Reviewerly/exHarmony huggingface dataset:
Novel LabVIEW Implementation of FREAK Based Image Mosaicing and Object Tracking Algorithms

In this paper, we propose implementations of two important vision algorithms on LabVIEW tool; which are image mosaicing and object tracking. Features are used as a starting point for our algorithms; so the overall algorithms will only be as good as their used feature detectors and features matching techniques, for that, we propose using Harris corner detector to find key points in overlapping images, then describing the detected features using FREAK descriptors for finding correspondences, after estimating homography parameters; either the second image is transformed to the frame of the first image for image mosaicing purpose, or four corners of the tracked object are transformed for object tracking purpose. The two algorithms are tested on Matlab, and then they are implemented on LabVIEW platform; in which a sophisticated interface is created to facilitate code execution for users, the built interface on LabVIEW can be used to be executed on embedded systems. The performance of our implementation is verified on real images from different data base.
Analyzing Algorithms to Detect Disaster Events using Social Media

Disasters are instabilities that occur on the interface between society and the environment. During disasters, people communicate to inform and request for support for themselves or their community. Social media is used as a medium for communication due to its wide reach and global audience. During disasters, people communicate via messages regarding similar or different types of emergencies in the same general location. Interpreting and validating these messages during the occurrence of a disaster costs a significant time and loss. Therefore, this study presents a comparison between three models, K-Nearest Neighbor (KNN), Naive Bayes (NB), and Support Vector Machine (SVM), to classify and label a message as a disaster event. In order to simulate the examining process further, a categorization system is introduced to categorize the severity of a disaster as described in each message in a disaster environment. performances are compared for each of the models using classification scores of supervised learning.
A Summary of Aspect-based Sentiment Analysis

Abstract Aspect-based sentiment analysis is a subclass of sentiment analysis tasks, focusing on judging the sentiment tendency of entities or attributes, and has received extensive attention due to fine-grained analysis results. The current research mostly summarizes the research from a single point of aspect extraction or sentiment classification, and lacks of research on the methods and problems in aspect-based sentiment analysis from the overall perspective. By using the method of literature investigation, an in-depth overview of the typical methods and solutions is given in the field of aspect-based sentiment analysis from three aspects: aspect information extraction, aspect information sentiment classification and aspect-based sentiment analysis joint modeling. Then, we discuss some of the most challenging problems existing in the field in terms of model adaptability, error transmission, knowledge and reasoning, and proposes future research opportunities in terms of strengthening knowledge representation, multi-task learning and integrating knowledge and reasoning from the overall perspective of practical application.
STM-GAN: Sequentially Trained Multiple Generators for Mitigating Mode Collapse

Generative adversarial networks have shown promise in generating images and videos. However, they suffer from the mode collapse issue which prevents it from generating complex multi-modal data. In this paper, We propose an approach to mitigate the mode collapse issue in generative adversarial networks (GANs). We propose to use multiple generators to capture various modes and each generator is encouraged to learn a different mode through a novel loss function. The generators are trained in a sequential way to effectively learn multiple modes. The effectiveness of the proposed approach is demonstrated through experiments on a synthetic data set, image data sets such as MNIST and fashion MNIST, and in multi-topic document modelling.
A Data Trading Scheme Based on Payment Channel Network for Internet of Things

In recent years, the Internet of Things has developed rapidly. With the popularity of 5G, the Internet of Things is about to enter every corner of our lives. With the popularization of IoT devices, a large amount of data will be generated, which includes all aspects of people's lives. Therefore, data has become one of the important commodities. This paper proposes a solution for Internet of Things data transactions through the "payment channel network" (PCN) technology based on blockchain. The use of PCN technology based on blockchain not only guarantees the security of data, conforms to the characteristics of IoT data decentralization, but also ensures the transaction rate, and solves the problem of long delay and slow transaction rate in traditional blockchain technology. In this article, we constructed a PCN-based transaction model, and compared the transaction delay based on blockchain and the transaction delay based on PCN through experiments.
Empirical Laws of Natural Language Processing for Hindi Language

Empirical laws are the statistical laws that describe the relation between entities in a large dataset. They are readily found in nature, and findings have been proven by observations [1]. The primary objective of this study is to verify some of the empirical laws such as Zipf’s law, Mandelbrot’s approximation, and Heap’s law for Hindi language corpus. This involves collecting a corpus, performing text normalization, tokenizing it to get a list of words, identifying word types and their frequency, sorting and ranking the data based on frequency, and representing the relation between the frequency and rank of the word types to validate Zipf’s law and Mandelbrot’s approximation. For Heap’s law, the relation between the number of word types and tokens for different subsets of the corpus is considered. Based on our observations, the Hindi language satisfies the laws mentioned above.
Dynamic Graph Generative Adversarial Networks for Skeleton-Based Human Motion Prediction

Human 3D skeleton contains important dynamic information. By learning human skeleton data, graph neural networks have achieved excellent performance in extracting motion features. And the effectiveness of generative adversarial networks in synthetic data generation attracts much interest. In this work, we propose a novel dynamic graph generative adversarial networks for skeleton-based motion prediction. Our model, which is called DG-GAN, consists of generator and discriminator networks. The generator networks use a traditional encoder-decoder architecture. The encoder uses dynamic graph structures with global spatiotemporal receptive fields to encode human skeleton sequences, and the decoder consists of a sequence of graph-based gate recurrent units to generate prediction. In addition, we train the discriminator networks to calculate the errors between the prediction and the ground truth, and propagate the errors back to improve the quality of prediction. On the Human 3. 6M dataset, DG-GAN achieves competitive performance compared to the state-of-the-art methods.
WeGo: An Efficient Travel Assistant Application using Android

The greatest infuriating task for a backpack traveller is to generate a well-organized and cost-effective trip plan. Even if the travel agency delivers some preplanned schedules, it will not be adequate for a particular customer. Many people likely to take rest and enjoy the period of holidays. However, the complex process of travel schedule arrangement is mostly unfavorable and results in the cancellation of travelling. Most people like travelling with fun. However, when it comes to planning it is always difficult to make it happen. It is the problem for every trip to be cancelled. The main aim of the proposed application is to collect information about the current traffic situations, food availability and accommodation. Travel assistant application, WeGo is all about planning a trip most effectively. It helps a user with travel routes, food, accommodation details, nearby gas stations etc., and helps to rent adventure-travelling gears. The proposed application organizes individual travel schedules, delivers particular data for banqueting, entertaining, and lodging. WeGo proposes a simple and rapid method, which diminishes the time that it takes travellers to plan their travel.
Enhanced p-Sensitive k-Anonymity Models for Achieving Better Privacy

To our best knowledge, the p-sensitive k-anonymity model is a sophisticated model to resist linking attacks and homogeneous attacks in data publishing. However, if the distribution of sensitive values is skew, the model is difficult to defend against skew attacks and even faces sensitive attacks. In practice, the privacy requirements of different sensitive values are not always identical. The “one size fits all” unified privacy protection level may cause unnecessary information loss. To address these problems, the paper quantifies privacy requirements with the concept of IDF and concerns more about sensitive groups. Two enhanced anonymous models with personalized protection characteristic, that is, (p,α <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">isg</sub> ) -sensitive k-anonymity model and (pi,α <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">isg</sub> )-sensitive k-anonymity model, are then proposed to resist skew attacks and sensitive attacks. Furthermore, two clustering algorithms with global search and local search are designed to implement our models. Experimental results show that the two enhanced models have outstanding advantages in better privacy at the expense of a little data utility.
Simulation of the Ronchi test for arbitrary wave aberrations in imaging systems

Abstract The Ronchi test is a simple qualitative method to identify aberrations in imaging systems: a binary grating is superimposed on its own image formed by the system to produce patterns, known as Ronchigrams, that manifest the aberrations in the system. In practice, the experimentally recorded Ronchigrams are compared with model-based simulations to infer the types and amounts of wave aberrations. In this article an effective algorithm to simulate the Ronchigrams in an imaging system under arbitrary third-order wave aberrations is introduced. The method uses simple coordinate interpolation to transfer the grating transmittance from the object to the image space. High-quality Ronchigrams for all types of wave aberrations can easily be produced. For demonstration purposes, experimental Ronchigrams obtained in real imaging systems are also provided and compared with the simulations. The results show that this technique can both be used as an educational tool, and also in a real test setup.
UPSILON PI EPSILONCoping with you know what

column Share on UPSILON PI EPSILONCoping with you know what Author: Jeffrey L. Popyack Drexel University Drexel UniversityView Profile Authors Info & Claims ACM InroadsVolume 11Issue 4December 2020 pp 9–11https://doi.org/10.1145/3429985Online:12 November 2020Publication History 1citation21DownloadsMetricsTotal Citations1Total Downloads21Last 12 Months6Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Sentiment Polarity Classification Using Conjure of Genetic Algorithm and Differential Evolution Methods for Optimized Feature Selection

Background: Sentiment Analysis (SA) has a big role in Big data applications regarding consumer attitude detection, brand/product positioning, customer relationship management and market research. SA is a natural language processing method to track the public mood on a specific product. SA builds a system to collect/examine opinions on a product in comments, blog posts, re- views or tweets. Machine learning applicable to Sentiment Analysis belongs to supervised classifi- cation in general. Methods: Two sets of documents, training and test set are required in machine learning based classification: Training set is used by classifiers to learn documents differentiating character- istics; it is thus called supervised learning. Results: Test sets validate the classifier’s performance. Se- mantic orientation approach to SA is unsupervised learning because it requires no prior training for mining data. It measures how far a word is either positive or negative. This paper uses a hybrid GA- DE optimization technique for sentiment classification to classify features from movie reviews and medical data. Conclusion: Our research has enhanced the variables on learning rate as well as momentum values which are optimized by genetic approach that in turn improve the accuracy of classification procedure.
DMA Regularization: Enhancing Discriminability of Neural Networks by Decreasing the Minimal Angle

Most of the discriminative feature learning methods are specifically developed for metric learning, however, the effectiveness may be not obvious for other tasks. In this letter, we propose a novel discrimination regularization method for image classification, which enhances the intra-class compactness and inter-class discrepancy simultaneously, through decreasing the minimalangle (DMA) between the feature vector and any one of the weight vectors in classification layer. This method can robustly improve the discriminability and generalizability of neural networks and easily exert its effect by plugging the DMA regularization term into the loss function with negligible computational overhead. The DMA regularization is simple, efficient, and effective. Therefore, it can be used as a basic regularization method for models based on neural networks. We evaluate DMA by applying it to various modern models on CIFAR10, CIFAR100, and TinyImageNet datasets, decreasing the test error rate by 0.2-0.4%, 0.2-1.5%, and 0.3-0.4% respectively. Code is available at: https://github.com/wznpub/DMA_Regularization.
An Interactive Approach of Rule Mining and Anomaly Detection for Internal Risks

Liu, Kun Wu, Yunkun Wei, Wenting Wang, Zhonghui Zhu, Jiaqi Wang, HonganHow to prevent internal risks to the information system, especially for undefined risks, is a great challenge. A reasonable approach is to mine the behavior rules of internal staff on historical data through various data mining algorithms and then use the behavior rules to detect abnormal behaviors. However, in practice, risk control officers are often not familiar with data mining technologies, so it is hard to make them effectively choose and adapt these algorithms to find internal risks. In this paper, we propose an interactive approach for behavior rule mining and anomaly detection. Firstly, we express behavior rules and abnormal behaviors as complex events uniformly to accommodate different mining algorithms. Then, the internal staff’s history behavior logs generated during production are used for mining behavior rules. Next, mined behavior rules are applied to new logs for anomaly detection. Finally, the detected abnormal behavior will be reported to the risk control officer for evaluation, and the feedback will be used for improving mining and detection settings to form a gradual and interactive process. The experiments on the real production data show that the approach is effective and efficient to detect abnormal behavior and can be used to prevent internal risks of the information system of big corporations such as banks.
How Many Stars Do You See in This Constellation?

With the increase of Knowledge Graphs available on the Web came the need of characterising them, adding for example provenance information. To facilitate adding statement-level metadata, an RDF syntax extension has recently been proposed to the Semantic Web community: RDF*. In this article, we examine the current coverage of RDF* by SPARQL engines. We identify a few issues arising even when performing simple operations such as counting the number of statements. We raise awareness on these issues and derive new research directions for the community.
Model NOx Emissions Emitted from Coal-fired Boilers Based on Differential Evolution Fast Learning Network

Abstract Low NOx combustion optimization is a simple, efficient and inexpensive NOx emission reduction technology for coal-fired power plants. Establishing NOx prediction model is an important part of this technology. Fast learning network (FLN) is an improved neural network proposed in recent years, which is simple and efficient. However, randomly generated hidden layer thresholds and input weights would affect the performance of FLN. To solve this problem, differential evolution (DE) algorithm is employed to optimize hidden layer thresholds and input weights. The proposed model was applied to some 330MW coal-fired boiler together with FLN. Each model was repeated 51 times to consider the randomness of both models caused by the randomness of DE algorithm and the stochastic initialization of the original FLN. Results showed that the model has better generalization ability, retaining the good approximation ability and stability. Besides, optimization process of the proposed model is very fast and fit for online combustion optimization.
Research on RLGA-based Hardware Evolution Optimization Technology

This paper discusses, comparing with the most commonly used genetic algorithms, how to obtain RLGA via researches on reinforcement learning and improves on genetic algorithms, so as to meet our requirements for hardware evolutionary operations, under the structure of the hard core microprocessor plus FPGA and the reconfigurable evolution circuit structure based on dynamic reconfiguration technology.
Structured sparse support vector machine with ordered features

In the application of high-dimensional data classification, several attempts have been made to achieve variable selection by replacing the ℓ2 -penalty with other penalties for the support vector machine (SVM). However, these high-dimensional SVM methods usually do not take into account the special structure among covariates (features). In this article, we consider a classification problem, where the covariates are ordered in some meaningful way, and the number of covariates p can be much larger than the sample size n. We propose a structured sparse SVM to tackle this type of problems, which combines the non-convex penalty and cubic spline estimation procedure (i.e. penalizing second-order derivatives of the coefficients) to the SVM. From a theoretical point of view, the proposed method satisfies the local oracle property. Simulations show that the method works effectively both in feature selection and classification accuracy. A real application is conducted to illustrate the benefits of the method.
Counting colorful necklaces and bracelets in three colors

A necklace or bracelet is colorful if no pair of adjacent beads are the same color. In addition, two necklaces are equivalent if one results from the other by permuting its colors, and two bracelets are equivalent if one results from the other by either permuting its colors or reversing the order of the beads; a bracelet is thus a necklace that can be turned over. This note counts the number K(n) of non-equivalent colorful necklaces and the number $$K'(n)$$ of colorful bracelets formed with n-beads in at most three colors. Expressions obtained for $$K'(n)$$ simplify expressions given by OEIS sequence A114438, while the expressions given for K(n) appear to be new and are not included in OEIS.
The Bitwise Hashing Trick for Personalized Search

Many real world problems require fast and efficient lexical comparison of large numbers of short text strings. Search personalization is one such domain. We introduce the use of feature bit vectors using the hashing trick for improving relevance in personalized search and other personalization applications. We present results of several lexical hashing and comparison methods. These methods are applied to a user’s historical behavior and are used to predict future behavior. Using a single bit per dimension instead of floating point results in an order of magnitude decrease in data structure size, while preserving or even improving quality. We use real data to simulate a search personalization task. A simple method for combining bit vectors demonstrates an order of magnitude improvement in compute time on the task with only a small decrease in accuracy.
A Benchmarking of the Effectiveness of Modular Exponentiation Algorithms using the library GMP in C language

This research aims to implement different modular exponentiation algorithms and evaluate the average complexity and compare it to the theoretical value. We use the library GMP to implement seven modular exponentiation algorithms. They are Left-to-right Square and Multiply, Right-to-left Square and Multiply, Left-to-right Signed Digit Square, and Multiply Left-to-right Square and Multiply Always Right-to-left Square and Multiply Always, Montgomery Ladder and Joye Ladder. For some exponent bit length, we choose 1024 bits and execute each algorithm on many exponent values and count the average numbers of squares and the average number of multiplications. Whenever relevant, our programs will check the consistency relations between the registers at the end of the exponentiation.
Class imbalance learning for Identity Management in Healthcare

Classifier learning with data sets comprising of imbalanced class distributions is a challenging problem in data mining. This has a significant impact on some of the real-world applications such as medical diagnosis, anomaly detection and fault diagnosis. This empirical investigation focus on two issues, i)Efficacy of machine learning deprived due to scarcity of observations in the class of interest ii) A vulnerability assessment in terms of privacy compromise when publishing health care data. The class balancing methods are analyzed for the suitability towards a suite of classification algorithms. The results indicate a positive synergy between the preprocessing method SMOTE- TL and the learning models. An identity management algorithm based on decomposition strategy is proposed for the class imbalance problem. This can have an impact on the healthcare industry where a published health record demands privacy without compromising machine learning results.
Visual-Based Positioning and Pose Estimation

Recent advances in deep learning and computer vision offer an excellent opportunity to investigate high-level visual analysis tasks such as human localization and human pose estimation. Although the performances of human localization and human pose estimation have significantly improved in recent reports, they are not perfect, and erroneous estimation of position and pose can be expected among video frames. Studies on the integration of these techniques into a generic pipeline robust to those errors are still lacking. This paper fills the missing study. We explored and developed two working pipelines that suited visual-based positioning and pose estimation tasks. Analyses of the proposed pipelines were conducted on a badminton game. We showed that the concept of tracking by detection could work well, and errors in position and pose could be effectively handled by linear interpolation of information from nearby frames. The results showed that the Visual-based Positioning and Pose Estimation could deliver position and pose estimations with good spatial and temporal resolutions.