Reviewerly/exHarmony huggingface dataset:
Low Power Unsupervised Anomaly Detection by Nonparametric Modeling of Sensor Statistics

This article presents anomaly detection by examining sensor stream statistics (AEGIS), a novel mixed-signal framework for real-time AEGIS. AEGIS utilizes kernel density estimation (KDE)-based nonparametric density estimation to generate a real-time statistical model of the sensor data stream. The likelihood estimate of the sensor data point can be obtained based on the generated statistical model to detect outliers. We present CMOS Gilbert Gaussian cell-based design to realize Gaussian kernels for KDE. For outlier detection, the decision boundary is defined in terms of kernel standard deviation (σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Kernel</sub> ) and likelihood threshold (P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Thres</sub> ). We adopt a sliding window to update the detection model in real time. We use time-series data set provided from Yahoo to benchmark the performance of AEGIS. A f1-score higher than 0.87 is achieved by optimizing parameters such as length of the sliding window and decision thresholds which are programmable in AEGIS. Discussed architecture is designed using 45-nm technology node and our approach on average consumes ~75-μW power at a sampling rate of 2 MHz while using ten recent inlier samples for density estimation.
A Survey of Time Series Data Visualization Research

Abstract Time series data visualization integrates data analysis and mining, computer graphics, interaction design and other technologies and methods. This paper first analyzes the characteristics of time series data, including time and data attributes. Secondly, two kinds of visualization methods are summarized: one is the visualization method of time attribute, including spiral chart, calendar view, theme river view and dynamic view; the other is the visualization method of high dimensional time series data, which mainly summarizes four kinds of parallel coordinate methods. And the visual interaction design method is analyzed. Finally, the visualization of time series data is summarized and prospected.
PinNPivot: Object Manipulation Using Pins in Immersive Virtual Environments

Object manipulation techniques in immersive virtual environments are either inaccurate or slow. We present a novel technique, PinNPivot, where pins are used to constrain 1DOF/2DOF/3DOF rotations. It also supports 6DOF manipulation and 3DOF translation. A comparison with three existing techniques shows that PinNPivot is significantly more accurate and faster.
A Novel Attribute-Based Symmetric Multiple Instance Learning for Histopathological Image Analysis

Histopathological image analysis is a challenging task due to a diverse histology feature set as well as due to the presence of large non-informative regions in whole slide images. In this paper, we propose a multiple-instance learning (MIL) method for image-level classification as well as for annotating relevant regions in the image. In MIL, a common assumption is that negative bags contain only negative instances while positive bags contain one or more positive instances. This asymmetric assumption may be inappropriate for some application scenarios where negative bags also contain representative negative instances. We introduce a novel symmetric MIL framework associating each instance in a bag with an attribute which can be either negative, positive, or irrelevant. We extend the notion of relevance by introducing control over the number of relevant instances. We develop a probabilistic graphical model that incorporates the aforementioned paradigm and a corresponding computationally efficient inference for learning the model parameters and obtaining an instance level attribute-learning classifier. The effectiveness of the proposed method is evaluated on available histopathology datasets with promising results.
Human Activity Recognition using RGB-D Sensors

Computer Vision (CV) is a very alive area of research. The CV has all around multiple applications. One application of CV Ambient Assisted Living (AAL). AAL is getting very much importance day by day because of its extraordinary social values. In this paper we were considering AAL we focused on human activity recognition for that we are using knight depth camera for capturing 3-D skeleton data. This paper is about human activity recognition by using knight depth camera. This camera will capture 3-dimensional skeleton data.[1] Human Activity Recognition deals with a bag of multiple frame sequences with the image caption. Many methodologies have been proposed previously. In this paper, Convolutional Neural Network is used which gives the accuracy 0.85 on SBU Kinect Interaction Dataset.
PhishStack: Evaluation of Stacked Generalization in Phishing URLs Detection

Stacked Generalization has been assessed and evaluated in the field of Phishing URLs detection. This field has become egregious area of information security. Recently, different phishing URLs detection systems have already proposed by several researchers. But due to the lack of proper machine learning algorithm selection, the performance of those systems can be affected. A details investigation on individual machine learning classifiers on level 1 and final prediction from level 2 along with three real datasets have been presented on this paper. The performance has been evaluated by precision-recall curve, AUC-ROC curve, accuracy, misclassification rate and mean absolute error (MAE). The best AUC area obtained from Random Forest and Multi Layer Perceptron (MLP) individually. But stacked generalization provides higher accuracy of 97.44% with numeric feature set in binary classification and in multiclass feature set (dataset three), provides the performance with 97.86% of accuracy. Stacked generalization provides minimum error rate and MAE of 2.142857% with multiclass feature set which leads to a strong basement of developing an anti-phishing tools.
Efficient and secure content dissemination architecture for content centric network using ECC-based public key infrastructure

Initially, Internet was designed with the goal of sharing resources. With the recent technological advancement and huge information need, Internet has gradually shifted from resource sharing mode to information sharing mode. To meet the demands of current Internet usage pattern, content centric network (CCN) is envisaged as a clean-slate Internet architecture. This paper proposes secure content dissemination architecture for CCN using elliptic curve cryptography based public key infrastructure (ECC-PKI). The proposed scheme restores the current business model of the Internet by decoupling the encrypted content from its access control specifications while maintaining the in-network content caching mechanism of the CCN. In the proposed security architecture, content will be securely communicated between each pair of entities of CCN using ECC-based protocols. While designing proposed protocols our main aim is to minimize the computation and communication cost and to increase the performance, efficiency and security. Finally, a formal security verification using well known AVISPA simulator and BAN logic concludes that the proposed scheme is secure against the existing relevant cryptographic attacks.
A New Class of Severity Regression Models with an Application to IBNR Prediction

Insurance loss severity data often exhibit heavy-tailed behavior, complex distributional characteristics such as multimodality, and peculiar links between policyholders’ risk profiles and claim amounts. To capture these features, we propose a transformed Gamma logit-weighted mixture of experts (TG-LRMoE) model for severity regression. The model possesses several desirable properties. The TG-LRMoE satisfies the denseness property that warrants its full versatility in capturing any distribution and regression structures. It may effectively extrapolate a wide range of tail behavior. The model is also identifiable, which further ensures its suitability for statistical inference. To make the TG-LRMoE computationally tractable, an expectation conditional maximization (ECM) algorithm with parameter penalization is developed for efficient and robust parameter estimation. The proposed model is applied to fit the severity and reporting delay components of a European automobile insurance dataset. In addition to obtaining excellent goodness of fit, the proposed model is shown to be useful and crucial for adequate prediction of incurred but not reported (IBNR) reserves through out-of-sample testing.
SMT-Friendly Formalization of the Solidity Memory Model

Solidity is the dominant programming language for Ethereum smart contracts. This paper presents a high-level formalization of the Solidity language with a focus on the memory model. The presented formalization covers all features of the language related to managing state and memory. In addition, the formalization we provide is effective: all but few features can be encoded in the quantifier-free fragment of standard SMT theories. This enables precise and efficient reasoning about the state of smart contracts written in Solidity. The formalization is implemented in the solc-verify verifier and we provide an extensive set of tests that covers the breadth of the required semantics. We also provide an evaluation on the test set that validates the semantics and shows the novelty of the approach compared to other Solidity-level contract analysis tools.
Q-Zilla: A Scheduling Framework and Core Microarchitecture for Tail-Tolerant Microservices

Managing tail latency is a primary challenge in designing large-scale Internet services. Queuing is a major contributor to end-to-end tail latency, wherein nominal tasks are enqueued behind rare, long ones, due to Head-of-Line (HoL) blocking. In this paper, we introduce Q-Zilla, a scheduling framework to tackle tail latency from a queuing perspective, and CoreZilla, a microarchitectural instantiation of our framework. On the algorthmic front, we first propose Server-Queue Decoupled Size-Interval Task Assignment (SQD-SITA), an efficient scheduling algorithm to minimize tail latency for high-disparity service distributions. SQD-SITA is inspired by an earlier algorithm, SITA, which explicitly seeks to address HoL blocking by providing an express-lane for short tasks, protecting them from queuing behind rare, long ones. But, SITA requires prior knowledge of task lengths to steer them into their corresponding lane, which is impractical. Furthermore, SITA may underperform an M/G/k system when some lanes become underutilized. In contrast, SQD-SITA uses incremental preemption to avoid the need for a priori task-size information, and dynamically reallocates servers to lanes to increase server utilization with no performance penalty. We then introduce Interruptible SQD-SITA, which further improves tail latency at the cost of additional preemptions. Finally, we describe and evaluate CoreZilla, wherein a multi-threaded core efficiently implements ISQD-SITA in a software-transparent manner at minimal cost. Our evaluation demonstrates that CoreZilla improves tail latency over a conventional SMT core with 2, 4, and 8 contexts by 2.25×, 3.23×, and 4.88×, on average, respectively.
Performance Comparison of Three Classifiers for the Classification of Breast Cancer Dataset

Breast Cancer is one of the threatening issues for women's existence nowadays. It is increasing in our society due to pursuing modern/western cultures and careless in food and living habits. It has some syndromes and based on those syndromes we can easily identify whether a patient has breast cancer or not. Support Vector Machine (SVM), Artificial Neural Network (ANN) and Naïve Bayes Algorithms are very popular and powerful supervised learning algorithms to classify an unknown label/result. We select a dataset from WBCD (Wisconsin Breast Cancer Diagnosis) which contains 9 attributes column and 1 class column. The attribute columns are the causes and the class column is the result of the attribute columns. In this paper, we trained different parts of SVM, ANN and Naïve Bayes based on a particular training dataset (WBCD). Based on the highest accuracy, we voted the best model from the described models in this paper and selected it to use in the future for the client dataset (clinical data). The best model is Linear SVM for the WBCD dataset and accuracy is 96.72%.
Saliency-guided automatic detection and segmentation of tumor in breast ultrasound images

The development of computer aided diagnosis (CAD) systems for the task of tumor detection and segmentation in breast ultrasound (BUS) images presents one of the most active research fields. In this paper, a saliency-guided approach for fast and automatic tumor segmentation in BUS images is proposed. We explore the ability of the concept of saliency to emulate radiologist expertise for tumor lesion detection in BUS images. For that, we compute BUS image saliency to generate effective tumor seeds and background seeds. Then, a graph-based interactive image segmentation method is applied automatically using the generated seeds to extract tumor region. For more accurate segmentation, we propose a novel saliency score measure to apply a post-processing step for result refinement. Our results have been compared with other approaches in challenged datasets and demonstrated the effectiveness of our saliency-guided approach for tumor detection and segmentation using different evaluation metrics.
<i>Sketch-R2CNN</i>: An RNN-Rasterization-CNN Architecture for Vector Sketch Recognition

Sketches in existing large-scale datasets like the recent QuickDraw collection are often stored in a vector format, with strokes consisting of sequentially sampled points. However, most existing sketch recognition methods rasterize vector sketches as binary images and then adopt image classification techniques. In this article, we propose a novel end-to-end single-branch network architecture RNN-Rasterization-CNN (Sketch-R2CNN for short) to fully leverage the vector format of sketches for recognition. Sketch-R2CNN takes a vector sketch as input and uses an RNN for extracting per-point features in the vector space. We then develop a neural line rasterization module to convert the vector sketch and the per-point features to multi-channel point feature maps, which are subsequently fed to a CNN for extracting convolutional features in the pixel space. Our neural line rasterization module is designed in a differentiable way for end-to-end learning. We perform experiments on existing large-scale sketch recognition datasets and show that the RNN-Rasterization design brings consistent improvement over CNN baselines and that Sketch-R2CNN substantially outperforms the state-of-the-art methods.
Knowledge Representation with Ontologies and Semantic Web Technologies to Promote Augmented and Artificial Intelligence in Systems Engineering

ABSTRACT This article discusses knowledge representation using ontologies and semantic web technologies to enable artificial intelligence (AI) for Systems Engineering. Technology trends indicate new methods and tools for digital engineering will incorporate AI and machine learning (ML) technologies. ML techniques support classification, clustering, and association identification, but struggle to explain the rationale for decision making, where multi‐domain semantic modeling and rule‐based reasoning can excel. Knowledge representation plays a key role in applying this type of AI. Ontologies are a means to domain modeling and reasoning required across Digital Thread domains instantiated in digital system models (DSM). These evolve over time as digital twins, which co‐evolve with physical instantiations of a DSM. Semantic technologies and ontologies formalize knowledge as an enabler for reasoning, with interoperable ontologies enabling reason about systems engineering across domains.
Decentralised and Distributed System for Organ/Tissue Donation and Transplantation

In today's era of digitisation, many technologies have evolved that every manual work can be digitally automatized. In the digital automatizing process, security and privacy are the most important and highly demanding aspects. Blockchain offers many features that can be used in almost every sphere of life. Features like decentralisation, transparency, privacy makes it an extremely useful technology. Therefore, by making use of all these features, several problems in healthcare sector can be solved like removing complex network of third parties and lack of traceability of transactions. This paper presents a decentralised, secure and transparent organ and tissue transplant web application (also called DApp), which not only nullifies the role of any third party involved in the organ transplantation, but also is a cost effective solution that saves the patient's from high cost of transplantation. The details and Electronic Medical Record(EMR) are hashed using the IPFS(a distributed file server), which reduces the cost of upload to a great extent as shown in the results section of this paper.
Semantic-related image style transfer with dual-consistency loss.

Recently, image style transfer has drawn great attention in computer vision area with its successful applications in digital entertainment and social media. Much of the research aimed at rendering an image with artistic style of single artwork. However in certain applications, the target is to render the image as it were painted by a certain artist. Even though collection-based image style transfer methods were proposed to address the single artwork problem, they ignored the relation between the subject and the style of an artwork, which is an important factor in mimicking an artist’s creation mechanism. Based on this consideration, we assume the semantic relationship between the content and style by constructing a subject-related photo-artwork dataset and seek to learn that relationship. We propose a dual-consistency loss to train an encoder decoder network with adversarial discriminator. The dual-consistency loss encourages the output to be semantically and stylistically consistent with subject-related content and style image pair. Thus the visual appearance of the stylized outputs would adapt to the subject of the content image, which increased the diversity of the generated results. Both quantitative and qualitative experimental results demonstrate the effectiveness of the proposed approach for retaining semantic and style consistency in style transfer process.
Inverse reinforcement learning-based time-dependent A* planner for human-aware robot navigation with local vision

In an environment where robots coexist with humans, mobile robots should be human-aware and comply with humans' behavioural norms so as to not disturb humans' personal space and activities. In this work, we propose an inverse reinforcement learning-based time-dependent A* planner for human-aware robot navigation with local vision. In this method, the planning process of time-dependent A* is regarded as a Markov decision process and the cost function of the time-dependent A* is learned using the inverse reinforcement learning via capturing humans' demonstration trajectories. With this method, a robot can plan a path that complies with humans' behaviour patterns and the robot's kinematics. When constructing feature vectors of the cost function, considering the local vision characteristics, we propose a visual coverage feature for enabling robots to learn from how humans move in a limited visual field. The effectiveness of the proposed method has been validated by experiments in real-world scenarios: using this approach robots can effectively mimic human motion patterns when avoiding pedestrians; furthermore, in a limited visual field, robots can learn to choose a path that enables them to have the larger visual coverage which shows a better navigation performance.
Target-Guided Structured Attention Network for Target-Dependent Sentiment Analysis

Target-dependent sentiment analysis (TDSA) aims to classify the sentiment of a text towards a given target. The major challenge of this task lies in modeling the semantic relatedness between a target and its context sentence. This paper proposes a novel Target-Guided Structured Attention Network (TG-SAN), which captures target-related contexts for TDSA in a fine-to-coarse manner. Given a target and its context sentence, the proposed TG-SAN first identifies multiple semantic segments from the sentence using a target-guided structured attention mechanism. It then fuses the extracted segments based on their relatedness with the target for sentiment classification. We present comprehensive comparative experiments on three benchmarks with three major findings. First, TG-SAN outperforms the state-of-the-art by up to 1.61% and 3.58% in terms of accuracy and Marco-F1, respectively. Second, it shows a strong advantage in determining the sentiment of a target when the context sentence contains multiple semantic segments. Lastly, visualization results show that the attention scores produced by TG-SAN are highly interpretable
CNN Algorithms for Detection of Human Face Attributes – A Survey

In recent years, CNN algorithms are being increasingly applied for various computer vision based applications such as disaster management systems using crowd-sourced images. Flood is one such frequent natural disaster that threatens human life and property. Research is in progress to find the extent of damage in flood hit areas by calculating the depth of the water using flood images containing humans captured by smartphone cameras. Algorithms, which can detect a human face and its attributes such as age, gender and ethnicity with these crowd-sourced images, can provide valuable information during such situations. A multitude of CNN algorithms is available for these tasks. Each one of them is different in their architecture which in turn influences the accuracy of the results. In this survey, we compare the state of the art CNN algorithms which perform each of these tasks, namely, face detection, age and gender classification, and ethnicity classification. We compare these algorithms with respect to their performance and accuracy so that an appropriate algorithm can be selected for the above application.
Developing Science Process Skills through Mobile Scientific Inquiry

The main purpose of science education is to provide students with Science Process Skills (SPS). Participation in scientific inquiry activities is the most effective method in developing these skills. This study uses a mixed-method design to investigate the effect of mobile scientific inquiry on pre-service teachers' SPS. For this purpose, a mobile application developed by the researchers and used by participants for eight weeks. 94 pre-service teachers participated in the study. "Science Process Skills Test", semi-structured interviews and reflective journals were the data collection tools of the study. Results showed that engaging mobile scientific inquiry affects development of science process skills significantly and positively. Qualitative data analysis also supported this positive change.