Reviewerly/exHarmony huggingface dataset:
Pheromone Accumulation and Iteration

In this chapter, the robot path-planning problemRobot path planning problem is explored under the vision–brain hypothesisVision–brain hypothesis, The , and meanwhile, the pheromonePheromone accumulation and iteration mechanisms and processes are explicitly illustrated. Based on the hypothesis, robots can recognize obstacles, and therefore, to solve the robot path-planning problem, it remains to decide the optimal path to the targets or the regions of interest. Differing from most studies on the robot path planning, the significance of pheromone paths (sub-paths) in full path generationPath generation is emphasized, employing the ant colony algorithm, where pheromonePheromone updates are directed through calculating the passed ants of the sub-paths in each iteration. This algorithm can be further improved by placing pheromone on the nodes to improve the efficiency of the pheromone storage and updates, where the ant colony (a series of pheromone points) becomes a pheromonePheromone trace. Utilizing localization rules and one-step optimization rules for local optimization, the time to construct the first complete solution can be shorten and a better solution of the problem of the robot path planning can be generated by establishing a mesh model of the navigation area with determined obstacles. Utilizing the locally compressive sensing algorithm in Chap. 2 and selecting a behavior-sensitive area for compressive trackingCompressive tracking , machine can recognize some special global behaviors (e.g., running, falling) and local behaviors (e.g., smiles and blinking) and the recognition rate and accuracy can be ensured. TheBroad learning system broad learning systemLearning systems with a vision–brain (the decision layerDecision layer ) introduced in Chap. 2 for face recognitionFace recognition is further utilized to tackling a series of challenging issues—illuminationIllumination changes, expression and posePose variations and occlusionOcclusion problems, respectively, utilizing some representative face databases. Results show that face recognition rates in 100 times of training and testing on each database can approach to 100%, including the database with real disguise occlusionDisguise occlusion .
Automatic Dynamic Web Service Composition Using AND/OR Directed Graphs

In this article, the authors propose a dynamic web service composition approach based on representing the semantic relationship between web services using a weighted directed AND/OR graph. The nodes in this graph represent available services while the arcs represent the semantic input/output dependencies among them. The novelty of this work consists of constructing the graph and computing offline the shortest paths between each pair of its nodes to disconnect this tedious task from the composition query process. A set of dynamic optimization techniques has been included to reduce the size of the graph and thus improve the scalability and performance of this approach. In addition to the sequence and fork relations between services, this solution also supports the parallel relation. Furthermore, a recovery mechanism is integrated to ensure the continuity of the execution of the composition.
How C++ Developers Use Immutability Declarations: An Empirical Study

Best practices for developers, as encoded in recent programming language designs, recommend the use of immutability whenever practical. However, there is a lack of empirical evidence about the uptake of this advice. Our goal is to understand the usage of immutability by C++ developers in practice. This work investigates how C++ developers use immutability by analyzing their use of the C++ immutability qualifier, const, and by analyzing the code itself. We answer the following broad questions about const usage: 1) do developers actually write non-trivial (more than 3 methods) immutable classes and immutable methods? 2) do developers label their immutable classes and methods? We analyzed 7 medium-to-large open source projects and collected two sources of empirical data: 1) const annotations by developers, indicating an intent to write immutable code; and 2) the results of a simple static analysis which identified easily const-able methods---those that clearly did not mutate state. We estimate that 5% of non-trivial classes (median) are immutable. We found the vast majority of classes do carry immutability labels on methods: surprisingly, developers const-annotate 46% of methods, and we estimate that at least 51% of methods could be const-annotated. Furthermore, developers missed immutability labels on at least 6% of unannotated methods. We provide an in-depth discussion on how developers use const and the results of our analyses.
Code Smells Enabled by Artificial Intelligence: A Systematic Mapping

Code smells are an indicator of poor design in software systems. Artificial intelligence techniques have been applied in several ways to improve soft-ware quality in code smells detection i.e. (detection rules or standards using a combination of object-oriented metrics and Bayesian inference graphs). Literature in the field has identified artificial intelligence techniques and compare different artificial intelligence algorithms, which are used in the detection of code smells. However, to the best of our knowledge, there is not a systematic literature review devoted to study in deep the interaction of these fields. In this paper, authors conduct a systematic mapping to get to know how artificial intelligence inter-acts with code smells. Results show the deep connection of Artificial Intelligence with code smells in a solid way, as well as, providing potential challenges and opportunities for future research.
Regulatory Ambiguity in the Market for Bitcoin

The standard economic approach to considering the effects of a policy tends to neglect the prospect of regulatory ambiguity. I describe four sources of regulatory ambiguity and survey the literature considering the effects of ambiguity on entrepreneurial activity. I also explain how jurisdictional redundancy, where multiple agencies regulate the same action or industry, increases the likelihood of regulatory ambiguity. I offer a brief description of the regulatory environment of bitcoin in the United States in order to provide specific examples of the sources of regulatory ambiguity identified herein.
Comparison of Convolutional Neural Networks and Search Based Approaches for Extracting Psychological Characteristics from Job Description

The paper discusses two machine learning approaches for analysis of job text descriptions and extracting some psychological characteristics which are required from employees. The Holland Codes (RIASEC model) are used as algorithms target variables. The first approach is based on TF-IDF and WMD. The second approach is based on a convolutional neural network. The results comparison of the proposed algorithms is given.
The Principles and the Conceptual Architecture of the Metagraph Storage System

This paper discusses an approach for active metagraph model storage. The formal definition of the metagraph data model is proposed. The example of data metagraph model is given. The formal definition of the metagraph function and rule agents are discussed. The example of a metagraph rule agent is given. It is shown that the distinguishing feature of the metagraph agent is its homoiconicity which means that it can be a data structure for itself. Thus, the metagraph agent can change both data metagraph fragments and the structure of other metagraph agents. The definition of active metagraph is given. The possible states of active metagraph elements and transitions between them are discussed. The conceptual architecture of the metagraph storage system based on active metagraph is proposed. The approaches for mapping the metagraph model to the flat graph, document-oriented, and relational data models are proposed. The experiments result for storing the metagraph model in different databases are given. It is shown that the flat graph model is most suitable for metagraph storage.
In Vivo or in Vitro? Influence of the Study Design on Crowdsourced Video QoE

Evaluating the QoE of video streaming and its influence factors has become paramount for streaming providers, as they want to maintain high satisfaction for their customers. In this context, crowdsourced user studies became a valuable tool to evaluate different factors which can affect the perceived user experience on a large scale.In general, we observed that most of these crowdsourcing studies either use an in vivo or an in vitro design. In vivo design means that the study participant has to rate the QoE of a video that is embedded in an application similar to a real streaming service, e.g., YouTube or Netflix. In vitro design refers to a setting, in which the video stream is separated from a specific service and thus, the video plays on a plain background. Although these designs vary widely, the results are often compared and generalized.Therefore, in this work, we investigate the influence of these two study design alternatives on the perceived QoE. In crowdsourced user studies, participants rate the video streaming with respect to different stalling patterns (no stalling, different positions) and study designs (in vivo or in vitro). Contrary to our expectations, the results indicate that there is statistically no significant influence of the study design on the perceived video QoE and acceptance. In addition, we found that the in vivo design does not reduce the test takers' attentiveness.
On Board Autonomy Operations for OPS-SAT Experiment

Upcoming space missions are requiring a higher degree of on-board autonomy operations to increase quality science return, to minimize close-loop space-ground decision making, and to enable new scenarios. Artificial Intelligence technologies like Machine Learning and Automated Planning are becoming more and more popular as they can support data analytics conducted directly on-board as input for the on-board decision making system that generates plans or updates them while being executed. This paper describes the planning and execution architecture under development at the European Space Agency to target this need of autonomy for the ops-sat mission to be launched in 2019.
How Does Feature Dependency Affect Configurable System Comprehensibility?

Background: Conditional compilation is often used to implement variability in configurable systems. This technique relies on #ifdefs to delimit feature code. Previous studies have shown that #ifdefs may hinder code comprehensibility. However, they did not explicitly took feature dependencies into account. Feature dependency occurs when different features refer to the same program element, such as a variable. Comprehensibility may be even more affected in the presence of feature dependency, as the developer must reason about different scenarios affecting the same variable. Aim: Our goal is to understand how feature dependency affects the comprehensibility of configurable system source code. Method: We carried out an experiment in which 30 developers debugged programs with different types of feature dependency. We recorded the time each of them had spent to find a bug. Also, we used an eye-tracking device to record developers' gaze movements while they debugged programs. Results: Debugging programs with global and interprocedural dependency required more time and higher visual effort. Conclusion: Our study showed that #ifdefs affect comprehensibility in different degrees depending on the type of feature dependency. Therefore, when possible, developers should take more care when dealing with code with global and interprocedural dependencies.
Deep Learning for Object Tracking in 360 Degree Videos

Object tracking is used to locate the position of an object over a period of time using the association of an object of interest over consecutive frames. In the last years, several methods were proposed to track objects in rectangular videos. This paper presents is an object tracking method within 360-degree videos using a state-of-the-art tracking-by-detection paradigm. This method uses two trackers namely Kalman filter and Lucas-Kanade methods to handle challenges in the 360-degree videos. The proposed method uses a deep learning object detector for extraction of prior information of the object of interest. The information is then used, to track the object of interest using a combination of the two trackers of the Kalman filter and Lucas-Kanade. The experiments show that this combination improves the tracker stability.
A Note on the Instantiability of the Quantum Random Oracle

In a highly influential paper from fifteen years ago [10], Canetti, Goldreich, and Halevi showed a fundamental separation between the Random Oracle Model (ROM) and the standard model. They constructed a signature scheme which can be proven secure in the ROM, but is insecure when instantiated with any hash function (and thus insecure in the standard model). In 2011, Boneh et al. defined the notion of the Quantum Random Oracle Model (QROM), where queries to the random oracle may be made in quantum superposition. Because the QROM generalizes the ROM, a proof of security in the QROM is stronger than one in the ROM. This leaves open the possibility that security in the QROM could imply security in the standard model. In this work, we show that this is not the case, and that security in the QROM cannot imply standard-model security. We do this by showing that the original schemes that show a separation between the standard model and the ROM are also secure in the QROM. We consider two schemes that establish such a separation, one with length-restricted messages, and one without, and show both to be secure in the QROM. Our results give further understanding to the landscape of proofs in the ROM versus the QROM or standard model, and point towards the QROM and ROM being much closer to each other than either is to standard model security.
TMPS: Ticket-Mediated Password Strengthening

We introduce the notion of TMPS: Ticket-Mediated Password Strengthening, a technique for allowing users to derive keys from passwords while imposing a strict limit on the number of guesses of their password any attacker can make, and strongly protecting the users' privacy. We describe the security requirements of TMPS, and then a set of efficient and practical protocols to implement a TMPS scheme, requiring only hash functions, CCA2-secure encryption, and blind signatures. We provide several variant protocols, including an offline symmetric-only protocol that uses a local trusted computing environment, and online variants that use group signatures or stronger trust assumptions instead of blind signatures. We formalize the security of our scheme by defining an ideal functionality in the Universal Composability (UC) framework, and by providing game-based definitions of security. We prove that our protocol realizes the ideal functionality in the random oracle model (ROM) under adaptive corruptions with erasures, and prove that security with respect to the ideal/real definition implies security with respect to the game-based definitions.
The Wiener Attack on RSA Revisited: A Quest for the Exact Bound

Since Wiener pointed out that the RSA can be broken if the private exponent d is relatively small compared to the modulus N (using the continued fraction technique), it has been a general belief that the Wiener attack works for $$d < N^{\frac{1}{4}}$$ . On the contrary, in this work, we give an example where the Wiener attack fails with $$d = \left\lfloor \frac{1}{2} N^{\frac{1}{4}} \right\rfloor + 1$$ , thus, showing that the bound $$d < N^{\frac{1}{4}}$$ is not accurate as it has been thought of. By using the classical Legendre Theorem on continued fractions, in 1999 Boneh provided the first rigorous proof which showed that the Wiener attack works for $$d < \frac{1}{3} N^{\frac{1}{4}}$$ . However, the question remains whether $$\frac{1}{3} N^{\frac{1}{4}}$$ is the best bound for the Wiener attack. Additionally, the question whether another rigorous proof for a better bound exists remains an elusive research problem. In this paper, we attempt to answer the aforementioned problems by improving Boneh's bound after the two decades of research. By a new proof, we show that the Wiener continued fraction technique works for a wider range, namely, for $$d \le \frac{1}{\root 4 \of {18}} N^{\frac{1}{4}} = \frac{1}{2.06...} N^{\frac{1}{4}}$$ . Our new analysis is supported by an experimental result where it is shown that the Wiener attack can successfully perform the factorization on the RSA modulus N and determine a private key d where $$d = \left\lfloor \frac{1}{\root 4 \of {18}} N^{\frac{1}{4}} \right\rfloor $$ .
A Study on the Job Trading volume of Teachers of Characterization High School and Meister High School and Students' Satisfaction in School Life

The purpose of this study is analyze difference career consciousness, career planning, satisfaction with career education of the job-Characterization high school according to the grade, gender, type of school. Research questions are as follows : First, what is the difference in job trade according to the background change of teachers? Second, what are the differences in job trade according to the type of school and Characterization classes of teachers? Third, what is the difference in satisfaction with school life according to the background change of students? Fourth, what is the difference in satisfaction with school life according to the type of school or Characterization classes of students? In order to achieve the purpose of the study, about 1170 questionnaires were distributed to 15 job-Characterization high school. The questionnaire used in this study can be divided into four main categories: general characteristics of teachers, teacher turnover, student general characteristics, and student satisfaction with school life.
A Benchmark Production Tool for Regular Expressions

We describe a new tool, named REgen, that generates regular expressions (RE) to be used as test cases, and that generates also synthetic benchmarks for exercising and measuring the performance of RE-based software libraries and applications. Each group of REs is randomly generated and satisfies a user-specified set of constraints, such as length, nesting depth, operator arity, repetition depth, and syntax tree balancing. In addition to such parameters, other features are chosen by the tool. An RE group may include REs that are ambiguous, or that define the same regular language but differ with respect to their syntactic structure. A benchmark is a collection of RE groups that have a user-specified numerosity and distribution, together with a representative sample of texts for each RE in the collection. We present two generation algorithms for RE groups and for benchmarks. Experimental results are reported for a large benchmark we used to compare the performance of different RE parsing algorithms. The tool REgen and the RE benchmark are publicly available and fill a gap in supporting tools for the development and evaluation of RE applications.
Industrial Fisheye Image Segmentation Using Neural Networks

Fisheye cameras have recently became very popular in computer vision applications due to their wide field of view. In addition to a better overview of the surrounding area, they enable to capture objects at extremely close ranges. These advantages come at a cost of strong image distortion, which cannot be removed completely maintaining image continuity. This complicates the use of traditional computer vision algorithms, which expect a single image as an input. This paper presents a performance evaluation of neural network algorithms for object detection and segmentation on fisheye camera images. Three approaches are evaluated: semantic image segmentation with Fully Convolutional Network (FCN) [13], a fully convolutional approach to instance segmentation with U-Net [18] and a region-based approach to instance segmentation with Mask R-CNN [10]. All of these networks successfully solved the task. However, as they were designed to different purposes, each of them has its own strengths and shortcomings. These three approaches are used to perform euro container image segmentation task. An image dataset was created in order to train and evaluate these algorithms. Huge part of this dataset was generated artificially, which simplified the task of ground truth labeling. The power of neural networks enable for fast and reliable image segmentation. As to our knowledge, this is the first neural networks application for euro container fisheye image detection and segmentation.
An adaptive plan‐oriented and continuous software migration to cloud in dynamic enterprises

Summary Replacing existing software/hardware components with their equivalent cloud services is an important decision faced by IT managers in today's enterprises. A variety of possible migration targets and cloud services with too many configurations and cost models, disparate and changing strategic objectives of the enterprise management that triggers the migration process, and the complex structure of the legacy applications make software migration to the cloud a challenging issue. In contrast to the existing approaches that model the migration process as an optimization problem to find the optimal deployment of software components on cloud services without presenting a practical migration plan, in this paper, a plan‐oriented migration approach is proposed by which the enterprise management is able to follow migration steps of a valid plan. All valid plans are modeled using a labeled transition system, and a recommender engine directs the management through the possible migration paths using predefined fitness functions. It was observed that, particularly in dynamic and changing conditions that a flexible migration plan is essential, the proposed plan‐oriented method is very much effective in satisfying the enterprise strategic objectives. Evaluations have been performed using two quality indicators: total cost of ownership and scalability index.
Stereo Vision-Based Gamma-Ray Imaging for 3D Scene Data Fusion

Modern developments of gamma-ray imagers by integrating multi-contextual sensors and advanced computer vision theories have enabled unprecedented capabilities in detection and imaging, reconstruction and mapping of radioactive sources. Notwithstanding these remarkable capabilities, the addition of multiple sensors such as light detection and ranging units (LiDAR), RGB-D sensors (Microsoft Kinect), and inertial measurement units (IMU) are mostly expensive. Instead of using such expensive sensors, we, in this paper, introduce a modest three-dimensional (3D) gamma-ray imaging method by exploiting the advancements in modern stereo vision technologies. A stereo line equation model is proposed to properly identify the distribution area of gamma-ray intensities that are used for two-dimensional (2D) visualizations. Scene data information of the surrounding environment captured at different locations are reconstructed by re-projecting disparity images created with the semi-global matching algorithm (SGM) and are merged together by employing the point-to-point iterative closest point algorithm (ICP). Instead of superimposing/overlaying 2D radioisotopes on the merged scene area, reconstructions of 2D gamma images are fused together with it to create a detailed 3D volume. Through experimental results, we try to emphasize the accuracy of our proposed fusion method.