Reviewerly/exHarmony huggingface dataset:
Mobile photo recommendation system of continuous shots based on aesthetic ranking

Image aesthetics have been a popular topic in the recent years. The users can obtain aesthetic score of any image by using the computational approaches based on photography rules. However, most approaches consider the task as an off-line process because those methods usually require high computational complexity. On the other hand, mobile devices (tablet and smart phone) have become an indispensable tool in our daily life. Many users are more willing to take photos with them instead of digital cameras. Thus, developing practical camera applications (APPs) for mobile devices has high application potential. In this paper, respect to the issue of hardware computation limitation of mobile devices, we first design an instant photo aesthetics evaluation method. It adopts several simple and effective features to predict photo scores. The preliminary results conducted on benchmark datasets verify its performance and efficiency. Based on this achievement, we develop a continuous-shot photo recommendation system. It includes two steps: dominant photo selection performed in partially-decoded compression domain and instant photo aesthetics ranking. The system can automatically list the photos with higher scores, and help users to select desired ones. Crowd sourcing tests are employed to evaluate system performance, which show that our system is effective for instant aesthetic rating and photo recommendation.1
Detection of Blood-Related Diseases Using Deep Neural Nets

Image processing offers medical diagnosis and it overcomes the shortcomings faced by traditional laboratory methods with the help of intelligent algorithms. It is also useful for remote quality control and consultations. As machine learning is stepping into biomedical engineering, there is a huge demand for devices which are intelligent and accurate enough to target the diseases. The platelet count in a blood sample can be done by extrapolating the number of platelets counted in the blood smear. Deep neural nets use multiple layers of filtering and automated feature extraction and detection and can overcome the hurdle of devising complex algorithms to extract features for each type of disease. So, this chapter deals with the usage of deep neural networks for the image classification and platelets count. The method of using deep neural nets has increased the accuracy of detecting the disease and greater efficiency compared to traditional image processing techniques. The method can be further expanded to other forms of diseases which can be detected through blood samples.
A Domain Adaptation Approach for Multistream Classification

In this paper, we formulate cross-domain multistream classification as a domain adaptation problem. Then we propose a novel algorithm that utilizes low-rank representation and graph embedding to preserve data structures, which benefits in dealing with concept drifts and concept revolution. In addition, we deploy MMD metric to minimize the distribution discrepancy between the source data stream and the target data stream. Experiment results on Office+Caltech dataset with DeCAF $$_6$$ features verified the effectiveness of our algorithm.
The “Game Hack” Scam

Game Hack Scam (GHS) is a cyberattack in which the attacker attempts to convince the victim, often a child or a young adult, that they will be provided with free, unlimited resources or other advantages for their favorite game. To obtain these claimed advantages, the victims are asked to complete one or more tasks, called “offers”. These so-called offers include, but are not limited to, subscriptions to questionable services and installation of executable files on the victim’s device. Although recent research has provided important insights into different types of scam such as “Technical Support Scam”, “Survey Scam”, and “Romance Scam”, to the best of our knowledge GHS has not been studied up to now. In this paper, we report the first systematic study of GHS. We use a data-driven approach to investigate and gain knowledge on this type of scam: we formulated GHS-related search queries, and used multiple search engines to collect data about the websites to which GHS victims are directed when they search online for various game hacks and tricks. We analyze the collected data to provide new insight into GHS, and research the extent of this scam. We show that GHS attackers abuse social media, streaming sites, blogs, and even unrelated sites such as change.org or researchgate.net to carry out their attacks and reach a large number of victims. We estimate that these attacks have been clicked close to 60 million times since mid-2014. Our data collection spans over nine months; over the last five months, we uncovered over 3,000 GHS domains and over 100 different offer domains. Furthermore, we find that GHS instances are on the rise and so is the number of victims. Finally, in keeping with similar large-scale scam studies, we find that the current public blacklists are inadequate and suggest that our method is more effective at detecting these attacks.
Shaping the Music Perception of an Automatic Music Composition: An Empirical Approach for Modelling Music Expressiveness

Expressiveness is an important aspect of a music composition. It becomes fundamental in an automatic music composition process, a domain where the Artificial Intelligent Systems have shown great potential and interest. The research presented in this paper describes an empirical approach to give expressiveness to a tonal melody generated by computers, considering both the symbolic music text and the relationships among the sounds of the musical text. The method adapts the musical expressive character to the musical text on the base of the “harmonic function” carried by every single musical chord. The article is intended to demonstrate the effectiveness of the method by applying it to some (tonal) musical pieces written in the 18th and 19th century. Future improvements of the method are discussed briefly at the end of the paper.
Scene Estimation From a Swiped Image

The image blurring that results from moving a camera with the shutter open is normally regarded as undesirable. However, the blurring of the images encapsulates information that can be extracted to recover the light rays present within the scene. Given the correct recovery of the light rays that resulted in a blurred image, it is possible to reconstruct images of the scene from different camera locations. Therefore, rather than resharpening an image with motion blur, the goal of this paper is to recover the information needed to resynthesize images of the scene from different viewpoints. Estimation of the light rays within a scene is achieved by using a layer-based model to represent objects in the scene as layers, and by using an extended level set method to segment the blurred image into planes at different depths. The algorithm described in this paper has been evaluated on real and synthetic images to produce an estimate of the underlying epipolar plane image.
Approximate String Matching of Power System Substation Names

When running simulations, we often use power system cases from different organizations. Each organization has its own naming schemes, which usually result in the same bus being numbered and spelled differently for different cases (e.g., through contractions). While humans can easily identify a contraction, the large number of buses requires an automated method, which is difficult. While research exists on approximate string matching, the application to power system bus names reveals unique challenges. This paper proposes an approximate string matching algorithm for bus names. The algorithm uses an optimally chosen string distance measure to quantify the difference between pairs of bus names from two different cases, and an improved Hungarian algorithm to find the overall minimum distance pairing of buses. Through testing, it was found that using the edit distance produced the best results. Using our algorithm, 76% of the matches in a training case were identified, with 99% accuracy. This is a large improvement over the 32% of exact matches.
An Improved Enhancement Technique for Mammogram Image Analysis: A Fuzzy Rule-Based Approach of Contrast Enhancement

This paper presents a new and automated image enhancement technique for mammogram images. The new enhancement technique is proposed where it has adopted the fuzzy rule-based approach to improve the contrast of the image while details are preserved and existing noises are not amplified. A new fuzzy contrast parameter is executed to distinguish the regions to rule bases that are specifically enhanced the distinguished regions. The performance of the proposed technique is compared with other state-of-the art enhancement techniques in terms of qualitative appearance as well as quantitative measurements. The proposed technique attains good results and outperforms the other techniques. The enhanced image appears brighter and appearance of significant feature of microcalcification is improved.
OCTVis: Ontology-Based Comparison of Topic Models

Evaluating topic modeling results requires communication between domain and NLP experts. OCTVis is a visual interface to compare the quality of two topic models when mapped against a domain ontology. Its design is based on detailed data and task models, and was tested in a case study in the healthcare domain.
Self-Organizing Mappings on the Flag Manifold

A flag is a nested sequence of vector spaces. The type of the flag is determined by the sequence of dimensions of the vector spaces making up the flag. A flag manifold is a manifold whose points parameterize all flags of a particular type in a fixed vector space. This paper provides the mathematical framework necessary for implementing self-organizing mappings on flag manifolds. Flags arise implicitly in many data analysis techniques for instance in wavelet, Fourier, and singular value decompositions. The proposed geometric framework in this paper enables the computation of distances between flags, the computation of geodesics between flags, and the ability to move one flag a prescribed distance in the direction of another flag. Using these operations as building blocks, we implement the SOM algorithm on a flag manifold. The basic algorithm is applied to the problem of parameterizing a set of flags of a fixed type.
DWT-Based Data Hiding Technique for Videos Ownership Protection

This chapter proposes a wavelet data hiding scheme for video authentication and ownership protection. A watermark in the shape of a logo image will be hidden. In this research, a discrete wavelet transform (DWT) process is implemented using orthonormal filter banks, where the Y components of the YUV color space of the video frames are decomposed using DWT, and a watermark is inserted in one or more of the resulting sub-bands in a way that is fully controlled by the owner. Then, the watermarked video is reconstructed. The filters used for the DWT decompositions are randomly generated to increase the security of the algorithm. An enhanced detection technique is developed to increase the reliability of the system. The overall robustness of this scheme is measured when common attacks are applied to the test videos. Moreover, the proposed algorithm is used with the high-efficiency video coding (HEVC) technique to examine the whole performance. Furthermore, a selective denoising filter is built to eliminate the effect of the noise. The simulation results show that the proposed algorithm achieves well under both the visual and the metric tests. Moreover, it performed well against intentional and unintentional attacks. The average normalized correlation achieved is 97%, while the mean peak signal-to-noise ratio (PSNR) is 45 dB.
Two-Input Gegenbauer Orthogonal Neural Network with Growing-and-Pruning Weights and Structure Determination

Based on the probability theory and polynomial interpolation and approximation theory, a two-input Gegenbauer orthogonal neural network (TIGONN) is investigated and constructed in this paper. In order to avoid inherent problems of back-propagation (BP) training algorithm, a weights-direct-determination (WDD) is applied to calculate the optimal connecting weights of TIGONN proposed. Then, based on WDD, a growing and pruning weights and structure determination (GPWSD) is developed to determine the optimal connecting weights and optimal number of neurons in hidden layer, by combining growing weights and structure determination (GWSD) and post-pruning scheme. Furthermore, numerical verifications are also conducted to substantiate the superiority and efficacy of TIGONN with GPWSD in terms of approximation and denoising.
Multi-scale Local Receptive Field Based Online Sequential Extreme Learning Machine for Material Classification

Surface material classification has attracted a lot of attention from the academic and industrial communities. The surface material classification methods are for static object material data. However, in real industrial production, data cannot be generated overnight. It is generated continuously. In this work, we propose an algorithm named Multi-Scale Local Receptive Field Based Online Sequential Extreme Learning Machine (MSLRF-OSELM) for material classification, which not only can make dynamic training of networks by using data that are generated online of material images, but also can extract highly representative features from complex texture by multi-scale local receptive field. We conduct experiments on the public texture ALOT dataset and MNIST dataset. Experimental results verify the effectiveness of our algorithm and has good generalization performance.
Area‐efficient and high‐speed hardware structure of hybrid cryptosystem (AES‐RC4) for maximizing key lifetime using parallel subpipeline architecture

Summary With the growth of wireless environments, confidential communication has become an important part of daily life. Generally, a wireless medium uses cryptography techniques when transmitting data to provide end‐to‐end protection. Most of the hardware‐efficient cryptosystems do not satisfy the security requirements. In this paper, we concentrate both on security issues and hardware efficiency and present an area‐efficient, high‐throughput hardware structure to implement a hybrid cryptosystem to avoid security problems. The security enhancements are done using a key enhancement process, and it is achieved by using a hybrid encryption cryptosystem. In this paper, we combine block and stream cipher encryption algorithms to frame the hybrid algorithm. We consider advanced encryption standard (AES) as the block cipher and Rivest cipher‐4 (RC4) as the stream cipher. Due to the hybrid systems, performance metrics, such as area, throughput, and power consumptions, are affected. Moreover, advanced arithmetic logic (ie, field arithmetic) combined with the on‐the‐fly key expansion technique is used to minimize area consumption, and parallel subpipeline technique is used to enhance the system throughput. The proposed hybrid architecture (AES‐RC4) is implemented in a field‐programmable gate array with a Xilinx tool.
Active Learning for Uneven Noisy Labeled Data in Mention-Level Relation Extraction

Mention-level relation extraction (mRE) plays an important role in extracting relational information from short texts such as those exchanged in a social network.Deep learning (DL) has made remarkable achievements; the main problem encountered with DL in mRE is a lack of training samples.In this paper, we present a design for a quick sample-marking method.First, we construct an uneven noisy labeled data (UNLD) set using a pattern matching algorithm, and then a relabeling framework is put forward for modifying the UNLD.With regard to the accuracy, the recall rates of categories with sufficient samples increased from 0.4 to nearly 1 using the relabeling framework.We have released our code and other resources for further research (https://github.com/curtainsky/UNLD).
Multi-device Adaptation with Liquid Media Queries

The design of responsive Web applications is traditionally based on the assumption that they run on a single client at a time. Thanks to CSS3 media queries, developers can declaratively specify how the Web application UI adapts to the capabilities of specific devices. As users own more and more devices and they attempt to use them to run Web applications in parallel, we propose to extend CSS media queries so that they can be used to adapt the UI of liquid Web applications while they are dynamically deployed across multiple devices. In this paper we present our extension of CSS media queries with liquid-related types and features, allowing to detect the number of devices connected, the number of users running the application, or the role played by each device. The liquid media query types and features defined in this paper are designed and suitable for liquid component-based Web architectures, and they enable developers to control the deployment of individual Web components across multiple browsers. Furthermore we show the design of liquid media queries in the Liquid.js for Polymer framework and propose different adaptation algorithms. Finally we showcase the expressiveness of the liquid media queries to support real-world examples and evaluate the algorithmic complexity of our approach.
Autoencoders Covering Space as a Life-Long Classifier

A life-long classifier that learns incrementally has many challenges such as concept drift, when the class changes in time, and catastrophic forgetting when the earlier learned knowledge is lost. Many successful connectionist solutions are based on an idea that new data are learned only in a part of a network that is relevant to the new data. We leverage this idea and propose a novel method for learning an ensemble of specialized autoencoders. We interpret autoencoders as manifolds that can be trained to contain or exclude given points from the input space. This manifold manipulation allows us to implement a classifier that can suppress catastrophic forgetting and adapt to concept drift. The proposed algorithm is evaluated on an incremental version of the XOR problem and on an incremental version of the MNIST classification where we achieved 0.9 accuracy which is a significant improvement over the previously published results.
La investigación en Bibliotecología y Ciencias de la Información en el Perú: un estado del arte

Objective: To present the current view of the research situation in Librarianship and Information Sciences in Peru taking into account the two universities where the information sciences career is taught. Design/Methodology/Approach: A review was made of the documents that have been published in Peru on research in Librarianship and Information Sciences, as well as on the research-related subjects offered at the two universities that teach the degree. Interviews were also conducted with six professors and researchers of the specialty. Results/Discussion: The research-oriented courses, subjects and methods used, graduation system and its relationship with research, professors, research lines and publications in Librarianship and Information Sciences are presented. Conclusions: It is concluded that the conditions for research have notoriously improved and that professors are involved in research activities more frequently and follow the standards currently required. However, there are still difficulties to be faced and suggestions are offered to help overcome them. Originality/Value: The study is a first approach to the current environment of research in Libraries and Information Sciences in Peru, whose new regulatory framework, added to stimuli and renewed study plans offer better conditions for the advancement of research issues.
Opinion Mining to Detect Irony in Twitter Messages in Spanish

Companies, among other sectors, require that the opinions generated on the web be extracted automatically, obtaining their polarity on products or services, to achieve their objectives. Since the opinions are subjective and unstructured, there are still many problems within this field that must be solved. To mention a few, the problem of ambiguity and the support of languages, directly affect in the time to make the right classification of opinions, because most of the tools used in the processing of texts, they only work well with data in English. With the aim of contributing to the solution of both problems and evaluating the real behavior of sentiment analysis for the Spanish language, a system is proposed that allows determining the positive or negative polarity, trying to detect the irony as a problem of ambiguity. For the classification, a supervised learning method was implemented, with the Naive Bayes algorithm. The evaluation of the results of the classification shows that the problem of detecting ironies in Spanish, using the classical techniques of opinion mining, is not completely resolved. However, we believe that these results can be improved by applying some strategies.
On the Web Platform Cornucopia

The evolution of the Web browser has been organic, with new features introduced on a pragmatic basis rather than following a clear rational design. This evolution has resulted in a cornucopia of overlapping features and redundant choices for developing Web applications. These choices include multiple architecture and rendering models, different communication primitives and protocols, and a variety of local storage mechanisms. In this position paper we examine the underlying reasons for this historic evolution. We argue that without a sound engineering approach and some fundamental rethinking there will be a growing risk that the Web may no longer be a viable, open software platform in the long run.
A Rough Sketch of the Freehand Drawing Process

Dynamic elements of the drawing process (e.g., order of compilation, speed, length, and pressure of strokes) are considered important because they can reveal the technique, process, and emotions of the artist. To explore how sensing, visualizing, and sharing these aspects of the creative process might shape art making and art viewing experiences, we designed a research probe which unobtrusively tracks and visualizes the movement and pressure of the artist's pencil on an easel. Using our probe, we conducted studies with artists and art viewers, which reveal digital and physical representations of creative process as a means of reflecting on a multitude of factors about the finished artwork, including technique, style, and the emotions of the artists. We conclude by discussing future directions for HCI systems that sense and visualize aspects of the creative process in digitally-mediated arts, as well as the social considerations of sharing and curating intimate process information.
Embedded Zero-Tree Wavelet Coding with Selective Decomposition Bands

Wavelets and its applications became widespread with the inception of embedded zerotree wavelet coding since past two decades. The wavelet transform decomposes any signal into different decomposition bands. As the level of decomposition progresses, the bands are further decomposed. But, the low frequency bands carry significant role in reconstruction. When a wavelet based coding scheme is to be applied, all the bands are being considered, which raises memory requirement on the encoded or compressed data. In this paper, EZW coding is exploited in depth to identify the ways of reducing number of symbols generated by which the Compression Ratio(CR) will be improved without reducing much of the quality. Now the proposal of the paper is that, as HH band constitutes a minor role in reconstruction, the band is supposed to be ignored, making all the coefficients in that band insignificant expecting a significant improvement in compression ratio and PSNR. Results show better PSNR and CR.