Reviewerly/exHarmony huggingface dataset:
Adaptive Fast Composite Splitting Algorithm for MR Image Reconstruction

Reconstruction of an image from undersampled k-space data using conventional sparsity priors use any of the pre-specified transforms like wavelet, which utilizes the global sparsity of images. Alternatively, methods such as block-matching and 3D filtering (BM3D) utilize the non-local sparsity of images, resulting in better preservation of finer details. However, use of either of the methods cannot utilize global and non-local sparsity at the same time. In this paper, we propose an efficient combination of the global and non-local sparsity realized using wavelet and BM3D, implemented using the fast composite splitting denoising (FCSD) algorithm. Furthermore, we use an adaptive regularization parameter for the wavelet to yield the best achievable performance using adaptive thresholded Landweber (ATL). The proposed adaptive fast composite splitting denoising algorithm (AFCSD) combines the advantages of both BM3D with adaptive TL through the use of FCSD and thus exploits the self-repetitive structure in images by enforcing non-local sparsity as well as entire consistency of the image. Experimental results reveal that the proposed method is superior to any of the individual methods in terms of reconstruction accuracy and computation complexity. Additionally, when used as an auxiliary constraint in dictionary learning (DL), this can result in further improvements in reconstruction with significant reduction in the computation time as compared to a conventional DL reconstruction.
Similarity-Guided Multimedia Recommendation in Heterogeneous Information Network

With the rapid growth in multimedia information, problems on how to discover the individual interests of users and recommend them with the proper goods have become increasingly difficult. Traditional recommendation algorithms simply utilize user rating logs for recommendations, but ignore lots of useful information which can be expressed as a Heterogeneous Information Network. In this paper, we propose a similarity measure, PW- PathSim, to calculate the relevance between two entities of the semi-symmetric weighted meta paths. Then a similarity regularization based recommendation algorithm is proposed to integrate the similarity of users and items with matrix factorization for recommendations. Furthermore, we compare the PWMFP algorithm with several benchmarks including FunkSVD, HeteFM and DSR. Experimental results with Douban dataset show that it outperforms other HIN-based algorithms in terms of recommendation accuracy.
Optimization Models in Cluster Analysis

This chapter presents different formulations of the clustering problem using various optimization approaches. They include the mixed integer programming, the general nonsmooth, and the nonsmooth DC formulations of the clustering problem. We introduce the auxiliary clustering problem and study optimality conditions for both the clustering and the auxiliary clustering problems. Finally, we discuss the smoothing of the clustering and the auxiliary clustering problems.
An Effective Content Based Image Retrieval Using Dot Diffusion Block Truncation Coding

Image processing applications such as medical diagnosis, crime prevention, publishing or advertising, utilizes the traditional image processing techniques. Simple browsing can easily identify images from a small collection of images but for large and different collection of images identification of images emerge as a critical issue. In this paper for image retrieval four different features are extracted using DDBTC technique. The first two features such as Color Co-occurrence Features (CCF) and Color Histogram Features (CHF) are obtained using color quantizers, Bit Pattern Feature (BPF) and Bit Histogram Feature (BHF) are obtained using Bitmap image. In order to remove the false counter problem and blocking effect different error diffusion kernels are employed. The results shows better accuracy.
Efficient Implementation and Computational Analysis of Privacy-Preserving Auction Protocols

Auctions are a key economic mechanism for establishing the value of goods that have an uncertain price. Nowadays, as a consequence of the ubiquitous emergence of technology, auctions can reach consumers, and as a result, drive market prices on a global scale. Collection of private information such as losing bids exposes more information than desired. In fact, the leaked information can be analyzed to provide auctioneers or competitors with advantages on future transactions. Therefore, the need to preserve privacy has become a critical concern to reach an accepted level of fairness and to provide market participants with an environment in which they can bid true valuations. This paper focuses on constructions of sealed-bid auctions based on cryptographic protocols. Instead of solely focusing on theoretical aspects of sealed-bid auctions, this paper dives into implementation details and demonstrates communication and computational analysis and how different settings affect performance.
Design of the Multimedia Teaching System for Literary Creation Based on B/S Model

Under the new educational situation, there are many disadvantages in the traditional classroom teaching, and the multimedia-assisted teaching has become a useful supplement to the classroom teaching. The visualization of the text presentation is combined with the images of the literary aesthetics. The interaction of the teaching links is combined with the expansion of students’ imagination by the literary aesthetics, and the integration of the teaching contents is combined with the cognitive function of the literary aesthetics. The integration strategy of the technical elements and the aesthetic elements in the literature teaching makes the teaching process of the literature courses three-dimensional, modern and diversified. The design of the multimedia teaching system for the literary creation based on the B/S mode can not only display the cultural connotation contained in the literary works more effectively and annotate the systematic thinking of the literary creation, but can also facilitate students to learn effectively, master the necessary skills of dealing with the literary emotions, and realize the comprehensive deepening of the literary creation mechanisms.
Category Independent Object Transfiguration with Domain Aware GAN

Object transfiguration aims to translate the domain of objects in an image. In this paper, we challenge a new task: category independent object transfiguration, which enables objects to be transfigured even for object categories not included in the training data. Conventional methods are based on the premise that the object categories in the test images are contained in the training images. Therefore, they can train transfer regions and magnitude implicitly, and they accurately estimate the transfer regions and magnitude in test images. However, when an image containing object categories not included in the training data is input, this premise breaks down. Consequently, undesired regions are converted with undesired magnitude. To tackle this problem, we propose a domain region and magnitude aware GAN that explicitly predicts transfer region and magnitude, and translates so that the predicted region and magnitude before and after translation are the same. Experimental results show that our method can more realistically and accurately translate the object domains than the state-of-the-art method.
Graph-Based Analysis of Similarities between Word Frequency Distributions of Various Corpora for Complex Word Identification

Complex word identification (CWI) is a fundamental task in educational NLP and applied linguistics which involves the identification of complex words in a text for various applications, including text simplification. Recent studies have independently reported that when word-frequency features from some uncommon corpora are used in combination with those from a general corpus, they improve the CWI accuracy; this suggests that they can be used as adjustments for a general corpus. However, although previous studies have analyzed similarity values between each pair of corpora, the significance of the similarity in the entire set of corpora is unclear. This complicates the analysis of the combination of general and uncommon corpora aimed at improving CWI accuracy; thus, the search for effective types of corpora would have to be exhaustive. To contribute to a better understanding and a non-exhaustive search, this paper proposes a novel graph-based analysis method. We first calculate various similarities among the word frequency distributions of various corpora in an unsupervised manner. Subsequently, we regard each similarity as a weighted graph and analyze the importance of a pair of corpora, or an edge, within the entire graph structure. Through our experiments, it was found that our analysis method can successfully explain why the previously reported combinations of corpora were effective; Furthermore, it can find effective corpus combinations.
Video Summarization Based on Optical Flow

The explosive growth in digital videos demands a technique that effectively identifies informative parts from the video. Video summarization refers to creating a video summary as a collection of keyframes that depicts key actions and events in the video. The authors propose to generate a video summary based on apparent motion information in the video, that is, optical flow. The proposed algorithm uses optical flow technique to estimate the change in the local flow of pixel intensities to identify the keyframes. The proposed algorithm is tested on two standard databases, such as Open Video Project and YouTube database. The results and the quantitative evaluation validate the effectiveness of the proposed algorithm for generation of a video summary.
How does Selecting a Benchmark Function Suite Influence the Estimation of an Algorithm's Quality?

This paper is focused on answering the question how the selection of a testbed on which the newly proposed algorithms are evaluated influence t he estimation of an algorithm's quality. New algorithms are usually tested on well-known benchmark function suites, where the goal is to achieve the best results of the algorithm in the shortest time. A lot of questions have arisen when looking for the most suitable testbed, for instance, which benchmark to take, and which version of it is the most representative for determining the best algorithms. In this study, the newly proposed algorithms introducing the coalition game concept for solving global optimization were tested by solving two different benchmark function suites, i.e., CEC-14 and CEC-18, in order to show that selecting the different CEC benchmark suites does not have a crucial impact on estimating the algorithm's quality.
Exploring Application of Knowledge Space Theory in Accessibility Testing

Knowledge space theory has so far most predominantly found applications in e-learning and educational assessment systems. The principles of knowledge space theory can however also be used in the assessment of a system, device, or application in terms of accessibility. A precedence order among the Web Content Accessibility Guidelines (WCAG) can be discovered through careful analysis, and expert knowledge can be used to construct a knowledge structure with each knowledge state representing a set of these guidelines. This is useful not only in gaining an understanding of where a system lies on its way toward becoming universally accessible but also helps in determining the next steps for improvement of the analyzed system’s accessibility from the outer fringe of the current knowledge state. This is a more feasible way of judging a system for its accessibility since it shows exactly in terms of which guidelines the assessed system is accessible. The standards for acceptable accessibility criteria specified and met by organizations or industries will thus be in terms of knowledge states instead of a numerical or graded value. This invokes comprehensiveness, clarity, and customization in practice of accessibility testing.
Cross-Level Matching Model for Information Retrieval

Recently, many neural retrieval models have been proposed and shown competitive results. In particular, interaction-based models have shown superior performance to traditional models in a number of studies. However, the interactions used as the basic matching signals are between single terms or their embeddings. In reality, a term can often match a phrase or even longer segment of text. This paper proposes a Cross-Level Matching Model which enhances the basic matching signals by allowing terms to match hidden representation states within a sentence. A gating mechanism aggregates the learned matching patterns of different matching channels and outputs a global matching score. Our model provides a simple and effective way for word-phrase matching.
Enhancing Open-Set Face Recognition by Closing It with Cluster-Inferred Gallery Augmentation

In open-set face recognition—as opposed to closed-set face recognition—it is possible that the identity of a given query is not present in the gallery set. In that case, the identity of the query can only be correctly classified as “unknown” when the similarity with the gallery faces is below a threshold that was determined a priori. However, in many use-cases, the set of queries contains multiple instances of the same identity, whether or not this identity is represented in the gallery. Thus, the set of query faces lends itself to identity clustering that could yield representative instances for unknown identities. By augmenting the gallery with these instances, we can make an open-set face recognition problem more closed. In this paper, we show that this method of Cluster-Inferred Gallery Augmentation (CIGA) does indeed improve the quality of open-set face recognition. We evaluate the addition of CIGA for both a private dataset of images taken in a school context and the public LFW dataset, showing a significant improvement in both cases. Moreover, an implementation of the suggested approach along with our experiments are made publicly available on https://gitlab.com/florisdf/acpr2019 .
Understanding Cohesion in Writings and Speech of Schizophrenia Patients

Schizophrenia is one of the mental disorders that impacts a person's thinking, speech, and actions. It can reduce a person's ability to process auditory information and make decisions. Analyzing this disorder correctly is important because it might help with different ways of reducing its negative effects on its patients. Linguists and psychiatrists have been investigating language impairments and speech disorder in people with schizophrenia disorder which can be challenging. In this study, we attempt to address this issue by analyzing linguistic features i.e. cohesion in the writings and speech scripts of schizophrenia patients. Our results show that using referential cohesion with text easability or situation model features provides the best performance for speech whereas for writing dataset, readability or a combination of situation model and readability yield the best performance.
Modified Contrast Enhancement Algorithm Using Numerical Integration Simpson’s Method

This paper presents an efficient method to improve the contrast of low contrast images and at the same time provides satisfactory performances in terms of image contrast and sharpness. It is based on numerical cubic interpolation formula known as Simpson’s $$\frac{3}{8}$$ th rule. The neighboring pixel information is used in interpolation method, and it gives the high contrast image and also preserves fine details of the images. Different quality-based parameters are used to measure the improvement of the contrast of the images. The obtained results are compared with various standard existing methods for further validation.
Direct and Indirect Evolutionary Designs of Artificial Neural Networks

Evolutionary Algorithms (EAs) and other kind of metaheuristics are utilized to either design or optimize the architecture of Artificial Neural Networks (ANNs) in order to adapt them for solving a specific problem; these generated ANNs are known as Evolutionary Artificial Neural Networks (EANNs). Their architecture components, including number of neurons or their kind of transfer functions, connectivity pattern, etc., can be defined through direct or indirect encoding schemes; the former, directly codifies ANN architecture components into the genotype of solutions, meanwhile the last one, requires to transform the solution’s genotype through mapping processes to generate an ANN architecture. In this work, architectures of three-layered feed-forward ANNs are designed by using both encoding schemes to solve several well-known benchmark datasets of supervised classification problems. The results of designed ANNs by using direct and indirect encoding schemes are compared.
Systematic Asset Identification and Modeling During Requirements Engineering

Risk management primarily targets the treatment of threats which might harm the assets of a system. Therefore, identifying such assets of a system and documenting them systematically in an asset model are the key activities in any risk management approach. Based on the ISO/IEC 27005 standard, the consideration of assets consists of two major activities: (i) asset identification, and (ii) asset valuation. However, despite the crucial role of asset identification and asset documentation, such documentation is often neglected during software development. In this paper, we aim to support security analysts in identifying and analyzing assets in the earliest stages of software development, i.e., during requirements engineering. Our contribution is two-fold: We first provide a conceptual model for assets that allows us to classify assets and to express the relations between assets. Second, we propose a method for a systematic identification of system assets and their documentation in an asset model. Our method is based on the functional requirements of software which are expressed by means of problem diagrams. We illustrate and evaluate our proposed approach by applying it to an application example from the smart home sector.
Outsourced Private Set Union on Multi-Attribute Datasets for Search Protocol using Fully Homomorphic Encryption

In the era of big data and cloud computing, outsourcing data storage to the cloud poses the risk of its abuse or leakage. Thus, we address the problem of delegating computation on outsourced private datasets while maintaining privacy. In this study, we consider a scenario involving two data owners outsourcing their datasets to a cloud service. The cloud performs a set union computation, after which the querier sends a query to obtain information from both datasets. We propose a protocol that uses fully homomorphic encryption (FHE) and Cartesian-join of Bloom filters (CBF) as proposed by Wang et al. The protocol obtains information on the existence of a particular set of elements without learning about the residing source. To the best of our knowledge, our protocol, by using the FHE and CBF matrix, is a novel approach to ensuring the security of outsourced set union operations.
3D Reconstruction for the Multiple Moving Objects Based on Phase Shifting Profilometry

Phase shifting profilometry (PSP) is one of the most popular technologies for 3D shape reconstruction. However, as multiple fringe patterns are used in PSP, the object is required to be kept static during the measurement. Errors will be introduced when moving object is reconstructed. This paper proposes a new method to reconstruct the multiple moving objects based on PSP. The multiple objects can have different movement. At first, the objects are tracked separately. Then, the rotation matrix and translation vector describing the movement for each object are obtained. With the relationship between the object movement and the phase value, the reconstruction model employing the movement information is used to eliminate the errors caused by the movement. At last, the objects are reconstructed separately with high accuracy. The effectiveness of the proposed method is verified by experiments.
OperativeCriticalPointBug Algorithm - Local Path Planning of Mobile Robot Avoiding Obstacles

For Autonomous Mobile Robot one of the biggest and interesting issues is path planning. An autonomous mobile robot should be able determine its own path to reach destination. This paper offers a new algorithm for mobile robot to plan a path in local environments with stationary as well as moving obstacles. For movable robots' path planning OperativeCriticalPointBug(OCPB) algorithm, is a new Bug algorithm. This algorithm is carried out by the robot throughout the movement from source to goal, hence allowing the robot to rectify its way if a new obstacle comes into the route or any existing obstacle changes its route. According as, not only the robot tries to avoid clash with other obstacle but also tries a series of run time adjustment in its way to produce roughly a best possible path. During journey the robot is believed to be capable to act in an unknown location by acquiring information perceived locally. Using this algorithm the robot can avoid obstacle by considering its own as well as the obstacle's dimension. The obstacle may be static or dynamic. The algorithm belongs to bug family.
A Chinese Remainder Theorem Based Perfect Secret Sharing Scheme with Enhanced Secret Range Values Using Tensor Based Operations

Protecting sensitive information is an increasingly difficult task due to the advances in hardware. Brute force attacks (BFA) have been successful in accessing protected data. As BFA is a trial and error process, and a natural solution against it consists of enhancing the range of possible secret values. One of the most used cryptographic techniques to protect sensitive data is the Secret Sharing Scheme (SSS), by means of which one can protect the secret by mathematically and individually distributing it into shares over n participants. Only when a minimal quantity of t participants combine their shares the secret is revealed to all of them. Among the several applications of the Chinese Remainder Theorem (CRT), it is also used as a SSS. Although the state-of-the-art Asmuth-Bloom's SSS is perfect in terms of secrecy, the candidate values for the secret are quite small, therefore enhancing the probability of a successful BFA, as less values are to be tested by the attacker. In this paper, we propose a new and perfect CRT based SSS based on sparse matrices. The secret is an integer that, by means of the Lehmer Code, has a bijective relationship with the permutations of the values in a vector constructed with the shares. In the proposed CRT based SSS, the secret can assume a set of values that largely outperforms the range of values obtained with the Asmuth-Bloom's SSS. Furthermore, it is mathematically proven to be potentially unlimited. Considering for instance a set of 6 co-prime numbers under 100, the gain in secret range compared with the Asmuth-Bloom's SSS per bits used is 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">103</sup> higher.
Methods of Protection of Soft Targets in Urban Area

The paper shortly presents the possibilities of soft targets protection using technical measures against the blast wave creating in explosion in terrorist attacks. Technical can be based on two principles: measures ensuring the reduction of the blast load increasing the perimeter using so called stand-off distance, i.e. to increase the distance between the protected asses and source of explosive and in that way to decrease the pressure affected the asses. Or if it is not possible, to increase the blast resistance using so called retrofit technique.
Professional Competence of A Foreign Language Teacher in A Technical University

The article is devoted to the problem of professional competence of a foreign language teacher in a technical university. The author has developed a model of professional competence based on the analysis of the conditions of a teacher's activity. A conclusion has been made about the importance of a professional component in the competence structure.