Reviewerly/exHarmony huggingface dataset:
Research on the Influence of Sampling Methods for the Accuracy of Web Services QoS Prediction

In recent years, as the number of Web services, increases dramatically, the personalized Web service recommendation has become a hot topic in both academia and industry.The quality-of-service (QoS) prediction plays a key role in Web service recommendation systems.However, how to further improve the accuracy of QoS prediction is still a problem.Traditional QoS predicting models do not consider the impact of sampling methods on the accuracy of QoS prediction.However, the outstanding sampling method can train the predicting model more effectively and obtain higher accuracy.Therefore, it is necessary to study sampling methods based on the QoS dataset in order to obtain sample distribution closer to the original distribution, so as to improve the accuracy of the predicting models.In this paper, we first discuss how to apply several existing sampling methods to QoS datasets and then analyze their advantages and disadvantages.Finally, a novel sampling method, enhanced importance resampling (EIRS), is proposed and applied.The experiments on the real-world datasets show that our method can not only sample efficiently and accurately but also can greatly improve the accuracy of Web service QoS prediction.
The Role of Business Simulator Games Implementation in Improving the Ability of Analysis and Business Decision Making Independently for Students (Literature Research)

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Establishing a Research Data Management Service on a Health Sciences Campus

<strong>Objective</strong><em>:</em> Given the increasing need for research data management support and education, the Spokane Academic Library at Washington State University (WSU) sought to determine the data management practices, perceptions, and needs of researchers on the WSU Spokane health sciences campus. <strong>Methods</strong><em>:</em> A 23-question online survey was distributed to WSU researchers and research support staff through the campus listserv. This online survey addressed data organization, documentation, storage & backup, security, preservation, and sharing, as well as challenges and desired support services. <em></em> <strong>Results</strong><em>: </em>Survey results indicated that there was a clear need for more instruction with regard to data management planning, particularly as data management planning addresses the areas of metadata design, data sharing, data security, and data storage and backup. <strong>Conclusions</strong>: This needs assessment will direct how RDM services are implemented on the WSU Spokane campus by the Spokane Academic Library (SAL). These services will influence both research data quality and integrity through improved data management practices.
Graph analysis of network flow connectivity behaviors

Graph-based approaches have been widely employed to facilitate in analyzing network flow connectivity behaviors, which aim to understand the impacts and patterns of network events. However, existing approaches suffer from lack of connectivity-behavior information and loss of network event identification. In this paper, we propose network flow connectivity graphs (NFCGs) to capture network flow behavior for modeling social behaviors from network entities. Given a set of flows, edges of a NFCG are generated by connecting pairwise hosts who communicate with each other. To preserve more information about network flows, we also embed node-ranking values and edge-weight vectors into the original NFCG. After that, a network flow connectivity behavior analysis framework is present based on NFCGs. The proposed framework consists of three modules: a graph simplification module based on diversified filtering rules, a graph feature analysis module based on quantitative or semiquantitative analysis, and a graph structure analysis module based on several graph mining methods. Furthermore, we evaluate our NFCG-based framework by using real network traffic data. The results show that NFCGs and the proposed framework can not only achieve good performance on network behavior analysis but also exhibit excellent scalability for further algorithmic implementations.
Graph Compression with Stars

Making massive graph data easily understandable by people is a demanding task in a variety of real applications. Graph compression is an effective approach to reducing the size of graph data as well as its complexity in structures. This paper proposes a simple yet effective graph compression method called the star-based graph compression. This method compresses a graph by shrinking a collection of disjoint subgraphs called stars. Compressing a graph into the optimal star-based compressed graph with the highest compression ratio is shown to be NP-complete. We propose a greedy compression algorithm called StarZip. We experimentally verify that StarZip achieves compression ratios of 3.8–45.7 and 2.9–241.6 in terms of vertex count and edge count, respectively. Besides, we study the shortest path queries on compressed graphs. On the real graphs, the StarSSSP algorithm for processing shortest path queries on compressed graphs is 4X–20X faster than Dijkstra's algorithm running on original graphs. The average absolute error between the query results of StarSSSP and the exact shortest distances is about 1. On the synthetic graphs, StarSSSP is up to 313X faster than Dijkstra's algorithm, and the average absolute error is also about 1.
Perancangan Sistem Informasi Penjualan Dengan Pendekatan Metode Waterfall

Along with the rapid development of science and technology, the need for fast and accurate information is needed by every company. To manage information requires good and sophisticated technology. The technology needed is not only in the form of hardware (Hardware) and also in the form of software (Software), but has great value for those who use it. One of the support to make it happen is to process company data using a computerized system. In this research method, the method used is the waterfall method as a pattern of system development. until now has not been computerized, while the information system is very supportive and provides satisfying services for customers. For this reason the author discusses the design of a sales information system at the Sono Snack Shop with a program that facilitates the shop. The design of this information system is the best solution to solve the problems that exist in this store, and with a computerized system can be achieved an activity that is effective and efficient in supporting the activities of this store.
Ethno Pedagogical Technologies In Regional Education Environment

This work is devoted to the problem of developing ethno pedagogical technologies. Ethno pedagogy is a separate branch of pedagogical science that studies the natural methods and traditions of education. The main problem of modern ethno pedagogy is the fragmentation of ethno pedagogical knowledge. The development of ethno pedagogical technologies makes it possible to systematize ethno pedagogical knowledge, consider their place in the pedagogical process, determine the conditions under which the ethno pedagogical process becomes effective. The place occupied by ethno pedagogical technologies in the regional educational space is considered. Regional educational space is an environment necessary for the implementation of ethno-pedagogical technologies. The components of ethno pedagogical technology are presented, the combination of which ensures the successful implementation of ethno pedagogical technology. The methodological component, including approaches to the consideration of the features of ethno-pedagogical technologies, is considered. The use of synergistic, systemic, competence and technological and other approaches provides a comprehensive consideration of the problem under study. The presented components of ethno pedagogical technology are considered in detail, taking into account the peculiarities of the regional educational space. The development of ethno pedagogical technologies allows the use of ethno pedagogical knowledge systematically, reasonably, taking into account the specifics of the regional educational space.
Design of an Interactive Spatial Augmented Reality System for Stage Performance Based on UWB Positioning and Wireless Triggering Technology

In this research, the authors designed an interactive spatial augmented reality system for stage performance based on the technologies of UWB positioning and Bluetooth® triggering. The position of the actor is obtained through the antenna tag carried by the actor and the signal base station placed on the stage. Special effects can be triggered through the Bluetooth® module according to the actor, and rendered at the relevant location on the screen, which has higher concealment. The system has a higher degree of freedom in practical applications, which can present an interactive spatial augmented reality effect, and therefore provide new possibilities for the application of spatial augmented reality in the stage performance.
Applying Diffie-Hellman Algorithm to Solve the Key Agreement Problem in Mobile Blockchain-based Sensing Applications

Mobile blockchain has achieved huge success with the integration of edge computing services.This concept, when applied in mobile crowd sensing, enables transfer of sensor data from blockchain clients to edge nodes.Edge nodes perform proof-of-work on sensor data from blockchain clients and append validated data to the chain.With this approach, blockchain can be performed pervasively.However, securing sensitive sensor data in a mobile blockchain (client/edge node architecture) becomes imperative.To this end, this paper proposes an integrated framework for mobile blockchain which ensures key agreement between clients and edge nodes using Elliptic Curve Diffie-Hellman algorithm.Also, the framework provides efficient encryption of sensor data using the Advanced Encryption Standard algorithm.Finally, key agreement processes in the framework were analyzed and results show that key pairing between the blockchain client and the edge node is a non-trivial process.
Identity Vector Extraction Using Shared Mixture of PLDA for Short‐Time Speaker Recognition

The state-of-the-art speaker recognition system degrades performance rapidly dealing with short-time utterances. It is known to all that identity vectors (i-vectors) extracted from short utterances have large uncertainties and standard Probabilistic linear discriminant analysis (PLDA) method can not exploit this uncertainty to reduce the effect of duration variation. In this work, we use Shared mixture of PLDA (SM-PLDA) to remodel the i-vectors utilizing their uncertainties. SM-PLDA is an improved generative model with a shared intrinsic factor, and this factor can be regarded as an identity vector containing speaker indentification information. This identity vector can be modeled by PLDA. Experimental results are evaluated by both equal error rate and minimum detection cost function. The results conducted on the National institute of standards and technology (NIST) Speaker recognition evaluation (SRE) 2010 extended tasks show that the proposed method has achieved significant improvements compared with ivector/ PLDA and some other advanced methods.
DeepCause: Hypothesis Extraction from Information Systems Papers with Deep Learning for Theory Ontology Learning

This paper applies different deep learning architectures for sequence labelling to extract causes, effects, moderators, and mediators from hypotheses of information systems papers for theory ontology learning.We compared a variety of recurrent neural networks (RNN) architectures, like long short-term memory (LSTM), bidirectional LSTM (BiLSTM), simple RNNs, and gated recurrent units (GRU).We analyzed GloVe word embedding, character level vector representation of words, and part-of-speech (POS) tags.Furthermore, we evaluated various hyperparameters and architectures to achieve the highest performance scores.The prototype was evaluated on hypotheses from the AIS basket of eight.The F1 result for the sequence labelling task of causal variables on a chunk level was 80%, with a precision of 80% and a recall of 80%.
RESTFUL WEB SERVICE UNTUK INTEGRASI SISTEM AKADEMIK DAN PERPUSTAKAAN UNIVERSITAS PERJUANGAN

Perjuangan University is developing an information system in its campus, but the system is partially built. This has resulted in its own problems, there is no connectivity between systems and the difficulty of developing data integration. So that in the business process that occurs many experience difficulties in data synchronization and the amount of duplicate data that must be entered. One solution to overcome this problem is to utilize web service technology. The web service architecture used is REST. REST as a bridge that is able to integrate data in a stateless format. The results of the application of the web service as a backend system are able to integrate the existing academic information systems and library information systems without changing existing structures, programs or architecture. Format data exchange between systems using the JSON format. In addition, officers do not need to input and verify data several times, when the data in the library information system changes automatically in the academic information system the changes are immediately read.
Deep learning approach for negation trigger and scope recognition

The automatic detection of negation elements is an active area of study due to its high impact on several natural language processing tasks. This article presents a system based on deep learning and a non-language dependent architecture for the automatic detection of both, triggers and scopes of negation for English and Spanish. The presented system obtains for English comparable results with those obtained in recent works by more complex systems. For Spanish, the results obtained in the detection of negation triggers are remarkable. The results for the scope recognition are similar to those obtained for English.
Neural algebra on “how does the brain think?”

The mathematical model employed in this essay attempts to explain how complex scripts of behaviour and conceptual content can reside in, combine and interact on large neural networks. The neural hypothesis attributes functions of the brain to sets of firing neurons dynamically: to sets of cascades of such firings, typically visualised by imaging technologies. Such sets are represented as the elements of what we call a neural algebra with their interaction as its basic operation. The neuro-algebraic thesis identifies “thoughts” with elements of a neural algebra and “thinking” with its basic operation. We argue for this thesis by a thought-experiment. It examines examples of human thought processes in proposed emulation by neural algebras. In particular we analyse problems such as controlling, classifying and learning. In neural algebras these may be posed as algebraic equations, whose solutions may lead to extensions of a neural algebra by new elements. The modelling of such extensions consists of formal analogues to familiar faculties such as reflection, distinction and comprehension which will be mades precise as operations on the algebra. An advantage of our approach is that this modelling leads directly to brain functions. From the cascades of such functions we obtain the neurons involved in them and their connective structure, and mathematically describe their behaviour.
Lightweight Conversion from Arithmetic to Boolean Masking for Embedded IoT Processor

A masking method is a widely known countermeasure against side-channel attacks. To apply a masking method to cryptosystems consisting of Boolean and arithmetic operations, such as ARX (Addition, Rotation, XOR) block ciphers, a masking conversion algorithm should be used. Masking conversion algorithms can be classified into two categories: “Boolean to Arithmetic (B2A)” and “Arithmetic to Boolean (A2B)”. The A2B algorithm generally requires more execution time than the B2A algorithm. Using pre-computation tables, the A2B algorithm substantially reduces its execution time, although it requires additional space in RAM. In CHES2012, B. Debraize proposed a conversion algorithm that somewhat reduced the memory cost of using pre-computation tables. However, they still require ( 2 ( k + 1 ) ) entries of length ( k + 1 ) -bit where k denotes the size of the processed data. In this paper, we propose a low-memory algorithm to convert A2B masking that requires only ( 2 k ) ( k ) -bit. Our contributions are three-fold. First, we specifically show how to reduce the pre-computation table from ( k + 1 ) -bit to ( k ) -bit, as a result, the memory use for the pre-computation table is reduced from ( 2 ( k + 1 ) ) ( k + 1 ) -bit to ( 2 k ) ( k ) -bit. Second, we optimize the execution times of the pre-computation phase and the conversion phase, and determine that our pre-computation algorithm requires approximately half of the operations than Debraize’s algorithm. The results of the 8/16/32-bit simulation show improved speed in the pre-computation phase and the conversion phase as compared to Debraize’s results. Finally, we verify the security of the algorithm against side-channel attacks as well as the soundness of the proposed algorithm.
Implementation of Homomorphic Encryption for Wireless Sensor Networks Integrated with Cloud Infrastructure

With the current explosion of cloud services, it is easy for everyone to store the prodigious amount of data on remote servers.On the other hand, service providers are given full access to our information, or in the worst case, can be intercepted by malicious people.The solution is to encrypt the data to make it completely secret.But then, we can no longer manipulate these data remotely (retouching the photos, looking for words in a text, performing calculations ...).A new form of cryptography, which is just beginning, promises precisely to offer this security while allowing the encrypted data to be manipulated by the authorized users, it named "Homomorphic Encryption".This paper presents the implementation of a homomorphic encryption to ensure the confidentiality of aggregated data from wireless sensor networks and also demonstrates how to use it through the cloud infrastructure in order to perform arithmetic operations directly on the encrypted data without decrypting them.After a theoretical comparative study on homomorphic encryption algorithms, we picked out the Domingo-Ferrer's cryptosystem on the basis of criteria mentioned during the survey.This cryptosystem requires to split the message into several fragments (at least two fragments) before the encryption process.For each implementation case performed with two, three and four fragments of the message, 10,000 messages were sent so that the battery levels of the sensor nodes dropped by 6%, 7% and 8% respectively, which led us to conclude that as long as the number of fragments increases, the power consumption in the network is increasingly significant because of excessive processing and a large amount of data to be transmitted over the wireless network, generated by the Domingo-Ferrer's cryptosystem.
Classification of URL into Malicious or Benign using Machine Learning Approach

Now days everything is digitalized and it's difficult to secure our data.Web security is a major issue because everything is connected through internet.The common way of launching an internet attack is by using malicious URLs.Hackers or Intruders leaks billions of confidential data every year by using malicious websites The traditional way of detecting these kinds of malicious URLs or websites is by the use of a web database called Blacklists.There are many URL shortening methods and Domain Generation Algorithms are available which make it difficult to detect the newly generated malicious URLs.To increase the efficiency and avoid database dependency we proposed the Machine learning approach to detect the malicious URLs.In machine learning approach there are so many algorithms available for the classification and feature extraction from that we select the best method that is Random forest which will gave us more accurate result than the past ones.So our proposed system will increase the efficiency and gave accurate prediction whether the URL entered is malicious or benign by using a well-defined dataset.It is also possible to implement the machine learning application in a proxy server or any network traffic controller system.
The Influence of The Big Book Media Has The Character of Conservation in Early Reading

The big book media with the character of conservation is the development of learning media adapted to the character of the child in early reading. The purpose of this study was to see the effect of big book media on conservation characteristics in early reading in the first grade of SD Labschool Unnes using a quasi-experimental method with a pretest-posttest design with one research class without any comparison class with data analysis using SPSS 17. The results of the study were correlation correlation pretest and posttest of 0.757 with a determinant coefficient or R Square of 0.573 that the big book media conservation character influences early reading with a magnitude of 0.548, to test the significance with Fcount = 22.821 with a significance level of 0.000 &lt; 0.005, so the significant influence of big book media on conservation early reading skills, to answer the research hypothesis then tested by looking at constant posttest of 48.861 while the regression value of 0.561 can be interpreted that the regression coefficient is positive, then the research hypothesis is answered by the conclusion that the media is big book conservation characteristics affect early reading.
Licensing by Design: A Systematic Approach

This article draws on a case study at the University of Connecticut Library to present best practices for managing license documents as well as tracking and communicating terms of use and performance obligations in an academic library context. This article tackles leveraging the Ex Libris Alma library services platform to manage licensing workflows and display relevant terms to end users through the Primo discovery service. The discussion also addresses ways to simplify and standardize license terms for consistent tracking and display, recommends guiding principles for effective licensing, reviews policy statements to maximize compliance and define expectations with vendors, and presents ideas for educating staff and users about terms of use.
Accelerating Qurán Reading Fluency through Learning Using QURÁNI Application for Students with Hearing Impairments

The ability to read Qurán of students with hearing impairments supported by special education is still not as expected due to the limited access to technology. The progress of technology such as a smartphone has led many people to adopt a view that the technology of application is very appropriate to be developed for their learning. In this study, we have developed an application called QUR'ANI to teach reading the Qur'an fluently and appropriately for students with hearing impairments. The method used was single subject research with the AB model. The effectiveness of this application in teaching the Qur’an for students with hearing impairments has been tested with positive effect. Besides, the teacher's subjective point of view in this application has also been consulted. This research found that there was a strong correlation between the components of reading fluency.