Reviewerly/exHarmony huggingface dataset:
Ultra-Short-Term Photovoltaic Power Prediction Model Based on the Localized Emotion Reconstruction Emotional Neural Network

Due to the intermittency and randomness of photovoltaic (PV) power, the PV power prediction accuracy of the traditional data-driven prediction models is difficult to improve. A prediction model based on the localized emotion reconstruction emotional neural network (LERENN) is proposed, which is motivated by chaos theory and the neuropsychological theory of emotion. Firstly, the chaotic nonlinear dynamics approach is used to draw the hidden characteristics of PV power time series, and the single-step cyclic rolling localized prediction mechanism is derived. Secondly, in order to establish the correlation between the prediction model and the specific characteristics of PV power time series, the extended signal and emotional parameters are reconstructed with a relatively certain local basis. Finally, the proposed prediction model is trained and tested for single-step and three-step prediction using the actual measured data. Compared with the prediction model based on the long short-term memory (LSTM) neural network, limbic-based artificial emotional neural network (LiAENN), the back propagation neural network (BPNN), and the persistence model (PM), numerical results show that the proposed prediction model achieves better accuracy and better detection of ramp events for different weather conditions when only using PV power data.
Eye-based Interaction Using Embedded Optical Sensors on an Eyewear Device for Facial Expression Recognition

Non-verbal information is essential to understand intentions and emotions and to facilitate social interaction between humans and between humans and computers. One reliable source of such information is the eyes. We investigated the eye-based interaction (recognizing eye gestures or eye movements) using an eyewear device for facial expression recognition. The device incorporates 16 low-cost optical sensors. The system allows hands-free interaction in many situations. Using the device, we evaluated three eye-based interactions. First, we evaluated the accuracy of detecting the gestures with nine participants. The average accuracy of detecting seven different eye gestures is 89.1% with user-dependent training. We used dynamic time warping (DTW) for gesture recognition. Second, we evaluated the accuracy of eye gaze position estimation with five users holding a neutral face. The system showed potential to track the approximate direction of the eyes, with higher accuracy in detecting position y than x. Finally, we did a feasibility study of one user reading jokes while wearing the device. The system was capable of analyzing facial expressions and eye movements in daily contexts.
State of Ohio v. Ross Compton: Internet-enabled medical device data introduced as evidence of arson and insurance fraud

The data generated by Internet of Things devices is increasingly being introduced as evidence in court. The first US case involving the introduction of medical data from a pacemaker as evidence of arson and insurance fraud was State of Ohio v Compton. The purpose of this article is three-fold. First, the article explores this case, looking in particular at the facts of the case and the charges brought against the defendant. Second, the article critically examines the decision of the trial court judge during the suppression hearing for the evidence from the pacemaker. In this hearing, the judge ruled that the search and seizure did not violate the Fourth Amendment rights of the defendant and allowed the pacemaker data to be entered as evidence against him. Third, the article considers the implications of this decision for future cases involving Internet-of-Things (IoT) medical data. Ultimately, the constitutional protections of IoT medical device data and the circumstances under which the data from these devices will be collected and used as evidence, are issues that currently demand the attention of legal and digital forensics professionals and warrant public debate.
A Multi-level Interactive Lifelog Search Engine with User Feedback

With the rise of portable wearable devices, it is easier for users to save their lifelog data. As lifelog is usually disorganized with multi-modal information (even noisy sometimes), an interactive search engine is crucial for users to review and explore their lifelog. Unlike traditional search engines, lifelog search includes multi-modality information of images, text and other data from sensors, which brings challenges to data arrangement and search. Accordingly, users' information need is also multi-level. Hence, a single interaction mechanism may not be able to satisfy users' requirements. As the data set is highly personalized, interaction and feedback from users should also be considered in the search engine. Therefore, in this paper we present an interactive multi-modality lifelog search engine to help users manage and find lifelog data. To this end, lifelog data is clustered and processed in multi-level processing. Then, we build an interactive search engine, includingtext as query, image as query, andtimeline view modules. Besides, the system is able to adopt user feedback mechanisms in multi-round queries. Our system shows promising experimental results on LSC'20 dataset and development topics. The text-based search module gives correct results on more than 60% of the development topics at LSC'20.
Dynamic noncontact surface profilometry using a fast eigenspace method in diffraction phase microscopy

Dynamic measurement of surface profile is an important requirement in nondestructive testing, especially for the inspection of large samples with consecutive area scans or test objects under translation. In this paper, we propose the application of an eigenspace signal analysis method in diffraction phase microscopy for reliable and noncontact dynamic surface metrology. We also propose the inclusion of a graphics processing unit (GPU) computing framework in our method to enable fast interferogram processing for dynamics-based investigations. The practical viability of the proposed method is demonstrated for noninvasive nanoscale topography of a test target.
A hidden Markov model to detect regime changes in cryptoasset markets

Abstract The objective of this work is to understand the dynamics of cryptocurrency prices. Specifically, how prices switch between different regimes, going from “bull” to “stable” and “bear” times. For this purpose, we propose a hidden Markov model that aims at explaining the evolution of Bitcoin prices through different, unobserved states. The implementation of the proposed model includes a likelihood ratio test that allows to compare models with different states and with different covariance structures. Our empirical findings show that the time movements of Bitcoin prices across different exchange markets are well‐described by the proposed model. In particular, a parsimonious model with a diagonal covariance matrix leads to better predictions, compared with a model with a full covariance matrix.
Deep Contextualized Medical Concept Normalization in Social Media Text

In recent times with more internet users, social media evolved as a rich source of information related to various domains including health. Mapping colloquial expressions in social media text to standard medical concepts is useful in applications like pharma-covigilance, medical question and answering etc. However, it is challenging owing to the noisy nature of social media text. To map colloquial expressions to standard medical concepts, we use BiLSTM with deep contextualized and traditional word vectors. Em-beddings from Language Models (ELMo) being a) char based, offer quality vector representations for rare and misspelled words which occur often in social media text and b) contextual and deep (i.e., contextual, as it models context into word vector representation and deep, as it is based on three layer representations), encode rich information. Evaluation on CADEC-MCN dataset shows that our model achieves 5.07% improvement in accuracy over the state-of-the-art method. Further, we show that clinical ELMo embeddings can better model medical terms compared to general and biomedical ELMo embeddings.
Learning fast and precise numerical analysis

Numerical abstract domains are a key component of modern static analyzers. Despite recent advances, precise analysis with highly expressive domains remains too costly for many real-world programs. To address this challenge, we introduce a new data-driven method, called LAIT, that produces a faster and more scalable numerical analysis without significant loss of precision. Our approach is based on the key insight that sequences of abstract elements produced by the analyzer contain redundancy which can be exploited to increase performance without compromising precision significantly. Concretely, we present an iterative learning algorithm that learns a neural policy that identifies and removes redundant constraints at various points in the sequence. We believe that our method is generic and can be applied to various numerical domains.
How Trusted Execution Environments Fuel Research on Microarchitectural Attacks

Trusted execution environments (TEEs) enabled research in scenarios where highest-privileged attackers had precise control over systems and microarchitecture. Insights gained from such attacks facilitated the discovery of non-TEE attacks, such as Spectre (as well as Foreshadow from within virtual machines).
Multi-Objective Scheduling using Logistic Regression for OpenStack-based Cloud

Efficient resource management in Cloud data center is a challenging problem. Resource monitoring has been reported as a tedious task in multi-tenant data centers. Implementing an effective technique for monitoring and scheduling of the cloud resources is the need of today's data centers. OpenStack is an open source distributed cloud computing platform. Default scheduler of OpenStack implements worst fit algorithm which assigns VM to host with only maximum available RAM which is one-dimensional so it leads to inefficient usage of resources. This paper proposes a new model considering multiple parameters such as memory, CPU utilization and current workload of the hosts. This work proposes for the use of machine learning classifier to classify whether host is overloaded or under loaded. Proposed scheduler uses the decision provided by the classifier and implements best fit algorithm which schedules VM to host by considering multiple parameters. Experimental results show that proposed algorithm outperforms default scheduler and it leads to efficient usage of underlying resources of hosts.
Machine Learning and Blockchain Techniques Used in Healthcare System

Information systems and computerization nowadays need very faster, secure & easier data analysis techniques. It is also required to maintain efficiency and accuracy in data analysis. So machine learning & Blockchain techniques have been continuously used in the data analysis & security in various fields from medicine to organization and education to energy applications. This study applies classification of machine learning & Blockchain techniques to process the data and survey of selection methods, query strategies, applications and security. In highly secure data, Security issues are solved by using Blockchain Technology. Blockchain technology is rapidly gaining attention towards the Security of confidential data. The healthcare industry is one of the fields of organization where high risk involves & its attracted attention of many technological organizations so this field required the security for securing their data. The Blockchain is generally used for providing the security to secure the high sensitive data. By using the Blockchain technology there are numerous opportunities for healthcare industry to achieve & gain. Such as reduced transaction costs, increased transparency for regulatory reporting, efficient healthcare data management and healthcare records universality as well as able to access data from any location. In the context of smart healthcare system blockchain may provide distinct benefits, particularly from a context-aware perspective where efficient and personalized solutions may be provided to citizens and the society. This paper provides a comprehensive survey of relationship between Machine learning and blockchain techniques related to smart healthcare system. In addition, we are going to discussed several challenges can comes for actually implementing machine learning secure healthcare system using blockchain based Technology.
Timber: An SDN-Based Emulation Platform for Experimental Research on Video Streaming

In this paper, we present an open source Software-Defined Networking (SDN) based emulation platform called Timber.We aim to provide the research community with an experimental tool for the design and evaluation of the new Quality of Experience (QoE) management and monitoring procedures for video streaming.To this aim, the main functionalities of Timber include: i) an SDN application for taking QoE-aware management decisions; ii) an SDN controller to monitor the network's QoS (Quality of Service) and implement network management actions, such as network slicing and Multiprotocol Label Switching (MPLS) based prioritization operations; iii) a complete video streaming application including a multimedia server and a DASH-based client video player; iv) a user-end probe at the client video player to monitor QoE-related video application parameters, which are stored in a database that can be accessed by the SDN application; v) data analysis tools, which enable easy data visualization of measured QoS and QoE metrics as well as execution of statistical analysis of experimental results.In this article, we introduce and describe the main characteristics and functionalities of Timber as well as the implementation details.Finally, we provide experimental results of a video streaming scenario to demonstrate the capability of Timber to implement and test QoE-aware management approaches.
Wavelet threshold based on Stein's unbiased risk estimators of restricted location parameter in multivariate normal

In this paper, the problem of estimating the mean vector under non-negative constraints on location vector of the multivariate normal distribution is investigated. The value of the wavelet threshold based on Stein's unbiased risk estimators is calculated for the shrinkage estimator in restricted parameter space. We suppose that covariance matrix is unknown and we find the dominant class of shrinkage estimators under Balance loss function. The performance evaluation of the proposed class of estimators is checked through a simulation study by using risk and average mean square error values.
Highly degenerate photonic waveguide structures for holonomic computation

We investigate an all-out optical setup allowing for generation of non-Abelian geometric phases on its large degenerate eigenspaces. The proposal has the form of an $M$-pod system and can be implemented in terms of integrated photonic waveguide structures. We show that by injecting a larger number of photons into the optical setup, the degeneracy of eigenspaces scales rapidly. After studying the spectral properties of our system for the general case, we show how arbitrary $\mathrm{U}(3)$ transformations can be generated on the dark subspace of an optical tripod filled with two photons. Moreover, a degeneracy in the bright subspaces of the system, absent in any atomic analog, allows for the generation of universal single-qubit manipulations. Finally, we address the complexity issue of holonomic computation. Particularly, we show how two-qubit and three-qubit states can be implemented on a photonic tripod, where a natural multipartite structure is inherited from the spatial mode structure of the waveguides.
Customized Approach to the Implementation of Educational Programs for Training Engineers

The article discusses peculiarities of the customized approach, which takes into account the requirements of specific employers to the process and content of specialists’ training. Since the final consumer of the educational service provided by tertiary educational organization is the employer who hired the graduate, it is necessary to focus on the specifics of a particular organization activity when training specialists. Customized training is based on the collaboration of the University and the partner organization-employer, which consists in coordinating and adjusting educational programs within the existing standards to form professionally significant skills of graduates demanded by the employer, and involves further employment of the graduate in this organization. In this case, the specified organization acts as an investor or co-investor in the training of a future specialist. The employer’s participation in the educational process includes cooperation with the university not only in development of curricula and work programs of disciplines, but also in organization of seminars, conferences, in providing platforms and equipment for students to have their practice and to do their projects in real production conditions while solving actual professional problems. Customized training is one of the mechanisms to solve the problem of overabundance of specialists in some industries and a shortage of highly qualified personnel in others.
Applications, Methodologies, and Technologies for Linked Open Data

Advances in semantic web technologies have rocketed the volume of linked data published on the web. In this regard, linked open data (LOD) has long been a topic of great interest in a wide range of fields (e.g. open government, business, culture, education, etc.). This article reports the results of a systematic literature review on LOD. 250 articles were reviewed for providing a general overview of the current applications, technologies, and methodologies for LOD. The main findings include: i) most of the studies conducted so far focus on the use of semantic web technologies and tools applied to contexts such as biology, social sciences, libraries, research, and education; ii) there is a lack of research with regard to a standardized methodology for managing LOD; and iii) a plenty of tools can be used for managing LOD, but most of them lack of user-friendly interfaces for querying datasets.
Protecting a whale in a sea of phish

Whaling is one of the most financially damaging, well-known, effective cyberattacks employed by sophisticated cybercriminals. Although whaling largely consists of sending a simplistic email message to a whale (i.e. a high-value target in an organization), it can result in large payoffs for cybercriminals, in terms of money or data stolen from organizations. While a legitimate cybersecurity threat, little information security research has directed attention toward whaling. In this study, we begin to provide an initial understanding of what makes whaling such a pernicious problem for organizations, executives, or celebrities (e.g. whales), and those charged with protecting them. We do this by defining whaling, delineating it from general phishing and spear phishing, presenting real-world cases of whaling, and provide guidance on future information security research on whaling. We find that whaling is far more complex than general phishing and spear phishing, spans multiple domains (e.g. work and personal), and potentially results in spillover effects that ripple across the organization. We conclude with a discussion of promising future directions for whaling and information security research.
Survey on Segmentation and Recognition of Handwritten Arabic Script

The issue of handwritten recognition in Arabic script nature has attracted many researchers from both academic and industrial fields. But their efforts have not reached satisfying outcomes till now. In this paper, a survey has been done in segmentation and recognition of handwritten documents in Arabic script. Most of the previous published works have been analyzed, and some remedies have been suggested. Various strategies used for creating a powerful recognition system have been summarized. This paper presents various algorithms with respect to text, word and characters segmentation and recognition of Arabic document. It analyzes the recognition stage of Arabic script depending on segmentation strategies.
“Identity Bracelets” for Deep Neural Networks

The power of deep learning and the enormous effort and money required to build a deep learning model makes stealing them a hugely worthwhile and highly lucrative endeavor. Worse still, model theft requires little more than a high-school understanding of computer functions, which ensures a healthy and vibrant black market full of choice for any would-be pirate. As such, estimating how many neural network models are likely to be illegally reproduced and distributed in future is almost impossible. Therefore, we propose an embedded `identity bracelet' for deep neural networks that acts as proof of a model's owner. Our solution is an extension to the existing trigger-set watermarking techniques that embeds a post-cryptographic-style serial number into the base deep neural network (DNN). Called a DNN-SN, this identifier works like an identity bracelet that proves a network's rightful owner. Further, a novel training method based on non-related multitask learning ensures that embedding the DNN-SN does not compromise model performance. Experimental evaluations of the framework confirm that a DNN-SN can be embedded into a model when training from scratch or in the student network component of Net2Net.
lifeXplore at the Lifelog Search Challenge 2020

Since its first iteration in 2018, the Lifelog Search Challenge (LSC) -- an interactive competition for retrieving lifelogging moments -- is co-located at the annual ACM International Conference on Multimedia Retrieval (ICMR) and has drawn international attention. With the goal of making an ever growing public lifelogging dataset searchable, several teams develop systems for quickly solving time-limited queries during the challenge. Having participated in both previous LSC iterations, i.e. LSC2018 and LSC2019, we present our lifeXplore system -- a video exploration and retrieval tool combining feature map browsing, concept search and filtering as well as hand-drawn sketching. The system is improved by including additional deep concept YOLO9000, optical character recognition (OCR) as well as adding uniform sampling as an alternative to the system's traditional underlying shot segmentation.
Integrating Security with DevSecOps: Techniques and Challenges

The delivery of software applications is key to the success of many development organizations. The software development process over the period increased its scope and included other areas like operations with core Information Technology. Software project practices with DevOps have demonstrated how to streamline these processes, improve the quality of products with present technologies and speed up the processes. In the project cell, a university students' organization which undertake software development projects and services implemented the DevOps methodology for student projects. This model showed results with effective completion of projects. However, what was missing was robust and secure applications, increasing concern and risk involved with insecure products that brought our attention to the implementation and integration of security model into our present development process. The goal of this article is to describe the integration of DevSecOps practice to our ongoing projects that will avoid insecure practices during development.