Reviewerly/exHarmony huggingface dataset:
Design of a Language for IoT Service Composition

In the Internet of Things (IoT) ecosystem, multiple smart devices communicate among them and with people.Similarly, they are primarily characterized by remarkable detection and processing capabilities.On the other hand, a service composition (SC) task involves performing the orchestration or choreography of services.SC is frequently studied in the context of Web services (WS), where a series of standards have been developed and used in real-world implementations to support SC.Unfortunately, these standards are inadequate in the IoT paradigm, since IoT devices are based on data/events and the resources are restricted.This research work proposes the design of a language for IoT SC, while simultaneously discussing important literature on SC, business process, IoT service orchestration, and IoT service choreography.Finally, as a proof-ofconcept, we present a case study of IoT service composition in the healthcare domain for patients with overweight or obesity.
Design of building equipment Internet of things system based on blockchain technology

At present, with the rapid development of Internet of things technology, there are more and more intelligent devices connected to the Internet of things, which are faced with many problems such as unsafe data, easy disclosure of privacy and difficult sharing of data. In order to solve these problems, this paper proposes a flat and center-less building equipment Internet of things system based on blockchain technology, so as to better realize accurate perception, reliable transmission and intelligent processing of the Internet of things. The application of blockchain technology and Internet of things technology in intelligent buildings ensures data security, improves system operation efficiency and provides a new method to solve the problems of Internet of things.
A Data Processing Method of UAV Detection Using Mechanical and Electronical Scanned Radar

In this paper, in order to detect the unmanned aerial vehicle (UAV) target with low-altitude, slow-speed and small-Radar-cross-section (RCS) characteristics, the mechanical scanning strategy in azimuthal dimension, electronical scanning strategy in elevational dimension and pulse-Doppler radar principle are adopted. A waveform design method is proposed for the precise detection of targets. By this way, the improved multi-objective processing capability, the optimal data processing, and the effective tracking of maneuvering state are achieved. Sequentially, the stable searching and tracking of the UAV target is implemented.
An Energy Efficient Run-Time Security Monitor for True Random Number Generators

True random number generators (TRNGs) are the most important building blocks of cryptographic systems. They generate random numbers for use in session keys, security protocols, and countermeasures. The statistical quality of the continuously generated random bits is highly crucial in order to guarantee the security of a cryptographic system. The mathematical, information-theoretic (entropy) properties aside, the statistical properties of the output bitstream generated by a TRNG are usually monitored against failures or physical attacks. Unfortunately, continuous monitoring of the output bitstream increases the power consumption, which is undesirable for light-weight cryptographic systems. In this paper, we present a hardware (in terms of area), and energy efficient implementation of a run-time operating security monitor for TRNGs which is very suitable for light-weight cryptographic systems.
Short Term Forecasting of Solar Power Using Harmony Search based Extreme Learning Machine

The grid incorporation of solar power production creates nonlinearity, instability and entails improper energy management, which is still a challenging situation while installing a large solar power plant. Thus prediction of solar power generation is highly essential in different time horizons for maintaining proper power adjustment techniques and management. In this work a smart and competent prediction model has been implemented on a real time photovoltaic power plant. The Extreme learning machine (ELM) which is a new prediction technique is applied in this work. The weights of ELM are selected randomly in general. The performance of the forecasting model also depends on proper weight selection. Thus a new optimization technique such as harmony search optimization is applied to select the optimized weights. The forecasting model is implemented on an historical data set of real time solar power plant whose geographical location is given in the last part of section-II. The ELM model is activated to mobilize the feed forward neural network, iteratively, to achieve better forecasting error in each step. ELM model and the Harmony Search optimized ELM model are simulated for error calculation and their results are compared in terms of different measuring indices and their forecasting errors are compared.
Heterogeneous 2-Phase Method for Strongly Connected Components Detection and Classification of Graphs

Different sequential and parallel methods exist for decomposing a directed graph into its Strongly Connected Components (SCCs). To speed up the execution for very large graphs, Graphics Processing Units (GPUs) have been used. The real-world graphs have small-world property with a single giant SCC and many small-sized SCCs. For such graphs, two-phase methods have been used with different parallelism strategies, both on CPU and GPU in the previous research. Our research indicates that the existing methods have great variance in their performances for different types of graphs. This research classifies the graphs on the basis of which method works best for them after analyzing their properties. Such classification ensures applying the best SCC detection method in case the type of the input graph is known. We have also developed a new method that has consistently better performance for all types of graphs. This method can be used when the type of the input graph is not known or it doesn't fit into the classification. In this method, the first phase is run on the GPU whereas the second phase is run on the CPU using either sequential or multithreaded routine.
Introduction to Python and Its Statistical Applications

This chapter introduces the history of Python and its IDEs (integrated development environment) and code editors as developing environment. The history tells how Python started from ABC programming language in the Netherlands to a community with developers from different areas, and later became one of the most popular programming languages in the world. Popular IDEs and Code Editor for professional developers and beginners are also introduced with their advantages and disadvantages. Later in this chapter, the authors introduce Python libraries, which could be used in statistical analysis, and give out a simple case on how these methods can be applied.
Optimal Path Planning for Construction Region Based on Potential Function

Rapidly Exploring Random Tree Star (RRT*) obtains the optimal path for high-dimensional complex dynamic environments by randomly sampling nodes. Although RRT* has been proven to have probabilistic completeness and asymptotic optimality, there is still a contradiction between optimal solution and rapidity. Based on the problem, we will introduce an algorithm, Potential Region RRT*. Region is constructed through the necessary information in the space, such as root nodes, the goal node, and various obstacles. The size of the sampling area is limited by the area and unnecessary sampling points are reduced. The reduction of sampling points influence the optimal solution of the path, therefore artificial potential field method is integrated into the algorithm to get a better solution.
Flora

Flora is an interactive projection installation that utilizes the HTC Vive system and a mini projector mounted to one of the tracked controllers. This installation allows for viewers to explore projected images in a physical space. It stimulates a participant's spatial-visual abilities, but also represents the fragmented quality of a memory that isn't quite accurate. This project was inspired by the lead artist's personal memories as a child being surrounded by the nature of Hawaii. In Flora, the experience is not limited to a singular perspective, but can be enjoyed by two participants and a surrounding audience. The technology encourages the sense of free exploration in an otherwise plain physical space and offers an advantage over the heads-on display of virtual reality.
Non-standard Distances in High Dimensional Raw Data Stream Classification

In this paper, we present a new approach for classifying high dimensional raw (or close to raw) data streams. It is based on k-nearest neighbour (kNN) classifier. The novelty of the proposed solution is based on non-standard distances, which are computed from compression and hashing methods. We use the term “non-standard” to emphasize the method by which proposed distances are computed. Standard distances, such as Euclidean, Manhattan, Mahalanobis, etc. are calculated from numerical features that describe data. The non-standard approach is not necessarily based on extracted features - we can use raw (not preprocessed) data. The proposed method does not need to select or extract features. Experiments were performed on the datasets having dimensionality larger than 1000 features. Results show that the proposed method in most cases performs better than or similarly to other standard stream classification algorithms. All experiments and comparisons were performed in a Massive Online Analysis (MOA) environment.
Modified Accelerated Bundle-Level Methods and Their Application in Two-Stage Stochastic Programming

The accelerated prox-level (APL) and uniform smoothing level (USL) methods recently proposed by Lan (Math Program, 149: 1–45, 2015) can achieve uniformly optimal complexity when solving black-box convex programming (CP) and structure non-smooth CP problems. In this paper, we propose two modified accelerated bundle-level type methods, namely, the modified APL (MAPL) and modified USL (MUSL) methods. Compared with the original APL and USL methods, the MAPL and MUSL methods reduce the number of subproblems by one in each iteration, thereby improving the efficiency of the algorithms. Conclusions of optimal iteration complexity of the proposed algorithms are established. Furthermore, the modified methods are applied to the two-stage stochastic programming, and numerical experiments are implemented to illustrate the advantages of our methods in terms of efficiency and accuracy.
Implementation of Target Tracking System Based on Small Drone

The small drone has the advantages of low cost, fast response, convenient carrying and good maneuverability, and has broad application prospects in military, civilian, video shooting and other fields.It has a wide application prospect in military, civil, video shooting and other fields. With the development of camera technology and the development of image processing technology, computer vision technology has been increasingly applied to small drones.In this paper, small drone is taken as an applicable platform, and relevant theoretical research and comprehensive experiments are carried out around monocular vision feature point extraction and matching, UAV target tracking strategy, etc. Finally, a target tracking system is built to realize real-time face tracking.
Study on Discrimination of Finger Motions based on EMG signals

In recent years, biological signals have attracted attention as tools for human interfaces. Researches on biological signals have been actively conducted. In this paper, we propose a method which distinguishes ten motions, such as “One” “Two” “Three” “Four” “Five” “Six” “Seven” “Eight” “Nine” and “Ten” by measured the electromyogram of the wrist. We measure data by installing 8 dry type sensors on the right wrist. We carry out frequency analysis using FFT and try to take 3 kinds of methods to remove noise. Finally, we use Support Vector Machine (SVM) for identification and classification. We conducted experiments with four subjects. In the experimental result, the accuracy of finger motions recognition was 65%. In the future, we will also add more methods to remove noise, and try to find other methods to improve the accuracy in the research.
Secure Data Quiry Using Unique Hash Indexing Utilizing MAC Address in a Remote Data Storage

Data privacy and protection is the hottest topic in cloud computing-based systems. Cloud based systems users keen on finding a way to ensure the security of information and maintaining privacy using remote document search to eliminate the need to retrieve data from the cloud. In this paper, we propose a novel model to provide a unique index using SHA-256 Hashing and to utilize the uniqueness of MAC address to produce a unique MAC-HASH with no collision and minimal processing time. The results has been proven to be irreversible and give a unique value for each user and lies in dispersing infiltrators of data and the hacker would have no access to the original data. Therefore, the files will be blind searched and the cloud administrator and the hacker would have no knowledge of your search.
Analysis of Web Usage Patterns to Identify Most Frequently Accessed Web Page by Multiple Users

All Data related to web sites that we access is stored in web logs. Increase in browsing these days has led to increase in size of these web log files. Web Mining is one technique that can be applied to these log files to mine navigational patterns. There are various types of web mining depending upon data mined Content, Usage or Structure. In this paper we focus on Mining of usage patterns: Web Usage Mining to discover most frequently accessed web page by multiple users after preprocessing of log file.
Research on High Efficiency IO Control Technology of Seismic Data Based on Data Segmentation

Abstract With the development of geophysical prospecting technology, seismic operation processing is terabytes of data in actual production. A seismic operation processing time can be as long as ten days. In order to complete these processing tasks in as short a time as possible, efficient use of node resources is needed. Therefore, it is necessary to segment and decouple the input data in order to improve the computational efficiency when carrying out such large amount of data, intensive calculation and long running time operations. IO control system can decompose such jobs into several sub-jobs for parallel processing, so as to shorten the running time, improve the efficiency of seismic data processing and enhance the computing performance of cluster system.
Sequence Generative Adversarial Network for Chinese Social Media Text Summarization

Although the sequence-to-sequence models have achieved state-of-the-art performance in many summarization datasets, there are still some problems in the processing of Chinese social media text, such as short sentences, lack of coherence and accuracy. These issues are caused by two factors: the principle of the RNN-based sequence-to-sequence model is maximum likelihood estimation, which will lead to gradient vanishing or exploding when generating long summaries; the text in the Chinese social media is long and noisy, for which it is very difficult to generate high-quality summaries. To solve these issues, we apply a sequence generative adversarial network framework. The framework includes generator and discriminator, in which generator is used to generate summaries and discriminator is used to evaluate generated summaries. The softargmax layer is used as a connection layer to guarantee the co-training of generator and discriminator. Experiments are carried out on Large Scale Chinese Social Media Text Summarization Dataset. The length of the sentence, ROUGE score and artificial score of summary's quality are used to evaluate the generated summaries. The result shows that the sentences in the summaries generated by our model are longer and have higher accuracy.
Learning Information Visualization Algorithms using Tulu

Information visualization libraries are toolkits that frequently require users to have a deep understanding of their internal structure and design. Their features sometimes include a wide range of visualization methods from simple low-level drawings all the way to high-level sophisticated visualizations. This complexity is an obstacle for researchers, students, and developers who need to design and implement appealing visual representations that can be incorporated into their products efficiently and easily. This paper presents Tulu, a library that runs on top of Diököl, a programming environment created with Lua and OpenVG. Tulu is aimed to serve as an alternative for creating visualization systems. This library offers several advantages over traditional libraries including its extensibility, portability, and size. With respect to extensibility, it allows the adaptation and extension of the logic of the visualization techniques implemented in its catalog. Concerning portability, it runs on several operating systems. Finally, in regard to size, Diököl's space requirement is less than 1 MB, which makes it fitting for small single-board computers, such as the Raspberry Pi device, aimed at teaching basic computer science concepts.
Welcome to AI matters 5(4)

Welcome to the fourth issue of the fifth volume of the AI Matters Newsletter. This issue is full of great new articles and stories! In our regular articles, we open with an event report by Louise Dennis on recent conferences relevant to SIGAI organized around the world. Louise Dennis then provides a summary of upcoming AI events. In the policy column, Larry Medsker covers recent policies on national strategy for AI laid down by the U.S. government, implications of AI on workplace transitions, President Trump's executive order on re-establishing the President's Council of Advisors on Science and Technology (PCAST), and more. Our final regular column is our AI crosswords from Adi Botea. Enjoy! We have three contributed articles in our current issue. In the first article, Sharad Sinha and Clint George provide a summary of the events and activities of a recent workshop conducted in India on AI for high school students. In the second article, Trevor Watkins describes the Cosmology of Artificial Intelligence Project (CAIP) that uses innovative visualization techniques to present the evolution of the field of AI. In the third article, Virginia Dignum discusses how AI is progressively trending to become a multi-disciplinary field of reseach and the implications of this trend on the future of AI.
CoNet: Compact and Low-Cost CNN for Image Classification

As the number of applications of Convolutional Neural Network increasing, the need for lightweight models to be able to run on embedded devices is also increasing. For that reason, a novel lightweight CNN is designed. Experiment on CIFAR-10 and CIFAR-100 shows that our method outperforms other state-of-the-art models with less parameters and FLOPs.
A Deep learning based evaluation method for feature detectors using satellite images

Feature detectors play an important role on computer vision. So far, feature-based and region-based methods, such as the repeatability, have been used to evaluate the performance of these detectors. Although these methods can provide an evaluation measure about the detection repeatability, they do not consider the informative part of the detected features as a whole set. In this work, we propose a novel method to evaluate feature detectors using a classification-based application. Kernel density estimation is used to represent the features sets and an effective SVM-based method is used to increase the number of variables on the feature space. An SVM classifier is then trained on the deep learning features which are generated using sparse autoencoders. Experimental results on satellite images show great difference among some common detectors and reveal the effect of their spaces on the classification accuracy.
A Shooting and Blending Style Ubiquitous Artwork Editor for Smartphones

Wide spread use of smartphones enables human to work, study, and even relax anytime and anywhere. If creative activities such as making artwork can be done on a smartphone just like playing an online game, the smartphone becomes an essential body part. We propose a method enabling users to smartly and easily producing aesthetically pleasing visual artwork using their preferred images. The users only need to select their preferred images taken by an embedded camera or downloaded from free archives on the Internet, and then ask the system to produce a new image with the selected ones. We elaborate the concept, design, implementation method, and preliminary evaluation of the proposed system in this paper.
Generative Video Face Reenactment by AUs and Gaze Regularization

In this work, we propose an encoder-decoder-like architecture to perform face reenactment in image sequences. Our goal is to transfer the training subject identity to a given test subject. We regularize face reenactment by facial action unit intensity and 3D gaze vector regression. This way, we enforce the network to transfer subtle facial expressions and eye dynamics, providing a more lifelike result. The proposed encoder-decoder receives as input the previous sequence frame stacked to the current frame image of facial landmarks. Thus, the generated frames benefit from appearance and geometry, while keeping temporal coherence for the generated sequence. At test stage, a new target subject with the facial performance of the source subject and the appearance of the training subject is reenacted. Principal component analysis is applied to project the test subject geometry to the closest training subject geometry before reenactment. Evaluation of our proposal shows faster convergence, and more accurate and realistic results in comparison to other architectures without action units and gaze regularization.
Research on predicting students' learning effectiveness with deep learning mode

This paper builds a model using the multi-layer perceptron (MLP) of artificial intelligence “deep learning”. Use the relevant family background information provided by US high school students when enrolling in school, Such as gender, race, education level of parents, economic conditions of students, whether to participate in exam preparation courses and other factors. Used to predict the effectiveness of student learning. The number of students is 1,000. Select at random 95 percent of the student data as a training group, 5 percent of the students as a group to predict. After the MLP model through training and learning. Under the standard of 100 points. Predict student achievement and actual test scores. Verification using the mean absolute deviation (MAD) commonly used to evaluate prediction accuracy. After the statistics, the average difference score was found to be less than 10 points. The results of this study show that the MLP prediction model can help teachers to find out early students who need remedial teaching. To assist students in effective learning.
Real-Time Face Detection Method Using Wavelet-Based for a Beautification System

In recently, the face detection is an emerging computer technology. It is a biometric identification that distinguishes organisms from the biological characteristics (fingerprints, palm prints, irises, etc.) of organisms (mainly humans). A real-time face detection and improve the accuracy of detection is present. We use several algorithms to identify faces will be written and we will explore the detection accuracy of the program. It uses a Haar wavelet transform to improve the beautification system to achieve real-time processing, and it adopts the face feature detection method to increase the accuracy ratio. As result, that show the verified on a captured images under several real indoor environments.