Reviewerly/exHarmony huggingface dataset:
Meta-Graph: Few shot Link Prediction via Meta Learning

We consider the task of few shot link prediction on graphs. The goal is to learn from a distribution over graphs so that a model is able to quickly infer missing edges in a new graph after a small amount of training. We show that current link prediction methods are generally ill-equipped to handle this task. They cannot effectively transfer learned knowledge from one graph to another and are unable to effectively learn from sparse samples of edges. To address this challenge, we introduce a new gradient-based meta learning framework, Meta-Graph. Our framework leverages higher-order gradients along with a learned graph signature function that conditionally generates a graph neural network initialization. Using a novel set of few shot link prediction benchmarks, we show that Meta-Graph can learn to quickly adapt to a new graph using only a small sample of true edges, enabling not only fast adaptation but also improved results at convergence.
Characterizing and automatically detecting smooth pursuit in a large-scale ground-truth data set of dynamic natural scenes

Eye movements are fundamental to our visual experience of the real world, and tracking smooth pursuit eye movements play an important role because of the dynamic nature of our environment. Static images, however, do not induce this class of eye movements, and commonly used synthetic moving stimuli lack ecological validity because of their low scene complexity compared to the real world. Traditionally, ground truth data for pursuit analyses with naturalistic stimuli are obtained via laborious hand-labelling. Therefore, previous studies typically remained small in scale. We here present the first large-scale quantitative characterization of human smooth pursuit. In order to achieve this, we first provide a methodological framework for such analyses by collecting a large set of manual annotations for eye movements in dynamic scenes and by examining the bias and variance of human annotators. To enable further research on even larger future data sets, we also describe, improve, and thoroughly analyze a novel algorithm to automatically classify eye movements. Our approach incorporates unsupervised learning techniques and thus demonstrates improved performance with the addition of unlabelled data. The code and data related to our manual and automated eye movement annotation are publicly available via https://web.gin.g-node.org/ioannis.agtzidis/gazecom_annotations/.
Gaussian Conditionally Markov Sequences: Dynamic Models and Representations of Reciprocal and Other Classes

Conditionally Markov (CM) sequences are powerful mathematical tools for modeling problems. One class of CM sequences is the reciprocal sequence. In application, we need not only CM dynamic models, but also know how to design model parameters. Models of two important classes of nonsingular Gaussian (NG) CM sequences, called $CM_L$ and $CM_F$ models, and a model of the NG reciprocal sequence, called reciprocal $CM_L$ model, were presented in our previous works and their applications were discussed. In this paper, these models are studied in more detail, in particular their parameter design. It is shown that every reciprocal $CM_L$ model can be induced by a Markov model. Then, parameters of each reciprocal $CM_L$ model can be obtained from those of the Markov model. Also, it is shown that a NG $CM_L$ ($CM_F$) sequence can be represented by a sum of a NG Markov sequence and an uncorrelated NG vector. This (necessary and sufficient) representation provides a basis for designing parameters of a $CM_L$ ($CM_F$) model. From the CM viewpoint, a representation is also obtained for NG reciprocal sequences. This representation is simple and reveals an important property of reciprocal sequences. As a result, the significance of studying reciprocal sequences from the CM viewpoint is demonstrated. A full spectrum of dynamic models from a $CM_L$ model to a reciprocal $CM_L$ model is also presented. Some examples are presented for illustration.
FCS-MBFLEACH: Designing an Energy-Aware Fault Detection System for Mobile Wireless Sensor Networks

Wireless sensor networks (WSNs) include large-scale sensor nodes that are densely distributed over a geographical region that is completely randomized for monitoring, identifying, and analyzing physical events. The crucial challenge in wireless sensor networks is the very high dependence of the sensor nodes on limited battery power to exchange information wirelessly as well as the non-rechargeable battery of the wireless sensor nodes, which makes the management and monitoring of these nodes in terms of abnormal changes very difficult. These anomalies appear under faults, including hardware, software, anomalies, and attacks by raiders, all of which affect the comprehensiveness of the data collected by wireless sensor networks. Hence, a crucial contraption should be taken to detect the early faults in the network, despite the limitations of the sensor nodes. Machine learning methods include solutions that can be used to detect the sensor node faults in the network. The purpose of this study is to use several classification methods to compute the fault detection accuracy with different densities under two scenarios in regions of interest such as MB-FLEACH, one-class support vector machine (SVM), fuzzy one-class, or a combination of SVM and FCS-MBFLEACH methods. It should be noted that in the study so far, no super cluster head (SCH) selection has been performed to detect node faults in the network. The simulation outcomes demonstrate that the FCS-MBFLEACH method has the best performance in terms of the accuracy of fault detection, false-positive rate (FPR), average remaining energy, and network lifetime compared to other classification methods.
Identification of Instantaneous Anomalies in General Aviation Operations Using Energy Metrics

The quantification and improvement of safety is one of the most important objectives among the general aviation community. In recent years, machine learning techniques have emerged as an important enabler in the data-driven safety enhancement of aviation operations, with a number of techniques being applied to flight data to identify and isolate anomalous (and potentially unsafe) operations. Energy-based metrics provide measurable indications of the energy state of the aircraft and can be viewed as an objective currency to evaluate various safety-critical conditions across a heterogeneous fleet of aircraft and operations. In this paper, a novel method of identifying instantaneous anomalies for retrospective safety analysis in general aviation using energy-based metrics is proposed. Each flight data record is processed by a sliding window across the multivariate time series of evaluated metrics. A Gaussian mixture model using energy metrics and their variability within each window is fit in order to predict the probability of any instant during the flight being nominal. Instances during flights that deviate from the nominal are isolated to identify potential increased levels of risk. The identified anomalies are compared with traditional methods of safety assessment, such as exceedance detection to highlight the benefits of the developed method. The methodology is demonstrated using flight data records from two representative aircraft for critical phases of flight.
Optimization model for QoS based task scheduling in cloud computing environment

Shortest job first task scheduling algorithm allocates task based on the length of the task, i.e the task that will have small execution time will be scheduled first and the longer tasks will be executed later based on system availability. Min- Min algorithm will schedule short tasks parallel and long tasks will follow them. Short tasks will be executed until the system is free to schedule and execute longer tasks. Task Particle optimization model can be used for allocating the tasks in the network of cloud computing network by applying Quality of Service (QoS) to satisfy user’s needs. The tasks are categorized into different groups. Every one group contains the tasks with attributes (types of users and tasks, size and latency of the task). Once the task is allocated to a particular group, scheduler starts assigning these tasks to accessible services. The proposed optimization model includes Resource and load balancing Optimization, Non-linear objective function, Resource allocation model, Queuing Cost Model, Cloud cost estimation model and Task Particle optimization model for task scheduling in cloud computing environement. The main objectives identified are as follows. To propose an efficient task scheduling algorithm which maps the tasks to resources by using a dynamic load based distributed queue for dependent tasks so as to reduce cost, execution and tardiness time and to improve resource utilization and fault tolerance. To develop a multi-objective optimization based VM consolidation technique by considering the precedence of tasks, load balancing and fault tolerance and to aim for efficient resource allocation and performance of data center operations. To achieve a better migration performance model to efficiently model the requirements of memory, networking and task scheduling. To propose a QoS based resource allocation model using fitness function to optimize execution cost, execution time, energy consumption and task rejection ratio and to increase the throughput. QoS parameters such as reliability, availability, degree of imbalance, performance and SLA violation and response time for cloud services can be used to deliver better cloud services.
Joint Motion Information Extraction and Human Behavior Recognition in Video Based on Deep Learning

Video surveillance has played a huge role in the safety and security tasks of various places for many years, and its application range is also expanding. The core problem that needs to be addressed in intelligent video surveillance is the identification and analysis of human behavior in video. In this paper, the structure and joint motion feature extraction of the two-channel deep convolutional neural network model are studied and designed. In the design of the network structure, the ventral and dorsal channels used in the processing of visual signals by the brain visual cortex were simulated. The spatial channel network and the time channel network are used to process static information and dynamic information respectively, and the two types of features are separately extracted. The superiority of the dual-channel structure is verified by comparing the recognition effects of the two-channel model with the single-channel model. Finally, experiments were performed on the KTH behavioral dataset. The results show that the human behavior recognition algorithm based on deep learning can achieve high recognition accuracy based on the good extraction of joint motion information.
Representation learning in intraoperative vital signs for heart failure risk prediction

The probability of heart failure during the perioperative period is 2% on average and it is as high as 17% when accompanied by cardiovascular diseases in China. It has been the most significant cause of postoperative death of patients. However, the patient is managed by the flow of information during the operation, but a lot of clinical information can make it difficult for medical staff to identify the information relevant to patient care. There are major practical and technical barriers to understand perioperative complications.In this work, we present three machine learning methods to estimate risks of heart failure, which extract intraoperative vital signs monitoring data into different modal representations (statistical learning representation, text learning representation, image learning representation). Firstly, we extracted features of vital signs monitoring data of surgical patients by statistical analysis. Secondly, the vital signs data is converted into text information by Piecewise Approximate Aggregation (PAA) and Symbolic Aggregate Approximation (SAX), then Latent Dirichlet Allocation (LDA) model is used to extract text topics of patients for heart failure prediction. Thirdly, the vital sign monitoring time series data of the surgical patient is converted into a grid image by using the grid representation, and then the convolutional neural network is directly used to identify the grid image for heart failure prediction. We evaluated the proposed methods in the monitoring data of real patients during the perioperative period.In this paper, the results of our experiment demonstrate the Gradient Boosting Decision Tree (GBDT) classifier achieves the best results in the prediction of heart failure by statistical feature representation. The sensitivity, specificity and the area under the curve (AUC) of the best method can reach 83, 85 and 84% respectively.The experimental results demonstrate that representation learning model of vital signs monitoring data of intraoperative patients can effectively capture the physiological characteristics of postoperative heart failure.
Classifying Skarns and Quantifying Metasomatism at the Antamina Deposit, Peru: Insights from Whole-Rock Geochemistry

Abstract At the Antamina deposit, Peru, accurate classification of exoskarns and endoskarns can be problematic when textures are mottled. In this study, we use whole-rock geochemical compositions (62 elements) of 221 samples to differentiate texturally similar endoskarns and exoskarns by comparing their compositions to least altered precursors (wall rocks and intrusive rocks). We present a simple method for discriminating these skarn types using immobile element bivariate plots. The most effective discriminators partition endoskarn and exoskarn into distinct domains defined by the composition of each precursor; these include Al2O3 versus heavy rare earth elements and some high field strength elements. Using these geochemical parameters, undifferentiated skarn samples can be more reliably classified as endoskarn or exoskarn. The effectiveness of these element pairs is attributed to their significantly different initial concentrations in wall rocks versus igneous precursors and their immobility during skarn formation. While immobile elements can differentiate the skarns, mobile element gains and losses (quantified using isocon analysis) provide insight on the bulk mineralogical and mass changes that take place during skarn formation.
Making Parameter Dependencies of Time‐Series Segmentation Visually Understandable

Abstract This work presents an approach to support the visual analysis of parameter dependencies of time‐series segmentation. The goal is to help analysts understand which parameters have high influence and which segmentation properties are highly sensitive to parameter changes. Our approach first derives features from the segmentation output and then calculates correlations between the features and the parameters, more precisely, in parameter subranges to capture global and local dependencies. Dedicated overviews visualize the correlations to help users understand parameter impact and recognize distinct regions of influence in the parameter space. A detailed inspection of the segmentations is supported by means of visually emphasizing parameter ranges and segments participating in a dependency. This involves linking and highlighting, and also a special sorting mechanism that adjusts the visualization dynamically as users interactively explore individual dependencies. The approach is applied in the context of segmenting time series for activity recognition. Informal feedback from a domain expert suggests that our approach is a useful addition to the analyst's toolbox for time‐series segmentation.
Exquisitor at the Video Browser Showdown 2020

When browsing large video collections, human-in-the-loop systems are essential. The system should understand the semantic information need of the user and interactively help formulate queries to satisfy that information need based on data-driven methods. Full synergy between the interacting user and the system can only be obtained when the system learns from the user interactions while providing immediate response. Doing so with dynamically changing information needs for large scale multimodal collections is a challenging task. To push the boundary of current methods, we propose to apply the state of the art in interactive multimodal learning to the complex multimodal information needs posed by the Video Browser Showdown (VBS). To that end we adapt the Exquisitor system, a highly scalable interactive learning system. Exquisitor combines semantic features extracted from visual content and text to suggest relevant media items to the user, based on user relevance feedback on previously suggested items. In this paper, we briefly describe the Exquisitor system, and its first incarnation as a VBS entrant.
Feature-map-level Online Adversarial Knowledge Distillation

Feature maps contain rich information about image intensity and spatial correlation. However, previous online knowledge distillation methods only utilize the class probabilities. Thus in this paper, we propose an online knowledge distillation method that transfers not only the knowledge of the class probabilities but also that of the feature map using the adversarial training framework. We train multiple networks simultaneously by employing discriminators to distinguish the feature map distributions of different networks. Each network has its corresponding discriminator which discriminates the feature map from its own as fake while classifying that of the other network as real. By training a network to fool the corresponding discriminator, it can learn the other network's feature map distribution. We show that our method performs better than the conventional direct alignment method such as L1 and is more suitable for online distillation. Also, we propose a novel cyclic learning scheme for training more than two networks together. We have applied our method to various network architectures on the classification task and discovered a significant improvement of performance especially in the case of training a pair of a small network and a large one.
Use of the Sentinel-2A Multispectral Image for Litho-Structural and Alteration Mapping in Al Glo’a Map Sheet (1/50,000) (Bou Azzer–El Graara Inlier, Central Anti-Atlas, Morocco)

Abstract The discovery of natural resources remains the main mission of Earth observation satellites, especially in geographical areas that have a very difficult accessibility as those of the Bou Azzer–El Graara inlier (Central Anti-Atlas, Morocco). This work investigates the use of different satellite data, such as Sentinel-2A’s multispectral imagery, in order to direct the prospection program in an efficient manner, saving both time and cost. The image processing methods of Landsat 7, 8, and “Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)” (30 m/15 m) were used to create methods for Sentinel-2A images (10 m). The red, green, blue (RGB) image 12.8.2, 11/12.11/2.11/8, principal component (PC) 1,2,3(11.12.2), and other new images were the result of principal component analysis (PCA), and classification by the Iterative Self-Organizing Data Analysis Technique (ISODATA) and K-Means allowed realization of a lithological cartography as well as maps of lineaments through directional filters and the ratio of 11/12 for hydrothermal alteration zone mapping. The assembly of lithological, structural, and hydrothermal alteration data gave an idea of the mineralogy of the study area. Validity of the results was tested by comparison with the field data and the geological maps of the studied site (62% for the hydrothermal alteration zone, 81% for the lithological map, and 74% for the structural map).
Going Deep: Graph Convolutional Ladder-Shape Networks

Neighborhood aggregation algorithms like spectral graph convolutional networks (GCNs) formulate graph convolutions as a symmetric Laplacian smoothing operation to aggregate the feature information of one node with that of its neighbors. While they have achieved great success in semi-supervised node classification on graphs, current approaches suffer from the over-smoothing problem when the depth of the neural networks increases, which always leads to a noticeable degradation of performance. To solve this problem, we present graph convolutional ladder-shape networks (GCLN), a novel graph neural network architecture that transmits messages from shallow layers to deeper layers to overcome the over-smoothing problem and dramatically extend the scale of the neural networks with improved performance. We have validated the effectiveness of proposed GCLN at a node-wise level with a semi-supervised task (node classification) and an unsupervised task (node clustering), and at a graph-wise level with graph classification by applying a differentiable pooling operation. The proposed GCLN outperforms original GCNs, deep GCNs and other state-of-the-art GCN-based models for all three tasks, which were designed from various perspectives on six real-world benchmark data sets.
Criteria as a Prelude for Guiding Taxonomy Evaluation

Taxonomies are design science artifacts used by researchers and practitioners to describe and classify existing or future objects of a domain.As such, they constitute a necessary foundation for theory building.Yet despite the great interest in taxonomies, there is virtually no guidance on how to rigorously evaluate them.Based on a literature review and a sample of 446 articles, this study explores the criteria currently employed in taxonomy evaluations.Surprisingly, we find that only a minority of taxonomy building projects actually evaluate their taxonomies and that there is no consistency across the multiplicity of criteria used.Our study provides a structured overview of the taxonomy evaluation criteria used by IS researchers and proposes a set of potential guidelines to support future evaluations.The purposeful and rigorous taxonomy evaluation our study advances contributes to DSR by bridging the gap between generic evaluation criteria and concrete taxonomy evaluation criteria.
Learning and Reasoning for Robot Sequential Decision Making under Uncertainty

Robots frequently face complex tasks that require more than one action, where sequential decision-making (sdm) capabilities become necessary. The key contribution of this work is a robot sdm framework, called lcorpp, that supports the simultaneous capabilities of supervised learning for passive state estimation, automated reasoning with declarative human knowledge, and planning under uncertainty toward achieving long-term goals. In particular, we use a hybrid reasoning paradigm to refine the state estimator, and provide informative priors for the probabilistic planner. In experiments, a mobile robot is tasked with estimating human intentions using their motion trajectories, declarative contextual knowledge, and human-robot interaction (dialog-based and motion-based). Results suggest that, in efficiency and accuracy, our framework performs better than its no-learning and no-reasoning counterparts in office environment.