Reviewerly/exHarmony huggingface dataset:
Rancang Bangun Aplikasi “Ngamenkuy” Berbasis Android

Band is a group of musicians who play music or sing, the band is divided into two types namely Label Band and Indie Band, label band is a band that is under the auspices of a company or management label while indie bands are bands that move independently or stand alone in producing or disseminating his work. In conducting promotions, label bands tend to be easier because they are assisted by a company or management so that the marketing will be wider. Actually through local media social bands can still interact with each other, using social media they can communicate well, but to get to know more details is not enough only through social media, so the design of ngamenkuy application is expected to be a solution to the problem. The method used in this study uses the prototype method, in the process of collecting data using interview observation techniques and literature studies. Then in the design stage using DFD (Data Flow Diagrams), and in making applications using the Kotlin Programming Language on Android Studio. For testing applications use the User Experience Questionnaire (UEQ). The results obtained from the design of the ngamenkuy application are the local band portal application in Sumedang Regency. Ngamenkuy is a local band portal in Sumedang Regency, aims to facilitate local bands and the public (users) to interact in this application.
Domain Adaptation of Unreal Images for Image Classification

Deep learning has been intensively researched in computer vision tasks like im-age classification. Collecting and labeling images that these neural networks aretrained on is labor-intensive, which ...
Prediction of Cocoa Productivity in Mamuju Regency with the K-Nearest Neighbor Algorithm

This study processes data using the Knowedge Discovery in Database (KDD) process flow. The test sample used was cocoa statistical data in Mamuju district from 2016-2019. The KDD process flow itself consists of data selection, preprocessing, transformation, data mining and evaluation. In the data mining process, determining the pattern of the selected data uses the K-Nearest Neighbor (K-NN) method, while the evaluation uses the Root Mean Square Error (RMSE) method. RMSE is used to measure the level of predictive accuracy of the error value obtained. If the resulting error value is low, it means that the prediction is close to its actual value. The results showed that using the K-NN method can be used to predict cocoa productivity, this is evidenced by the RMSE evaluation of the error value obtained in low districts, with an average error of 6,395.
Characterizing ensemble perception through variations in multiple statistical parameters

Many ensemble studies utilize uniform distributions, often with fewer than 10 items per set. This poses a challenge to real-world generalizability, given that naturally occurring ensemble distributions can contain many items and can vary considerably in shape. To that end, we evaluated the influence of set size and various statistical moments on ensemble processing. Across three experiments, participants viewed an ensemble of isosceles triangles with varying orientations for 250 ms. In Experiment 1, we examined the effect of varying set size (10, 20, 40, and 50 items) and range (60°, 90°, 120°, 150°, and 180°) on reports of average orientation for uniform distributions. Accuracy increased with larger set sizes and smaller ranges, with no interaction between the two distribution parameters. Next we evaluated performance using more naturalistic and differently shaped distributions. Specifically, we generated normally-distributed ensembles, and then changed their shape by manipulating skewness and kurtosis. In Experiment 2, participants had difficulty explicitly discriminating whether two ensembles had the same or different values of skew or kurtosis. In Experiment 3, we examined the effect of manipulating multiple distribution parameters (set size, range, skewness, and kurtosis) on reports of average orientation. We again found no interaction between range and set size, but interestingly, participants had higher accuracy for leptokurtic compared with platykurtic distributions, and for skewed compared with non-skewed (i.e., normal) distributions, despite the lack of explicit sensitivity to these statistical moments in Experiment 2. Kurtosis interacted with both range and set size, but not with skew. Importantly, performance for skewed, kurtotic, and normal distributions was more accurate than performance for uniform distributions in Experiment 1. These results reveal the differential contributions of various distribution parameters on ensemble encoding, and, importantly, highlight the need to use naturalistic statistics over artificial uniform distributions when studying ensemble processing.
Nigerian youths in a global world: Active music learning as a paradigm

The young people belong to the largest peer group in history. They live in a rapidly changing world faced with many pressures and challenges and are ill-equipped to tackle their problems. Education is the bedrock of development but in a global world, it is bisected with myriads of problems ranging from poor teaching approaches to paucity of quality teachers and polluted learning environment. The teachers’ approaches should be able to prepare learners for challenges of the 21st century. This qualitative study is an attempt to explore the interactive approaches to music education in this era of information technology. This study which is anchored on constructivism theory argues that learning is a process of making meaning and that young people in this global world can develop their existing knowledge and understanding in music education through active interactive approaches to achieve deeper levels of understanding. It concludes by recommending that music teachers at all levels should brace themselves with the life challenges of Nigerian youths to develop in the students, life skills, entrepreneurial thinking and workforce.&#x0D; Keywords: Globalization, Youths, Music education, Entrepreneurship and Active teaching and learning approaches.
Innovative technologies for knowledge transmission in the days of Covid 19

The aim of this paper is to investigate the contribution of technological innovation in the educational-training process, with a particular reference to the COVID-19 situation. It must be considered that, in this perspective, innovation has taken on a fundamental role in transferring knowledge and educational values. The starting point of the paper was the definition of technology and innovation, and then the focus was shifted to the digital aspects at school.
Regression testing of object-oriented systems using UML state machine diagram and sequence diagram

The software requirements are modelled using UML state machine diagram and UML sequence diagram. The different features of both the diagrams are combined and an intermediate graph, i.e., state sequence graph (SSG) is generated. The affected nodes, due to different changes in the past versions of the applications are stored for further analysis. Whenever, a new version of the software is developed, and it is under regression testing, test scenario prioritisation is carried out by finding the frequent pattern from the stored modification history. Different other factors like number of message passing, number of state changes etc. also contribute in prioritising the test cases. The proposed approach is applied on different case studies and the results are recorded. This approach is found to be very efficient when evaluated using prioritisation metric and compared with other related work.
IMPLEMENTASI EDMS DALAM PENATAAN DOKUMEN DI RAIL DOCUMENT SYSTEM PT. KERETA API INDONESIA (PERSERO) DAERAH OPERASI 1 JAKARTA

To ensure the availability of information needed by employees of PT KAI (Persero) Operating Area 1, EDMS becomes a solution that utilizes information technology and communication in its management. This research uses a qualitative approach with descriptive case study research method. The research data was obtained through observation, interviews and document studies. The results of this study indicate that the implementation of EDMS in structuring documents of PT KAI Daerah Operasi 1 Jakarta using the Rail Document System serves to cut time in the bureaucratic process; saving resources; and more efficient and effective access. This can be reflected in the findings obtained by researchers indicating that these components have been implemented in electronic document processing activities, although in practice there are still things that need to be improved to improve the effectiveness and efficiency of the RDS system.
Combined DWT-DCT Digital Image Watermarking

A blended discrete wavelet transform and discrete cosine rework based watermarking method with low frequency watermarking with weighted correction is proposed. Discrete wavelet transform has exceptional spatial localization, frequency unfold and multi-resolution traits, that are almost a bit just like the theoretical models of the human sensory gadget. Discrete cosine transform primarily based watermarking techniques provide compression while discrete wavelet transform based watermarking techniques offer scalability. Those relevant Houses are utilized on this blended watermarking technique. Within the proposed method watermark bits are embedded in the low waveband of every discrete cosine remodel block of selected discrete wavelet transform sub-band. The weighted correction is moreover used to improve the imperceptibility. The extracting approach reverses the embedding operations without the reference of the number one image. In comparison with the same technique with the help of discrete cosine rework primarily based absolutely technique and discrete wavelet remodel primarily based method.
A Method for Place Name Recognition in Tang Poetry Based on Feature Templates and Conditional Random Field

Tang poetry is an important aspect of ancient Chinese culture. Given that Tang poetry has unique features in text structure, how to use entity recognition, knowledge graph and other information processing technologies to research poetry is of great importance. However, the existing artificial neural network methods for named entity recognition require a large number of labeled training sets, while Chinese Tang poetry has not been labeled with a good training set. Besides, the grammatical structure of Tang poetry is far from modern Chinese. Therefore, for place name recognition in poetry, the existing neural network methods do not perform well. This article studies and analyzes the metrical form of Tang poetry, finds the metrical rules of place names, and summarizes the feature templates based on the metrical rules. According to the feature templates of Tang poetry, a method of combining feature templates with conditional random field is proposed. Experimental results prove the effectiveness of the proposed method.
Analisis Litensi Metode PCC, NTH dan ECMP untuk Load Balance dan Failover

Abstract — Failover and Load Balance are needed to handle disaster on the server. Failover has a function as a backup system when the main system is interrupted. Whereas load balance functions as a server balancer when a request from a client causes overload on one of the servers when the traffic is not balanced. In this study analyze the comparative advantages and disadvantages of the three load balancing and failover methods, namely Equal Cost Multi Path (ECMP), Nth, and Per Connection Classifier (PCC). These three methods will be implemented in a virtual network topology using two ISPs using Winbox software and MikroTik RB-941-2nD-TC. Testing is done using Webstress Tools, Internet Download Manager (IDM), Ping Tools. The results showed the PCC method because classifying connection traffic in and out of the router into several groups. This grouping can be distinguished by src-address, dst-address, src-port and dst-port. Mikrotik will remember the gateway path that has been passed at the beginning of the connection traffic. So that the next data packet that is still related will pass on the same gateway path as the previous data packet that was sent.
EXPERIENCIA TRANSFORMADORA EN LA FORMACIÓN DOCENTE: DE LAS AULAS A LAS PANTALLAS EN TIEMPOS DE PANDEMIA

Since the confinement began worldwide, due to the presence of COVID-19, teachers were forced to continue their academic training from their homes through online or virtual courses and workshops, always in favor of continuity university education. Education in emergency situations ensures learning opportunities for all ages in safe spaces with qualified teaching staff, from the onset of a crisis to recovery, ensuring physical, psychosocial and cognitive protection, for quality learning (INEE, 2004). The purpose of the research was to explain the transformative experience in teacher training: from classrooms to screens in times of pandemic. A qualitative research was developed, with an interpretive approach. Likewise, after analyzing and interpreting the data using an interview with two key informants, it was evidenced as the main conclusions that in times of emergencies, teachers should not paralyze their professional training; on the contrary, they must adapt it to the present adversities. From the knowledge and actions of the pedagogical experience, it was considered that some aspects should be taken into account for educational continuity: the exploration of new pedagogical strategies and resources; as well as constant teacher training through virtuality.
Recommendation of web services using implicit feedback and collaborative filtering technique

A usual activity of recommendation systems has been to increase customer interests by means of customized recommendations supported by implicit feedback data. These systems track user behaviors passively, according to user preferences. In this paper we identify different properties of implicit feedback datasets which are unique and use collaborative filtering to recommended web services. We conjointly recommend a scalable improvement procedure for recommendation, which scales linearly with the size of data. It favors well - tuned implementation of other known methods such as collaborative filtering and implicit feedback. User’s history is given as an input to the recommender system for the prediction of webservices. Providing recommendations to users with minimal past history becomes an onerous problem for collaborative filtering as their predictive ability is limited. In this project we propose a method which is a Hybridization of implicit feedback and collaborative filtering technique gives optimal solution for web services recommendation system. several Collaborative Filtering -based Web service prediction methods and approaches have been proposed, the performances needs significant improvement because existing methods uses information about of users and services once measure the similarity among users and among services. Furthermore, web services factors on qualities such as reaction time and throughput, regularly rely upon where web services and clients are found.
Metrics Based Feature Selection for Software Defect Prediction

Nowadays, software is very influential on various sectors of life, both to solve business needs, as well as personal needs. To have a Software with high quality, testing is needed to avoid software defect. Research on software defects involving Machine Learning is currently being carried out by many researchers. This method contains one important step, which is called feature selection. In this study, researchers conducted a feature selection based on the software metric category to determine the level of accuracy of the prediction of software defects by utilizing 13 (thirteen) datasets from NASA MDP namely CM1, JM1, KC1, KC3, KC4, MC1, MC2, MW1, PC1, PC2, PC3, PC4, and PC5. To classify, the researchers involved 5 (five) classifiers, namely Naive Bayes, Decision Trees, Random Forests, K-Nearest Neighbor, and Support Vector Machines. The research result shows that each attribure on software metric categories has effect on each dataset. Naive Bayes Algorithm and Random Forest Algorithm can give better performance than other algorithm in classifieng software defect with feature selection based on metrics. On the other hand, the best metrics category on each classifier algorithm is metric Misc. From average AUC value, it can be concluded that metrics category which can give best performance is metric LoC, followed by metric Misc. Both categories have achieved highest AUC value in Random Forest classifier.
Digital Library, Features, Strategies, Infrastructure and their Benefits for Users in Digital Era

A digital library, unlike other forms of media such as print or microform, is a specialized form of library that encompasses a collection of digital assets. Such digital objects can be in the form of visual material, text, and audio or video that are in electronic media forms. As it is a library, it also has features to organize, store and retrieve media or files that make up the collection. Content in a digital library may also be stored locally or accessed by means of networks when stored remotely (Techopedia),thispaper is mainly high lights for digitalization of library based on their benefits, limitations, planning, infrastructure, hardware and software and so in details for library users.
Sistem Klasifikasi Penumpang Bus Trans Padang Berdasarkan Pakaian Menggunakan Metode Image Processing

Public transportation is one of the things that is often used by everyone. One type of public transportation that is often used in Indonesia is the Bus Rapid Transit (BRT) or commonly known as the busway. The city of Padang, West Sumatra has a BRT to serve the community called the Trans Padang Bus. However, in this bus service there are still several shortcomings that make the bus service ineffective. The gate system which is still manual on this bus makes service slow and inefficient. By using a flap barrier gate that has an image processing system for suit passengers, the service on this bus will be more effective. Utilizing the Raspberry Pi and You Only Look Once (YOLO), this system can be made as efficient as possible for the place and use. Passengers who will enter will be immediately detected by the PIR sensor and the system will start with a camera that will take pictures of passenger clothes. The image will be processed using YOLO and the results of the process in the form of passenger categories and prices will be displayed on the LCD. The author uses YOLO and the Fast R-CNN algorithm to determine passenger categories. With this method, the test results show an average success percentage of 98.3%.
Cultivating Safety in the Information Technology Era

In the information technology era, the banking industry must compete in an external environment characterized by high levels of uncertainty, complexity and change. Accidents in high-risk manufacturing organizations are generally related to safety studies. The researcher attempted to study "accidents" due to network vulnerabilities in IT-based organizations using the safety concept. This qualitative study is important because it provides analysis of IT support for organizational development. This study used multi-case study and grounded research approaches. The process of developing an information safety climate is considered an alternative solution other than technology. The study revealed that normal accident theory can be used to explain accidents in IT-based organizations. The process of developing an information security climate in banking organizations is categorized as the emergency type. The manifestation of information safety climate is attentiveness, accountability, ethical sensitivity, integrity and sustainability. Phases that need to be undergone in cultivating the safety climate are: adaptation, learning, awareness to risk, and resilience. Thus, it can be concluded that the climate of an IT-based organization is different from the climate of a non technology-based organization.
Data-driven materiality analysis

A materiality assessment is a systematic analysis of societal needs and expectations, enabling the identification by a company of those issues that are crucial for both internal and external stakeholders, as well as gaps between business and society priorities. The lack of a definition of what a robust materiality process is leads to an even deeper limitation of current materiality assessments, being that the purpose of the analysis is predominantly restricted to reporting only. In words, a materiality assessment should go beyond the sole purpose of identifying issues that should be included in the non-financial report. All evidence leads to the fact that companies should carry out a continuous data-driven materiality analysis, no longer confined in the reporting area but thoroughly embedded in risk management processes. The sooner an organization understands this, the sooner it will be able to future-proof its strategy and stay ahead of the competition.
Unconstrained Face Identification Based on 3D Face Frontalization and Support Vector Guided Dictionary Learning

Face identification aims at putting a label on an unknown face with respect to some training set. Unconstrained face identification is a challenging problem because of the possible variations in face pose, illumination, occlusion, and facial expression. This paper presents an unconstrained face identification method based on face frontalization and learning-based data representation. Firstly, the frontal views of unconstrained face images are automatically generated by using a single, unchanged 3D face model. Then, we crop the face relevant regions of the frontal views to segment faces from the backgrounds. At last, to enhance the discriminative capability of the coding vectors, a support vector-guided dictionary learning (SVGDL) model is applied to adaptively assign different weights to different pairs of coding vectors. The performance of the proposed method FSVGDL (frontalization-based support vector guided dictionary learning) is evaluated on the Labeled Faces in the wild (LFW) database. After decision fusion, the identification accuracy yields 97.17% when using 7 images per individual for training and 3 images per individual for testing with 158 classes in total.
A Method for Webpage Classification Based on URL Using Clustering

Background: Pattern mining is the mechanism of extracting useful information from a large dataset of information. A sub-field of web mining is sequential Noisy data extraction from user query, which is considered along with redundancy handling. This redundancy handling mechanism employed in the existing literature is known as ambiguity handling. The clustering mechanism employed in the existing system includes k means, semantic search and incremental growth of the internet. Aims: The proposed works comprise an analysis of techniques used to extract useful URLs to replace noisy data. Methods: We consider noisy data extraction from user query considered along with the redundancy handling. This redundancy handling mechanism employed in the existing literature is known as ambiguity handling. The clustering mechanism used in the existing system includes k means and semantic search. These mechanisms are static, causing performance degradation in terms of execution time. It suggests the performance improvement mechanism in this literature. Results: The methods MPV (Most-Probable-Values) clustering and N-gram techniques for improvement considered in existing literature can further be improved using the research methodology specified through this literature. Conclusion: In the proposed system, results are based on MPV clustering with N-grams techniques. N-gram analyzes the instances of a word or phrase across all query data. The parameters fetch the results in terms of execution time and the number of URLs retrieves for web page classification.
LibGuides: How to use the Barnard Library - Information Session for FLI Students: Remote Library Services

Here is information for the August 27, 2020 FLI Pre-Orientation Program: BLAIS Info Session.