Reviewerly/exHarmony huggingface dataset:
Markerless detection of ancient rock carvings in the wild: rock art in Vathy, Astypalaia

In this paper, we discuss the problem of object detection in a cultural heritage application context. In particular, the objects to be detected are ancient rock carvings, discovered at the archaeological site of Vathy, Astypalaia in Greece. Without the help of a marker or a human expert, the rock carvings are extremely difficult for a visitor of the site to discern from their surroundings. We explore the possibility of using a computational method that could replace the human expert and detect the rock carvings of interest without the aid of a specific marker. We present a dataset of images that is comprised of annotated photographs of the rock carvings, taken in situ and under differing poses and lighting parameters. Two methods for detection are applied; the first method makes use of a supervised, deep learning-based model, while the other relies on feature point-based matching to an annotated template, in the context of which we propose a simple image matching distance. We show that each method is applicable under different conditions, and evaluate their effectiveness with numerical trials.
Layerwise Buffer Voltage Scaling for Energy-Efficient Convolutional Neural Network

In order to effectively reduce buffer energy consumption, which constitutes a significant part of the total energy consumption in a convolutional neural network (CNN), it is useful to apply different amounts of energy conservation effort to the different levels of a CNN as the buffer energy to total energy usage ratios can differ quite substantially across the layers of a CNN. This article proposes layerwise buffer voltage scaling as an effective technique for reducing buffer access energy. Error-resilience analysis, including interlayer effects, conducted during design-time is used to determine the specific buffer supply voltage to be used for each layer of a CNN. Then these layer-specific buffer supply voltages are used in the CNN for image classification inference. Error injection experiments with three different types of CNN architectures show that, with this technique, the buffer access energy and overall system energy can be reduced by up to 68.41% and 33.68%, respectively, without sacrificing image classification accuracy.
Particle swarm optimization algorithm based on Beetle Antennae Search algorithm to solve path planning problem

Particle Swarm Optimization (PSO), as a swarm intelligence optimization algorithm, researching and applying in the track planning of robot in large range, but it has the defects of low search accuracy and easy fall into a locally optimal path. To overcome its shortcomings, this paper proposes an evolutionary algorithm combining beetle algorithm search (BAS) and particle swarm algorithm (PSO) and named BAS -PSO. This method is applied to multi-objective optimization path planning. In the optimization process, the update rule of each particle is derived from the BAS. In each iteration, each particle has its own judgment of the space of the environment, which reduces the number of iterations and improves the search speed. Finally, BAS-PSO algorithm and PSO algorithm are respectively used to simulate the path planning and the results demonstrate the BAS-PSO algorithm is more stable and effective.
Migrating a Recommendation System to Cloud Using ML Workflow

Inference is the production stage of machine learning workflow in which a trained model is used to infer or predict with real world data. A recommendation system improves customer experience by displaying most relevant items based on historical behavior of a customer. Machine learning models built for recommendation systems are deployed either on-premise or migrated to a cloud for inference in real time or batch. A recommendation system should be cost effective while honoring service level agreements (SLAs). In this work we discuss on-premise implementation of our recommendation system called iPrescribe. We show a methodology to migrate on-premise implementation of recommendation system to a cloud using ML workflow. We also present our study on performance of recommendation system model when deployed on different types of virtual instances.
Transformation of SysML Requirement Diagram into OWL Ontologies

The Requirement Diagrams are used by the System Modeling Language (SysML) to depict and model non-functional requirements, such as response time, size, or system functionality, which cannot be accommodated in the Unified Modeling Language (UML).Nevertheless, SysML still lacks the capability to represent the semantic contexts within the design.Web Ontology Language (OWL) can be used to capture the semantic context of system design; hence, the transformation of SysML diagrams into OWL is needed.The current method of SysML Diagrams transformation into OWL is still done manually so that it is very vulnerable to errors, and the translation process requires more time and effort for system engineers.This research proposes a model that can automatically transform a SysML Requirement Diagram into an OWL file so that system designs can be easily understood by both humans and machines.It also allows users to extract knowledge contained in the previous diagrams.The transformation process makes use of a transformation rule and an algorithm that can be used to change a SysML Requirement Diagram into an OWL ontology file.XML Metadata Interchange (XMI) serialization is used as the bridge to perform the transformation.The produced ontology can be viewed in Protégé.The class and subclass hierarchy, as well as the object properties and data properties, are clearly shown.In the experiment, it is also shown that the model can conduct the transformation correctly.
Progressive Face Super-Resolution via Attention to Facial Landmark.

Face Super-Resolution (SR) is a subfield of the SR domain that specifically targets the reconstruction of face images. The main challenge of face SR is to restore essential facial features without distortion. We propose a novel face SR method that generates photo-realistic 8x super-resolved face images with fully retained facial details. To that end, we adopt a progressive training method, which allows stable training by splitting the network into successive steps, each producing output with a progressively higher resolution. We also propose a novel facial attention loss and apply it at each step to focus on restoring facial attributes in greater details by multiplying the pixel difference and heatmap values. Lastly, we propose a compressed version of the state-of-the-art face alignment network (FAN) for landmark heatmap extraction. With the proposed FAN, we can extract the heatmaps suitable for face SR and also reduce the overall training time. Experimental results verify that our method outperforms state-of-the-art methods in both qualitative and quantitative measurements, especially in perceptual quality.
Memory Reorganization: A Symmetric Memory Network for Reorganizing Neighbors and Topics to Complete Rating Prediction

Using pre-trained topic information to assist in training neural networks can effectively support the completion of the rating prediction task. However, existing neural-topic methods consider only the use of topic information corresponding to current users and items without neighbors, whereas existing memory-based neighborhood approaches are inappropriate for the direct modeling of neighbors with topics. To address the limitations, we argue that memory networks have the ability to organize neighbors with corresponding topics well and can provide a general solution to this problem. To confirm our hypothesis, we propose two approaches. One is an augmented memory network to couple with and enhance existing neural-topic models. The other is a symmetric memory network activated by a memory reorganization mechanism, which is a compact and generalized method for rating prediction. The experimental results demonstrate the effectiveness of the memory reorganization mechanism and show that the two proposed methods have advantages over existing state-of-the-art topic modeling approaches.
EL DAPP – An Electrıcıty Meter Trackıng Decentralızed Applıcatıon

The electricity industry has always been under scrutiny in order to improve the quality of electricity supply, measurement and billing services to have the at most user transparency, while providing these services with the highest efficiency. Although many solutions have emerged, of which the smart meter was considered a viable option, it was quick to perish under the prodigious complications with the real-life feasibilities. El DApp – An electricity power consumption tracking application solution, harnessing both the IoT and Blockchain utilities to provide a decentralized and secure recording mechanism, that provides an improved architecture to the smart meter is proposed in this article. The El DApp provides a high security and cost efficient decentralized live electricity power consumption recording of the user that is maintained by a Raspberry Pi based Ethereum network.
The Effects of Avatar Visibility on Behavioral Response with or without Mirror-Visual Feedback in Virtual Environments

Existing studies have shown that increasing avatar visibility could not improve perceptual responses. With the recent advances of VR technology, full body tracking avatars have been adopted to social interactions and games with light weight head-mounted displays. However, it is unknown about the effects of full-body avatars on behavioral responses. Hence, in this study, we designed a full-body avatar visibility with or without virtual-mirror feedback, and investigated their effects on presence, embodiment, and task performance in a bow-shooting game. This study provides initial results of using avatar visibility to enhance behavioral responses in virtual environments
Visual Object Tracking Based on Mutual Learning Between Cohort Multiscale Feature-Fusion Networks With Weighted Loss

The deep convolutional neural network (CNN) based tracking-by-detection framework recently has become one of the most popular trackers. However, these methods are either time consuming or have greatly reduced performance. This article aims to achieve nearly identical tracking accuracy with the state-of-the-art CNN tracking-by-detection algorithm with relatively faster speed. We study the existing excellent trackers under the CNN tracking-by-detection framework and introduce the following: a multiscale feature pyramid fusion neural network based on dilated convolutions is constructed to learn a scale-invariant discriminative representation for tracking small objects, a hard-threshold weighted cross-entropy loss function is proposed to decrease the gap between object classification and tracking, and a mutual learning-based training policy is used to fuse the information from the network trained by image patches with different contextual regions to further improve the tracking performance. We conduct comprehensive experiments on visual object tracking benchmarks that validate the achievement of competitive performance of the proposed tracker with relatively faster speed both on qualitative and quantitative criteria. Additionally, the experimental results reveal that the proposed mutual learning-based training policy can accelerate the convergence speed and achieve better generalization performance.
Solar irradiation regionalization in Uruguay: Understanding the interannual variability and its relation to El Niño climatic phenomena

A regionalization of different characteristics of the solar resource is performed for Uruguay and surrounding areas (Southeastern South America). The input information consists of daily satellite estimates of Global Horizontal Irradiation (GHI) generated in a regular grid using a low uncertainty empirical satellite-based model which was specifically adapted for the region. Clusters are derived from the climatological annual cycle of monthly irradiation and clearness index and, separately, from time-series of monthly variability. The solar irradiation variability in each cluster is compared with El Niño South Oscillation (ENSO) signal. A high negative correlation is observed between ENSO and solar irradiance, most predominantly over February to May and November to December, particularly for the latter. This means that in a strong El Niño/La Niña year, solar irradiation values for the November–December period in Uruguay will be smaller/higher than the climatological average. These results are in agreement with the ones obtained for rainfall in other studies.
HIN_DRL: A random walk based dynamic network representation learning method for heterogeneous information networks

Learning the low-dimensional vector representation of networks can effectively reduce the complexity of various network analysis tasks, such as link prediction, clustering and classification. However, most of the existing network representation learning (NRL) methods are aimed at homogeneous or static networks, while the real-world networks are usually heterogeneous and tend to change dynamically over time, therefore providing an intelligent insight into the evolution of heterogeneous networks is more practical and significant. Based on this consideration, we focus on the dynamic representation learning problem for heterogeneous information networks, and propose a random walk based Dynamic Representation Learning method for Heterogeneous Information Networks (HIN_DRL), which can learn the representation of network nodes at different timestamps. Specifically, we improve the first step of the existing random walk based NRL methods, which generally include two steps: constructing node sequences through random walk process, and then learning node representations by throwing the node sequences into a homogeneous or heterogeneous Skip-Gram model. In order to construct optimized node sequences for evolving heterogeneous networks, we propose a method for automatically extracting and extending meta-paths, and propose a new method for generating node sequences via dynamic random walk based on meta-path and timestamp information of networks. We also propose two strategies for adjusting the quantity and length of node sequences during each random walk process, which makes it more effective to construct the node sequences for heterogeneous information networks at a specific timestamp, thus improving the effect of dynamic representation learning. Extensive experimental results show that compared with the state-of-art algorithms, HIN_DRL achieves better results in Macro-F1, Micro-F1 and NMI for multi-label node classification, multi-class node classification and node clustering on several real-world network datasets. Furthermore, case studies of visualization and dynamic on Microsoft Academic dataset demonstrate that HIN_DRL can learn network representation dynamically and more effectively.
Managing Streaming Video: The Experiences of Six Academic Libraries

Librarians from six diverse public higher education institutions in Maryland describe the challenges their libraries have experienced in managing streaming video platforms and services and how they have attempted to address them.
Rethinking Assumptions in Deep Anomaly Detection

Though anomaly detection (AD) can be viewed as a classification problem (nominal vs. anomalous) it is usually treated in an unsupervised manner since one typically does not have access to, or it is infeasible to utilize, a dataset that sufficiently characterizes what it means to be anomalous. In this paper we present results demonstrating that this intuition surprisingly seems not to extend to deep AD on images. For a recent AD benchmark on ImageNet, classifiers trained to discern between normal samples and just a few (64) random natural images are able to outperform the current state of the art in deep AD. Experimentally we discover that the multiscale structure of image data makes example anomalies exceptionally informative.
Precise Positioning of Circular Mark Points and Transistor Components in Surface Mounting Technology Applications

The visual inspection algorithms are the core of automatic optical inspection system on surface mounting machines. This article is concerned with the development of precise positioning algorithms for circular mark points and transistor (TR) components on surface mounting devices. To handle nonuniform illumination or occlusion of other components, a polar coordinate transform and smoothness selection based circular mark point location method is proposed. The TR components are fundamental chips in electronic products and have various package types. The illumination changes, background disturbance, and the diversity of package types have imposed great challenges on the development of the uniform algorithm for detection and location of TR components. To deal with these issues, the 1-D integral image based TR component detection and location algorithm is proposed and the coordinates and orientation of the component are calculated simultaneously. The efficiency of the proposed methods is tested on real images and compared with classical Hough transform method, commercial algorithms on SMT482 device, and two methods of Halcon software.
Strategic Management of Smart University Development

In modern market conditions, each organization needs an effective management system. Such a management system is needed that meets the laws of the economic system, scientific, and technological progress and is associated with the achievement of strategic goals, ensuring the interest of employees in the highest final results. Managing a smart university requires multilateral knowledge, a research approach, strategic, analytical thinking, organizational skills, communication skills, responsibility, innovation, and enterprise. Management Theory is constantly evolving. In practice, there are no two organizations with absolutely identical management models since management methods are constantly being adjusted to reflect changes in the external environment. Success in the activities of a smart university depends on the effectiveness of strategic management of its development. The main goal of the article is to improve the strategic management of the development of the smart university. As a result of the study, a strategic university model of smart university is proposed. The study made it possible to single out the project-planning and program approaches to the strategic management of the development of the smart university, which takes into account the features of its activities. The result of the study is the recommended main stages of strategic management of the development of the smart university. The results obtained were tested by the strategic development department of the smart division of the university on the example of the department.
Edge and region segmentation in high-resolution aerial images using improved kernel density estimation: A hybrid approach

Edge detection and segmentation are the two main approaches being used since last three decades for successful image analysis in remote sensing domain. Although many intensive studies were undertaken, they all were limited to high-resolution aerial images and none addressed this problem exhaustivel y. The purpose of this study was to investigate both edge detection and segmentation by employing a novel hybrid method combining probability density function and partial differential equation to obtain accurate estimations. The newly proposed method is implemented in two phases: the first phase deals with smoothening that include improved kernel density estimation (KDE) with anisotropic diffusion coefficient function kernel with both adaptive bandwidth and constant threshold selection using Shannon entropy, in addition to a weighting parameter of 3 × 3 window for lower probability of the whole image in diffusion function; whereas in the second phase, edge detection and segmentation are dealt with by incorporating two prominent techniques, namely diffusion coefficient equation and six-sigma control limit. We carried out a cross-sectional analysis using different datasets such as SIPI database and ground truth images for smoothing, edging and segmentation. Afterward, the results were compared with the other state-of-the-art techniques. Finally, the performance measures of the implemented technique were evaluated by means of entropy, fractal dimension, and an equivalent number of looks for smoothened images, by the Pratt metric for edge detection, and in the case of segmentation, misclassification error was considered. The experimental results demonstrated that the proposed scheme outperforms its counterparts in all aspects. Hence, the proposed hybrid scheme is better and robust, and results in accurate estimation for the given datasets.
A Survey on Generative Adversarial Networks: Variants, Applications, and Training

The Generative Models have gained considerable attention in the field of unsupervised learning via a new and practical framework called Generative Adversarial Networks (GAN) due to its outstanding data generation capability. Many models of GAN have proposed, and several practical applications emerged in various domains of computer vision and machine learning. Despite GAN's excellent success, there are still obstacles to stable training. The problems are due to Nash-equilibrium, internal covariate shift, mode collapse, vanishing gradient, and lack of proper evaluation metrics. Therefore, stable training is a crucial issue in different applications for the success of GAN. Herein, we survey several training solutions proposed by different researchers to stabilize GAN training. We survey, (I) the original GAN model and its modified classical versions, (II) detail analysis of various GAN applications in different domains, (III) detail study about the various GAN training obstacles as well as training solutions. Finally, we discuss several new issues as well as research outlines to the topic.
Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses

Uniform stability is a notion of algorithmic stability that bounds the worst case change in the model output by the algorithm when a single data point in the dataset is replaced. An influential work of Hardt et al. (2016) provides strong upper bounds on the uniform stability of the stochastic gradient descent (SGD) algorithm on sufficiently smooth convex losses. These results led to important progress in understanding of the generalization properties of SGD and several applications to differentially private convex optimization for smooth losses. Our work is the first to address uniform stability of SGD on {\em nonsmooth} convex losses. Specifically, we provide sharp upper and lower bounds for several forms of SGD and full-batch GD on arbitrary Lipschitz nonsmooth convex losses. Our lower bounds show that, in the nonsmooth case, (S)GD can be inherently less stable than in the smooth case. On the other hand, our upper bounds show that (S)GD is sufficiently stable for deriving new and useful bounds on generalization error. Most notably, we obtain the first dimension-independent generalization bounds for multi-pass SGD in the nonsmooth case. In addition, our bounds allow us to derive a new algorithm for differentially private nonsmooth stochastic convex optimization with optimal excess population risk. Our algorithm is simpler and more efficient than the best known algorithm for the nonsmooth case Feldman et al. (2020).