Reviewerly/exHarmony huggingface dataset:
Call to Action: Mobilizing Community Discussion to Improve Information-Sharing About Vulnerabilities in Industrial Control Systems and Critical Infrastructure

Vulnerability management remains a significant challenge for organizations that handle critical infrastructure worldwide. Hallmark cyber-physical incidents with disruptive and destructive capabilities like Stuxnet (2010) and Triton (2017) have exploited known vulnerabilities in information technology (IT) and operational technology (OT) assets throughout the attack lifecycle. However, the global critical infrastructure security community is still nascent in the field of industrial control systems (ICS) vulnerability management, especially in information-sharing. While their counterparts in IT security have spent years elaborating multiple resources to track and disseminate information about known vulnerabilities, the ICS community lacks specialized mechanisms for knowledge-sharing. Multiple challenges exist when addressing this issue: a general lack of awareness about ICS cybersecurity, the need to consider multiple industry sectors and unique network architectures, and the need to find a balance between protecting and releasing sensitive information regarding critical infrastructure organizations or proprietary vendor knowledge. Through a multiphase research initiative based on the user-centered design process, we intend to test and evaluate the feasibility and effectiveness of various information-sharing platform designs for streamlining the discussion of ICS vulnerabilities. In the first phase of this research, we surveyed ICS and critical infrastructure security stakeholders to gain insight into the range of cogent, shared, and divergent views of the community relating to the need for specialized resources to share information about ICS vulnerabilities. We then evaluated what these different perspectives imply for the adoption and success of certain information-sharing platform frameworks. Finally, utilizing these insights, we demonstrated possible alternative paths forward for addressing the challenge of sharing information about ICS vulnerabilities to keep critical infrastructure safe.
Informing Hybrid System Design in Cyber Security Incident Response

Computer security incident response is a complex socio-technical environment that provides first line of defense against network intrusions, but struggles to obtain and keep qualified analysts at different levels of response. Practical approaches have focused on the larger skillsets and myriad supply channels for getting more qualified candidates. Research approaches to this problem space have been limited in scope and effectiveness, and may be partially or completely removed from actual security operations environments. As low-level incident response (IR) activities move towards automation, context-based research may provide valuable insights for developing hybrid systems that can both execute IR tasks and coordinate with human analysts. This paper presents insights originating from qualitative research with the analysts who currently perform IR functions, and discusses challenges in performing contextual inquiry in this setting. This article also acts as the first in a series of papers by the authors that translate these findings to hybrid system requirements.
A Framework to Predict Failures for Ground Tests on Aircrafts

The failures (known in the terminology of the Airbus DS Company as "incidences") in the test process of an aircraft create delays and costs for the company. Our team from the Electronic Technology Department (Spain) worked with the Airbus DS Company and developed a framework for incidence prediction from a data mining process. Specifically, the framework is designed for predicting the incidences generated in the ground testing process of the A400M aircraft.
Blockchain and Trust: A Practice-Based Inquiry

Blockchain technologies, such as the distributed cryptocurrency Bitcoin, present us with a vast array of possible applications. They promise to fundamentally transform traditional systems of managing property, conducting governance, organising smart devices, and establishing online identities. At the same time, few large scale real-world applications of the technology exist apart from Bitcoin. This makes designing Blockchain systems difficult, since there are few guidelines and frameworks available for interaction designers. Furthermore, due to the allegedly disruptive nature of these systems, user studies and observations based on current data are of limited utility. In this paper, we propose and discuss a design methodology combining practice-based research methods with elements of speculative design. In so doing we focus on one key aspect of Blockchain systems: trust. We thus want to show how practice-based methods can inform blockchain designs while providing an elucidative conceptualisation of the key category of trust.
Improved Algorithm for Circuit Diagram Image Recognition

This paper improves the traditional algorithm for circuit diagram image recognition, which identifies each component as well as the topological relationship over an entire circuit diagram. This paper uses generalized nodes as the basis for analysis of the circuit connection relationship, and compares recognition speed and accuracy of the proposed method against the traditional recognition algorithm for image recognition of printed and handdrawn circuit diagrams. The comparison results show that the algorithm proposed in this paper is superior to the traditional algorithm.
A Study of Interrelation Between Ratings and User Reviews in Light of Classification

Natural language processing (NLP) is a broad area of study where human language and their emotion can be characterized and analyzed by computer. Types of NLP problems can be, but not limited to, categorization, classification, analytical, etc. A major application of NLP is at sentiment analysis of rich texts. In this study, we have worked on sentiment analysis and categorization aspects of NLP. Here, we have performed analysis, lemmatization, sampling, and classification on Yelp text reviews, which is an open-source dataset, and then compared the results of the different types of classifiers. Categorization of different texts has been considered as a response of classification problems. After validation, we have achieved a significant level of robustness in these methods. An attempt was made to study NLP classification problems under various situations.
Research on Path Planning of Mobile Robot Based on Improved A* in Special Environment

Aiming at the problems of A* algorithm in a large environment with density of obstacles, such as large memory overheads, long calculation times, an novel method based on the fusion of static weight method and jump point search is proposed in this paper. Static weight method improves heuristic function, then the search space is limited and brute force search is reduced. Jump point search filters key points, then unnecessary nodes are reduced and search speed is accelerated The experimental results demonstrate that compared with traditional A* algorithm, the number of invisited nodes is reduced, and the search speed is accelerated. Therefore, the proposed algorithm can meet the requirements of fast path finding in special environments.
Low-Power Floating-Point Adaptive-CORDIC-Based FFT Twiddle Factor on 65-nm Silicon-on-Thin-BOX (SOTB) With Back-Gate Bias

In this brief, a silicon-on-thin-BOX (SOTB) implementation of single-precision floating-point fast-Fourier-transform (FFT) twiddle factor (TF) is presented. The architecture of the proposed TF is developed based on the adaptive method of the coordinate rotation digital computer (CORDIC) algorithm. The 65-nm SOTB technology was chosen because of its ultra-low-power advantage. Furthermore, the back-gate bias technique can be applied on an SOTB chip to adjust the operation for high-performance or low-power requirement. The layout of the SOTB 65-nm TF core is about 22869 gate-count on the die area of 86721 μm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The measurement results show that the core reached its highest operating frequency of 55 MHz at the 1.2-V supply voltage (VDD) with the forward back-gate bias (FBB) 1.5 V. The power and energy consumptions at this point were 1.54 mW and 27.91 pJ/cycle, respectively. The lowest operating VDD was at 0.5 V with the FBB 0.5 V. In the standby mode, when the clock-gating technique was deployed, the leakage current can be reduced to 0.4 nA at the 0.4 V VDD and -2.5-V reverse back-gate bias (RBB).
Consensus-Based Human-Agent Interaction Model for Emotion Regulation in ASD

This paper presents a human-agent interaction framework for emotional regulation through emotional interaction with a virtual agent that uses a consensus-based algorithm. This model-based framework provides a parameterized and dynamic approach for emotional interaction with an agent that can be applied to social robot-based therapies and virtual agent-based socio-emotional interaction studies. The extended goal of this study is to develop a human-robot interaction framework that can implement emotion regulation (ER) for children with Autism spectrum disorder (ASD). This paper describes the algorithmic approach for the design of this framework, as well as a user study to evaluate its effectiveness as a tool to provide emotional interaction and emotion regulation to children with ASD.
An Interactive Visualization System for Large Sets of Phase Space Trajectories

Abstract We introduce a visual analysis system with GPU acceleration techniques for large sets of trajectories from complex dynamical systems. The approach is based on an interactive Boolean combination of subsets into a Focus+Context phase‐space visualization. We achieve high performance through efficient bitwise algorithms utilizing runtime generated GPU shaders and kernels. This enables a higher level of interactivity for visualizing the large multivariate trajectory data. We explain how our design meets a set of carefully considered analysis requirements, provide performance results, and demonstrate utility through case studies with many‐particle simulation data from two application areas.
Chinese spam filtering based on Stacked Denoising Autoencoders

Abstract Aimed at the problem that the traditional feature selection method extracts the feature items and the filtering accuracy is degraded in the Chinese spam filtering process, this paper proposes a Chinese spam filtering method based on Stacked Denoising Autoencoder (SDA). Firstly, use the Continuous Bag-of-Words (CBOW) model training the Word2vec tool set for the processed corpus to transform word segments into vectors; the inputs are the word vectors; then apply the Stacked Denoising Autoencoder (SDA) to effectively extract the features text in unsupervised learning. Finally, the improved softmax classifier is used for regression classification. The test was carried out on the TREC06C dataset, the experimental results show that compared with Bayesian model, KNN classification algorithm and traditional Stacked Denoising Autoencoder, the accuracy, precision, recall, and f1 score of the method reached 93.5%, 94.8%, 92% and 93.2%, and had better dichotomous effect and robustness in the application.
How to Observe Users’ Movements in Virtual Environments: Viewpoint Control in a Power Wheelchair Simulator

Objective We describe a networked, two-user virtual reality (VR) power wheelchair (PWC) simulator system in which an actor (client) and an observer (clinician) meet. We then present a study with 15 observers (expert clinicians) evaluating the effect of three principal forms of viewpoint control (egocentric-egomotion, egocentric-tethered, and client-centric) on the observer’s assessment of driving tasks in a virtual environment (VE). Background VR allows for the simulation and assessment of real-world tasks in a controlled, safe, and repeatable environment. Observing users’ movement behavior in such a VE requires appropriate viewpoint control for the observer. The VR viewpoint user interface should allow an observer to make judgments equivalent or even superior to real-world situations. Method A purpose-built VR PWC simulator was developed. In a series of PWC driving tasks, we measured the perceived ease of use and sense of presence of the observers and compared the virtual assessment with real-world “gold standard” scores, including confidence levels in judgments. Results Findings suggest that with more immersive techniques, such as egomotion and tethered egocentric viewpoints, judgments are both more accurate and more confident. The ability to walk and/or orbit around the view significantly affected the observers’ sense of presence. Conclusion Incorporating the observer into the VE, through egomotion, is an effective method for assessing users’ behavior in VR with implications for the transferability of virtual experiences to the real world. Application Our application domain serves as a representative example for tasks where the movement of users through a VE needs to be evaluated.
A Sentiment Classification Model Based on Multiple Multi-classifier Systems

With the fast development of the Internet, especially the development of the Web technique, more and more people express their views on the Internet. The content of these views involve various aspects of our life, such as books, movies, musics, policies, commodities, technical services, etc. Since these views are usually subjective and possess great potential commercial and social value, the study of sentiment analysis is followed. At the early stage of sentiment analysis, researchers used lexicon based method to solve sentiment analysis problem. With the improvement in machine learning and Natural Language Processing technology, machine learning based method are becoming more and more popular in the field of sentiment analysis. In this study, we propose an ensemble sentiment classification model based on three multi-classifier systems. The proposed model uses majority voting as an ensemble method of multiple multi-classifier systems. The effectiveness of the classification model is verified on five public datasets. Experimental results show that the proposed model is superior to traditional sentiment classification methods and simple multi-classifier systems in the performance of classification accuracy. By using the sentiment classification model proposed in this paper, the accuracy improvement in different domains also show that our proposed model has certain generalization ability.
News2meme: An Automatic Content Generator from News Based on Word Subspaces from Text and Image

Internet users engage in content creation by using various media formats. One of the most popular forms is the “internet meme”, which often depicts the general opinion about events with an image and a catchphrase. In this paper, we propose news2meme, a method for automatically generating memes from a news article, where we aim to match words and images efficiently. We approach this as two multimedia retrieval problems with the same input news text: (1) An image retrieval task where the output is a meme image; (2) A text retrieval task where the output is a catchphrase. These two outputs are combined to generate the meme for the news article. We represent texts and catchphrases as sets of word vectors through the word2vec representation. To handle images similarly, we extract sets of tags from the images using a deep neural network. These tags are then translated to word vectors in the same vector space through word2vec. Finally, we represent the intrinsic variability of features in a set of word vectors with a word subspace. Through word subspaces comparison, we can directly compare images and texts, making retrieval across media formats possible. A preliminary experiment was performed to evaluate our framework.
Utilizing a Vulnerable Software Package to Teach Software Security Design Analysis

As the number of threats and attacks to software systems increases, more attention is given to secure software engineering practices, such as secure coding and security testing. More abstract activities, such as security design analysis, require extensive security expertise from software engineers. Unfortunately, such knowledge is scarcely available, as it is an area that is both difficult to teach and learn. We developed a framework for teaching security design analysis, which is built around the hybrid flipped classroom and case study analysis. This paper enhances our framework by utilizing freely available vulnerable software packages as case studies for security design analysis. We illustrate the enhancement by using a mature vulnerable software package to construct a laboratory exercise dedicated to the security design analysis of threats originating from injection-based attacks. We provide guidance for the usage of our enhanced framework and outline a lab that can be utilized for a university course or a corporate training program dedicated to secure software engineering.
The use of Reflexive Approach in Distance Education of Students at Teacher-training Institutions

The article deals with the features of distance education in the system of teacher training. The aim of the work was to show the effectiveness of the use of reflective techniques in distance teaching students. It presents the arguments in favor of the need for the formation of skills and reflection as the basis for successful professional self-realization. Distance training on the Moodle platform used in the training of teachers, allowed us to use a variety of reflective techniques, including formative assessment, online forums, discussions and conferences, which increased the efficiency of independent work of students. Резу льтатами такого обу чения стали хор ошо р азвитые у большей части сту дентов (90-100%) у мения использовать в пр офессиональной деятельности активные методы обу чения, выстр аивать индивиду альные обр азовательные тр аектор ии для себя исвоих у чеников, пр именять пр офессиональные з нания на пр актике. The results of such training are well-developed skills of the majority of students (90-100%) to use active teaching methods in their professional activities, to build individual educational trajectories for themselves and their students, to apply professional knowledge in practice.
Simulation of Apple Picking Path Planning Based On Artificial Potential Field Method

This paper aims at the problem of picking path design for apple harvesting robot manipulator in dynamic and unstructured environmen, and according to the obstacle avoidance situation and non obstacle avoidance situation, the picking path design is achieved respectively. The non obstacle avoidance path is designed by the default path planning method and the artificial potential field method is used to avoid obstacles. By combining the characteristics of obstacles in the growth environment of fruit trees, the principle of two-dimensional potential application in artificial potential field is transferred to three-dimensional space, and a more smooth obstacle avoidance motion curve is obtained. The proposed picking path planning method is verified by Java graphical interface and MATLAB. The results show that picking path planning based on artificial potential field can effectively avoid obstacles and achieve timely and reliable picking.
Cyclic Shift Matrix—A New Tool for the Translation Matching Problem

For numerous applications in image registration, sub-pixel translation estimation is a fundamental task, and increasing attention has been given to methods based on image phase information. However, we have found that none of these methods is universal. In other words, for any one of these methods, we can always find some image pairs which will not be well matched. In this paper, by introducing the cyclic shift matrix (CSM), we present a new model for the translation matching problem and derive a least squares solution for the model. In addition, by repeatedly applying the CSM to the matching image, an iterative CSM method is proposed to further improve the matching accuracy. Furthermore, we show that the traditional phase-based matching algorithms can only achieve an exact solution when there is a cyclic shift relationship between the images to be matched. The proposed method is evaluated using simulated and real images and demonstrates a better performance in both accuracy and robustness compared with the state-of-the-art methods.
Comparison of contemporary evolutionary algorithms on the rotated Klee-Minty problem

The Rotated Klee-Minty Problem represents an advancement of the well-known linearly constrained Klee-Minty Problem that was introduced to illustrate the worst case running time of the Simplex algorithm. Keeping the linearity as well as the complexity of the original Klee-Minty Problem, the Rotated Klee-Minty Problem remedies potential biases with respect to Coordinate Search and turns out to be a suitable constrained test environment for randomized search heuristics. The present paper is concerned with the comparison of recent evolutionary algorithm variants for constrained optimization on this respective test bed. The considered algorithm variants include the most successful participants of the CEC Competition on Single Objective Real-parameter optimization and other selected strategies that are not directly applicable to the CEC test suite. Taking into account the diverse constraint handling approaches, the performance results of all search heuristics are interpreted. It turns out that most strategies that have been successful in the CEC competitions do have severe problems on the RKMP.
Neuron Image Synthesizer Via Gaussian Mixture Model and Perlin Noise

Microscopy imaging provides valuable information to neuroscientists. There are many different algorithms to analyze these images but due to an absence of ground truths, it is difficult to quantitatively compare the algorithm performance. Realistic synthetic images with known ground truths offer a means of assessing their performance. This approach has been successfully applied in several areas such as segmentation and classification. In this paper, we propose a synthesizer for neuron nucleus micro-environment using Gaussian mixture model (GMM) and Perlin noise function. Nucleus shapes are generated by spline interpolation of random points on elliptical shapes. The textures of the foreground (nuclei) and the background are generated via Perlin noise, and then assigned intensities generated by applying GMM to the real data. The cell orientations are also implemented via Perlin noise to mimic the behavior of the actual neuron cells.
Fast and Power-Analysis Resistant Ring Lizard Crypto-Processor Based on the Sparse Ternary Property

Ring Lizard (RLizard) is a quantum-resistant public-key cryptosystem based on the ideal lattice. RLizard uses a sparse ternary polynomial, which facilitates implementation with lower complexity. The Lizard scheme’s proposal for the National Institute of Standards and Technology’s post-quantum cryptography standardization included its reference hardware design using the sparse ternary property; however, in this paper, we present the RLizard crypto-processor with the improved processing speed and security level against power analysis attacks. By additionally utilizing unused values for each memory access in the conventional RLizard crypto-processor, the processing speed of the proposed RLizard crypto-processors can increase by a factor of two or up to four times. The implementation results with three different FPGA devices show that the area overhead is approximately 50–100 flip-flops (FFs) and 50–300 lookup tables (LUTs), occupying approximately 2%–3% of the total area. The vulnerability to power analysis attacks and the proposed countermeasures were also analyzed. The experimental results prove the vulnerability of unprotected implementation, and the implementation results show that the masking and hiding countermeasures additionally require approximately 50–120 FFs and 100–360 LUTs. In addition, our idea can be applied to other ideal-lattice-based cryptosystems using a sparse binary or ternary polynomial, such as NTRU and Round5.