Miracle-dz/UniFAR-FTUs huggingface dataset:
How Do the Users Show their Interest on Line?------ Eye Movement and Browsing Behaviour

Nowadays, increasing number of people prefer to shop online which has become a central part in their daily life. However, this precipitates a need to get knowledge about how people show their interests in the product which may contribute to recommend the right product for them. In this paper, an experiment was conducted in a clothing website which aimed to find out how and when people were interested in certain product in terms of eye movement and browsing behaviour. The results show that for eye movement, there is no direct relationship between pupil diameter and interests, and user’s interests have direct correlativity with blink frequency for some users. Moreover, eye movement can predict the user’s interests, and the accuracy is about 62%; for browsing behaviour, scroll times and display time also has something to do with the user’s interest; as for the relationship between eye movement and interactive behaviour, when scroll times increases, then saccade will increases too, but fixation time will reduce. Therefore, the conclusion can be drawn that when users shop online, they always need to interact with online store, and eye movement and interactive behaviour occurs in a particular pattern. This result could contribute to choose the corresponding parameters for recommendation and avoid using the parameters which can reflect users’ interests in the same degree or choose the one which can reflect users’ interest effectively to greatest extent.
An optimization strategy of massive small files storage based on HDFS

Nowadays, Hadoop distributed file system as a distributed storage system, has a good effect on the storage of large files. However, there is a natural flaw in the storage of small files: storing a large number of small files will produce excessive metadata, resulting in namenode memory bottlenecks; frequent RPC communications will cause time consumption due to over-provisioning. To solve these problems, this paper presents a merging algorithm based on two factors: the distribution of files and the correlation of files. The algorithm can not only reduce the HDFS blocks, but also make relevant files close. Experimental results show that the algorithm effectively improves the storage efficiency of HDFS on small files and help to optimize the access of small files.
SUPPORT MODEL OF MATERIAL REQUIREMENTS PLANNING IN A COAL MINE

This paper presents a multiple criteria decision making model of material requirements planning in a coal mine. The model is built on the basis of historical data of material consumption and two criteria are considered in the model: (a) probability of a lack of material and (b) deviations of the order size from the material consumption volumes in the last n periods. The work assumes that the volume of material consumed is a continuous random variable with a known distribution. In the model proposed it is also assumed that the costs of material order volume are limited. The problem analyzed with the two criteria is solved by using goal programming and as a result, two non-dominated solutions are received. The game theory approach to multiple attribute decision problems is used to choose the final solution.
Sentiment classification of twitter data belonging to renewable energy using machine learning

Abstract The paper disccuses about the sentimenal analysis process which is a classification problem. To study the emotions of people about alternate energy sources, we carried out sentiment analysis on Twitter data. Sentiment analysis is used to analyze opinions of the user for decision making with the help of natural language processing techniques. In this paper, we carried out sentiment analysis and classification task of tweets belonging to #RenewableEnergy. We applied five different machine learning algorithms for the classification of tweets into three categories. We have carried classification without feature selection technique and with feature selection techniques. We have used CfsSubsetEvaluation and Information Gain feature selection methods to reduce the number of features from the dataset. The result obtained through the techniques followed in this paper, shows that the accuracy of sentiment classification is better with feature selection methods. The best accuracy (92.96%) is achieved with Support Vector Machine(Using PUK Kernel) and CfsSubsetEval feature selection method.
Design of Microstructure for Stimulating Mechanical Torque to Cells

In this study, we proposed the design for microstructure which can apply bending or torsion stimulus to cell using an external magnetic field. First, we defined “ideal bending stimulus” and “ideal torsion stimulus” for cell on a microstructure. In order to apply ideal bending or torsion stimulus to cells, the thickness of the microstructure of cell-culturing region is important. We designed and microfabricated the microstructure which consists of a thin silicon beam as cell-culturing region and a ferromagnetic material, nickel film for magneto-active structure. Then, fabricated microstructures actuated by external magnetic field and deformation of the microstructures was measured. From the results of the measurements, we calculated radius of curvature and angle of torsion respectively and we confirmed the platform almost actuated in theory. Our design of the platform can contribute to applying new kinds of mechanical stimuli to cultured cells.
A monocular ORB-SLAM in dynamic environments

The well-known ORB-SLAM is capable to deal with the localization and mapping tasks in static scenes. But in a dynamic environment, it is hard to perform well as the positions of 3D points in the map may change over time. In order to solve the problem, we propose a new slam framework, i.e. DORB-SLAM, which extends the application range of the ORBSLAM in dynamic circumstances. We compared the appearance of the image blocks centered at the projection of the 3D points in current frame and the reference frame to decide whether the points are dynamic points or not. We test the DORB-SLAM in a public dataset and the results indicate that the algorithm is more robust than the origin ORB-SLAM in dynamic enviroments.
Towards Platform Defined Business – Complementarity at the Spotlight

As markets become increasingly more competitive firms systematically move away from hierarchical integrated supply chains toward fragmented networks of strategic partnerships with external partners. Business practice indicate a growing number of busi-nesses relying on the platform organizational structures. For such constructs superior pro-duct quality and customer appeal maintain necessary but it is the breadth of the ecosystem of related product and services that has become a prerequisite for success. It implies the focus on third parties, complementors, who develop and deliver diverse content to plat-forms as well as enhance platform’s generativity. Although complementary relations should be the main reference while considering the network dynamics from different angles, the attention in the extant research gravitates toward inter-platform competition with platform owners as the central object. Thus, with the objective to contribute to the emerging lite-rature on industry platforms, this conceptual article discusses main challenges concerned with orchestrating arm’s length relationships with complementors, by departing from plat-form-owner-centered approach and focusing on behavior of interdepended contributors.
Public-Key Encryption With Keyword Search via Obfuscation

Public-key encryption with keyword search (PEKS) enables users to search on encrypted data, which is applicable to the scenario of sharing data in the cloud storage. In this paper, we focus on how to construct a PEKS scheme via obfuscation. Our basic scheme is built on the differing-inputs obfuscation (diO) and can be considered as an initial attempt to apply diO in the PEKS field. The scheme supports searching on encrypted data by providing to the cloud server an obfuscated simple “decrypt-then-compare” circuit with the secret key and the queried keyword hardwired in it. More interestingly, the scheme can be simply improved to resist off-line keyword guessing attacks (KGAs) as the standard PEKS scheme rather than a designated tester one. For complex search conditions, our scheme can be easily extended to multiple functionalities, such as conjunctive and fuzzy keyword search. Furthermore, it can be extended to the PEKS scheme in the multi-user setting.
Saw-Mark Defect Detection in Heterogeneous Solar Wafer Images using GAN-based Training Samples Generation and CNN Classification

This paper presents a machine vision-based scheme to automatically detect saw-mark defects in solar wafer surfaces. A saw-mark defect is a severe flaw when cutting a silicon ingot into wafers. A multicrystalline solar wafer surface presents random shapes, sizes and orientations of crystal grains in the surface and, thus, results in a heterogeneous texture. It makes the automatic visual inspection task extremely difficult. The deep learning technique is an ideal choice to tackle the problem, but it requires a huge amount of positive (defect-free) and negative (defective) samples for the training. The negative samples are generally not sufficient enough in a manufacturing process. We thus apply a GAN-based model to generate the defective samples for training, and then use the true defect-free samples and the synthesized defective samples to train a CNN model. It solves the imbalanced data arising in manufacturing inspection. The preliminary experiment has shown promising results of the proposed method for detecting various saw-mark defects including black line, white line, and impurity in multicrystalline solar wafers.
Bias detection in Philippine political news articles using SentiWordNet and inverse reinforcement model

Not all information posted on the internet is deemed ‘trustworthy. ’ Some articles, especially those related to politics, seem to display traces of bias, whether they be for or against the Philippine administration. This research aims to determine if a news article—and by extension, a news outlet—is biased based on its sentiments and use of lexica. Data were harvested from chosen websites and news outlets provided by Alexa. These data underwent pre-processing and were scored based on their sentiments with the use of SentiWordNet. The resulting scores were then fed into the Inverse Reinforcement Model, which determined whether an article is biased or not. With the use of Inquirer, Philstar, Manila Bulletin, The Manila Times, and Journal Online news articles, the system was able to detect bias with an accuracy rating of 0.89, precision of 1, recall of 0.60 and F-Measure of 0.75.
Epidemiology of acute kidney injury

Acute kidney injury (AKI) is a clinical syndrome with an acute decline or loss of glomerular filtration function caused by different reasons. It is an important disease affecting human health. In recent years, the incidence and mortality of AKI are rising continuously. Researchers both in China and other countries have performed many studies on the epidemiology of AKI. However, due to the difference in diagnostic criteria, diagnostic indicators, patient population, geographical location, and observation timing, the obtained data are quite different. In the early years, there were few studies on AKI epidemiology in China, and many of them reported single-center and small-sample data. The reported incidence of AKI is significantly lower than that in developed countries. In recent years, great progress has been made in the epidemiological research of AKI in inpatients and ICU patients. In this article, we review the current status of epidemiology of AKI in various clinical settings, aiming to explore its incidence and severity. Key words: Acute kidney injury; Epidemiology; Incidence
NLP based sentiment analysis for Twitter's opinion mining and visualization

In many of today’s big data analytics applications, it might need to analyze social media feeds as well as to visualize users’ opinions. This will provide a viable alternative source to establish new metrics in our digital life. Social interaction with people in Twitter is open-ended, making media analysis in Twitter easier in comparison with other social media. That is because the interaction in those media is often different since most of them are private. This work is therefore devoted to focus merely on Twitter and deemed to be within the confines of Data Mining. It is concerned with Natural Language Processing (NLP)-based sentiment analysis for Twitter’s opinion mining. As such, the objective of this work is to use a data mining approach of text-feature extraction, classification, and dimensionality reduction, using sentiment analysis to analyze and visualize Twitter users’ opinion. The utilized methodology is based on applying sentiment analysis NLP on a large number of tweets in order to get word scoring of the tweet and thus to exploit public tweeting for knowledge discovery. This will moreover serve for fake news detection. The pertinent mechanism involves several consecutive steps, namely: dataset collection stage, the pre-processing stage, NLP stage, sentiment analysis stage, and prediction and classification stage using BNN. The U.S. Airlines Sentiment Analysis Twitter dataset has been utilized which is already provided with Data for Everyone. The presented system is monitoring Twitter streams from both the media and the public. It is capable to extract meaningful data from tweets in real-time and store them into a relational model for analysis. And then use our dimension reduction method. This will help people discover the correlation of the leading role between them, which also reflects news media’s focuses and people’s interests. This system has proved better results with respect to accuracy and efficiency in comparison with some other similar works. It is convenient for a wide application spectrum involving: big data analytics solutions, predicting e-commerce customer’s behavior, improving marketing strategy, getting market competitive advantages, besides visualization in various data mining applications.
Brain MRI Image Classification for Cancer Detection Using Deep Wavelet Autoencoder-Based Deep Neural Network

Technology and the rapid growth in the area of brain imaging technologies have forever made for a pivotal role in analyzing and focusing the new views of brain anatomy and functions. The mechanism of image processing has widespread usage in the area of medical science for improving the early detection and treatment phases. Deep neural networks (DNN), till date, have demonstrated wonderful performance in classification and segmentation task. Carrying this idea into consideration, in this paper, a technique for image compression using a deep wavelet autoencoder (DWA), which blends the basic feature reduction property of autoencoder along with the image decomposition property of wavelet transform is proposed. The combination of both has a tremendous effect on sinking the size of the feature set for enduring further classification task by using DNN. A brain image dataset was taken and the proposed DWA-DNN image classifier was considered. The performance criterion for the DWA-DNN classifier was compared with other existing classifiers such as autoencoder-DNN or DNN, and it was noted that the proposed method outshines the existing methods.
Research on the Positioning in WSN Based on an Improved DV-HOP Algorithm

In order to reduce the positioning error of the sensor nodes, this paper first analyzes the reasons for the low accuracy of DV - HOP algorithm currently, and then proposes an improved DV - HOP algorithm. The algorithm can be divided into four stages, including data information overlapping stage, average skip distance calculation stage, optimum choice of anchor nodes and redundant node positioning. In the data information overlapping stage, the joint probability density is used for improvement; in the average skip distance calculation, the zero deviation estimation value is introduced; in the optimal selection of anchor nodes, optimal strategy is used; and the nodes are positioned in the redundant node positioning with the method of gradually increasing the skip distance. Simulation results show that the algorithm of this paper can effectively reduce the position error in calculation and improve the positioning accuracy.
Oscillating Mobile Neurons with Entropic Assembling

Functionality of neural networks is based on changing connectivity between the neurons. Usually, such changes follow certain learning procedures that define which neurons are interconnected and what is the strength of the connection. The connected neurons form the distinguished groups also known as Hebbian ensembles that can act during long time or can disintegrate into smaller groups or even into separate neurons. In the paper, we consider the mechanism of assembling / disassembling of the groups of neurons. In contrast to the traditional approaches, we set ourselves to “the neuron’s point of view” and assume that the neuron chooses the neuron to connect with following the difference between the current individual entropy and the expected entropy of the ensemble. The states of the neurons are defined by the well-known Hodgkin-Huxley model and the entropy of the neuron and the neuron’s ensemble is calculated using the Klimontovich method. The suggested model is illustrated by numerical simulations that demonstrate its close relation with the known self-organizing systems and the dynamical models of the brain activity.
Data Sensification: beyond representation modality, toward encoding data in experience

Humans have represented data for thousands of years, yet the design process we use to encode data remains almost exclusively related to modalities such as visual, haptic, auditory, olfactory and gustatory. This paper proposes a novel approach to designing data representations, where we move beyond mapping data to a modality or combination of modalities and instead facilitate an understanding of the underlying data through people’s overall experience of it. Based on a review of existing data representations that fall into this research area, but have not been discussed under one common term, this paper defines, for the first time, the term Data Sensification. Data Sensification is an emerging form of representation that encodes data in the behaviour, performance, affordances and resulting experience of a data representation. This research contributes to the on-going research on data representation beyond the visual paradigm as well as conceptualizing a new approach to representing data beyond representational modalities.
An investigation into a denial of service attack on an ethereum network

Apart from its much-publicised use in crypto-currency, blockchain technology is used in a wide range of application areas, from diamonds to wine. The most common application of this technology is in smart contracts in supply chain management, where assurance of delivery and provenance are important. One problem for an Ethereum consortium is the potential for disruption caused by a Denial-of-Service attack across the consortium nodes. Such an attack can be launched from a single source or multiple sources to amplify the effect. This paper investigates the impact of various Denial-of-Service attacks on an Ethereum Consortium deployed on the Azure Cloud platform. Our experiments demonstrated that a Denial-of-Service attack on some nodes can be successful. We found that an Ethereum Transaction Server is vulnerable to both Flood and Bandwidth Depletion attacks, but that Ethereum Mining Server nodes appear to be resilient to a Bandwidth Depletion attack.
Markov Model-Based Energy Storage System Planning in Power Systems

Battery energy storage systems (BESSs) can regulate the supply of energy from an electric utility, and improve the reliability and stability of the power system, which has high penetration of renewable power generation. This paper determines the optimal sizes (MWh) and capacities (MW) of BESSs in a distribution system that is composed of photovoltaic (PV) arrays. The objective is to minimize the investment cost, replacement cost, operation/maintenance cost, and the net present values of both the energy loss cost and to maximize the annual arbitrage benefit (AAB). The energy loss cost and AAB incorporate time-of-use (TOU) tariffs. The objective is subject to equality constraints (power flow equations and chronological ESS balance constraints) and inequality constraints (voltage, line flow, and BESS operational limits). Because the studied problem involves many constraints and variables in the chronological domain, traditional optimization methods cannot be applied directly to this large-scale problem. Assume that the chronological PV powers and loads are stochastic process, where the description of present state fully contains the information for the future evolution. Thus, Markov models are implemented by considering probability/duration/frequency of PV powers and loads to reduce the numbers of constraints and variables. The Markov states are attained using a clustering algorithm that is based on fuzzy-c-means. Simulation results obtained from a 33-bus system consisting of a 1 MW PV farm and six BESSs reveal the applicability of the proposed method. Simulation results indicate that the TOU tariff markedly affects the allocation of BESSs and the AAB benefit is generally smaller than the costs of the BESSs and energy losses.
Learning Mixtures of Smooth Product Distributions: Identifiability and Algorithm

We study the problem of learning a mixture model of non-parametric product distributions. The problem of learning a mixture model is that of finding the component distributions along with the mixing weights using observed samples generated from the mixture. The problem is well-studied in the parametric setting, i.e., when the component distributions are members of a parametric family – such as Gaussian distributions. In this work, we focus on multivariate mixtures of non-parametric product distributions and propose a two-stage approach which recovers the component distributions of the mixture under a smoothness condition. Our approach builds upon the identifiability properties of the canonical polyadic (low-rank) decomposition of tensors, in tandem with Fourier and Shannon-Nyquist sampling staples from signal processing. We demonstrate the effectiveness of the approach on synthetic and real datasets.
Robust Matrix Completion from Quantized Observations

1-bit matrix completion refers to the problem of recovering a real-valued low-rank matrix from a small fraction of its sign patterns. In many real-world applications, however, the observations are not only highly quantized, but also grossly corrupted. In this work, we consider the noisy statistical model where each observed entry can be flipped with some probability after quantization. We propose a simple maximum likelihood estimator which is shown to be robust to the sign flipping noise. In particular, we prove an upper bound on the statistical error, showing that with overwhelming probability n = O(poly(1-2E[tau])^{-2} rd log d) samples are sufficient for accurate recovery, where r and d are the rank and dimension of the underlying matrix respectively, and tau in [0, 1/2) is a random variable that parameterizes the sign flipping noise. Furthermore, a lower bound is established showing that the obtained sample complexity is near-optimal for prevalent statistical models. Finally, we substantiate our theoretical findings with a comprehensive study on synthetic and realistic data sets, and demonstrate the state-of-the-art performance.