Reviewerly/exHarmony huggingface dataset:
A novel hybrid feature selection based on ReliefF and binary dragonfly for high dimensional datasets

High dimensionality is a common challenge in large datasets. Combination of the filter and wrapper methods is used to select the appropriate set of features in these datasets. The hybrid method is desirable, which uses the advantages of both the methods and covers the disadvantages. In this paper, a hybrid method for feature selection in high dimension data is presented. In proposed algorithm, the ReliefF algorithm is used as a filter method for ranking features. Next, the binary dragonfly algorithm (BDA) is applied as a wrapper method. The BDA algorithm uses the ranked features to find optimal set of features incrementally and iteratively. Minimizing the cross-validation loss and decreasing the number of features is considered to evaluate the solution, hierarchically. The proposed algorithm and other compared algorithms run over 5 datasets and the results indicated that the proposed algorithm not only reduce the dimension of dataset but also improve the performance of classifiers on the test data.
New stabilizer codes from the construction of dual-containing matrix-product codes

It is known from the CSS code construction that an [[m,2k−m,≥d]]q stabilizer code can be obtained from a (Euclidean) dual-containing [m,k,d]q code. In [5], Blackmore and Norton introduced an interesting code called matrix-product code, which is very useful in constructing new quantum codes of large lengths. Recently, Galindo et al. [16] constructed several classes of stabilizer codes from the dual-containing matrix-product codes of (generalized) Reed-Muller, hyperbolic and affine variety ones. In this paper, we first provide a more general approach to construct dual-containing matrix-product codes and then further study it in two cases. The first case generalizes the result by Galindo et al. and constructs dual-containing matrix-product codes more explicitly since the matrices involved are not restricted to be orthogonal. The second case presents a different way to construct dual-containing matrix-product codes in which some of the constituent codes are not required to be dual-containing. Through the construction of dual-containing matrix-product codes of Reed-Muller and affine variety ones, the CSS code construction and Steane's enlargement, we supply several classes of new stabilizer codes over the fields F5, F7 and F9 either having minimum distances larger than the ones achieved from the first case or the technique in [16], or having lengths that are not studied in [16].
Hierarchical Binary Classification for Monocular Depth Estimation

Extracting dense depth from a single image is an important and challenging task in computer vision. Recently, significant progress has been made in monocular depth estimation (MDE) by formulating this problem as a multi-label classification instead of traditional regression. However, to discretize depth into hundreds of labels results in complicate network output and a large number of parameters. In this paper, we propose to encode the discretized depth into a binary code and formulate MDE as a hierarchical binary classification (HBC) problem to reduce the complexity. Besides, by studying the distribution of individual bit of the encoded depth codes, we find that our encoding scheme can also solve the problem of depth data imbalance. We conduct experiments on KITTI and NYU Depth V2 datasets, which show that our simplified approach still achieves a comparable performance with state-of-the-art methods.
An Efficient Human-Following Method by Fusing Kernelized Correlation Filter and Depth Information for Mobile Robot

Human-following ability enables mobile robots to cooperate with human beings smartly. The kernelized correlation filter (KCF) was typically adopted as a human tracker during the human-following process due to its advantages of fast speed and high precision. However, it is easy to drift away owing to the changes of human pose. In this study, we proposed an efficient human-following method by fusion of KCF and depth information for the mobile robot. The target human was separated from the background regions based on the seeded region growing. KCF tracker outputs and segmentation results were fused by using the adaptive weighted fusion method. The weighting factors were adjusted based on the tracking quality indicators, which reflect the tracking reliability of KCF and the distribution of depth information. Experimental results verify the effectiveness of the proposed method in terms of tracking speed and stability. The center location error of all frames was less than 48 pixels and the bounding box overlap rate of all frames was larger than 0.62.
Modulation Recognition of Radar Signal Based on an Improved CNN Model

In order to further improve the performance of radar signal modulation recognition, the radar signal modulation recognition algorithm based on an improved convolutional neural networks (CNN) model is proposed in this paper. As the CNN model has some shortcomings in the signal modulation recognition, such as long training time and poor generalization, the dense connection block layer and the global pooling layer are added in the CNN model to improve its performance. In the experiment, eight types of radar signals are used to verify the feasibility of the proposed algorithm, and the results show that the algorithm based on the improved CNN has the advantages of high recognition rate, short training time and good generalization.
An Ontology and Knowledge Graph Infrastructure for Digital Library Knowledge Representation

New technologies for storing and handling knowledge provide unprecedented opportunities for enhanced fruition of digital libraries and archives. Going beyond document retrieval based on lexical content or metadata, using the context of documents, and/or of their content, may provide very new ways to put them in perspective and grasp a deeper understanding thereof, also for non-technical users. Several components are needed to support this new perspective: suitable ontological resources to describe such variated knowledge, collaborative tools to collect the precious knowledge scattered across many scholars and practitioners spread all over the world, and to store it in a knowledge base, fruition tools to make the collected knowledge available to all interested stakeholders (scholars, researchers, but also common people). This paper proposes the GraphBRAIN environment as a possible infrastructure. It is a general-purpose tool that allows its users to design and populate knowledge graphs, to collaboratively enrich them, and to exploit advanced fruition tools, consultation and analysis tools. Its functionality may also be provided as a set of Web services to end-user applications. An initial version of the ontology and knowledge graph for digital libraries and archives are also presented and discussed in the paper.
Macropixel-constrained Collocated Position Search for Plenoptic Video Coding

The plenoptic video recording light fields varying with time has the large-range motion and complex macropixel structure, which brings a great challenge to efficient compression. Based on the analysis of the relationship between temporal motion and macropixel arrangement, an efficient motion estimation algorithm is proposed for plenoptic video compression. It finetunes motion vector predictor (MVP) by the macropixel-constrained collocated position of the current prediction unit in the nearest macropixel, and uses all the macropixel-constrained collocated positions, derived from the finetuned MVP, as motion search candidates for better complexity and compression-efficiency trade-off. The experimental results demonstrate that the proposed algorithm outperforms multi-view based compression and the pseudovideo based compression by an average of 56.40% and 78.72% bitrate reduction, respectively. Compared with HEVC, the proposed method can also save 7.65% bitrate.
Cyber Forensics Evolution and Its Goals

This chapter includes the evolution of cyber forensics from the 1980s to the current era. It was the era when computer forensics came into existence after a personal computer became a viable option for consumers. The formation of digital forensics is also discussed here. This chapter also includes the formation of cyber forensic investigation agencies. Cyber forensic life cycle and related phases are discussed in detail. Role of international organizations on computer evidence is discussed with the emphasize on Digital Forensic Research Workshop (DFRWS), Scientific Working Group on Digital Evidence (SWDGE), chief police officers' involvement. Authenticity-, accuracy-, and completeness-related pieces of evidence are also discussed. The most important thing that is discussed here is the cyber forensics data.
Hole-filling approach based on convolutional neural network for depth image-based rendering view synthesis

Depth image-based rendering (DIBR) has recently received significant attention as an efficient concept for generating virtual views. One of the main challenges in the view synthesis process is that holes will appear in the background (BG) of the virtual views because of occlusion. The existing methods either utilize spatial or temporal information from the original views and depth maps to fill the holes. However, the performance of the existing methods is seriously affected by the quality of the depth maps. Foreground (FG) texture will be wrongly introduced to the BG when the depth maps are not accurate enough. We develop a hole-filling approach for DIBR based on convolutional neural network, which shows reduced dependency on the quality of the depth map. More specifically, FG objects are detected on the RGB image by combining the Laplacian operator with a graph cut algorithm to avoid dependencies on the depth map quality. Furthermore, we learn an end-to-end mapping between the warped virtual view and the ground-truth view image with FG awareness. The learning-based approach exhibits excellent superiority in terms of saving time. The experimental results also show that the proposed approach outperforms current state-of-the-art techniques quantitatively and qualitatively.
Novel Motion Models for Time-Optimal Risk-Aware Motion Planning for Variable-Speed AUVs

In this paper, we develop new motion models for curvature-constrained and variable-speed Autonomous Underwater Vehicles (AUVs) for time-optimal risk-aware motion planning. AUVs are becoming increasingly useful and cost-effective for a variety of tasks in many underwater applications including surveillance and scientific expeditions. Despite recent advances, the autonomy of AUVs is limited, especially for vehicles that operate in environments with obstacles. In particular, there is limited research for time-optimal risk-aware motion planning. In contrast, there has been significant research for finding the time-optimal paths in environments without obstacles; however, adapting these models to environments with obstacles yields sub-optimal results, since these models force the AUV to operate at extremal speeds at close proximity to obstacles. Specifically, moving at maximum speed increases the risk of collision, while moving at minimum speed dramatically increases travel time. As such, this paper presents new motion models for AUVs that enable the selection of intermediate speeds to provide a better balance between time and risk near obstacles. These models enhance the agility and maneuverability of the AUV and provide motion planners the flexibility to select appropriate speeds and therefore construct time-optimal risk-aware paths. Additionally, the models are simple to compute and are suitable for on-demand real-time computation. The performance of the proposed model is compared against existing models using our recently developed T* algorithm for time-optimal risk-aware motion planning. The results show that our new model yields paths that are shorter in obstacles-rich scenarios with substantially lower risks.
Building An Open Source Linux Computing System On RISC-V

As is commonly known, hardware in a computer system requires a processor to run the operating system. The primary architectures that form the basis of most processors are CISC and RISC. CISC includes long sub-level commands for effective memory management and simple compiler implementations to be developed. Thus CISC has a complex set of commands; hence the name Complex Instruction Set Computer. On the other hand, CISC has had backward compatibility since the 1970s. Reduced Instruction Set Computer (RISC), the other popular architecture, simplifies command sets and handles access to memory with load-store commands. Since the instruction set is simplified, the hardware runs faster. In addition, RISC-based processors contain less chip-sets and may be designed faster. Currently, with the prevalence of the Internet of Things (IoT), the demand for RISC architectures has increased and its usage in embedded devices has become widespread. The ARM processor is a successful example of the RISC architecture adaptation. However, ARM processor production has certain license term limitations and contains a few design flaws in its architecture [1]. In 2010, the RISC-V processor architecture, which was developed as an open source instruction set at Berkeley University, paves the way for open source hardware in terms of its BSD license and design professionalism. As a result, it will be possible to produce their own processors for communities and organizations without license restrictions. In this paper, we discuss the building blocks of the development life-cycle of an open source operating system on the RISC-V architecture. As an operating system base, we implement MILIS Linux, an open source operating system development project. It has an independent user space and its own package manager. For a completely independent open source computing system, open source RISC-V hardware architecture provides the freedom that is needed.
Capable guardianship against identity theft

Purpose This paper aims to examine demographic differences between individuals who do not take measures to protect themselves from identity theft victimization and those who do. A majority of the research on identity theft has focused on predictors of victimization, reporting behaviors of the victims and their health and mental outcomes. However, little remains known about the individuals who choose to take any identity-theft measures despite concerns over this fast-growing breed of crime. Design/methodology/approach Guided by Felson and Cohen’s routine activities theoretical framework (1979), this study uses the 2014 Identity Theft Supplement of the National Crime Victimization Survey to identify the demographic characteristics that influence the use of self-protection measures among individuals in the general population. Findings This study finds that these individuals are much more likely to be white, older, female and highly educated. The decision to undertake protection against identity theft is also influenced by the following factors: prior experience of misuse, possession of a bank account in the prior 12 months, current possession of at least one credit card and awareness that one is entitled to a free copy of one’s credit report. Originality/value This study addresses the gap in scholarship on identity theft prevention by applying the concept of guardianship in Cohen and Felson’s routine activity theory (1979) to the usage of self-protection measures in a general population. Future findings will identify the areas which agencies and researchers can focus on to inform policies that foster individuals’ own initiatives to take self-protection measures against potential identity theft.
A study of a digital sticky note design environment

In order to advance our understanding of digital alternatives to analogue sticky notes we have developed a high-fidelity prototype system called Cards & Boards. Its design is informed by a study of the current design practice at a Danish design agency. The Cards & Boards system is a real-time collaborative system that can be accessed from any device with a browser, e.g. phones, tablets, laptops, and large displays, and it allows for drawing on digital whiteboards and creating not only digital sticky notes, but also other generalized kinds of “notes” like image and hyperlink cards. We present a study of Cards & Boards in a design project spanning two weeks, and we identify challenges as well as benefits from using this particular kind of design environment. We observe how this enables to work with content that is persistent and collaboratively accessible both across time and space. We also observe how the system changes the collaborative dynamics of the design meeting.
Low Illumination Enhancement For Object Detection In Self-Driving

Object detection plays an important role in the field of self-driving. Illumination has a great impact on object detection, but most of the current methods do not solve the problem of object detection in poor light environment well. We propose a network that can optimize the image conversion which is based on the Cycle Generative Adversarial Networks (CycleGAN). We redesign the discriminator network of Cycle-GAN, add additional discriminators, optimize multiple parts of the network such as loss functions, and add object detection networks after converting the network. The Robot Car dataset of Oxford University is utilized to verify the effectiveness of the proposed method, and the results proved that our method can significantly improve the detection accuracy and increase the detected object number in low illumination environment.
Camera Vignetting Model and its Effects on Deep Neural Networks for Object Detection

In this paper, we describe a new approach for synthetic image augmentation and its advantages in training Deep Neural Networks (DNNs) for object classification and localization. To address the need for a significant amount of data when training DNNs, for image-based ADAS functions, our method relies on virtually generated scenarios augmented via a physics-based camera model. The camera model implements various optical effects on ideal-synthetic images. For the scope of this paper, we illustrate the performance differences associated with the vignetting effect when training DNNs with and without image augmentation. We show that training on images altered by our camera vignetting model yield to a better performance than using ideal-synthetic images, additionally we illustrate the relationship between the network's performance results and the implemented effect (vignetting in this case). For a start, our results open the possibility for using camera models for training neural networks on synthetic data and pave the way toward further investigations on significant optical and image sensor effects to be modeled/implemented for performance enhancement during the training process. The approach is conducted and evaluated by training a DNN for car detection using the Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago (KITTI) and Virtual KITTI (VKITTI) datasets.
Context and knowledge aware conversational model and system combination for grounded response generation

End-to-end neural-based dialogue systems can potentially generate tailored and coherent responses for user inputs. However, most of existing systems produce universal and non-informative responses, and they have not gone beyond chitchat yet. To tackle these problems, 7th Dialog System Technology Challenges (DSTC7-Track2) was developed to focus on building a dialogue system that produces informational responses that are grounded on external knowledge. In this study, we propose a Memory-augmented Hierarchical Recurrent Encoder-Decoder, called MHRED, that grounded on both multi-turn dialogue context and external knowledge. Furthermore, we apply a combination of multiple dialogue systems. Our final system is an ensemble that combines three modules: a generation-based module, a retrieval-based module, and a reranking module. First, responses are generated by MHRED, and retrieved from a pre-defined database focusing on informativeness. Next, the reranking module sorts these candidates using several hand-crafted features, and finally it selects a response with the highest score. Therefore, this system can return diverse and meaningful responses from various perspectives. Experimental results show that our proposed MHRED outperforms strong baseline models and combining multiple dialogue systems significantly improves the automatic evaluation and human evaluations.
Residual Networks Behave Like Boosting Algorithms

We show that Residual Networks (ResNet) is equivalent to boosting feature representation, without any modification to the underlying ResNet training algorithm. A regret bound based on Online Gradient Boosting theory is proved and suggests that ResNet could achieve Online Gradient Boosting regret bounds through neural network architectural changes with the addition of a shrinkage parameter in the identity skip-connections and using residual modules with max-norm bounds. Through this relation between ResNet and Online Boosting, novel feature representation boosting algorithms can be constructed based on altering residual modules. We demonstrate this through proposing decision tree residual modules to construct a new boosted decision tree algorithm and demonstrating generalization error bounds for both approaches; relaxing constraints within BoostResNet algorithm to allow it to be trained in an out-of-core manner. We evaluate convolution ResNet with and without shrinkage modifications to demonstrate its efficacy, and demonstrate that our online boosted decision tree algorithm is comparable to state-of-the-art offline boosted decision tree algorithms without the drawback of offline approaches.
Constraint Path Planning for an Autonomous Wall Spray Coating Robot

The wall spray coating task requires robot to handle a spray-head to cover the whole interior wall surface. In this paper a manipulator is used to complete such task where a Cartesian constraint path is firstly generated by breadth first search (BFS) based on the boustrophedon decomposition, and then the Cartesian path will be converted to joint sequences. In addition, corners formed by two walls as the non-differentiable area are analyzed and fitted by Bezier curve. Finally, a sphere cap constraint planner is proposed to deal with wall suspended with multiple aligned pipelines. A 6DOF KUKA robot arm was employed to do the spray coating task in a building, and the experiments will demonstrate the effectiveness and efficiency of the proposed approach.
Highly Reflective Surface Measurement Based On Dual Stereo Monocular Structured Light System Fusion

3D surface measurement plays an important role in robotics. In this paper, we provide a highly reflective surface measurement method based on combination of dual stereo monocular structured light system fusion. A predetermined rigid transformation matrix is utilized to improve fusion quality for the fusion area. Moreover, a final decision map is created to avoid the reconstruction of repeating regions of left and right system for ensuring the accuracy of the non-fusion area to achieve monocular system accuracy. The validity and accuracy of our algorithm are verified by designed experiments. Furthermore, various complex objects are measured including a metal surface, a phone case and a motor component, which show the proposed method has the capability to solve the highly reflective surface measurement problem and the occlusion problem.