Reviewerly/exHarmony huggingface dataset:
A simple but practical method: How to improve the usage of entities in the Chinese question generation

Answer-aware question generation aims to generate answerable questions from a given paragraph and answer. Most of the current models concatenated entity information into word embeddings to improve the model's learning ability for special entities, but this method is inefficient for utilizing these information and has accumulated errors. In addition, the majority of research focuses on English, with less exploration in languages such as Chinese. Combining the differences between languages, we propose three methods for incorporating entity information in paragraphs and answers into the training corpus. The corpus processed by these methods can enable the model to have the ability to learn entities autonomously. The experimental results show that our methods can improve most mainstream models and enhance the learning ability of the model for special entities.
Translating children's books from the Ukrainian Diaspora as a way to carry out the Ukrainian educational ideal overseas

This study explores how children's books by authors such Dhan Gopal Mukerji, Louis Henri Boussenard, Oscar Wilde, Daniel Defoe, Rudyard Kipling, etc. have been translated into Ukrainian by members of the Ukrainian Diaspora. I contend that the Ukrainian World Congress served as the bridge between many cultural centers where this ideal was developed in the 20th century. The fundamental tenets of the educational ideal were endorsed by the Second Ukrainian World Congress in 1973, summarizing earlier successes. It is founded on universal principles of tolerance, respect, and love. These principles all have a Ukrainian flavor. In this essay, I want to emphasize how the translation of children's literature from the Ukrainian Diaspora satisfies these idealistic pedagogical objectives.
Robust &amp; Compact End-to-End Hindi Language ASR System

Deep neural network architectures are highly in-corporated in modern (progressive) automatic speech recognition(ASR) systems which outperforms conventional and hybrid systems. Even after adapting to such system we still facing a problem to make balance between accuracy and model size (no. of trainable parameters). We present our experiment in building a robust encoder-decoder based end-to-end ASR model for the Hindi language. Starting with, we have trained the QuartzNet model with two different architectures QuartzNet-5x5 and QuartzNet-15x5 by keeping the same training parameters and dataset. The speech corpus consists of 2648.9 hours of Hindi labelled audio data collected from various domains and sources. We have analyzed the performance of both models and observed that QuartzNet-15x5 has a radical improvement of 24% in accuracy. For building and training the models, we have used the PARAM SIDDHI AI system and training recipes from the open source NeMo toolkit.
Capability-based Component Security Mechanism for Microkernel OS

Most of the services on microkernel architecture operating system are decoupled and built as independent component modules in user-mode. Therefore, how to use these independent service modules to efficiently build a secure microkernel architecture operating system has become an important proposition. However, the existing methods often select some system modules according to the requirements and configuration to build at compile time, which rarely considers the security of user-mode components outside the kernel. This paper proposes a construction mechanism of the security component based on the capability, constructing the system from the point of system security. In order to verify the construction mechanism, sufficient experiments and analysis are carried out on the All winner T3 platform. The experiments prove that the construction mechanism meet the construction principles proposed in this paper. The construction mechanism has certain versatility and continuity for building a microkernel-based operating system ecosystem.
Context Modeling with Hierarchical Shallow Attention Structure for Document-Level NMT

It is acknowledged that neural machine translation(NMT) can be improved by considering context information. Nevertheless, the progress of context-aware NMT has encountered some challenges. Firstly, effectively utilizing valuable information contained in context is still challenging. Moreover, as the number of sentences increases, the parameters of context-aware NMT models will surge, which costs computing power and prevents them from transferring to other translation tasks. Therefore, we propose a hierarchical shallow attention structure for document-level NMT to tackle the problems above. We employ hierarchical encoders to extract both sentence and context information. Then the integration of hierarchical attention is incorporated with the self-attention of the target sentences in the decoding phase. Moreover, we employ shallow attention to reduce model complexity. Assessments of several linguistic phenomena demonstrate that the proposed approach can balance model complexity and translation performance while getting SOTA BLEU scores in several translation tasks.
Data Distribution Transfer for Out Of Distribution Generalization

Modern deep neural networks suffer from performance degradation when evaluated on testing data under different distributions from training data. The goal of out-of-distribution generalization is to solve this problem by learning transferable knowledge from source domains to generalize to invisible target domains. This paper presents a data augmentation method for out-of-distribution generalization. The main assumption is that the main data distribution of an image mostly contains domain-related information, such as color, illumination, texture content, etc, which hurts the domain shifts. To force the model to pay less attention to this part of the information, we propose a new data augmentation method based on the main distribution transition. Extensive experiments on two data set have demonstrated that the proposed method is able to achieve state-of-the-art performance for domain generalization. At the same time, our method can not only combine with other methods to produce a superposition generalization effect but also generate obfuscation data cheaply.
The Impact of the "Teams Games Tournament" Cooperative Model on the Learning Motivation of Grade 2 Elementary School Students

This study aims to determine the effect of the TGT type cooperative model (Teams Games Tournament) on student learning motivation. Researchers conducted research at SDN Cangkring Turi Prambon. The type of research used is a quantitative approach to experimental methods with Pre Experimental design. The type used is One Group Pretest-Posttest Design. The subjects taken were 21 grade II students at SDN Cangkring Turi Prambon. TGT learning model (Teams Games Tournament) is used to find out the effect of the TGT learning model (Teams Games Tournament) on the learning motivation of class II students of SDN Cangkring Turi Prambon. The results of this study indicate that the analysis of the data obtained is the count of 20.80 and a table of 1.725. Thus count &gt; table, namely 20.80&gt; 1.725, there is the effect of the TGT model (Teams Games Tournament) on the learning motivation of class II students of SDN Cangkring Turi Prambon. While the results of the eta squared test obtained 0.955 ≥ 0.14 has the meaning of great influence.
SorTEE: Service-Oriented Routing for Payment Channel Networks With Scalability and Privacy Protection

Payment channel networks (PCNs) are emerged as the most widely deployed solution to mitigate the scalability problem of permissionless cryptocurrencies, allowing vast payments to be carried out off-chain. Routing, which finds feasible paths between the senders and receivers, is critical for PCNs. However, existing solutions either fail to achieve high scalability that can maintain low storage/computation/network communication overhead, or they are susceptible to privacy disclosure. In this paper, we propose SorTEE, a service-oriented routing solution for PCNs, which adopts a set of service nodes to alleviate the per-user burden of routing and achieves more comprehensive privacy guarantees than the state-of-the-art by leveraging trusted execution environments (TEEs). SorTEE demands users communicate with the TEE by the secure channel to protect the privacy of transaction value. Then, an oblivious path mechanism is designed to construct redundant paths with the pseudo senders and receivers generated by TEEs to confuse its untrusted controller. Further, we report a novel attack that allows malicious service nodes to drop the valid paths for profit, and design a feedback mechanism to relieve it. Moreover, SorTEE hides the identities of the senders/receivers for the intermediate nodes of payment paths by introducing a novel identity information transfer scheme called encrypted identity chain. Based on security analysis and performance evaluation, our results demonstrate that SorTEE is able to achieve sufficient privacy-preserving payment and low per-user overhead.
Ways to increase the efficiency of the organization independent work of higher applicants education

The article is devoted to the study of the problem of organizing independent work of students of higher educational institutions and the determination of the main ways to increase its effectiveness. The main attention is paid to the analysis of the motivational sphere as an internal factor that significantly affects the success of independent educational activity, its focus and goals. Utilitarian, social, cognitive, professional motives are highlighted and characterized. Special attention is paid to achievement motivation; the role of cognitive and professional motives. Based on the analysis of psychological and pedagogical sources, the role of the teacher's personality and his professional competence in ensuring the effectiveness of the organization of independent work is shown. It has been proven that the development of interest and perseverance in achieving high educational results will be promoted by: the teacher's ability to create a cognitive atmosphere in the classroom, to convince students that they “meet with him not to teach, but to share their own knowledge, skills and scientific hobbies "; the ability to design various types of independent work, including problematic, search questions, involving in-depth text processing, etc.; creative combination of traditional and innovative technologies, active and interactive teaching methods; timeliness of control, a variety of its forms and improvement of criteria for evaluating independent work; the art of inspiring and supporting students' aspirations to achieve their goals.
Development of Hotspots Ground Check Module in the Mobile Application for Forest and Land Fires Prevention Patrol

Forest and land fire yearly occurs in some area in Indonesia. One of the efforts to reduce forest and land fire occurrences is to conduct patrol prevention in prone-fire areas. The previous study has successfully developed a mobile application for forest and land fire prevention patrol. The application records and manages patrol data collected from the field. However, the application currently can not be used to report the ground check activity of hotspots during patrols. This study aims to build a hotspot ground check module in the mobile application for forest and land fire prevention patrol, directly integrated with the database using the Ionic V3 framework. We adopt the prototyping approach to develop the module that consists of 5 stages: communication, quick plan, quick design modeling, construction of the prototype, and deployment delivery and feedback. The users tested the application using the black box method, namely patrol teams in South Sumatera on March 28, 2022, and Jambi on May 12, 2022. In the last iteration test, the application meets all the user needs. The mobile application of forest and land fire prevention patrol can now report ground checks on hotspot locations during patrols. The hotspot ground check module is expected to help patrol teams conduct patrols effectively to prevent forest and land fires. Therefore, the negative effects of forest and land fires can be minimized.
Automatic vehicle cognizance identification

The main objective of the automatic vehicle cognizance identification is to recognize and store the registration number of the vehicles travelling on the roads. This project is an application of data science. The automatic vehicle cognizance identification system uses the concept of optical char- acter recognition and Yolo algorithm to read the characters on the vehicle number plate. This is used to recognize and store the vehicle registration number. It saves time to find whether a particular vehicle moved in a particular way or not by entering the vehicle number. Also, the insurance and fitness status of the vehicle crossing the roads can be found and either fine or charge sheet can be filed.
Path Planning for Mobile Robots Based on Improved A* Algorithm

Aiming at the problem of low efficiency of mobile robot path planning in complex environments, based on the traditional A* algorithm and combined with the divide and conquer strategy algorithm, A four-way A* algorithm for a two-dimensional raster map is proposed in this paper. First, use random sorting and preprocessing to optimize the traditional A* algorithm and change the termination condition of the two-way A* algorithm expansion. Finally, use the start and end points to calculate the third node. The original problem is decomposed into a subproblem that simultaneously extends the four search trees from the starting point, intermediate point, and target point. After the pathfinding is successful, the paths of the subproblems are merged to get the optimal path. In addition, termination conditions have been added. While planning the four-way A* algorithm, it will also judge the one-way and two-way path planning so that the algorithm can find a path faster in a complex space. In order to verify the effectiveness of the improved algorithm, the improved algorithm and other algorithms are simulated in Matlab. The simulation results show that the path planning efficiency of the algorithm has been significantly improved, and as the scale of the environment increases, the advantages of the improved algorithm are more prominent.
Spectrometry Analysis Techniques for LCT Pegmatite Halo Identification: The Role of European Projects

Lithium-Cesium-Tantalum (LCT) pegmatites are enriched in several raw materials. However, their small size and the limited penetration depth of the sensors, limits remote sensing approaches. This study evaluates the usefulness of hyperspectral data to identify geochemical halos related to LCT pegmatites by exploiting the information acquired in European projects. It was possible to identify key minerals and related mineralogical changes that can be due to hydrothermal alteration. Partial Least Squares Regression (PLSR) was used to model the abundance of Li, Rb and Cs. The most reliable results were obtained for Cs, with results being influenced by lithological and weathering factors. New outcomes are expected, namely mineral chemistry studies that will complement the hyperspectral results.
Expanding Export Control-related Data for Expert System

In this paper we describe an extension of question answering dataset to be utilized for testing security export control expert system. Differently to the long questions and answers with rich context mostly describing exceptional interpretation of regulatory texts, our addition contains short questions and answers which were manually created by an expert who often answers similar questions from academic researchers. This dataset extension should allow broader and more realistic experiments for evaluating dialog-based expert systems with more realistic input. The dataset itself could be used broadly in other studies as a testbed for testing both text classification methods and QA systems requiring external data processing. We introduce the task, problems of previously used data, and describe how they are tackled with the additional set. Examples are presented together with term frequency analysis. Unlike the government-made QAs, the newly created dataset is freely available upon request.
Application of GTTM

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Lightweight and CCA2-Secure Hardware Implementation of Binary Ring-LWE

Due to increasing the number of connected devices to IoT network, providing end-to-end security is essential. Lattice-based cryptography (LBC) is a promising method for IoT by providing the reasonable security against classic and quantum attacks. Binary Ring-LWE is a type of LBC that is suitable for IoT devices. However, a reliable cryptosystem should also be secure against different side-channel attacks, such as power analysis or fault injection ones. In this work, a fault resilient hardware implementation for an optimized hardware design of Ring Binary LWE for resource-constraint IoT devices is presented. The design was implemented on the FPGA platform. The maximum frequency and occupied slices on Virtex-7 are 210.5MHz and 423, respectively. Based on the result, the proposed design occupied only 1% of the total available slices.
An Empirical Analysis of Boosting Deep Networks

Boosting is a method for finding a highly accurate classifier by linearly combining many “weak” classifiers, each of which may be only moderately accurate. Thus, boosting is a method for learning an ensemble of classifiers. While boosting has been shown to be very effective for decision trees, its impact on neural networks has not been extensively studied. Using standard object recognition datasets, we verify experimentally the well-known result that a boosted ensemble of decision trees usually generalizes much better on testing data than a single decision tree with the same number of parameters. In contrast, using the same datasets and boosting algorithms, our experiments show the opposite to be true when using neural networks (both convolutional neural networks (CNNs) and multilayer perceptrons (MLPs)). We find that a single neural network usually generalizes better than a boosted ensemble of smaller neural networks with the same total number of parameters. While this is an experimental investigation, more theoretical research is warranted to understand the role of boosting in deep learning-based classifiers.
Standard Collaborative Development and Revision System Based on Web 2.0 Technologies

In order to effectively improve the standard development and revision efficiency by means of information technology, the collaborative mode and system is designed based on Web 2.0 technologies especially Wiki mode. Based on the hierarchical model of the development and revision process, the overall framework and functions of the system is constructed, which concludes user management, permission management, comment, Knowledge retrieval, collaborative editing of standardized documents and auxiliary information collection. Finally, based on the system architecture and function module, a prototype system with the functions of document structure development and reuse, content collaborative editing, visual collaborative revision and so on is developed to improve the standardization efficiency and reduce the cost.
Transformer-Based Multilingual Language Models in Cross-Lingual Plagiarism Detection

In this work, we study the effectiveness of 6 pretrained Transformer-based language models in the task of crosslingual sentence alignment. We evaluate and compare the models on 10 language pairs, defining the task as a binary classification of two input sentences in English and another Indo-European language. The main objective is to determine the best model in terms of processing speed, accuracy, and cross-lingual transferability. For the latter, we test the language models in three different settings: (i) when one fine-tuned model is used for all considered languages, (ii) when a separately fine-tuned model is used for each group of closely related languages, and (iii) when a separate model is fine-tuned for each language pair. For the experiments we use challenging translation identification datasets containing paraphrases and near-paraphrases.
Normalizing Weights

As stated in Chap. 2, normalizing the weights can implicitly normalize the activations by imposing constraints on the weight matrix, which can contribute to preserving the activations (gradients) during forward (backpropagation).
Automated Recognition of Oracle Bone Inscriptions Using Deep Learning and Data Augmentation

Oracle bone inscriptions (OBIs) are the earliest Chinese writing system. However, deciphering OBIs is a very challenging task because of the lack of data and time- and resource-consuming manual classification process. In this paper, I apply the technology of deep learning to solve the problem of OBI recognition, proposing a method for merging incompatible OBI classification datasets and implementing it successfully, significantly raising the training and testing accuracy of the neural networks tested. Another major contribution of this paper is the inclusion of a residual module on the AlexNet convolutional neural network, which achieves an accuracy of 89.51% after hyperparameter optimization on the merged dataset, about 1% better than the classical AlexNet under the same conditions and meets the expectation.
User Perspectives on the Acceptability of Realtime Data Capture for Design Research by Connected Products

Connected products present new opportunities for conducting in-the-wild design research, where live data is transmitted by devices about their use and function. However, industry data gathering practices have raised public concerns around privacy and security. Thus, we need to account for users’ perspectives on how data is gathered and used. A technology provocation was used to spark discussion on the acceptability of physical devices collecting information for design research. Attitudes ranged from extreme unease to lack of concern, with varying beliefs about the trustworthiness and capability of researchers and companies. A range of real and speculative contexts prompted participants to examine value trade-offs between themselves and corporations, privacy and ethical issues, agency, and informed consent. Based on this we set out implications for carrying out data-driven design in order to unlock potential value while respecting user privacy and time.
Design Research Need in Helping to Address International Conflict Intersections in Southeast Asia

Design-led research on conflicts arising from anthropogenic and environmental factors, such as that from forest management in Indonesia, are largely under-represented in the design research community. In this position paper we discuss the need for more design research in this area. We present the complex intersecting challenges in health, education, local communities and environment, surrounding conflicts arising from forest management in Indonesia and their spillovers into other countries in Southeast Asia, such as Malaysia. We propose a design-led community-based approach to conflict resolution that anticipates the human-drivers and threats behind potential emerging conflicts in such contexts; by applying participatory design principles and methods, informing preventive measures to those conflicts. We envisage that this will generate debate on the strategic need to embed more design research practices in international conflict intersections research.