Reviewerly/exHarmony huggingface dataset:
Cyber-physical application monitoring across multiple clouds

Cyber-physical systems (CPS) integrate cyber-infrastructure comprising computers and networks with physical processes. The cyber components monitor, control, and coordinate the physical processes typically via actuators. As CPS are characterized by reliability, availability, and performance, they are expected to have a tremendous impact not only on industrial systems but also in our daily lives. We have started to witness the emergence of cloud-based CPS. However, cloud systems are prone to stochastic conditions that may lead to quality of service degradation. In this paper, we propose M2CPA - a novel framework for multi-virtualization, and multi-cloud monitoring in cloud-based cyber-physical systems. M2CPA monitors the performance of application components running inside multiple virtualization platforms deployed on multiple clouds. M2CPA is validated through extensive experimental analysis using a real testbed comprising multiple public clouds and multi-virtualization technologies.
Mannequette

Drawing upon multiple disciplines, avant-garde fashion-tech teams push the boundaries between fashion and technology. Many are well trained in envisioning aesthetic qualities of garments, but few have formal training on designing and fabricating technologies themselves. We introduce Mannequette, a prototyping tool for fashion-tech garments that enables teams to experiment with interactive technologies at early stages of their design processes. Mannequette provides an abstraction of light-based outputs and sensor-based inputs for garments through a DJ mixer-like interface that allows for dynamic changes and recording/playback of visual effects. The base of Mannequette can also be incorporated into the final garment, where it is then connected to the final components. We conducted an 8-week deployment study with eight design teams who created new garments for a runway show. Our results revealed Mannequette allowed teams to repeatedly consider new design and technical options early in their creative processes, and to communicate more effectively across disciplinary backgrounds.
Fast Keyed-Verification Anonymous Credentials on Standard Smart Cards

Cryptographic anonymous credential schemes allow users to prove their personal attributes, such as age, nationality, or the validity of a ticket or a pre-paid pass, while preserving their privacy, as such proofs are unlinkable and attributes can be selectively disclosed. Recently, Chase et al. (CCS 2014) observe that in such systems, a typical setup is that the credential issuer also serves as the verifier. They introduce keyed-verification credentials that are tailored to this setting. In this paper, we present a novel keyed-verification credential system designed for lightweight devices (primarily smart cards). By using a novel algebraic MAC based on Boneh-Boyen signatures, we achieve the most efficient proving protocol compared to existing schemes. To demonstrate the practicality of our scheme in real applications, including large-scale services such as public transportation or e-government, we present an implementation on a standard, off-the-shelf, Multos smart card. While using significantly higher security parameters than most existing implementations, we achieve performance that is more than 44% better than the current state-of-the-art implementation.
A multimodal smartphone sensor system for behaviour measurement and health status inference

Smartphones are becoming increasingly pervasive in almost every aspect of daily life. With smartphones being equipped with multiple sensors, they provide an opportunity to automatically extract information relating to daily life. Information relating to daily life could have major benefits in the area of health informatics. Research shows that there is a need for more objective and accurate means of measuring health status. Hence, this work investigates the use of multi-modal smartphone sensors to measure human behaviour and generate behaviour profiles which can be used to make objective predictions related to health status. Three sensor modalities are used to compute behaviour profiles for three different components of human behaviour. Motion sensors are utilised to measure physical activity, location sensors are utilised to measure travel behaviour and sound sensors are used to measure voice activity related behaviour. Sensor fusion, using a genetic algorithm, is performed to find complementary and co-operative features. Using a behaviour feature composed of motion, sound and locations data, results show that a Support Vector Machine (SVM) can predict 10 different health metrics with an error that does not exceed a clinical error benchmark.
Automated Attendance Marking and Management System by Facial Recognition Using Histogram

In proposed system an automated attendance marking and management system is proposed by using face detection and recognition algorithms. Identification of human faces by the unique characteristics or features of their face is known as Face recognition. Currently, Face recognition technology is the fastest growing technology. Instead of using the traditional methods, this proposed system aims to develop an automated system that records the student's attendance by using facial recognition technology for those who are present during lecture hours. The main objective of this work is to make the attendance marking and management system fully automatic, simple and easy. In this work the facial recognition of face is done by image processing techniques. The processed image is used to match with the existing stored record and then attendance is marked in the database correspondingly. Compared to existing system traditional attendance marking system, this system reduces the workload of people and also saves times. This proposed system is been implemented with 4 modules such as Image Capturing, Segmentation of group photo and Face Detection, Face comparison and Recognition, Updating of Attendance in database.
Fully automated prostate whole gland and central gland segmentation on MRI using holistically nested networks with short connections

Accurate and automated prostate whole gland and central gland segmentations on MR images are essential for aiding any prostate cancer diagnosis system. Our work presents a 2-D orthogonal deep learning method to automatically segment the whole prostate and central gland from T2-weighted axial-only MR images. The proposed method can generate high-density 3-D surfaces from low-resolution ( z axis) MR images. In the past, most methods have focused on axial images alone, e.g., 2-D based segmentation of the prostate from each 2-D slice. Those methods suffer the problems of over-segmenting or under-segmenting the prostate at apex and base, which adds a major contribution for errors. The proposed method leverages the orthogonal context to effectively reduce the apex and base segmentation ambiguities. It also overcomes jittering or stair-step surface artifacts when constructing a 3-D surface from 2-D segmentation or direct 3-D segmentation approaches, such as 3-D U-Net. The experimental results demonstrate that the proposed method achieves 92.4%±3% Dice similarity coefficient (DSC) for prostate and DSC of 90.1%±4.6% for central gland without trimming any ending contours at apex and base. The experiments illustrate the feasibility and robustness of the 2-D-based holistically nested networks with short connections method for MR prostate and central gland segmentation. The proposed method achieves segmentation results on par with the current literature.
SQL Database with physical database tuning technique and NoSQL graph database comparisons

Relational databases are used in many organizations of various natures from last three decades such as Education, health, businesses and in many other applications. SQL databases are designed to manage structured data and show tremendous performance. Atomicity, Consistency Isolation, Durability (ACID) property of Relational databases is used to manage data integrity and consistency. Physical database techniques are used to increase the performance of relational databases. Tablespaces also called subfolder is one of the physical database technique used by Oracle SQL database. Tablespaces are used to store the data logically in separate data files. Now-a-days huge amount and varied nature (unstructured and semi structured) of data is generated by the various organizations i.e., videos, images, blogs etc. This large amount of data is not handled by the SQL databases efficiently. NoSQL databases are used to process and analyze the large amount of data efficiently. Four different types of NoSQL databases are used in the industry according to the organization requirement. In this article, first, we do the physical database tuning of the Oracle Relational database and then compared with NoSQL Graph database. Relational database performance is increased up to 50% due to physical database tuning technique (Tablespaces). Besides, physical database tuning approach of relational database NoSQL graph database performed better in all our proposed scenarios.
Massively Parallel Computation via Remote Memory Access

We introduce the Adaptive Massively Parallel Computation (AMPC) model, which is an extension of the widely popular Massively Parallel Computation (MPC) model. At a high level, the AMPC model strengthens the MPC model by storing all messages sent within a round in a distributed data store. In the following round all machines are provided with random read access to the data store, subject to the same constraints on the total amount of communication as in the MPC model. Our model is inspired by the previous empirical studies of distributed graph algorithms~\citecc-beyond,nips17 using MapReduce and a distributed hash table service~\citebigtablepaper. This extension allows us to give new graph algorithms with much lower round complexities compared to the best known solutions in the MPC model. In particular, in the AMPC model we show how to solve maximal independent set in $O(1)$ rounds, and connectivity/minimum spanning tree in $O(łog łog_m/n n)$ rounds, which is an exponential improvement upon the best known algorithms in the MPC model with sublinear space per machine. Our results imply that the 2-Cycle conjecture, the most popular hardness conjecture in the MPC model, does not hold in the AMPC model.
An automated computational biomechanics workflow for improving breast cancer diagnosis and treatment

Clinicians face many challenges when diagnosing and treating breast cancer. These challenges include interpreting and co-locating information between different medical imaging modalities that are used to identify tumours and predicting where these tumours move to during different treatment procedures. We have developed a novel automated breast image analysis workflow that integrates state-of-the-art image processing and machine learning techniques, personalized three-dimensional biomechanical modelling and population-based statistical analysis to assist clinicians during breast cancer detection and treatment procedures. This paper summarizes our recent research to address the various technical and implementation challenges associated with creating a fully automated system. The workflow is applied to predict the repositioning of tumours from the prone position, where diagnostic magnetic resonance imaging is performed, to the supine position where treatment procedures are performed. We discuss our recent advances towards addressing challenges in identifying the mechanical properties of the breast and evaluating the accuracy of the biomechanical models. We also describe our progress in implementing a prototype of this workflow in clinical practice. Clinical adoption of these state-of-the-art modelling techniques has significant potential for reducing the number of misdiagnosed breast cancers, while also helping to improve the treatment of patients.
Physical discomfort and eye movements during arbitrary and optical flow-like motions in stereo 3D contents

Users of stereo 3D technology commonly report physical discomfort during or after exposure of stereo 3D contents. The discomfort has been associated with sensation of arbitrary and optical flow-like self-motion. However, there is no information on whether arbitrary motion induces stronger physical discomfort compared with optical flow-like motion. To address this research gap, we investigate physical discomfort among players and spectators of stereo 3D contents using eye tracking and Simulator Sickness Questionnaire. Thirty participants ($$N=30$$) acted as players and spectators of a first-person shooter (FPS) and a car racing game. The FPS and the car racing game produce a sensation of arbitrary and optical flow-like self-motion, respectively. Experimental results show that the FPS game induces more severe physical discomfort than its racing counterpart ($$p<0.0083$$, with a Bonferroni correction to the p value). We also found that severeness of oculomotor symptoms can be predicted using two eye movements metrics: the amount of fixational eye movements and viewing duration at the center of the screen. Our study implies that one should pay particular attention to different types of self-motion in stereo 3D contents regardless of whether the user controls or solely watches the contents. Our study also suggests that physical discomfort can be reduced by decreasing the frequency of fixational eye movements while prolonging the duration of each fixation at the center of screen.
Presence Is the Key to Understanding Immersive Learning

Presence as the subjective feeling of ‘being there’ is one of the main psychological components in immersive virtual environments. Research shows that presence can have an effect on learning outcomes in educational virtual environments. As presence can be considered as an individual psychological variable, its crucial role in the process of immersive learning is influenced by numerous subjective and objective factors. On the basis of the Educational Framework for Immersive Learning (EFiL), we developed a research model including the factors presence, immersion, cognitive abilities, motivation, and emotion. The hypotheses of the research model have been examined in a study with 23 students testing three different immersive educational virtual environments for learning computer science. The results of 67 presence questionnaires could confirm the hypotheses of the research model deriving from the EFiL partly. The factors immersion, emotion, and cognitive abilities were predictors for presence. An assumed, predictive effect of intrinsic motivation towards learning computer science on presence could not be verified.
Adiabatic Fock-state-generation scheme using Kerr nonlinearity

We propose a theoretical scheme to deterministically generate Fock states in a Kerr cavity through adiabatic variation of the driving field strength and the cavity detuning. We show that the required time to generate an $n$-photon Fock state scales as the square root of $n$. Requirements for the Kerr coefficient relative to the system decoherence rate are provided as a function of desired state fidelity, indicating that the scheme is potentially realizable with the present state of the art in microwave superconducting circuits.
Model-Based Clustering of Nonparametric Weighted Networks With Application to Water Pollution Analysis

Water pollution is a major global environmental problem, and it poses a great environmental risk to public health and biological diversity. This work is motivated by assessing the potential environmental threat of coal mining through increased sulfate concentrations in river networks, which do not belong to any simple parametric distribution. However, existing network models mainly focus on binary or discrete networks and weighted networks with known parametric weight distributions. We propose a principled nonparametric weighted network model based on exponential-family random graph models and local likelihood estimation, and study its model-based clustering with application to large-scale water pollution network analysis. We do not require any parametric distribution assumption on network weights. The proposed method greatly extends the methodology and applicability of statistical network models. Furthermore, it is scalable to large and complex networks in large-scale environmental studies. The power of our proposed methods is demonstrated in simulation studies and a real application to sulfate pollution network analysis in Ohio watershed located in Pennsylvania, United States.
IoT Based Safety and Health Monitoring for Construction Workers

Safety is a major problem in construction works. There is no proper solution to solve the problem. People’s safety is not ensured in the construction works. In most of the cases, the problem occurs due to work stress or poor health conditions. Some of the accidents occur where people fall down from heights and left unnoticed which leads to death due to lack of medical attention. According to the international report almost 48000 workers die every year due to workplace accidents and the construction site tops the list with 24.20 percent. However, most deaths occurred were preventable. This project aims to develop smart wearable devices such as band and helmet using various sensors that will help in monitoring the health and safety of workers. The devices constructed using IoT help in detecting the fall of any workers and sends SMS notification for immediate aid. Moreover, the workers vitals such as heart rate and temperature are also monitored and warned regarding abnormal health conditions. The project aims to provide a secure and safer working environment for worker thus reducing the number of deaths happening in construction sites. The prototype developed was tested on various conditions and showed high accuracy in the performance.
Content-Aware Trust Propagation Toward Online Review Spam Detection

With the increasing popularity of online review systems, a large volume of user-generated content becomes available to help people make reasonable judgments about the quality of services and products from unknown providers. However, these platforms are frequently abused since fraudulent information can be freely inserted by potentially malicious users without validation. Consequently, online review systems become targets of individual and professional spammers, who insert deceptive reviews by manipulating the rating and/or the content of the reviews. In this work, we propose a review spamming detection scheme based on the deviation between the aspect-specific opinions extracted from individual reviews and the aggregated opinions on the corresponding aspects. In particular, we model the influence on the trustworthiness of the user due to his opinion deviations from the majority in the form of a deviation-based penalty, and integrate this penalty into a three-layer trust propagation framework to iteratively compute the trust scores for users, reviews, and review targets, respectively. The trust scores are effective indicators of spammers, since they reflect the overall deviation of a user from the aggregated aspect-specific opinions across all targets and all aspects. Experiments on the dataset collected from Yelp.com show that the proposed detection scheme based on aspect-specific content-aware trust propagation is able to measure users’ trustworthiness based on opinions expressed in reviews.
Building a Broad Knowledge Graph for Products

A knowledge graph (KG) describes entities and relations between them; for example, between entities Amazon and Seattle, there can be a headquarter_located_at relation. Recent years have witnessed broad applications of KG in search (e.g., by Google and Bing) and question answering (e.g., by Amazon Alexa or Google Home). In this talk, we ask the question: Can one build a knowledge graph (KG) for all products in the world?
Syntax-aware Semantic Role Labeling without Parsing

In this paper we focus on learning dependency aware representations for semantic role labeling without recourse to an external parser. The backbone of our model is an LSTM-based semantic role labeler jointly trained with two auxiliary tasks: predicting the dependency label of a word and whether there exists an arc linking it to the predicate. The auxiliary tasks provide syntactic information that is specific to semantic role labeling and are learned from training data (dependency annotations) without relying on existing dependency parsers, which can be noisy (e.g., on out-of-domain data or infrequent constructions). Experimental results on the CoNLL-2009 benchmark dataset show that our model outperforms the state of the art in English, and consistently improves performance in other languages, including Chinese, German, and Spanish.
A deep learning methodology for automatic extraction and discovery of technical intelligence

It is imperative and arduous to acquire product and business intelligence of global technical market. In this paper, a deep learning methodology is proposed to automatically extract and discover vital technical information from large-scale news dataset. More specifically, six kinds of technical elements are first defined to provide the concrete syntax information. Next, the CRF-BiLSTM approach is used to automatically extract technical entities, in which a conditional random field (CRF) layer is added on top of bidirectional long short-term memory (BiLSTM) layer. Then, three indicators including timeliness, influence and innovativeness are designed to evaluate the value of intelligence comprehensively. Finally, as a case study, technical news on three military-related websites is utilized to illustrate the efficiency and effectiveness of the foregoing methodology with the result of 80.82 (F-score) in comparison to four other models. In more detail, data on unmanned systems are extracted to summarize the state-of-the-art, and track up-to-the-minute innovations and developments in this field.
Empowering video players in cellular

Today's HTTP adaptive streaming applications are designed to provide high levels of Quality of Experience (QoE) across a wide range of network conditions. The adaptation logic in these applications typically needs an estimate of the future network bandwidth for quality decisions. This estimation, however, is challenging in cellular networks because of the inherent variability of bandwidth and latency due to factors like signal fading, variable load, and user mobility. In this paper, we exploit machine learning (ML) techniques on a range of radio channel metrics and throughput measurements from a commercial cellular network to improve the estimation accuracy and hence, streaming quality. We propose a novel summarization approach for input raw data samples. This approach reduces the 90th percentile of absolute prediction error from 54% to 13%. We evaluate our prediction engine in a trace-driven controlled lab environment using a popular Android video player (ExoPlayer) running on a stock mobile device and also validate it in the commercial cellular network. Our results show that the three tested adaptation algorithms register improvement across all QoE metrics when using prediction, with stall reduction up to 85% and bitrate switching reduction up to 40%, while maintaining or improving video quality. Finally, prediction improves the video QoE score by up to 33%.