Reviewerly/exHarmony huggingface dataset:
Development of a Smart Education System for Analysis and Prediction of Students’ Academic Performance

The aim of this article is to research the key characteristics of the smart education system, prepare some requirements for its main components, and develop a set of tasks in order to ensure the higher level of education, communication, analysis and prediction of students’ academic performance. Based on the research of the smart education system main components, there was built an account interface for a student to login into this system using web development tools. Moreover, to implement this smart education system, there was recommended to use software and hardware system that will allow quickly to access educational materials, smart simulators, interactive personal assistant, virtual boards, and other educational tools. Overall, the development of the smart education system will provide the higher-quality learning content, make the educational process more individualized, help to keep track of changes in the academic performance, provide recommendations for its improvement, as well as improve the knowledge quality for the future specialists.
LibGuides: Information Technology Help: Blackboard

Resources to help Central Penn College students navigate the technology required in their courses.
Fast and Accurate Anchor Graph-based Label Prediction

Anchor graphs are a popular tool used in label prediction of sparsely labeled data. In anchor graphs, labels of labeled data are propagated to unlabeled data via anchor points; anchor points are the centers of k-means clusters. Anchor graph-based label prediction determines local weights between data points and anchor points by exploiting Nesterov's method to obtain the graph's adjacency matrix, and it inverts a matrix obtained from the adjacency matrix to predict labels., however, incurs high computation cost since (1) Nesterov's method is applied to all closest anchor points to compute local weights, and (2) the computation cost of the inversion matrix is cubic in the number of anchor points. We propose an approach that can efficiently perform anchor graph-based label prediction because of its two key advances: (1) it prunes unnecessary anchor points so they are not passed to Nesterov's method, and (2) it applies the conjugate gradient method in computing labels of data points to avoid matrix inversion. In addition, we propose to exploit basis vectors computed by SVD as anchor points to improve label prediction accuracy. Experiments show that our approach outperforms the previous approaches in terms of efficiency and accuracy.
Kernel-Based Partial Permutation Test for Detecting Heterogeneous Functional Relationship

We propose a kernel-based partial permutation test for checking the equality of functional relationship between response and covariates among different groups. The main idea, which is intuitive and easy to implement, is to keep the projections of the response vector Y on leading principle components of a kernel matrix fixed and permute Y’s projections on the remaining principle components. The proposed test allows for different choices of kernels, corresponding to different classes of functions under the null hypothesis. First, using linear or polynomial kernels, our partial permutation tests are exactly valid in finite samples for linear or polynomial regression models with Gaussian noise; similar results straightforwardly extend to kernels with finite feature spaces. Second, by allowing the kernel feature space to diverge with the sample size, the test can be large-sample valid for a wider class of functions. Third, for general kernels with possibly infinite-dimensional feature space, the partial permutation test is exactly valid when the covariates are exactly balanced across all groups, or asymptotically valid when the underlying function follows certain regularized Gaussian processes. We further suggest test statistics using likelihood ratio between two (nested) Gaussian process regression models, and propose computationally efficient algorithms utilizing the EM algorithm and Newton’s method, where the latter also involves Fisher scoring and quadratic programming and is particularly useful when EM suffers from slow convergence. Extensions to correlated and non-Gaussian noises have also been investigated theoretically or numerically. Furthermore, the test can be extended to use multiple kernels together and can thus enjoy properties from each kernel. Both simulation study and application illustrate the properties of the proposed test.
Graphic QR code Combined with Stamp

Stamp is one of the most distinctive and representative elements in Chinese culture. However, with the gradual advances in science and technology, verification remains to be the only function of the stamp. Today in some important documents, in addition to the stamp, QR codes are widely used to carry information, which makes the layout of the whole document look unsystematic and cluttered. Therefore, this study aims to beautify normal QR codes into Graphic QR codes combining in stamps, and expects to find out the effect of different brightness on the picture on the error rate of QR codes. In this study, QR codes with different information intensities are combined into two stamp images with different brightness through the error diffusion method by using applications then the digital files of the QR-code combined stamp are printed and confirmed for their reading effect. After scanned, the information contained in the printed barcode is recognized through the anchor dots by geometrical transformation, then comparing the error rate with the original QR code's information. The experiment finds that a higher information intensity of the QR code leads to a higher decoding rate. Besides, the content recognition rate of brighter stamp images is better. Therefore stamps with appropriate brightness have been imported to the experiments separately to analyze the effect of different brightness on the picture with graphic QR code error and find out the best position under different conditions. The above findings show that this study effectively integrates the image characteristics and contents of graphic QR codes with different brightness and information intensities, and also combines technology with the stamp image, indicating a new development direction for stamps —the combination of physical stamp and the virtual online space.
Short Text Clustering Using Joint Optimization of Feature Representations and Cluster Assignments

The application of traditional text clustering methods to short text data is inefficient owing to the high dimensionality and semantic sparseness of such data. Contrastingly, convolutional neural networks can capture the local information between consecutive words in a sentence and extract the semantic features of the text. In this paper, we propose a short text clustering method based on convolutional autoencoders (CAE-STC) that jointly optimizes feature representations and cluster assignments. The proposed method employs a convolutional autoencoder to learn deep text feature representations and preserve the local structure of text generation distribution. By integrating the clustering loss and convolutional autoencoder’s reconstruction loss, a unified loss function is formulated to update the network parameters and cluster centers iteratively, improving the performance of the feature learning and clustering tasks. The results of extensive experiments conducted on three public short text datasets demonstrate that the proposed method outperforms several popular clustering methods in terms of the normalized mutual information and clustering accuracy.
LoCo-VAE: Modeling Short-Term Preference as Joint Effect of Long-Term Preference and Context-Aware Impact in Recommendation

User preference modeling is an essential task for online recommender systems. Recently, methods have been applied to model short-term user preferences within a short-term period. These approaches use recent user behavior as the context to determine the current short-term preferences. However, we argue that short-term user preferences are related to more complex contexts, e.g., the seasons or the time of the day. Furthermore, we make the hypothesis that short-term preferences of a user is actually a joint effect of his/her stable long-term preferences and the context-aware impact. Therefore, we propose LoCo-VAE, a unified model of this joint effect with Variational Auto-Encoder (VAE) based strategies. First, we utilize a Multilayer Perceptron(MLP) to capture long-term user preferences. Second, we improve the traditional VAE by distributing user interactions with respect to different contexts to introduce the context-aware impact. Finally, the long-term preferences and context-aware impact are combined with a joint generative training process to generate the embedding of short-term user preferences. Experiments on real-world datasets of Amazon consumption and music selection demonstrate the superiority of our model compare with state-of-the-art methods in recommendation system.
Experimental Demonstration of a Local Active Phase Compensation Method for the Phase Encoding Quantum Key Distribution System

Phase drifting is an intrinsic problem in the various phase-encoding quantum key distribution (QKD) system. This letter presents a novel local active phase compensation (LAPC) method, in which the phase drift of the interferometer for each node is tracked and compensated separately. The proof-of-principle demonstration is implemented based on the two common BB84 prepare-and-measure QKD systems, and the experiment result shows that our LAPC setup can run steadily for above 10 hours. A test quantum key distribution based on the Mach–Zehnder interferometer QKD system and the Faraday–Michelson interferometer QKD system with an average quantum bit error rate of 3.8% and 1.9% was achieved, respectively. This study demonstrates the validity of the LAPC method for the BB84 phase-encoding prepare-and-measure QKD system, and then the potential for its use in large-scale QKD networks, while offering a simpler scheme for the practical operation of the MDI-QKD system and the star-type MDI QKD network.
CBIAAT: Context Based Image Analyzer for Alzheimer’s Therapy

Alzheimer’s is an irreversible progressive brain disorder which affects the Cognitive health of the older adults. Eventually it will destroy their memory, thinking abilities and destroy the ability to perform even a simple task. This is the common course of dementia. Unlikely, any drugs can successfully treat this. Keeping patients mind active will reduce the risk of this disease and delay the cognitive decline. Traditional way of therapy is letting them to engage in some memory activities and recalling their memory by showing past photographs with the help of a caretaker. Sentiments are very important in human interaction, but Alzheimer patients are facing difficulties in identifying it. In this work we proposed an Automated system which contains an intelligent image analyzer and a sentiment analysis game. The aim of this project is to help Alzheimer patients to recall memories through their life events with the visual sentiment and to keep the patient’s brain active.
Object Detection Based Software System for Automatic Evaluation of Cursogramas Images

The aim of this work is to describe the tasks performed to carry out the development of a software system capable of detecting and recognizing the symbols of Cursogramas in images by using a Deep Learning model that has been trained from scratch. In this way, we seek to assist teachers of an undergraduate subject to automatically evaluate diagrams made as part of the practical exercise of their students. For this purpose, in addition to having carried out a process of understanding the problem and identifying the available data, tasks of technology selection and construction of each of the components that are part of the system are also carried out. Therefore, although the problem domain belongs to the field of university education, this work is more related to the engineering and technological aspect of the application of Artificial Intelligence to solve complex problems.
Being Hidden and Anonymous

Adversaries use many nefarious techniques to stay hidden and anonymous during their online activities. But there are still loopholes in the identity hiding services that can be exploited at a granular level to deanonymize the user. The Internet has gone through significant advancement in the last few decades. This technology change provides useful data and information via websites and blogs, but it gives add-on services that help users while on a route. For the last few years, such third-party services were continuously grabbing and capturing our personal information for their own benefits, such as advertisement or providing more recommendations without the consent of users. Furthermore, there is a possibility of sharing data with traders of the black market, where personal information such as mail, phone, or physical address can be sold and used for illegal access to various systems. Such information may also be used for any atrocious act. Many anonymous services are rendered over the Internet to provide low-level anonymity and privacy to communicated data. However, those services still have flaws that can be easily exploited. Such systems are susceptible to various attacks, including exploiting hardware devices, traffic analysis, footprinting, and many more. Proxy servers that help alter IP addresses and location details are also receptive in nature to collect data by authorities. Virtual private network (VPN), which uses the same features as proxy servers, adds an extra encryption layer. This functionality makes VPN a more secure technology compared to proxy servers. Though it has security, VPN has another serious issue of untrustworthy of VPN servers located around the globe. These VPN servers can collect users' data, or they can be used as network security monitoring (NSM) media. This chapter focuses on anonymous systems like Invisible Internet Project (I2P), Freenet, and JonDonym, services that hide the user identity from the surface Internet. It also includes an introduction to The Onion Router (TOR) without going into details. Moreover, the chapter demonstrates the configure of each system and its crucial elements to make the users' identity safe from different nemesis.
A Comparison of Bagging and Boosting on Classification Data: Case Study on Rainfall Data in Sultan Syarif Kasim II Meteorological Station in Pekanbaru

Abstract A frequent way for classification data is using a machine learning algorithm alongside ensemble methods like bagging and boosting. In earlier studies, these two algorithms have shown to be very accurate. The aim of this research is to discover performance of bagging and boosting to classify rainfall data obtained at the Sultan Syarif Kasim II Meteorological Station in Pekanbaru from 1 January 2018 until 31 July 2021. Rainfall data are classified into two categories: rainy and non-rainy. The parameters are average temperature, average humidity, sunshine duration, wind direction at maximum speed, and average wind speed. For comparison, this study developed Stochastic Gradient Boosting with Gradient Boosting Modelling and C5.0 from boosting, as well as Bagged Classification and Regression Tree (CART) and Random Forest from bagging. In order to generate reliable conclusions, each algorithm is run 30 times with repeated cross validation. The result demonstrates that Stochastic Gradient Boosting with Gradient Boosting Modelling is the best algorithm based on average accuracy.
Design and Implementation of a Robot Pose Predicting Recurrent Neural Network for Visual Servoing Application

Visual servoing is the method of controlling a robot using image input from one or more image sensors to complete a predefined task. This paper examines the effectiveness of a Recurrent Neural Network (RNN) to predict the position and orientation (pose) of an industrial robot manipulator for automatic pick and place applications mainly in unstructured environment. The robot manipulator moves to the target object based on the pose commands obtained from the trained neural network. Various images obtained from the camera attached to the end-effector and corresponding pose of the end-effector are the input and the output data for training the neural network. The performance of the RNN in predicting the robot pose is compared with the feedforward neural (FFN) network and cascade forward neural (CFN) network. The proposed method is validated experimentally using ABB IRB 1200 6-DOF industrial robot manipulator.
Research on Basketball Robot Recognition and Localization Based on MobileNet-SSD and Multi-sensor

The rules of basketball robot game and the structure level of basketball robot system are introduced, and it is clear that the main task requirement of the perception subsystem is to identify and locate the target. The traditional target recognition method is sensitive to natural light, and the generalization ability and robustness are poor. To address the problems of traditional algorithms, this paper implements a target detection model with good robustness and high accuracy based on the application of migration learning on deep learning, using the SSD (Single Shot Multibox Detector) algorithm with a lightweight MobileNet as the feature extraction network. In the target localization method, the localization strategy of "using laser sensors for long distance and short distance" is proposed. Finally, the above research results are integrated and tested in the basketball robot by target recognition experiments, and the fast and accurate target recognition is successfully achieved.
On the (M)iNTRU Assumption in the Integer Case

In AsiaCrypt 2019, Genise, Gentry, Halevi, Li and Micciancio put forth two novel and intriguing computational hardness hypotheses: The inhomogeneous NTRU (\(\mathsf {iNTRU}\)) assumption and its matrix version \(\mathsf {MiNTRU}\). In this work, we break the integer case of the \(\mathsf {iNTRU}\) assumption through elementary lattice reduction, and we describe how the attack might be generalized to polynomial rings and to the low dimensional \(\mathsf {MiNTRU}\) assumption with small noise.
Improvisation of spectral clustering through affinity propagation

Pairwise requirements determine whether two examples must to be in one group. In spite of actual fact that it has been achievement full to consolidate them into conventional grouping techniques, for example, K-implies, little advancement has been made in combining them with phantom bunching. The numerous test in planning a viable compelled phantom bunching is a reasonable mix of the scant pairwise limitations with the first fondness network. By generating pairwise constraints data over the primary fondness network, we propose to combine the two sources of partiality. For the new liking grid, we used a Gaussian cycle translation and ended up with a closed structure articulation. Experiments show it outflanks innovative compelled grouping strategies in getting great clustering with less limitations, and yields great picture division with client determined pairwise requirements.
Polynomial approximation to manifold learning

Manifold learning plays an important role in nonlinear dimensionality reduction. But many manifold learning algorithms cannot offer an explicit expression for dealing with the problem of out-of-sample (or new data). In recent, many improved algorithms introduce a fixed function to the object function of manifold learning for learning this expression. In manifold learning, the relationship between the high-dimensional data and its low-dimensional representation is a local homeomorphic mapping. Therefore, these improved algorithms actually change or damage the intrinsic structure of manifold learning, as well as not manifold learning. In this paper, a novel manifold learning based on polynomial approximation (PAML) is proposed, which learns the polynomial approximation of manifold learning by using the dimensionality reduction results of manifold learning and the original high-dimensional data. In particular, we establish a polynomial representation of high-dimensional data with Kronecker product, and learns an optimal transformation matrix with this polynomial representation. This matrix gives an explicit and optimal nonlinear mapping between the high-dimensional data and its low-dimensional representation, and can be directly used for solving the problem of new data. Compare with using the fixed linear or nonlinear relationship instead of the manifold relationship, our proposed method actually learns the polynomial optimal approximation of manifold learning, without changing the object function of manifold learning (i.e., keeping the intrinsic structure of manifold learning). We implement experiments over eight data sets with the advanced algorithms published in recent years to demonstrate the benefits of our algorithm.
DS-YOLOv5: Deformable and Scalable YOLOv5 for Mathematical Formula Detection in Scientific Documents

Mathematical formula detection (MFD) is a prerequisite step for the digitization of scientific documents. The MFD task has two key challenges, i.e. a large scale span between embedded formula and isolated formula, and a huge variation of the ratio between height and width. However, the detection accuracy of the most existing approaches rely on page segmentation still needs improvement due to the errors of complex documents. In this work, to solve the important problem of scale variation, we aim to assess the performance of a multi-scaled deformable method for the MFD task based on deformable convolution, image representation, and YOLOv5 detector. For the experimental study, the proposed method has been evaluated on the Marmot dataset, which is an existing benchmark. In our evaluation, the experimental results show that the proposed method outperforms previous methods on the Marmot dataset by a large margin. Moreover, we accomplished correct detection accuracy of 82.42% on embedded formulas and 90.69% on isolated formulas on the Marmot dataset, which results in a significant error reduction.
Blockchain Technology and Intellectual Property – A Basic Introduction

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
State-of-the-Art Technological Developments

In the previous chapter we analysed the impact of implementing the GDPR, and in particular the RtbF, in established IT environments and business processes. However, two advanced technological trends of our times used increasingly nowadays for storing and processing personal data, have been emerged in parallel and independently of the GDPR: the ubiquitous mobile computing and the decentralized p2p networks. The revolution of sensor capabilities embedded into the latest smartphones and the rapid evolution of machine learning algorithms have paved the way towards the recent advancements in mobile computing and sensing. At the same time, the re-introduction of decentralization nowadays is demonstrated by the boom of the blockchain and decentralized file storage and sharing networks such as the IPFS. Notwithstanding the unquestionable benefits that these state-of-the-art technologies bring to our daily lives and to our society, they simultaneously pose great risks to our privacy and data protection rights. Before proceeding in investigating the relevant privacy threats and how these technologies can comply with the GDPR principles in the next chapters, we present and examine hereafter the main characteristics and properties of the ubiquitous mobile computing and two commonly used decentralized p2p networks, the blockchain and the IPFS.