Reviewerly/exHarmony huggingface dataset:
A Resource Design Framework to Realize Intent-Based Cloud Management

More businesses and organizations are choosing clouds to accommodate their workload, e.g., annalistic departments of enterprises use public/private cloud to deploy the analysis function of data, and telecommunication operators deploy Virtual Network Functions (NFVs) such as virtual Evolved Packet Core (vEPC) on clouds to provide telecommunication services. When utilizing the cloud, the cloud user is concerned about the functionality, reliability, performance, etc. of the cloud-based function, which we call the intent or the service requirements. However, to meet the intent, the user needs to tell the cloud resource orchestrator/controller what kinds of Virtual Machines (VMs) are needed and the amount of computing resources necessary to allocate to each VM, which we call resource requirements. In our preliminary work [1], we addressed this problem and proposed an Intent-Based Cloud Management (IBCM) framework to "translate" the cloud user's service requirements into resource requirements. In this work, we have focused on realizing a Resource Design Framework (RDF) for IBCM that decides the needed computing resource amount to fulfill the cloud user' intent/ service requirements. The main contributions of this work are as follows: (1) We have systematically analyzed factors that need to be taken into consideration when designing the resources. (2) On the basis of the analysis, we propose the architecture of RDF that is applicable for various cloud-based applications and scenarios. To the best of knowledge, this is the first work to address this issue. (3) We have validated the proposed RDF in an experimental cloud-based machine learning scenario. The experiment results show our RDF is able to infer the performance and operation state with over 90% precision, thus enabling resources to be precisely designed in accordance with performance requirements and operation policy. (4) In addition to precise resource design, we also proposed a service level agreement (SLA) violation prevention mechanism for RDF and verified its effectiveness.
HIBEChain: A Hierarchical Identity-Based Blockchain System for Large-Scale IoT

Internet-of-Things enables interconnection of billions of devices, which perform autonomous operations and collect various types of data. These things, along with their generated huge amount of data, need to be handled efficiently and securely. Centralized solutions are not desired due to security concerns and scalability issue. In this article, we propose HIBEChain, a hierarchical blockchain system that realizes scalable and accountable management of IoT devices and data. HIBEChain consists of multiple permissioned blockchains that form a hierarchical tree structure. To support the hierarchical structure of HIBEChain, we design a decentralized hierarchical identity-based signature (DHIBS) scheme, which enables IoT devices to use their identities as public keys. Consequently, HIBEChain achieves high scalability through parallel processing as blockchain sharding schemes, and it also implements accountability by use of identity-based keys. Identity-based keys not only make HIBEChain more user-friendly, they also allow private key recovery by validators when necessary. We provide detailed analysis of its security and performance, and implement HIBEChain based on Ethereum source code. Experiment results show that a 6-ary, (7,10)-threshold, 4-level HIBEChain can achieve 32,000 TPS, and it needs only 9 seconds to confirm a transaction.
A Robust Pupil Detection Algorithm based on a New Adaptive Thresholding Procedure

This paper presents a pupil detection algorithm based on a global adaptive thresholding procedure. The binarization threshold for each image is determined by analyzing the derivative of the percentile function. Additional morphological transformations are used to obtain the set of possible pupil contours. Afterwards, the algorithm selects the largest compact contour, fits an ellipse to it by using the least square method and delivers its center as the output. By using this method, the algorithm achieves good results even in non-uniform and variable lighting conditions. For two representative databases of eye images, the 5 pixels detection rate is higher than 92%.
Automatic Caption Generation of Retinal Diseases with Self-trained RNN Merge Model

Retinal diseases may lead to blindness. So, early detection of the diseases may prevent the scenario. Automatic caption generation from the retinal images is the challenging method to diagnose the diseases. In the recent era, deep learning is very popular in analyzing the medical images effectively. In this paper, we propose an automatic caption generation method based on CNN and self-trained bidirectional LSTM model. We build the model based on merge architecture. The model is trained with normal and abnormal retinal images and their labeled diseases. The target is to generate the caption of true disease from a given test image. We use the STARE database to build the model. After the experiment, it can be said that our attempts are promising and experimental result shows that the model performs well in generating the caption from the new given retinal image. We also evaluate the score using BLEU metrics.
Dynamically Recommending Repositories for Health Data: a Machine Learning Model

Recently, a wide range of digital health record repositories has emerged. These include Electronic Health record managed by the government, Electronic Medical Record (EMR) managed by healthcare providers, Personal Health Record (PHR) managed directly by the patient and new Blockchain-based systems mainly managed by technologies. Health record repositories differ from one another on the level of security, privacy, and quality of services (QoS) they provide. Health data stored in these repositories also varies from patient to patient in sensitivity, and significance depending on medical, personal preference, and other factors. Decisions regarding which digital record repository is most appropriate for the storage of each data item at every point in time are complex and nuanced. The challenges are exacerbated with health data continuously streamed from wearable sensors. In this paper, we propose a recommendation model for health data storage that can accommodate patient preferences and make storage decisions rapidly, in real-time, even with streamed data. The model maps health data to be stored in the repositories. The mapping between health data features and characteristics of each repository is learned using a machine learning-based classifier mediated through clinical rules. Evaluation results demonstrate the model's feasibility.
An OpenCL-Based Hybrid CNN-RNN Inference Accelerator On FPGA

Recently, Convolution Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and CNN-RNN hybrid networks have demonstrated great success in many deep learning scenarios. Although many dedicated FPGA accelerators for a certain kind of network have been proposed, few of them combine CNN and RNN acceleration together. In this paper we propose a high-throughput and resource-efficient CNN-RNN fusion accelerator on FPGA with commercial OpenCL to support general-purpose DNNs. It utilizes a novel streaming architecture and mapping strategy to implement the most computationintensive and resource-demanding parts in DNNs on the same computation logic. By such a hardware reuse method, it realizes resource efficiency in accelerating CNNs, RNNs and their hybrid networks. Our accelerator follows a layer-by-layer, subgraph-by-subgraph or subnetwork-by-subnetwork execution mode, which facilities it to deploy most DNNs flexibly during runtime with best performance. YOLOv2, LSTM and CRNN are tested with our work on Intel Arria10 GX1150 FPGA. It achieves 646 GOPS throughput on CRNN, which is the best performance on CNNRNN hybrid networks among high-level-synthesis (HLS) based FPGA accelerators. Moreover, its throughput for CNNs and RNNs is competitive to the state-of-the-art specialized FPGA accelerators.
Visualization in the preprocessing phase: Getting insights from enterprise professionals

The current information age has increasingly required organizations to become data-driven. However, analyzing and managing raw data is still a challenging part of the data mining process. Even though we can find interview studies proposing design implications or recommendations for future visualization solutions in the data mining scope, they cover the entire workflow and do not fully focus on the challenges during the preprocessing phase and on how visualization can support it. Moreover, they do not organize a final list of insights consolidating the findings of other related studies. Hence, to better understand the current practice of enterprise professionals in data mining workflows, in particular, during the preprocessing phase, and how visualization supports this process, we conducted semi-structured interviews with 13 data analysts. The discussion about the challenges and opportunities based on the responses of the interviewees resulted in a list of 10 insights. This list was compared with the closest related works, improving the reliability of our findings and providing background, as a consolidated set of requirements, for future visualization research articles applied to visual data exploration in data mining. Furthermore, we provide greater details on the profile of the data analysts, the main challenges they face, and the opportunities that arise while they are engaged in data mining projects in diverse organizational areas.
A Node Rating Based Sharding Scheme for Blockchain

The incumbent sharding schemes usually assign the nodes to different committees randomly to meet the demands of security and efficiency at the same time. For example, Elastico protocol obtains a random value by letting the node perform proof of work, and then uses this value for sharding. However, the strategy of random sharding ignores the objective differences between nodes, causing performance gaps between different committees in blockchain. This creates a bottleneck in the transaction throughput of the blockchain. In the paper, we propose a node rating based sharding scheme for blockchain system called NRSS. The key idea of NRSS is to evaluate nodes in the network by both the speeds and results of transactions verification before, and then assign them into different committees by balancing the score to reduce the performance gap between committees and increase the speed of transaction process. We implement NRSS in a local blockchain system, and the experiment results show that NRSS can increase the sharding effect of a blockchain, with an average throughput increase of 32.2% in the simulation environment where the node performance difference is up to 75%, depending on the number of nodes in the committee that are preset in the blockchain.
Air To Ground Collaboration For Energy-efficient Path Planning For Ground Robots

We study a fundamental motion planning problem of navigating a ground robot to a goal position with minimum energy consumption. Most existing solutions for this problem require an energy consumption model as a function of the environment and the robot motion. Obtaining such models is difficult which prevents the practical applicability of path planning algorithms for energy optimization. To address this issue, we present a new approach based on the assumption that the energy consumption for the ground robot is correlated with ground appearance. The first main contribution of this paper is the validation of the ground appearance assumption by experiments using actual energy consumption data obtained by ground robots. We then show how aerial images collected by an unmanned aerial vehicle can be used to generate the energy cost map of a given environment, which can further be used for planning energy-efficient paths for ground robots.
Implementation of LSTM Accelerator for Speech Keywords Recognition

The speech keyword detection system is generally used to detect a specific wake-up word spoken by the user, as a simple interactive command between the user and the device, or a front end of a larger voice recognition system for determining whether to open a large speech recognition system at the back end. Yet, such keyword recognition systems are always active, hence have strict power and latency constraints. This paper proposes an implementation of an LSTM accelerator based on an iterative linear approximation scheme, which optimizes the circuit implementation while maintaining high accuracy and reliability. The scheme of this paper has been verified on the FPGA board, and real-time speech recognition power consumption is only less 150$\mu$w.
Cascaded Gaussian Processes for Data-efficient Robot Dynamics Learning

Motivated by the recursive Newton-Euler formulation, we propose a novel cascaded Gaussian process learning framework for the inverse dynamics of robot manipulators. This approach leads to a significant dimensionality reduction which in turn results in better learning and data efficiency. We explore two formulations for the cascading: the inward and outward, both along the manipulator chain topology. The learned modeling is tested in conjunction with the classical inverse dynamics model (semi-parametric) and on its own (non-parametric) in the context of feed-forward control of the arm. Experimental results are obtained with Jaco 2 six-DOF and SARCOS seven-DOF manipulators for randomly defined sinusoidal motions of the joints in order to evaluate the performance of cascading against the standard GP learning. In addition, experiments are conducted using Jaco 2 on a task emulating a pouring maneuver. Results indicate a consistent improvement in learning speed with the inward cascaded GP model and an overall improvement in data efficiency and generalization.
Tailoring Infographics on Water Resources Through Iterative, User‐Centered Design: A Case Study in the Peruvian Andes

Abstract Effective communication and knowledge sharing across stakeholder groups (e.g., science, government, business, civil society, farmers, and the general public) are essential for more informed water resource management. Visualizations and graphics are powerful tools to engage diverse groups with unfamiliar information. Despite this potential, the design of visuals within applied science settings often does not involve end‐user interaction or explicit consideration of their existing knowledge systems, perspective, requirements, and context of use. As a result, products are often difficult for users to understand and contextualize. While user interaction and the development of tailored visualizations is increasingly promoted as a potential remedy, limited empirical evidence exists that shows the potential impact and can guide the development of specific approaches. We piloted an iterative and user‐centered design methodology toward the tailoring of infographic‐style posters in the context of Peruvian water governance. To test whether tailoring demonstrably improves the perceived effectiveness of products, we designed three products that conveyed similar information but were tailored to three different audiences (an Andean agricultural, urban professional, and urban general). We then compared the tailored posters to those tailored to other audiences by means of interviews and user grading. We found that end‐users perceive products that have undergone tailoring as more interesting, clearer, and more useful than products designed without explicit user consideration. Our findings indicate that identifying groups with shared characteristics and requirements is key for effective tailoring. Our research provides empirical evidence to support the incorporation of user‐centered design methods in water resource management contexts.
A Novel method for IDC Prediction in Breast Cancer Histopathology images using Deep Residual Neural Networks

Invasive ductal carcinoma (IDC), which is also sometimes known as the infiltrating ductal carcinoma, is the most regular form of breast cancer. It accounts to about 80% of all breast cancers. According to American Cancer Society [1], more than 180, 000 women in the United States are diagnosed with invasive breast cancer each year. The survival rate associated with this form of cancer is about 77% to 93% depending on the stage at which they are being diagnosed. The invasiveness and the frequency of the occurrence of these disease makes it one of the difficult cancers to be diagnosed. Our proposed methodology involves diagnosing the invasive ductal carcinoma with a deep residual convolution network to classify the IDC affected histopathological images from the normal images. The dataset for the purpose used is a benchmark dataset known as the Breast Histopathology Images [2]. The microscopic RGB images are converted into a seven channel image matrix, which are then fed to the network. The proposed model produces a 99.29% accurate approach towards prediction of IDC in the histopathology images with an AUROC score of 0.9996. Classification ability of the model is tested using standard performance metrics. The following methodology has been described in the next sections.
A Robust Hybrid Approach for Textual Document Classification

Text document classification is an important task for diverse natural language processing based applications. Traditional machine learning approaches mainly focused on reducing dimensionality of textual data to perform classification. This although improved the overall classification accuracy, the classifiers still faced sparsity problem due to lack of better data representation techniques. Deep learning based text document classification, on the other hand, benefitted greatly from the invention of word embeddings that have solved the sparsity problem and researchers focus mainly remained on the development of deep architectures. Deeper architectures, however, learn some redundant features that limit the performance of deep learning based solutions. In this paper, we propose a two stage text document classification methodology which combines traditional feature engineering with automatic feature engineering (using deep learning). The proposed methodology comprises a filter based feature selection (FSE) algorithm followed by a deep convolutional neural network. This methodology is evaluated on the two most commonly used public datasets, i.e., 20 Newsgroups data and BBC news data. Evaluation results reveal that the proposed methodology outperforms the state-of-the-art of both the (traditional) machine learning and deep learning based text document classification methodologies with a significant margin of 7.7% on 20 Newsgroups and 6.6% on BBC news datasets.
Preliminary Findings of using Chat-bots as a Course FAQ Tool

Students learn by asking questions and as instructors we encourage students to ask questions. However, not all questions are of equal importance. Questions related to the mechanics of a course are unavoidable but when the same question is asked and answered multiple times by an instructor it can become a burden to the instructor to personally answer these questions multiple times. This paper presents the preliminary findings of an admin-updatable Dialogflow-based chat-bot deployed on an instant messaging platform (Telegram) to handle all course queries. The chat-bot uses the in-built natural language processing module by Dialogflow to understand a student's query and match it to the provided predefined answers. The method outline in this paper describes the process to create a chat-bot from a limited set of course related questions, use the chat-bot as a data collection tool to create a question bank and use the new questions to improve the functionality of the chat-bot to support the course. Our observations and findings from the first iteration of deployment shows that chat-bots are a viable tool to disseminate information to students and it is possible to build such an agent with a limited set of question.
Person Retrieval in Surveillance Videos Using Deep Soft Biometrics

In the world of security and surveillance, it is very common to localize the person in the video. The localization turns to be non-trivial when the search is based on a linguistic query. This is due to the semantic gap between language based query and its processing by a machine. Typically query uses attributes like height, cloth color, cloth type, gender, and hair color, e.g., a tall male with a black t-shirt and pink short. Such attributes are known as soft biometrics. Conventionally, searching the person in the surveillance video is done manually by scouring through hours of videos, which is inefficient and time-consuming. Thus, an automatic person retrieval algorithm is an active area of research. The chapter discusses automatic person retrieval algorithm using soft biometric attributes namely height, cloth color, gender. The algorithm uses Mask R-CNN for detection and semantic segmentation of the person. This removes background clutter and allows a precise color patch extraction for better classification. The algorithm uses the geometric model for height classification while color and gender models are built using the AlexNet: a deep neural network. The proposed algorithm is tested on the AVSS 2018 challenge II dataset and it analyzes person retrieval in challenging conditions.
Conditional Recurrent Flow: Conditional Generation of Longitudinal Samples With Applications to Neuroimaging

We develop a conditional generative model for longitudinal image datasets based on sequential invertible neural networks. Longitudinal image acquisitions are common in various scientific and biomedical studies where often each image sequence sample may also come together with various secondary (fixed or temporally dependent) measurements. The key goal is not only to estimate the parameters of a deep generative model for the given longitudinal data, but also to enable evaluation of how the temporal course of the generated longitudinal samples are influenced as a function of induced changes in the (secondary) temporal measurements (or events). Our proposed formulation incorporates recurrent subnetworks and temporal context gating, which provide a smooth transition in a temporal sequence of generated data that can be easily informed or modulated by secondary temporal conditioning variables. We show that the formulation works well despite the smaller sample sizes common in these applications. Our model is validated on two video datasets and a longitudinal Alzheimer's disease (AD) dataset for both quantitative and qualitative evaluations of the generated samples. Further, using our generated longitudinal image samples, we show that we can capture the pathological progressions in the brain that turn out to be consistent with the existing literature, and could facilitate various types of downstream statistical analysis.