Reviewerly/exHarmony huggingface dataset:
Speed profile optimisation for intelligent vehicles in dynamic traffic scenarios

In the autonomous navigation of intelligent vehicles, collision avoidance is essential for driving safety. Similar to the driving preference of human, the driving path and speed can be determined separately. This paper is concerned with speed profile optimisation problem for dynamic obstacle avoidance given the reference path. The optimisation consists of smoothness, risk, and efficiency terms with obstacle constraints. For task formulation, the s-t motion space is constructed to describe the motion of the ego vehicle and obstacles. Then the high-dimensional trajectory space is mapped to the low-dimensional s-t space for computational efficiency. The speed optimisation problem is transformed into a path searching problem considering collision avoidance and searching efficiency. RRT-based algorithm is proposed to search for the optimal speed profile in the s-t space asymptotically. In each searching step, node extension strategy is designed for the space exploring efficiency; then the tree structure is locally refined for asymptotic optimisation. The optimal speed profile is generated after the searching process converges and the speed profile is planned periodically. For performance evaluation, simulation tests in typical traffic conditions are conducted based on the SUMO (Simulation of Urban MObility) platform. Results show the effectiveness and efficiency of this method.
Modelling a Leaf and Spine Topology for VM Migration in Fog Computing

As many IoT environments consider mobility features for the devices, their virtual computing capabilities allocated in hosts within a fog or edge datacentre may follow it to a closer location as the IoT device changes its position in order to minimise both latency and bandwidth use. In this paper, a model for VM migration in a Leaf and Spine Data Center topology is proposed, being that model expressed in both an algorithmic and an algebraic manner. Furthermore, the devices and their ports being part of each of the redundant paths available between any two given hosts are obtained by applying the model.
Robust approach of video steganography using combined keypoints detection algorithm against geometrical and signal processing attacks

To secure the secret communication, a robust video steganography algorithm is proposed. The major objectives of the proposed method (PM) are: (1) perceiving the region of interest (ROI) keypoints’ locations in video frames for concealing the secret data and (2) determining the appropriate amount of data to be embedded into the perceived ROI keypoints’ region. In the PM, keypoints are initially extracted from each video frame using scale-invariant feature transform (SIFT) and a speeded-up robust features (SURF) descriptor against a set of predefined geometrical and signal processing attacks. Next, ROI keypoints are generated by comparing the SIFT and SURF keypoint descriptors of the original frame and attacked versions of the frame. Then ROI keypoints are divided into four least significant bit (LSB) groups to determine the embedding capacity of each ROI keypoint. Subsequently, the secret data are encrypted using a symmetric key-based shift cipher to provide an additional security layer for secret communication. Finally, the encrypted secret data have been embedded into the ROI keypoints using the LSB substitution method based on the four LSB groups’ values. The PM is tested on 22 standard benchmark videos. The efficiency of the PM is evaluated in terms of the perceptual invisibility, robustness, and concealing capacity. From experimental results, it is observed that the PM outperforms contemporary methods by attaining significant outcomes.
Augmenting Node‐Link Diagrams with Topographic Attribute Maps

Abstract We propose a novel visualization technique for graphs that are attributed with scalar data. In many scenarios, these attributes (e.g., birth date in a family network) provide ambient context information for the graph structure, whose consideration is important for different visual graph analysis tasks. Graph attributes are usually conveyed using different visual representations (e.g., color, size, shape) or by reordering the graph structure according to the attribute domain (e.g., timelines). While visual encodings allow graphs to be arranged in a readable layout, assessing contextual information such as the relative similarities of attributes across the graph is often cumbersome. In contrast, attribute‐based graph reordering serves the comparison task of attributes, but typically strongly impairs the readability of the structural information given by the graph's topology. In this work, we augment force‐directed node‐link diagrams with a continuous ambient representation of the attribute context. This way, we provide a consistent overview of the graph's topological structure as well as its attributes, supporting a wide range of graph‐related analysis tasks. We resort to an intuitive height field metaphor, illustrated by a topographic map rendering using contour lines and suitable color maps. Contour lines visually connect nodes of similar attribute values, and depict their relative arrangement within the global context. Moreover, our contextual representation supports visualizing attribute value ranges associated with graph nodes (e.g., lifespans in a family network) as trajectories routed through this height field. We discuss how user interaction with both the structural and the contextual information fosters exploratory graph analysis tasks. The effectiveness and versatility of our technique is confirmed in a user study and case studies from various application domains.
Time-optimal bang-bang control for the driven spin-boson system

In this paper we investigate the application of optimal control of a single qubit coupled to an ohmic heat bath. For the weak bath coupling regime, we derive a Bloch-Redfield master equation describing the evolution of the qubit state parametrized by vectors in the Bloch sphere. By use of the optimal control methodology we determine the field that generates an arbitrary single qubit rotation in minimum time. In particular we develop an efficient method for solving the time-optimal control problem, namely the implementation of $X$ gate, which consists of a rotation about the $\stackrel{P\vec}{ox}$ axis through angle $\ensuremath{\pi}$. The time-optimal control problem requires a bounded control. If upper and lower bounds are imposed for the external control, the optimal solution is of bang-bang type and switches from the upper to the lower values of the control bounds. We compare our results using the techniques of automatic differentiation to compute the gradient for the cost functional with some known results using Pontryagin's minimum principle. We use an alternative numerical approach for the optimization strategy.
Sea-surface Floating Small Target Detection Based on Joint Features

This paper proposes a method for sea-surface floating small targets detection. Eight features (relative average amplitude, relative Doppler peak height, relative Doppler spectral amplitude entropy, time-frequency ridge energy, the number of binary connection areas, the largest pass size, relative average power and relative Doppler spectral entropy) are extracted from the radar returns. The detector based on the principle component analysis (PCA) is used to distinguish target bins from sea clutters in the feature space. Experiments using IPIX data sets show that this method achieves better detection performance than several existing detectors.
Usage-based instruction, systems thinking, and the role of Language Mining in second language development

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Solving Jigsaw Puzzles via Nonconvex Quadratic Programming With the Projected Power Method

Jigsaw puzzles consist of reconstructing a picture that has been divided into many interlocking pieces. This paper describes an automatic global method for solving the square-piece jigsaw puzzle problem in which neither the orientations nor the locations of the jigsaw pieces are known. This hard combinatorial sorting task is formulated as a nonconvex quadratic programming problem that is solved via the projected power method. Specifically, this work aims to specify the locations and orientations of puzzle pieces by maximizing a constrained quadratic function that resolves an optimized permutation matrix composed of the noisy pairwise affinities between jigsaw pieces. The experimental results obtained in the MIT, McGill and Pomeranz datasets indicate that our method outperforms state-of-the-art techniques.
Reversible Fragile Watermarking for Multichannel Images with High Redundancy Channels

The paper presents a methodology to protect the integrity of multichannel images, having some highly redundant channels, by means of a reversible fragile watermarking algorithm. The watermark embedding phase uses a lossless compression method to compress the high redundancy channels, stores the compressed stream into their most significant bits, then embeds a secret fragile watermark by modifying the least significant bits of the high redundancy channels. In case the watermarked image is not modified, the host image can be perfectly reconstructed; otherwise, the modified area can be detected and located with very high probability and the area that has not been forged can be restored as in the original host image. The embedding of the watermark is performed by a Genetic Algorithm in the Karhunen-Loève Transform (KLT) domain: the use of a secret space defined by the KLT guarantees both security of the method and a high sensitivity in the detection of the forged areas.
An Analysis of Transfer Learning Methods for Multilingual Readability Assessment

Recent advances in readability assessment have lead to the introduction of multilingual strategies that can predict the reading-level of a text regardless of its language. These strategies, however, tend to be limited to just operating in different languages rather than taking any explicit advantage of the multilingual corpora they utilize. In this manuscript, we discuss the results of the in-depth empirical analysis we conducted to assess the language transfer capabilities of four different strategies for readability assessment with increasing multilingual power. Results showcase that transfer learning is a valid option for improving the performance of readability assessment, particularly in the case of typologically-similar languages and when training corpora availability is limited.
Technical Threat Intelligence Analytics: What and How to Visualize for Analytic Process

Visual Analytics uses data visualization techniques for enabling compelling data analysis by engaging graphical and visual portrayal. In the domain of cybersecurity, convincing visual representation of data enables to ascertain valuable observations that allow the domain experts to construct efficient cyberattack mitigation strategies and provide useful decision support. We present a survey of visual analytics tools and methods in the domain of cybersecurity. We explore and discuss Technical Threat Intelligence visualization tools using the Five Question Method. We conclude the analysis of the works using Moody's Physics of Notations, and VIS4ML ontology as a methodological background of visual analytics process. We summarize our analysis as a high-level model of visual analytics for cybersecurity threat analysis.
Evaluation of Full-Body Gestures Performed by Individuals with Down Syndrome: Proposal for Designing User Interfaces for All Based on Kinect Sensor

The ever-growing and widespread use of touch, face, full-body, and 3D mid-air gesture recognition sensors in domestic and industrial settings is serving to highlight whether interactive gestures are sufficiently inclusive, and whether or not they can be executed by all users. The purpose of this study was to analyze full-body gestures from the point of view of user experience using the Microsoft Kinect sensor, to identify which gestures are easy for individuals living with Down syndrome. With this information, app developers can satisfy Design for All (DfA) requirements by selecting suitable gestures from existing lists of gesture sets. A set of twenty full-body gestures were analyzed in this study; to do so, the research team developed an application to measure the success/failure rates and execution times of each gesture. The results show that the failure rate for gesture execution is greater than the success rate, and that there is no difference between male and female participants in terms of execution times or the successful execution of gestures. Through this study, we conclude that, in general, people living with Down syndrome are not able to perform certain full-body gestures correctly. This is a direct consequence of limitations resulting from characteristic physical and motor impairments. As a consequence, the Microsoft Kinect sensor cannot identify the gestures. It is important to remember this fact when developing gesture-based on Human Computer Interaction (HCI) applications that use the Kinect sensor as an input device when the apps are going to be used by people who have such disabilities.
Active Learning Based Framework for Image Captioning Corpus Creation

Image captioning aims at analyzing the content of an image in order to subsequently generate a textual description through verbally expressing the important aspects of it. In spite of the fact that the task of automatic image description is not bound to the English language, yet, the recent advances mostly focus on English descriptions. Collecting captions for images is an expensive process that requires time and labor cost. In this paper, we introduce a novel active learning framework with human in the loop for image captioning corpus creation, using a translated version of existing datasets. We implemented this framework to create a new dataset called ArabicFlickr1K. This dataset has 1095 images, each is associated with three to five descriptions. We also propose a neural network architecture to automatically generate Arabic captions for images. This architecture relies on an encoder-decoder framework. Our model scored 47% on BLUE-1.
A systematic review of fault tolerance solutions for communication errors in open source cloud computing

Cloud systems, as any other system, must be reliable. This means that the system should respond correctly in presence of failures, which are quite probable in a distributed, largely independent, system as cloud systems are. Thus, it is important that cloud systems become fault tolerant, ensuring safe recovery from failures. Since failures in clouds may come from several different sources, although a major role comes from communication failures, the techniques that can be applied to assure reliability are also very different. This survey presents a systematic review of solutions to provide fault tolerance in open source clouds. Our goal with this review is to provide to cloud managers a guided approach to choose a solution for a given problem or system.
Second Order Expansions for High-Dimension Low-Sample-Size Data Statistics in Random Setting

We consider high-dimension low-sample-size data taken from the standard multivariate normal distribution under assumption that dimension is a random variable. The second order Chebyshev–Edgeworth expansions for distributions of an angle between two sample observations and corresponding sample correlation coefficient are constructed with error bounds. Depending on the type of normalization, we get three different limit distributions: Normal, Student’s t-, or Laplace distributions. The paper continues studies of the authors on approximation of statistics for random size samples.
Lyapunov-based unified control method for closed quantum systems

A Lyapunov-based unified control method for closed quantum systems is proposed in this paper for the state transfers from an arbitrary initial state to a desired target state. Especially, the proposed control method can be used in the cases that the free Hamiltonian of the system is not strongly regular, and (or) the control Hamiltonian is not fully connected. Three types of control laws are proposed simultaneously based on the implicit Lyapunov control theory, in which two implicit functions and one Lyapunov function with virtual mechanical quantity are well-designed. The largest invariant set of the quantum control system is investigated, and the convergence of the control system designed is proved. A numerical simulation is implemented in detail to instruct the design procedure and demonstrate the effectiveness of the control method proposed.
Gas Path Fault Diagnosis of Aeroengine Based on Soft Square Pinball Loss ELM

The ELM constructed based on the least squares loss function and ±1 label has poor generalization in the classification of data containing noise.The introduction of square pinball loss function can improve the robustness of ELM.However, the algorithm based on the squared loss function and the ±1 label imposes a margin of 1 for all training samples.At the same time, due to the unbounded nature of the loss function, the generalization of the algorithm in the classification problem is reduced.This paper proposes a soft threshold square pinball loss (SSP-Loss) function.This function can set more flexible thresholds for training samples while maintaining the robustness of the square pinball loss function.The soft-threshold square pinball loss function can approximate the bounded loss function in stages to further improve the classification performance of the algorithm.The performance of ELM based on the soft pinball loss function on several benchmark data sets proves the effectiveness of our proposed algorithm.More importantly, the excellent robustness and classification performance of the algorithm is very suitable for aeroengine gas path fault diagnosis, and is expected to become its candidate technology.
Classification of the Requirement Sentences of the US DOT Standard Specification Using Deep Learning Algorithms

This aim of this study is to classify requirement sentences from the specifications of US DOT using natural language processing (NLP) and a deep neural network. At the contract phase of the project, the requirements analysis of contract documents is a significant task to prevent claims or disputes caused by ambiguous or missing clauses, but it is highly human-intensive and difficult to identify requirements within a given short period. In this article, the requirement sentences identification model was proposed based on deep-learning algorithms. First, the critical terms that define what the requirement sentence is were identified, and then all sentences were labeled using the pre-defined critical terms. Second, three vectorizing methods were used, including two pre-trained methods—GloVe and Word2Vec—and a self-trained method to produce word embedding. Third, the automated classification of requirements sentences was experimented using three deep-learning models: the convolutional neural network (CNN), the long-short-term memory (LSTM), and the combination of CNN+LSTM. In the evaluation of nine total experiments, the results showed that the F1 scores of the CNN model were the highest at 92.9% and 92.4% for both the Word2Vec model and the Glove model. This study provided a way to achieve a high level of classification accuracy with simple deep-learning models and pre-trained embedding models.
Collaborative collection development: current perspectives leading to future initiatives

As academic libraries continue to face acquisition budget challenges, collaborative collection development (CCD) offers greater opportunities to fulfill the core role of library collecting and collection management, namely, to provide enhanced access to the widest variety of relevant resources in the most cost-responsible manner possible. Libraries have successfully implemented CCD projects of various types, and as a result, have achieved these needed cost savings. The authors conducted survey research to investigate current CCD activities and librarians' perceptions of its benefits, drawbacks, elements contributing to successful CCD programs, and possible obstacles to success. Library collections consist of a variety of material formats and librarians have applied CCD models to maintain needed access to these resources, shifting from ownership to access, all in support of building collective collections. The survey results found that, although challenges can exist, application of CCD activities have realized substantial benefits, financial and otherwise, for academic libraries overall.
Multi-criteria usability evaluation of symmetric data encryption algorithms in fuzzy environment

Effective use of parameters such as time, resources, and energy is a crucial subject in daily life. These parameters directly influence the selection strategy in decision-making problems. While using encryption algorithms, it may be a problem to choose which algorithm to use according to file types in order to use resources efficiently. In this study, a C#-based program has been developed to measure various performance parameters and to compare symmetric data encryption algorithms. Accordingly, an intelligent selection system has been created that allows the most efficient encryption algorithm to be selected when encrypting text, audio, and video files. In this system, the user is presented with three profiles as “Quick,” “Performance,” and “Secure.” Thanks to these profiles, the user can find the answer by selecting the desired profile according to the purpose. The data obtained from the program have been converted to fuzzy values using fuzzy logic. The generated fuzzy values have been evaluated separately using FAHP, TOPSIS, and PROMETHEE multi-criteria decision-making methods, and the PROMETHEE method has been found as the ranking method giving the closest result to the order created by the expert. The developed system provides efficient use of time, resources, and security.
A transition-based neural framework for Chinese information extraction

Chinese information extraction is traditionally performed in the process of word segmentation, entity recognition, relation extraction and event detection. This pipelined approach suffers from two limitations: 1) It is prone to introduce propagated errors from upstream tasks to subsequent applications; 2) Mutual benefits of cross-task dependencies are hard to be introduced in non-overlapping models. To address these two challenges, we propose a novel transition-based model that jointly performs entity recognition, relation extraction and event detection as a single task. In addition, we incorporate subword-level information into character sequence with the use of a hybrid lattice structure, removing the reliance of external word tokenizers. Results on standard ACE benchmarks show the benefits of the proposed joint model and lattice network, which gives the best result in the literature.