Reviewerly/exHarmony huggingface dataset:
Single Sign-on Implementation: Leveraging Browser Storage for Handling Tabbed Browsing Sign-outs

Organizations provide multiple web-based services to its users. To manage easy provisioning and deprovisioning of users, they offer single sign-on (SSO) service as an access control mechanism. To access any SSO service, users need to remember only one set of credentials. These credentials are managed by organization’s identity and access management system (IAM). The identity provider (IDP) is the federated identity management platform to authenticate users, for service providers (SPs), to access and use the services. Users may access the services provided by organization via web browsers. Modern web browsers provide a feature called tabbed browsing, where tabs are widgets within the browser window, so that the users can stay organized when browsing multiple Web sites. In this paper, we analyzed how SSO works in a tabbed browsing environment. Our analysis shows that, in some scenarios, a user may not sign out from some services that he or she was using. This situation may lead to information security attacks. Also, we propose a solution that can be implemented on the browser to ensure a safe sign-out process.
Workflow scheduling with data transfer optimization and enhancement of reliability in cloud data centers

Infrastructure as a service (IaaS) clouds offer huge opportunities to solve large-scale scientific problems. Executing workflows in such environments can be expensive in time if not scheduled rightly. Although scheduling workflows in the cloud is widely studied, most approaches focus on two user's quality of service requirements namely makespan (i.e., completion time) and costs. Other important features of cloud computing such as the heterogeneity of resources and reliability must be considered. In this paper, we present a reliability-aware method based on discrete particle swarm optimisation (RDPSO) for workflow scheduling in multiple and heterogeneous cloud data centres. Our aim is to optimise data transfer time while minimising makespan and enhancing reliability. Based on simulation, our results show a significant improvement in terms of makespan, transferred data and reliability relative to reliability-aware HEFT method (heterogeneous earliest finish time), for the real-world workflows.
Estimation of State-Space Models with Gaussian Mixture Process Noise

State-space models are widely used to estimate latent dynamic processes from noisy and low-dimensional observations. When applying these models to real data, it is commonly assumed that the state dynamics are governed by Gaussian statistics. However, this assumption does not hold in applications where the process noise is composed of various exogenous components with heterogeneous statistics, resulting in a multimodal distribution. In this work, we consider a state-space model with Gaussian mixture process noise to account for such multimodality. We integrate the Expectation Maximization algorithm with sequential Monte Carlo methods to jointly estimate the Gaussian mixture parameters and states from noisy and low-dimensional observations. We validate our proposed method using simulated data inspired by auditory neuroscience, which reveals significant gains in state estimation as compared to widely used techniques that assume Gaussian state dynamics.
Research on Application Model of Unconsciousness in User Interface

The aim of the paper is to make the human-computer interaction more efficient and natural. Based on Jung’s unconscious theory, the paper combines unconscious cognition and unconscious behavior to discuss how unconsciousness works between users and User Interface on the methods of literature research, observation, interview and questionnaire. As a result, the study elaborates the working mechanism of unconsciousness in User Interface. Finally, an abstract model is established to show the two aspects of unconsciousness in Natural User Interface,the task execution and feedback. The former is about the unconscious behavior and cognition formed by acquired learning, while the latter is about unconscious cognition of congenital instinct. At the end of the paper, the next research direction that how Visual adaptability, grip feeling and information location influence users’ unconscious cognition, is put forward by questionnaire analysis.
A Generative Adversarial Distribution Matching Framework for Visual Domain Adaptation

One of the difficulties in computer vision is how to build an accurate classifier for a new target domain with insufficient labeled images from a related source domain with labeled images. Adversarial learning is a novel domain adaptation method that tackles this challenge by training robust deep networks and reducing the distribution difference between source and target domains, thus improving the classification performance on a target task. However, most prior adversarial adaptation learning approaches merely reduce the distribution difference across domains through GAN (Generative Adversarial Networks)-based loss, but when the performance of a generator or discriminator in GAN is degraded, the distribution difference between source and target domains are difficult to decrease. In this paper, we propose a novel generalized framework for adversarial domain adaptation, referred to as Generative Adversarial Distribution Matching. Our idea is to add the data discrepancy distance between source and target domains to the objective function of the generator so as to reduce distribution difference across domains through a Generator and a Discriminator compete against each other. Comprehensive experimental results confirm that it can well outperform several state-of-the-art methods for cross-domain image classification problems.
DUOLINGO GAMIFICATION: DOES IT REDUCE STUDENTS’ GRAMMATICAL ERRORS IN WRITING?

The study is aimed to examine the effectiveness of Duolingo Gamification Platform to reduce the students' gramma tical errors in writing a report text. The participant of this study were 25 students in the second grade of Methodist Senior High School in Banda Aceh, Indonesia. The research utilized a pre-experimental design with one group pre-test post-test. The instrument of this study is a test divided into pre and post-test. The data were analyzed using the t-test. The study revealed that the mean score of the pre-test is 53.8, and the mean score of post-test is 55.4. The standard deviation for pre-test was 7.18 and for post-test were 7.29 and the score of the standard of error of pre-test was 1.43 and post-test was 1.45. The t-score of the study was 4.0, with the degree of freedom 24, and the t-table was 1.69. The data showed that the t-score was higher than t-table, which leads us to the interpretation that the H a was accepted and H 0 was rejected. I t indicated that the use of Duolingo Gamification platform was sufficient to reduce the students' gramma tical errors in writing report text . Therefore, the platform was highly recommended to use to increase the quality of students’ writing skill, especially in the report text. Keywords: Duolingo, Gamification, Writing, Technology
Predictive Analytics In Weather Forecasting Using Machine Learning Algorithms

Agriculture is the backbone of every economy.In a country like India, which has ever increasing demand of food due to rising population, advances in agriculture sector are required to meet the needs.To add to it, the present economic conditions and government policies of India are such that it necessitates the adoption of Precision farming or smart farming.It will enable the farmers to maximize their crop yields and minimize the input costs as well as the losses due to reasons like uncertain rainfall, droughts etc. from this model.For Predicting weather forecasting we will use machine learning Algorithms like Linear Regression, Decision tree.
Image Encryption Using Modified Rubik’s Cube Algorithm

For multimedia applications, data are developed and transmitted through the networks. These multimedia data should not be accessed by the unauthorized persons as it contains information. So, in current scenarios image security and privacy become a major issue in communication. In this work, we proposed an advanced encryption scheme based on modified Rubik’s cube algorithm. First, the original image is scrambled using two secret keys, which is generated using logistic function and shift register method, respectively. Then, with XOR operator, rows and columns of the scrambled image are again mixed using various means. Performance of the proposed work is assessed with correlation coefficient and information entropy. From the experimental analysis and security parameter evaluation, it can be observed that the proposed scheme can resist exhaustive attack, statistical attack and differential attack.
Intelligent Management of Secondary Water Supply Systems in Downtown Shanghai

Secondary water supply, as the final component of the urban water supply system, is critical for ensuring the safety of urban water supply, and is directly related to customers’ water use. However, water stored in secondary water supply facilities will more likely to lose disinfectants (chlorine residual) which can lead to bacterial regrowth and water quality deterioration. Therefore, it is important to solve problems associated with secondary water supply and manage the system more effectively. In the context of the Internet of Things, the intelligent management model based on information technology has become the growing trend. This paper introduces the application of the secondary water supply intelligent management model in Shanghai.
Image Hashing Based on CS-LBP and DCT for Copy Detection

In order to improve the accuracy and speed of copy detection, we propose an image hashing algorithm based on local feature and global feature in this paper. Firstly, input image is converted to a normalized image. Then, wavelet decomposition is applied to the preprocessed image to produce approximate image and high-frequency information. Center symmetric local binary pattern (CS-LBP) is applied to approximate image to produce a CS-LBP texture image. The local feature is extracted by dividing blocks and selecting mean, variance, third moment, fourth moment statistics feature in CS-LBP texture image. The global feature is extracted by selecting the discrete cosine transform (DCT) coefficients of high frequency-information. Finally, local features and global features are combined to generate image hashing. Experiment with open images are carried out and the results show that the proposed algorithm is robust and discriminative. The precision rate and the recall rate (P-R) curve comparisons show that our hashing algorithm outperforms some existing hashing algorithms in copy detection.
Seismic Sensors Probe Lipari’s Underground Plumbing

An international team of scientists installed a novel, dense network of 48 seismic sensors on the island of Lipari to investigate the active magma system underground.
Some issues of improving the training of masters of the field 44.04.01 Pedagogical Education under the program “Mathematical education”

И. Ю. Покорная, С
Journal Classification Using Cosine Similarity Method on Title and Abstract with Frequency-Based Stopword Removal 

Classification of economic journal articles has been done using the VSM (Vector Space Model) approach and the Cosine Similarity method. The results of previous studies are considered to be less optimal because Stopword Removal was carried out by using a dictionary of basic words (tuning). Therefore, the omitted words limited to only basic words. This study shows the improved performance accuracy of the Cosine Similarity method using frequency-based Stopword Removal. The reason is because the term with a certain frequency is assumed to be an insignificant word and will give less relevant results. Performance testing of the Cosine Similarity method that had been added to frequency-based Stopword Removal was done by using K-fold Cross Validation. The method performance produced accuracy value for 64.28%, precision for 64.76 %, and recall for 65.26%. The execution time after pre-processing was 0, 05033 second.
A Secure Data Aggregation Protocol in VANETs Based on Multi-key FHE

For sake of data aggregation in VANETs, a protocol is devised based on multi-key fully homomorphic encryption (MFHE). In order to introduce practical properties, such as scalability, into the proposed protocol, a dynamic topology is utilized to structure the very-basic framework. To address the problem of dynamic changes with respect to floating nodes, linear secret sharing scheme is applied to multi-key fully homomorphic encryption with threshold decryption, and then the partial sharing decryption is proposed. Performance analysis illustrated that the proposed scheme is feasible and the complexity expands. Under the universal composability frame, the proposed protocol is also proved to be semantically secure.
Research on SQL Injection and Defense Technology

With the rapid development of Internet technology, more and more dynamic web sites based on the B/S three-tier architecture have been established. At the same time, the security issues exposed by the websites are increasing, and the situation is not optimistic. Today, a large number of Web systems use a database to store various data of a website, which may be the user’s personal information, or may be a company’s trade secret information. If this information is leaked, it is a huge loss and risk to the individual or the company. SQL injection attacks can achieve the purpose of obtaining illegal data, so it is conceivable that the harm of SQL injection is huge. From the point of view of SQL injection, SQL injection attacks are still one of the most common and most dangerous attacks. This paper introduces the concept and technical principle of SQL injection attack, introduces the type of SQL injection, analyzes the basic implementation process of SQL injection attack, and finally gives the defense method of preventing SQL injection and summarizes some researches on SQL injection.
Characterizing the Cognitive Impact of Tangible Augmented Reality

This study examines how cognitive processes that support mission planning are influenced by the physical ability to touch and manipulate sand, compared to passively observing the same action. It employs a systematic investigation on terrain conceptual knowledge, terrain recognition and, landmark memory using the ARES sand table. Sand tables are topographic models that support learning through the physical creation of scenarios. In the military, sand tables support strategic exercises for soldiers to practice the process of collective decision making and communication. Operational mission planning typically occurs with one person shaping the sand, followed by a larger group of individuals observing. It is the relationship between the person shaping terrain and those observing that is of specific interest. A total of 96 participants were recruited, from the University of Central Florida (UCF) and the Center for Applied Brain & Cognitive Sciences (CABCS). Results indicate that physically shaping the terrain improved recognition but did not have an effect on conceptual knowledge or recollection of landmarks compared to observers. This experiment supports the need for further investigation to determine how tangible interaction can contribute to cognitive understanding.
Agile, Continuous Integration, and DevOps

The following topics are dealt with: learning (artificial intelligence); Internet; mobile computing; security of data; pattern classification; program testing; Internet of Things; smart phones; neural nets; data mining.
Stellar mass black hole optimisation for utility mining

Major challenges in mining high utility itemsets from the transaction databases requires exponential search space and database-dependent minimum utility threshold. The search space is very large because of the large number of distinct items and size of the database. Data analysts need to specifying appropriate minimum utility thresholds for their data mining tasks though they may have no knowledge pertaining to their databases. To get rid of these problems, Stellar mass black hole optimisation (SBO) method is proposed to mine Top-K HUIs from the transaction database without specifying minimum utility threshold. To know the performance of SBO, the experiment results are compared with GA.
Utilizing Recorded English Dialogues in Teaching English Word Stress to Islamic Higher Education Students in Indonesia

The objective of this study is to know the effectiveness of recorded English dialogues in developing the students understanding of English word stress for Islamic Education students. The study employed the classroom action research design. The subject of this study was undergraduate EFL students at the Islamic Education Department in 2018/2019 academic year. The data were collected through four research instruments: observation checklist, questionnaire, field notes, and test. The result of study shows that teaching English word stress through the utilizing recorded English dialogue performed by the researcher is effective to develop students understanding of English word stress. The primary criteria to judge that the students have development are that from the score they gained from two phases of test. In the first test, the students’ development reached 56.25%. Meanwhile, in the second test, students’ development attained 81.25%. Since learning achievement is calculated based on the number of students who get the target score, so it can be stated that the learning achievement has been attained.
Measuring Network User Trust via Mouse Behavior Characteristics Under Different Emotions

Authentication based on mouse behavior is a guarantee for network information security. But the mouse behavior is affected by the user’s emotions. Therefore, this study aims to analyze the user’s mouse behavior characteristics to measure the identity trust of users under different emotions, and to verify whether there is a significant difference. To achieve this goal, an experiment was conducted. A total of 18 college students participated in this study. The results show that there are differences in the accuracy of authentication based on the user’s mouse sliding behavior in three different emotional states, but the difference is not significant. The average accuracy of authentication under neutral, positive and negative emotions were 83.6%, 80.3% and 81.9%, respectively. The results also show that although the user performs human-computer interaction under different emotions, it will not essentially affect user authentication. Therefore, it can conclude that measuring network user trust via mouse behavior characteristics under different emotions is credible.
An Intelligent Traffic Signal Control System Based on Fuzzy Theory

Probabilistic fuzzy logic control is developed for the signalized control of a diamond interchange, where the signal phasing and green time extension are decided in response to real-time traffic conditions, aimed at improving traffic flows on surface streets and highways. The probabilistic fuzzy logic for the diamond interchange (PFLDI) comprises two modules: probabilistic fuzzy phase timing (PFPT) that controls the green time extension process of the current running phase, and phase selection (PSL) which decides the next phase based on the pre-setup phase logic by the local transport authority. We used advanced interactive microscopic simulator for urban and non-urban network (AIMSUN) software for modeling various performances of the PFLDI algorithm. Simulation results show that the PFLDI surpasses the traffic actuated and conventional fuzzy models with lower system total travel time, average delay, and improvements in downstream average speed and downstream average delay.
Constructing National Geospatial Big Data Platform: Current Status and Future Direction

With the increasing predominance of Internet of Things (IoT) applications, a considerable amount of geospatial information has been accumulating from various sources. In addition to the common features of big data, the unique characteristics of spatial data make the treatment of big spatial data even more complicated. To facilitate developers creating big spatial data applications, it is imperative to develop new technologies to efficiently handle the massive amount of big spatial data. Given this impetus, the Korean government launched a five-year national project involving businesses, government, and the research community. The goal is to develop a platform for efficiently storing, extracting, processing, and analyzing geospatial big data. This paper explains the expected outcome from the project including the overall architecture of the platform, along with its current status and future direction.
Proof-of-What? Detecting Original Consensus Algorithms in Cryptocurrencies with a Four-Factor Model

This study applies Fama-French-style factor loading analysis to cryptocurrency financial performance data to determine the originality of 32 reportedly novel consensus algorithms (“proofs”) and 20 hybrid consensus mechanisms as compared to conventional proof-of-work and proof-of-stake using a sample of 302 cryptocurrencies. Only 14 out of 32 new consensus algorithms and 12 out of 20 hybrid mechanisms are found to be truly original. Innovative consensus protocols are not associated with superior returns while original hybrid solutions are. The findings allow investors to select coins with original “proofs” and to explore performance implications of consensus algorithms. For future research, the applicability of market, size, proof and age factors for risk and attribution analysis of cryptocurrency markets is evidenced.
Testing Isomorphism of Lattices over CM-Orders

A CM-order is a reduced order equipped with an involution that mimics complex conjugation. The Witt--Picard group of such an order is a certain group of ideal classes that is closely related to the “minus part” of the class group. We present a deterministic polynomial-time algorithm for the following problem, which may be viewed as a special case of the principal ideal testing problem: given a CM-order, decide whether two given elements of its Witt--Picard group are equal. In order to prevent coefficient blow-up, the algorithm operates with lattices rather than with ideals. An important ingredient is a technique introduced by Gentry and Szydlo in a cryptographic context. Our application of it to lattices over CM-orders hinges upon a novel existence theorem for auxiliary ideals, which we deduce from a result of Konyagin and Pomerance in elementary number theory.