Reviewerly/exHarmony huggingface dataset:
On the Optimal Tradeoff Between Computational Efficiency and Generalizability of Oja’s Algorithm

The Oja's algorithm is widely applied for computing principal eigenvectors in real problems, and it is practically useful to understand the theoretical relationships between the learning rate, convergence rate, and generalization error of this algorithm for noisy samples. In this paper, we show that under mild assumptions of sampling noise, both the generalization error and the convergence rate reveal linear relationships with the learning rate in the large sample size and small learning rate regime. In addition, when the algorithm nearly converges, we provide a refined characterization of the generalization error, which suggests the optimal design for the learning rate for data with noise. Moreover, we investigate the minibatch variation of Oja's algorithm and demonstrate that the learning rate of minibatch training is decayed by a factor characterized by the batch size, which provides theoretical insights and guidance for designing the learning rate in minibatch training algorithms. Finally, our theoretical results are validated by experiments on both synthesized data and the MNIST dataset.
Per-Image Super-Resolution for Material BTFs

Image-based appearance measurements are fundamentally limited in spatial resolution by the acquisition hardware. Due to the ever-increasing resolution of displaying hardware, high-resolution representations of digital material appearance are desireable for authentic renderings. In the present paper, we demonstrate that high-resolution bidirectional texture functions (BTFs) for materials can be obtained from low-resolution measurements using single-image convolutional neural network (CNN) architectures for image super-resolution. In particular, we show that this approach works for high-dynamic-range data and produces consistent BTFs, even though it operates on an image-by-image basis. Moreover, the CNN can be trained on down-sampled measured data, therefore no high-resolution ground-truth data, which would be difficult to obtain, is necessary. We train and test our method's performance on a large-scale BTF database and evaluate against the current state-of-the-art in BTF super-resolution, finding superior performance.
An Optimized SUCDDES to Control Access in Cloud

Cloud computing is employed as integration means in several application domains, covering from the manufacturing to the healthcare, point towards attaining flexible storage of data and easy to access and interchange amongst geographically and heterogeneous sparse organizations. The existing technique explained encrypted data through Uncrackable Cipher Dynamic Double Encryption Standard (SUCDDES) reduces the issue of unauthorized users/ hackers accessing data. In this proposed method, used a secure uncrackable cipher dynamic double encryption standard (SUDDES) algorithm to secure the data access control and privacy-preserving. To randomly select the key length reduced the data security issues. After dynamically selecting the key length the data governor sent the key request to the authority. Then based on the obtained key length the data governor generated the partial secret key. It is further used to decrypt the data and stored in the cloud storage. The outcome of the study enhances the data access control and increases the effectiveness of the cloud security and reduces the decryption and encryption time and also it prevents from dictionary attacks, brute force attacks, collision attacks, SQL injection attacks and so on
Updating the Information Systems Curriculum

The Association of Computing Machinery (ACM) and the Association for Information Systems (AIS), two global professional and academic societies with a stake in Information Systems (IS) education, are engaged in a project to revise the Information Systems Curriculum for bachelor's degrees. A joint taskforce on the Information Systems (IS2020) Model Curriculum was created following the report and recommendation of an Exploratory Taskforce. This panel seeks to introduce the work of this taskforce as well as engage the information systems community in this effort. The taskforce seeks to facilitate broad feedback during the IS2020 development process through surveys and open feedback requests. Panelists will introduce the key components of this process and seek input and feedback. This session should be of interest to all attendees, especially faculty developing college-level curricula in Information Systems.
Social Project as a Technology for Developing Leadership Qualities in Modern Students

The article shows the potential of a social project in the formation of leadership qualities in students. The essence of a social project is revealed, the stages of its implementation are shown. The author focuses on the concept of leadership, considers the key qualities of a leader. One of the demanded qualities of a leader in modern life is his active life position, which is expressed in the conscious realization of his social roles. Social project facilitates the development of such activity. We used social project as a technology for the development of leadership qualities in students. To that end, we have conducted a survey among the students of the faculty of service, tourism, and technology of public catering (N=69) with the aim to reveal the leadership skills. The results of testing showed insufficient development of these skills. We have assumed that the participation of students in the development and realization of the social project would foster more intensive development of leadership qualities. The realization of the social project “Supporting of persons with impaired mobility in city infrastructure” under the guidance of teachers and experts has justified the efficiency of the implemented technology. Upon the project completion the students demonstrated better results in four positions.
LME SSD

For a long time, the accuracy of the object detection methods has been continuously improved, but the computational cost and resource occupation have not received enough attention. In recent years, with the development of some excellent lightweight object detection methods for compressing model parameters and computational quantities, the object detection framework can run on resource-constrained mobile devices. This also makes reducing computational cost and resource occupation an important direction to improve the performance of the object detection methods. In this study, we have proposed an efficient detection framework named LME (Light-Multi Extract) SSD, which maintains detection accuracy while reducing the occupation of computing resources. We propose the LME module, which compresses parameters and calculations of the architecture by applying depth-wise convolutions with different convolution kernel sizes to extract features. In addition, we have further improved the accuracy of the detection framework through LF (Light-Fusion) module. LF performs bidirectional fusion of feature map information by up-sampling of bilinear interpolation and down-sampling of depth-wise convolution, thereby improving the accuracy while maintaining the scale of the detection framework. We follow the SSD method to predict by setting anchor boxes on feature maps of different sizes.
An Object Detection Algorithm Based on Attention Mechanism and Lightweight Network (AMLN)

In recent years, with the widely accepted and in-depth research of deep network and deep learning, the techniques of object detection have been greatly promoted. Visual attention mechanism is an important visual signal processing approach of human beings. The incorporated of attention mechanism into computer vision applications based on deep learning, for various tasks such as image classification, object detection, image segmentation and motion recognition, has reached remarkable achievement with excellent effects. In current, object detection algorithms based on deep learning can be implemented by two main methods: candidate region method (two-stage method) and regression method (one-stage method). The former method -- two-stage object detector can acquired more detection accuracy than the later method -- one-stage object detector, however with inherent drawback -- slower speed to derive detection conclusion. This paper proposes a new object detection algorithm (AMLN) which incorporating the Attention Mechanism into the two-stage detection model, replacing the heavy design architecture for detection component with Lightweight Networks, and adopting soft-NMS instead of NMS as post-processing for prediction component. The experiments are evaluated on the COCO dataset, and the results show that the proposed object detection algorithm based on Attention Mechanism and Lightweight Network (AMLN) can achieve significantly higher detection efficiency while maintaining higher accuracy.
Study on the cost allocation method to maximize the benefit of e‐commerce enterprises in the cloud computing environment

Summary Cloud computing is a brand‐new IT service delivery model. It is a key strategic technology and means to lead the innovation of the information industry in the future. It can effectively solve the dynamic demand and final cost of e‐commerce enterprise resources, and it can reduce costs for e‐commerce enterprises to improve management, promote information development, and achieve resource integration. It is a key factor in shaping and enhancing the competitiveness of enterprises. Fuzzy paradigm should be understood and used at the level of world view and methodology, and should be recognized that it has different forms and properties in different disciplines. Based on the cloud computing environment, this article proposes a task allocation model, taking into account the utility functions of different users, and maximizing the overall terminal benefits by assigning computational tasks on a large number of terminals to virtual machine instances. The simulation results show that the overall benefit of the terminal under this allocation model is significantly higher than that of the overall terminal under the centralized task allocation model.
Research on Core Algorithms of 3-D Aided UHV Transmission Line Selection Design Platform

UHV power transmission lines are playing very important role in the UHV power system, and the design of transmission line selection is the leading part of the power transmission line project, which is playing a most important role. The rationality of transmission line design impacts cost, difficulty and security of the whole UHV project construction, also impacts overall benefit of power transmission project. Furthermore, it also impacts the whole planning of grid system and country’s security and stability. Along with the development of data gathering technology and hardware of computer, 3-D Aided UHV transmission Line Selection Design Platform (based on 3-D topographic data and 3-D device modeling data,) is already available from both theoretical and technology prospect. And its most key point is the algorithm of 3-D visible Aided transmission Line Selection Design Platform, this paper investigates the algorithm related researches, also includes the technical selection of the algorithm.
User interface of smart environment based on human body gestures

This paper deals with the problematics of user interfaces that are controlled by human body gestures. It analyses the possibilities of capturing gestures, presents the architecture of the proposed interface for performing actions and controlling devices in an intelligent environment. In the proposed interface gestures are used to control the intelligent environment while the proposed mobile application displays the current state of the environment. The proposed interface was verified by using the empirical testing method.
To the Question of Motivation in Teaching Foreign Languages to the Students of Technical Specialities: Psychological And Pedagogical Aspect

In the modern society, in the era of European integration processes, the status of a foreign language as a subject is changing.Taking into account the increasing interest to a foreign language as a means of communication, the modern methodology focuses on achieving higher results.One of the key elements of the language learning is the motivation of students to study this subject.Accordingly, the purpose of the publication is to summarize the constituent elements of motivation while learning English.It has been established that the motivational aspect is crucial for the activation of all psychological processes of intellectual activity: thinking, perception of information, understanding and analyzing of knowledge, learning the material, developing dialogical and monological speaking.The reasons for students' high motivation to dialogical speaking (communication on various topics, a large number of creative tasks that make the classes interesting) are identified.It has been found that the creation of positive motivation to learn a foreign language is facilitated by the creation of psychological comfort during the organization of the educational process (atmosphere of good will in class, hand-holding to students, conversations on interesting and modern topics).Based on the analysis of scientific literature, several approaches to the teaching a foreign language (behavioral, inductive and conscious, cognitive, integrated) can be distinguished.
Analysis of images to identify specific user actions

The study of any writing process, such as translation or programming, typically involves collecting data about user actions including keystrokes and mouse clicks. A tool designed for this type of data collection is ResearchLogger and, in addition to keystrokes and clicks, it collects screenshots at predefined intervals and images around the mouse pointer. The main roadblock for researchers using this tool is analyzing the images it generates, which are very diverse and usually need to be processed manually. In particular, one action that is very hard to recognize without using the images is text selection; this action is very important since it might indicate copying, replacing or formatting of a text passage. In this paper, we present two solutions that support researchers in the review of such images and we test them using data from a previous pilot study. Results show that they can be useful to detect images with the specific user action of text selection.
Timing Considerations in Test Development and Administration

In this chapter, the authors discuss timing considerations in developing tests, beginning with defining the construct to be measured by a test and continuing through test administration. They also discuss how test developers can assess whether power tests are unnecessarily speeded. The International Test Commission's Guidelines on Computer-Based and Internet Delivered Testing also provide useful suggestions for timing considerations. Regardless of the test administration model, sufficient numbers of quality items must be developed to support a testing program. Although some testing programs cling to the 20th century paper-based format, a hallmark of contemporary educational assessments is the use of technology to allow examinees to interact with and respond to items. Authors address several issues related to testing time. These issues included determining whether speed of responding to items is part of the construct intended to be measured on test, factors to consider in establishing time limits on test and analysis of item response time data for test development and test evaluation.
The Definitive Guide to Blockchain for Accounting and Business

Blockchain is a disruptive technology potentially impacting how economic transactions are recorded, stored, and verified. Despite such ramifications, there is a lack of literature discussing this from the accountant’s perspective. Through real-world cases this book distils an abstract technology to relatable experiences for business professionals.
Application of Criteria Aggregation Techniques for the Selection of Innovative Products

The peculiarity of innovative products requires taking into account a large number of criteria that should be aggregated into generalized ones and building a convolution tree with qualitative, quantitative and fuzzy rules. The paper proposes a methodology for the optimal partitioning of criteria scales into generalized gradations for using combined methods of multicriteria analysis of alternatives. The transition to fewer criteria leads to a signifi cant increase in the dimension of the scales of generalized criteria. Scales have to be converted to new scales with fewer gradations. To solve the problem of minimizing the information loss occurring when converting the scales picked Bellman function and applied method of dynamic programming. Computational experiments have shown the effectiveness of the proposed approach.
Virtual Reality Game in Education: An Pragmatic

Virtual Reality is becoming a recent trend on entertaining world, also reached greater elevations in Science, Technology, Engineering and Management. Virtual Reality (VR) technologies have been widely applied over all educational settings for years and proved that integrating education over visual modalities is becoming more impactful these days. An experimental study over the impact of using virtual reality games on education is analyzed and compared with the spatial presence in this article. Study proves that virtual reality based experiments creates more impact when it is brought through visual and auditory modalities. And it also proves that VR is more immersive and engaging compared to all other educational system parameters. Low cost VR headsets such as Google Cardboard, Samsung VR Gear and so on, have made it more accessible in the society which provides the feeling of being psychologically occupied in a virtual environment tend to bring out more technology in the environment. Thus, an important inference for design is that to bring out more visual modalities for educational contents.
Prediction of maximum in-cylinder pressure by adaptive neuro-fuzzy inference system method

Abstract A widely used method to substituting expensive experimental method in order to optimizing different parameters of technological application of equipment is using of modelling these phenomena by intelligent techniques. Hence, in this paper, an ANFIS (adaptive neuro-fuzzy inference system architecture) model has been used to predict one of the most important of the diesel engine which is cylinder pressure. Measurement of this parameter requires expensive and time consuming methods. Therefore, application of the mathematical method to prediction of this parameter is necessary. The inputs of this model are injection time, engine speed and engine load. The testing performance of the proposed ANFIS model revealed a good predictive capacity to yield acceptable error measures with, R 2 =0.99 and MSE=6.8. This model is not developed based on complicated mathematical formula and is easy to use. The result of study recommends that the ANFIS model can be successfully used to perdition of cylinder pressure according to effective parameters.
New Discoveries on Integer Factorization by Valuated Binary Tree

The paper proves that an odd composite integer N can be factorized in O(logN) time if N's big divisor is in the form 2α u + 1 or 2α u − 1 and its small divisor does not exceed 2α + 1 Theorems and corollaries around the conclusion are proved with detail mathematical reasoning. The results in the paper again demonstrate the significance of applying the valuated binary tree on analyzing the odd integers.
Research on Key Technologies of synonym automatic extraction based on multithreaded web crawler

In the power intelligent customer service system, it mainly includes semantic analysis, intelligent voice, speech recognition, intelligent search, text knowledge base analysis and so on. In order to improve the service efficiency and accuracy of the intelligent knowledge base and enhance the user's satisfaction with the experience of the customer service robot, this paper mainly focuses on the text knowledge base, according to the segmentation results of power users, through the multi-threaded network crawler technology to expand the thesaurus in the intelligent knowledge base, making the knowledge of the knowledge base management background more comprehensive. For the power users, the retrieval terms can touch the knowledge in the knowledge base more likely, which greatly improves the accuracy of Knowledge Q & A, and improves the intelligent search effect of the intelligent robot; For the knowledge base managers, in the process of knowledge editing and management, only the part of speech synonyms need to be audited and certified, then they can be edited and stored in the database, serving the knowledge search engine, and improving the work efficiency and effectiveness of knowledge base managers.
Development and implementation of a software layer for generic skill descriptions of versatile autonomous vehicles

This paper describes a control architecture for versatile autonomous vehicles (VAV). As a part of a hybrid architecture, a behaviour engine is developed which offers two main skills, forecast and acting, which are used for order coordination and execution. Furthermore, we introduce time dependent Key Performance Indicators (KPIs) as basic information quantities. These KPIs are transmitted as scheduled values for order coordination and as real values after the execution to offer feedback on the performance of the VAV. An experimental setup shows that the architecture is well suited for a VAV.
Field Note on CVE-2019-11510

This Field Note describes the case of a critical unauthenticated RCE vulnerability in an SSL-VPN product that remained unpatched at a large scale-up and until after exploits became public. Approximately 14,500 systems worldwide were reportedly unpatched at the end of August 2019. Two weeks after exploits emerged in public, both GCHQ and NSA released notices that the vulnerability was being exploited by APT actors. The present Field Note describes observations from the Netherlands and includes reflections in an attempt to stimulate thinking on how to improve the status quo, such as through coordinated proactive measures by CSIRTs.
Resilient Algorithm In Predicting Fertilizer Imports by Major Countries

Abstract In the last five years (2013-2017) Indonesia’s fertilizer production experienced volatile growth, but overall tended to increase at a rate of 1.7% per year. The research aims to optimize artificial neural networks with a resilient backpropagation algorithm (Rprop), artificial neural networks are one of the artificial representations of the human brain that always tries to simulate the learning process in the human brain. Sample data used for optimization is fertilizer import data according to the main country of origin and uses 4 architectures, the best results are obtained between architectures 6-8-1, 6-12-1, 6-16-1, and 6-32-1 is architecture 6-32-1 with 100% accuracy.