Reviewerly/exHarmony huggingface dataset:
The National Library of Latvia in the early stage of Covid-19 exit

The National Library of Latvia had to close for two and a half months when emergency situation was declared in Latvia. The library took proactive steps to protect its staff and ensure continuous on-line services to readers, as well as prepared re-opening plan with physical service transformation according to existing health and safety protocols. The National Library of Latvia engaged in different initiatives to diversify its products and services, including contribution of library data centre processing power to crowdsourcing initiative battling Covid-19. Library major achievement was to receive the license from the state copyright agency that allowed public access to copyright protected collections of books and periodicals from the Latvian National Digital Library throughout the entire Covid-19 crisis. Unlimited access to library digital collections was significant contribution in supporting general public, students and researchers during homestay period. The library re-opened in late May adapting to new normal and has strong future vision of improving information and communication technologies (ICT) of state cultural sector and implementing several international projects.
Using The Jakarta Post (Daily News) To Improve Academic English Vocabulary of The United English Forum (UEF) Members In Makassar

The objective of this research is to discover whether using The Jakarta Post newspaper could improve the academic vocabulary of UEF members in Makassar or not. The population was 100 with 40 samples. Samples were determined using random sampling technique. I used quasi experiment design where 20 members were in the control class and 20 were in the experimental class. Members of UEF Makassar in control class did not receive a treatment while those in experimental class were treated up to three meetings and given an extra article to study at home. The tests consist of 20 questions from rearranging words to translating sentences. The result shows that using English newspaper from The Jakarta Post can improve academic vocabulary of UEF members in Makassar. It can be seen from the average test scores of students before and after given treatments. The improvement was quite significant from 47.75 to 81. It was proven by the result of the statistical analysis of the significant level, p= 5% dan p= 1%, with degree of freedom (df) = 38 indicating that the t-test value of the students’ academic vocabulary (3,246) was higher than t-table value (2,02 and 2,71) or 2,02 &lt; 3,246 &gt; 2,71.
Combining Agile and DevOps to Improve Students’ Tech and Non-tech Skills

The goal of this ongoing study is to understand how the development of soft skills is approached in the existing computer science and software engineering curricula.Based on the findings and improvement opportunities, we propose a high-level course structure to be used as a framework for those higher education organizations who want to support the development of students technical and non-technical skills.With this proposal, we believe computer science and software engineering students would be better prepared for the most recent IT market requirements, fostering the development of the 21st century's main competencies.In order to develop the course structure presented in this study, we conducted a literature research, which showed the development of students' soft skills still requires improvement and more efficient approaches.Our solution proposal combines DevOps, Scrum and Challenge Based Learning approaches into one single course, which uses Agile DevOps culture and values, along with continuous feedback, to promote students' soft skills development.
Data Clustering for Fuzzyfier Value Derivation

The fuzzifier value m is improving significant factor for achieving the accuracy of data. Therefore, in this chapter, various clustering method is introduced with the definition of important values for clustering. To adaptively calculate the appropriate purge value of the gap type −2 fuzzy c-means, two fuzzy values m1 and m2 are provided by extracting information from individual data points using a histogram scheme. Most of the clustering in this chapter automatically obtains determination of m1 and m2 values that depended on existent repeated experiments. Also, in order to increase efficiency on deriving valid fuzzifier value, we introduce the Interval type-2 possibilistic fuzzy C-means (IT2PFCM), as one of advanced fuzzy clustering method to classify a fixed pattern. In Efficient IT2PFCM method, proper fuzzifier values for each data is obtained from an algorithm including histogram analysis and Gaussian Curve Fitting method. Using the extracted information form fuzzifier values, two modified fuzzifier value m1 and m2 are determined. These updated fuzzifier values are used to calculated the new membership values. Determining these updated values improve not only the clustering accuracy rate of the measured sensor data, but also can be used without additional procedure such as data labeling. It is also efficient at monitoring numerous sensors, managing and verifying sensor data obtained in real time such as smart cities.
Show and Tell: A Neural Visual Story-Teller

Automatically narrating the idea of an image is an elemental problem in artificial intelligence that links computer vision and natural language processing. RNN, CNN and NLP are powerful techniques that help us to work toward generation of captions and further generating a story. This model will describe complex images using technologies like Recurrent Neural Network (RNN) and Natural Language Processing (NLP). RNN consists of memory: it feeds its inputs back into itself, so it can remember past information. RNNs are used for sequential data. But they do have some flaws in remembering information. This is why CNN is used for generating captions as they are good at identifying patterns.
Online Learning Media on Biology Conservation: Rawa Danau Nature Reserve Website

The use of digital technology in education is currently important as for increased access to education through the perform various digital platforms, as well as by providing website as online learning media. The website can load text information, images, videos, animation, sound or a combination of these, and can be widely accessed. This research aims to develop an interactive website of Rawa Danau natural reserve, in Banten, to support conservation education through online learning. The 3D research and development (R &amp;amp; D) methodology approach was used which includes 3 stages; define, design, and develop. The define stage was the process of collecting data through the requirements analysis, curriculum analysis for biology conservation, and content analysis. The website of CARD biology conservation was developed in the design stage. In the develop stage, the website was assessed by the expert on both content material and learning media specialist. The results of the expert’s validations showed that the website was feasible to be used as online learning media for biology conservation education on both content material and learning media.
Approach to combining different methods for detecting insiders

The paper deals with the problem of internal intruders (insiders) in the organization. It presents Top-7 methods of insider detection and substantiates the necessity of their joint usage. A technique to combine different methods of insider detection is proposed. A combination of methods means using the results of only one of them, union or/and intersecting it with the results of others. The technique formalization and graphic interpretation are given, as well as expressions for completeness, precision, accuracy, error and F-measure. Visualization of the third method combination is provided as an example. The results of experiments on insider detection at the real corporate network using human and machine-based methods are presented.
Research on Library Big Data Service Based on Blockchain Technology

With the continuous development of China's economic construction, various new technologies have been created in this new era, and the operation methods of all sectors of society have also undergone earth-shaking changes on the original basis. At the same time, staff in each field need to think from multiple angles when performing work tasks, and actively use more innovative science and technology to fundamentally improve the original work efficiency. Among them, the library can provide everyone with an environment for learning cultural knowledge and effectively increase the cultural literacy of all citizens. This is extremely important for promoting social development. However, there are a large number of books in the library, and a large number of readers visit the library every day. This has also led to the decline of service effectiveness if the work in the library field continues to use the past work methods. The use of blockchain technology can solve various work problems that existed in the past, so that all work can provide readers with a better reading experience in the process of execution, so that readers can find their favorite content in many books. To this end, the relevant staff should be deeply aware of the important responsibilities they shoulder, innovate their work methods in a timely manner, and organically integrate their valuable work experience and brand-new blockchain technology. In this way, data information can be used to provide staff with more valuable information and make library services more humane.
Universal quantum control based on parametric modulation in superconducting circuits*

As superconducting quantum circuits are scaling up rapidly towards the noisy intermediate-scale quantum (NISQ) era, the demand for electronic control equipment has increased significantly. To fully control a quantum chip of N qubits, the common method based on up-conversion technology costs at least 2 × N digital-to-analog converters (DACs) and N IQ mixers. The expenses and complicate mixer calibration have become a hinderance for intermediate-scale quantum control. Here we propose a universal control scheme for superconducting circuits, fully based on parametric modulation. To control N qubits on a chip, our scheme only requires N DACs and no IQ mixer, which significantly reduces the expenses. One key idea in the control scheme is to introduce a global pump signal for single-qubit gates. We theoretically explain how the universal gates are constructed using parametric modulation. The fidelity analysis shows that parametric single-qubit (two-qubit) gates in the proposed scheme can achieve low error rates of 10 −4 , with a gate time of about 60 ns (100 ns).
On-line detection device for high temperature forgings based on laser triangulation

Abstract This paper takes the online detection equipment of high temperature forgings in the forging environment as the research goal, and uses laser triangulation to perform 3D point cloud imaging of high temperature forgings, and then conducts online detection of the defects, temperature, position, size and other parameters of the forgings. Real-time information feedback of measurement results, and timely adjustment of forging equipment and improvement of production process routes based on the feedback results, thereby effectively improving forging accuracy and forging efficiency, and reducing the work intensity of workers.
The Empirical Evaluation of Semantic Alignment Quality Metrics for Vehicle Domain Component Frameworks Interface Ontologies

Semantic alignment of application software components’ ontologies represents a great interest in vehicle application domains that manipulate heterogeneous overlapping knowledge application frameworks. In the past few years, with the growth in the novel vehicle service requirements such as autonomous driving, V2X (Vehicle-to-Vehicle communication) and many others, automotive application software component models are becoming increasingly collaborative with other qualified cross-enterprise industrial partners to accomplish these complex service requirements. The most daunting impediment to this cross-enterprise collaboration is semantic interoperability. For efficient services collaboration through cross-enterprise semantic interoperability between the vehicle application frameworks’ software components, aligning the interface ontologies of these components by identifying the depth of semantic alignment relationships between the concepts of the interface ontologies is the major focus of this paper. In contrast to several existing ontology structural metrics, this work defines, evaluates and validates ontology metrics to measure the depth of semantic alignment between the vehicle domain software component frameworks’ interface ontological models. To emphasize the substantial role of semantic alignment of software component frameworks’ interface ontologies in semantic interoperability, a typical vehicle domain case study involving vehicle applications is considered for demonstration.
Computer Guts and Swallowed Sensors: Ingestibles Made Palatable in an Era of Embodied Computing

Picture the following three scenarios. You have an elderly uncle named Buck. Given his advanced age, on rare occasions, Uncle Buck forgets to take his prescribed dosages of phenytoin and azathioprine, both lifesaving medications. Stephanie, your best friend's niece, has spent the last two weeks in pain and requires risky surgery to dislodge a foreign object that has become stuck somewhere in her gastrointestinal tract. Once a week, you worry about an increased heartrate, shortness of breath, and begin to wonder whether you should slow down your run. A company, Visceral Data, begins producing smart pills to assist in each of the above three cases. Currently, numerous partners and organizations in academia, government, and the private sector are in fact building ingestible computational technologies that would feature prominently in each of the above scenarios, including the policy frameworks that govern them.1 As former Alphabet executive chair Eric Schmidt once stated, “You will—voluntarily, I might add—take a pill, which you think of as a pill but is in fact a microscopic robot, which will monitor your systems” and share information about what is happening in your body (Bilton 2013).
Brave New World? Bitcoin is not the New Gold: Understanding Cryptocurrency Price Dynamics

While the many commonalities shared by Bitcoin and gold raise a question of whether Bitcoin is a safe-haven like gold, relevant empirical evidence to date is mixed. Unlike existing empirical studies, we derive a simple estimable model of Bitcoin price dynamics from the quantity equation, which allows for structural interpretation of our findings; we then estimate the dynamic effects of macro factors, including income, inflation, and interest rates on Bitcoin prices at a weekly frequency. Unlike gold, Bitcoin prices are vulnerable to financial risk or uncertainty shocks, which is inconsistent with safe-haven quality. When the empirical model is augmented with Bitcoin-specific variables, such as its supply, transactions, and velocity, a major share of Bitcoin price dynamics is explained by these variables. We also find an interesting nonlinearity in the drivers of Bitcoin price dynamics between bullish and bearish market: the role of Bitcoin-idiosyncratic shocks increases when it appreciates, while the effects of macro factors dominate when it depreciates.
Abstractive text summarization for Hungarian

In our research we have created a text summarization software tool for Hungarian using multilingual and Hungarian BERT-based models.Two types of text summarization method exist: abstractive and extractive.The abstractive summarization is more similar to human generated summarization.Target summaries may include phrases that the original text does not necessarily contain.This method generates the summarized text by applying keywords that were extracted from the original text.The extractive method summarizes the text by using the most important extracted phrases or sentences from the original text.In our research we have built both abstractive and extractive models for Hungarian.For abstractive models, we have used a multilingual BERT model and Hungarian monolingual BERT models.For extractive summarization, in addition to the BERT models, we have also made experiments with ELECTRA models.We find that the Hungarian monolingual models outperformed the multilingual BERT model in all cases.Furthermore, the ELECTRA small models achieved higher results than some of the BERT models.This result is important because the ELECTRA small models have much fewer parameters and were trained on only 1 GPU within a couple of days.Another important consideration is that the ELECTRA
Spatial‐tree filter for cost aggregation in stereo matching

The desired solution to many labelling problems in computer vision is a spatially smooth result where label changes are aligned with the edges of the guidance image. It can be obtained traditionally by smoothing the label costs using edge-aware filters. However, local filters incorporate the information in a local support region to obtain locally-optimal and non-local tree-based filters, which often overuse piece-wise constant assumptions. In this paper, we propose a spatial-tree filter for cost aggregation. The tree model incorporates the spatial affinity into the tree structure. The tree distance between two pixels on our spatial tree is an approximated geodesic distance, which acts as a pixel similarity metric. The filtering process was implemented by recursively techniques in four directions: Top-to-bottom, left-to-right, and vice-versa. Thus, the complexity of our approach is linear to the number of image pixels. Extensive experiments demonstrate the effectiveness and efficiency of our spatial-tree filter in image smoothing and stereo matching. Our method performs better than the existing tree-based non-local method in cost aggregation.
Automated Assessment of Quality of Jupyter Notebooks Using Artificial Intelligence and Big Code

We present in this paper an automated method to assess the quality of Jupyter notebooks. The quality of notebooks is assessed in terms of reproducibility and executability. Specifically, we automatically extract a number of expert-defined features for each notebook, perform a feature selection step, and then trained supervised binary classifiers to predict whether a notebook is reproducible and executable, respectively. We also experimented with semantic code embeddings to capture the notebooks' semantics. We have evaluated these methods on a dataset of 306,539 notebooks and achieved an F1 score of 0.87 for reproducibility and 0.96 for executability (using expert-defined features) and an F1 score of 0.81 for reproducibility and 0.78 for executability (using code embeddings). Our results suggest that semantic code embeddings can be used to determine with good performance the reproducibility and executability of Jupyter notebooks, and since they can be automatically derived, they have the advantage of no need for expert involvement to define features.
The Effectiveness of Learning Models and Achievement Motivation for Students' English Reading Comprehension in a State University in Medan

This study aims to determine: the effectiveness of learning models and achievement motivation for English reading comprehension. This research was conducted on PGSD FIP UNIMED students at the Jln. William Iskandar Pasar V Medan Estate. The instrument used in this study was a test and observation. Samples were taken from two classes totalling 78 students. The research method was using quasi experiment with 2x2 factorial design, data analysis technique was using Anava 2 paths. The results of the study are as follows: 1) English reading comprehension of students who are given a PQ4R learning model is higher than the SQ3R learning model; 2) There is a significant influence between reading comprehension of students who have low and high achievement motivation. 3) There is no interaction between PQ4R and SQ3R Models and Motivation having achievements in understanding students' English texts.
Text Encryption Based on Huffman Coding and ElGamal Cryptosystem

Background: Data that are in the form of text, audio, image, and video are used everywhere in our modern scientific world. These data are stored in physical storage, cloud storage and other storage devices. Some of these data are very sensitive and require efficient security while storing as well as in transmitting from the sender to the receiver. Objective: With the increase in data transfer operation, enough space is also required to store these data. Many researchers have been working to develop different encryption schemes, yet there exist many limitations in their works. There is always a need for encryption schemes with smaller cipher data, faster execution time and low computation cost. Methods: A text encryption based on Huffman coding and ElGamal cryptosystem is proposed. Initially, the text data is converted into its corresponding binary bits using Huffman coding. Next, the binary bits are grouped and again converted into large integer values which will be used as the input for the ElGamal cryptosystem. Results: Encryption and Decryption are successfully performed where the data size is reduced using Huffman coding and advance security with the smaller key size is provided by the ElGamal cryptosystem. Conclusion: Simulation results and performance analysis specify that our encryption algorithm is better than the existing algorithms under consideration.
Pengujian Usability untuk Mengetahui Kepuasan Pengguna pada Website Perpustakaan Institut Teknologi Telkom Purwokerto

The coronavirus pandemic makes human activities, in particular work, change. At first, when working face-to-face for discussions or other supporting activities, now everyone requires using an online virtual system that can be accessed from anywhere to prevent the spread of the coronavirus. Website-based information systems are no stranger to society because most have accessed information through certain websites. Institut Teknologi Telkom Purwokerto created a web-based application to support book lending activities or libraries' activities. This website requires testing to determine the level of user satisfaction in accessing the website, whether it is according to user expectations or not. One of the testing techniques to assess the feasibility of a website from user responses is usability testing. We created a questionnaire to look at several aspects of the test: learnability, flexibility, effectiveness, and attitude. The test results from 12 respondents showed that the ITTP library website usability test reached 79.45%, which means that this website is suitable for use.
Defending and Detecting Audio Adversarial Example using Frame Offsets

Machine learning models are vulnerable to adversarial examples generated by adding a deliberately designed perturbation to a benign sample.Particularly, for automatic speech recognition (ASR) system, a benign audio which sounds normal could be decoded as a harmful command due to potential adversarial attacks.In this paper, we focus on the countermeasures against audio adversarial examples.By analyzing the characteristics of ASR systems, we find that frame offsets with silence clip appended at the beginning of an audio can degenerate adversarial perturbations to normal noise.For various scenarios, we exploit frame offsets by different strategies such as defending, detecting and hybrid strategy.Compared with the previous methods, our proposed method can defense audio adversarial example in a simpler, more generic and efficient way.Evaluated on three state-of-the-arts adversarial attacks against different ASR systems respectively, the experimental results demonstrate that the proposed method can effectively improve the robustness of ASR systems.