Reviewerly/exHarmony huggingface dataset:
A Formal Approach to Physics-based Attacks in Cyber-physical Systems

We apply formal methods to lay and streamline theoretical foundations to reason about Cyber-Physical Systems (CPSs) and physics-based attacks, i.e., attacks targeting physical devices. We focus on a formal treatment of both integrity and denial of service attacks to sensors and actuators of CPSs, and on the timing aspects of these attacks. Our contributions are fourfold. (1) We define a hybrid process calculus to model both CPSs and physics-based attacks. (2) We formalise a threat model that specifies MITM attacks that can manipulate sensor readings or control commands to drive a CPS into an undesired state; we group these attacks into classes and provide the means to assess attack tolerance/vulnerability with respect to a given class of attacks, based on a proper notion of most powerful physics-based attack. (3) We formalise how to estimate the impact of a successful attack on a CPS and investigate possible quantifications of the success chances of an attack. (4) We illustrate our definitions and results by formalising a non-trivial running example in U PPAAL SMC, the statistical extension of the U PPAAL model checker; we use U PPAAL SMC as an automatic tool for carrying out a static security analysis of our running example in isolation and when exposed to three different physics-based attacks with different impacts.
A New Hybrid Convolutional Neural Network and eXtreme Gradient Boosting Classifier for Recognizing Handwritten Ethiopian Characters

Handwritten character recognition has been profoundly studied for many years in the field of pattern recognition.Due to its vast practical applications and financial implications, the handwritten character recognition is still an important research area.In this research, a Handwritten Ethiopian Character Recognition (HECR) dataset is prepared to train a model.Images in the HECR dataset were organized with more than one color pen RGB main spaces that are size normalized to 28 × 28 pixels.The dataset is a combination of scripts (Fidel in Ethiopia), numerical representations, punctuations, tonal symbols, combining symbols, and special characters.These scripts have been used to write ancient histories, science, and arts of Ethiopia and Eritrea.In this study, a hybrid model of two super classifiers: Convolutional Neural Network (CNN), as well as eXtreme Gradient Boosting (XGBoost), are proposed for classification.In this integrated model, CNN works as a trainable automatic feature extractor from the raw images and XGBoost takes the extracted features as an input for recognition and classification.The output error rates of the hybrid model and CNN with a fully connected layer are compared.A 0.4630 and 0.1612 error rates were achieved in classifying the handwritten testing dataset images, respectively.The XGBoost as a classifier gave better results than the traditional fully connected layer.
New-Product Portfolio Management with Agile

Overview: While Agile development approaches to physical products have allowed companies to respond more quickly to change and increase R&D productivity, implementing these approaches also poses new challenges for new-product portfolio management. When Agile and the traditional way of measuring, evaluating, and managing projects clash, companies must reassess how portfolios are managed, how go/kill and prioritization decisions are made, and how performance is measured. This article explores the new solutions and emerging challenges at the boundary between project and portfolio management and suggests tools and approaches to deal with these new challenges.
Highly-scalable traffic management of autonomous industrial transportation systems

In this paper, we present a novel method for highly-scalable coordination of free-ranging automated guided vehicles in industrial logistics and manufacturing scenarios. The primary aim of this method is to enhance the current industrial state-of-the-art multi-vehicle transportation systems, which, despite their long presence on the factory floor and significant advances over the last decades, still rely on a centralized controller and predetermined network of paths. In order to eliminate the major drawbacks of such systems, including poor scalability, low flexibility, and the presence of a single point of failure, in the proposed control approach vehicles autonomously execute their assigned pick-up and delivery operations by running a fully decentralized control algorithm. The algorithm integrates path planning and motion coordination capabilities and relies on a two-layer control architecture with topological workspace representation on the top layer and state-lattice representation on the bottom layer. Each vehicle plans its own shortest feasible path toward the assigned goal location and resolves conflict situations with other vehicles as they arise along the way. The motion coordination strategy relies on the private-zone mechanism ensuring reliable collision avoidance, and local negotiations within the limited communication radius ensuring high scalability as the number of vehicles in the fleet increases. We present experimental validation results obtained on a system comprising six Pioneer 3DX robots in four different scenarios and simulation results with up to fifty vehicles. We also analyze the overall quality of the proposed traffic management method and compare its performance to other state-of-the-art multi-vehicle coordination approaches.
Video Instance Segmentation 2019: A Winning Approach for Combined Detection, Segmentation, Classification and Tracking

Video Instance Segmentation (VIS) is the task of localizing all objects in a video, segmenting them, tracking them throughout the video and classifying them into a set of predefined classes. In this work, divide VIS into these four parts: detection, segmentation, tracking and classification. We then develop algorithms for performing each of these four sub tasks individually, and combine these into a complete solution for VIS. Our solution is an adaptation of UnOVOST, the current best performing algorithm for Unsupervised Video Object Segmentation, to this VIS task. We benchmark our algorithm on the 2019 YouTube-VIS Challenge, where we obtain first place with an mAP score of 46.7%.
A Structural Graph Representation Learning Framework

The success of many graph-based machine learning tasks highly depends on an appropriate representation learned from the graph data. Most work has focused on learning node embeddings that preserve proximity as opposed to structural role-based embeddings that preserve the structural similarity among nodes. These methods fail to capture higher-order structural dependencies and connectivity patterns that are crucial for structural role-based applications such as visitor stitching from web logs. In this work, we formulate higher-order network representation learning and describe a general framework called HONE for learning such structural node embeddings from networks via the subgraph patterns (network motifs, graphlet orbits/positions) in a nodes neighborhood. A general diffusion mechanism is introduced in HONE along with a space-efficient approach that avoids explicit construction of the k-step motif-based matrices using a k-step linear operator. Furthermore, HONE is shown to be fast and efficient with a worst-case time complexity that is nearly-linear in the number of edges. The experiments demonstrate the effectiveness of HONE for a number of important tasks including link prediction and visitor stitching from large web log data.
Task scheduling mechanisms in cloud computing: A systematic review

Summary Today, cloud computing has developed as one of the important emergent technologies in communication and Internet. It offers on demand, pay per use access to infrastructure, platforms, and applications. Due to the increase in its popularity, the huge number of requests need to be handled in an efficient manner. Task scheduling as one of the challenges in the cloud computing supports the requests for assigning a particular resource so as to perform effectively. In the resource management, task scheduling is performed where there is the dependency between tasks. Many approaches and case studies have been developed for the scheduling of these tasks. Up to now, a systematic literature review (SLR) has not been presented to discover and evaluate the task scheduling approaches in the cloud computing environment. To overcome, this paper presents an SLR‐based analysis on the task scheduling approaches that classify into (a) single cloud environments that evaluate cost‐aware, energy‐aware, multi‐objective, and QoS‐aware approaches in task scheduling; (b) multicloud environment that evaluates cost‐aware, multi‐objective, and QoS‐aware task scheduling; and (c) mobile cloud environment that is energy‐aware and QoS‐aware task scheduling. The analytical discussions are provided to show the advantages and limitations of the existing approaches.
Zero-Shot Hyperspectral Image Denoising With Separable Image Prior

Supervised learning with a convolutional neural network is recognized as a powerful means of image restoration. However, most such methods have been designed for application to grayscale and/or color images; therefore, they have limited success when applied to hyperspectral image restoration. This is partially owing to large datasets being difficult to collect, and also the heavy computational load associated with the restoration of an image with many spectral bands. To address this difficulty, we propose a novel self-supervised learning strategy for application to hyperspectral image restoration. Our method automatically creates a training dataset from a single degraded image and trains a denoising network without any clear images. Another notable feature of our method is the use of a separable convolutional layer. We undertake experiments to prove that a separable network allows us to acquire the prior of a hyperspectral image and to realize efficient restoration. We demonstrate the validity of our method through extensive experiments and show that our method has better characteristics than those that are currently regarded as state-of-the-art.
On Lipschitz Bounds of General Convolutional Neural Networks

Many convolutional neural networks (CNN's) have a feed-forward structure. In this paper, we model a general framework for analyzing the Lipschitz bounds of CNN's and propose a linear program that estimates these bounds. Several CNN's, including the scattering networks, the AlexNet and the GoogleNet, are studied numerically. In these practical numerical examples, estimations of local Lipschitz bounds are compared to these theoretical bounds. Based on the Lipschitz bounds, we next establish concentration inequalities for the output distribution with respect to a stationary random input signal. The Lipschitz bound is further used to perform nonlinear discriminant analysis that measures the separation between features of different classes.
Personalized Reason Generation for Explainable Song Recommendation

Personalized recommendation has received a lot of attention as a highly practical research topic. However, existing recommender systems provide the recommendations with a generic statement such as “Customers who bought this item also bought…”. Explainable recommendation, which makes a user aware of why such items are recommended, is in demand. The goal of our research is to make the users feel as if they are receiving recommendations from their friends. To this end, we formulate a new challenging problem called personalized reason generation for explainable recommendation for songs in conversation applications and propose a solution that generates a natural language explanation of the reason for recommending a song to that particular user. For example, if the user is a student, our method can generate an output such as “Campus radio plays this song at noon every day, and I think it sounds wonderful,” which the student may find easy to relate to. In the offline experiments, through manual assessments, the gain of our method is statistically significant on the relevance to songs and personalization to users comparing with baselines. Large-scale online experiments show that our method outperforms manually selected reasons by 8.2% in terms of click-through rate. Evaluation results indicate that our generated reasons are relevant to songs and personalized to users, and they attract users to click the recommendations.
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We here present a Digital Image Correlation toolkit, formulated as a Python package. This package aims at providing a complete toolkit for performing DIC analysis on experimental data, performing virtual experiments, as well as a framework for further development. A suite of tools for generating synthetic speckle images, modelling of sensor artefacts and deformation of images by displacement fields, are included. The virtual experiments are used as a part of the accuracy assessment of the toolkit as well as for testing during development. B-spline elements are employed for the discretisation of the displacement fields and allow the polynomial order and degree of continuity to be controlled by the user.
Leveraging Active Learning and Conditional Mutual Information to Minimize Data Annotation in Human Activity Recognition

A difficulty in human activity recognition (HAR) with wearable sensors is the acquisition of large amounts of annotated data for training models using supervised learning approaches. While collecting raw sensor data has been made easier with advances in mobile sensing and computing, the process of data annotation remains a time-consuming and onerous process. This paper explores active learning as a way to minimize the labor-intensive task of labeling data. We train models with active learning in both offline and online settings with data from 4 publicly available activity recognition datasets and show that it performs comparably to or better than supervised methods while using around 10% of the training data. Moreover, we introduce a method based on conditional mutual information for determining when to stop the active learning process while maximizing recognition performance. This is an important issue that arises in practice when applying active learning to unlabeled datasets.
KISS+ for Rapid and Accurate Pedestrian Re-Identification

Pedestrian re-identification (Re-ID) is a very challenging and unavoidable problem in the field of multi-camera surveillance in smart transportation. Among many ways to solve this problem, keep it simple and straightforward (KISS) metric learning (KISSME) stands out since it has unbeatable advantages in running time while maintaining highly acceptable matching rate. It can be used to realize effective pedestrian Re-ID in an open world. Although it has achieved highly acceptable performance in some applications, it encounters a small sample size (S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) problem that causes too small eigenvalues of its covariance matrix, thus resulting in an instability issue. Its large eigenvalues are overestimated; while its small ones are underestimated. In order to solve this problem, we use an orthogonal basis vector to generate virtual samples to overcome the S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> problem. The resulting algorithm named KISS+ is experimentally shown to have the eigenvalues of its covariance matrix significantly larger than those of the original KISSME. In order to show its advantage in pedestrian Re-ID, this work uses multi-feature fusion to extract more discriminant features, and obtain a low-dimensional expression of features through dimension reduction. Experiments based on several well-known databases show that our method can improve the matching rate, while maintaining the advantage of fast computation. Compared with deep learning algorithms, our algorithm does not achieve their matching rate, but it is highly suitable for real-time pedestrian Re-ID of an open world due to its simplicity, easy operation and fast execution.
OMLML: a helpful opinion mining method based on lexicon and machine learning in social networks

Identification of users' polarities and mining their opinions in various areas, especially social networks, has become one of the popular and useful research fields. Although opinion mining and analyzing methods based on machine learning or lexicon have been useful, high training cost based on time or memory used, lack of enriched and complete lexicons, high dimensions of feature space and ambiguity in positive or negative detection of some sentences in these methods are examples of their downsides. To cope with these problems, in this paper a helpful method based on lexicon and machine learning called OMLML is proposed by using social networks. The main superiority of the proposed method compared to other methods is addressing these challenges simultaneously. According to the proposed method, the polarity of the opinions toward a target word is first determined using a method based on lexicon and textual features of words and sentences. Next, having mapped feature space into a 3-D vector, opinions are analyzed and classified based on a new machine learning method. The results of quantitative and qualitative experiments show that mapping data into a new space decreases training cost and that the performance of the proposed method than is acceptable particularly from the perspective of accuracy, F-measure and runtime.
Regexes are Hard: Decision-Making, Difficulties, and Risks in Programming Regular Expressions

Regular expressions (regexes) are a powerful mechanism for solving string-matching problems. They are supported by all modern programming languages, and have been estimated to appear in more than a third of Python and JavaScript projects. Yet existing studies have focused mostly on one aspect of regex programming: readability. We know little about how developers perceive and program regexes, nor the difficulties that they face. In this paper, we provide the first study of the regex development cycle, with a focus on (1) how developers make decisions throughout the process, (2) what difficulties they face, and (3) how aware they are about serious risks involved in programming regexes. We took a mixed-methods approach, surveying 279 professional developers from a diversity of backgrounds (including top tech firms) for a high-level perspective, and interviewing 17 developers to learn the details about the difficulties that they face and the solutions that they prefer. In brief, regexes are hard. Not only are they hard to read, our participants said that they are hard to search for, hard to validate, and hard to document. They are also hard to master: the majority of our studied developers were unaware of critical security risks that can occur when using regexes, and those who knew of the risks did not deal with them in effective manners. Our findings provide multiple implications for future work, including semantic regex search engines for regex reuse and improved input generators for regex validation.
An Authentication Information Exchange Scheme in WSN for IoT Applications

Recently, in IoT, wireless sensor network has become a critical technology with which many applications in industries and human life can achieve smart IoT control.However, for those daily applications using WSN technology, malicious users can capture the sensor nodes much easily since these wireless sensor nodes are usually deployed in easily touched places.Once this node captured attack occurs, wireless sensor network soon faces various security risks.In this paper, in order to resist node captured attack, we propose a novel authentication information exchange scheme in WSN, which is very different from the previous authentication researches.Our idea is to add an authentication information exchange scheme in previous authentication scheme but not propose new one.We develop this scheme based on the idea of the association scheme of Home GWN and local sensor nodes.In this study, HGWN should contact all local sensor nodes and meanwhile is responsible for performing an authentication information exchange scheme for resisting security risk.In order to prevent the attacker from guessing communication period between HGWN and the sensor, we also design a dynamic contacting mechanism.We give a detail discussion of this scheme and validate it by three ways, security evaluation, BAN logic and performance evaluation, which proves that our authentication information exchange scheme can achieve security features and goals.
Measuring the Performance Impact of Using the Microsoft HoloLens 1 to Provide Guided Assembly Work Instructions

Abstract The human performance benefits of four types of guided assembly instructions, including the Microsoft HoloLens 1, were analyzed in the context of a realistic assembly task. Several studies have confirmed the benefits of using augmented reality (AR) work instructions over traditional digital or paper instructions, but few have compared the effects of different AR hardware, including head-mounted displays, for complex assembly tasks. Participants completed a mock wing assembly task using the Microsoft HoloLens 1, and completion time, error count, and net promoter score (NPS) were recorded. These data were compared to data from previous research which employed desktop model-based instructions (MBIs), tablet MBI, and tablet AR instructions for the same task. The use of HoloLens AR instructions led to time saving of 16% over the tablet AR instructions. HoloLens users also had lower error rates than non-AR users. Despite the performance benefits of the HoloLens AR instructions, this condition had a lower NPS than the tablet AR group. The qualitative data showed that some users thought the HoloLens device was uncomfortable and that the tracking was not always exact. Although the users favored the tablet AR condition, the HoloLens condition had significantly faster assembly times. The authors recommend using the HoloLens for complex guided assembly instructions with minor changes, such as allowing the user to toggle the AR instructions on and off at will. The results of this paper can help manufacturing stakeholders understand the benefits of different AR technologies for manual assembly tasks.
Bone cancer detection using machine learning techniques

Bone cancer is one of the life threatening diseases which may cause death to many individuals. There must be an accurate detection and classification system available to diagnose bone cancer at early stage. Early detection of cancer seems to be the important factor in increasing the chance of cancer patient survival. Classification of cancer is one of the most challenging tasks in clinical finding and diagnosis. This work elaborates different machine learning techniques for tumor detection and classification. Machine Learning has a vast area of research, out of which medical image processing is significant area of work. In medical diagnosis like ulcer, fracture, tumor etc image processing made the work easier in finding the exact cause and best possible solution. In this work, bone Computed Tomography (CT) dataset in digital Imaging and communication in medicine (DICOM) format are used. Machine learning techniques applied on medical images for abnormality detection. It is observed that satisfactory level of improvement has been achieved by applying Machine Learning techniques. In this work different machine learning techniques for classification are elaborated.