Reviewerly/exHarmony huggingface dataset:
REPD: Source Code Defect Prediction As Anomaly Detection

In this paper, we present a novel approach to defect prediction within project source code. Since defect prediction datasets are typically imbalanced, and there are few defective examples, we treat defect prediction as anomaly detection. We present our Reconstruction Error Probability Distribution (REPD) model and compare it on five different datasets to five standardly used models: Gaussian Naive Bayes, Logistic regression, k-nearest-neighbors, decision tree, and SVM. For the main performance results we use F1-scores. Using statistical means, we show that our model produces significantly better results, improving F1-score up to 10.11%.
A constraint modeling framework for domain-specific languages

The growing usage of Domain Specific Modeling Languages (DSML) for architecture view frameworks induces a need for automatic verification of non-functional model properties like completeness and consistency. However, we argue that the high demand for tailored architecture view frameworks is not complemented by appropriate constraint specification facilities. OCL is a common language for defining modeling constraints, but industry user reports indicate that despite its accuracy, it is too complex to be adopted in industrial scale. Approaches that were proposed to simplify the use of OCL either operate on technical formalisms or lack tool support to express new, or more complex types of constraints that can be validated automatically on the model. To address this challenge, we present a constraint modeling framework for the specification and validation of constraints on DSMLs. A Constraint Modeling Language (CML) created based on this framework provides a high level constraint specification en- vironment by using extensible template implementations to enable the automatic validation in computer aided software engineering (CASE) tools. We evaluate the approach in different industry projects and observe that using the proposed framework enhances understandability and effectiveness of constraint specification.
Gaussian Forensic Detection using Blur Quantity of Forgery Image

For a design of the Gaussian forensic detection (GFD) in the altered digital images, this paper presents a feature vector that is defined as a blurring quantity by the window size of the Gaussian filtering. The window size is prepared ten types, and their blur quantity is computed by the Gaussian filtering, respectively. In the proposed scheme of the GFD, the defined 10-dim. feature vector of the image is trained in a SVM (Support Vector Machine) classifier for the Gaussian forensic detection of the forged images. In the experiment, the measured area under the curves (AUC) are above 0.9 from a classification point of view between the altered and Gaussian filtered image. Thus, the grade evaluation of the proposed method is rated as "Excellent (A)".
Voice Conversion of Synthesized Speeches Using Deep Neural Networks

Voice conversion is the transform technique of the individuality between source and target speakers. In the previous studies, we proposed the voice conversion using synthesized speeches based on formant or line spectral information. The suggested method used the piecewise linear transform function of formant or LSP(line spectral pairs) features on formant intervals. In this paper, we propose the conversion of the individuality between speakers using a deep neural network. Along with improvements in deep neural network research, end-to-end speech conversion methods have been proposed and the results are suitable for generating more natural utterances compared to the past. Among them, we explore the representative speech generation methods and propose the voice conversion system to use formant features as additional information for local and global conditioned deep neural networks.
Hybrid Cryptosystems for Protecting IoT Smart Devices with Comparative Analysis and Evaluation

There are limited numbers of reliable hybrid cryptosystems that can be used to protect IoT smart devices, specifically in smart cities, smart hospitals, smart homes, and industrial fields. Therefore, much related work has to be performed. The aim is to achieve more secure hybrid cryptosystem with high demanded performance. This paper investigates some recommended types of hybrid cryptosystems for protecting IoT smart devices from internet attacks. Then, a robust hybrid cryptosystem is proposed. These hybrid cryptosystems combine symmetric encryption algorithms such as TEA, XTEA, XXTEA, and asymmetric encryption algorithms such as RSA and ECC. They should have the capability to protect IoT smart devices from internet attacks. Since, they could efficiently achieve confidentiality, authenticity, integrity, and non-repudiation. Comparative analysis and evaluation are achieved. The analysis included the most important factors that have to be investigated in case of using lightweight ciphers to suit limited resources IoT smart devices. Among these factors are security level, memory size, power consumption, encryption time, decryption time, and throughput. Results show that the proposed hybrid cryptosystem that combined ECC and XXTEA gives better security and performance than RSA and XXTEA.
Detection of Dynamic Objects in Videos Using LBSP and Fuzzy Gray Level Difference Histograms

This paper presents a new motion detection algorithm called Fuzzy Gray Difference Histogram (FGDH). The method consists of three main stages. The first stage obtains the gray level differences of a pixel centered in a LxL window with respect to its neighbors by the local binary similarity patterns (LBSP) operator. Then, these differences are fuzzified by a Gaussian function that carries out a weighting from 0 to 1, which indicates the degree of similarity of the neighborhood pixels with respect to the central one. The second stage obtains a background model based on fuzzy local histograms that are made from small neighborhoods in order to provide spatial and gray level information. The third stage updates the background histograms based on the similarity between the current and past histograms. Experimental results indicate that FGDH presents a competitive performance in the Change detection database considering the elimination of shadows and noise in video scenarios from the categories Baseline, Badweather and Shadow. The implementation was attained under an optimization scheme that reduced the computational cost and a parallelizable scheme of the method which allows real time speed.
A Tickless AMP Distributed Core-Based Microkernel for Big Data

Operating system kernels are designed to manage the underlying hardware resources of an environment and avail them to applications in a unified secure way. General purpose kernels perform average over different diversified application profiles and workloads. Their policies are not designed to be a perfect-fit for Big Data applications. In this paper, we present a special purpose tickless AMP distributed kernel designed with Big Data in mind. Design goals and decisions are presented together with the proposed kernel architecture and main subsystems. A prototype of the proposed kernel was used to conduct experiments for assessing the performance gain in Big Data applications in comparison with the open source Linux kernel. Experiment results show a gain in performance that ranges from 1.52 to 4.89 folds based on the target application profile and workload. The new proposed kernel was able to achieve 2.34 folds speed up over Linux/Hadoop using the TeraSort benchmark. Finally, a factorial ANOVA and regression analysis was conducted to assess the different factors affecting the gain in performance, and we were able to show and quantify the positive influential effect of our proposed new kernel on the performance.
An Efficient EUF-ID-CMA Secure Identity-based Short Signature Scheme using Discrete Logarithm

In this article, we devise an EUF-ID-CMA protected ID-based short signature protocol. Its security is firmly identified with the Discrete Logarithm (DL) assumption in the random oracle. This protocol offers a more well safety assurance than other competing established ID-based signature protocol. The presented new ID-based short signature protocol is suitable for low-storage, low-bandwidth communication, and low-computation environments.
User-Guided Clustering for Video Segmentation on Coarse-Grained Feature Extraction

Video segmentation is the task of temporally dividing a video into semantic sections, which are typically based on a specific concept or a theme that is usually defined by the user's intention. However, previous studies of video segmentation have that far not taken a user's intention into consideration. In this paper, a two-stage user-guided video segmentation framework has been presented, including dimension reduction and temporal clustering. During the dimension reduction stage, a coarse granularity feature extraction is conducted by a deep convolutional neural network pre-trained on ImageNet. In the temporal clustering stage, the information of the user's intention is utilized to segment videos on time domain with a proposed operator, which calculates the similarity distance between dimension reduced frames. To provide more insight into the videos, a hierarchical clustering method that allows users to segment videos at different granularities is proposed. Evaluation on Open Video Scene Detection(OVSD) dataset shows that the average F-score achieved by the proposed method is 0.72, even coarse-grained feature extraction is adopted. The evaluation also demonstrated that the proposed method can not only produce different segmentation results according to the user's intention, but it also produces hierarchical segmentation results from a low level to a higher abstraction level.
Active and passive computational imaging for tracking and prediction of three-dimensional MUAV flight paths

Micro unmanned aerial vehicles (MUAV) have become increasingly popular during the last decade due to their access to a wide consumer market. With the increasing number of MUAV, the unintended and intended misuse by flying close to sensitive areas has risen as a potentially increasing risk. To counter this threat, surveillance systems are under development which will monitor the MUAV flight behavior. In this context, the reliable tracking and prediction of the MUAV flight behavior is crucial to increase the performance of countermeasures. In this paper, we discuss electro-optical computational imaging methods with a focus on the ability to perform a tracking of the three dimensional (3D) flight path. We evaluate the analysis of different imaging methods performed with active laser detection as well as with passive imaging using advanced scenario analysis. In first experimental investigation, we recorded and analyzed image sequences of a MUAV quad-copter flying at low altitude in laboratory and in outdoor scenario. Our results show, that we are able to track the three dimensional flight path with high accuracy and we are able to give a reliable prediction of the MUAV flight behavior within the near future.
A Logical and Empirical Study of Right-Nested Counterfactuals

The paper focuses on a recent challenge brought forward against the interventionist approach to the meaning of counterfactual conditionals. According to this objection, interventionism cannot in general account for the interpretation of right-nested counterfactuals, the problem being its strict interventionism. We will report on the results of an empirical study supporting the objection, and we will extend the well-known logic of actual causality with a new operator expressing an alternative notion of intervention that does not suffer from the problem (and thus can account for some critical examples). The core idea of the alternative approach is a new notion of intervention, which operates on the evaluation of the variables in a causal model, and not on their functional dependencies. Our result provides new insights into the logical analysis of causal reasoning.
Security Risk Modeling

A statistical analysis can be thought of as a story. Every story has a theme, characters, and a plot that ties everything together. For an analysis project, the goal of the project is the theme, the data being used are the characters in the story, and the analysis is the plot. While a story may not directly state its theme, a statistical analysis always should state its theme, that is, it must have a clearly defined goal and/or hypothesis/hypothesis. The project goal should include the study population and the outcome variable(s). The goal is used to determine what data needs to be collected and what type of statistical methods are needed to analyze the data. Much as characters are important to a story, data is fundamental for any statistical analysis. Thinking about data as a statistician thinks about data can give a smoother flow to the data analysis process.
Ubiquitous Healthcare Systems and Medical Rules in COPD Domain

Chronic Obstructive Pulmonary Disease (COPD) is a severe lung illness that causes a progressive deterioration in the function and structure of the respiratory system. Recently, COPD became the fifth cause of mortality and the seventh cause of morbidity in Canada. The advancement of context-aware technology creates a new and important opportunity to transform the standard shape of healthcare services into a more dynamic and interactive form. This research project design and validates a rule-based ontology-reasoning framework that provides a context-aware system for COPD patients. The originality of the proposed approach consists in its methodology to prove the efficiency of this model in simulated examples of real-life scenarios based on collaborative data analysis, recognized by specialized medical experts.
Multiclass Classification Based on the Convolutional Fuzzy Neural Networks

The paper presents a solution to the multiclass classification problem based on the Convolutional Fuzzy Neural Networks. The proposed model includes a fuzzy self-organization layer for data clustering (in addition to convolutional, pooling and fully-connected layers). The model combines the power of convolutional neural networks and fuzzy logic, it is capable of handling uncertainty and imprecision in the input pattern representation. The Convolutional Fuzzy Neural Networks could provide better accuracy in multiclass classification tasks when classified objects are often characterized by uncertainty and inaccuracy in their representation.
Multi‐frame track‐before‐detect algorithm for disambiguation

In practice, medium pulse repetition frequency (PRF) radar system is widely used as it takes both the advantages of low and high PRF radars. However, range and Doppler ambiguities appear in such a complicated system. Traditional target tracking algorithms first threshold raw measurements and obtain points from different PRF, then perform ambiguity resolutions over them to obtain the unambiguous measurements. It leads to a rather poor performance when target signal-to-noise ratio is low. While multi-frame track-before-detect (MF-TBD) is an advantageous method to track weak targets, but it fails to be applied in multiple PRF radar system directly. In this study, two algorithms based on MF-TBD are proposed. Classical ambiguity resolution (CAR) TBD first resolves ambiguities by means of a robust Chinese Remainder Theorem, where the first detection threshold is below the normal threshold, and then MF-TBD is applied for further tracking. The simulation shows that the proposed method significantly outperforms the CAR detect-before-track algorithm.
A Stealth Migration Approach to Moving Target Defense in Cloud Computing

A stealth migration protocol is proposed in this paper that obfuscates the virtual machine (VM) migration from intruders and enhances the security of the MTD process. Starting by encrypting the VM data and generating a secret key that is split along with the encrypted data into small chunks. Then the fragments are transmitted through intermediate VMs on the way to the destination VM. As a result, the chances of an intruder detecting the VM migration is reduced. The migration traffic is maintained close to normal traffic by adjusting the chunk size, thereby avoiding the attention of the intruder. Finally, the normal and migration traffic patterns are analyzed with the proposed protocol.
3D Cubic Bezier Curves for Multi-target Path Planning for Autonomous Underwater Vehicles

This paper presents a new method to design the shortest smooth 3D path for multiple Autonomous Underwater Vehicles (AUVs) to visit multiple targets. This path planning method can be separated into the target assignment problem and path designing problem. For the target assignment problem, we compare the genetic algorithm and ant colony optimization algorithm to get target sequence for each AUV with less distance cost. For the given target sequence for each AUVs, the path designing problem is to design shortest smooth path for visiting each target with constraint of G <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> continuity. This paper designs 3D Bezier curves by selecting the control points along the incoming and out-going heading direction. Each two-target segment has a set of discrete in-coming and out-going heading direction. For multi-target path planning, the in-coming heading direction and the out-going heading direction at each target have to be the same and the shortest path is selected via back propagation algorithm. It is proved that the combined 3D cubic Bezier curves ensure G <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> continuity along the entire multi-target path. Back propagation algorithm is applied to find the shortest curve sequence for multiple targets.
Construction Industry Pursuit Intel Modeling Insights with FCM

This paper describes new interactive mental models applied to the pursuit of Construction Management (CM) project opportunities using Fuzzy Cognitive Maps (FCM). The CM-FCM models provide a basis for new decision support tools capable of providing Construction Industry Practitioners (CIP) support throughout the Project Life Cycle (PLC). It presents two novel CM-FCM models based on real-world construction engineering and management experience, specifically designed to support key decisions in the PLC. The interactive CM-FCM validates the application of FCM to this domain and demonstrate a method capable of helping manage the complexity and uncertainty inherent in construction management. The models offer a foundation for interactive intelligent decision support tools to assist with construction management.
Decentralized System Intelligence in Data Driven Networks for Shipping Industrial Applications: Digital Models to Blockchain Technologies

Data driven networks applicable for shipping industrial applications to create decentralized system intelligence are considered in this study. Such system intelligence can facilitate to improve the respective operational efficiency in local (i.e. vessel operations) and global (i.e. logistics operations) scales in shipping as the main advantage. The main features of these data driven networks are summarized in the first part of this study. Two applications of digital models and blockchain technologies are discussed and compared with their features to illustrate their similarities and differences in the second part of this study. A digital model represents a vector based mathematical structure derived from ship performance and navigation data sets and has categorized as a low-level information model. It is also believed that the respective data sets from industrial IoT (internet of things) should go through such low-level models to improve their quality. These data driven networks can be used to quantify ship performance and navigation conditions, where the outcome can also be used to improve vessel energy efficiency and reduce engine emissions in a local scale. A blockchain represents a decentralized, distributed and digital ledger system in a public domain and can handle and record transactions executed by many users. That has categorized as a high-level information model due the high quality data sets from industrial processes that these networks are handling. Such data driven networks can be used to formulate various logistics operations in shipping and optimize their operational conditions in a global scale. The outcomes of these data driven networks can be used to improve operational efficiency and reduce the respective costs in the shipping industry.
On the Learning Properties of Dueling DDQN in Parameter Control for Evolutionary and Swarm-based Algorithms

This work is intended to assess the learning capability of an agent implemented with a Dueling Double Deep Q-Network in the problem of parameter control for Evolutionary and Swarm-based algorithms. The objective is to build a general parameter control method for these algorithms, that can be used for any Population Based Algorithm (PBA) to solve any numerical optimization problem, implemented for any computing platform, and is able to choose a good sequence of parameter values for the PBA, given a time budget constraint. For the experiments, an implementation of the Particle Swarm Optimization for CUDA devices was chosen as the PBA and a set of well-known highly complex numerical minimization problems were used for the benchmark. The experiments showed that the agent is clearly able to evolve from a completely random decision policy to a fitness-minimization-oriented policy for most of the functions.
Constrained Kalman Filtering: Improving Fused Information Retention During Constraining

Kalman filtering can produce unrealistic values and can prevent accurate convergence as the technique does not naturally include safeguards that exclude unphysical states. It can be demonstrated that without implementing constraints, or even some existing constraint strategies, that the filter could converge incorrectly. Currently available approaches to constraining the estimated state variables are arbitrary. For example, a simple way to constrain a violating state variable, is to reset its value to the constraint limit, the effect of which is a reduction of the importance of the measurement. The proposed constraining method attempts to preserve the importance of the observation/measurement in the fused estimate. This method compensates the changes in the constrained state variables by adjusting the non-constrained state variables in order to force the net change in measurement estimate to zero. The approach is implemented for the extended Kalman filters. The method is using a gas phase reaction in a Continuously Stirred Tank Reactor, with the state variables consisting of three species concentrations and the measurement is a pressure measurement with a known relationship to the state variables. The performance of the method is compared to currently available constraining techniques.
Enabling Empirical Research: A Corpus of Large-Scale Python Systems

The Python programming language has been picking up traction in Industry for the past few years in virtually all application domains. Python is known for its high calibre and passionate community of developers. Empirical research on Python systems has potential to promote a healthy environment, where claims and beliefs held by the community are supported by data. To facilitate such research, a corpus of 132 open source python projects have been identified, basic information, quality as well as complexity metrics has been collected and organized into CSV files. Collectively, the list consists of 36, 635 python modules, 59, 532 classes, 253, 954 methods and 84, 892 functions. Projects in the selected list span various application domains including Web/APIs, Scientific Computing, Security and more.