Reviewerly/exHarmony huggingface dataset:
ReadOnce Transformers: Reusable Representations of Text for Transformers

We present READONCE Transformers, an approach to convert a transformer-based model into one that can build an informationcapturing, task-independent, and compressed representation of text.The resulting representation is reusable across different examples and tasks, thereby requiring a document shared across many examples or tasks to only be read once.This leads to faster training and evaluation of models.Additionally, we extend standard text-to-text transformer models to Representation+Text-to-text models, and evaluate on multiple downstream tasks: multihop QA, abstractive QA, and long-document summarization.Our one-time computed representation results in a 2x-5x speedup compared to standard text-to-text models, while the compression also allows existing language models to handle longer documents without the need for designing new pre-trained models.* The author's work was primarily done during an internship at the Allen Institute for AI.1 By "extract information",
Bayesian Network Model Averaging Classifiers by Subbagging

When applied to classification problems, Bayesian networks are often used to infer a class variable when given feature variables. Earlier reports have described that the classification accuracy of Bayesian network structures achieved by maximizing the marginal likelihood (ML) is lower than that achieved by maximizing the conditional log likelihood (CLL) of a class variable given the feature variables. Nevertheless, because ML has asymptotic consistency, the performance of Bayesian network structures achieved by maximizing ML is not necessarily worse than that achieved by maximizing CLL for large data. However, the error of learning structures by maximizing the ML becomes much larger for small sample sizes. That large error degrades the classification accuracy. As a method to resolve this shortcoming, model averaging has been proposed to marginalize the class variable posterior over all structures. However, the posterior standard error of each structure in the model averaging becomes large as the sample size becomes small; it subsequently degrades the classification accuracy. The main idea of this study is to improve the classification accuracy using subbagging, which is modified bagging using random sampling without replacement, to reduce the posterior standard error of each structure in model averaging. Moreover, to guarantee asymptotic consistency, we use the K-best method with the ML score. The experimentally obtained results demonstrate that our proposed method provides more accurate classification than earlier BNC methods and the other state-of-the-art ensemble methods do.
Application of the Relief-f Algorithm for Feature Selection in the Prediction of the Relevance Education Background with the Graduate Employment of the Universitas Sriwijaya

Career Development Center (CDC) at Universitas Sriwijaya provided a tracer study dataset for graduates. The data contained feature questions about the relevance of background education and graduate employment, namely about lectures, research projects experience, internships experience, English skill, internet knowledge, computer skill and others. the data was filled in by graduates in 2014, 2015, and 2016. Applying the Relief-f algorithm was to select the pattern features that most influence the relevance of education background and graduate employment. This study used Naive Bayes and KNN methods to measure the success rate of the Relief-f algorithm. The results of the accuracy of the data before the feature selection process for the naïve Bayes method were 73.43% and the KNN method was 66.24%, after the feature selection process the accuracy obtained in both methods increased to 74.38% for the Naive Bayes method and 72.22% for the KNN method. The best pattern features selected were 8 features: department relationship with work, the competence of education background, English skill, research projects experience, extracurricular activities, the competence of education background, internships experience, and communication skills. Based on the accuracy obtained, it was concluded that the Relief-f algorithm worked well in the feature selection and improved the accuracy.
Smart Surveillance System using Face Tracking

The face is seen as a key component of the human body, and humans utilise it to identify one another. Face detection in video refers to the process of detecting a person's face from a video sequence, while face tracking refers to the process of tracking the person's face throughout the video. Face detection and tracking has become a widely researched issue due to applications such as video surveillance systems and identifying criminal activity. However, working with videos is tough due to problems such as bad illumination, low resolution, and atypical posture, among others. It is critical to produce a fair analysis of various tracking and detection strategies in order to fulfil the goal of video tracking and detection. Closed-circuit television (CCTV) technology had a significant impact on how crimes were investigated and solved. The material used to review crime scenes was CCTV footage. CCTV systems, on the other hand, just offer footage and do not have the ability to analyse it. In this research, we propose a system that can be integrated with the CCTV footage or any other video input like webcam to detect, recognise, and track a person of interest. Our system will follow people as they move through a space and will be able to detect and recognise human faces. It enables video analytics, allowing existing cameras to be combined with a system that will recognise individuals and track their activities over time. It may be used for remote surveillance and can be integrated into video analytics software and CCTV security solutions as a component. It may be used on college campuses, in offices, and in shopping malls, among other places.
REST and GraphQL comparative analysis

The article presents a comparative analysis of the two most commonly used API web design standards - REST and GraphQL. The time and size of HTTP responses returned by applications were tested. Two applications with the same functionalities, performing CRUD operations, on data stored in the non-relational MongoDB database were used for the research. Both applications were based on NodeJS technology. The JMeter tool was used to collect and analyze the data. On the basis of the obtained results, it was found that there were no significant differences in reading the data with a small number of queries and when removing resources. With the increase in the number of queries, a clear advantage of the REST standard was observed. The advantage of GraphQL, both in response time and size, was demonstrated when retrieving specific data.
Fault-Tolerant Operation of a Quantum Error-Correction Code

Quantum error correction protects fragile quantum information by encoding it into a larger quantum system. These extra degrees of freedom enable the detection and correction of errors, but also increase the operational complexity of the encoded logical qubit. Fault-tolerant circuits contain the spread of errors while operating the logical qubit, and are essential for realizing error suppression in practice. While fault-tolerant design works in principle, it has not previously been demonstrated in an error-corrected physical system with native noise characteristics. In this work, we experimentally demonstrate fault-tolerant preparation, measurement, rotation, and stabilizer measurement of a Bacon-Shor logical qubit using 13 trapped ion qubits. When we compare these fault-tolerant protocols to non-fault tolerant protocols, we see significant reductions in the error rates of the logical primitives in the presence of noise. The result of fault-tolerant design is an average state preparation and measurement error of 0.6% and a Clifford gate error of 0.3% after error correction. Additionally, we prepare magic states with fidelities exceeding the distillation threshold, demonstrating all of the key single-qubit ingredients required for universal fault-tolerant operation. These results demonstrate that fault-tolerant circuits enable highly accurate logical primitives in current quantum systems. With improved two-qubit gates and the use of intermediate measurements, a stabilized logical qubit can be achieved.
Saying No is An Art: Contextualized Fallback Responses for Unanswerable Dialogue Queries

Despite end-to-end neural systems making significant progress in the last decade for taskoriented as well as chit-chat based dialogue systems, most dialogue systems rely on hybrid approaches which use a combination of rulebased, retrieval and generative approaches for generating a set of ranked responses.Such dialogue systems need to rely on a fallback mechanism to respond to out-of-domain or novel user queries which are not answerable within the scope of the dialogue system.While, dialogue systems today rely on static and unnatural responses like "I don't know the answer to that question" or "I'm not sure about that", we design a neural approach which generates responses which are contextually aware with the user query as well as say no to the user.Such customized responses provide paraphrasing ability and contextualization as well as improve the interaction with the user and reduce dialogue monotonicity.Our simple approach makes use of rules over dependency parses and a text-to-text transformer fine-tuned on synthetic data of question-response pairs generating highly relevant, grammatical as well as diverse questions.We perform automatic and manual evaluations to demonstrate the efficacy of the system.
Comparative Study of Effective Augmentation Method for Bangla ASR Using Convolutional Neural Network

Data scarcity is the main obstacle to get top-notch accuracy in neural network-based automatic speech recognition (ASR). To solve this problem, using data augmentation is a very familiar phenomenon nowadays. But in Bangla ASR, this technique is seldom used. This paper illustrates the benefits of data augmentation. In this work, the most effective augmentation methods have been explored for Convolutional Neural Network (CNN)-based Bangla word recognition system. A few types of augmentation methods have been implemented in this task, e.g., time stretching, background noise injection and pitch shifting. This experiment is performed on Linear Prediction Cepstral Coefficients (LPCC) and Mel Frequency Cepstral Coefficients (MFCC) feature extraction methods for comparative analysis. In LPCC extraction, slow-down augmentation is the most efficient one, whereas, in MFCC, positive pitch shifting augmentation is the hugely successful one at raising the accuracy rate. Overall, time-stretching is the most effective augmentation method that has consistently given better accuracy in both features. Contrariwise, noise injection is a less effective method in both cases. The consequence of using all these augmentation techniques is the escalation of accuracy up to 27.27%. To the best of our knowledge, this research paper is a pioneer work to inquire about the most effective augmentation method for Bangla ASR.
ON OPTIMAL RECOVERY OF FUNCTIONS FROM THE KOROBOV CLASS IN THE FRAMEWORK OF C(N)D – STATEMENT

With optimal recovery of an operator T mapping a functional class F into a normed space Y by computing units constructed from inaccurate numerical information l(N), the problems С(N)D - 1, С(N)D –2 and С(N)D –3 are successively solved. In this work, when we consider the unit operator as the operator T , the multidimensional oneperiodic Korobov class as the class F , the normalized space of functions continuous on the unit cube as the space Y , the trigonometric Fourier coefficients of the function being restored as numerical information l(N) , problems С(N)D – 2 and С(N)D –3 are solved. Namely, in the problem С(N)D – 2 the marginal error of the optimal computing unit (l¯(N),ϕ¯¯¯N) from the previously solved problem С(N)D - 1 was found, and in the problem С(N)D - 3 it was proved, that any computing unit with respect to trigonometric Fourier coefficients has a better marginal error, than a computing unit (l¯(N),ϕ¯¯¯N)
Optimization of Task Scheduling and Cloudlets Cost Scheduling Algorithms on Cloud Using Cloud Simulator

Cloud computing, one of the emerging technologies, is advantageous for its simplicity and efficiency, and recommended for a vast pool of services where it can be implemented. Being scalable and cost-effective, it is being offered by a vast number of execution points. For maximizing the efficiency of use of resources in fulfilling the requirements of the users, managing schedules is one of the major challenges in cloud computing. For scheduling the task submitted, scheduling selects the best possible resource, taking into account the static and dynamic parameters of the particular task. To optimize the efficiency of use of resources to the fullest in cloud systems, this paper represents a scheduling algorithm which evaluates the tasks in line and prepares their order of execution. The upcoming job are evaluated on the basis of the goal, which may be minimizing the cost of execution, or minimizing the time of completion. Scheduling criteria is executed on the result of evaluation which generates the order of execution of all the tasks in line. It follows greedy approach to select a suitable resource and schedule the task. This has lead to considerable improvement in utilization of resources and efficiency has been gained over sequential scheduling algorithms. Simulator has been used to implement, test, validate and verify the proposed work.
Controlling Text Edition by Changing Answers of Specific Questions

In this paper, we introduce the new task of controllable text edition, in which we take as input a long text, a question, and a target answer, and the output is a minimally modified text, so that it fits the target answer.This task is very important in many situations, such as changing some conditions, consequences, or properties in a legal document, or changing some key information of an event in a news text.This is very challenging, as it is hard to obtain a parallel corpus for training, and we need to first find all text positions that should be changed and then decide how to change them.We constructed the new dataset WIKIBIOCTE for this task based on the existing dataset WIKIBIO (originally created for table-to-text generation).We use WIKIBIOCTE for training, and manually labeled a test set for testing.We also propose novel evaluation metrics and a novel method for solving the new task.Experimental results on the test set show that our proposed method is a good fit for this novel NLP task.
OBJECT TRACKING CONTROL USING A GIMBAL MECHANISM

Abstract. This work describes a control solution for real time object tracking in images acquired for a RPAS on an object inspection environment. This, controlling a 3-axis gimbal mechanism to control a camera orientation embedded to a RPAS, using its image processed for feedback. The objective of control is to maintain the target of interest at the center of the image plane. The proposed solution uses a YOLOv3 object detection model in order to detect the target object and determine, thru rotation matrices, the new desired angles to converge the object’s position to the center of the image. To compare results of the proposed control, a linear control was tuned using a linear PI algorithm. Simulation and practice experiments successfully tracked the desired object in real time using YOLOv3 in both control approaches presented.
Power and Branding: Language Commodification of Tourist Resorts

Exploitation of language in commercial sector is inexorable. Language does not only work to articulate meaning but also to send and build image. In this sense, language has been commodified. This fact becomes more obvious in tourism industry in which language plays vital role to construct brand as well as to reach targeted profit. Based on these premises, this research aims to unravel the phenomenon of language commodification in Baturraden Tourism areas within the framework of Linguistic Landscape. The descriptive qualitative research was conducted through observation, documentation and interview. Data in the forms of photographs were gathered to be analyzed and interpreted to capture the language commodification manifested in the selected areas. The analysis shows that language commodification is materialized through the use of Indonesian, English and Javanese. English serves a dominant language in this tourism industry. Most tourist destinations prefer using English to Indonesian as national language to label their place. The issue of branding therefore becomes obvious to elevate the market and effectively attract the visitors. English, rather than functions as referential meaning, serves economic functions. Here, English is more powerful than other given languages. English offers higher prestige, modernity and promising brand to be beneficial for this industry. Then, Javanese is maintained to introduce the history or local legend as well as to bring the sense of authenticity.
Calibration Method for Central Catadioptric Camera Using Multiple Groups of Parallel Lines and Their Properties

This paper presents an approach for calibrating omnidirectional single-viewpoint sensors using the central catadioptric projection properties of parallel lines. Single-viewpoint sensors are widely used in robot navigation and driverless cars; thus, a high degree of calibration accuracy is needed. In the unit viewing sphere model of central catadioptric cameras, a line in a three-dimensional space is projected to a great circle, resulting in the projections of a group of parallel lines intersecting only at the endpoints of the diameter of the great circle. Based on this property, when there are multiple groups of parallel lines, a group of orthogonal directions can be determined by a rectangle constructed by two groups of parallel lines in different directions. When there is a single group of parallel lines in space, the diameter and tangents at their endpoints determine a group of orthogonal directions for the plane containing the great circle. The intrinsic parameters of the camera can be obtained from the orthogonal vanishing points in the central catadioptric image plane. An optimization algorithm for line image fitting based on the properties of antipodal points is proposed. The performance of the algorithm is verified using simulated setups. Our calibration method was validated though simulations and real experiments with a catadioptric camera.
24sata news article archive 1.0

The 24sata news portal consists of a portal with daily news and several smaller portals covering news from specific topics, such as automotive news, health, culinary content, and lifestyle advice. The dataset contains over 650,000 articles in Croatian from 2007 to 2019, as well as assigned tags.Description of the DatasetThe dataset consists of 11 columns and 657806 rows. Each row represents a single news article published on the 24sata news portals. Besides the 'www.24sata.hr', the biggest news portal, articles from other niche portals affiliated with 24sata are also included.Columns:'article_id' - Public id of the article on the new site. The article can be accessed by concatenating the site URL and article_id. For example, to access the article with article_id 614684, you can access it on 'www.24sata.hr/--614684'. This id is, by itself, not unique across the dataset - articles from different portals can share the same article_id.'site' - The location of the portals where the article came from. There are eight different portals covering topics of daily news, to the more focused portals about automotive technologies and trends, health and wellness, culinary trends and recipes, or lifestyle advices.'title' - The title of the news article.'lead' - Lead text, a short introduction to the content of an article. Can be empty.'content' - The content of the news article, contains the bulk of the text. Can be empty if the whole article could fit in the lead text.'tags' - Tags, zero or more, separated with a '|' character. Article tags are chosen by the author of the article.'section' - The main section of the news portal where the article was posted (does not need to be set). The most frequent section is 'Vijesti' (News).'subsection' - The subsection of the section where the article was posted (does not need to be set). Each section can have multiple subsections.'authors' - Article authors, zero or more, separated with a '|' character. The author does not need to sign the article if he chooses not to so this can be empty.'published_from' - A date when this article appeared on the portal. Journalists can write the article in advance and pick a future date and time when it will appear on the site. Due to this strategy, the 'published_from' can be much later than the 'date_created'.'date_created' - A date when this article was originally written. For all articles published before 2nd Feb 2010 the 'date_created' is set to 2nd Feb 2010 - this is the date when the portal was redesigned and the database with news articles recreated.
Business model implications of privacy-preserving technologies in data marketplaces: The case of multi-party computation.

Privacy-preserving technologies could allow data marketplaces to deliver technical assurances to companies on data privacy and control. However, how such technologies change the business model of data marketplaces is not fully understood. This paper aims to bridge this gap by focusing on multi-party computation (MPC) as a cryptographic technology that is currently being hyped. Based on interviews with privacy and security experts, we find that MPC enables data marketplaces to employ a “privacy-as-a-service” business model, which goes beyond privacy-preserving data exchange. Depending on the architecture, MPC could transform data marketplaces into data brokers or data aggregators. More complex architectures might lead to more robust security guarantees and lower trust requirements towards data marketplace operators. Furthermore, MPC enables new offerings of privacy-preserving analytics and services as new revenue sources. Our findings contribute to developing business models of privacy-preserving data marketplaces to unlock the potential of data sharing in a digitized economy.
Analysis of Voice Recognition System on Translator for Daily Use

This research paper’s aim is to analyse the usage and the implementation of voice recognition system in translator device by reviewing some of literatures in aspect of linguistic and voice recognition system. This research purpose is to understand the procedure of how the voice recognition system is implemented and what the impact of using the translator device for daily use is in a lot of field. This paper also include a review about what is the importance of multilinguistic skill, what is the problem that we usually have in multilinguistic communication usually in global environment, How the basic system that usually use in voice recognition system so the reader can learn by describing the procedure of voice recognition system,In what way the implementation of voice recognition in translator device by providing some example of the devices, and How is the development of the translation devices and the example of translator device usage in society.