Reviewerly/exHarmony huggingface dataset:
Predictive model for multistage cyber-attack simulation

Abstract Adoption of information and communication technologies (ICT) in railway has improved the reliability, maintainability, operational efficiency, capacity as well as the comfort of passengers. This adoption introduces new vulnerabilities and entry points for hackers to launch attacks. Advanced cybersecurity threats with automated capabilities are increasing in such sectors as finance, health, grid, retail, government, telecommunications, transportation, etc. These cyber threats are also increasing in railways and, therefore, it needs for cybersecurity measures to predict, detect and respond these threats. The cyber kill chain (CKC) model is a widely used model to detect cyber-attacks and it consists of seven stages/chains; breaking the chain at an early stage will help the defender stop the adversary’s malicious actions. Due to lack of real cybersecurity data, this research simulates cyber-attacks to calculate the attack penetration probabilities at each stage of the cyber kill chain model. The objective of this research is to predict cyber-attack penetrations by implementing various security controls using modeling and simulation. This research is an extension of developed railway defender kill chain which provides security controls at each stage of CKC for railway organizations to minimize the risk of cyber threats.
An information-rich sampling technique over spatio-temporal CNN for classification of human actions in videos

We propose a novel video sampling scheme for human action recognition in videos, using Gaussian Weighing Function. Traditionally in deep learning-based human activity recognition approaches, either a few random frames or every kth frame of the video is considered for training the 3D CNN, where k is a small positive integer, like 4, 5, or 6. This kind of sampling reduces the volume of the input data, which speeds-up the training network and also avoids overfitting to some extent, thus enhancing the performance of the 3D CNN model. In the proposed video sampling technique, consecutive k frames of a video are aggregated into a single frame by computing a Gaussian-weighted summation of the k frames. The resulting frame preserves the information in a better way than the conventional approaches and experimentally shown to perform better. In this paper, a 3-Dimensional deep CNN is proposed to extract the spatio-temporal features and follows Long Short-Term Memory (LSTM) to recognize human actions. The proposed 3D CNN architecture is capable of handling the videos where the camera is placed at a distance from the performer. Experiments are performed with KTH, WEIZMANN, and CASIA-B Human Activity and Gait datasets, whereby it is shown to outperform state-of-the-art deep learning based techniques. We achieve 95.78%, 95.27%, and 95.27% over the KTH, WEIZMANN, and CASIA-B human action and gait recognition datasets, respectively.
Design and Implementation of a General Chess Game System Client Based on Electron Framework

The traditional chess game system is usually designed for a single chess type, which has low versatility and can not be well compatible with different chess type. Based on this problem, a general chess game system based on electron framework is designed and implemented in JavaScript language. Through two typical game systems, Surakarta and Amazons, the universality and expansibility of the platform are verified. The system is divided into two parts: client and server. This paper mainly introduces the design and implementation of client.
EEG as a tool to measure cognitive load while playing Sudoku: A preliminary study

Cognitive load or mental workload in human beings is an important parameter associated with the task being performed. The level of task and the learning curve for any task has a certain cognitive load or mental workload. Apart from causing stress and mental exhaustion, increase in cognitive load beyond a critical limit may affect the performance on the end task. There is a need to explore noninvasive and non- intrusive physiological means of measuring cognitive load to identify the subjective performance and well been. This paper discusses identification of EEG as one of the means, identification of suitable EEG frequency bands and spatial locations to assess the cognitive load based on available literature and also to demonstrate measured load based on an experimental study performed using a commonly played Sudoku game.
Improving Accuracy of Key Information Acquisition for Social Media Text Summarization

The attention-based sequence-to-sequence (Seq2Seq) model for abstractive summarization has achieved good performance, but it still suffers from generating inaccurate information and repetitive content when applied to noisy social media text. To address these issues, we propose a novel model that includes a feature extractor and a semantic enhancer. In our model, the feature extractor produces feature vectors that contain key information, filtering out noise in the source text. The semantic enhancer takes the concatenation of the feature vectors and the encoder hidden states as input to learn long-range dependencies, further improving semantic relevance and reducing repetition. Experimental results on a Chinese social media dataset show that our model can capture the key information from noisy text and achieve improvement as compared to state-of-the-art baseline models.
Social Sentiment Analysis Using Fuzzy Logic: Addressing context effect on sentiment analysis by fuzzy lexicon dynamism

Fuzzy Logic revolution started during 1965 by Prof Lotfi A. Zadeh [1], [2] have evolved uncertainty and indeterminacy analytics forever. Sentiment analysis of published content on social networks is an essential key to analyze and predict social engagement polarities determined by provider's attitude and viewpoint toward a subject, being a profile, topic or single post. This paper uses precious works of Prof Lotfi A. Zadeh to propose a quantitative sentiment calculation method by introducing fuzziness and context dynamism on language-specific lexicons found on published short informal textual content on three holistic contexts of owner, user and the post itself.
Implicit Runge-Kutta Methods for Accelerated Unconstrained Convex Optimization

Accelerated gradient methods have the potential of achieving optimal convergence rates and have successfully been used in many practical applications.Despite this fact, the rationale underlying these accelerated methods remain elusive.In this work, we study gradient-based accelerated optimization methods obtained by directly discretizing a second-order ordinary differential equation (ODE) related to the Bregman Lagrangian.We show that for sufficiently smooth objectives, the acceleration can be achieved by discretizing the proposed ODE using s-stage q-order implicit Runge-Kutta integrators.In particular, we prove that under the assumption of convexity and sufficient smoothness, the sequence of iteration generated by the proposed accelerated method stably converges to the optimal solution at a rate of O((1 -Cp,q • µ L ) N N -p ), where p ≥ 2 is the parameter in the second-order ODE and Cp,q is a constant depending on p and q.Several numerical experiments are given to verify the convergence results.
A Military Named Entity Recognition Method Combined with Dictionary

Military named entity recognition is the basis of the military intelligence analysis and operational information service. In order to solve the problems of inaccurate word segmentation, various forms of existence and the lack of corpus in military texts, the author proposes a method of military named entity recognition by combining with dictionaries. By constructing authoritative dictionary in the military field, and taking advantage of Bi-directional Long Short-Term Memory (BiLSTM) neural network in dealing with the wide range of contextual information, the Dic+ BiLSTM-CRF named entity recognition model was constructed. The experimental results on the tagged military text corpus show that the extraction effect of this method is further improved than the traditional method.
Using Visual Intelligence to Automate Maintenance Task Guidance and Monitoring on a Head-mounted Display

Maintenance is a required process in many commercial domains. Commonly, paper-based instruction manuals are digitised to improve accessibility to maintenance information. Among the different forms of digital media, augmented reality (AR) combined with machine learning has the potential to provide real-time user guidance and interaction. In this paper, we present an Augmented Reality Visual Intelligence (ARVI) framework, which combines visual perception with cognitive task reasoning to monitor user performance and provide contextualised guidance for maintenance tasks. The framework is implemented in Microsoft Hololens for a wheel removal operation. The implemented software can recognise aspects of the environment, objects and a user's hands, as well as infer the task being completed. The evaluation of visual perception models for object detection using a single task video for training obtained relatively high F1 scores, and a small-scale usability study reported some positive feedback on the application development.
A Generalized Densely Connected Encoder-Decoder Network for epithelial and stromal regions segmentation in histopathological images

Identification of epithelial and stromal regions by a computerized system is quite challenging due to their irregular shape and size in histopathological images. Nowadays, although convolutional neural networks(CNNs) have significantly push forward this field, the typical use of CNNs in histopathological images is to classify an image block into a corresponding category. It fails to enable a better adherence of class boundaries due to the trade-off between networks' localization accuracy and input context. Larger blocks require more pooling layers that reduce localization accuracy, while tiny blocks allow networks to see only a small amount of context. So in this paper, we propose a generalized densely connected encoder-decoder network to deal with this problem. The main idea behind our network is captured in dense skip connections to compensate for resolution loss induced by pooling layers. So our network can have a large amount of context as input without losing localization accuracy. Finally, we show that the proposed network can outperform U-Net on Stanford Tissue Microarray Database without any further post-processing module or pretraining. Moreover, due to smart construction of the model, our approach has much fewer parameters than currently published best entries for this dataset. This means our approach is much faster than currently published entries. Segmentation of a $512\times 512$ image only takes 34ms on an Nvidia Tesla P100 GPU.
Cyber Security Aspects of Virtualization in Cloud Computing Environments

Cybercrime continues to emerge, with new threats surfacing every year. Every business, regardless of its size, is a potential target of cyber-attack. Cybersecurity in today's connected world is a key component of any establishment. Amidst known security threats in a virtualization environment, side-channel attacks (SCA) target most impressionable data and computations. SCA is flattering major security interests that need to be inspected from a new point of view. As a part of cybersecurity aspects, secured implementation of virtualization infrastructure is very much essential to ensure the overall security of the cloud computing environment. We require the most effective tools for threat detection, response, and reporting to safeguard business and customers from cyber-attacks. The objective of this chapter is to explore virtualization aspects of cybersecurity threats and solutions in the cloud computing environment. The authors also discuss the design of their novel ‘Flush+Flush' cache attack detection approach in a virtualized environment.
A Spray Path Planning Algorithm based on 3D Point Cloud

Robotic spray path planning for complex surface has always been a challenging problem, especially when it comes to flexible manufacturing system. This paper presents a new method to plan the spray path based on the point cloud obtained by vision sensors. In order to select the direction of slice adaptively, a method based on principal component analysis (PCA) is adopted to calculate the dominant eigenvector of point cloud. The intersection algorithm is also discussed to obtain points for constructing spraying path. The normal vector calculation and sorting problem of slice points are also solved. The experimental results show that the proposed path planning method can automatically generate the spraying path according to the surface of spray target. This method can also be extended to other applications.
Related Tasks Can Share! A Multi-task Framework for Affective Language

Expressing the polarity of sentiment as ‘positive’ and ‘negative’ usually have limited scope compared with the intensity/degree of polarity. These two tasks (i.e. sentiment classification and sentiment intensity prediction) are closely related and may offer assistance to each other during the learning process. In this paper, we propose to leverage the relatedness of multiple tasks in a multi-task learning framework. Our multi-task model is based on convolutional-Gated Recurrent Unit (GRU) framework, which is further assisted by a diverse hand-crafted feature set. Evaluation and analysis suggest that joint-learning of the related tasks in a multi-task framework can outperform each of the individual tasks in the single-task frameworks.
Integrated Exploit Kit for Web Application

In this period, data and information became the most important things to be protected in the organization. Unfortunately, based on SANS 2017 [2], not only small but also large-scale organizations suffered from the incident. That phenomenon happened because of increasing vulnerability, which was not handled carefully. On the other hand, exposed vulnerability increases the risk of assets such as data and information so it needs to be fixed as soon as possible. As a security engineer team in the organization, doing vulnerability identification took time and some time produced many false positives. This paper proposed a solution to decrease false positive in vulnerability identification result and fasten its process by integrating vulnerability identification tool as an exploit kit. In the end, our solution can reduce vulnerability identification time by 50% for two targets and increase vulnerability identification certainty by using manual analysis and proof of concept feature.
COMPARATIVE ANALYSIS OF IMAGE RETRIEVAL TECHNIQUES IN CYBERSPACE

Purpose: With the popularity and remarkable usage of digital images in various domains, the existing image retrieval techniques need to be enhanced. The content-based image retrieval is playing a vital role to retrieve the requested data from the database available in cyberspace. CBIR from cyberspace is a popular and interesting research area nowadays for a better outcome. The searching and downloading of the requested images accurately based on meta-data from the cyberspace by using CBIR techniques is a challenging task. The purpose of this study is to explore the various image retrieval techniques for retrieving the data available in cyberspace. &#x0D; Methodology: Whenever a user wishes to retrieve an image from the web, using present search engines, a bunch of images is retrieved based on a user query. But, most of the resultant images are unrelated to the user query. Here, the user puts their text-based query in the web-based search engine and compute the related images and retrieval time.&#x0D; Main Findings: This study compares the accuracy and retrieval-time of the requested image. After the detailed analysis, the main finding is none of the used web-search engines viz. Flickr, Pixabay, Shutterstock, Bing, Everypixel, retrieved the accurate related images based on the entered query. &#x0D; Implications: This study is discussing and performs a comparative analysis of various content-based image retrieval techniques from cyberspace.&#x0D; Novelty of Study: Research community has been making efforts towards efficient retrieval of useful images from the web but this problem has not been solved and it still prevails as an open research challenge. This study makes some efforts to resolve this research challenge and perform a comparative analysis of the outcome of various web-search engines.
Performance Comparisson Human Activity Recognition Using Simple Linear Method

Human activity recognition (HAR) with daily activities have become leading problems in human physical analysis. HAR with wide application in several areas of human physical analysis were increased along with several machine learning methods. This topic such as fall detection, medical rehabilitation or other smart appliance in physical analysis application has increase degree of life. Smart wearable devices with inertial sensor accelerometer and gyroscope were popular sensor for physical analysis. The previous research used this sensor with a various position in the human body part. Activities can classify in three class, static activity (SA), transition activity (TA), and dynamic activity (DA). Activity from complexity in activities can be separated in low and high complexity based on daily activity. Daily activity pattern has the same shape and patterns with gathering sensor. Dataset used in this paper have acquired from 30 volunteers. Seven basic machine learning algorithm Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, Gradient Boosted and K-Nearest Neighbor. Confusion activities were solved with a simple linear method. The purposed method Logistic Regression achieves 98% accuracy same as SVM with linear kernel, with same result hyperparameter tuning for both methods have the same accuracy. LR and SVC its better used in SA and DA without TA in each recognizing.
Active Player Detection in Handball Videos Using Optical Flow and STIPs Based Measures

In handball videos recorded during the training, multiple players are present in the scene at the same time. Although they all might move and interact, not all players contribute to the currently relevant exercise nor practice the given handball techniques. The goal of this experiment is to automatically determine players on training footage that perform given handball techniques and are therefore considered active. It is a very challenging task for which a precise object detector is needed that can handle cluttered scenes with poor illumination, with many players present in different sizes and distances from the camera, partially occluded, moving fast. To determine which of the detected players are active, additional information is needed about the level of player activity. Since many handball actions are characterized by considerable changes in speed, position, and variations in the player's appearance, we propose using spatio-temporal interest points (STIPs) and optical flow (OF). Therefore, we propose an active player detection method combining the YOLO object detector and two activity measures based on STIPs and OF. The performance of the proposed method and activity measures are evaluated on a custom handball video dataset acquired during handball training lessons.
Sounds of Infinity: Playful Illusions with Performative Interaction

Sounds of Infinity is an interactive, low-resolution lighting display that portrays a magnified variation of the infinity mirror. Developed for an outdoor light and music festival, the installation provides a retro-futuristic experience for audiences and explores how playful interactions might impact the behaviour of people in public spaces. Using multiple layers of LED lights, the concept enhances the infinity mirror illusion with a variety of audio-to-visual effects to create a tunnel of interactive light visuals. Due to its intuitive design with sound input, Sounds of Infinity also allows for open exploration of the body to produce sound through voice and movements. It illustrates the timeless quality of light and sound to promote social harmony, connecting and engaging people in collaborative, fun and expressive play.
Arbitrary-Oriented Ship Detection Based on Rotation Region Locating Networks in Large Scale Remote Sensing Images

Although deep learning has dominated the ship detection domain, it still has two challenges: arbitrary-oriented densely arranged ships cause detection omissions and large scale image contains redundant areas. This paper proposes an effective convolutional neural network framework for arbitrary-oriented ship detection in large scale and complex scenes. In this framework, we propose Cumulative Feature Pyramid Networks for multi-receptive-field feature fusion, which enhances high-level semantic information at all scales. Based on the outputs of Region Proposal Networks, Rotation Region Locating Network predicts rotation bounding box of arbitrary-oriented ships and adopts rotation intersection over union to avoid the effect of ship dense arrangement. For large scale scenes, No-Ship Area Suppression uses OTSU algorithm to generate binary mask and then filter out non-ship regions to reduce redundant computations. Additionally, we firstly build a remote sensing image dataset for ship detection, which contains 4237 images and over 21200 ships. Experimental comparisons with the state-of-the-art approaches validate the effectiveness, accuracy and robustness of the proposed method.
A New Smooth Approximation to the Zero One Loss with a Probabilistic Interpretation

We examine a new form of smooth approximation to the zero one loss in which learning is performed using a reformulation of the widely used logistic function. Our approach is based on using the posterior mean of a novel generalized Beta-Bernoulli formulation. This leads to a generalized logistic function that approximates the zero one loss, but retains a probabilistic formulation conferring a number of useful properties. The approach is easily generalized to kernel logistic regression and easily integrated into methods for structured prediction. We present experiments in which we learn such models using an optimization method consisting of a combination of gradient descent and coordinate descent using localized grid search so as to escape from local minima. Our experiments indicate that optimization quality is improved when learning metaparameters are themselves optimized using a validation set. Our experiments show improved performance relative to widely used logistic and hinge loss methods on a wide variety of problems ranging from standard UC Irvine and libSVM evaluation datasets to product review predictions and a visual information extraction task. We observe that the approach is as follows: (1) more robust to outliers compared to the logistic and hinge losses; (2) outperforms comparable logistic and max margin models on larger scale benchmark problems; (3) when combined with Gaussian–Laplacian mixture prior on parameters the kernelized version of our formulation yields sparser solutions than Support Vector Machine classifiers; and (4) when integrated into a probabilistic structured prediction technique our approach provides more accurate probabilities yielding improved inference and increasing information extraction performance.