Reviewerly/exHarmony huggingface dataset:
The Application of Computer Database System in Educational Information Management

The development and progress of mankind promote continuous innovation in science and technology. It is the age of information and the age of science and technology at 21st century. The appearance of computers is an important milestone in the development of science and technology. It has greatly improved the efficiency of people's work and study and has also promoted the globalization of information. Among computer science and technology, database system is very important, and its development speed is very fast, today, the application of database system is also more and more common. A good application of computer database system in education information management can effectively improve the management level and work efficiency. This article analyzes and discusses computer database system application in education information management and explains it.
An Unending Data Breach Immune to Audit? Can the TCF and RTB Be Reconciled with the GDPR?

The majority of Internet advertising is served using a system called Real-Time Bidding (RTB). RTB exposes the personal data of Internet users to large numbers of companies without any means of control over what happens to that data. This is a security problem and is irreconcilable with the European legal requirement that processing of personal data must be secure, accountable, and transparent. For several years the RTB industry used the “Transparency & Consent Framework” (TCF) to provide legal cover. However, in February 2022 European authorities made a landmark decision declaring the use of the TCF for RTB illegal. The TCF’s creator, IAB Europe, was ordered to bring the TCF into compliance with the GDPR by demonstrating that it can account for what happens to TCF data, including in RTB. IAB Europe claims two new initiatives enable it to do so: the “Vendor Compliance Programme” and the “Global Accountability Platform”. We examine both in this paper. Our conclusion is that the use of the TCF for RTB is impossible to monitor, audit, or secure.
Towards Building Economic Models of Conversational Search

Various conceptual and descriptive models of conversational search have been proposed in the literature – while useful, they do not provide insights into how interaction between the agent and user would change in response to the costs and benefits of the different interactions. In this paper, we develop two economic models of conversational search based on patterns previously observed during conversational search sessions, which we refer to as: Feedback First where the agent asks clarifying questions then presents results, and Feedback After where the agent presents results, and then asks follow up questions. Our models show that the amount of feedback given/requested depends on its efficiency at improving the initial or subsequent query and the relative cost of providing said feedback. This theoretical framework for conversational search provides a number of insights that can be used to guide and inform the development of conversational search agents. However, empirical work is needed to estimate the parameters in order to make predictions specific to a given conversational search setting.
Research and Construction of Classical Formulas Knowledge Graph Based on Ontology

Classical formula is an important part and basis of traditional Chinese prescriptions. The effective storage and expression of classical formula knowledge in ancient books and modern documents is a key issue for studying and using classical formulas. This paper standardized and normalized the contents of the classic formula in "Treatise on Exogenous Febrile Disease" and "The Synopsis of the Golden Chamber", collected and organized classical formulas related documents from China National Knowledge Infrastructure (CNKI). Ontology construction tool Protégé is used to establish the domain ontology of classical formulas, and Neo4j graph database is used to construct the knowledge graph of ancient books and modern CNKI documents. 296 ancient classical formula items and 11175 modern documents from CNKI are collected, normalized and stored in database as the data source of this research. On the basis of these work, constructed the ontology and knowledge graph of classical formulas and built an application system for knowledge query. Constructing knowledge graph from top to bottom based on ontology can express and visualize the related knowledge of classical formulas accurately and efficiently. The construction strategy mentioned in this paper has got a good result and showed great potential in traditional Chinese medicine knowledge domain.
AFTTM

Cloud Computing has now successfully emerged as an integral trend in Information Technology market. It is internet-based concept of virtualization. Many failures can occur in computing environment that actually leads to non-availability and non-reliability upon service providers. To ensure this, a mechanism has been proposed in this paper which can deploy the checkpoint module so that faults can be detected timely and shall be removed in due course of time so that reliable and fault free cloud services can be provided to consumers. In this, fault tolerance agent has been used which actually is an intelligent and reliable agent that helps in detecting the malicious faults on time. The proposed framework considers throughout overheads, checkpoints overheads, monetary costs overheads and availability overheads in its performance validation. The proposed framework has been compared with other two frameworks and has been identified that AFTTM outperforms several on-going techniques in fault tolerant techniques.
A Speech-Based Model for Tracking the Progression of Activities in Extreme Action Teamwork

Designing computerized approaches to support complex teamwork requires an understanding of how activity-related information is relayed among team members. In this paper, we focus on verbal communication and describe a speech-based model that we developed for tracking activity progression during time-critical teamwork. We situated our study in the emergency medical domain of trauma resuscitation and transcribed speech from 104 audio recordings of actual resuscitations. Using the transcripts, we first studied the nature of speech during 34 clinically relevant activities. From this analysis, we identified 11 communicative events across three different stages of activity performance-before, during, and after. For each activity, we created sequential ordering of the communicative events using the concept of narrative schemas. The final speech-based model emerged by extracting and aggregating generalized aspects of the 34 schemas. We evaluated the model performance by using 17 new transcripts and found that the model reliably recognized an activity stage in 98% of activity-related conversation instances. We conclude by discussing these results, their implications for designing computerized approaches that support complex teamwork, and their generalizability to other safety-critical domains.
Multi-dimensional joint clustering and auxiliary abnormal judgment of state data of power transmission and transformation equipment

research-article Share on Multi-dimensional joint clustering and auxiliary abnormal judgment of state data of power transmission and transformation equipment Author: Jian Zhong Sinohydro Bureau 10 Co., Ltd, China Sinohydro Bureau 10 Co., Ltd, ChinaView Profile Authors Info & Claims ICASIT 2021: 2021 International Conference on Aviation Safety and Information TechnologyDecember 2021 Pages 797–802https://doi.org/10.1145/3510858.3511391Published:04 March 2022Publication History 1citation8DownloadsMetricsTotal Citations1Total Downloads8Last 12 Months8Last 6 weeks1 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 SiteGet Access
Research on architecture and smart contract of household intelligent power service platform based on blockchain

Household intelligent power service (HIPS) is an interaction service between power grid companies and household users. Power grid companies release interactive electricity demand and response subsidy price through HIPS platform to attract users to participate in orderly electricity consumption, so as to reduce the peak debt ratio of power consumption in stations or improve the consumption rate of new energy. This paper proposes an architecture of HIPS platform based on the Consortium blockchain, and then designs smart contracts for three types of participants: power grid companies, load aggregators and users. Finally, the proposed smart contract is implemented.
Zero-Watermark Method Based on Non-Uniform BTC Partition

Zero-watermarking is an effective method to better pro tect the copyright of images. This paper proposes a new ze ro-watermarking algorithm based on Non-uniform Block Tr uncation Coding (NUBTC). In the proposed scheme, the ori ginal image is divided into different rectangular grids accor ding to the non-uniform block-truncation-coding partition, a nd the feature matrix is formed by calculating number of rectangles in each 8*8 sub-area. The obtained feature matri x is further mapped to a binary watermark after Arnold s crambling, and both of them are stored as the zero water mark. Experimental results indicate that the extracted zero-watermark could keep more than 90% similarity to the un attacked zero-watermark under JPEG com pression attack a nd other various noise attacks, thus proving that the propo sed zero-watermark scheme is very robust when sufferring attacks.
Ultra-short-Term PV Power Generation Prediction Based on Gated Recurrent Unit Neural Network

As a clean and renewable energy source, solar energy makes photovoltaic (PV) generation become one of the most viablepower generation resources. However, photovoltaic power generation is greatly affected by weather changes and random changes, which limits the development of photovoltaic grid-connected systems. To timely control the impact and fluctuation brought by photovoltaic grid connection, it is very necessary to establish a reliable photovoltaic power generation forecasting model. This paper utilizes the Gated Recurrent Unit (GRU) neural network to predict the photovoltaic power generation in ultra-short time. The proposed method is applied to the DC competition dataset and a smart microgrid photovoltaic dataset. After the data set is analyzed and preprocessed, different loss functions are used to train the GRU model. The comparison analysis with the Deep Neural Network (DNN) model and the Support Vector Machine (SVR) model shows that the proposed method exhibits high performance in forecasting accuracy method achieve more accurate prediction result. In addition, experiment under different weather conditions shows that the GRU network model could adapt to weather changes.
Multimodal Unsupervised Image-to-Image Translation Without Independent Style Encoder

The multi-modal image-to-image translation frameworks often have the problems of complex model structure and low training efficiency. In addition, we find that although these methods can maintain the structural information of the source image well, they cannot transfer the style of the reference image well. To solve these problems, we propose a novel framework called Multimodal-No-Independent-Style-Encoder Generative Adversarial Network (MNISE-GAN) that simplifies the overall network structure by reusing the front part of the discriminator as the style encoder so it can achieve multi-modal image translation more effectively. At the same time, the discriminator directly uses the style code to classify real and synthetic samples, so it can enhance the classification ability and improve training efficiency. To enhance the style transfer ability, we propose a multi-scale style module embedded in the generator, and propose an Adaptive Layer-Instance-Group Normalization (AdaLIGN) to further strengthen the generator’s ability to control texture. Extensive experiments on four popular image translation benchmarks quantitative and qualitative results demonstrate that our method is superior to state-of-the-art methods
A Study on Native American English Speech Recognition by Indian Listeners with Varying Word Familiarity Level

In this study, listeners of varied Indian nativities are asked to listen and recognize TIMIT utterances spoken by American speakers. We have three kinds of responses from each listener while they recognize an utterance: 1. Sentence difficulty ratings, 2. Speaker difficulty ratings, and 3. Transcription of the utterance. From these transcriptions, word error rate (WER) is calculated and used as a metric to evaluate the similarity between the recognized and the original sentences. The sentences selected in this study are categorized into three groups: Easy, Medium and Hard, based on the frequency of occurrence of the words in them. We observe that the sentence, speaker difficulty ratings and the WERs increase from easy to hard categories of sentences. We also compare the human speech recognition performance with that using three automatic speech recognition (ASR) under following three combinations of acoustic model (AM) and language model (LM): ASR1) AM trained with recordings from speakers of Indian origin and LM built on TIMIT text, ASR2) AM using recordings from native American speakers and LM built on text from LIBRI speech corpus, and ASR3) AM using recordings from native American speakers and LM build on LIBRI speech and TIMIT text. We observe that HSR performance is similar to that of ASR1 whereas ASR3 achieves the best performance. Speaker nativity wise analysis shows that utterances from speakers of some nativity are more difficult to recognize by Indian listeners compared to few other nativities.
Camera Lens Dust Detection and Dust Removal for Mobile Robots in Dusty Fields

In this paper, a new method of detection of the dust particle in camera lens placed on a moving robot is implemented. Then further for correction of recorded/live data a dust removal technique using adaptive tiling-based selective dust area coverage scheme has been implemented. An artifact such as dust is a main problem for camera lenses, which are used in many fields for example in photogrammetry, robotics inspection, geospatial data capture etc… The goal is to enhance the dust detection and correct the image data by comparing consecutive frames captured by moving robots and cleaning it by using the technique specified in the proposed system without much affecting the original data frame. Finally, our simulation results demonstrates that the proposed method was able to remove the dust particles with an accuracy of 90-92%. This accuracy without affecting the actual data is quite significant.
Aspect-Based Sentiment Analysis with a Position-Aware Multi-head Attention Network

Aspect-based sentiment analysis aims to predict the sentiment polarities of the given aspects in its context. A gated recurrent unit (GRU) network can effectively obtain the temporal feature of the context, and the multi-head attention (MHA) mechanism can effectively obtain the vertical connection of the context. This paper proposes a position-aware multi-head attention network, which combines a GRU network with MHA. First, the input layer contains position information of aspect terms, word embedding, and aspect embedding. Second, we process the input information separately through the GRU network and MHA to obtain vertical space information of context and time-series information. Finally, we employ an interactive approach to learn aspect terms and context, generating a more efficient representation for aspect and context. Extensive experiments on the dataset of SemEval 2014 demonstrate the effectiveness of our proposed model.
Interpreting and Comparing Convolutional Neural Networks: A Quantitative Approach

A convolutional neural network (CNN) is sometimes understood as a black box in the sense that while it can approximate any function, studying its structure will not give us any insights into the nature of the function being approximated. In other terms, the discriminative ability does not reveal much about the latent representation of a network. This research aims to establish a framework for interpreting the CNNs by profiling them in terms of interpretable visual concepts and verifying them by means of Integrated Gradient. The interpretability profiling has been done by evaluating the correspondence between individual hidden neurons and a set of human-understandable visual semantic concepts. An integrated gradient-based class-specific relevance mapping approach is proposed that verifies interpretability profiling. Moreover, it is insightful to examine the correlation between the different input classes in terms of an overlapping set of highly active neurons. The result suggests the existence of a structured set of neurons inclined to a particular class. Finally, network ablation is performed to illustrate the performance of the network based on our approach.
Continuous Nonlinear State Prediction by Finite Volume Method on Logically Rectangular Grids

This paper deals with the state estimation of the nonlinear stochastic models with continuous dynamics and discrete measurement. Namely, the numerical solution to the Fokker-Planck equation, representing the estimator time update, is explored. Unlike the classical approaches, which use the finite difference method and operate on a standard rectangular grid, this paper utilizes finite volume method on the logically rectangular grid. In particular, the circular grid is chosen, which better approximates an estimated conditional probability density function support and improves time update accuracy without any impact on the its computational complexity. The benefits of the solution are illustrated in a numerical example. Keywords: State estimation, Bayesian inference, Nonlinear systems.
Unsupervised Blur Kernel Estimation and Correction for Blind Super-Resolution

Blind super-resolution (blind-SR) is an important task in the field of computer vision and has various applications in real-world. Blur kernel estimation is the main element of blind-SR along with the adaptive SR networks and a more accurately estimated kernel guarantees a better performance. Recently, generative adversarial networks (GANs), comparing recurrence patches across scales, have been the most successful unsupervised kernel estimation methods. However, they still involve several problems. ① Their sharpness discrimination ability has been noted as being too weak, causing them to focus more on pattern shapes than sharpness. ② In some cases, kernel correction processes were omitted; however, these are essential because the optimally generated kernel may be narrower than a point spread function (PSF) except when the PSF is ideal low-pass filter. ③ Previous studies also did not consider that GANs are affected by the thickness of edges as well as PSF. Thus, in this paper, 1) we propose a degradation and ranking comparison process designed to induce GAN models to became sensitive to image sharpness, and 2) propose a scale-free kernel correction technique using Gaussian kernel approximation including a thickness parameter. To improve the kernel accuracy further, we 3) propose a combination model of the proposed GAN and DIP(deep image prior) for more supervision, and designed a kernel correction network to propagate gradients through developed correction method. Several experiments demonstrate that our methods enhanced the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{2}$ </tex-math></inline-formula> error and the shape of the kernel significantly. In addition, by combining with ordinary blind-SR algorithms, the best reconstruction accuracy was achieved among unsupervised blur kernel estimation methods.
Evaluating Factors for Successful Technological Implementation in the Indian Banking Industry Using DEMATEL

The banking system is a crucial constituent of the financial ecosystem. Over the last two decades, there is a continuous movement in the banking industry from physical banking towards digital banking. The banks have started recognising that adopting new technologies is essential to remain competitive. Yet, the process of digital transformation is slow. The paper aims to identify and analyse the factors for successful technological implementation in the Indian Banking Industry. Further, this research seeks to establish a cause-effect relationship among them using the DEMATEL approach. For the purpose of the study, twelve factors were identified that are contributing towards successful implementation of technology using literature reviews and opinion of an expert. The study highlighted the crucial component for the successful technological implementation in the Indian Banking Sector are: regulations, government support, bank size, top management support.
Corpus Creation for Anaphora Resolution in Punjabi Language

Annotated corpora are greatly required for the development of various applications of NLP. This paper concentrates on creating corpus for anaphora resolution which is required in automatic question answering, machine translation, and summarization. The work presented in this study is based on annotating Punjabi corpora with morphological information, POS tagging, and anaphoric links. We also propose to add additional features like animacy, NER, and pronoun type which will be used for machine learning module. Our aim is to build a real corpus that will be used for training and evaluation of anaphora resolution for Punjabi language focusing on pronominal resolution.
ParsiNorm: A Persian Toolkit for Speech Processing Normalization

In general, speech processing models consist of a language model along with an acoustic model. Regardless of the language model&#x0027;s complexity and variants, three critical pre-processing steps are needed in language models: cleaning, normalization, and tokenization. Among mentioned steps, the normalization step is so essential to format unification in pure textual applications. However, for embedded language models in speech processing modules, normalization is not limited to format unification. Moreover, it has to convert each readable symbol, number, etc., to how they are pronounced. To the best of our knowledge, there is no Persian normalization toolkits for embedded language models in speech processing modules, So in this paper, we propose an open-source normalization toolkit for text processing in speech applications. Briefly, we consider different readable Persian text like symbols (common currencies,#,@,URL, etc.), numbers (date, time, phone number, national code, etc.), and so on. Comparison with other available Persian textual normalization tools indicates the superiority of the proposed method in speech processing. Also, comparing the model&#x0027;s performance for one of the proposed functions (sentence separation) with other common natural language libraries such as HAZM and Parsivar indicates the proper performance of the proposed method. Besides, its evaluation of some Persian Wikipedia data confirms the proper performance of the proposed method.