Reviewerly/exHarmony huggingface dataset:
Musical acoustics education in Indonesia - a case study of the bundengan

As the world’s largest archipelago, Indonesia has a vast natural and cultural diversity, and hence a huge variety of musical instruments. Musical acoustics is, therefore, an important field of research and study in Indonesia. However, this subject is still rarely touched in undergraduate education. To help improve the quality of musical acoustics education in Indonesia, we have developed several methods. First, we design a series of final projects for undergraduate engineering students, where they can implement various methods and techniques previously studied in class (e.g., system identification, experimental methods, computational analysis, engineering design) into actual practice. These projects help the students to gain a more com- prehensive perspective on the various applications of their knowledge. Second, we form inter-disciplinary collaborations, where the students can learn with musicians, craftsmen, scholars, and conservators. Such collaborations help the students to understand that, by studying the musical instruments, they are supporting the conservation of their own heritage. We have been applying these methods with our students in Uni- versitas Gadjah Mada, particularly in our research on the bundengan, a very unique bamboo-based musical instrument. Our experience indicates that these approaches can be effective to help improve the quality of musical acoustics education in Indonesia.
Topic Modelling Extraction of “Mann Ki Baat”

The purpose of this study is to give an insight about Textual Data analytics and its application in the analysis of unique public relations campaign”Mann Ki Baat” that was initiated by incumbent Prime Minister of India,honourable “Mr.Narendra Modi” which was initially aired on All India Radio Programme on Vijaya Dashami on October 3rd , 2014 followed by second on November 2nd, 2014 of the same year till December 2019. In this paper, an analytical framework is designed using a powerful technique of textual data analytics “Topic Modelling based on LDA (Latent Dirichlet Allocation)” to accomplish the study. The proposed framework is applied to the corpus of 60 episodes(October 2014 to December 2019) of Mann ki Baat gathered from PMindia website and was analyzed in greater detail. The terms used frequently and recurrence of the topics spoken in his popular monthly radio address program were determined and analyzed from both in statistical and dynamic perspectives.In this context the present study is a first approach of application under the conventional technique “topic modelling” on Mann Ki Baat.Further, this is the principal endeavour to excerpt the themes discussed in radio programme using statistical modelling.
Insight from a Containerized Kubernetes Workload Introspection

Developments in virtual containers, especially in the cloud infrastructure, have led to diversification of jobs that containers are used to support, particularly in the big data and machine learning spaces.The diversification has been powered by the adoption of orchestration systems that marshal fleets of containers to accomplish complex programming tasks.The additional components in the vertical technology stack, plus the continued horizontal scaling have led to questions regarding how to forensically analyze complicated technology stacks.This paper proposed a solution through the use of introspection.An exploratory case study has been conducted on a bare-metal cloud that utilizes Kubernetes, the introspection tool Prometheus, and Apache Spark.The contribution of this research is two-fold.First, it provides empirical support that introspection tools can acquire forensically viable data from different levels of a technology stack.Second, it provides the ground work for comparisons between different virtual container platforms.
Error Analysis in Using Simple Past Tense Found in Students’ Writing of Recount Text

This research was to find out the types of error analysis and the most dominant type of error analysis found in Studentsâ€™ Writing Composition inÂ simple past tenseÂ especially in Recount Text. This study used a mixed-method, both qualitative and quantitative approach. From the analysis, the studentsâ€™ error in writing composition ofÂ simple past tenseÂ consists of some aspects of writing, they were in grammar, punctuation and spelling. In grammar, there were 57 data or 53.37 %, which consist of errors in class words like in determiner/article (25 data or 43.86 %), in verb (14 data or 24.56 %), in preposition (11 data or 19.30 %) and the last in noun (7 data or 12.28 %). While, the most dominant error wasÂ in the determiner/article. Second, in the aspect of spelling, there were 26 data or 21.70 %). Third, in the aspect of punctuation, there were (23 data or 24.53 %). Based on the frequency of each aspect of error, the most dominant error found in studentsâ€™ writing composition was in the aspect of grammar as 57 data or 53.37 %.
A Legal Analysis of Cybercrimes and Cybertorts: Lessons for Nigeria

The dawn of the cyber age and cyberspace inclusive of computers, computer networks, the internet, information technology, and virtual reality, means much of a person’s personal and financial life is transacted over the cyberspace. States have also taken advantage of these modern technologies. This has afforded the opportunity for these tools to become the objects and media through which crimes and torts are committed.Cyber-crimes or computer-oriented crimes include but are not restricted to the hacking of computers or computer networks, cyber-stalking, cyber-squatting, child pornography, computer-related fraud, identity theft, impersonation, and cyber-terrorism. On the other hand, cyber-torts are torts which are committed on computer systems or a network of computer systems such as trespass to chattels, conversion, cyber defamation, negligent enablement of cyber-crimes, etc. Recent global happenings including, ransomware and malware attacks, cyber-squatting, cyber-bullying and cyber harassment, cyber-warfare, and child pornography have shown how these actions affect the private and financial life of citizens as well as the economic development and international reputation of states. In light of these, States have recognized the pertinent need to have in place laws combating cyber-crimes and cyber-torts.This thesis aims at examining the response of legal systems to these crimes and torts. The international position will be looked at in respect of responsibilities of states, jurisdiction, and international cooperation, treaties and conventions dealing with cyber-crimes and cyber-torts. The position of other jurisdictions specifically the United States of America, the United Kingdom, India, and Kenya will be analysed. The position of Nigerian law will also be examined and its inadequacies will be highlighted, and lessons from other jurisdictions on better ways to combat these crimes and torts will be extracted, resulting in recommendations.
Correcting experience replay for multi-agent communication

We consider the problem of learning to communicate using multi-agent reinforcement learning (MARL). A common approach is to learn off-policy, using data sampled from a replay buffer. However, messages received in the past may not accurately reflect the current communication policy of each agent, and this complicates learning. We therefore introduce a 'communication correction' which accounts for the non-stationarity of observed communication induced by multi-agent learning. It works by relabelling the received message to make it likely under the communicator's current policy, and thus be a better reflection of the receiver's current environment. To account for cases in which agents are both senders and receivers, we introduce an ordered relabelling scheme. Our correction is computationally efficient and can be integrated with a range of off-policy algorithms. We find in our experiments that it substantially improves the ability of communicating MARL systems to learn across a variety of cooperative and competitive tasks.
Image Noise Estimation with Using Pre-trained Conventional Neural Network

Noise level estimation becomes a necessary step when the noise level of the white Gaussian noise images is unknown in a realistic situation. Machine learning has brought out a series of high-performance convolutional neural networks (CNN) that work well in noise level estimation. However, studies on the pre-trained convolutional neural network are still lacking. As a result, a model based on a pre-trained Visual Geometry Group-16 network with additional regression layers was created in this research. It was then trained with a dataset that contained a series of images with different levels of additive white Gaussian noise. The results showed high performances in all ranges of the noise levels and were relatively competitive among some other algorithms. In all, a pre-trained convolutional network can estimate the noise level as accurately as some classic algorithms, and it even performed better in the high noise level range. The whole processing would be far simpler, compared to the traditional method, since CNN does not require setting parameters once the training is done.
Glowworm Swarm Optimization Algorithm- and K-Prototypes Algorithm-Based Metadata Tree Clustering

The main objective of this paper is to present a new clustering algorithm for metadata trees based on K-prototypes algorithm, GSO (glowworm swarm optimization) algorithm, and maximal frequent path (MFP). Metadata tree clustering includes computing the feature vector of the metadata tree and the feature vector clustering. Therefore, traditional data clustering methods are not suitable directly for metadata trees. As the main method to calculate eigenvectors, the MFP method also faces the difficulties of high computational complexity and loss of key information. Generally, the K-prototypes algorithm is suitable for clustering of mixed-attribute data such as feature vectors, but the K-prototypes algorithm is sensitive to the initial clustering center. Compared with other swarm intelligence algorithms, the GSO algorithm has more efficient global search advantages, which are suitable for solving multimodal problems and also useful to optimize the K-prototypes algorithm. To address the clustering of metadata tree structures in terms of clustering accuracy and high data dimension, this paper combines the GSO algorithm, K-prototypes algorithm, and MFP together to study and design a new metadata structure clustering method. Firstly, MFP is used to describe metadata tree features, and the key parameter of categorical data is introduced into the feature vector of MFP to improve the accuracy of the feature vector to describe the metadata tree; secondly, GSO is combined with K-prototypes to design GSOKP for clustering the feature vector that contains numeric data and categorical data so as to improve the clustering accuracy; finally, tests are conducted with a set of metadata trees. The experimental results show that the designed metadata tree clustering method GSOKP-FP has certain advantages in respect to clustering accuracy and time complexity.
A Hybrid Cryptographic Scheme for Improving Cloud Security Using ECC and TDES Algorithms

The evolving cloud technology is capable of accommodating volumes of business processes. This feature attracts many individuals and organizations to store their data over cloud. But there are many security issues that require a deep insight. In this research, articles and surveys on cloud security have been reviewed to identify the issues. Efforts have been made to increase cloud data security by devising a hybrid cryptographic algorithm namely Hybrid Elliptic Curve Cryptography -Triple Data Encryption Standard (ECC-TDES). The hybridized algorithms make data more secure and immune to malicious attacks. The proposed algorithm was applied to database of a cloud-based web application and tested with audio, video, image and text files ranging between 10-100 kb file sizes to record performance metrics such as: encryption time, decryption time and accuracy. The recorded parameter values were compared with individual ECC and TDES algorithms. The findings indicated that ECC-TDES takes more time to encrypt/decrypt files but gives highest accuracy with 0.01% error rate.
Neural Machine Translation for Conditional Generation of Novel Procedures

Procedural knowledge is generally dispersed across many experts within or across organizations which might lead to inefficiencies and redundancy.Historically, computers have been well suited to store procedural knowledge but they have lacked the capability to produce natural language text.Nonetheless, recent advances in machine learning permit a higher linguistic coherence which benefits applications with longer text outputs such as procedures.This work closes the gap between human experts and computers by proposing a framework for automatic, computer generation of procedures based on neural machine translation and the BART model.Furthermore, we define two benchmark problems for procedure generation and establish a set of evaluation metrics that can be used as a reference in further work.We demonstrate the potential of this solution on the task of generating cooking recipes based on available ingredients.The evaluation results on the Recipe1M dataset showcase the method's superiority over other, fairly novel, neural architectures.
Coded Dynamic Consent framework using blockchain for healthcare information exchange

As personalized digital healthcare is attracting attention, recognition of data sovereignty has been essential in healthcare data exchange. Dynamic consent is a new approach that patients enable to tailor and manage their consent. It expands patients’ autonomy by giving the right to intervene in the healthcare data exchange process. This study proposes a Coded Dynamic Consent framework based on blockchain. We developed five design criteria to support dynamic consent and represent consent preferences as a coded format. The framework ensures data sovereignty and autonomy of a data provider so that patient-centered healthcare data exchange environment could be achieved.
An Efficient High-Speed Lifting Based 1D/2D-DWT VLSI Architecture Using CDF-5/3 Wavelet Transform For Image Processing Applications

There are various Discrete Wavelet Transform architectures that are designed to fulfil certain requirements and criteria’s. The convolution method which is an old traditional method which requires more multipliers, hardware resources and huge memory storage which is not apt to yield high speed and efficient image processing, signal processing application designs when compared to lifting method. In this paper, we have proposed an architecture for lifting scheme based CDF-5/3 2D-DWT, which includes less mathematical computations and high speed is achieved compared to existing works. The proposed architecture design is multiplier-less and utilizes lesser number of components hence it is area efficient and also achieves high throughput rate. The RTL of this design is described in VHDL language and is synthesized in Xilinx 14.5 and target device is Virtex-5 series XC5VLX110T for testing the resulting parameters like LUTs, flip-flops and slices. Higher PSNR values and lesser MSE values are achieved when tested for different kind of images. The maximum operating frequency achieved for the designed 2D-DWT architecture is 258.358MHz. Hence the proposed 2D-DWT architecture has increased speed, less computational complexity and good hardware utilization when compared to other existing works.
Software evolution: the lifetime of fine-grained elements

A model regarding the lifetime of individual source code lines or tokens can estimate maintenance effort, guide preventive maintenance, and, more broadly, identify factors that can improve the efficiency of software development. We present methods and tools that allow tracking of each line’s or token’s birth and death. Through them, we analyze 3.3 billion source code element lifetime events in 89 revision control repositories. Statistical analysis shows that code lines are durable, with a median lifespan of about 2.4 years, and that young lines are more likely to be modified or deleted, following a Weibull distribution with the associated hazard rate decreasing over time. This behavior appears to be independent from specific characteristics of lines or tokens, as we could not determine factors that influence significantly their longevity across projects. The programing language, and developer tenure and experience were not found to be significantly correlated with line or token longevity, while project size and project age showed only a slight correlation.
The Influence of Lecturer’s Personality on Work Performance Through Mediation of Motivation (Case Studies in Private and Public Higher Institute of Education in Díli)

Work Performance of lecturer becomes a determinant factor that gives an influence to the lecturer’s personality through the mediation of motivation. The purposes of this study as follows: (1) Test of the influence of lecturer’s personality on work achievement. (2) Test of lecturer’s personality in terms of motivation; (3) Test of lecturer’s personality in terms of work achievement; (4) Test of the impact of motivation mediation on relationship of lecturer’s personality and work achievement. The method used in this study is quantitative study through survey done for some lecturers as study correspondents in both Private and Public Institute of Higher Education in Dili. There are 1.805 total population and used Solving Formula in order to find 100 samples that become respondents. The research instrument of obtaining the data is via questionnaire that developed form previous research, that’s why there is no need to have both validation and reliability test instrument. The data analysis used SPSS and Smart-PLS 3.0 to analyze that data and hypothesis. The result shows that it does have positive relationship between the lecturer’s personality toward work achievement and it does have significant relationship between both variables. It also has positive and significant relationship between lecturer’s personality and motivation. Also, there is positive and significant relationship between variable of motivation and variable of of work achievement. Therefore, motivation can be a good mediation to influence positively and significantly of both lecturer’s personality and work achievement.
Mobile Phone Based Contact Tracing Applications for Combating Covid-19 Pandemic

Sheikh Mohammad Idrees and Mariusz Nowostawski* Author Affiliations Department of Computer Science (IDI), Norwegian University of Science and Technology, Norway Received: December 07, 2020| Published: December 21, 2020 Corresponding author:Mariusz Nowostawski, Department of Computer Science (IDI), Norwegian University of Science and Technology, Norway DOI: 10.26717/BJSTR.2020.32.005286
Developing an Architecture-independent Graph Framework for Modern Vector Processors and NVIDIA GPUs

This paper describes the first-in-the-world attempt to develop an architectural-independent graph framework named VGL, designed for different modern architectures with high-bandwidth memory. Currently VGL supports two classes of architectures: NEC SX-Aurora TSUBASA vector processors and NVIDIA GPUs. However, VGL can be easily extended to other architectures due to its flexible software structure. VGL is designed to provide users with the possibility of selecting the most suitable architecture for solving a specific graph problem on a given input data, which, in return, allows to significantly outperform existing frameworks and libraries, developed for modern multicore CPUs and NVIDIA GPUs. Since VGL uses an identical set of computational and data abstractions for all architectures, its users can easily port graph algorithms between different target architectures without any source code modifications. Additionally, in this paper we show how graph algorithms should be implemented and optimised for NVIDIA GPU and NEC SX-Aurora TSUBASA architectures, demonstrating that both architectures have multiple similar properties and hardware features.
Why resampling outperforms reweighting for correcting sampling bias with stochastic descents

A data set sampled from a certain population is biased if the subgroups of the population are sampled at proportions that are significantly different from their underlying proportions. Training machine learning models on biased data sets requires correction techniques to compensate for the bias. We consider two commonly-used techniques, resampling and reweighting, that rebalance the proportions of the subgroups to maintain the desired objective function. Though statistically equivalent, it has been observed that resampling outperforms reweighting when combined with stochastic gradient algorithms. By analyzing illustrative examples, we explain the reason behind this phenomenon using tools from dynamical stability and stochastic asymptotics. We also present experiments from regression, classification, and off-policy prediction to demonstrate that this is a general phenomenon. We argue that it is imperative to consider the objective function design and the optimization algorithm together while addressing the sampling bias.
A Group Recommendation Model Using Diversification Techniques

In daily life groups are formed naturally, such as watching a movie with friends, or going out for dinner.In all these scenarios, using Recommendation Systems can be helpful by suggesting pieces of information (e.g.movies or restaurants) that satisfies all rather than a single member in the group.To do so, it is crucial to aggregate individual preferences of the group members aiming at satisfying all.Although there are consensus techniques to create the group profile, the recommendations still may be repetitive and overspecialized.This drawback sets precedent for adopting diversification techniques to group recommendations.In this paper, we propose a group recommendation model using diversification techniques that exploits different aggregation techniques over group preferences matrix.The experiments evaluate accuracy and diversity goals for the group recommendations.Results from the experiments point out that our approach achieved 1.8% of diversity increase and 3.8% of precision improvement over compared methods.
Why Phishing Works on Smartphones: A Preliminary Study

Phishing is a form of fraud where an attacker attempts to acquire sensitive information from a target by posing as trustworthy.One strategy to fool the target is spoofing of a legitimate website.But why do people fall for phishing, and what security indicators are utilized or not utilized when deciding the legitimacy of a website?Hitherto, two studies have been conducted in 2006 and 2015.As time has passed since then, we like to check if people are meanwhile more certain in identifying spoofed websites.Therefore, 20 participants were observed when they analyzed and classified websites as legitimate or spoofed.On average participants had a success rate of 69 %, like previous studies results.The URL was used as an indicator by most of the participants (80 %), indicating user behavior and ease of identifying spoofed and legitimate websites is not very different on a smartphone compared to a desktop.Almost all participants used the content of the website at least once when deciding if a website was spoofed or legitimate.These findings will be used to conduct a bigger study to create more resilient results.