Reviewerly/exHarmony huggingface dataset:
Automatic Music Completion Based on Joint Optimization of Harmony Progression and Voicing

This paper proposes automatic music completion - the automatic generation of music pieces from any incomplete fragments of music - as a new class of music composition assistance tasks. This is a generalization of conventional music information problems such as automatic melody generation and harmonization. The goal is to turn musical ideas of a user into music pieces, allowing users to quickly explore new ideas and enabling inexperienced users to create their own music. This principle is applicable to a wide variety of music, and as a first step, we present a system that automatically fills in missing parts of a four-part chorale, as well as the underlying harmony progression. The user can input any combination of melody fragments, and freely constrain the harmony. Our system searches for harmonies and melodies that adhere to music-theoretical principles, which requires extensive knowledge and practice for human composers. Accounting for the mutual influence of melodic and harmonic development in music composition, the system is based on a joint model of harmony and voicing. The system was evaluated by analyzing generated music with respect to music theory, in addition to a subjective evaluation experiment. The readers are invited to experiment with our system at http://160.16.202.131/music_completion.
TIS'19

In this workshop, we focus on physical attacks and their countermeasures. With the advent of the Internet of Things, the interest in embedded cryptographic systems and physical attacks on these systems is steadily increasing, both in academia and industry. Sophisticated security certification and evaluation methods have been established to give assurance about the security claims by independent evaluation and testing. The certification has a drawback that it is time consuming and expensive. There is a need for further developing provably secure protection methods and automated verification tools, but also improving the efficiency and quality of certification by integrating these tools and methods. All these challenges motivate even more research on the Theory of Implementation Security.
Robot Path Planning Method Based on Improved Double Deep Q-Network

Considering the slow convergence speed of common deep Q-learning algorithm used in robot exploration strategy, an improved double deep Q-network method based on dueling network is proposed. The use of dueling network structure contributes to the more accurate function estimation, which makes the robot can identify the optimal behavior more quickly, and the network model is more stable. The combination of Boltzmann strategy and ε-greedy strategy improves the speed of robot exploring environment and avoids the constant in-situ movement of robot in local environment. And the use of resampling optimization mechanism solves the problem that samples can’t be recovered and the over-estimation of training caused by lack of diversity. The experimental results show that the robot trained with proposed method can adapt to complex environment faster, the convergence speed of the network is improved, and the success rate of reaching target point is increased, which means proposed method can help the robot to obtain the optimal path better, compared with the double deep Q-network.
Medical social network content analysis for medical image retrieval purpose

Summary Background and objective: Medical social networking platforms provide virtual spaces ensuring the interaction between different healthcare participants. As a part of the exchange, these spaces allow subscribers to upload medical images, describing different medical cases for an analysis or an interpretation proposal. Facing this expected huge amount of uploaded images generated daily, it is needed to engage new mechanisms to effectively deal with this circumstance, for enhancing the search function process of medical images, based on what is uploaded. To overcome this issue, setting up of images visual searching based on a content‐based medical image retrieval scheme is the solution. More clearly, such mechanism will help and motivate medical social networking subscribers to find visually similar stored images. Methods: To ensure this task, the development of this mechanism, technically, is based mainly on a fusion of three visual features, which offers a flexible and more precision. It is reinforced by a weighted distance approach through attributing weights for feature vectors to scale up the performance. Indeed, the displayed results of this system can be updated based on user's intention by a user interactive feedback mechanism to indicate the truly relevant images. Results: We provide the theoretical performance of our scheme. Extensive experiments were conducted on a categorically classified collection containing 500 images. We conduct a practical evaluation on this dataset classes, putting returned results in a comparative study with other models results, existing in the literature. Conclusions: The proposed scheme preserves the efficiency of the search task. As theoretically and experimentally established, our scheme offers an effective image retrieval model that can support different subscribers' expectations. The relevance feedback mechanism can keep the dynamism of the system, thus offering a continuous searching result evolution. Experimentation outcomes indicate better findings compared with the other models.
A Joint Radar Signal Sorting Method For Multi-Radar Reconnaissance Station

Abstract Passive Detection Network Composed of Multi-Radar Reconnaissance Station, which can receive multiple sets of radar signal pulse descriptor word at the same time, joint signal sorting becomes an inevitable measure of such data. This paper proposes a multi-radar time-aligned association sorting method, and builds scenario close to actual conditions for testing. The results show that method can overcome the problem of partial pulse loss leading to sorting failure, and can quickly complete complex radar signal sorting in real electromagnetic environment.
Teaching English for young learners at primary school based on the environmental approach

Abstract This study aims to create an English language learning model for children who are concerned about the environment in elementary schools. This research is a qualitative descriptive study that aims to create a conceptual model of learning English based on environmental love. This research was conducted at SD 1 Bone-Bone, Baubau City, Southeast Sulawesi, in May. The data in this study are primary data, among others, the results of observations as long as researchers make observations, the results of interviews with English teachers. Secondary data include documentation about student name data, documentation on English language teaching materials based on Environmental Education, banners, photos, and other available data as research supporting data. The data analysis technique in this study uses Miles and Huberman’s interactive data analysis method which includes three stages, namely data reduction, data presentation, and conclusion/verification. The researcher analyzed the data obtained from the observations and the results of interviews with the principal, English teacher and student.
Improvement of semi-supervised learning in real application scenarios

Due to the high demand of deep learning for data quantity, semi-supervised learning (SSL) has a very important application prospect because of its successful use of unlabeled data. Existing SSL algorithms have achieved high accuracy on MINIST, CIFAR-10 and SHVN datasets, and even outperform fully supervised algorithms. However, because the above three datasets have the characteristics of balanced data categories and simple identification tasks which can’t be ignored for classification problems, the SSL algorithm has uncertainties of effectiveness in the case of unbalanced datasets and specific recognition tasks. We analyze the datasets and find that the number of “disgust” in expressions dataset is less than other categories, and so is “discussion” in the classroom action recognition dataset. Therefore, we use a novel SSL model: Deep Co-Training (DCT) model to experiment on the expression recognition database (FER2013), as well as our own classroom student action database (BNU-LCSAD) and analyze the effectiveness of the algorithm in specific application scenarios. Moreover, we use a training strategy of TSA when train our model to solve the problem of being easily overfitting which is more likely to occur when data categories are not balanced. The experimental results prove the effectiveness of the SSL algorithm in practical application and the significance of using TSA.
Multi-word Similarity and Retrieval Model for a Refined Retrieval of Quranic Sentences

This paper addresses the task of learning sentence similarity on pairs of relevant sentences retrieved from a Quranic Retrieval Application (QRA). With the existing keywords and semantic concepts extraction, a long list of relevant verses (sentences) is retrieved that matches the query. However, as Quranic concepts are repeatedly conveyed on scattered sentences, it is important to classify which of the retrieved sentences are similar not only in word function but in context with subsequence words. Information context on similar sentences is realized with the evaluation of both word similarity and relatedness. This paper proposed a multi-word Term Similarity and Retrieval (mTSR) model that uses the n-gram score function that measures the relatedness of subsequent words. Bigram similarity scores are constructed between every pair of the relevant Quranic sentences, which boost the conventional keyword matched QRA. A similarity score is established to refine the list of relevant sentences aimed to help the user to understand the scattered content of the documents. The results are presented to the user as a refined list of similar sentences, by ranking the first-retrieved results from a keyword search. The ranking is done using a bigram score. When the score is tested on the Malay Quranic Retrieval Application (myQRA) prototype, results show that the refined results accurately matched the manually perceived similar sentences (iS) extracted by the three volunteers.
BNU-LCSAD: A video database for classroom student action recognition

With the development and application of digital cameras, especially in education, a great number of digital video recordings are produced in classrooms. Taking Beijing Normal University as an example, 3.4 TB of videos are recorded every day in more than 200 classrooms. Such huge data is beneficial for us, computer vision researchers, to automatically recognize students' classroom actions and even evaluate the quality of classroom teaching. To focus action recognition on students, we propose Beijing Normal University Large-scale Classroom Student Action Database version 1.0(BNU-LCSAD) which is the first large-scale classroom student action database for student action recognition and consists of 10 classroom student action classes from digital camera recordings at BNU. We introduce the construct and label Processing of this database in detail. In Addition , we provide baseline of student action recognition results based our new database using C3D network.
Saliency Assessment Using Selective Features Based on Entropy and Wavelet Transform

In this paper, we present a novel framework for an image saliency assessment based on an adaptive model. This later evaluates the image-content importance using a tuning strategy based on information theoretic concepts coupled with wavelet multiscale image representation. Our saliency-based encoding form can greatly characterize both regular/irregular structures within the image. The performance of the proposed model is benchmarked with those available in the literature. The saliency assessment mechanism we propose can potentially offer up to very promising results.
A Calibration Method of CBCT Geometric Parameters Based on the Visual Imaging Model

In this paper, a framework for calibrating and correcting geometric parameters of CBCT based on visual imaging model is designed. Based on this framework, an automatic detection and recognition method for markers and a calibration and correction method for geometric parameters of CBCT under complex conditions are designed. Specifically, an algorithm for detecting and recognizing markers with high robustness and accuracy to rotation, illumination and marking imaging deformation is proposed. The geometric parameters of visual imaging model are estimated by using the corresponding relationship of high-precision markers, and the geometric parameters are optimized and corrected based on the minimum re-projection error.
Research on Robot's Road Detection Technology Based on Machine Vision

In order to enhance the early-warning performance for the walk-aided service robot, this paper reviews the current states of the road detection method, and does an analysis on the principle of road detection method based on machine vision.Firstly, the recent advances of the road detection method based on machine vision are reviewed, including the structured and unstructured road, as well as the application of artificial intelligence algorithm in road detection system. Secondly, analyses the principle of road detection technology based on machine vision.Finally, analyses the problems of the road detection method based on vision technology, some solutions to improve detection efficiency and accuracy of the vision method are given, which have the extensive applicability and practical significance to improve the autonomous navigation design of the walk-aided service robot.
COACHING APPROACH WHEN WORKING WITH STUDENTS- CONCERTMASTERS

The article is devoted to a relevant problem of the coaching approach in higher musical education, which is examined by the example of working with students-concertmasters. The author points out that the concertmaster’s professional activity fits well with the basic principles of the coaching approach. The functions of a concertmaster-coach are identified; the basic methods and forms of his work are described. Two aspects of applying the coaching approach are outlined: in the work of a concertmaster class teacher and in further professional activity of a student- concertmaster.
False memories produced by tentatively organized categorical representations: The Deese–Roediger–McDermott (DRM) paradigm using lists of ad hoc category exemplars

The study presented here investigated whether tentatively organized categorical representations resulting from learning ad hoc category lists produced false memories as well as whether learning a list with themes (category labels) increased the prevalence of false memories. A sample of university students (N = 48) participated in an experiment using the Deese–Roediger–McDermott paradigm, which presented them with word lists consisting of atypical exemplars of ad hoc categories designed to obscure the themes. The participants studied the lists with or without category labels, and then engaged in a recognition test. The lure items for each recognition list were category exemplars that the participants did not learn. The results indicated that false recognition occurred as a result of learning ad hoc category lists and increased when learning a list with category labels. In addition, participants who noticed a theme reported false recognition more frequently than those who did not, even in the condition where labels were not presented. These findings suggest that noticing themes promotes false recognition regardless of the presence of category labels.
Effective Transform Domain Denoising of Oceanographic SAR Images for Improved Target Characterization

Synthetic Aperture Radar (SAR) images are widely used for a variety of applications such as surveillance, agricultural assessment and classification, planetary and celestial investigations, geology and mining, etc., due to its remarkable characteristic of capturing it under all weather conditions. SAR images are highly prone to speckle noise due to the ingrained nature of radar backscatter. Speckle removal is highly essential to limit the difficulty encountered while processing the SAR images. An exhaustive work has been done by researchers to despeckle SAR images using spatial filters, wavelet transform, and hybrid approaches. This work aims at exploring the different despeckling techniques to identify the best and suitable methodology. On measuring the despeckling performance using Peak Signal-to-Noise Ratio, Edge Preservation Ratio, Speckle Suppression Index, Speckle Suppression and Mean Preservation Index, and Structural Similarity Index simultaneously for the various techniques experimented, ridgelet transform-based thresholding works well. It gives better results by applying ridgelet transform and processing the subbands with minimax thresholding. The type and characteristics of the scene imaged also influence the result.
A Vital Study of Digital Ledger: Future Trends, Pertinent

Digital Ledger is an innovative technology that has changed the way we work with data. Digital Ledger otherwise popularly known as blockchain enables us to store data in the truest form which avoids the necessity to gather, collect, examine and represent data for various platforms and systems. Blockchain is revolutionizing the digital world with numerous applications breaking out in different fields, predominantly finance. Apart from crypto currency, blockchain has also found its use in healthcare, travel and voting system, to name a few. With the advent of blockchain, several sectors have begun using and experimenting with this technology. The implementation of blockchain in emerging fields of Internet of Things, Cyber Physical Systems, edge computing, social networking, crowd sourcing is being studied and tested. Blockchain has enormous potential to make lasting changes in the world. The purpose of this document is to highlight the need of current technologies, algorithms, platforms which can be made suitable to support the understanding of blockchain. This paper presents a comprehensive overview on blockchain technology. It also presents a brief description about the current and future trends of this revolutionary technology.
Leveraging Machinima to Characterize Comprehension of Character Motivation

Deliberation-driven reflective sequences, or DDRSs, are cinematic idioms used by film makers to convey the motivations for characters adopting a particular course of action in a story. We report on an experiment where the cinematic generation system Ember was used to create a cinematic sequence with variants making different choices for DDRS use around a single decision point for a single character.
Peningkatan Aktivitas Pembelajaran dan Hasil Belajar Visual Merchandising Melalui Metode Penyelesaian Masalah

&#x0D; The purpose of this study is to improve learning activities and learning outcomes for visual merchandising through problem solving methods. This research uses action research with a cycle of three times. Each cycle consists of four stages: design, activity and observation, reflection, and refission. The subject of this research is the students of class XI Data Marketing. From the results of the analyst found that student achievement has increased from cycle I to cycle III. Cycle I 64.00%, cycle II 76.00%, cycle III 88.00%. The conclusion of this study is that the problem solving method can positively influence the activities and learning outcomes of Class XI Marketing Students in Visual Merchandising subjects.&#x0D;
NIOECM: A Network I/O Event Control Mechanism to Provide Fairness of Network Performance Among VMs with Same Resource Configuration in Virtualized Environment

In the virtualization environment, a hypervisor scheduler determines the degree of shared resource occupancy of the virtual machine (VM) according to the degree of CPU processing and it provides a fair CPU processing on virtual machines (VMs). But VMs are experiencing unfair network performance due to the hypervisor scheduler’s policy occupying resources based on CPU processing time. In this paper, we present NIOECM which is a network IO event control technique that controls the network-intensive VM’s network IO event to guarantee a fairness network performance of VMs which have the same resource configuration. The NIOECM performs a network delay processing on the network-intensive VMs which have a high network I/O event set. As a result, the network-intensive VMs which have a low network I/O event set will have more chance to occupy the network resource. In the result of experiments, our approach provides more fairness of network performance and does not give a performance interference on VM which is performing another task.
Privacy Preserving Approach for Proficient User Revocation in Cloud Environments

Cloud environments can afford dynamic infrastructure for the mobile users. Due to the lack of security in cloud, auditing it with public concern and privacy for the distributed information available on the cloud remains as the challenging task. The user revocation plays a vital role for maintaining the integrity for the user’s privacy. For this reason, we generate a proficient the public auditing methodology is propose and implemented to maintain the user revocation with the privacy for the user in the unsecured cloud environment.
Regression analysis with compositional data using orthogonal log-ratio coordinates

Compositional data frequently arise when data refer to components which are proportions or fractions of a whole. Within the log-ratio approach, the analysis of compositional data can be conducted in terms of log-ratio transformations of components. These transformations make it possible to overcome the problem of the constant-sum constraint, making standard statistical methods applicable. In the present work, the log-ratio approach based on orthogonal log-ratio coordinates is adopted to show how it can lead to considerable improvements in the interpretation of the results of regression modeling with compositional data, both as explanatory or response variables. In order to demonstrate its practical usefulness, the methodology presented in this paper is applied to the analysis of air pollution produced by vehicles traveling through road intersections, with a specific focus on the effect of the type of traffic control (traffic signal vs. roundabout) on CO2 emissions.
On the Okamoto-Uchiyama cryptosystem: (A brief essay on basic mathematics applied in cryptography)

Abstract The Okamoto-Uchiyama cryptosystem applies many consepts of basic abstract algebra, discrete mathematics and number theory. Many of these concepts are elementary and used in other branches of cryptography. However, those elementary concepts are not enough provided in the mathematics curriculla in under developed and developing countries despite the fact that in early stages teaching of those basic mathematics concepts, there is no need to provide sophiticated nor rigorous treatments beyond computations of integers. By exploring the basic mathematics applied in Okamoto-Uchiyama’s algorithm and its re-lated concepts, this exploration should make mathematics teachers, educators and instructors to be aware of the important teaching the basic abstract algebra, discrete mathematics and number theory as early as high school senior level or at the first year in colleges and universities.