Reviewerly/exHarmony huggingface dataset:
THE USE OF A GENETIC ALGORITHM FOR MUSIC HARMONIZATION OF TRANSITIONAL FRAGMENTS IN COMPUTER GAMES

The problem of musical accompaniment harmonization for adjacent transitional fragments in computer games is considered. A harmonization method based on a tonal model of the musical composition in conjunction with a genetic algorithm seeking the global extremum of the harmonic function is proposed. For the method under consideration, all operators of the genetic algorithm and fitness functions are defined. A scheme was developed for the formation of transitional musical compositions and a flowchart was proposed for using the genetic algorithm in its implementation. Using examples of well-known musical compositions, a study was conducted of the genetic algorithm features and its most appropriate parameters were found in the context of the problem under consideration. This allowed us to obtain expression for the computational costs of the proposed method, which are necessary for balancing the computational load in the computer between the game algorithm itself and the musical harmonization algorithm. Moreover, the study showed that using genetic algorithm allows even for a small number of iterations to achieve melody harmonization level no worse than professional composer achieved, and even exceed this level when performing extra iterations.
On-Line STEAM Outreach with Remote Robot Access

Hands-on programming of humanoid robots can be effectively utilized to motivate interest in science, technology, engineering, art, and mathematics for K-12 students. Constrained by pandemic restrictions, our academic and industry team developed and deployed an Internet-accessible capability for K-12 students to remotely program Misty II robots using the Blockly language. Utilizing configurations with both stationary and mobile robots, the students were able to create and run programs on the robots and watch the resulting behaviors via a live video stream. This remote mechanism provides both tracking of accesses and usage of specific programming features as well as protection from accidental or purposeful destructive actions. Initial use of this capability with five groups and a total of 170 3rd grade through high school students demonstrated that the approach can effectively motivate student interest in both robotics and programming. This remote robot access mechanism demonstrates how cost effective access to advanced robots can be provided at a potentially large scale.
The Cross Domain Social Media Collective Behavior of Social Networking Sites

The valuable learning from aCross domain can be exchanged through the social domain to an objective domain. Notwithstanding, some of the time we may experience the ill effects of thing cold start problem in the objective domain. To ease this issue we apply cross domain algorithm alongside page positioning algorithm. The cross domain algorithm is separated into two phases; in the primary stage we apply the TrAdaBoost algorithm to choose a few things which are being prescribed to users in the objective domain. While, in the second stage we receive nonparametric pairwise bunching algorithm to settle on a choice whether to prescribe a thing to client or not. The algorithm finds the prescribed or not suggested client bunches for one thing through the two phases and afterward with the assistance of page positioning algorithm we give relevant and unsearched data to the users. Over the most recent couple of years recommender systems has developed overwhelmingly as an intriguing and new research field. Many research articles have been distributed in setting to the zones like User Modeling, Information Retrieval and Knowledge Management and so on that are identified with recommender systems. A large portion of the exploration ponders in this field manage prescribing things identified with a solitary domain (like books, motion pictures and so on.). With each new research there comes a couple of issues as well. This paper examines one such issue identified with the field of recommender systems i.e. cross domain suggestions (nuts and bolts, assignments, objectives and so forth.)
Road Offence Information Management System in Nigeria: a Most Needed Technology Intervention

This study is concerned with road traffic offence information management in Nigeria. It focused on trends in road traffic offences information and carried out a critical review of current information and communication and technology compliance state of FRSC with a view to identifying its defects in road traffic offence information management. A system to correct road traffic offence information management failure as identified in the existing system was then proposed. Road traffic offence records and details of current safety measures obtained from FRSC and online in addition to research works, provided the basic data for the study. The system was designed using the object OOAD methodology and implemented using rapid Php IDE on a Windows 7 system, using PHP, HTML,CSS and mySQL technologies and Apache server as the application server. The result showed the high rate of road traffic offence as a result of poor road traffic offence information management and failure to improve on the existing road traffic information management.
Media Pembelajaran Pengenalan Alat Transportasi, Musik, dan Benda di Sekolah bagi Siswa Tunarungu Sekolah Dasar

A remarkable school – B (SLB) YRTRW Surakarta is a spesial school for children with special school for children with special needs to help provide education. According to a survey of SLB-B YRTRW Surakarta, it is difficult for students to get the names of objects right anf there are still mistakes, and it is also difficult for students to play because the learing methods are less interesting and therefore not focus on the material presented by teachers. An alternative or solution is to provide the learning media in the form of applications. The appication is called BELMA (together learning) abaout the introduction of transportation tools, Musical Instruments, and objects in the school environment for fourth-grade elementary school students. The founding of the media of education is a method for observation, analysis or problem identification, and interviews. The result of research based on questionnaires are 87.871%, and this application can thus help the learning process. The purpose of this study is to be expected to help both tearchers and students in the learning process in class so that students can be quick to understand and enthusiastic about material delivery.
CLOUD COMPUTING PARADIGM AND USE CASES

The economical of cloud computing as well as its compelling scaling provides it with anadvancement motorist for small firms, especially in the establishing world. Cloud deployedventure source preparation, supply chain control apps, customer connection administrationfunctions, medical apps and mobile functions possess the prospective to get to countless users.Within this paper, our experts check out the various principles associated with cloud computing.Leveraging our adventures on a variety of clouds, our company take a look at clouds fromspecialized, and also service aspects. Our experts highlight some of the possibilities in cloudcomputing, underscoring the relevance of clouds as well as presenting why that moderntechnology should be successful.
Using of Smart Contracts in Logistic Information Systems Services and the Ways to Review it

There are many changes related to the development of the Internet and e-business, which significantly impact the logistics sector's service sectors. Electronic logistics is not a new segment or a new standard in the classification process for logistics companies. It leads to the development and improvement of a wide range of logistics services. Blockchain technologies can be used to improve and develop services, which are an integral part of this evolution. This research sheds light on the mechanism and application of these technologies in logistics services and the extent of their impact on logistical activities where some technical aspects of Blockchain are studied in detail, including smart contracts that use the Ethereum platform as an example.
육군 부사관의 보직관리 인식에 관한 연구 : 근거이론적 접근

This study actually examined how Army noncommissioned officers were aware of position management. In consideration of the selection of interviewers, non-commissioned officers of the front and rear units were selected who worked for more than 20 years and who conducted at least three exchanges of troops. In order to enhance the veracity and accuracy of the data, a number of people who are currently serving as the chief executive officer or experienced the position of the chief executive officer were included.In this study, the contents of the interview were analyzed using the evidence theoretical approach presented by Strauss & Corbin (1998), and the results of open coding and axial coding resulted in 90 concepts, 20 subcategories and 10 categories. In addition, all six paradigms were found, and as a result of the selective coding, the key category was ''expectations for improving the non-commissioned officer''s position management system to improve professionalism and service satisfaction''.Therefore, this study was meaningful in that it presented a new direction for research recognizing the feasibility and importance of institutional improvement in position management so that noncommissioned officers can do their best in their original duties through interviews.It is hoped that the perception of position management, which was confirmed through this study, will lead to the improvement of non-commissioned officers'' policies and systems in the future, and various practical studies will be conducted on non-commissioned officers, such as research that clearly redefines non-commissioned officers'' job management standards and develops a job management model.
Extracting Features of Solving Behavior in Handwriting on Tablets

This paper proposes a method to estimate the learner's solving behavior during the exercises from the handwriting situation on the tablet. The proposed method extracts the handwriting characteristics in the solving behavior of each learner. It is important to give appropriate guidance according to the understanding of the learner. It is difficult to grasp the understanding status of learners in cases where teachers cannot observe learners directly as in distance learning. Teachers need an online system that estimates the solving behavior of individual learners to notify it of them. In the proposed method, the solving behavior is estimated from logs of both the handwriting and actions seeking supports using the moving window for 30 seconds. The method constructs a random forest model to estimate the solving behavior. Writing characteristics in each solving state are extracted to examine the values of the important variables in a random forest model. Handwriting data were collected from learners in an experiment. The learner's solving behavior was estimated with high accuracy. The constructed model precisely classifies not only states in which the hands stay still from ones in which the hands are moving smoothly but also states of answering correctly from ones of answering incorrectly. Since learners repeating to answer incorrectly are detected on the fly, teachers can take immediate measures for learners who need assistance during exercises.
SoC-based Design and Optimization of Robotic Arm Collision Avoidance Control System

The research on robotic arm represents the development of mechatronics technology in recent years. This study presents a robotic arm control system based on system-on-chip (SoC). For the robotic collision avoidance control, this paper builds kinematic model for 6-degree-of-freedom (6-DOF) robotic arm and implement probabilistic roadmap (PRM) algorithm on the SoC. Furthermore, an optimized PRM is presented to improve motion path consistency by introducing a fixed nodes generating function. The experimental results are given to prove the efficiency of the robotic arm control system and the optimized PRM algorithm can improve the consistency of the motion path.
Interactive Image Segmentation of MARS Datasets Using Bag of Features

In this article, we propose a slice-based interactive segmentation of spectral CT datasets using a bag of features method. The data are acquired from a MARS scanner that divides up the X-ray spectrum into multiple energy bins for imaging. In literature, most existing segmentation methods are limited to performing a specific task or tied to a particular imaging modality. Therefore, when applying generalized methods to MARS datasets, the additional energy information acquired from the scanner cannot be sufficiently utilized. We describe a new approach that circumvents this problem by effectively aggregating the data from multiple channels. Our method solves a classification problem to get the solution for segmentation. Starting with a set of labeled pixels, we partition the data using superpixels. Then, a set of local descriptors, extracted from each superpixel, are encoded into a codebook and pooled together to create a global superpixel-level descriptor (bag of features representation). We propose to use the vector of locally aggregated descriptors as our encoding/pooling strategy, as it is efficient to compute and leads to good results with simple linear classifiers. A linear support vector machine is then used to classify the superpixels into different labels. The proposed method was evaluated on multiple MARS datasets. Experimental results show that our method achieved an average of more than 10% increase in the accuracy over other state-of-the-art methods.
Series Editorial: Data Science and Artificial Intelligence for Communications

Welcome to the February issue of the Data Science and Artificial Intelligence for Communications series of IEEE Communications Magazine. The series continues to grow in popularity among academicians, researchers and practitioners who apply artificial intelligence (AI) and machine learning (ML) techniques to solving problems across diverse domains of networking and communications. With the rapid developments in AI and ML theory and practice, and the abundant availability of data, this trend continues to establish itself as a viable approach to providing efficient and practical solutions for automation in tethered and untethered environments, in small or large systems, and in local or wide areas.
Self-Attention Meta-Learner for Continual Learning

Continual learning aims to provide intelligent agents capable of learning multiple tasks sequentially with neural networks. One of its main challenging, catastrophic forgetting, is caused by the neural networks non-optimal ability to learn in non-stationary distributions. In most settings of the current approaches, the agent starts from randomly initialized parameters and is optimized to master the current task regardless of the usefulness of the learned representation for future tasks. Moreover, each of the future tasks uses all the previously learned knowledge although parts of this knowledge might not be helpful for its learning. These cause interference among tasks, especially when the data of previous tasks is not accessible. In this paper, we propose a new method, named Self-Attention Meta-Learner (SAM)1, which learns a prior knowledge for continual learning that permits learning a sequence of tasks, while avoiding catastrophic forgetting. SAM incorporates an attention mechanism that learns to select the particular relevant representation for each future task. Each task builds a specific representation branch on the top of the selected knowledge, avoiding the interference between tasks. We evaluate the proposed method on the Split CIFAR-10/100 and Split MNIST benchmarks in the task agnostic inference. We empirically show that we can achieve a better performance than several state-of-the-art methods for continual learning by building on the top of selected representation learned by SAM.We also show the role of the meta-attention mechanism in boosting informative features corresponding to the input data and identifying the correct target in the task agnostic inference. Finally, we demonstrate that popular existing continual learning methods gain a performance boost when they adopt SAM as a starting point.
PENERAPAN MODEL PEMBELAJARAN PENEMUAN TERBIMBING UNTUK MENINGKATKAN KEMAMPUAN MENULIS DIALOG SESUAI UNGGAH-UNGGUH BASA DAN SIKAP SANTUN

This research was aimed to improve student’s ability in writing appropriate with unggah-ungguh basa and well-mannered attitude in class VII E of  SMP 5 Madiun, using guided discovery learning model. This class action research was conducted in two cycles, which every cycles consisted of action planning, action, observing, and reflecting. The subject of the research were the students of class VII E SMP Negeri 5 Madiun which consist of 34 students. The sources of the data were from the teacher, the students, and teaching-learning process. The technique of collecting data used were test, observation, interview, and documentation. Triangulation technique, which were triangulation data and triangulation method, was used  to test the validity of the data. The technique to analize the quantitative data was statistic descriptive comparative technique, whereas to analize qualitative data was critical analysis technique. The research model used spiral model (planning, acting, observing, and reflecting). The result of this research showed that after implemented guided discovery learning model, students’s ability in writing appropriate with unggah-ungguh basa and well-mannered attitude have changed to be better. Based on the test of students’s ability in writing appropriate with unggah-ungguh basa, the percentage of the class passing grade increased from 37,5% in pre-cycle, became 66,7% in cycle 1 and 79,2% in cycle 2 from the total of 24 students. Meanwhile the improvement of well-mannered attitude based on observation and questionnaire about well-mannered attitude, was 41,7% during pre-cycle, then improved to 70,8% in cycle 1 and 87,5% in cycle 2. Based on the result and the discussion of the research, the conclusion that can be drawn is that the implementation guided discovery learning model can improve students’s ability in writing appropriate with unggah-ungguh basa and well-mannered attitude in class VII E of SMP 5 Madiun.
Estimating the Annual Global Solar Radiation In Three Jordanian Cities by Using Air Temperature Data

Estimating solar radiation is an imperative requirement for solar energy development in Jordan. In this paper, a quantitative approach, based on Artifiial Neural Network, was developed for estimating the annual global solar radiation of three Jordanian cities: Amman, Irbid and Aqaba. Thse cities are currently witnessing huge development and increasing demand for energy supply. Using a set of known meteorological parameters, two Artifiial Neural Network (ANN) models with diffrent architectures, called case 1 and case 2, fed with three types of learning algorithms for data training and testing, were designed to identify the optimum conditions for obtaining reliable and accurate prediction of the solar radiation. Th results showed that model case 1 performed generally better in terms of predicting the annual GSR (96%) compared to model case 2 (95%). Furthermore, the algorithms LM and SCG in general, ensured the highest effiency in training and testing the data in the designed models compared to the GDX algorithm. Threfore, model case 1, designed with one of these two algorithms, is selected as the optimal model design that is able to compute with high accuracy the annual solar radiation for the three studied cities.
Differentially Oblivious Turing Machines

Oblivious RAM (ORAM) is a machinery that any RAM from leaking information about its secret input by observing only the access pattern. It is known that every ORAM must incur a logarithmic overhead compared to the non-oblivious RAM. In fact, even the seemingly weaker notion of differential obliviousness, which intuitively protects a single access by guaranteeing that the observed access pattern for every two neighboring logical access sequences satisfy (e,δ)-differential privacy, is subject to a logarithmic lower bound. In this work, we show that any Turing machine computation can be generically compiled into a differentially oblivious one with only doubly logarithmic overhead. More precisely, given a Turing machine that makes N transitions, the compiled Turing machine makes O(N ⋅ log log N) transitions in total and the physical head movements sequence satisfies (e,δ)-differential privacy (for a constant e and a negligible δ). We additionally show that Ω(log log N) overhead is necessary in a natural range of parameters (and in the balls and bins model).As a corollary, we show that there exist natural data structures such as stack and queues (supporting online operations) on N elements for which there is a differentially oblivious implementation on a Turing machine incurring amortized O(log log N) overhead per operation, while it is known that any oblivious implementation must consume Ω(log N) operations unconditionally even on a RAM. Therefore, we obtain the first unconditional separation between obliviousness and differential obliviousness in the most natural setting of parameters where e is a constant and δ is negligible. Before this work, such a separation was only known in the balls and bins model. Note that the lower bound applies in the RAM model while our upper bound is in the Turing machine model, making our separation stronger.
The Obstacles Facing Jordanian Universities in the Use of Mobile Learning in Instruction from the Point of View of Experts and Students

This study aims to uncover the obstacles facing Jordanian Universities in the use of Mobile learning in Instructional from the point of view of experts and students. The study followed the qualitative curriculum as required by the nature of the study, the number of study members (19) faculty members and (25) students of Bachelor's and Graduate students from public and private universities for the Academic Year (2019-2018), the interview questions were built to reveal obstacles faced by Jordanian Universities in the use of Mobile Learning in Instruction. The results of the study revealed that the training by holding workshops and courses for faculty and students, and the encouraging environment, received the highest percentage of disabilities faced by Jordanian Universities in the use of Mobile Learning in Instruction from the point of view of experts, as it turns out that the slowness of the internet package, and the availability of a learning material is designed in line with the principles of mobile learning and has the highest percentage of disabilities faced by Jordanian Universities in the use of Mobile Learning in Instruction from the point of view of students. The study also concluded with a number of recommendations which are: the need for faculty and students in Jordanian Universities to receive training courses in the use of Mobile Learning in Instruction, and the study of the obstacles facing Jordanian Universities in the use and reduction of Mobile Learning in Instruction.
An Error Analysis of the Durative Aspect Mark “Zhe” (着) by Vietnamese Students at Elementary Chinese Level

The article is based on the “Chinese Interlanguage Corpus of Vietnamese Students” (2018 edition) to investigate the errors in the durative aspect “zhe” (着) by Vietnamese students at the elementary Chinese level. The study found that students’ errors can be classified into three types: over-inclusion, misselection and omission. Among them, over-inclusion is the most common type of error, followed by misselection, and omission is the least common type of error. Excessive generalization of target language rules is the main reason for errors. On this basis, we made some teaching suggestions.