Reviewerly/exHarmony huggingface dataset:
An Efficient Convolutional Neural Network With Attached Accelerating Strategy

With the development of convolution neural network, people invented a deeper network and gained higher accuracy. But with the increasing demand in mobile technology of this field, how to save the computing resource consumption, enhance the training speed has become an important question. In this paper, based on some of the classic acceleration strategy, we established a new multi-branch network called FRINet, the model lowers computing power of the hardware requirements and enhancing training efficiency. By experiments on ISIC dataset, as compared to no acceleration strategy model InceptionV3, we achieve 2.3 times speedup in training speed, while the loss of the correct rate is only 1.7%.
InverseNet: Solving Inverse Problems of Multimedia Data with Splitting Networks

We propose a novel network architecture, namely InverseNet, to solve the inverse problems of multimedia data. The inverse problem is cast in the form of learning an end-to-end mapping from observed multimedia data to the ground-truth data. Inspired by the splitting strategy to tackle inverse problems, the mapping is learned by InverseNet, a composition of two networks, with one handling the inversion of the physical forward model and the other handling the denoising of the output from the former network. Training InverseNet is annealing as the intermediate variable between these two networks bridges the gap between the input and output and progressively approaches to the ground-truth. Extensive experiments on synthetic and real multimedia datasets on the tasks, e.g., motion deblurring, super-resolution, and colorization, demonstrate the efficiency and accuracy of the proposed method compared with other image processing algorithms.
&lt;b&gt;An Analytical Model of Relation between Browsing and Entry Activities on an Internet Portal Site for Job-hunting&lt;/b&gt;

In recent years, many companies conducting recruitment activities and many students looking for a job use internet portal sites for job-hunting in Japan. Companies can post their basic information on individual company pages and recruit applications from students. On the other hand, student users can gauge corporate attractiveness by browsing individual company pages on a job-hunting site and can make entries to companies of interest. Therefore, a large amount of their behavior history data is accumulated on the site. There are several studies on prediction of users' entries to companies and analysis of preference using user attribute information and entry history data. However, in the conventional researches, browsing activities on individual company pages existing in the background of the user's entry were not considered, so the relation between browsing and making entries has not been studied. This research proposes a latent class model for analyzing the relation between browsing company pages and making entries to companies. The proposed model enables clarification of target users and consideration of effective promotion activities. Through a demonstrative analysis using actual data on a major job-hunting website in Japan, we show the effectiveness of the proposed model.
Analyzing Trajectories Using a Path-based API

Large vehicle trajectory data sets can give detailed insight into traffic and congestion that is useful for routing as well as transportation planning. Making information from such data sets available to more users can enable applications that reduce travel time and fuel consumption. However, extracting such information efficiently requires deep knowledge of the underlying schema and indexing methods. To enable more users to extract information from trajectory data, we have developed an API that removes the need to be familiar with the schema. Furthermore, when giving access to trajectory data, privacy concerns often call for the application of anonymization methods before analysis results are made available. In our demonstration, owners of trajectory data are able to experiment with different levels of anonymization to see how this affects the quality of different types of trajectory analysis services implemented on top of a large trajectory data set.
Reducing the execution time of unit tests of smart contracts in blockchain platforms

Smart Contracts are software code that resides within a blockchain, using its infrastructure as an advantage and guarantee of execution. Blockchain and smart contracts are enabling new business models and standards to information systems. However, a smart contract needs to be well tested before to be published in a blockchain, since it cannot be changed after being deployed. The execution time to deploy smart contracts and run their tests is considerable because all transactions must be mined before being added to a new block. This work proposes an approach to reuse the execution of the deployment and the setup of unit test in smart contracts to reduce the execution time of these tests. Experiments have shown a large reduction in the execution time of smart contract unit tests, without breaking the principle of test independency.
Watch Out for that Tree! A Tutorial on Shortcut Deforestation

Functional programmers are strong enthusiasts of modular solutions to programming problems. Since software characteristics such as readability or maintainability are often directly proportional to modularity, this programming style naturally contributes to the beauty of functional programs. Unfortunately, in return of this beauty we often sacrifice efficiency: modular programs rely, at runtime, on the creation, use and elimination of intermediate data structures to connect its components. In this tutorial paper, we study an advanced technique that attempts to retain the best of this two worlds: (i) it allows programmers to implement beautiful, modular programs (ii) it shows how to transform such programs, in a way that can be incorporated in a compiler, into programs that do not construct any intermediate structure.
Finite algebras of Bernoulli distributions

Abstract The paper is concerned with sets of Bernoulli distributions which are closed under substitutions of independent random variables into Boolean functions from a given set (an algebra of Bernoulli distributions). A description of all finite algebras of Bernoulli distributions is given.
A Dataset for Evaluating Pedestrian Event Classification Algorithms

This paper proposes a pedestrial video dataset, called the MAD pedestrian, for evaluating performance of predestrial event classification algorithms. By our preliminary experiments, “location of event” and “distance from camera position to the event” are two major obstacles for high accuracy. Therefore, we intend to create a dataset which can be used to test on both issues. The dataset composed of 829 clips of four pedestrial events which are approximately nine hours long. Pedestrian moving path detection algorithm is proposed. Moving paths of all objects in the scenes are available to be referred as the ground truth.
Intelligent Assessment of 95598 Speech Transcription Text Quality Based on Topic Model

Abstract The quality of speech transcripts is of great significance to power system management and is an important basis for supporting subsequent data analysis. In this paper, based on the characteristics of speech transcription texts of customer service, this paper proposes an analysis method combining manual processing and latent Dirichlet allocation topic model, analyzing the transcribed texts. First, data preprocessing is performed on the State Grid’s work order data, and then the text topic distribution calculation is performed by the LDA topic model, and the topic parameter is set to a total of 100 topics. Next, the unsupervised clustering of the documents is performed by the k-means method, and the similarity between the files is obtained. Finally, the quality of the data is analyzed by combining manual labeling and manual evaluation. For the first time, this paper marks and identifies the State Grid’s work order analysis data, which is a pioneering work for natural language processing technology in the field of power grid.
Neuromorphic Photonics with Laser Dynamics

We report on the activation, inhibition and propagation of controllable neuron-like spiking signals at sub-nanosecond speeds (>7 orders of magnitude faster than neurons) in artificial optical neurons based upon Vertical-Cavity Surface-Emitting Lasers. These results offer great prospects for future ultrafast photon-enabled neuromorphic computing platforms.
Unsupervised Learning of Visual Odometry with Depth Warp Constraints

Abstract Visual Odometry (VO) is one of the important components of Visual SLAM system. Some impressive work on the end-to-end deep neural networks for 6-DoF VO has appeared. We propose two-part cascade network structure to learn depth from binocular image and to infer ego-motion from consecutive frames. We propose depth warp constraints to make the Network learning more geometrically information. A lot of experiments on KITTI data set show that our model is superior to previous unsupervised methods and has comparable results with the supervised method, verifying that such a depth warp constraints perform successfully in the unsupervised deep method which is an important complement to the geometric method.
A stereo matching network with a cascade spatial pyramid pooling (CSPP) substructure

We propose a novel end-to-end supervised convolutional neural network(CNN) to compute disparity from a pair of stereo images. To solve the current problem of computing the high-quality disparity in ill-areas, our cascade spatial pyramid pooling (CSPP) substructure is able to gather global context information by aggregating the context information in different positions and different feature block scales from coarse to fine. We also introduce a warp layer, the right feature map is warped with the previously predicted disparity, and then is compared with the left feature map to form a cost volume. We learn the disparity from the cost volume with different level features information. We evaluate our method on three stereo datasets, and results show our method has advantages in textured areas, target edge areas and efficiency. We also achieve a high ranking performance.
Management elements of conception and development of scientific research projects

Abstract The concept of a scientific research project offer must be based on the analysis of the four types of resources: the human resources, the financial resources, the material resources and the informational resources. We note the importance of the human resource quality. In the following figure we present the main elements of the design algorithm of a research project. The feasibility study (otherwise very complex) is of great importance for the success and viability of the project. We have developed the algorithm for designing a scientific research project. Thus, we have devised several items, in a non-fortuitous order: formulation of the theme, resources analysis, feasibility study, offer formulation, project drawing, project implementation, project evaluation, dissemination. We have critically analyzed several definitions of a scientific research project. For example, “a set of investments and other planned activities to achieve specific objectives within a predetermined time frame and budget”, or, “a planned action that is a set of interrelated and coordinated activities to achieve specific objectives within a given budget and a period of determined time”. We also analyze the key functions of a scientific research project: specifying the issue to be addressed, specifying the objectives to be achieved, specifying the activities to be undertaken to achieve the objectives, specifying the revenue (or resources) needed for the deployment activities, identifying and specifying the responsibilities of the different organizations involved in implementation of the project, allowing technical and administrative reviews of the project, the basis for monitoring and evaluating the project that can be done either during the project’s implementation, or after the project has been completed. We have formulated in the article specific concepts on: “a development program is sustainable when it is able to provide an appropriate level of benefits over a longer period of time after the financial, managerial and technical assistance from the external donor ended”, or, “for most projects sustainability is a default, and donors and recipients wait and take responsibility that certain aspects of the activity will continue. Failure to make explicit sustainability as part of the project’s training and implementation process may lead to lack of benefit”.
Random gradient algorithms for convex minimization over intersection of simple sets

In this paper we consider constrained convex optimization problems, where the constraints are described as the intersection of a finite number of convex sets. Each set of the intersection is assumed to be simple, that is the projection can be computed efficiently. For these problems we derive gradient methods with random projections, that is we perform a gradient step for minimizing the objective function and then access the feasible set only through the projection onto a random individual set. Thus, our algorithms can easily address streaming settings, where the whole feasible set is not known in advance, but it is rather learned in time through observations, as in machine learning applications. Also, these algorithms are of interest for optimization problems having a very large number of given constraints, as in constrained predictive control. We analyze the convergence behavior of the proposed algorithms for the case when the objective function is strongly convex and with Lipschitz continuous gradients. We prove linear convergence rate into a noise dominated region for the expected quadratic distance of the iterates from the optimal set for constant stepsize. However, our analysis allows to also provide global sublinear rates for diminishing stepsize. Numerical evidence supports the effectiveness of our methods in real-world problems.
DepthMove: Hands-free Interaction in Virtual Reality Using Head Motions in the Depth Dimension

Hands-free interactions are very handy for virtual reality (VR) head-worn display (HWD) systems because they allow users to interact with VR environments without the need for a hand-held device. This paper explores the potential of a new approach that we call DepthMove to allow hands-free interactions that are based on head motions towards the depth dimension. With DepthMove, a user can interact in a VR system proactively by moving towards the depth dimension with an HWD. We present the concept and implementation of DepthMove in VR HWD systems and demonstrate its potential applications. We further discuss the advantages and limitations of using DepthMove.
About Sound and Vision: CLEF Beyond Text Retrieval Tasks

CLEF was initiated with intention of providing a catalyst to research in Cross-Language Information Retrieval (CLIR) and Multilingual Information Retrieval (MIR). Focusing principally on European languages, it initially provided CLIR benchmark tasks to the research community within an annual cycle of task design, conduct and reporting. While the early focus was on textual data, the emergence of technologies to enable collection, archiving and content processing of multimedia content led to several initiatives which sought to address search for spoken and visual content. Similar to the interest in multilingual search for text, interest arose in working multilingually with multimedia content. To support research in these areas CLEF introduced a number of tasks in multilingual search for multimedia content. While investigation of image retrieval has formed the focus of the ImageCLEF task over many years, this chapter reviews tasks examining speech and video retrieval carried out within CLEF during its first 10 years, and overviews related work reported at other information retrieval benchmarks.
Practice of Constructing Name Authority Database Based on Multi-Source Data Integration

Name authority is a common issue in digital library. This paper mainly summarizes the practice of constructing name authority database based on multi-source data in NSTL. Firstly, we load, integrate different source data and convert them into unified structure. Then, we extract scientific entities and relationships from these data, according to metadata model. For different entities, we use different disambiguation rules and algorithms. As a result, we have constructed author name authority database, institution authority database, journal authority database, and fund authority database. Compared with Incites, taking six institutions name authority data as a sample, the result shows that the average accuracy can reach 86.8% <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Database Processing-in-Memory: A Vision

The recent trend of Processing-in-Memory (PIM) promises to tackle the memory and energy wall problems lurking in the data movement around the memory hierarchy, like in data analysis applications. In this paper, we present our vision on how database systems can embrace PIM in query processing. We share with the community an empirical analysis of the pros/cons of PIM in three main query operators to discuss our vision. We also present promising results of our ongoing work to build a PIM-aware query scheduler that improved query execution in almost 3 $$\times $$ and reduced energy consumption in at least 25%. We complete our discussion with challenges and opportunities to foster research impulses in the co-design of Database-PIM.
Feature Selection Based on Extreme Learning Machine

Feature selection (FS) algorithm based on neural networks has been widely implemented due to its good nonlinear mapping ability and accurate approximation ability to arbitrary functions. However, the learning speed of the neural networks is very slow. To solve this problem, we propose a feature selection algorithm based on extreme learning machine (ELM), which determines the feature weights by analyzing the input weights and output weights, due to ELM randomly chooses the input weights and analytically determines the output weights. The proposed algorithm mainly considers the output weights and the feature weights are mainly determined by the output weights. According to the feature weights, many experiments are taken to obtain the optimal feature subsets for ELM. The results of several experiments demonstrate that by feature selection, the ELM achieves higher classification accuracy, and the training and testing process is much faster.
Prediction model for day-ahead solar insolation using meteorological data for smart grid

In the overseas market, power generation and energy service companies have been engaged in the business of providing personalized trading services for the production of electric power through the Internet platform. This is, so that the electric power sharing system between individuals is being developed through the Internet platform. The prediction of insolation is essential for the prediction of power generation for photovoltaic systems. In this study, we present a prediction model for insolation from data observed at the Meteorological Administration. We also present basic data for the development of the insolation prediction model through meteorological parameters provided in future weather forecasts. The prediction model presented is for five years of observation of weather data in the Seoul area. The proposed model was trained by using the feed-forward neural networks, taking into account the daily climatic elements. To validate the reliability of the model, the root mean square error (RMSE), mean bias error (MBE), and mean absolute error (MAE) were used for estimation. The results of this study can be used to predict the solar power generation system and to provide basic information for trading generated output by photovoltaic systems.
Construction and Application of Risk Ontology in Logistics Finance from the Perspective of Risk Correlation

Abstract Previous research on logistics financial risk pre-warning and pre-control focuses on the linear causal relationship between risk and risk events. In fact, risk events in logistics financial field are often caused by multiple risk factors, which are directly or indirectly related to these risk factors. Therefore, it is helpful for the healthy development of logistics finance to find out the related risks of each logistics financial risk event and screen and control them one by one. This paper proposes OntoLFR(Logistics Financial Risk Ontology), and constructs the logistics financial risk ontology model to adapt to the variability, complexity and relevance of risk in early warning and pre-control. Taking the risk event (RW_risk) of the financing enterprise to escape, the feasibility of using the logistics financial risk ontology model for risk-related reasoning and analysis is verified.
Flexible and efficient agent-based metaheuristic computing

Agent-based metaheuristics computing paradigm (EMAS) has been proposed over 20 years ago by Cetnarowicz. Since then, many efforts were made in order to evaluate, formally analyze and further develop this paradigm towards creating new algorithms as EMAS hybrids, or EMAS-inspired techniques. However, at the same time a significant work has been done in order to build efficient software frameworks supporting this (and similar) computing paradigms. These frameworks were based not only on classic object-oriented programming, but also on functional approach and recently also utilizing heterogeneous infrastructure. This paper presents an overview of the most important findings in this area, including novel ways of processing the agents and component orientation, which allow for both high flexibility and high efficiency of provided solutions. The discussed concepts are illustrated with a case study of a system solving hard computational problem leveraging GPGPU.
Modelling and implementing an app for an exhibition in a museum

Nowadays mobile technologies have become prominent in many aspects of our lives. The cultural world needs to follow the technological advances by finding ways to exploit some of the capabilities made available from the mobile world. The key is to enhance the users' experience and to give more value to the exhibition that they are going to enjoy. This principle can be applied for every museum or place of an exhibition. The aim of this paper is to present the mobile application, created for the exhibition "Women in the mirror"1, held at Palazzo Coronini Museum in Gorizia (Italy); based on best practices derived from the surveyed exhibitions and associated apps, the proposed model and implementation include a structure of the contents' organization and a list of key features and functions for this typology of app. An evaluation of the app has been performed, during the exhibition, in terms of usability.