Reviewerly/exHarmony huggingface dataset:
Maintenance Scheduling for Cloud Infrastructure with Timing Constraints of Live Migration

In this paper, we propose an implementation of a maintenance scheduler for cloud infrastructures. Live migration associated with maintenance work is important to ensure service continuity for all virtual machines in an infrastructure. However, executing the migration process when the machines are under heavy load negatively affects cloud users' businesses, such as by degrading performance and extending downtime. We can avoid this by finding an appropriate time period for live migration and performing the migration then. This idea is convenient for cloud users but inconvenient for cloud providers, that is, maintenance work should be completed as soon as possible for security reasons. To satisfy both the users' convenience and providers' requirements, we designed a maintenance scheduling problem to find the appropriate time period and to shorten the maintenance work period. Since it is a large-scale combinatorial optimization problem with complex constraints on maintenance requirements, we described the constraints by using answer set programming and implemented a maintenance scheduler on the basis of a divide-and-conquer approach to reduce the computational complexity exponentially. We evaluated our scheduler by using information on a real configuration of a commercial cloud infrastructure. While a naive approach to solving the maintenance scheduling problem could not find any feasible solutions within a realistic amount of time and memory, our implementation generated the best maintenance schedule for 1032 physical machines and 14208 virtual machines in 206 s with a memory usage of 1086 MB.
Types and annotation of reply relations in computer-mediated communication

Abstract This paper presents types and annotation layers of reply relations in computer- mediated communication (CMC). Reply relations hold between post units in CMC interactions and describe references from one given post to a previous post. We classify three types of reply relations in CMC interactions: first, technical replies, i. e. the possibility to reply directly to a previous post by clicking a ‘reply’ button; second, indentations, e. g. in wiki talk pages in which users insert their contributions in the existing talk page by indenting them and third, interpretative reply relations, i. e. the reply action is not realised formally but signalled by other structural or linguistics means such as address markers ‘@’, greetings, citations and/or Q-A structures. We take a look at existing practices in the description and representation of such relations in corpora and examples of chat, Wikipedia talk pages, Twitter and blogs. We then provide an annotation proposal that combines the different levels of description and representation of reply relations and which adheres to the schemas and practices for encoding CMC corpus documents within the TEI framework as defined by the TEI CMC SIG. It constitutes a prerequisite for correctly identifying higher levels of interactional relations such as dialogue acts or discussion trees.
Design of sparse cosine modulated filter banks based on Hopfield neural network

Abstract Cosine modulated filter bank (CMFB) is the most basic and important module in multi-rate signal processing system. Sparse FIR linear-phase CMFB can not only reduce the complexity of hardware design, but also ensure good performance. In this paper, a new design method of CMFB is proposed. First, the set of sparse coefficients of the prototype filter is found by using the orthogonal matching pursuit (OMP) algorithm. Then the Hopfield neural network (HNN) is employed to optimize the non-zero coefficients. The analytical and simulation results demonstrate that the new approach not only ensures that the sparse CMFBs satisfy near perfect reconstruction (NPR), but also improves the efficiency of hardware operation.
Applying an Adaptive Generative Representation to the Investigation of Affordances in Puzzles

This study uses a real-coded representation to encode discrete strategies for playing a simple grid based game. This representation is able to adapt itself on the fly to the local situation in the game. This adaptability makes the representation particularly suitable for finding good play strategies which, in turn, permit us to explore biases in the play strategies and even to compare instances of the game for difficulty. Variability in the best results achieved by the solver can be used to gauge difficulty, while the shape of the distribution of best results can indicate how interesting an instance is. Results indicate that the variety and combination of affordances produce instances of the game with varying degrees of anticipated difficulty and interestingness, and confirm that the solver can be used to evaluate the quality of different affordance combinations for producing good game instances. Design principles may also be discovered through post hoc analysis of instances with high or low average best score.
Revisiting sonic imaging with 3D slowness time coherence

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2019Revisiting sonic imaging with 3D slowness time coherenceAuthors: Nicholas BennettAdam DonaldTakeshi EndoAsbjørn Lund JohansenGabriela MartinezErik WielemakerNicholas BennettSchlumbergerSearch for more papers by this author, Adam DonaldSchlumbergerSearch for more papers by this author, Takeshi EndoSchlumbergerSearch for more papers by this author, Asbjørn Lund JohansenSchlumbergerSearch for more papers by this author, Gabriela MartinezSchlumbergerSearch for more papers by this author, and Erik WielemakerSchlumbergerSearch for more papers by this authorhttps://doi.org/10.1190/segam2019-3213539.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractA new approach to sonic imaging is presented in which an automated workflow detects and characterizes the events in the filtered wavefield directly and produces a 3D model of the reflectors as well as a log of reflector true dip and azimuth that can be directly integrated into downstream modeling and interpretation workflows. The new workflow can be used in addition to the more well-known practice of employing a migration to create 2D images of near wellbore reflectors.Presentation Date: Wednesday, September 18, 2019Session Start Time: 1:50 PMPresentation Time: 1:50 PMLocation: 217CPresentation Type: OralKeywords: borehole acoustics, fracture imaging, reservoir characterization, time-domain, wireline loggingPermalink: https://doi.org/10.1190/segam2019-3213539.1FiguresReferencesRelatedDetailsCited byUnderstand the Unconventional Reservoir Heterogeneity via Far-Field Sonic Imaging and Chemical Tracer Evaluations: A Case Study from Wulalike Shale Gas Exploration in Ordos Basin14 October 2022 SEG Technical Program Expanded Abstracts 2019ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2019 Pages: 5407 publication data© 2019 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 10 Aug 2019 CITATION INFORMATION Nicholas Bennett, Adam Donald, Takeshi Endo, Asbjørn LundJohansen, Gabriela Martinez, and Erik Wielemaker, (2019), "Revisiting sonic imaging with 3D slowness time coherence," SEG Technical Program Expanded Abstracts : 839-843. https://doi.org/10.1190/segam2019-3213539.1 Plain-Language Summary Keywordsborehole acousticsfracture imagingreservoir characterizationtime-domainwireline loggingPDF DownloadLoading ...
Mixed 3D-(2+1)D convolution for action recognition

2D CNNS for video-based action modeling ignore the temporal information and treat the multiple frames analogously to channels. In view of this, a mixed convolution structure implemented with ResNet-18 residual network is designed for video feature extracting. The 3D convolution and the (2+1)D convolution are interleaved in sequence throughout the network. Firstly, 2D convolution is performed on input multiple video frames one by one in the spatial. Then, 1D convolution of temporal is performed on the output of 2D convolution. Finally, 3D convolution is performed for spatiotemporal modeling simultaneously. Results show that the mixed convolution structure enhances the transmission of temporal information, improves the ability of video feature extraction and the action recognition accuracy obviously
Proposal and Evaluation of Authentication Method Having Shoulder-Surfing Resistance for Smartwatches Using Shift Rule

Recently, mobile devices having small touchscreen such as smartwatches has been increasing due to miniaturization of electronic devices. Currently, PIN and pattern lock are used for personal authentication of these devices, but there is possibility of leakage of authentication information by shoulder-surfing attack. Many authentication methods having shoulder-surfing resistance are proposed until now. However, these methods are for smartphones or tablets having middle-size screen. Hence, when these authentication methods apply for smartwatches, the usability reduces because a user cannot touch the screen accurately. Therefore, in this paper, we propose personal authentication method having shoulder-surfing resistance for smartwatches. In this method, the user selects alternative icon to registered icon on $$3\times 3$$ matrix using shift rule. In addition, we implemented the proposed method on smartwatch, and performed two experiments to confirm usability and shoulder-surfing resistance. As a result, average authentication time and authentication success rate was 13.8 s and 89.4%, and touch success rate was 96.2% when using shift rule. Also, the leakage rate of authentication information was 0.0%.
A method for feature extraction based on SVD and machine learning

By studying the shortcomings of feature, which extracted from Radar-Cross Section(RCS),using mathematical and statistical method, using the idea of extracting abstract features in image recognition and speech recognition by artificial intelligence for reference[2][3]. This paper explores the possibility of extracting abstract features of target's RCS sequence, and proposes an abstract feature extraction method of RCS sequence based on singular value decomposition(SVD) feature decomposition. Because of the poor interpretability of abstract features, four different machine learning algorithms are used to classify the extracted Abstract features. The experimental results show that the machine learning algorithm can classify different types of spatial objects with high accuracy, which shows that the RCS features of different spatial objects can be characterized by abstract features.
Video Saliency Detection Based on Eye-Movement Guided Region Matching and LDP Embedded Optical Flow

Video saliency detection has many potential applications in computer vision field and attracts more and more attention recently. In this paper, a video saliency detection method based on eye-movement guided region matching and local directional patterns (LDP) embedded optical flow is proposed. We use the eye fixation of human beings to match the regions of the adjacent frames to overcome the inaccuracy of the dynamic characteristics constructed by the traditional optical flow method when the object displacement is too large or the velocity is too fast. The dynamic LDP embedded optical flow features between two consecutive frames are captured to reduce the influence of illumination. And the final video saliency map to be detected can be generated by the support vector regression (SVR) model. The proposed method achieved better results on several video databases in objective and subjective evaluation compared with stat-of-arts, which demonstrates the effectiveness and robustness of our algorithm.
Importance of Application-Level Resource Management in Multi-Cloud Deployments

Cloud service providers started with Infrastructure as a Service (IaaS) offerings and over time expanded into Platform as a Service (PaaS) and Software as a Service (SaaS). Even though each provider has a rich product offering, there are many scenarios where a multi-cloud strategy is desirable: utilizing economic dynamics, preventing data lock-in with one vendor, circumventing geographic restrictions, complying with local regulations, or combining on-premise and public-cloud resources. The challenge from a consumer perspective with multi-cloud deployments is the lack of a common abstraction for the offered products and a standardized way to express all of the application requirements for the resulting deployments. In this paper, we contribute by making yet another case for multi-cloud deployments and by predicting the emergence of a new generation of application-level resource managers which will natively support multi-cloud for enterprise applications. We identify three main components of the feedback loop controlled application-level resource managers: the software life-cycle manager, the data storage and access manager, and the service execution manager.
Double-coupling learning for multi-task data stream classification

Data stream classification methods exploiting cohesion in a single data stream have demonstrated promising performance. However, scenarios involving multiple data streams are indeed common in practice. They involve several correlated data streams and can be viewed as multi-task data streams. Instead of considering them separately as individual data streams, it is beneficial to leverage the correlations among the multi-task data streams in data stream modeling. In this regard, a novel classification method called double-coupling support vector machines (DC-SVM) is proposed to classify multiple data streams simultaneously, where both the external correlations between multiple data streams and the internal relationships within each individual data stream are considered. Experimental results on synthetic and real-world multi-task data streams show that the proposed method outperforms traditional data stream classification methods.
Anchored neighborhoods search based on global dictionary atoms for face photo-sketch synthesis

Example-based face sketch synthesis technology generally requires face photo-sketch images with face alignment and size normalize. To break through the limitation, we propose a global face sketch synthesis method: In training, all training photo-sketch patch pairs are collected together and a photo feature dictionary is learned from the photo patches. For each atom of the dictionary, its K closest photo-sketch patch pairs are clustered, namely “Anchored Neighborhood”. In testing, for each test photo patch, we search its nearest photo patch in the Anchored Neighborhood determined by its closest atom, then the corresponding sketch patch is the output. By the same way, we train and test in the high-frequency domain and synthesis the high-frequency results. Finally, the fusion of the initial and the high-frequency results is the final sketch. The experiments on three public face sketch datasets and various real-world photos demonstrate the effectiveness and robustness of the proposed method
On Sharing Physical Geometric Space between Augmented and Virtual Reality Environments

Despite the expected synergistic effects, augmented and virtual reality (AR and VR, respectively) technologies still tend to be discrete entities. In this paper, we describe our effort to enable both AR and VR users to share the same physical geometric space. The geometric transformation between two world spaces, defined separately by AR and VR systems, are estimated using a specially designed tracking board. Once initially obtained, the transformation will allow users to collaborate with each other within an integrated physical environment while making the best use of both AR and VR technologies.
Stoneware Ceramic Synthesis with Iron Sand as a Filler Material

In an effort to explore the use of natural resources, stoneware ceramics have been made using iron sand as a filler to replace quartz Kalimantan.The results showed that iron sand can be well used in making stoneware ceramics.This is shown by the measurement of water absorption which is less than 5% and has compressive strength slightly smaller compared to stoneware ceramics made with quartz as a filler.
Unsupervised classification of lava flows in Harrat Lunayyir using remote sensing and GIS

Mapping of lava flows based on remote sensing data of high accuracy has become a common tool for exploring volcanic eruptions in greater detail. Mapping data based on remote sensing data provides information on the location and flow direction of lava flows as well as their areas and enables the localisation of volcanic vents—valuable knowledge for pre- and post-eruption volcanic activity and behaviour estimations. The current research seeks to expand the understanding of the volcanic and tectonic processes in Harrat Lunayyir, Saudi Arabia. In view of this, remote sensing data was classified using iterative self-organising data analysis technique (ISODATA) algorithms. The aim of the classification is to identify spectral top-of-atmosphere (TOA) reflectance values to distinguish layers of old and recent lava flows based on differences. As a result, three distinct basaltic units were identified to have the following ages: 15.1 ± 6.1 ka (4%), 15.0 ± 8.4 ka (6%), and 14.6 ± 23.1 ka (10%). The differences in the calculated areas of the lava flows as mapped based on the remote sensing data and earlier modelling are captured and identified in relation to the local geomorphologic and geologic structures. The differences can be partly explained as being related to a considerable weathering of the observed geological formations at 800–1600 nm, with reflectance of 12%. The limitation of the methodology relates to the lack of accurate geochronological timeline, that is, an inability to identify accurate age of the samples.
Background subtraction via time continuity and texture consistency constraints

Background subtraction has attracted enormous interest in the field of moving object detection. However, when there are complex scenarios such as illumination changes, dynamic background, and noise, the moving object area obtained by background subtraction often has holes, noise, and shadows. This paper proposes a novel background update model based on matrix factorization, which uses the temporal continuity of video content to solve the problems of holes, noise, and shadows. Moreover, in some cases, the texture consistency of the object is also a factor worth considering. The neighborhood weighed local binary pattern (NWLBP) is introduced to optimize the background update model, which is very effective for suppressing background or foreground shadow. The effectiveness of our method is confirmed by extensive experiments on public data sets and real shot video. Compared with the existing state-of-the-art moving object detection methods, the proposed methods can accurately establish the background model and locate the moving object region robustly.
Writing process restoration of Chinese calligraphy images based on skeleton extraction

Abstract In this paper, a writing process restoration method for static Chinese calligraphy works is proposed. Through this method, the initial skeleton of the Chinese character calligraphy image is extracted first. Then, the distortion elimination and sorting processes are performed for the initial skeleton, and the accurate skeleton of the Chinese character image is obtained. Finally, the appropriate ink dots are selected to move and render along the skeleton, thus obtaining the dynamic writing effects. Regarding the aspect of skeleton distortion elimination, the initial skeletons are first classified into three different types; then, the processing algorithms are designed respectively according to their morphological features, thus realizing highly efficient recognition and elimination for the skeleton distortion. This method can realize rapid dynamic restoration for calligraphy fonts, such as regular script.
[DC] Multi-user (Social) Virtual Reality Commnunication

Virtual Reality (VR) applications are currently suffering several gaps to allow a full breakthrough in the consumer market.One aspect is the social isolation.Many VR experiences do not represent yourself or other users, making it lonely rather than a shared experience.VR applications that do offer a user representation mostly use artificial avatars.These applications may not be well suited for all communication use cases (e.g. at work or with your family).In this paper I present our current work, at TNO and in my PhD research, to create Social VR experiences where both the user and the environment are represented in photo-realistic quality.The aim is not only to allow users to experience VR together, but also to allow new ways of natural communication within VR, with an extended presence and immersion.My PhD research focuses on 3 aspects that are building a Social VR system: capture and processing, transmission and client composition.The main goal of my research is to move processing from the capture and orchestration into the cloud (and particularly into the edge network) to allow Social VR with a large set of users (100+) in one session and to support mobile end devices while maximizing the QoS and QoE.
Bind Intra-pulse Modulation Recognition based on Machine Learning in Radar Signal Processing

Intra-pulse modulation recognition is one of the radar reconnaissance key technologies; it is especially a hot point of recent researching under low SNR. This thesis propounds a novel way for radar intra-pulse modulation characteristic recognition based on machine learning means of extreme learning machine (ELM), which is widely applied in the region of pattern recognition. As a novel learning framework, the ELM attracts increasing draws in the regions of large-scale computing, high-velocity signal processing, and artificial intelligence. The aim of the ELM is to break the barriers down between the biological learning mechanism and conventional artificial learning techniques and represent a suite of machine learning methods in which hidden neurons need not to be tuned. This algorithm has a trend to provide perfect generalization performance at staggering learning rate. This article focuses on the high frequency (HF) channel environment and Wavelet transform algorithm with the lower computational complexity. The simulation results imply that the ELM could reap a perfectly satisfactory acceptance performance and therefore supplies a substantial ground structure for dealing with intra-pulse modulation challenges in inadequate channel conditions.
What Matters for Chatbots? Analyzing Quality Measures for Facebook Messenger’s 100 Most Popular Chatbots

Chatbots are becoming mainstream. This work aims at ascertaining what are the enablers behind this popularity. To this end, we introduce four quality attributes, namely, “support of a minimal set of commands”, “foresee language variations”, “human-assistance provision” and “timeliness”. These criteria are applied to the 100 most popular Facebook Messenger chatbots. We review and measure both capacities and performance in order to find correlations between quality attribute fulfilment and popularity (chatbots’ ’likes’). Results show no significance correlations between quality attributes and chatbot popularity. However, the experiment comes up with three main contributions. First, a detailed description of how to measure these four quality attributes. Second, insights about how this assessment can be automatized, paving the way towards chatbot-evaluation platforms. Third, a checklist of frequently committed interaction errors as found in the revised chatbots. This might help developers to double-check their development.