Reviewerly/exHarmony huggingface dataset:
K-Covers for Active Learning in Image Classification

Deep learning has shown its effectiveness in various computer vision tasks. However, a large amount of labeled data is usually needed for deep learning approaches. Active learning can help reduce the labeling efforts by choosing the most informative samples to label and thus achieves a comparable performance with less labeled data. In this paper, we argue that only choosing samples based on some uncertainty function would lead to an unbalanced distribution of the selected samples, especially when the initial set of labeled samples are unbalanced. Following the intuition of reducing the repetitive sampling for similar images, we propose a novel K-Covers method to partition the feature space into several clusters and then choose one sample with the largest uncertainty in each cluster. Our method can constantly outperform the state-of-the-art with a clear margin.
Survey of Testing Methods of O2O Catering Platform

The particularity of the Web application of catering O2O platform makes its testing challenging, but the current research on its testing is relatively weak compared with the research on its design and development.This paper summarizes the research progress of Web platform testing methods in recent years.The Basic test contents and technologies, typical test models and automated testing tools of existing platforms are summarized.The current research hotspots and difficulties are analyzed, including the optimization of test models, the improvement and development of automated testing tools, the guarantee of test comprehensiveness, safety, stability and efficiency.Finally, the future research directions of the testing methods of catering O2O Web platform are discussed from three aspects: testing content, testing methods and testing tools.
The Effect of Elastic Feedback on the Perceived User Experience and Presence of Travel Methods in Immersive Environments

Mid-air interaction achieved through handtracking or controller in Virtual Reality (VR) is performed without an explicit spatial reference. One solution for creating such a reference is elastic feedback, which we realized via tension springs attached to a tracked VR-controller. Thereby, directional force is exerted and perceived by the user. A study was conducted to investigate the effects of elastic feedback on users in virtual travel tasks. Therefore, the elastic mode of the input device was compared to an isotonic mode, without any perceivable force. Also, two virtual travelling methods, a driving mode and a flight mode, were investigated in the study. The main goal of the study was to analyze, how elastic feedback influences user experience and presence perception. Statistical analysis with ANOVA of data from 24 subjects revealed significant main effects for the elastic mode on both, the User Experience Questionnaire (UEQ) and the Igroup Presence Questionnaire (IPQ). In addition, the subjects missed fewer target objects on average during the travel task. However, analysis of task completion times indicated no relevant differences. Although not all individual scales of UEQ and IPQ showed significant outcomes, the result indicates that immersive travel interfaces could benefit from the use of elastic feedback.
Application of Single Shot MultiBox Detector in Logistics Safety Testing

With the rapid development of the Internet economy, the load of the logistics industry has gradually increased, and people are paying more and more attention to the detection of restricted items in logistics packages. Using Convolution Neural Network can quickly and accurately identify the restrictions in the package, thus minimizing the pressure on personnel in logistics monitoring. This paper introduces the application method of SSD-based target detection model in the detection of logistics goods restriction products. Firstly, it analyses the characteristics of data sets and preprocesses the data and expounds the feasibility of SSD model in this respect. Different from the traditional upsampling method, this paper uses cubic spline interpolation to adjust the image resolution and expands the number of positive samples to balance the number of positive and negative samples. Next, batch normalization is added after the convolution layer and Softmax is replaced with multiple logistic classifiers so that the performance of the model can be improved. Finally, by comparing the results of SSD and Fast R-CNN, this paper proves that SSD, which obtains a mAp of 0.476 at more than 30 FPS, is better on this dataset. It owns both high accuracy and real-time speed.
Sentiment Classification Using Recurrent Neural Network

Sentiment basically represents a person’s attitude, expressing thoughts or an expression triggered by a feeling. Sentiment analysis is the study of sentiments on a given piece of text. Users can express their sentiment/thoughts on internet which may have impact on the user reading it [7]. This expressed sentiment are usually available in unstructured format which needs to be converted. Sentiment analysis is referred to as organizing text into a structured format [7]. The challenge for sentiment analysis is insufficient labelled information, this can be overcome by using machine learning algorithms. Therefore, to perform sentiment analysis we have employed Deep Neural Network.
Chaotic Evolution Algorithms Using Opposition-Based Learning

We propose a method for accelerating chaotic evolution (CE) search using the triple and quadruple comparison mechanisms. We utilize some performance measurements to analyse and verify our proposed algorithm with benchmark functions. The CE is one of evolutionary computation (EC) algorithms that fuses the iteration of evolution and the ergodicity of a chaos system for optimization. We apply the opposition-based learning (OBL) mechanism to CE algorithm to obtain opposite vectors in its search process. Besides the target vectors and chaotic vectors in the conventional CE algorithm, the opposite vectors are also examined during determining the offspring individual for the next generation. Generally, one of drawbacks for the conventional EC algorithm is that premature convergence towards a local optimum instead of a global optimum. The advantage of our proposed algorithm is that it has a higher possibility to avoid being trapped in a premature convergence so that it can reduce a lot of unnecessary computational costs. We also evaluate these algorithms using 12 benchmark functions and some performance measurements. The experiments found that applying OBL mechanism to the CE algorithm can obtain a better optimization performance than the conventional one, especially in the high dimensional optimization tasks.
The Challenges of Language Variation in Information Access

This chapter will give an overview of how human languages differ from each other and how those differences are relevant to the development of human language understanding technology for the purposes of information access. It formulates what requirements information access technology poses (and might pose) to language technology. We also discuss a number of relevant approaches and current challenges to meet those requirements.
Librarians' Perceptions on Skills/Knowledge and Resources Needed for Research Data Services: Preliminary Results

This poster presents partial findings from a survey study that explored academic librarians' perceptions on research data services. Preliminary results reveal that librarians perceive knowledge and skills related to education and consultation more important than technical skills, and they find collaboration with other organizations on campus and full-time data librarians the most important resources needed for research data services.
Spatially-Coherent Segmentation Using Hierarchical Gaussian Mixture Reduction Based on Cauchy-Schwarz Divergence

Gaussian mixture models (GMM) are widely used for image segmentation. The bigger the number in the mixture, the higher will be the data likelihood. Unfortunately, too many GMM components leads to model overfitting and poor segmentation. Thus, there has been a growing interest in GMM reduction algorithms that rely on component fusion while preserving the structure of data. In this work, we present an algorithm based on a closed-form Cauchy-Schwarz divergence for GMM reduction. Contrarily to previous GMM reduction techniques which a single GMM, our approach can lead to multiple small GMMs describing more accurately the structure of the data. Experiments on image foreground segmentation demonstrate the effectiveness of our proposed model compared to state-of-art methods.
RoPose-Real: Real World Dataset Acquisition for Data-Driven Industrial Robot Arm Pose Estimation

It is necessary to employ smart sensory systems in dynamic and mobile workspaces where industrial robots are mounted on mobile platforms. Such systems should be aware of flexible and non-stationary workspaces and able to react autonomously to changing situations. Building upon our previously presented RoPose-system [1], which employs a convolutional neural network architecture that has been trained on pure synthetic data to estimate the kinematic chain of an industrial robot arm system, we now present RoPose-Real. RoPose-Real extends the prior system with a comfortable and targetless extrinsic calibration tool, to allow for the production of automatically annotated datasets for real robot systems. Furthermore, we use the novel datasets to train the estimation network with real world data. The extracted pose information is used to automatically estimate the observing sensor pose relative to the robot system. Finally we evaluate the performance of the presented subsystems in a real world robotic scenario.
Behavioral Analysis of Service Composition Patterns in ECBS Using Petri-Net-Based Approach

The service composition and scheduling activities are facing performance and complexity issues because of (a) large number of heterogeneous clouds, (b) integrating various service components into composite service. In order to facilitate such issues, the advent of automatic dynamic web service composition and scheduling methodology is required, such that the current trends of problem like service reusability, flexibility, statelessness, efficiency, etc. can be addressed. This work focus on the web service composition process in multi-cloud architecture, where various types of service composition patterns are discussed. The service composition patterns are classified according to the degree of heterogeneity of the services. It also helps to design the dynamic facets of composition patterns using Enterprise Service Composition Petri net (ESCP) model. Further, using the concepts of ESCP model and the reachability graph, several key properties like safeness, boundedness, fairness, etc. are analyzed formally.
Static Parameter Estimation on SO(3) using Stochastic Gradient Descent for Visual Tracking

This paper presents a method for parameter estimation using a particle filter, where the parameters are elements on the manifold SO(3). The presented method is based on Stochastic Gradient Descent, which makes it possible to perform the static parameter estimation on-line. The presented method is coordinate-free and is therefore not prone to singularities. The proposed method is demonstrated for estimating the motion of a crane load, where markers on the crane load facilitate visual tracking. The pose of each marker relative to the crane load is initially unknown, but are found within four seconds using the proposed method.
Reducing Localization Error of Vision-Guided Industrial Robots

In many manufacturing applications, such as automated drilling or inspection of large parts, accurate knowledge of both position and orientation of the robot end-effector is critical. In this paper, a method for reducing robot end-effector position and orientation error is presented. Experimental results show that the method can reduce the median position error by 97% (to 0.3 mm) and the median orientation error by 57% (to 0.27 deg). Limitations of the method caused by the hand-eye calibration are discussed.
VR-Fit: Walking-in-Place Locomotion with Real Time Step Detection for VR-Enabled Exercise

With recent advances in mobile and wearable technologies, virtual reality (VR) found many applications in daily use. Today, a mobile device can be converted into a low-cost immersive VR kit thanks to the availability of do-it-yourself viewers in the shape of simple cardboards and compatible software for 3D rendering. These applications involve interacting with stationary scenes or moving in between spaces within a VR environment. VR locomotion can be enabled through a variety of methods, such as head movement tracking, joystick-triggered motion and through mapping natural movements to translate to virtual locomotion. In this study, we implemented a walk-in-place (WIP) locomotion method for a VR-enabled exercise application. We investigate the utility of WIP for exercise purposes, and compare it with joystick-based locomotion in terms of step performance and subjective qualities of the activity, such as enjoyment, encouragement for exercise and ease of use. Our technique uses vertical accelerometer data to estimate steps taken during walking or running, and locomotes the user’s avatar accordingly in virtual space. We evaluated our technique in a controlled experimental study with 12 people. Results indicate that the way users control the simulated locomotion affects how they interact with the VR simulation, and influence the subjective sense of immersion and the perceived quality of the interaction. In particular, WIP encourages users to move further, and creates a more enjoyable and interesting experience in comparison to joystick-based navigation.
Context Attention Module for Human Hand Detection

Human hand detection is still a challenging problem in a real world setting because the hand appearance features are less and varied form. So it is very important to make good use of context and enhance hand feature through each other. In this paper, we propose Context Attention Feature Pyramid Networks CA-FPN for human hand detection, where Context Attention Module (CAM) is introduced as a new component of the pyramid architecture. The CAM aims to: (1) capture context information and reserve local edge feature, (2) figure out the relative context for hands. The proposed model achieves state-of-the-art performance on two challenging hand detection datasets, i.e. the Oxford hand dataset and the Vision for Intelligent and Applications (VIVA) Challenge dataset. What's more, processing time of CA-FPN is about 8.5FPS on one TITAN X GPU, which could be applied for real time application.
Linked Data Queriesby a Trialogical Learning Approach

Querying Linked (Open) Data (LOD) by directly using SPARQL could be a painful task for most potential users of semantic data. Several approaches have been proposed to help users in query formulation. They succeed in hiding the underlying complexity but exploit only the monological - individual - approach. Information seeking and retrieval is not merely an individual effort, but it inherently involves various collaborative activities. For this reason, our proposal is to facilitate the exploitation of LODs by wrapping the querying and visualization tool in a social platform environment. In this way, we enable the dialogical approach. Moreover, since the users can collaboratively create datasets and visualizations, and reuse them also out of the social platform, we reach the trialogical learning. In this paper, we present our design approach, our tool, and related tests.
Lightweight Computation to Robust Cloud Infrastructure for Future Technologies (Workshop Paper)

Hardware and software lightweight solutions became the mainstream for current and future emerging technologies. Container-based virtualization provides more efficient and faster solutions than traditional virtual machines, offering good scalability, flexibility, and multi-tenancy. They are capable of serving in a heterogeneous and dynamic environment across multiple domains, including IoT, cloud, fog, and multi-access edge computing. In this paper, we propose a lightweight solution for LCC (Live Container Cloud) that permits the user to access live/remote cloud resources faster. LCC can be embedded as a fog/edge node to permit the users to allocate and deallocate cloud resources. The performance of such a containerization technology is presented.
A Multimedia Based Movie Style Model

Movie style is one abstract representation of a movie and is used in recommendation systems. In this paper, we argue that movie style model is more than just emotional attribute represented by genre, but also includes visual, auditory and compositional attributes. We propose a movie model that takes all these attributes into account and generate a comprehensive movie model by automatically analyzing the contents of the movie. The proposed model does not require manual tagging and is independent of the length of the movie. Using a case study of a set of Hollywood directors we show that the attributes proposed by us are important to model a movie style. We show that predictor using these features recognize the correct director with an accuracy of 76.4%.
An Efficient Approach to Optimize the Learning Rate of Radial Basis Function Neural Network for Prediction of Metastatic Carcinoma

This paper is based on the optimization of linear weights in a radial basis function neural network that connects the hidden layer and the output layer. A new optimization algorithm called dualist algorithm is applied for choosing an optimal learning parameter. A conventional strategy of random selection of radial basis function (RBF) centers and the width, as well as the weights, are estimated by gradient descent method and least square methods, respectively. The ideology behind this study is to predict the occurrence of metastatic carcinoma in human cells by computational approaches. Our simulation consists of comparing the predictive accuracy of harmony search-radial basis function network (RBFN) and dualist-RBFN by optimizing the weight factor. The Wisconsin breast cancer dataset is used as a benchmark to experiment our training pattern. The learning rate (weight factor) is taken as the optimized parameter to obtain the best possible solution.
A Domain-Sensitive Threshold Derivation Method

Software metrics provide means to quantify several attributes of information systems. The effective measurement is dependent of appropriate metric thresholds as they allow characterizing the quality of information systems. Several methods have been proposed to derive metric thresholds, however, previous methods do not take characteristics of software domains into account, such as the difference between size and complexity of systems from different domains. Instead, they rely on (generic) thresholds that are derived from heterogeneous systems. Although derivation of reliable thresholds has long been a concern, we also lack empirical evidence about threshold variation across distinct mobile software domains. This paper proposes a domain-sensitive method to derive thresholds that respects metric statistics and is based on benchmarks of systems from the same domain. To evaluate our method, we manually mined one hundred mobile systems from Fossdroid, measured them using a set of seven well-known metrics, derived thresholds, and validated them through qualitative and quantitative analyses. As a result, we observed that our method gathered more reliable thresholds considering software domain as a factor when building benchmarks for threshold derivation.
Effectiveness Evaluation of Wartime Equipment Maintenance Support Based on Bayesian Network

Abstract Starting with the definition of wartime equipment maintenance support effectiveness, the paper deeply analyses the difference between equipment maintenance support effectiveness and equipment maintenance support capability. Based on Bayesian network theory and experts experience knowledge in the field of equipment maintenance, an evaluation model of wartime equipment maintenance and support effectiveness is established, which takes equipment command efficiency, equipment maintenance efficiency and supply efficiency as main evaluation indicators. Based on Netica software, the Bayesian network for evaluating the effectiveness of wartime equipment maintenance support is constructed. The gradient descent method is used to learn network parameters. Finally, the feasibility of the model is proved by a practical case.
Design on four-axis aircraft control system based on Somatosensory interaction

Abstract The four-axis aircraft has the characteristics of small size and symmetrical structure, and has important application value in complex environmental monitoring such as block, tunnel, indoor and close to the ground. However, the aircraft uses remote control and the overall control is difficult. To this end, a four-axis aircraft control system based on somatosensory interaction is presented in this paper. The Leap Motion controller is used to identify and read the hand motion data, and the control system reads the hand motion to realize the flight control of the four-axis aircraft. Research shows that the four-axis aircraft control system can effectively recognize the operator’s gestures. The gesture control target attitude value is very close to the aircraft attitude angle, the control rate is high and the corresponding speed is fast, which has certain application value.
Database Intrusion Detection Using Adaptive Resonance Network Theory Model

In the current automation world, every organizations starting from education to industry and also in research organization are maintaining database and several security breaches are found in these databases. Traditional database security mechanism cannot handle the malicious access toward database. Although, various researches have been done in database intrusion detection, but most of the researches are limited in efficiency and accuracy. Inspired by human cognitive system, we present a database intrusion detection system using Adaptive Resonance Theory which is accompanied with some data mining techniques for preprocessing data. The proposed model can learn easily and cope up with the dynamic environment which entitles the system to detect both known and unknown patterns accurately with low false positive cost. The calculated simulation result shows that the database intrusion detection based on Adaptive Resonance Theory can accelerate the detection process with higher accuracy as compared to Self Organizing Map and Radial basis functional neural network.
Coordination of Cyclic Motion Processes in Free-Ranging Multiple Mobile Robot Systems

We consider a Multiple Mobile Robot System (MMRS) viewed as a group of autonomous robots sharing a common 2D motion space. Each robot performs a mission that requires it to travel a number of times along a specific, independently planned closed path. The robots operate asynchronously and are able to control their motion with path-following algorithms that allow each of them to correctly perform its mission when alone on the stage. When sharing the motion space, the robots must refine their motion strategies in order to avoid collisions, through modification of their paths, velocity profiles or both. Following our earlier contributions, we represent MMRS as a class of RAS (Resource Allocation System) that abstracts in a discrete form the motion space and the motion processes of the robots. A model of the feasible dynamic behavior of the robot system is then obtained by mapping the distinguished RAS into a DFSA (Deterministic Finite State Automaton) that ensures collision avoidance among the robots. Based on this model, we formulate the deadlock avoidance problem, discuss its complexity, and demonstrate relevant algorithms to solve it. Finally, we propose a control architecture that implements the described control logic and combines it with the priority control, thus receiving a flexible controller for MMRS.