Reviewerly/exHarmony huggingface dataset:
Critical Literacy Approach in the teaching of literary appreciation using Indonesian short stories

This paper presents experimental results about the use of the Critical Literacy Approach (CLA) in literary appreciation using Indonesian short stories. The purpose of the study is to find better teaching strategies to improve students’ critical thinking skills and critical awareness to comprehend the conditions of the world, including social relationships involving disproportionate power relations. This study uses the randomized pretest-posttest control group design (RPPCGD), which is a randomized design by giving pretest and posttest to the experimental group (CLA) and control group (expository). The effect of both teaching strategies is calculated through the difference between the pretest and posttest of both groups. This research involved 170 students in the Department of Indonesian Language and Literature Education of an education university in Indonesia. The results are presented in the form of analytical descriptions of paired samples test and paired samples correlations of each group. The results show that students who did a short story analysis with CLA had significantly increased critical thinking skills and critical awareness compared to students of expository strategy. The average posttest of the experimental group is 80.33, which is considerably higher than the control group, with an average of 76.13. The average increase in skills (posttest-pretest) of the experimental group is 38.71 points, and the control groups have an average increase of 31.19 points. Therefore, it is clear that the teaching of literary appreciation using Indonesian short stories with CLA is effective. The use of CLA strategies in increasing students’ critical thinking skills and critical awareness through literary appreciation of Indonesian short stories shows positive results. The results of this study can contribute to the field of learning design with new ideas to improve critical thinking skills and critical awareness of Indonesian students through effective short story analysis or fictional prose analysis.
Challenges to Manage the Educational Process in the HEI during the Pandemic

The article describes the features of distance learning and key aspects of organizing the educational process in higher education institutions during the pandemic. The relevance of the material presented in this publication is due to the need for further development of technologies and creation of models of distance education in Ukraine. The authors analyzed scientific sources and, based on our own experience, describe the advantages and disadvantages of distance learning. It is shown that through distance learning students can provide quality educational services. In particular, students have the opportunity to master educational content with the help of modern information and communication technologies. It has been noted that the educational platform Moodle plays a significant role in distance education. Emphasis is placed on the fact that teachers also have the opportunity to improve their skills by using educational platforms and distance education networks. The authors outline the positives and negatives of distance learning, and their comparisons are presented in the article.
Chinese Document Classification with Bi-directional Convolutional Language Model

By setting a typeface, each character of the Chinese text can be converted to a glyph pixel matrix. We propose to conduct text classification with such glyph features using bi-directional convolution. Although the pixel embedding can be applied to all languages, it is much more convenient to be used to represent Chinese scripts due to the square shape of Chinese characters. We extract both the forward and backward n-gram features of the text via bi-directional convolutional operations and then concatenate them. A subsequent 1-dimensional max-over-time pooling is applied to the bi-directional feature maps, and then three fully connected layers are used for conducting text classification. The proposed model has a light-weight architecture that only contains a single-layer convolutional neural network. Experiments on several Chinese text classification datasets demonstrate surprisingly excellent results for the training speed and superior performance of the proposed model in comparison with traditional methods.
When are Non-Parametric Methods Robust?

A growing body of research has shown that many classifiers are susceptible to {\em{adversarial examples}} -- small strategic modifications to test inputs that lead to misclassification. In this work, we study general non-parametric methods, with a view towards understanding when they are robust to these modifications. We establish general conditions under which non-parametric methods are r-consistent -- in the sense that they converge to optimally robust and accurate classifiers in the large sample limit. Concretely, our results show that when data is well-separated, nearest neighbors and kernel classifiers are r-consistent, while histograms are not. For general data distributions, we prove that preprocessing by Adversarial Pruning (Yang et. al., 2019) -- that makes data well-separated -- followed by nearest neighbors or kernel classifiers also leads to r-consistency.
Irony detection in Twitter with imbalanced class distributions

Irony detection is a not trivial problem and can help to improve natural language processing tasks as sentiment analysis. When dealing with social media data in real scenarios, an important issue to address is data skew, i.e. the imbalance between available ironic and non-ironic samples available. In this work, the main objective is to address irony detection in Twitter considering various degrees of imbalanced distribution between classes. We rely on the emotIDM irony detection model. We evaluated it against both benchmark corpora and skewed Twitter datasets collected to simulate a realistic distribution of ironic tweets. We carry out a set of classification experiments aimed to determine the impact of class imbalance on detecting irony, and we evaluate the performance of irony detection when different scenarios are considered. We experiment with a set of classifiers applying class imbalance techniques to compensate class distribution. Our results indicate that by using such techniques, it is possible to improve the performance of irony detection in imbalanced class scenarios.
Publicly Verifiable Shared Dynamic Electronic Health Record Databases With Functional Commitment Supporting Privacy-Preserving Integrity Auditing

Electronic health record (EHR) is a system that collects patients' digital health information and shares it with other healthcare providers in the cloud. Since EHR contains a large amount of significant and sensitive information about patients, it is required that the system ensures response correctness and storage integrity. Meanwhile, with the rise of IoT, more low-performance terminals are deployed for receiving and uploading patient data to the server, which increases the computational and communication burden of the EHR systems. The verifiable database (VDB), where a user outsources his large database to a cloud server and makes queries once he needs certain data, is proposed as an efficient updatable cloud storage model for resource-constrained users. To improve efficiency, most existing VDB schemes utilize proof reuse and proof updating technique to prove correctness of the query results. However, it ignores the “real-time” of proof generation, which results in an overhead that the user has to perform extra process (e.g., auditing schemes) to check storage integrity. In this article, we propose a publicly verifiable shared updatable EHR database scheme that supports privacy-preserving and batch integrity checking with minimum user communication cost. We modify the existing functional commitment (FC) scheme for the VDB design and construct a concrete FC under the computational <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> -BDHE assumption. In addition, the use of an efficient verifier-local revocation group signature scheme makes our scheme support dynamic group member operations, and gives nice features, such as traceability and non-frameability.
Study of Supervised Learning and Unsupervised Learning

Today's artificial intelligence has become a commodity of and experimental studies, such as mathematics. Machine learning is an application of artificial intelligence that allows machines to learn from prior data. The paper explains two modes of learning, supervised learning and unsupervised learning, used in machine learning. There is a need for these learning strategies if there is a kind of calculations are undertaken. This paper engineering narrates the supervised learning and unsupervised learning from beginning. It also focuses on a variety of Supervised learning methods and unsupervised learning methods. This paper sheds light on the basic construction of these two learning strategies. Comparative studies of Supervised and unsupervised learning have also been presented in the paper and allow the researcher to look more closely at these programs to understand which might be the solution to a particular problem.
A fuzzy-PID controller in shell and tube heat exchanger simulation modeled for temperature control

&lt;span&gt;Shell and tube heat exchanger is the most generally utilized types of heat exchanger for heat transfer in many industrial purposes. Shell and tube heat exchanger comprises a set of unit. One unit includes mechanical parts and another unit consists of controlling part. Both the unit has to be modelled to ensure the efficient operation of shell and tube heat exchanger. The mechanical modelling is completely established on the type of applications. The controller modelling is independent of the kind of applications. The controller only needs the input fluid and output fluid properties such as temperature and flow rate. Hence the primary objective of the paper is to focus on the controller part for enhancing the heat exchanger performance. This paper proposes the novel fuzzy-PID controlling technique based on the multiplication operation to make the settling time and overshoot of setpoint temperature to be less to a greater extent and the results are compared with the conventional PI method with various tuning algorithms.&lt;/span&gt;
A Real-time Robust Approach for Tracking UAVs in Infrared Videos

Object tracking has been studied for decades, but most of the existing works are focused on the RGB tracking. For an infrared video, the object is often textureless, especially for far-range drone planar targets. Furthermore, motion of camera and unexpected movement of the drones make tracking more difficult, causing existing object tracking algorithms lose the targets. In this paper a robust and realtime tracking algorithm is proposed for infrared drones, in which a feature attention module and an expansion strategy for searching the target are added to the fully convolutional classifier. Experiments on the Anti-UAV infrared dataset show its robustness to the different challenges of real infrared scenes with a high efficiency.
A Very Condensed Survey and Critique of Multiagent Deep Reinforcement Learning

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has led to a dramatic increase in the number of applications and methods. Recent works have explored learning beyond single-agent scenarios and have considered multiagent learning (MAL) scenarios. Initial results report successes in complex multiagent domains, although there are several challenges to be addressed. The primary goal of this extended abstract is to provide a broad overview of current multiagent deep reinforcement learning (MDRL) literature, hopefully motivating the reader to review our 47-page JAAMAS survey article. Additionally, we complement the overview with a broader analysis: (i) We revisit previous key components, originally presented in MAL and RL, and highlight how they have been adapted to multiagent deep reinforcement learning settings. (ii) We provide general guidelines to new practitioners in the area: describing lessons learned from MDRL works, pointing to recent benchmarks, and outlining open avenues of research. (iii) We take a more critical tone raising practical challenges of MDRL.
Advances in Using Grammars with Latent Annotations for Discontinuous Parsing

We present new experiments that transfer techniques from Probabilistic Context-free Grammars with Latent Annotations (PCFG-LA) to two grammar formalisms for discontinuous parsing: linear context-free rewriting systems and hybrid grammars.In particular, Dirichlet priors during EM training, ensemble models, and a new nonterminal scheme for hybrid grammars are evaluated.We find that our grammars are more accurate than previous approaches based on discontinuous grammar formalisms and early instances of the discriminative models but inferior to recent discriminative parsers. 1
Rast: Resource-Aware Session Types with Arithmetic Refinements (System Description).

Traditional session types prescribe bidirectional communication protocols for concurrent computations, where well-typed programs are guaranteed to adhere to the protocols. Recent work has extended session types with refinements from linear arithmetic, capturing intrinsic properties of processes and data. These refinements then play a central role in describing sequential and parallel complexity bounds on session-typed programs.The Rast language and system provide an open-source implementation of session-typed concurrent programs extended with arithmetic refinements as well as ergometric and temporal types to capture work and span of program execution. Type checking relies on Cooper’s algorithm for quantifier elimination in Presburger arithmetic with a few significant optimizations, and a heuristic extension to nonlinear constraints. Rast furthermore includes a reconstruction engine so that most program constructs pertaining the layers of refinements and resources are inserted automatically. We provide a variety of examples to demonstrate the expressivity of the language.
From Multi-Agent Pathfinding to 3D Pipe Routing

The 2D Multi-Agent Path Finding (MAPF) problem aims at finding collision-free paths for a number of agents, from a set of start locations to a set of goal positions in a known 2D environment. MAPF has been studied in theoretical computer science, robotics, and artificial intelligence over several decades, due to its importance for robot navigation. It is currently experiencing significant scientific progress due to its relevance in automated warehousing (such as those operated by Amazon) and in other contemporary application areas. In this paper, we demonstrate that some recently developed MAPF algorithms apply more broadly than currently believed in the MAPF research community. In particular, we describe the 3D Pipe Routing (PR) problem, which aims at placing collision-free pipes from given start locations to given goal locations in a known 3D environment. The MAPF and PR problems are similar: a solution to a MAPF instance is a set of blocked cells in x-y-t space, while a solution to the corresponding PR instance is a set of blocked cells in x-y-z space. We show how to use this similarity to apply several recently developed MAPF algorithms to the PR problem, and discuss their performance on real-world PR instances. This opens up a new direction of industrial relevance for the MAPF research community.
Mobile Assisted Language Learning in Intensive English Course for Freshmen Year Students

Learning on twenty first century has influenced the use of technology especially as contextual learning media in the classroom. Moreover, digital generation students have already known the role of technology and have adapted it successfully. Therefore, it was very important to introduce attractive and active learning by using authentic activities and one of these activities was called Mobile Assisted Language Learning (MALL). This learning brought the world’s information only to the students’ smart phone. In contrast to traditional learning in the classroom, the students did not fully gain knowledge in the classroom. In fact, the implementation of MALL could give students’ freedom to learn in terms of time and place. The result of this research showed that students had obstacles in learning especially in terms of activities and the used of teaching media. Students needed authentic learning media that could improve their four skills.
Deepfake Forensics, an AI-synthesized Detection with Deep Convolutional Generative Adversarial Networks

Recently, artificial intelligence, deep learning and Generative Adversarial Networks (GANs) adaptabilities for deepfake detection and forensics have become an emerging field of research interest.GANs have been widely studied since it was first proposed, and many applications have been produced to generate contents such as videos and images.The application of these new technologies in many fields makes it more and more difficult to distinguish between true and fake content.This study analyzes more than hundred published papers related to the application of GANs technology in various fields to generate digital multimedia data and expounds the technologies that can be used to identify deepfakes, the benefits and threats of deepfake technology, and how to crack down deepfakes.The findings indicate that although deepfakes pose a major threat to our society, politics and commerce, a variety of means are listed to limit the production of unethical and illegal deepfakes.Finally, the study also puts forward its limitations and possible future research directions and recommendations.
Textual Adversarial Attack as Combinatorial Optimization

Adversarial attacks are carried out to reveal the vulnerability of deep neural networks. Textual adversarial attacking is challenging because text is discrete and a small perturbation can bring significant change to the original input. Word-level attacking, which can be regarded as a combinatorial optimization problem, is a well-studied class of textual attack methods. However, existing word-level attack models are far from perfect, largely because unsuitable search space reduction methods and inefficient optimization algorithms are employed. In this paper, we propose a novel attack model, which incorporates the sememe-based word substitution method and particle swarm optimization-based search algorithm to solve the two problems separately. We conduct exhaustive experiments to evaluate our attack model by attacking BiLSTM and BERT on three benchmark datasets. Experimental results demonstrate that our model consistently achieves much higher attack success rates and crafts more high-quality adversarial examples as compared to baseline methods. Also, further experiments show our model has higher transferability and can bring more robustness enhancement to victim models by adversarial training. All the code and data of this paper can be obtained on this https URL.
Personalized Standard Deviations Improve the Baseline Estimation of Collaborative Filtering Recommendation

Baseline estimation is a critical component for latent factor-based collaborative filtering (CF) recommendations to obtain baseline predictions by evaluating global deviations for both users and items from personalized ratings. Classical baseline estimation presupposes that the user’s factual rating range is the same as the system’s given rating range. However, from observations on real datasets of movie recommender systems, we found that different users have different actual rating ranges, and users can be classified into four kinds according to their personalized rating criterion, including normal, strict, lenient, and middle. We analyzed ratings’ distributions and found that the proportion of user ratings’ local standard deviation to the system’s global standard deviation is equal to that of the user’s actual rating range to the system’s rating range. We propose an improved and unified baseline estimation model based on the standard deviation’s proportion to alleviate the influence of classical baseline estimation’s limitation. We also apply the proposed baseline estimation model in existing latent factor-based CF recommendations and propose two instances. We performed experiments on full ratings of datasets by cross evaluations, including Flixster, Movielens (10 M), Movielens (latest small), FilmTrust, and MiniFilm. The results prove that the proposed baseline estimation model has better predictive accuracy than the classical model and is efficient in improving prediction performance for existing latent factor-based CF recommendations.
Hyperledger Fabric: Evaluating Endorsement Policy Strategies in Supply Chains

Hyperledger Fabric is a permissioned blockchain solution, in which network participation is controlled by predefined rules. This makes it an attractive platform for enterprise settings. In Fabric, endorsement policies are used to specify the peers that must confirm a transaction before it can be considered as valid and appended to the ledger. This work examines various implications of Fabric's endorsement policy component, for which different endorsement policy strategies (and subsequent trade-offs) are evaluated by modeling two real-world supply chain case studies. This work discusses how vulnerable endorsement policies can lead to admitting inauthentic data on the ledger. To address this issue, several approaches are proposed to balance integrity with limited disclosure of confidential information, with or without hosting a network peer directly. Furthermore, the concept of multi-tenancy in blockchain networks is introduced as a way of reducing the technological barrier in technology adoption.
Enhancing EFL Students’ Active Learning by Using ‘Formative’ on Mobile Devices

The present study was aimed at analyzing the factors that contribute to engage students in active learning through Formative on mobile devices. It also focused on English as a Foreign Language (EFL) students’ perceptions of using this tool for enhancing active learning. The participants were 82 adult on-site learners en-rolled in five different courses of an English program at a university level. In ad-dition, 5 English instructors were in charge of teaching the aforementioned stu-dents. An action-research design along with a mixed-method approach were used to analyze the data in this study. The information was gathered through diagnostic questionnaires, observations, and perceptions questionnaires. The results evi-denced that engagement, motivation, participation, and reduction in students’ anx-iety constitute the most relevant factors that contributed to involve students in ac-tive learning through the use of Formative on mobile devices. The tool was per-ceived as motivating and engaging because it allowed students to enhance active learning and get directly involved in the class activities to demonstrate their knowledge.
A Survey on Using Gaze Behaviour for Natural Language Processing

Gaze behaviour has been used as a way to gather cognitive information for a number of years. In this paper, we discuss the use of gaze behaviour in solving different tasks in natural language processing (NLP) without having to record it at test time. This is because the collection of gaze behaviour is a costly task, both in terms of time and money. Hence, in this paper, we focus on research done to alleviate the need for recording gaze behaviour at run time. We also mention different eye tracking corpora in multiple languages, which are currently available and can be used in natural language processing. We conclude our paper by discussing applications in a domain - education - and how learning gaze behaviour can help in solving the tasks of complex word identification and automatic essay grading.