Reviewerly/exHarmony huggingface dataset:
Solar Radiation Interpolation

Geographic information systems provide different options to analyze and represent the spatial heterogeneity of solar radiation incident on a certain area. This chapter presents a description of the main and well-known methods for determining interpolation surfaces from a data sample. Moreover, using 3D model of the analyzed area, computer models of spatial analysis are precise techniques to adjust the results to the variability of surfaces in a geographic area. Both alternatives offer a great analysis capacity. The selection of a procedure will depend on the objective of the study and the available information.
Clinical, Consumer Health, and Visual Question Answering

This work presents an overview of three research directions that support clinical and health-related decisions: clinical question answering using both the Electronic Health Record data and the literature; consumer health question answering; and an emerging area of biomedical visual question answering (VQA).
HOGHS and Zernike Moments Features-Based Motion-Blurred Object Tracking

Motion-blurred object tracking method integrating Histogram of Oriented Gradients and Hue Saturation (HOGHS) with Zernike moments features and based on ECO_HC tracker was proposed in this study to deal with object blur caused by the motion of the camera or the object itself. HOGHS was constructed by integrating fHOG with color features, and the properties of Zernike moments were introduced. The object was represented by combining HOGHS and Zernike moments. Furthermore, a novel quality evaluation method of response map considering both positioning accuracy and robustness was proposed and based on the method an adaptive fusion strategy utilizing the complementary properties of HOGHS and Zernike moments was implemented. Experiments were performed on the motion blur sequences from OTB-100 datasheet. Our method was compared with four other state-of-the-art methods. The precision as well as success rate were 0.849 and 0.827, respectively. Speed was 38.4 FPS. It is superior to VOT-2016’s excellent tracker ECO_HC, with relative gains of 2.3% for Pre-20 and 2.4% for AUC. The results show that the proposed method can effectively achieve the objective of motion-blurred object tracking.
The Poetry of Strange Connections: An Interview with Bill Verplank

Bill Verplank is an interaction designer educated at Stanford and MIT in Mechanical Engineering (Design and Man-Machine Systems). He did user testing at Xerox (“Star”), interaction design at IDTWO (now IDEO), research at Interval. He has taught design at MIT, Stanford (ME, Music, CS), ACM-SIGCHI, NIME, IDII (Ivrea), CIID (Copenhagen).
Research on library long-tail server based on K-means algorithms

The rapid development of Internet affects all walks of life in our society, And the library service industry is the first to be affected, Electronic information publishers, together with Internet content providers are all competing in taking over the book information market actively, the development of the situation the Library facing is very serious, Taking advantages of the combination of library’s traditional superiority with advanced technology will be the key pose to mend matters, Needless to say that it is a very arduous task, This paper uses the method of data mining to explore how to apply the long tail theory to the library service field and put forward the concept of "library long tail service", On the basis of analyzing the applicability of the long tail theory in the field of library service, this paper puts forward the library long tail service data mining system based on the keyword text clustering, and according to the characteristics of the data generated by the library service, In the library long tail service research theory, methods and techniques, and carried out the algorithm improvement.
Outlier Detection Based on Cluster Outlier Factor and Mutual Density

Outlier detection is an important task in data mining with numerous applications. Recent years, the study on outlier detection is very active, many algorithms were proposed including that based on clustering. However, most outlier detection algorithms based on clustering often need parameters, and it is very difficult to select a suitable parameter for different data set. In order to solve this problem, an outlier detection algorithm called outlier detection based on cluster outlier factor and mutual density is proposed in this paper which combining the natural neighbor search algorithm of the Natural Outlier Factor (NOF) algorithm and based on the Density and Distance Cluster (DDC) algorithm. The mutual density and γ density is used to construct decision graph. The data points with γ density anomalously large in decision graph are treated as cluster centers. This algorithm detect the boundary of outlier cluster using cluster outlier factor called Cluster Outlier Factor (COF), it can automatic find the parameter. This method can achieve good performance in clustering and outlier detection which be shown in the experiments.
Application of Regularized Online Sequential Learning for Glucose Correction

Glucose measurement by using handheld devices is applied widely due to their comfortabilities. They are easy to use and can give results quickly. However, the accuracy of measurement results is affected by interferences, in which hematocrit (HCT) is one of the most highly affecting factors. In this paper, an approach for glucose correction based on the neural network is presented. The regularized online sequential learning is utilized for hematocrit estimation. The transduced current curve which is produced by the chemical reaction during glucose measurement is used as an input feature of neural network. The experimental results shown that the proposed approach is promising.
Non-black-box Simulation in the Fully Concurrent Setting, Revisited

We give a new proof of the existence of $$O(n^{\epsilon })$$ -round public-coin concurrent zero-knowledge arguments for $$\mathcal {NP}$$ , where $$\epsilon >0$$ is an arbitrary constant. The security is proven in the plain model under the assumption that collision-resistant hash functions exist. The existence of such concurrent zero-knowledge arguments was previously proven by Goyal (STOC’13) in the plain model under the same assumption. In the proof, we use a new variant of the non-black-box simulation technique of Barak (FOCS’01). An important property of our simulation technique is that the simulator runs in a “straight-line” manner in the fully concurrent setting. Compared with the simulation technique of Goyal, which also has such a property, the analysis of our simulation technique is (arguably) simpler.
Active Descriptor Learning for Feature Matching

Feature descriptor extraction lies at the core of many computer vision tasks including image retrieval and registration. In this paper, we present an active learning method for extracting efficient features to be used in matching image patches. We train a Siamese deep neural network by optimizing a triplet loss function. We develop a more efficient and faster training procedure compared to the state-of-the-art methods by increasing difficulty during batch training. We achieve this by adjusting the margin in the loss and picking harder samples over time. The experiments are carried out on Photo Tourism dataset. The results show a significant improvement on matching performance and faster convergence in training.
PHENOMENON OF VOCAL COACHING IN MODERN MUSICAL EDUCATION

The article reveals the content of the notion of vocal coaching, which has become a new phenomenon in the modern musical-educational space. The focus is on examining the role of the coach for students-vocalists given to the accompanist. The author studies the problem of expanding the range of the professional functions of the accompanist, who becomes a coach, analyses the specialist-coach’s necessary skills and competencies. The idea of the integrity of a student’s independent work and the accompanist-coach’s directing, vector role is substantiated.
Mitigation of Cross-Site Scripting Attacks in Mobile Cloud Environments

Cross-Site Scripting (XSS) is one of the dangerous and topmost web attacks as stated by recent surveys. XSS vulnerability arises, when an application deployed in a cloud, accept information from uncertain origin without an input validation, allowing the execution of dynamic content. XSS vulnerabilities may cause serious security violations in web and mobile cloud-based applications. In general, Cross-Site Scripting bugs are very easy to accomplish, but hard to discover and mitigate, because of the flexibility of encoding schemes like HTML encoding, which offers the adversary numerous chances to bypass the filters that should block dangerous content from being inserted into relied websites. In order to mitigate XSS vulnerability of a web application in the mobile cloud, a novel approach is presented, which successfully identifies the JavaScript-driven XSS attacks. In addition, we focus on, initiating a client-side Cross-Site Scripting attack discovery and mitigation technique known as Secure XSS layer based on the placement of sanitizers in the inserted malicious code.
Linguistic-Based Security in Fog and Cloud Computing

This paper will describe new classes of linguistic secret division procedures created with application of different classes of sequential, tree and graph grammars. Description and comparison of such method will be done, as well as possible application for data management in Fog and Cloud Computing will be presented. New classes of linguistic threshold schemes will be proposed for data (service, information, etc.) protection by split secret parts between selected groups of thrusted participants.
Adding Haystacks to Our Needles? Assessing Changes in Collection Composition after the Introduction of Uncurated Packages

Although academic library print collections are traditionally carefully selected, collection strategies for online materials often involve the addition of large, uncurated packages of eBooks and other online materials. This study examines how adding such packages has altered the composition of one library’s collection in terms of publication date, format, and subject. It then utilizes WebPAC and Summon search results to understand how the collection is experienced by library searchers. Results show rapid growth in uncurated collections. Whereas WebPAC heavily favored newer items and eBooks from uncurated collections, Summon promoted print books from cultivated collections to a degree greater than expected.
Simple Learning with a Teacher via Biased Regularized Least Squares

In the paradigm of learning with a teacher, introduced by Vapnik, a supervised learner is trained on an augmented features space, and a student is requested to match the teacher accuracy as much as possible in a reduced feature space. In particular, in the transfer learning mode proposed by Vapnik, a method was formalized to move the knowledge from the teacher to the student. In this paper, we use biased regularized least squares as a simple yet effective method to transfer the knowledge from one learner to another, and to assess its accuracy. We achieve this by further generalizing a semi-supervised learning method, which we previously introduced. We will show that, with this approach, the teacher can be any classifier. In particular, we will employ the Relevance Vector Machine (RVM) as teacher to assess the method’s capability in transferring the knowledge in terms of classification accuracy, and in reproducing the probabilities coming from RVM. We validate the method against standard UCI datasets and systematically compare it with Vapnik’s original method in terms of accuracy and execution time. We thus demonstrate the feasibility and speed of this new approach.
Reflecting on How Artworks Are Processed and Analyzed by Computer Vision

The intersection between computer vision and art history has resulted in new ways of seeing, engaging and analyzing digital images. Innovative methods and tools have assisted with the evaluation of large datasets, performing tasks such as classification, object detection, image description and style transfer or assisting with a form and content analysis. At this point, in order to progress, past works and established practices must be revisited and evaluated on the ground of their usability for art history. This paper provides a reflection from an art historical perspective to point to erroneous assumptions and where improvements are still needed.
Improved Dictionary Learning with Enriched Information for Biomedical Images

With dictionary learning using k-means or k-means++, the optimal value of k is traditionally determined empirically using a validation set. The optimal k, which should depend on the particular problem, is chosen with previously determined values from prior work. We argue that there is rich information from clustering with a number of values of k. We propose a novel method to extract information from clustering with all reasonable values of k at the same time. It is shown that our method improves the performance of dictionary learning for the popular bag-of-features model in image classification with simple patterns like cells such as biomedical images. Our experiments demonstrate that, our proposed dictionary learning method outperforms popular methods, on two well-known datasets by 12.5 $$\%$$ and 8.5 $$\%$$ compared to k-means/k-means++ dictionary learning and by 8.9 $$\%$$ and 6.1 $$\%$$ compared to sparse coding.
Solving a Special Case of the Generalized Assignment Problem Using the Modified Differential Evolution Algorithms: A Case Study in Sugarcane Harvesting

We proposed and created a methodology to solve a real-world problem, which is a special case of the generalized assignment problem. The problem consists of assigning drivers to harvesters, which will then be assigned to harvest sugarcane in order to maximize daily profit. A set of drivers have various levels of experience. Therefore, a different capability to harvest sugarcane leads to a range of daily wages. Each harvester has different operating years and engine size, which affects its fuel consumption rate and capacity to harvest sugarcane, respectively. Assigning a worker to a harvester can improve the fuel consumption and efficiency of the harvester. We developed a mathematical model to reflect this problem and to solve it to find the maximum outcome using Lingo v.11 commercial optimization software. Since Lingo v.11 is limited to solving only small-size test instances, for medium to large test instances, four modified differential evolution (MDE) algorithms were used to solve the problem: MDE-1, MDE-2, MDE-3, and MDE-4. MDE-2 was found to be the best proposed heuristics because it has intensification and diversification ability. MDE has been tested with the case study. We tried to increase the daily profit by implementing three strategies: (1) change all harvesters that are more than five years old, (2) train drivers to reach maximum capacity, and (3) a combination of 1 and 2. Each strategy has a different investment. The breakeven point (number of days) to return the investment was calculated from the increase of daily profit. The computational results show that strategy 2 is the best because it has the quickest rate of investment return rate. However, this strategy has a disadvantage, since it is possible that drivers may leave the company if they have been highly trained. Moreover, strategy 1 has an acceptable break-even point at 392 days.
Panning and Jitter Invariant Incremental Principal Component Pursuit for Video Background Modeling

Video background modeling is an important preprocessing stage for various applications, and principal component pursuit (PCP) is among the state-of-the-art algorithms for this task. One of the main drawbacks of PCP is its sensitivity to jitter and camera movement. This problem has only been partially solved by a few methods devised for jitter or small transformations. However, such methods cannot handle the case of moving or panning cameras in an incremental fashion. In this paper, we greatly expand the results of our earlier work, in which we presented a novel, fully incremental PCP algorithm, named incPCP-PTI, which was able to cope with panning scenarios and jitter by continuously aligning the low-rank component to the current reference frame of the camera. To the best of our knowledge, incPCP-PTI is the first low-rank plus additive incremental matrix method capable of handling these scenarios in an incremental way. The results on synthetic videos and Moseg, DAVIS, and CDnet2014 datasets show that incPCP-PTI is able to maintain a good performance in the detection of moving objects even when panning and jitter are present in a video. Additionally, in most videos, incPCP-PTI obtains competitive or superior results compared to state-of-the-art batch methods.
Personalized, Browser-Based Visual Phishing Detection Based on Deep Learning

Phishing defense mechanisms that are close to browsers and that do not rely on any forms of website reputation may be a powerful tool for combating phishing campaigns that are increasingly more targeted and last for increasingly shorter life spans. Browser-based phishing detectors that are specialized for a user-selected set of targeted web sites and that are based only on the overall visual appearance of a target could be a very effective tool in this respect. Approaches of this kind have not been very successful for several reasons, including the difficulty of coping with the large set of genuine pages encountered in normal browser usage without flooding the user with false positives. In this work we intend to investigate whether the power of modern deep learning methodologies for image classification may enable solutions that are more practical and effective. Our experimental assessment of a convolutional neural network resulted in very high classification accuracy for targeted sets of 15 websites (the largest size that we analyzed) even when immersed in a set of login pages taken from 100 websites.
Resolution of Max-Product Fuzzy Relation Equation with Interval-Valued Parameter

Considering the application background on P2P network system, we investigate the max-product fuzzy relation equation with interval-valued parameter in this paper. Order relation on the set of all interval-valued numbers plays key role in the construction and resolution of the interval-valued-parameter fuzzy relation equation (IPFRE). The basic operations supremum (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>a</mml:mi><mml:mo>∨</mml:mo><mml:mi>b</mml:mi></mml:math>) and infimum (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mi>a</mml:mi><mml:mo>∧</mml:mo><mml:mi>b</mml:mi></mml:math>) in the IPFRE should be defined depending on the order relation. A novel total order is introduced for establishing the IPFRE. We also discuss some properties of the IPFRE system, including the consistency and structure of the complete solution set. Concepts of close index set and open index set are defined, helping us to construct the resolution method of the IPFRE system. We further provide a detailed algorithm for obtaining the complete solution set. Besides, the solution set is compared to that of the classical max-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mrow><mml:mi>T</mml:mi></mml:mrow></mml:math> fuzzy relation equations system.
An Empirical Study on Performance Server Analysis and URL Phishing Prevention to Improve System Management Through Machine Learning

This paper tackles some important matters such as the server performance and the URL phishing. Nowadays the system management is a crucial issue and any potential failure needs to be detected quickly and, at the same time, to avoid URL phishing via defining rules in the firewall setting. An empirical study through data mining is conducted covering different prediction techniques. Lastly, some guidelines are provided to emit a critical view about what may happen and how to act immediately.
Blockchain Based Distributed Network Architecture

Blockchain, as an emerging information technology, can transmits trusted data in untrusted networks. As a overlay network, blockchain based network can be used to construct low-cost distributed networks. It has the characteristics of non-destructive and transparent transmission. Blockchain based network architecture can be divided into: infrastructure layer, network layer, transport layer and application layer, and it has the potential changing the network security paradigm and the existing Internet operating mode. But as an immature technology, the blockchain network still faces great challenge in information security, privacy protection and communication bandwidth.
Methods to Evaluate Lifecycle Models for Research Data Management

Abstract Lifecycle models for research data are often abstract and simple. This comes at the danger of oversimplifying the complex concepts of research data management. The analyses of 90 different lifecycle models lead to two approaches to assess the quality of these models. While terminological issues make direct comparisons of models hard, an empirical evaluation seems possible.
Dynamic Eyes and Mouth Reinforced LBP Histogram Descriptors Based on Emotion Classification in Video Sequences

In the world of visual technology, classifying emotions from the face image is a challenging task. In the recent surveys, they have focused on capturing the whole facial signatures. But the mouth and eyes are the utmost vital facial components involved in classifying the emotions. This paper proposes an innovative approach to emotion classification using dynamic eyes and mouth signatures with high performance in minimum time. Initially, each eye and mouth image is separated into non-intersecting regions from this video sequences. The regions are further separated into small intersecting sub-regions. Dynamic reinforced local binary pattern signatures are seized from the sub-region of eyes and mouth in the subsequent frames which shows the dynamic changes of eyes and mouth aspects, respectively. In each sub-region, the dynamic eyes and mouth signatures are normalized using Z-score which is further converted into binary form signatures with the help of threshold values. The binary signatures are obtained for each pixel in a region on eyes and mouth computing histogram signatures. Concatenate the histogram signature which is captured from all the regions in the eye and mouth into a single enhanced signature. The discriminative dynamic signatures are categorized into seven emotions utilizing multi-class AdaBoost categorizer algorithm.
Design of AL-13 Block Cipher Algorithm Based On Extended Feistel Network

In this paper, we design a new block cipher algorithm called AL-13. The structure of AL-13 is simple. We use Extended Feistel Network Type II with input-output 128 bits and key input 256 bits. We also use Dragon's key schedule, binary addition, modulus addition, and XOR to give diffusion, and two functions F and N for confusion. We conducted Strict Avalanche Criterion (SAC) tests to determine whether AL-13 satisfy cryptographic measurement parameters.