Reviewerly/exHarmony huggingface dataset:
Flood detection by using FCN-AlexNet

Floods are the natural disasters which can give serious damage to properties, roads, vehicles and even people. These damages bring huge payload both to individuals and governments. Thus, a system which can detect floods at early stage and warn the related offices immediately will be very useful for public. Detecting flooding early can save human lives, time, money for the government, as well as an important step to move towards smarter cities. In this paper, we propose the use of a deep learning architecture to detect floods in certain susceptible areas. We used FCN AlexNet deep learning architecture to train and test our dataset. Images of our dataset are collected from two PTZ cameras with different view angles. According to the experimental results, used system gets above 95% classification accuracy on both cameras.
Affordable person detection in omnidirectional cameras using radial integral channel features

Omnidirectional cameras cover more ground than perspective cameras, at the expense of resolution. Their comprehensive field of view makes omnidirectional cameras appealing for security and ambient intelligence applications. Person detection is usually a core part of such applications. Conventional methods fail for omnidirectional images due to different image geometry and formation. In this study, we propose a method for person detection in omnidirectional images, which is based on the integral channel features approach. Features are extracted from various channels, such as LUV and gradient magnitude, and classified using boosted decision trees. Features are pixel sums inside annular sectors (doughnut slice shapes) contained by the detection window. We also propose a novel data structure called radial integral image that allows to calculate sums inside annular sectors efficiently. We have shown with experiments that our method outperforms the previous state of the art and uses significantly less computational resources.
A method for spatially weighted image brightness normalization for face verification

Despite the major advances, the accuracy of modern face verification systems depends on the lighting conditions. The variability of illumination can be compensated either by performing image preprocessing or by training more robust verification models. Nowadays, great priority is given to the development of neural network classifiers, while the importance of image preprocessing is being undeservedly neglected. This article proposes a method for spatially weighted brightness normalization of grayscale face images which preserves the relevant image information. An experimental study is performed to demonstrate the effects of various methods for brightness normalization on the accuracy of the neural network classifier in the application of face verification. It is shown that brightness normalization can improve the face verification accuracy for images captured in complex illumination conditions, that is, to compensate for samples that were not fully present in the training data.
The 2.0 Critic: Blended Discourses in Blogging

Although traditional critics remain a reliable source, they are losing influence to online reviews produced by technology-empowered consumers. This study aims to analyze this new form of critic that have emerged with Web 2.0 and who post technical reviews based on personal experiences. We conducted a qualitative study, following netnographic and content analysis protocols in a case study of the Brazilian blog 2Beauty.com.br, which has product reviews as core content. As a contribution, we define the 2.0 Critic and its composed narrative forms—Friend, Salesperson, Connoisseur, Hacker and Examiner—which point to an expert alternative position in the ecosystem.
AMOUN: Lightweight Scalable Multi-recipient Asymmetric Cryptographic Scheme

Securing multi-party communication is very challenging particularly in dynamic networks. Existing multi-recipient cryptographic schemes pose variety of limitations. These include: requiring trust among all recipients to make an agreement, high computational cost for both encryption and decryption, and additional communication overhead when group membership changes. To overcome these limitations, this paper introduces a novel multi-recipient asymmetric cryptographic scheme, AMOUN. This scheme enables the sender to possibly send different messages in one ciphertext to multiple recipients to better utilize network resources, while ensuring that each recipient only retrieves its own designated message. Security analysis demonstrates that proposed scheme is secure against well-known attacks. Evaluation results demonstrate that lightweight AMOUN outperforms RSA and Multi-RSA in terms of computational cost for both encryption and decryption. For a given prime size, in case of encryption, AMOUN achieves 86% and 98% lower average computational cost than RSA and Multi-RSA, respectively; while for decryption, it shows performance improvement of 98% compared to RSA and Multi-RSA.
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.
Partial sums of binomials, intersecting numbers, and the excess bound in Rosenbloom–Tsfasman space

In this work, the sphere-covering bound on covering codes in Rosenbloom–Tsfasman spaces (RT spaces) is improved by generalizing the excess counting method. The approach focuses on studying the parity of a Rosenbloom–Tsfasman sphere (RT sphere) and the parity of the intersection of two RT spheres. We connect the parity of an RT sphere with partial sums of binomial coefficients and p-adic valuation of binomial coefficients. The intersection number of RT spaces is introduced and we determinate its parity under some conditions. Numerical applications of the method are discussed.
Sentimental Analysis of Social Media to Find Out Customer Opinion

Social media sites provide us facility to get the data which is generated by the customers, this data is available in large amount. Companies not only need to analyze the data generated on social media sites by their own customers, but they also need to analyze the data generated by the competitor’s customers. In this research paper, we collected the data of six fast food chains KFC, Pizza Hut, Subway, Dunkin Donuts, Domino’s Pizza and McDonald from twitter. We analyze 25,000 English tweets of each company. We used the lexicon for sentiment analysis, for each tweet we compare every word of the tweet with lexicon and determine that either this tweet contains more positive words or negative words. The main concern of this research is to calculate the reputation from the collected tweets in Twitter. To do so, an English sentiment lexicon has been used to classify tweets then beta probability function used to calculate reputation score of every restaurant based on the calculating of number of positive/negative words and tweets. Experimental results show some statistical information such as comparing the number of positive/negative tweets, the occurrence of top 10 positive/negative word, and reputation of every restaurant based on word-level and sentence-level statistics calculation.
Application of Fractal Analysis in Lichenoindication of Atmospheric Air Pollution of Technogenically Disturbed Territories

A method for modeling and assessing the degree of air pollution in Technogenically disturbed areas based on fractal analysis is proposed.
Estimation of Wetness and Color from a Single Multispectral Image

Recognizing wet surfaces and their degrees of wetness is essential for many computer vision applications. Surface wetness can inform us slippery spots on a road to autonomous vehicles, muddy areas of a trail to humanoid robots, and the freshness of groceries to us. The fact that surfaces darken when wet, i.e., monochromatic appearance change, has been modeled to recognize wet surfaces in the past. In this paper, we show that color change, particularly in its spectral behavior, carries rich information about surface wetness. We first derive an analytical spectral appearance model of wet surfaces that expresses the characteristic spectral sharpening due to multiple scattering and absorption in the surface. We present a novel method for estimating key parameters of this spectral appearance model, which enables the recovery of the original surface color and the degree of wetness from a single multispectral image. Applied to a multispectral image, the method estimates the spatial map of wetness together with the dry spectral distribution of the surface. To our knowledge, this is the first work to model and leverage the spectral characteristics of wet surfaces to decipher its appearance. We conduct comprehensive experimental validation with a number of wet real surfaces. The results demonstrate the accuracy of our model and the effectiveness of our method for surface wetness and color estimation.
Exploring constructivism learning theory using mobile game

Experience as our way of constructing our understanding of the world we live in is what constructivism theory advocates. In this study, the Decimal FunPro, a mobile game application which is designed to learn number systems was used to explore the occurrence of constructivist learning theory by employing clustering algorithm on the dataset by means of RapidMiner instrument. The performance of the students in using the mobile game shows that during the first tries from the different game categories, students spent more time finishing the activity as compared to the other trials. This means that the students learned from the first trials. A t-test was also conducted to define the significant transformation between the post-test and pre-test. The result showed that there is a significant difference between the posttest as well as the pre-test signifying that students acquired knowledge in number systems when they used the mobile game application. With the evidence stated, it can be concluded that using the Decimal FunPro mobile game application reveals that students can build new knowledge based on prior experience.
Querying informative constraints for data clustering: An embedding approach

Querying high quality constraints for clustering is a difficult task in clustering research due to the lack of an appropriate criterion for measuring the quality of constraints. In this paper, we propose a novel idea of tracking the quality of candidate constraints in various embedding spaces. Unlike previous methods that estimate the quality of candidate constraints in the input data space, the proposed method embeds a distance matrix obtained from a random walk on the proximity graph into different embedding spaces and assigns each candidate constraint a utility by tracking its presence in the clusters’ skeleton. The most useful constraints are then chosen in terms of their quality and presented to the clustering technique. The results show that the proposed method improves over existing algorithms for constraints selection in term of quality of constraints, when used in conjunction with two well-known clustering methods.
Optimization Techniques for the Multilevel Thresholding of the Medical Images

Multilevel thresholding is segmenting the image into several distinct regions. Medical data like magnetic resonance images (MRI) contain important clinical information that is crucial for diagnosis. Hence, automatic segregation of tissue constituents is of key interest to clinician. In the chapter, standard entropies (i.e., Kapur and Tsallis) are explored for thresholding of brain MR images. The optimal thresholds are obtained by the maximization of these entropies using the particle swarm optimization (PSO) and the BAT optimization approach. The techniques are implemented for the segregation of various tissue constituents (i.e., cerebral spinal fluid [CSF], white matter [WM], and gray matter [GM]) from simulated images obtained from the brain web database. The efficacy of the thresholding technique is evaluated by the Dice coefficient (Dice). The results demonstrate that Tsallis' entropy is superior to the Kapur's entropy for the segmentation CSF and WM. Moreover, entropy maximization using BAT algorithm attains a higher Dice in contrast to PSO.
Analysis and improvement of neighborhood topology of particle swarm optimization

Particle swarm optimization (PSO) is a population-based intelligent algorithm for solving optimization problems. Since the fast convergence and easy implementation, PSO has been successfully applied in some areas. However, the standard PSO also has s
Which Body Is Mine?

In the light of the human studies that report a strong correlation between head circumference and body size, we propose a new research problem: head-body matching. Given an image of a person's head, we want to match it with his body (headless) image. We propose a dual-pathway framework which computes head and body discriminating features independently, and learns the correlation between such features. We introduce a comprehensive evaluation of our proposed framework for this problem using different features including anthropometric features and deep-CNN features, different experimental setting such as head-body scale variations, and different body parts. We demonstrate the usefulness of our framework with two novel applications: head/body recognition, and T-shirt sizing from a head image. Our evaluations for head/body recognition application on the challenging large scale PIPA dataset (contains high variations of pose, viewpoint, and occlusion) show up to 53% of performance improvement using deep-CNN features, over the global model features in which head and body features are not separated or correlated. For T-shirt sizing application, we use anthropometric features for head-body matching. We achieve promising experimental results on small and challenging datasets.
Transaction and Identity Authentication Security Model for E-Banking: Confluence of Quantum Cryptography and AI

In any online banking system the major concern is to achieve ultimate security and privacy of customer’s personal information or transactions being carried out in addition to some other banking services. Unfortunately, with the day-by-day advancements in technology, it has become really challenging to withstand such security breaches as banks are the holders of very sensitive data that can cause havocs if misused. At present, modern encryption techniques are said to be sufficient to secure information but still there is a need to enhance these encryption techniques as these are uncertain in providing unconditional security causing some communication security issues. In this paper we have proposed quantum solution to address such security issues by engaging AI. By replacing SSL or SET connections having classical encryption techniques with the quantum cryptographic security systems, privacy and authenticity of data can be ensured and will minimize the chances of attacks. Features of AI like fuzzy logic and knowledge-base are also exploited. By the combination of the two, a robust and responsive security model for E-Banking is proposed.
Shadow Patching: Guided Image Completion for Shadow Removal

Removing unwanted shadows is a common need in photo editing software. Previous methods handle some shadows well but perform poorly in cases with severe degradation (darker shadowing) because they rely on directly restoring the degraded data in the shadowed region. Image-completion algorithms can completely replace severely degraded shadowed regions, and perform well with smaller-scale textures, but often fail to reproduce larger-scale macrostructure that may still be visible in the shadowed region. This paper provides a general framework that leverages degraded (in this case shadowed) data in a region to guide image completion by extending the objective function commonly used in current state-of-the-art energy-minimization methods for image completion to include not only visual realism but consistency with the original degraded content. This approach achieves realistic-looking shadow removal even in cases of severe degradation where precise recovery of the unshadowed content may not be possible. Although not demonstrated here, the generality of the approach potentially allows it to be extended to other types of localized degradation.
Self-Paced Dense Connectivity Learning for Visual Tracking

When misalignment, deformation, and tracking failures occur, the appearance of the target tends to change significantly. How to effectively learn the change of target's appearance is an essential problem in visual tracking. Recently, most recent trackers based on convolutional neural networks update the tracker online to learn the change of target's appearance. These methods collect tracking results as online training samples. Thus, the reliability of training samples is very important for online updates. We propose a self-paced selection model, which integrates the self-paced learning model into the tracking framework for the goal of distinguishing the reliable samples from the tracking results. It estimates the reliability of the tracking results by the self-paced function. We design a method that adaptively calculates the value of the pace, which determines the number of samples selected. And this method is based on the number of tracking results. At the same time, the quality of the target's features plays a key role in the performance of the tracker. We employ dense connectivity learning to enhance the flow of information throughout the network, which makes the target's features represent better. The extensive experiments demonstrate that our self-paced dense connectivity learning tracker (SPDCT) performs favorably against the state-of-the-art trackers over four benchmark datasets.
Identification of Relevant Hashtags for Planned Events Using Learning to Rank

Lots of planned events (e.g. concerts, sports matches, festivals, etc.) keep happening across the world every day. In various applications like event recommendation, event reporting, etc. it might be useful to find user discussions related to such events from social media. Identification of event related hashtags can be useful for this purpose. In this paper, we focus on identifying the top hashtags related to a given event. We define a set of features for (event, hashtag) pairs, and discuss ways to obtain these feature scores. A linear aggregation of these scores is used to finally output a ranked list of top hashtags for the event. The aggregation weights of the features are obtained using a learning to rank algorithm. We establish the superiority of our method by performing detailed experiments on a large dataset containing multiple categories of events and related tweets.
IRelics

We present IRelics, a tangible interaction platform for the popularization of field archeology. IRelics allows users to experience archaeological field work activities as a serious game by using a set of tangible tools. We developed an innovative LWIR (Long Wavelength Infrared Rays) sensing system, which implements the design of tangible tools that provide real manipulation experiences. By interacting with IRelics, a player may experience different archaeological activities such as excavation and cleaning. We conducted two observations to evaluate the usability and effectiveness at archeology popularizing. Findings suggest that the IRelics platform can enhance the engagement of the participants by providing a positive and interactive environment while teaching them unfamiliar knowledge.
Assisting Health Consumers While Searching the Web through Medical Annotations

Health consumers usually face difficulties on their online searches, mainly because of the differences between terminologies used by laypeople and health professionals. This work presents a tool, HealthTranslator, available as a Google Chrome extension that intends to reduce this terminological gap while users are searching the Web for health information. HealthTranslator automatically annotates medical concepts in web documents, providing additional information, such as concept definition, related concepts and links to external references. The solution was evaluated regarding its: (a) performance - the document processing is done gradually, typically from the top to the bottom of the document and performance was not an issue raised by the users; (b) concept coverage - the solution was compared to a similar extension performing in English recognizing significantly more concepts. A comparison with a corpus of Portuguese documents manually annotated with medical concepts showed an average F-measure between 27% and 33%, depending on the type of concepts being recognized; (c) users' receptivity to HealthTranslator and its usability - many aspects were surveyed on a user study. In general, the extension has a good acceptance and users find it useful.