Reviewerly/exHarmony huggingface dataset:
Use of Machine Learning Services in Cloud

Machine learning services are the comprehensive description of integrated and semiautomated web devices covering most facilities problems such as preprocessing information, design preparation, and design assessment, with the further forecast. REST APIs can bridge the outcomes of predictions with one’s inner IT infrastructure. Like the original SaaS, IaaS, and PaaS cloud delivery models, ML and AI fields cover high-level services to provide infrastructure and platform, exposed as APIs. This article identifying the most used Cloud Technologies for Machine Learning as a Service (MLaaS): Google Cloud AI, Amazon, and Microsoft Azure.
Noah: Neural-optimized A* Search Algorithm for Graph Edit Distance Computation

Graph Edit Distance (GED) is a classical graph similarity metric that can be tailored to a wide range of applications. However, the exact GED computation is NP-complete, which means it is only feasible for small graphs only. And therefore, approximate GED computation methods are used in most real-world applications. However, traditional practices and end-to-end learning-based methods have their shortcomings when applied for approximate GED computation. The former relies on experience and usually performs not well. The latter is only capable of computing similarity scores between graphs without an actual edit path, which is crucial in specific problems (e.g., Graph Alignment, Semantic Role Labeling). This paper proposes a novel approach Noah, which combines A* search algorithm and graph neural networks to compute approximate GED in a more effective and intelligent way. The combination is mainly reflected in two aspects. First, we learn the estimated cost function h(·) by Graph Path Networks. Pre-training GEDs and corresponding edit paths are also incorporated for training the model, therefore helping optimize the search direction of A* search algorithm. Second, we learn an elastic beam size that can help reduce search size and satisfy various user settings. Experimental results demonstrate the practical effectiveness of our approach on several tasks and suggest that our approach significantly outperforms the state-of-the-art methods.
A Comparative Evaluation of Spatio Temporal Deep Learning Techniques for Crime Prediction

This paper presents a detailed evaluation of three spatio-temporal deep learning architectures for crime prediction. These network architectures are as follows: the Spatio Temporal Residual Network (ST-ResNet), the Deep Multi View Spatio Temporal Network (DMVST-Net), and the Spatio Temporal Dynamic Network (STD-Net). The architectures were trained using the Chicago crime data set. The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were used as performance metrics to evaluate the models. Results show that the STD-Net achieved the best results of the three approaches, with an accuracy of 0.89, RMSE of 0.2870, and MAE of 0.2093. The ST-ResNet and DMVST-Net also showed considerable promise. The ST-ResNet achieved an accuracy of 0.83, RMSE of 0.4033 and an MAE of 0.3278 while the DMVST-Net achieved an accuracy of 0.79, RMSE of 0.4171 and an MAE of 0.3455. Future work will include training these algorithms with crime data, which is augmented with external data such as climate and socioeconomic data. Hyperparameter optimization of these algorithms using techniques, such as evolutionary computation, will also be explored.
Learning Triadic Belief Dynamics in Nonverbal Communication from Videos

Humans possess a unique social cognition capability [43], [20]; nonverbal communication can convey rich social information among agents. In contrast, such crucial social characteristics are mostly missing in the existing scene understanding literature. In this paper, we incorporate different nonverbal communication cues (e.g., gaze, human poses, and gestures) to represent, model, learn, and infer agents’ mental states from pure visual inputs. Crucially, such a mental representation takes the agent’s belief into account so that it represents what the true world state is and infers the beliefs in each agent’s mental state, which may differ from the true world states. By aggregating different beliefs and true world states, our model essentially forms "five minds" during the interactions between two agents. This "five minds" model differs from prior works that infer beliefs in an infinite recursion; instead, agents’ beliefs are converged into a "common mind" [31], [47]. Based on this representation, we further devise a hierarchical energy-based model that jointly tracks and predicts all five minds. From this new perspective, a social event is interpreted by a series of nonverbal communication and belief dynamics, which transcends the classic keyframe video summary. In the experiments, we demonstrate that using such a social account provides a better video summary on videos with rich social interactions compared with state-of-the-art keyframe video summary methods.
A Multi-agent and Content-Based Course Recommender System for University E-learning Platforms

E-learning has recently become widespread all over the world. All countries are working more and more to integrate information and communication technology in their educational systems. In this concern, recommender systems have played a crucial role for suggesting items that best match user’s preferences in different area. Currently, in order to prevent the spread of coronavirus pandemic and to ensure continuity of education during the confinement period likewise our country Morocco, distance learning has become an imperative necessity. In this regard, we are going to delve into the role of e learning in achieving quality education and the difficulties of its implementation in Moroccan context. Several digital courses have been published on e-learning platforms. However, learners, who do not have enough experience to choose the courses they need, often face many challenges. In this paper, we present Multi-agent system as a helpful tool in recommendation, then we review the current recommendation methods. Moreover, we implement a recommendation system for e-learning platforms that recommends university courses for students.
Review: Breast Cancer Detection Using Deep Learning

Breast cancer or ductal carcinoma is prevalent in women and is the leading cause of women's death worldwide. Delaying breast cancer or tumor growth has long-term effects, can also become life-threatening. So the tumor should be identified as early as possible to control the growth and preventing to spread to other tissues. Several types of researches have been done to detect breast cancer early so that the treatment can be started to increase the chance of survival. A mammography procedure for early detection and diagnosis of breast cancer is commonly advised. There are so many techniques that are used for malignancy prediction. Using Artificial Intelligence-powered machine learning is widely recommended. Researches are conducted in machine learning to detect cancerous tumors in the human body. Mainly used algorithms which give high accuracy are SVM, naïve bays, decision trees, KNN. Deep learning, the machine learning sub-branch, can also be used to classify breast cancer. Deep learning is a method that is mostly used to clear, rectify, and detect machine learning errors or disadvantages. Convolutional Neural Networks are the perfect deep learning method for overcoming the drawbacks of machine learning in malignancy detection; however, other strategies such as a recurrent neural network and a deep belief network are being used to overcome the shortcomings of machine learning. As a consequence, using deep learning rather than machine learning yields better results. This review paper's primary motivation is to make budding researchers aware that breast cancer is a serious issue among women and we need to be swift in using different technologies to detect and to improve accuracy as efficiently as possible, it is very important to save our mothers, sisters, loved ones and our society from this dangerous predator. In this review paper, we have discussed all the techniques used by different authors.
Unsupervised Learning for Robust Fitting: A Reinforcement Learning Approach

Robust model fitting is a core algorithm in a large number of computer vision applications. Solving this problem efficiently for datasets highly contaminated with outliers is, however, still challenging due to the underlying computational complexity. Recent literature has focused on learning-based algorithms. However, most approaches are supervised (which require a large amount of labelled training data). In this paper, we introduce a novel unsupervised learning framework that learns to directly solve robust model fitting. Unlike other methods, our work is agnostic to the underlying input features, and can be easily generalized to a wide variety of LP-type problems with quasi-convex residuals. We empirically show that our method out-performs existing unsupervised learning approaches, and achieves competitive results compared to traditional methods on several important computer vision problems <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Accelerated evolutionary induction of heterogeneous decision trees for gene expression-based classification

Decision trees (DTs) are popular techniques in the field of eXplainable Artificial Intelligence. Despite their effectiveness in solving various classification problems, they are not compatible with modern biological data generated with high-throughput technologies. This work aims to combine evolutionary induced DT with a recently developed concept designed directly for gene expression data, called Relative eXpression Analysis (RXA). We propose a new solution, termed Evolutionary Heterogeneous Decision Tree (EvoHDTree), which uses both classical univariate and bivariate tests that focus on the relative ordering and weight relationships between the genes in the splitting nodes. The search for the decision tree structure, node representation, and splits is performed globally by the evolutionary algorithm.
Privacy-Preserving Energy Storage Sharing with Blockchain

Energy storage provides an effective way of shifting temporal energy demands and supplies, enabling significant cost reduction under dynamic energy pricing. Despite its promising benefits, the cost of present energy storage remains expensive, presenting a major obstacle to practical deployment. A more viable solution to improve cost-effectiveness is by sharing energy storage, such as community sharing, cloud energy storage and peer-to-peer sharing. However, revealing private energy demand data in energy storage sharing may compromise user privacy, susceptible to data misuses and breaches. In this paper, we explore a novel approach to support energy storage sharing with privacy protection, based on privacy-preserving blockchain and secure multi-party computation. We present an integrated solution to enable privacy-preserving energy storage sharing, such that energy storage service scheduling and cost-sharing can be attained without the knowledge of individual users' demands. It also supports auditing and verification by the grid operator via blockchain. Furthermore, our privacy-preserving solution can safeguard against a dishonest majority of users, who may collude in cheating, without requiring trusted third-parties. We implemented our solution as a smart contract on real-world Ethereum blockchain platform, with empirical evaluation.
Rooftop PV: Potential and Impacts in a Complex Territory

When developing a sustainability plan in a complex and heavily urbanized territory, one of the most relevant options available is installing rooftop photovoltaic (PV) panels. Thus, it is essential to determine the amount of available surface and the potential impact of such installations on the energy and emission budget of the area. Instead of processing remotely sensed imagery, which is a long process and does not allow considering the buildings’ ownership, this study develops an approach based on a cluster analysis of the urban/morphological characteristics of the municipalities. Once a clear group diversification is obtained, the roof surface of the center of gravity of each cluster is extrapolated to all similar settlements. This, together with the information of local solar irradiation, allowed us to compute each cluster’s potential solar energy production and its capability to respond to the local energy demand, a key parameter to decide about the possibility of a local smart electricity network. Finally, the emissions avoided thanks to solar PV development are computed in terms of carbon dioxide and other relevant pollutants. This approach is applied to the residential rooftop of Lombardy, a Northern Italy region with a wide variety of urban morphologies and landscapes. The potential production of rooftop PV exceeds the estimated electricity consumption of residential buildings and would allow sparing almost 4 M ton of CO2 equivalent or 5% of the overall regional emissions.
Quantum Radar – What is it good for?

We consider the implications for the practical usefulness of a quantum enhanced radar system in the scenario where it is able to outperform any classical counterpart. In this context, we analyze the system from two perspectives, in the frequency range from microwaves to terahertz. On the one hand, we discuss the theoretical implications of a quantum radar system, and on the other hand, we consider technologies for realizing an experimental proof of concept. Our main conclusion is that, while realizable experimentally, useful application of microwave quantum radar protocols to any conventional setting is unrealistic because of fundamental restrictions on power levels.
Cloud advection model of solar irradiance smoothing by spatial aggregation

Solar generation facilities are inherently spatially distributed and therefore aggregate solar irradiance in both space and time, smoothing its variability. To represent the spatiotemporal aggregation process, most existing studies focus on the reduced correlation in solar irradiance throughout a plant's spatial distribution. In this paper, we derived a cloud advection model that is instead based upon lagging correlations between upwind/downwind portions of a distributed plant, induced by advection of a fixed cloud pattern over the plant. We use the model to calculate a plant transfer function that can be used to predict the smoothing of the time series. The model was validated using the distributed HOPE-Melpitz measurement dataset, which consisted of 50 solar irradiance sensors at 1 s temporal resolution over a 3 × 2 km2 bounding area. The initial validation showed that the advection-based model outperforms other models at predicting the smoothed irradiance time series during manually identified, advection dominated conditions. We also conducted validation on the model against additional advection dominated periods in the dataset that were identified algorithmically. The cloud advection model's performance compared well to models in literature, but degraded slightly as larger cross-wind plant distributions were investigated. The results in this paper highlight the need to incorporate advection effects on spatial aggregation during advection dominated conditions. Future development of spatiotemporal aggregation models is needed to unify advective models with existing correlation reduction models and to identify regimes where each dominate.
mTeeth

Ensuring that all the teeth surfaces are adequately covered during daily brushing can reduce the risk of several oral diseases. In this paper, we propose the mTeeth model to detect teeth surfaces being brushed with a manual toothbrush in the natural free-living environment using wrist-worn inertial sensors. To unambiguously label sensor data corresponding to different surfaces and capture all transitions that last only milliseconds, we present a lightweight method to detect the micro-event of brushing strokes that cleanly demarcates transitions among brushing surfaces. Using features extracted from brushing strokes, we propose a Bayesian Ensemble method that leverages the natural hierarchy among teeth surfaces and patterns of transition among them. For training and testing, we enrich a publicly-available wrist-worn inertial sensor dataset collected from the natural environment with time-synchronized precise labels of brushing surface timings and moments of transition. We annotate 10,230 instances of brushing on different surfaces from 114 episodes and evaluate the impact of wide between-person and within-person between-episode variability on machine learning model's performance for brushing surface detection.
Acceleration-Aware Fine-Grained Channel Pruning for Deep Neural Networks via Residual Gating

Deep neural networks have achieved remarkable advancement in various intelligence tasks. However, the massive computation and storage consumption limit applications on resource-constrained devices. While channel pruning has been widely applied to compress models, it is challenging to reach very deep compressions for such a coarse-grained pruning structure without significant performance degradation. In this article, we propose an acceleration-aware fine-grained channel pruning (AFCP) framework for accelerating neural networks, which optimizes trainable gate parameters by estimating residual errors between pruned and original channels with hardware characteristics. Our fine-grained concept consists of both algorithm and structure levels. Different from existing methods that leverage a predefined pruning criterion, AFCP explicitly considers both zero-out and similar criteria for each channel, and adaptively selects the suitable one via residual gate parameters. For structure level, AFCP adopts a fine-grained channel pruning strategy for residual neural networks and a decomposition-based structure, which further extends the pruning optimization space. Moreover, instead of using theoretical computation costs, such as floating-point operations, we propose the hardware predictor that bridges the gap between realistic acceleration and pruning procedure to guide the learning of pruning, which improves the efficiency of model pruning when deployed on accelerators. Extensive evaluation results demonstrate that AFCP outperforms state-of-the-art methods, and achieves a favorable balance between model performance and computation cost.
Making Graphs Compact by Lossless Contraction

This paper proposes a scheme to reduce big graphs to small graphs. It contracts obsolete parts, stars, cliques and paths into supernodes. The supernodes carry a synopsis S_Q for each query class Q to abstract key features of the contracted parts for answering queries of Q. The contraction scheme provides a compact graph representation and prioritizes up-to-date data. Better still, it is generic and lossless. We show that the same contracted graph is able to support multiple query classes at the same time, no matter whether their queries are label-based or not, local or non-local. Moreover, existing algorithms for these queries can be readily adapted to compute exact answers by using the synopses when possible, and decontracting the supernodes only when necessary. As a proof of concept, we show how to adapt existing algorithms for subgraph isomorphism, triangle counting and shortest distance to contracted graphs. We also provide an incremental contraction algorithm in response to updates. We experimentally verify that on average, the contraction scheme reduces graphs by 71.2%, and improves the evaluation of these queries by 1.53, 1.42 and 2.14 times, respectively.
Fusing the Old with the New: Learning Relative Camera Pose with Geometry-Guided Uncertainty

Learning methods for relative camera pose estimation have been developed largely in isolation from classical geometric approaches. The question of how to integrate predictions from deep neural networks (DNNs) and solutions from geometric solvers, such as the 5-point algorithm [37], has as yet remained under-explored. In this paper, we present a novel framework that involves probabilistic fusion between the two families of predictions during network training, with a view to leveraging their complementary benefits in a learnable way. The fusion is achieved by learning the DNN un- certainty under explicit guidance by the geometric uncertainty, thereby learning to take into account the geometric solution in relation to the DNN prediction. Our network features a self-attention graph neural network, which drives the learning by enforcing strong interactions between different correspondences and potentially modeling complex relationships between points. We propose motion parmeterizations suitable for learning and show that our method achieves state-of-the-art performance on the challenging DeMoN [61] and ScanNet [8] datasets. While we focus on relative pose, we envision that our pipeline is broadly applicable for fusing classical geometry and deep learning.
Exploring Task Placement for Edge-to-Cloud Applications using Emulation

A vast and growing number of IoT applications connect physical devices, such as scientific instruments, technical equipment, machines, and cameras, across heterogenous infrastructure from the edge to the cloud to provide responsive, intelligent services while complying with privacy and security requirements. However, the integration of heterogeneous IoT, edge, and cloud technologies and the design of end-to-end applications that seamlessly work across multiple layers and types of infrastructures is challenging. A significant issue is resource management and the need to ensure that the right type and scale of resources is allocated on every layer to fulfill the application's processing needs. As edge and cloud layers are increasingly tightly integrated, imbalanced resource allocations and sub-optimally placed tasks can quickly deteriorate the overall system performance. This paper proposes an emulation approach for the investigation of task placements across the edge-to-cloud continuum. We demonstrate that emulation can address the complexity and many degrees-of-freedom of the problem, allowing us to investigate essential deployment patterns and trade-offs. We evaluate our approach using a machine learning-based workload, demonstrating the validity by comparing emulation and real-world experiments. Further, we show that the right task placement strategy has a significant impact on performance - in our experiments, between 5% and 65% depending on the scenario.
Automatic Improvement of Continuous Colormaps in Euclidean Colorspaces

Abstract Colormapping is one of the simplest and most widely used data visualization methods within and outside the visualization community. Uniformity, order, discriminative power, and smoothness of continuous colormaps are the most important criteria for evaluating and potentially improving colormaps. We present a local and a global automatic optimization algorithm in Euclidean color spaces for each of these design rules in this work. As a foundation for our optimization algorithms, we used the CCC‐Tool colormap specification (CMS); each algorithm has been implemented in this tool. In addition to synthetic examples that demonstrate each method's effect, we show the outcome of some of the methods applied to a typhoon simulation.