Reviewerly/exHarmony huggingface dataset:
Multi-Agent Mapping and Navigation of Unknown GPS-Denied Environments Using a Relative Navigation Framework

When generating 3D maps with unmanned aerial vehicles (UAVs) in GPS-denied environments, it is important to correctly handle path planning, estimation, and mapping techniques. Because multirotor UAVs are limited in flight time, using multiple UAVs to map an environment collaboratively can significantly improve the mapping efficiency. This paper addresses the following key issues required to enable mapping with multiple agents: Combining a reactive path planner with an obstacle avoidance algorithm to handle navigation in complex environments. Estimating the relative and global states of a UAV separately with a relative navigation framework to allow for loop closures in the mapping process without causing the estimation to diverge. Adapting a graph-based simultaneous localization and mapping (graph-SLAM) technique for multiple UAVs flying simultaneously and merging their maps in real-time. We were able to use these strategies to generate dense maps in complex GPS-denied environments with multiple UAVs.
Design of a portable toxic gas monitor

To solve the problem of real-time monitoring of the distribution of toxic gases in high-risk pollution areas and ensure the safety of field workers, a portable toxic gas monitoring system based on u/COS-II is designed by combining embedded system and Internet of things technology. The system can collect six kinds of toxic gases such as carbon monoxide and sulfur dioxide in the environment in real-time; display the gas concentration through the human-computer interface, set the gas alarm threshold; and use the wireless transmission unit to upload the data to the cloud server. The experimental data show that the system has stable performance and small error, which can meet the requirements of real-time, accurate and comprehensive monitoring of gas distribution in high-risk pollution sites, and remind the site staff to evacuate in time.
Research on Mongolian-Chinese Machine Translation Based on Generative Method

Neural machine translation methods based on auto-regressive Recurrent Neural Networks (Auto-regressive RNN) are constrained by sequential decoding methods during translation prediction, which in turn results in severe translation delays. In addition, the error transmission during training will lead to error accumulation, which will affect the quality of the model. Therefore, in this paper, using the generative method, the latent vector space based on the source language and the target language is constructed. The advantage is that the translation decoding is only subject to the independent distribution in the potential space, and is not affected by the historical target translation, so the translation delay of the target language is alleviated, and the error accumulation problem of the traditional method is also avoided. Moreover, the CWMT2019 Mongolian-Chinese translation task is used to verify the constructed model, and the results show that it has excellent results, in which way its effectiveness is fully demonstrated.
Minimum fuzzy divergence based image cosegmentation

Multi-view cosegmentation for the same object is the basis of true three-dimensional imaging. Due to changes in the foreground and interferences from the background of the images, traditional cosegmentation algorithms often cannot fully and effectively extract common areas. To solve this problem, in this paper,we propose a new image cosegmentation algorithm which incorporates the minimum fuzzy divergence and active contours model.Considering the foreground similarity and background consistency between multiple images,the energy functions of images are generated. We lead color information covered by an image into the energy function of another image to enhance the robustness of curve evolution.Then we minimize the energy function value via the minimum fuzzy divergence. The experimental demonstrate that the proposed method can effectively segment the common objects from multi-view image pairs with generating lower error rates than that of traditional cosegmentation methods.
POSTER: Mitigating Memory Sharing-based Side-channel Attack by Embedding Random Values in Binary for Cloud Environment

Memory deduplication is a technique that eliminates duplicate physical pages among virtual machines (VMs). Despite the advantage of efficient memory utilization in cloud computing, it leads to memory sharing-based side-channel attacks. Accordingly, most cloud service providers nowadays disable the memory deduplication on their servers to mitigate threats of the attacks, which comes at the cost of the efficiency loss. In this paper, we propose a novel mitigation technique for cloud computing environments against memory sharing-based side-channel attacks. Our technique converts vulnerable applications to secure ones that resist against the attacks. In particular, we utilize binary instrumentation to embed a secret random value inside an executable binary of the application. The random value will prevent memory sharing of security-sensitive applications with other from different security domains. On the other hand, the application-specific approach allows the systemwide memory deduplication, which preserves the efficient memory usage. We present the design and implementation of proposed mitigation as well as its evaluation results.
Poincaré Embeddings in the Task of Named Entity Recognition

Hyperbolic embeddings have become important in many natural language processing tasks due to their great ability to capture latent hierarchical data and to encode valuable syntactic and semantic information. We study and consider the ability of Poincaré embeddings to get the most similar nodes to a given node when trying to recognize named entities in a set of text documents. In this paper, we propose a classifier model for the NER (Named Entity Recognition) task by implementing Poincaré embeddings and by using the most frequent n-grams and their Part-of-Speech (POS) structures from the training dataset. We found that POS structures and n-grams help to map possible named entities, while using Poincaré embeddings manage to affirm and refine this recognition, improving the recognition of named entities.
A Profile of SIGITE/RIIT Authors, 2nd Ed.

The Special Interest Group in IT Education (SIGITE) was formed in 2003 and includes an annual conference, paper proceedings and poster presentations. Many authors and presenters from the first conference have continued to regularly publish in SIGITE and the community has continued to grow since this time.
Privacy Preservation with Machine Learning

In this chapter, we present various privacy preservation features applied to the Internet of Things (IoT) theme. The IoT framework consists of smart sensing devices, communication systems, and data processing units. The implementations of privacy features across the IoT framework were deliberated. The first section explores the limitations of implementing certain privacy features to the IoT smart devices. The next section focuses on applying privacy features for communication technologies in the IoT system. The third section presents various artificial intelligence data processing techniques for preserving the privacy of data that is originated in the IoT ecosystem. The privacy procedures, such as multi-party computation (MPC), generative adversarial network (GAN), and long short-term memory networks (LTSM), are explained for practical privacy preservation. The fourth section presents the trade-off between the privacy, usage, and efficiency of the presented models. In the end, the machine learning-based privacy preservation techniques and their adaptability to various IoT applications are summarized.
A bilateral-driven multi-centers clustering method for brain MRI segmentation

Magnetic resonance imaging (MRI) is a common medical imaging technology in modern medicine. MRI images can provide valuable information for doctors to diagnose and treat patients, in which the segmentation of the brain into gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) is an important step for neural image analysis. Clustering methods are widely developed for this task. However, the traditional clustering segmentation method is easy to be affected by the initial clustering centers, which cause great trouble to accurately identify and extract the tissue. In this paper, we propose a bilateral-driven multi-centers clustering (BMC) method to segment brain MRI images, which integrates the pixel feature and spatial relationship. Firstly, the traditional fuzzy c-means (FCM) is employed to perform an initial rough segmentation. Secondly, we propose multi-centers seeking strategy based on the algorithm of clustering by fast search and find of density peaks (CFDP) to get primary multi-centers for each cluster. Thirdly, an iterative procedure is proposed to seek secondary centers and link all potential centers for each cluster, and in this process, the cluster labels of some pixels in the neighborhood of the secondary centers are determined accordingly. The proposed method is validated on the public simulated brain data from BrainWeb, experimental results show the proposed method can achieve a better segmentation performance than traditional methods. It is shown that our main strategy by multicenters can reasonably reflect the tissue distribution, which is advantageous than the traditional clustering segmentation method with an only single center.
Making Use of the Capability and Process Concepts – A Structured Comparison Method

There are discussions in the business architecture, enterprise architecture, process management, and IT communities regarding the relation between the concepts of capability and processes, their similarities and differences. Some practitioners claim that capability and processes are the same concept while other emphasize the differences. In this paper, we present a method that generates an explanatory model of the similarities and differences between the two concepts. To demonstrate the method, we apply the method to well-known definitions of both concepts. The aim of this paper is to present a pragmatic method that enables explanations of differences between the two concepts of process and capability and thereby support comparative discussions by business modelers, enterprise analysts and architecture framework designers. Besides explaining the differences between the two concepts the explanatory model highlights the particular benefits of using the capability concept.
Asymmetric convolution kernel for deep optical flow estimation

Deep optical flow estimation is a hot topic in computer vision in recent years. In this paper, an asymmetric convolution kernel is introduced to improve the accuracy of optical flow. Then, in order to alleviate the rasterization problem caused by dilated convolution in optical flow results, a method of hybrid dilated convolution is introduced. At the same time, the proposed method can keep a small number of parameters without using additional occlusion and bidirectional information. Finally, experiments on the open standard datasets MPI-Sintel and KITTI-15 are carried out, and the results demonstrate the effectiveness of the proposed approach.
Feature Space Based Loss for Face Photo-Sketch Synthesis

Learning-based face photo-sketch synthesis has made great progress in the past few years because of the development of the generate adversarial networks (GANs) []. However, these existing GAN-based methods mostly yield poor texture and details on the synthesized sketch/photo which leads low perceptual similarity between the synthesized sketch/photo and real sketch/photo. In order to tackle this problem, we first introduce the perceptual loss into our objective loss function which can measure the difference of content and style between synthesized sketch/photo and real sketch/photo in feature level. Second, we propose a feature map based loss termed content feature loss, which are utilized to supervise the generator in our network to make the synthesized sketch/photo have more perceptual quality. To achieve this, we use the pre-trained VGG network to extract the feature maps of the sketch/photo as a feature extractor, and calculate the Euclidean difference between these feature maps and the feature maps from the hidden layers of the generator. Extensive experiments both synthesis quality and recognition ability assessment of the public face photo-sketch database are conducted to show that our method can obtain better results in comparison with existing state-of-the-art methods.
Ant colony optimization for 2 satisfiability in restricted neural symbolic integration

2 Satisfiability (2SAT) is a language that bridges real life application to neural network optimization. 2SAT is an interesting paradigm because the outcome of this logical rule is always positive. Hopfield Neural Network (HNN) is a type of neural network that finds the solution based on energy minimization. Interesting intelligent behavior has been observed when logical rule is embedded to HNN. Increasing the storage capacity during learning phase of HNN has been a challenging problem for most neural network researchers. Ant Colony Optimization (ACO) is a population-based and nature-inspired algorithm to decipher several combinatorial optimization problems. In this paper, metaheuristic algorithm ACO will be proposed in learning 2SAT programming. To this end, all the learning model will be tested in a new restricted learning environment. In circumstances of where HNN model was not given unlimited iteration until all 2SAT clauses were fully satisfied, we would like to determine the better performing search method between exhaustive search and ACO. Results acquired from the computer simulation showed that ACO outperformed exhaustive search in restricted learning environment.
Training for Media and Public Relations: Challenges of the Modernization Era

Abstract The article analyzes the key tasks of training specialists in the field of communication. The authors consider the results of research in the professional environment and among students, analyze the possibilities of digital technologies in the development of the specialty. The result of the article is a comprehensive model of digital resource aimed at improving the quality of information interaction between public relations and media communications, expanding the knowledge and skills of communicators, the formation of an information base and professional community for the development of specialists in the field of communications, increasing the efficiency of their activities, increasing public importance and relevance of the profession in the new conditions of the digital economy.
Edge Filtering Method for Brain MRI using CED

Edge detection is an image processing technique for finding the boundaries of objects within images. Its performance greatly influences the overall output of the computer vision application. Though it is impossible to remove all effects of noise, it usually presents itself as an unstructured collection of points in an image rather than forming straight lines. The Canny algorithm is considered as an optimal edge detector. In this paper, an algorithm is proposed to measure the probability of a point being an edge point based on the distribution of points in its surroundings. Under this condition, it was marked as an edge point by the Canny algorithm. Here, the probability is used as a threshold to remove false edges. Also, analysis of various parameters like the size of the neighborhood is carried out and their effects on the output produced are measured.
Model Parameter Learning for Real-Time High-Resolution Image Enhancement

Deep learning-based methods have achieved remarkable performance in image enhancement but generally require huge GPU memory, and computational cost when enhancing the high-resolution images. We explore to enhance high-resolution images like manual retouching by digital artists to obtain stable, and excellent performance in real-time. We extract the features from their thumbnails, and utilizes these features to guide the enhancement of the high-resolution images. Our designed light-weight convolution, and polynomial attention consume limited computational cost on the full-resolution image to retouch the image from both pixel-level, and global-level. Compared with the contemporary methods, our proposed method can work in real-time but surpass the state-of-the-art methods over 1.0 dB on the high-resolution image enhancement benchmark.
Post-processing Algorithms for Performance Improvement of Substitution Boxes Based on Chaotic System

Substitution boxes are an important cryptographic primitive. Design techniques based on mathematical transformations, a design technique of these cryptographic primitives, have played a particularly important role in the block encryption algorithm known as the Advanced Encryption Standard (AES). Because AES substitution box structure has the best cryptographic properties. However, various weaknesses arise when the implementation attacks are considered. Although the cryptographic properties of substitution box structures based on random selection techniques are worse than the AES substitution box structure, they are more successful to prevent implementation attacks. The aim of this study is to investigate the performance improvement algorithms of substitution boxes based on random selection. In this study, it has been shown that performance improvements of substitution boxes based on random selection can be achieved with the proposed post-processing algorithms. These results are thought to make significant contributions in the future, especially as a countermeasure for the prevention of implementation attacks.
An interactive punch power tracker for heavy bag training

For a martial artist wanting to have a good insight into their progress it is necessary to regularly measure and evaluate their training performance. Unfortunately, most of today's performance tracking devices applicable in martial arts training are expensive and thereby available only to professional sportsmen.This paper describes the model and prototype implementation of a device which facilitates information gathering and automated feedback provision during or after a heavy bag training session. Its main features are measuring relative punch strength, frequency, and spatial distribution with a minimalist setup and affordable hardware components. Device prototype implementation is evaluated in authentic settings and shows promising results.
Implementasi Sistem Informasi Monitoring Pengolahan Data Inventory Gudang Pada PT.Talaga Mulya Indah

Monitoring of warehouse inventory data processing is an important thing for companies. PT Talaga mulya indah is still manual using paper media, causing problems that have an effect on existing information, namely: problems with data processing of incoming and outgoing goods. And the difference between data on the amount of stock of goods available with physical data, often occurs inputting data more than once for the same item, searching for available data, and making reports so that it impedes companies in monitoring inventory of existing stock of goods. Which aims to create a system that can provide updated information to facilitate the warehouse admin in making inventory reports, and reduce errors in input by means of integrated control. In this study, the authors used the data collection method used in this analysis using the method of observation, interviews, and literature review (literature study). For analysis using the PIECES analysis method. Furthermore, the system design used is UML (Unified Modeling Language). The results of this study are expected to produce the right data in the process of monitoring inventory data processing, also can provide the right information and make it easier to control the overall availability of goods.
An Animation Design of an Adventure through Time and Space

Since the reform and opening up, China's rapid economic development, the people in food, clothing and accommodation and other aspects have been improved by leaps and bounds. In these 40 years, the progress of transport has freed the feet of mankind, the way human travel is more and more convenient and diverse. Bicycles in the 80's became one of the “three pieces”, the motorcycle in the 90's swept the north and south of the river, the early 20th century car has become an irreplaceable position in people's hearts. And now, on the basis of speed, we are pursuing high-efficiency, low-carbon environmental protection of the way of travel, so the sharing of bicycles, new energy vehicles emerged. High-speed railway speed is constantly updated, civil aviation boom and development, in the military aspect, the creation of a series of Chinese fighter jets and aircraft carriers. Forty years is not long, short is not short. We are fortunate to live in this prosperous era, fortunately, we should learn about the extraordinary times, to remember those special significance of the moment, those buried in the spirit of the years continue. The continuation of life carries the continuation of the spirit, and mankind always places its hopes on a new generation of children, which makes us determined to 1create such a work. We chose a fun pixel style and unfolded in an adventurous game, using break-ins to show the changes in transportation, which are more acceptable to children. We use Photoshop to design characters and scenes, combining Flash's synthesis and post-production capabilities to animate them. Compared to other types of animation, in this era our work can be aimed at more people of an age, interesting but not childish. Through our animation, we hope that our children and young people can understand the changes in life since the reform and opening up, and understand more deeply the great achievements of reform and opening up.
Research on the Method of Position Recognition of Titanium Alloy Pipeline Weld

Aiming at the problem of space pose recognition of titanium alloy pipe weld, this paper designs a pose recognition method based on the combination of ORB and RANSAC algorithm. First of all, preprocess the collected image, then detect the significant feature points in the image quickly, assign the direction to the feature points, and finally match the left and right two images of binocular vision system to realize pose recognition. The experimental results show that compared with SIFT and SURF, the recognition accuracy and efficiency of the designed method are significantly improved.
CAUX-Based Mobile Personas Creation

In order to solve some problems occurred when creating mobile personas, especially at data collection and data analysis period, a CAUX(Context-awareness User Experience)-based method is used in this paper. We use CAUX tool to collect objective data such as APP using APP usage information, mobile phone power, Call/SMS information etc., and then doing data visualization and analyze data, display user’s mobile phone usage with Gantt chart. Next, summarize behaviors of different users when using mobile APP, and classify according to a certain dimension. Finally, form a set of personas. We create mobile personas with CAUX tools, which compared with traditional method is more concentrate on users‘real behaviors. The CAUX-based personas focused on data collection and data analysis, which can effectively solve the problems when creating personas and leads to a better performance when using.