Reviewerly/exHarmony huggingface dataset:
Development of an Unmanned Traffic Management Simulation with Robot Operating System and Gazebo

his paper emphasizes the need of a simulation environment to test the development of UTM(Unmanned Traffic Management) systems being configured currently in the United States. This paper provides results of 2 methods done for the simulation of a UTM system that provides a Third Party Landing service. One simulation is conducted with Monte Carlo to analyze the probability of success of the Third Party Landing Service in guiding 4 to 10 UAS for landing. The other simulation was done through Robot Operating System (ROS) and Unreal Engine in a higher fidelity visualization of the UTM with the Third Party Landing Service.
Giza Pyramids Construction Algorithm with Centroid Opposition-Based Learning

Giza pyramids construction (GPC) algorithm is a recently developed metaheuristic algorithm inspired by the construction mechanism of Giza pyramids in Egypt. GPC algorithm is directed by the continuous changing of the workers and the motion of the stone blocks on the slide. GPC algorithm shows better performance than its competitive algorithms in benchmark numerical function optimization and image segmentation. But GPC gets trapped in local optima and suffers from premature convergence for some problems due to a lack of exploration of the search space. In this paper, we improved GPC by incorporating an opposition-based learning (OBL) scheme called centroid opposition-based computing (COBC) to enhance its exploration capability. The proposed algorithm is termed by GPC-COBL in this paper. It is tested on 28 IEEE CEC2013 benchmark problems. The results of GPC-COBL are compared with GPC. The experimental results demonstrate that the GPC-COBL statistically performs better than or equal to GPC in solving most of the problems.
The Behavioural and Topological Effects of Measurement Noise on Evolutionary Neurocontrollers

The disparity in performance between simulated and real systems is a major problem in robotics and other fields. Simulating measurement noise is one method of reducing these performance differences. Here, we examine the effect of measurement noise on the behaviour and topology of evolved neuromodulated neurocontrollers applied to control evader agents in a pursuit-evasion game. Measurement noise in the form of a zero-mean, normally distributed random signal is applied to the evader’s radar range and angle signals. The results indicate that increasing the levels of measurement noise increases the number of generations required to evolve fit agents. Noise at the neurocontroller outputs is of lesser amplitude than that at the inputs, suggesting a low-pass filtering operation. When levels of measurement noise different to those with which they were evolved were applied to the neurocontrollers, greater amplitude in the measurement noise signal increased the average length of time required to capture the evader. When the level of measurement noise was changed during evolution, after a few generations of further evolution, the neurocontrollers were able to adapt to both increases and decreases in the amount of noise. The evolutionary neurocontrollers are robust to high levels of measurement noise and can adapt to large changes in noise amplitude. This suggests that the neurocontrollers will be robust when used in the field on real robots, and that they may be a good solution to bridging the gap between simulation and reality.
SPCNet: A Panoramic image depth estimation method based on spherical convolution

As an emerging media format, virtual reality (VR) has attracted the attention of researchers. 6-DoF VR can reconstruct the surrounding environment with the help of the depth information of the scene, so as to provide users with immersive experience. However, due to the lack of depth information in panoramic image, it is still a challenge to convert panorama to 6-DOF VR. In this paper, we propose a new depth estimation method SPCNet based on spherical convolution to solve the problem of depth information restoration of panoramic image. Particularly, spherical convolution is introduced to improve depth estimation accuracy by reducing distortion, which is attributed to Equi-Rectangular Projection (ERP). The experimental results show that many indicators of SPCNet are better than other advanced networks. For example, RMSE is 0.419 lower than UResNet. Moreover, the threshold accuracy of depth estimation has also been improved.
XSP: Fast SSSP Based on Communication-Computation Collaboration

Single-source shortest path (SSSP) is an important graph search algorithm for data-intensive applications which finds the minimum distance from a source vertex to any other vertex in a given graph. Although having been extensively studied for both single- and multi-node scenarios, SSSP search still brings severe challenge to communication when processing large graphs that consist of billions of vertices involving hundreds of computing nodes. To address this problem, in this paper we propose XSP, a fast SSSP search method based on communication-computation collaboration, which optimizes the communication of parallel SSSP in two aspects. First, we design a group-based scalable batching mechanism which effectively reduces the inter-machine communication overhead. Second, we propose a CCO (Communication-Computation Overlapping) method which realizes non-blocking execution of communication and computation. We have implemented XSP and extensive evaluation results show that the performance of XSP is significantly higher than that of the state-of-the-art parallel SSSP methods.
Research and Analysis Of Extraction and Recognition INS Criptions of Bronzebased On Improved K-Means and Convo Lutional Neural Network

The natural language processing of Bronze inscriptions is the pivotal step to the study of the historical use of Bronze inscriptions, and the recognition of Bronze inscription words relics is the most important part. Due to its long history and complex font, the difficulty of recognition is increased a lot. At present, convolutional neural network has been widely used in the field of photo recognition, but it has few application in the field of Bronze inscriptions recognition. This paper proposes a method of Bronze inscriptions' extract and recognition based on improved k-means and convolutional neural network, using the improved k-means algorithm to extract characters, which makes preparations for the recognition of convolutional neural network. Experiments has shown that the improved method has significantly improved the accuracy and speed of Bronze inscriptions recognition, and it is also considerably helpful to the Bronze inscriptions research.
The Education System of the United States of America: ICT and Digitalization

This chapter aims at assisting and guiding educators, researchers, and policymakers who are interested in learning about Information and Communication Technology (ICT) and digitization in education in the United States (USA). With a historical context covering the past 20 years, the chapter provides an overview of research, trends, and issues related to ICT and digitization in K-12 school settings. It presents a wide range of developments in ICT and digitization impacting the field of public education, exploring and analyzing the following four key issues: what technology integration is and what isn’t; why technology integration is important; emerging technologies in education; and funding for technology and the “digital divide.” The chapter ends describing the most recent stage on the key issues discussed and the implications for policy and practice these issues introduce.
A Frequency-first Heuristic for Shortest Linear Programs

The shortest linear program has been proved to be a NP-hard problem. In order to obtain the better approximate solution, a frequency-first heuristic method is proposed, which can optimize the number of XOR gates required by linear components while ensuring the stability of the algorithm. Firstly, we combine the pre-emptive gate strategy with frequency-first (FQCY) in the selection stage to reduce the increase of time complexity caused by exhaustive search, so that the high-density matrix can obtain the optimal result within a reasonable time. Secondly, minimization of vector and appropriate randomization are added to deal with the tie, so as to give full play to the advantages of cancellation-allowed circuit and increase the possibility of obtaining the optimal solution. Finally, compared with Paar, BP, RNBP, RSDF algorithms on random matrices of various sizes and densities, it is proved that the probability of obtaining the optimal solution of the proposed algorithm in circuit depth is more than 30% higher than RNBP and RSDF. In terms of the number of XOR gates, especially for larger matrix, the probability of obtaining the optimal solution increases by more than 10%. The stability of the optimal circuit generated by this algorithm is about 90%.
An End-to-End Learning Architecture for Efficient Image Encoding and Deep Learning

Learning-based image/video codecs typically utilize the well known auto-encoder structure where the encoder transforms input data to a low-dimensional latent representation. Efficient latent encoding can reduce bandwidth needs during compression for transmission and storage. In this paper, we examine the effect of assigning high level coarse grouping labels to each latent vector. Designing coding profiles for each latent group can achieve high compression encoding. We show that such grouping can be learned via end-to-end optimization of the codec and the deep learning (DL) model to optimize rate-accuracy for a given data set. For cloud-based inference, source encoder can select a coding profile based on its learned grouping and encode the data features accordingly. Our test results on image classification show that significant performance improvement can be achieved with learned grouping over its non-grouping counterpart.
Multi-task based Image Aesthetics Quality Evaluation

Image aesthetics quality evaluation, which allows computers to judge "beauty and ugliness", is widely used in fields of image recommendation and image editing etc.. Most aesthetic evaluation methods can only output one type of evaluation result, thus, the scope of their application scenarios is limited. To solve this problem, we proposed a multi-task based aesthetic evaluation system, which can output the image style label and three forms of the aesthetic evaluation results for a image. The training procedure is divided into two stages to realize the aesthetic evaluation tasks from coarse to fine. Experiments on AVA dataset show that it can accomplish an efficient and comprehensive image aesthetic quality evaluation.
Fraud Prediction in Pakistani E-commerce Market

With Pakistan being ranked as the 46 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> largest revenue generator in terms of the E-commerce industry, online frauds have increased proportionally. The process of online shopping has changed drastically as the seller and buyer can now communicate directly through social media applications without needing a specific platform. It implies that all fraud prevention techniques, already in place, fail in such scenarios as they are only applicable to their platform. So, for an easily attainable input to the fraud prevention pipeline, our research focuses on analyzing the fraudulent activities in this market by using commonly available customer, product, and seller traits, as features. For this research, a product-based fraud detection dataset was collected through a survey and various feature selection techniques and ML models were applied to it. In prospect, our approach can be used to develop a utility that automatically extracts relevant features, calculates risk scores, and facilitates customers in purchase decisions, given a threshold on the risk score.
Aerial Manipulation while in Contact with the Environment by Multilinked Aerial Robot with Thrust Vectoring Module

In order to manipulate objects using aerial robots, conventional way using quadcopter with a robotic arm has two problems; Underactuation and Weak manipulation force. To solve underactuation, 1-DoF thrust vectoring mechanism is attached to each propeller and enables fully-actuated flight control. Flight control uses online non-linear optimization which takes thrust forces and vectoring angles as inputs. To increase manipulation force, this paper proposes whole-body manipulation by multilinked robot taking advantage of environmental adherence. Motion planning for manipulation which includes contact-detection, mode transition, end-effector position control was proposed. The robot successfully opened the door in real machine experiment, and showed its ability to manipulate large objects.
Research on New Virtualization Security Protection Management System Based on Cloud Platform

Abstract As an emerging product under the condition of informatization, the utilization of cloud platform in many industries has brought fundamental changes to the production and business model in related fields. The cloud platform provides rich and diverse utilization services to terminals through multi-dimensional integration of different IT resources. With the in-depth utilization of cloud platform, the security problems it faces are becoming more and more prominent. The traditional network security protection means have been difficult to effectively adapt to and deal with the security threats under the new situation of cloud platform utilization. As a prominent part of building cloud platform, the construction level of virtualization security protection system will have an intuitive impact on the security of cloud platform. At present, the virtualization security protection management system under cloud platform is facing direct threats from virtual machine deployment, virtual machine communication and virtual machine migration. Based on this, this paper studies the virtualization security protection management system of cloud platform from the perspective of virtualization security tech, so as to ameliorate the stability, reliability and security of cloud platform.
DENNI: Distributed Neural Network Inference on Severely Resource Constrained Edge Devices

Pervasive intelligence promises to revolutionize society from Industrial Internet of Things (IIoT), to smart infrastructure and homes, to personal health monitoring. Unfortunately, many edge devices that are pervasively embedded into infrastructure or implanted into humans are severely resource-constrained. As performing computations at the edge becomes increasingly important to meet latency deadlines and retain sensitive data locally, severe resource constraints present a challenge because many algorithms are too large to fit on a single edge device. In this paper, we focus on distributing inference for neural networks (NNs) with convolution and fully connected layers across multiple edge nodes. In order to improve efficiency on severely resource-constrained edge nodes for diverse NN architectures we present an end-to-end, automated approach, DENNI, that optimizes NN distribution with minimal nodes while meeting memory constraints. When targeting a network of edge nodes with 256KB of non-volatile memory connected with Bluetooth Low Energy, DENNI successfully distributes NN inference for a variety of machine learning algorithms across multiple edge nodes where other, static approaches cannot.
Incorporation of Contextual Information into BERT for Dialog Act Classification in Japanese

Recently developed Bidirectional Encoder Representations from Transformers (BERT) outperforms the state-of-the-art in many natural language processing tasks in English. Although contextual information is known to be useful for dialog act classification, fine-tuning BERT with contextual information has not been investigated, especially in head final languages such as Japanese. This paper investigates whether BERT with contextual information performs well on dialog act classification in Japanese open-domain conversation. In our proposed model, not only the utterance itself but also the information about previous utterances and turn-taking are taken into account. Results of experiments on a Japanese dialog corpus showed that the incorporation of the contextual information improved the F1-score by 6.7 points.
Human pose estimation based on region refined network

With the development of detection technology based on deep learning, the keypoint detection of the human body has gradually formed a theoretical system based on convolutional neural networks. In this context, the keypoints of the predicted and real values in the model follow the statistical deviation law of neighbourhood, and an improved model for refined prediction of keypoints is proposed. The original one-stage detection network is transformed into a two-stage end-to-end detection network. The detection error of the typical model in the keypoint neighbourhood is reduced, and the AP of the model on the COCO2017 dataset has an increase of 1 percentage point. This paper will detail in the improvement work the structure and parameters of the network, the training and prediction process of the network, the network supervision and loss function, and finally the experimental results on the COCO2017 dataset.
DCT Coefficients Weighting (DCTCW)-Based Gray Wolf Optimization (GWO) for Brightness Preserving Image Contrast Enhancement

Image contrast enhancement (CE) is a frequent image enhancement requirement in diverse applications. Histogram equalization (HE), in its conventional and different further improved ways, is a popular technique to enhance the image contrast. The conventional as well as many of the later versions of HE algorithms often cause loss of original image characteristics particularly brightness distribution of original image that results artificial appearance and feature loss in the enhanced image. Discrete Cosine Transform (DCT) coefficient mapping is one of the recent methods to minimize such problems while enhancing the image contrast. Tuning of DCT parameters plays a crucial role towards avoiding the saturations of pixel values. Optimization can be a possible solution to address this problem and generate contrast enhanced image preserving the desired original image characteristics. Biological behavior-inspired optimization techniques have shown remarkable betterment over conventional optimization techniques in different complex engineering problems. Gray wolf optimization (GWO) is a comparatively new algorithm in this domain that has shown promising potential. The objective function has been formulated using different parameters to retain original image characteristics. The objective evaluation against CEF, PCQI, FSIM, BRISQUE and NIQE with test images from three standard databases, namely, SIPI, TID and CSIQ shows that the presented method can result in values up to 1.4, 1.4, 0.94, 19 and 4.18, respectively, for the stated metrics which are competitive to the reported conventional and improved techniques. This paper can be considered a first-time application of GWO towards DCT-based image CE.
A Deep Reinforcement Learning Approach to Learning Tree Edit Policies on Binary Trees

Modeling sequential transformations between two trees is a fundamental task in domains such as bioinformatics. Traditional methods to address this usually rely on hand-coded heuristics and algorithms which are often hard to derive. The large advances in deep learning technologies and computational power have recently opened up new potential avenues. In particular, representation and policy learning provides an interesting opportunity to model the dynamic evolution of two trees, where each tree can be embedded in a Euclidean space and its evolution can be modeled by an embedding trajectory in this space.In this paper, we propose a representation and policy learning framework that learns a representation for arbitrary sized binary tree pairs using recurrent LSTM networks and a policy to transfer one tree in to the corresponding target tree using Reinforcement Learning. Here, the representation is pre-trained on tree transfer similarity to transform pairs of tree-structured data into an approximate numerical multidimensional vector which encodes the original structure information. This model, used with a deep reinforcement learning approach, yields a constructive method for generating basis functions for approximating value functions and permits to learn an efficient, general tree transfer policy that incrementally transforms the source tree into the target tree.
A Cybersecurity Curricular Framework for IT Undergraduates in South Africa

Many organisations have reported a dramatic shortage of cybersecurity skills to counteract cyberattacks. Academia and industry should be responsible for working together to investigate possibilities in closing this cybersecurity skills gap. The IT curricula of many South African universities are based on the outdated IT2008 curricular guidelines, which make no mention of cybersecurity. In order to address this increasing cybersecurity skills gap, universities offering computer-related qualifications need to adapt their curricula accordingly. This paper proposes a framework for integrating cybersecurity into IT undergraduate curricula in South Africa. As the cybersecurity needs of industry continue to evolve, the proposed framework will provide a structure that aligns IT curricula to the cybersecurity workforce needs by considering the NIST NICE framework, CSEC2017, and IT2017. Furthermore, the proposed framework discussed in this paper will serve as an academic guide for communicating cybersecurity content to address specific industry work roles. The implementation of this IT Cybersecurity Curricular Framework aims to impact the development of the cybersecurity workforce positively and reduce the cybersecurity skills gap between academia and industry.
IPR-GAN: Identity Preserving Representation GAN for Multi-view Face Synthesis and Recognition

Photo-realistic and identity preserving multi-view synthesis from a single face image is a challenging and essential problem. The common challenges faced in multi-view face synthesis are that the serious appearance distortion suffering from face synthesis and the generated face images may keep “incomplete” identity information due to a single-pathway encoder-decoder network. This paper proposes Identity Preserving Representation Generative Adversarial Network (IPR-GAN) for photo-realistic multi-view face synthesis. IPR-GAN combats the challenging synthesis problems with a recognizing while generating framework and reserves the postural invariance identity data for downstream tasks like face recognition and pose estimation. Exhaustive experiments substantiate that the proposed method not only represents the improvement of multi-view face synthesis on visual realism, but also preserves identity information for face recognition.
Challenges and Trends of Agile

Rapidly increasing of requirements of business pushed researchers to define new approaches and methodologies to meet marketing needs. Agile methodology has been created and replaced the traditional-driven development methods that focus on soliciting, documenting a complete set of requirements, and take a long time comparing to market change. On the other hand, customers need to be closer to the development process and collaborate with team development. Despite agile advantages, introducing new tools, coordination, and collaboration concepts, some challenges still need to be discussed and improved. These challenges relate to achieve balanced IT service development process in the organization. As a result, new trends have been created to facilitate new changes in software development. This chapter will study agile methodologies and different challenges with suggested solutions generated from agile philosophy itself.