Reviewerly/exHarmony huggingface dataset:
Analytical study on fraud detection in healthcare insurance claim data using machine learning classifiers

Prediction of medical insurance fraud has become an active research topic. It is a demanding task among the business and academic researchers. The anonymous activities prevailing in the insurance claims have affected the financial growth of the health insurance companies. In this paper, an analytical study is done on the conventional Machine Learning (ML) classifiers like k-mean clustering, Support Vector Machine (SVM) and Naive Bayes (NB) are analyzed on a healthcare provider fraud detection dataset. The collected data is pre-processed by filtering techniques. The detection of fraudulent claims is achieved from the diagnosis and the total charged amount on a claim given by the different providers. Thus, the proposed claim data consist of claim amount; diagnosis and the providers. By using diagnosis attribute, as control (or) decision variables and the provider attribute as target class, the performances of the considered ML classifiers are presented in terms of classification accuracy, recall and precision. In addition to that, False Positive Rate (FPR) is also analyzed to evaluate the classifiers.
An Asterisk-shaped Patch Attack for Object Detection

With the development of artificial intelligence, deep neural networks (DNNs) have been widely used, and the ability to solve some complex problems even exceeds that of humans. However, recent research shows that deep neural networks face multiple security threats. By adding some noise to the input data, the attacker can make a well-performing deep neural network make wrong decisions, or even make the model produce the same recognition results for completely different input data. However, it is difficult for the human eye to distinguish the difference between the samples before and after the perturbation is added, so the adversarial samples have strong concealment. Such attacks are called adversarial attacks, and the carefully constructed data used to fool deep neural networks are called adversarial examples. Most of the existing researches on adversarial attacks are aimed at image classification problems, and few works have shifted their attention to object detectors. Object detection is the basis of many computer vision tasks and has been applied in many life-related applications, such as autonomous driving, pedestrian recognition, pathological detection, etc. Therefore, it is of great significance to study the vulnerability of object detection models. Attacks against object detection models are more difficult because it combines multi-object localization and multi-object classification problems at the same time. In this paper, we study some adversarial attacks against object detection models, and we propose an asterisk-shaped Adversarial patch generation algorithms that make current state-of-the-art object detectors undetectable. Extensive experimental results show that our method can ensure good attack performance while modifying a small number of image pixels.
Compressed Particle-Based Federated Bayesian Learning and Unlearning

Conventional frequentist federated learning (FL) schemes are known to yield overconfident decisions. Bayesian FL addresses this issue by allowing agents to process and exchange uncertainty information encoded in distributions over the model parameters. However, this comes at the cost of a larger per-iteration communication overhead. This letter investigates whether Bayesian FL can still provide advantages in terms of calibration when constraining communication bandwidth. We present compressed particle-based Bayesian FL protocols for FL and federated “unlearning” that apply quantization and sparsification across multiple particles. The experimental results confirm that the benefits of Bayesian FL are robust to bandwidth constraints.
Deep learning for image forgery classification based on modified Xception net and dense net

This paper deals with the problem of image forgery detection because of the problems it causes. Where The Fake images can lead to social problems, for example, mislead society, divert people's mentality, denigrating an individual, and using them as evidence in law may mislead the court. This work proposes a deep learning approach based on Deep CNN (Convolutional Neural Network) Architecture, to detect fake images .the network is based on a modified structure of Xception net, The CNN based on depthwise separable convolution layers, The CNN feature maps can be separated. After extracting the feature maps, pooling layers used with dense connection with Xception output, to increase feature maps. Inspired by the idea of a densenet network. On the other hand, the work uses the YCbCr color system for images, which gave better Accuracy up to 99.41, more than RGB, HSV, and XYZ color systems.
Migrating on-premise application workloads to a hybrid cloud architecture

Organizations are moving towards adapting cloud strategies for various reasons. The important benefits realized with the adaption of cloud computing are - scalability, availability, performance and cost. With the benefits, come the challenges such as - security, privacy, regulatory compliances, trust, etc. With adoption of cloud, organizations are relieved from hardware infrastructures up to a large extent as well as overheads of managing, operating and physical security of infrastructure hardware. Having legacy applications on-premise, enterprises are looking forward to modernize the applications in terms of planning to strategize the future vision, need for scaling those applications, be competitive in the market with an ability as well as capability to cope and adapt the new demand and changes with ease, flexibility and cost effectiveness. One of the immediate adaptions with the cloud that the enterprises are looking forward is migrating their on-premise workloads to the cloud infrastructure, so that they can realize the benefit of cost-effective solutions along with minimizing operational and management overheads. In this paper we are proposing a hybrid cloud architecture that an enterprise may adapt while considering migrating one of its legacy applications’ workload to the cloud environment; with minimal changes and migration effort.
Attention-Based Auto-Encoder Framework for Abnormal Driving Detection

With the popularity of smartphones, abnormal driving detection via smartphone sensors has been proposed in recent years. However, existing methods are insufficient in exploring feature extraction, so the practical value is limited due to the low accuracy. To address this problem, we propose an attention-based auto-encoder framework for abnormal driving detection that combines the advantages of bi-directional long short-term memory and self-attention. Specifically, these two modules are embedded in the auto-encoder for modeling latent vector and exploring the internal correlations of spatial-temporal features, respectively, so as to improve the capability of reconstructing driving time series using small and representative features. We conduct experiments on the real-world datasets, and the results show that the proposed framework achieves significant performance with recall and F1-score of 96.2% and 95.0%, superior to the other baselines.
Approaches Involving Big Data Analytics (BDA) Using Machine Learning, Described

In computer engineering and several other fields across the globe, big data analytics seems to be a developing research discipline. These would have made significant progress in numerous different applications. This includes online company networks, finance, healthcare, energy, agriculture, and many other industries. Various innovative machine learning methods have been established to provide big data projecting complex algorithms. The surveillance systems regularly gather a vast amount of data. Different forecasting methods are suggested for estimating energy consumption. The information on various machine learning approaches is investigated in this work. Additionally, this article examined modern prediction methodology. Furthermore, big data combined with traditional knowledge are used to test prediction models. In light of the findings of this research, gaps in existing studies are highlighted, and directions for future research are suggested.
Texture Image Recognition Based on Deep Convolutional Neural Network and Transfer Learning

The traditional texture image recognition methods have a complex design process, and the existing methods based on deep learning can’t effectively solve the problem of insufficient texture image samples which lead to unsatisfying recognition accuracy. To solve the above problems, a texture image recognition method based on deep convolutional neural network and transfer learning is proposed. Firstly, a new transfer learning model is constructed by using the deep learning model pretrained on the large-scale ImageNet image dataset. Secondly, the reasonable model super-parameters are set, and the weighted sum of the training loss, the validation loss, and the deep feature distance between the training set and the validation set is taken as the cost function of training process. Finally, the best transfer learning model is determined by layer-by-layer training and validation. The experimental results show that the proposed method achieves 99.76%, 99.87%, 99.80%, 100.00% and 94.01% recognition accuracies on the CUReT, KTH-TIPS, UIUC, UMD and NewBarkTex texture datasets respectively, and has good robustness and recognition ability.
Skipped Nonsynaptic Backpropagation for Interval-valued Long-term Cognitive Networks

The recently published Interval-valued Long-term Cognitive Networks have shown promising results when reasoning under uncertainty conditions. In these recurrent neural networks, the interval weights are learned using a nonsynaptic backpropagation learning algorithm. Similar to traditional propagation-based algorithms, this variant might suffer from vanishing/exploding gradient issues. This paper proposes three skipped learning variants that do not use the backpropagation process to deliver the error signal to intermediate abstract layers (iterations in the recurrent neural network). The numerical simulations using 35 synthetic datasets confirm that the skipped variants work as well as the nonsynaptic backpropagation algorithm.
CIDER

In this paper, we introduce CIDER, a Concept-based Interactive DEsign Recovery tool that recovers a software design in the form of hierarchically organized concepts. In addition to facilitating design comprehension, it also enables designers to assess design quality and identify design problems. It integrates multiple clustering algorithms to reduce the complexity of the recovered design structure, leverages information retrieval techniques to name each cluster using the most relevant topic terms to ease design comprehension, and identifies and labels highly-coupled file clusters to reveal possible design problems. It enables interactive selection of concepts of interest and recovers partial design structures accordingly. The user can also interactively change the levels of recovered hierarchical structure to visualize the design at different granularities.
Quasi-orthogonality and intrinsic dimensions as measures of learning and generalisation

Finding the best architectures for learning machines, such as deep neural networks, is a well-known technical and theoretical challenge. Recent work by Mellor et al [1] showed that there may exist correlations between the accuracies of trained networks and the values of some easily computable measures defined on randomly initialised networks which may enable the search of tens of thousands of neural architectures without training. Mellor et al [1] used the Hamming distance evaluated over all the ReLU neurons as such a measure. Motivated by these findings, in our work, we ask the question of the existence of other and perhaps more principled measures which could be used as determinants of the potential success of a given neural architecture. In particular, we examine if the dimensionality and quasi-orthogonality of neural networks' feature space could be correlated with the network's performance after training. We showed, using the setup as in Mellor et al [1], that dimensionality and quasi-orthogonality may jointly serve as networks' performance discriminants. In addition to offering new opportunities to accelerate neural architecture search, our findings suggest important relationships between the networks' final performance and properties of their randomly initialised feature spaces: data dimension and quasi-orthogonality.
Entity Representation by Neighboring Relations Topology for Inductive Relation Prediction

Inductive relation prediction is to predict relations between unseen entities. The current methods implicitly learn the logical rules in the knowledge graph through the local subgraph structures, and obtain the latent semantic representation of the predicted triples. However, existing methods lack relation information of neighboring triples due to the incompleteness of the knowledge graph, and the representation of entities does not consider the connection structures between relations which contain different semantic information. To address these challenges, we propose a novel entity representation by Neighboring Relations Topology Graph (NRTG) for inductive relation prediction. Specifically, we divide connection structures between relations into several topological patterns, and design a module to extract relations of all neighboring triples for constructing Neighboring Relations Topology Graph (NRTG). In NRTG, the nodes represent the relations and the edges represent the topological patterns. Afterward, we design an information aggregation module to encode the NRTG as the entity representation, and then use the scoring network to predict relations between unseen entities. Experiments demonstrate that our model can effectively capture relation information of neighboring triples and semantic information of connection structures between relations. Moreover, it outperforms existing methods on benchmark datasets for the inductive relation prediction task.
Evaluation of design guidelines for the development of intuitive virtual reality authoring tools: a case study with NVIDIA Omniverse

Virtual reality software might be challenging to utilize for new and inexperienced individuals who lack a background in programming and computer graphics. There are existing authoring tools that aim to democratize the usage of virtual reality by many specialists in various situations, such as educational or industrial. However, these authoring tools are frequently insufficient, with lack of support and standard operating procedures. This study proposes design guidelines for the development of intuitive virtual reality authoring tools. The findings indicate that most of the design guidelines might be effective in assisting the development of more intuitive virtual reality authoring tools.
Federated Semi-Supervised Learning Through a Combination of Self and Cross Model Ensembling

Medical image segmentation (MIS) plays a vital role in modern computer-aided diagnosis systems. Deep learning technology has achieved promising results in MIS in recent years. However, deep learning (DL) is a data-hungry technology and in the domain of healthcare, a single hospital usually cannot afford to collect adequate medical images to train a robust DL model. Moreover, a medical image usually contains sensitive data related to patients' privacy, making it's infeasible to build a larger dataset by collecting images from different hospitals. Federated learning (FL), a recently proposed privacy-protecting collaborative paradigm aiming at allowing different data owners to collaboratively train a model without exposing raw data, seems to be a proper solution to these problems. Some recent works have verified the feasibility of applying FL to MIS but most of these works only confine to fully supervised scenarios. Unfortunately, most hospitals in realistic usually cannot provide fully labeled data due to lack of labor. In this paper, we study a challenging but more practical problem in which each hospital can only provide a few labeled data combined with some other unlabeled data. To effectively handle such a problem, we propose a novel and robust federated semi-supervised learning (FSSL) framework, which improves over the mean teacher mechanism with a cross-clients ensemble module and a model-wise self-ensembling module. We evaluate our method on two public medical image datasets and the results show that, in the challenging FSSL scenario, our method can effectively leverage unlabeled data to boost the model performance by a considerable margin. Notably, our method also outperforms other existing FSSL approaches designed for MIS.
Trying to Elicit and Assign Goals to the Right Actors

At the beginning of a project, an organisation may define very abstract goals. These high-level goals describe organisation characteristics that all projects must fulfil. Due to the very generic and abstract nature of these goals, it is sometimes not easy to break them down into more concrete goals and to decide who should be responsible for what. For many years, goal modelling approaches have proposed frameworks for eliciting and defining stakeholders’ goals in an organisation. In the context of an aeronautical company, we conducted an application on a case of study of a goal modelling method. From high-level goals, we have supported business experts in eliciting more concrete goals, assigning them to the right actors and identifying possible organisational needs. For this, we started from an existing method that we have adapted to fit our purposes.
Social Sensing for Monuments Recognition using Convolutional Neural Networks: A Case Study

Social media are revolutionizing various areas of our society, including the cultural heritage one. The same goes for artificial intelligent strategies and machine learning algorithms, which are exploited in many contexts, and could play an interesting role in promoting and sharing cultural heritage and touristic activities and points of interests in urban environments. An interesting application of these techniques in the cultural and touristic domain could be represented by the automatic recognition and classification of monuments and points of interest. In this work, we present a study on monuments recognition, starting from pictures taken by users by means of their mobile devices, while visiting an urban environment. In particular, we employed images collected by the citizens and tourists with the aim of contributing to valorize monuments or places in a specific case study, that is the city of Cesena (Italy). In this paper, we compared several architectures of convolutional neural networks, that are able to discriminate among a set of outdoor monuments with an accuracy of over 90%.
The Complete Subtour Order Crossover in Genetic Algorithms for Traveling Salesman Problem Solving

The traveling salesman problem (TSP) is one of many well-known combinatorial problems. TSP could be ex-plained as difficulty of finding the shortest distance to travel from the first city, via all cities, and then return to the starting point. Among the standard problems, TSP is often used for de-termining the efficiency of new algorithms. One of the successful algorithms for TSP solving is the genetic algorithm (GA). The key mechanism for GA efficiency for solution finding is the crossover operator. Many studies have suggested new crossover or even improved previous methods to solve TSP including other related problems. Nevertheless, the solution quality is the foremost factor. This paper proposes Complete Subtour Order Crossover (CSOX) for GA to search the solution for TSP. CSOX employs the Order crossover (OX) principle and expands the crossover to mainly cover the parts of the quality solution. Notably, one operation can generate six new solutions. This outcome increases the variety of solutions to the problem and the quality while lowering computing times. From three studies, five types of crossover operators are compared with CSOX. The summarized results from the experiments revealed that CSOX outperforms all in terms of solution quality and good computing times quality.
Regression with an Ensemble of Noisy Base Functions

Ensemble methods achieve state-of-the-art performance in many real-world regression problems while enjoying structural compatibility for modern decentralized computing architectures. However, the implementation of ensemble regression on distributed systems may compromise its cutting-edge performance due to computing and communication reliability issues. This paper introduces robust ensemble combining techniques designed to integrate multiple noisy predictions into a single reliable prediction. Experiments conducted with synthetic and real-world datasets in various noise regimes illustrate our robust methods' superiority over non-robust counterparts.
Adaptive Enhanced Micro-expression Spotting Network Based on Multi-stage Features Extraction

When micro-expressions are mixed with normal or macro-expressions, it becomes increasingly challenging to spot them in long videos. Aiming at the specific time prior of Micro-expression (ME)s, a ME spotting network called AEM-Net (Adaptive Enhanced ME Detection Network) is proposed. The network improves the spotting performance in the following four aspects. First, the multi-stage channel feature extraction module is proposed to extract feature information of different depths. Then, an attention spatial-temporal module was used to obtain salient and discriminative micro-expression segments while suppressing the generation of excessively long or short suggestions. Thirdly, a ME-NMS (Non-Maximum Suppression) network is developed to reduce redundancy and decision errors. Finally, two spotting mechanisms named anchor_based and anchor_free are combined in our method. Extensive experiments have done on prevalent $$\mathrm CAS(ME)^2$$ and the SAMM-Long ME databases to evaluate the spotting performance. The results show that the AEM-Net achieves an impressive performance, which outperforms other state-of-the-art methods.
SwinT-YOLOv5s: Improved YOLOv5s for Vehicle-mounted Infrared Target Detection

Infrared vehicle-mounted target detection is an important research direction in assisted driving, but also a very challenging topic. Existing infrared target detection methods often have problems such as high missed detection rate and false alarm in complex background, small target size and occlusion scene. A SwinT-YOLOv5s algorithm is proposed by the fusion of attention mechanism and convolutional network. Based on YOLOv5s algorithm, a detection layer is added to enhance the detection ability of small target objects. The CBAM modules are inserted into the backbone network to make the model pay more attention to the useful information and resist the interference of redundant information, so as to improve the detection ability in dense scenes. In addition, the Swin Transfomer encoders are used to replace some part of C3 modules to improve the model's ability of mining potential feature details and further improve the detection accuracy of the model. Experimental results show that the improved algorithm increases the average precision (IOU=0.5) and precision rate by 5.60% and 4.20% compared with the original YOLOv5s model, and has good generalization ability in remote small target and overlapping target scenarios.