Reviewerly/exHarmony huggingface dataset:
Big Data Technology Based Ecological Characteristics and Teaching Mode for the Teaching of College Public English

Nowadays, the new English-teaching model for the application of big-data-technology(BDT) has received wide attention. This model combines online learning and classroom face-to-face teaching to improve the efficiency of classroom teaching significantly. However, the application of big-data-technology(BDT) based English teaching not only brings opportunities for modern English teaching but also imposes challenges. One of the practical challenges is that the intervention of the application of big-data-technology (BDT) affects the traditional College public English education ecology. Therefore, It is obliged to redefine the ecological characteristics of the teaching of college public English under the application of big-data-technology (BDT) and explore the corresponding teaching model. In this context, firstly, we analyzed the ecological characteristics of the teaching of college public English and then constructed the Teaching model of College public English-(TMCPE) under BDT. The research finding contributes to expanding the database of English teaching and contributes to building a comprehensive, balanced and reasonable environment, or the College public English education.
A Russian Continuous Speech Recognition System Based on the DTW Algorithm under Artificial Intelligence

In order to improve the effect of continuous speech recognition, this paper combines the DTW algorithm to construct a continuous Russian speech recognition system and proposes a continuous Russian speech detection method based on VGDTW-MPCA with an unequal interval process. Moreover, considering the influence of the correlation between variables on the synchronization of the DTW algorithm, this paper constructs a DTW algorithm on a local data set to synchronize in different variable groups. Then, this paper integrates the obtained data into complete 3D data for modeling. It can be seen from the simulation research that the Russian continuous speech recognition system based on DTW proposed in this paper has a high continuous Russian speech recognition accuracy.
Dance Action Recognition Model Using Deep Learning Network in Streaming Media Environment

Dance movement recognition is a video technology that has a significant impact on intelligent applications and is widely applied in many industries. In the training of intelligent dance assistants, this method can be used. Dancers' postures can be reconstructed by taking the features out of their images. Examine and correct dancers' postures in order to recognise their dance movements. The most crucial aspect of this technology is effectively extracting features, and deep learning is currently one of the best ways to do this for video features. In this paper, the dance movement recognition method is studied using a convolution neural network based on a deep learning network. The deep-learning-network-based convolution neural network is also used to conduct a simulation test, confirming the viability of this method for the recognition of dance movements. Due to the addition of manually extracted time-domain optical flow information, the convolution neural network's accuracy in recognising dance movements has increased by 30.65% and 19.49% for InceptionV3 and 3D-CNN networks, respectively. It is evident from this that the convolution neural network suggested in this paper is more effective at identifying dance movements. Dance movements will continue to develop quickly thanks to the improvement of the in-depth recognition system for them. This technology has a wide range of applications in the instruction and practise of dance movements, and this research has promising application potential.
Reinforcement Shrink-Mask for Text Detection

Existing real-time text detectors reconstruct text contours by shrink-masks only. Though they simplify the framework and can make the model run fast, the strong dependence on shrink-masks leads to unreliable detection results (e.g., miss detection and overdetection). Moreover, these methods ignore the information from surrounding pixels, which causes sensitive shrink-masks and accelerates the reliability decline of detection results. Considering the above problems, we construct an effective and efficient text detection network, termed as Reinforcement Shrink-Mask for Text Detection (RSMTD), which strengthens the model's ability to recognize texts while enjoying a high detection speed. Specifically, an effective text representation strategy (Reinforcement Shrink-Mask, RSM) is designed to decouple texts and shrink-masks. RSM builds texts through shrink-masks and reinforcement offsets to ensure stable detection results encountering shrink-masks that deviate from the ground-truth. It is worth noting that reinforcement offsets can force our method to focus on the foreground shapes to bring precise shrink-mask edges. For the robustness improvement of shrink-masks, Super-pixel Window (SPW) is proposed to encourage RSMTD to utilize the surroundings of each pixel to predict shrink-masks. Particularly, SPW treats the interval regions between texts and shrink-masks as background, which helps to suppress interval regions and to avoid text adhesion. Moreover, a lightweight feature merging branch is constructed to further accelerate the inference process. As demonstrated in the experiments, our method is superior to existing state-of-the-art (SOTA) methods in both detection accuracy and speed on multiple benchmarks.
Modeling Temporal-Sensitive Information for Complex Question Answering over Knowledge Graphs

Question answering over temporal knowledge graphs (TKGQA) has attracted great attentions in natural language processing community. One of the key challenges is how to effectively model the representations of questions and the candidate answers associated with timestamp constraints. Many existing methods attempt to learn temporal knowledge graph embedding for entities, relations and timestamps. However, these existing methods cannot effectively exploiting temporal knowledge graph embeddings to capture time intervals (e.g., “WWII” refers to 1939–1945) as well as temporal relation words (e.g., “first” and “last”) appeared in complex questions, resulting in the sub-optimal results. In this paper, we propose a temporal-sensitive information for complex question answering (TSIQA) framework to tackle these problems. We employ two alternative approaches to augment questions embeddings with question-specific time interval information, which consists of specific start and end timestamps. We also present auxiliary contrastive learning to contrast the answer prediction and prior knowledge regarding time approximation for questions that only differ by the temporal relation words. To evaluate the effectiveness of our proposed method, we conduct the experiments on $$\textbf{C}$$ RON $$\textbf{Q}$$ UESTION. The results show that our proposed model achieves better improvements over the state-of-the-art models that require multiple steps of reasoning.
Agile Software Development Methodologies in Cloud Computing

Department of Computer Science Faculty of Computing, University of Delta, Agbor, Nigeria E-mails: justin.ogala@unidel.edu.com; s.mughele@unidel.edu.ng ABSTRACT In the software sector, agile development approaches have a lot of promise. Agile development methodologies are highly realistic in their awareness of the fact that business requirements change all the time. By executing software releases iteratively and collecting user feedback more often, agile development approaches maximize the benefits of cloud computing. This study is a cloud computing and Agile Methods analysis. This article examines Agile management and development approaches, as well as their benefits when used in conjunction with cloud computing. By executing software releases iteratively and obtaining user feedback very often, agile development practices maximize the benefits of cloud computing. The basic proposition of this study is that Small and medium enterprises should use a framework that guides their migration to cloud computing to reduce all the issues that come with cloud computing infrastructures Keywords: Agile Methodologies, Cloud Computing, Software Development, Integration
Self-supervised Learning for Unintentional Action Prediction

Distinguishing if an action is performed as intended or if an intended action fails is an important skill that not only humans have, but that is also important for intelligent systems that operate in human environments. Recognizing if an action is unintentional or anticipating if an action will fail, however, is not straight-forward due to lack of annotated data. While videos of unintentional or failed actions can be found in the Internet in abundance, high annotation costs are a major bottleneck for learning networks for these tasks. In this work, we thus study the problem of self-supervised representation learning for unintentional action prediction. While previous works learn the representation based on a local temporal neighborhood, we show that the global context of a video is needed to learn a good representation for the three downstream tasks: unintentional action classification, localization and anticipation. In the supplementary material, we show that the learned representation can be used for detecting anomalies in videos as well.
XSPIR: Efficient Symmetrically Private Information Retrieval from Ring-LWE

Private Information Retrieval (PIR) allows a client to retrieve one entry from a database held by a server, while hiding from the server which entry has been retrieved. Symmetrically Private Information Retrieval (SPIR) additionally protects the privacy of the data, requiring that the client obtains only its desired entry, and no information on other data entries. In recent years, considerable effort has been expanded towards making PIR practical, reducing communication and computation. State-of-the-art PIR protocols are based on homomorphic encryption from the ring-LWE assumption. However, these efficient PIR protocols do not achieve database privacy, and leak a lot of information about other data entries, even when the client is honest. Generic transformation of these PIR protocols to SPIR have been suggested, but not implemented. In this paper, we propose XSPIR, a practically efficient SPIR scheme. Our scheme is based on homomorphic encryption from ring-LWE like recent PIR works, but achieves a stronger security guarantee with low performance overhead. We implement XSPIR, and run experiments comparing its performance against SealPIR (Angel et al., IEEE S &P 2018) and MulPIR (Ali et al., USENIX SECURITY 2021). We find that, even though our scheme achieves a stronger security guarantee, our performance is comparable to these state-of-the-art PIR protocols.
From Therbligs to Visual Assets: A Technique to Convey Work Instructions in Augmented Reality Technical Documentation

In this work, we present a technique to simplify the authoring of Augmented Reality Technical Documentation, allowing technical writers with limited knowledge in Augmented Reality (AR) to produce this new type of documentation in their companies. Contrarily to Traditional Technical Documentation, AR offers the opportunity to provide the exact amount of information needed through a careful design of the AR interface. However, in the literature, there are no established techniques to break work instructions down into elemental pieces of information and define how to convey this information through visual assets in AR. In this work, we proposed identifying the information contained in work instructions of technical documentation, extending the method of “Therbligs,” already used in the literature to describe assembly tasks. First, we defined six classes of information types: identity, location, order, way-to, notification, and orientation. Then, we showed how to use these information types to break down the work instructions of an assembly manual used as a case study. We found that the six information types were enough to analyze the complete manual. The second contribution of this work is the proposal of the most suitable visual asset and its properties for each information type. This goal was accomplished through a technical discussion in a focus group with ten experts in the design of AR technical documentation.
Immersive Virtual Reality as a Training Tool for Safety Working Procedure in Confined Spaces

Confined spaces cause fatal and serious injuries that tragically recur with similar dynamics every year. A survey carried out on the Italian territory by the National Institute for Insurance against Accidents at Work on the years from 2001 to 2019 shows that a total of 184 accidents occurred in confined spaces mostly because workers were poorly informed and trained, the risk assessment lacking, and that the provisions of the law were not respected. Often fatal events affect those involved in the primary incident and those who intervene in an attempt to assist. Consequently, it is necessary to apply a proper training process and extend it to all those involved in operations both inside and next to confined spaces. Traditionally, the training approach consists of classroom lectures and simulations in real environments. However, real simulation scenarios are costly and time-consuming as real scenarios training simulations require expensive purpose-built physical simulators. To overcome these limitations, we designed and implemented an Immersive Virtual Reality based platform to support and enforce the traditional confined space safety training approach. Finally, we defined an experimental validation procedure.
Ring Signatures with User-Controlled Linkability

Anonymous authentication primitives, e.g., group or ring signatures, allow one to realize privacy-preserving data collection applications, as they strike a balance between authenticity of data being collected and privacy of data providers. At PKC 2021, Diaz and Lehmann defined group signatures with User-Controlled Linkability (UCL) and provided an instantiation based on BBS+ signatures. In a nutshell, a signer of a UCL group signature scheme can link any of her signatures: linking evidence can be produced at signature time, or after signatures have been output, by providing an explicit linking proof. In this paper, we introduce Ring Signatures with User-Controlled Linkability (RS-UCL). Compared to group signatures with user-controlled linkability, RS-UCL require no group manager and can be instantiated in a completely decentralized manner. We also introduce a variation, User Controlled and Autonomous Linkability (RS-UCAL), which gives the user full control of the linkability of their signatures. We provide a formal model for both RS-UCL and RS-UCAL and introduce a compiler that can upgrade any ring signature scheme to RS-UCAL. The compiler leverages a new primitive we call Anonymous Key Randomizable Signatures (AKRS)—a signature scheme where the verification key can be randomized—that can be of independent interest. We also provide different instantiations of AKRS based on Schnorr signatures and on lattices. Finally, we show that an AKRS scheme can additionally be used to construct an RS-UCL scheme.
Application and Analysis of RGB-D Salient Object Detection in Photographic Camera Vision Processing

To identify the most visually salient regions in a set of paired RGB and depth maps, in this paper, we propose a multimodal feature fusion supervised RGB-D image saliency detection network, which learns RGB and depth data by two independent streams separately, uses a dual-stream side-supervision module to obtain saliency maps based on RGB and depth features for each layer of the network separately, and then uses a multimodal feature fusion module to fuse the latter 3 layers of RGB and depth high-dimensional information to generate high-level significant prediction results. Experiments on three publicly available datasets show that the proposed network outperforms the current mainstream RGB-D saliency detection models with strong robustness due to the use of a dual-stream side-surveillance module and a multimodal feature fusion module. We use the proposed RGB-D SOD model for background defocusing in realistic scenes and achieve excellent visual results.
Efektivitas Implementasi Pelayanan Publik Digital: Studi Kasus Pelayanan Perpustakaan Digital Puslatbang PKASN LAN

One of Puslatbang PKASN LAN’s digital services is the library application which started in 2010. This application was developed internally and has been used for more than a decade. This study aims to analyze how effective this application is, the management of the library, and analyze the driving factors as well as the obstacles encountered during use. A descriptive qualitative method was used with data triangulation. Deployment of questionnaires filled by the users to find out the effectiveness of the services and interview conducted with the librarian of Puslatbang PKASN LAN. The results showed that 93.34% of users felt helped by the digital service of the Puslatbang PKASN Library which shows that the level of effectiveness is very high. The supporting factor, as well as the inhibiting factor, is the leadership's commitment to this digitizing library services. Other agencies that are willing to adopt and/or replicate this service should pay attention to some of the things, among others are (1) availability of competent human resources for library administrators such as librarians or other administrators; (2) availability of supporting infrastructure, and (3) leadership policies that support the implementation of digital services.
An Algorithm for Locating the Educational Function of Rural Primary Schools under the Background of Rural Revitalization by Neural Networking

The functional orientation of agricultural primary schools in China has not been “rewritten” under the Rural Revitalization Strategy but has been given new connotation and new requirements. With the help of qualitative research design, taking three representative agricultural primary schools as the research field, a semistructured interview was conducted with 12 teachers engaged in management, teaching, and scientific research. According to the analysis of NVivo data, the Rural Revitalization Strategy promotes the expansion of agricultural primary schools in the four functional areas of teaching, scientific research, social services and cultural inheritance, and innovation, that is, to provide the “three rural” identity and value construction of agricultural primary school students under the curriculum reform; build first-class disciplines and first-class research and become the booster of Rural Revitalization; create a “decent” campus culture for agriculture, rural areas, and farmers; and cultivate new talents with morality, feelings, knowledge and ability.
Digital Point Solutions for Extending Contact Tracing Capacity

Background Initial COVID-19 surges in the United States created a need for technology to supplement human resources to increase efficiency and efficacy. Methods Resolve to Save Lives worked with jurisdictions to co-design four technology solutions—Epi Viaduct data pipeline, Epi Contacts contact elicitation webform, Epi Locator contact information search plugin, and Epi Viewpoint case management system (CMS)—to expand the capacity of case investigation and contact tracing (CI/CT) teams. We assessed impact on reducing CI/CT time intervals for COVID-19 using product data and user feedback. Results Epi Viaduct accelerated the transfer of approximately 7,400,000 records from an electronic laboratory reporting system in a single jurisdiction to the respective CMS from more than 2.5 hours to less than 1 minute and reduced time to remove duplicate laboratory results from multiple days to less than 6 hours. Epi Contacts focused on increasing the efficacy of contact elicitation, and during a single period, 10% of index cases (9,440 of 96,319) completed Epi Contacts for a total of approximately 18,700 contacts elicited. User interviews indicated the tool increased speed of CI/CT workflows. In total, 134,410 searches were run in Epi Locator by 7320 distinct users—75% of which returned 1 or more person matches. A simple CMS, Epi Viewpoint, was developed and completed, but not deployed. Conclusions Systems to mount large-scale population-based contact tracing programs were developed and implemented during the COVID-19 pandemic and can be adapted for CI/CT programs aiming to control the spread of other communicable diseases such as sexually transmitted diseases.
A Multi-task Learning Model for Fine-Grain Dialogue Social Bias Measurement

In recent years, the use of NLP models to predict people’s attitudes toward social bias has attracted the attention of many researchers. In the existing work, most of the research is at the sentence level, i.e., judging whether the whole sentence has a biased property. In this work, we leverage pre-trained models’ powerful semantic modeling capabilities to model dialogue context. Furthermore, to use more features to improve the ability of the model to identify bias, we propose two auxiliary tasks with the help of the dialogue’s topic and type features. In order to achieve better classification results, we use the adversarial training method to train two multi-task models. Finally, we combine the two multi-task models by voting. We participated in the NLPCC-2022 shared task on Fine-Grain Dialogue Social Bias Measurement and ranked fourth with the Macro-F1 score of 0.5765. The codes of our model are available at github ( https://github.com/33Da/nlpcc2022-task7 ).
Dimension Reduction Method by Principal Component Analysis in the Prediction of Final User Satisfaction

User Experience (UX) issues are widely used for product or service evaluation. Although UX data provide abundant information about behavior log data, feeling and user satisfaction, high dimensionality and long time periods substantially increase the computational burden. In UX evaluation, one challenging task in pre-processing is to reduce the redundancy of the UX data without losing valuable details. In this paper, we present a dimension reduction method by Principal Component Analysis (PCA) in the prediction of final user satisfaction. The dimension reduction method in our research not only serves as the first step in UX data pre-processing that leads to a significant reduction in computational complexity, it is also an invaluable research tool for retaining key information in UX data. In this study, we tested the dimension reduction method with PCA based on satisfaction questionnaires about travel website usage. The proposed method achieves good results in terms of accuracy when compared with original UX data.