Reviewerly/exHarmony huggingface dataset:
Remote Anomaly Detection in Industry 4.0 Using Resource-Constrained Devices

A central use case for the Internet of Things (IoT) is the adoption of sensors to monitor physical processes, such as the environment and industrial manufacturing processes, where they provide data for predictive maintenance, anomaly detection, or similar. The sensor devices are typically resource-constrained in terms of computation and power, and need to rely on cloud or edge computing for data processing. However, the capacity of the wireless link and their power constraints limit the amount of data that can be transmitted to the cloud. While this is not problematic for the monitoring of slowly varying processes such as temperature, it is more problematic for complex signals such as those captured by vibration and acoustic sensors. In this paper, we consider the specific problem of remote anomaly detection based on signals that fall into the latter category over wireless channels with resource-constrained sensors. We study the impact of source coding on the detection accuracy with both an anomaly detector based on Principal Component Analysis (PCA) and one based on an autoencoder. We show that the coded transmission is beneficial when the signal-to-noise ratio (SNR) of the channel is low, while uncoded transmission performs best in the high SNR regime.
Deep Rapid Class Augmentation; A New Progressive Learning Approach that Eliminates the Issue of Catastrophic Forgetting

This paper discusses a new progressive learning approach for deep neural networks called Deep RCA that eliminates the issues associated with Catastrophic Forgetting. Across multiple experiments and datasets, we show that Deep RCA achieves the optimal joint training performance when sequentially trained on new tasks using just the new task data. It does this by utilizing a modified recursive least squares algorithm and a novel recursive null-class initialization vector that together allow Deep RCA to jointly update old tasks and new tasks sequentially. Deep RCA’s ability to train only on the new task data while jointly optimizing across all tasks is shown to offer the practical benefits of significantly reduced augmentation training times and data storage requirements compared with a standard sequential learning implementation. Furthermore, it allows Deep RCA to decouple the time required to learn a new task from the number of previously learned tasks which is essential for scaling continuous learning applications. These traits make Deep RCA well suited for many practical applications that require rapid sequential learning on the edge where data storage and compute power are often limited.
Dynamic Reconfiguration via Typed Modalities

Modern software systems are increasingly exhibiting dynamic-reconfiguration features analogous to naturally occurring phenomena where the architecture of a complex changes dynamically, at run time, on account of interactions between its components. This has led to a renewed interest in modal logics for formal system development, building on the intuitive idea that system configurations can be regarded as local models of a Kripke structure, while reconfigurations are captured by accessibility relations. We contribute to this line of research by advancing a modal logic with varying quantification domains that employs typed modalities and dedicated modal operators to specify and reason about a new generation of Kripke structures, called dynamic networks of interactions, that account for the context of a system’s dynamics, identifying which actants have triggered a reconfiguration and what are its outcomes. To illustrate the expressiveness of the formalism, we provide a specification of the biological process of membrane budding, which we then analyse using a sound and complete proof-by-translation method that links dynamic networks of interactions with partial first-order logic.
Local Path Planning of the Outdoor Blind Guiding Robot

In this paper, an outdoor blind guiding robot is designed to meet the travel needs of the blind. Aiming at the problem of path planning of outdoor blind guiding robot, this paper adopts the idea of combining artificial potential field method and Bug algorithm to carry out local path planning and realizes the off-line aimless obstacle avoidance of outdoor blind guiding robot.
Smart Learning Communities for Bridging the Gender Gap in STEM

This paper presents the results achieved in four years of activity within the NERD? project, which was created by IBM Italy and La Sapienza University of Rome and is carried on in collaboration with several universities and schools, in Italy. The long-term objective of the project is bridging the gender gap in the so-called STEM disciplines (science, technology, engineering and mathematics) starting from the secondary education. Specifically, we refer to the experience conducted in the field of informatics and computer engineering by the Federico II University of Naples, which empowered the original educational model of the NERD? project, introducing collaboration issues within a smart learning community. This involved people from industry, university and secondary schools, including both teachers and students at all the levels.
Combination of graphics, uncertainty, and semantics: A survey

Abstract Graphics, uncertainty, and semantics are three approaches to building models. The combination of the three approaches is a way to develop a stronger modeling method. This article surveys the research efforts toward combining these aspects, which can be divided into two routes: One is to combine graphics and uncertainty as probabilistic graphical models and then incorporate semantics, and the other is to combine graphics and semantics and then incorporate probability to handle uncertainty. The models and methods involved in these efforts are introduced and their expressiveness, pros, and cons are discussed.
Non-Linear Gaussian Smoothing With Taylor Moment Expansion

This letter is concerned with solving continuous-discrete Gaussian smoothing problems by using the Taylor moment expansion (TME) scheme. In the proposed smoothing method, we apply the TME method to approximate the transition density of the stochastic differential equation in the dynamic model. Furthermore, we derive a theoretical error bound (in the mean square sense) of the TME smoothing estimates showing that the smoother is stable under weak assumptions. Numerical experiments are presented in order to illustrate practical use of the method.
Bayesradar : Bayesian Metric-Kalman Filter Learning for Improved and Reliable Radar Target Classification

Automotive radar sensors offer a promising and effective modality for perception and assessment of the surrounding environment. A key element of environment sensing in automotive radars is the reliable detection, classification and tracking of vulnerable road users such as pedestrians and cyclists. In this paper, we propose an integrated Bayesian framework dubbed BayesRadar, which improves the overall classification accuracy by tracking the embedding vector of a neural network and its prediction uncertainty via recursive Kalman filtering over time. Apart from the classification accuracy of a model, a critical measure includes the analysis of statistical confidence over the target class score. Such measures for predicting the true correctness likelihood of the classification estimates are essential in safety-critical automotive applications. Therefore, in this paper, we present and evaluate the classification, embedding cluster score and statistical confidence performance of the proposed framework in the context of classifying vulnerable road users compared to state-of-art deep learning approaches. Furthermore, we demonstrate superior performance of the BayesRadar for unseen classes compared to long short-term memory based temporal tracking of the embedding vectors.
Comparative Analysis of Modern Mobile Operating Systems

The importance of smartphones has grown exponentially since their emergence. Smartphones have fundamentally modified the way people live by allowing them to easily access information and communicate with one another. With this meteoric rise, the operating systems run by these mobile devices have also come a long way in terms of their functionalities and their features. The operating system plays a vital and essential role in the use of these devices and cannot be overlooked. The operating system acts as the foundation of the device. The quality of the operating system directly impacts the quality of the device and also determines the usability of the device. A wide range of mobile operating systems, each with its own set of characteristics and features, currently exist in the market. This paper looks into some of the popular operating systems used in mobile devices and aims to compare and evaluate their different characteristics like architecture, security, and other attributes. This paper also analyzes a few of the advantages and disadvantages of these operating systems.
Mutism Guide: A Real-Time Sign Spelling Recognition using Skeleton based Improved Haar Support Vector

The deaf and dumb peoples mostly communicate with normal people through the standard nonverbal American gesture. But the sign languages are difficult to recognize and understandable by the common people. The existing computerized sign recognition methodologies suffer from the sign object classification. The false-positive test result reduces the prediction accuracy or increases the training overhead. The proposed approach named Improved Haar Feature (IHF) locate the skeleton of the hand pose and recognize the sign by the Angle Support Vector (ASV). The skeleton is estimated by the series of cyclic connected points of the box model. The object's centroid is estimated, and the cut-vertex is defined through the support vector. The number of vertices and the distance between the point-pin are also considered for region mapping. The number of segregation classes will differ based on the nature of the sign skeleton. The angle of the centroid and the connection points are examined to extract the right feature. The custom gesture is trained using the deep convolutional neural network models. The proposed methodology will observe the hand sign and produce an optimal output in the form of verbal speech or text. The algorithm produces a good prediction accuracy of 96.2 percent.
Bounds for List-Decoding and List-Recovery of Random Linear Codes

A family of error-correcting codes is list-decodable from error fraction <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> if, for every code in the family, the number of codewords in any Hamming ball of fractional radius <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> is less than some integer <inline-formula> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula>. It is said to be list-recoverable for input list size <inline-formula> <tex-math notation="LaTeX">$\ell $ </tex-math></inline-formula> if for every sufficiently large subset of at least <inline-formula> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula> codewords, there is a coordinate where the codewords take more than <inline-formula> <tex-math notation="LaTeX">$\ell $ </tex-math></inline-formula> values. In this work, we study the list size of <i>random linear codes</i> for both list-decoding and list-recovery as the rate approaches capacity. We show the following claims hold with high probability over the choice of the code (below <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> is the alphabet size, and <inline-formula> <tex-math notation="LaTeX">$ \varepsilon &gt; 0$ </tex-math></inline-formula> is the gap to capacity). (1) A random linear code of rate <inline-formula> <tex-math notation="LaTeX">$1 - \log _{q}(\ell) - \varepsilon $ </tex-math></inline-formula> requires list size <inline-formula> <tex-math notation="LaTeX">$L \ge \ell ^{\Omega (1/ \varepsilon)}$ </tex-math></inline-formula> for list-recovery from input list size <inline-formula> <tex-math notation="LaTeX">$\ell $ </tex-math></inline-formula>. (2) A random linear code of rate <inline-formula> <tex-math notation="LaTeX">$1 - h_{q}(p) - \varepsilon $ </tex-math></inline-formula> requires list size <inline-formula> <tex-math notation="LaTeX">$L \ge \left \lfloor{ {h_{q}(p)/ \varepsilon +0.99}}\right \rfloor $ </tex-math></inline-formula> for list-decoding from error fraction <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula>. (3) A random <i>binary</i> linear code of rate <inline-formula> <tex-math notation="LaTeX">$1 - h_{2}(p) - \varepsilon $ </tex-math></inline-formula> is list-decodable from <i>average</i> error fraction <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> with list size with <inline-formula> <tex-math notation="LaTeX">$L \leq \left \lfloor{ {h_{2}(p)/ \varepsilon }}\right \rfloor + 2$ </tex-math></inline-formula>. Our lower bounds follow by exhibiting an explicit subset of codewords so that this subset&#x2014;or some symbol-wise permutation of it&#x2014;lies in a random linear code with high probability. Our upper bound follows by strengthening a result of (Li, Wootters, 2018).
White Blood Cell Segmentation and Classification Using Deep Learning Coupled with Image Processing Technique

White blood cell (WBC) segmentation and classification contribute significantly to developing computer-aided detection system. Several approaches have been proposed by different researchers. In this manuscript, we investigate in an approach for segmenting and classifying WBCs using image processing technique and deep learning. The WBC segmentation is a combination of the thresholding method and Watershed algorithm. The types of leukocytes (WBCs) are classified by a deep learning model. The experiments were evaluated on a publicly available dataset with large number of images. The segmentation result is promising. The proposed approach attained the average classification accuracy among five WBC types of 99.4%, the minimum accuracy was 98.6% corresponding to monocyte while the highest accuracy was 99.8% corresponding to eosinophil. The proposed approach also attained the classification accuracy of 99.6%, 99.2%, and 99.4% respectively for basophil, lymphocyte, and neutrophil.
Motion and Meaning: Sample-Level Nonlinear Analyses of Virtual Reality Tracking Data

Behavioral data is the “gold standard” for experiments in psychology. The tracking component of virtual reality systems captures data on nonverbal behavior both covertly and continuously at high spatial and temporal fidelity, enabling what is called behavioral tracing. With previous research analyzing this type of data, however, inference has primarily been limited to linear relationships of subject-level aggregates. In this work, we suggest these rough aggregations are often neither the best according to theory nor do they make use of the rich data available from behaviorally traced experiments. We also explore the relationships between motion and subjective experiences with a previously published dataset of 360-degree video and emotion, and we find evidence for nonlinear sample-level relationships. In particular, reported valence relates with head pitch and pitch velocity, among others, and reported arousal relates with head rotation speed and yaw velocity, among others. The role of these sample-level nonlinear relationships for future work are discussed.
Security Enhancement of A5/1 Stream Cipher in GSM Communication &amp; its Randomness Analysis

Global System for Mobile (GSM) is still widely used cellular standard providing many services like Voice, SMS & Data Service with mobility. To secure information in the GSM cellular network, security is implemented at two levels i.e. initially to authorize the valid users for every location update and then during call, encryption is applied over the information being transmitted on GSM channel to protect from being intercepted and decoded by unauthorized persons. This protection is achieved by converting the original message into an encoded form (cipher form) that appears to be a random stream of symbols. Under “Challenge-Response” mechanism, A3 and A8 algorithms are used to generate triplet (RAND, SRES & Kc) for authentication of any user at GSM network. But the actual information is encrypted using A5 algorithm to generate cipher stream for better protection from interception. This A5 stream cipher has three versions: A5/1, A5/2 and A5/3. A5/1 works on Liner Feedback Shift Registers (LFSRs) with irregular clocking and generates pseudo random binary stream. These three versions of A5 algorithm are being used for encryption of information over GSM since the launching of GSM services and have been cryptographically analyzed by Reverse-Engineering. With passage of time, many hackers & crackers are becoming able to break this encryption identifying some weaknesses of these algorithms and can decrypt the original information. These stream ciphers are facing some weaknesses like poor Liner Complexity (LC) & clocking mechanism (Majority Rule), short clocking period, weak choice of clocking taps and collision problem. Because of such weaknesses, these stream ciphers can be decrypted by intruders. In this papers an attempt has been made to reduce these weaknesses and enhance the security by introducing non-linear combinational generator (NLFSRs), reuse of 32 bits SRES generated by A3 algorithm and finally combining the output stream with last 32 bits of CGI. The randomness analysis of proposed stream cipher is carried out by NIST Statistical Test Suite and it is confirmed by comparison of the randomness parameters results that the randomness of bit-stream produced by the proposed stream cipher has improved significantly hence the enhanced security can be achieved.
Normalization Framework for Vulnerability Risk Management in Cloud

Vulnerability Risk Management (VRM) is a critical element in cloud security that directly impacts cloud providers' security assurance levels. Today, VRM is a challenging process because the dramatic increase of known vulnerabilities (+26% in the last five years), and because it is even more dependent on the organization's context. Moreover, the vulnerability's severity score depends on the Vulnerability Database (VD) selected as a reference in VRM. All these factors introduce a new challenge for security specialists in evaluating and patching the vulnerabilities. This study provides a framework to improve the classification and evaluation phases in vulnerability risk management while using multiple vulnerability databases as a reference. Our solution normalizes the severity score of each vulnerability based on the selected security assurance level. The results of our study highlighted the role of the vulnerability databases in patch prioritization, showing the advantage of using multiple VDs.
A New Firefly Algorithm with Enhanced Attractiveness

In the process of continuous optimization of complex problems, the firefly algorithm (FA) has the disadvantages of poor convergence behavior and easy to fall into local optimality. In order to overcome the shortcomings, we propose a new firefly algorithm with enhanced attractiveness (EA-LFA). It is achieved by reconstructing a new and enhanced attractive item and introducing Lévy flight to FA at the same time. Compared with the FA and its variants, the EA-LFA has best convergence behavior and global optimization efficiency.
Detection of Stop Line Violations Using the Hough Transform

The number of road users, in particular those using motor vehicles, is constantly increasing. It is imperative that these users obey road markings, in order to ensure traffic safety. However, the number of traffic violations is still very high. One example is violation of stop line before a pedestrian crossing. This paper proposes an automatic detection of this type of traffic violation. The approach is based on the Hough transform. This experiment show that the approach can achieve accuracy rate for the morning and afternoon dataset are 89% and for the evening dataset is approximately 69% (or 71% using an alternative set of parameters). So, the overall average of accuracy rate of the system is 82.33 % (or 83 %, with an alternative set of parameters). The main factors affecting the system performance is the availability of adequate lighting and the quality of the stop line marking.
SEDA: A Self-Explaining Decision Architecture Implemented Using Deep Learning for On-Board Command and Control

Machine learning (ML) is a powerful tool for solving stochastic optimization problems. The aerospace and defense sectors have a number of stochastic optimization problems that would benefit from the application of ML; however, people often have difficulties interpreting solutions arrived at via ML, which undermines trust, producing an obstacle to widespread adoption in these sectors. This paper introduces the Self-Explaining Decision Architecture (SEDA) for ML-based decision-making systems capable of generating intuitive explanations for their decisions in real time. SEDA makes use of a feature extraction subsystem and a sequence interpretation subsystem to identify patterns in data followed by a decision generation subsystem that determines appropriate actions based on those patterns. Internal state information from each of these subsystems is used to generate explanations of the system’s decisions. Using this information to create explanations provides insight as to the data elements the system focused on when making decisions as well as the reasoning that was used. As a proof-of-concept, we present a first implementation of SEDA using start-of-the-art deep learning components including a combined convolutional neural network and long short-term memory network with attention mechanisms and demonstrate its use on both standard and custom datasets.
Artifact Removal of Eye Tracking Data for the Assessment of Cognitive Vigilance Levels

In this paper, we present a preprocessing pipeline of Eye tracking data to assess cognitive vigilance levels. We introduced two different levels of vigilance state; alertness and vigilance decrement while subjects were performing Stroop Color-Word Task (SCWT) for approximately 45 minutes. We assessed the levels of vigilance by utilizing Eye tracking data and five machine learning (ML) classifiers. Our preprocessing pipeline consists of baseline correction, and artifacts, and noise removal. We extracted six features namely: fixation duration, pupil size, saccade duration, saccade amplitude, saccade velocity, and blink duration. These features were then used as an input to the five ML classifiers for vigilance level classification. We achieved the highest classification accuracy of 76.8% in differentiating between the two vigilance levels using all features with a selected Support vector machine classifier. Other classifiers have also achieved comparable accuracy.
Hybrid Classification Method for the Human Activity Detection

In person-to person collaboration and social relations, the identification of human activity plays a significant role to obtain information of the individuality of people, their character and emotional state. It is difficult to extract this information. The research of the scientific fields of computer vision and Machine Learning is useful to recognize the actions of other people. A hybrid technique is proposed in this research for the human activity recognition. This hybrid technique has integrated the Principal Component Analysis, K-mean and voting classification. The voting classification improves results in comparison with the existing logistic regression classification with regard to precision and recall etc.