Reviewerly/exHarmony huggingface dataset:
Outlier Analysis for Telecom Fraud Detection

Every year, the number of telecommunication fraud cases increases dramatically, and companies providing such services lose billions of euros worldwide. It has been receiving more and more attention lately mobile virtual network operators (MVNOs) which operate on top of existing cellular infrastructures of the basic operators, and at the same time are able to offer cheaper call plans. This paper is aimed to identify suspicious customers with unusual behaviour, typical to potential fraudsters in MVNO. In this study, different univariate outlier detection methods are applied. Univariate outliers are obtained using call detail records (CDR) and payments records information which is aggregated by users. A special emphasis in this paper is put on the metrics designed for outlier detection in the context of suspicious customer labelling which may support the fraud experts in evaluating customers and revealing fraud. In this research, we identified specific attributes that could be applied for fraud detection. Threshold values were found for the attributes examined, which could be used to compile lists of suspicious users.
Uncertainty Quantification for Object Detection: Output- and Gradient-Based Approaches

Abstract Safety-critical applications of deep neural networks require reliable confidence estimation methods with high predictive power. However, evaluating and comparing different methods for uncertainty quantification is oftentimes highly context-dependent. In this chapter, we introduce flexible evaluation protocols which are applicable to a wide range of tasks with an emphasis on object detection. In this light, we investigate uncertainty metrics based on the network output, as well as metrics based on a learning gradient, both of which significantly outperform the confidence score of the network. While output-based uncertainty is produced by post-processing steps and is computationally efficient, gradient-based uncertainty, in principle, allows for localization of uncertainty within the network architecture. We show that both sources of uncertainty are mutually non-redundant and can be combined beneficially. Furthermore, we show direct applications of uncertainty quantification by improving detection accuracy.
Feedback Control for Collision-Free Nonholonomic Vehicle Navigation on SE(2) With Null Space Circumvention

In this article, we present a novel feedback controller for the collision-free navigation of a class of nonholonomic vehicles based on navigation functions in convex environment. This controller utilizes a high-level principle— <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">null space circumvention</i> —in nonholonomic navigation control, allowing us to involve different vehicle kinematics and navigation functions with respect to the task and environment. For a single vehicle, we have proved that the controller can steer it to the target on SE(2) while avoiding collision from almost all the initial states. The controller can also close the gap between holonomic vehicle navigation and nonholonomic vehicle navigation, allowing the navigation functions created for holonomic vehicles to be used for nonholonomic vehicles. Furthermore, we extend the controller to multivehicle formation control. Within this task, our controller shows generality without much modification except for updating the gradient of the chosen navigation functions. Simulation and experimental results demonstrate that the proposed controllers can achieve collision-free navigation for a single vehicle and multiple vehicles.
Quotient reflective subcategories of the category of bounded uniform filter spaces

&lt;abstract&gt;&lt;p&gt;Previously, several notions of $ T_{0} $ and $ T_{1} $ objects have been studied and examined in various topological categories. In this paper, we characterize each of $ T_{0} $ and $ T_{1} $ objects in the categories of several types of bounded uniform filter spaces and examine their mutual relations, and compare that with the usual ones. Moreover, it is shown that under $ T_{0} $ (resp. $ T_{1} $) condition, the category of preuniform (resp. semiuniform) convergence spaces and the category of bornological (resp. symmetric) bounded uniform filter spaces are isomorphic. Finally, it is proved that the category of each of $ T_{0} $ (resp. $ T_{1} $) bounded uniform filter space are quotient reflective subcategories of the category of bounded uniform filter spaces.&lt;/p&gt;&lt;/abstract&gt;
Blockchain-Based Light-Weighted Provable Data Possession for Low Performance Devices

Provable Data Possession (PDP) schemes have long been proposed to solve problem of how to check the integrity of data stored in cloud service without downloading. However, with the emerging of network consisting of low performance devices such as Internet of Things, we find that there are still two obstacles for applying PDP schemes. The first one is the heavy computation overhead in generating tags for data blocks, which is essential for setting up any PDP scheme. The other one is how to resist collusion attacks from third party auditors with any possible entities participating the auditing. In this paper, we propose a novel blockchain-based light-weighted PDP scheme for low performance devices, with an instance deployed on a cloud server. We design a secure outsourced tag generating method for low performance devices, which enables a kind of “hash-sign-switch” two-phase tag computing. With this method, users with low performance devices can employ third party auditors to compute modular exponential operations that accounts for the largest portion of computation overhead in tag generation, without leaking their data content. Chaincodes in blockchain network ensure the correctness of such outsourcing and prevent collusion attacks. The security analysis and performance evaluation prove that our scheme is both secure and efficient.
Deep Rejoining Model for Oracle Bone Fragment Image

Image based object fragments rejoining can avoid touching and damaging objects, and be applied to recover fragments of oracle bones, artifacts, paper money, calligraphy and painting files. However, traditional methods are insufficient in terms of judging whether two images’ texture are rejoinable. In this paper, we propose a deep rejoining model (DRM) for automatic rejoining of oracle bone fragment images. In our model, an edge equal distance rejoining method (EEDR) is used to locate the matching position of the edges of two fragment images and crop the target area image (TAI), then a convolution neural network (CNN) is used to evaluate the similarity of texture in TAI. To improve the performance of similarity evaluation, a maximum similarity pooling (MSP) layer is proposed in CNN, and the fully connected layer outputs the two-class probability of whether the rejoining is eligible or not. Our experiments show that DRM achieved state-of-the-arts performance in rejoining oracle bone fragment images and has stronger adaptability.
AI-Driven Human Motion Classification and Analysis Using Laban Movement System

Human movement classification and analysis are important in the research of health sciences and the arts. Laban movement analysis is an effective method to annotate human movement in dance that describes communication and expression. Technology-supported human movement analysis employs motion sensors, infrared cameras, and other wearable devices to capture critical joints of the human skeleton and facial key points. However, the aforementioned technologies are not mainstream, and the most popular form of motion capture is conventional video recording, usually from a single stationary camera. Such video recordings can be used to evaluate human movement or dance performance. Any methods that can systematically analyze and annotate these raw video footage would be of great importance to this field. Therefore, this research offers an analysis and comparison of AI-based computer vision methods that can annotate the human movement automatically. This study trained and compared four different machine learning algorithms (random forest, K neighbors, neural network, and decision tree) through supervised learning on existing video datasets of dance performances. The developed system was able to automatically produce annotation in the four dimensions (effort, space, shape, body) of Laban movement analysis. The results demonstrate accurately produced annotations in comparison to manually entered ground truth Laban annotation.
Solution Algorithms for the Bounded Acceleration Shortest Path Problem

The purpose of this article is to introduce and characterize the bounded acceleration shortest path problem (BASP), a generalization of the shortest path problem (SP). This problem is associated to a graph: nodes represent positions of a mobile vehicle and arcs are associated to preassigned geometric paths that connect these positions. The BASP consists in finding the minimum-time path between two nodes. Differently from the SP, the vehicle has to satisfy bounds on maximum and minimum acceleration and speed, which depend on the vehicle’s position on the currently traveled arc. Even if the BASP is NP-hard in the general case, we present a solution algorithm that achieves polynomial time-complexity under some additional hypotheses on problem data.
An Integrated Novel Framework for Coping Missing Values Imputation and Classification

This work presents an integrated framework for imputation of missing values and prediction of class label of unseen samples by using the best features of rule based inductive decision tree (DT) and Support Vector Machine (SVM) classifier (DT-SVM).In this work, the decision tree is used for imputation of missing values of the datasets containing both categorical and numerical valued attributes.In addition, some of the other popular and simple missing value imputation techniques like drop, mean, median, mode, and k-nearest neighbor (kNN) are used for a comparative analysis.The imputed datasets are then classified using SVM.The performance of the proposed integrated novel framework DT-SVM has been compared with Drop-SVM, Mean-SVM, Median-SVM, Mode-SVM, and kNN-SVM and it is found that DT-SVM outperforms others.Further, a new variant of kNN named it as approximated kNN (A-kNN) has been proposed to overcome some of the shortcomings of canonical kNN while learning from a training set imputed by DT.Unlike canonical kNN, A-kNN does not scan the entire training set.Instead, it processes some of the representative instances from the training dataset to identify the nearest neighbor.The class centroid approach is adopted to find the representative instances of the training set.The effectiveness in term of accuracy as well as computational time of A-kNN is examined by comparing with canonical kNN.It is found that computational time of the proposed A-kNN is drastically reduced as compared to canonical kNN without compromising with the classification accuracy.
Novel Edutainment Learning Concept via Augmented Reality Approach

Augmented reality (AR) empowers virtual and interactive experience through smartphone devices. Nowadays, technologies have become more advanced, and AR is generally compatible with smartphones, thus being more accessible globally. Furthermore, many developers have started to create AR-based applications in fields such as edutainment, games, furniture, biomedicine, geology, and entertainment. AR-based edutainment applications allow students to observe experiments in a safe environment and improve understanding through manipulation and multi-angle observation of virtual three-dimensional (3D) objects. In addition, AR has become increasingly cost-friendly, accessible, and effective. This paper thus aimed to design and create a novel edutainment learning application based on AR targeted at children. This is because AR enhances the learning content so as to be more interactive and exciting as it is projected in a 3D realm. The powerful visual models enable users to have a better understanding to learn a specific theory through visualizations. This research used experimental analysis consisting of a pre-and post-test survey. The pre-test results demonstrated that some respondents were not aware of AR technology and were not supportive of it. In contrast, the post-test result has revealed that the respondents felt that the proposed application is easy to use, user friendly and the learning content is interactive.
An Adjusting Strategy after DBSCAN

DBSCAN is a popular density-based clustering algorithm in data mining. However, its principle of first come, first served to deal with the cross-border points of multiple clusters will make some cross-border points not belong to the best clustering. To solve this problem, an adjusting strategy after DBSCAN to change the positions of the points of every cluster’s convex hull is proposed in this paper. At the end, the effectiveness of proposed adjustment strategy is verified by an experiment.
Efficient and Secure Deep Learning Inference in Trusted Processor Enabled Edge Clouds

Edge intelligence has emerged as a prevalent enabling technology to support various intelligent applications. Along with the prosperity, it also raises great concern on the security and privacy since the edge servers are usually shared and untrusted. The security-sensitive code (i.e., the pre-trained model) and data may be easily stolen by malicious tenants, and even untrusted infrastructure providers. To this end, Software Guard Extensions (SGX) is proposed to provide an isolated Trust Execution Environment (TEE) for security and privacy guarantee. However, we find that running tasks in SGX suffer certain performance degradation due to the limited Enclave Page Cache (EPC) size. This further leads to frequent page swapping operations and the high enclave call overhead, which are also influenced by the task (i.e., DNN layer) dispatching and scheduling. To this end, in this paper, we design <b>Lasagna</b> , as an SGX based secure DNN inference acceleration framework, which explores the layered-structure of DNN models to well balance the usage of the scarce EPC resources and the computation resources. Lasagna mainly consists of a global task balancer and a local task scheduler, responding for task dispatching across distributed edge servers and task scheduling in local server, respectively. We evaluate Lasagna over different well-known DNN models, and the results show that Lasagna effectively speeds up the inference performance by <inline-formula><tex-math notation="LaTeX">$1.11\times -1.51\times$</tex-math></inline-formula> .
Autoencoders and variational autoencoders in medical image analysis

This chapter introduces two popular methods for unsupervised representation learning using neural networks, namely autoencoders and variational autoencoders. Both methods rely on a bottleneck encoder–decoder network architecture where the encoder maps an input to a low-dimensional latent space representation from which the decoder aims to reconstruct the input as accurately as possible. This latent space representation can also be used to systematically analyze or manipulate certain properties of the input data, which makes them a key tool for biomedical image synthesis tasks such as image reconstruction, data augmentation, or modality transfer. While autoencoders and variational autoencoders share the same general idea, they differ significantly in their theoretical foundations and abilities. Classical autoencoders are purely deterministic and their training usually solely focuses on minimizing the data reconstruction error. Variational autoencoders are deeply rooted in Bayesian statistics and aim to learn a rich probabilistic model to explain the data being analyzed. This chapter outlines the theoretical foundations of both methods, discusses their advantages and practical challenges, outlines some of their various extensions, presents selected example applications, and concludes with a concise discussion of directions for future research in this area.
Towards Disturbance Rejection in Feature Pyramid Network

The multiscale problem is one of the challenging topics in the object detection field. Among the current studies toward this problem, feature pyramid network (FPN) has been shown a superior performance. The core principle of FPN is a mapping for the detectable objects into hierarchy features. These features are designed with different size of receptive fields, which brings good scale invariance in the classification and localization subtasks. However, a disturbance of information dilution is generated by the assignment for the detectable objects into different features. This dilution disturbance is associated with the convolution or pooling operation during the hierarchy feature extraction process. Both of the imprecise localization information in the top features and the fuzzy classification information in the bottom ones limit the performance of FPN. Aiming at the disturbance rejection problem of information dilution, this article presents a feature compensation method. A cross compensation between the semantic information and the localization information in hierarchy features is used to enhance the vanilla FPN. Then, the evaluation metrics in an object detection task are examined. Not only the overall performance, but also the respective metrics for multiscale objects validate a considerable improvement of the FPN with a disturbance compensation mechanism. The experimental results demonstrate an effectiveness of the proposed method in this article.
Demystifying Content-Blockers: Measuring Their Impact on Performance and Quality of Experience

With the evolution of the online advertisement and tracking ecosystem, content-blockers have become the reference tool for improving the security, privacy and browsing experience when surfing the Internet. It is also commonly believed that using content-blockers to stop unsolicited content decreases the time needed for loading websites. In this work, we perform a large-scale study on the actual improvements of using content-blockers in terms of performance and quality of experience. For measuring it, we analyze the page size and loading times of the 100K most popular websites, as well as the most relevant QoE metrics, such as the Speed Index, Time to Interactive or the Cumulative Layout Shift, for the subset of the top 10K of them. Our experiments show that using content-blockers results in small improvements in terms of performance. However, contrary to popular belief, this has a negligible impact in terms of loading time and quality of experience. Moreover, in the case of small and lightweight websites, the overhead introduced by content-blockers can even result in decreased performance. Finally, we evaluate the improvement in terms of QoE based on the Mean Opinion Score (MOS) and find that two of the three studied content-blockers present an overall decrease between 3% and 5% instead of the expected improvement.
Analytical Solution of the Space Charge Limited Current Using Lambert–Tsallis <i>W<sub>q</sub> </i> Function

The space charge limited current (SCLC) has been studied since more than a century ago, and up to now, it remains a hot topic playing a key role in charge transport in microelectronic and nanoelectronic devices. Therefore, it would be useful for device engineers the existence of mathematical tools able to work with the different models of SCLC proposed in the literature. In this direction, the present work shows the applications of the Lambert–Tsallis <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${W}_{q}$ </tex-math></inline-formula> function for solving some problems analytically related to different models of SCLC, like some generalizations of the 1-D Child–Langmuir (CL) law and SCLC of solids with carrier-density-dependent mobility.
Object Description Using Visual and Tactile Data

With the development of vision and haptic sensor technologies, robots have become increasingly capable of perceiving their external environments. Although machine vision and haptics have surpassed humans in some aspects of perception, it is difficult for robots to describe objects from multiple viewpoints by using a combination of visual and haptic modalities. In this paper, we use convolutional neural networks to separately extract visual and haptic features and then fuse these two types of features. Then, multitask learning is combined with multilabel classification to form a multitask-multilabel classification method. The developed method is used to identify the color, shape, material attributes, and class of an object from the visual-haptic fused feature vector. To verify the effectiveness of the proposed object description method, experiments are conducted on the PHAC-2 dataset and the collected VHAC dataset. The experimental results show that the proposed method produces the most accurate object descriptions with the smallest number of parameters.
Blockchain-Assisted Comprehensive Key Management in CP-ABE for Cloud-Stored Data

Public clouds have drawn increasing attention from academia and industry due to their high computational and storage performance. Attribute-based encryption (ABE) is the most promising technology to simultaneously achieve confidentiality and fine-grained access control of the cloud-stored data. However, traditional ABE that relies on centralized authority faces several key management issues, such as the key escrow, key distribution, key tracking, key update, and heavy communication and computing overhead for users, which will cause security concerns and impede its widespread application. On the other hand, blockchain technology preserves distributed ledgers to ensure the immutability and transparency of data, which can further solve the security vulnerabilities caused by system centralization. This paper proposes a blockchain-assisted transformation method to solve all the key management problems mentioned above in ciphertext-policy ABE by utilizing technologies such as secret sharing protocols. In addition, our transformation method realizes two additional benefits: outsourced decryption and efficient user revocation, which are extremely valuable for practical implementations. We simulate a demonstration by adopting the most popular permissioned blockchain, Hyperledger Fabric. The security and efficiency analysis reveals that the scheme obtained from our transformation method can achieve replayable chosen-ciphertext security with extremely efficient decryption.
Instrumented Timed Up and Go Test Using Inertial Sensors from Consumer Wearable Devices

Precision medicine pursues the ambitious goal of providing personalized interventions targeted at individual patients. Within this vision, digital health and mental health, where fine-grained monitoring of patients form the basis for so-called ecological momentary assessments and interventions, play a central role as complementary technology-based and data-driven instruments to traditional psychological treatments. Mobile devices are hereby key enablers: consumer smartphones and wearables are ubiquitously present and used in daily life, while they come with the necessary embedded physiological, inertial and movement sensors to potentially recognise user’s activities and behaviors. In this article, we explore whether real-time detection of fine-grained activities - relevant in the context of wellbeing - is feasible, applying machine learning techniques and based on sensor data collected from a consumer smartwatch device. We present the system architecture, whereby data collection is performed in the wearable device, real-time data processing and inference is delegated to the paired smartphone, and model training is performed offline. Finally, we demonstrate its use by instrumenting the well-known Timed Up and Go (TUG) test, typically used to assess the risk of fall in elderly people. Experiments show that consumer smartwatches can be used to automate the assessment of TUG tests and obtain satisfactory results, comparable with the classical manually performed version of the test.