Reviewerly/exHarmony huggingface dataset:
Base de Dados Referencial de Artigos de Periódicos em Ciência da Informação (Brapci)

The objective of this study is to develop a methodology for incorporating models of Functional Requirements for Authority Records (FRBR) and the Semantic Web (WS) into an experimental basis in the Reference Database of Information Science Journals (Brapci). The study design is characterized by a descriptive and methodological research, consisting of two aspects, which can be deployed after the institutionalization and expansion of the knowledge necessary for future applications of the project. The first aspect is necessary for the consolidation of a theoretical framework, appropriating the concepts of WS and FRBR to conceive the empirical model to be developed. The second strand, concerning the practical application, embodied in the modeling of the prototype based on semantic data structures, in order to support the obtaining of the methodology that integrates existing bibliographic repertoires. With these characteristics, it is believed that better ways of presenting the collections to users will be provided and the possibility of creating “inferences” that contribute to the refinement of the queries. The results obtained in the research will help in the construction of new knowledge of the FRBR model centered on user experiences. The application of the FRBR model in Brapci will enable a better understanding of the model, as well as contribute to the qualification and expansion of the quality of Brapci's indexing and authority tools, expanding the user's experience and satisfaction in the search and retrieval of information. Mainly with the possibility of exporting RDF data to support Semantic Web studies and inferences from Brazilian scientific production. Being a project a laboratory for the engagement of undergraduate and postgraduate students, as well as possible agreements with other national and Latin American educational and research institutions.
$γ$-ABC: Outlier-Robust Approximate Bayesian Computation Based on a Robust Divergence Estimator

Approximate Bayesian computation (ABC) is a likelihood-free inference method that has been employed in various applications. However, ABC can be sensitive to outliers if a data discrepancy measure is chosen inappropriately. In this paper, we propose to use a nearest-neighbor-based $γ$-divergence estimator as a data discrepancy measure. We show that our estimator possesses a suitable theoretical robustness property called the redescending property. In addition, our estimator enjoys various desirable properties such as high flexibility, asymptotic unbiasedness, almost sure convergence, and linear-time computational complexity. Through experiments, we demonstrate that our method achieves significantly higher robustness than existing discrepancy measures.
A hybrid statistical approach for texture images classification based on scale invariant features and mixture gamma distribution

Image classification refers to an important process in computer vision.The purpose of this paper is to propose a novel approach named GGD-GMM and based on statistical modeling in the wavelet domain to describe textured images and rely on a number of principles that give its internal coherence and originality.Firstly, we propose a robust algorithm based on the combination of the wavelet transform and Scale Invariant Feature Transform.Secondly, we implement the aforementioned algorithm and fit the result using the finite mixture gamma distribution (GMM).The results, obtained for two benchmark datasets show that the proposed algorithm has a good relevance as it provides higher classification accuracy than some other well-known models (Kohavi, 1995).Moreover, it shows other advantages relied upon Noise-resistant and rotation invariant.
GraphFormers: GNN-nested Language Models for Linked Text Representation.

Linked text representation is critical for many intelligent web applications, such as online advertisement and recommender systems. Recent breakthroughs on pretrained language models and graph neural networks facilitate the development of corresponding techniques. However, the existing works mainly rely on cascaded model structures: the texts are independently encoded by language models at first, and the textual embeddings are further aggregated by graph neural networks. We argue that the neighbourhood information is insufficiently utilized within the above process, which restricts the representation quality. In this work, we propose GraphFormers, where graph neural networks are nested alongside each transformer layer of the language models. On top of the above architecture, the linked texts will iteratively extract neighbourhood information for the enhancement of their own semantics. Such an iterative workflow gives rise to more effective utilization of neighbourhood information, which contributes to the representation quality. We further introduce an adaptation called unidirectional GraphFormers, which is much more efficient and comparably effective; and we leverage a pretraining strategy called the neighbourhood-aware masked language modeling to enhance the training effect. We perform extensive experiment studies with three large-scale linked text datasets, whose results verify the effectiveness of our proposed methods.
THE CORRELATION BETWEEN STUDENTS’ ANXIETY AND SPEAKING SKILL AT STKIP KUSUMA NEGARA JAKARTA (A survey research first semester academic year 2018/2019)

&lt;p&gt;The purpose of this research is to know whether there is any correlation between students’anxiety and speaking skill at STKIP Kusuma Negara Jakarta, first semester academic year 2018/2019.&lt;/p&gt;&lt;p&gt;This research used quantitative method and the design was correlational research. The sample of this research consists of 25 students. The data of the students’anxiety were collected by using the questionaire which was adopted from foreign Language Classroom Anxiety Scale Horwitz et.at FLCAS, while the data of students’speaking skill were collected by using speaking test. In this research, researcher has two variables, independent variable which variable students’anxiety and dependent variable consists of students’ speaking skill.&lt;/p&gt;&lt;p&gt;The researcher use SPSS program, before doing the analysis the data normality was tested by using Kolmogorf-Smirnov test to see if the population was distributed normal. The result of this research concluded that there is significant correlation between variable (X) students’anxiety and variable (Y) speaking skill. Sig. (2-tailed) is 0.001 &amp;lt; 0.05. It means that students who have higher speaking skill, they was influenced by anxiety. The value of correlation coefficient is -0.629 &amp;lt;0.5. It means that there is negative correlation between students’anxiety and speaking skill. If the students’anxiety is high so speaking skill is low and if the students’anxiety is low so speaking skill is high.&lt;/p&gt;
Recent Development of Theory and Application on Homomorphic Encryption

With the popularization of various cloud services such as cloud computing and cloud storage, privacy preservation issues in the cloud environment have gradually become the focus of industrial applications. Homomorphic encryption has become an important method to solve this issue. Among them, how to construct an efficient fully homomorphic encryption scheme is one of the hotspots at present. Firstly, the development of homomorphic encryption is introduced. The homomorphic encryption schemes are analyzed and classified from different perspectives. The research progress of verifiable fully homomorphic encryption schemes is discussed in detail. By analyzing the property rights literature on homomorphic encryption that has been published in recent years, the progress in the theoretical research and application about homomorphic encryption are summarized. Secondly, the working performances of three typical homomorphic encryption libraries, Helib, SEAL and TFHE, are compared and analyzed. Finally, various application scenarios of homomorphic encryption technology are sorted out, and possible research and development directions in the future are proposed.
State-level Racially Motivated Hate Crimes Contrast Public Opinion on the #StopAsianHate and #StopAAPIHate Movement

#StopAsianHate and #StopAAPIHate are two of the most commonly used hashtags that represent the current movement to end hate crimes against the Asian American and Pacific Islander community. We conduct a social media study of public opinion on the #StopAsianHate and #StopAAPIHate movement based on 46,058 Twitter users across 30 states in the United States ranging from March 18 to April 11, 2021. The movement attracts more participation from women, younger adults, Asian and Black communities. 51.56% of the Twitter users show direct support, 18.38% are news about anti-Asian hate crimes, while 5.43% show a negative attitude towards the movement. Public opinion varies across user characteristics. Furthermore, among the states with most racial bias motivated hate crimes, the negative attitude towards the #StopAsianHate and #StopAAPIHate movement is the weakest. To our best knowledge, this is the first large-scale social media-based study to understand public opinion on the #StopAsianHate and #StopAAPIHate movement. We hope our study can provide insights and promote research on anti-Asian hate crimes, and ultimately help address such a serious societal issue for the common benefits of all communities.
QuNet: Cost vector analysis & multi-path entanglement routing in quantum networks

Entanglement distribution will form the backbone of many future distributed quantum technologies, especially the quantum internet. The act of purifying multiple noisy entangled states into a single one of higher quality has no analogue in classical networking and as such, this transforms the way in which we will consider future algorithms for routing entanglement. We outline the differences that arise because of this, demonstrate some elementary formalisms for `multi-path entanglement routing', and discuss the philosophical differences that arise when comparing this regime to conventional digital network theory. We also present a software package, QuNet, that uses novel `quantum cost-vector analysis' to simulate and benchmark routing in multi-user entanglement networks in a way that is is highly scalable in network size and the number of competing users. Our software accommodates both ground- and space-based networks, and implements efficient multi-user time-optimisation for mitigating congestion when quantum memories are available.
Target Robust Discriminant Analysis

In practice, the data distribution at test time often differs, to a smaller or larger extent, from that of the original training data. Consequentially, the so-called source classifier, trained on the available labelled data, deteriorates on the test, or target, data. Domain adaptive classifiers aim to combat this problem, but typically assume some particular form of domain shift. Most are not robust to violations of domain shift assumptions and may even perform worse than their non-adaptive counterparts. We construct robust parameter estimators for discriminant analysis that guarantee performance improvements of the adaptive classifier over the non-adaptive source classifier.
The Sample Complexity of Distribution-Free Parity Learning in the Robust Shuffle Model

We provide a lowerbound on the sample complexity of distribution-free parity learning in the realizable case in the shuffle model of differential privacy. Namely, we show that the sample complexity of learning $d$-bit parity functions is $\Omega(2^{d/2})$. Our result extends a recent similar lowerbound on the sample complexity of private agnostic learning of parity functions in the shuffle model by Cheu and Ullman. We also sketch a simple shuffle model protocol demonstrating that our results are tight up to $poly(d)$ factors.
Sistem Informasi Eksekutif Pelelangan dengan Metode Iconix Process Pada PT. Pelindo I Berbasis Web

The advancement of information technology has had a tremendous impact in assisting executives in making decisions. The executive information system is one of the best methods to help the leader or executive to make decisions effectively and efficiently. Not all work and procurement of goods is carried out by employees or staff of PT. Pelindo I, however, was carried out through an auction process. So far, the data collection process has been done using Microsoft Excel. Many weaknesses that occur, namely the data is not realtime and the leader cannot see the information directly and the data displayed cannot help the leader make decisions quickly. Therefore, an Executive Information System is needed so that the leadership can see directly the status of the existing auction at PT. Pelindo I. The purpose of this research is to design and build an executive information system at PT. Pelindo I with the iconix process method to display the status of the auction. The stages in designing an application using the Iconix Process method consist of nine stages, namely the process of requirements analysis, preparation of a domain model, GUI storyboard, use case diagrams, robustness diagrams, sequence diagrams, domain updates model, class diagram, implementation. The result of this research is to produce an application that displays a report on the status of the existing auction at PT. Pelindo in 2019 which is more efficient by using the iconix process method. The report that is displayed is in the form of data on contract completion, still in process, failed and returned.
GAP: Quantifying the Generative Adversarial Set and Class Feature Applicability of Deep Neural Networks

Recent work in deep neural networks has sought to characterize the nature in which a network learns features and how applicable learnt features are to various problem sets. Deep neural network applicability can be split into three sub-problems; set applicability, class applicability, and instance applicability. In this work we seek to quantify the applicability of features learned during adversarial training, focusing specifically on set and class applicability. We apply techniques for measuring applicability to both generators and discriminators trained on various data sets to quantify applicability.
An Efficient Method of Image Encryption Using Rossler Chaotic System

In this study, a new approach of image encryption has been proposed. This method is depends on the symmetric encryption algorithm RC4 and Rossler chaotic system. Firstly, the encryption key is employed to ciphering a plain image using RC4 and obtains a ciphered-image. Then, the same key is used to generate the initial conditions of the Rossler system. The system parameters and the initial conditions are used as the inputs for Rossler chaotic system to generate the 2-dimensional array of random values. The resulted array is XORed with the ciphered-image to obtain the final encrypted-image. Based on the experimental results, the proposed method has achieved high security and less computation time. Also, the proposed method can be resisted attacks like (statistical, brute-force, and differential).
Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets

Data is the engine of modern computer vision, which necessitates collecting large-scale datasets. This is expensive, and guaranteeing the quality of the labels is a major challenge. In this paper, we investigate efficient annotation strategies for collecting multi-class classification labels for a large collection of images. While methods that exploit learnt models for labeling exist, a surprisingly prevalent approach is to query humans for a fixed number of labels per datum and aggregate them, which is expensive. Building on prior work on online joint probabilistic modeling of human annotations and machine-generated beliefs, we propose modifications and best practices aimed at minimizing human labeling effort. Specifically, we make use of advances in self-supervised learning, view annotation as a semi-supervised learning problem, identify and mitigate pitfalls and ablate several key design choices to propose effective guidelines for labeling. Our analysis is done in a more realistic simulation that involves querying human labelers, which uncovers issues with evaluation using existing worker simulation methods. Simulated experiments on a 125k image subset of the ImageNet100 show that it can be annotated to 80% top-1 accuracy with 0.35 annotations per image on average, a 2.7x and 6.7x improvement over prior work and manual annotation, respectively. Project page: https://fidler-lab.github.io/efficient-annotation-cookbook
Semi-On-Policy Training for Sample Efficient Multi-Agent Policy Gradients

Policy gradient methods are an attractive approach to multi-agent reinforcement learning problems due to their convergence properties and robustness in partially observable scenarios. However, there is a significant performance gap between state-of-the-art policy gradient and value-based methods on the popular StarCraft Multi-Agent Challenge (SMAC) benchmark. In this paper, we introduce semi-on-policy (SOP) training as an effective and computationally efficient way to address the sample inefficiency of on-policy policy gradient methods. We enhance two state-of-the-art policy gradient algorithms with SOP training, demonstrating significant performance improvements. Furthermore, we show that our methods perform as well or better than state-of-the-art value-based methods on a variety of SMAC tasks.
Hidden Backdoors in Human-Centric Language Models

Natural language processing (NLP) systems have been proven to be vulnerable to backdoor attacks, whereby hidden features (backdoors) are trained into a language model and may only be activated by specific inputs (called triggers), to trick the model into producing unexpected behaviors. In this paper, we create covert and natural triggers for textual backdoor attacks, \textit{hidden backdoors}, where triggers can fool both modern language models and human inspection. We deploy our hidden backdoors through two state-of-the-art trigger embedding methods. The first approach via homograph replacement, embeds the trigger into deep neural networks through the visual spoofing of lookalike character replacement. The second approach uses subtle differences between text generated by language models and real natural text to produce trigger sentences with correct grammar and high fluency. We demonstrate that the proposed hidden backdoors can be effective across three downstream security-critical NLP tasks, representative of modern human-centric NLP systems, including toxic comment detection, neural machine translation (NMT), and question answering (QA). Our two hidden backdoor attacks can achieve an Attack Success Rate (ASR) of at least $97\%$ with an injection rate of only $3\%$ in toxic comment detection, $95.1\%$ ASR in NMT with less than $0.5\%$ injected data, and finally $91.12\%$ ASR against QA updated with only 27 poisoning data samples on a model previously trained with 92,024 samples (0.029\%). We are able to demonstrate the adversary's high success rate of attacks, while maintaining functionality for regular users, with triggers inconspicuous by the human administrators.
The Development Concept of Mobile Augmented Reality (MAR) as an Innovation to Improve Tourism Experience (Study Case in Denpasar City, Bali)

The evolution of technology has impacted many fields, especially in the tourism sector which has a positive trend every year in Indonesia.Enhancement of internet and mobile phone users is also one of the reasons why the sectors are digitalized, it also certainly opens opportunities for mobile app developers especially for the games category as the top global mobile apps ranked to explore tourism.Tourism has taken steps to use AR to enhance the tourism experience.Some studies also state that AR is an effective way to promote and deliver information to a broad audience compared to other technologies.However, the existing features are considered less attractive to attracting tourists and have not provided value-added more than just providing information.It has become a problem yet an opportunity to develop the concept of Mobile Augmented Reality (MAR) in the tourism sector, especially in Denpasar's city case with low tourist visits.We propose a Design Thinking theory with a Text Mining approach to explore hidden information based on the user's perspective, and descriptive methods to find new design solutions based on their needs and desires.We find that lack of information is one that causes Denpasar's case.The majority (91%) of respondents stated that they would be interested in using Tourism MAR to enhance their experience, which the features can fulfill three realms of experiences; entertainment, aesthetic, and escapism with visual information as an additional aspect.
Robustness Enhancement of Object Detection in Advanced Driver Assistance Systems (ADAS)

A unified system integrating a compact object detector and a surrounding environmental condition classifier for enhancing the robustness of object detection scheme in advanced driver assistance systems (ADAS) is proposed in this paper. ADAS are invented to improve traffic safety and effectiveness in autonomous driving systems where object detection plays an extremely important role. However, modern object detectors integrated in ADAS are still unstable due to high latency and the variation of the environmental contexts in the deployment phase. Our system is proposed to address the aforementioned problems. The proposed system includes two main components: (1) a compact one-stage object detector which is expected to be able to perform at a comparable accuracy compared to state-of-the-art object detectors, and (2) an environmental condition detector that helps to send a warning signal to the cloud in case the self-driving car needs human actions due to the significance of the situation. The empirical results prove the reliability and the scalability of the proposed system to realistic scenarios.
A product quality impacts of a mobile software product line: an empirical study

Background The software product lines (SPL) enable development teams to fully address a systematic reuse of shared assets to deliver a family of similar software products. Mobile applications are an obvious candidate for employing an SPL approach. This paper presents our research outcomes, based on empirical data from an industry-level development project. Two development teams were confronted with the same functionalities set to be delivered through a family of native mobile applications for Android and iOS. Methods Empirical data was gathered before, during and after a year of full-time development. The data demonstrate the impact of a SPL approach by comparing the SPL and non-SPL multiple edition development. One family of products (Android apps) was developed using an SPL approach, while another (iOS apps), functionally the same, was developed without employing an SPL approach. The project generated a volume of raw and aggregated empirical data to support our research questions. Results The paper reports a positive impact of an SPL approach on product quality (internal and external) and feature output per week. As data shows, it also increases the delivery of functionalities (240% in 6 more editions), while investing the same amount of effort needed for a single-edition development. As a result of system-supported separation of development and production code, developers had a high confidence in further development. On the other hand, the second team delivered less new functionalities, only two new application editions, and lower software quality than the team that manages multi-edition development by employing an SPL approach.