Reviewerly/exHarmony huggingface dataset:
Personalized Learning Model based on Deep Learning Algorithm for Student Behaviour Analytic

The learning processes are unique for each individual. Thus, the appropriate learning methods are essential to boost the student performance and success in their study. The current e-learning system is focusing on producing tools for collaborative study or facilitating the course for the discussion forum and direct communication such as chat service. However, the problem of boosting up individual achievements remains challenging. This paper proposed a personalized learning model to find suitable learning methods based on a deep learning algorithm. The factors of personalized learning such as adaptive, individualized, differentiated and competence-based learning have been taken into account when build and tested the model. These factors will act as a feeder for the network then it will be processed to identify which model of learning has the biggest chance on augmenting student achievement. The testing result has shown that the proposed model with deep learning approach has overcome other classifier with 72% success rate The model is believed to bring a great benefit for student and the teacher as well to manage the studying process more convenient for both parties.
Novel Protocols to Mitigate Network Slice Topology Learning Attacks and Protect Privacy of Users’ Service Access Behavior in Softwarized 5G Networks

In the softwarized fifth generation (5G) networks, authentication protocols are deployed by both the mobile network operators and the third-party service providers to enable secure slice formation at the network side and user validation at the service provider side, respectively. However, the usage of static key-based authentication protocols in 5G networks gives rise to two major problems: (i) network slice topology learning attack during the formation of slice and (ii) exposure of users’ service access behavior to the third-party service providers and device-to-device communication peers. In this article, we propose group anonymous (i) privacy preserving mutual authentication (PPMA) and (ii) privacy preserving one-way authentication (PPOA) protocols to address the aforementioned problems, respectively. We also prove the security of the PPMA and PPOA protocols against the chosen ciphertext attack in the random oracle model. The PPMA and PPOA protocols are implemented using the JPBC library and the computation overhead is measured. For 1024 bits discrete logarithm security, the computation overhead of the PPMA and PPOA protocols is respectively 48.54 and 68.65 percent lower than an existing privacy preserving authentication protocol on an Intel processor. The PPMA and PPOA protocols are generic and can be applied in peer-to-peer authentication scenarios where group anonymity and privacy preservation are considered.
Comparative study of permissioned blockchain solutions for enterprises

Digital world is shifting towards distributed systems due to scalability and reliability issues of centralized systems. Blockchain is one of rapidly growing technology of distributed nature. Most of people have information about public blockchain due to bitcoin and other cryptocurrencies, but there is another face of blockchain which is private or permissioned blockchain. Permissioned blockchain is a distribute ledger for consortium enterprises network. The escalation in popularity of permissioned blockchain platforms in recent era is remarkable. There are multiple solutions available for permissioned blockchain, but there are three leading houses which deals enterprise blockchain, Fabric, Corda and Quorum. All developing communities have their contribution in market and claim their solution is best but blockchain just stepin permissioned era there are multiple plus and negative point in these frameworks. In this paper, I have discussed permissioned blockchain frameworks and analyze them comparatively on the bases of consensus range, modularity, language support, privacy, transaction rate, currency, and accumulate the adoption rate from these filters.
An Efficient Topic Modeling Approach for Text Mining and Information Retrieval through K-means Clustering

Topic modeling is an effective text mining and information retrieval approach to organizing knowledge with various contents under a specific topic. Text documents in form of news articles are increasing very fast on the web. Analysis of these documents is very important in the fields of text mining and information retrieval. Meaningful information extraction from these documents is a challenging task. One approach for discovering the theme from text documents is topic modeling but this approach still needs a new perspective to improve its performance. In topic modeling, documents have topics and topics are the collection of words. In this paper, we propose a new k-means topic modeling (KTM) approach by using the k-means clustering algorithm. KTM discovers better semantic topics from a collection of documents. Experiments on two real-world Reuters 21578 and BBC News datasets show that KTM performance is better than state-of-the-art topic models like LDA (Latent Dirichlet Allocation) and LSA (Latent Semantic Analysis). The KTM is also applicable for classification and clustering tasks in text mining and achieves higher performance with a comparison of its competitors LDA and LSA.
Panoramic Image Generation: From 2-D Sketch to Spherical Image

The 360-degree video/image, also called an omnidirectional video/image or panoramic video/image, is very important in some emerging areas such as virtual reality (VR). Therefore, corresponding image generation algorithms are urgently needed. However, existing image generation models mainly focus on 2-D images and do not consider the spherical structures of panoramic images. In this article, we propose a panoramic image generation method based on spherical convolution and generative adversarial networks, called spherical generative adversarial networks (SGANs). We adopt the sketch map as the input, which is a concise geometric structure representation of the panoramic image, e.g., comprising approximately 7% of the pixels for a 583 × 1163 image. Through adversarial learning, a realistic-looking, plausible and high-fidelity spherical image can be obtained from the sparse sketch map. In particular, we build a dataset of the sketch maps using a visual computation-based sketching model. Then, by optimizing SGANs with GAN loss, feature matching loss and perceptual loss, realistic textures and details are recovered gradually. On one hand, it is an improvement using the sparse sketch map as input rather than the denser input, e.g., the features of the textures and colors. On the other hand, spherical convolution helps to remedy space-varying distortions of the planar projection. We conduct extensive experiments on some public panoramic image datasets and compare them with state-of-the-art techniques to validate the superior performance of the proposed approach.
A Distributed Fair Machine Learning Framework with Private Demographic Data Protection

Fair machine learning has become a significant research topic with broad societal impact. However, most fair learning methods require direct access to personal demographic data, which is increasingly restricted to use for protecting user privacy (e.g. by the EU General Data Protection Regulation). In this paper, we propose a distributed fair learning framework for protecting the privacy of demographic data. We assume this data is privately held by a third party, which can communicate with the data center (responsible for model development) without revealing the demographic information. We propose a principled approach to design fair learning methods under this framework, exemplify four methods and show they consistently outperform their existing counterparts in both fairness and accuracy across two real-world data sets. We theoretically analyze the framework, and prove it can learn models with high fairness or high accuracy, with their trade-offs balanced by a threshold variable.
Constructing Educational Concept Maps with Multiple Relationships from Multi-Source Data

Concept map is an useful tool to help people organize and improve knowledge. Particularly in educational domain, it is beneficial for students and teachers to improve the learning and teaching quality. Traditionally, manual educational concept maps, provided by teachers, are quite time-consuming and limited to teachers' experience. Thus, it is meaningful to automatically construct high-quality concept maps. However, existing data-driven solutions only focus on either separate data source or single pedagogic relationship, which are not sufficient to satisfy actual demands. To this end, we propose a novel framework, named Extracting Multiple Relationships Concept Map (EMRCM), to construct multiple relations concept maps from Multi-source Data. Specifically, we design various targeted evidences to explore diverse information of multi-source data from different perspectives. Then, we employ three classic classifiers to bulid the predictive model for extracting key concepts and multiple concept relationships using the proposed evidences. We create a real dataset for empirically studying this problem. Extensive experiments on a real-world dataset show the effectiveness of our method.
Performance evaluation of clustering algorithms for varying cardinality and dimensionality of data sets

Clustering is the most widely used unsupervised machine learning technique, having extensive applications in statistical analysis. We have multiple clustering algorithms available in theory and many more implementations available in practice. A bunch of literatures can be found focusing on the quality of clustering algorithms using various internal and external evaluation techniques. The motivation behind this work is the scarcity of literatures dealing with performance of clustering algorithms in terms of turnaround time. This paper summarizes the experimental analysis conducted on the performance of multiple clustering algorithms based on cardinality and dimensionality. The analysis is performed in R, which is a free and open source programming language mainly used for statistical computing. This work evaluates nine key algorithms coming under partitioning, hierarchical, density-based and model-based clustering approaches using different social media data sets. We captured performance trends of these algorithms in terms of turnaround time by varying the cardinality and dimensionality parameters of the data sets. Based on our experiments, CLARA, CLARANS, and k-means algorithms demonstrate best performances with varying cardinality. It is also observed that changes in dimensionality do not impact hierarchical clustering approaches whereas there is a positive influence on the execution time for partitioning, density-based and model-based clustering approaches.
Decision Support in Medical Data Using 3D Decision Tree Visualisation

Decision making is a difficult task, especially in the medical domain. Decision trees are among the most often used supervised machine learning algorithms, primarily due to their straightforward interpretation through rules, they are composed of. Visualizations of decision trees can help with the understanding of data and decision rules on top of them. This paper proposes a 3D decision tree visualization method that allows effective decision tree exploration and help with decision tree interpretation. The main contribution of this visualization is the ability to display global tree structure and the difference in the distributions of values between subsets created by individual decision nodes. The approach is evaluated using a design study on a typical medical dataset and on a synthetic dataset with controlled properties. It shows the visualization's ability to display individual components of the decision rule and the ability to see other correlated factors in the decision and the change it causes in subsets of data created by individual decision nodes.
Interpretation of Discourse Connectives Is Probabilistic: Evidence From the Study of <i>But</i> and <i>Although</i>

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
A Fast Filtering Mechanism to Improve Efficiency of Large-Scale Video Analytics

Surveillance cameras are ubiquitous around us. Emerging full-feature object-detection models can analyze surveillance videos with high accuracy but consume much computation. Directly applying these models for practical scenarios with large-scale cameras is prohibitively expensive. This, however, is wasteful and unnecessary considering that user-defined anomalies occur rarely among these videos. Therefore, we propose FFS-VA, a multi-stage Fast Filtering Mechanism for Video Analytics, to make video analytics much cost-effective. FFS-VA filters out the frames without the user-defined events by two stream-specialized filters and a cheap full-function model, to reduce the number of frames reaching the full-feature model. FFS-VA presents a global feedback-queue approach to balance the processing speeds of different filters in intra-stream and inter-stream processes. FFS-VA designs a dynamic batch technique to achieve a trade-off between throughput and latency. FFS-VA can also efficiently scale to multiple GPUs. We evaluate FFS-VA against the state-of-the-art YOLOv3 under the same hardware and video workloads. The experimental results show that under a 12.88 percent target-object occurrence rate on two GPUs, FFS-VA can support up to 30 concurrent video streams (15× more than YOLOv3) in the online case, and obtain 10× speedup when offline analyzing a stream, with an accuracy loss of less than 2 percent.
BeBOT: Bernstein Polynomial Toolkit for Trajectory Generation

We present a method and an open-source implementation (BeBOT) for the generation of trajectories for autonomous system operations using Bernstein polynomials. Bernstein polynomials possess convenient geometric properties that enable the trajectory planner to efficiently evaluate and impose constraints along the vehicles' trajectories, such as maximum speed and angular rates, minimum distance between trajectories and between the vehicles and obstacles. Thus, the proposed method is particularly suitable for generating trajectories in real-time for safe operations in complex environments and multiple vehicle missions.
Nearest Neighbor Ensembles: An Effective Method for Difficult Problems in Streaming Classification with Emerging New Classes

This paper re-examines existing systems in streaming classification with emerging new classes (SENC) problems, where new classes that have not been used to train a classifier may emerge in a data stream. We identify that existing systems have an unspecified assumption that emerging new classes are geometrically far from known classes, or instances of known classes are densely distributed, in the feature space. Using a class separation indicator alpha, we refine the SENC problem into an alpha-SENC problem, where alpha indicates a geometric distance between two classes in the feature space. We show that while most existing systems work well in high-alpha SENC problems (i.e., a new class is geometrically far from a known class or instances of known classes are densely distributed), they perform poorly in low-alpha SENC problems. To solve low-alpha SENC problems effectively, we propose an approach using nearest neighbor ensembles or SENNE. We demonstrate that SENNE is able to handle both the low-alpha and high-alpha SENC problems which can appear at different times in a single data stream.
Lightweight Attention Pyramid Network for Object Detection and Instance Segmentation

Feature pyramids of convolutional neural networks (ConvNets)—from bottom to top—are used by most recent researchers for the improvement of object detection accuracy, but they seldom aim to address the correlation of each feature channel and the fusion of low-level features and high-level features. In this paper, an Attention Pyramid Network (APN) is proposed, which mainly contains the adaptive transformation module and feature attention block. The adaptive transformation module utilizes the multiscale feature fusion, and makes full use of the accurate target location information of low-level features and the semantic information of high-level features. Then, the feature attention block strengthens the features of important channels and weakens the features of unimportant channels through learning. By implementing the APN in a basic Mask R-CNN system, our method achieves state-of-the-art results on the MS COCO dataset and 2018 WAD database without bells and whistles. In addition, the structure of the APN makes the network parameters lighter, and runs at 4 ms on average, which is ignorable when compared to the inference time of the backbone of ConvNet.
Catching the Phish: Detecting Phishing Attacks Using Recurrent Neural Networks (RNNs)

The emergence of online services in our daily lives has been accompanied by a range of malicious attempts to trick individuals into performing undesired actions, often to the benefit of the adversary. The most popular medium of these attempts is phishing attacks, mainly through emails and websites. In order to defend against such attacks, there is an urgent need for automated mechanisms to identify this malicious content before it reaches users. Machine learning techniques have gradually become the standard for such classification problems. However, identifying common measurable features of phishing content (e.g., in emails) is notoriously difficult. To address this problem, we engage in a novel study into a phishing content classifier based on a recurrent neural network (RNN), which identifies such features without human input. At this stage, we scope our research to emails, but our approach can be extended to apply to websites. Our results show that the proposed system outperforms state-of-the-art tools. Furthermore, our classifier is efficient and takes into account only the text and, in particular, the textual structure of the email. Since these features are rarely considered in email classification, we argue that our classifier can complement existing classifiers with high information gain.
SPUD: simultaneous phase unwrapping and denoising algorithm for phase imaging

Recent methods for phase unwrapping in the presence of noise include denoising algorithms to filter out noise as a preprocessing stage. However, including a denoising stage increases the overall computational complexity resulting in long execution times. In this paper, we present a noniterative simultaneous phase unwrapping and denoising algorithm for phase imaging, referred to as SPUD. The proposed method relies on the least squares discrete cosine transform (DCT) solution for phase unwrapping with an additional sparsity constraint on the DCT coefficients of the unwrapped solution. Simulation results with different levels of noise and wrapped phase fringe density reveal the suitability of the proposed method for accurate phase unwrapping and restoration. When compared to the 2D windowed Fourier transform filter, SPUD performs better in terms of phase error and execution times. The processing of experimental data from synthetic aperture radar showed the capability for processing real images, including removing phase dislocations. An implementation of the proposed algorithm can be accessed and executed through a Code Ocean compute capsule.
CRNN Based Jersey-Bib Number/Text Recognition in Sports and Marathon Images

The primary challenge in tracing the participants in sports and marathon video or images is to detect and localize the jersey/Bib number that may present in different regions of their outfit captured in cluttered environment conditions. In this work, we proposed a new framework based on detecting the human body parts such that both Jersey Bib number and text is localized reliably. To achieve this, the proposed method first detects and localize the human in a given image using Single Shot Multibox Detector (SSD). In the next step, different human body parts namely, Torso, Left Thigh, Right Thigh, that generally contain a Bib number or text region is automatically extracted. These detected individual parts are processed individually to detect the Jersey Bib number/text using a deep CNN network based on the 2-channel architecture based on the novel adaptive weighting loss function. Finally, the detected text is cropped out and fed to a CNN-RNN based deep model abbreviated as CRNN for recognizing jersey/Bib/text. Extensive experiments are carried out on the four different datasets including both bench-marking dataset and a new dataset. The performance of the proposed method is compared with the state-of-the-art methods on all four datasets that indicates the improved performance of the proposed method on all four datasets.
Email Spam Detection : An Empirical Comparative Study of Different ML and Ensemble Classifiers

Recent Development in Hardware and Software Technology for the communication email is preferred. But due to the unbidden emails, it affects communication. There is a need for detection and classification of spam email. In this present research email spam detection and classification, models are built. We have used different Machine learning classifiers like Naive Bayes, SVM, KNN, Bagging and Boosting (Adaboost), and Ensemble Classifiers with a voting mechanism. Evaluation and testing of classifiers is performed on email spam dataset from UCI Machine learning repository and Kaggle website. Different accuracy measures like Accuracy Score, F measure, Recall, Precision, Support and ROC are used. The preliminary result shows that Ensemble Classifier with a voting mechanism is the best to be used. It gives the minimum false positive rate and high accuracy.
Privacy-Aware Cloud Ecosystems and GDPR Compliance

Understanding how cloud providers support the European General Data Protection Regulation (GDPR) remains an imporant challenge for new providers emerging on the market. GDPR influences access to, storage, processing and tranmission of data, requiring these operations to be exposed to a user to seek explicit consent. A privacy-aware cloud architecture is proposed that improves transparency and enables the audit trail of providers who accessed the user data to be recorded. The architecture not only supports GDPR compliance by imposing several data protection requirements on cloud providers, but also benefits from a blockchain network that securely stores the providers' operations on the user data. A blockchainbased tracking approach based on a shared privacy agreement implemented as a smart contract is described - providers who violate GDPR rules are automatically reported through a voting mechanism.
Hardware Implementation of an Improved Stochastic Computing Based Deep Neural Network Using Short Sequence Length

By introducing stochastic computing (SC), the hardware consumption of Deep Neural Network (DNN) can be reduced significantly. However, long SC sequence length is needed to maintain the accuracy, which results in extended computation time. To shorten the sequence length, we propose two high-accuracy SC computation units to improve the precision of SC-DNN, which are the rematching-based correlation-independent multiplier and the accumulator-based Rectified Linear Unit. Moreover, a length-adaptive method that uses variable lengths for different images is adopted to decrease the average sequence length. Software simulation shows that the SC design has just 0.01% accuracy loss with only the sequence length of 20 for MNIST dataset comparing to the binary system. The ASIC layout results demonstrate the area efficiency is improved by 16X comparing to the latest SC-DNN with similar accuracy loss.