Reviewerly/exHarmony huggingface dataset:
An Analysis of Translation Error that Occurs among English Foreign Language Students

The quality of translation can affect the quality of information conveyed. However, errors often occur. It is evidenced by the various errors that can be found in this study. The objectives of this study are: 1) To check types of translation errors in Indonesian to English translation. 2) To find the possible explanations for the sources of errors made by the students in translation. The researcher hopes that it can help students to enrich their knowledge about learning translation. The methodology of this study was qualitative in case study design. The data was obtained from student tests. Researchers also conducted interviews to find out the source of the translation error. It analyzed the data and calculated each error with frequency and percentage. The respondents of this study were English Foreign Language students of Class TBI-E 2016 of UIN SMH Banten. They made some errors when they translated text from Indonesian into English. The researcher found that each error was a syntax error (68.13%), then a semantic error (31.87%). Researchers also found sources of translation errors: lack of linguistic understanding, translation procedures, unconfident, and inattention. The recommendation from the researcher is to inform students about the correct translation procedure. Moreover, more tasks about vocabulary and structure hope to be able to increase their English skill
The Analysis of Conversational Implicature in an Extensive Listening of BBC Learning English Podcast

Using qualitative content analysis, this study attempts to investigate the occurrence of conversational implicature in an extensive listening of BBC Learning English Podcast. This study was conducted to find out how conversational implicature is employed by the speakers, including the types and functions of the implicature. Following Grice’s theory of conversational implicature and cooperative principle, this study revealed that out of 200 samples taken, the implicature occurred in 104 utterances which were generated through flouting and violation with Particularized Conversational Implicature (PCI) dominating the exchange rather than Generalized Conversational Implicature (GCI). As for the function of implicature produced, the researcher utilized Searle’s theory of Speech Act which comprises Assertive, Commissive, Declarative, Directive, and Expressive. The result showed that Assertive function dominates the utterance produced, meanwhile Declarative function was absent. The findings and result of this study is expected to be able to give some instances to the learners of English to the theory of conversational implicature, so that they will learn to avoid misunderstanding during the conversation itself. It is also expected to give contribution to the field of pragmatics, since, to the researcher’s knowledge, the study of conversational implicature on the same media is still scarcely conducted.&#x0D; &#x0D;
The use of artificial intelligence in education

Artificial Intelligence (AI) is nowadays increasingly used in various fields to improve efficiency and productivity. The purpose of this article is to study the main aspects concerning the application of artificial intelligence in modern education. The paper analyzes the latest technologies that can be used or are already used in the educational process by both students and teachers. The article also lists the main pros and cons of using this kind of technology in education.
Multi-party anonymous authentication scheme in heterogeneous network slicing

Network slicing is a logical network function that supports communication service requirements for specific usage scenarios or business models. It has become an essential factor in solving future network architecture. In recent years, heterogeneous network slicing between multiple autonomous domains has emerged, with significant limitations compared to single-domain networks. Data sharing in heterogeneous network slices challenge the confidentiality of sensitive data significantly. Preventing sensitive data from leaking and protecting core data have become urgent problems to be solved. Therefore, we propose a multi-party (multi-sender to multi-receiver) anonymous authentication scheme on certificateless cryptography(CLC) and identity-based cryptosystem(IBC) institutional domain Internet of Vehicles(IoV) to smart healthcare. Our scheme realizes heterogeneous communication between multiple senders and multiple receivers, and uses different system parameters for heterogeneous cryptographic systems. In this work, the limitation of one-to-many or many-to-one communication in existing heterogeneous networks is broken through. The proposed work utilizes the pseudo-identity generated by the trusted authority to communicate in IoV slices, ensuring the anonymity of the sender; The authorization Center TA can uniquely determine the true identity of the vehicle through pseudo-random values for traceability. What is more, the Lagrange interpolation formula is employed to prevent the leakage of the identities of smart healthcare slices, ensuring the anonymity of the receiver. Under the random oracle model, the proposed scheme has proved to satisfy unforgeability, confidentiality, and anonymity under the computational Diffie–Hellman problem. Experimental results indicate that our proposal achieves better performance than existing schemes.
Sistem Pakar Diagnosa Penyakit Katarak dengan Metode Certainty Factor

The eye is one of the most vital parts of the human body, which of course must be maintained. Unhealthy eyes will have a bad impact on the sufferer because it can interfere with the sufferer's activity process. One of the simplest and most common eye diseases suffered by humans is cataracts. Symptoms of cataracts occur painlessly so that many sufferers do not realize that they have cataracts. In Indonesia, the main cause of visual impairment and blindness is cataracts. Cases of blindness caused by cataracts occur due to a condition where the lens of the eye becomes cloudy and cataracts do not only occur at an early age but at all ages even though cataracts are a type of blindness that can be avoided and can be cured through treatment. Therefore an expert system is needed to help diagnose cataracts so that prevention can be done as much as possible. One method that can be used by expert systems is the assurance factor method. The advantage of the assurance factor method is that it can provide settlement solutions with the value of disease symptoms given by experts. With the superiority of the assurance factor method, this study will discuss the application of the assurance factor in the diagnosis of cataracts. To diagnose cataracts, there are 18 symptoms with 3 types of disease, namely congenital, juvenile, and traumatic cataracts. This study used 5 data tests based on the symptoms felt by people with cataracts to produce accurate predictions for each type of cataract where 86.0762% congenital cataracts in the first test data, 94.0595% juvenile cataracts in the second test data, 92.5128% traumatic cataracts in the third test data, 92.2440 % juvenile cataracts in the fourth test data, and 90.2080% juvenile cataracts in the fifth test data.
A Study on the Effect of Motivation for Using Private Message Service on Satisfaction and Loyalty: Focus on Chinese Female K-Pop Fans

This study is focused on 'Private Message Service', a monthly subscription service that allows users to receive and reply to messages sent by artists. This study extracted the motivation for using private message service(pseudo feeling of love, entertainment and rest, friendliness, star identification, information seeking, idol support, and reference group conformance desire) and examined how the motivation for using private message service affects satisfaction and loyalty. This study conducted a survey of 354 Chinese female K-Pop fans who had experiences using the private message service. It was found that entertainment and rest, friendliness, star identification, and information seeking motivation have positive effects on satisfaction, while psuedo feeling of love was found to have negative effects on satisfaction. Among the motivational factors for using private message service, entertainment and rest, friendliness, star identification, and information seeking motivation was found to have positive effects on loyalty. And satisfaction was found to have positive effects on loyalty. In addition, there is a complete mediating effect of satisfaction between entertainment and rest, friendliness, star identification, information-seeking motivation, respectively with loyalty. It is hoped that findings of this study would contribute to enhancing the quality of the private message services.
Breaking the Echo Chamber Effect in Social Media 🤔: An Impossibility Result

Abstract As scientists, we are proud of our role in developing the current digital age that enables billions of people to communicate rapidly with others via social media. However, when things go wrong, we are also responsible for taking an ethical stand and trying to solve problems, and this work aims to take a step in this direction. Our goal is to set the foundation for a mathematically formal study of how we might regulate social media and, in particular, address the problem of the echo chamber effect.An echo chamber is a closed system where other voices are excluded by omission, causing your beliefs to become amplified or reinforced. In turn, these bubbles can boost social polarization and extreme political views, and, unfortunately, there is strong evidence that echo chambers exist in social media. The fundamental question we try to answer is: how and can a regulation “break” or reduce the echo chamber effect in social media?Sadly, the paper’s main result is an impossibility result: a general regulation function that achieves this goal while obeying the core values of democratic societies (freedom of expression and user privacy) does not exist, which leaves us with hard future choices to make.
PERBANDINGAN PERFORMA METODE INTERPOLASI POLINOMIAL NEWTON-GREGORY MAJU DAN NEWTON-GREGORY MUNDUR DALAM MENGESTIMASI JUMLAH PENDUDUK DI PROVINSI PAPUA

Population data is one of the absolute requirements that must be fulfilled by the Central Statistics Agency (Badan Pusat Statistik) to determine the grand design of development in the Province of Papua. However, the most complete and accurate source of population data comes from the results of a population census carried out every ten years. With long intervals and requiring costs, time, and effort, it will be more efficient and save time and effort if the number of residents can be estimated. Estimating the population required the proper method. Therefore, in this article, a comparison of Forward Newton-Gregory Polynomial Interpolation and Backward Newton-Gregory Polynomial Interpolation techniques is carried out to estimate the population of Papua Province. The estimated results of the two methods are compared by comparing the relative amount of error. The comparison results show that the relative error average of the Forward Newton-Gregory Polynomial Interpolation 0,014200679 is smaller than the relative error average of the Backward Newton-Gregory Polynomial Interpolation 0,047163677. So, it can be concluded that the Forward Newton-Gregory Polynomial Interpolation method is better than Backwards Newton-Gregory Polynomial Interpolation in predicting the population of Papua Province.
A Benchmark Study on Knowledge Graphs Enrichment and Pruning Methods in the Presence of Noisy Relationships

In the past few years, knowledge graphs (KGs), as a form of structured human intelligence, have attracted considerable research attention from academia and industry. In this very active field of study, a widely explored problem is that of link prediction, the task of predicting whether two nodes should be connected, based on node attributes and local or global graph connectivity properties. The state of the art in this area is represented by techniques based on graph embeddings. However, KGs, especially those acquired using automated or partly automated techniques, are often riddled with noise, e.g., wrong relationships, which makes the problem of link deletion as important as that of link prediction. In this paper, we address three main research questions. The first is about the true effectiveness of different knowledge graph embedding models under the presence of an increasing number of wrong links. The second is to asses if methods that can predict unknown relationships effectively, work equally well in recognizing incorrect relations. The third is to verify if there are systems robust enough to maintain primacy in all experimental conditions. To answer these research questions, we performed a systematic benchmark study in which the experimental setting includes ten state-of-the-art models, three common KG datasets with different structural properties and three downstream tasks: the widely explored tasks of link prediction and triple classification, and the less popular task of link deletion. Comparative studies often yield contradictory results, where the same systems score better or worse depending on the experimental context. In our work, in order to facilitate the discovery of clear performance patterns and their interpretation, we select and/or aggregate performance data to highlight each specific comparison dimension: dataset complexity, type of task, category of models, and robustness against noise.
Directional Graph Attention Networks

In recent years, graph neural networks (GNNs) have become a promising method for analyzing data structured in graph format. By considering connections between entities in a graph, GNNs are able to extract valuable insights. One notable variation of GNN is the graph attention network (GAT), which employs the attention mechanism and has demonstrated promising performance in various applications. However, its ability to incorporate feature information from nodes beyond the immediate neighborhood is limited, leading to degraded performance on heterophilic data. To address this limitation, this thesis proposes a novel attention-based model, namely the Directional Graph Attention Network (DGAT). This model combines the feature-based attention with the global directional information extracted from the graph topology, as inspired by the Directional Graph Network (DGN). A new class of Laplacian matrices is proposed and an existing theoretical result on DGN is extended. This extension bridges a gap in the literature. The experimental results presented in the thesis, based on nine real-world benchmarks and ten synthetic data sets, demonstrate the superiority of the proposed DGAT model compared to the GAT baseline model. Particularly on heterophilic data sets, DGAT showed a notable average increase of approximately 35% in node classification tasks across all heterophilic real-world data sets. In addition, DGAT outperforms GAT by an average margin of around 51% in all ten synthetic data sets with various levels of heterophily
Image Synthesis in Multi-Contrast MRI with Generative Adversarial Networks

From unconditional synthesis to conditional synthesis, GANs have shown that they are a perfect fit for such problems thanks to their ability to learn probability distributions. For unconditional synthesis, the objective is to stochastically generate MR images of target contrast. Conditional synthesis refers to the case where the model learns nonlinear mapping to the different MR tissue contrasts without altering the physiological information. Furthermore, by merging collaborative information of multiple contrast images, missing data imputation among many different domains is also effectively solved with GANs. Although promising results are seen, development in the area is still at its early stage. Interesting research directions are proposed from prior work, including the application of more advanced methods and rigorous validation in clinical settings. In effect, MRI image synthesis techniques should be able to reduce the burden of costly MR scans, benefiting both patients and hospitals.
Research on the Application and Performance Optimization of GPU Parallel Computing in Concrete Temperature Control Simulation

With the development of engineering technology, engineering has higher requirements for the accuracy and the scale of simulation calculation. The computational efficiency of traditional se-rial program can not meet the requirements of engineering。Therefore, reducing the calcula-tion time of temperature control simulation program has important engineering significance for real-time simulation of temperature field and stress field, and then adopting more reasona-ble temperature control and crack prevention measures. GPU parallel computing is introduced into the temperature control simulation program of massive concrete to solve this problem and the optimization is carried out. Considering factors such as GPU clock rate, number of cores, parallel overhead and Parallel Region, The improved GPU parallel algorithm analysis indicator formula is proposed. It makes up for the shortcomings of traditional formula that focus only on time. According to this formula, when there are enough threads, the parallel effect is limited by the size of the parallel domain, and when the parallel domain is large enough, the efficiency is limited by the parallel overhead and the clock rate. This paper studies the optimal Kernel execu-tion configuration. Shared Memory is utilized to improve memory access efficiency by 155%. After solving the problem of bank conflicts, an accelerate rate of 437.5x was realized in the sub-routine of the matrix transpose of the solver. The asynchronous parallel of data access and logi-cal operation is realized on GPU by using CUDA Stream , which can overlap part of the data access time. On the basis of GPU parallelism, asynchronous parallelism can double the compu-ting efficiency. Compared with the serial program, the accelerate rate of inner product matrix multiplication of the GPU asynchronous parallel program is 61.42x. This study further proposed a theoretical formula of data access overlap rate to guide the selection of the number of CUDA streams to achieve the optimal computing conditions. The GPU parallel program compiled and optimized by CUDA Fortran platform can effectively improve the computational efficiency of the simulation program for concrete temperature control, and better serve for engineering computing.
Spatial Cognition

Now in its fifth edition, this accessible and comprehensive text highlights the most important theoretical, conceptual and methodological issues in cognitive neuroscience. Written by two experienced researchers who excel at teaching, the consistent narrative ensures that concepts are linked across chapters, and the careful selection of topics enables readers to grasp the big picture without getting distracted by details. Clinical applications such as developmental disorders, brain injuries and dementias are highlighted. In addition, the analogies and examples, opening case studies, and 'In Focus' boxes both engage and demonstrate the relevance of the material to real-world concerns. Revised for even greater clarity, the fifth edition features new and updated artwork, 'Key Questions' to review concepts, and 'Thought Questions' which develop the critical thinking skills needed to evaluate future developments in this fast-moving field. An expanded set of online resources is also available.
Formalizing Deep Neural Networks

We provide a short, self-contained introduction to deep neural networks that is aimed at mathematically inclined readers. We promote the use of a vect--matrix formalism that is well suited to the compositional structure of these networks and that facilitates the derivation/description of the backpropagation algorithm. We present a detailed analysis of supervised learning for the two most common scenarios, (i) multivariate regression and (ii) classification, which rely on the minimization of least squares and cross-entropy criteria, respectively.
Scalable Deep Q-Learning for Session-Based Slate Recommendation

Reinforcement learning (RL) has demonstrated great potential to improve slate-based recommender systems by optimizing recommendations for long-term user engagement. To handle the combinatorial action space in slate recommendation, recent works decompose the Q-value of a slate into item-wise Q-values, using an item-wise value-based policy. However, the common case where the value function is a parameterized function taking state and action as input results in a linearly increasing number of evaluations required to select an action, proportional to the number of candidate items. While slow training may be acceptable, this becomes intractable when considering the costly evaluation of the parameterized function, such as with deep neural networks, during model serving time. To address this issue, we propose an actor-based policy that reduces the evaluation of the Q-function to a subset of items, significantly reducing inference time and enabling practical deployment in real-world industrial settings. In our empirical evaluation, we demonstrate that our proposed approach achieves equivalent user session engagement to a value-based policy, while significantly reducing the slate serving time by at least 4 times.
HUMMUS: A Linked, Healthiness-Aware, User-centered and Argument-Enabling Recipe Data Set for Recommendation

The overweight and obesity rate is increasing for decades worldwide. Healthy nutrition is, besides education and physical activity, one of the various keys to tackle this issue. In an effort to increase the availability of digital, healthy recommendations, the scientific area of food recommendation extends its focus from the accuracy of the recommendations to beyond-accuracy goals like transparency and healthiness. To address this issue a data basis is required, which in the ideal case encompasses user-item interactions like ratings and reviews, food-related information such as recipe details, nutritional data, and in the best case additional data which describes the food items and their relations semantically. Though several recipe recommendation data sets exist, to the best of our knowledge, a holistic large-scale healthiness-aware and connected data sets have not been made available yet. The lack of such data could partially explain the poor popularity of the topic of healthy food recommendation when compared to the domain of movie recommendation. In this paper, we show that taking into account only user-item interactions is not sufficient for a recommendation. To close this gap, we propose a connected data set called HUMMUS (Health-aware User-centered recoMMendation and argUment-enabling data Set) collected from Food.com containing multiple features including rich nutrient information, text reviews, and ratings, enriched by the authors with extra features such as Nutri-scores and connections to semantic data like the FoodKG and the FoodOn ontology. We hope that these data will contribute to the healthy food recommendation domain.
Incorporating Time in Sequential Recommendation Models

Sequential models are designed to learn sequential patterns in data based on the chronological order of user interactions. However, they often ignore the timestamps of these interactions. Incorporating time is crucial because many sequential patterns are time-dependent, and the model cannot make time-aware recommendations without considering time. This article demonstrates that providing a rich representation of time can significantly improve the performance of sequential models. The existing literature treats time as a one-dimensional time-series obtained by quantizing time. In this study, we propose treating time as a multi-dimensional time-series and explore representation learning methods, including a kernel based method and an embedding-based algorithm. Experiments on multiple datasets show that the inclusion of time significantly enhances the model’s performance, and multi-dimensional methods outperform the one-dimensional method by a substantial margin.