Reviewerly/exHarmony huggingface dataset:
UPAYA GURU BK DALAM MENGATASI KESULITAN BELAJAR PESERTA DIDIK DI KELAS XI IPS SMA NEGERI 1 VII KOTO SUNGAI SARIAK KABUPATEN PADANG PARIAMAN

This research was motivated by the learning difficulties experienced by students at school and saw how the efforts of the counseling teachers in overcoming the learning difficulties of students. This study aims to describe the counseling teachers' efforts in overcoming the learning difficulties of students in class XI IPS at SMA Negeri 1 VII Koto Sungai Sariak. This research was conducted through a qualitative descriptive approach. This study took 3 respondents consisting of 1 key informant and 2 additional informants. The data collection method used interview techniques. Based on the results of research on the efforts of counseling teachers in overcoming learning difficulties of students in class XI IPS at SMA Negeri 1 VII Koto Sungai Sariak, namely: 1) BK teachers have good abilities in identifying learning difficulties of students. 2) BK teachers have good abilities in responding to learning difficulties of students. 3) The counseling teacher's efforts are successful in overcoming the learning difficulties of students in class XI.
Contactless access control system for critical objects based on deep learning neural networks

The paper presents a software product that enables contactless identity verification at various sites using Leap Motion controller and neural network module, which will improve security at critical sites. The authors present the results of a study of numerical sequence generation for identification through two-factor authentication and a predictive hand model recognition module to perform automatic identification of an individual. The process of verification of an identifiable fingerprint is based on a decision support system-by means of a fuzzy rule base, the percentage coefficient of accessibility for an identifiable person is determined. In addition, the algorithm has been optimised to work with devices based on ARM single board computers-the deployment is in this case an independent customer authorisation unit at a remote distance from the information processing server with the varied possibility of working offline and online modes.
How to Trick a Neural Network? Synthesising Noise to Reduce the Accuracy of Neural Network Image Classification

The study aims to develop an algorithm and then software to synthesise noise that could be used to attack deep learning neural networks designed to classify images. We present the results of our analysis of methods for conducting this type of attacks. The synthesis of attack noise is stated as a problem of multidimensional constrained optimization. The main features of the attack noise synthesis algorithm proposed are as follows: we employ the clip function to take constraints on noise into account; we use the top-1 and top-5 classification error ratings as attack noise efficiency criteria; we train our neural networks using backpropagation and Adam's gradient descent algorithm; stochastic gradient descent is employed to solve the optimisation problem indicated above; neural network training also makes use of the augmentation technique. The software was developed in Python using the Pytorch framework to dynamically differentiate the calculation graph and runs under Ubuntu 18.04 and CentOS 7. Our IDE was Visual Studio Code. We accelerated the computation via CUDA executed on a NVIDIA Titan XP GPU. The paper presents the results of a broad computational experiment in synthesising non-universal and universal attack noise types for eight deep neural networks. We show that the attack algorithm proposed is able to increase the neural network error by eight times
The management application design of digital archiving letters

Abstract Demands for speed and reliable access to services in the management of archive activities are the demands of today’s industry and business. Managing and controlling documentary documents is a vital business process because it is related to the accessibility of company data and information. This study aims to design and create a digital mail-based archive management application. The method used in this research is a systematic approach in the design and manufacture of software with the RUP (Rational Unified Process) method with Unified Modelling Language (UML) modelling. The results of this application are expected to help management in archiving services more effectively and efficiently.
Find Name of Any Song Without Knowing Full Lyrics (3 Methods)

We all have been in several moments where a new song is stuck in our mind, but we arent able to recite the exact music. Its annoying when we loved a ...
Affiliation Information in DataCite Dataset Metadata: a Flemish Case Study

This article aims to evaluate how and to what extent metadata of datasets indexed in DataCite offer clear human- or machine-readable information that enables the research data to be linked to a particular research institution. Two main pathways are explored. First, researchers can encode their affiliation information at the moment of data submission. This can be done by means of free-text metadata fields or via the inclusion of identifiers such as GRID/ROR and ORCID. Second, affiliation information can be traced indirectly through linking between a dataset and associated publications, given that the metadata of publications is often more explicit about affiliation information than the metadata of datasets. Both pathways of affiliation information encoding are evaluated on the basis of metadata pertaining to datasets created at the five Flemish universities. It is shown that good practices such as encoding of affiliation information in a dedicated metadata field or inclusion of ORCID in the metadata are on the rise, but could be expanded further. Finally, the establishment of links between datasets and related publications is often lacking in dataset metadata, although there are important differences between data repositories, as is also demonstrated in a more data-intensive follow-up analysis based on random samples of metadata records. It is important that data repositories address this issue by providing a metadata field clearly dedicated to associated publications, prominently displayed on the landing page of the dataset.
Design and Fabrication of Intelligent Training Javelin Based on Embedded Technique

The angle of throwing and the speed of throwing are very important technical indicators in the javelin throwing training process. It is helpful to improve the effect of training according to the feedback information in real time for each training session. In view of this situation, based on the embedded technique, we designed and produced the javelin-throwing posture measuring system. A new intelligent training javelin with an embedded moving posture measurement system was manufactured through further mechanical design and processing. This intelligent javelin can realize the real-time inspection and transmission of key javelin-throwing parameters and quantitatively describe the regular moving pattern of a javelin thrower. Coaches and athletes may analyze the training results according to the parameters they get from our system, which realize quantification and modification of javelin training and provide technical support to the improvement of the athletic level of javelin throw.
Detección y representación de eventos ambientales basada en agentes

An Intelligent environment can respond to the necessities of the users according to the context, this is so that the individuals can have the ideal climatic conditions in order to go about their activities, these conditions are related through a series of special norms. The events that we describe in this paper are in relation to the events of the environment (temperature, humidity, brightness, and presence), also involved are different variables like time, space, or person, such are important in order to be able to model what is occurring in a determined place. In this project, we propose a personalized ontological design for the academic dominion. The ontological model is utilized for the identification of environmental events according to the data acquired from the environment through the simulation of intellectual agents. Also, our ontological model is used to rationalize with the information obtained from the identified events. The model of ontologies based on events considers four contextual questions like a perspective modular: person, seasonality (weather), spatiality (location), network (resources in order to acquire environmental data) and event (academic events). And the detector is based on rules obtained from the standards of optimum climatic conditions of a physical space.
Fine-tuning techniques and data augmentation on transformer-based models for conversational texts and noisy user-generated content

Transfer learning and Transformer-based language models play important roles in modern natural language processing research community. In this paper, we propose Transformer model's fine-tuning and data augmentation (TMFTDA) techniques for conversational texts and noisy user-generated content. We use two NTCIR-15 tasks, namely the first Dialogue Evaluation (DialEval-1) task and the second Numeral Attachment in Financial Tweets (FinNum-2) task, to evaluate the efficacy of TMFTDA. Experimental results show that TMFTDA substantially outperforms the baselines model of Bidirectional Long Short-Term Memory (Bi-LSTM) in multi-turn dialogue system evaluation at DialEval-1's Dialogue Quality (DQ) and Nugget Detection (ND) subtasks. Moreover, TMFTDA performs to a satisfactory level at FinNum-2 with a model of Cross-lingual Language Models using a Robustly Optimized BERT Pretraining Approach (XLM-RoBERTa). The research contribution of this paper is that, we help shed some light on the usefulness of TMFTDA, for conversational texts and noisy user-generated content in social media text analytics.
The non-excavation corrosion prediction model of grounding grid based on particle swarm optimization extreme learning machine

Abstract The grounding grid is an indispensible part in the eletrical system. Nevertheless, the grounding grid materials are susceptible. Considering the small sample size and strong nonlinear feature of the grounding grid corrosion, we introduced a non-excavation corrosion prediction model based on particle swarm optimization extreme learning machine. This model utilized the extreme learning machine to fast deal with the nonlinear relationship, and utilized the particle swarm optimization to search global optimal solution. Compared with generalized regression neural network and BP neural network, the prediction results of this model are more accurate. Thus, this model might have bright future in improving the accuracy of corrosion prediction of grounding grids.
Text Line Segmentation: A FCN Based Approach

Text line segmentation is a prerequisite for most of the document processing systems. However, for handwritten/warped documents, it is not straightforward to segment the text lines. This work proposes a learning-based text line segmentation method from document images. This work can tackle complex layouts present in a camera captured or handwritten document images along with printed flat-bed scanned English documents. The method also works for Alphasyllabrary scripts like Bangla. Segmentation of Bangla handwritten text is quite challenging because of its unique characteristics. The proposed approach of line segmentation relies on fully convolutional networks (FCNs). To improve the performance of the method, we introduce a post-processing step. The model is trained and tested on our dataset along with the cBAD dataset. We develop the model in such a way that it can be trained and tested in a machine that has limited access to highly computational accessories like GPU. The results of our experiments are encouraging.
Clearness Index Determination in Santo Domingo To Predict the Solar Radiation

In order to establish the correct dimensions of any device that works with alternative energy it is necessary to evaluate the available resources. Daily solar radiation was measured in Santo Domingo city using a pyranometer. Simultaneously, for every day, the time evolution of the total solar irradiance was determined. The relationship between solar radiation and total solar irradiance was analyzed for a defined period, obtaining the average hourly clearness index global to statistically predict the solar irradiance that will reach the surface on our location considering the cloudiness based on the Julian day. This parameter will enable a more exact simulation of the operation of any device working with solar energy in Santo Domingo, particularly the solar refrigerator of our research group.
Android-based learning media design simulation license driving motor vehicles

Abstract Driving License has become an essential factor in traffic, is also one of the requirements that must be possessed by motorcyclists. In obtaining a Driving License is not as easy as it seems, not a few people who fail in making a Driving License. The reasons vary, most frequently found is due to the difficulty of the field for the practice test and lack of ignorance in completing each of the listing tests. Apart from problems, needs be a solution, such as a learning medium for people who often fail or even for those who first want to make a Driving License. The development method used is the Multimedia Development Life Cycle (MDLC) system, such as concepts, design, material collection, assembly, testing, and distribution. This article produces a simulation design of a motor vehicle driving license that can be used on an android smartphone. The instructional media for simulating motor vehicle driving licenses to be e pected to be able to illustrate making a driving license before a real practice test at the City Resort Police Station.
Autonomous Navigation in Complex Environments using Memory-Aided Deep Reinforcement Learning

Mobile robots have gained increased importance within industrial tasks such as commissioning, delivery or operation in hazardous environments. The ability to navigate in unknown and complex environments is paramount in industrial robotics. Reinforcement learning approaches have shown remarkable success in dealing with unknown situations and react accordingly without manually engineered guidelines and overconservative measures. However, these approaches are often restricted to short range navigation and are prone to local minima due to a lack of a memory module. Thus, the navigation in complex environments such as mazes, long corridors or concave areas is still an open frontier. In this paper, we incorporate a variety of recurrent neural networks to cope with these challenges. We train a reinforcement learning based agent within a 2D simulation environment of our previous work and extend it with a memory module. The agent is able to navigate solely on sensor data observations which are directly mapped to actions. We evaluate the performance on different complex environments and achieve enhanced results within complex environments compared to memory-free baseline approaches.
Discipline Construction Plan of Undergraduate Studies in Applied Universities for China's Guangdong Baiyun University Based on the McKinsey 7S model

Disciplinary development of undergraduate studies in applied universities is affected by a confluence of factors. This article studies the problems in current disciplinary development, and takes Guangdong Baiyun University as an example to discuss the development paths, from the perspective of the McKinsey 7S model, i.e. strategy, structure, systems, style, staff, skills and shared values, and give these seven elements special meanings in application scenarios of discipline construction. The aim is to promote the reform of the system and mechanism of discipline construction and provide strategic support of and reference to decision-making to enhance such universities' disciplinary, and even comprehensive, strengths.
Fast Multiple Object Tracking Using Relevant Motion Vector

Multiple object tracking is a crucial task in the field of computer vision. In conventional tracking algorithms, frequent detections are required to achieve a good tracking performance, which makes the process time consuming and unable to be applied in real-time applications. Since the adjacent frames are highly relevant and the relevant motion vector can be extracted directly from compressed videos without extra calculation, we present a fast tracking algorithm based on the relevant motion vector to reduce the detection frequency. In the proposed algorithm, the video is divided into key and non-key frames. For the key frames, the objects are detected on the RGB images based on detection method. For the non-key frames, the objects are tracked based on transformation information calculated on motion vector. In order to combine the detection results and the tracking results, data association is performed for the key frames based on Hungarian algorithm. Evaluations on a video dataset show that our proposed algorithm achieves better efficiency and comparable accuracy than the previous algorithm.
A SPEA-Based Group Trading Strategy Portfolio Optimization Algorithm

Trading strategies are usually employed for finding trading signals for increasing returns as well as reducing risks. As a result, many approaches have been proposed for obtaining trading strategy portfolio. The group trading strategy portfolio (GTSP) optimization approaches that can be used to provide various trading strategy portfolios were also proposed. Because different criteria should be considered to derive GTSPs, a MOGA (multi-objective genetic algorithm) based approach has been presented for searching non-dominated solutions. In this paper, to extract a better set of non-dominated solutions, we propose a SPEA-based algorithm for deriving GTSPs with two objective functions. Since the goal of trading is to get profit, the first objective function is utilized to evaluate the return and risk of a candidate GTSP. The second objective function is used to evaluate whether the numbers of strategies between groups are similar and weights of groups as well. Experiments were conducted on a financial dataset to show the effectiveness of the proposed approach and comparison results of the proposed approach and the previous approach.
Sewing-enabled electric button for smart fabric

A new button-shaped electrical device was developed for a smart fabric. This electric button can be sewn anywhere on the garment, similar to a traditional button fastener. t not only performs a decorative function but also makes the fabric suitable for use in Internet of Things (IoT) applications. It has metallic through-holes such that it can be fastened onto a fabric by conductive sewing threads. When threaded through metallic holes, the button can communicate with the external device by transmitting and receiving data. In addition, it adds specific functions by stacking a detachable application layer on the base layer. It is robust to frequent washing, and thus has excellent repeatability for use as an IoT device. The feasibility of the electric button was successfully demonstrated by its ability to identify the physical activities of walking and running, monitoring ambient temperature, and turning on LED lights.
Prototyping online learning media using SCORM

Abstract The online learning model is now widely used by various institutional sectors aimed at learning can be done anytime and anywhere without being limited by space and time, but many institutions or institutions are constrained in presenting standardized material so that the impact of teaching materials that have been made is limited its use. In the Information Technology Education study program of Indonesian Education Institute still uses the file attach system to store teaching materials. This results in the difficulty of lecturers knowing the learning activities carried out by students. Therefore, we need to design an online learning media based on SCORM in the e-learning web institute of institupendidikan.ac.id that can solve the problem so far. The design model in this study uses the Unifed Modelling Language (UML) model which is a structural model and techniques for modelling object-oriented program (OOP) design. The results of this study are expected to be implemented by all lecturers in making teaching materials in the SCORM format.
Energy efficient task scheduling using adaptive PSO for cloud computing

Cloud computing is an important research domain where all computational resources are networked globally and shared to users easily. Cloud service provider (CSP) wants the eco-friendly solution to resolve these issues. To enhance the performance of cloud computing resources, task scheduling is of prime concern. Further, the growth of cloud computing resources leads to a large amount of energy consumption and carbon footprints. Thus, this paper aims to reduce the makespan along with energy consumption for independent tasks. For this purpose, we proposed energy efficient adaptive particle swarm optimisation (EE-APSO) algorithm for independent tasks scheduling decision. Each particle represents a potential solution, and small position value (SPV) rule is used to change continuous particle position vector to discrete particle position vector. PSO is made adaptive by varying acceleration coefficients and inertia weight. We also introduced mutation operation to avoid the PSO algorithm getting stuck in local minima and explore the whole search space efficiently. Result analysis demonstrated that our proposed algorithm EE-APSO using SPV rule gives better results than min-min, max-min and genetic algorithm (GA) in terms of makespan and energy consumption.
To learn from … different generations: some important issues

The article presents one of the current directions of development and optimisation of modern education – a features of each of the current generations and the use of such teaching tools that reflect and embody these features. We are talking about both the didactic and technological features of the learning process and the semantic, essential aspects of the learning process that make the learning process itself relevant, meaningful, promising for each of the generations, not formally required. The result of such an approach to optimising the learning process is to increase the effectiveness of both the learning process and the education system as a whole in each specific historical period in the development of society.
Usage of RMarkdown and Bookdown for Educational and Scientific Writing

In this article we discuss the usage of lightweight Markdown markup language for educational and scientific writing. We start with a brief analysis of current methods and tools for document editing and publishing. Then we switch to the simple Markdown markup language and describe its extensions - Markdown Extra, RMarkdown and bookdown. The use of the latter significantly facilitates the writing of various documents for educational and scientific purposes, such as materials on practical classes for students, lecture notes, teaching materials, scientific articles and books.
Video Categorization Based on Sentiment Analysis of YouTube Comments

With recent development in digital technologies, the amount of multimedia statistics is increasing everyday. Abusive video constitutes a hazard to public safety and thus constructive detection algorithms are in urgent need. In order to improve the detection accuracy here, Sentiment analysis-based video classification is proposed. Sentiment analysis-based video classification system is used to classify video content into two different categories, i.e., Abusive videos, nonabusive videos. We are using YouTube comments of a video as source of input, which is analyzed by our sentiment analysis model and the model determines the category to which that particular video belongs. Many techniques such as Bag of Words, Lemmatization, logistic regression and NLP are used. The proposed scheme obtains competitive results on abusive content detection. The empirical outcome shows that our method is elementary and productive.