Reviewerly/exHarmony huggingface dataset:
RF network analysis of the WEST ICRH antenna with the open-source python scikit-RF package

Scikit-rf is an open-source Python package developed for RF/Microwave engineering. The package provides a modern, object-oriented library for network analysis and calibration which is both flexible and scalable. Besides offering standard microwave network physics and operations, it is also capable of advanced operations such as interpolating between an individual set of networks or deriving network statistical properties. The package also allows direct plotting of rectangular plots, Smith Charts or automated uncertainty bounds. In this paper, the scikit-rf package is used to simulate the WEST ICRH antennas. The antenna is modelled by connecting the various elements that compose it, separately full-wave modelled. Tunable elements, such as the matching capacitors, can be either created from ideal lump components or from interpolating full-wave calculations performed at various capacitance configurations. Finally, a numerical antenna model of a WEST ICRH antenna can be used offline or online by using operating-system-level virtualization services.
Improving Machine Reading Comprehension with Contextualized Commonsense Knowledge

To perform well on a machine reading comprehension (MRC) task, machine readers usually require commonsense knowledge that is not explicitly mentioned in the given documents.This paper aims to extract a new kind of structured knowledge from scripts and use it to improve MRC.We focus on scripts as they contain rich verbal and nonverbal messages, and two relevant messages originally conveyed by different modalities during a short time period may serve as arguments of a piece of commonsense knowledge as they function together in daily communications.To save human efforts to name relations, we propose to represent relations implicitly by situating such an argument pair in a context and call it contextualized knowledge.To use the extracted knowledge to improve MRC, we compare several fine-tuning strategies to use the weakly-labeled MRC data constructed based on contextualized knowledge and further design a teacher-student paradigm with multiple teachers to facilitate the transfer of knowledge in weakly-labeled MRC data.Experimental results show that our paradigm outperforms other methods that use weaklylabeled data and improves a state-of-the-art baseline by 4.3% in accuracy on a Chinese multiple-choice MRC dataset C 3 , wherein most of the questions require unstated prior knowledge.We also seek to transfer the knowledge to other tasks by simply adapting the resulting student reader, yielding a 2.9% improvement in F1 on a relation extraction dataset DialogRE, demonstrating the potential usefulness of the knowledge for non-MRC tasks that require document comprehension.
Contact tracers as knowledge-makers

Contact tracing is one of the primary tools in interrupting the transmission of infectious disease, including COVID-19. Contact tracing has been shown to be a set of varied roles and contextualized practices. It is essential that contact tracer training reflect this complexity, and prepare and support contact tracers to engage in this multilayered work. However, there are potential obstacles to preparing and supporting contact tracers beyond necessary knowledge and skills, including dominant discourses which position contact tracers as mere technicians responsible for delivering information. Contact tracing involves professional decision-making, negotiation and deliberation. Contact tracers have first-hand knowledge of these roles and practices. Those who do the work of contact tracing need to be able to learn from each other and to generate knowledge. Suggestions are given for how contact tracer training may invite and structure such learning, and recognize contact tracers as knowledge-makers.The knowledge generated by contact tracers can inform future health communication and public health workforce development efforts.
Pay-per-use Sensor Data Exchange between IoT Devices by Blockchain and Smart Contract based Data and Encryption Key Management

This paper proposes a smart contract based approach enabling Internet of Things devices to exchange data in a secure and automatic way. This leads to new digital business models as pay-per-use establishing the vision of the Economy of Things. By using a blockchain there is no need for a trusted third party to secure transactions. We propose a novel use of smart contracts for assurance of data integrity, encryption key provision and payment. Thereby, a three layer architecture consisting of physical layer, on-chain layer and off-chain layer is designed. Proof of concept is based on an Ethereum Blockchain using Bosch XDK devices.
Sentiment Analysis of Tweets on Social Issues using Machine Learning Approach

Sentiment analysis plays an important role in analyzing people's opinions, emotions, and feelings with the help of Natural Language Processing and Artificial Intelligence.This paper presents five major social issues of the world like Corruption, Women violence, Poverty, Child abuse, Illiteracy that are the most critical in the world.These evils are barriers to the social development of the peoples.Tweets from the years 2006 to July 2020 are collected to analyze the sentiments of peoples related to these social issues with Machine learning tools and techniques.In this proposed study different Classification algorithms, NLP techniques like tokenization, Stemming, stop words, Word Cloud are applied for effective enhancement of dataset.Sentiments are analyzed as positive, negative, and neutral from the twitter dataset.Naïve Bayes, Support Vector Machine, Logistic Regression, Random Forest Classifier, Decision Tree, and Stochastic Gradient Descent classifier applied to build the model and also measure the performance by precision, recall, and F1 score parameters.
HART: Hardware-Assisted Kernel Module Tracing on Arm

While the usage of kernel modules has become more prevalent from mobile to IoT devices, it poses an increased threat to computer systems since the modules enjoy high privileges as the main kernel but lack the matching robustness and security. In this work, we propose HART, a modular and dynamic tracing framework enabled by the Embedded Trace Macrocell (ETM) debugging feature in Arm processors. Powered by even the minimum supports of ETM, HART can trace binary-only modules without any modification to the main kernel efficiently, and plug and play on any module at any time. Besides, HART provides convenient interfaces for users to further build tracing-based security solutions, such as the modular AddressSanitizer HASAN we demonstrated. Our evaluation shows that HART and HASAN incur the average overhead of 5% and 6% on 6 widely-used benchmarks, and HASAN detects all vulnerabilities in various types, proving their efficiency and effectiveness.
A VFDT algorithm optimization and application thereof in data stream classification

Abstract Data stream mining is a new technology for dynamically extracting feature patterns from data streams. Its core technology is data stream mining algorithms. Data stream mining algorithms are divided into clustering and classification algorithms. In data stream classification algorithms, VFDT (The Concept-adapting Very Fast Decision Tree algorithm is an effective classification decision tree algorithm. The algorithm dynamically constructs a decision tree based on the improvement of the Hoefdding tree. With the inflow of data, new branches are continuously added and outdated Branch. However, the algorithm has the problem of concept drift, and some nodes may no longer meet the Hoefdding bound, which affects the classification mining effect. This paper proposes an optimization algorithm (VFDT), which reduces the impact of concept drift and network noise by adding sliding window technology and fuzzy technology to the VFDT classification mining algorithm. Experimental results show that the algorithm can effectively improve the accuracy of stream data classification mining. Reduce classification errors.
Multi-Label Multi-Task Learning with Dynamic Task Weight Balancing

Data collected from real-world environments often contain multiple objects, scenes, and activities. In comparison to single-label problems, where each data sample only defines one concept, multi-label problems allow the co-existence of multiple concepts. To exploit the rich semantic information in real-world data, multi-label classification has seen many applications in a variety of domains. The traditional approaches to multi-label problems tend to have the side effects of increased memory usage, slow model inference speed, and most importantly the under-utilization of the dependency across concepts. In this paper, we adopt multi-task learning to address these challenges. Multi-task learning treats the learning of each concept as a separate job, while at the same time leverages the shared representations among all tasks. We also propose a dynamic task balancing method to automatically adjust the task weight distribution by taking both sample-level and task-level learning complexities into consideration. Our framework is evaluated on a disaster video dataset and the performance is compared with several state-of-the-art multi-label and multi-task learning techniques. The results demonstrate the effectiveness and supremacy of our approach.
UAV Path Planning Based on Shuffled Frog-Leaping Algorithm and Dubins Path

In this paper, a novel path planning algorithm for the fixed-wing UAV based on shuffled frog-leaping algorithm(SFLA) and Dubins path is proposed. First, the Dubins path planning is transformed into a discrete combinatorial optimization problem. Then the SFLA is used to search the optimal Dubins path, so that the generated flight path directly satisfies the minimum turning radius constraint of UAV. In order to perfectly combine the SFLA and the Dubins path, a novel coding method and the worst frog update strategy are presented. Finally, the simulation results prove that the algorithm is effective for path planning of single UAV and multiple UAVs.
Class-imbalanced Domain Adaptation: An Empirical Odyssey

Unsupervised domain adaptation is a promising way to generalize deep models to novel domains. However, the current literature assumes that the label distribution is domain-invariant and only aligns the feature distributions or vice versa. In this work, we explore the more realistic task of Class-imbalanced Domain Adaptation: How to align feature distributions across domains while the label distributions of the two domains are also different? Taking a practical step towards this problem, we constructed the first benchmark with 22 cross-domain tasks from 6real-image datasets. We conducted comprehensive experiments on 10 recent domain adaptation methods and find most of them are very fragile in the face of coexisting feature and label distribution shift. Towards a better solution, we further proposed a feature and label distribution CO-ALignment (COAL) model with a novel combination of existing ideas. COAL is empirically shown to outperform the most recent domain adaptation methods on our benchmarks. We believe the provided benchmarks, empirical analysis results, and the COAL baseline could stimulate and facilitate future research towards this important problem.
Extending the Basic Local Independence Model to Polytomous Data

Abstract A probabilistic framework for the polytomous extension of knowledge space theory (KST) is proposed. It consists in a probabilistic model, called polytomous local independence model , that is developed as a generalization of the basic local independence model. The algorithms for computing “maximum likelihood” (ML) and “minimum discrepancy” (MD) estimates of the model parameters have been derived and tested in a simulation study. Results show that the algorithms differ in their capability of recovering the true parameter values. The ML algorithm correctly recovers the true values, regardless of the manipulated variables. This is not totally true for the MD algorithm. Finally, the model has been applied to a real polytomous data set collected in the area of psychological assessment. Results show that it can be successfully applied in practice, paving the way to a number of applications of KST outside the area of knowledge and learning assessment.
Modeling and estimating the subjects’ diversity of opinions in video quality assessment: a neural network based approach

Abstract Subjective experiments are considered the most reliable way to assess the perceived visual quality. However, observers’ opinions are characterized by large diversity: in fact, even the same observer is often not able to exactly repeat his first opinion when rating again a given stimulus. This makes the Mean Opinion Score (MOS) alone, in many cases, not sufficient to get accurate information about the perceived visual quality. To this aim, it is important to have a measure characterizing to what extent the observed or predicted MOS value is reliable and stable. For instance, the Standard deviation of the Opinions of the Subjects (SOS) could be considered as a measure of reliability when evaluating the quality subjectively. However, we are not aware of the existence of models or algorithms that allow to objectively predict how much diversity would be observed in subjects’ opinions in terms of SOS. In this work we observe, on the basis of a statistical analysis made on several subjective experiments, that the disagreement between the quality as measured by means of different objective video quality metrics (VQMs) can provide information on the diversity of the observers’ ratings on a given processed video sequence (PVS). In light of this observation we: i) propose and validate a model for the SOS observed in a subjective experiment; ii) design and train Neural Networks (NNs) that predict the average diversity that would be observed among the subjects’ ratings for a PVS starting from a set of VQMs values computed on such a PVS; iii) give insights into how the same NN based approach can be used to identify potential anomalies in the data collected in subjective experiments.
STYLOMETRIC COMPARISON OF PROFESSIONALLY GHOST-WRITTEN AND STUDENT-WRITTEN ASSIGNMENTS

We report a stylometric investigation of a portfolio of 20 assignments submitted by an individual student over two consecutive academic years.This investigation followed a formal disciplinary investigation which had identified that eight of the assignments had been ghostwritten, with seven of those showing explicit ghost-writer ID information and three of those showing ID information from the same commercial provider.The stylometric investigation involved a conventional word and bigram frequency analysis and a prototype word complexity analysis.The word and bigram analysis identified four consistent groups of assignments, which associate other assignments with the eight known to have been ghost-written, indicating that those were probably also ghost-written.One of those groups comprises the three assignments from the same provider, plus another assignment, implying that the provider has a 'house style' and that the other assignment also came from that provider.The prototype analysis clearly categorised the core members of two of those same groups, including the group from the identified provider, adding further weight those associations.More generally, this investigation shows that it is possible to categorise assignments according to aspects of writing style: we would have obtained the same groups even if we had not possessed the ghost-writer ID information.Where such consistent groups are identified it implies, on balance of probabilities, multiple authorship of assignments and that the student concerned cannot have written all the submitted assignments and that some were ghost-written.
Text Complexity Classification Based on Linguistic Information: Application to Intelligent Tutoring of ESL

The goal of this work is to build a classifier that can identify text complexity within the context of teaching reading to English as a Second Language (ESL) learners. To present language learners with texts that are suitable to their level of English, a set of features that can describe the phonological, morphological, lexical, syntactic, discursive, and psychological complexity of a given text were identified. Using a corpus of 6171 texts, which had already been classified into three different levels of difficulty by ESL experts, different experiments were conducted with five machine learning algorithms. The results showed that the adopted linguistic features provide a good overall classification performance (F-Score = 0.97). A scalability evaluation was conducted to test if such a classifier could be used within real applications, where it can be, for example, plugged into a search engine or a web-scraping module. In this evaluation, the texts in the test set are not only different from those from the training set but also of different types (ESL texts vs. children reading texts). Although the overall performance of the classifier decreased significantly (F-Score = 0.65), the confusion matrix shows that most of the classification errors are between the classes two and three (the middle-level classes) and that the system has a robust performance in categorizing texts of class one and four. This behavior can be explained by the difference in classification criteria between the two corpora. Hence, the observed results confirm the usability of such a classifier within a real-world application.
Identification of Abusive Sinhala Comments in Social Media using Text Mining and Machine Learning Techniques

With the technology revolution, most of the natural languages that are used all over the world have won the digital world. Therefore, people use modern technologies such as Social Media and the Internet with their native languages. As a result, people who are with self-ego on their tradition, race, caste, religion and other social factors, tend to make abusiveness on others who do not belong to the same social group by their native languages. Since the Social Media platforms do not have centralized control, it has become a good platform to advertise their backward ideas without being governed and monitored. The Sinhala language has also been added to most famous Social Media platforms. Though the Sinhala language has more than 2500 years of history, it does not have rich resources for computer-based natural language processing. Therefore, it has been a very difficult task to automatically detect Sinhala abusive comments which are being published and shared among Social Media platforms. Therefore, here, we have used evenly distributed 2000 Sinhala comment corpus among offensive and neutral classes to train three different models: Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM) and, Random Forest Decision Tree (RFDT) and the features were extracted from Bag of Word model, word n-gram model, character n-gram model, and word skip-gram model to automatically detect Sinhala abusive comments. After the training process, each model was tested with 200 evenly distributed comment corpus and MNB showed the highest accuracy of 96.5% with 96% average recall for both character tri-gram and character four-gram models. Further, two lexicon-based approaches called cross-lingual lexicon approach and corpus-based lexicon approach were considered to detect Sinhala abusive comments. From these two lexicon based approaches, the corpus-based lexicon gave the highest accuracy of 90.5% with an average recall of 90.5%.
Pairwise meta-analysis of aggregate data using metaan in Stata

A few years ago, we developed metaan, a package to perform fixedor random-effects meta-analysis. In terms of random-effects meta-analysis, it offered a wide choice of models, including maximum likelihood, profile likelihood, or restricted maximum-likelihood, in addition to the established DerSimonian–Laird method available in metan or Cochrane’s RevMan software. Other unique features included a wide range of reported heterogeneity measures and a plot of the maximum likelihood function. Since then, metaan has been continuously updated to offer improved graphics, more options, and more meta-analysis models. In this necessary update, we describe these additions and discuss the new models and the evidence behind them.
Exploring wearable technology for supporting couples in long-distance relationships

Fulfilling one of the most crucial human needs -relatedness -is regarded primarily as a positive experience that technology can create to make a relationship flourish (Hassenzahl, Heidecker, Eckoldt, Diefenbach, & Hillmann, 2012).There are increasingly many couples in long-distance relationships (LDRs) who cannot interact physically or talk to each other face-to-face on a daily basis.Their interaction and communication depends on different computer-mediated communication (CMC) tools, such as phone, email, instant messaging and video chat (Neustaedter & Greenberg, 2012), to mediate intimacy at a distance (Jiang & Hancock, 2013).Despite that CMC tools provide a wide variety of communication channels, evidence suggests that the current mainstream technologies are unable to support the full spectrum of communication needed in intimate relationships (Hassenzahl et al., 2012).Commonly used CMC tools, e.g.text messaging and email, are not designed to support
A Comparison of GA Crossover and Mutation Methods for the Traveling Salesman Problem

The traveling salesman problem is a very popular combinatorial optimization problem in fields such as computer science, operations research, mathematics and optimization theory. Given a list of cities and the distances between any city to another, the objective of the problem is to find the optimal permutation (tour) in the sense of minimum traveled distance when visiting each city only once before returning to the starting city. Because many real-world problems can be modeled to fit this formulation, the traveling salesman problem has applications in challenges related to planning, routing, scheduling, manufacturing, logistics, and other domains. Moreover, the traveling salesman problem serves as a benchmark problem for optimization methods and algorithms, including the genetic algorithm. In this paper, we examine various implementations of the genetic algorithm for solving two examples of the traveling salesman problem. Specifically, we compare commonly employed methods of partially mapped crossover and order crossover with an alternative encoding scheme that allows for single-point, multipoint, and uniform crossovers. In addition, we examine several mutation methods, including Twors mutation, center inverse mutation, reverse sequence mutation, and partial shuffle mutation. We empirically compare the implementations in terms of the chosen crossover and mutation methods to solve two benchmark variations of the traveling salesperson problem. The experimental results show that the genetic algorithm with order crossover and the center inverse mutation method provides the best solution for the two test cases.