Reviewerly/exHarmony huggingface dataset:
Model-Centric Data Manifold: The Data Through the Eyes of the Model

We show that deep ReLU neural network classifiers can see a low-dimensional Riemannian manifold structure on data.Such structure comes via the local data matrix, a variation of the Fisher information matrix, where the role of the model parameters is taken by the data variables.We obtain a foliation of the data domain, and we show that the dataset on which the model is trained lies on a leaf, the data leaf, whose dimension is bounded by the number of classification labels.We validate our results with some experiments with the MNIST dataset: paths on the data leaf connect valid images, while other leaves cover noisy images.
Research on the Innovation of Graduate Educational Administration Management Mode under the Background of Big Data

Abstract In recent years, big data technology has been developed rapidly, among which data management and data analysis in big data technology are widely used at present, and have been successfully applied in all walks of life. Applying big data technology to the field of education is the current mainstream trend, and the use of big data technology for graduate education management is also one of the current hot topics. However, there are still many deficiencies in the existing graduate educational administration management mode. Therefore, this paper puts forward the innovation research of graduate educational administration management mode under the background of big data. This paper makes an in-depth investigation on the main problems existing in the traditional mode of graduate educational administration management in Colleges and universities. According to the results, the main problems are the lack of sense of belonging to the class, poor information transmission, and the disconnection between class management and tutor management. In view of these problems, this paper proposes an optimization and improvement scheme based on big data technology. According to the actual needs of the current graduate education management in China’s colleges and universities, combined with the advanced big data management and analysis technology, the program establishes a new big data technology university graduate educational administration management mode. The improvement scheme of this paper makes up for the shortcomings of the traditional mode of graduate educational administration. The corresponding survey results show that the graduate management mode based on big data technology can better integrate the school network resources, improve the management ability of the school and meet the needs of students.
Privacy Preserving Distributed Energy Trading

The smart grid incentivizes distributed agents with local generation (e.g., smart homes, and microgrids) to establish multi-agent systems for enhanced reliability and energy consumption efficiency. Distributed energy trading has emerged as one of the most important multi-agent systems on the power grid by enabling agents to sell their excessive local energy to each other or back to the grid. However, it requests all the agents to disclose their sensitive data (e.g., each agent's fine-grained local generation and demand load). In this paper, to the best of our knowledge, we propose the first privacy preserving distributed energy trading framework, Private Energy Market (PEM), in which all the agents privately compute an optimal price for their trading (ensured by a Nash Equilibrium), and allocate pairwise energy trading amounts without disclosing sensitive data (via novel cryptographic protocols). Specifically, we model the trading problem as a non-cooperative Stackelberg game for all the agents (i.e., buyers and sellers) to determine the optimal price, and then derive the pairwise trading amounts. Our PEM framework can privately perform all the computations among all the agents without a trusted third party. We prove the privacy, individual rationality, and incentive compatibility for the PEM framework. Finally, we conduct experiments on real datasets to validate the effectiveness and efficiency of the PEM.
Multi-feature fusion refine network for video captioning

Describing video content using natural language is an important part of video understanding. It needs to not only understand the spatial information on video, but also capture the motion information. Meanwhile, video captioning is a cross-modal problem between vision and language. Traditional video captioning methods follow the encoder-decoder framework that transfers the video to sentence. But the semantic alignment from sentence to video is ignored. Hence, finding a discriminative visual representation as well as narrowing the semantic gap between video and text has great influence on generating accurate sentences. In this paper, we propose an approach based on multi-feature fusion refine network (MFRN), which can not only capture the spatial information and motion information by exploiting multi-feature fusion, but also can get better semantic aligning of different models by designing a refiner to explore the sentence to video stream. The main novelties and advantages of our method are: (1) multi-feature fusion: Both two-dimension convolutional neural networks and three-dimension convolutional neural networks pre-trained on ImageNet and Kinetic respectively are used to construct spatial information and motion information, and then fused to get better visual representation. (2) Sematic alignment refiner: the refiner is designed to restrain the decoder and reproduce the video features to narrow semantic gap between different modal. Experiments on two widely used datasets demonstrate our approach achieves state-of-the-art performance in terms of BLEU@4, METEOR, ROUGE and CIDEr metrics.
Visual Localization Based on Remote Sensing Scene Matching with Siamese Feature Aggregation Network

This paper presents a new framework with a siamese feature aggregation network (SFANet) for visual localization based on remote sensing scene matching. Specifically, the presented framework predicts the location of a query image by finding the matching remote sensing images with geographical information. We employ the fully convolutional networks (FCNs) and a siamese network of NetVLAD to aggregate local features and learn the global representations for images from different sources. A new soft margin loss function is established for the network. Geographic coordinates of the query images are obtained by calculating the similarity with satellite images. We also collect a multi-scale dataset that contains 136959 images from 45653 locations. Various experiments are carried out on it. Experimental results show the effectivity of the proposed method.
Optimization of Multi-LED Setups for Underwater Robotic Vision Systems

In deep water conditions, vision systems mounted on underwater robotic platforms require artificial light sources to illuminate the scene. The particular lighting configurations significantly influence the quality of the captured underwater images and can make their analysis much harder or easier. Nowadays, classical monolithic Xenon flashes are gradually being replaced by more flexible setups of multiple powerful LEDs. However, this raises the question of how to arrange these light sources, given different types of seawater and-depending-on different flying altitudes of the capture platforms. Hence, this paper presents a rendering based coarse-to-fine approach to optimize recent multi-light setups for underwater vehicles. It uses physical underwater light transport models and target ocean and mission parameters to simulate the underwater images as would be observed by a camera system with particular lighting setups. This paper proposes to systematically vary certain design parameters such as each LED’s orientation and analyses the rendered image properties (such as illuminated image area and light uniformity) to find optimal light configurations. We report first results on a real, ongoing AUV light design process for deep sea mission conditions.
Contextualizing Security Innovation: Responsible Research and Innovation at the Smart Border?

Abstract Current European innovation and security policies are increasingly channeled into efforts to address the assumed challenges that threaten European societies. A field in which this has become particularly salient is digitized EU border management. Here, the framework of responsible research and innovation (RRI) has recently been used to point to the alleged sensitivity of political actors towards the contingent dimensions of emerging security technologies. RRI, in general, is concerned with societal needs and the engagement and inclusion of various stakeholder groups in the research and innovation processes, aiming to anticipate undesired consequences of and identifying socially acceptable alternatives for emerging technologies. However, RRI has also been criticized as an industry-driven attempt to gain societal legitimacy for new technologies. In this article, we argue that while RRI evokes a space where different actors enter co-creative dialogues, it lays bare the specific challenges of governing security innovation in socially responsible ways. Empirically, we draw on the case study of BODEGA, the first EU funded research project to apply the RRI framework to the field of border security. We show how stakeholders involved in the project represent their work in relation to RRI and the resulting benefits and challenges they face. The paper argues that applying the framework to the field of (border) security lays bare its limitations, namely that RRI itself embodies a political agenda, conceals alternative experiences by those on whom security is enacted upon and that its key propositions of openness and transparency are hardly met in practice due to confidentiality agreements. Our hope is to contribute to work on RRI and emerging debates about how the concept can (or cannot) be contextualized for the field of security—a field that might be more in need than any other to consider the ethical dimension of its activities.
Leveling up: A Checklist Comparison of Pennsylvania Public Libraries’ Web Presence in 2010 and 2018

In a technology-driven world, libraries must have quality ­websites that deliver meaningful information in a structured way. Measuring the ease of access to information through website evaluation studies can help site creators assess the efficacy of presented information. This study, performed in 2018, recognizes the importance of maintaining quality ­websites for successful communication of information to the public. It replicates Powers’s 2010 content analysis of Pennsylvania public libraries’ websites. The author employs Powers’s evaluation checklist and seeks to discover how current levels of web presence for Pennsylvania public libraries ­compare with the results of Powers’s original study. A sufficient number of websites were reviewed to allow for generalizability to all Pennsylvania public libraries. This study finds that public libraries’ web presence in Pennsylvania has increased since Powers evaluated websites in 2010, in terms of the range of available information. In addition, the 2018 results show which website development areas public libraries must improve in to maintain relevance.
Quantum scheme of dissipative two qubits in a squeezed field: Entanglement and Fisher information

A clear relationship between the quantum version of Fisher information and the bipartite system entanglement has been explored recently. Known existing results for some quantum systems having strongly nonlocal correlations are still limited. In this paper, based on the interaction between a two-qubit and squeezed field, we investigate the correlation between the dynamical behavior of qubit-qubit entanglement and the quantum Fisher information (QFI). We focus on the effect of a number of photon multiplicity, low and high squeezing regime of the field on the dynamics of the QFI and entanglement quantifier. We show how the QFI and entanglement quantifier can be affected by the number of photon multiplicity and squeeze parameter during the time evolution of cases of absence and presence of dissipation effect. Our results exhibit the correlation between the entanglement quantifiers and the QFI with respect to the appearance of collapse and revival phenomena of the atomic inversion.
Helping to detect legal swimming pools with Deep Learning and Data Visualization

The municipalities have, as a primary responsibility, the administrative management of their territory. The use of geographic information systems (GIS) and orthophoto maps can help to handle this task. One of the great difficulties they face is related to the continuous and quick changes that the territory suffers, and whose inspection is challenging to support. An example of this is the maintenance of the swimming pool registration system, in order to validate or license them. Indeed, it requires a substantial manual intervention, yet. This paper describes a system prototype for helping on detecting swimming pools on aerial images. It explores and integrates artificial intelligence (AI), systems integration, GIS, and data visualization. The AI improves the detection of objects, and middleware supports the integration of the detection results with other municipality systems for georeferencing and private property data licensing data crossing. To the innovative interoperability services, visualization libraries were explored, and an advanced visualization and analysis system was constructed. A dataset of aerial images of swimming pools and correspondent classification metadata was created. The model was trained with several convolutional neural networks in order to obtain and compare the precision results. The more accurate model is described.
Learning Discriminative Features for Fine-grained Image Retrieval

The main problem of fine-grained image retrieval is the small inter-class variance as well as large intra-class variance. Therefore, it is important to pay attention to the local details of the image to identify subtle differences in fine-grained retrieval. In this paper, we combine global and local features in different scales to improve discriminative performance in fine-grained image retrieval tasks. A new deep network architecture with three branches is designed, one branch for global feature representation and the others for local feature representation with different granularities. Further, we also combine classification loss function and metric learning loss function to improve the performance of the feature extractor. Comprehensive experiments conducted on the two public evaluation datasets including CUB200-2011 and CARS196 demonstrate that our method can achieve superior performance compared with existing methods.
CIDER: A Low Overhead Approach to Privacy Aware Client-side Deduplication

In cloud storage systems, malicious users may exploit client-side deduplication responses to infer what other users are storing in the cloud. We propose CIDER, a low overhead approach to mitigate this side channel by obfuscating the existence status of data stored in the cloud. We analyze the scheme's ability to obfuscate the side-channel and discuss attacks that a user may still employ and what he could learn from such attacks. Finally, we use simulated and real data to examine the performance under realistic deduplication workloads, revealing that CIDER ends up transmitting less redundant information than similar methods, thus enabling a more efficient utilization of the available bandwidth.
Application of Intelligent Information Technology in Intelligent Home Decoration Design

Abstract With the rapid development of social economy and the continuous progress of science and technology, people’s living standards have been greatly improved, and at the same time, people have put forward higher requirements for the quality of life. Especially, with the deepening of the concept of smart home, it has become a hot research topic to combine intelligent information technology in smart home decoration design. In view of this situation, this paper focuses on analyzing the impact of this technology on the existing smart home decoration environment, and makes a detailed analysis and discussion on the intelligentization of the home environment.
First-Order Learning Models With the GDINA: Estimation With the EM Algorithm and Applications

In learning environments, understanding the longitudinal path of learning is one of the main goals. Cognitive diagnostic models (CDMs) for measurement combined with a transition model for mastery may be beneficial for providing fine-grained information about students' knowledge profiles over time. An efficient algorithm to estimate model parameters would augment the practicality of this combination. In this study, the Expectation-Maximization (EM) algorithm is presented for the estimation of student learning trajectories with the GDINA (generalized deterministic inputs, noisy, "and" gate) and some of its submodels for the measurement component, and a first-order Markov model for learning transitions is implemented. A simulation study is conducted to investigate the efficiency of the algorithm in estimation accuracy of student and model parameters under several factors-sample size, number of attributes, number of time points in a test, and complexity of the measurement model. Attribute- and vector-level agreement rates as well as the root mean square error rates of the model parameters are investigated. In addition, the computer run times for converging are recorded. The result shows that for a majority of the conditions, the accuracy rates of the parameters are quite promising in conjunction with relatively short computation times. Only for the conditions with relatively low sample sizes and high numbers of attributes, the computation time increases with a reduction parameter recovery rate. An application using spatial reasoning data is given. Based on the Bayesian information criterion (BIC), the model fit analysis shows that the DINA (deterministic inputs, noisy, "and" gate) model is preferable to the GDINA with these data.
Evasive entrepreneurship: Circumventing and exploiting institutional impediments for new profit opportunity in an emerging market

Evasive entrepreneurship (circumvention and exploitation of institutions by entrepreneurs) is a prevalent practice in many developing economies. Extant literature on the topic falls short of providing adequate theories to explain its triggers, mechanisms, and consequences. Leveraging extensive survey data from the World Bank, we used structural equation modeling to examine the relationship between evasive entrepreneurial behavior-tax evasion and bribery-and the relative payoff of such practices. Of the 2599 Nigerian entrepreneurs in our sample, the majority admitted to engaging in evasive entrepreneurship. The data suggest that institutional factors thought to constrain entrepreneurship in emerging markets are counter-intuitively perceived by founders as opportunities to earn large rents and improve firm performance. Our results emphasize the urgent need to eliminate institutional constraints that paradoxically enable the growth of evasive entrepreneurship in emerging economies. Our results also suggest that prevailing local conventions involving evasive behavior may motivate nascent entrepreneurs to imitate bribery and tax evasion, normalizing malfeasance as 'best practice.'
An enhanced personal credit identification coin-day destruction model based on blockchain technology fuzzy sets for region of China pearl river delta

Nowadays, the Expansion and evolution of the global financial system oblige lenders to develop stricter requirements for assessing creditworthiness of borrowers. This paper analyses the problems prevalent in the existing credit models of coastal cities in China Pearl River Delta, including data centralization, difficulties in detecting forged data and delay in data transmission; we constructed a CDDC model based fuzzy sets that employs all the issues. The related results showed that the technology fuzzy sets decentralizes and expands data sources, acquires and processes data automatically and self-perfects its ability to rank borrowers into cohorts of creditworthiness. Moreover, the CDDC model out-performs the traditional model in assessing creditworthiness and reducing delinquencies and defaults. That means our fuzzy sets model employs decentralized data sources, destroys historical data regularly and facilitates training and improvement. It ranks creditworthy borrowers in a better fashion than the statistics-based traditional credit model.
Touching the Past

My research explores the use of tangible user interfaces and augmented reality to interact with virtual representations of historical artefacts within the cultural heritage domain. As a practice-led research, this research project aims to design, develop and evaluate tangible user interfaces combined with augmented reality to provide cultural heritage professionals and archaeology experts with an accessible and intuitive tangible augmented reality interface for the visualization, analysis and interpretations of historical artefacts. This research is interdisciplinary in nature combining the fields of design, HCI and heritage; and the newly explored interaction methods will suggest new possibilities for the future of Tangible User Interfaces and augmented reality research in general and cultural heritage in particular. In the first year of my PhD, I developed a prototype that showcased a tangible interface to interact with augmented historical artefacts, and preliminary testing with experts yielded very positive results. In the second year, the research will investigate how different tangible interaction methods in augmented reality can promote realism and immersion and enhance the user experience.
High Speed Moving Target Tracking Algorithm based on Mean Shift for Video Human Motion

Abstract As an important research field of computer vision, there are a lot of achievements of human action recognition in recent Most of the researches are confined to the daily gentle action of human, such as walking jogging, sitting down. In sports video there were many specific characteristics about human actions, such as the upside pose of human, high speed, moving viewpoint. For human motion characteristics of sports video, a highspeed moving target tracking algorithm was proposed. Mean shift was an effective tracking algorithm, which was employed to track moving object in video, but it loses object frequently when the tracked object in the video moves rapidly. We advanced an approach to tracking object moving rapidly, and it can work effectively when the object was in cluttered environment. Experimental shows that the proposed algorithm can improve the tracking effect of mean shift algorithm and effectively track high-speed moving target.
Artificial intelligent third umpire runout decision making using image processing

Abstract Cricket is the most interesting game watched by the people all over the world. The decisions given by the umpire play a vital role in deciding the game winning situation. As human decisions are prone to error, the need of smart technology intervention is mandate. The advances in image processing and computer vision techniques has now paved the way for making effective and correct decisions on all phases of the game and in automatic scoring. This paper proposes a new approach for detecting the run out using stump detection and from the intersection of the bat and the crease line, named Artificial Intelligent third Umpire (AITU). The accuracy of decision making is improved thereby reducing the third umpire work.