Reviewerly/exHarmony huggingface dataset:
DSN: A New Deformable Subnetwork for Object Detection

Although deep convolutional neural networks have achieved great success in object detection, they depend heavily on considerable training data and do not have a specific mechanism to handle related challenging problems, such as object deformation. In this paper, we design a deformable subnetwork (DSN) to introduce the deformable part-based model (DPM) in the deep object detection framework. It is effective for handling object deformation and is composed of two significant parts: the deformation coefficient part and the deformation pooling part. The deformation coefficient part is responsible for generating the deformation coefficients for each position of the input. The deformation pooling part calculates the final score for each position which takes into account its displacement penalty relative to the root position. DSN is convenient for being embedded into the most prevalent object detection frameworks such as faster-RCNN or RFCN. More importantly, it does not impair the integrity of the original framework and only causes little time consumption. We show the effectiveness of DSN via experiments on the PASCAL VOC and COCO datasets, achieving state-of-the-art results, 82.7% for PASCAL VOC and 32.1% for COCO.
Analysis of Image Feature Characteristics for Automated Scoring of HER2 in Histology Slides

The evaluation of breast cancer grades in immunohistochemistry (IHC) slides takes into account various types of visual markers and morphological features of stained membrane regions. Digital pathology algorithms using whole slide images (WSIs) of histology slides have recently been finding several applications in such computer-assisted evaluations. Features that are directly related to biomarkers used by pathologists are generally preferred over the pixel values of entire images, even though the latter has more information content. This paper explores in detail various types of feature measurements that are suitable for the automated scoring of human epidermal growth factor receptor 2 (HER2) in histology slides. These are intensity features known as characteristic curves, texture features in the form of uniform local binary patterns (ULBPs), morphological features specifying connectivity of regions, and first-order statistical features of the overall intensity distribution. This paper considers important properties of the above features and outlines methods for reducing information redundancy, maximizing inter-class separability, and improving classification accuracy in the combined feature set. This paper also presents a detailed experimental analysis performed using the aforementioned features on a WSI dataset of IHC stained slides.
Applying queue theory for modeling of cloud computing: A systematic review

Summary The cloud computing paradigm is an important service in the Internet for sharing and providing resources in a cost‐efficient way. Modeling of a cloud system is not an easy task because of the complexity and large scale of such systems. Cloud reliability could be improved by modeling the various aspects of cloud systems, including scheduling, service time, wait time, and hardware and software failures. The aim of this study is to survey research studies done on the modeling of cloud computing using the queuing system in order to identify where more emphasis should be placed in both current and future research directions. This paper follows the goal by investigating the articles published between 2008 and January 2017 in journals and conferences. A systematic mapping study combined with a systematic literature review was performed to find the related literature, and 71 articles were selected as primary studies that were classified in relation to the focus, research type, and contribution type. We classified the modeling techniques of cloud computing using the queuing theory in seven categories based on their focus area: (1) performance, (2) quality of service, (3) workflow scheduling, (4) energy savings, (5) resource management, (6) priority‐based servicing, and (7) reliability. A majority of the primary articles focus on performance (37%), 15% of them focus on resource management, 14% of them focus on quality of service, 13% of them focus on workflow scheduling, 13% of them focus on energy savings, 4% of them focus on priority‐based servicing for requests, and 4% of them focus on reliability. This work summarizes and classifies the research efforts conducted on applying queue theory for modeling of cloud computing (AQTMCC), providing a good starting point for further research in this area.
2D-3D Registration With Weighted Local Mutual Information in Vascular Interventions

Registration between the intraoperative 2D digital subtraction angiography (DSA) and the pre-operative 3D computed tomography angiography (CTA) is useful in the guidance of vascular interventional surgery (VIS). Because of the flow and diffusion of contrast in vessels, not all the vessels are visible in the DSA, which leads to difficulty in registration. Although the conventional mutual information (MI) and the normalized MI (NMI) perform well in orthopedic surgery, it cannot perform as well in VIS because of the missing vessel in DSA. In this paper, a novel similarity measure, WLMI (Weighted Local Mutual Information), is proposed to perform 2D-3D registration, which uses the patches selected in DSA to find the best match in the DRRs (digital radiography reconstruction) with weighted MI. At first, we choose several ways to measure the richness of block information to select small and scattered patches in the DSA image. Subsequently, different weights are assigned to these patches according to their information abundance by calculating the local MI. With the proposed WLMI, the 2D-3D registration experiments are conducted with the synthetic and real DSA images to CTA vessel model. In synthetic registration, the optimal way to measure information abundance and the optimal weight parameter value are selected. The registration with intact and partially absent vessels are compared with the conventional methods using MAE, mTREproj, and IoU metrics. In the registration of real DSA to CTA model, only IoU is used to compare the difference since the ground-truth cannot be derived. In synthetic registration with vessel excalation test, the mTREproj is 2.9 mm and the IoU is 96.7%. In a real registration test, the IoU reaches 81.3%. Both the synthetic and real DSA registration results show that the proposed WLMI can cope with the problem that partial vessel is invisible in the DSA, and outperformed the conventional methods such as MI and NMI. Therefore, the WLMI is more suitable to be used in the 2D/3D registration task in VIS.
A Spammer Identification Method for Class Imbalanced Weibo Datasets

Nowadays, Weibo has become a significant and popular information sharing platform in China. Meanwhile, spammer identification has been a big challenge for it. To mitigate the damage caused by spammers, classification algorithms from machine learning have been applied to distinguish spammers and non-spammers. However, most of the previous studies overlook the class imbalance problem of real-world data. In this paper, by analyzing the characteristics of spammers in Weibo, we select microblog content similarity, the average number of links, and the other 12 features to construct a comprehensive feature vector never seen before. Considering the existence of imbalance problems in spammer identification, an ensemble learning method is used to combine multiple base classifiers for improving the learning performance. During the training stage of base learners, fuzzy-logic-based oversampling and cost-sensitive support vector machine are considered to tackle imbalanced data at both data and algorithmic levels. The experimental results demonstrate that compared with the existing state-of-the-art methods, the recall rate of our proposed approach increases by 6.5% and reaches the precision value of 87.53% when used to deal with real-world Weibo datasets we collected.
Sentiment Analysis of Student�s Comment by using Long-Short Term Model

Objectives: Teacher’s evaluation in education system is quite important to improve the learning experience ininstitutions. For this purpose, sentiment analysis model is developedto identify the student sentiments from the piece of text. Methods/ Statistical Analysis: Long Short-Term Memory Model (LSTM) is used for analyzing the sentiments expressed by students through textual feedback. For this purpose, dataset has been built through student’s feedback and then divided into 70% and 30% for training and testing. The proposed model has been trained using softmax and adam along with drop out values 0.1 and 0.2. Obtained results showed that our model provides 99%, and 90% accuracy over training and validation with 0.2 and 0.5 losses respectively. Findings: It was found that proposed model provides an efficient way for sentiment analysis for teacher’s evaluation. Model used input as word embedding over the LSTM for mapping the words. Andmoreover, the model is collected significant semantic and syntactic information by implementing pre-trained word vector model. Hence, this model has the prospective to overcome several flaws in traditional methods e.g., bag-of-words, n-gram, Naïve Bayes and SVM models where order and information about word is vanished. The experimental results show that the model can achieve state-ofthe-art accuracy on student feedback dataset. Application/Improvements: The study helps for improving the quality of teaching in education system. And moreover,it will be upgrade by increasing the data samples of neutral comments in dataset. Keywords: Course Evaluation, Opinion Mining, Sentiment Analysis, Student’s Feedback, LSTM, RNN
Automated prostate segmentation of volumetric CT images using 3D deeply supervised dilated FCN

Segmentation of the prostate in 3D CT images is a crucial step in treatment planning and procedure guidance such as brachytherapy and radiotherapy. However, manual segmentation of the prostate is very time-consuming and depends on the experience of the clinician. On the contrary, automated prostate segmentation is more helpful in practice, whereas the task is very challenging due to low soft-tissue contrast in CT images. In this paper, we propose a 3D deeply supervised fully-convolutional-network (FCN) with dilated convolution kernel to automatically segment prostate in CT images. A deep supervision strategy could acquire more powerful discriminative capability and accelerate the optimization convergence in training stage, while concatenating the dilated convolution enlarges the receptive field to extract more global contextual information for accurate prostate segmentation. The presented method was evaluated using 15 prostate CT images and obtained a mean Dice similarity coefficient (DSC) of 0.85&plusmn;0.04 and mean surface distance (MSD) of 1.92&plusmn;0.46 mm. The experimental results show that our approach yields accurate CT prostate segmentation, which can be employed for the prostate-cancer treatment planning of brachytherapy and external beam radiotherapy.
Accuracy of Distinguishing Atypical Ductal Hyperplasia From Ductal Carcinoma In Situ With Convolutional Neural Network–Based Machine Learning Approach Using Mammographic Image Data

OBJECTIVE. The purpose of this study was to test the hypothesis that convolutional neural networks can be used to predict which patients with pure atypical ductal hyperplasia (ADH) may be safely monitored rather than undergo surgery. MATERIALS AND METHODS. A total of 298 unique images from 149 patients were used for our convolutional neural network algorithm. A total of 134 images from 67 patients with ADH that had been diagnosed by stereotactic-guided biopsy of calcifications but had not been upgraded to ductal carcinoma in situ or invasive cancer at the time of surgical excision. A total of 164 images from 82 patients with mammographic calcifications indicated that ductal carcinoma in situ was the final diagnosis. Two standard mammographic magnification views of the calcifications (a craniocaudal view and a mediolateral or lateromedial view) were used for analysis. Calcifications were segmented using an open-source software platform and images were resized to fit a bounding box of 128 × 128 pixels. A topology with 15 hidden layers was used to implement the convolutional neural network. The network architecture contained five residual layers and dropout of 0.25 after each convolution. Patients were randomly separated into a training-and-validation set (80% of patients) and a test set (20% of patients). Code was implemented using open-source software on a workstation with an open-source operating system and a graphics card. RESULTS. The AUC value was 0.86 (95% CI, ± 0.03) for the test set. Aggregate sensitivity and specificity were 84.6% (95% CI, ± 4.0%) and 88.2% (95% CI, ± 3.0%), respectively. Diagnostic accuracy was 86.7% (95% CI, ± 2.9). CONCLUSION. It is feasible to apply convolutional neural networks to distinguish pure atypical ductal hyperplasia from ductal carcinoma in situ with the use of mammographic images. A larger dataset will likely result in further improvement of our prediction model.
Prediction of Volleyball Trajectory Using Skeletal Motions of Setter Player

In this paper, we present a method that predicts the ball trajectory of a volleyball toss 0.3 s before the actual toss by observing the motion of the setter player. We input 3D data of body joints obtained using Kinect into a simple neural network, and 2D data estimated using OpenPose is used for comparison. We created simple neural networks for the two players and tested them. The trajectory of a volleyball toss is properly predicted by the proposed method and the error of the toss trajectory was approximately equal to the size of the ball. This technology can provide a new spectating experience in sports by superimposing the predicted images onto a live broadcast. We also show that this method can be used to identify the important body parts that contribute to the toss prediction. A professional volleyball analyst stated that this technology can be used for analyzing the peculiarities of opponent players.
Improving Model Inference in Industry by Combining Active and Passive Learning

Inferring behavioral models (e.g., state machines) of software systems is an important element of re-engineering activities. Model inference techniques can be categorized as active or passive learning, constructing models by (dynamically) interacting with systems or (statically) analyzing traces, respectively. Application of those techniques in the industry is, however, hindered by the trade-off between learning time and completeness achieved (active learning) or by incomplete input logs (passive learning). We investigate the learning time/completeness achieved trade-off of active learning with a pilot study at ASML, provider of lithography systems for the semiconductor industry. To resolve the trade-off we advocate extending active learning with execution logs and passive learning results. We apply the extended approach to eighteen components used in ASML TWINSCAN lithography machines. Compared to traditional active learning, our approach significantly reduces the active learning time. Moreover, it is capable of learning the behavior missed by the traditional active learning approach.
PoWaP: Proof of Work at Proximity for a crowdsensing system for collaborative traffic information gathering

Blockchain technologies has become popular since the appearance of the first electric value exchange system on the Internet: Bitcoin. This technologies enable reliable data structure against tampering even in the absence of any authority to rely on, instead to check consistency with everyone who joins in the system. At present, however, the practical application of the blockchain technologies is to issue currency mainly traded for market speculation or money game. On the other hand, many researchers and software developers believe its applicability not only to improving today’s bank systems but also to creating a new solution to nonbank areas without relying on any centralized control system. In the field of transportation technologies for smart city, the combination of self-driving connected cars and IoT have been recently studied to achieve more efficient and safer transportation. To encourage a reduction of traffic jams by controlling mobility efficiently, it is necessary to automatically gather high-definition traffic information about traffic counts and road closures on urban roads. In this paper, a distributed crowdsensing system for collaborative gathering of traffic information is proposed. In the system, special beacon devices are asumed to be deployed along road segments by many unspecified individuals like today’s Wi-Fi routers. These devices at geographical proximity collaboratively and automatically record which vehicles passed in the road segment into the blockchain using Proof of Work (PoW). This process is incentivized by issuing its own digital currency. The system also enhances anti-tampering performance by cross-referencing blockchains of multiple road segments with each other. By chronologically saving traffic data into blocks in the blockchain, each beacon can refer traffic count and abnormal traffic, such as traffic jam and accidents. To evaluate the proposed system, we demonstrate some simple numerical simulations to show how the system makes localized PoW based consensus, and also the blockchain responds to quasi-real-time traffic status in the road segment.
Improving the energy efficiency of relational and NoSQL databases via query optimizations

As big data becomes the norm of various industrial applications, the complexity of database workloads and database system design has increased significantly. To address these challenges, conventional relational databases have been constantly improved and NoSQL databases such as MongoDB and Cassandra have been proposed and implemented to compete with SQL databases. In addition to traditional metrics such as response time, throughput, and capacity, modern database systems are posing higher requirements on energy efficiency due to the large volume of data that need to be stored, queried, updated, and analyzed. While decades of research in the database and data processing communities has produced a wealth of literature that optimize for performance, research on optimizations for energy efficiency has been historically overlooked and only a few studies have investigated the energy efficiency of database systems. To the best our knowledge, there are currently no comprehensive studies that analyze the impact of query optimizations on performance and energy efficiency across both relational and NoSQL databases. In fact, the energy behavior of many basic database operations (e.g. insertion, deletion, searching, update, indexing, etc) remains largely unknown due to the lack of accurate power measurement methodologies for various databases and queries. In this paper, we investigate a series of query optimization techniques for improving the energy-efficiency of relational databases and NoSQL databases. We use both widely acceptable benchmarks (e.g. Yahoo! Cloud Server Benchmark) and customized datasets (converted from ˜100GB of Twitter data) in our experiments to evaluate the effectiveness of various optimization techniques. We conduct cross database analysis on relational database (MySQL) and NoSQL based databases (MongoDB and Cassandra) to compare their performance and energy efficiency. Additionally, we study a variety of optimization techniques that can improve energy efficiency without compromising performance on the databases derived from the Twitter data. Using these techniques, we are able to achieve significant energy savings without performance degradation. Moreover, we investigate the impact of Dynamic Voltage and Frequency Scaling (DVFS) on the performance and energy efficiency of MySQL, MongoDB and Cassandra.
New Landscapes of the Data Stream Processing in the era of Fog Computing

The "New Landscapes of the Data Stream Processing in the era of Fog Computing" special issue aims to present new research works on topics related to recent advances in Data Streaming Processing (DSP) computing paradigm in the emerging environments of Fog Computing and Internet of Things (IoT). The papers included in this special issue are relevant examples of recent research achievements in the definition of new DSP applications in the Fog Computing context, of run-time systems mechanisms and techniques targeting DPS frameworks, and also of new high-level interfaces for data streaming in highly dynamic IT environments.
Data Science Support at the Academic Library

Data science is a rapidly growing field with applications across all scientific domains. The demand for support in data science literacy is outpacing available resources at college campuses. The academic library is uniquely positioned to provide training and guidance in a number of areas relevant to data science. The University of Arizona Libraries has built a successful data science support program, focusing on computational literacy, geographic information systems, and reproducible science. Success of the program has largely been due to the strength of library personnel and strategic partnerships with units outside of the library. Academic libraries can support campus data science needs through professional development of current staff and recruitment of new personnel with expertise in data-intensive domains.
A Secure G-Cloud-Based Framework for Government Healthcare Services

Within the literature, we have witnessed in the healthcare sector, the growing demand for and adoption of software development in the cloud environment to cope with and fulfill current and future demands in healthcare services. In this paper, we propose a flexible, secure, cost-effective, and privacy-preserved cloud-based framework for the healthcare environment. We propose a secure and efficient framework for the government EHR system, in which fine-grained access control can be afforded based on multi-authority ciphertext-policy attribute-based encryption (CP-ABE), together with a hierarchical structure, to enforce access control policies. The proposed framework will allow decision-makers in Saudi Arabia to develop the healthcare sector and to benefit from the existing e-government cloud computing platform “Yasser,” which is responsible for delivering shared services through a highly efficient, reliable, and safe environment. This framework aims to provide health services and facilities from the government to citizens (G2C). Furthermore, multifactor applicant authentication has been identified and proofed in cooperation with two trusted authorities. The security analysis and comparisons with the related frameworks have been conducted.