Reviewerly/exHarmony huggingface dataset:
Multi-task learning approach for utilizing temporal relations in natural language understanding tasks

Various studies have been conducted on multi-task learning techniques in natural language understanding (NLU), which build a model capable of processing multiple tasks and providing generalized performance. Most documents written in natural languages contain time-related information. It is essential to recognize such information accurately and utilize it to understand the context and overall content of a document while performing NLU tasks. In this study, we propose a multi-task learning technique that includes a temporal relation extraction task in the training process of NLU tasks such that the trained model can utilize temporal context information from the input sentences. To utilize the characteristics of multi-task learning, an additional task that extracts temporal relations from given sentences was designed, and the multi-task model was configured to learn in combination with the existing NLU tasks on Korean and English datasets. Performance differences were analyzed by combining NLU tasks to extract temporal relations. The accuracy of the single task for temporal relation extraction is 57.8 and 45.1 for Korean and English, respectively, and improves up to 64.2 and 48.7 when combined with other NLU tasks. The experimental results confirm that extracting temporal relations can improve its performance when combined with other NLU tasks in multi-task learning, compared to dealing with it individually. Also, because of the differences in linguistic characteristics between Korean and English, there are different task combinations that positively affect extracting the temporal relations.
Improving Arabic Sentiment Analysis Using LSTM Based on Word Embedding Models

In recent times, online users freely express their sentiments in different life aspects because of the huge increase in social networks. Sentiment Analysis (SA) is one of the main Natural Language Processing (NLP) fields thanks to its important role in identifying sentiment polarities and making decisions from the public’s opinions. The Arabic language is one of the most challenging languages for SA due to its various dialects, and morphological and syntactic complexities. Deep Learning (DL) models have shown significant capabilities, especially in SA. In particular, Long Short-Term Memory (LSTM) networks have proven perfect abilities to learn sequential data. This paper proposes a comparative study result of Word2Vec and FastText word embedding models that are used to create two Arabic SA (ASA) LSTM-based approaches. The experimental results confirm that the LSTM model with FastText can significantly ameliorate the Arabic classification accuracy.
An Improved Method for Hiding Text in Image Using Header Image

The necessities of steganography methods for hiding secret message into images have been ascend. Thereby, this study is to generate a practical steganography procedure to hide text into image. This operation allows the user to provide the system with both text and cover image, and to find a resulting image that comprises the hidden text inside. The suggested technique is to hide a text inside the header formats of a digital image. Least Significant Bit (LSB) method to hide the message or text, in order to keep the features and characteristics of the original image are used. A new method is applied via using the whole image (header formats) to hide the image. From the experimental results, suggested technique that gives a higher embedding of several stages of complexity. Also, LSB method via using the whole image is to increase the security and robustness of the proposed method as compared to state-of the-art methods.
Building a decentralized industrial alliance with the information system empowered by blockchain

The traditional industrial chain consists of a group of entities that are familiar with each other, and the relationship between the entities is maintained by signing contracts. However, on the one hand, the inability to reach mutual trust between unfamiliar entities limits the collaboration of entities, and on the other hand, most of the profits in the industrial chain are grabbed by the dominant enterprises. In this paper, an information system for collaboration among business entities is developed based on blockchain, based on which a business model called Decentralized Industry Alliance (DIA) is proposed. Transactions and profit distribution in the DIA are executed by smart contracts, which enables unfamiliar entities to trust each other without relying on a third party. Any entity that voluntarily complies with the smart contracts automatically becomes a member of the DIA, thereby promoting the expansion of the industry scale. The smart contracts distribute profits according to each entity’s contribution to the alliance, so as to ensure the fairness of value distribution. A case about the alliance of elderly care industry is carried out, which shows that the DIA achieves rapid expansion on the premise of ensuring fairness. As a general business model in the web 3.0 era, the DIA can be referenced by other industries.
Analysis of the K-Means Algorithm for Clustering School Participation Rates in Central Java

One indication of the development of educational services in Indonesia is the School Enrollment Rate (SER). Higher the rate of enrolment, the better a location offers access to training. The dataset source was collected from the Central Java Statistical Agency website. The analysis object is the percentage of SERs for ages 7-12 years, 13-15 years, and 16-18 years in the Central Java region during 2017-2019. In the Central Java province, the aim of which is the third largest province after West Java and East Java, was to analyze the level of school participation as mapped. The created research product is a mapping of locations in the District and City areas in the form of clusters. The solution is the clustering algorithm k-means. In this study, there were two groups: high (C1) and low. The clusters were separated into (C2). Cluster-mapping studies results for the years 7-12 were, that in a high cluster, 24 provinces (cluster 0) and 11 provinces (cluster 1) were in a lower cluster, whereas the 13-15-year-old cluster mapping results from 23 provinces (cluster 0) and 12 provinces (cluster 1) and the 16-18-year-old cluster mapping results from 15 provinces. Final centroid value is the basis for the determination of the clusters where the final centroid value for a cluster aged 7-12 years were high (cluster 0) {99.81, 99.87, 99.75} and low (cluster 1) {99.73, 99.43, 99.25}, whereas the final centroid value of a cluster aged 13-15 years was high (cluster 0). For all age categories, the mapping findings reveal a good proportion, that is, over 50% in the top class. In particular, 24 provinces (57%) were in the low cluster of the 16-18-year age group. Research results information can provide a macro-image of the level of SER development in recent years.&#x0D; Keywords: K-Means, algorithm, clustering
Indistinguishability under adaptive chosen-ciphertext attack secure double-NTRU-based key encapsulation mechanism

In this article, we propose a double-NTRU (D-NTRU)-based key encapsulation mechanism (KEM) for the key agreement requirement of the post-quantum world. The proposed KEM is obtained by combining one-way D-NTRU encryption and Dent's KEM design method. The main contribution of this article is to construct a D-NTRU-based KEM that provides indistinguishability under adaptive chosen-ciphertext attack (IND-CCA2) security. The IND-CCA2 analysis and primal/dual attack resistance of the proposed D-NTRU KEM are examined in detail. A comparison with similar protocols is provided regarding parameters, public/secret keys, and ciphertext sizes. The proposed scheme presents arithmetic simplicity and IND-CCA2 security that does not require any padding mechanism.
Retrieval of Weighted Lexicons Based on Supervised Learning Method

Lexicons are a lexical resource that has been used successfully in sentiment analysis and other areas of natural language processing. Although there are several unweighted lexicons and weighted lexicons, they all achieve poor performance in many applications. This is because they are created in general contexts, and adding more terms to an existing lexicon is complicated. Furthermore, current methods for generating weighted lexicons are complex and not very intuitive. In this article, we show the results of a method to generate weighted lexicons from a tagged corpus. The terms that make up the lexicon, as well as the corresponding weights, are obtained by means of a distance measure that is closely related to the probability that a document belongs to its label. The preliminary results obtained with a corpus of 405 documents show that the method reaches an accuracy of 92.3%.
Securely Computing Protocol of Set Intersection under the Malicious Model

Private set intersection (PSI) is a valuable technique with various practical applications, including secure matching of communication packets in the Internet of Things. However, most of the currently available two-party PSI protocols are based on the oblivious transfer (OT) protocol, which is computationally expensive and results in significant communication overhead. In this paper, we propose a new coding method to design a two-party PSI protocol under the semi-honest model. We analyze possible malicious attacks and then develop a PSI protocol under the malicious model using the Paillier cryptosystem, cut-and-choose, zero-knowledge proof, and other cryptographic tools. By adopting the real/ideal model paradigm, we prove the protocol’s security under the malicious model, which is more efficient compared to the existing related schemes.
Text classification and gradation in Arabic textbooks

Text classification and text gradation are important for language teachers. Profiling and readability studies examine textual and linguistic features that determine text difficulty. Arabic, as an under-resourced language, suffers from a lack of such studies which results in material developers and textbook writers relying on their intuitions and experiences in selecting appropriate texts. At the classroom level, teachers often supplement or adapt the texts they use to suit their students’ developmental stages. However, they often do this based on their intuition and experience as well. This study aims to sensitise material developers and particularly teachers to a range of textual, linguistic and contextual features that affect text difficulty so, they can make informed decisions about the suitability of the texts they use and how to adapt and/or supplement them. Although this is most important for teachers of under-resourced languages such as Arabic; the study will be useful for teachers of resource-rich languages as well since research shows that even with systematically classified texts, discrepancies exist between texts and their assumed level.
Maximizing total yield in safety hazard monitoring of online reviews

Many firms face challenges in monitoring their products for evidence of safety hazards, which can have profoundly negative effects both on consumers and on firms’ financial standings. Monitoring online reviews has the potential to unveil vital safety insights, but the volume of the data poses practical challenge. A popular recent approach to safety surveillance has involved utilizing “smoke terms” customized to the safety-related language in each product category. In this work, we suggest two new methods for deriving smoke terms: piecewise smoke terms and sum of ranks smoke terms. In addition, we address the question of whether smoke terms can be trained to perform well across product categories. We find that our new smoke term methods show improvement in performance upon traditional approaches, such as sentiment analysis, for detecting mentions of safety hazards. We find that piecewise smoke terms in particular performed well across all multiple product categories. We also found that while it is difficult for a cross-category technique to match the performance of a category-specific technique, our novel set of cross-category smoke terms performed with great promise and offer substantial improvement upon sentiment analysis.
An Improved Model on the Vague Sets-Based DPoS's Voting Phase in Blockchain

As a common consensus mechanism used in Blockchain, DPoS uses voting to select the committee members to generate blocks. In order to elect the committee members more fairly, vague sets are introduced into the voting phase of DPoS. In the original vague sets-based model proposed in 2020, the voting nodes can vote yes, no or abstain. In this paper, we improve this model by introducing a new mapping from the vague set to the fuzzy set and assigning weights to each node. In our new model, we consider that the influence factor of abstention vote increases with the increase of effective votes. Moreover, several nice properties of the improved model are proved which make our model conform to the real situation. Through our experiments, the fuzzy value's distribution of our scheme is more uniform and reduce the probability of the same fuzzy value. If the ratio of non-abstention votes is higher, the influence of abstention vote will be greater, and the chance of being selected will increase. This makes the voting phase of DPoS fairer.
The plausibility transition model for sensemaking

When people make plausibility judgments about an assertion, an event, or a piece of evidence, they are gauging whether it makes sense that the event could transpire as it did. Therefore, we can treat plausibility judgments as a part of sensemaking. In this paper, we review the research literature, presenting the different ways that plausibility has been defined and measured. Then we describe the naturalistic research that allowed us to model how plausibility judgments are engaged during the sensemaking process. The model is based on an analysis of 23 cases in which people tried to make sense of complex situations. The model describes the user's attempts to construct a narrative as a state transition string, relying on plausibility judgments for each transition point. The model has implications for measurement and for training.
Interval Type 2 Fuzzy PD+G Control for Position Regulation of Manipulator Robots

This paper presents an alternative to enhance the classical PD+G Control for position regulation of robot manipulators, using interval type-2 fuzzy logic to compensate variations in the gravitational forces vector of the dynamic model of the robot. The proposed control strategy detects the variation in the gravity vector and performs a Takagi-Sugeno type fuzzy mapping using the mentioned variation as the input and a compensation gain as the output using interval type-2 fuzzy sets. Furthermore, the passivity property of the dynamic model of robot manipulators is used for the controller design by means of the direct Lyapunov's method in order to guarantee asymptotic stability. The experiments were performed using numerical simulations with a model of a 2 DOF robot manipulator, showing successful results for the position regulation of the robot joints.
An investigation of camera movements and capture techniques on optical flow for real-time rendering and presentation

Abstract New and interesting uses for portable devices include the creation and viewing of 3D models and 360-degree photos of real landscapes. To provide a 3D model and a 360-degree view of a scenario, these apps search for real-time rendering and presentation. This study examines the impact of real-time image processing on movements in camera view and the application of optical flow algorithms. The optical flows are affected by how the objects involved motion. The way the image is recorded and the camera movements affect the relative motion. As a consequence, camera motions are responsive to optical flow algorithms. To record an image, the camera may pan around, move in a straight path, or move randomly. We have captured datasets of videos produced in different contexts to better replicate real-world scenarios. Each dataset was captured in a variety of illumination situations, camera movements, and indoor and outdoor recording sites. Here, to determine the most effective optical flow algorithms for use in near real-time applications, such as augmented reality and virtual reality, we compare results based on quality and processing delay for each video frame. We conducted a comparison study to better understand how random camera motion affects real-time video processing. These methods can be used to handle a variety of real-world issues, such as object tracking, video segmentation, structure from motion, gesture tracking, and so on.
Bandit Algorithm Driven by a Classical Random Walk and a Quantum Walk

Quantum walks (QWs) have a property that classical random walks (RWs) do not possess-the coexistence of linear spreading and localization-and this property is utilized to implement various kinds of applications. This paper proposes RW- and QW-based algorithms for multi-armed-bandit (MAB) problems. We show that, under some settings, the QW-based model realizes higher performance than the corresponding RW-based one by associating the two operations that make MAB problems difficult-exploration and exploitation-with these two behaviors of QWs.
Digital Tales Media to Improve Reading Literacy for II Grade Elementary School Students

The problem occurs in the learning process, where elementary school students are less motivated to understand material in the form of stories without pictures. Therefore, the researcher conducts this study to develop digital fairy tale media that is applied to second grade of elementary school students. This research is a type of research and development (R&amp;D) or research and development according to Bord and Gall. The steps taken in this research and development, namely: research and data collection, planning, initial product development, initial field trials, revision of trial results, field trials, product enhancements that have been refined, field implementation trials, product improvement final, cementation and implementation. The research subjects were elementary school students. The product produced in this research is digital fairy tale learning media which was developed to add a variety of media. The results of the study show that the digital fairy tale learning media developed using the Bord and Gall model has been declared valid. Based on the material assessment it obtained a percentage of 75% with valid criteria, language assessment obtained a percentage of 95.45% with very valid criteria, and the media assessment obtained a percentage of 86.45% with very valid criteria. From students obtained through product trials in small groups, namely obtaining a score percentage of 90.52% with very valid criteria, and large group field trials obtaining a score percentage of 95.40% with very valid assessment criteria, and has a level of effectiveness of 93.63%.
Hidden Grid Background Oriented Schlieren in studying convective flows

Abstract In this paper, we present a new BOS (Background Oriented Schlieren), based on a hidden grid, for studying heat flows. In the setup, we record a grid-based intensity pattern whose phase map carries information about the temperature gradient. The background (undistorted) pattern is hidden in the light source. Quantitative analysis is obtained by a windowed Fourier transform approach. Some experimental results are given to demonstrate the feasibility of the technique.
A Hybrid Parallel Balanced Phasmatodea Population Evolution Algorithm and Its Application in Workshop Material Scheduling

The phasmatodea population evolution algorithm (PPE) is a recently proposed meta-heuristic algorithm based on the evolutionary characteristics of the stick insect population. The algorithm simulates the features of convergent evolution, population competition, and population growth in the evolution process of the stick insect population in nature and realizes the above process through the population competition and growth model. Since the algorithm has a slow convergence speed and falls easily into local optimality, in this paper, it is mixed with the equilibrium optimization algorithm to make it easier to avoid the local optimum. Based on the hybrid algorithm, the population is grouped and processed in parallel to accelerate the algorithm's convergence speed and achieve better convergence accuracy. On this basis, we propose the hybrid parallel balanced phasmatodea population evolution algorithm (HP_PPE), and this algorithm is compared and tested on the CEC2017, a novel benchmark function suite. The results show that the performance of HP_PPE is better than that of similar algorithms. Finally, this paper applies HP_PPE to solve the AGV workshop material scheduling problem. Experimental results show that HP_PPE can achieve better scheduling results than other algorithms.
Formal verification for event stream processing: Model checking of BeepBeep stream processing pipelines

Event stream processing (ESP) is the application of a computation to a set of input sequences of arbitrary data objects, called “events”, in order to produce other sequences of data objects. In recent years, a large number of ESP systems have been developed; however, none of them is easily amenable to a formal verification of properties on their execution. In this paper, we show how stream processing pipelines built with an existing ESP library called BeepBeep 3 can be exported as a Kripke structure for the NuXmv model checker. This makes it possible to formally verify properties on these pipelines, and opens the way to the use of such pipelines directly within a model checker as an extension of its specification language.
Distribution Network Disaster Early Warning and Production Decision Support System Based on Multisource Data

Aiming at the problems of long warning time and low warning accuracy in the traditional distribution network disaster early warning and production decision support systems, a distribution network disaster early warning and production decision support system based on multisource data is designed. The forecast information is collected through the data collector, the wind load and lightning trip rate of the line are calculated, all of the information is integrated together for multisource data fusion processing, and the distribution network disaster early warning model is constructed in accordance with the system hardware, which is designed with a data collector, gateway, man-machine interface, fault analysis module, disaster early warning module, and expert decision support module. According to the system hardware and software design, the design of a distribution network disaster early warning and production decision support system based on multisource data is realized. The simulation results show that the system has high accuracy and a short warning time.