Reviewerly/exHarmony huggingface dataset:
Shenanigans (Internet Takedown Edition)

Protecting one’s own reputation and livelihood—whether protecting it against lies, against opinions, or against the truth—is likely high on many people’s willing-to-lie-for lists. Making money is, too. Yet though I don’t think of myself as naive on this score, the sheer magnitude and brazenness of these schemes surprised me. My sense is that it surprised many of my colleagues. Perhaps it surprised you. And this reminder of just how common fraud can be might help keep us alert to shenanigans in many other fields as well— and might help us design systems that deal better with such risks.
Refining Network Alignment to Improve Matched Neighborhood Consistency.

Network alignment, or the task of finding meaningful node correspondences between nodes in different graphs, is an important graph mining task with many scientific and industrial applications. An important principle for network alignment is matched neighborhood consistency (MNC): nodes that are close in one graph should be matched to nodes that are close in the other graph. We theoretically demonstrate a close relationship between MNC and alignment accuracy. As many existing network alignment methods struggle to preserve topological consistency in difficult scenarios, we show how to refine their solutions by improving their MNC. Our refinement method, RefiNA, is straightforward to implement, admits scalable sparse approximation, and can be paired post hoc with any network alignment method. Extensive experiments show that RefiNA increases the accuracy of diverse unsupervised network alignment methods by up to 90%, making them robust enough to align graphs that are 5x more topologically different than were considered in prior work.
Produktivitas Pengarang Menggunakan Hukum Lotka Dengan Teknik Complete Count Pada Jurnal Kedokteran YARSI Tahun 2009-2018

Researchers research to develop science. Research results are published as a form of scientific communication. One type of scientific publication is a journal article. YARSI Medical Journal is a peer-reviewed journal in the field of medicine and health that is open to the public owned by YARSI University. The author's productivity patterns in the YARSI Medical Journal so far are unknown. Author productivity is the number of papers produced by the author on a particular subject and published in scientific journals in a certain period. This study aims to (1) Determine the author's productivity patterns using Lotka's Law with complete count techniques in YARSI Medical Journal 2009-2018; (2) Testing the suitability of the frequency of Lotka's Law with the author's frequency distribution using the complete count technique in YARSI Medical Journal 2009-2018. The research method used in this study is a quantitative method with bibliometric analysis. Data collection uses documentation method. The results of this study include (1) the pattern of author productivity with an exponent value (n) 2.874110535 and the constant (C) 0.820080499 meaning that the author contributing to one article is 82.00% of the total number of authors, it can be said that the YARSI Medical Journal Author Productivity Pattern 2009- 2018 is low or less productive. (2) Based on the K-S test the maximum deviation is 0 and the critical value is 0.0829206. Dmax is smaller than the critical value, so H0 is accepted. That is, the Author Productivity Pattern uses the Complete Count Technique in YARSI Medical Journal 2009-2018 following Lotka's Law.
Workshop Pembuatan E-Modul Interaktif Pada MGMP Sejarah Kota Lubuklinggau

Teachers in schools in general have never received training on e-learning using e-modules. Through e-learning, students not only listen to material descriptions from educators but also actively observe, perform, demonstrate, and so on. Teaching materials can be virtualized in various formats so that they are more interesting and more dynamic so that they can motivate students to go further in the learning process. One form of utilization of e-learning is to use interactive e-modules. The purpose of this service is to provide workshops so that teachers can make interactive emodules well. The method used is through 3 (three) stages, namely the preparation stage, the implementation stage and the evaluation stage. The results of this activity are that the participants consist of the achievement of the objectives of community service activities, namely: the achievement of the material delivered, and the teacher's motivation seems to increase to learn to make interactive emodules.
Tight State-Restoration Soundness in the Algebraic Group Model.

Most efficient zero-knowledge arguments lack a concrete security analysis, making parameter choices and efficiency comparisons challenging. This is even more true for non-interactive versions of these systems obtained via the Fiat-Shamir transform, for which the security guarantees generically derived from the interactive protocol are often too weak, even when assuming a random oracle.
Investigating the Foreign Direct Investment, Intellectual Property Rights and Economic Growth: A Panel Study from Arabic Countries

This study examines the impact of intellectual property rights (IPR) protection and FDI inflows on economic growth. The empirical analysis is carried out using a model of simultaneous equations applied to a panel of Arab countries over the period 1970-2011. Empirical results suggest that FDI has responded positively to the level of IPR protection among Arab countries. They indicate that strengthening IPR protection also increases economic growth. FDI inflows negatively affect the growth of total factor productivity. The results of this study can be of great use to policy makers.
Effective Push Tamper Restoration and Pop Tamper Detection of Pixel Set in CCTV Images Using Image Processing Techniques

Image tampering detection and restoration is a tedious process due to its combinational complexities with exact calculation and prediction analysis towards the implementation of restoring the originality. The recent technologies in image manipulation tools support with advanced features and enhancements for higher level tampered image creation. Effective restoration of images represents the detection of tampered portions and the maximum effort for getting back the original pixels from its neighborhood. The anti tampering image processing tools are also developed in an effective way for the well established recovering of images in forensic image sciences. This paper proposed efficient restoration of pixel set in tampered CCTV images using image processing techniques. In future this paper will be extended with artificial intelligence techniques of implementation through machine learning to attain an accurate restoration of tampered CCTV image footage.
NetzDG and the German Precedent for Authoritarian Creep and Authoritarian Learning

In our digital age, how do governments both combat hate speech and terrorism, while continuing to protect civil liberties? And who makes the rules – platforms themselves? National governments? International institutions? When it comes to establishing policies to protect people on and off-line, many open questions remain. Governments have largely outsourced content moderation, platform policing, and digital policy to the private sector, often leaving the bulk of the work to the platforms themselves. Against this backdrop, governments have traditionally used two main tools to shape policy: threats of regulation and voluntary compliance programs. However, increasingly frustrated with the ineffectiveness of voluntary commitments, governments are taking a larger role in both defining hate and violent speech online, and clarifying the role that platforms must play.In 2017, after a series of right-wing extremist attacks linked to online hate speech, Germany passed the “Network Enforcement Act” (NetzDG), with the aim of improving the enforcement of law in social networks. NetzDG requires social media platforms with more than two million users to establish user-friendly complaint-reporting mechanisms, remove manifestly unlawful conduct within 24 hours, and deliver transparency reports twice a year. Importantly, NetzDG forces platforms to proactively report hate speech to law enforcement without a court order under penalty of fine. It is the first example of a European state using an aggressive approach to social media regulation, by making content moderation laws compulsory, rather than voluntary. This paper examines NetzDG's global proliferation. It argues that NetzDG’s reproduction in various hybrid and authoritarian regimes is doubly problematic––it is both an indication of authoritarian creep into democratic regimes and an instance of authoritarian learning from democratic regimes. Part One describes NetzDG’s history and specificities. Part Two provides background on the challenges of content moderation. It discusses the big questions that inform the debate about NetzDG specifically and digital regulation more broadly. Part Three explores the global repercussions. First, it describes NetzDG as an example of democratic backsliding in Germany. It then uses the case studies of Russia, Singapore, and Turkey to demonstrate how authoritarian and hybrid regimes justify illiberal actions by invoking the practices of liberal democracies. Throughout, this paper underscores the dangers of a one-size-fits-all digital governance regime, urging states to consider the global and long-term effects of their policies
A Study on Emerging Strategic Performance Management Trends for a Competitive Advantage among IT Employees in Post Covid'19

Due to the COVID-19 pandemic, the economic condition of the country has faced a great downfall and millions of employees have lost their jobs. However the IT sector initiative “ Work from Home” is a saviour for many companies which would have otherwise shut down in this crisis. It is becoming the “New Normal” now. The study aims to bring out various emerging strategic performance management trends in post COVID-19 situation. In this study both primary and secondary data have been used. Primary data was collected from 55 respondents. Statistical tools are used for analyzing the data such as Chi-square test, One-way Anova and frequency. This paper puts light on the various strategies which can be followed by the employers to improve the work performance of the employees during pandemic situations.
SMILE: Set Membership from Ideal Lattices with Applications to Ring Signatures and Confidential Transactions.

In a set membership proof, the public information consists of a set of elements and a commitment. The prover then produces a zero-knowledge proof showing that the commitment is indeed to some element from the set. This primitive is closely related to concepts like ring signatures and “one-out-of-many” proofs that underlie many anonymity and privacy protocols. The main result of this work is a new succinct lattice-based set membership proof whose size is logarithmic in the size of the set.
The Influence of Pre-service Early Childhood Teachers' Educational Service on Teacher-Child Interaction : Focusing on the mediator effect of teacher efficacy

The purpose of this study is to identify the appropriate time of educational service and to verify the effect of educational service on teacher efficacy and teacher-child interaction. For this purpose, the following were investigated : 1. difference of teacher efficacy and teacher-child Interaction by educational service time, 2. relationship between educational service, teaching efficacy and teacher-child interaction and 3. mediation effect of teacher efficacy in educational service and teacher-infant interaction. The subjects of this study were 272 students in 3rd grade at 4-year university in Seoul and Kyonggi–do. Through questionnaires, the educational service, teacher efficacy, and teacher-child interaction were investigated. And The data were analyzed statistically. The results of the study are as follows. First, teachers efficacy and teacher-child interaction were significantly different according to the hours of service. Second, there was a statistically significant relationship between educational service hours, teacher efficacy, and teacher-child interaction. Third, teacher efficacy has a partial mediating effect between the educational service hours and the teacher-child interaction. The results of this study are meaningful in that it provides basic data on educational service programs for pre-service early childhood teachers.
2D object detection and semantic segmentation in the Carla simulator

The subject of self-driving car technology has drawn growing interest in recent years. Many companies, such as Baidu and Tesla, have already introduced automatic driving techniques in their newest ...
AutoPunct: Sistema de Puntuación y Mayusculización Automático basado en BERT para Castellano y Euskera

espanolLa salida en bruto de un sistema de Reconocimiento Automatico del Habla generalmente consiste en una secuencia de palabras sin mayusculas ni signos de puntuacion. Para mejorar la legibilidad y posibilitar posteriores usos de esta salida es necesario incluir la puntuacion y las mayusculas. En este contexto, presentamos AutoPunct, un modelo para puntuacion y mayusculizacion basado en arquitecturas de Transformers que combina tanto informacion acustica (silencios) como lexica (palabras). Hemos comparado su desempeno con un sistema basado en redes neuronales recursivas bidireccionales (BRNN) en euskera (un idioma de pocos recursos) y castellano, asi como combinando ambos idiomas. El resultado es un sistema que obtiene buenos resultados aplicando mayusculizacion y puntuacion de manera simultanea en dos idiomas diferentes, con una velocidad de proceso que alcanza varios miles de palabras por segundo en una GPU estandar. Englishhe raw output of an Automatic Speech Recognition system usually consists in a stream of words without any casing nor punctuation. In order to improve the readability and enable further uses of this output, punctuation and capitalisation have to be included. In this context, we present AutoPunct, a Transformers-based automatic punctuation and capitalisation model that combines both acoustic (i.e. silences duration) and lexical information (the words themselves). We compared its performance with a system based on Bidirectional Recurrent Neural Networks (BRNN) on Basque (a low-resource language) and Spanish, both individually and simultaneously. The result is a system that achieves high accuracy for punctuation and capitalisation in both languages at the same time, with a throughput of several thousand words per second using a standard GPU.
Priming PCA with EigenGame.

We introduce primed-PCA (pPCA), an extension of the recently proposed EigenGame algorithm for computing principal components in a large-scale setup. Our algorithm first runs EigenGame to get an approximation of the principal components, and then applies an exact PCA in the subspace they span. Since this subspace is of small dimension in any practical use of EigenGame, this second step is extremely cheap computationally. Nonetheless, it improves accuracy significantly for a given computational budget across datasets. In this setup, the purpose of EigenGame is to narrow down the search space, and prepare the data for the second step, an exact calculation. We show formally that pPCA improves upon EigenGame under very mild conditions, and we provide experimental validation on both synthetic and real large-scale datasets showing that it systematically translates to improved performance. In our experiments we achieve improvements in convergence speed by factors of 5-25 on the datasets of the original EigenGame paper.
Noise-Response Analysis of Deep Neural Networks Quantifies Robustness and Fingerprints Structural Malware.

The ubiquity of deep neural networks (DNNs), cloud-based training, and transfer learning is giving rise to a new cybersecurity frontier in which unsecure DNNs have `structural malware' (i.e., compromised weights and activation pathways). In particular, DNNs can be designed to have backdoors that allow an adversary to easily and reliably fool an image classifier by adding a pattern of pixels called a trigger. It is generally difficult to detect backdoors, and existing detection methods are computationally expensive and require extensive resources (e.g., access to the training data). Here, we propose a rapid feature-generation technique that quantifies the robustness of a DNN, `fingerprints' its nonlinearity, and allows us to detect backdoors (if present). Our approach involves studying how a DNN responds to noise-infused images with varying noise intensity, which we summarize with titration curves. We find that DNNs with backdoors are more sensitive to input noise and respond in a characteristic way that reveals the backdoor and where it leads (its `target'). Our empirical results demonstrate that we can accurately detect backdoors with high confidence orders-of-magnitude faster than existing approaches (seconds versus hours).
Bubble Testing: An In-Depth Study on Digital Currency MONERO

The formation of a Bubble happens because current owners tend to make irrational decisions believing that they can resell that asset at even higher prices. The time period in consideration is very short and also collapses in a matter of an almost equal or even lesser period of time. But the area of concern is that such repetitive shocks cause the stock markets or commodity markets to break down in avalanches. This study is conducted to verify such a bubble formed on MONERO from August 2016 to September 2017due to a Darknet. Three variants of the Right tailed Augmented Dickey-Fuller tests (ADF, SADF, and RADF) are used to arrive at the conclusion.
Search efficiency in parallel search is not impacted (or only minimally so) by background complexity.

In efficient visual search tasks, response times increase logarithmically as a function of set size. This reflects an evidence accumulation process. The accumulation rate is indexed by the slope of this function. The less similar are the target and the item, the faster is the accumulation rate, the flatter is the slope. Here, we examined whether background complexity impacts search efficiency in parallel search in real-world scenes. In Experiment 1, a relatively unstructured background was studied. Observers searched for a green turtle presented against one of three unstructured backgrounds which varied in complexity: a solid-colored background (lowest complexity), a beach, and a phase-scrambled beach background (highest complexity). For half the participants, the distractors were yellow turtles (high target-distractor similarity). For the other half, the distractors were black turtles (low target-distractor similarity). Although background did not affect accumulation rates in the low-similarity condition, in the high-similarity condition, a significant but small reduction in accumulation rates was observed as background complexity increased. The magnitude of this effect on the log slope was relatively small (an increase of about 12.7%). In Experiment 2, a more structured background scene (a bedroom) was studied. Observers searched for a teddy bear presented against one of three backgrounds: the bedroom, a phase-scrambled version of the bedroom, and an upside-down version of the bedroom. For half the participants, the distractors were red dolls (high-similarity). For the other half, the distractors were white reindeers (low-similarity). In this experiment, the search slopes did not vary as a function of background. Overall, we concluded that background complexity does not have a meaningful effect on search efficiency, and when effects are observed, they tend to be small in magnitude.
Applying generalizability theory in language testing: Comparing nested and crossed scoring designs in the assessment of speaking skills: Comparing nested and crossed scoring designs in the assessment of speaking skills

Scoring language learners’ speaking skills is open to a number of measurement errors since raters’ personal judgements could involve in the process. Different grading designs in which raters score a student’s whole speaking skills or a specific dimension of the speaking performance could be settled to control and minimize the amount of the error in grading foreign language speaking skills. Therefore, the present study aims to compare G and Phi coefficients gained from the scores of a full factorial (fully crossed) model versus a nested model where rubric components are nested in graders. Four experienced raters and 116 intermediate level language learners studying at a Turkish state university’s language school voluntarily participated in this exploratory study. Findings revealed that the G and Phi values obtained with a full factorial grading model were higher. In addition, checking the variance components according to the source of variation, the variance associated with the student's main effect of the full factorial pattern was higher, while the variance value of the residual effect was lower. These findings reveal that full factorial designs could generate more reliable results in speaking exams, thus, it is recommended for language schools to implement the full factorial design in speaking exams when practical conditions such as enough time or sufficient number of graders are available.
Avalanche Analysis of Variant Polynomials for AES

Advanced Encryption Standard (AES) is the most commonly used symmetric cipher nowadays. An AEScryptosystem chooses a specific irreducible polynomial to compute its values. All computations of the cryptosystem are basedon this chosen polynomial. The selection of the polynomial creates the interesting opportunity to see how the cryptosystemperforms when the polynomial is replaced by other irreducible polynomials. The objective of this paper is to examine theimpact on the avalanche level when utilizing other polynomials to develop AES cryptosystems. To implement those AEScryptosystems easily, MATLAB® codes were written for creating 30 different AES cryptosystems. Then, the avalancheanalysis was conducted on all the cryptosystems created. The results of the analysis show that choosing different polynomialsas a component of the AES cryptosystems has a minor impact on the avalanche level.
Exploring Conditional Language Model Based Data Augmentation Approaches for Hate Speech Classification

Deep Neural Network (DNN) based classifiers have gained increased attention in hate speech classification. However, the performance of DNN classifiers increases with quantity of available training data and in reality, hate speech datasets consist of only a small amount of labeled data. To counter this, Data Augmentation (DA) techniques are often used to increase the number of labeled samples and therefore, improve the classifier’s performance. In this article, we explore augmentation of training samples using a conditional language model. Our approach uses a single class conditioned Generative Pre-Trained Transformer-2 (GPT-2) language model for DA, avoiding the need for multiple class specific GPT-2 models. We study the effect of increasing the quantity of the augmented data and show that adding a few hundred samples significantly improves the classifier’s performance. Furthermore, we evaluate the effect of filtering the generated data used for DA. Our approach demonstrates up to 7.3% and up to 25.0% of relative improvements in macro-averaged F1 on two widely used hate speech corpora.
BISMF Algorithm based on Probability Matrix Decomposition Model

In order to alleviate the problem of data sparsity and cold start, and improve the prediction accuracy, in this paper, we combine the user’s social network and scoring records, based on the existing work, based on the existing Bayesian probabilistic matrix factorization model, a Bayesian improved social matrix factorization algorithm based on probabilistic matrix factorization model is proposed. The experimental results show that the prediction accuracy of our method is higher than the existing methods.