Reviewerly/exHarmony huggingface dataset:
Obstacle Avoidance Strategy for Wheeled Mobile Robots with a Simplified Artificial Potential Field

Obstacle avoidance is one of the most basic path planning strategies particularly for mobile robot navigation in unknown environments. In this work, the focus is on design and development of an Artificial Potential Field (APF) based obstacle avoidance strategy of a nonholonomic wheeled mobile robot. A simplified APF-based algorithm is proposed to accommodate for the constraints of the robot due to its limited mobility. The method was validated via a series of experiments with different goal coordinates and number of obstacles. From the numerical and experimental results, we showed the effectiveness of the proposed strategy as it was able to drive the mobile robot to the goal coordinates with very small steady-state position errors without collisions with enroute obstacles.
Performance analysis and formative assessment of visual trackers using PETS critical infrastructure surveillance datasets

Surveillance of perimeter of critical infrastructures (CIs) has gained a particular attention worldwide due to rising threats of terrorist incidents. However, the generation and dissemination of real-world, challenging datasets concerning CIs for performance assessment of surveillance tasks, particularly tracking, have widely been overlooked in the community. Recent performance evaluation of tracking and surveillance (PETS) workshops have sought to address this issue by introducing ample CI surveillance datasets. We demonstrate the effectiveness of these publicly released real-world PETS CI visual datasets by providing a comprehensive statistically significant performance analysis as well as formative assessment of several state-of-the-art multitarget trackers using well-known and recent performance assessment criteria.
Augmented Reality Based Assembly Guidance for Spacecraft Conductive Network

Conductive network is an important component that affects the electromagnetic compatibility (EMC) of spacecraft. Manual work-based paste and overlap of copper tape is the main installation way of conductive network. Work patterns dominated by human experience have problems of low efficiency, high error rate and high learning cost, which cannot guarantee the operation requirements of speed, quality and precision. In order to solve these problems, the augmented reality (AR) based guidance technology for assembling spacecraft conductive network is proposed. Firstly, the virtual-real fusion method and the guidance information modeling method are studied. Then an AR based guidance system is built with HoloLens and Unity3D, which can help worker paste and overlap copper tape efficiently and verify the superior experience of applying AR technology to conductive network assembly.
Heterogenic Multi-Core System on Chip for Virtual Based Security

The paper describes the process of coding information in the heterogenic multi-core system on chip for virtual-based security designed For image processing, signal processing and neural networks emulation. The coding of information carried out in assembly language according to the GOST. This is an implementation of the GOST - a standard symmetric key block cipher has a 64-bit block size and 256-bit key size.
Alternative Financing Approaches and Regulation in Africa

Quite interesting developments are occurring on the African financial landscape. The growth of mobile payments services; agent banking; the increase in the use of crowdfunding mechanisms to raise finance; and the growing interest in the use of cryptocurrencies by Africans to transact have all contributed to this trend. A common factor running through these recent developments is their ability to facilitate financial inclusion. Another characteristic they all share is that they all have cross-border dimensions. This therefore requires a coordinated approach to regulation, of which a regional regulatory approach is likely to play a prominent role. The increasing speed of these developments in this era of financial technology calls for the urgent institution of a robust regional financial regulatory framework that addresses key issues raised by them and which this chapter considers.
Depth Estimation for Instrument Segmentation from a Single Laparoscopic Video toward Laparoscopic Surgery Support

It is necessary to extract surgical instruments such as forceps from laparoscopic images in order to improve the safety of laparoscopic surgery using a surgery support system. For image segmentation for surgical instruments, a deep learning technique such as a fully-convolutional neural network (FCN) is effective. It is known that the segmentation accuracy can be improved by using a stereo camera, because the depth information as well as color information on surgical instruments should be useful. This paper proposes a FCN-based depth estimation method from a single laparoscopic image captured by a monocular camera. And also proposes a U-Net-based image segmentation method using on the estimated depth information as well as color information. In experiments with the dataset of the MICCAI challenge, our method improved both the average IOU and Dice coefficient by about 2%, comparing with a comparative method using only color information. We confirmed the effectiveness of our method.
Interactive Search Profiles as a Design Tool

Interactive Information Retrieval (IIR) research studies how users interact with IR systems and evaluates the users’ satisfaction with the retrieval process. Thus, it focuses on how users behave when they interact with IR systems. The involvement of potential users, and access to dynamic and individual information needs are essential elements in IIR studies. User-oriented evaluations investigate the ability of users to engage with a system, thus requiring real users to be involved in the evaluation process. Despite its importance in driving the design of better and more usable IR systems, the user-oriented evaluation approach has been criticised for: (i) being expensive and time consuming in its application, (ii) failing to capture a holistic understanding of the context, as it focuses on users, (iii) delivering not reusable data, often of qualitative nature, and (iv) generating experiments and results that are not reproducible. In this paper, we propose an alternative approach, based on data extracted from Log Files (LF) and automatically extracted Usage Pattern (UP)s. Interactive Search Profiles (ISP)s are then created as tools to guide the design of more usable IIR systems, specifically considering Digital Libraries (DL).
Clothing Classification with Multi-attribute Using Convolutional Neural Network

Convolutional Neural Network (CNN) has demonstrated great efficiency in image classification tasks. In this paper, CNN is used with multi-label classification to extract features from clothing images and classify clothing types and colors. In our method, we first develop a neural network for types and colors separately then combine into a multi-output model to identify them. The experimental results show that the proposed method achieves practical performance in classify precision.
High Sensitivity Layer Feature Analysis in Food Market

It is not uncommon to conduct test marketing for the purpose of market research when dropping new products to the market. However, if you actually drop it you will need a lot of money. This study, we pay attention to innovation theory. In Japan, the study reported a long sales period. However, the study didn’t report a short sales period. Therefore, we report of a short sales period, especially food. This study, we call “High Sensitivity Layer” the innovators and the early adopters in innovation theory in term of to be interested in the innovation of products, sensitive to trends and constantly collecting new information by themselves and to have greater influence on other consumers. We think that those that collect a lot of empathy in the “High Sensitivity Layer” are diffusive in the innovators and the early adopters, and grab the characteristics of highly sensitive consumers who gather many empathies. I think that it may be able to fulfill the purpose of test marketing by seeing the response of new products of food to this consumer. We prepare a generalized model with a deep learning model and report features of highly sensitive consumers, visually and numerically clearly, using decision tree analysis from that model. From the analysis results, attached more images, and the older, the better it got a report that empathizes with sensitive consumers. When conducting test marketing, it is predicted that high-sensitivity consumers will be able to obtain preferable results by targeting people with this characteristic. Also, it was found that gender and emotion are not related to the characteristics of the person who writes the report sympathized with the consumer. In the future, I would like to further accurate classification by text mining of posted characters and analysis of posted images.
CloneTM: A Code Clone Detection Tool Based on Latent Dirichlet Allocation

A plethora of clone detection techniques have been proposed in the literature to support a variety of programming languages and adopt different clone detection strategies at different levels of complexity. However, despite these major advances, these techniques are still far from achieving optimal accuracy. This requires developers to manually classify and verify the detected candidate clones, a process that is often described as time-consuming and error-prone. This paper describes CloneTM, a code clone detection tool based on Latent Dirichlet Allocation (LDA). We discuss the key features of CloneTM and present our evaluation on two datasets.
User Experience and Perceived Usability of Traditional and Multimodal Interfaces for Scientific Information Seeking

The current study was carried out to investigate users’ preferences for different input modalities (i.e., mouse or gaze) when involved in scientific information-seeking tasks. According to the task that they had to perform (i.e., abstract opening, gaze bookmarking, or keyword highlighting), participants were randomly assigned to three different conditions. They had to carry out the same task twice with both means of interaction (i.e., mouse or gaze); input modalities were counterbalanced across participants. The findings showed that users were faster and perceived the system as more pleasant when they performed the tasks using their gaze. However, when participants controlled the system with a more familiar mouse interface, they perceived the system as more accurate, easy to use, and efficient. Future studies will evaluate how increasing users’ familiarity with alternative input modalities (i.e., gaze) can affect their perception of accuracy, easiness, and efficiency.
Multi-Confirmations and DNS Graph Mining for Malicious Domain Detection

The Internet criminals use domain name service (DNS) as cornerstone for variety of malicious activities. Typically, attackers register domains name to locate their $$ C{ \& }C$$ (command and control servers) for spam mails, ddos attacks, etc. There are thousands of cyber attacks happening every day and the number of those increase with exponential growth, which becomes an urgent matter for cyber security. To tackle this issue, several recent techniques have been proposed to identify malicious domains. Most of them use DNS data which contains much of global information cannot be controlled by attackers. Hence, using labeled domains to identify malicious domains relying on their association by analyzing history log is reasonable. Besides, the graph is usually used to present the relationship between objects and many powerful algorithms have been successfully implemented on graph in literature. So, a method with modified version of the Belief-Propagation algorithm to detect malicious domains based on a graph of domains and hosts IPs were proposed in our previous paper. In addition, graph inference is also applied with many different methods with very impressive results. However, these methods cannot handle all domains on the graph because the domains blacklist does not cover all graph components. This can be considered a bottle neck of the methods proposed. To solve this problem, we propose a new method by extending the previous work. This paper proposes a new approach which employs the law of total probability based on relationship between domains and clients to calculate malicious score of domains. This paper not only uses new score to label for domain but also combines with the previous score. The scores from the two methods are combined via a weighted sum to arrive at a score for each domain. The study shows high performance in malicious domain prediction on test case with accuracy, precision and recall as: 97.6%, 99.2% and 93.1%. This technique can discover huge potential malicious domains by mining on small knowledge data (black and white list domain) with highly precision. It indicates that the proposal technique is strongly effective in detecting malicious domains.
A Target Tracking Application On Mobese Videos

The development of digital communication and image technologies is becoming increasingly widespread. The high resolution rates used in image technologies increased the quality of the recordings. These developments reveal the necessity of interpretation studies on digital images. Image processing applications were first created by the interpretation of still images. In order to achieve better image processing after the stages of obtaining, improving and decontamination of the image, certain features have been determined and highlighted. Nowadays, it continues with the processing and interpretation of moving images or videos. In recent studies, object tracking has gained importance in order to interpret visual inspection systems.
Design of Mobile Learning System for Courses of Computer Science and Technology

Abstract In recent years, mobile learning system is increasingly applied in colleges and universities due to its promising capability. This paper proposed a design of mobile learning system for courses of computer science and technology. The architecture was described, and the detailed scheme of the system was given including server and client solution. The system adopted Client/Server structure, and it supported Android and iOS operating systems. The relationship of each level and the data structure of each model were designed according to the business logic. Consequently the client program was developed based on real time video transmission protocols of RTP/RTCP/RTSP. The main functions of mobile learning system included curriculum center, teacher-student interaction, assignment and project. This design provided a learning platform for the students majoring computer science and technology. Using hand-hold mobile terminal devices, students can study courses anywhere and anytime through video, voice, picture, text and other media.
Predicting Daily Physical Activity Level for Older Adults Using Wearable Activity Trackers

In recent years, there is an increasing trend towards using wearable activity trackers to help monitor and track physical activities (PA) for older adults, with the purpose of motivating regular PA for better health. However, existing activity trackers are frequently abandoned within a short period of time. One of the major reasons is that they do not differentiate individual PA habits and provide PA recommendations based on a unified standard, which may lead to unrealistic suggestions and thus cause frustrations. In order to motivate long-term use of activity trackers and promote PA progression in older adults, PA recommendations should adapt to the changes of an individual’s PA habits. As a step towards achieving this, we introduce in this paper an innovative multi-scale personalized LSTM model that can predict an individual’s daily PA level with satisfied accuracy. This model is verified through a series of experimental studies.
A Stealth Key Exchange Protocol

Cryptography, the science of secret writing, is in use since long to inarticulate the legitimate contents to prevent their unauthorized exposure. To ensure secure communication between communicators, cryptographic contrivances such as encryption and authentication relies on the conjecture of prior sharing of some secret called cryptographic or secret key that reign the security of the underneath algorithms. Hence, safekeeping and handling of such keys at all stages of encryption and decryption inclusive of its generation, storage, distribution, archiving and destruction are imperative. Kirchhoff, in 1883, has already elucidated on the significance of such keys by expounding that the security of a public domain encryption algorithm lies in protecting the key used in communication. Regardless of the key being symmetric or asymmetric, its distribution/exchange over an insecure channel such as the internet, together with the increasing concern among technologically advanced countries on suppressing encryption, calls for an oblivious mechanism for its undercover traversal. Steganography, the purpose of which is to embed information seamlessly inside some carrier, is the technique used for stealth information exchange, and hence, seems ideal for key management and its successive dissemination. Through this research endeavor, we have proposed a novel stealth key-exchange solution, which makes use of interactive computer-mediated technologies, cryptography, and steganography. Our proposed method adlibs the Diffie-Hellman key exchange algorithm by disregarding the direct routing of any data relating to key exchange, and hence protects it from man-in-the-middle attack by keeping intact the anonymity of communicators.
Short-Term Photovoltaic Generation Forecasting Based on LVQ-PSO-BP Neural Network and Markov Chain Method

Abstract With the rapid development of solar photovoltaic generation, the effective prediction of photovoltaic is of great significance to mitigate its impact on power system. According to the analysis of main factors which affect power output of photovoltaic system, a short-term power forecasting model based on back propagation(BP) neutral network and LVQ-PSO-BP neural network and Markov chain method was established. The weather is clustered and distinguished by using learning vector quantization(LVQ) and the particle swarm optimization(PSO) is used to optimize BP neural network weights and thresholds, improving forecasting network training speed. Finally, daily predictive value is corrected by Markov chain method to improve short-term photovoltaic generation forecasting precision. The simulation results indicate that the proposed method can accelerate the speed of searching optimums, improving the classification accuracy of weather types and the precision of the photovoltaic generation output effectively.
Investigating the effects of population size and the number of subcomponents on the performance of SHADE algorithm with random adaptive grouping for LSGO problems

Large-scale global optimization (LSGO) problems are known as hard problems for many evolutionary algorithms (EAs). LSGO problems are usually computationally costly, thus an experimental analysis for choosing an appropriate algorithm and its parameter settings is difficult or sometimes impossible. In this study, we have investigated the performance of novel EA for LSGO based on Adaptive Differential Evolution with Success-History (SHADE) and cooperative coevolution (CC) with random adaptive grouping (RAG). SHADE is a self-adaptive DE algorithm. RAG approach is able to identify effective combinations of variables (subcomponents) in the problem decomposition stage. Thus, the proposed approach contains only two controlled parameters: the population size and the number of subcomponents. We have evaluated the performance of CC-SHADE-RAG with different settings on the IEEE CEC LSGO benchmark. The experimental results show that the approach outperforms some state-of-the-art LSGO techniques. CC-SHADE-RAG performs better with a small number of subcomponents and large size of population. We have performed statistical analysis for the experimental results and have established that the population size has greater effect on the algorithm performance than the number of subcomponents. This information can be used for further development of a completely self-adaptive LSGO technique by introducing an adaptive population sizing scheme in CC-SHADE-RAG.
Translation of Image Edge Detection Based on Python

Matlab software is often used in image processing, with the development of science and technology, a lot of data to be efficient and real-time processing is valued. Python as a new interpretation scripting language, the program is simple, easy tounderstand, and maintain real-time processing. Using python in image processing, can well keep the requirements of the designer, because of the open and free program, reduce the difficulty of programming, and enhance the interest of programmers. In this paper, through the comparison between canny operators, Sobel operator, lapla operator in image processing, the simulation results verify that the canny operator has good detection effect. The simulation results show the advantages of python, is suitable for the use in image processing.
Should Do, Can Do, Can Know

This paper characterizes six years of final projects from an interaction design master's program at our School of Design in Hong Kong. Our reporting includes a thematic analysis of the specific application areas in which these diverse designers made their choices, along the dimensions of values and vision (Should Do), concepts and approaches (Can Do), domain knowledge (Can Know), and interactive Forms. The dimensions of Should Do and Can Do are particularly privileged in this paper. In this particular reporting of our analysis, we are especially interested in trends relating to sustainability and its relation to other values-orientations. This interest owes to our concern as a faculty for fostering a school of thinking and practice that balances all of these dimensions. Our analysis demonstrates the use and value of the Should Do, Can Do, Can Know framing as an analytical tool for design. Moreover, our work characterizes the common strategies among our 101 designers. As such, it provides a lens and points of inspiration for others, more generally. As a service, we present our entire analysis in table form as an Appendix.
Fine-Grained Formal Specification and Analysis of Buddy Memory Allocation in Zephyr RTOS

Bugs in memory management of Operating Systems may lead to crashing. This paper presents a case study of formal verification on the buddy memory allocation component of the Zephyr RTOS kernel. The algorithm of the component allows memory blocks of 4-power sizes to be dynamically allocated by efficiently partitioning larger blocks into smaller ones, and then be released supporting immediate and automatic combining of smaller blocks. The execution of memory allocation is preemptive, which means that the allocation may invoke rescheduling when there is no block available for memory requests. In this paper, we provide a fine-grained formal specification of buddy memory allocation and formally verify its safety via invariants and functional correctness. The specification covers all the elements of the data structure as well as all statements of memory initialization, allocation, and release presented in the C source code. During the formal verification, we found a functional flaw in the C code. To the best of our knowledge, this paper is the first effort of formal verification at a fine-grained level on buddy memory allocation in Operating Systems.