Reviewerly/exHarmony huggingface dataset:
Development of Recommendation System of Place of Employment for Students with Decision Rules in Rough Set using the Condition Relaxation based on User’s Aptitude

Conditional search system in employment supporting website is well used in Japan, if college or university students search place of employment. However, this system is not possible to respond flexibly to user’s undefined preference, and recommends only a few place of employment with strict conditions setup.
Movie Video Summarization- Generating Personalized Summaries Using Spatiotemporal Salient Region Detection

Video summarization condenses a video by extracting its informative and interesting segments. In this article, a novel video summarization approach is proposed based on spatiotemporal salient region detection. The proposed approach first segments a video into a set of shots which are ranked with spatiotemporal saliency scores. The score for a shot is computed by aggregating the frame level spatiotemporal saliency scores. This approach detects spatial and temporal salient regions separately using different saliency theories related to objects present in a visual scenario. The spatial saliency of a video frame is computed using color contrast and color distribution estimations and center prior integration. The temporal saliency of a video frame is estimated as an integration of local and global temporal saliencies computed using patch level optical flow abstractions. Finally, top ranked shots with the highest saliency scores are selected for generating the video summary. The objective and subjective experimental results demonstrate the efficacy of the proposed approach.
Effective L-Diversity Anonymization Algorithm Based on Improved Clustering

Mass data has been collected and released everyday, at the same time, the published data contains a lot of sensitive information related to individuals. K-anonymity privacy preserving mechanisms can prevent the disclosure of individual privacy information in the scenarios of data publication. L-diversity further considers the distribution of sensitive attributes in equivalence classes to avoid homogeneity attacks. In this paper, we propose an improved L-diversity algorithm based on clustering, and we consider the L-diversity demand of sensitive attributes while clustering to achieve K-anonymity. We minimize the total information loss of each equivalence class by choosing records which has minimal loss of information, regardless of whether they have different sensitive attributes, until the number of distinct values of sensitive attribute in the equivalence class reaches L. This algorithm we conduct experiments on UCI Adult data set and compared with traditional (K,L)-member algorithm. Theoretical analysis and the experimental results demonstrate that the improved L-diversity algorithm can not only improve the privacy protection degree of sensitive data, but also effectively reduce the information loss.
Selenium-enriched soil mapping using airborne SASI images

Selenium (Se) is an important trace element that is essential to human beings. In the past, the Se concentration has mostly been obtained by field sampling and analysed under laboratory conditions. Unfortunately, this process is expensive, and the number of available samples is usually relatively small. A soil geochemical survey was conducted in conjunction with an airborne survey via hyperspectral remote sensing in the Chuangye Farm area, China. Twenty-five elements/oxides including Se were analysed in the samples, and the results showed that Se has a highly negative correlation with K. Using hyperspectral Shortwave Infrared Airborne Spectrographic Imager (SASI) data, the abundances of clay minerals were obtained through the sequential maximum angle convex cone (SMACC) classification operation. According to the abundances of clay minerals, the reflectance of clay minerals was obtained using the spectral retrieval method. Due to the correlation among K, Se, clay minerals and their spectral characteristics, a stepwise regression model was established using the results from the geochemical survey data and retrieved hyperspectral SASI data; then, the K and Se concentrations were predicted. The results of this study show that predicting the Se content in soil by using SASI images through the spectral retrieval of clay minerals in soil in conjunction with actual geochemical analysis results boasts a higher prediction accuracy than the use of the raw SASI images, and this prediction approach has been proven to be feasible.
Big Data and Query Optimization Techniques

With rise in new technologies like Big Data, Internet of Things, Sensor Networks, etc. new query processing and optimization techniques have been evolved which handles the complex and interactive queries. Traditional query processing systems are not capable enough to handle queries on big data. In this paper, we represent many of the big data querying techniques along with the comparative analysis. Finally, the paper concludes with list of tools/techniques that are used for big data query optimization.
A Hybrid of Sentence-Level Approach and Fragment-Level Approach of Parallel Text Extraction from Comparable Text

Parallel texts are essential resources in linguistics, natural language processing, and multilingual information retrieval. Many studies attempt to extract parallel text from existing resources, particularly from comparable texts. The approaches to extract parallel text from comparable text can be divided into sentence-level approach and fragment-level approach. In this paper, an approach that combines sentence-level approach and fragment-level approach is proposed. The study was evaluated using statistical machine translation (SMT) and neural machine translation (NMT). The experiment results show a very significant improvement in the BLEU scores of SMT and NMT. The BLEU scores for SMT for the test in computer science domain and news domain increase from 17.45 and 41.45 to 18.56 and 48.65 respectively. On the other hand, the BLEU scores for NMT in the computer science domain and news domain increase from 14.42 and 19.39 to 21.17 and 41.75 respectively.
Inductive Multi-view Semi-Supervised Anomaly Detection via Probabilistic Modeling

This paper considers anomaly detection with multi-view data. Unlike traditional detection on single-view data which identifies anomalies based on inconsistency between instances, multi-view anomaly detection identifies anomalies based on view inconsistency within each instance. Current multi-view detection approaches are mostly unsupervised and transductive. This may have limited performance in many applications, which have labeled normal data and prefer efficient detection on new data. In this paper, we propose an inductive semi-supervised multi-view anomaly detection approach. We design a probabilistic generative model for normal data, which assumes different views of a normal instance are generated from a shared latent factor, conditioned on which the views become independent. We estimate the model by maximizing its likelihood on normal data using the EM algorithm. Then, we apply the model to detect anomalies, which are instances generated with small probabilities. We experiment our approach on nine public data sets under different multi-view anomaly settings, and show it outperforms several state-of-the-art multi-view detection methods.
Analysis of accuracy parameters of ANN backpropagation algorithm through training and testing of hydro-climatology data based on GUI MATLAB

Abstract The authors have developed a GUI Matlab to simplify the process of predicting Hydro-climatology data using ANN Back Propagation method. Five data for training, testing, and prediction were used. The data, i.e. rainfall, air humidity, duration of shine, temperature, and wind speed are taken from the last ten years with matrix input size m x n . Each data is trained 21 times using a combination of the activation functions (logsig, tansig, and purelin) and training methods (traingda, traingdx, and trainrp). The result of the training data was that the logsig function and trainrp on each layer are the best formulas in conducting training, testing, and predictions with an accuracy of 99.71%. This result is obtained from parameter settings including epochs of 1000, learning rate of 0.7, goal error of 0.0001, and training steps of 1.
Exploring Privacy Breaches and Mitigation Strategies of Occupancy Sensors in Smart Buildings

Occupancy sensors are an integral part of many smart building applications, including energy and space optimizations. However, as more occupancy data is collected at an increasingly fine-grained level, possibilities of potential privacy breaches also increase. In this paper, we propose an attack in which the adversary associates an individual with the occupancy sensor by combining the occupancy data with other public information that could easily be obtained online. We conduct an experiment using real-world data to demonstrate that the proposed attack is not only feasible, but the adversary can efficiently identify a small set of sensor IDs that contains the ID of interest given a relatively small amount of additional information. We also summarize a number of mitigation strategies against the proposed attack.
Connected Subsets of an n × 2 Rectangle

Suppose an n × m grid is printed on an n × m note card. Cutting only along grid lines, in how many ways may a connected piece be cut from the card? In how many ways may the card be cut into two con...
Fast Root Finding for Interpolation-Based Decoding of Interleaved Gabidulin Codes

We show that the root-finding step in interpolation-based decoding of interleaved Gabidulin codes can be solved by finding a so-called minimal approximant basis of a matrix over a linearized polynomial ring. Based on existing fast algorithms for computing such bases over ordinary polynomial rings, we develop fast algorithms for computing them over linearized polynomials. As a result, root finding costs O <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">~</sup> (ℓ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ω</sup> M(n)) operations in F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> m, where ℓ is the interleaving degree, n the code length, F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> m the base field of the code, 2 ≤ ω ≤ 3 the matrix multiplication exponent, and M(n) ∈ O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1.635</sup> ) is the complexity of multiplying two linearized polynomials of degree at most n. This is an asymptotic improvement upon the previously fastest algorithm of complexity O(ℓ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), in some cases O(ℓ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ).
Adaptive Structural Co-regularization for Unsupervised Multi-view Feature Selection

With the advent of big data, there is an urgent need for methods and tools for integrative analyses of multi-modal or multi-view data. Of particular interest are unsupervised methods for parsimonious selection of non-redundant, complementary, and information-rich features from multi-view data. We introduce Adaptive Structural Co-Regularization Algorithm (ASCRA) for unsupervised multi-view feature selection. ASCRA jointly optimizes the embeddings of the different views so as to maximize their agreement with a consensus embedding which aims to simultaneously recover the latent cluster structure in the multi-view data while accounting for correlations between views. ASCRA uses the consensus embedding to guide efficient selection of features that preserve the latent cluster structure of the multi-view data. We establish ASCRA's convergence properties and analyze its computational complexity. The results of our experiments using several real-world and synthetic data sets suggest that ASCRA outperforms or is competitive with state-of-the-art unsupervised multi-view feature selection methods.
Combining Opinions for Use in Bayesian Networks: A Measurement Error Approach

Summary Bayesian networks (BNs) are graphical probabilistic models used for reasoning under uncertainty. These models are becoming increasingly popular in a range of fields including engineering, ecology, computational biology, medical diagnosis and forensics. In most of these cases, the BNs are quantified using information from experts or from users' opinions. While this quantification is straightforward for one expert, there is still debate about how to represent opinions from multiple experts in a BN. This paper proposes the use of a measurement error model to achieve this. The proposed model addresses the issues associated with current methods of combining opinions such as the absence of a coherent probability model, the loss of the conditional independence structure of the BN and the provision of only a point estimate for the consensus. The proposed model is applied to a subnetwork (the three final nodes) of a larger BN about wayfinding in airports. It is shown that the approach performs well than do existing methods of combining opinions.
Smart Tracking and Fall Detection for Golden Age’s Citizen

Senior Citizen is an elderly human in a golden age who has many limitations. The number of older people is increasing in almost every country, including in Indonesia. One of the diseases that were suffered is senile or known as Dementia. Therefore, to protect the elderly, the special care is needed in order to be able to monitor the located and to see the condition of the elderly when they are travelling outside the home. Based on that condition, a prototype of the detector is proposed in this paper namely Smart tracking for the Golden Age’s Citizen. This system consists of a GPS module, a vibration sensor, a GSM Module, and a voice recorder module. GPS module will detect the location, and the vibration sensor will detect the vibration if the elderly falls. The GSM module will send the information in the form of SMS to the family. The voice recorder module is used to record and play the recorded sound. Based on the results of the prototype test, the prototype is successfully detecting the location if the elderly is outside the range. The testing was concluding that the older people position can be tracked approximately around 2-5 meters from the determination in google map application and send an SMS in 3-5 seconds, if the user falls or lost.
Hand gesture understanding by weakly-supervised fusing shallow/deep image attributes

Accurately recognizing human hand gestures is a useful component in many modern intelligent systems, such as identification authentication, human–computer interaction, and sign language recognition. Conventional approaches are typically based on shallow visual features and relatively simple backgrounds, which cannot readily recognize partially occluded hand gestures with sophisticated backgrounds. In this work, we propose a unified hand gesture recognition framework by optimally fusing a set of shallow/deep finger-level image attributes, based on which a weakly-supervised ranking algorithm is designed to select semantically salient regions for gesture understanding. More specifically, given a rich number of hand gesture images, we employ the well-known BING object proposal generator to extract hundreds of object patches that potentially draw human visual attention. Since the hundreds of object patches are still too many for building an effective recognition system, a weakly-supervised metric is proposed to rank them by extracting multiple shallow/deep features. And visual semantics are encoded at region-level by transferring the image-level semantic tags into various human gesture image regions by a weakly-supervised learning paradigm Apparently, the top-ranking highly salient object patches are highly indicative to human visual perception of human hand gesture, Thus we extract their ImageNet-CNN features and further concatenate them. Finally, the concatenated deep feature is fed into a multi-class SVM for classifying each hand gesture image into a particular type. Comprehensive experimental validations have demonstrated the effectiveness and robustness of our proposed hybrid-feature-based hand gesture categorization.
Research on Improved k-Means Clustering Algorithm Based on Hadoop Platform

In this paper, aiming at the problems of traditional K-means clustering algorithm in big data processing, such as performance and determination of initial clustering center, an improved k-means clustering algorithm based on Hadoop platform is proposed. This algorithm uses canopy algorithm and cosine similarity to calculate, optimizes the determination of initial clustering center by K-means algorithm, and uses parallel computing framework to expand the algorithm in parallel. To adapt to big data processing. The experimental results show that the improved k-means clustering algorithm based on Hadoop platform has better clustering effect, and also has good speedup and scalability when processing a large number of data.
Phishing Detection: Malicious and Benign Websites Classification Using Machine Learning Techniques

In past few years, various fraudulent online websites have been created by attackers to imitate trustworthy websites, in order to gain private information from users and organizations. This act of creating fake websites and sending emails that erroneously claims to be from a real association is known as phishing. Many organizations as well as Internet users have been victims of phishing and have faced huge loss costing them a million dollars. These malicious websites and emails have been the most popular and easy way for data theft; hence it becomes difficult to identify phishing websites. This paper aims at using machine learning techniques to perform data analysis and construct predictive models to detect phishing in various websites. Along with these, deep learning model such as artificial neural network has also been used to perform this task. The task is basically to employ these models to classify the websites into legit and fraudulent websites.
Token-Based Sharing Control for IPFS

Blockchains can now be used to distribute digital assets independent of specific organizations. One way to handle digital assets on a blockchain is through tokens. Tokens define chunks of data on a blockchain, allowing it to be distributed like virtual currency. However, storing all data related to digital assets in a token is difficult. Data is currently managed separately because storing a large amount of data in a blockchain can cause the ledger to grow large. When the management of data depends on a particular organization, the stability of the token's value depends on that particular organization. In this paper, we propose an organization-independent management method that combines a distributed content-addressable file system with a blockchain. Our proposed method places the data associated with the token and the access information to the token into the distributed content-addressable file system. As a result, the token associated with the data has the sharing control of the data stored in the distributed content-addressable file system, and a configuration independent of the specific contract is realized.
Intelligent Color Image Watermarking Using Phase Congruency

In this paper, an invisible color image watermarking technique has been developed as a copyright protection tool. This proposed scheme embeds a color watermark into a color cover image. Maximum watermark data is implanted into the most informative region and the amount of hidden data gradually decreases with lesser informative regions. The fragmentation of regions according to their business is performed through a phase congruency based feature map, followed by the generation of an intelligent hiding capacity map. The experimental results can show that this adaptive data embedding process is able to deal with the tradeoffs among imperceptibility, robustness, and data hiding capacity.
Non-stationary Data Stream Analysis: State-of-the-Art Challenges and Solutions

In today’s digital era, data is playing crucial role which is increasing at tremendous growth at very high speed and processing this data for analysis is becoming important. Analyzing of this huge, continuous, and non-stationary data is difficult using conventional machine learning and statistical techniques where most of the time it is assumed that underlying data is stationary or drawn from fixed, unknown probability distribution. Classification of data streams is one of the important tasks to be done during the analysis which is distressed with many research challenges like concept drift, concept evolution, class imbalance, and new concept detection which are the most significant. To extend clear perspective data stream classification, this study reviews the approaches used to deal with these challenges. In addition, based on comparison of different classification approaches, the key findings, advantages, and disadvantages of some methods are also presented. The objective of this study is to give an overview of non-stationary data analysis and reference by researchers which will be helpful to recognize further research directions in the same.
OB-WSPES: A Uniform Evaluation System for Obfuscation-based Web Search Privacy

Web search queries reveal extensive sensitive information about users’ interests and preferences to the search engines and eavesdroppers. Obfuscation-based private web search solutions automatically generate dummy queries and send the obfuscated queries to the search engine to hide users’ search intentions. Despite many obfuscation methods and tools have been developed, there is no practical system for evaluating their utility performance and the vulnerability against modern privacy attacks. In this article, we propose and develop OB-WSPES, a uniform evaluation system for obfuscation-based web search privacy, which allows researchers to conduct fair analysis and evaluation of existing or newly developed web search privacy protection/attack techniques. Leveraging OB-WSPES, we model the obfuscation activities and systematically implement and evaluate five obfuscation schemes and 10 modern web search attacks on the public AOL dataset. Our results demonstrate that, counter-intuitively, adding more fake queries to a user’s real data does not necessarily yield better privacy. The query utility of obfuscated queries declines with the increasing amount of dummy queries, while the application utility does not. We discuss the experimental results and point out the four important factors that affect the web search privacy and utility. Further, we propose possible directions for future research.
Algorithm Comparisons among Space Time Volume Velocimetry, Horn-Schunk, and Lucas-Kanade for the Analysis of Water Surface Velocity Image Sequences

Performance of three optical flow algorithm variants has been evaluated to obtain the most optimal measurement for water surface velocity. The algorithms are Space Time Volume Velocimetry (STVV), Horn-Schunk (HS) and Lucas-Kanade (LK). The algorithms were applied to the same video of waterflow, whose direction was rotated by 0°, 90°, 180°, and 270°. Since the water velocity in the video are unknown, the evaluation parameter is varian of estimated water velocity and their repoducibility when the direction of water flow is changed. The results show that the STVV had the smaalest varian 7% to 13%, HS is 11% to 41 % and LK is 17% to 30%. However, the STVV has the most disadvantages compared to the other two methods in terms of reproduciblity when the direction of water flow is changed. The reproducibility of STVV result when using En number as testing method could reach 1,7 while HS and LK is 0 for all experiment.