Reviewerly/exHarmony huggingface dataset:
Electricity Corpus Construction based on Data Mining and Machine Learning algorithm

With the exponential increase of the Internet information, it is more difficult to collect and distinguish the useful information online. Corpus plays an important role in information retrieval and recognition. This paper proposes a corpus construction method based on data mining and machine learning for the energy and power industry. Firstly, the information data of the power industry are obtained by web crawler technology. Then they are stored in a database in structured manner. In order to construct a useful power industry corpus, the Long Short-Term Memory - Conditional Random Fields (LSTM-CRF) model are adopted for word segmentation, the Inverse Document Frequency (IDF) feature and Accessor Variety feature are used for new word discovery. The final electricity corpus consists of words, paragraphs and chapters. Based on the information retrieval and knowledge map, the built electricity corpus is demonstrated that is benefit to energy and power research.
Stair Climbing Robot Based on Convolutional Neural Networks for Visual Impaired

When a person loses the sense of sight, in general, it is suggested to use a white cane to perform daily activities. However, using a white cane limits the movement of a person. In addition, guide dogs can be served in this impairment. However, the acquisition and maintenance of a guide dog is extremely high for people in development countries. In this regard, this paper presents a proof-of-concept of a low-cost robotic system able to guide a visual impaired, as a guide dog. The robot is specially designed for climbing stairs at indoors, and it uses convolutional neural networks (CNN) for both object detection and hand gesture recognition for special instructions from the user. Experimental results showed that our prototype robot can climb stairs with 86.7% of efficiency in concrete stair surfaces. Also, the visual representation by CNN performed more than 98% accuracy.
An improved cell situation identification approach with convolutional neural network and wavelet extreme learning machine

In the aluminum reduction process, the flame hole is an influential index of the whole production situation, which reflects the distribution of the physical field of the reduction cell, the current efficiency and the lifespan of the aluminum reduction cell. Therefore, flame hole situation detection and identification are critical and significant in the whole process of aluminum electrolysis. However, in the practical industrial production, flame hole identification result is coming from the experimental operation workers, with the loss of workers and different experimental levels, the real-time measurement of the flame hole index is still a challenge beyond solution. This article develops a flame hole image classification method based on wavelet extreme learning machine. First, a deep feature set is extracted from the original images with a convolutional neural network. Then, a classification model based on an extreme learning machine with wavelet activation function is developed. Finally, the proposed convolutional neural network–weighted extreme learning machine model is applied to superheat degree real-time detection in the industrial electrolysis cell. The proposed method is evaluated on aluminum production which outperforms existing other superheat degree methods in accuracy and robustness.
Adaptive Algorithm For Object Tracking In Video Image

A method for solving the problem of stable tracking is presented, based on a universal approach to the construction of adaptive tracking algorithms, which includes three components: tracking, learning and detection (TLD). The problem of robust object tracking was solved. An algorithm has been developed for tracking the contour of an object, used as a tracking component in a complex algorithm that implements the described approach. The object's trajectory is tracked by a certain short-term tracking algorithm, which works parallel with the detector, allowing it to be re-initialized after a failure. The proposed tracking algorithm was implemented as a program in C++, which is part of the software developed for automatic tracking of objects in the video image. The experimental and comparison results of the TLD algorithm using the Lucas-Kanade tracking algorithm and the proposed algorithm are shown in this paper.
Study of IMU Installation Position for Posture Estimation of Excavators

Posture estimation provides basic mechanical information for remote control of construction equipment, safety analysis of construction sites and productivity analysis of construction projects. The rapid development of micro-machined electromechanical systems (MEMS) inertial sensors enables wide application of posture estimation in the construction industry. Previous studies have put efforts to orientation estimation, trajectory tracking, and activity recognition of construction equipment based on Inertia Measurement Units (IMU). As excavators are common and important heavy construction equipment on site, posture estimation of excavators is closely related to the safety of construction sites and automation of construction processes. Although there have been studies on IMU-based posture estimation of excavators, there is still a lack of studies that rigorously discuss the impact of IMU installation positions on posture estimation of construction equipment, especially excavators. Placing IMU sensors in appropriate positions on excavators would affect the accuracy of posture estimation. More practically, the installability issue also governs the installation positions. Hence, this paper investigates the influence of different IMU installation positions on posture estimation of excavators. Firstly, a systematic framework is proposed to estimate posture of excavators based on data collected by IMUs. Our experiments verify the feasibility of our proposed framework. Furthermore, the in-fluences of different installation positions of IMU are investigated, on the basis of accuracies represented by Euler angle. Considering accuracy and applicability, a scheme of installing IMU on excavators is discussed. This paper will provide a theoretical basis and a reliable operation scheme for IMU-based posture estimation of excavators in the future.
Software Vulnerabilities Integrated Management System

In simple terms, a vulnerability can be an error or a flaw in code design which can determine malfunction of an application. Software vulnerabilities are the main cause of computer security problems. In order to avoid an attack or to minimize the damages produced by a cyberattack, a good vulnerability management plan should be implemented. This paper proposes a solution for vulnerability management by implementing a software vulnerabilities integrated management system (SV-IMS) that can perform security test in order to detect software vulnerabilities, the results of vulnerability tests being processed and viewed using a dedicated platform. The SV-IMS has a dedicated database for configuration management.
From Autonomous Systems to Symbiotic System Science

Today, disruptive technologies are appearing at an accelerating pace. Economic and societal forces, enabled by the evolution and convergence of several technologies, are driving the development of a novel generation of systems. We are at a crossroads, on the cusp of significant transformative changes that will impact society worldwide, revolutionizing global business operations and fundamentally altering how inanimate objects are perceived in a world increasingly reliant on autonomous systems. Symbiotic System Science (SSS) is a growing scientific area which is taking a leadership role in fostering consensus on how best to bring about symbiotic relationships between autonomous systems. Capitalizing on SSS insights and development, the recognition that SAS (Symbiotic Autonomous Systems) are poised to have a revolutionary impact on society over the coming years is quite straightforward. In present paper we discuss main implications of Autonomous System (AS), SAS and SSS to the incoming technological society. In order to achieve an antifragile behavior, next generation human-made system must have a new fundamental component, able to address and to face effectively the problem of multiscale ontological uncertainty management, in an instinctively sustainable way: wisdom by design!
A Semantically Integrated Conceptual Modelling Method for Business Process Reengineering

Information system methodologies project different modelling aspects into disparate dimensions and it creates difficulties to achieve integrity in various diagram types. Concepts cannot be analysed in isolation from interactions and state changes, which is the case in current traditional system development methods. The lack of an integrated graphical modelling approach creates problems in rethinking and changing business processes. In this chapter, we present Semantically Integrated Conceptual Modeling (SICM) method and demonstrate how the method can be applied for business process re-engineering. A well-known case study is analyzed to demonstrate how service interactions among enterprise organizational components can be represented to detect the essential similarities and differences of business processes. Service architecture is graphically described in terms of interactions among organizational and technical enterprise system components. One of the advantages of SICM method is based on a single type of diagram, which represents interactive, transitional and the structural aspects of concepts. State changes result from processes that are represented by creation and termination events of various concepts.
Optimising the tower-defense games with advanced local cultural content and a greedy algorithm

Abstract The aims of our games is preserving the local culture of Madura island that many people may have left behind. By utilizing mobile technology that is currently growing rapidly, it is expected that the promotion of local cultural content can be more effective. The game-based application is an attractive media with fun content, so it is normally more easy to attract interest and to be familiar with people. Our contribution is to participate in the campaign of familiarization of local wisdom to re-establish the living tradition of regional cultural values as a national identity. Save The Madura Island game is a strategy game which carries out the Madura culture theme. The app allows players to learn Madurese culture happily by playing a game. Save The Madura Island game is inspired by tower-defense games and uses a greedy algorithm as artificial intelligence which is inserted in character of defense to help the character of defense in determining which enemy is more effective to be attacked first. From the experiments, it was concluded that the use of greedy algorithms in the application proved effective in helping reduce the number of enemies who escaped, which decreased from 41.8% to 30.4%.
Towards smart rooms for children

Children's bedrooms are private spaces for identity exploration and self-expression, defined in literature as "bedroom culture". With the rise of smart-home and Internet of Things (IoT) technologies, comes a great opportunity to create a "digital bedroom culture" that captures the rich meaning that bedrooms can have in children's lives. To properly understand how children perceive their bedrooms, and how they think smart-home technologies can be integrated into their rooms, we conducted interviews with 17 children in the context of their bedrooms. Using thematic coding, we mapped children's needs related to their bedroom into Emotional and Practical themes. When discussing "smart-room" technologies, children strongly associated them with practical needs and much less with emotional ones. We argue that smart-home and IoT designers should consider this gap and explore the possibilities of designing IoT technologies that will augment children's emotional needs in the context of their bedroom.
Developing and Evaluating a Jejakatua Virtual Campus Tour Prototype Using Auto-stitching Technique

Abstract This paper describes the design and manufacture of virtual campuses with auto-stitching techniques. The automatic stitching technique using Insta360 allows getting a panoramic view of the 360 0 campus area. The creation of a virtual campus is built by each campus to support campus promotion more fully and more closely. The prototype of the Virtual Campus Exploration (Jejakatua) application was implemented at the Faculty of Computer Science UPN “Veterans” Jatim the Jejakatua application page and evaluated by users using a set of questionnaires distributed using Google Forms. The results of testing by the black box test the built system is in accordance with the design and can be operated properly. Studying the questionnaire to 30 respondents from UPN “Veteran” Jatim academicians gave a pretty good average response. So Jejakatua is fully built using the auto-stitching method using Insta360 and is quite feasible to be implemented on Campus.
Generating and Editing Arbitrary Facial Images by Learning Feature Axis

There are mainly three limitations of the traditional facial attribute editing techniques: 1) incapability of generating an arbitrary facial image with high-resolution; 2) being unable to generate and edit new facial images synthesized by the computer and 3) limited diversity of edited images. This paper presents a method for generating and editing images simultaneously. It incorporates a high-resolution facial image generator, a multi-label classifier, and a Generalized Linear Model (GLM). Experimental results show that our method can generate arbitrary high-resolution facial images, edit computer-synthesized images, perform multi-attribute editing, and effectively control the intensity and style of the generated images. Besides, the approach has high efficiency and flexibility, allowing rapid migration of attribute information from the data set. We design a graphical interface program, which can be integrated as a mobile application.
Attention-Based Convolutional LSTM for Describing Video

Video description technique has been widely used in the computer community for many applications.The typical approaches are mainly based on the encode-decode framework: the fixed-length video representation vectors are extracted by the encoder using the upper layer output of pre-trained convolutional neural networks (CNNs); The decoder uses the recurrent neural networks to generate a textual sentence.However, the upper layers of convolutional neural networks contain low-resolution, semantically strong, while the lower layers contain high-resolution, semantically weak features.In the existing method, the multi-scale information of CNNs is hardly considered to be used in the video description.Ignoring this information will lead to the problem that the video description is not detailed and comprehensive.This paper applies the hierarchical convolutional long short-term memory (ConvLSTM) in the encode-decode framework to conduct feature extraction of the upper and lower layers in CNNs.Moreover, multiple network structures are designed to explore the Spatio-temporal feature extraction performance of ConvLSTM, which can approach the optimal accuracy in the three-layer ConvLSTM.In order to efficiently improve the language quality of video description, the attention mechanism focuses on visual feature output by ConvLSTM.The extensive experimental results demonstrate that the proposed method outperforms the existing approaches.
Fast Depth Estimation for View Synthesis

Disparity/depth estimation from sequences of stereo images is an important element in 3D vision. Owing to occlusions, imperfect settings and homogeneous luminance, accurate estimate of depth remains a challenging problem. Targetting view synthesis, we propose a novel learning-based framework making use of dilated convolution, densely connected convolutional modules, compact decoder and skip connections. The network is shallow but dense, so it is fast and accurate. Two additional contributions - a non-linear adjustment of the depth resolution and the introduction of a projection loss, lead to reduction of estimation error by up to 20% and 25% respectively. The results show that our network outperforms state-of-the-art methods with an average improvement in accuracy of depth estimation and view synthesis by approximately 45% and 34% respectively. Where our method generates comparable quality of estimated depth, it performs 10 times faster than those methods.
Automatic Prostate Segmentation on 3D MRI Scans Using Convolutional Neural Networks with Residual Connections and Superpixels

Automatic and reliable prostate segmentation is an essential prerequisite for assisting the diagnosis and treatment, such as guiding biopsy procedure and radiation therapy. Notwithstanding, automatic segmentation is challenging due to the lack of clear prostate boundaries owing to similar appearance of prostate and surrounding tissues and the wide variation in size and shape among different patients. Therefore, this paper proposes an automatic method for prostate segmentation on 3D MRI scans using a content-sensitive superpixels technique, deep convolutional neural networks with residual connections (ResCNN), and the particle swarm optimization (PSO) algorithm. The proposed method has been evaluated on the Prostate 3T and PROMISE12 databases, presenting a Dice similarity coefficient of 86.68 %, Jaccard index of 76.58%, relative volume difference of 3.92%, volumetric similarity of 96.69 %, sensitivity of 88.36 %, specificity of 93.43%, accuracy of 91.97% and an area under ROC curve of 90.90 %. Experimental results demonstrate the high performance-potential of the proposed method comparable to those previously published.
Interactive Information Retrieval: Models, Algorithms, and Evaluation

Since Information Retrieval (IR) is an interactive process in general, it is important to study Interactive Information Retrieval (IIR), where we would attempt to model and optimize an entire interactive retrieval process (rather than a single query) with consideration of many different ways a user can potentially interact with a search engine. This tutorial systematically reviews the progress of research in IIR with an emphasis on the most recent progress in the development of models, algorithms, and evaluation strategies for IIR. It starts with a broad overview of research in IIR and then gives an introduction to formal models for IIR using a cooperative game framework and covering decision-theoretic models such as the Interface Card Model and Probability Ranking Principle for IIR. Next, it provides a review of some representative specific techniques and algorithms for IIR, such as various forms of feedback techniques and diversification of search results, followed by a discussion of how an IIR system should be evaluated and multiple strategies proposed recently for evaluating IIR using user simulation. The tutorial ends with a brief discussion of the major open challenges in IIR and some of the most promising future research directions.
Modeling of Prediction Bandwidth Density with Backpropagation Neural Network (BPNN) Methods

Abstract Using computer networks in campus area which is open access will cause some problems at the speed to access the information. The allocation of bandwidth that provided sometimes does not match the needs of the client, so it takes an accurate prediction of bandwidth usage. This research obtained that Neural Network backpropagation modeling can solve the problem. The stages of research conducted the stage of training and testing phase. Data training is traffic data weekly and conducted by feed-forward back method, with max error 0.001, max hidden layer neuron 5000, constant momentum 0.95 and increase ratio 0.1. Before the data train is conducted, the scaling of the input and target values in the range of 0.1-0.9, then resumes the denormalization after the data train to return the data into Kb form. The results obtained from the training process in the form of comparison data, training performance, and regression. Furthermore, data testing, conducted by using a network that has been developed from the previous results. The test results are shown in the form of real data and predictive data using 8 input layers. In the prediction process, the mean square error generated is 0.0031792 which indicates a low error rate, so it can be stated that the resulting modeling has a level of output accuracy in predicting the use of computer network bandwidth is very high.
Co-designing immersive VR with and for adolescents in elementary school

This paper reports from the early phase of a Participatory Design (PD) process where the goal is to design and use an immersive virtual reality (VR) application in elementary school for teaching first aid, where pupils are involved as co-designers in the design process. In this paper we report on two conducted co-design sessions in which twelve 13-14-year old pupils participated. The pupils were invited to create interactive storylines that represent recognizable and relevant characters, themes, events, references, scripts, and scenarios. From our viewpoints as facilitators, we describe the conducted co-design workshops and present a set of lessons learnt connected to (1) group selection and group dynamics, (2) methods and artefacts and (3) dissemination and adaptation to VR.
Forensic Investigation of Ransomware Activities—Part 1

Techniques employed by malware authors evolve and become more advanced each day in an effort to bypass defences and evade detection. From 2013 to the present, a type of malware known as ransomware has increased exponentially in popularity with cyber criminals. Ransomware encrypts files on a victim’s filesystem and subsequently demands a ransom payment to release the files. The exponential growth of ransomware poses a serious and real threat to end-users and organisations worldwide. The exponential growth also poses serious challenges to the security industry, such as the need to analyse and study the large volume of emerging ransomware families. A problem exists in that new ransomware families may use previously unseen techniques to evade detection and detonate successfully. A second problem exists for security analysts when it comes to analysing the ever increasing volume of emerging ransomware families. Malware analysis generally falls into one of two categories: static and dynamic analysis. Dynamic analysis is effective at classifying malware, however it’s ineffective at discovering newly developed techniques or functionality. On the contrary static analysis is effective at discovering newly developed techniques and functionality, however it requires significantly more time to complete than dynamic analysis. The information gathered from static analysis is essential to enable organisations better defend against these new attacks. The information obtained from this research can be used to help defend against future threats using similar techniques and highlight the effectiveness of manual analysis to discover new and advanced techniques.
Attack and defense in the layered cyber-security model and their (1 ± ϵ)-approximation schemes

Let M=(T,C,P) be a security model, where T is a rooted tree, C is a multiset of costs and P is a multiset of prizes and let (T,c,p) be a security system, where c and p are bijections of costs and prizes. The problems of computing an optimal attack on a security system and of determining an edge e∈E(T) such that the maximum sum of prizes obtained from an optimal attack in (T,c,p) is minimized when c(e)=∞ are considered. An O(G2n)-time algorithm to compute an optimal attack as well as an O(G2n2)-time algorithm to determine such an edge are given, in addition to a (1-ϵ) FPTAS with the time bound O(1ϵ2n3log⁡G) and a (1+ϵ) FPTAS with the time bound O(1ϵ2n4log⁡G) for the first and second problems, respectively.