Reviewerly/exHarmony huggingface dataset:
Cloud-Cubing: Computing Iceberg Cubes Based on Cloud Computing

Data cubes reduce the amount of data by pruning nonessential ones. Iceberg-cube is one of the algorithms used to calculate data cubes. This paper proposes a high-speed algorithm to construct Iceberg-cube in parallel computing systems. The algorithm proposed in this paper builds Iceberg-cube in cloud and presents a new data transmission strategy. It can not only ensure the accuracy in the adverse condition of asynchronous data collection, but greatly decrease running time and the amount of data transmission as well.
Matching Cost Calculation Model Based on Multi-Scale Convolutional Neural Network

Aiming at the problem that the pixel matching cost is difficult to accurately calculate in complex images, a matching cost calculation model based on multi-scale convolutional neural network is proposed in this paper. The proposed calculation model optimizes the existing model based on feature fusion idea. This model improves the feature extraction ability by extracting and fusing different scale feature information. The experiment results show that the pixel matching accuracy of the matching cost calculation model based on multi-scale convolutional neural network is 8% higher than the existing matching cost calculation model.
BDI logic applied to a dialogical interpretation of human–machine cooperative dialogues

Abstract We first define a language for the dialogical logic and the models that the language generates. This language is thought to capture the structure of the dialogues captured by Lekta—a software framework oriented to the design and implementation of natural language processing-related applications—and its users. These dialogues are defined as cooperative dialogues in which the dialogical logic perspective of a dialogue seen as a competition is shifted into a perspective in which both agents cooperate towards a common goal. Later we define a BDI temporal logic based on a modal framework that we will use to study the beliefs, desires and intentions of both agents based on the model generated by the dialogical logic.
Design of No-Go Game Algorithm Based on Reinforcement Learning

The research focus of computer chess is how to use computer to abstract and formalize the logical thinking activities of human beings in chess game. This paper designs and implements a No-Go algorithm based on reinforcement learning. During the game, MCTS algorithm is used to explore the legal situation under the current situation. In this process, the convolutional neural network is used to evaluate the explored situation. Then, the neural network is trained to evaluate the situation with the results of the match, so as to achieve the effect of self-improving algorithm without human experience. In addition, the author used the Union-Find to find the legal landing point on the chessboard. Finally, the experiment shows that this type of No-Go algorithm based on reinforcement learning can be improved by itself and reach a certain level of chess ability, which improves the efficiency of searching legal moves compared with the traditional No-Go chessboard traversal method.
Improved Weighted Learning Support Vector Machines (SVM) for High Accuracy

Support Vector machine (SVM) is a linear model designed for classification problem and popular due to a number of their attractive features such as high generalization ability and promising performance. However, the high generalization ability of SVM is only achieved by depending on a small part of the data points to determine an optimal hyperplane. During the learning process, the noise still exists to deviate severely the corresponding decision boundary from the ideal hyperplane. Two different weighted SVM such as one-step WSVM (OWSVM) and iteratively WSVM (iWSVM) has been reviewed besides the standard SVM. This method assigns relative important weights to achieve optimal margin hyperplane. In this study, an improved WSVM using moving weighted average is introduced to generate useful weighted and unweighted support vector for the optimal margin hyperplane. The methods are compared based on correctly labeled, mislabeled data within margin and classification accuracy using three datasets in KEEL repository with 20% noise. The results show that the proposed method yields better classification accuracy compared to OWSVM and iWSVM.
A Novel High-speed FPGA-based True Random Number Generator Based on Chaotic Ring Oscillator

This paper presents a novel true random number generator with entropy source based on swappable matrix feedback ring oscillator (SMFRO). The proposed SMFRO structure utilizes multiple feedback mechanism to produce reliable chaotic oscillation. Three SMFROs implemented on FPGA with different resource configurations are tested. All of them can pass chaotic oscillation and NIST tests. Proposed design can generate random bits at 500MHz without extra post-processing.
Decision Making on Critical Component Using Combined Approach

The most promising strategy that will smooth out the maintainability issues of the software in software industry is Refactoring. The code often inherits some bad smell due to lack of proper design approach which may lead to improper functioning of code, especially when new functionalities is added to the code due the requirement change by the customer in maintenance phase. Improving the code by refactoring is an expensive process; a lot of research has been conducted to optimize the refactoring. In this chapter, a medium-size component-based system is considered and an integrated approach using Bayes hypothesis is applied to find out the critical component, so that refactoring effort should be applied on the critical component first. Integrated weights involve weights from different approaches, namely subjective approach which is been evaluated with fuzzy TOPSIS approach and objective weights are evaluated by entropy approach.
School of aerospace engineers

The authors discuss the project of creating a school of aerospace engineers, as well as the need to take additional measures to expand the opportunities of space education for children. These are promotion of schoolchildren’ s career guidance in the aerospace sphere, popularization of cosmonautics and involvement of young people in engineering and space activities, as well as developing the motivation of the younger generation to choose technical professions related to Cosmonautics, space research and the use of space technologies. Space activities are necessary as a significant incentive for the developing technologies and basic science. The authors propose to promote the intellectual development of students through competitions, research, scientific and project activities. The implementation of the project is a systematic, consistent work of the project team and is to contribute to solving the problem of the students’ career guidance in aerospace education to perform the tasks. Today, the relevance of technical education is very high and there is a need to improve the level of the students’ knowledge in the field of exact Sciences, to emphasize the importance of technical education.
Metrics to gauge the success of a manufacturing ontology

Ontologies are structures of concepts that define a high-level representation of a system or area. They can serve as a foundational understanding for developing or integrating software representations of said system. And while the construction of can be a complex, multi-party exercise, the assessment of ontologies is often defined less on usage and more on completeness and coverage. In manufacturing, ontology development ranges from supply chain to production to design. Owing to the computer science foundations of ontology design and representation, the value or quality of an ontology can be assessed on notions of completeness and coverage. Recently, researchers have posited that usage should factor into the Ontology Lifecycle. Similar to how the market, and not technology, defines the success of a product or technology, this paper will examine how utilization of an ontology can define the value or quality of the ontology
Embodied Atmospheres

This paper introduces a Ph.D. research project about wearable computer artifacts that intertwine digital information from both the human body and the environment. These artifacts use content-related sensors to make air constituents perceptible. In addition, the artifacts' system design is based on metaphorical representations to provide a conceptual system of thought and action for the user. Through the intuitive use of these wearable computer artifacts, participants are able to explore information about themselves and their surroundings with their enhanced body. To provide insights into these human-computer interactions, this Ph.D. research project conducts a user experience study as a 'real-life' ethnographic enactment.
Contrast Enhancement using Efficient Gray-Level Grouping

A novel contrast enhancement scheme, named efficient gray-level grouping has been proposed. The proposed scheme performs better than the existing gray-level grouping approach. The contrast improvement of the scheme has been evaluated in terms of average distance between pixels on gray scale and root-mean square contrast. The simulation results suggest that the proposed scheme performs better than the gray-level grouping in terms of contrast measures. The proposed algorithm is also faster than gray-level grouping algorithm.
Locking Object Position using Camshift Algorithm for Automatic Trolley

Today, the development of science and technology produces many innovative devices, that can facilitate people to make their work easier. One of the devices is a trolley, which can help people to bring goods from one place to another place in a modern market. This paper proposed a method to develop an automatic trolley control, which can follow a customer based on the clothes color. The control system is built with Jetson Nano, where a webcam and motors are connected. The webcam has a function as the trolley's eye, which is used to view and retrieve the color data of the customer's clothes. These data are processed by Jetson Nano and the results are sent to the motors for the robot movement. The unique point of this paper is that the trolley has ability to lock the position of a certain customer. To improve the performance of locking object position, the Camshift Algorithm was used. This research conducted three experiments to lock the object position using proposed algorithm. The result of these experiments were: (1) minimum distance between main object and another object to be detected by the camera was 5 cm; (2) the main object was successfully detected by the camera with maximum distance of 360 cm; (3) the camera successfully locked the main object in front of the camera as far as 360 cm while another object stood in front of the camera as far as 20 cm. The advantage of this system is that the trolley can remember the customer that is supposed to be followed even though there are some customer use similar color. This study shows that the trolley can identify the position of customer and follows successfully.
Research on Remote Sensing Image Data Attack Method Based on Machine Deep Learning Network

At present, with the breakthrough and application of in-depth learning technology in the field of artificial intelligence, the content of information acquired through interpretation of remote sensing images is more and more abundant, and how to guard information security has become a new technology hotspot. [1] In order to camouflage and conceal our important objects, it is necessary to attack the acquired remote sensing images to remove and confuse the target information and mislead the "enemy" to make wrong image analysis, which is a means to protect information security.[2] In this paper, the mainstream algorithm principle is introduced based on machine deep learning network technology. Aiming at remote sensing image data poisoning attack and sample attack in the process of deep learning network training, the purpose of tampering with original image data and hiding characteristic targets is realized.
Recommendation System for Big Data Software Using Popularity Model and Collaborative Filtering

A recommender system is a model which has the ability to predict the list of items according to the user's preferences or ratings. Recommender systems have huge areas of application ranging from music, books, movies, search queries, and social sites to news. A recommender system filters information into various categories which may be personalized or non-personalized. Recommendation systems are very useful for the user in discovering those items which they may not have found else ways. These above-mentioned systems are quite similar to search queries except that search queries need to be explicitly provided by the user but recommender system may do it implicitly without the user having prior knowledge about its working. There are many algorithms used in recommendation systems such as restricted Boltzmann machines (RBM), slope one, singular value decomposition, alternating least squares. This paper presents an in-depth study and analysis of the following two algorithms: Review-based popularity model and collaborative filtering model. Popularity model is a straightforward model based on the popularity and rating of an item given to it by the user. Collaborative filtering model is a very efficient and popular model and is used by many popular organizations like Netflix, Amazon, and Facebook.
Chinese Address Standardization Based on seq2seq Model

Address information is important in many industries especially express delivery. However, since address is usually an unstructured text that has multiple expressions, the use of the address is confusing in the real world, for example, people usually simplify and express addresses according to their own habits. This leads to the challenge of missing and error of important information, which will reduce the efficiency for most of industries. In this paper, we propose an address standardization method based on the seq2seq model to solve the above challenges. We use the Gated Recurrent Unit (GRU) to learn the intrinsic link in the Chinese address and use attention mechanism to determine the weight of different parts of the address. To our knowledge, it is the first method that can complete the missing administrative address information and correct the error of address information without using additional information such as standard address database and geological element table. It is also the first time to use deep learning and attention mechanism in Chinese address standardization task. The experiment verifies that our method achieves high accuracy in the industry.
Prediction of Optical Transmittance Loss In Solar Panels Using Lambert-Beer Law

The energy industry is extensively using the photovoltaic generators. The output of the panels during day time depends on various parameters solar irradiation, temperature, wind speed, humidity, fog and dust deposition, etc. In the outdoor photovoltaic generators under the natural weather conditions, the deposition of dust particles and pollutants reduce the energy efficiency. The deposition rate of dust particles are highly dependent on characteristics of the aerosol, humidity, wind direction, geographical location, tilt angle and cleaning time of panel. The dust density keep on increasing with the exposure time if cleaning is not done regularly. The dust covering the solar cell glaze leads to the degradation in the light beam optical transmittance, which causes the shadowing effect and reduction in the energy conversion efficiency. In the paper, particles with different dust densities are considered and reduction in the optical transmittance recorded experimentally and mathematically. In the study, dust particles of 4.2 to 1.4micrometer size are considered at different density level from 2g/m2 to 40g/m2. The paper provides the estimate of the number of particles present during different dust densities responsible for the optical transmittance loss. The results show that the transmittance is linearly decreasing in the initial dust densities, but after 20g/m2 the reduction is exponential and is followed by the saturation in transmittance. The result shows that the radius and density of the dust particles has a significant impact on the light beam transmittance.
Key Frame Extraction with Face Biometric Features in Multi-shot Human Re-identification System

Although achieving a good accuracy, the multi-shot human re-identification (MS Re-ID) experiences long processing time and large memory consumption, because it uses all of the frames it receives. The usage of all of the frames for reidentification does not only weigh the system's process, but also gives redundant information. To enable a faster and lighter execution of the MS Re-ID system, the present study proposed a key frame extraction method using face biometric feature for MS Re-ID system. Key frames were responsible to provide important information of an individual's face for re-identification, and avoid collecting the redundant features. The proposed key frame extraction method consists of two phases: face detection with facial landmark extraction and face tilt angle calculation. Multitask Cascaded Convolutional Networks (MTCNN) was used for face detection with facial landmark extraction, while 10 was used as the optimal number of key frames and the optimal face tilt angle range was -10° until 10°. We also compared MS Re-ID system with the proposed method to the normal single-shot and multi-shot systems to examine the proposed method's impact on the performance. We found that the proposed key frame extraction method successfully retained 100% accuracy while reducing the memory consumption by 2% and the execution time by 19%, compared to MS Re-ID system without key frame extraction.
Construction of the Integrated Network Service Platform for Smart City Community Home Care Under the Background of “Internet+”

“Filial piety is the most important of all virtues” is a traditional virtue of our country. Today, with the aging of our population becoming more and more serious, the intelligent home-based pension service platform can provide a more comfortable, safe and healthy living environment for the elderly, and help them spend their old age safely. Aiming at the potential safety hazards and the living needs of the elderly at home, this paper designs a set of the intelligent home care service platform based on the related technology of the Internet of Things. It extracts the eigenvalues of the original data and sends them to the remote server by means of the multi-sensor information fusion through the intelligent terminal device. It processes the data on the server side accordingly, realizes the remote monitoring and care of the elderly, and finally realizes the purpose of the intelligent home-based pension.
Design of a Mini Robot for the Automation of 3D Winding Machines Axes and Self-correction by Artificial Vision Using Deep Learning

Object recognition is among the most important subjects in computer vision, it has undergone a huge evolution during these last decades, but in the last years artificial intelligence has seen the appearance of Deep Learning, and through the efforts of researchers, Deep Learning is having great success, its applications have touched on different fields, such as robotics, industry, automotive. In this context, in collaboration with an Automotive components manufacturer and FST faculty of sciences and technologies of tangier (UAE University) they have taken the initiative to develop an object recognition and self-correction system for a winding machine that requires a good accuracy of location of the needle using the Deep Learning which is the purpose of this paper. This report summarizes the work done within this Company concerning the development and implementation of a system for automating and self-correcting the location of the needles of winding machines using artificial vision with Deep learning. The convolutional neural networks have become advanced methods for classification and detection of objects over the last five years. At present, they work better than conventional image processing method set, on many image classification data sets. Most of these datasets are based on the notion of concrete classes. In this paper, we present a new set of image classification data as well as object detection data, which should be easy for humans to solve, but its variations are difficult for CNN. The classification performance of popular CNN architectures is evaluated on this dataset and variations of this dataset may be of interest for future research.
An adjustable amplitude and pulse-width laser modulation driver with active feedback for QKD experiments

This paper presents a laser modulation driver using active feedback in a 130-nm CMOS process. The driver is designed for laser modulator in quantum key distribution (QKD) experiments. The laser modulator requires a drive signal with adjustable amplitude and pulse-width. We employ a delay-adjustable delay chain and a multi-stage amplifier with active feedback to provide a current pulse signal with an adjustable pulse-width from 110 ps to 3.3 ns and an amplitude adjustment range of 0 mA to 14.4 mA to drive the laser modulation. By employing the active feedback, the rising/falling edge of the output current in the proposed circuit can reach 70 ps, while the data rate is up to 625 Mb/s. The post layout simulation results indicate that the modulation driver can meet the requirements of the QKD experiments.
Multi-source Situation Information Fusion Based on Particle Swarm Optimization Evidence Theory

According to the problems of existing situation information fusion algorithms in fusion efficiency, detection rate and false detection rate, a multi-source situation information fusion algorithm based on particle swarm optimization evidence theory is proposed. The algorithm combines the particle swarm optimization algorithm and evidence theory, uses the optimized particle swarm optimization algorithm to obtain the index weight of multi-evidence, and uses the simplified alarm as evidence fusion to improve the efficiency and accuracy of alarm fusion. Experimental results show that the proposed multi-source fusion algorithm has higher efficiency and lower false alarm rate, and its performance is better than the typical fusion methods.
In-Plane Solar Irradiance Calculation for Various Type of PV Arrays

PV array can operate in various types, fixed arrays, single-axis tracking and double axis tracking. How to correctly calculate the in-plane solar irradiance and irradiation is the key issue in estimating the PV power generations. This paper derives the mathematical models and formulas of various type of PV arrays from the geometry relationship between Sun and PV array and spherical triangle rules, so that people can use such formulas to calculate in-plane solar irradiance and irradiation for both front side and backside surfaces.
Where is geoscience going?

Abstract This short article provides my views – and not necessarily views that are shared by the British Geological Survey, where I was executive director from 2006–19, or by the Earth science community in general. I have outlined some of the trends that I see as important for the geosciences, largely from a solid-Earth perspective. I stress that fundamental discovery science in this sector must be, and will largely continue to be, led by the academic community but that Earth sciences research needs to be more focused on problem solving rather than refining our knowledge of the problems that face the Earth system. Academics and government laboratories have distinct but complementary roles in the pursuit of discovery and in applied geoscience research and training of geoscientists.