Reviewerly/exHarmony huggingface dataset:
Ameliorating Accuracy Using Dual Dimensionality Reduction on a Classification Data set

In this era of globally interconnected networks, we have huge amounts of data. The data is increasing not in gigabytes or terabytes but in terms of trillions of zettabytes and yottabytes. This data can be used in the field of machine learning and artificial intelligence so as to get intelligent machines that can have virtual brains to take and execute decisions. In practice, machine learning is used to train our machines with a specific training set of data in order to develop an efficient algorithm. Once an efficient algorithm is developed, our machine is smart enough to predict the results further. Hence, data is the source food that needs to be supplied to our algorithm and the efficiency of our algorithm is directly proportional to the amount of data that we provide to it. But not all the data is important for the study, and we need to eliminate the unnecessary and redundant information out of that data. In order to do that the concept of dimensionality reduction comes into play. By using dimensionality reduction, we extract only the essential features of our data and use it for our study. We get the motivation behind this study from the fact that the dimensionality reduction techniques used commonly do not increase the accuracy significantly and the problem of overfitting was not rectified too. Hence, in this paper we have used the concept of dual dimensionality reduction to increase the efficiency even more and combat the problem of overfitting. In this paper, we have improved the accuracy of the dimensionality reduction by using the concept of dual dimensionality reduction in which we have used a combination of various other dimensionality reduction algorithms
Adversarial Collusion on the Web: State-of-the-Art and Future Directions

The growth and popularity of online media has made it the most important platform for collaboration and communication among its users. Given its tremendous growth, social reputation of an entity in online media plays an important role. This has led to users choosing artificial ways to gain social reputation by means of blackmarket services as the natural way to boost social reputation is time-consuming. We refer to such artificial ways of boosting social reputation as collusion. In this tutorial, we will comprehensively review recent developments in analyzing and detecting collusive entities on online media. First, we give an overview of the problem and motivate the need to detect these entities. Second, we survey the state-of-the-art models that range from designing feature-based methods to more complex models, such as using deep learning architectures and advanced graph concepts. Third, we detail the annotation guidelines, provide a description of tools/applications and explain the publicly available datasets. The tutorial concludes with a discussion of future trends.
Problems Encountered in Doing Research by the Grade 12 Students of Schools in San Manuel District, San Manuel, Isabela S.Y. 2018–2019

Research is a challenging endeavor. Thus, this study determined the factors and challenges that were experienced by the 178 total Grade 12 student-researchers of schools in San Manuel District, San Manuel, Isabela for the second semester in the school year 2018-2019, from Callang National High School, Sandiat National High School, and Malalinta National High School. The descriptive survey method was utilized in this study. The researcher employed a structured questionnaire which was adapted, revised, and used from the designed questionnaire by Ignacio (2017) in her study. The study showed that the income of parents really affects their children’s research activity, particularly the male student-researchers which deliberates with financial support. While the researcher’s preparedness and time-management seemed to be the most dominant problem encountered problem by the researchers. On the other hand, Computer Literacy and Services appeared to be the most dominant solution.
Go Beyond Plain Fine-Tuning: Improving Pretrained Models for Social Commonsense

Pretrained language models have demonstrated outstanding performance in many NLP tasks recently. However, their social intelligence, which requires commonsense reasoning about the current situation and mental states of others, is still developing. Towards improving language models' social intelligence, in this study we focus on the Social IQA dataset, a task requiring social and emotional commonsense reasoning. Building on top of the pretrained RoBERTa and GPT2 models, we propose several architecture variations and extensions, as well as leveraging external commonsense corpora, to optimize the model for Social IQA. Our proposed system achieves competitive results as those top-ranking models on the leaderboard. This work demonstrates the strengths of pretrained language models, and provides viable ways to improve their performance for a particular task.
Detection of Forgery in the JPEG Images Using Forward Quantization Noise Method

In this work, the presence of forgery was detected using the forward quantization noise method. Required threshold to achieve maximum sensitivity, specificity and precision was derived for JPEG images. Seam carving dataset with a quality factor of 75% was used in order to demonstrate the method. The threshold was varied from 0.005 to 0.0005 and the corresponding maximum sensitivity, specificity and precision were estimated. It has been demonstrated that a threshold of 0.0005 yields that highest maximum sensitivity, specificity and precision.
HILPS: Human-in-Loop Policy Search for Mobile Robot Navigation

Reinforcement learning has obtained increasing attention in mobile robot mapless navigation in recent years. However, there are still some obvious challenges including the sample efficiency, safety due to dilemma of exploration and exploitation. These problems are addressed in this paper by proposing the Human-in-Loop Policy Search (HILPS) framework, where learning from demonstration, learning from human intervention and Near Optimal Policy strategies are integrated together. Firstly, the former two make sure that expert experience grant mobile robot a more informative and correct decision for accomplishing the task and also maintaining the safety of the mobile robot due to the priority of human control. Then the Near Optimal Policy (NOP) provides a way to selectively store the similar experience with respect to the preexisting human demonstration, in which case the sample efficiency can be improved by eliminating exclusively exploratory behaviors. To verify the performance of the algorithm, the mobile robot navigation experiments are extensively conducted in simulation and real world. Results show that HILPS can improve sample efficiency and safety in comparison to state-of-art reinforcement learning.
Scoring Vulnerabilities After Seeing a Chained Vulnerability Demonstration

The general problem was the NIST SP 800-40r3 (Souppaya &amp; Scarfone, 2013) or the CVSS (FIRST, 2018a) did not provide enough information to prioritize vulnerability remediation. The specific problem was CVSS severity rankings were specific to individual vulnerabilities, which limited organizations to remediate vulnerabilities based on the potential downstream impact to other systems (Franklin, Wergin, &amp; Booth, 2014). The purpose of this quantitative study was to use a pre-test / pro-test experiment to compare how cybersecurity professionals in the USMC rate vulnerabilities before and after seeing examples of vulnerability chaining using the CVSS calculator. The research question was, what score would cybersecurity professionals in the USMC give individual vulnerabilities before and after seeing vulnerabilities used in combination to create a more severe cyberattack? The research method used a quasi-experimental method with a pre-test / post-test design to identify how vulnerabilities would be scored before and after seeing a chained vulnerability demonstration. The results of the vulnerability scores were compared between the control and treatment groups, as well as the CVSS scores provided in versions 2.0 and 3.0 for each vulnerability. Participants from the control group changed two vulnerabilities from a Medium score to a High score; CSRF (from 7.5 to 9.0) and XSS (8.3 to 9.0). The treatment group did not change any vulnerability scores in a statistically significant manner, but the researcher found this was due to the overall higher scores for each vulnerability.
Finite Time Guarantees for Continuous State MDPs with Generative Model

In this paper, we present Online Empirical Value Learning (ONEVaL), an `online' reinforcement learning algorithm for continuous MDPs that is `quasi-model-free' (needs a generative/simulation model but not the model per se) that can compute nearly-optimal policies and comes with nonasymptotic performance guarantees including prescriptions on required sample complexity for specified performance bounds. The algorithm relies on use of a `fully' randomized policy that will generate a β-mixing sample trajectory. It also relies on randomized function approximation in an RKHS for arbitrarily small function approximation error, and an `empirical' estimate of value from the next state by several samples of the next state from the generative model. We demonstrate its' good numerical performance on some benchmark problems. We note that the algorithm requires no hyper-parameter tuning, and is also robust to other concerns that seem to plague Deep RL algorithms.
Creating intelligent content with lightweight DITA

In Creating Intelligent Content with Lightweight DITA, Evia introduces readers to an open source information standard that can be used to write structured content; coordinate collaborative workflow...
Statistical Machine Translation Approach for Lexical Normalization on Indonesian Text

Lexical normalization is an important task to be performed on noisy data, such as social media posts, before using the data for further analysis. We examine the potential of Statistical Machine Translation (SMT) for normalization of Indonesian text using the translation unit on both phrase and character levels. We also used an external corpus to generate additional language model data and pre-normalization rules to enhance the SMT system. The result shows the SMT systems on both phrase and character levels are outperforming various baseline in Word Error Rate (WER) score and Bilingual Understudy Evaluation (BLEU) score. This research also demonstrates the effect of using an external language model and applying pre-normalization rules can further enhance the effectiveness of SMT systems in normalizing Indonesian text.
Design and Implementation of Software Bus Based on Object Model Operating System

Abstract At present, the commercial operating system VxWorks is the first choice of leading international intelligent equipment manufacturers. They implement their own equipment controller based on VxWorks. However, VxWorks is not specifically designed for the control field, so there are many deficiencies in the control field, such as: using a dynamic model, dynamically calling various resources at runtime, making the system less predictable and real-time. Aiming at the typical application scenarios of the object operating system, this paper gives the overall software architecture of the object bus. The designed object bus provides a transparent mechanism for communication between components. In view of the highly dynamic adjustment of the objects in the object bus, this article has designed a corresponding set of migration mechanisms for the object bus to solve the various negative effects of cache invalidation after dynamic adjustment. In the last part of the article, the functional verification of the implemented object bus is carried out, and the analysis and summary are carried out.
A Novel Multi-Knowledge Distillation Approach

Knowledge distillation approaches can transfer information from a large network (teacher network) to a small network (student network) to compress and accelerate deep neural networks. This paper proposes a novel knowledge distillation approach called multi-knowledge distillation (MKD). MKD consists of two stages. In the first stage, it employs autoencoders to learn compact and precise representations of the feature maps (FM) from the teacher network and the student network, these representations can be treated as the essential of the FM, i.e., EFM. In the second stage, MKD utilizes multiple kinds of knowledge, i.e., the magnitude of individual sample's EFM and the similarity relationships among several samples' EFM to enhance the generalization ability of the student network. Compared with previous approaches that employ FM or the handcrafted features from FM, the EFM learned from autoencoders can be transferred more efficiently and reliably. Furthermore, the rich information provided by the multiple kinds of knowledge guarantees the student network to mimic the teacher network as closely as possible. Experimental results also show that MKD is superior to the-state-of-arts.
Two-stage Unsupervised Approach for Combating Social Spammers

Spammers use Online Social Networks (OSNs) as a popular platform for spreading malicious content and links. The nature of OSNs allows the spammers to bypass the combating techniques by changing their behaviours. Classification based approaches are the most common technique for spam detection. “Data labelling” “spam drift” “imbalanced datasets” and “data fabrication” are the most common limitations of classification techniques that hinder the accuracy of spam detection. The paper presents a two-stage fully unsupervised approach using a user's peer acceptance within OSN to distinguish spammers from genuine users. User's common shared interest over multiple topics and the mentioning behaviour are used to derive the peer acceptance. The contribution of the paper is a pure unsupervised method to detect spammers based on users' peer acceptance without labelled datasets. Our unsupervised approach is able to achieve 95.9% accuracy without the need for labelling.
Semantic Rule-based Automatic Code conversion System

Most software employees are facing challenges on integrating programs written in different languages for implementing the techniques for their software development. This can be achieved by automating the conversion of programs using natural language programming techniques. This research presents a novel `Semantic Rule-based Automatic Code conversion System (SRACS)' that uses semantic layering, keyword identification, and a semantic rule-based constructor. The code snippets for `Hello World', `For Loop', `While Loop', `If else', `Factorial' and `Travelling Salesman Program' are converted from Java to Python and vice versa, and the accuracies are presented. An average accuracy of 71.57% is achieved for the conversion of the code snippets from Java to Python, and a 77.07% is achieved for Python to Java. The accuracy is based on the `accuracy in the conversion of the variables', `accuracy in the conversion of the attributes' and on the `proper indentation of the code in the target code'.
Supervised Imbalanced Multi-domain Adaptation for Text-independent Speaker Verification

Speaker verification is an important recognition task in speech signal processing. Domain adaptation for speaker verification is challenging and it is one of the practical problems put forward in the INTERSPEECH2020 Short-duration Speaker Verification (SdSV) Challenge 2020. Although there are several previous researches focused on the domain mismatch problem of the speaker verification task, many methods are not easy to show effectiveness in the real conditions. This is due to the suboptimal loss design, as well as the real-world datasets could contain multiple domains and imbalanced data in each domain. We have explored various domain adaptation methods and proposed one that is both effective and robust in this task by optimizing loss design and explicitly considering the data-imbalance problem. The proposed method is also designed to fit the scenarios where the datasets contain multiple domains. Significant single-model performance improvements have been observed by evaluating on the SdSV20 challenge testbench with our proposed method.
Explainable and Actionable Machine Learning Models for Electronic Health Record Data

State-of-the art machine learning (ML) methods show immense promise for medical applications, offering significant improvement in prediction capabilities for electronic health record (EHR) data. However, the models’ black-box nature make it difficult for an end-user to trace the decision-making process, a key challenge for the application of machine learning in healthcare. Explainable machine learning (ex-ML) methods is a complementary tool, identifying key features associated with a model’s predictions. Extending from ex-ML methods, we show how EHR data can be used to make explainable machine learning predictions, and generate insights to minimise an unfavourable healthcare outcome. Firstly, our clinical explainability step outlines how feature importance values change across the range of possible physiological variables, for a single patient and across a population. The output is a target feature value for which the feature importance values are minimised. Secondly, we show that explanations across different machine learning models are not guaranteed, and we provide a method for aggregation. Finally, we develop a set of actionable recommendations on features that can be changed, providing a target treatment order that doctors can follow, to reduce a patient’s likelihood of disease.
The development of a system for automated ontology building based on heterogeneous ontology design patterns

Abstract Currently, ontologies have become the main means of formalizing and systematizing knowledge and data in scientific subject domains (SSDs). In this regard, there is an urgent need for the methods and software tools allowing to involve specialists from different scientific subject domains in the process of ontology building. A possible way to solve this problem is to use ontology design patterns (ODPs). The paper presents an approach to the development of a system supporting the automated construction of the SSD ontologies based on ontology design patterns, which are the formal descriptions of field-proven solutions to typical ontological modeling problems. The main components of this system are repositories of heterogeneous ODPs and basic ontologies, including concepts common to most SSDs, and editors of patterns and ontologies used to build and populate ontologies.
Social Media and Changing Trends in Everyday Languages

The emergence of the internet along with the consequential array of social media networks have, undoubtedly, resulted in an exponential increase in new types of language: blogs, tweets, Facebook posts and LinkedIn profiles are just a few to mention. There’s no denying that social media has had a drastic impact on the sheer volume of people we are now able to communicate with, it’s also had an impact on the frequency with which we are able to communicate with them. This has led us being exposed to a myriad of different personalities, perspectives, and approaches when we use social media to communicate. People who use the same social network build their own community with common interests, activities, and topics for discussions. With its global reach social media has also resulted in faster communication. The need for speed and easy understanding has thus altered the common used words and phrases. Some of the more popular social media sites limit the number of characters one can use in a post. Twitter, limits the “Tweet” to 280characters, thus pushing its users to become more adept at saying what they want or need to say with fewer words resulting new trending terms. It has even introduced new terms in English. This paper tries to highlight the changes in language and communication pattern with the introduction of social media in everyday life. The acceptances as well as the impact of the new vocabulary are also the areas where this paper has tried to focus. For this purpose, the main objective of this paper is to understand the changes introduced in English Language with the advent of social media, to analyse the acceptance of the new words in modern vocabulary as well as to analyse the impact that social media has brought in our everyday terms.
ATM: Attentional Text Matting

Image matting is a fundamental computer vision problem and has many applications. Previous image matting methods always focus on extracting a general object or portrait from the background in an image. In this paper, we try to solve the text matting problem, which extracts characters (usually WordArts) from the background in an image. Different from traditional image matting problems, text matting is much harder because of its foreground's three properties: smallness, multi-objectness, and complicated structures and boundaries. We propose a two-stage attentional text matting pipeline to solve the text matting problem. In the first stage, we utilize text detection methods to serve as the attention mechanism. In the second stage, we employ the attentional text regions and matting system to obtain mattes of these text regions. Finally, we post-process the mattes and obtain the final matte of the input image. We also construct a large-scale dataset with high-quality annotations consisting of 46,289 unique foregrounds to facilitate the learning and evaluation of text matting. Extensive experiments on this dataset and real images clearly demonstrate the superiority of our proposed pipeline over previous image matting methods on the task of text matting.
Corpora in Text-Based Russian Studies

Abstract This chapter opens with a discussion about what a corpus is and proceeds with an introduction of the main types of textual resources: the Web as a corpus, electronic libraries, and linguistic corpora. Among the last-mentioned, two are of particular interest. The first is the Russian National Corpus, a deeply annotated and well-designed Russian-language resource ranging from early Old Russian chronicles up to modern internet communication. The second is Integrum, the largest resource by some margin, which covers most of the newspapers and journals published both abroad and domestically, as well as a significant amount of TV, radio, and internet media. The chapter includes two case studies that demonstrate how word choice reflects shared memory patterns and how changes in language usage reflect political and social mutations.
Gaussian approximation of Gaussian scale mixtures

For a given positive random variable V > 0 and a given Z ∼ N (0, 1) independent of V , we compute the scalar t0 such that the distance in the L 2 (R) sense between ZV 1/2 and Z √ t0 is minimal.We also consider the same problem in several dimensions when V is a random positive definite matrix.