Reviewerly/exHarmony huggingface dataset:
Design of Real-time Database in Small Satellite Integration Testing System

Aim at reducing the degree of difficulty in data access and data migration of the real-time database, this paper presents a design method for real-time database in small satellite integration testing system based on SQL Server relational database. This real-time database adopts JSON serialization and WriteToServer method in SqlBulkCopy class to batch import the telemetry data into the database. This method realizes real time telemetry data storage and then sets up the real-time database.
Unsupervised Spatial-Temporal Model Based on Region Alignment for Person Re-identification

Abstract Person re-identification(re-ID) refers to find a specific pedestrian across disjoint camera views. Recently, person re-ID rely on supervised learning to train network by labeled information. Resulting poor generalization in real-world environment because of the lack of pedestrian labels. At the same time, person images are easily affected by background, illumination and pose variations. And these factors make it difficult to extract discriminative features to distinguish different pedestrians. In order to resolve this research problem, we proposing an unsupervised learning alignment method called Region Alignment of Spatial-Temporal Fusion(RASTF) which joints global features with local aligned features to get more discriminative features. Local features are aligned by calculating the shortest distances between regions. Our proposed framework integrates a novel region alignment method in unsupervised network and the experiment results indicate that can outperform the state-of-the-art unsupervised methods.
Breast mass image retrieval based on multimodality similarity estimation

Retrieval of similar cases can help radiologists in efficient diagnosis, treatment planning, and preparation of reports for new cases. In this study, similarities of pairs of lesions were estimated using convolutional neural networks with subjective similarity data. The network was trained with pairs of mammograms (MG), pairs of ultrasound images (US), and both as input data and the corresponding similarity ratings by expert radiologists as teacher data. Based on the estimated similarity, the cases with the highest similarities were retrieved for a test case. The precisions of selecting pathology-matched relevant cases were compared for the networks using different input data. In this study, rather a simple network architecture, which takes a pair or pairs of input images and has one regression output layer corresponding to the similarity, provided higher precisions. The precisions using mammograms, ultrasound images, and both modalities were 0.72, 0.68, and 0.80, respectively. The highest precision was obtained by the use of one network with multimodality image inputs than combining the outputs by two separate networks for MG and US data. Relatively high precision indicates that the presentation of reference images can be useful for assisting breast cancer diagnosis.
A Similarity Metric for Face Verification

In order to improve the accuracy of similarity Metric, the method of combining Mobilenetv2 and SSIM is used to solve the problem that Mobilenetv2 is inaccurate in the similarity measure of different faces. We used the face dataset provided by the University of Essex, UK to test and the effectiveness of the method has been verified.
An Analysis Model of Project-Based Teaching Effect Using DNNs

This paper describes the structure and implementation of a teaching quality assurance system and depicts the process of teaching quality assessment of teachers. The results show that project-based teaching (PBL) can bring significant improvement to students' mathematics, communication, team-working, and other abilities. In this paper, a deep neural network (DNN) is used as a data fitting model to quantitatively analyze the effect of PBL teaching.
Measurement and Optimization Models of Risk Management System Usability

Systematization of issues related to the approach to defining and measuring the usability of a Risk Management System (RMS) is one of the key milestones on the way to understand it. A number of models and methods exists in this area. Author attempted to propose the model of the RMS usability and methodology of its application, as a results of in-depth studies carried out by himself in multiple organizations. The outcomes of research are aligned to the requirements that allow their use in any organization regardless of its field of operations, location, size and type of ownership. The model provides fundamentals that enables optimization of the RMS functional configuration in cases where it is possible to generate several acceptable its configuration’s variants for specific organization.
The TaiwanICDF is cooperating with the Swiss-based INGO Terre des hommes (Tdh) to conduct Contact Tracing Technology Applications in Tackling COVID-19 Webinar

Event: Webinar: Contact Tracing Technology Applications in Tackling COVID-19Date: 19 May 2020 19:00-20:00 Taipei | 13:00-14:00 GenevaIn response to the COVID-19 pandemic, governments play a leading role in pandemic prevention and control as well as r
A Study of Online Scams

This chapter presents the overview of how the online fraud began; and how in starting to spread around the world. Mastering the motivation factor fraudster to do scamming. Also the perspectives of victim attitude, social media; the emerging trends of community attitudes and IT among public community among youngster or even among elders; the concept of motivation factors, by fraudsters; the characteristics of victim by dividing to gender and age stages; sustainable online media ;sustainable long hours of consuming internet; the importance of sustainable consumption, sustainability, and sustainable deterrence to avoid people being online scam victim. Mastering how this case working is essential for modern police organizations that seek to serve public and nation, reduce the loses are facing by victim in the future, strengthen strategy in combating online scam. It's also emphasize thoroughly the effect, and the consequences after victim got cheated by online. The chapter argues that mastering fraudster and victim attitude and knowledge to enhance the awareness and reduce online scam.
Non-Global Shared Recursive Network for Image Super-Resolution

Abstract In Super-Resolution(SR) reconstruction, some recursive networks use shared parameters to keep lightweight. But these methods lack flexibility while use shared parameters to process different feature maps. We propose a non-global shared recursive network that consists of recursive units and channel attention modules. Firstly, we propose multi-scale channel attention module to learn the inter-channel interdependence and spatial information within the channel. The multi-scale channel attention module is composed of a multi-scale pooling layer, fully connected layers and activation functions. In order to flexibly process different feature maps in different layers, the attention module parameters are not shared, but only a small number of parameters are used. Secondly, residual dense recursive unit is used to accelerate convergence and feature reuse. In order to further reduce parameters, bottleneck layer is added to the dense connection. Based on the above mechanism, we propose a lightweight and efficient network structure. Experiments show that the proposed method has better performance than other lightweight networks in the standard dataset.
Reasoning About Shared-Variable Concurrency: Interactions Between Research Threads

Most research on concurrency involves either communication-based approaches or accepts the shared-variable model. This paper addresses the latter approach and traces the research from Hoare’s axiomatic approach, through Sue Owicki’s work up to separation logics and rely/guarantee methods. Researchers in these last two approaches have been involved in a friendly rivalry and cooperation. The focus is on the insights that have arisen rather than technical details.
Foreword to special section on motion, interactions and games

• Basset et al. present an optimization-based method for motion retargeting. • Ferstl et al. present a deep neural network for hand gesture generation • van Toll et al. present a topology-driven method for crowd simulation.
Fuzzy Optimized Gravitational Search Algorithm for Disease Prediction

In this article, fuzzy logic and gravitational search algorithms have been amalgamated and explored for feature selection in the automated prediction of diseases. The gravitational search algorithm has been used for search optimization while fuzzy logic had been used for its parameter tuning. Feature selection has been considered as a dual objective problem in the article, i.e. selecting minimum number of features without compromising the accuracy of classification, which is performed using K-Nearest Neighbour classifier. The improved algorithm has been tested with various publicly available medical datasets to analyse its effectiveness. The results indicate that the approach not only reduces the feature set by an average of 67.66% but also increases the accuracy by an average of 12%. Further, the results have also been compared with the prior work wherein the feature selection has been done using other evolutionary techniques. It is observed that the proposed approach is able to generate better results in most of the cases.
Model transferring for unsupervised object localization

Object localization becomes considerable research with the rapid expansion of computer vision applications. How to locate an efficient object for vision recognition tasks is constantly a pivotal issue. Unsupervised object localization is extremely challenging because of the deletion of label information. In the last decade, based on a large amount of label information, a lot of localization algorithms have been put forward, some of which are capable of obtaining desirable performance. Whereas, it is unaffordable for providing sufficient labelled data in plenty of real-world applications, which is the reason why unsupervised object localization captured growing attention. In such cases, model transfer would dramatically improve the performance by evading much expensive labelling efforts. Towards this end, this paper focuses on model transferring for solving the unsupervised object localization problem. We propose an effective method for utilizing the pre-trained deep convolutional models to evaluate the correlation of features and predict results between original image and sub-images which generated via object proposal. The most similar sub-image can precisely locate the object for the unlabeled images. There is one more point, extensive experiments validate the effectiveness of the proposed method. On publicly available object localization datasets, this method invariably outperforms existing state-of-the-art methods.
Tense use in dialogue

This paper reports on parallel corpus research that shows that there are differences in tense use in written texts between parts that represent dialogue, and parts that narrate the story. This calls for further study of tense use in dialogue, both in written representations, and in spoken dialogue. Yet, in the dialogue semantics literature almost no prior work exists that is devoted to tense use, neither from a formal, nor from a computational angle. We argue that this gap in dialogue research should be filled by further empirical investigation, as well as the development of computational tools for automated annotation of tense and temporal structure in dialogue. This will not only help understand how speakers track the temporal structure of dialogue, but also give theoretical linguistic literature on tense a wider empirical and computational dimension.
Near-Data Prediction Based Speculative Optimization in a Distribution Environment

Apache Hadoop is an open source software framework that supports data-intensive distributed applications and is distributed under the Apache 2.0 licensing agreement, where consumers will no longer deal with complex configuration of software and hardware but only pay for cloud services on demand. So how to make the performance of the cloud platform become more important in a consumer-centric environment. There exists imbalance between in some distribution of slow tasks, which results in straggling tasks will have a great influence on the Hadoop framework. By monitoring those tasks in real-time progress and copying the potential Stragglers to a different node, the speculative execution (SE) realizes to improve the probability of finishing those backup tasks before the original ones. The Speculative execution (SE) applies this principle and thus proposed a solution to handle the Straggling tasks. At present, the performance of the Hadoop system is unsatisfying because of the erroneous judgement and inappropriate selection for the backup nodes in the current SE policy. This paper proposes an SE optimized strategy which can be used in prediction of near data. In this strategy, the first step is gathering the real-time task execution information and the remaining runtime required for the task is predicted by a local prediction method. Then it chooses a proper backup node according to the near data and actual demand in the second step. On the other side, this model also includes a cost-effective model in order to make the performance of SE to the peak. The results show that using this strategy in Hadoop effectively improves the accuracy of alternative tasks and effects better in heterogeneous Hadoop environments in various situations, which is beneficial to consumers and cloud platform.
A General Framework for Depth Compression and Multi-Sensor Fusion in Asymmetric View-Plus-Depth 3D Representation

We present a general framework which can handle different processing stages of the three-dimensional (3D) scene representation referred to as “view-plus-depth” (V+Z). The main component of the framework is the relation between the depth map and the super-pixel segmentation of the color image. We propose a hierarchical super-pixel segmentation which keeps the same boundaries between hierarchical segmentation layers. Such segmentation allows for a corresponding depth segmentation, decimation and reconstruction with varying quality and is instrumental in tasks such as depth compression and 3D data fusion. For the latter we utilize a cross-modality reconstruction filter which is adaptive to the size of the refining super-pixel segments. We propose a novel depth encoding scheme, which includes specific arithmetic encoder and handles misalignment outliers. We demonstrate that our scheme is especially applicable for low bit-rate depth encoding and for fusing color and depth data, where the latter is noisy and with lower spatial resolution.
Efficient GPU Acceleration for Computing Maximal Exact Matches in Long DNA Reads

The seeding heuristic is widely used in many DNA analysis applications to speed up the analysis time. In many applications, seeding takes a substantial amount of the total execution time. In this paper, we present an efficient GPU implementation for computing maximal exact matching (MEM) seeds in long DNA reads. We applied various optimizations to reduce the number of GPU global memory accesses and to avoid redundant computation. Our implementation also extracts maximum parallelism from the MEM computation tasks. We tested our implementation using data from the state-of-the-art third generation Pacbio DNA sequencers, which produces DNA reads that are tens of kilobases long. Our implementation is up to 9x faster for computing MEM seeds as compared to the fastest CPU implementation running on a server-grade machine with 24 threads. Computing suffix array intervals (first part of MEM computation) is up to 3x faster whereas calculating the location of the match (second part) is up to 9x faster. The implementation is publicly available at https://github.com/nahmedraja/GPUseed
A Proposal Of A Multi-agent Modeling Based On A Parallel Model Of The PSO Algorithm

The objective of this paper is to propose a new multiagent modeling based on a parallel model of the Particle Swarm Optimization algorithm (MAPPSO). This model combines multi-agent systems and parallelism in optimization. Multi-Agent Systems promote the concept of coordination and communication of computational agents. Supporting the parallel PPSO model by multi-agent modeling improves the performance of the proposed model.
A Fast and Accurate Face Alignment Method Based on Deep Learning on Mobile Device

Face alignment is a crucial prerequisite for many face analysis applications. Hence, the algorithm with high speed, high precision and small model becomes very important for applications on mobile devices. Addressing this issue, we propose a new lightweight face alignment method which could run fast on mobile devices by coarse-to-fine deep convolution network. For a given face image, our method firstly detects face landmarks by a global convolutional network to predict initial local landmarks with rich texture and accurate residue landmarks. Then the second local network is used to adjust the accuracy of local landmarks according to the initial locations. Simultaneously, for video-based face alignment, we proposed a simple but effective tracking method by using LK optical flow to ensure the accuracy and stability of facial landmark in video.
A Study on the Robustness of Pitch-Range Estimation from Brief Speech Segments

Pitch-range estimation from brief speech segments could bring benefits to many tasks like automatic speech recognition and speaker recognition. To estimate pitch range, previous studies have proposed to utilize deep-learning-based models with spectrum information as input. They demonstrated that such method works and could still achieve reliable estimation results when the speech segment is as brief as 300 ms. In this study, we evaluated the robustness of this method. We take the following scenarios into account: (1) a large number of training speakers; (2) different language backgrounds; and (3) monosyllabic utterances with different tones. Experimental results showed that: (1) The use of a large number of training speakers improved the estimation accuracies. (2) The mean absolute percentage error (MAPE) rate evaluated on the L2 speakers is similar to that on the native speakers. (3) Different tonal information will affect the LSTM-based model, but this influence is limited compared to the baseline method which calculates pitch-range targets from the distribution of [Formula: see text]0 values. These experimental results verified the efficiency of the LSTM-based pitch-range estimation method.
Instance Segmentation Based on Superpixel Module and Attention Module

Instance segmentation has become a hot topic in computer vision, which is a combination of semantic image segmentation and object detection. In this work, we propose a new network MaskSAN for instance segmentation, which has three outputs: box prediction, semantic segmentation, and classification score. In this network, Superpixel-pooling is used to increase the spatial prior information of features and enhance the edge performance of segmentation. In addition, the network also combines the recently popular attention modules to enhance the feature extraction module of the network and the context information in feature extraction. Without reducing the speed, the accuracy of segmentation and detection is improved. The network makes full use of the image's own information and enhances the constraints. Our proposed network performs experiments on the public coco dataset and has excellent performance in detection and segmentation.
Face Attribute Manipulation Based on Self-Perception GAN

Abstract Manipulating human facial images between two domains is an important and interesting problem in computer vision. Most of the existing methods address this issue by applying two generators or one generator with extra conditional inputs to generate face images with manipulated attribute. In this paper, we proposed a novel self-perception method based on Generative Adversarial Networks (GANs) for automatic face attribute inverse, where giving a face image with an arbitrary facial attribute the model can generate a new face image with the reversed facial attribute. The proposed method takes face images as inputs and employs only one single generator without being conditioned on other inputs. Profiting from the multi-loss strategy and modified U-net structure, our model is quite stable in training and capable of preserving finer details of the original face images. The extensive experimental results have demonstrated the effectiveness of our method on generating high-quality and realistic attribute-reversed face images.
An Improved Whale Swarm Algorithm with Nonlinear Weighting and Convergence Factor

A whale optimization algorithm based on nonlinear convergence factor and inertial weight is proposed to solve the problem of slow convergence and low convergence accuracy. The improved Logistic chaotic map was first used to initialize the swarm and increase the diversity of the swarm. Then the convergence factor of linear change is improved to a piecewise nonlinear convergence factor. Meanwhile, the nonlinear inertial weight is added to enhance the exploration and development ability of the algorithm. In the end, 7 benchmark functions were selected for testing, and the experiment showed that the improved algorithm had fast convergence speed and high precision.
On biased random walks, corrupted intervals, and learning under adversarial design

We tackle some fundamental problems in probability theory on corrupted random processes on the integer line. We analyze when a biased random walk is expected to reach its bottommost point and when intervals of integer points can be detected under a natural model of noise. We apply these results to problems in learning thresholds and intervals under a new model for learning under adversarial design.
Real-time human hand following system based on multiple vision sensors

This paper designs a real-time human hand following system based on multi-vision sensors. Through the KinectV2 camera, human hand behavior can be observed; the pose information of the human hand joint can also be obtained by using OpenNI2 and NITE2 interface; calculating the angle information of each joint of the six-degree-of-freedom robot can be achieved by stereo geometric correlation calculation. Through LeapMotion, accurate human joint information is obtained, then calculating the finger distance to control manipulator's tension, which is smoothed by Kalman filtering. Control, simulation and visualization for real-time follow-up of the robotic arm and the human hand are achieved under the Robot Operating System(ROS). The final experimental results are verified by Moveit's simulation environment and Kinova's Jaco arm.
From Whole Slide Tissues to Knowledge: Mapping Sub-cellular Morphology of Cancer

Digital pathology has made great strides in the past decade to create the ability to computationally extract rich information about cancer morphology with traditional image analysis and deep learning. High-resolution whole slide images of cancer tissue samples can be analyzed to quantitatively extract and characterize cellular and sub-cellular phenotypic imaging features. These features combined with genomics and clinical data can be used to advance our understanding of cancer and provide opportunities to the discovery, design, and evaluation of new treatment strategies. Researchers need reliable and efficient image analysis algorithms and software tools that can support indexing, query, and exploration of vast quantities of image analysis data in order to maximize the full potential of digital pathology in cancer research. In this paper we present a brief overview of recent work done by our group, as well as others, in tissue image analysis and digital pathology software systems.