNSW‐NB15</scp> computer security dataset: Analysis through visualization

Abstract Class imbalance refers to a major issue in data mining where data with unequal class distribution can deteriorate classification performance. Although it alone affects the performance of the classifiers, the joint‐effect of class imbalance and overlap is more damaging. Data overlap happens when multiple classes are assigned to a single data point causing the classifiers to misidentify the class boundaries. This study offers a deep insight into the intricacies of the UNSW‐NB15 dataset and two issues that may lead the data‐driven models to demonstrate poor performance. The most commonly used visualization methods such as bar chart, 3D and 2D scatter plots, intercluster distance map, and parallel coordinate diagram were employed to depict the data imbalanced and overlap. However, their limitations in capturing the overlapping issue led us to propose an accurate, easy‐to‐interpret, and scalable overlapping visualization method. The method clearly detects the data overlap and illustrates the effect of several data scalers in dealing with the data overlap. To verify the accuracy of the proposed method, a number of classifiers were implemented along with the scalers and the calculated AUC scores were compared to those calculated from the classifiers that were implemented on the original dataset.
Improved Robustness to Open Set Inputs via Tempered Mixup

Supervised classification methods often assume that evaluation data is drawn from the same distribution as training data and that all classes are present for training. However, real-world classifiers must handle inputs that are far from the training distribution including samples from unknown classes. Open set robustness refers to the ability to properly label samples from previously unseen categories as novel and avoid high-confidence, incorrect predictions. Existing approaches have focused on either novel inference methods, unique training architectures, or supplementing the training data with additional background samples. Here, we propose a simple regularization technique easily applied to existing convolutional neural network architectures that improves open set robustness without a background dataset. Our method achieves state-of-the-art results on open set classification baselines and easily scales to large-scale open set classification problems.
Field Comparison Study of Fixed-Tilted and Single-Axis Tracking PV Structures in the Desert Environment of Dubai, UAE

This paper evaluates a fixed-tilted and a single-axis sun tracking photovoltaic (PV) test setups located within a harsh desert environment. We compare multiple parameters, including energy yields, temperature losses, and soiling losses. Four monocrystalline (sc-Si) PV modules and one thin-film (TF) module are considered in this study during the period January-December 2019. The improvement in the energy yield from the east-west sun tracking system compared to the fixed-tilted system at 25° was found to be 16% and 24% on average for the sc-Si and TF PV technologies, respectively. The single-axis tracking modules showed higher month-to-month variability in energy yields, where the highest yields occurred in summer months. The differences in temperature losses between the two test setups were found to be negligible; however, the losses due to soiling were significantly lower for the single-axis tracker as characterized by an average daily soiling rate of -0.156%/day compared to -0.366%/day for the fixed-tilted modules.
Phishing Website Prediction: A Comparison of Machine Learning Techniques

Phishing is a well-known cyber-crime. It is an act of stealing a user’s information such as usernames and passwords which are crucial for them to access the web resources on the Internet. It is a way of getting a user trapped who is somehow unaware of this kind of threat, and in absence of the adequate tools, most of the times the users submit their information unknowingly to the webpages that look very much like the original one. This paper presents a machine learning-oriented comparison containing many efficient classifier algorithms which may result in helping the users to accurately detect and predict the phishing websites. Present paper used various machine learning-based data mining classifiers for prediction of phishing websites. Data mining is a technique all over the world in almost every kind of business and financial organisation for analysing, classifying the data for their purposes. This paper includes algorithms such as logistic regression (LRC), decision tree (DTC), random forest (RFC), support vector machine (SVMC), k-nearest neighbour (KNNC), and Gaussian Naïve Bayes (GNBC) classifiers. The dataset used in this comparative study is taken from UCI online repository (Dua and Graff in UCI machine learning repository. Irvine, CA, 2019) []. The results of all the predictions done by these algorithms are displayed in tabular as well as graphical format. The most accurate results found in case of random forest classifier, i.e. 97.56%. This comparison may be helpful in future studies of phishing website detection and prevention through correct prediction.
CNN-BPSO Model for Multi Classification of Tweets

Twitter is a widely used platform for sharing views and opinions about every ongoing topic in public. A huge amount of opinion oriented data are posted on this platform so that every other person can know and respond to it. Classification of tweet data is a recent field of research. Researchers are developing different types of models for classifying tweet data into several classes. In this paper, Convolutional Neural Network (CNN) is proposed to classify tweet data into four different classes such as business, entertainment, health and politics. The performance of CNN depends on different hyper parameters such as input batch size, learning rate, number of filters, filter size and architecture. Binary Particle Swarm optimization is used in this work to optimize the hyper parameters of CNN. The proposed CNN-BPSO approach is evaluated over a real twitter data set and experimental results show that CNN with BPSO is able to provide better accuracy than CNN model.
Natural Text-to-Speech Synthesis by Conditioning Spectrogram Predictions from Transformer Network on WaveGlow Vocoder

Text to Speech (TTS) is a form of speech synthesis where the text is converted into a spoken human-like voice output. The state of the art methods for TTS employs a neural network based approach. This work aims to look at some of the issues and limitations present in the current works, specifically Tacotron-2, and attempts to further improve its performance by modifying its architecture. The modified model uses Transformer network as a Spectrogram Prediction Network (SPN) and WaveGlow as an Audio Generation Network (AGN). For the modified model, performance improvements are seen in terms of the speech output generated for corresponding texts, the inference time taken for audio generation, and a Mean Opinion Score (MOS) of 4.10 (out of 5) is obtained.
Recent Trends and Challenges in Blockchain Technology

With the increasing utilization of Internet in majority areas such as banking and social security, which produces an enormous amount of data, the data integrity of the customers has turned to be a major challenge for the organizations which are dependent on third-party software organizations. This challenge led to evolve the expert’s interest in Blockchain technology which focuses on transaction decentralization with effective data management. Hence, this paper tries to concentrate on facilitating with an effective study of various works related to Blockchain technology. This technology acts as a backbone of the crypto-currency system. The main objective of this review is to provide a complete overview of the current research, challenges and their solutions related to Blockchain technology that mainly focuses on wide range of applications. This review helps the researchers to understand the technology and its advancements in the Blockchain mechanism.
Leet Usage and Its Effect on Password Security

Text-based passwords have long acted as the dominating authentication method. Leet, as one of the significant components in password, has not been paid enough attention yet. In this paper, we systematically study the presence of Leet in passwords. We define single and pattern forms of Leet and propose a matching approach to check whether a user password contains Leet. We extract the most prevalent counterpart pairs of Leet manifestations. Afterward, we examine the effect of Leet in passwords by incorporating Leet transformation into the probabilistic context-free grammar(PCFG) method to crack passwords. We construct the first comprehensively analyzed dictionary of Leets for passwords, which is confirmed suitable for most datasets by user survey. Experiments on four leaked password sets demonstrate that distinguished Leet usage accumulates to account for around 1% of the total dataset. Only 5% of high-frequency Leets replacement could increase the cracking rate by 0.55%. For crackers, incorporating popular Leets aids to improve password cracking performance. For users, adopting low-frequency Leets could strengthen their passwords. This research provides a new perspective to investigate Leet transformations in passwords.
Multi-Language Web Vulnerability Detection

Most web applications are compromised due to vulnerable source code. Static code analysis tools that are often used to find security vulnerabilities in code have two main problems: they are language-specific, and they have to be programmed, or at least configured manually, to deal with new types of vulnerabilities.
NPSCS: Non-Preemptive Stochastic Coflow Scheduling With Time-Indexed LP Relaxation

Coflows model a scheduling setting that is commonly found in a variety of applications in distributed and cloud computing. A stochastic coflow task contains a set of parallel flows with randomly distributed sizes. Further, many applications require non-preemptive scheduling of coflow tasks. This article gives an approximation algorithm on the weighted expected completion time for non-preemptive stochastic coflow scheduling. The proposed approach uses a time-indexed linear program relaxation, and uses its solution to come up with a feasible schedule. This algorithm is shown to achieve an approximation ratio of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(2\log {m}{+}1)(1{+}\sqrt {m {\Delta }})(1{+}m\sqrt {{\Delta }}){(1{+}{\Delta })}$ </tex-math></inline-formula> for zero-release times, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2(2\log {m}{+}1)(1{+}\sqrt {m{\Delta }})(1{+}m\sqrt {{\Delta }})(1{+}{\Delta })$ </tex-math></inline-formula> for general release times, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\Delta }$ </tex-math></inline-formula> represents the upper bound of squared coefficient of variation of processing times, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula> is the number of servers.
Cross-Correlation Based Automatic Segmentation of Medial Phonemes

This paper describes a cross-correlation based method aimed at extracting homogeneously-trimmed target medial phonemes from a reference pronunciation (a word or logatome pronounced by the Speech Language Pathologist (SLP)) and a sample utterance (the same word/logatome pronounced by a subject). The newly-generated audio segments are then fed to an Information Entropy based classification stage in order to assess the (dis)similarities between them and to serve as a valid, automated Speech Sound Disorder (SSD) Screening tool. The input for the study consisted in audio recordings of a Romanian word containing the target phoneme /r/ in medial position pronounced by a population of 44 preschoolers and primary schoolers, aged 6-7.
Automatic acquisition of particle orientation by interferometric out-of-focus image

In this paper, the automatic acquisition of particle orientation is enabled by interferometric out-of-focus image. Based on optical transfer matrix theory, the slope expression of the speckle orientation and autocorrelation result of an out-of-focus image are acquired. The calculation formula of the particle orientation angle is derived. The processing algorithm which can automatically obtain the particle orientation from the out-of-focus image is proposed. Hough transform is utilized to improve the measurement accuracy. The out-of- focus images with different speckle orientations are acquired and the algorithm is applied to process the simulative and experimental out-of-focus images. The results show that the absolute measurement error of the algorithm is 2.7°. The algorithm has potential applications in particle measurements.
Making DensePose fast and light

DensePose estimation task is a significant step forward for enhancing user experience computer vision applications ranging from augmented reality to cloth fitting. Existing neural network models capable of solving this task are heavily parameterized and a long way from being transferred to an embedded or mobile device. To enable Dense Pose inference on the end device with current models, one needs to support an expensive server-side infrastructure and have a stable internet connection. To make things worse, mobile and embedded devices do not always have a powerful GPU inside. In this work, we target the problem of redesigning the DensePose R-CNN model's architecture so that the final network retains most of its accuracy but becomes more light-weight and fast. To achieve that, we tested and incorporated many deep learning innovations from recent years, specifically performing an ablation study on 23 efficient backbone architectures, multiple two-stage detection pipeline modifications, and custom model quantization methods. As a result, we achieved 17× model size reduction and 2× latency improvement compared to the baseline model. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Controller Tuning by Metaheuristics Optimization

Metaheuristics are suitable for optimizing a broad class of problems. Subsequently, metaheuristics have attracted the attention of researchers from diverse engineering fields, including control. Indeed, their popularity has increased worldwide over the past three decades, which is substantiated by many new metaheuristics publications. There exist hundreds of metaheuristics. Consequently, selecting an optimization method to solve the problem at hand might be difficult. Therefore, this chapter aims to give an overview of the metaheuristics field and provide enough details to empower the reader to apply metaheuristics to control tuning. This chapter presents a brief history of metaheuristics and their origin. A comprehensive list of published metaheuristics is presented as well as their first application. To deepen understanding, the main principles behind any metaheuristics are presented together with a general skeleton/framework that fits most metaheuristics. The general assets of metaheuristics are provided and some insight into how to select the most suitable method for the problem at hand. Two metaheuristics, GA and SA, are introduced as they are employed in the subsequent chapter for controller tuning.
Image Detection of Foreign Body Intrusion in Railway Perimeter Based on Dual Recognition Method

In order to ensure the safety of railway operation, it is urgent to strengthen the detection and protection of railway perimeter safety. This paper proposes a method for detecting foreign body intrusion in railway perimeter based on double recognition. The Gaussian Mixture Model (GMM) is used to process the video image of the railway scene, and the foreign objects are pre-screened, and the foreign object existence frames are extracted, and then the YOLOv3 algorithm is used to perform secondary detection and recognition on the foreign object existence frames. This method can improve the accuracy of target recognition, reduce the false alarm rate and false alarm rate of foreign object invasion in railway scenes, and occupy less on-site computing resources, which is suitable for on-site requirements. The results show that, compared with the GMM, the false negative rate of the algorithm in this paper is lower, and the algorithm is more suitable for railway site requirements than the deep learning algorithm.
SPA

Deep neural networks are susceptible to trojan attacks due to the lack of their interpretability in training process. The trained models can be purposely polluted by the attackers using training data with special patterns called trojan triggers, which are also called poisoned samples. When the model is put in use, the trojan trigger will be stamped on the testing samples to manipulate the output so as to achieve trojan attack. In previous work that achieved high attack success rate, the fixed patterns of the trojan trigger in poisoned samples make them easily detected and eliminated by defense algorithms. We propose a novel stealthy trojan attack approach called SPA, which exploits the generative adversarial network to generate poisoned samples and models that can be triggered by benign samples without trojan triggers. The generated poisoned samples are stealthy, namely, look natural thus are less likely to be detected easily. Our experiments have shown that SPA can achieve trojan attack success rate as high as 91.74%, with only 7% poisoned samples in public dataset LFW and CASIA. We have experimented with a few defense algorithms such as autodecoder defense and DBSCAN cluster detection and showed the resilience of SPA.
A useful variance decomposition for destructive Waring regression cure model with an application to HIV data

Motivated by the works of Irwin and Rodríguez-Avi et al., a destructive Waring regression cure model is developed here. This model enables the patients to be protagonists for the treatment and also facilitates an understanding of the nature of overdispersion of competing risk factors to prevent higher risk of the event of interest. The cure rate and the destructive mechanism (immune system) are personalized and the overdispersion of risk factors is explained through the decomposition of variance components: randomness, external frailty (unknown covariates) and internal frailty (destructive mechanism). A simulation study demonstrates the effectiveness of the proposed model and associated inferential method. Finally, an illustrative example shows that the internal frailty is an important factor in recurrent sinus disease among HIV-positive patients.
Red Carpet to Fight Club: Partially-supervised Domain Transfer for Face Recognition in Violent Videos

In many real-world problems, there is typically a large discrepancy between the characteristics of data used in training versus deployment. A prime example is the analysis of aggression videos: in a criminal incidence, typically suspects need to be identified based on their clean portraitlike photos, instead of their prior video recordings. This results in three major challenges; large domain discrepancy between violence videos and ID-photos, the lack of video examples for most individuals and limited training data availability. To mimic such scenarios, we formulate a realistic domain-transfer problem, where the goal is to transfer the recognition model trained on clean posed images to the target domain of violent videos, where training videos are available only for a subset of subjects. To this end, we introduce the "WildestFaces" dataset, tailored to study cross-domain recognition under a variety of adverse conditions. We divide the task of transferring a recognition model from the domain of clean images to the violent videos into two sub-problems and tackle them using (i) stacked affine-transforms for classifier-transfer, (ii) attention-driven pooling for temporal-adaptation. We additionally formulate a self-attention based model for domain-transfer. We establish a rigorous evaluation protocol for this "clean-to-violent" recognition task, and present a detailed analysis of the proposed dataset and the methods. Our experiments highlight the unique challenges introduced by the WildestFaces dataset and the advantages of the proposed approach.
Maximizing Privacy in MIMO Cyber-Physical Systems Using the Chapman-Robbins Bound

Privacy breaches of cyber-physical systems could expose vulnerabilities to an adversary. Here, privacy leaks of step inputs to linear time-invariant systems are mitigated through additive Gaussian noise. Fundamental lower bounds on the privacy are derived, which are based on the variance of any estimator that seeks to recreate the input. Fully private inputs are investigated and related to transmission zeros. Thereafter, a method to increase the privacy of optimal step inputs is presented and a privacy-utility trade-off bound is derived. Finally, these results are verified on data from the KTH Live-In Lab Testbed, showing good correspondence with theoretical results.
Unlinkable and Revocable Secret Handshake

Abstract In this paper, we introduce a new construction for unlinkable secret handshake that allows a group of users to perform handshakes anonymously. We define formal security models for the proposed construction and prove that it can achieve session key security, anonymity and affiliation hiding. In particular, the proposed construction ensures that (i) anonymity against protocol participants (including group authority) is achieved since a hierarchical identity-based signature is used in generating group user’s pseudonym-credential pairs and (ii) revocation is achieved using a secret sharing-based revocation mechanism.
An Algorithm to Recognize and Classify Circular Objects from Image on Basis of Their Radius

For the computer vision, fast and accurate detection of an object is challenging. Detecting a circular object in a cluttered image has always been a problem. Circular object detections has wide applications in the field of biometrics, automobile and other mechanical production industries. The traditional existing circular object detection are maximum likelihood estimation (MLE) and voting-based methods. The voting based methods have high memory requirements and more computational complexity while these are less sensitive to noise. MLE approach consumes less memory and are efficient in terms of computational complexity but these approaches are more prone to noise. This paper proposes modified Hough transform based algorithm for detection of circular objects within other shaped objects also it can identify circular objects on basis of diameter. The proposed algorithm worked efficiently and detected the circular objects on basis of diameters with very less computational time and less memory consumption.