Reviewerly/exHarmony huggingface dataset:
Sharpening Kubernetes for the Edge

Kubernetes has become the most popular cluster manager during the past 5 years. It is used primarily for orchestrating data center deployments running web applications. Its powerful features, e.g., self-healing and scaling, have attracted a huge community, which in turn, is inducing a meteoric rise of this open source project. We venture to shape Kubernetes to be suited for edge infrastructure. As mostly delay-sensitive applications are to be deployed in the edge, a topology-aware Kubernetes is needed, extending its widely-used feature set with regard to network latency. Moreover, as the edge infrastructure is highly prone to failures and is considered to be expensive to build and maintain, self-healing features must receive more emphasis than in the baseline Kubernetes. We therefore designed a custom Kubernetes scheduler that makes its decisions with applications' delay constraints and edge reliability in mind. In this demonstration we show the novel features of our Kubernetes extension, and describe the solution that we release as open source.
Differential Grouping with Spectral Clustering for Large Scale Global Optimization

Cooperative co-evolution (CC) is an effective strategy for large scale global optimization (LSGO) problems, which divides the problem into several smaller sub-problems based on the idea of divide-and-conquer. The grouping of decision variables has an important impact on the optimization results. Differential grouping (DG) and its improved versions detect the interaction relationship between variables according to the differential values, achieving competitive grouping results on the CEC'2010 benchmark functions which have uniform subcomponent sizes. However, these algorithms still have difficulty to obtain good decomposition for the more complex functions, especially for overlapping functions. Due to some degree of overlap between subcomponents, there will be no unique optimal decomposition of the decision variables. We draw lessons from the idea of clustering that objects in the same cluster are similar to each other and objects in different clusters are quite different. In this paper, we propose a differential grouping with spectral clustering (DGSC) algorithm. All the variables are treated as edge-connected points in space to construct an undirected weighted graph. The design structure matrix derived from the differential values is used as the similarity matrix of spectral clustering such that the weight of edges is expressed by the interaction relationship between these variables. This graph is divided into several subcomponents by clustering to decompose decision variables, making the degree of interaction between subcomponents as weak as possible and within the subcomponent as strong as possible. The experimental results show that DGSC has promising performance on the latest LSGO benchmark functions.
<i>t</i> -wise Coverage by Uniform Sampling

Efficiently testing large configuration spaces of Software Product Lines (SPLs) needs a sampling algorithm that is both scalable and provides good t-wise coverage. The 2019 SPLC Sampling Challenge provides large real-world feature models and asks for a t-wise sampling algorithm that can work for those models.
Geologic legacy spanning &gt;90 years explains unique Yellowstone hot spring geochemistry and biodiversity

Little is known about how the geological history of an environment shapes its physical and chemical properties and how these, in turn, influence the assembly of communities. Evening primrose (EP), a moderately acidic hot spring (pH 5.6, 77.4°C) in Yellowstone National Park (YNP), has undergone dramatic physicochemical change linked to seismic activity. Here, we show that this legacy of geologic change led to the development of an unusual sulphur-rich, anoxic chemical environment that supports a unique archaeal-dominated and anaerobic microbial community. Metagenomic sequencing and informatics analyses reveal that >96% of this community is supported by dissimilatory reduction or disproportionation of inorganic sulphur compounds, including a novel, deeply diverging sulphate-reducing thaumarchaeote. When compared to other YNP metagenomes, the inferred functions of EP populations were like those from sulphur-rich acidic springs, suggesting that sulphur may overprint the predominant influence of pH on the composition of hydrothermal communities. Together, these observations indicate that the dynamic geological history of EP underpins its unique geochemistry and biodiversity, emphasizing the need to consider the legacy of geologic change when describing processes that shape the assembly of communities.
Knowledge is Never Enough: Towards Web Aided Deep Open World Recognition

While today's robots are able to perform sophisticated tasks, they can only act on objects they have been trained to recognize. This is a severe limitation: any robot will inevitably see new objects in unconstrained settings, and thus will always have visual knowledge gaps. However, standard visual modules are usually built on a limited set of classes and are based on the strong prior that an object must belong to one of those classes. Identifying whether an instance does not belong to the set of known categories (i.e. open set recognition), only partially tackles this problem, as a truly autonomous agent should be able not only to detect what it does not know, but also to extend dynamically its knowledge about the world. We contribute to this challenge with a deep learning architecture that can dynamically update its known classes in an end-to-end fashion. The proposed deep network, based on a deep extension of a non-parametric model, detects whether a perceived object belongs to the set of categories known by the system and learns it without the need to retrain the whole system from scratch. Annotated images about the new category can be provided by an `oracle' (i.e. human supervision), or by autonomous mining of the Web. Experiments on two different databases and on a robot platform demonstrate the promise of our approach.
A survey on visual analysis of ocean data

A major challenge in analysis of huge amounts of ocean data is the complexity of the data and the inherent complexity of ocean dynamic processes. Interactive visual analysis serves as an efficient complementary approach for the detection of various phenomena or patterns, and correlation exploring or comparing multiple variables in researchers daily work. Firstly, this paper presents a basic concept of the ocean data produced from numerous measurement devices or computer simulations. The characteristics of ocean data and the related data processing techniques are also described. Secondly, the main tasks of ocean data analysis are introduced. Based on the main analysis tasks in the field of oceanography, the survey emphasizes related interactive visualization techniques and tools from four aspects: visualization of multiple ocean environmental elements and multivariate analysis, ocean phenomena identification and tracking, patterns or correlation discovery, ensembles and uncertainties exploration. Finally, the opportunities are discussed for future studies.
Effects of Self-Avatar and Gaze on Avoidance Movement Behavior

The present study investigates users' movement behavior in a virtual environment when they attempted to avoid a virtual character. At each iteration of the experiment, four conditions (Self-Avatar LookAt, No Self-Avatar LookAt, Self-Avatar No LookAt, and No Self-Avatar No LookAt) were applied to examine users' movement behavior based on kinematic measures. During the experiment, 52 participants were asked to walk from a starting position to a target position. A virtual character was placed at the midpoint. Participants were asked to wear a head-mounted display throughout the task, and their locomotion was captured using a motion capture suit. We analyzed the captured trajectories of the participants' routes on four kinematic measures to explore whether the four experimental conditions influenced the paths they took. The results indicated that the Self-Avatar LookAt condition affected the path the participants chose more significantly than the other three conditions in terms of length, duration, and deviation, but not in terms of speed. Overall, the length and duration of the task, as well as the deviation of the trajectory from the straight line, were greater when a self-avatar represented participants. An additional effect on kinematic measures was found in the LookAt (Gaze) conditions. Implications for future research are discussed.
A Novel Forgery Detection Algorithm for Video Foreground Removal

Video processing software is often used to remove specific moving foreground from a video. Existing forgery algorithms for detecting this type of tampering generally suffer from inefficiency and are not effective for the forged videos under complex background. To address these problems, we propose a novel forgery detection algorithm for detecting video foreground removal. The algorithm first calculates the energy factor (EF) of each frame to identify forged frames. An adaptive parameter-based visual background extractor (AVIBE) algorithm is then designed to detect suspected regions from the forged frames determined in the first stage. After eliminating false detection by calculating the difference of EF between suspected regions in the forged frames and the corresponding regions in the authentic frames, the algorithm finally locates the tampering traces. The experimental results show that our proposed algorithm has higher computational efficiency and accuracy as well as better robustness than those of previous algorithms.
The Influence of Size in Augmented Reality Telepresence Avatars

In this work, we explore how advances in augmented reality technologies are creating a new design space for long-distance telepresence communication through virtual avatars. Studies have shown that the relative size of a speaker has a significant impact on many aspects of human communication including perceived dominance and persuasiveness. Our system synchronizes the body pose of a remote user with a realistic, virtual human avatar visible to a local user wearing an augmented reality head-mounted display. We conducted a two-by-two (relative system size: equivalent vs. small; leader vs. follower), between participants study (N = 40) to investigate the effect of avatar size on the interactions between remote and local user. We found the equal-sized avatars to be significantly more influential than the small-sized avatars and that the small avatars commanded significantly less attention than the equal-sized avatars. Additionally, we found the assigned leadership role to significantly impact participant subjective satisfaction of the task outcome.
Human-skeleton based Fall-Detection Method using LSTM for Manufacturing Industries

According to the statistics of the Korea Occupational Safety & Health Agency, the incidences of falls in the manufacturing industry are increasing. In this paper, we introduce a fall-detection method based on skeleton data obtained from a 2D RGB CCTV Camera installed on the manufacturing floor. We proposed feature-extraction methods to improve of fall-detection accuracy and the construction of a fall-detection system using LSTM. Experiments were conducted through public datasets (URFD and SDUFall) to find feature-extraction methods that can achieve high classification accuracy. The experimental results showed that the proposed method is more effective in detecting falls than raw skeleton data which are not processed anything.
Self-Improving System Integration - On a Definition and Characteristics of the Challenge

The self-improving system integration (SISSY) initiative-sometimes also called self-integration-started over six years ago. Recently, we have seen contributions approaching parts of the scope from different directions. In order to provide a better understanding how these attempts support the development of novel SISSY technology, this paper presents a definition of the term. In addition, it discusses the fundamental challenges stemming from the increasingly interconnected character of large-scale distributed and adaptive systems typically referred to as "Interwoven Systems", with these systems describing the basic motivation for SISSY research.
SARM: Salah Activities Recognition Model Based on Smartphone

Alzheimer’s is a chronic neurodegenerative disease that frequently occurs in many people today. It has a major effect on the routine activities of affected people. Previous advancement in smartphone sensors technology enables us to help people suffering from Alzheimer’s. For people in the Muslim community, where it is mandatory to offer prayers five times a day, it may mean that they are struggling in their daily life prayers due to Alzheimer’s or lack of concentration. To deal with such a problem, automated mobile sensor-based activity recognition applications can be supportive to design accurate and precise solutions with an objective to direct the Namazi (worshipper). In this paper, a Salah activities recognition model (SARM) using a mobile sensor is proposed with the aim to recognize specific activities, such as Al-Qayam (standing), Ruku (standing to bowing), and Sujud (standing to prostration). This model entails the collection of data, selection and placement of sensor, data preprocessing, segmentation, feature extraction, and classification. The proposed model will provide a stepping edge to develop an application for observing prayer. For these activities’ recognition, data sets were collected from ten subjects, and six different features sets were used to get improved results. Extensive experiments were performed to test and validate the model features to train random forest (RF), K-nearest neighbor (KNN), naive Bayes (NB), and decision tree (DT). The predicted average accuracy of RF, KNN, NB, and DT was 97%, 94%, 71.6%, and 95% respectively.
The bow tie structure of the Bitcoin users graph

The availability of the entire Bitcoin transaction history, stored in its public blockchain, offers interesting opportunities for analysing the transaction graph to obtain insight on users behaviour. This paper presents an analysis of the Bitcoin users graph, obtained by clustering the transaction graph, to highlight its connectivity structure and the economical meaning of the different obtained components. In fact, the bow tie structure, already observed for the graph of the web, is augmented, in the Bitocoin users graph, with the economical information about the entities involved. We study the connectivity components of the users graph individually, to infer their macroscopic contribution to the whole economy. We define and evaluate a set of measures of nodes inside each component to characterize and quantify such a contribution. We also perform a temporal analysis of the evolution of the resulting bow tie structure. Our findings confirm our hypothesis on the components semantic, defined in terms of their economical role in the flow of value inside the graph.
An Attention-Based Spatiotemporal Gated Recurrent Unit Network for Point-of-Interest Recommendation

Point-of-interest (POI) recommendation is one of the fundamental tasks for location-based social networks (LBSNs). Some existing methods are mostly based on collaborative filtering (CF), Markov chain (MC) and recurrent neural network (RNN). However, it is difficult to capture dynamic user’s preferences using CF based methods. MC based methods suffer from strong independence assumptions. RNN based methods are still in the early stage of incorporating spatiotemporal context information, and the user’s main behavioral intention in the current sequence is not emphasized. To solve these problems, we proposed an attention-based spatiotemporal gated recurrent unit (ATST-GRU) network model for POI recommendation in this paper. We first designed a novel variant of GRU, which acquired the user’s sequential preference and spatiotemporal preference by feeding the continuous geographical distance and time interval information into the GRU network in each time step. Then, we integrated an attention model into our network, which is a personalized process and can capture the user’s main behavioral intention in the user’s check-in history. Moreover, we conducted an extensive performance evaluation on two real-world datasets: Foursquare and Gowalla. The experimental results demonstrated that the proposed ATST-GRU network outperforms the existing state-of-the-art POI recommendation methods significantly regarding two commonly-used evaluation metrics.
Why Open-Endedness Matters

Rather than acting as a review or analysis of the field, this essay focuses squarely on the motivations for investigating open-endedness and the opportunities it opens up. It begins by contemplating the awesome accomplishments of evolution in nature and the profound implications if such a process could be ignited on a computer. Some of the milestones in our understanding so far are then discussed, finally closing by highlighting the grand challenge of formalizing open-endedness as a computational process that can be encoded as an algorithm. The main contribution is to articulate why open-endedness deserves a place alongside artificial intelligence as one of the great computational challenges, and opportunities, of our time.
Worlds-in-Wedges: Combining Worlds-in-Miniature and Portals to Support Comparative Immersive Visualization of Forestry Data

Virtual reality (VR) environments are typically designed so users feel present in a single virtual world at a time, but this creates a problem for applications that require visual comparisons (e.g., forest scientists comparing multiple data-driven virtual forests). To address this, we present Worlds-in-Wedges, a 3D user interface and visualization technique that supports comparative immersive visualization by dividing the virtual space surrounding the user into volumetric wedges. There are three visual/interactive levels. The first, worlds-in-context, visualizes high-level relationships between the worlds (e.g., a map for worlds that are related in space). The second level, worlds-in-miniature, is a multi-instance implementation of the World-in-Miniature technique extended to support mutlivari-ate glyph visualization. The third level, worlds-in-wedges, displays multiple large-scale worlds in wedges that act as volumetric portals. The interface supports navigation, selection, and view manipulation. Since the techniques were inspired directly by problems facing forest scientists, the interface was evaluated by building a complete multivariate data visualization of the US Forest Service Forest Inventory and Analysis public dataset. Scientist user feedback and lessons from iterative design are reported.
Cross-Database Micro-Expression Recognition: A Style Aggregated and Attention Transfer Approach

Deep learning systems, such as the Residual Network (ResNet), can infer a hierarchical representation of input data that facilitates categorization. In this paper, we propose a Style Aggregated and Attention Transfer framework (SA-AT) based on ResNet for cross-database Micro-Expression Recognition (MER). The training of SA-AT has two stages. In the first stage, facial expression samples are used as the auxiliary database to train a ResNet teacher model. To benefit from the significant classification ability of teacher model which is trained on large scale data, in the second step, the attention of the teacher model is transferred to train the student model on style aggregated micro-expression databases with limited samples. Our experiments demonstrate that compared with the baseline method in Micro-Expression Grand Challenge 2019, our proposed technique achieves more promising performance.
OVPC Mesh: 3D Free-space Representation for Local Ground Vehicle Navigation

This paper presents a novel approach for local 3D environment representation for autonomous unmanned ground vehicle (UGV) navigation called On Visible Point Clouds Mesh (OVPC Mesh). Our approach represents the surrounding of the robot as a watertight 3D mesh generated from local point cloud data in order to represent the free space surrounding the robot. It is a conservative estimation of the free space and provides a desirable trade-off between representation precision and computational efficiency, without having to discretize the environment into a fixed grid size. Our experiments analyze the usability of the approach for UGV navigation in rough terrain, both in simulation and in a fully integrated real-world system. Additionally, we compare our approach to well-known state-of the-art solutions, such as Octomap and Elevation Mapping and show that OVPC Mesh can provide reliable 3D information for trajectory planning while fulfilling real-time constraints.
Application of Natural Language Processing Algorithms to the Task of Automatic Classification of Russian Scientific Texts

This work is devoted to the study of applicability of modern methods of machine learning to the task of automatic classification of scientific articles and abstracts. For this purpose, the study of such models of machine learning as artificial neural networks, random forest, logistic regression, and support vector machine was carried out with taking into account such a feature of scientific texts as a large number of terms specific for various categories. Separately, the stages of data collection and extraction of text characteristics are considered. The results of research are used in development of a decision support system for assignment of scientific texts to the code of the department or abstract journal of All-Russian Institute of Scientific and Technical Information of Russian Academy of Sciences.
Deep Face Image Retrieval: a Comparative Study with Dictionary Learning

Facial image retrieval is a challenging task since faces have many similar features (areas), which makes it difficult for the retrieval systems to distinguish faces of different people. With the advent of deep learning, deep networks are often applied to extract powerful features that are used in many areas of computer vision. This paper investigates the application of different deep learning models' (layers) for face image retrieval, namely, Alexlayer6, Alexlayer7, VGG16layer6, VGG16layer7, VGG19layer6 and VGG19layer7, with two types of dictionary learning techniques, namely K-means and K-SVD. We also investigate some coefficient learning techniques such as the Homotopy, Lasso, Elastic Net and SSF and their effect on the face retrieval system. The comparative results of the experiments conducted on three standard face image datasets show that the best performers for face image retrieval are Alexlayer7 with K-means and SSF, Alexlayer6 with K-SVD and SSF and Alexlayer6 with K-means and SSF. The APR and ARR of these methods were further compared to some of the state-of-the-art methods based on local descriptors. The experimental results show that deep learning outperforms most of those methods and therefore can be recommended for use in practice of face image retrieval.
Portable technology and multi-domain energy practices

This article complements the concept of embedded security by proposing disembedded security to capture consumers’ energy practices when travelling across multiple domains of energy accessibility. Consumer mobility outside the home produces misalignments between infrastructure and portable technology experienced as ‘hysteresis of the battery’. Hysteresis captures how respondents are subject to ‘unpleasant unpredictability’ about battery-based technology and infrastructure, which spurs hermeneutic reflection about energy, location and sociality. Multi-domain energy practices therefore bring energy consumers to ‘reembed’ or create a sense of psychological comfort on the move. Charge levels on battery icons not only structure daily patterns of consumer life through planning efforts but become interpretively entangled in issues of duration, distance and sociality as energy demands in portable technology push consumers to avoid disruption.