Reviewerly/exHarmony huggingface dataset:
Pre-processing for UAV Based Wildfire Detection: A Loss U-net Enhanced GAN for Image Restoration

In this paper, a U-net with feature loss enhanced generative adversarial network (GAN) is designed for the wildfire or smoke images restoration which is captured by unmanned aerial vehicles in a serious environment. Based on the concepts of GAN, feature loss, and fastai API, we firstly crappy the target images, and train a U-net architecture based generator, then load the adaptive loss of discriminator and the mean square error together to train the GAN model. After the GAN, a second U-net grabs the feature loss from an Imagenet pre-trained loss network to generate the GAN output images with one more step. This U-net enhanced the generator of GAN and helped to get the main features in human conception. Comparing with other restoration methods, this model used the adaptive loss to train the GAN and perceptual loss to train the next U-net. Learning rate with simulation annealing helped jumping out of the local minimum. The result proved the good performance of this model.
Adversarial Training on Joint Energy Based Model for Robust Classification and Out-of-Distribution Detection

Deep neural networks tend to be erroneous when the training and test distribution differ. Especially, neural classifiers are brittle to adversarial examples, and highly overconfident to out-of-distribution examples. Hybrid modeling of generative and discriminative distribution shown to be effective for out-of-distribution detection, but is not robust to adversarial attacks. Otherwise, defense methods for adversarial attacks cannot distinguish out-of-distribution examples. In this work, we present a hybrid model that can deal with both adversarial and out-of-distribution examples. Our method is built upon the joint energy based model and adversarial training. Through experiments on CIFAR-10 dataset, we show that our method has state-of-the-art performanced among hybrid models. Furthermore, we show that our model exhibits more perceptually-aligned feature than other methods, by showing the gradient sensitivity map with newly proposed score function.
Depicting uncertainty in 2.5D treemaps

A truthful and unbiased display of data using information visualization requires detecting and communicating uncertainty. Uncertainty is often inherent in data or is introduced by data processing and visualization (e.g., visual display of accumulated data) but frequently not accounted for. This paper discusses the suitability of advanced visual variables such as sketchiness, noise, nesting-level contouring, and color weaving for communicating uncertainty.
Security Analysis of Storage, Virtualization, Infrastructure and Standardization in Cloud Computing

Nowadays the cloud computing grows in high rate with the help of recent technological advancement. It provides the on demand and low cost services to the customer. Cloud computing distributes the services to various location based on the demand. The services can cross various layers and location which leads the security problem. The existing security system provides reliable solutions which are not enough to secure the resource and services from unauthorized access. Three types of security is available in the cloud namely Infrastructure security, Platform security and application security. The proposed analysis gives the complete information about these security and its issues. Data security management process has various steps for securing the data around the cloud network with reliable access. Virtualization level security is analyzed with different parameter based on the number issues present in the cloud. Authentication types are also identified and analyzed over security level. Various types of cloud framework and vulnerabilities are assessed in order to achieve better security.
High-Speed Human Arm Projection Mapping with Skin Deformation

Augmenting the human arm surface via projection mapping can have a great impact on our daily lives with regards to entertainment, human-computer interaction, and education. However, conventional methods ignore skin deformation and have a high latency from motion to projection, which degrades the user experience. In this paper, we propose a projection mapping system that can solve such problems. First, we newly combine a state-of-the-art parametric deformable surface model with an efficient regression-based accuracy compensation method of skin deformation. The compensation method modifies the texture coordinate to achieve high-speed and highly accurate image generation for projection using joint-tracking results. Second, we develop a high-speed system that reduces latency from motion to projection within 10 ms. Compared to the conventional methods, this system provides more realistic experiences.
Understanding VR Software Testing Needs from Stakeholders’ Points of View

Software testing is a critical activity to ensure that software complies with its specification and guarantee for delivering high quality products. However, there are a number of challenges to be faced as new software domains are created. Although there are some works in the literature that explore the application of software testing practices specific to the context of Virtual Reality (VR), these works tend to reflect only the researcher's point of view, leaving aside the players involved in the VR development process, contributing to a gap that makes it difficult to popularize such practices. Aiming to reduce this gap, this paper presents an analysis under the perception of stakeholders involved in the process of developing and using VR applications. We conducted a survey with 88 respondents, comprising different roles, interviewed during a virtual reality scientific conference and later complemented by electronic dissemination, reaching five different profiles. The results showed that there is a major concern among all the interviewed profiles regarding the impact of faults in VR applications. In addition, we investigated these results in both software and hardware aspects. The results related to software pointed to a greater concern of the players regarding visual aspects and non-functional requirements. As for hardware-related aspects, the biggest concern is related to limitations in mechanisms that are responsible for providing input data to applications.
VizWick

In this paper we present a web-based interactive tool for visualizing hierarchical data. Our main purpose is to facilitate the visualization of datasets. We achieve this by offering VizWick in a browser environment, with no requirement of additional software. We provide the option to view the same dataset from multiple coordinated perspectives, thus providing the possibility to gain more analytical insight than if the dataset was visualized in a single view. We focus on several hierarchy visualization techniques which can be either in 2D, 3D, or a virtual reality environment. The choice of programming language is JavaScript, with the aid of PixiJS and Three.js libraries. We demonstrate the usefulness of our tool by applying it to the NCBI taxonomy, a hierarchically structured dataset which contains over 300,000 elements.
Image Guided Visual Tracking Control System for Unmanned Multirotor Aerial Vehicle with Uncertainty

This paper presents a wavelet-based image guided tracking control system for unmanned multirotor aerial vehicle system with the presence of uncertainty. The visual signals for the visual tracking process are developed by using wavelet coefficients. The design uses a multiresolution interaction matrix with half and details images to relate the time-variation of wavelet coefficients with the velocity of the aerial vehicle and controller. The proposed design is evaluated on a virtual quadrotor aerial vehicle system to demonstrate the effectiveness of the wavelet-based visual tracking system without using an image processing unit in the presence of uncertainty. In contrast to the classical visual tracking technique, the wavelet-based method does not require an image processing task.
Achieving Pairing-Free Aggregate Signatures using Pre-Communication between Signers

Most aggregate signature schemes are relying on pairings, but high computational and storage costs of pairings limit the feasibility of those schemes in practice. Zhao proposed the first pairing-free aggregate signature scheme (AsiaCCS 2019). However, the security of Zhao’s scheme is based on the hardness of a newly introduced non-standard computational problem. The recent impossibility results of Drijvers et al. (IEEE S&P 2019) on two-round pairing-free multi-signature schemes whose security based on the standard discrete logarithm (DL) problem has strengthened the view that constructing a pairing-free aggregate signature scheme which is proven secure based on standard problems such as DL problem is indeed a challenging open problem. In this paper, we offer a novel solution to this open problem. We introduce a new paradigm of aggregate signatures, i.e., aggregate signatures with an additional pre-communication stage. In the pre-communication stage, each signer interacts with the aggregator to agree on a specific random value before deciding messages to be signed. We also discover that the impossibility results of Drijvers et al. apply if the adversary can decide the whole randomness part of any individual signature. Based on the new paradigm and our discovery of the applicability of the impossibility result, we propose a pairing-free aggregate signature scheme such that any individual signature includes a random nonce which can be freely generated by the signer. We prove the security of our scheme based on the hardness of the standard DL problem. As a trade-off, in contrast to the plain public-key model, which Zhao’s scheme uses, we employ a more restricted key setup model, i.e., the knowledge of secret-key model.
Comparative Studies of Several Methods for Building Simple Traceability and Identifying The Quality Aspects of Requirements in SRS Documents

In the software development process, the requirements traceability in the Software Requirements Specification (SRS) is an important aspect to trace fulfillment of requirement. Generally, the implementation of traceability is done manually, and it requires considerable time and money. Many methods have been proposed to identify traceability automatically, therefore, we conducted a comparative study of several existing methods to find out which method is better. In this study, we made a comparison of 4 methods, namely the Information Retrieval (IR), Ontology, Combination of Information Retrieval with Topic Modeling (LSA or LDA) methods. From the comparison of those methods, we found that the IR method gets the highest accuracy score, but the precision, recall and fl-score are unequal between dataset 1 and dataset 2, while other methods (ontology, IR+LSA, IR+LDA) have more stable metrics. The average score of precision from highest to lowest consecutively is in the IR + LDA method, Ontology, then IR+LSA.
Surveillance Missions Deployment on the Edge by Combining Swarm Robotics and Blockchain

Current swarm robotics systems are not utilized as frequently in surveillance missions due to the limitations of the existing distributed systems' designs. The main limitation of swarm robotics is the absence of a framework for robots to be self-governing, secure, and scalable. As of today, a swarm of robots is not able to communicate and perform tasks in transparent and autonomous ways. Many believe blockchain is the imminent future of distributed autonomous systems. A blockchain is a system of computers that stores and distributes data among all participants. Every single participant is a validator and protector of the data in the blockchain system. The data cannot be modified since all participants are storing and watching the exact same records. In this study, we will focus on blockchain applications in swarm robotics using Ethereum smart contracts because blockchain can make a swarm globally connected and secure. A decentralized application (DApp) is used to deploy surveillance missions. After mission deployment, the swarm uses blockchain to communicate and make decisions on appropriate tasks within Ethereum private networks. We conclude that, although blockchain enables a swarm to be globally connected and secure, there are performance limitations that can become a critical issue.
Comparative Performance of Machine Learning Methods for Text Classification

This paper studies the integration of modular arithmetic, matrix algebra and complex number arithmetic. It describes implementation strategies of matrix transformations for residue matrices over complex plane that can be applied in the computer science fields which deal with cryptographic applications for more secure. Their mathematical properties over complex plane are analyzed to apply in cryptographic matrix transformation techniques. A new implementation approach to Hill cipher is proposed using the residue matrices over complex plane.
Convolutional neural networks for detection of hand-written drawings

Convolutional Neural Networks (CNN) have been used since the late 80's. Nevertheless, until the 2000's when they begun to be popular for image classification tasks, thanks to the improvements in computation performance of electronic devices and new algorithms development. However, most of the classifiers are oriented towards the processing of real-world images. This document presents a CNN for hand-written drawings recognition. The dataset consists of 710,000 images that correspond to 71 different classes, each one with around 10,000 samples. The dataset was randomly divided into a training set (80%) and a testing set (20%). The CNN classifier achieved an accuracy of 84.79% for classifying the samples on the testing set. The classification results showed perfect identification for 10 classes, whereas 6 classes were poorly classified. It is foreseen that the results presented here can fuel applications where identification of hand-made drawings are critical, such as neuropsychological tests.
Data Mining Approach to Job Recommendation Systems

Employability has been a major concern for society, due to rapidly changing technology and skillsets expected by the industry. Sometimes due to lack in having skillsets and sometimes as we can't get through all the options for employability. In the last decade, job recommender system provide the high growth in the job recruiting domain. User profile increade the high attention ain due to the job recommender system based on the recommendationtechnologies. This paper presents a review of some typical job recommender systems and proposes a data mining approach to job recommendation systems.
A lightweight convolutional neural network model for target recognition

Abstract Convolutional neural networks have achieved excellent performance in a wide range of applications, but the huge resource consumption makes a great challenge to their application on mobile terminals and embedded devices. In order to solve such problems, it is necessary to balance the size, speed and accuracy of the network model. This study proposed a new shallow neural network on the bases of ResNet and DenseNet. We use different size convolution kernels to obtain feature maps and then concat them. Afterwards we build two convolution layers to reduce the size of the feature maps and increase the depth of the network. By stacking this structure, we get our net model. Experiments show that our nine-layers network recognition performance is better than 18-layers ResNet and 19-layers DenseNet, and its training time is shorter. The final recognition rate of our network is 97.37%, ResNet recognition rate is 96.93%, and DenseNet is 96.31%.
Towards Using Universal Big Data in Artificial Intelligence Research and Development to Gain Meaningful Insights and Automation Systems

The increasing number of human activities using information and communication technology (ICT) generates a tremendous amount of data. It brings the opportunity to use universal big data for further computation that may deliver new insights and automation system. The technology that enables this work is artificial intelligence (AI). As known, the utilization of AI technology is becoming more and more pervasive in our daily life. Furthermore, discussion related to AI has played an essential role in an organization's decision-making process. Thus, this paper discusses the utilization of AI technology that penetrates end-to-end various aspects of human activities, such as in education, health, business, social life and so forth. The end-to-end process begins with data collection covering various types of data (text, picture, audio, video, animation) and various methods (survey, observation, interview, experiment). Then, it continues with the pre-process data cleansing and process to determine data features, and seeks the relationship within them, using machine learning process with appropriate algorithms. The results may then be used for applications portfolio in order to improve organization strategies and programs. The organization's performance can be visualized from time to time for continuous quality improvement. The end-to-end cycles of processes continue, from data collection, data pre-processing and processing, performance computation monitoring (identification, classification, prediction, and prescription), improving strategy and program, and implementing in the real-life activities until generating more behavioral data, finding pattern and design the systems. As a whole, this end-to-end cycle leads us to an AI automation system, with the power to generate meaningful insights suited to solve current problems, predicting trending issues, and understanding phenomena.
Control System Synthesis Based on Optimal Trajectories Approximation by Symbolic Regression for Group of Robots

The paper considers the solution of the problem of optimal control system synthesis. It is proposed to solve this problem based on the approximation of the set of optimal trajectories using symbolic regression methods. At the first step the optimal control problem is solved for various initial states; at the second step symbolic regression method is used to approximate the obtained set of optimal trajectories. In the suggested approach the proximity of the solution to the optimal one is determined by the accuracy of the approximation. A computational experiment of solving the applied problem of optimal control system synthesis for a group of car-like mobile robots in space with dynamic and static phase constraints is presented. The experiment showed that the found synthesized control function allows to move robots by the trajectory close to the optimal one for any initial state from a given domain.
Design of convolutional neural network SoC system based on FPGA

With the continuous development of neural network technology, it has been paid more and more attention in digital image processing. In this paper, the convolution neural network is designed on the programmable logic device (FPGA). Using the characteristics of the hardware circuit, the convolution kernel is implemented with the parallel data processing in the core and the parallel processing between the convolution cores. The double buffer is used to reduce the access to memory devices. At the same time, the characteristics of cyclic block and sparse matrix are used to optimize the network structure, improve the network speed and reduce the power consumption. ARM processor is used to preprocess the images and configure the corresponding registers to control the number of network layers of CNN network. The test results show that the recognition rate of handwritten numeral can reach 97% by using CNN accelerator based on FPGA. Meanwhile, the power consumption and speed are significantly improved, which meets the requirements of portable mobile devices.
LidarPhone: acoustic eavesdropping using a lidar sensor

Private conversations are an attractive target for malicious actors intending to conduct audio eavesdropping attacks. Previous works discovered unexpected vectors for these attacks, such as analyzing high-speed video of objects adjacent to sound sources, or using WiFi signal information. We propose LidarPhone, a novel side-channel attack that exploits the lidar sensors in commodity robot vacuum cleaners to perform acoustic eavesdropping attacks. LidarPhone is able to detect the minute vibrations induced on objects that are near audio sources, and extract meaningful signals from inherently noisy raw lidar returns. We evaluate a realistic scenario for potential victims: recovering privacy-sensitive digits (e.g., credit card numbers, social security numbers) emitted by computer speakers during teleconferencing calls. We implement LidarPhone on a Xiaomi Roborock vacuum cleaning robot and perform a comprehensive series of real-world experiments to determine its performance. LidarPhone achieves up to 91% accuracy for digit classification.
Rule-Guided Graph Neural Networks for Recommender Systems

To alleviate the cold start problem caused by collaborative filtering in recommender systems, knowledge graphs (KGs) are increasingly employed by many methods as auxiliary resources. However, existing work incorporated with KGs cannot capture the explicit long-range semantics between users and items meanwhile consider various connectivity between items. In this paper, we propose RGRec, which combines rule learning and graph neural networks (GNNs) for recommendation. RGRec first maps items to corresponding entities in KGs and adds users as new entities. Then, it automatically learns rules to model the explicit long-range semantics, and captures the connectivity between entities by aggregation to better encode various information. We show the effectiveness of RGRec on three real-world datasets. Particularly, the combination of rule learning and GNNs achieves substantial improvement compared to methods only using either of them.
Playing Catan with Cross-Dimensional Neural Network

Catan is a strategic board game with many interesting properties, including multi-player, imperfect information, stochasticity, a complex state space structure (hexagonal board where each vertex, edge and face has its own features, cards for each player, etc.), and a large action space (including trading). Therefore, it is challenging to build AI agents by Reinforcement Learning (RL), without domain knowledge nor heuristics. In this paper, we introduce cross-dimensional neural networks to handle a mixture of information sources and a wide variety of outputs, and empirically demonstrate that the network dramatically improves RL in Catan. We also show that, for the first time, a RL agent can outperform jsettler, the best heuristic agent available.
Fine grained sentiment analysis based on Bert

Abstract The fine-grained sentiment mining based on online review data is helpful to analyze user pain points and improve user experience and business marketing. A fine-grained sentiment analysis model based on Bert is proposed. Commodity feature words and emotion words are extracted by dependency parsing; the polarity of emotion was analyzed by the combination of Bert and emotional words; through the corresponding emotional polarity of feature words, we can get the emotional tendency of commodity features. The experimental results in movie review data set and film review data set show that, the accuracy and F1 value of Bert with feature words and emotional words were 94.67% and 94.55%, which were better than other models.