Reviewerly/exHarmony huggingface dataset:
Fraud Detection at Self Checkout Retail using Data Mining

Self-service checkout is now increasingly used in supermarkets and retail. Customers scan all items with a barcode reader to buy and pay for them. This type of payment carries the risk of fraud by not scanning all items in the cart.. Food retailers hope to be able to identify cases of fraud to minimize losses. Data mining techniques and algorithms are used to check the classification of large amounts of information and data recovery. One group of these methods is the Anomaly Detection Method. The history of customer behavior is considered routine. If the new action is seen from customers who are not compatible with the movement pattern, the likelihood of fraud will be even more substantial. The aim is to predict fraud in one retail self check out using classification techniques and visualize the results to obtain new insight. The results of this study are oversampling on unbalanced data that can improve the performance of a model. The model with the best performance is J48 with F-measure 0.981. New insight from the visualization of prediction results is that customers with low trust levels are vulnerable to fraud.
The Rhodes electric piano: Analysis and simulation of the inharmonic overtones

The Rhodes piano is an electromechanical keyboard instrument, released for the first time in 1946 and subsequently manufactured for at least four decades, reaching an iconic status and being now generally referred to as the electric piano. A few academic works discuss its operating principle and propose different physical modeling strategies; however, the inharmonic modes that characterize the attack transient have not been subject of a dedicated study before. This study addresses this topic by first observing the spectrum at the pickup output, applying a psychoacoustic model to assess perceptual relevance, and then conducts a series of scanning laser Doppler vibrometry (SLDV) experiments on the Rhodes asymmetric tuning fork. This study compares the modes of the Rhodes piano to those of its individual parts, allowing for the extraction of important information regarding role and natural modes. On the basis of this study, numerical experiments are conducted that show the intermodulation of the modes due to the magnetic pickup and allow the tones produced by the Rhodes from the collected data to be closely matched. Finally, this study is able to extract the distribution of the most important modes found on the whole keyboard range of a Rhodes piano, which can be useful for sound synthesis.
Development and application of standard data schema for interoperability between manufacturing application systems

In recent years, successful manufacturing activities such as managing production efficiency, managing yields and responding to changes in demand are becoming more difficult as boundary breakdown among manufacturing application systems accelerates. To solve this problem, the importance of manufacturing information management and control is increasing, and the establishment of manufacturing application IT systems is becoming necessary. To match the speed of technological development centered on the 4th Industrial Revolution, smart factories in manufacturing industries are being introduced without methods for linking them with existing operating systems. In other words, to apply various manufacturing application systems to manufacturing sites, customizations that reflect different production processes, reference information, and site requirements are required by industry and company. Solving the problems of matching individual interfaces is required for compatibility with other manufacturing application systems. In this paper, a manufacturing data integration schema was established as a collaborative tool to secure consistency in the meaning of data and to enhance the reuse of different data models among manufacturing application systems such as Product Lifecycle Management (PLM), Manufacturing Execution System(MES) and Enterprise Resource Planning(ERP).
Blind Photograph Watermarking with Robust Defocus-Based JND Model

Just noticeable distortion (JND) is widely employed to describe the perception redundancy in the quantization-based watermarking framework. However, the existing JND models are generally constructed to treat every region of the photograph with an equal focus level, whereas the defocus effect has never been considered. In this paper, the defocus feature, which can portray the aesthetic emphasis in the photograph, is provided to improve the perceptual JND model. Firstly, two indicators which consider the block energy in the defocus measurement (DM) are proposed. Then, the defocus feature map (DFM) is obtained by integrating the influence of the circumambient blocks, and it is applied to the proposed JND contrast masking (CM) processing. In this way, a new blind photograph watermarking method, with emphasis on defocus-JND estimation combined with the proposed CM, is presented. Simulations show that the proposed JND is more suitable for watermarking framework than some exiting JND models, and the proposed watermarking scheme with the improved defocus-based JND model has superior robustness compared with some watermarking schemes.
Spatial-Temporal Topic Model for Cold-Start Event Recommendation

Event recommendation has attracted an increasing attention with the popularity of eventbased social networks (EBSNs).The previous studies mainly focus on exploiting various contextual information to alleviate the cold-start problem in event recommendation.However, the interactions between different contextual factors, such as time, location and content, have not been modeled jointly.In this paper, we investigate the relationships among time, location and organizer in EBSNs, and propose a Spatial-Temporal Topic Model (STTM) for cold-start event recommendation.STTM can capture user's interests on content and geographical space changing over time, and users have different event and venue topic distributions at different times in STTM.We perform an experimental evaluation on the real-world EBSNs dataset.The experimental results show the significant improvements of our method over other comparison methods, especially when dataset is more sparse.
SPNets: Human-like Navigation Behaviors with Uncertain Goals

Most path planning techniques use exact, global information of the environment to make optimal or near-optimal plans. In contrast, humans navigate using only local information, which they must augment with their understanding of typical building layouts to guess what lies ahead, while integrating what they have seen already to form mental representations of building structure. Here, we propose Scene Planning Networks (SPNets), a neural network based approach for formulating the long-range navigation problem as a series of local decisions similar to what humans face when navigating. Agents navigating using SPNets build additive neural representations of previous observations to understand local obstacle structure, and use a network-based planning approach to plan the next steps towards a fuzzy goal region. Our approach reproduces several important aspects of human behavior that are not captured by either full global planning or simple local heuristics.
An automatic self‐initialized clustering method for brain tissue segmentation and pathology detection from magnetic resonance human head scans with graphics processing unit machine

Abstract The proposed work introduces a fully automatic modified fuzzy c‐means (MFCM) algorithm for segmenting brain tissue into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) which identifies the pathological conditions of magnetic resonance human head scans. The present work implements histogram smoothing using Gaussian distribution for finding the number of clusters ( K ) and cluster centers ( C ) to initialize modified FCM algorithm (MFCM). The modification includes the local impact of each pixel based on the median of local neighborhoods. This needs more computational power to reduce the processing time and requires a parallel programming environment like the Graphics Processing Unit. The parallel MFCM is performed with the help of compute unified device architecture language and reduced the processing time up to 80 speedup folds than the serial implementation in Matlab and 20 speedup folds than C programming implementation. The method is examined with the Internet Brain Segmentation Repository (IBSR20) T1W dataset. The quantitative and qualitative results of the proposed method are compared with state‐of‐the‐art‐methods using the Dice coefficient (DC). Proposed method yields high DC 0.84 ± 0.03 for GM, 0.83 ± 0.04 for WM, and 0.41 ± 0.12 for CSF segmentation. In post‐processing, 3D volumes of segmented regions have been constructed and compared with the gold standard quantitatively and qualitatively.
Large-Scale Kernel Method for Vertical Federated Learning

For nowadays real-world data mining task, multiple data holders usually maintain the different feature part of the common data which is called as vertically partitioned data. Accompanying by the emerging demand of privacy persevering, it is hard to do data mining over this kind of vertically partitioned data by legacy machine learning methods. In consideration of less literature for non-linear learning over kernels, in this chapter, we propose a vertical federated kernel learning (VFKL) method to train over the vertically partitioned data. Specifically, we first approximate the kernel function by the random feature technique, and then federatedly update the predict function by the special designed doubly stochastic gradient without leaking privacy in both data and model. Theoretically, our VFKL could provide a sublinear convergence rate, and guarantee the security of data under the common semi-honest assumption. We conduct numerous experiments on various datasets to demonstrate the effectiveness and superiority of the proposed VFKL method.
<scp>High‐dimensional</scp> data classification model based on random projection and <scp>Bagging‐</scp>support vector machine

Abstract Aiming at the long training time when classifying high‐dimensional data, a parallel classification model is proposed based on random projection and Bagging‐support vector machine (SVM) to process high‐dimensional data. The model first uses random projection to project the input data into the low‐dimensional space. Then, we used the Bagging method to construct multiple training data subsets and used SVM to train the training subset in parallel and generate several subclassifiers. Finally, various classifiers vote to determine the category of the test sample. The model has been verified using two standard datasets. The experimental results show that the model can significantly improve the training speed and classification performance of high‐dimensional data with little accuracy loss.
Multi-factor Authentication-Based E-Exam Management System (EEMS)

With the explosion of Internet-based technologies like online examination system, a lot of manual tasks have been automated, thereby saving time and effort. But deployment of such automated system has made the process more vulnerable to various risks, such as confidentiality loss, intrusion of critical data, and impersonation. In this paper, we proposed an online exam system using multi-factor authentication that allows instructors to set exams for different subjects via an integrated high text editor. Students can take exams after code-based verification and biometric authentication. It automates the process of exam scheduling, grading, and reporting, thereby reducing the workload of the instructors. A survey has been conducted to prove the efficacy of the system in terms of reliability, scalability, and availability.
Consistency Guided Scene Flow Estimation

Consistency Guided Scene Flow Estimation (CGSF) is a self-supervised framework for the joint reconstruction of 3D scene structure and motion from stereo video. The model takes two temporal stereo pairs as input, and predicts disparity and scene flow. The model self-adapts at test time by iteratively refining its predictions. The refinement process is guided by a consistency loss, which combines stereo and temporal photo-consistency with a geometric term that couples disparity and 3D motion. To handle inherent modeling error in the consistency loss (e.g. Lambertian assumptions) and for better generalization, we further introduce a learned, output refinement network, which takes the initial predictions, the loss, and the gradient as input, and efficiently predicts a correlated output update. In multiple experiments, including ablation studies, we show that the proposed model can reliably predict disparity and scene flow in challenging imagery, achieves better generalization than the state-of-the-art, and adapts quickly and robustly to unseen domains.
Autoencoder-Based Graph Construction for Semi-supervised Learning

We consider graph-based semi-supervised learning that leverages a similarity graph across data points to better exploit data structure exposed in unlabeled data. One challenge that arises in this problem context is that conventional matrix completion which can serve to construct a similarity graph entails heavy computational overhead, since it re-trains the graph independently whenever model parameters of an interested classifier are updated. In this paper, we propose a holistic approach that employs a parameterized neural-net-based autoencoder for matrix completion, thereby enabling simultaneous training between models of the classifier and matrix completion. We find that this approach not only speeds up training time (around a three-fold improvement over a prior approach), but also offers a higher prediction accuracy via a more accurate graph estimate. We demonstrate that our algorithm obtains state-of-the-art performances by respectful margins on benchmark datasets: Achieving the error rates of 0.57% on MNIST with 100 labels; 3.48% on SVHN with 1000 labels; and 6.87% on CIFAR-10 with 4000 labels.
Avatar’s Lip Synchronization in Talking Involved Virtual Reality

Having a virtual talking face along with a virtual body increases immersion in VR applications. As virtual reality (VR)techniques develop, various applications are increasing including multi-user social networking and education applications that involve talking avatars.Due to a lack of sensory information for full face and body motion capture in consumer-grade VR, most VR applications do not show a synced talking face and body.We propose a novel method, targeted for VR applications, for talking face synced with audio with an upper-body inverse kinematics.Our system presents a mirrored avatar of a user himself in single-user applications.We implement the mirroring in a single user environment and by visualizing a synced conversational partner in multi-user environment.We found that a realistic talking face avatar is more influential than an un-synced talking avatar or an invisible avatar.
Drop the packets

Understanding end-user video Quality of Experience (QoE) is important for Internet Service Providers (ISPs). Existing work presents mechanisms that use network measurement data to estimate video QoE. Most of these mechanisms assume access to packet-level traces, the most-detailed data available from the network. However, collecting packet-level traces can be challenging at a network-wide scale. Therefore, we ask:"Is it feasible to estimate video QoE with lightweight, readily-available, but coarse-grained network data?" We specifically consider data in the form of Transport Layer Security (TLS) transactions that can be collected using a standard proxy and present a machine learning-based methodology to estimate QoE. Our evaluation with three popular streaming services shows that the estimation accuracy using TLS transactions is high (up to 72%) with up to 85% recall in detecting low QoE (low video quality or high re-buffering) instances. Compared to packet traces, the estimation accuracy (recall) is 7% (9%) lower but has up to 60 times lower computation overhead.
Highlight on Cryptocurrencies Mining with CPUs and GPUs and their Benefits Based on their Characteristics

Nowadays cryptography and their technologies have been with us for a long time. This field of science is developing day by day. Blockchain has come a long way since the birth of its first application. This application is Bitcoin which depends on Blockchain or DLT (Distributed Ledger Technology). Bitcoin is the most famous cryptocurrency intended for mass which appeared the first time in 2008. This innovation led to sparked in the digital revolution, which provides for many sectors and industries the security features, decentralization, and a reliable mechanism for transmission and storing data. This new technology led to the meteoric rise of Bitcoin and other such cryptocurrencies, so this has aroused the interest of governments and emerging companies to get an effective role in competition. We can apply blockchain in different sectors and different Internet of things (IoT). Cryptocurrency mining has become a major concern for computer professionals and also for those who earn more money from an additional source. Mining is a process for settle mathematical issues that depend on the strength and speed of the devices to solve those problems, which by resolving them leads to getting rewards in the form of a digital currency. In this paper, we will highlight the benefits of using both CPU (Central Processing Unit) and GPU (Graphical Processing Unit) and a practical comparison between them to find out the best and fastest based on previous studies and on the characteristics and architecture of each.
DoFNet: Depth of Field Difference Learning for Detecting Image Forgery

Recently, online transactions have had an exponential growth and expanded to various cases, such as opening bank accounts and filing for insurance claims. Despite the effort of many companies requiring their own mobile applications to capture images for online transactions, it is difficult to restrict users from taking a picture of other's images displayed on a screen. To detect such cases, we propose a novel approach using paired images with different depth of field (DoF) for distinguishing the real images and the display images. Also, we introduce a new dataset containing 2,752 pairs of images capturing real and display objects on various types of displays, which is the largest real dataset employing DoF with multi-focus. Furthermore, we develop a new framework to concentrate on the difference of DoF in paired images, while avoiding learning individual display artifacts. Since DoF lies on the optical fundamentals, the framework can be widely utilized with any camera, and its performance shows at least \(23\%\) improvement compared to the conventional classification models.
Parallel Algorithm for the Path Planning of Multiple Unmanned Aerial Vehicles

This paper presents a parallel algorithm for the path planning of multiple unmanned aerial vehicles (UAVs) in the context of a surveillance mission. The UAVs are tasked to visit a set of points of interest (POIs) dispersed in a 3D environment and the algorithm allocates the POIs to the UAVs and computes optimal paths in between the POIs. The algorithm following a four-step approach and relies on a single source shortest path (SSSP) algorithm to compute the optimal paths between the POIs and a genetic algorithm to assign the POIs to the UAVs and find the order in which the POIs are visited. The algorithm is parallelized on a graphics processing unit and a multicore CPU to reduce the computing time and to allow for in-flight planning. The proposed algorithm is able to calculate paths for 3 UAVs and 10 POIs in just 0.6 seconds which represents a speedup of 48x compared to a sequential implementation on CPU.
BanglaMusicMooD: A Music Mood Classifier from Bangla Music Lyrics

This research work is aimed at using word-level features for mood classification. The goal of this project is to build a classifier model to detect the mood of the song by analyzing the lyrics of Bangla song. We have used the most famous approach “Bagging” (standing for “bootstrap aggregating”) that targets at constructing an ensemble model that is more robust than the individual models composing it. We used this learning to predict the probability of different classes based on various attributes. Our work proposed support vector machine (SVM) ensembles with bagging that outperforms better performance in terms of classification accuracy greatly. We have worked on two types of Bangla moods such as happy and sad, corresponding to the Bangla meaning. At the end of the experiment, our proposed method performed well to classify the mood.
Mode I stress intensity factors of printed and extruded specimens based on Digital Image Correlation method (DIC): case of ABS material

In the present paper, the Digital Image Correlation (DIC) method, which helps to find the stress intensity factors (SIF), is examined for the Single Edge Notch Tension (SENT) specimens under mode I loading for Acrylonitrile Butadiene Styrene (ABS) material for both extruded and printed samples. First, the analytical mode I stress, the displacement fields around the tip of the SENT specimen and the concept of SIF are explained. Then, the experimental setup for DIC method is performed and different loading speeds are considered over these specimens. Furthermore, the DIC method is employed to determine the experimental displacement fields around crack tips. Finally, the experimental displacement fields obtained through the DIC method and the analytical ones are leading to identify the same SIF.
A systematic review of security threats and countermeasures in SaaS

Among the service models provided by the cloud, the software as a service (SaaS) model has had the greatest growth. This service model is an attractive option for organizations, as they can transfer part or all of their IT functions to a cloud service provider. However, there is still some uncertainty about deciding to carry out a migration of all data to the cloud, mainly due to security concerns. The SaaS model not only inherits the security problems of a traditional application, but there are unique attacks and vulnerabilities for a SaaS architecture. Additionally, some of the attacks in this environment are more devastating due to nature of shared resources in the SaaS model. Some of these attacks and vulnerabilities are not yet well known to software designers and developers. This lack of knowledge has negative consequences as it can expose sensitive data of users and organizations. This paper presents a rigorous systematic review using the SALSA framework to know the threats, attacks and countermeasures to mitigate the security problems that occur in a SaaS environment. As part of the results of this review, a classification of threats, attacks and countermeasures in the SaaS environment is presented.