Reviewerly/exHarmony huggingface dataset:
The Effect of Weighted Moving Windows on Security Vulnerability Prediction

Vulnerability prediction models strive to identify vulnerable modules in large software systems. Consequently, several vulnerability prediction approaches have been proposed to identify such susceptible units by using software metrics, historical data, and machine learning techniques. However, in spite of the key role seasonal trends of vulnerabilities play in estimating the resources needed for developing corrective measures, most of the proffered models fail to examine the trend, level, and seasonality of security vulnerability. To address this lacuna, this paper examines the statistical significance of the annual seasonal patterns and trends in vulnerability discovery using the weighted moving window. Our approach takes into account the chronological order within vulnerability data and assigns different weights of importance to projects in a window to effectively portray current security practices. Specifically, we used three regression-based models as vulnerability predictors for historical vulnerability data mined from five open-source applications offered by the Common Vulnerability Exposures and the National Vulnerability Database (CVE-NVD). In addition, we evaluate the performance and reliability of the models with symmetric mean absolute percent error (SMAPE). The preliminary results suggest that weighting the moving window based on Gaussian function yields improved accuracy and the recommended forecasting model is the robust regression.
Driver Assistance for Safe Navigation Under Unstructured Traffic Environment

This research work aims at developing a system using a vision sensor, which aids in detecting and warning driver about road anomalies on unstructured traffic environment like jaywalkers, two wheelers and other vehicles whose sudden entry into the drive region causing mishaps due to lack of attention by the driver while driving in unstructured traffic. Drive region on the road is predicted based on vanishing Point (VP) which is calculated based on camera calibration. AdaBoost machine learning algorithm with Local Binary Pattern (LBP) features is used to detect road vehicles. To remove false positives and improve detection and classification accuracy, Histogram of Oriented Gradients (HOG) features with Radial Basis Function (RBF) Support Vector Machine (SVM) is been developed. Visual alert of the type of the object that is entering and is in the drive region within the distance threshold of 15 meters from the vehicles is been developed and tested on road at accuracy of 0.73 F1 score.
Supply Chain and Logistics Management and an Open Door Policy Concerning Cyber Security Introduction

The need for integrity and reliability in the supply chain and logistics management operations has a well-established array of theoretical frameworks that guide organizations and managers in the field. However, what is missing is a comprehensive, established framework for logistics and supply chain cybersecurity. The emerging area of cyber-supply chain security continues to lack suitable models to help secure critical data and systems. In all industries and fields today, cybersecurity is no longer just an information technology issue. It is a business sustainability and business strategy issue. The human factors in the cyber-supply chain operations represent the actions or events when human error results in a successful hack or data breach. Today, protecting logistics and supply chain organizations from cyber and data security risks is no longer just an information technology employee function. Every organizational employee has a responsibility in the data protection process. Progressive organizations are ones that can create methods, policies, and approaches that encourage employees to play an active role in the information and cybersecurity process. This paper explores innovative approaches around open-door systems as a proactive data security and cybersecurity risk reduction organizational strategy. This research provides cyber-supply chain practitioners and scholars an array of concepts to help them understand and describe the dynamics of cyber-supply chain and logistics management data security vulnerabilities and opportunities for process improvements.
Re-sculpturing Semantic Web of Things as a Strategy for Internet of Things’ Intrinsic Contradiction

In this paper, based on annotating objects’ information and reasoning semantically with ontologies, the strategy of Semantic Web of Things (SWoT) for Internet of Things’ intrinsic contradiction is extended, and machine learning is introduced so that a novel hierarchical structure and the dynamic relationship between entities are proposed on SWoT. Specific contributions: (1) Establish of vertical and hierarchical framework of SWoT; (2) Definite on composite ontologies and (3) construct computational framework of Agent-based dynamic relationship model. And this paper proposes the essence that SWoT should be attributed to the interoperability between its various objects, which constructs inter-objects’ certain dynamic relationships.
Distribution-Free Testing of Linear Functions on ℝⁿ

We study the problem of testing whether a function f:ℝⁿ → ℝ is linear (i.e., both additive and homogeneous) in the distribution-free property testing model, where the distance between functions is measured with respect to an unknown probability distribution over ℝⁿ. We show that, given query access to f, sampling access to the unknown distribution as well as the standard Gaussian, and e > 0, we can distinguish additive functions from functions that are e-far from additive functions with O((1/e)log(1/e)) queries, independent of n. Furthermore, under the assumption that f is a continuous function, the additivity tester can be extended to a distribution-free tester for linearity using the same number of queries. On the other hand, we show that if we are only allowed to get values of f on sampled points, then any distribution-free tester requires Ω(n) samples, even if the underlying distribution is the standard Gaussian.
Survey of Progressive Era of Text Summarization for Indian and Foreign Languages Using Natural Language Processing

The last few years of Data Science definitely show the upward trend in growth of popularity, different industries which are effectively relating with data science & the transformation of world with e-commerce sites, social networking sites, travel aggregators, Google assistants. Here the need of text summarization comes in existence. This text summarization is a conception which actually deals with time saving and giving user the output with minimum text without changing its meaning. This approach is very impressive as the e-contents reading is very important aspect in many fields like academics, industries, hospitals, clinical notes, news papers, crime records, cyber security and many other. This paper shows the advancements which has initiated research for text summarization in many global and local languages. Text mining is coping up with many dimensions like text summarization, question generation, sentiment analysis, and translators. Natural Language Processing is one of the demanding areas now a day, which will definitely improve the human machine interaction.
Heuristic-based Multiple Mobile Depots Route Planning for Recharging Persistent Surveillance Robots

Persistent surveillance of a target space using multiple unmanned aerial vehicles (UAVs) has multiple applications such as geographical surveys, air quality monitoring, and security monitoring. The limited onboard battery capacity challenges the continuous operation in these applications of persistent robots. We consider the problem for recharging persistent robots using mobile depots. The mobile depots collectively compute a set of tours to recharge all persistent robots with the minimum total cost. Compared to other works, the persistent UAVs are not required to detour to a static depot for energy replenishment such as recharging or battery swapping. We formulate this problem as a Generalized Multiple Depots Travelling Salesman Problem (GMDTSP) on a complete graph. A heuristic-based algorithm Multiple Depots Random Select (RSMD) is proposed to solve the recharging problem efficiently. The RSMD has proved to have an analytical constant upper bound in the worst-case scenario. We also propose a post-processing heuristic (RSMD-IM) to improve the solution quality further. We demonstrate the efficiency and effectiveness of our algorithm via benchmark on multiple instances from TSPLIB and GTSPLIB. The simulation results show that RSMD and RSMD-IM will perform significantly faster than the state of the art heuristic solver LKH with a loss of about 10% of solution quality.
ZFP-V: Hardware-Optimized Lossy Floating Point Compression

Lossy floating point compression algorithms are critical components of reducing the cost and improving the performance of many modern applications, including machine learning and scientific computing. Data compression is widely used to reduce data storage requirements and transfer overhead, but traditional data-oblivious lossless compression schemes are very inefficient for floating point data. On the other hand, recently proposed lossy compression algorithms like ZFP and SZ achieve very high rates of compression while controlling the tolerable error margin. To the best of our knowledge, no efficient hardware implementation of ZFP exists yet, partially due to the inherently serial nature of the algorithm. In this paper, we present the design and implementation of ZFP-V, which identifies the serial portion of the ZFP algorithm and modifies it for more efficient hardware implementation. ZFP-V replaces the "group testing" part of ZFP with a variable-length header, which allows our hardware implementation to achieve up to 2x performance improvement compared to our best-effort hardware implementation of the original algorithm while using less on-chip resources, at a marginal reduction of compression ratio. We evaluate an OpenCL implementation of ZFP-V on an Intel Arria 10 FPGA using a variety of real-world scientific datasets, and show a single-pipeline throughput of 1 GB/s - 4 GB/s compression and 2 GB/s - 10 GB/s decompression on real-world datasets. Our implementation often outperforms a 32-thread software implementation on a high-end Intel Xeon CPU, and significantly outperforms a state-of-the-art FPGA implementation of SZ.
Escape Room como propuesta de gamificación en educación

The present paper approaches gamma classification as a learning strategy, presenting the benefits derived from its implementation in the classroom and specifying a gamma classification model such as the Escape Room. The objective of the research is to deepen and examine the advantages of gamification in the educational field, alluding to the need to present the content in an attractive and playful way. Specifically, this document presents a group gamification model such as the Escape Room. The methodology used is based on the analysis of secondary sources related to the phenomenon, through the review of scientific publications of impact. The results obtained make it possible to approach the subject of the study. The present text defines the concept of gamification, its origin, evolution and the multiple benefits derived from its application in the classroom. It also alludes to the need for methodological innovations to be in line with the student profile, making it necessary for teachers to explore new tools to increase motivation, responsibility and commitment in students. The Escape Room is also presented as an optimal resource for the teaching-learning process. Finally, conclusions are presented showing the importance of the teacher designing the tasks in advance knowing the singularities of the students to whom the intervention is addressed.
Novel Fuzzy System Identification: Comparative Study and Application for Data Forecasting

A novel fuzzy clustering technique for system identification based on the Takagi-Sugeno fuzzy inference is proposed. In this paper, the identification algorithm differs from others found in the literature, in the manner that the inference’s antecedents and consequents are built. Gaussian fuzzy sets with support on the cluster’s ordinal set of a determined α-level are unidimensional antecedents that have as a consequent an affine function, which recovers the attributes of the collected data. Two simulations examples are performed to compare to the new method: the comparisons are done with well-known algorithms in terms of the normalized root mean square goodness of the ﬁt measure, and the computational speed in the identiﬁcation process. For that purpose, Matlab System Identiﬁcation toolbox is used in order to consolidate a unique source of comparison. The method has been tested in the prediction ﬁeld, as part of a project supporting Brazilian local companies.
Algorithms and Rhetorical Inquiry: The Case of the 2008 Financial Collapse

Abstract Algorithms have never been more influential, yet our collective understanding of how they transform massive networks of cultural power has not kept pace. This is especially true when it comes to economic algorithms, which operate as black boxes largely inaccessible to the majority of citizens whose worlds they continuously reshape. This essay offers a rhetorical approach to reading algorithms—not only to challenge the positivism and mathematical realism that naïvely apotheosizes algorithms and algorithmic culture but more importantly to become critical informants, scholars who can open up these black boxes for fellow citizens, examine the hidden assumptions therein, and study how they actively transform our social-material worlds. The essay’s exemplar is the 2008 financial crisis and a little-known algorithm called the Li Guassian copula, which played a major role in the spread of subprime mortgages. I argue that this copula puts on spectacular display the power of algorithms as principles of composition—actants that materially expand our social collectives even as they marginalize human agency and practical judgment with forms of technological rationality that, in the case of the Li copula, concentrated the networks of structured finance around a single decision apparatus, rendering those networks both larger and, contra conventional wisdom, more fragile.
X-Ray Mammary Image Segmentation Based on Convolutional Neural Network

Medical images segmentation is a challenging research topic in the field of computer vision, especially when applied to breast cancer. X-ray examination is the best method to diagnose breast cancer at present, however, X-ray imaging used to diagnose mass is heavily dependent on the clinician’s experience. To address those problems, this paper implements an automated method for revealing mass in a mammary X-ray image based on a convolutional neural network. Traditional deep convolutional neural network and machine learning methods not only lead to poor performance, but also fail to make full use of the long-term dependence between pixels. According to the characteristics of X-ray images, we propose a novel convolutional neural network framework based on one of the most successful medical images segmentation frameworks, U-net. The experimental results show how to improve models by the use of the pre-trained encoder.
Colour Compression of Plenoptic Point Clouds Using Raht-Klt with Prior Colour Clustering and Specular/Diffuse Component Separation

The recently introduced plenoptic point cloud representation marries a 3D point cloud with a light field. Instead of each point being associated with a single colour value, there can be multiple values to represent the colour at that point as perceived from different viewpoints. This representation was introduced together with a compression technique for the multi-view colour vectors, which is an extension of the RAHT method for point cloud attribute coding. In the current paper, we demonstrate that the best-proposed RAHT extension, RAHT-KLT, can be improved by performing a prior subdivision of the plenoptic point cloud into clusters based on similar colour values, followed by a separation of each cluster into specular and diffuse components, and coding each component separately with RAHT-KLT. Our proposed improvements are shown to achieve better rate-distortion results than the original RAHT-KLT method.
Archaeology and computers: a long story in the making of modern archaeology

The growing success, for more than fifty years, of the scientific contribution of computer applications and quantitative methods in archaeology may be now reviewed and analyzed from different technological and sociological points of view. This examination allows us to appreciate the material importance of such contributions and how the community of specialists in computational archaeology should play a major role in the future of 21st-century archaeology.
Navigation Landmark Recognition and Matching Algorithm Based on the Improved SURF

The satellite measurement landmark information is a new autonomous navigation information source which can provide a new method to improve the accuracy of satellite autonomous orbit determination. The landmark image recognition and matching performance is one of the key factors affecting the measurement accuracy. Aiming at the application limitations of the traditional speeded up robust features (SURF) algorithm in image matching, an improved SURF algorithm is proposed combined with image preprocessing technique, Harris feature point detection method and mismatching point elimination technique to recognize and match the landmark. Harris feature point detection method can improve the feature points extraction stability and the robustness of affine transformation. The low pass filter is used to preprocess the image to reduce the influence of noise on the recognition. The relative distance method is used to eliminate the mismatching points. According to the satellite camera parameters, Bohai Bay, Zhaling Lake and Penghu Island are chosen as the images to be matched. Then the matching points and the matching accuracy are chosen as the indicators to verify the performance of the proposed algorithm. The simulation results indicate that the matching accuracy of the traditional SURF algorithm is over 70% and the matching accuracy of the improved SURF algorithm is over 90%.
A Method for UAV Tracking Target in Obstacle Environment

Aiming at the problem of autonomous tracking of moving target on the ground by UAV in the environment of dense obstacles, a real-time obstacle avoidance tracking method for UAV is proposed. In this method, Kalman filter is used to predict the state of the target, so that the UAV can track the target more effectively. The characteristics of concave obstacles with feasible area of depression and the problems caused by traditional genetic algorithm in practical application are analyzed. An improved Bayesian genetic algorithm is proposed in the process of UAV tracking. This method combines tracking and obstacle avoidance model, so that the UAV will not fall into the sag area and bypass the concave obstacles. The simulation results show that the proposed method can make UAV avoid obstacles in the process of tracking ground moving targets.
Performance of Tip Selection Schemes in DAG Blockchains

In this paper we investigate the impact of transaction validation of two tip selection mechanisms in DAG blockchains such as the tangle of IOTA on the performance of the consensus mechanism. The tip selection algorithm determines which prior transactions are validated by a transaction. With validating a tip a transaction is appended to the tangle. We present TangleSim, our simulator based on OMNeT++, which allows to evaluate the transaction validation time and the time a transaction will spend as a tip in DAG blockchains. We find that the weighted random walk selection can achieve a lower transaction validation time than the random tip selection algorithm in many cases.
An Improved Faster R-CNN Algorithm for Gesture Recognition in Human-Robot Interaction

Gesture recognition technology has been recognized as a communication method between human and robot systems. To our best knowledge, however, no study has been reported in open literature regarding the faster region-based convolutional neural network (Faster R-CNN) algorithm to improve the accuracy of gesture recognition. This paper proposes an improved Faster R-CNN algorithm that combines three key insights: (1) one can use Gaussian filter as image pre-processing method in order to remove image noise, (2) VGG16, compared with the residual network, is used as the feature extraction network of the improved Faster R-CNN algorithm to improve the gesture recognition accuracy, (3) five-fold cross-validation is used to evaluate the generalization performance of the algorithm. The experimental results show that an improved Faster R-CNN algorithm significantly improves mean average precision to 99.89%, which provides a better method for gesture recognition in human-robot interaction applications.
Pedestrian Detection and Behaviour Characterization for Video Surveillance Systems

Nowadays, to identify potentially dangerous situations created by pedestrians, performing video surveillance systems are more than necessary. The application presented in this paper is designed to automatically detect individual pedestrians and analyze and characterize their behavior. Based on this result, an informed security decision can be made. The implementation uses YOLO (You-Only-Look-Once) [1] to detect people in a real-time video stream and evaluates and characterizes their movement, based on data collected from the current video frame and a certain number of past video frames. A series of tests revealed that our system successfully detects both regular and irregular pedestrian trajectory type and also the type of movement: walk, run or stand. The processing speed of up to 77 FPS for YOLOv3-tiny and 21 FPS for YOLOv3 qualifies our solution for real-time operation in a surveillance system, if it is running on a computer platform equipped with a NVIDIA GPU with CUDA capabilities.
Time Optimal Trajectories for a Mobile Robot Under Nonsliding and Radius-of-Turn Constraints

Abstract The time-optimal path problem for a point mass mobile robot is considered. Given initial and target states, we seek the time optimal path subject to the following constraints: (1) A limitation on its maximal linear acceleration; (2) a speed-dependent nonsliding condition; and (3) a minimal radius of turn. The paper formulates and analyzes the time optimal path problem using standard optimal control formulation with extensive use of the classical Hodograph method. Based on the analysis, the time optimal path consists of five path primitives. Numerical solutions are obtained to support and illustrate the analysis.
Blind Adversarial Network Perturbations.

Deep Neural Networks (DNNs) are commonly used for various traffic analysis problems, such as website fingerprinting and flow correlation, as they outperform traditional (e.g., statistical) techniques by large margins. However, deep neural networks are known to be vulnerable to adversarial examples: adversarial inputs to the model that get labeled incorrectly by the model due to small adversarial perturbations. In this paper, for the first time, we show that an adversary can defeat DNN-based traffic analysis techniques by applying \emph{adversarial perturbations} on the patterns of \emph{live} network traffic.