Reviewerly/exHarmony huggingface dataset:
Detection and cycle frequency estimation

Detection strategies and estimators of cycle frequencies for almost-cyclostationary signals are presented. The spectral line regeneration through homogeneous quadratic time-invariant transformation is treated. A structure for joint maximum likelihood detection and source location is considered in the weak-signal scenario. A detection strategy based on the asymptotic normality of cyclic statistical function estimators is presented for signals exhibiting second-order cyclostationarity or spectral correlation. Detectors based on subsampling or block-bootstrap techniques, robust detectors for signals with heavy tail distributions, and detectors based on higher-order cyclic statistics are considered. Several cycle frequency estimation techniques and their properties are also presented.
Efficient Counter-factual Type Error Debugging

Type inference is an important part of functional programming languages and has been increasingly adopted to imperative programming. However, providing effective error messages in response to type inference failures (due to type errors in programs) continues to be a challenge. Type error messages generated by compilers and existing error debugging approaches often point to bogus error locations or lack sufficient information for removing the type error, making error debugging ineffective. Counter-factual typing (CFT) addressed this problem by generating comprehensive error messages with each message includes a rich set of information. However, CFT has a large response time, making it too slow for interactive use. In particular, our recent study shows that programmers usually have to go through multiple iterations of updating and recompiling programs to remove a type error. Interestingly, our study also reveals that program updates are minor in each iteration during type error debugging. We exploit this fact and develop eCFT, an efficient version of CFT, which doesn't recompute all error fixes from scratch for each updated program but only recomputes error fixes that are changed in response to the update. Our key observation is that minor program changes lead to minor error suggestion changes. eCFT is based on principal typing, a typing scheme more amenable to reuse previous typing results. We have evaluated our approach and found it is about 12.4× faster than CFT in updating error fixes.
Web-Based Internship Information System

Abstract The purpose of this study is to solve all the problems that exist in each stage of the internship process, starting from registration, selecting an internship place, selecting a supervisor, submitting an internship report and appraisal of internship reports. This study used descriptive research methods, the system approach method is an object oriented approach and the system development method is the Prototype Method. Data collection techniques are interviews, observation and literature. The result of this study is web-based internship information systems. This web-based information system can minimize errors in the process of managing the internship as a whole. This system can also provide information about internships to students in quick time.
The formation of doctoral schools in Europe in the condition of cross-border education

Training of doctoral students is of special attention in the modern integration conditions of the higher education development. Trends of study of the doctoral schools development are necessary and practically in demand; absence of such investigations makes forecasting of the higher education in Russia difficult. Main tendencies of the doctoral schools formation and development were identified in this article: focus shifting of the questions solving from the national into the European level; applying of the modern methodological approaches system (student-centered, interdisciplinary and competency based ones); responsibility increasing of the academic community for the doctoral students’ training quality; implementation of thematic principles; interdisciplinary; interuniversity and international integration.
Digital stories: improving the process using smartphone technology

With smartphone saturation at 100% among Japanese university students, educators are developing new and innovative ways to bring them to the forefront of learning, ensuring students are as engaged with their technology in their formal learning as they are with their informal learning. Smartphones of today are small, portable, have high spec cameras, microphones, and a large storage capacity. These devices also allow for videos to be edited within applications on the smartphone itself, without the need for a separate and expensive computer and editing software. Aspects such as these make using smartphones to make Digital Stories (DS) one possible way to effectively use this technology for formal learning purposes. This paper shows the results of a paper-based survey and discusses preliminary observations conducted with 38 Japanese university students undertaking a short-term study abroad experience in Australia and New Zealand.
Permutation on binary de bruijn sequence

Binary de bruijn sequences are studied by many researchers since it has many applications such as in DNA sequencing, robotics, and radar. In this paper we study the properties of binary de bruijn sequences in term of permutation. We showed that it has a nice property that we hope it will be useful in further research.
Application of Data Mining in Recommending Mode of Agricultural Products Market System

Abstract The agricultural product trading system is a system that involves multiple links and multi-agency network-based work management methods. The article describes the overall design, system architecture, network architecture and related modules of the system through flow charts and structure diagrams. The design is implemented, and the database diagrams and attribute fields of related modules are given. The application process of data mining algorithm in the system is studied. Firstly, the data mining workflow is analyzed. Then the data collected by the system is studied. The data preprocessing process is studied, which makes the data set suitable for data mining. According to the characteristics of the data, the decision tree method is used to perform data mining. The data has been preprocessed. Based on the analysis of data and build a decision tree.
Metrological Assurance of Environmental Estimation of Soil Pollution by Heavy Metals by a Geoinformation Cyber-Physical System

The article is devoted to estimation of the concentration of heavy metals in soils. A Geo-Information Cyber-Physical system was used to visualize the measurement results. The metrological assurance of the reliability of pollution boundaries selection has been evaluated.
SocialRT: A Recommendation Method Based On Social Trust

With the advent of online social networks, the approach of recommendation based on social network has emerged. However, some recommendation algorithms based on the trust network do not fully mine the information of user's trust relationships. To alleviate such problems, we propose a socialRT method, which is a social recommendation trust method based on joint matrix decomposition. The proposed socialRT method collective factorizes the following relationship matrix and the social trust relationship matrix to obtain the recommendation model. We have conducted experiments on Sina Weibo dataset, the experimental results demonstrate that the proposed recommendation method leads to a substantial increase in recommendation quality.
Web-based student master book information system in vocational school of Muhammadiyah Banyuresmi

Abstract The main student book information system is a web-based application that comes as a form of modernization from a manual student main book that can streamline, and streamline the time in importing, searching, etc. This application is made as simple as possible so that it is easy to use by the user, in terms of interfaces and interactions in the application so even for novice users it will be easy to understand, but even though simple, this application can contribute a lot of reasons, the Student Parent Book is the most important book in school administration. Especially data management of students, the Student Parent Book Information System Application is based on localhost (local network), meaning that when you want to access it does not need to be connected to the internet, just connect to the local network and can only be accessed by users who are connected with the admin, or recorded by admin as someone who has access rights.
Mobile Node Path Planning Based on Particle Swarm Optimization

In Wireless Sensor Networks, Path planning for mobile nodes can effectively select the optimal path and improve the performance of wireless sensor networks. There are some problems in current path planning of mobile nodes in Wireless Sensor Networks, for example, it takes too long to complete the path planning and excessive energy consumption, etc. A path planning method for mobile nodes based on particle swarm optimization is proposed. According to the stop location of the mobile node, the energy consumption of each grid node is calculated and the path planning model of mobile node is established. Get the position and speed for each mobile node by using particles cooperate with each other. Fitness function is found by referring obstacle repulsive field in network mobile nodes path. Solving the global optimal path of mobile nodes based on the fitness value. The experimental results show that using the proposed method to plan the path of mobile nodes in wireless sensor networks requires shorter completion time and less energy consumption.
Particle Filter and Qualitative Reasoning Based Multi-modal Interval Information Processing

Derived from look-ahead control in magnetic navigation, a new mode called look-back control is defined. In this mode, a mobile robot is controlled to move forward tracking a magnetic tape with a magnetic sensor mounted on its rear bottom, while the sensor is mounted on the robot’s front bottom in look-ahead control. To generate control commands, an estimation of a front bias between the robot’s pivot and the magnetic tape is required first. We propose a Grey Qualitative Particle Filter (GQPF) combining the grey qualitative theory and the particle filter to process involved multi-modal interval uncertain information. An existed motion model is applied to get a prior estimation in the particle filter prediction stage. In the updating stage, the proposed approach utilizes qualitative reasoning and quantitative computing to obtain a quantified interval observation, which is merged with the prior estimation to obtain a final estimation of the front bias. A proportion controller takes the estimation as an input to generate control commands. Online experimental results have verified the effectiveness of the proposed approach.
Methods for Determining the Initial State of a Projectile Equipped with Onboard Strapdown Sensors

Projectile aerodynamics for long-range and indirect-fire shots may be characterized using onboard magnetometer, gyroscope, and accelerometer sensors. Previous researchers have developed ways to match projectile aerodynamic parameters to the collected measurements; however, accurate reconstruction of the projectile trajectory was hindered by the ability to accurately estimate projectile initial states. In this work, we demonstrate the application of several models to better match the trajectory and sequentially update estimates of the initial projectile state. The result is a set of algorithms that renders accurate initial-condition estimates that can then be merged with an existing high-fidelity ballistics code in order to provide precise reconstruction of the projectile trajectory. Models for both 3- and 6-degree-of-freedom projectiles are demonstrated. The algorithms are exercised on actual and virtual data sets, and convergence to realistic initial conditions, parameters, and trajectories is reliable even in the presence of unmodeled sensor errors.
IEEE ICHI Data Analytics Challenge on Missing data Imputation by Amelia II

There are some traditional tools for the imputation of missing data, such as imputeTS [1], multiple imputation with chained equations (MICE) [2] and Gaussian process (GP) [3]. The imputeTS package and GP specializes on univariate time series imputation, MICE could not take the advantage of time information. 3D-MICE [4] algorithm combining MICE-based with GP-based predictions to impute missing data based on both cross-sectional and longitudinal information. The reason we chose Amelia II [5] was that it could utilize both time series and multivariable information at one model, implying that Amelia II may be a more suitable tool for MIMIC dataset. Amelia II uses the bootstrap-based EMB algorithm to impute many variables with many observations. EMB algorithm combines the classic EM algorithm with a bootstrap approach.
Adopting a Method to Evaluate Bibliographic Electronic Resource Title Sets of Metadata

Welcome to another edition of E-Resource Round Up! This issue’s covers a wide range of e-resource topics, including: Evaluation of bibliographic electronic resource title sets of metadataAutomatic ...
Saliency Guided Data Association Measure for Multiple-Object Tracking

The association of correct object locations to its respective track over time is vital for robust tracking results when employing a detection-based tracking approach. It is important for handling object track identity switches which is one of the limiting issues when designing multiple-object trackers. This paper expresses an object model by its saliency-colour histogram. Dissimilarity between a reference track model and input candidate detection (obtained by an object detector) is quantified by a measure derived from the Bhattacharyya coefficient. The Bhattacharyya distance measure is used for validating the confidence in tracks based on an adaptively obtained threshold value. This approach results in improved tracker results under a track-identity loss scenario, especially under short-term partial or full occlusions, clutter or scale variations. The suggested method for track assignment, combined with a state-of-art real-time object detector YOLO (i.e. “You only look once”) led to improved online results by reducing the number of miss-assignments within tracks.
An Accelerated Linear Approximation Method in Deep Actor-Critic Framework

Reinforcement learning is considered to be one of the main methods of general artificial intelligence, which can realize self-learning of machines through interaction with the environment. In this paper, a modified version of deep reinforcement learning algorithm based on the Actor-Critic framework is proposed. Unlike traditional updated methods, the algorithm proposed in this paper adopts a special on-policy method, which we called Accelerated Linear Approximation Method in Deep Actor-Critic Framework (ALA-AC). When the network is trained to a certain extent, the networks' parameters of some layers are frozen, and the remaining layers' parameters are trained for better strategy and faster training speed.
Online K-Means Clustering with Lightweight Coresets

Coresets are representative samples of data that can be used to train machine learning models with provable guarantees of approximating the accuracy of training on the full data set. They have been used for scalable clustering of large datasets and result in better cluster partitions compared to clustering a random sample. In this paper, we present a novel approach of constructing lightweight coresets on subsets of data that can fit in memory while performing a streaming variant of k-means clustering known as online k-means. Experimental results show that this approach generates cluster partitions of comparable accuracy to the regular online k-means algorithm in less time, or superior partitions in comparable time.
Forecasting using predictor selection from a large set of highly correlated variables

The potential of correlation-based feature selection has been explored in selecting an optimal subset from a set of highly correlated predictors. This problem occurs, for example, in time series forecasting of economic indicators using regression models on multiple lags of a large number of candidate leading indicators. Greedy algorithms (forward selection and backward elimination) in such cases fail. To obtain the globally optimal solution, the feature selection problem is formulated as a mixed integer programming problem. To solve it, we use the binary cut-and-branch method. The results of simulation studies demonstrate the advantage of using the binary cut-and-branch method in comparison with heuristic search algorithms. The real example of the selection of leading indicators of consumer price index growth shows the acceptability of using the correlation-based feature selection method.
Coherence as a Resource – An Overview

Coherence is the resource that allows quantum technologies to go beyond what is possible in the classical world. Due to its usefulness, coherence deserves to be quantified, just as we do for petrol, electricity, etc. Explaining how to do so in rigorously and how the resulting theory relates to experiments will be the subject of this lecture.
Application of Online Ticket as a Method in Purchasing Bus Tickets

Abstract The purpose of this research is to identify whether the application of online ticket as a method in purchasing bus tickets can be fully implemented or not. The research method used a descriptive method to analyse the application of online ticket in the XYZ autobus companies. XYZ autobus company is one of the company in Bandung that engaged in bus-based transport service especially tourism. The results of this study are to determine the effect of the online ticket on consumers and autobus companies. Also, the research was obtained based on observations and exploring sources related to the bus ticket purchase process by conducting surveys, interviews, and analysis. After doing the research, it can be concluded by applying online ticket is influential for consumers and companies. For consumers, the online ticket makes it easy to purchase tickets, because the consumers do not need to come to the sales agent for purchasing bus tickets. As for the companies, online tickets affect managing transaction data, safer and as a promotional media for the companies.
A Distributed Scheduling Framework of Service Based ETL Process

The use of service oriented computing paradigm and ETL (Extract-Transform-Load) technology has recently received significant attention to enable data warehouse construction and data integration. Aiming at improving scheduling and execution efficiency of service based ETL process, this paper proposes a distributed scheduling and execution framework for ETL process and a corresponding method. Firstly, add different weights to the ETL process to ensure the loading efficiency of core business data. Secondly, the scheduler selects the executors according to the performance and load, then allocates the ETL process execution request based on the greedy balance (GB) algorithm to make the load of the executor balancing. Thirdly, the executors parses ETL process to ETL services, then selects one or more executors to deploy and execute the ETL service according to the locality-aware strategy, that is, the amount of data involved and the distance of the node network which service involved, which can reduce the network overhead and improve execution efficiency. Finally, the effectiveness of the proposed method is verified by experimental comparison.
Technology in Japanese Language Private

Abstract The purpose of this study is to find out the use of technology in private learning services. The technology that referred in this study is an Online Site that provides learning services. This study used qualitative methods, by focusing on socio-cultural conditions. The results show that how important the use of technology in the field of education is seen from Indonesian citizens who are very enthusiastic in learning foreign languages to increase their abilities and knowledge. This is also due to the development of Japanese companies in Indonesia and the many offers of Japanese scholarships to students in Indonesia. Of course, foreign languages are very important for Indonesian education nowadays.
Dynamics of self-efficacy in the context of the problem of professional formation of future teacher

The problem of dynamics of development of self-efficacy at students of pedagogical specialties is considered. The analysis of the theoretical material on the topic is under study. And we try to reveal the considered types, levels and conditions of formation of perceived self-efficacy in future teachers. We present the results of empirical research of dynamic characteristics of self-efficacy of a sample of 83 students identified statistical indicators of development of subject self-efficacy and self-efficacy in communicating their relationship with motivation of choice of profession and level of self-evaluation. The integrity, dynamism and multicomponent self-efficacy; nonlinear nature of its development at different stages of training are revealed.
Technological Terms of Monitoring the Readiness of Musician Students for Artistic-Interpretational Activities

The article describes the requirements for a man of the new time, expressed in the need for his personal self-development, the full disclosure of creative potential. The specificity of the professional activity of a teacher-musician as a subject of culture capable of establishing a spiritual level interaction with students is determined, which is based on the actualization of the creative self-consciousness of all subjects of the educational process. The author considers the possibility of the technologization of the process of mastering the teacher-musician artistic and interpretative activities as a necessary component of his professional training, starting with the development of diagnostics of the success of mastering it. For this purpose, the concept of “artistic and interpretational competence of a teacher-musician” is introduced, and the stages of mastering it are defined: motivational-indicative, procedural-activity-related, and effective-corrective. The article proposes a detailed description of these stages in terms of content and diagnosis of the achievement of the appropriate level. In accordance with this, to diagnose the process of forming artistic and interpretative competence, the author proposes a level principle of diagnosis and an exemplary system of tasks that reveal knowledge and skills that are characterized as standard, key or leading.
Joint Coarse-and-Fine Semantic Segmentation

The issue of image semantic segmentation is renowned within computer vision and artificial intelligence. The ground truth in image segmentation is hard to produce and is time- and resource-intensive. Recent research on real-time image semantic segmentation based on deep learning has reduced image resolution through pooling operations, resulting in detail loss in the scene. In order to generate high-quality annotated data, in this paper, we propose a joint coarse-and-fine (JCF) architecture that can repair fragment defects based on a coarse module, and also produce fine details based on a fine module. The experiments show promising results compared to state-of-the-art methods.