Reviewerly/exHarmony huggingface dataset:
Sarcasm Detection in Tweets: A Feature-based Approach using Supervised Machine Learning Models

Sarcasm (i.e., the use of irony to mock or convey contempt) detection in tweets and other social media platforms is one of the problems facing the regulation and moderation of social media content.Sarcasm is difficult to detect, even for humans, due to the deliberate ambiguity in using words.Existing approaches to automatic sarcasm detection primarily rely on lexical and linguistic cues.However, these approaches have produced little or no significant improvement in terms of the accuracy of sentiment.We propose implementing a robust and efficient system to detect sarcasm to improve accuracy for sentiment analysis.In this study, four sets of features include various types of sarcasm commonly used in social media.These feature sets are used to classify tweets into sarcastic and nonsarcastic.This study reveals a sarcastic feature set with an effective supervised machine learning model, leading to better accuracy.Results show that Decision Tree (91.84%) and Random Forest (91.90%) outperform in terms of accuracy compared to other supervised machine learning algorithms for the right features selection.The paper has highlighted the suitable supervised machine learning models along with its appropriate feature set for detecting sarcasm in tweets.
L1 Identification from L2 Speech Using Neural Spectrogram Analysis

The main objective of this project is to model L1-L2 interaction and uncover discriminative speech features that can identify the L1 background of a speaker from their non-native English speech. Traditional phonetic analyses of L1-L2 interaction tend to use a pre-selected set of acoustic features. This, however, may not be sufficient to capture all traces of the L1 in the L2 speech to make an accurate classification. Deep learning has the potential to address this by exploring the space of features automatically.In this talk I report a series of classification experiments involving a deep convolutional neural network (CNN) based on spectrogram pictures. The classification problem consists of determining whether English speech samples from a large spontaneous speech corpus are spoken by a native speaker of SSBE, Japanese, Dutch, French or Polish.
THE USE OF ANIMATION VIDEO AS LEARNING MEDIA FOR YOUNG LEARNERS TO IMPROVE EFL STUDENTS' MOTIVATION IN LEARNING ENGLISH

Currently, the use of learning media has an essential role in the educational field. Students and teachers have used the implementation of media in the classroom. The performance of media in the academic field is limited to adult learners and young learners. One of the media commonly used in the learning process is the Animation Video, such as the Pow Toon Animation Video. The implementation of Animation Video in the classroom brings several perceptions toward students. This study was designed using the library research method. Therefore, this paper aims to share some theoretical concepts and pedagogical practices using Animation video towards the young learners' perceptions.
A New Distributed, Decentralized Privacy-Preserving ID Registration System

Blockchain, a distributed ledger technology, is used in various fields, including many types of critical infrastructure, such as smart grids. Recently, studies have dealt with privacy preservation in smart grids. One such study presented a privacy-preserving aggregation system that provides integrity and anonymity using blockchain technology in a smart grid. In earlier work, a Bloom filter was used for rapid authentication on the blockchain, and a key management center was used for user key management. However, due to its high dependence on the key management center, it was not able to provide a completely distributed environment, and there was a linkability problem. In addition, there was a limitation in that key revocation and update functions through the Bloom filter could not be provided. In this article, we propose a decentralized privacy-preserving ID scheme that provides a fully distributed environment to address the limitations of existing research. The proposed scheme provides unlinkability to protect the anonymity of users using a blind signature. It also provides update or revocation functionalities of the pseudonym by the counting Bloom filter or the revoked-pseudonym Bloom filter. In addition, fully distributed operation is guaranteed through the certification authority responsible for user key management. Our scheme is applicable not only to blockchain-based decentralized privacy-preserving systems such as billing systems in a smart grid or smart cities, but also to any critical infrastructure, such as banks, hospitals, and systems including e-bulletins, e-surveys, e-voting, and so on.
A deep learning method for video‐based action recognition

In this paper, a deep learning method for video-based action recognition is proposed. On the one hand, boundary compensation on the basis of a deep neural network is performed to achieve action proposal. Boundary compensation considering non-maximum suppression according to sliding window priority is applied to remove redundant windows. To accurately detect boundaries, a boundary compensation network is established with multiple networks to process different numbers of segments. On the other hand, action recognition based on the resultant action proposals is performed. To further utilise boundary compensation, three methods are introduced for key frame selection. Optical flow and RGB features are combined via a channel fusion to realise feature representation. A two-stream network with a spatiotemporal structure is adopted for action recognition. The proposed method is evaluated on three public datasets. The experimental results demonstrate that the proposed method achieves a superior performance to that of state-of-the-art methods.
STC: FoV Tracking Enabled High-Quality 16K VR Video Streaming on Mobile Platforms

The ultra-high-definition 16K Virtual Reality (VR) video is coming to ages with more ”real” virtual experience and less cybersickness. However, the huge bitrate and decoding overhead would overwhelm today’s network and mobile hardware. The widely-known Field-of-View (FoV) adaptation streaming method still has severe bitrate wastes and decoding overhead as it delivers FoV areas with grid-like static tiles. Inspired by this, we present a novel ShiftTile-traCking (STC) streaming scheme, which crops and delivers tiles by tracking FoV movement. It is equivalent to deliver an FoV planar video instead of VR videos. This would save huge bit-rate and reduce decoding complexity. We mainly entail three contributions. 1) To reduce projection distortions, a novel FoV-centric sphere projection is proposed, which projects VR videos with the center of users’ FoVs. 2) To cover diverse FoV movement trajectories with a limited number of tiles, we propose an optimal tiling algorithm by trajectory clustering. 3) To be resilient to FoV prediction errors, we propose an accuracy-sensitive streaming algorithm, which scales FoV areas by the prediction accuracy. The evaluation shows that under the same network conditions, STC improves up to 1.3dB V-PSNR, reduces up to 13.2% buffering ratio, and achieves 60% faster decoding speed (61.5 frames per second) compared with state-of-the-art solutions.
WHICH IS MORE EFFECTIVE, A MIND MAP OR A CONCEPT MAP LEARNING STRATEGY?

A mind map and a concept map learning strategy are two learning strategies that are often used to improve students’ learning achievement. This study aimed to describe the differences in learning achievement between students who studied with a mind map learning strategy and those who used the concept map learning strategy, as well as the students’ responses toward the learning strategy applied. This type of study was a quasi-experiment with a pretest-posttest control group design. The methods used in this study were a test and a questionnaire. Data of students’ learning achievement were analyzed using inferential statistics of covariance analysis, while data of students’ responses were analyzed by descriptive statistics. The results of the study indicated that there were significant differences in the learning achievement between students who used the mind map learning strategy and those who studied with the concept map learning strategy. Those who used the mind map learning strategy had better achievements than those using the concept map learning strategy. In addition, the students’ responses were more positive toward the mind map learning strategy than to the concept map learning strategy.
A network approach to expertise retrieval based on path similarity and credit allocation

Abstract With the increasing availability of online scholarly databases, publication records can be easily extracted and analysed. Researchers can promptly keep abreast of others’ scientific production and, in principle, can select new collaborators and build new research teams. A critical factor one should consider when contemplating new potential collaborations is the possibility of unambiguously defining the expertise of other researchers. While some organisations have established database systems to enable their members to manually produce a profile, maintaining such systems is time-consuming and costly. Therefore, there has been a growing interest in retrieving expertise through automated approaches. Indeed, the identification of researchers’ expertise is of great value in many applications, such as identifying qualified experts to supervise new researchers, assigning manuscripts to reviewers, and forming a qualified team. Here, we propose a network-based approach to the construction of authors’ expertise profiles. Using the MEDLINE corpus as an example, we show that our method can be applied to a number of widely used data sets and outperforms other methods traditionally used for expertise identification.
Training object detectors from few weakly-labeled and many unlabeled images

Weakly-supervised object detection attempts to limit the amount of supervision by dispensing the need for bounding boxes, but still assumes image-level labels on the entire training set. In this work, we study the problem of training an object detector from one or few images with image-level labels and a larger set of completely unlabeled images. This is an extreme case of semi-supervised learning where the labeled data are not enough to bootstrap the learning of a detector. Our solution is to train a weakly-supervised student detector model from image-level pseudo-labels generated on the unlabeled set by a teacher classifier model, bootstrapped by region-level similarities to labeled images. Building upon the recent representative weakly-supervised pipeline PCL [1], our method can use more unlabeled images to achieve performance competitive or superior to many recent weakly-supervised detection solutions. Code will be made available at https://github.com/zhaohui-yang/NSOD.
Convolutional neural networks framework for human hand gesture recognition

Recently, the recognition of human hand gestures is becoming a valuable technology for various applications like sign language recognition, virtual games and robotics control, video surveillance, and home automation. Owing to the recent development of deep learning and its excellent performance, deep learning-based hand gesture recognition systems can provide promising results. However, accurate recognition of hand gestures remains a substantial challenge that faces most of the recently existing recognition systems. In this paper, convolutional neural networks (CNN) framework with multiple layers for accurate, effective, and less complex human hand gesture recognition has been proposed. Since the images of the infrared hand gestures can provide accurate gesture information through the low illumination environment, the proposed system is tested and evaluated on a database of hand-based near-infrared which including ten gesture poses. Extensive experiments prove that the proposed system provides excellent results of accuracy, precision, sensitivity (recall), and F1-score. Furthermore, a comparison with recently existing systems is reported.
Bin2vec: learning representations of binary executable programs for security tasks

Abstract Tackling binary program analysis problems has traditionally implied manually defining rules and heuristics, a tedious and time consuming task for human analysts. In order to improve automation and scalability, we propose an alternative direction based on distributed representations of binary programs with applicability to a number of downstream tasks. We introduce Bin2vec, a new approach leveraging Graph Convolutional Networks (GCN) along with computational program graphs in order to learn a high dimensional representation of binary executable programs. We demonstrate the versatility of this approach by using our representations to solve two semantically different binary analysis tasks – functional algorithm classification and vulnerability discovery. We compare the proposed approach to our own strong baseline as well as published results, and demonstrate improvement over state-of-the-art methods for both tasks. We evaluated Bin2vec on 49191 binaries for the functional algorithm classification task, and on 30 different CWE-IDs including at least 100 CVE entries each for the vulnerability discovery task. We set a new state-of-the-art result by reducing the classification error by 40% compared to the source-code based inst2vec approach, while working on binary code. For almost every vulnerability class in our dataset, our prediction accuracy is over 80% (and over 90% in multiple classes).
Validating static warnings via testing code fragments

Static analysis is an important approach for finding bugs and vulnerabilities in software. However, inspecting and confirming static warnings are challenging and time-consuming. In this paper, we present a novel solution that automatically generates test cases based on static warnings to validate true and false positives. We designed a syntactic patching algorithm that can generate syntactically valid, semantic preserving executable code fragments from static warnings. We developed a build and testing system to automatically test code fragments using fuzzers, KLEE and Valgrind. We evaluated our techniques using 12 real-world C projects and 1955 warnings from two commercial static analysis tools. We successfully built 68.5% code fragments and generated 1003 test cases. Through automatic testing, we identified 48 true positives and 27 false positives, and 205 likely false positives. We matched 4 CVE and real-world bugs using Helium, and they are only triggered by our tool but not other baseline tools. We found that testing code fragments is scalable and useful; it can trigger bugs that testing entire programs or testing procedures failed to trigger.
Scratching the Surface of Clinical Deterioration With Deep Learning*

1 Department of Medicine, University of Wisconsin, Madison, WI 2 Department of Medicine, University of Chicago, Chicago, IL *See also p. 1312. Drs. Churpek and Edelson’s institutions received funding from the National Institute of General Medical Sciences (R01 GM123193) and the Department of Defense Technology/Therapeutic Development Award (W81XWH-21-1-0009); they received funding from a patent pending (ARCD. P0535US.P2) for risk stratification algorithms for hospitalized patients and have received research support from EarlySense (Tel Aviv, Israel). Dr. Edelson has received research support and honoraria from Philips Healthcare (Andover, MA), as well as research support from the American Heart Association (Dallas, TX) and Laerdal Medical (Stavanger, Norway). She has ownership interest in Quant HC (Chicago, IL), which is developing products for risk stratification of hospitalized patients.
The Strategic Perceptron

The classical Perceptron algorithm provides a simple and elegant procedure for learning a linear classifier. In each step, the algorithm observes the sample's position and label and updates the current predictor accordingly if it makes a mistake. However, in presence of strategic agents that desire to be classified as positive and that are able to modify their position by a limited amount, the classifier may not be able to observe the true position of agents but rather a position where the agent pretends to be. Unlike the original setting with perfect knowledge of positions, in this situation the Perceptron algorithm fails to achieve its guarantees, and we illustrate examples with the predictor oscillating between two solutions forever, making an unbounded number of mistakes even though a perfect large-margin linear classifier exists. Our main contribution is providing a modified Perceptron-style algorithm which makes a bounded number of mistakes in presence of strategic agents with both $\ell_2$ and weighted $\ell_1$ manipulation costs. In our baseline model, knowledge of the manipulation costs (i.e., the extent to which an agent may manipulate) is assumed. In our most general model, we relax this assumption and provide an algorithm which learns and refines both the classifier and its cost estimates to achieve good mistake bounds even when manipulation costs are unknown.
Efficient assessment of process fidelity

The accurate implementation of quantum gates is essential for the realization of quantum algorithms and digital quantum simulations. This accuracy may be increased on noisy hardware through the variational optimization of gates, however the experimental realization of such a protocol is impeded by the large effort required to estimate the fidelity of an implemented gate. With a hierarchy of approximations we find a faithful approximation to the quantum process fidelity that can be estimated experimentally with reduced effort. Its practical use is demonstrated with the optimization of a three-qubit quantum gate on a commercially available quantum processor.
Sentiment Analysis of Customer Comments in Banking using BERT-based Approaches

Customer comments collected by companies through various channels are useful resources for understanding customer satisfaction. The continuous increase in the amount of comments makes manual analysis infeasible. In this study, the comments of customers, written in Turkish, regarding banking services collected through NPS questionnaires were analyzed using Natural Language Processing methods. BERT-based sentiment classification models were developed and compared with traditional methods for the banking domain. The effectiveness of the methods was investigated in a low-resource setting, where (i) there is a small amount of labeled training data and (ii) there is no labeled training data in the target domain. For the first case, the results showed that BERTurk-based model performs better than the traditional models and its performance is affected less from the decrease in training data size. For the second case, training with out of domain data from Twitter was explored. In addition, zero-shot learning with XLM-Roberta, which was pertained for natural language inference, was investigated. While using out of domain data resulted in poor performance, the zero-shot learning approach achieved promising results for sentiment classification in the banking domain.
Human Evaluation of Creative NLG Systems: An Interdisciplinary Survey on Recent Papers

We survey human evaluation in papers presenting work on creative natural language generation that have been published in INLG 2020 and ICCC 2020.The most typical human evaluation method is a scaled survey, typically on a 5 point scale, while many other less common methods exist.The most commonly evaluated parameters are meaning, syntactic correctness, novelty, relevance and emotional value, among many others.Our guidelines for future evaluation include clearly defining the goal of the generative system, asking questions as concrete as possible, testing the evaluation setup, using multiple different evaluation setups, reporting the entire evaluation process and potential biases clearly, and finally analyzing the evaluation results in a more profound way than merely reporting the most typical statistics.
Energy-Efficient Adaptive Sensing Scheduling in Wireless Sensor Networks Using Fibonacci Tree Optimization Algorithm

Wireless sensor networks are appealing, largely because they do not need wired infrastructure, but it is precisely this feature that renders them energy-constrained. The duty cycle scheduling is perceived as a contributor to the energy efficiency of sensing. This paper developed a novel paradigm for modeling wireless sensor networks; in this context, an adaptive sensing scheduling strategy is proposed depending on event occurrence behavior, and the scheduling problem is framed as an optimization problem. The optimization objectives include reducing energy depletion and optimizing detection accuracy. We determine the explicit form of the objective function by numerical fitting and found that the objective function aggregated by the fitting functions is a bivariate multimodal function that favors the Fibonacci tree optimization algorithm. Then, with the optimal parameters optimized by the Fibonacci tree optimization algorithm, the scheduling scheme can be easily deployed, and it behaves consistently in the coming hours. The proposed “Fibonacci Tree Optimization Strategy” (“FTOS”) outperforms lightweight deployment-aware scheduling (LDAS), balanced-energy scheduling (BS), distributed self-spreading algorithm (DSS) and probing environment and collaborating adaptive sleeping (PECAS) in achieving the aforementioned scheduling objectives. The Fibonacci tree optimization algorithm has attained a better optimistic effect than the artificial bee colony (ABC) algorithm, differential evolution (DE) algorithm, genetic algorithm (GA) algorithm, particle swarm optimization (PSO) algorithm, and comprehensive learning particle swarm optimization (CLPSO) algorithm in multiple runs.
QED driven QAOA for network-flow optimization

We present a general framework for modifying quantum approximate optimization algorithms (QAOA) to solve constrained network flow problems. By exploiting an analogy between flow-constraints and Gauss' law for electromagnetism, we design lattice quantum electrodynamics (QED)- inspired mixing Hamiltonians that preserve flow constraints throughout the QAOA process. This results in an exponential reduction in the size of the configuration space that needs to be explored, which we show through numerical simulations, yields higher quality approximate solutions compared to the original QAOA routine. We outline a specific implementation for edge-disjoint path (EDP) problems related to traffic congestion minimization, numerically analyze the effect of initial state choice, and explore trade-offs between circuit complexity and qubit resources via a particle-vortex duality mapping. Comparing the effect of initial states reveals that starting with an ergodic (unbiased) superposition of solutions yields better performance than beginning with the mixer ground-state, suggesting a departure from the ``short-cut to adiabaticity" mechanism often used to motivate QAOA.
Mitigating Biases in Toxic Language Detection through Invariant Rationalization

Automatic detection of toxic language plays an essential role in protecting social media users, especially minority groups, from verbal abuse.However, biases toward some attributes, including gender, race, and dialect, exist in most training datasets for toxicity detection.The biases make the learned models unfair and can even exacerbate the marginalization of people.Considering that current debiasing methods for general natural language understanding tasks cannot effectively mitigate the biases in the toxicity detectors, we propose to use invariant rationalization (INVRAT), a game-theoretic framework consisting of a rationale generator and predictors, to rule out the spurious correlation of certain syntactic patterns (e.g., identity mentions, dialect) to toxicity labels.We empirically show that our method yields lower false positive rate in both lexical and dialectal attributes than previous debiasing methods.