Reviewerly/exHarmony huggingface dataset:
An Interactive Learning System for Large-Scale Multimedia Analytics

Analyzing multimedia collections in order to gain insight is a common desire amongst industry and society. Recent research has shown that while machines are getting better at analyzing multimedia data, they still lack the understanding and flexibility of humans. A central conjecture in Multimedia Analytics is that interactive learning is a key method to bridge the gap between human and machine. We investigate the requirements and design of the Exquisitor system, a very large-scale interactive learning system that aims to verify the validity of this conjecture. We describe the architecture and initial scalability results for Exquisitor, and propose research directions related to both performance and result quality.
Cardiac Arrhythmia Detection Using Computational Intelligence Techniques Based on ECG Signals

The article proposes a study of cardiac arrhythmia detection, because besides the elderly this type of anomaly can occur with adolescents as well. During an arrhythmia the heart may beat very fast, very slow, or with an irregular rhythm. Often the physician may have difficulty detecting which type of arrhythmia, so a system will be proposed to assist in the medical diagnosis to detect arrhythmia in the patient. For this will be a database of ECG signal of several patients, where was applied an ANN (Artificial Neural Network with ELM training) to make the classification. Both standardized and raw data were used during the training stage, as well as several neuron architectures in the hidden layer were tested to have a good accuracy that will be the metric used in the work.
A Comparative Study on Music Teacher Training Programs of Ontario, Quebec, British Columbia in Canada

본 연구는 캐나다의 온타리오, 퀘벡, 브리티시컬럼비아 주의 교사 자격제도와 음악교사 양성과정의 특징을 조사하고, 각 주를 대표하는 대학을 선정하여 음악교사 교육 프로그램을 분석하였다. 연구결과 각 주는 교육체계, 교사 자격 및 양성제도를 각각 다르게 운영하였으며, 교사자격증은 취득 요건의 충족 정도에 따라 다양한 종류로 발행하였다. 각 주를 대표하는 대학에서는 교과목 및 학점조건에 따라 초·중등 음악교사를 복수자격으로 준비할 수 있었으며, 다양한 주제의 강의와 현장중심의 연계성 있는 실습 프로그램을 동시형 및 복수학위 프로그램으로 운영하고 있었다. 본 연구는 캐나다를 대표하는 3개의 주에 대한 음악교사 양성과정에 대한 연구로서 그 의의를 두는 것은 물론 향후 우리나라 음악교사 양성과정 프로그램 개발 및 개편에 기초 자료로서 역할을 하길 기대한다.The purpose of this study was to compare the teacher qualification system and the characteristics of music teachers training of Ontario, Quebec, British Columbia in Canada as well as the music teacher programs of these representing universities. The result was that the teacher qualification system in each state was operated to obtain a teacher’s certificate and showed differences based on the education system, teacher qualifications, training systems, and types of teacher’s certificate. Each university provided music teacher programs for elementary and middle school with enough time for practical programs and projects in a concurrent program and a dual degree. This study expects to play a role as basic data for the training system of music education in Korea.
An Accounting Ledger System Using the Hyperledger Fabric-Based Blockchain

Data related to money, or in other words, financial data, is encrypted as well as obfuscated and stored through various processes. The validity of this data can then be verified by using corresponding values. In this paper, chaincode generation, storage, and validation were used to verify whether the input data is reliable using a blockchain open source based on Hyperledger Fabric. The user has a hash value for the membership fee paid by himself/herself, is able to use the hash value to check whether the correct data was entered or not, and/or to detect the presence of irregularities. This allows transparent disclosure of the account book while corresponding data is provided with the function that only users belonging to a specific group can check, resulting in increased financial reliability within each group.
Research on Waveform Table Compression Algorithm Based on Direct Digital Frequency Synthesizer (DDS)

Direct digital frequency synthesizer (DDS) requires configuration of waveform data tables of hundreds of thousand or even a few million words. Integrating such a large amount of high-speed ROM with current integrated circuit technology is difficult and uneconomical. In order to reduce hardware resources, this paper proposes an improved parabolic interpolation algorithm, which is a low-power direct digital frequency synthesizer structure, using a smaller lookup table to find the sine function. The algorithm performs interpolation calculation according to four waveform data points near the interpolation interval, and can automatically identify the singularity of waveform and select three different parabolic interpolation algorithms, so as to reduce the interpolation error to the lowest level. The algorithm can obviously reduce the maximum interpolation error for different waveforms containing singularities. The experimental results show that the improved parabolic interpolation algorithm has smaller interpolation error than other algorithms when compressing the same waveform under the same compression conditions.
Trend Analyses for Blockchain Technology Innovations Using Data Analytics

Data Visualisations have been identified to play a critical role in business decision making, wherein it acts as a bridge between decision makers and the vast amount of unstructured data. Blockchain applications are being carefully assimilated into the technological space due to Bitcoin's rapid rise in the financial market. Consequently, researchers, technologists and cryptocurrency investors can understand the posture of this technology that leads to articulate technological and financial investment strategies. This paper investigates the role of data analyses in the prominent implementation of Blockchain. We characterise the adoption of Blockchain technology by analysing two types of datasets which are text and multivariate by fitting multiple time series and geographical location to reveal unrealised opportunities for potential income and innovation. In addition, to obtain the useful trends over time a temporal data analyses to fit the Twitter dataset, was conducted. With the automated knowledge discovered this will supplement the decision makers' demand to have a valuable data-driven business strategy, which in return will deliver business or investment opportunities.
ERP system implementation with accounting modules in national amil zakat institutions

Abstract Rumah Yatim is an authorized zakat institution in Indonesia with national-level task of collecting and distributing mandatory zakat to the eligible person fairly and evenly. Therefore, there are many issues that prevent zakat optimization such as lack of awareness to pay zakat, inaccessibility of required channel, disrupted of productive program and overlapping funds as the cause of incoordination. This study offers the system design in related to the accounting module to present the compliance and standards in order to convince relevant party and public eyes upon the credibility and reliability of zakat management. It was carried out by developing an Enterprise Resource Planning (ERP) system using the Odoo Quick Start method as the popular mechanism within small and medium enterprises (SMEs). The results of this study are an ERP system designed based on the customized Odoo module that has been aligned with the business processes and function within the institution. It has the purposes to make it easier and quicker for solving problem compare to the previous process in order to create effective and efficient management for the institution to carry out their agenda and activities based on meaningful and resourceful accounting performance.
Gaussian Process Regression with Average Hyperparameter

Abstract Gaussian regression process (GPR) is a non-parametric analysis approach that is more flexible than simple linear regression analysis. Linear and non linear patterned data can be modeled by GPR. In big data the use of GPR is constrained by the increasing size of the data. Data partitioning approach can be done as an alternative to facilitate data processing. The partitioned data then shifts one data, and so on until the last training data. In each data partition an optimum hyperparameter will be obtained. The average hyperparameter is used in the prediction process. The results obtained are that by partitioning the data and then using the average hyperparameter, the predicted results obtained are better than doing with the overall training data. In the data partition approach, small partitions provide better predictive results than large partitions. The resulting RMSE value is also getting smaller. Thus the average hyperparameter approach with small data partitions can be used as an alternative in making predictions on GPR.
Digital Assistants Conveying Emotion

Artificial intelligence is evolving at an unprecedented pace. The ability to convey emotion is one of the largest roadblocks preventing artificially intelligent agent from becoming truly anthropomorphic and integral in modern society. There are several works that have made a connection between anthropomorphism and emotion but none have directly tested the effect of anthropomorphism on a user's perceived emotion of the technology. Prior research showed that users prefer to interact with agents who can convey emotion as they perceive them to be more intelligent and humanlike. By being able to systematically increase perceived emotion in intelligent agents, we can further advance the domain of smart devices and increase adoption rates. Higher adoption rates leads to more research and open source projects, which ultimately translates to a better experience using them. This proposed research aims to explore the relationship between users' perceived anthropomorphism and perceived emotion of digital assistants.
Using the Uncalibrated Eye Tracker Signal in the Gaming Environment

It seems that controlling games with the eyes should be very intuitive and obvious. However, eye-controlled games have not become very popular yet. One of the reasons is – in our opinion – the necessity of eye tracker calibration before its every usage. This process is not very long, but it is inconvenient and requires focusing on the particular task. Moreover, sometimes the calibration fails and must be repeated.
Applying clustering algorithm to analyze the data from different dimensions

In today's data-driven panorama, the ability to analyze information to drive decision-making and resolve problems is fundamental for success. This requires a robust, effective, flexible, data analytics that assists to build accurate predictive versions quickly and intuitively. Data analysis is a common technique used to analyze data in various fields of modern scientific research, which includes different divisions of Data analytics include many techniques. Clustering is considered as one of the unsupervised learning technique for analyzing the data. With the increase in number of disciplines, the amount of data is also increased. This results in the development of various tools and algorithms for applying cluster analysis. Each of the clustering algorithm has its own advantages and limitations and it completely depends on the complexity of available information. The current research is an attempt to analyze the data using clustering techniques. The researcher use python language to compile a program to collect the data from an enterprise's information management system. Python is used to analyze and clusters are interpreted accordingly. The results of clustering data based on different dimensions will lead to improve knowledge about the data accordingly.
A Convolutional Neural Network Model for Object Detection Based on Receptive Field

The mainstream methods for object detection can be divided into two types: one-stage (based on Integrated Convolutional Network) and two-stage (based on Candidate Box Convolutional Network). The one-stage method is fast but not accurate. While the two-stage method is accurate but slow. Thus, this paper proposes a novel convolutional neural network model that can satisfy both efficiency and accuracy needs for real-time object detection. Based on Single Shot Detector (SSD) and Feature Pyramid Networks (FPN), the proposed model addresses the issue of small object detection. The introduction of receptive field block (RFB) and RefineDet network improves the accuracy of the model. The experiment results show that the mAP value of the model exceeds 80%, and the FPS is above 30, when the size of the input image is 320 * 320.
Modeling Probability Weighting Functions in Prospect Theory using a Class of Modifier Operators of Continuous-valued Logic

In this study, it is demonstrated that applying the Dombi modifier operator can be turned into a general approach for modeling probability weighting functions of prospect theory including the well-known ones. Furthermore, a two-phase regression method for fitting a probability weighting function generated by the modifier operator to empirical data is also presented.
rw*-closed sets in Alexandroff Spaces

Abstract This paper explained and defined the notion of regular weakly-star closed (briefly known as rw *-closed) sets in alexandroff spaces in which every point has a minimal neighbourhood. We discuss the characterizations and study their properties based on set theory along with the notion of rw *-open sets.
Enhanced Mechanism for Efficient Storage, Retrieval and De-Duplication in Cloud

Cloud storage is a service model where data is stored, managed, remotely backed up, and made available to users over a network (usually the Internet). Users generally pay per-consumption per month for their cloud data storage. Although the cost per gigabyte has been driven down drastically, cloud storage companies have introduced operating expenses that can make the service more costly than consumers who have been negotiating for. Therefore, the more room you use, the greater the price that you are liable to pay. The main objective of this project is to reduce the need to purchase additional storage area. Measures have been proposed to avoid duplicate files in the centralized cloud storage area. For this, secured auditing methodologies have been introduced. This will enhance the maintenance and management of files in the storage area. In addition, file classification techniques have been introduced wherein the users can upload the file into one of the following classes : private, public, sensitive and confidential. Files that are categorised as private and public are deleted periodically thereby saving storage space and the files that are confidential and sensitive are stored free of tampering unless the user wishes to delete them manually. At any instance, any file's class can be modified. This will help in reducing the need to buy extra storage space. Further, customised file tagging techniques are proposed where each file can be saved with three tags which aids efficient management of files. Through these approaches, files can be efficiently stored, accessed and managed in the centralised cloud storage area.
A Fish-swarm Algorithm Based Energy-saving Scheduling Strategy of Distributed Embedded Systems

Aiming at the real-time energy-saving scheduling problem in multiprocessor systems, a new scheduling strategy based on artificial fish-swarm algorithm is designed. Firstly, the height values of the task nodes are defined to determine the execution order of the task nodes. The task graph is split based on the critical path, the resulting paths are assigned based on the idea of load balancing, and then the task assignment scheme is obtained. Secondly, aiming at the multiprocessor system and its energy consumption model, an effective encoding strategy is proposed to determine the voltage regulation scheme. The deadline constraint is processed based on the feasibility rules to ensure the real-time performance of the system. On this basis, the corresponding problem model is established and solved by AFSA. The simulation results show that compared with the related scheduling strategies, the application of this scheduling strategy can achieve better load balancing, and then get better energy-saving effect.
Design of BeiDou-based data acquisition system for oil and gas wells in remote areas

Abstract The real-time operation status monitoring of natural gas wells in the wilderness area has problem such as construction suffering, high maintenance cost, and high data transmission cost. In view of the above problems, this paper studies the design of a multi-node data transmission system using Beidou satellite short message as the data transmission means. Firstly, analysed the advantages and disadvantages of several common data transmission methods and introduced the Beidou short message; Secondly, introduced the main working parameters of the oil and gas well; Thirdly, The data acquisition and transmission system was designed in detail and the prototype system was developed. The experimental results show that the data transmission success rate of the system can reach more than 98%. This system solution can provide a useful reference for engineering with similar requirements.
A Comparative Study of Semantic Search Systems

Today’s internet consists of mostly unstructured data, most of it being unusable for average users. With increase in the number of smart devices that are getting access to the web, we have a large set of unlinked data that is not able to communicate. Indirectly, it can be said that the Web is broken. Semantic Web focuses on making the meaning explicit instead of fetching results with the help of word matching. Semantic Web is an extension to the current Web that provides an easier way to find, share, reuse and combine information. In this paper, we are presenting an analysis of the different approaches taken by various semantic web search engines and the comparison between them, thus identifying the advantages and limitation of each search engine.
A Novel Image Edge Detection Method Based on Multi-Population Ant Colony Optimization

Edge detection is a fundamental problem in image processing and computer vision. Recently ant colony optimization (ACO) algorithm has been used in edge detection, most existing ACO-based edge detection methods used single ant population and a fixed number of neighborhood pixels to calculate the gradient in the heuristic information for each pixel in transition probability. Those make the algorithm tend to obtain local optima. To enhance the accuracy of the ACO-based edge detection methods, a multiple-population strategy is utilized in this paper. The artificial ants are divided into two populations to obtain both advantages of global search and local search, one trends to make the ants move around optima to focus on local search, the other population make the ants move sharply from current position to jump out local optima and explore global optima. The experimental results show the effectiveness of the proposed method.
On the Design of a Natural Logic System for Knowledge Bases

Natural logics are logics that take form of stylized natural language sentences within a selected fragment of natural language. Natural Logics are at the same time formal logics with a well-defined syntax and semantics. Therefore, natural logics may be advanced as knowledge base logics enhancing explainability of query answers. This paper is concerned with a natural logic, NaturaLog, having been proposed as a deductive knowledge base language. The paper briefly reviews and brings together in compact form the main points in our previously but separately published design proposals, systems functionalities and implementation principles.
A Comparison of Fast Non-negative Dictionary Update Strategies

Fast optimization strategies are frequently used to repeatedly solve the Non-negative Dictionary Update (NDU) problem in many well-known intelligent computing algorithms, e.g., Non-negative Matrix Factorization (NMF) and its variants for different applications. However, few comparisons are available on their rationale, efficiency and practical performance. In view of this, we present a theoretical and experimental comparison of four representative fast NDU strategies, i.e., the Multiplicative Update rules, the Rank-one Residue Iteration (RRI) method, the Nesterov's Optimal Gradient method and the Modified RRI (MR-RI) method. In the theoretical part, we compare their procedures and flop-counts per iteration, which indicate different rationales but similar efficiency. Furthermore, in the experimental part, we compare the four NDU strategies based Graph regularized NMF (GNMF) algorithms in terms of image clustering performance, convergence, efficiency and sparseness of dictionary. Under our experimental setting, it can be observed that (i) the column-by-column update way of RRI and MRRI is more time-consuming in practice, (ii) when achieving similar clustering performance, MRRI based GNMF converges to a stationary point with a much larger objective value than the other three, and (iii) MRRI based GNMF always generates dictionaries without interpretability, and it learns almost single-pixel based image representations, which are, however, discriminative.
Digitization planning for museum exhibition the learning museum of Universitas Negeri Malang

Abstract The digitization design of the Learning Museum of Universitas Negeri Malang aims to realize the achievement of the Learning Museum’s vision so that it becomes a reference in terms of learning development. The Learning Museum as a source of learning is expected to provide a concrete picture of the development of learning from the past and present. This museum is present as a place for conservation, education, and recreation. For this reason, the museum is equipped with several story lines that illustrate the long journey of Universitas Negeri Malang from 1954-present. As part of educational and recreational tourism, the museum management team also designed the digitization of museum collections following the needs of the industrial revolution 4.0 era, the goal is that visitors feel comfortable and enjoy learning through the museum. This study uses qualitative research and data collection techniques through observation, comparative studies and literature studies. This research resulted in digitizing the UM Learning Museum design with Augmented Reality technology, Virtual Reality, Touchscreen, Flash, Green screen, Video Animation, Games and also resulted in a visitor flow plan of the Learning Museum of Universitas Negeri Malang.
A Multi-Radar Track Fusion Methodology Based on Random Forest Regression

This paper introduces a multi-radar track fusion method based on random forest regression and provides an accurate and stable fusion track. The increasing number of aircraft will lead to congested routes, further leading to safety issues. Therefore, an effective track fusion method can accurately locate the aircraft, thereby ensuring the safety of the aircraft in the case of crowded routes. The basic idea of the method proposed in this paper is to select the radar data of a certain track of a certain day to train the model, and predict the position of the aircraft on the next day of the track through the trained model. As a traditional track fusion algorithm, the Kalman filtering has the problem of requiring accurate error estimation, insensitivity to noise, and long calculation time in the case of large data volume. The neural network method that compensates for these shortcomings also has the disadvantage of poor generalization ability in the case of a large amount of noise.