Reviewerly/exHarmony huggingface dataset:
Increasing the effectiveness of educational technologies in the foreign languages learning process by linguistic students (comparative analysis of Russian, Indonesian and Egyptian experience)

The aim of the study is to investigate the effectiveness of educational technologies in the foreign language learning process by linguistic students. The focus is to compare the experiences of Russia, Indonesia, and the Egyptian language experience regarding effective of educational technologies. The role of educational technologies has gained significant importance recently; therefore, considering this aspect, the study focused on a comprehensive discussion on this aspect using previous literature and argues what has been examined previously. The rationale and significance of the study are provided, along with the brief structure of the research. Besides, the study opts for a quantitative methodology to present empirical insights into the use of educational technologies in the foreign language learning process by linguistic students in Russia, Indonesia and Egypt. To analyze and interpret the research data, a descriptive technique is used where data was collected from 90 students (30 from each country). The collected data was analyzed using statistical tools, namely SPSS, where descriptive analysis was used to determine the effectiveness of educational technologies in the foreign language learning by Russian, Indonesian, and Egyptian students. The data findings revealed that the use of educational technologies in foreign language learning facilitated the learning process for the students with the integration of various technologies ranging from computer-assisted activities and audio-visual devices to online resources and social media. The findings also revealed that the utilization of technology-assisted tools proliferated the foreign language learning, making the language learning process highly engaging and interactive, while enabling the students to have better control and autonomy over their learning, wider resource accessibility, helping improve the pronunciation, and enabling to interact with native speakers. Hence, the study concluded that technology positively contributed to the language learning experience of the students, irrespective of the location.
Towards an Intelligent Decision Support System for Aircraft Troubleshooting

Currently, troubleshooting an aircraft remains a promising area for automation and intellectualization. At the same time, existing solutions in this area in the form of electronic technical manuals do not always meet the requirements of technical personnel when searching and troubleshooting aircraft. In this regard, the development of another class of systems based on artificial intelligence methods is relevant. These systems can provide not only the search and elimination of failures and malfunctions but also self-learning by accumulating the experience. This paper proposes the basic principles of such an intelligent system, called the AirTech Assistant, designed for use by technical personnel engaged in the maintenance and repair of the power supply system of the Sukhoi Superjet (RRJ-95) aircraft. In particular, a fragment of the conceptual model of the domain is given, functional, operational, and quality requirements are formulated, architecture, as well as fundamental algorithms and a stack of implementation technologies, are defined. The main components of the designed system will be expert systems implementing case-based and rule-based reasoning. An additional study was conducted in terms of testing the formalism of event trees for knowledge base engineering.
Witnesses for Answer Sets of Logic Programs

In this article, we consider Answer Set Programming (ASP). It is a declarative problem solving paradigm that can be used to encode a problem as a logic program whose answer sets correspond to the solutions of the problem. It has been widely applied in various domains in AI and beyond. Given that answer sets are supposed to yield solutions to the original problem, the question of “why a set of atoms is an answer set” becomes important for both semantics understanding and program debugging. It has been well investigated for normal logic programs. However, for the class of disjunctive logic programs, which is a substantial extension of that of normal logic programs, this question has not been addressed much. In this article, we propose a notion of reduct for disjunctive logic programs and show how it can provide answers to the aforementioned question. First, we show that for each answer set, its reduct provides a resolution proof for each atom in it. We then further consider minimal sets of rules that will be sufficient to provide resolution proofs for sets of atoms. Such sets of rules will be called witnesses and are the focus of this article. We study complexity issues of computing various witnesses and provide algorithms for computing them. In particular, we show that the problem is tractable for normal and headcycle-free disjunctive logic programs, but intractable for general disjunctive logic programs. We also conducted some experiments and found that for many well-known ASP and SAT benchmarks, computing a minimal witness for an atom of an answer set is often feasible.
Classifying Sentiments from Movie Reviews Using Deep Neural Networks

Sentiment analysis has become crucial for the building of opinion mining systems due to the daily creation, sharing, and transfer of massive volumes of data and opinions via the Internet and other media. The sentiment analysis for movie recommendation is the main focus of this study. There are too many reviews and comments for movies to be manually processed. Therefore, to effectively process, we utilized user assessments of movies (whether they were favorable or unfavorable) to construct an overall assessment of the movie, which we then used to suggest it to other users. Even the most sophisticated review algorithms have been baffled by the enormous volume of reviews that are currently available. As a result, a strategy for extracting knowledge from the existing reviews and using it more skillfully needs to be created. This study utilized Artificial Neural Network (ANN), Convolutional Neural Network (CNN) and Long short-term memory (LSTM) to perform movie sentiment analysis on a dataset of 50k reviews from IMDB movies. Initially, the data was pre-processed using a GloVe word embedding algorithm. The testing outcomes showed that, with accuracy of 87.11% and an F1-score of 87%, LSTM beats other strategies. However, ANN recorded accuracy of 73.95%, while CNN reported accuracy of 85.17%. In addition, our models performed better than Naive Bayes (NB) and Support Vector Machine (SVM).
Entropy regularized actor-critic based multi-agent deep reinforcement learning for stochastic games

Multi-agent reinforcement learning (MARL) is an abstract framework modeling a dynamic environment that involves multiple learning and decision-making agents, each of which tries to maximize her cumulative reward. In MARL, each agent discovers a strategy alongside others and adapts her policy in response to the behavioural changes of others. A fundamental difficulty faced by MARL is that every agent is dynamically learning and changing to improve her reward, making the whole system unstable and agents’ policies difficult to converge. In this paper, we introduce the entropy regularizer into the Bellman equation and utilize Lagrange approach to optimize the entropy regularizer. We then propose a MARL algorithm based on the maximum entropy principle and the actor-critic method. This algorithm follows the policy gradient approach and uses a policy network and a value network. We call it Multi-Agent Deep Soft Policy Gradient (MADSPG). Then by using the Lagrange approach and dynamic minimax optimization, we propose the AUTO-MADSPG algorithm with an automatically adjusted entropy regularizer. These algorithms make multi-agent learning more stable while sufficient exploration is guaranteed. Finally, we also incorporate MADSPG with the recently proposed opponent modeling component into an integrated framework. This framework outperforms many state-of-the-art MARL algorithms in conventional cooperative and competitive game settings.
A hybrid optimization algorithm and its application in flight trajectory prediction

In order to improve the optimization performance of differential evolution (DE), a hybrid optimization algorithm (abbreviated as DEHHO) based on DE and Harris hawks optimization (HHO) is proposed. Firstly, the local search operator “HHO/SB” of HHO is combined with and classic mutation operator “DE/RAND” of DE to form a mutation link. Under the influence of the historical evolution state, each individual chooses a more suitable mutation operator to improve the possibility of successful evolution. Secondly, under the control of the historical evolution state, the updating of control parameters at the individual level assists the hybrid mutation operator to balance the population diversity and convergence rate during the evolution process. The performance of DEHHO is verified by a set of universal test benchmarks. On this basis, back propagation neural network (the initial parameters of which are optimized by DEHHO) is used to predict the flight trajectory, which further verifies the performance of DEHHO. Both validation results show that DEHHO outperforms other competitors under the same conditions.
Dialogue-adaptive language model pre-training from quality estimation☆

Pre-trained language models (PrLMs) have achieved great success on a wide range of natural language processing tasks by virtue of the universal language representation ability obtained by self-supervised learning on a large corpus. These models are pre-trained on standard plain texts with general language model (LM) training objectives, which would be insufficient to model dialogue-exclusive attributes like specificity and informativeness reflected in these tasks that are not explicitly captured by the pre-trained universal language representations. In this work, we propose dialogue-adaptive pre-training objectives (DAPO) derived from quality estimation to simulate dialogue-specific features, namely coherence, specificity, and informativeness. As the foundation for model pre-training, we synthesize a new dialogue corpus and build our training set with two unsupervised methods: 1) coherence-oriented context corruption, including utterance ordering, insertion, and replacement, to help the model capture the coherence inside the dialogue contexts; and 2) specificity-oriented automatic rescoring, which encourages the model to measure the quality of the synthesized data for dialogue-adaptive pre-training by considering specificity and informativeness. Experimental results on widely used open-domain response selection and quality estimation benchmarks show that DAPO significantly improves the baseline models and achieves state-of-the-art performance on the MuTual leaderboard, verifying the effectiveness of estimating quality evaluation factors into pre-training.
New trends in retail payments: How technological changes are reshaping the payments system. Introducing a proposal for a new pan-European instant payment system

Since the institution of a unified European market, the retail payment system has changed significantly. In September 2020, the European Commission adopted the Digital Finance Package, that includes a digital finance strategy, legislative proposals on crypto-assets and digital resilience, as well as a renewed strategy for retail payments. This work proposes the framework for a new pan-European retail payment system based on public identity recognition and disintermediation of currently used devices: Euro-PaID. The aim is to have a competitive EU financial sector that gives consumers access to innovative financial products whilst ensuring consumer protection and financial stability. The new system is designed in compliance with existing regulations. However, because certain aspects do not fall within the scope of applicable EU law, ad hoc regulation should be enacted. We concluded that a holistic approach is essential in rethinking regulation to address the numerous social implications of a hypothetical change in the payments system.
Multi-Task Learning and Improved TextRank for Knowledge Graph Completion

Knowledge graph completion is an important technology for supplementing knowledge graphs and improving data quality. However, the existing knowledge graph completion methods ignore the features of triple relations, and the introduced entity description texts are long and redundant. To address these problems, this study proposes a multi-task learning and improved TextRank for knowledge graph completion (MIT-KGC) model. The key contexts are first extracted from redundant entity descriptions using the improved TextRank algorithm. Then, a lite bidirectional encoder representations from transformers (ALBERT) is used as the text encoder to reduce the parameters of the model. Subsequently, the multi-task learning method is utilized to fine-tune the model by effectively integrating the entity and relation features. Based on the datasets of WN18RR, FB15k-237, and DBpedia50k, experiments were conducted with the proposed model and the results showed that, compared with traditional methods, the mean rank (MR), top 10 hit ratio (Hit@10), and top three hit ratio (Hit@3) were enhanced by 38, 1.3%, and 1.9%, respectively, on WN18RR. Additionally, the MR and Hit@10 were increased by 23 and 0.7%, respectively, on FB15k-237. The model also improved the Hit@3 and the top one hit ratio (Hit@1) by 3.1% and 1.5% on the dataset DBpedia50k, respectively, verifying the validity of the model.
Robustness meets accuracy in adversarial training for graph autoencoder

Graph autoencoder (GAE) is an effective deep method for graph embedding, while it is vulnerable to the graph adversarial attacks. Adversarial training, which generates adversarial examples in the adversarial scope(neighborhood of natural examples), is effective to improve the robustness of GAE. However, it may lead to degradation of natural accuracy (accuracy on natural examples) due to the extra training examples generated in the adversarial scope (the reasonable scope of adversarial examples). Therefore, considering robustness and natural accuracy is crucial to GAE. In this paper, an improved GAE model is formulated by combining the Structure and Feature encoders, and a novel Adversarial Training strategy (GAE-SFAT) is proposed based on improved GAE. GAE-SFAT has a smaller but more reasonable adversarial scope for adversarial training, which keeps the robustness and reduces the degradation of natural accuracy compared with ordinary adversarial training. In addition, a novel algorithm considering the robustness and accuracy is designed to optimize the GAE-SFAT. We conduct experiments both on the natural graphs as well as perturbed graphs for three datasets. The results show that GAE-SFAT can perform better than state of arts adversarial training model under different kinds of perturbations.
Enhancing access to high quality tangible information through machine embroidered tactile graphics

Public Access Share on Enhancing access to high quality tangible information through machine embroidered tactile graphics Authors: Margaret Ellen Seehorn Grinnell College, USA Grinnell College, USAView Profile , Gene S-H Kim Stanford University, USA Stanford University, USAView Profile , Aashaka Desai University of Washington, USA University of Washington, USAView Profile , Megan Hofmann Northeastern University, USA Northeastern University, USAView Profile , Jennifer Mankoff University of Washington, USA University of Washington, USAView Profile Authors Info & Claims SCF '22: Proceedings of the 7th Annual ACM Symposium on Computational FabricationOctober 2022Article No.: 23Pages 1–3https://doi.org/10.1145/3559400.3565586Published:26 October 2022Publication History 3citation140DownloadsMetricsTotal Citations3Total Downloads140Last 12 Months104Last 6 weeks12 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
Deep Collaborative Recommendation Algorithm Based on Attention Mechanism

Representation learning-based collaborative filtering (CF) methods address the linear relationship of user-items with dot products and cannot study the latent nonlinear relationship applied to implicit feedback. Matching function learning-based CF methods directly learn the complicated mapping functions that map user-item pairs to matching scores, which has limitations in identifying low-rank relationships. To this end, we propose a deep collaborative recommendation algorithm based on attention mechanism (DACR). First, before the user-item representations are input into the DNNs, we utilize the attention mechanism to adaptively assign different weights to the user-item representations, which captures the hidden information in implicit feedback. After that, we input the user-item representations with corresponding weights into the representation learning and matching function learning modules. Finally, we concatenate the prediction vectors learned from different dimensions to predict the matching scores. The results show that we can improve the expression ability of the model while taking into account not only the nonlinear information hidden in implicit feedback, but also the low-rank relationships of user-item pairs to obtain more accurate predictions. Through detailed experiments on two datasets, we find that the ranking capability of the DACR model is enhanced compared with other baseline models, and the evaluation metrics HR and NDCG of DACR are increased by 0.88–1.19% and 0.65–1.15%, respectively.
The application of multiple metrics in deformable image registration for target volume delineation of breast tumor bed

For postoperative breast cancer patients, deformable image registration (DIR) is challenged due to the large deformations and non-correspondence caused by tumor resection and clip insertion. To deal with it, three metrics (fiducial-, region-, and intensity-based) were jointly used in DIR algorithm for improved accuracy.Three types of metrics were combined to form a single-objective function in DIR algorithm. Fiducial-based metric was used to minimize the distance between the corresponding point sets of two images. Region-based metric was used to improve the overlap between the corresponding areas of two images. Intensity-based metric was used to maximize the correlation between the corresponding voxel intensities of two images. The two CT images, one before surgery and the other after surgery, were acquired from the same patient in the same radiotherapy treatment position. Twenty patients who underwent breast-conserving surgery and postoperative radiotherapy were enrolled in this study.For target registration error, the difference between the proposed and the conventional registration methods was statistically significant for soft tissue (2.06 vs. 7.82, p = 0.00024 < 0.05) and body boundary (3.70 vs. 6.93, p = 0.021 < 0.05). For visual assessment, the proposed method achieved better matching result for soft tissue and body boundary.Comparing to the conventional method, the registration accuracy of the proposed method was significantly improved. This method provided a feasible way for target volume delineation of tumor bed in postoperative radiotherapy of breast cancer patients.
Parallel multiple watermarking using adaptive Inter-Block correlation

People pay more and more attention to copyright protection and digital watermarking technology is a reliable method for copyright identification. In this paper, we propose the parallel multiple watermarking method using adaptive inter-block correlation. Considering the image texture characteristics, the texture complexity method is designed, based on which, the Circular-shaped Adjacent Block Selection and the Arch-shaped Adjacent Block Selection will be adaptively selected for adjacent block selection. The multiple watermarks are alternately embedded into the low and middle frequency band in DCT domain of the image blocks to good imperceptibility and high robustness. By adjusting the difference of projection of embedding coefficients and reference coefficients on the spreading vector, the multiple watermarks can be embedded, so the embedding capacity increases. In addition, we improve the multiple watermarks embedding based on orthogonal spreading vectors so that it can be adapted to embedding multiple watermarks in parallel based on inter-block correlation, saving embedding and extraction time. We theoretically analyze the rationality of parallel orthogonal embedding of multiple watermarks and experimentally verify the high efficiency of parallelism. Cover images of different texture complexity are included in the test dataset, and various attacks have been simulated in the experiments. Experimental results on imperceptibility, robustness, time cost and watermark capacity show the satisfied performance of the proposed scheme.
CSA-Net: Cross-modal scale-aware attention-aggregated network for RGB-T crowd counting

Albeit recent cross-modal crowd counting methods have achieved promising performance, most of them only focus on how to combine the RGB modality and thermal modality, and ignore the adverse effect of the scale variation issue on the cross-modal feature fusion process, where the scale variation is extensively investigated in unimodal crowd counting but less-studied in RGB-T crowd counting. Therefore, this paper aims to explore the aggregated multi-scale context in both the modality-specific and modality-shared feature extraction. Specially, we firstly introduce a novel combining way of the multi-scale analysis and attention aggregation to realize a deeply aggregated scale-aware feature representation. Then based on this combining way, the Scale-aware channel attention aggregation(SCAA) module and Scale-aware Cross-modal Feature Aggregation(SCFA) module are designed for the modality-specific feature extraction and cross-modal feature fusion process. Finally, based on SCAA module and SCFA module, we construct our cross-modal crowd counting architecture, and importantly, the backbone structure of the counting model could be substituted by other structures and thus the SCAA and SCFA are plug-and-play modules. Extensive experiments on the challenging RGB-T and RGB-D crowd counting benchmarks demonstrate the proposed method realizes the state-of-the-art RGB-T counting performance and could also be extended into the RGB-D crowd counting task.
BRN: A belief rule network model for the health evaluation of complex systems

A belief rule base (BRB) expert system provides a generic inference framework for approximating the complicated nonlinear relationships between inputs and outputs. Such systems have been widely applied in the system health management community. However, limited by the method of construction rules, existing methods encounter the combinatorial explosion problem in the modeling of complex systems. Thus, determining how to effectively use the collected signals, system mechanism, and expert knowledge to realize the health evaluation of complex systems has become a key issue limiting the development of BRB systems. To solve this problem, a modified micro belief rule structure is proposed, and a new belief rule network (BRN) model is developed. Then, the cautious conjunctive rule is introduced to realize the fusion and reasoning of the nonindependent nodes in the BRN. In addition, the structure and parameter learning strategies of the proposed BRN are given, thereby providing a systemic mechanism to enhance the capability of the BRN when both expert knowledge and observed data are available. The effectiveness of the BRN model is verified in an aerospace relay experiment.