Reviewerly/exHarmony huggingface dataset:
A Hybrid ACO-SVM Approach for Detecting and Classifying Malaria Parasites

The major growth in computing technology has done wonders for the medical field. Being a significantly favourable research domain, many algorithms have been proposed in the near past to detect the presence of malaria parasites. The target of this work was to originate a technique which detects the parasites of malaria easily and classifies in just a few seconds. A hybrid ant colony optimization and support vector machine approach is developed for this task. In this work, ant colony optimization was used for the detection of the boundaries of microscopic images of malaria parasites and by using multi-class support vector machine, classification of malaria parasites is achieved. In SVM various parameters were taken (HSV colour values, auto correlogram, colour moments, mean amplitude, area and wavelet moments), which are used to build different classes or groups. The performance of this approach is verified by simulation results.
Image-enhanced Adaptive Learning Rate Handwritten Vision Processing Algorithm Based on CNN

Handwritten images are an important source of information in the human world, and machine learning has always been an important method for dealing with handwritten visual problems. Most of today's machine learning uses shallow structures such as SVM (Support Vector Machine) and kernel regression. These algorithms cannot be complex. The image information is more accurately predicted, and the recognition method of machine learning has quite high requirements on the quality of the image. This paper takes the MNIST data set of the National Institute of Standards and Technology as an example to enhance the data of the handwritten image data. The Convolutional Neural Network (CNN) algorithm builds the model and uses the adaptive learning rate algorithm (Adadelta) to enhance the model accuracy. The experimental results on the MNIST dataset show that the proposed method can effectively improve the recognition of handwritten images and quickly and correctly identify handwritten images. Its accuracy of 99.6% is significantly better than neural network and logistic algorithm.
An Improved Brainstorm Optimization Algorithm Based on the Strategy of Random Perturbation and Vertical Variation

The brainstorming optimization algorithm (BSO) is a swarm intelligence algorithm based on human creative thinking, which is inspired by the brainstorming process and proposed by Professor Yuhui Shi. It has been successfully applied to a lot of engineering problems involving optimization. In this paper, random perturbation strategy and vertical crossover variation are introduced to BSO to improve its performance. The specific idea is to increase the random disturbance and the vertical cross variation on the same variable when the individual is updated. The proposed algorithm Accelerating BSO (ABSO) is compared with BSO and other three algorithms(PSO, DE, CS) on 9 benchmark functions. From the results, both final solutions and convergence speed show the superiority of ABSO.
Learning based Probabilistic Model for Migration of Industrial Control Systems

The updating and upgrading of control systems is a cumbersome, expensive and time consuming task. From a software perspective, control system migration is a collective task of migrating the control logic, Human Machine Interface (HMI) and auxiliary software applications. Migrating control logic is the most challenging task owing to constraints on hard real-time behavior and execution order. Control logic typically contains engineering artifacts that specify the functionality of industrial devices taking into account various parameters. Therefore, to migrate from one Distributed Control System (DCS) system to another or to upgrade the existing DCS, one needs to map the source control entity and their parameters to the appropriate control entities in the target DCS.In this paper, we propose a machine learning based suggestion management system that identifies control entities and parameters for a source DCS and suggests the use of similar control entities and corresponding parameters for the target DCS. This in effect saves effort required in mapping of control parameters and reduces the dependence on subject matter experts. Our system uses a probabilistic approach to find these similarity mappings based on meta-data stored in an Ontology. We further describe a case study implemented for mapping heritage and legacy systems to a modern control system to verify and validate our approach.
Using Neural Architecture Search to Optimize Neural Networks for Embedded Devices

Recent advances in the field of Neural Architecture Search (NAS) have made it possible to develop state-of-the-art deep learning systems without requiring extensive human expertise and hyperparameter tuning. In most previous research, little concern was given to the resources required to run the generated systems. In this paper, we present an improvement on a recent NAS method, Efficient Neural Architecture Search (ENAS). We adapt ENAS to not only take into account the network’s performance, but also various constraints that would allow these networks to be ported to embedded devices. Our results show ENAS’ ability to comply with these added constraints. In order to show the efficacy of our system, we demonstrate it by designing a Recurrent Neural Network (RNN) that predicts words as they are spoken, and meets the constraints set out for operation on an embedded device.
Multi-USVs Coordinated Detection in Marine Environment with Deep Reinforcement Learning

In recent years, with the rapid development of deep reinforcement learning, numerous researches have begun taking more and more attention in military and civilian fields. Compared with ship monitoring and other technical means, USVs have more significant advantages in marine environment and is gradually becoming a concern of academic and marine management departments. However, single agent reinforcement learning cannot fit well in the multi-USVs cases because of the non-stationary environment and complex multi-agent interactions. In order to learn cooperation models among USVs, we propose a multi-USVs coordinated detection method based on DDPG and LSTM is used for storage about the sequence of states and actions. Besides, in order to adapt to the algorithm, we model the marine environment where every USV is considered as an agent. Experiments are constructed in simulation conditions and the results verify the effectiveness of the proposed method.
End to End Person Re-Identification Based on Attention Mechanism

Abstract This paper treats person re-identification (re-id) as a sequential model, guide person re-id with person detection, combines recurrent neural network (RNN) with attention mechanism, and proposed an end to end person re-id method for surveillance scenarios. The feature of target person is firstly extracted using ResNet, and then the feature is added to the long short-term memory (LSTM) network to guide the attention model for the region of interest in the surveillance image. Finally, combined with the information observed from the image multiple times, the most similar candidate person in the image is deduced, and the feature distance is calculated and ranked for person re-id. This paper strengthens the relationship between person detection and person re-id, and reduces the error between models. Because the number of candidate person matched with target person is reduced, this method can process person re-id task with less calculation and time. This paper also verified the effectiveness of the proposed method by experiments comparing a variety of person detection and re-id methods on several person re-id datasets.
The Relationship Between Listening Ability and the Ability to Write News Using Audiovisual Media for Middle School Students

Listening skills can help students develop their knowledge of the language. Writing skills can determine the success of students in following the teaching and learning process. This study aims to get an overview of the relationship between listening ability and the ability to write news using audiovisual media for middle school students. This type of research is quantitative research using the correlation method. Data collection uses research instruments in the form of objective questions and performance. The results of the study there is a significant positive relationship between the ability to listen to the news and the ability to write news using audiovisual media in junior high school students. This is evidenced by then-2 degree, t count is greater than t table which is 3,17>2,51 with the calculated r-value 0.57 which means it has a rather low level of correlation. It can be concluded that H1 is accepted, namely, there is a relationship between the ability to listen to the news and the ability to write news using audiovisual media for middle school students. So, the higher the ability to listen to student news, the higher the ability to write student news.
A Reinforcement Learning based End-to-End Algorithm for Confrontation Problem

In this paper, a confrontation problem between two agents is investigated in which one agent is required to find then eliminate the other agent through projecting bullets. A reinforcement learning based algorithm is designed to realize an end-to-end strategy for one agent to confront the other. Self-play method is used to enhance the algorithm. Simulation results show the effectiveness of the proposed algorithm in confrontation with FSM based strategy and human player.
Interaction can hurt - Exploring gesture-based interaction for users with Chronic Pain

Chronic Pain is a universal disorder affecting millions of people, influencing even the most basic decisions in their lives. With the computer becoming such an integral part of our society and the ever-expanding interaction paradigm, the need to explore potential computer interactions for people with Chronic Pain has only increased. In this paper we explore the used of gesture-based interaction as a medium with which these users can perform the base operations of computer interaction. We show that, for gestural pointing and selection, modeling users' interaction space and multimodel interaction performed the best in terms of throughput.
YUE Virtual Park: Multi-sensory experience creative cultural products based on AR technology

This paper presents a study in which mobile technology was used to build a mobile app for the promotion of regional Chinese culture.We used traditional Cantonese nursery rhymes as the basis for our work.These were recorded as sound files, illustrated and combined with Augmented Reality technology to create a multi-sensory experience for the user.We used Android Studio to build the application and implement the main functions.Unity3D was used to implement the AR functions.The purpose of developing this app is to promote the culture of Cantonese nursery rhymes and assist in classroom teaching.For the testing, the primary school students are mainly supplemented by foreign friends.In the process of using this app, there are three small games built in, each of which can learn Cantonese nursery rhymes.The app was evaluated in a primary school classroom-teaching environment.More than 1,000 students were exposed to it over a period of five days.After a week of trials we returned to the school to conduct a return visit survey.We are constantly making changes and improvements to the app following feedback from schools.Augmented reality.
Generating Artificial Images by Generative Adversary Network

This paper presents a case study of the structure, generative adversary network (GAN). The primary goal of this project is to apply the concept of GAN (Generative Adversary Network) to generate a group of artificial images with same theme, which are expected to give a realistic view to human eyes in final stage. This independent research mainly focuses on basic structure of GAN and the improvement of its quality via implementing a variation, DCGAN. Thus, it can offer solid foundations and help to our team to focus on exploring a possibility of this fair new technology.
The Morality of Transparency: Clarity versus Emptiness

With transparency being either a state of clarity or the exposure of emptiness, this paper focuses on the practices of transparency, termed ‘transparenting’. Focus on transparenting practices can provide a better tool for understanding the ‘rise of transparency’, and its moral, practical and political aspects. Three types of transparenting are defined: the voluntary transparenting conducted by organizations, the “flashlight” transparenting of suspicious outsiders, and renegade transparenting by the whistleblower. Examples are given from the field of anti-corruption programs and business ethics and compliance. Through a focus on transparenting, we can better understand both the risks and dark sides of transparency.
Dual Low Identification Target Recognition in Complex Environment based on Neural Network

In order to solve the problem that the traditional neural network has low recognition rate in the complex environment, this paper designed a low recognition target recognition system based on Faster-RCNN and YOLO neural network. Firstly, a synchronous acquisition system for collecting RGB and thermal dual datasets was designed. Then, a Dual Faster-RCNN and a Dual YOLO neural network were established, and three sets of information feature fusion experiments were carried out to establish the optimal network parameters. Finally, the two networks that were trained and tested using a separate test sets. The test data shows that the average recognition rate of personnel and vehicles in the Dual Faster-RCNN neural network is about 82%, and the recognition rate of the Dual YOLO neural network is about 80.3%. The low-recognition target recognition system studied in this paper can accurately identify the key information in low-recognition pictures, which has important research significance and practical application value for future development of intelligent transportation, remote sensing image analysis and security system protection.
Test System for Control Algorithms on a DC Motor

This work implements a system for testing control algorithms. The objective is to find an optimal regulator for the closed-loop speed control of a DC Motor. The system consists of a DC motor, an encoder, the Arduino card and a computer. The speed of the motor is the control variable. First, the transfer function is obtained based on the electrical and mechanical characteristics of the system. The function is reduced based on data obtained experimentally from plant input and output using Matlab’s IDENT tool. The equation that governs the system found allows to simulate the different controllers: proportional, proportional derivative, proportional integral, proportional integral derivative and fuzzy control. The results are displayed in a graphical interface in Labview. In the experiments, closed-loop and open-loop control subsystems are simulated. The experiments indicated that the proportional integral derivative controller has a better response.
Evaluation of target acquisition performance in photosimulation test

The photosimulation test is one of preferred method to evaluate the effectiveness of the camouflage cover. The arrangement of the photosimulation test is in principal similar to the way of testing the target acquisition performance. The deal of this paper is to refer about our experiences with the application of methods of target acquisition performance to the results of the robust set of the photosimulation trials.
N-dimensional M-ary Constant Energy Modulation for Nonlinear Communications Channels

In this paper, we present a multidimensional constant energy modulation, which utilizes N-dimensional signal space to provide a very large set of M-ary signal constellations. The modulation is termed as (ND-MCEM). This modulation provides good spectral efficiency while constraining all possible communications symbols to a constant energy with the goal of improving performance over nonlinear communications channels.
Research on Multi-objective clustering Optimization of Logistics Distribution Line

Abstract In order to improve the ability of logistics distribution line optimization, a multi-objective clustering algorithm based on particle clustering is proposed. The maximum density sparse detection and adaptive optimization method are used to schedule the logistics distribution route. The particle swarm optimization (PSO) algorithm is used to construct the multi-objective optimization model of the logistics distribution line. The global optimization characteristics of the particle swarm optimization algorithm are used to optimize the logistics distribution route. PSO adaptive clustering method is used to realize multi-objective optimization of logistics distribution path. The simulation results show that the method has better performance in the optimization control of logistics distribution line, improves the efficiency of logistics, reduces the loss of logistics line, and improves the throughput performance of logistics line.
Salaat Ontology: A Domain Ontology for Modeling Information Related to Prayers in Islam

Objectives: This work is directed towards development of a domain ontology and a semantic tool for Muslim's prayer called Salaat.Methods/Statistical Analysis: The proposed ontology has been developed on Protégé tool based on TODE, a test driven ontology development methodology.A prototype application has been developed on JENA semantic web toolkit for the utilization of Salaat ontology.Findings: The proposed ontology comprises 113 concepts and 85 properties.A set of 40 queries were run to analyze the accuracy of proposed ontology.All the queries were successfully executed and extracted the desired information.Application/Improvements: The proposed tool can help a novice user to gain knowledge about Salaat (Islamic prayer).It becomes very easy to get the precise information through keyword based search.
One-Handed Interaction Technique for Single-Touch Gesture Input on Large Smartphones

We propose a one-handed interaction technique using cursor based on touch pressure to enable users to perform various single-touch gestures such as a tap, swipe, drag, and double-tap on unreachable targets. In the proposed technique, cursor mode is started by swiping from the bezel. Touch-down and touch-up events occur at the cursor position when users increase and decrease touch pressure, respectively. Since touch-down and touch-up event triggers are different but easily performed by just adjusting the touch pressure of the thumb from low to high or vice versa, the user can perform single-touch gestures at the cursor position with the thumb. To investigate the performance of the proposed technique, we conducted a pilot study; the results showed that the proposed technique is promising for one-handed interaction technique.
Blended Learning Supports in Chinese Architectures with 360 Degree VR Contents and 3D Modelling VR Contents

General Education (GE) module is a basic requirement for students who are studying undergraduate programmes in City University of Hong Kong (CityU). Several study areas are included in the GE curriculum design to achieve the all-round training objectives, although College-specified courses might be varied for different college or school. It is always a challenge for teaching staff to engage students in science and engineering GE module; particular VR activities are designed in supporting blended learning approach. The study survey was undertaken with 88 samples while the students finished both VR activities with a 5-point Likert scale, and the mean comparison on the "ease of use" study was conducted with a t-test. The acceptance level with high mean values is found with using VR activities to enhance students' learning experience. Also, there is no significant difference in the "ease of use" studies while comparing two types of VR contents. Both the VR contents using 360-degree production and 3D modelling production have their limitations, which are deficient interactive features and lack of real-world image projection. Moreover, the development of teaching pedagogies with virtual tours are discussed; production workflow, features, and limitations of these two VR content types are recorded for review and future enhancement.
Impact of quantum algorithms on time complexity of classical-data problems

We explore the impact of quantum algorithms on classical-data problems if quantum algorithms can be implemented on quantum computers. It is underlined in this paper that both state estimator design problems for classical linear systems and quantum state tomography can be considered as classical-data problems. Our recent research indicated that the time complexity of state estimator design problems can be reduced to O(qn) in comparison to the time complexity O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> ) in traditional classical algorithms when quantum states can be efficiently prepared, the system matrix is sparse, and both the condition number κ and the reciprocal of precision ε are small in size O(poly log(n)), where n is the dimension of the state x(t) and q is the dimension of the input u(t). This is in contrast to the further observation that the time complexity of state estimator design problems is O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) when quantum states cannot be efficiently prepared. The time complexity of quantum state tomography with dimension d is O(d <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> ) when classical algorithms are adopted. We have demonstrated that if the system matrix is sparse, and both the condition number κ and the reciprocal of precision ε are small in size O(poly log(d)), quantum algorithms can reduce the time complexity of quantum state tomography to O(dpoly log d) or O(d <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) when quantum states can or cannot be efficiently prepared. In other words, preparing quantum states efficiently has become a bottleneck constraint for quantum acceleration. Our recent researches can be regarded as a novel attempt of enriching application area of quantum computation.
Towards Context-Aware DNA Sequence Compression Algorithms

High throughput DNA sequencing instruments significantly decreases sequencing costs and thus produces large volumes of genomic data. Thus for efficient storage and faster transfer of genomic datasets, compression is inevitable. DNA sequences are rarely accessed alone and DNA of same species have high degree of similarity. Thus considering each sequence separately and utilizing intra-similarity alone for compression fails to achieve impressive compression ratios when compared to direct encoding. Better compression ratios can be achieved if compression is done based on a reference sequence and encoding only the differences. This paper aims to overview various techniques developed so far for DNA sequence compression ranging from the initial general purpose compression algorithms to the latest multi-reference sequence based compression algorithms.