Reviewerly/exHarmony huggingface dataset:
Learning Internal Dense But External Sparse Structures of Deep Convolutional Neural Network

Recent years have witnessed two seemingly opposite developments of deep convolutional neural networks (CNNs). On the one hand, increasing the density of CNNs (e.g., by adding cross-layer connections) achieves better performance on basic computer vision tasks. On the other hand, creating sparsity structures (e.g., through pruning methods) achieves a more slim network structure. Inspired by modularity structures in the human brain, we bridge these two trends by proposing a new network structure with internally dense yet externally sparse connections. Experimental results demonstrate that our new structure could obtain competitive performance on benchmark tasks (CIFAR10, CIFAR100, and ImageNet) while keeping the network structure slim.
Hierarchically engineering quality-related perceptual features for understanding breast cancer

Breast cancer is generally acknowledged as the second leading cause of cancer death among women. Therefore, accurately understanding breast cancer from X-ray images is an indispensable technique in medical sciences and image analysis. In the work, we propose a novel perceptual deep architecture that hierarchically learns deep features from large-scale X-ray images, wherein human visual perception is naturally encoded. More specifically, given a rich number of breast cancer images, we first employ the well-known BING objectness measure to identify all possible visually/semantically salient patches. Due to the relatively huge number of BING object patches, a weakly-supervised ranking algorithm is designed to select high quality object patches according to human visual perception. Subsequently, an aggregation scheme is utilized to derive the deep features of high quality object patches within each brain cancer image. Based on the aggregated deep feature, a multi-class SVM is trained to classify each breast cancer into multiple levels. Extensive comparative studies and visualization results have demonstrated the effectiveness and efficiency of our proposed deep architecture.
Quality-guided video aesthetics assessment with social media context

Media aesthetic assessment is a key technique in computer vision, which is widely applied in computer game rendering, video/image classification. Low-level and high-level features fusion-based video aesthetic assessment algorithms have achieved impressive performance, which outperform photo- and motion-based algorithms, however, these methods only focus on aesthetic features of single-frame while ignore the inherent relationship between adjacent frames. Therefore, we propose a novel video aesthetic assessment framework, where structural cues among frames are well encoded. Our method consists of two components: aesthetic features extraction and structure correlation construction. More specifically, we incorporate both low-level and high-level visual features to construct aesthetic features, where salient regions are extracted for content understanding. Subsequently, we develop a structure correlation-based algorithm to evaluate the relationship among adjacent frames, where frames with similar structure property should have a strong correlation coefficient. Afterwards, a kernel multi-SVM is trained for video classification and high aesthetic video selection. Comprehensive experiments demonstrate the effectiveness of our method.
NARPCA: Neural Accumulate-Retract PCA for Low-Latency High-Throughput Processing on Datastreams

The increasingly interconnected and instrumented world, provides a deluge of data generated by multiple sensors in the form of continuous streams. Efficient stream processing needs control over the number of useful variables. This is because maintaining data structure in reduced sub-spaces, given that data is generated at high frequencies and is typically follows non-stationary distributions, brings new challenges for dimensionality reduction algorithms. In this work we introduce NARPCA, a neural network streaming PCA algorithm capable to explain the variance-covariance structure of a set of variables in a stream through linear combinations. The essentially neural-based algorithm is leveraged by a novel incremental computation method and system operating on data streams and capable of achieving low-latency and high-throughput when learning from data streams, while maintaining resource usage guarantees. We evaluate NARPCA in real-world data experiments and demonstrate low-latency (millisecond level) and high-throughput (thousands events/second) for simultaneous eigenvalues and eigenvectors estimation in a multi-class classification task.
Simultaneous detection of deep-sea earthquake and magnetic field using three-axis fiber optic accelerometer-magnetometer

We present the field trial of three-axis deep-sea earthquake and magnetic detection. The fiber optic accelerometer-magnetometer with the control system based on phase generation carrier (PGC) method are integrated in a titanium alloy chamber. The experiment is carried out in 1000 meter deep sea in the south China sea. The results show that the proposed fiber optic sensors can record the air gun signal and submarine magnetic field noise signal.
Tagging emotions using a wheel user interface

Assessing users' emotional reactions plays an important role in modern technologies - from image classification and crowd-sourcing, through assessing user experience, to sentiment analysis for social media and marketing data. Providing easy-to-use and enjoyable emotion tagging tools is of key importance in encouraging users to share their reactions. This study tests the usability and accessibility of a mobile wheel interface for reporting emotional reactions based on Plutchik's emotion wheel. The study includes problem discovery, learnability and benchmark tests and is performed on both young, middle-aged and elderly participants, with various mobile device operation experience. The respondents include disabled users suffering from visual impairments and fine motor skill loss.
Incentives for Harvesting Attack in Proof of Work Mining Pools

PoW consensus largely depends on mining that mostly happens in the pools where Pay Per Share (PPS) and Pay Per Last N Shares (PPLNS) are the most common reward schemes that are offered to the affiliated miners by pool managers. In this paper, we demonstrate that in the system consisting of PPS and PPLNS pools, manager who governs the both pools may have incentive for a new type of “pool harvesting” attack that is harmful for honest miners. In order to profit from the attack on PPLNS pool manager declares that a non-existent miner A joins that pool. She then collects the portion of reward that corresponds to the mining power of the proclaimed miner A. We demonstrate that for the mining community, such unfavorable outcome is worsened by the manager incentives to misrepresent (or not report) the true power of PPS pools, which complicates unified estimation of the level of decentralization in blockchain.
iSCAN

Speaker recognition is a key component for emerging Internet of Things (IoT) smart services, such as voice-control and personalized applications. Although speaker recognition systems can attain excellent performance on synthetic datasets, operation in the real-world can lead to a significant degradation in performance. The key reason for this is the lack of enough labeled datasets for model adaptation, primarily due to the cost of manual annotation and enrollment. A recent solution to this problem is to use cross-modal identifiers e.g. WiFi sniffing to gradually associate an identity with a certain vocal feature e.g. Simultaneous Clustering and Naming (SCAN). In this paper we demonstrate how to further improve performance of these cross-modal systems in the wild by iteratively adapting the feature extractor based on the output of the noisy association and clustering step. We show how this feedback loop can not only improve overall accuracy, but also labeling coverage in association result. iSCAN is a further step towards a robust and zero-effort speaker recognition system for the IoT.
Emerging technologies in the health-care supply chain

In this chapter, the background and organization of the health-care supply chain are reviewed, and the impact of emerging technologies is described. Maturing technologies, including optimization software, sensors/telematics, cloud computing, data warehouse systems, and automated storage and retrieval, are examined. Growth technologies, including mobility, wearable devices, data analytics, and social media, are examined as they potentially relate to the health-care supply chain. Emerging technologies, including 3D printing, drone delivery, and autonomous vehicles, are presented and examples provided on their use in the health-care supply chain. Exponential technologies, including blockchain, the Internet of Things, virtual/augmented reality, and artificial intelligence, are described with respect to potential applications in the health-care supply chain. Future changes in the external environment of health care, including decentralization, new competitors, and the increased use of telemedicine, are described with respect to impacts on the health-care supply chain.
Structural Models in Corporate Social Responsibility: Attraction of Investment in Tunisia

The attraction of foreign direct investment is a common objective in developing countries, and this broad aim is carried out with different approaches in public policies. Corporate social responsibility is very common in international corporations, and it tends to produce a positive image for investors and in the surrounding society. This study aims to clarify the influence of the enhancement of corporate social responsibility by companies established in Tunisia as a consequence of the host country government general policies on the attraction of direct foreign investment. We propose the testing of a conceptual framework that describes this influence and explains the benefits of the social commitment, especially when it will be encouraged by public policies which can favor the attraction of foreign investments. The paper opted for an exploratory analysis on a sample of foreign companies with subsidiaries in the country. It contains a descriptive analysis, a study of the reliability of the scales of measurement and a principal components analysis. This approach is completed by an analysis of moment structures (AMOS) through a structural equation model linking the interactions of public policies with the development of strategies in social responsibility in companies, and their induced effects on the investment decisions in their subsidiaries. This approach tends to be associated with the sustainability and the commitment in the country, which is especially important in the present moment, with the political changes in the Maghreb region. With the models proposed, it has been shown that public policies, in addition to having a direct impact on investment decisions, can produce positive effects when they are carried out with the aim of promoting sustainable growth, and using indirect tools like the promotion of corporate social strategies in the companies that are already established in the country.
A Fusion Algorithm of Gravitational Search and Tabu Search

In order to overcome the shortcomings of gravitational search algorithm (GSA), such as easily falling into local optimum traps and needing to be improved in accuracy, a fusion algorithm of gravitational search and tabu search (TS-GSA) is proposed. Firstly, the optimal solution of each iteration operation of GSA is regarded as a tabu search object. Secondly, a neighborhood search method is designed to search in depth between two historical extremes to avoid missing global optimum solution. Thirdly, tabu list is used to reduce the frequency of circuitous search and improve search efficiency. The simulation results show that TS-GSA has stronger global optimization ability than GSA, new hybrid populational-based algorithm with combination of particle swarm optimization and GSA (PSOGSA), adaptive gbest-guided GSA (GGSA), and can effectively overcome local optimum problem. Results also show that TS-GSA can find the theoretical optimum solutions of some high-dimensional multi-modal functions, and has stronger robustness for low-dimensional multi-modal functions.
On Chow-Liu Forest Based Regularization of Deep Belief Networks

In this paper we introduce a methodology for the simple integration of almost-independence information on the visible (input) variables of the restricted Boltzmann machines (RBM) into the weight decay regularization of the contrastive divergence and stochastic gradient descent algorithm. After identifying almost independent clusters of the input coordinates by Chow-Liu tree and forest estimation, the RBM regularization strategy is constructed. We show an example of a sparse two hidden layer Deep Belief Net (DBN) applied on the MNIST data classification problem. The performance is quantified by estimating misclassification rate and measure of manifold disentanglement. Approach is benchmarked to the full model.
Industrial Sector Growth, Macroeconomic Performance, and Corruption in the Sub Sahara Africa

Abstract The study investigates the impacts of macroeconomic performance and corruption on the industrial growth of the SSA. The industrial sector is seen as the engine of the economic development of any country and hence policies that will promote the growth of the sector cannot be over emphasized. The study investigated effects of macroeconomic variables such as exchange rate, economic growth, inflation rate and unemployment rate as measures of economic performance in the SSA on the industrial sector growth. Also quality of institutions effects on industrial sector is investigated using control of corruption as proxy. The preliminary diagnostic results show that Panel Auto-Regressive Distributed Lags P-ARDL is appropriate for the estimation and the results show that both macroeconomic performance and corruption have significant impacts of the industrial sector growth in SSA. However, an exchange rate that will encourage domestic production, minimum inflation, and unemployment rates, will guarantee sustainable growth in the industrial sector, while tightening grip on control of corruption.
An Improved Adaptive Weighted Gaussian Nearest Neighbor Classification Method

The category of a test sample is decided by the template sample which has the smallest distance to the tested one for the traditional nearest neighbor algorithm, which may cause the sample to be misjudged. In order to improve the correct classification rate, more template samples should participate in decision-making. Therefore, the item of class distance used to classify the test sample is proposed in this paper, which is relative to several template samples selected adaptively. The Gaussian function is employed in the class distance because of its better clustering. To verify the merit of the improved methods, experiments of grading 1588 tobacco leaves belonging to 41 levels by the nearest neighbor algorithm, its improved methods, SVM, SRC, BP, RF and Adaboost have been implemented under the same conditions. The experimental results show that the adaptive weighted Gaussian nearest neighbor has better recognition ability than other methods.
Modified Ant Colony Algorithm For Swarm Multi Agent Exploration on Target Searching in Unknown Environment

The ant colony algorithm (ACA) method basically aims to solve target search problems strategy. In previous researchers, the method of ant colony have been use to solve optimization path to target, where the location of target is known. But this method cannot solve if the position of target is unknown. To solve unknown target, swarm agent can explore entire environment using ant colony algorithm. This research proposed a new method for swarm multi agent exploration on unknown target using modified ant colony algorithm based on Anti-pheromone. First strategy making swarm agent motion move direction, so that swarm agent can avoid the obstacle until find the target and the second by making swarm agent explore the entire environment using Anti-pheromone algorithm for information, the purpose of using Anti-pheromone algorithm is to make ant colony always making of decision to choose a path with a small number of pheromones. The simulated results present that modified algorithm of ant colony system can make swarm agent to explore in searching for find target position while the swarm avoid the obstacle. First simulation with 1-5 agent, using criteria (1) better solution to explore entire node, The second simulation with 1-5 agent, can finding target position more effectively using parameter (2) with minimum iterasi and minimum mileage. Proposed method can effectively enable swarm agent to search unknown target position quickly.
Practice and Evaluation of Model Based Development (MBD) Education

This study presents the practical results concerning model-based development educational practices implemented by Hiroshima University and Mazda Motor Corporation, an automobile manufacturer, and the consequent educational effects. Importance of model-based development is increasing in industrial world. Model-based development is performed according to a development process called the "V-type development process (V-process)." However, several professional engineers involved in automobile development have insufficient experience in conducting model-based development. In this study, the authors propose a program that allows these professional engineers to learn the V-process using a motor control system via a hands-on approach. The program is implemented to target 159 professional engineers belonging to Mazda Motor Corporation. From the questionnaire surveys taken by the learners, it is revealed that the V-process exercises considerably contribute to the self-efficacy of the learners for operational performance using model-based development.
mobEYEle

The eyes are a particularly interesting modality for cognitive industrial assistance systems, as gaze analysis can reveal cognition- and task-related aspects, while gaze interaction depicts a lightweight and fast method for hands-free machine control. In this paper, we present mobEYEle, a body-worn eye tracking platform that performs the entire computation directly on the user, as opposed to primarily streaming the data to a centralized unit for online processing and hence restricting its pervasiveness. The applicability of the platform is demonstrated throughout extensive performance and battery runtime tests. Moreover, a self-contained calibration method is outlined that enables the usage of mobEYEle without any supervisor nor digital screen.
The Power of Communication: Increasing Patron Engagement through Outreach and Assessment

National trends suggest that many library services are under-used due to lack of effective marketing, as opposed to lack of community interest. This paper uses Dibner Library’s instructional workshop program as an example of a low-attendance library offering transformed by assessing a number of communication efforts. In order to tailor outreach to Dibner patrons, librarians tracked patron surveys, Bitly clicks across all forms of digital publicity, and registration and attendance statistics. The resulting increase in engagement shows that customized communication is at the root of successful library programming.
ICAROSmuIti - A VR Test Environment for the Development of Multimodal and Multi-User Interaction Concepts

English - With 'ICAORSmulti' we present a cooperative Virtual Reality application that was developed for the presentation and validation of multimodal interaction techniques. To have an application scenario at our disposal that is attractive and can be demonstrated at trade shows, we chose a flying-based virtual environment for multiple users. The latter are able to interact and assume different roles. As input devices, we use up to two ICAROS flying machines that were specially designed to simulate a flight in VR. The users can move and navigate through the VR environment in a horizontal position (see figure 1). By means of a head-mounted display (HMD) they can see their virtual surroundings, while the sensor technology attached to the Icaros detects and communicates their movements. Actuator devices such as wind machines, heat lamps and others enable a multimodal output and improve the immersion of the flight simulation. Beyond that, it is also possible to integrate other devices and 3D interaction techniques such as running, climbing etc. into the current scene. All users are able to see each other and to communicate. For the simple development and testing of 3D interaction techniques based on predefined templates in Unity3D, several different components have already been developed and integrated. They are briefly described in this paper and can be tried out in a demonstration at the conference 'Mensch und Computer 2019'. One of the unique features of this project are components that perceive a measurable decline in immersion, e.g. a limitation of the viewing area, the latency or the frame rate.
Mobile computing and well-being in the outdoors

The inclusion of mobile computing in outdoor recreation raises important questions about its ability to contribute meaningfully to activities without detracting from their benefits to well-being. In this paper, we present results from our research, which seeks to explore and set directions for computing's place in outdoor recreation. Our work addresses smartphone use while hiking. Our position is that computing already has a place in outdoor recreation and can contribute meaningfully to well-being in the outdoors now and in the future.
CityAtmosphere

This paper presents a novel virtual reality demonstration program that provides people immersive urban experience, i.e. helps people understand characteristics of the city atmosphere from big data analysis on GPS location logs and search query logs. In contrast to the other demonstration systems that show the characteristics of areas of interests by using tag cloud, or VR systems using 3D computer graphics, our system synthesizes both the functionalities by showing 3D point clouds, where each particle shows search queries made by people staying at the area of interests. This synthesis invokes people feel the atmosphere of the city while traditional VR systems could not offer it. To make demonstration system effective, a new feature extraction process on search query logs is proposed by focusing on the users who visit the area of interests. In the demonstration at the conference, visitors could enjoy immersive urban experience with VR headset. Furthermore, the paper also shows the empirical evaluation on our new feature representation from search query logs.
Visualization of a Software System in Virtual Reality

IslandViz is a software visualization tool which represents the architecture of OSGi-based software systems in virtual reality. Using an island metaphor, each module is represented as an independent island. The resulting island system is displayed on a virtual table where users can interact with the visualization and explore the island system. IslandViz allows users to get an initial overview of the complexity of a software system by examining the modules and their dependencies.
Cybercartography in Indigenous Language Education

Over the last few centuries, languages in every part of the world have undergone dramatic changes in the ways they are transmitted from one generation of speakers to the next. Changes in language transmission reflect cultural shifts and, in the case of Indigenous languages, there have been major, overwhelmingly negative, disruptions as colonial languages and technologies come into contact with Indigenous societies. In this article, we reflect on the community-based language transmission that has characterized Indigenous cultures, introduce the collaborative mapping process Cybercartography, and discuss ways in which the holistic nature of Indigenous Language Education can be strengthened in contemporary communities.