Reviewerly/exHarmony huggingface dataset:
Information Usage Behavior and Importance: Korean Scientist and Engineer Users of a Personalized Recommendation Service

Background: We conducted research on the newly developed personalized recommendation service (PRS) of the global network of Korean scientists and engineers (KOSEN) in order to explore the information usage behavior and importance of the PRS used by Korean scientists and engineers. Methods: In order to understand information usage behavior, we gathered data from 513 survey results and analyzed them in terms of information usage behavior and the corresponding importance in each of the service quality areas. Results: We analyzed the 321 outcomes that indicated non-use of the PRS in order to understand the underlying reason(s); we employed 192 results that demonstrated the use of functionality to examine information usage behavior and importance. They found that the predominant motive for non-use of the service resulted from the respondents not knowing how to use it. According to demographic characteristics, the usage behavior of the PRS showed a difference regarding the purpose of using the service in the categories of gender and major field of study. Furthermore, users were concerned with various components of the PRS such as ease of use, design, relevance of content, user support, and interactivity. Conclusions: We suggest reinforcing user education degree and promotion to enhance the PRS. Since users were concerned with ease of use, design, relevance, user support, and interactivity, we recommend these as major points for improvement.
Enterprise wi-fi

Seeking to increase awareness of WPA2 Enterprise network security technology flaws and reduce risk to users.
Preimage Attacks on Reduced Troika with Divide-and-Conquer Methods

Troika is a recently proposed sponge-based hash function for IOTA’s ternary architecture and platform, which is developed by CYBERCRYPT. In this paper, we introduce the preimage attack on 2 and 3 rounds of Troika with a divide-and-conquer approach. Instead of directly matching a given hash value, we propose equivalent conditions to determine whether a message is the preimage before computing the complete hash value. As a result, for the two-round hash value that can be generated with one block, we can search the preimage only in a valid space and efficiently enumerate the messages which can satisfy most of the equivalent conditions with a guess-and-determine technique. For the three-round preimage attack, an MILP-based method is applied to separate the one-block message space into two parts in order to obtain the best advantage over brute force. Our experiments show that the time complexity of the preimage attack on 2 (out of 24) rounds of Troika can be improved to $$3^{79}$$ , which is $$3^{164}$$ times faster than the brute force. For the preimage attack on 3 (out of 24) rounds of Troika, we can obtain an advantage of $$3^{25.7}$$ over brute force. In addition, how to construct the second preimage for two-round Troika in seconds is presented as well. Our attacks do not threaten the security of Troika.
A gray box manifesto for evolutionary combinatorial optimization

It is almost an article of faith that most evolutionary algorithms utilize random mutation and random recombination operators. Often the mutation is uniform random and sometimes the recombination operator is also a type of "Uniform Crossover." However when solving classic combinatorial optimization problems, random mutation and recombination operators are often both unnecessary and unproductive.
CNN Customizations With Transfer Learning for Face Recognition Task

Several kinds of pretrained convolutional neural networks (CNN) exist nowadays. Utilizing these networks with the new classification task requires the retraining with new data sets. With the small embedded device, large network cannot be implemented. The authors study the use of pretrained models and customizing them towards accuracy and size against face recognition tasks. The results show 1) the performance of existing pretrained networks (e.g., AlexNet, GoogLeNet, CaffeNet, SqueezeNet), as well as size, and 2) demonstrate the layers customization towards the model size and accuracy. The studied results show that among the various networks with different data sets, SqueezeNet can achieve the same accuracy (0.99) as others with small size (up to 25 times smaller). Secondly, the two customizations with layer skipping are presented. The experiments show the example of SqueezeNet layer customizing, reducing the network size while keeping the accuracy (i.e., reducing the size by 7% with the slower convergence time). The experiments are measured based on Caffe 0.15.14.
Typicality-Based Across-Time Mapping of Entity Sets in Document Archives

News archives constitute a rich source of knowledge about the past societies. In order to effectively utilize such large and diverse accounts of the past, novel approaches need to be proposed. One of them is comparison of the past and present entities which can lay grounds for better comprehending the past and the present, as well as can support forecasting techniques. In this paper, we propose a novel research task of automatically generating across-time comparable entity pairs given two sets of entities, as well as we introduce an effective method to solve this task. The proposed model first applies the idea of typicality analysis to measure the representativeness of each entity. Then, it learns an orthogonal transformation between temporally distant entity collections. Finally, it generates a set of typical comparables based on a concise integer linear programming framework. We experimentally demonstrate the effectiveness of our method on the New York Times corpora through both qualitative and quantitative tests.
Novel Scheme for Image Encryption and Decryption Based on a Hermite-Gaussian Matrix

Media security is an issue of great concern over the internet and during wireless transmissions. In this paper, a novel scheme for image encryption and decryption is proposed based on a Hermite-Gaussian matrix and an array of subkeys. The proposed scheme includes a secret key that is processed to extract an array of subkeys, which are employed with the extracted phase part of the inverse Fourier transform of a Hermite-Gaussian matrix to encrypt and decrypt a grayscale image. After key generation and the production of two columns of subkeys, the Hermite-Gaussian matrix is multiplied by the first group of subkeys and subjected to a modulus operation (the remainder) with the second group, and the resulting matrix is verified for singularity. If the singularity test is passed, then the resulting image is multiplied by the original image and the output is subjected to a modulus operation and is used for the following subkeys. However, if the singularity test fails, then a new subkey is chosen and the process is repeated until all subkeys are tested and used to produce the encrypted image. For subsequent decryption, the reverse process is implemented to recover the original image from the encrypted one. Statistical analysis shows that the proposed scheme is robust and is strong against attacks. The correlation factor, among other tests, shows that reshuffling the image pixels reduces the correlation between neighboring pixels to very low values (i.e., <1%).
Evolutionary Genes Algorithm to Path Planning Problems

Genes are fundamental pieces for reproductive processes and one force field creator of the evolutionary mechanisms of the species, whose laws and mechanism are not well known. In this paper a new evolutionary optimization strategy that combines the standard genetics algorithms (GA) with selfish perspective of evolution of genes is presented. Natural selection theory is explained by a mechanism, which is centred in individuals, that are the elements of a population, characterized by their chromosomes. The primary variables are the genes (characters or words), which are non-autonomous entities, grouped in a Chromosome structure (phrases of live). However, genes make their influence felt far beyond the chromosome structure (entity of the individual). Based on this paradigm, we propose the Evolutionary Genes algorithm (EGA) that enriches the GA with a new line field generating of evolutions. Genes-centred evolution (GCE) improve the search engine of Chromosome-centred evolution (CGE) of the GA. Its impact is apparent on the increased algorithm speed, but mainly on the improvement of genetics solutions, which may be useful to solve complex problems. This approach was used to path-planning problems, in a continuous search space, to show its effectiveness in complex and interdependent sub-paths and evolution processes. GCE improved local sub-paths search as sub-processes that catalyse the CCE engine to find an optimal trajectory solution, task that the standard genetic algorithm have no ability to solve.
Gaussian Noise Removal Algorithm using Statistical Method

In this paper, a new efficient Gaussian noise removal algorithm is proposed that removes the Gaussian noises from the noisy images. This proposed method depends on statistical parameters like local standard deviation and local mean. It preserves the fine details of images along with the image restoration. In proposed method, a smoothing factor is introduced which determines the level of restoration. This factor is automatically calculated using standard deviation. From various experimental results, it is confirmed that the proposed method yields better results than the different existing methods.
A Hotel Review Corpus for Argument Mining

With the development of the network, the research of user reviews has become more important in academia and industry, because user reviews gradually influence the reputation of products and services. Argument mining has recently become a hot topic, and it is currently in the center of attention of the text mining research community. We can deeply dig out information contained in the user reviews with argument mining technology. This paper makes a corpus of hotel reviews and presents a novel scheme to model arguments, their components and relations in hotel reviews in English. In order to capture the structure of argumentative discourse, the annotation scheme includes the annotation of Major Claim, Claim, Premise, Background and Recommendation as well as Support and Attack relations. The sentiment polarity of argument components contains Positive, Negative and Neutral. We conduct a manual annotation study with 300 annotators on 1427 hotel reviews. And the final corpus collects 85 hotel reviews according to inter-rater agreement and it will encourage future study in argument recognition.
SCENet: Secondary Domain Intercorrelation Enhanced Network for Alleviating Compressed Poisson Noises

In real image coding systems, block-based coding is often applied on images contaminated by camera sensor noises such as Poisson noises, which cause complicated types of noises called compressed Poisson noises. Although many restoration methods have recently been proposed for compressed images, they do not provide satisfactory performance on the challenging compressed Poisson noises. This is mainly due to (i) inaccurate modeling regarding the image degradation, (ii) the signal-dependent noise property, and (iii) the lack of analysis on intercorrelation distortion. In this paper, we focused on the challenging issues in practical image coding systems and propose a compressed Poisson noise reduction scheme based on a secondary domain intercorrelation enhanced network. Specifically, we introduced a compressed Poisson noise corruption model and combined the secondary domain intercorrelation prior with a deep neural network especially designed for signal-dependent compression noise reduction. Experimental results showed that the proposed network is superior to the existing state-of-the-art restoration alternatives on classical images, the LIVE1 dataset, and the SIDD dataset.
Chapter 5 Modeling Public Mood and Emotion: Blog and News Sentiment and Politico-economic Phenomena

Given the maturation of the internet and virtual communities, an important emerging issue in the humanities and social sciences is how to accurately analyze the vast quantity of documents on public and social network websites. Therefore, this chapter integrates political blogs and news articles to develop a public mood dynamic prediction model for the stock market, while referencing the behavioral finance perspective and online political community characteristics. The goal of this chapter is to apply a big data and opinion mining approach to a sentiment analysis for the relationship between political status and economic development in Taiwan. The proposed model is verified using experimental datasets collected from ChinaTimes.com, cnYES.com, Yahoo stock market news, and Google stock market news, covering the period from January 1, 2016 to June 30, 2017. The empirical results indicate the accuracy rate with which the proposed model forecasts stock prices.
Contemporary Challenges in Street Trader-Customer Interaction Through Mobile Devices in Dodoma, Tanzania

Street trading is a common form of informal work carried out by almost one million Tanzanians. Majority of street traders use mobile devices to interact with customers. Despite this interaction, there is no abundant information showing if their interaction is mainly effective and does not face challenges. This study investigated the challenges faced by street traders and customers interacting through mobile devices in Dodoma, Tanzania. Qualitative data were collected using in-depth interviews with 42 street traders and 32 customers, followed by focus group discussion with eight street traders and six customers. Thematic analysis was used to analyze the data. The results show that street traders and customers occasionally interact using mobile phones. However, that interaction is challenged by issues connected to financial, technical and social aspects. These challenges are; lack of reliability among mobile phone interacting customers, mobile network problems, lack of business communication transparency, deep-rooted customary practices and perceptions of street trading, poor customer care, lack of consensus over mobile business etiquette, poor quality of product pictures, short mobile internet bundle validity, mobile phone battery life, and costs of mobile handset, transactions, vouchers, packages, and transport. The results call for the option of bundle and transaction cost reduction, network infrastructure improvement and provision of education to street traders and customers so that they realize the significance of business interaction using mobile devices contrary to what is happening recently, as well as abiding by communication ethics to minimize the likely challenges.
Secure Deduplication Scheme for Cloud Encrypted Data

Cloud storage (CS) is gaining much popularity nowadays because it offers low-cost and convenient network storage services. In this big data era, the explosive growth in digital data moves the users towards CS but this causes a lot of storage pressure on CS systems because a large volume of this data is redundant. Data deduplication is an effective data reduction technique. The dynamic nature of data makes security and ownership of data as a very important issue. Proof-of-ownership schemes are a robust way to check the ownership claimed by any owner. However, this method affects the deduplication process because encryption methods have varying characteristics. A convergent encryption (CE) scheme is widely used for secure data deduplication. The problem with the CE-based scheme is that the user can decrypt the cloud data while he has lost his ownership. This article addresses the problem of ownership revocation by proposing a secure deduplication scheme for encrypted data. The proposed scheme enhances the security against unauthorized encryption and poison attack on the predicted set of data.
Classification and diagnosis of diabetic with neural network algorithm learning vector quantizatin (LVQ)

Determining the type of Diabetes Mellitus (DM) is very important to determine what treatment is suitable for a patient. Unfortunately patient information about what type of diabetes is often ignored, so the patient gets a wrong diagnosis. This study aims to build a classification model in determining a DM patient diagnosed with one type of DM, namely type 1 DM, type 2 DM, Gestational DM or special type DM. The indicators used in determining the classification for diagnosing patients are age, sex, blood pressure, levels of blood glucose, weight, and height. The classification method used is the Neural Network method with Learning Vector Quantization (LVQ) algorithm. Algorithm LVQ provides results 96% accuracy for training data with final epoch is 759 and 90% accuracy for testing data.
Simultaneous Estimation of Hidden State and Unknown Input Using Expectation Maximization Algorithm

An expectation maximization (EM) algorithm-based simultaneous state and input estimator for nonlinear systems is developed. This study uses a Bayesian solution to estimate the states and unknown inputs simultaneously. It was assumed that a joint distribution between states and inputs exists. The joint distribution was estimated sequentially using an EM algorithm. The EM algorithm has two steps: expectation step (E-step) and maximization step (M-step). In the E-step, a particle filter was used to estimate the conditional probability of states. The conditional distribution of the measurements conditioned on the estimated states was maximized with respect to inputs in the M-step, and inputs were estimated. These two steps were performed alternatively until both states and inputs converge to steady values. The effectiveness of the proposed method was demonstrated using simulation and experimental case studies.
A Fast and Robust Re-Identification Method for Large Video Data

This paper proposes a fast and robust person re-identification method from videos. Naive re-identification methods may falsely recognize images of a same person as different people due to continuous change of environments around cameras even in a same place. This failure causes multiple result sets of images of different people, which should be one. This paper addresses the problem to propose a method that enhances the robustness and computation speed of the re-identification. The robustness of the re-identification is improved by employing video tracking technologies. The speed of that is increased with a Luigi index, which is a data structure for fast similarity search. Experimental results showed that the proposed method shortens execution time by 10% and reduces the number of redundant result sets by a factor of 12.6 compared to the naive one.
SAT-Based Generation of Optimum Circuits with Polymorphic Behavior Support

This paper presents a method for generating optimum multi-level implementations of Boolean functions based on Satisfiability (SAT) and Pseudo-Boolean Optimization (PBO) problems solving. The method is able to generate one or enumerate all optimum implementations, while the allowed target gate types and gates costs can be arbitrarily specified. Polymorphic circuits represent a newly emerging computation paradigm, where one hardware structure is capable of performing two or more different intended functions, depending on instantaneous conditions in the target operating environment. In this paper we propose the first method ever, generating provably size-optimal polymorphic circuits. Scalability and feasibility of the method are documented by providing experimental results for all NPN-equivalence classes of four-input functions implemented in AND–Inverter and AND–XOR–Inverter logics without polymorphic behavior support being used and for all pairs of NPN–equivalence classes of three-input functions for polymorphic circuits. Finally, several smaller benchmark circuits were synthesized optimally, both in standard and polymorphic logics.
Twittener: An Aggregated News Platform

The Internet offers an abundance of online sources for trending topics and news. However, this gives rise to the issue of content overload, where users must filter through large amount of content to find those that are of relevance or interest to them. This project aims to solve this issue by creating a web application called Twittener. Twittener aims to improve users' experience and time-efficiency when reading news online. Methods used include text-to-speech technology, sentiment analysis and recommender system. Text-to-speech technology enables users to listen to tweets and news without paying attention to their screens. This could also be useful for populations with visual impairments. Sentiment analysis on Twitter trends provides useful information on general sentiment towards each trend and a hybrid recommender system is deployed to recommend news that would likely be of interest to users. This paper seeks to document the development, implementation, design and implications of Twittener.
Fuzzy based cell generalization to improve the data utility with minimal loss of information

Nowadays people are sharing their information in digitally, and their information plays the significant role in public sectors and business world assist in understanding the social behavior, statistical analysis and drafting the business policies for future direction. At the same time, users’ pers onal information should be preserved from adversaries while data publishing. Privacy preservation is one of the main agenda of data publishing, K-anonymity and its families likel-diversity and t-closeness which are the most familiar models to preserve the sensitive information of individuals in data publishing. Besides, data reliability is also the main factor of reliable anonymized data. Various anonymization techniques which are performed the data protection against attack models; however, the privacy models satisfy the privacy preservation effectively. The data utilization is also a vital account to achieve the purpose of data publishing. Moreover; Publisher focuses on the utilization of anonymized dataset that might be helpful to analyze the meaningful data. Otherwise, the potency of anonymization leads to diminishing the data utility of anonymized dataset and weakening the purpose of data publishing. In this proposed method aims to improve the utilization of anonymized data in data publishing with minimal information loss based on fuzzy alpha-cuts. Triangular membership function has applied over in the proposed method to generalize the quasi-identifiers by constructed alpha cut sets and taxonomy tree. The evaluation results are compared with an existing model concerning information loss and time execution time metrics. The performance of the proposed method is encouraging that based on the experimental results.
Development of Youth Motivation for Leadership and Participation in R&amp;D Activities

Our paper tackles the problems in the development of mechanisms stimulating youth activity, especially youth activity in the field of science and innovation. The history of the development of human civilization proves that all significant changes in the world community were successful to some extent due to the fact that the youth actively participated in them as the most pro-active and promising social group. We show that participation of competent, enterprising, entrepreneurial, and moral young people who are able to make their own decisions and take responsibility for their decisions is significant for modern society. The outcomes of our analytical study are based on the implementation of our novel research project on the development of state incentive mechanisms for current and priority areas of youth policy in the field of science, technology, and innovation in the Russian Federation. Our results might be of a special interest for educators as well as policy-makers in science, education, and R&D sector.