Reviewerly/exHarmony huggingface dataset:
Improving Super Resolution Methods Via Incremental Residual Learning

Recently, Convolutional Neural Networks (CNNs) have shown promising performance in super-resolution (SR). However, these methods operate primarily on Low Resolution (LR) inputs for memory efficiency but this limits, as we demonstrate, their ability to (i) model high frequency information; and (ii) smoothly translate from LR to High Resolution (HR) space. To this end, we propose a novel Incremental Residual Learning (IRL) framework to address these mentioned issues. In IRL, first we select a typical SR pre-trained network as a master branch. Next we sequentially train and add residual branches to the main branch, where each residual branch is learned to model accumulated residuals of all previous branches. We plug state of the art methods in IRL framework and demonstrate consistent performance improvement on public benchmark datasets to set a new state of the art for SR at only ≈ 20% increase in training time.
Zero-Shot Detection with Transferable Object Proposal Mechanism

Zero-shot detection (ZSD) aims to locate and recognize unseen objects in pictures or videos to address the shortage of labeled training data. While most existing ZSD methods lay their emphasis on object classification, challenges lie in both object proposal and category prediction for detectors to get over domain shift. In this paper, we first design an experiment to verify the impact of transfer ability of the object proposal step on detection recall and further introduce a transferable mechanism to relate the co-occurrence among categories. We use a confidence distribution over all the classes for object confidence prediction. Experimental results show our method outperforms other zero-shot detectors on PASCAL VOC and MSCOCO datasets, even with a simple linear approach for classification.
Building multimedia repository for composing images perspective

Multimedia repository is helpful for educational activities since it offers several illustrations that facilitate the learning process and the text understanding. In this paper, we propose to build a multimedia repository from collected images using object extraction techniques. Then, we associate Arabic captions to all extracted objects. These extracted objects are used to compose new scenes that could illustrate efficiently the most important events in an Arabic story. We extend, thereby the notion of image composing to our approach as the task of constructing new images based on a set of toolboxes and a set of extracted objects as our multimedia repository of common animal behaviors. Our preliminary results show that the composed scenes using single objects provided a fair understanding of the main events of the stories as well as a coherent visual layout of all single objects. The diversity and the precision of the single objects images for the domain of animals have shown a great impact on composing new scenes either manually or dynamically.
Traffic Light Detection and Recognition Using Image Processing and Convolution Neural Networks

Automatic traffic light detection and mapping is an open research problem. In this paper, a method for detecting the position and recognizing the state of the traffic lights in video sequences is presented and evaluated using LISA Traffic Light Dataset which contains annotated traffic light video data. The first stage is the detection, which is accomplished through image processing technics such as image cropping, Gaussian low-pass filtering, color transformation, segmentation, morphological dilation, Canny edge detection, and Circle Hough transform to estimate the position and radius of possible traffic lights. The second stage is the recognition, whose purpose is to identify the color of the traffic light and is accomplished through deep learning, using a Convolutional Neural Network. Day and night images were used in both training and evaluation, giving excellent location rates in all conditions.
RIT2FIS: A Recurrent Interval Type 2 Fuzzy Inference System and its Rule Base Estimation

Two of the major challenges associated with time series modelling are handling uncertainty present in the data and tracing its dynamical behaviour. A Recurrent Interval Type 2 Fuzzy Inference System or RIT2FIS is presented in this paper. RIT2FIS adopts an interval type 2 fuzzy inference mechanism for superior handling of uncertainty. The memory neurons employed in its hidden and output layer, retain the temporal information, making RIT2FIS highly proficient in tracing system dynamics at a granular level. RIT2FIS also benefits from incorporating a k-means algorithm inspired approach to cluster the data in an unsupervised manner. An 'Elbow Method' is utilized next to determine the optimal clustering which is then employed as the optimal fuzzy rule base for RIT2FIS, eliminating the necessity of expert knowledge for fuzzy initiation. The antecedent and consequent parameters of RIT2FIS are updated using a gradient descent based back-propagation through time algorithm where the learning is made self-regulatory to avoid over-fitting and ensure generalization. Performance of RIT2FIS is evaluated against popular neuro-fuzzy methods on different benchmark and real-world time series problems which distinctly indicates an improved accuracy and a parsimonious rule base.
Mask Captioning Network

Nowadays, attention mechanisms have been widely adopted in image captioning task due to its outstanding performance. In this paper, we propose a Mask Captioning Network (MaC) that consists of an object layer and a background layer to capture the objects and scene of an image, independently to generate a much richer sentence. To this end, we leverage on Mask RCNN to detect the salient regions in pixel level in the object layer; while, in the background layer, a CNN model is used to encode the scene features. Experimental results show that our model significantly outperforms baseline models and achieves comparable results with the state-of-the-art methods on MSCOCO and Flickr30k datasets.
Research on the construction of quantum induction universe network

Summary The universe is filled with electromagnetic fields, quantum fields, and the other different field. They penetrate and interact with each other. Just as the interaction between magnetic materials are due to the magnetic field around the magnet contacting another magnet, between the quanta can also interact with each other through magnetic fields. Through the study of quantum mechanics and related theories of brain science, the article puts forward the following viewpoints: between quantum, which has entanglement state, cannot only interact through magnetic fields but also generate quantum induction fields to generate quantum induction waves. The entangled quantum generated spin waves are connected to each other based on quantum inductive waves to form a quantum inductive network. Then, the information is transmitted through resonance to realize the communication between all things. Thus, all things in the universe can be interconnected based on quantum entanglement effects. By analyzing the construction mode of the internet network, the paper proposes the architecture of the quantum induction communication system and the basic topology of a quantum induction internet network. Several thought experiment schemes were designed, and the information transmission between the ordinary objects and between the people and ordinary objects is studied as an example. The analysis points out the main problems faced by the construction of the quantum induction internet. It points out that the significance of constructing of the network of All things interconnect based on quantum entanglement effect is that all living bodies and nonliving bodies in the heaven and earth can be connected and communicate with each other.
Integration of large-scale data processing systems and traditional parallel database technology

In 2009 we explored the feasibility of building a hybrid SQL data analysis system that takes the best features from two competing technologies: large-scale data processing systems (such as Google MapReduce and Apache Hadoop) and parallel database management systems (such as Greenplum and Vertica). We built a prototype, HadoopDB, and demonstrated that it can deliver the high SQL query performance and efficiency of parallel database management systems while still providing the scalability, fault tolerance, and flexibility of large-scale data processing systems. Subsequently, HadoopDB grew into a commercial product, Hadapt, whose technology was eventually acquired by Teradata. In this paper, we provide an overview of HadoopDB's original design, and its evolution during the subsequent ten years of research and development effort. We describe how the project innovated both in the research lab, and as a commercial product at Hadapt and Teradata. We then discuss the current vibrant ecosystem of software projects (most of which are open source) that continued HadoopDB's legacy of implementing a systems level integration of large-scale data processing systems and parallel database technology.
2D Qubit Placement of Quantum Circuits Using LONGPATH

In order to achieveGhosh, Mrityunjay speedup over conventional classical computing forDey, Nivedita finding solution of computationally hard problems, quantumMitra, Debdeep computing was introduced. Quantum algorithms can beChakrabarti, Amlan simulated in a pseudo quantum environment, but implementation involves realization of quantum circuits through physical synthesis of quantum gates. This requires decomposition of complex quantum gates into a cascade of simple one-qubit and two-qubit gates. The methodological framework for physical synthesis imposes a constraint regarding placement of operands (qubits) and operators. If physical qubits can be placed on a grid, where each node of the grid represents a qubit, then quantum gates can only be operated on adjacent qubits, otherwise SWAP gates must be inserted to convert nonlinear nearest neighbour architecture to linear nearest neighbour architecture. Insertion of SWAP gates should be made optimal to reduce cumulative cost of physical implementation. A schedule layout generation is required for placement and routing a priori to actual implementation. In this paper, two algorithms are proposed to optimize the number of SWAP gates in any arbitrary quantum circuit. The first algorithm is intended to start with generation of an interaction graph followed by finding the longest path starting from the node with maximum degree. The second algorithm optimizes the number of SWAP gates between any pair of non-neighbouring qubits. Our proposed approach has a significant reduction in number of SWAP gates in 1D and 2D NTC architecture.
Ultrasonic image processing based on fusion super-resolution reconstruction of familiar models

Ultrasound image technology is to measure the energy and time of arrival of the reflected echo after the pulse acoustic signal is sent out by the ultrasonic wave. Usually, the distance between the ultrasonic source and the reflector is measured. Super-resolution image reconstruction aims to recover high-resolution images from one or more low-resolution images. Super-resolution image reconstruction is a software approach to solve the problem of low-resolution images by overcoming the limitations of hardware. In this paper, an image super-resolution reconstruction method based on sparse representation model and multi-feature fusion is proposed. Sparse dictionary is used to learn and reconstruct the luminance details of ultrasonic images, and edge interpolation is used to improve the edge clarity. Experimental results show that the proposed method is superior to Bicubic interpolation, SCSR and JOR in PSNR, and is 0.35 dB higher than RAISR. On the FSIM index, this method is also slightly better than other comparison methods, which can get better reconstruction results. Visual effects and numerical evaluation results of reconstructed images are better than several comparison methods.
Let them fail

An important purpose of managed runtime environments like virtual machines is to provide built-in behavior to their programs, that is, behavior that cannot or may not be expressed in the program's language and is implemented inside the VM. For performance, stability, and security, such built-in behavior is typically rigorous, and if it deems its invocation to be erroneous, ill-suited, or just not quite right, it communicates this failure via exceptions at best. This implies that a vm's sphere of influence extends rather far into the realm of the program executed: at best, exceptions can be reacted to when built-in behavior fails, even though a program might be able to provide alternatives, improvements, or other ways of handling a failure.
A User Profile Based Pension Service Recommendation Algorithm

Recommending suitable service for elderly customers has become an important issue of modern pension service industry. In this paper, we proposed a user profile based recommendation algorithm, which consider the users' short-term behavior in pension service system such as browsing, searching, and long-term objective conditions (health status, living address, and economic condition) together to make accurate prediction. We also improved L-BFGS algorithm to enhance the convergence speed of algorithm. The extensive experiments on the real data set show that the proposed method improves the efficiency of the algorithm and the accuracy of recommendation.
Ontology Based Software Design Documentation For Design Reasoning

Designing a quality software product adhering to all the functional and non-functional requirements is a challenging task in software architecture designing. Selecting the best designs to apply in the project will be included in the design reasoning. The discussion on the selections are important, but it dies when the discussion ends. The reasoning is important to be documented for maintenance purpose and to accommodate any changes that would result in architectural evolution. There are tools, which have been proposed for this purpose, but, using an ontological approach with implementation and evaluation has not been conducted. Hence an ontology-based approach has been chosen for documenting the software architecture design reasoning under this research. As software designing is a vast area of design decisions, the research was narrowed down to the RESTful web service domain. First, an ontology was created, capturing the key terms and the architectural elements of the domain. A tool was created using this ontology to generate the design reasoning for a given software document. Three techniques were used when creating the tool, which comprised of a key term matching, deriving elements based on parts of speech tagging, and ontology reasoning. The reasoning captured from these techniques will be documented in a user-friendly manner. A prototype of this approach was developed and evaluated to prove its usability and accuracy. The overall precision of 0.58 was calculated with the use of the prototype application developed.
Augmentations in the Palace of Culture: Animate Soundscapes at the Carnegie Library of Pittsburgh

Collectively referred to as the Palace of Culture, the Carnegie Library and Museums of Pittsburgh, PA are among the United States' oldest philanthropic cultural institutions, housing some of the most significant and varied public collections endowed by industrialist, Andrew Carnegie [1]. The institution's diverse cultural offerings include Carnegie Museum of Art, Carnegie Music Hall, Carnegie Museum of Natural History, and a renowned public library. Nearly 125 years after the institution's founding in 1895, the Carnegie Public Library of Pittsburgh has endured generations of transformation. Specifically the library, as an architectural site for learning, has evolved its facilities and collections in response to periods of war, local socio-economic changes, and global shifts in industry and technology. Despite such transformation, the library remains steadfastly adjacent to the Carnegie Museum of Art, the Museum of Natural History, and the Carnegie Music Hall, committed to offering full service cultural experiences to the public, free of charge. As the library expands its collections to reach a digitally native public with expanded offerings, including electronic music instruments and digital media collections, we ask how user experience might be specifically impacted by animated virtual media augmentations that bridge the library's historic and new media collections.
SDANet: A stacked dense adaptive network for real-time and accurate super resolution

Recent advances in single-image super-resolution are mostly based on convolutional neural networks. However, the high computational cost is a critical obstacle to achieve real-time performance(24 fps). In this paper, we proposed the Stacked Dense Adaptive Network (SDANet) to reduce the computational cost and improve the computational density of CPUs/GPUs. Especially, SDANet consists of (1) a dense adaptive learning (DAL) module, which harnesses adaptive gate units (AGU) to retain the high-frequency information in the prior layers. (2) a stacked bottleneck (STB) module, that stacks the information of all previous layers to improve the computational density and reduce the computation dramatically. (3) a spatial-channel transform (SCT) module, which transforms bilinear spatial information to channels to improve the computational density via parallel computing. With the proposed three modules, our algorithm takes full advantage of the high-frequency information of all the layers, resulting in reducing the computational complexity dramatically. Extensive experiments verify the necessity of the SDANet, not only yielding the state-of-the-art results with a considerable margin on five benchmark datasets in terms of PSNR and visual quality but also achieving real-time performance( > 24 fps) with a generic CPU.
Synergistic graph and SQL analytics inside IBM Db2

To meet the challenge of analyzing rapidly growing graph and network data created by modern applications, a large number of specialized graph databases have emerged, such as Neo4j, JanusGraph, and Sqlg. At the same time, RDBMSs and SQL continue to support mission-critical business analytics. However, real-life analytical applications seldom contain only one type of analytics. They are often made of heterogeneous workloads, including SQL, machine learning, graph, and other analytics. In particular, SQL and graph analytics are usually accompanied together in one analytical workload. This means that graph and SQL analytics need to be synergistic with each other. Unfortunately, most existing graph databases are standalone and cannot easily integrate with relational databases. In addition, as a matter of fact, many graph data (data about relationships between objects or people) are already prevalent in relational databases, although they are not explicitly stored as graphs. Performing graph analytics on these relational graph data today requires exporting large amount of data to the specialized graph databases. A natural question arises: can SQL and graph analytics be performed synergistically in a same system? In this demo, we present such a working system called IBM Db2 Graph. Db2 Graph is an in-DBMS graph query approach. It is implemented as a layer inside an experimental IBM Db2™, and thus can support synergistic graph and SQL analytics efficiently. Db2 Graph employs a graph overlay approach to expose a graph view of the relational data. This approach flexibly retrofits graph queries to existing graph data stored in relational tables. We use an example scenario on health insurance claim analysis to demonstrate how Db2 Graph is used to support synergistic graph and SQL analytics inside Db2.
Analysis of Metacrawler approach for URL based DUST removal by knowledge engineering systems

Multiple copies of URLs gathered by the web crawlers responsible for pages with similar or near about to similar content. Few pages combined by the web crawlers consist of similar content. Different URLs with Similar Text are generally known as DUST. Result of this is crawl data, to store the data and use such duplicated data results in building of less quality marking, waste of resources and destitute naive user experiences.To study with such problem, multiple studies have been observed. Previous studies focus only on URL based DUST removal .The proposed method removes content depended DUST and URL based DUST. To crawl the documents we are using a new method of metacrawler which fetches results from three Search engine. We are going to compare each website content with the other linked content to remove duplicates using k- gram paraphrased technique.
“Madonna of Demidovs” and “Madonna of Stroganovs”: to the Question of the Sociocultural Space Forming

This work demonstrates results of study of museum and exhibition activity, which forming modern sociоcultural space at the examples of Renaissance and Mannerism art works from the Stroganovs and the Demidovs collections. The members of these families of Ural and Siberian industrialists, patrons of arts and philanthropists possessed the richest collections of world-class art works. Authors pay attention on such work as “Madonna del Popolo”, also known as “The Holy Family”. Now this masterpiece is stored in the Nizhny Tagil Municipal Museum of Fine Arts. Academician Igor Grabar, who saved it from destroy, considered that this artwork belongs to the authorship of Raphael Santi. The second masterpiece “The Holy Family with Infant Saint John the Baptist” by Agnolo Bronzino now is in the Pushkin State Art Museum (Moscow). Each painter in its own way have revealed the images of the Holy Family, especially Madonna and Infant Christ. Raphael did it in the traditions of the High Renaissance and Bronzino followed the best achievements of Mannerism. These paintings are especially important and valuable because Italian painter, architect and writer Giorgio Vasari, known as the founder of art history as branch of science, have paid his attention on them. The researchers give characteristics to members of the Stroganovs and the Demidovs families on the context of the stated problem. The President of the Academy of Arts and the Director of the Imperial Public Library, the Count A. S. Stroganov bought for his collection the “Holy Family with Infant Saint John the Baptist” by Agnolo Bronzino. The owner of Nizhny Tagil factories N. N. Demidov, who also was a Russian envoy to the Duchy Tuscany (Florence), is connected with inclusion of “Madonna” by Raphael in his art collection. In the study the materials of Grabar’s monograph are analyzed, the results of author’s own scientific research and the detailed section on the Demidovs’ Madonna attribution are given. There are used cultural research and art criticism methods of analysis, for instance, with attraction of “Madonna Doni” by Michelangelo. The acquisition and transfer to Russia of this unique incarnations of the “Holy family” enriched the historical and cultural space not only of the owners and people close to the families, but also of the wider social circles, because these paintings were becoming an integral part of museum and exhibition activities. As a modern example authors analyze the exhibition “Madonna by Raphael from Nizhny Tagil”, where the samples from the collection of the Demidovs, Ural and Sibirian magnates, in the Hall of Arts of the South Ural State University were presented. It is established that, using them, the university academic exhibition has fulfilled the important task of the complex formation of cultural identity of students in the interaction of its regional, national and supranational (universal) aspects