Reviewerly/exHarmony huggingface dataset:
The Design and Implementation of the Client for the Integrated Office Platform

End-users in our hospital operate the integrated office platform (IOP) with too many steps. In detail, it lacks automatic detection of new mails or large files, and a fast access way to IOP. This paper designs and implements a client for IOP (CIOP): a) CIOP designs and implements a session handover mechanism between two kinds of browsers: CIOP implements the former browser, which is used to detect the numbers of new mails and large files automatically periodically or manually at any time, and CIOP subsequently launches the latter browser, IE, which joins the same session generated by the former browser and IOP. And IE can quickly enter the homepage, the mail page or the large-file transferring page of IOP. b) CIOP detects and repairs errors of the CIOP configuration file automatically. c) CIOP detects and prompts the errors of the sound card or network automatically. CIOP has a good robustness, which has been promoted in the 8th Medical Center of Chinese PLA General Hospital. And the application effect is perfect. This paper is of valuable reference for network engineers to make further studies for the interaction between browsers and a Web Server.
Incorporating Domain Knowledge and Semantic Information into Language Models for Commonsense Question Answering

Commonsense question answering (CSQA) aims to answer questions which require the system to understand related commonsense knowledge that is not explicitly expressed in the given context. Recent advance in neural language models (e.g., BERT) that are pre-trained on a large-scale text corpus and fine-tuned on downstream tasks has boosted the performance on CSQA. However, due to the lack of domain knowledge (e.g., in social situations), these models fail to reason about specific tasks. In this work, we propose an approach to incorporate domain knowledge and semantic information into language model based approaches for better understanding the related commonsense knowledge. Firstly, we extract the knowledge from existing resources by jointly learning to ask and answer as well as semantic role labeling based answering. These two tasks are correlated and can reinforce each other to discover the domain knowledge. Then, we utilize Semantic Role Labeling to enable the system to gain a better understanding of relations among relevant entities. Experimental results on several CSQA benchmarks demonstrate the effectiveness of the proposed approach.
Exploiting Structured Error to Improve Automated Scoring of Oral Reading Fluency

In order to track the development of young readers’ oral reading fluency (ORF) at scale, it is necessary to move away from hand-scoring responses to automating the assessment of ORF, while retaining the quality of the scores. We present a method for improving automated ORF scoring that utilizes an observed systematicity in machine error, namely, that cases with low estimated reading accuracy are harder to score correctly for fluency. We show that the method yields an improved performance, including on out-of-domain data.
Exploring Lexical Irregularities in Hypothesis-Only Models of Natural Language Inference

Natural Language Inference (NLI) or Recognizing Textual Entailment (RTE) is the task of predicting the entailment relation between a pair of sentences (premise and hypothesis). This task has been described as “a valuable testing ground for the development of semantic representations” [1], and is a key component in natural language understanding evaluation benchmarks. Models that understand entailment should encode both, the premise and the hypothesis. However, experiments by Poliak et al. [2] revealed a strong preference of these models towards patterns observed only in the hypothesis, based on a 10 dataset comparison. Their results indicated the existence of statistical irregularities present in the hypothesis that bias the model into performing competitively with the state of the art. While recast datasets provide large scale generation of NLI instances due to minimal human intervention, the papers that generate them do not provide fine-grained analysis of the potential statistical patterns that can bias NLI models. In this work, we analyze hypothesis-only models trained on one of the recast datasets provided in Poliak et al. [2] for word-level patterns. Our results indicate the existence of potential lexical biases that could contribute to inflating the models' performance.
Self managed data protection for containers

Container frameworks have been gaining popularity in recent years, with container native storage being one of the fastest growing segment. According to IDC report [1], 90% of applications on cloud platforms and over 95% of new microservices are being deployed in containers. The growth of container native storage is largely driven by stateful applications [2, 3], the mainstay of enterprise IT environments. As organizations are increasingly adopting containerized deployments, they must also deal with data protection to maintain business continuity.
Synergy Between Convergence and Divergence—Review of Concepts and Methods

Modern Industry 4.0 technologies face a challenge in dealing with billions of connected devices, petabyte-scale of generated data, and exponentially growing internet traffic. Artificial Intelligence and Evolutionary algorithms can resolve variety of large optimisation problems. Many methods employed in search for solutions often fall in stagnation or in unacceptable results, which reminds for classical dilemma exploration versus exploitations closely related with convergence and diversity of the explored solutions. This article reviews convergence and divergence centred algorithms and discuses synergy between convergence and divergence in adaptive heuristics.
Low-budget 3D scanning and material estimation using PyTorch3D

In this paper, we present recent results on our effort to develop low-budget, high precision 3D scanning solutions for art museum conservators. There is a real need for this in cultural heritage applications where the potential to detect and monitor degradations is of critical importance, but financial resources for high-quality 3D scanning devices is limited to a select few larger institutions. The technology we have developed uses just a screen and camera to recover sub-mm 3D surface features in paintings, drawings, and stained-glass artworks (see Figs. 1 - 3 ). Our mobile Multi-view phase-shifting deflectometry system allows for the three-dimensional measurement of extended specular surfaces with high surface normal variations [1] . It consists only of a mobile handheld device (such as a tablet) and exploits screen and front camera for reflectance-based surface measurements.
Analysis of Banks with State Participation Functioning the Role of Development Banks in the Global and National Dimensions

The chapter deals with results of evaluating the development of banks with state participation and the functioning of development banks in the global and national dimensions. It presents and interprets theories of banking with state participation—social, political and agency. It is concluded that a bank with state participation is the result of positioning the need to achieve the following goals: ensuring the security and stability of the banking system; levelling market failures due to information asymmetry; financing social investments; ensuring financial development and access to competitive banking products and services for customers. It is proposed to use the potential of new development banks; this could achieve national interests and solve the problem of expanding the set of financing sources for capital-intensive infrastructure projects.
Diagnosis, Problems, and Alternatives for College Journalism Education in South Korea

This study reviewed the programs of college journalism education in South Korea. The review on journalism education programs in ten universities showed that journalism education models in South Korea are different from both the American model that emphasizes field practices and the European model that emphasizes contextual knowledge for journalism. College journalism education in South Korea can be characterized as “the department store model of journalism education,” which focuses on various media and communication theories, with a small number of journalism courses. Journalism education programs in South Korea do not provide a systematic curriculum for practical skills required in the field. Only small proportion of faculty members was found to dedicate themselves to journalism-related courses. There are few journalism-related courses and instructors for the digital media environment. Goals of journalism education are found to be rather abstract and ambiguous, aiming at both the practical skills and theories. New models of journalism education for the new media and digital environment are discussed.
Containerization Design for Autonomous and Controllable Cloud Distributed System

With the relative popularity of cloud computing, a large number of services and applications have been deployed in cloud environments. However, the existing cloud technology architecture still has many problems. This paper summarizes the problems existing in the communication module in the development of cloud technology so far, such as the three major problems of complicated cloud’s architecture, homogeneous service, and pseudo-saturation of communication. Based on these problems, we give the corresponding solutions for our system under a distributed architecture container. This solution is implemented under autonomous and controllable conditions and can ensure the robustness and relative security of the system and ensure the smoothness of external communications. Our work has made a certain contribution to the current cloud computing environment and made up for some of the shortcomings of existing cloud environment communication modules.
DCDFNE: build dataset to evaluate binary codes diffing tools and train deep learning models

As comparison tools, BinDiff, PatchDiff2, Darun-Grim, eEye Binary Diffing Suite provide diffing functionality for binary codes files. In this paper, we propose one framework DCDFNE for building dataset. The built dataset could be used in two aspects: evaluate the performance of the binary codes comparison tools; train deep learning models which predict the binary codes are similar or not. DCDFNE includes four sections: the first section collects information about multiple projects from GitHub, the collected information is stored in the database which includes source codes of different versions and commit information between parent and child versions; the second section implements the function of distributed compilation of vim source codes; the third section extracts the differential function name which represents the modification between the two versions of the commit files; the fourth section combines the results of the second section and the third section, namely the binary codes and the differential function names, then extracts the binary codes of the differential functions. The experiment results show that the distributed compilation algorithm improves 99.80% compared to serial compilation algorithm; the extraction method extracts the differential function names effectively.
Challenges and Applications of Multimedia Big Data Computing

Multimedia is taken into inspection as one of the highest challenging and stimulating instruments in the remaining day collection of statistics. Every 2d a lot of program material are formed and communicated all around the domain thru one of a kind forms of secure networks. Big records gadget varieties a subject of documented content-based completely analytics. Multimedia Big Data Computing (MMBD) concentration on all of the essentials of distributed manipulative system and as a result documentations considerable quantity photograph and video analytics. There are various presentation of multimedia those are within the area of social media, internet data recovery, and engineering, autonomous petrol engine knowing cities, forensics and biomedicine. This impoverishment will hold the implication of multimedia big data computing, its applications and the frequent experiments that upset upon.
An Automatic Digital Modulation Classifier Using Higher-Order Statistics for Software-Defined Radios

Automatic modulation recognition (AMR) plays a vital role in various applications such as software-defined radios (SDRs), cognitive radio (CR) receivers, and surveillance systems. This paper is based on the recognition and classification of digital modulation schemes. The designed system is capable of recognizing nine digital modulation schemes, namely 2FSK, 4FSK, 8FSK, 2PSK, 4PSK, 8PSK, 16QAM, 64QAM, and 256 QAM in additive white Gaussian noise (AWGN) environment. The system includes two steps: feature extraction and classification. In feature extraction step has used seven higher-order cumulants, namely C40, C42, C44 C51, C53, C62, and C80 as features for statistical analysis of signals. In the classification, the step has used the principal component analysis technique on the feature set for compression of data and for classification of different modulation schemes. The simulation results show that the presented system has 100% classification accuracy at 16 dB SNR.
An Experimental Analysis to Learn Data Imbalance in Scholarly Data

Data imbalance is a key challenge in the majority of real-world classification problems. It refers to the disparity of data instances corresponding to either of the class labels. Data imbalance is studied in detail with respect to many data domains such as transaction data, medical data, e-commerce data, meteorological data, social media data, and web data. But the scholarly data domain is yet to be analyzed pertaining to data imbalance. In this chapter, the scholarly data domain is explored with a focus to study various forms of data imbalance. A well-known and popular scholarly platform, ResearchGate (RG), is targeted to extract real scholarly data. An extensive experimental analysis is performed on the extracted data in order to identify the existence of both data-level and network-level imbalance. The outcome contributes to the learning of various types of data imbalance that exist in scholarly data. Resolving the existing data imbalance will substantially help in achieving efficient and accurate outcomes in many real-world scholarly literature applications.
Optimization method of acoustic measurement station placement for wide-area miss distance

Abstract This paper focuses on the issue of how to select the optimal deployment scheme for the dispersal of miss distance during acoustic rendezvous measurements. Firstly, the measurement theory of acoustic rendezvous is introduced, and then the error model of acoustic rendezvous measurement is deduced and theoretical analysis is carried out. Then, an error evaluation index is established based on the dispersion characteristics of miss distance. Finally, simulation experiments are conducted for the miss distance obeying uniform distribution and normal distribution. Simulation experiments verify the correctness of the method in this paper, which has important guiding significance for the miss distance test.
Research on Computer Teaching in Universities Based on Computational Thinking

Abstract With the development of computer technology, basic computer application has become a basic course in various professional courses. Of course, with the development of network information technology, computer teaching is facing new challenges and opportunities. This paper mainly focuses on the current situation and reform of computer teaching.
Lesion Detection and Classification Using Sematic Deep Segmentation Network

Innovation of modern algorithms directed to development of effective segmentation and classification for liver segments. The lesion segmentation is essential in pre-operative liver surgical planning. In addition, the segmentation of liver lesions is considered as a crucial step for deriving qualitative biomarkers for precise clinical diagnosis. The aim of the research is to model an effective method that comprises of the extraction of the liver vessels and classification of liver segments for which Computed Tomography (CT) images is utilized. In classification module, the lesion detection and classification are performed using newly designed optimization algorithm named Adaptive Adam optimization algorithm along with DeepSegNet model. The proposed Adaptive Adam is designed by integrating adaptive concepts in the Adam algorithm. The liver image undergoes segmentation, wherein the tumors present in the liver are extracted based on an optimization strategy. Moreover, the accuracy is attained based on the training of the DeepSegNet using proposed Adaptive Adam algorithm. In this strategy, the probability for lesion or non-lesion is identified effectively. The performance of proposed Adaptive Adam algorithm found superior in terms maximal accuracy 0.846, sensitivity value of 0.881, and specificity value of 0.828, respectively.
Analysis of the Messages from Social Network for Emergency Cases Detection

Analysis of the publicly available data from social networks may benefit many practical applications, and analysis of text messages to reveal emergent cases is an urgent and important task. Wide spread of social media may be used in order to detect and manage rescue operations. In this paper, an approach to analysis of public messages from social network for understanding the emergency scenario developing and rescue operation managing is proposed. This approach allows filtering data according to the settings chosen by researcher and visualizing the result of analysis using two options: placing number of messages on the map and creating a pie diagram. The designed application was tested on data from VAST Mini-Challenge 3 2019 describing social network response on earthquake. The experiments showed that the proposed approach allows detecting the most dangerous areas in the city and highlighting them visually.
A power sum formula by Carlitz and its applications to permutation rational functions of finite fields

A formula discovered by L. Carlitz in 1935 finds an interesting application in permutation rational functions of finite fields. It allows us to determine all rational functions of degree three that permute the projective line $\mathbb {P}^{1}(\mathbb {F}_{q})$ over $\mathbb {F}_{q}$ , a result previously obtained by Ferraguti and Micheli through a different method. It also allows us to determine all rational functions of degree four that permute $\mathbb {P}^{1}(\mathbb {F}_{q})$ under a certain condition. (A complete determination of all rational functions of degree four that permute $\mathbb {P}^{1}(\mathbb {F}_{q})$ without any condition will appear in a separate forthcoming paper.)
Computer Technology in the Network Construction and Practice of Management Equipment in Gymnasium

In the construction of stadium management network, advanced computer technology occupies a major position. Computer network provides the possibility of network construction for stadium management. This article aims to study the application of computer technology in the network construction and practice of management equipment in gymnasiums. This article uses the method of supplementing points to solve the problem of breakpoints in the system, so as to prompt the user to check the device hardware environment and network environment. The experiment uses computer technology in the construction of management networks, strengthens the relationship between various modules, and transforms tedious and disorderly management work into orderly tasks, thereby changing the carrier form of management. Experimental data shows that due to the platform request test limit, 100 threads are used concurrently in this test, each time increasing by 20, to obtain the delay time data of the device management function. The experimental results show that the system’s service quality can be measured by the system response time, and the needs of users can be met. In this experiment, when the number of concurrent threads is less than 520, 95% will respond within 0.75 s, and the average response time is 0.48 s. Speeding up the construction of the stadium's management network has greatly improved the management of the stadium’s information resources, deepened the management level, and carried out network work in line with the management needs, so that the work of the stadium has a qualitative leap.
One Line Soft Keyboard with T9-keys Layout Proposal on Smartwatch Touch Screen Device

Abstract A smartwatch is a mini-sized digital computer device that functions apart from being a watch and can also perform intelligent computing tasks. Due to screen size limitations, a smartwatch requires special design techniques on its input and output to allow interaction with its users. Text entry is often required by many applications, but because the smartwatch screen is small, text entry techniques commonly used on personal computers are impractical when applied to smartwatches. In this study, we introduce a design concept proposal for a soft keyboard called One Line Soft Keyboard with T9-Keys Layout. The soft keyboard design concept is expected to reduce the space on the keyboard on the smartwatch screen, along with the use of widely used button layouts that are familiar to users.
Development and Application of Online English Education Platform Based on Digital Teaching Resources

The application of digital resources is very important in the design of English teaching platform. Traditional English teaching resources can not be matched precisely because of the narrow source of digital teaching resources. Web mining technology makes personalized teaching possible. The web mining technology to track and collect a large amount of data left by learners in the online course of modern English grammar is used in this paper. Through the analysis of the learners' learning habits, learning progress, learning effect and so on, we can customize a reasonable personalized learning platform for them. The experimental results show that the platform can improve the personalized online teaching service system, optimize teaching resources, and build a customized online teaching platform.
Isolated Kannada Character Recognition Using Transfer Learning

Kannada is a Dravidian Language spoken by over 80 million people all over the world; Kannada script’s character recognition system has wide range applications in education, healthcare, finance and many more sectors. Building it involves many phases and makes it more challenging because of the nature of script including horizontal skews and curves. Although challenging, several attempts have been made in the past to develop methods to either avoid the intermediate steps or reduce the complexity involved, thereby effecting the time and cost factors. The objective of this work is to understand Why, achieving effective recognition rates using recent technology is still challenging? And Is it possible to overcome the existing challenges and drawbacks by retraining different deep neural networks, using different Transfer Learning techniques? Four different Transfer Learning techniques, two different pre-trained deep neural networks and different dataset were employed, resulting in an exceptional recognition rate of 92.5%.