Reviewerly/exHarmony huggingface dataset:
Performance Comparison of Clustering Algorithm Based Collaborative Filtering Recommendation System

Importance of recommendation Systems (RS), based on collaborative filtering, is escalating with exponential growth of e-commerce application, e.g., on-line shopping, internet movie and music sites, tourism, news portals, to name a few. The target of recommendation system is to predict user preferences based on their previous activities, and associating users of similar behavior. Of the two main approaches, Content Based (CB) and Collaborative Filtering (CF), CF is increasingly popular because there is no need of domain knowledge and it scales well for large datasets. CF uses archives, like user-item purchase data or user-movie review data, which are large and sparse. Traditional approaches using matrix factorization like singular valued decomposition (SVD), is inefficient, for very a large review data. It is more efficient to cluster item-vectors, where elements of an item-vector are entries from users. As the dimension of an item-vector is large and elements are sparse, conventional clustering algorithms fails. We created item-item adjacency matrix, where the elements are similarity between item-vectors. There are various clustering algorithms that work directly on the adjacency matrix. We used spectral clustering, K-means++, Agglomerative Clustering. Considering item-vectors as nodes and adjacency matrix elements as link weights, we performed graph-clustering using Louvain Algorithm, to discover groups. We used synthetic data, added different levels of noise and run different algorithms to compare their accuracies to restore the original data from the noisy one. Louvain algorithm and Spectral clustering could achieve the highest accuracies.
Coupling matrix manifolds assisted optimization for optimal transport problems

Optimal transport (OT) is a powerful tool for measuring the distance between two probability distributions. In this paper, we introduce a new manifold named as the coupling matrix manifold (CMM), where each point on this novel manifold can be regarded as a transportation plan of the optimal transport problem. We firstly explore the Riemannian geometry of CMM with the metric expressed by the Fisher information. These geometrical features can be exploited in many essential optimization methods as a framework solving all types of OT problems via incorporating numerical Riemannian optimization algorithms such as gradient descent and trust region algorithms in CMM manifold. The proposed approach is validated using several OT problems in comparison with recent state-of-the-art related works. For the classic OT problem and its entropy regularized variant, it is shown that our method is comparable with the classic algorithms such as linear programming and Sinkhorn algorithms. For other types of non-entropy regularized OT problems, our proposed method has shown superior performance to other works, whereby the geometric information of the OT feasible space was not incorporated within.
Maximizing the Time Value of Cryptocurrency in Smart Contracts with Decentralized Money Markets

Smart contracts on the Ethereum blockchain can be utilized as secure places to store cryptocurrency if they are designed carefully. However, while cryptocurrency is kept in smart contracts, such as being paid to a blockchain service, no extra profit is generated. The time value of cryptocurrency serves only as collateral, which is an issue of capital inefficiency. Therefore, we propose to maximize the time value of cryptocurrency in smart contracts by supplying it to decentralized money markets of Compound to earn supply interest. Furthermore, by utilizing the interest generated, we can mitigate the liquidity risk of Compound and thus do not diminish the flexibility of the original service much. Experiments are conducted to testify the proposed method, and the implementation cost is estimated to be 2.1 US dollars. We also discuss the potential systemic risk in the decentralized finance system.
Specialized Applications Used in the Mobile Application Security Implementation Process

In the paper, authors aim to determine the extent to which open source applications can replace specialized security applications for mobile applications. In this respect, open source components are selected at which level the implemented security features are identified, based on which a completeness-determining indicator is calculated. The value of this indicator is then compared to the value obtained for the .NET CF3 framework, with a hierarchy of the components being treated according to this indicator. Based on the same selected components or components derived from them, to which other components have been added, the issue of ensuring their quality is dealt with by calculating the complexity of each.
3D Human Pose Estimation with a Catadioptric Sensor in Unconstrained Environments Using an Annealed Particle Filter

The purpose of this paper is to investigate the problem of 3D human tracking in complex environments using a particle filter with images captured by a catadioptric vision system. This issue has been widely studied in the literature on RGB images acquired from conventional perspective cameras, while omnidirectional images have seldom been used and published research works in this field remains limited. In this study, the Riemannian varieties was considered in order to compute the gradient on spherical images and generate a robust descriptor used along with an SVM classifier for human detection. Original likelihood functions associated with the particle filter are proposed, using both geodesic distances and overlapping regions between the silhouette detected in the images and the projected 3D human model. Our approach was experimentally evaluated on real data and showed favorable results compared to machine learning based techniques about the 3D pose accuracy. Thus, the Root Mean Square Error (RMSE) was measured by comparing estimated 3D poses and truth data, resulting in a mean error of 0.065 m when walking action was applied.
A Compiler-based Approach for Natural Language to Code Conversion

There is a gap observed between the natural language (NL) of speech and writing a program to generate code. Programmers should know the syntax of the programming language in order to code. The aim of the proposed model is to do away with the syntactic structure of a programming language and the user can specify the instructions in human interactive form, using either text or speech. The designed solution is an application based on speech recognition and user interaction to make coding faster and efficient. Lexical, syntax and semantic analysis is performed on the user's instructions and then the code is generated. C is used as the programming language in the proposed model. The code editor is a web page and the user instructions are sent to a Flask server for processing. Using Python libraries NLTK and ply libraries, conversion of human language data to programmable C codes is done and the code is returned to the client. Lex is used for tokenization and the LALR parser of Yacc processes the syntax specifications to generate an output procedure. The results are recorded and analyzed for time taken to convert the NL commands to code and the efficiency of the implementation is measured with accuracy, precision and recall.
A Calibration Method for the Generalized Imaging Model with Uncertain Calibration Target Coordinates

The developments in optical metrology and computer vision require more and more advanced camera models. Their geometric calibration is of essential importance. Usually, low-dimensional models are used, which however often have insufficient accuracy for the respective applications. A more sophisticated approach uses the generalized camera model. Here, each pixel is described individually by its geometric ray properties. Our efforts in this article strive to improve this model. Hence, we propose a new approach for calibration. Moreover, we show how the immense number of parameters can be efficiently calculated and how the measurement uncertainties of reference features can be effectively utilized. We demonstrate the benefits of our method through an extensive evaluation of different cameras, namely a standard webcam and a microlens-based light field camera.
A Novel Data-to-Text Generation Model with Transformer Planning and a Wasserstein Auto-Encoder

Existing methods for data-to-text generation have difficulty producing diverse texts with low duplication rates. In this paper, we propose a novel data-to-text generation model with Transformer planning and a Wasserstein auto-encoder, which can convert constructed data to coherent and diverse text. This model possesses the following features: Transformer is first used to generate the data planning sequence of the target text content (each sequence is a subset of the input items that can be covered by a sentence), and then the Wasserstein Auto-Encoder(WAE) and a deep neural network are employed to establish the global latent variable space of the model. Second, text generation is performed through a hierarchical structure that takes the data planning sequence, global latent variables, and context of the generated sentences as conditions. Furthermore, to achieve diversity of text expression, a decoder is developed that combines the neural network with the WAE. The experimental results show that this model can achieve higher evaluation scores than the existing baseline models in terms of the diversity metrics of text generation and the duplication rate.
A Hybrid Hash-Stego for Secured Message Transmission Using Stegnography

Abstract Steganography and cryptography is utilized together to fuse different layers security. Cryptography is a cryptographic framework and which is utilized to scramble the visual information. Cryptography is utilized in information stowing perpetually, secure the pictures, hidden picture, sight and sound and different fields. Steganography is the procedure for concealing mystery information into another information so it is basically more guaranteed about. In steganography the conundrum messages install in an innocuous looking spread, for example, a genuine picture chronicle, by then the picture is passed on. For scramble and unscramble the information we can utilize the cryptography procedure. The information are changed over into some other rubbish structure, and some time later the mixed information are passed on. Straightforwardly a days we have seen a smart improvement of correspondences security and the antonymous individual access conundrum information for the information correspondence experts. Cryptography and Steganography are the all around utilized procedures to vanquish this danger. LSB[1] method is utilized to camouflage the mixed message into annals. This undertaking is to improve another strategy for concealing riddle messages into video, the space of tending to the characters parallelly we are isolating the video chart utilizing steganalysis.
Growing Organizations with Patterns

Organizational patterns of software development have been mined in highly successful software organizations. Yet, no case of their application in a production setting has been reported. Our fear is that this is so because practitioners are expected to apply organizational patterns out of their descriptions. Organizational patterns of software development inspired explicitly stating and documenting drama patterns. We think that drama patterns have something to give back: interactivity and involvement exhibited by the process of building drama plays with drama patterns can be introduced into applying organizational patterns of software development. The key to this is that organizing people in software development is like creating a drama play. As in drama plays, the members of software development organizations take different roles and go through a number of situations in which they communicate and collaborate with each other. Although their drama plays will never be performed in front of the audience, these drama plays are going to determine the software systems they build. Coming out of two particular cases of pattern application related to people, one of which is about a software development organization built out of patterns with the applier not being aware of the notion of an organizational pattern, and the other about drama play built by intentionally applying drama patterns, some lessons have been drawn on how to apply organizational patterns of software development.
HoG Multi-face Detection

Human face detection is important in many applications, such as human–machine interface, automatic surveillance, and facial recognition. This work exposes a solid and general face detection system capable of detecting multiple faces in the same image, even in low light situations and chaotic backgrounds. The detection system uses a representation of the Gaussian pyramid and evaluates it in all scales the existence of faces using descriptors of HoG characteristics and linear classifiers SVM. The system shows that the gradient distribution in the face contours is sufficiently discriminative to distinguish faces and non-faces and the use of cascade detectors improves overall system performance by decreasing the number of false positives. Employing experimental tests, the methodology was applied to facial and non-facial test images, allowing the evaluation of the effectiveness of the face detection system and the influence of adjustable parameters on the accuracy and performance of the system.
Impact of Optimized Operations $$A\cdot B$$, $$A\cdot C$$ for Binary Field Inversion on Quantum Computers

The inversion circuit based on the Itoh-Tsujii algorithm, used for many cryptography functions, requires a number of multiplication and squaring operations in circuits. In the past, the optimized inversion implementation has been actively studied in modern computers. However, there are very few works to optimize the inversion on the quantum computer. In this paper, we present the optimized implementation of binary field inversion in quantum circuits. Reversible and non-reversible multiplication circuits are finely combined to reduce the number of CNOT gate. In particular, we optimized the reversible circuit for $$A\cdot B$$ and $$A \cdot C$$ case in the inversion operation. Afterward, the multiplication and squaring routine efficiently initializes some of the qubits used for the routine into zero value. Lastly, the-state-of-art multiplication and squaring implementation techniques, such as Karatsuba algorithm and shift-based squaring are utilized to obtain the optimal performance. In order to show the effectiveness of the proposed implementation, the inversion is applied to the substitute layer of AES block cipher. Furthermore, the proposed method can be applied to other cryptographic functions, such as binary field inversion for public key cryptography (i.e. Elliptic Curve Cryptography).
Fuzzy Identification of Nonlinear Dynamic System Based on Input Variable Selection and Particle Swarm Optimization Parameter Optimization

Input variable selection is an essential step in the development of data-driven models. In order to establish a fuzzy model with high identification accuracy for complex nonlinear systems (such as variable load pneumatic loading system) in engineering, a novel fuzzy identification method is proposed, which is based on the selection of important input variables. Firstly, the simplified Two Stage Fuzzy Curves and Surfaces method is used to rank the original input variables according to their significance, and the variables which are most relevant to the output are selected as the input of the T-S fuzzy model. Then, the Fuzzy c-Means clustering algorithm and Particle Swarm Optimization algorithm are used to identify the antecedent parameters, and the Recursive Least Square method is used to identify the consequent parameters. The validity of the proposed fuzzy identification method is verified by two benchmark problems, and the results show that the accuracies of identified models have been improved significantly compared with the other existing models. Finally, the proposed approach is implemented to the practical data of an actual variable load pneumatic loading system, and preponderant trajectory matching performance is achieved.
Smart Contracts and Blockchain: An Application Model for Traceability in the Beef Supply Chain

While demand for food increases as a result of the world population growth, supply chains face bigger challenges concerning the quality and traceability of goods. Thus, this paper focuses on the beef supply chain and its current traceability tools, which have shown several flaws as they fail to provide reliable means to track products back to their source. Since blockchain and smart contracts technology have emerged providing a decentralized and secure platform for information exchange, it has changed how people and a wide range of institutions handle assets transactions such as currency and data. Therefore, this paper demonstrates an application model using blockchain and a smart contract algorithm as a viable and trustworthy traceability tool for the beef supply chain.
Occlusion-aware light field depth estimation using side window angular coherence

Depth estimation is crucial in many light field applications. However, the accuracy of light field depth estimation is prone to be affected by occlusions. In this paper, a method of side window angular coherence is proposed to handle different types of occlusions, and the ability of the proposed method to resist occlusions is theoretically analyzed. The angular patch is divided into several discrete side window subsets. These subsets are a pure occluder-type subset, a pure object point–type subset, and a hybrid-type subset. The photo-consistency of the pure object point–type subset can reflect the true depth. Meanwhile, the occlusion edges can be detected to identify occluded points and nonoccluded points so the robustness of the algorithm can be further enhanced by processing the two types of points. Moreover, fast guided filtering is applied to cost volume for improving the accuracy of depth estimation. Experimental results demonstrate that our method outperforms the state-of-the-art depth estimation methods on both synthetic and real scenes, especially near occlusion boundaries.
SAEKCS: Sentiment Analysis for English – Kannada Code SwitchText Using Deep Learning Techniques

Usage of social media has become more widespread to express sentiment, emotion about public events, government policies, product reviews etc. Performing Sentiment Analysis (SA) on social media data will give more and more insights about user's behavior. Multilingual society like India, it is very common to use code switch text in social media to express their views. Switching between languages while communicating is refer as code mixing or code switching. Analyzing this code switch text and getting the useful information from this too harder because of its unstructured linguistic nature. In this paper, we proposed a hybrid model called SAEKCS for sentiment analysis on Kannada-English code switch text. Our proposed model uses deep learning techniques like Convolutional Neural Network (CNN) and Bidirectional Long Short Term Memory (BiLSTM) for sentiment analysis in code switch text. Our experimental results shows that 77.6% of accuracy and 69.6% of coverage. These results are much better than existing works [17] [18].
A multimodal multi-objective optimisation approach to deal with the phylogenetic inference problem

The phylogenetic inference strategies aim to propose hypotheses to explain the evolutionary relationships for different organisms. These resultant evolutionary histories are often represented as phylogenetic trees. In computer science, the phylogenetic inference has been treated as an optimisation problem. The literature has proposed different criteria to select the optimal tree between the possible topologies. In order to reduce the bias associated to the dependency on the selected criterion, different multi-objective optimisation strategies have been proposed during the last decade. These strategies search by solutions using operators and metrics based on the objective space. However, a recent work concluded that the topological features of the trees (decision space) and the objective space in the multi-objective phylogenetic inference context are not related, becoming phylogeny in a multimodal problem. It means that the current multi-objective strategies could discard solutions from different regions of the decision space, limiting the searching process and the resultant topologies. In this work, we propose a new version of the Memetic algorithm based on an NSGA-II scheme for phylogenetic inference, which include a multimodal operator that considers the diversity of the topologies of the trees based on the decision space to rank the solutions. The inclusion of this operator improved the diversity of solutions according to the decision and the objective space, increasing the hypervolume metric compared to the base version of this memetic algorithm.
Adaptive Control of Subpopulations in Evolutionary Dynamic Optimization

Multipopulation methods are highly effective in solving dynamic optimization problems. Three factors affect this significantly: 1) the exclusion mechanisms to avoid the convergence to the same peak by multiple subpopulations; 2) the resource allocation mechanism that assigns the computational resources to the subpopulations; and 3) the control mechanisms to adaptively adjust the number of subpopulations by considering the number of optima and available computational resources. In the existing exclusion mechanisms, when the distance (i.e., the distance between their best found positions) between two subpopulations becomes less than a predefined threshold, the inferior one will be removed/reinitialized. However, this leads to incapability of algorithms in covering peaks/optima that are closer than the threshold. Moreover, despite the importance of resource allocation due to the limited available computational resources between environmental changes, it has not been well studied in the literature. Finally, the number of subpopulations should be adapted to the number of optima. However, in most existing adaptive multipopulation methods, there is no predefined upper bound for generating subpopulations. Consequently, in problems with large numbers of peaks, they can generate too many subpopulations sharing limited computational resources. In this article, a multipopulation framework is proposed to address the aforementioned issues by using three adaptive approaches: 1) subpopulation generation; 2) double-layer exclusion; and 3) computational resource allocation. The experimental results demonstrate the superiority of the proposed framework over several peer approaches in solving various benchmark problems.
Question-Answering System with Linguistic Terms over RDF Knowledge Graphs

Resource Description Framework (RDF) is an important way of representing data on the Web. Although RDF is a data format suitable for publishing individual pieces of information together with relations between them, it represents a challenging format for answering questions. Thus, a system and a user interface that are easy and intuitive for users to access and operate on RDF data are of significant importance.In this paper, we introduce a Question-Answering (QA) system that allows users to ask questions in English. The uniqueness of this system is its ability to answer questions containing linguistic terms, i.e., concepts such as SMALL, LARGE, or TALL. Those concepts are defined via membership functions drawn by users using a dedicated software designed for entering `shapes' of these functions. The system is built based on an analogical problem solving approach, and is suitable for providing users with comprehensive answers. We demonstrate the capability of the proposed QA system by answering questions asked over two RDF stores: DBpedia and Wikidata.