Reviewerly/exHarmony huggingface dataset:
The Analysis of Shape Features for the Purpose of Exercise Types Classification Using Silhouette Sequences

This paper presents the idea of using simple shape features for action recognition based on binary silhouettes. Shape features are analysed as they change over time within an action sequence. It is shown that basic shape characteristics can discriminate between short, primitive actions performed by a single person. The proposed approach is tested on the Weizmann database using a various number of classes. Binary foreground masks (silhouettes) are replaced with convex hulls, which highlights some shape characteristics. Centroid locations are combined with some other simple shape descriptors. Each action sequence is represented using a vector with shape features and Discrete Fourier Transform. Classification is based on leave-one-sequence-out approach and employs Euclidean distance, correlation coefficient or C1 correlation. A list of processing steps for action recognition is explained and followed by some experiments that yielded accuracy exceeding 90%. The idea behind the presented approach is to develop a solution for action recognition that could be applied in a kind of human activity recognition system associated with the Ambient Assisted Living concept, helping adults increasing their activity levels by monitoring them during exercises.
Communicaton On Mobile Phone For The Deaf Using Image Recognition

Sign language is an absolute thing that is mastered by some people, especially people who have hearing or deaf problems. Problems will occur if they want to communicate via mobile devices. They have to use an application that translate sign language to text and vice versa. The research in this field still open because each language will have different sign language. In the other hand the image that was captured by the mobile devices depend on the environment such as brightness and the background. The sign language captured with static background has high accuracy compared with dynamic background that has colorful background. Most of research do not include the dynamic background. Therefore in this study introduced an application used image recognition that can translate sign language with dynamic background into a text so that it can be used to communicate among people who have hearing problems and also between deaf and normal people. The experimental showed that accuracy of our system is 87%.
Multi-Image Blind Deblurring Using a Smoothed NUV Prior and Iteratively Reweighted Coordinate Descent

A new method for blind image deblurring is proposed that relies on a smoothed-NUV (normal with unknown variance) prior for images, which promotes piecewise smooth images with crisp edges. The proposed method can use multiple blurred versions of the same image. The variational representation of the prior allows the joint estimation of the image and the blurring kernel(s) to be decomposed into descent steps in reweighted least-squares problems and nonlinear scalar updates of the individual variances of the prior. Specifically, we propose an iteratively reweighted coordinate descent algorithm that has no parameters. Simulation results demonstrate that the proposed approach compares favorably to state-of-the-art methods.
Performance Modeling and Prediction of Big Data Workflows: An Exploratory Analysis

Many next-generation scientific and business applications feature large-scale data-intensive workflows, which require massive computing resources for execution on high-performance clusters in cloud environments. Such computing resources (e.g., VCores and virtual memory) requested through parameter setting in big data systems, if not fully utilized by workloads, are simply wasted due to the nature of exclusive access made possible by containerization. This necessitates accurate modeling and prediction of workflow performance to make an effective recommendation of appropriate parameter settings to end users. However, it is challenging to determine optimal workflow and system configurations due to the large parameter space and the interaction between various technology layers of big data systems. Towards this goal, we propose a machine learning-based feature selection method to identify influential parameters based on historical performance measurements of Spark-based computing workloads executed in big data systems with YARN. We first identify a comprehensive set of parameters across multiple layers in the big data technology stack including workflow input structure, Spark computing engine, and YARN resource management. We then conduct an in-depth exploratory analysis of their individual and coupled impact on workflow performance, and develop a performance-influence model using random forest for prediction. Experimental results show that the proposed approach identifies important features for performance modeling and achieves high accuracy in performance prediction.
IT-Block: Inverted Triangle Block embedded U-Net for Medical Image Segmentation

Convolutional neural network (CNN) such as U-Net has demonstrated excellent performance for medical image segmentation. However, there are some limitations of its scalability. Specifically, the memory would increase significantly when embedding other functional modules into U-Net. Moreover, the kernel size used in U-Net is unitary, which makes it difficult to obtain the multi-level information and extract the target completely. In this paper, we only use 12 convolutional layers of U-Net as a backbone and design a novel architecture named Inverted Triangle (IT) Block embedded into it to address these problems. The IT-Block consists of Dense Connection, Residual Connection, and Inception, aiming to help the network obtain multi-level features and reuse them comprehensively. Furthermore, we optimize the dice loss to alleviate the butterfly effect, making the training process more stable during the backpropagation. The experimental results state that our framework is superior to U-Net in running time and accuracy.
Speech-based interaction for map editing on mobile devices

Speech-based interfaces on mobile phones are growing in popularity, yet mostly limited to helping users retrieve information. There is an untapped potential for using speech to help users contribute new information. This work discusses opportunities and challenges of map editing on mobile devices. It also presents results from a study (N=20) that evaluated 11 speech-based commands for map data enriching and urban fault reporting on mobile devices. Feedback from the users indicated that speech-based information editing is feasible: usability was rated as `good', while user experience ratings produced slightly mitigated results.
Hierarchical Model For Long-Length Video Summarization With Adversarially Enhanced Audio/Visual Features

In this paper, we propose a novel supervised method for summarizing long-length videos. Many recent approaches presented promising results in video summarization. However, videos in most benchmark datasets are short in duration (<; 10 minutes), and the methods often do not work well for very long-length videos (>1 hour). Furthermore, most approaches only use visual features, while audios provide useful information for the task. Based on these observations, we present a model that exploits both audio and visual features. To handle long videos, the hierarchical structure of our model captures both the short-term and long-term temporal dependencies. Our model also refines the extracted features using adversarial networks. To demonstrate our model, we have collected a new dataset of 28 baseball (~3.5 hours) videos, accompanied by an editorial summary video that is 5% in length of the original video. Evaluation on the dataset suggests that our method produces quality summaries for very long videos.
Director-360: Introducing Camera Handling to 360 Cameras

This work introduces the concept of camera handling for a 360 camera and proposes Director-360, a 360 camera enhanced with two novel handling techniques. Pointer and field-of-view (FoV) are designed to explicitly and implicitly capture the 360 photographer's subject of interest within the 360 media at capture time. Pointer lets users specify a subject of interest about the scene by directly pointing the 360 camera as if using the camera as a flashlight, while FoV captures the user's subject of interest within the 360 scene by mapping the user's face direction to the 360 media. We described an implementation using deep-learning algorithms. We also presented the Director-360 Editor, which incorporates the handling data to streamline the post-editing process. To understand how Director-360 helps to compose 360 media, a pilot study was carried out to create video storytelling in three target scenarios. The results and user feedback from a pilot study were reported.
Analyzing the Impact of Assessing Requirements Specifications on the Software Development Life Cycle

Developing an efficient and quality Software Requirements Specification (SRS) is based on software quality characteristics assessment such as completeness, consistency, feasibility and testability. These characteristics or attributes provide reasonably accurate predictions about system-free bias requirements and hidden assumptions and limit subsequent redesign. They additionally give realistic estimates for costs, risks, and timing of the product. This paper aims to identify possible rules and methods for measuring SRS quality in order to help the engineers to improve the quality of their SRS. The impact of these rules and methods on the software development lifecycle is also reviewed. In this paper, some methods of SRS quality assessment were analyzed from the literature and how to measure the impact of these SRS quality assessment methods on the software development lifecycle are also presented.
Data Allocation and Replication in Data Center: Tradeoff and Solutions

Data centers are important platforms and resources for many data intensive applications. A data center typically consists of many servers, which store a huge amount of data and perform computing tasks. Initial data needs to be transferred to the data center for users to access. It is usually necessary to replicate data for better reliability and fault tolerance. In this paper, we consider that the data center loads data from external storage devices. We investigate how to allocate and replicate data. We particularly emphasize the impact of data replication and present three goals, i.e., maximizing total data access frequencies, maximizing total replicas, and balancing the number of replicas. We propose different solutions to explore the tradeoff. Performance evaluation shows these solutions can handle various goals well and efficiently replicate data.
Virtual Reality: Prospective Catalyst for Restorative Justice

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Automatic segmentation algorithm for breast cell image based on multi-scale CNN and CSS corner detection

As the shapes of breast cell are diverse and there is adherent between cells, fast and accurate segmentation for breast cell remains a challenging task. In this paper, an automatic segmentation algorithm for breast cell image is proposed, which focuses on the segmentation of adherent cells. First of all, breast cell image enhancement is carried out by the staining regularization. Then, the cells and background are separated by Multi-scale Convolutional Neural Network (CNN) to obtain the initial segmentation results. Finally, the Curvature Scale Space (CSS) corner detection is used to segment adherent cells. Experimental results show that the proposed algorithm can achieve 93.01% accuracy, 93.93% sensitivity and 95.69% specificity. Compared with other segmentation algorithms of breast cell, the proposed algorithm can not only solve the difficulty of segmenting adherent cells, but also improve the segmentation accuracy of adherent cells.
Cheating Death

We apply survival analysis methods to a dataset of publicly-available software projects in order to examine the attributes that might lead to their inactivity over time. We ran a Kaplan-Meier analysis and fit a Cox Proportional-Hazards model to a subset of Software Heritage Graph Dataset, consisting of 3052 popular Python projects hosted on GitLab/GitHub, Debian, and PyPI, over a period of 165 months. We show that projects with repositories on multiple hosting services, a timeline of publishing major releases, and a good network of developers, remain healthy over time and should be worthy of the effort put in by developers and contributors.
A New Local Radon Descriptor for Content-Based Image Search

Content-based image retrieval (CBIR) is an essential part of computer vision research, especially in medical expert systems. Having a discriminative image descriptor with the least number of parameters for tuning is desirable in CBIR systems. In this paper, we introduce a new simple descriptor based on the histogram of local Radon projections. We also propose a very fast convolution-based local Radon estimator to overcome the slow process of Radon projections. We performed our experiments using pathology images (KimiaPath24) and lung CT patches and test our proposed solution for medical image processing. We achieved superior results compared with other histogram-based descriptors such as LBP and HoG as well as some pre-trained CNNs.
Multi-target regression via output space quantization

Multi-target regression is concerned with the prediction of multiple continuous target variables using a shared set of predictors. Two key challenges in multi-target regression are: (a) modelling target dependencies and (b) scalability to large output spaces. In this paper, a new multi-target regression method is proposed that tries to jointly address these challenges via a novel problem transformation approach. The proposed method, called MRQ, is based on the idea of quantizing the output space in order to transform the multiple continuous targets into one or more discrete ones. Learning on the transformed output space naturally enables modeling of target dependencies while the quantization strategy can be flexibly parameterized to control the trade-off between prediction accuracy and computational efficiency. Experiments on a large collection of benchmark datasets show that MRQ is both highly scalable and also competitive with the state-of-the-art in terms of accuracy. In particular, an ensemble version of MRQ obtains the best overall accuracy, while being an order of magnitude faster than the runner up method.
Active Learning of Decomposable Systems

Active automata learning is a technique of querying black box systems and modelling their behaviour. In this paper, we aim to apply active learning in parts. We formalise the conditions on systems---with a decomposable set of actions---that make learning in parts possible. The systems are themselves decomposable through non-intersecting subsets of actions. Learning these subsystems/components requires less time and resources. We prove that the technique works for both two components as well as an arbitrary number of components. We illustrate the usefulness of this technique through a classical example and through a real example from the industry.
NASCon: Network-Aware Server Consolidation for server-centric wireless datacenters

Servers in datacenter are underutilized due to over-provisioning, which contributes heavily toward high-power consumption of the datacenters. Server consolidation can drastically reduce power consumption of datacenters by reducing the number of active servers. However, server consolidation alone can adversely affect network performance. Recently, wireless datacenters using millimeter-wave communications have been proposed and shown to drastically reduce power consumption of networking equipment. Therefore, server-consolidation strategies should leverage novel datacenter network-architectures to minimize power consumption with minimal impact on performance. We present network-aware bandwidth-constrained server consolidation algorithm called Network-Aware Server Consolidation (NASCon), for wireless datacenters that can reduce the power consumption up to 37%. In wireless datacenter, NASCon server consolidation consumes comparable power compared to consolidation optimized with exhaustive search, while being much less computationally expensive and also improving the network performance. However, due to arrival of new-tasks and completion of existing-tasks, consolidated utilization profile of servers change, which may have adverse effect on overall power consumption over time. To overcome this, NASCon algorithm needs to be executed periodically. We propose a mechanism to find optimal inter-consolidation time and derived a mathematical model for the same. The model reduces the computing overheads for determining the optimal inter-consolidation time enabling real-time implementation.
Towards molecular musical instruments

Scientists increasingly rely on computational models of atoms and molecules to observe, understand and make predictions about the microscopic world. Atoms and molecules are in constant motion, with vibrations and structural fluctuations occurring at very short time-scales and corresponding length-scales. But can these microscopic oscillations be converted into sound? And, what would they sound like? In this paper we present our initial steps towards a generalised approach for sonifying data produced by a real-time molecular dynamics simulation. The approach uses scanned synthesis to translate real-time geometric simulation data into audio. The process is embedded within a stand alone application as well as a variety of audio plugin formats to enable the process to be used as an audio synthesis method for music making. We review the relevant background literature before providing an overview of our system. Simulations of three molecules are then considered: 17-alanine, graphene and a carbon nanotube. Four examples are then provided demonstrating how the technique maps molecular features and parameters onto the auditory character of the resulting sound. A case study is then provided in which the sonification/synthesis method is used within a musical composition.
Upright Adjustment With Graph Convolutional Networks

We present a novel method for the upright adjustment of 360° images. Our network consists of two modules, which are a convolutional neural network (CNN) and a graph convolutional network (GCN). The input 360° images is processed with the CNN for visual feature extraction, and the extracted feature map is converted into a graph that finds a spherical representation of the input. We also introduce a novel loss function to address the issue of discrete probability distributions defined on the surface of a sphere. Experimental results demonstrate that our method outperforms fully connected-based methods.
Corruption-Robust Enhancement of Deep Neural Networks for Classification of Peripheral Blood Smear Images

Deep learning emerges as a promising technology for automated peripheral blood smear analysis and hematologic diagnosis. A big challenge to deep learning model is the accuracy drop when facing image corruptions caused by different smear preparation and digitalization operations. In order to serve the real applications of peripheral blood analysis, a practical deep learning classifier needs to be robust against a wide range of corruptions. In this paper, we first investigate the comprehensive corruption types for peripheral blood smear image and establish the benchmark dataset Smear-C simulating the real physical cause factors. Then we propose a novel method SmearRobust which can easily fit into the existing neural networks and improve the robustness. Experimental results show that SmearRobust can significantly enhance the robustness upon Smear-C dataset. Furthermore, the proposed corruption simulation algorithms and the robust learning method can be potentially applied to bone marrow smear and general pathology areas.
Retraining Quantized Neural Network Models with Unlabeled Data

Running neural network models on edge devices is attracting much attention by neural network researchers since edge computing technology is becoming more powerful than ever. However, deploying large neural network models on edge devices is challenging due to the limitation in available computing resources and storage space. Therefore, model compression techniques have been recently studied to reduce the model size and fit models on resource-limited edge devices. Compressing neural network models reduces the size of a model, but also degrades the accuracy of the model since it reduces the precision of weights in the model. Consequently, a retraining method is required to recover the accuracy of compressed models. Most existing retraining methods require the original labeled training datasets to retrain the models, but labeling is a time-consuming process. In particular, we cannot always access the original labeled datasets because of privacy policies and license limitations. In this paper, we propose a method to retrain a compressed neural network model with an unlabeled dataset that is different from the original labeled dataset. We compress the neural network model using quantization to decrease the size of the model. Subsequently, the compressed model is retrained by our proposed retraining method without using a labeled dataset to recover the accuracy of the model. We compared the proposed retraining method against the conventional retraining. The proposed method reduced the size of VGG-16 and ResNet-50 by 81.10% and 52.45%, respectively without significant accuracy loss. In addition, our proposed retraining method is clearly faster than the conventional retraining method.
Application of Data Mining Algorithms for Tourism Industry

This paper presents different Data Mining Algorithms to be used to access data patterns in Tourism Industry and their applications using Data Mining Tool like WEKA which is a popular software, and useful patterns can be found efficiently. Data Mining, as the name implies is the science by which hidden and interesting information can be found from large datasets analogous to Gold Miners who extract precious metal sieving through a lot of raw material. Normal day to day business operations create huge voluminous data which is needed to be utilized properly to find information to make efficient, effective, and prompt decisions otherwise businesses can not sustain in competitive environment. There are some techniques and algorithms used in data mining for determining interesting hidden patterns. data mining algorithms are becoming popular day by day in various applications like Travel and Tourism. Nowadays, a number of data mining applications are available for Public use. Therefore, we have to judge which particular algorithm or application suits a problem domain. The idea behind applying data mining is very straight forward, it is similar to a human being becoming intelligent from learning by examples and experience. Similarly, in data mining the behavior of given system (training dataset) is learned by application, and certain rules are developed. Then these rules are used to evaluate the behavior/result for the given situation (test dataset). The overall goal of this paper is to classify some data mining algorithms and applications, draw a comparative study that which one is better for tourism industry and under what circumstances.