Reviewerly/exHarmony huggingface dataset:
Visibility Improvement of Hazy Image Using Fusion of Multiple Exposure Images

Haze is an atmospheric phenomenon, in which various dry particles such as dust, smoke or air pollutants, etc. and wet particles such as water droplets accumulated in the air. These particles obscure the visibility of the image. This paper proposes an image dehazing method to restore the visibility and contrast of the hazy images using image enhancement based approaches. The restoration based methods of image dehazing compute the transmission map of a hazy image based on some priors or assumptions. However, these methods fail, if assumptions or priors do not hold. The proposed method avoids the need to compute transmission map. Rather, we investigate the approaches of image enhancement that can be used in the image dehazing. We found that combination of exponential distribution, local histogram equalization and sharpening filter all together have the capability to remove the haze from the image without color distortion and texture loss. The proposed method is evaluated on several challenging hazy images of different degree. The experimental results demonstrate that it performs better in terms of qualitative and quantitative parameters and outperforms many existing dehazing methods.
Large scale biomedical data analysis with tree-based automated machine learning

Tree-based Pipeline Optimization Tool (TPOT) is an automated machine learning (AutoML) system that recommends optimal pipeline for supervised learning problems by scanning data for novel features, selecting appropriate models and optimizing their parameters. However, like other AutoML systems, TPOT may reach computational resource limits when working on big data such as whole-genome expression data. We develop two novel features for TPOT, Feature Set Selector and Template, which leverage domain knowledge, greatly reduce the computational expense and flexibly extend TPOT's application to biomedical big data analysis.
OWL2Go: Auto-generation of Go data models for OWL ontologies with integrated serialization and deserialization functionality

The Web Ontology Language (OWL) is a formal language for the description of terms and their relationship in a certain domain. It enables information exchange among heterogeneous applications and devices in a machine-readable format. However, in software development the usage of data models is common. In order to facilitate the usage of ontologies encoded in OWL also in software development we present OWL2Go. OWL2Go is a code-generator that parses an OWL ontology and generates a Go package implementing a data model compliant with the ontology as well as a serializer and deserializer for conversion between the Go data model and Turtle or JSON-LD documents. We demonstrate the generation process and the usage of the resulting Go package with the Smart Appliances REFerence (SAREF) ontology.
Research on Cost Management of Information Engineering

With the rapid development of the “One Belt, One Road” policy in China's economy, cost information management is an important part of the era of calculating the profit of construction engineering. This paper analyzes the construction cost information management, discusses the shortcomings of construction cost and the informationization ideas, and hopes to provide a certain reference value for the construction cost information management.
Cryptanalysis of Jain-Singh’s identity-based RSA encryption

Identity-based Encryption (IBE) solves the problem of requiring key certificates in Public Key Encryption (PKE) by replacing the public key with the user’s identity. However, most IBE systems depend on Elliptic Curve Cryptography (ECC) which makes them time inefficient. Since Adi Shamir presented the idea of IBE, designing an IBE using an efficient encryption algorithm such as RSA still remains an open challenge. In 2017, Jain and Singh presented an identity-based variant of RSA. They claimed that their system is secure without any more computational overhead. In this paper, we analyze the security of Jain Singh’s identity-based RSA encryption. We launch two attacks against their system and prove that their system is not secure.
A Hybrid Framework for Security in Cloud Computing Based on Different Algorithms

Cloud computing is the concept used to decode Daily Computing Issues. It is essentially a virtual pool of resources and also provides these tools to customers through the Internet. It is the net-based advancement and utilized in computer technology. The widespread problem connected with cloud computing is information privacy, protection, anonymity, and dependability, etc. However, the main issue involving them is safety and how the cloud supplier guarantees it. Securing the cloud means to secure the treatments (calculations) and storage (databases hosted by the Cloud provider). The paper reviews concurrent articles on security in cloud computing. By conducting research, we have managed to identify and analyze different security issues associated with the cloud as well as various cryptographic algorithms adaptable to better security for the cloud, and based on those algorithms, we have proposed a hybrid framework for security in cloud computing.
ALeS: Adaptive-length spaced-seed design

MOTIVATION Sequence similarity is the most frequently used procedure in biological research, as proved by the widely used BLAST program. The consecutive seed used by BLAST can be dramatically improved by considering multiple spaced seeds. Finding the best seeds is a hard problem and much effort went into developing heuristic algorithms and software for designing highly sensitive spaced seeds. RESULTS We introduce a new algorithm and software, ALeS, that produces more sensitive seeds than the current state-of-the-art programs, as shown by extensive testing. We also accurately estimate the sensitivity of a seed, enabling its computation for arbitrary seeds. AVAILABILITYAND IMPLEMENTATION The source code is freely available at github.com/lucian-ilie/ALeS. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Missing Value Imputation Approach Using Cosine Similarity Measure

Mining incomplete datasets containing missing values can produce various problems in data mining, particularly with the large-scale dataset. Missing data is one of the major factors affecting the quality of data. Missing value imputation provides optimal solutions to deal with incomplete datasets containing missing values. Although the class center-based approach for imputing missing value provides good results, however, they use Euclidean distance which works on linear distances which generate the linearity in distance: hence making the missing data imputation less accurate. In this paper, missing value imputation approach using cosine similarity measure is used. The proposed model uses the implicit classification label estimation and cosine similarity ratio formulation which provides better results than class center-based imputation approach.
Text Classification and Mark Key Fragment

In this paper, a novel architecture is proposed to classify website essays to two classes, "acception" or "need-improvement", and at the same time mark the key text fragment that influence the final prediction. All essays come from an important popular official website. They are submitted by users around China, and reviewed by human reviewers to decide which one is accepted to be published on website or needing more improvement. In the past eight years human reviewers have accumulated a large labeled dataset, which can be used as the train data and test data for training an artificial intelligence (AI) model to aid human reviewers. Because the AI model can't be 100% reliable, the model is designed not only predict the classification, but also mark the key fragment that influence the final prediction. For human perspective, it seems like the AI model gives out the reason why an essay is accepted or where needs improvement. This will definitely help human reviewers to accelerate the examine process. The final trained model can correctly classify 93.6% sample essays in test set and the positive samples (accepted essay) recall is 96.7%. The most interesting thing is even human reviewers never tell why an essay is rejected (where needs improvement) in train data, the model can give out the reason by marking the key fragment, where need improvement. After checked by human reviewers, it works really well.
Highlighting the user's primary criterion in text mining-based recommendation system

Recommendation systems (RS) are proposed to detect and suggest items that best meet the needs of users. Most of the existent works are based on recommendation algorithms are proposed to capture numerical data. Therefore, this may not provide consistent results (i.e. users may not have the same rating scale). The semantic web is proposed as an extension within the framework of RS in order to process data which are now the most used on the net, unstructured data (i.e. textual data). The unstructured data is used for the purpose of improving the personalization task in different application fields. As a matter of fact, textual data analysis has got the ability to reveal hidden information which enable us to further capture user's likes, preferences and interests and as a result a correct way to determine the user's profiles. However, the existing solutions unfit to take into account the multi-criteria feature of items which is essential to represent more complex user's preferences. This work proposes a text mining-based RS that exploit the users' comments in order to highlight the important criterion of the active user. Finally, the recommendation task is based on the criterion chosen thought exploiting the user's reviews. The choice of the criterion takes into consideration its importance regarding the percentage of its existence into the items reviews content. Several enhancement versions of this work were proposed that were tested on a real database extracted from the TripAdvisor website. The accuracy and quality of the recommendation has been improved considerably in this work by compared with traditional approaches.
Practical dependent type checking using twin types

People writing proofs or programs in dependently typed languages can omit some function arguments in order to decrease the code size and improve readability. Type checking such a program involves filling in each of these implicit arguments in a type-correct way. This is typically done using some form of unification.
Cursor Control Using Face Gestures

This paper presents the software implementation for the detection of facial landmarks with the data trained on iBUG 300 – W dataset by dlib library in python and implementing them for controlling mouse operations. The proposed method aims at catering to the needs of differently – abled people (for example, people with locked – in syndrome), who are not able to operate a computer. Blinking of the left eye will result in a left click, the blinking of the right eye will result in right-click and scroll mode is enabled by opening the mouth. The same is done for disabling the scroll mode. Concept of Eye Aspect Ratio in the case of eyes and mouth aspect ratio in the case of mouth has been used for checking whether that particular mouse operation is performed or not. Image enhancement has been done primarily by Contrast Limited Adaptive Histogram Equalization (CLAHE) and Gaussian Filtering.
The Design of Deep Learning Patterns in Mobile Scenarios

Deep learning has a gradient construction in mobile learning including acquisition, correlation, questioning, collaboration, practice, and micro-innovation. This paper combines related learning theories and principles to design deep learning patterns according to gradients for mobile scenarios.
Identification of Online Auction Bidding Robots Using Machine Learning

Maan, Pooja Eswari, R.The aim of this project is to identify the bidding robots using machine learning, which bids in an online auction. Bidding robot is basically an application which helps to place a bid or click automatically on a website. So, this project will help the site owners to prevent unfair auction by easily flag the robots and remove them from their sites. The major steps are feature extraction, feature selection, model implementation, and classification. Feature engineering is done which includes feature extraction, dropping unnecessary features, and selecting necessary features. Various machine learning classification models are applied with new features to classify human and robot online auction bids and the best performance achieved is ROC score 0.954 using Random Forest.
Emergency broadcasting message transmission based on CDR system

As a modern technical means, emergency broadcasting plays an important role in responding to public emergencies, and is the best possible way to deal with these emergencies as recognized internationally. Based on the characteristics of China's digital audio broadcasting system CDR, this article proposes an emergency broadcasting transmission scheme and adaptation protocol that enables the dissemination of emergency broadcasting information using digital audio broadcasting channel. Compared with the use of RDS channel, using CDR channel for emergency broadcasting information transmission can reduce the emergency broadcast response time of the terminal by its high data transmission capability.
A Deep Learning Approach in Early Prediction of Lungs Cancer from the 2D Image Scan with Gini Index

Digital Imaging and Communication in Medicine (DiCoM) is one of the key protocols for medical imaging and related data. It is implemented in various healthcare facilities. Lung cancer is one of the leading causes of death because of air pollution. Early detection of lung cancer can save many lives. In the last 5 years, the overall survival rate of lung cancer patients has increased, due to early detection. In this paper, we have proposed Zero-phase Component Analysis (ZCA) whitening and Local Binary Pattern (LBP) to enhance the quality of lung images which will be easy to detect cancer cells. Local Energy based Shape Histogram (LESH) technique is used to detect lung cancer. LESH feature extracts a suitable diagnosis of cancer from the CT scans. The Gini coefficient is used for characterizing lung nodules which will be helpful in Computed Tomography (CT) scan. We propose a Convolutional Neural Network (CNN) algorithm to integrate multilayer perceptron for image segmentation. In this process, we combined both traditional feature extraction and high-level feature extraction to classify lung images. The convolutional neural network for feature extraction will identify lung cancer cells with traditional feature extraction and high-level feature extraction to classify lung images. The experiment showed a final accuracy of about 93.27%.
An Adaptive Kinematic Filter Extended by the Additionally Measured Velocity or Acceleration

A previously described adaptive-gain tracking filter with the position only measurements is extended to models with additionally measured velocity or acceleration. The devised filter adapts the full matrix gain, copes with sensors cross-correlation, and improves the position-only prototype. By using a prediction error method, the adaptive gain always follows the Kalman gain. The filter proves efficient in scenarios with piecewise constant acceleration.
The Use of Factorization and Multimode Parametric Spectra in Estimating Frequency and Spectral Parameters of Signal

A multiplicative autoregressive model is constructed based on the linear prediction model. The expressions describing such a model are derived. A formula for the parametric spectral estimation of a random signal multiplicative model is given. Relations are obtained for the decomposition of the multimode power spectral density into simple spectral components. Examples of the decomposition of multimode spectra of random signals into simpler components are considered. Keywords - power spectral density, multiplicative autoregressive model, parametric spectral analysis, characteristic equation.
Handling uncertainty in code smells detection using a possibilistic SBSE approach

Code smells, also known as anti-patterns, are indicators of bad design solutions. However, two different experts may have different opinions not only about the smelliness of a particular software class but also about the smell type. This causes an uncertainty problem that should be taken into account. Unfortunately, existing works reject uncertain data that correspond to software classes with doubtful labels. Uncertain data rejection could cause a significant loss of information that could considerably degrade the performance of the detection process. Motivated by this observation and the good performance of the possibilistic K-NN classifier in handling uncertain data, we propose in this paper a new evolutionary detection method, named ADIPOK (Anti-pattern Detection and Identification using Possibilistic Optimized K-NN), that is able to cope with the uncertainty factor using the possibility theory. The comparative experimental results reveal the merits of our proposal with respect to four relevant state-of-the-art approaches.
Making Recommendations from Web Archives for "Lost" Web Pages

When a user requests a web page from a web archive, the user will typically either get an HTTP 200 if the page is available, or an HTTP 404 if the web page has not been archived. This is because web archives are typically accessed by Uniform Resource Identifier (URI) lookup, and the response is binary: the archive either has the page or it does not, and the user will not know of other archived web pages that exist and are potentially similar to the requested web page. In this paper, we propose augmenting these binary responses with a model for selecting and ranking recommended web pages in a Web archive. This is to enhance both HTTP 404 responses and HTTP 200 responses by surfacing web pages in the archive that the user may not know existed. First, we check if the URI is already classified in DMOZ or Wikipedia. If the requested URI is not found, we use machine learning to classify the URI using DMOZ as our ontology and collect candidate URIs to recommended to the user. The classification is in two parts, a first-level classification and a deep classification. Next, we filter the candidates based on if they are present in the archive. Finally, we rank candidates based on several features, such as archival quality, web page popularity, temporal similarity, and URI similarity. We calculated the F1 score for different methods of classifying the requested web page at the first level. We found that using all-grams from the URI after removing numerals and the top-level domain (TLD) produced the best result with F1 =0.59. For the deep-level classification, we measured the accuracy at each classification level. For second-level classification, the micro-average F1=0.30 and for third-level classification, F1=0.15. We also found that 44.89% of the correctly classified URIs contained at least one word that exists in a dictionary and 50.07% of the correctly classified URIs contained long strings in the domain. In comparison with the URIs from our Wayback access logs, only 5.39% of those URIs contained only words from a dictionary, and 26.74% contained at least one word from a dictionary. These percentages are low and may affect the ability for the requested URI to be correctly classified.
Mixed Noise Removal of Image with Interpolation

Nowadays, natural images in the real world can be affected by more than one type of noise during the process of image acquisition and transmission. Many researchers have been trying to remove the mixed noise because it is a major problem to be considered in image processing. Therefore, they utilized many denoising methods for removal of mixed noise because different noises have different properties. In this paper, we consider the removal of mixed noise composed of Additive White Gaussian Noise (AWGN) and Random-Valued Impulse Noise (RVIN). Although most mixed-noise removal methods can successfully suppress the noise, some image details are lost in edge and texture regions because of the miss-detection of the image details as the impulse noise. Hence, we propose a mixed-noise removal method to preserve the image details. Our method is divided into two steps. The first step is to estimate the denoised image by integrating interpolation, DWM filter, down-sampling and BM3D. The second step is to preserve the image details lost in the first step by calculating the absolute difference between the input noisy image and the pre-estimated image obtained from the first step. The core of this paper is that the input noisy image is initially interpolated by multi-surface fitting for single frame before impulse noise detection of DWM filter in the first step to maintain the image details in the edge and texture regions. Experimental results show that our mixed noise removal method is superior to the state-of-the-art image denoising methods in terms of both quantitative measure and visual perception quality.
Locabulary

Mixed reality creates exciting new opportunities for computer-aided language learning. By combining markerless tracking technology with a user's geolocation, software systems can dynamically locate and generate personalized interactive language practice exercises. The Locabulary mobile app uses a combination of markerless tracking and metadata from the user's location information to construct that utilize the learner's physical surroundings to provide unique and relevant content. Additionally, Locabulary employs context-aware spaced repetition to help language learners develop mastery over the material in the exercises it creates.
Implicit Products in the Decentralized eCommerce Ecosystems

Detecting dark businesses in a decentralized eCommerce ecosystem (e.g. eBay, eBid, and Taobao) is a critical research problem. In this paper, we investigate the characteristics of dark implicit products, the associated buyer seeking behaviors, and features of classification model. Results demonstrate that dark implicit product detection is a challenging problem, while buyer seeking behavior information could be useful as a critical alternative to address this problem.
Visualization of Granblue Fantasy Game Traffic Pattern Using Deep Packet Inspection Method

Granblue Fantasy is one of Role Playing Games (RPG). It’s a video role-playing game developed by Cygames. This research to observes the Granblue Fantasy Game. The purpose is to analyze the traffic data of the Granblue Fantasy to find the pattern using Deep Packet Inspection (DPI), Capturing the Data Traffic, Feature Extraction Process and Visualize the Pattern. The Pattern are Gacha, Solo Raid, Casino and Multiraid. This research demonstrate that Multiraid battle has more data than other pattern with TTL 237.
Legally Binding Anonymous Multiparty Commitments on a Blockchain

In recent times, many blockchain solutions have been proposed for automatic electronic contracts on a blockchain. The most prominent example of it is Ethereum smart contracts. In many cases, it is necessary to be able to sign contracts involving things in the physical world that are not represented in the form of crypto-assets. Such a contract can be legally enforced after all the parties have signed them in case any party backs out after signing the contract. We propose a solution to advertise and sign anonymous multi-party contracts that are designed to be acceptable and enforceable in a court of law once they are signed. To enable this, we also provide a designated deanonymizer ring signature that allows some designated deanonymizers to be able to break the anonymity of the signer and also allows them to prove the true identity of the signer to any third party of their choice.