Reviewerly/exHarmony huggingface dataset:
Using Big Data Analytics to Detect Fraud in Healthcare Provision

Big Data technologies can contribute to medical fraud detection. The aim of this paper is to present by an example, the methodological approach of the Hellenic National Organization for the Provision of Health Services (EOPYY) in data analysis to detect financial or medical fraud in claims. To analyze the data for fraud detection, a selection of prescription data from the year 2018 were examined. The Local Correlation Integral algorithm was applied to detect any outliers on the dataset. The results revealed that 7 out of 879 products could be characterized as outliers. These outliers must be further investigated to determine if they represent fraud cases. According to the results of this study, this outliers detection approach can support and help the fraud detection process conducted by the auditing services in Healthcare sector.
A Day on Campus - An Anomaly Detection Dataset for Events in a Single Camera

Detecting anomalies in videos is a complex problem with a myriad of applications in video surveillance. However, large and complex datasets that are representative of real-world deployment of surveillance cameras are unavailable. Anomalies in surveillance videos are not well defined and the standard and existing metrics for evaluation do not quantify the performance of algorithms accurately. We provide a large scale dataset, A Day on Campus (ADOC (Dataset available at qil.uh.edu/datasets)), with 25 event types, spanning over 721 instances and occurring over a period of 24 h. This is the largest dataset with localized bounding box annotations that is available to perform anomaly detection. We design a novel metric to evaluate the performance of methods and we perform an evaluation of the state-of-the-art methods to ascertain their readiness to transition into real-world surveillance scenarios.
Towards Query-by-eXpression Retrieval of Cuneiform Signs

Intermediate representations based on binary attributes are widely used for image retrieval tasks. Especially for the task of word spotting, attribute representations continually achieves state-of-the-art results. In contrast to Latin scripts, the cuneiform writing system is still a less well-known domain for the document analysis community. This writing system consists of wedge impressions, with the constellations and relative positions of wedges representing sign classes. While for Latin scripts characters are intuitive modeled as visual attributes, cuneiform signs do not reveal an evident approach to model visual attributes for cuneiform signs. However, in this work we introduce an attribute representation based on the so-called Gottstein-System. The idea is decompose signs according to wedge typology and enable a logical expression for sign classes, while sharing these expressions across visually similar cuneiform signs. We adapt this idea of describing wedge expressions in order to introduce to the Query-by-eXpression (QbX) retrieval scenario. Compared to queries based on sign IDs, our approach is capable of representing queries for an open-set retrieval scenario. Furthermore, we extend the Gottstein-System by expressions representing visual hints like wedge crossings or position relations. This way, promising results are achieved, as shown in our experiments.
A Generic Flexible and Scalable Framework for Hierarchical Parallelization of Population-Based Metaheuristics

Population-based metaheuristics -such as Evolutionary Algorithms (EAs)- are one of the most popular methods for solving highly complex and large-scale optimization problems. Nevertheless, finding an adequate solution with such approaches often requires computationally intensive fitness function evaluations especially in real-world applications. To speed up the computation, exploiting modern software techniques for parallelizing population-based metaheuristics on a cluster or a cloud is a viable approach. In the present paper, a generic, flexible and scalable framework for hierarchical hybridization of distributed population-based metaheuristics in a cluster environment is introduced. Three lightweight technologies, namely microservices, container virtualization and the publish/subscribe messaging paradigm are used to develop this framework. The combination of these technologies enables easy hybridizations of different parallelization models of population-based metaheuristics, a full decoupling between services providing basic building blocks of the algorithm and a seamless deployment in a scalable runtime environment. For evaluation purposes, the EA GLEAM (General Learning Evolutionary Algorithm and Method) is exemplarily integrated into the framework and successfully deployed in a cluster environment. Scalability and applicability of the framework are explored by hybridizing the Coarse-Grained Model with the Global Model for solving the problem of unit commitment of distributed energy resources utilizing renewable energy generation. The results show that the new proposed framework introduces an excellent performance for scaling up the optimization speed of complex unit commitment optimization problems.
Autonomous docking of an Unmanned Surface Vehicle based on Reachability Analysis

Autonomous docking is an essential capability to complete maritime missions for unmanned surface vehicles (USVs). This study presents a control procedure of autonomous docking for a USV based on reachability analysis which is a formal method to guarantee the feasibility of reaching goal states safely and design a sequence of optimal control inputs with constraints while overcoming disturbances. The USV's kinematic model is considered and a set of control inputs that guarantees successful docking in disturbances while satisfying the constraints is determined using reachability analysis. For this, the target pose and the dock are defined as level sets of signed distance functions, and the Hamilton-Jacobi partial differential equation with constraints is solved numerically.
Simple Fine-Tuning Attention Modules for Human Pose Estimation

The convolution neural networks (CNNs) have achieved the best performance not only for human pose estimation but also for other computer vision tasks (e.g., object detection, semantic segmentation, image classification). Then this paper focuses on a useful attention module (AM) for feed-forward CNNs. Firstly, feed the feature map after a block in the backbone network into the attention module, split into two separate dimensions, channel and spatial. After that, the AM combines these two feature maps by multiplication and gives it to the next block in the backbone. The network can capture the information in the long-range dependencies (channel) and the spatial data, which can gain better performance in accuracy. Therefore, our experimental results will illustrate how different between when using the attention module and the existing methods. As a result, the predicted joint heatmap maintains the accuracy and spatially better with the simple baseline. Besides, the proposed architecture gains 1.0 points in AP higher than the baseline. Moreover, the proposed network trained on COCO 2017 benchmarks, which is an accessible dataset nowadays.
Crack propagation in 3PB specimen made from welded joint

Crack propagation in 3PB specimen has been investigated experimentally by using Digital Image Correlation (DIC) technique. For this purpose, specimens were taken from butt welded plates, made of P460NL1 micro-alloyed steel. Fatigue pre-crack of 3PB specimens was introduced in the weld metal. Specimens were then statically loaded and analyzed using digital image correlation software ARAMIS to assess strain fields and crack propagation paths. Bifurcation of cracks was noticed as the main characteristics of crack prop-agation in the analyzed case.
Grasping Everyday Automation –A Design Space for Ubiquitous Automated Systems

No longer only experts are confronted with (semi-)automated systems, yet automation has founds its way into our everyday lifes in various forms and applications. Based on a thorough literature review, we introduce a design space for “everyday automation” to uncover eight core dimensions of respective systems. These dimensions include the domain, the task type, the type of user interaction, and the automation level, among others. Visualized as a “morphological box”, this design space is particularly supposed to support the ideation of novel automated systems for everyday life.
Component Detection and Evaluation Framework (CDEF): A Semantic Annotation Tool

Abstract Image labeling is the process of manually assigning a class to subregions within an image for machine learning applications. When these subregions are complex shapes, this process is known as semantic segmentation. We propose a new software application, the Component Detection and Evaluation Framework (CDEF), for creating such semantic labels. The benefits of CDEF over existing tools are highlighted, and further improvements are proposed.
DRACO: Weakly Supervised Dense Reconstruction And Canonicalization of Objects

We present DRACO, a method for Dense Reconstruction And Canonicalization of Object shape from one or more RGB images. Canonical shape reconstruction— estimating 3D object shape in a coordinate space canonicalized for scale, rotation, and translation parameters—is an emerging paradigm that holds promise for a multitude of robotic applications. Prior approaches either rely on painstakingly gathered dense 3D supervision, or produce only sparse canonical representations, limiting real-world applicability. DRACO performs dense canonicalization using only weak supervision in the form of camera poses and semantic keypoints at train time. During inference, DRACO predicts dense object-centric depth maps in a canonical coordinate-space, solely using one or more RGB images of an object. Extensive experiments on canonical shape reconstruction and pose estimation show that DRACO is competitive or superior to fully-supervised methods.
Multi-class Multi-label Classification for Cooking Activity Recognition

In this paper, we present an automatic approach to recognize cooking activities from acceleration and motion data. We rely on a dataset that contains three-axis acceleration and motion data collected with multiple devices, including two wristbands, two smartphones and a motion capture system. The data is collected from three participants while preparing sandwich, fruit salad and cereal recipes. The participants performed several fine-grained activities while preparing each recipe such as cut and peel. We propose to use the multi-class classification approach to distinguish between cooking recipes and a multi-label classification approach to identify the fine-grained activities. Our approach achieves 81% accuracy to recognize fine-grained activities and 66% accuracy to distinguish between different recipes using leave-one-subject-out cross-validation. The multi-class and multi-label classification results are 27 and 50% points higher than the baseline. We further investigate the effect on classification performance of different strategies to cope with missing data and show that imputing missing data with an iterative approach provides 3% point increment to identify fine-grained activities. We confirm findings from the literature that extracting features from multi-sensors achieves higher performance in comparison to using single-sensor features.
WGIN: A Session-Based Recommendation Model Considering the Repeated Link Effect

Session-based recommendation systems have high application value. Determining how to make better use of anonymous user sessions to recommend items of interest is a considerable challenge for current recommendation systems. Existing research has mainly focused on sequential session patterns; however, due to the complexity and diversity of user interests, such interests cannot be effectively modeled in this way. Therefore, in this paper, we investigate the transition patterns between items by constructing a session graph and propose a novel model called Weighted Graph Interest Networks (WGIN) that collaboratively considers hidden user preference information and the potential order of items in the session graph for a session-based recommendation system. Specifically, we propose a repetitive weighted graph neural network (RWGNN), which pays attention to the transitions between frequent items in a session to deeply explore the preferences of users. In addition, we establish a new Transformer structure to model long-term and short-term user preferences and obtain rich session embeddings. Extensive experiments on two real datasets illustrate that the proposed model outperforms other state-of-the-art session-based recommendation methods.
A Survey of Bayesian Statistical Approaches for Big Data

The modern era is characterised as an era of information or Big Data. This has motivated a huge literature on new methods for extracting information and insights from these data. A natural question is how these approaches differ from those that were available prior to the advent of Big Data. We present a survey of published studies that present Bayesian statistical approaches specifically for Big Data and discuss the reported and perceived benefits of these approaches. We conclude by addressing the question of whether focusing only on improving computational algorithms and infrastructure will be enough to face the challenges of Big Data.
Activity Recognition from Skeleton and Acceleration Data Using CNN and GCN

Most of the existing methods of activity recognition are based on single-label classification, however, these methods cannot be used in this challenge which focuses on multi-label classification-based micro-activity recognition. To address this, we propose a GCN model using the binary cross-entropy loss function, which enables multi-label classification and achieves an average accuracy of 83.1% on the Cooking Activity Dataset. In addition, to utilize the advantages of multi-modal data, we propose a joint training CNN model that combines the acceleration and skeleton data together. Finally, the proposed CNN model achieves an average accuracy of 82.8% for macro-activity recognition on Cooking Activity Dataset.
A Flight Rule Checker for the LADEE Lunar Spacecraft

As part of the design of a space mission, an important part is the design of so-called flight rules. Flight rules express constraints on various parts and processes of the mission, that if followed, will reduce the risk of failure. One such set of flight rules constrain the format of command sequences regularly (e.g. daily) sent to the spacecraft to control its next near term behavior. We present a high-level view of the automated flight rule checker Frc for checking command sequences sent to NASA’s LADEE Lunar mission spacecraft, used throughout its entire mission. A command sequence is in this case essentially a program (a sequence of commands) with no loops or conditionals, and it can therefore be verified with a trace analysis tool. Frc is implemented using the TraceContract runtime verification tool, an internal Scala DSL for checking event sequences against “formal specifications”. The paper illustrates this untraditional use of runtime verification in a real context, with strong demands on the expressiveness and flexibility of the specification language, illustrating the advantages of an internal DSL.
Adversarial Patches-based Attacks on Automated Vehicle Make and Model Recognition Systems

The recent works on automated vehicle make and model recognition (VMMR) have embraced the use of advanced deep learning models such as convolutional neural networks. In this work, we introduce an adversarial attack against such VMMR systems through adversarially learnt patches. We demonstrate the effectiveness of the adversarial patches against VMMR through experimental evaluations on a real-world surveillance dataset. The developed adversarial patches achieve reductions of upto 37% in VMMR recall scores. It is hoped that this work shall motivate future studies in developing VMMR systems that are robust to adversarial learning-based attacks.
Transcription Alignment for Highly Fragmentary Historical Manuscripts: The Dead Sea Scrolls

Most of the Dead Sea Scrolls have now been digitally transcribed and imaged to very high standards. Our goal is to align the transcriptions with the text visible in the image, glyph by (often fragmentary) glyph. This involves several tasks, normally considered in isolation: (A) Baseline segmentation. (B) Line polygon extraction. (C) Automated transcription by handwritten character recognition, to aid in alignment. (D) Alignment of the Unicode characters in a line transcription with the characters in the image of that line. The task is frustrated by the degraded nature of the frequently very small and/or warped fragments with many broken letters, substantially different allographs, ligatures, and scribal idiosyncrasies. Furthermore, a great number of inconsistencies between current cataloguing systems for the data need to be resolved. For each task, we apply state-of-the-art machine-learning methods in addition to more traditional techniques, each presenting significant difficulties on account of the poor state of most fragments' preservation. We have built ground-truth datasets and have managed to achieve good results with well-preserved fragments by leveraging heavily augmented transfer learning from prior work with medieval manuscripts.
Establishing key performance indicators for measuring software-development processes at a large organization

Developing software systems in large organizations requires the cooperation of various organizational units and stakeholders. As software-development processes are distributed among such organizational units; and are constantly transformed to fulfill new domain regulations, address changing customer requirements, or adopt new software-engineering methods; it is challenging to ensure, measure, and steer—essentially monitor—the quality of the resulting systems. One means to facilitate such monitoring throughout whole software-development processes are key performance indicators, which provide a consolidated analysis of an organizations' performance. However, it is also challenging to introduce key performance indicators for the software development of a large organization, as they must be implemented at and accepted by all relevant organizational units. In this paper, we report our experiences of introducing new key performance indicators for software-development processes at Volkswagen Financial Services AG, a large organization in the financial sector. We describe i) our methodology; ii) how we customized and use key performance indicators; iii) benefits achieved, namely improved monitoring and comparability, which help to define quality-improving actions; iv) and six lessons learned. These insights are helpful for other practitioners, providing an overview of a methodology they can adopt to assess the feasibility of key performance indicators as well as their benefits. Moreover, we hope to motivate research to investigate methods for introducing and monitoring key performance indicators to facilitate their adoption.
Classroom Attendance Monitoring Using CCTV

In Institutions/schools attendance maintaining is one of the major work for the faculty's to check the strength of a class. The faculties are provided with paper based attendance. They separately have it for various periods of subjects known log book. They mark the attendance every time when they go to class for their periods. To avoid manual paper based attendance system nowadays smart attendance monitoring system like biometric facial recognition system is being suggested. It is enormously used in much application such as monitoring the class room using CCTV, Computer -human interaction, Accurate Attendance maintaining and in security issues. This system rectifies the problems in marking the student's entry as absent even they are inside the classroom. In the implementation process, detecting the face, identifying and marking the attendance automatically whether the student is present or not is done. Principle Component Analysis (PCA), Eigen face value detection, Convolutional Neural Network (CNN) are the methods being used in this paper to create an automatic attendance management system. This model is successfully done in comparison with database of student's face to control the movement of people with a predefined protocol.
Benchmarking In Situ Triggers Via Reconstruction Error

This work considers evaluating in situ triggers using reconstruction error. Our experiments use data from the Nyx and Cloverleaf simulation codes, and focus on two key topics. The first topic aims to increase understanding of total reconstruction error, both with respect to the impact of adding more time slices and with respect to the variation from different time slice selections. The second topic evaluates performance for two current approaches: entropy-based triggers and evenly spaced time slices. Finally, we use these study components to construct a benchmarking system that enables visualization scientists to reason about triggers.
Opinion Mining using Machine Learning Approaches: A Critical Study

With the advancement of internet technology, there is a large volume of information exists on the web for users of the internet. These users not just utilize the accessible resources on the web yet, in addition, give their feedback, consequently creating more useful data. Opinion mining (OM) is contemplated as a subfield of NLP, information retrieval, and data mining. It is the way toward extricating people's thoughts and observations from unformed content, which with respect to the development of online social media and the huge amount of user feedback, has gotten an effective, attractive and furthermore complex issue. The fundamental in OM is classifying the polarity of text as far as positive (good), neutral (surprise) or negative (bad). This paper is proposed to review the methods and applications of opinion mining. This paper presents a review of OM and related strategies and additionally discusses the applications and difficulties for OM with knowledge into previous researchers.
Modelling of photovoltaic system power prediction based on environmental conditions using neural network single and multiple hidden layers

Abstract The solar power plant is an alternative to the provision of environmentally friendly renewable electricity, especially in the tropics, which are sufficiently exposed to the sun throughout the year. However, environmental conditions such as rainfall, solar radiation, or clouds may affect the output power of photovoltaic (PV) systems. These factors make it difficult to know whether PV can meet the needs of the existing load. This research develops a model to predict the output power of a 160 x 285W PV system located in the tropics and has certain environmental conditions. The prediction development is supported by the Python programming language with a single hidden layer and two hidden layers Neural Network, as well as the traditional Multiple Linear Regression tools. The simulation results show that the two hidden layers Neural Network method has a higher level of accuracy compared to the single hidden layer and Multiple Linear Regression as seen from the value of R 2 , MSE, and MAE.