Reviewerly/exHarmony huggingface dataset:
Blockchain Technology and New Business Models for Banks in the Financial Services Industry

In recent years, the financial technology of blockchain has become a disruptive innovation that is transforming the management of banks. If blockchain represents an opportunity for financial services, it also represents a severe threat of financial disintermediation. What are the business models available to banks when deploying a business model to integrate the blockchain technology? In this chapter, the authors surveyed the strategic intent of 45 international banks for blockchain technology and analyzed the different business models that might facilitate a management transformation for the banking industry. They developed a taxonomy of five different business models. They were labeled as the following: the coordinators, the integrators, the solution providers, the disrupters, and the explorers.
Energy Efficient Workload Processing Technology for Distributed Computing Environment

The energy consumption of modern data centers is currently growing rapidly, due to the development of new services and the spread of the ICT (Information & Communication Technologies) in all areas of human life. It causes a huge demand in new energy efficient computing technologies which would simultaneously improve data processing performance meeting the requirements of SLA.
A Growing Self-Organising Maps Implementation for Coherency Identification in a Power Electronics Dominated Power System

The presence of power electronics in today's power systems strengthens due to the wider integration of renewable energy and energy storage systems. Subsequently, the dynamical response becomes harder to model and understand. As a possible solution, coherency identification, among other applications, can reduce complexity. However, conventional tools possess limitations related to the assumptions need to be taken beforehand. In this paper, we propose a fully unsupervised variation of neural networks called the growing self organising maps (GSOM). The main advantage of GSOM over traditional methods is that network structure is not fixed, thus previous assumptions about the number of coherent groups or data structure are not necessary. A spreading factor controls the growth rate of the network allowing the analyst to choose the level of granularity whilst ensuring topology preservation. The effectiveness of the proposed algorithm is tested on the Nordic 32 power system.
On automated workflow for fine-tuning deepneural network models for table detection in document images

Nowadays methods and software for extracting tables from document images and portable documents (PDF) continue to be actively developed. One of the promising approaches to this task is the usage of fine-tuned object detection models. However, this approach involves many manipulations with data preparation and training process configuration. This paper proposes an automated workflow for fine-tuning deep neural network models for the table detection in document images. It enables us to automate two sub-tasks: (i) preparing a training dataset in the PascalVOC format with image transformation and augmentation; (ii) training a table detection model by using the well-known Faster R-CNN architecture. Implementation of the workflow design simplifies the use of the approach proposed by decreasing the number of required manipulations.
LSimRank: Node Similarity in a Labeled Graph

The notion of node similarity is useful in many real-world applications. Many existing similarity measurements such as SimRank and its variants have been proposed. Among these measurements, most capture the structural information of a graph only, and thus they are not suitable for graphs with additional label information. We propose a new similarity measurement called LSimRank which measures the similarities among nodes by using both the structural information and the label information of a graph. Extensive experiments on datasets verify that LSimRank is superior over SimRank and other variants on labeled graphs.
Character-Based LSTM-CRF with Semantic Features for Chinese Event Element Recognition

Event element recognition is a significant task in event-based information extraction. In this paper, we propose an event element recognition model based on character-level embedding with semantic features. By extracting character-level features, the proposed model can capture more information of words. Our results show that joint character Convolutional Neural Networks (CNN) and character Bi-directional Long Short-Term Memory Networks (Bi-LSTM) is superior to single character-level model. In addition, adding semantic features such as POS (part-of-speech) and DP (dependency parsing) tends to improve the effect of recognition. We evaluated different methods in CEC (Chinese Emergency Corpus), and the experimental results show that our model can achieve good performance, and the F value of element recognition was 77.17%.
WITHDRAWN: Remote Music Teaching Classroom Based on FPGA High-Performance Algorithm

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn at the request of the Editor in Chief. Subsequent to acceptance of this special issue paper by the responsible Guest Editor Sundhararajan Mahalingam, the integrity and rigor of the peer-review process was investigated and confirmed to fall beneath the high standards expected by Microprocessors & Microsystems. There are also indications that much of the Special Issue includes unoriginal and heavily paraphrased content. Due to a configuration error in the editorial system, unfortunately the Editor in Chief did not receive these papers for approval as per the journal's standard workflow.
The Scope of Variational Translation

This chapter introduces the use of variational translation in literary and artistic, social science, and sci-tech works. It reveals that, in translation practice, the scope of the application of variational translation is extensive, except for some extremely important documents, such as eminent leaders’ works, national leaders’ speeches, and legal documents, for which a translator should be cautious when adopting variational translation.
Health Communication in Digital Era: A Study of Sub-Urban City of Jaipur

Development Support Communication' is to ensure the development inputs reach the beneficiaries.Communication for development has three basic factors, Advocacy, Social Mobilization and Behavioural change.Health communication generally deals with the process of communicating promotional health information on personal health and hygiene and educational campaigns on public health and sanitation.Health communication pursues to increase target audience knowledge on health issues, demonstrate healthy practices, support health services and dispel the misconceptions about health and so on.To ensure that messages on health communication to reach target audiences accurately and quickly, health communication professionals must develop a health information package designed for the particular sections of the population in the society.Development Communication is the knowledge of social transformation carried out by the exploration, model, and skills in the communication to carry growth.Development communication normally refers to communication policies and principles in the underdeveloped and developing countries.Development communication is resulting .
Machine Learning Performance Comparison for Toxic Speech Classification : Online Payday Loan Scams in Indonesia

The recent advancement of Machine Learning (ML) has brought us to many implementations. Online payday loan scam is a phenomenon which interestingly containing toxic speech in conversation. Toxic speech means implying threat toxic speech, offensive language, and hate speech. toxic speech would ultimately trigger such responses, namely loss of work ethic, alienation from the social, even suicidal thought. Despite the unnerving impact of toxic speech, there is still little known research regarding toxic speech, one of them is how to classify toxic speech. This research aims to make a comparison of various ML techniques with the means of classifying toxic speech found in the online payday loan scam phenomenon. For this experiment, we employed Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Random Forest (RF), and k-Nearest Neighbour (k-NN). All data were taken, filtered, and normalized manually from YouTube. Many reported the incident of online payday loan scam via YouTube in the form of two-way call communication. In total, there are 79 fraud report records converted into *.wav files, followed by the feature extraction process using openSMILE, and are classified using machine learning. We get the MLP result which has an acquisition value of 97.9%, below that received SVM 97.2%.
On Geometric Goppa Codes from Elementary Abelian p-Extensions of $${{\mathbb{F}}}_{{p}^{s}}(x)$$

Let p be a prime number and s > 0 an integer. In this short note, we investigate one-point geometric Goppa codes associated with an elementary abelian p-extension of $${{\mathbb{F}}}_{{p}^{s}}(x)$$ . We determine their dimension and exact minimum distance in a few cases. These codes are a special case of weak Castle codes. We also list exact values of the second generalized Hamming weight of these codes in a few cases. Simple criteria for self-duality and quasi-self-duality of these codes are also provided. Furthermore, we construct examples of quantum codes, convolutional codes, and locally recoverable codes on the function field.
A Software MTD Technique of Multipath Execution Protection

Attackers exploit vulnerabilities to attack target software relying on accurate mastery of its code implementation details and exact prediction of its runtime state transitions. Especially, when and where a sensitive pointer is allocated, what struct a sensitive object is and how to hijack the victim program's execution flow. Software randomization technique, based on the idea of moving target defense, introduces uncertainty into the target program and offers probabilistic protection similar to cryptography. In this paper we present the multipath execution, a software randomization technique based on LLVM framework. Multipath execution is designed and implemented to achieve not only randomness of memory layout and diversity of binary files, but also randomness of program code paths executed, which is unique in our work. The processed software shows uncertainty from static and dynamic aspects, which makes it difficult to analyze and attack. Both qualitative and quantitative assessments illustrate the effectiveness of our method.
Spherical isochrone models revisited

ABSTRACT A short derivation of all isochrone models using complex analysis.
Frequency of Different Types of Artifacts among Oral and Maxillofacial Histopathological Slides in Zanjan Dental School from 2015 to 2017

Background: Artifact refers to an artificial or replaced structure in histopathological slides as a result of an extraneous factor.Given the influence of identification and awareness of the types of artifacts on the correct diagnosis, the frequency of artifacts in oral and maxillofacial histopathological slides was assessed.Methods: In this cross-sectional study, census method was used to assess 119 oral and maxillofacial histopathological slides retrieved from the archive of Zanjan Dental School from 2015 to 2017.Artifacts were divided into three groups arising from the surgeon's performance, technician's performance, and specimen transfer to the laboratory.Statistical analysis of data was performed using an independent t test in SPSS software version 18.0.Results: The average numbers of artifacts arising from the surgeon's performance, technician's performance, and specimen transfer to the laboratory were 3.90 ± 1.14, 3.08 ± 1.10, and 0, respectively.The mean number of artifacts arising from the surgeon's performance was significantly higher compared to the other two groups (P < 0.01) and the most common ones included fragmentation, split, and tear.The most common artifacts arising from the technician's performance were fold/wrinkle, chaffer, and floater.There was no artifact arising from specimen transfer to the laboratory.Conclusions: The results indicated a high frequency of various artifacts in the studied slides.Therefore, paying more attention to slide preparation protocols and proficient performance during the biopsy procedure as well as further cooperation between the surgeon, pathologist, and laboratory technician can be useful in reducing the frequency of artifacts and achieving a better diagnosis.
EVALUATION OF THE EFFECTS OF INFORMATION SECURITY TRAINING ON EMPLOYEES: A STUDY FROM A PRIVATE HOSPITAL

Aim: Human error is known as the biggest threat to information security in healthcare organizations. Training on the information security is important to the mission of establishing sustainable information security. The aim of the study was to evaluate the effect of a training program for information security in a private hospital. &#x0D; Materials and Methods: In this cross-sectional study, 66 medical unit employees (M/F: 53/13, mean age: 30,27±11,12 years) and 34 administrative unit employees (M/F: 11/23, mean age: 31,5±10,84 years) using the Hospital Information Management System (HIMS) were included. Data were collected by a questionnaire regarding the validated Information Security Scale before and after the training program. &#x0D; Results: Scores of three subgroups of the scale (Security Policy, Access and Authorization, Security Applications) were used in the study. Scores of them were significantly improved by the training program in both medical and administrative staff (p0.05). In addition, there was no positive effect of HIMS training on scores of these subgroups (p&gt;0.05). &#x0D; Conclusion: Well-designed training programs are necessary for improving information security culture in hospitals. Since ensuring the appropriate protection of organizational assets, it is essential to design an effective training program regarding information security and privacy in the perspective of health managers.
Short Paper: Weak Runtime-Irrelevant Typing for Security

Types indexed with extra type-level information are a powerful tool for statically enforcing domain-specific security properties. In many cases, this extra information is runtime-irrelevant, and so it can be completely erased at compile-time without degrading the performance of the compiled code. In practice, however, the added bureaucracy often disrupts the development process, as programmers must completely adhere to new complex constraints in order to even compile their code.
Parallel Implementation of Algebraic Curve for Data Security in Cloud Computing Environment

In recent years data security plays an important role in cloud environment. The running time of secure algorithms has found to be time- consuming and lengthy in key size. In this paper, we present a parallel implementation to reduce the running time to achieve the same level of security using hyperelliptic curve. Due to small operand size as compared to other cryptographic methods like RSA and elliptic curve, hyperelliptic curve was found to be more suitable for security applications. Our work comprised of two parts: (i) sequential implementation of group addition of elliptic curves and hyperelliptic curves of genus-2 over prime fields, (ii) parallel implementation of the sequential version to speed-up the computational time. Here, thread-level parallelism has been carried out using GNU MP, openMP libraries, and a speed-up of \(\approx \)32% achieved on dual core processor over the sequential implementation.
'The co-': feminisms, power and research cultures

opinion Share on 'The co-': feminisms, power and research cultures: a dialogue Authors: Jaz Hee-jeong Choi RMIT RMITView Profile , Ann Light University of Sussex and Malmö University University of Sussex and Malmö UniversityView Profile Authors Info & Claims InteractionsVolume 27Issue 6November - December 2020 pp 26–28https://doi.org/10.1145/3429697Published:02 November 2020Publication History 0citation2,606DownloadsMetricsTotal Citations0Total Downloads2,606Last 12 Months131Last 6 weeks11 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
Social E-Commerce and Marketing Strategy Based on Data Analysis

As a derivative and transformation of traditional e-commerce, social e-commerce mine business information and user relationships through the networks, establish some models to provide personalized e-commerce services for consumers, which has become an important development direction. This paper summarizes the definition of social e-commerce through reference review, and compares the characteristics of social and traditional e-commerce, and deepens the understanding of social e-commerce business mode. Based on the analysis of the data of the article pushed by Jingdong WeChat public account, mining the marketing strategy of social e-commerce, which has certain significance for the actual social e-commerce operation.
Obstacles to Depth Compression of Neural Networks

Massive neural network models are often preferred over smaller models for their more favorable optimization landscapes during training. However, since the cost of evaluating a model grows with the size, it is desirable to obtain an equivalent compressed neural network model before deploying it for prediction. The best-studied tools for compressing neural networks obtain models with broadly similar architectures, including the depth of the model. No guarantees have been available for obtaining compressed models with substantially reduced depth. In this paper, we present fundamental obstacles to any algorithm achieving depth compression of neural networks. In particular, we show that depth compression is as hard as learning the input distribution, ruling out guarantees for most existing approaches. Furthermore, even when the input distribution is of a known, simple form, we show that there are no local algorithms for depth compression.
The Implementation and Application of Computer Simulation Technology in PE Teaching

Implementation method of computer simulation technology in physical education as a subject to study. First of all, the paper discusses the basic concepts of computer simulation technology, the application scope and were briefly reviewed in this paper, the main process. Secondly, the obstacle factors of computer simulation technology in physical education teaching are summarized and analyzed from three perspectives. Thirdly, the application method of computer simulation technology in the field of sports science is introduced from two aspects. Finally, combining with the teaching practice of physical education, this paper comprehensively demonstrates the implementation method of computer simulation technology in the practice of physical education from three dimensions. The research results can provide positive and beneficial inspiration and help for related experts and researchers.
Fast Algorithm for Sequence Edit Distance Computation

Dynamic programming plays an important role in biomedical signal processing and it can determine the similarity between two nucleotide sequences. In this paper, we propose an algorithm to improve the computation efficiency of dynamic programming for global edit distance computation. Computation efficiency is a very important issue for dynamic programming since in practice the length of a nucleotide sequence is 1,000–100,000. Therefore, we propose several techniques, including the slope rule and the magic number rule, to simplify the procedure of dynamic programming. Simulations show that, with these rules, only 10% of the entries in the dynamic programming matrix should be computed and the computation time is much less than that of the original algorithm.
The Identification of the Apples (Malus Sylvestris) Skin Wax Coating Using the Edge Detection Method

Wax coating is generally used for the post-harvesting process of Malang apples (malus sylvestris). So far, the process of examining the wax coating still uses the sense of touch, which is found difficult to do. This research aims to identify the layers of wax using a computerized process. The edge detection method was tested to recognize the patterns on the skin of the wax-coated and uncoated apple. This method was possible to use since the wax coating is sensitive to light although it is transparent. It makes the wax-coated apple skin tend to be shinier than the uncoated one. After recognizing the patterns, the standard deviation of the edge detection results was measured. The results showed that uncoated apples had a standard deviation value between 17.4992 and 42.2488. Whereas the wax-coated apple had a standard deviation value between 32.7809 and 64.2292. The deviation appeared to be between 5.1079 to 39.3531. These results indicated that the standard deviation value for wax-coated apples tended to be higher than the uncoated ones. Based on these results, it was shown that the edge detection method was able to show the significant patterns to identify the presence of the wax coating on the apple's skin.
Structural Position Network for Aspect-Based Sentiment Classification

Aspect-based sentiment classification aims to discriminate the polarity of each aspect term for a given sentence. Previous works mainly focus on sequential modeling and aspect representations. However, the syntactical information and relative structural position of aspect in sentence are neglected, resulting in some irrelevant contextual words as clues during the identification process of aspect sentiment. This paper proposes a structural position network (SPNet) based on bidirectional long short-term memory (LSTM) for further integrating syntactical information. Specifically, we first utilize the dependency tree to represent the grammatical structure of the aspect in sentence. Then, a structural weighted-layer is applied after LSTM. In this situation, the syntactically relevant context is formulated. Besides, the sequential position is combined to reduce the impact of noise caused by imperfect grammatical analysis tools. SPNet not only significantly improves the ability of encoding syntactical information and word dependencies, but also provides a tailor-made representation for different aspect in a sentence. On three public ABSC datasets, SPNet produces a competitive performance compared with some existing state-of-the-art methods.
Programming Method of a Robot to Cut Automotive Upholsteries

To cut automotive upholsteries by using a robot, it is needed to compose a user program which is required to teach the robot in order to complete the task. For the purpose of studying the programming method, the water-cutting KUKA robot to cut automotive upholsteries used in Brilliance Jinbei is used as the research object. This paper probes into the problems and influencing factors to be considered in the overall design of program structure and the programming of main program and sub-program, and a good deal of work need to be carried out and solved on the spot according to objective conditions after the completion of main program and sub-program, which is program debugging. The research conclusion on robot programming method is of great significance to an enterprise's process optimization and trial-manufacture of new products.