Reviewerly/exHarmony huggingface dataset:
An Improvement of FP-Growth Mining Algorithm Using Linked list

Frequent pattern mining such as association rules, clustering, and classification is one of the most central areas in the data mining research. One of the foremost processes in association rule mining is the discovering of the frequent pattern. To draw on all substantial frequent patterns from the sizable amount of transaction data, various algorithms have been proposed. The proposed research aims to mine frequent patterns from the sizable amount of transaction database by using linked list. In this method, first scanning the database, the count of frequent 1-itemsets is searched using the hash map and for next itemsets, it is stored in the linked list, second scanning the database. The frequent 2-itemsets is generated using hash table and so on. So, the proposed research needs only two scans and this proposed method requires shorter processing time and smaller memory space.
Hybridization of Evolutionary and Swarm Intelligence Algorithms for improved performance

This paper conducts the hybridization of Swarm intelligence and Evolutionary Algorithm for Continuous and Discrete optimization. Optimization is the process of selecting the best element by following some rules and criteria from some set of available alternatives. Function optimization means finding the best available value of some given objective function in a defined domain. In this work we have proposed an innovative approach, by hybridizing Genetic Algorithm (GA) and Swarm Intelligence Algorithm (SIA). In this paper work we have implemented one evolutionary programming based algorithm - Improved First Evolutionary Programming (IFEP) and one swarm intelligence algorithm - Ant Colony Optimization (ACO). We have also used Travelling Salesman Problem (TSP) as a discrete problem. We have implemented both GA and ACO also to solve the Travelling Salesman Problem. We have compared the result produced by IFEP and ACO for Continuous Optimization. From the comparative study we have found that ACO is the better among the two. We also have compared the result produced by GA and ACO for Discrete Optimization and from the comparative study we have found that ACO often works better. We have conducted some experiments to optimize the parameters of ACO and GA and the amount of exploration and exploitation needed for ACO to produce the best result. using the best found parameter we have implemented a hybrid of Genetic Algorithm and Swarm Intelligence Algorithm and tested it with different strategies. Then we have conducted a comparative study between the hybrid and two other conventional Genetic and Swarm Intelligence Algorithms to observe the performance of our proposed hybrid algorithm. In some cases we have observed better performance from our proposed hybrid algorithm.
Implementation of an Approximator Based on a Multilayer Perceptron and Wavelet-Neural Network on the STM32 Microcontroller

Neural network approximators are used in digital signal processing, in particular when automating the process of their analysis. The paper is devoted to the implementation of an approximator based on a multilayer perceptron and a wavelet-neural network. Implementation is carried out on the STM32F401RE microcontroller. In the article the speed of training neural networks during the approximation of fragments of biomedical signals is estimated and the influence of the selected activation function of the wavelet-neural network on the approximation error is evaluated. The experimental method shows that for the approximation of electrocardiogram and electroencephalogram when implementing a neural network approximator on the selected microcontroller, the most justified use of the wavelet neural network using the Mexican hat as the activation function of the wavelet is used.
A Temporal Caching-Aware Dummy Selection Location Algorithm

Along with the increased convenience of our daily life thanks to the proliferation of location-based service (LBS), such as finding restaurants and booking taxi, concerns on privacy disclosure risks in sharing our locations with LBS have also increased and become a major bottleneck that obstacles the widespread of adoption of LBS [1]. To preserve privacy in LBS, k-anonymity was applied to conceal people's sensitive information against re-identification attacks [2]. Unfortunately, the k-anonymity technique relies on predefined background knowledge of an adversary. Once the adversary has different auxiliary information, we cannot guarantee any privacy preservation against such an adversary. To address the privacy leakage problem of the naive k-anonymity, a combination of k-anonymity and location's query frequency algorithm, the Caching-aware Dummy Selection Algorithm (CaDSA), were proposed [3]. CaDSA anonymises locations in a given area by grouping them with similar query frequency during a fixed time period, say one day. However, considering in the real-life situation location's query frequency often varies in different time slots even in a single day, privacy will clearly lose if we roughly group locations according to a fixed time period as CaDSA. Consequently, in this paper, we propose a Temporal Caching-aware Dummy Location Selection Algorithm (T-CaDLSA) that considers the differences among location's query frequencies over different time slots within a given time period (day). Both mathematical and experimental evaluations show that to achieve the same data utility, our method outperforms the existing work in privacy guarantee.
Graph Filtering for Recommendation on Heterogeneous Information Networks

Various kinds of auxiliary data in web services have been proved to be valuable to handler data sparsity and cold-start problems of recommendation. However, it is challenging to develop effective approaches to model and utilize these various and complex information. Due to the flexibility in modelling data heterogeneity, heterogeneous information network (HIN) has been adopted to model auxiliary data for sparsity recommendation, named HIN based recommendation. But most of these HIN based methods rely on meta path-based similarity or graph embedding, which cannot fully mine global structure and semantic features of users and items. Besides, these methods, utilizing extended matrix factorization model or deep learning model, suffer expensive model-building problem and cannot treat personal latent factors carefully since their global objective functions. In this paper, we model both rate and auxiliary data through a unified graph and propose a graph filtering (GF) recommendation method on HINs. Distinct from traditional HIN based methods, GF uses a rate pair structure to represent user's feedback information and predict the rating that says, “a predicted rating depends on its similar rating pairs.” Concretely, we design a semantic and sign value-aware similarity measure based on SimRank, named Constrained SimRank, to weight rating pair similarities on the unified graph and compute the predicting rate score for an active user via weighted average of all similar ratings. Various semantics behind edges of the unified graph have different contributions for the prediction. Thus, an adaptive framework is proposed to learn the weights of different semantic edges and products an optimized predicted rating. Finally, experimental studies on various real-world datasets demonstrate that GF is effective to handler the sparsity issue of recommendation and outperforms the state-of-the-art techniques.
TSBuilder 2.0: Improving the Identification Accuracy Due to Synonymy

The paper reviews a project on the automation of term system construction. TSBuilder (Term System Builder) was developed in 2014 as a multilayer Rosenblatt's perceptron for supervised machine learning, namely 1-3 word terms identification in natural language texts and their rigid categorization. In the next version we expanded the range of categorization parameters, removed the term length restriction, used convolution for a continuous sequence of terms, and analyzed the probability of term's falling into different categories.In the current release, we added the use of synonymic matching for single- and multiple-word terms. This allows us to replenish the database with new words close in meaning to the existing ones but having dropped out of our radar for not being cognit to the ones included into the database. Algorithms for translating natural language words into a vector form were used to estimate the degree of synonymy.
Predicting Behavior Trends among Students Based on Personality Traits

With the development of online activities, a remarkable change is observed on human behavior and personality. With the development of machine learning and other statistical techniques, we can define user's personality, preferences and other interests based on their online and daily life activities. In this paper we have wanted to see if machine learning techniques can be applied to predict certain user behavior patterns based on their personality traits. The goal of this paper is to find students preferences on online tutorial vs class lecture and to find particular pattern on students personality traits. We also try to find a student is happy or not based on his personality. From our research we can say that given enough data personality traits can be used to predict other behaviors among humans.
Summarizing Long-Length Videos with GAN-Enhanced Audio/Visual Features

In this paper, we propose a novel supervised method for summarizing long-length videos. Many recent works presented successful results in video summarization. However, most videos in those works are short in duration (~5 minutes), and the methods often break down on very long videos (~30 minutes). Moreover, most works only use visual features, while audios provide useful features for the task. Based on these observations, we present a model that exploits both visual and audio features. To handle long videos, our model also refines the extracted features using adversarial networks. To demonstrate our model, we have collected a new dataset of 63 e-sports (~30 minutes) videos, each accompanied by an editorial summary video that is about 10% in length of the original video. Evaluation on this dataset suggests that our method produces quality summaries for very long videos.
A Deep Learning Framework for Recognizing Vertical Texts in Natural Scene

Text detection and recognition of various text orientation in natural scene images is important to understand the surrounding. Various research has been carried out on detecting horizontal text, oriented text, curved text and vertical text. However, there are limited research carried out on detecting and recognizing vertical scene text. Vertical scene text can be differentiated into top-to-bottom vertical text, bottom-to-top vertical text and horizontal-stacked vertical text. Apart from detecting horizontal text and oriented text, the model proposed in this research detects all types of vertical scene text. In this paper, the modelling of a hybrid vertical scene texts recognition is proposed. The model contains Multidirectional Text Detector (MTD) for detecting text region in the scene image, Two-dimensional Decoder (2DD) for recognizing texts in the text region, while Vertical Oriented Network (VON) is applied to determine the types of vertical texts.
Computational Holographic Image Watermarking Algorithm Based on Discrete Cosine Transform

In order to improve the robustness and invisibility of watermark, a grayscale computing holographic image watermarking algorithm based on discrete cosine transform has been proposed. First, the watermark image is calculated to generate a hologram to improve the security of the watermark. The carrier image is divided into 4×4 blocks, and each block is separately subjected to discrete cosine transform, and the low-frequency coefficients of each block are extracted to form a new matrix.Then, the watermark image is subjected to discrete cosine transform, and embed it into the carrier image according to prescript embedding rules, thereby completing the embedding process of the watermark.The extraction process is the inverse of the embedding process. The obtained image is decomposed into 4×4 blocks, and each block is subjected to discrete cosine transform, and extracted according to the inverse law of the embedding.Through test, the experimental results of the method show that the proposed algorithm has strong robustness in image clipping, rotation, translation and Gaussian noise attack. It ensures that the watermark is hidden while resisting a certain degree of attack to meet the needs of copyright identification.
Converging Security Threats and Attacks Insinuation in Multidisciplinary Machine Learning Applications: A Survey

Security threats and attacks are increasing day by day with an inclination that insinuates most of the multidisciplinary Machine Learning (ML) applications. Artificial Intelligence (AI) is playing a major role in most of these multidisciplinary ML applications which help applications to sample, learn and decide to act wisely depending on the situation. The intelligence required for processing the data and take decisions opens the door towards vulnerabilities. This will take place when it converges with the world of Internet. Design of a secure realm that mitigates the attacks against privacy preservation in multidisciplinary ML applications is of paramount concern. Thus, it is a must to have an understanding of the security threats and attacks that can affect the ML applications or ML models. In this work we have listed various research work that has developed security models to defend the attacks on multidisciplinary ML applications including the traditional or existing ML and deep learning solutions. Then, we have categorized various security threats and attacks against some of the existing multidisciplinary ML applications. Finally, we have listed some defensive strategies that can enrich and complement to build a secure and efficient multidisciplinary ML model or application.
Multi-Tenant Big Data Analytics on AWS Cloud Platform

Big Data analytics is a crucial part of today's internet world and specifically analyzing the data for the multi-tenant systems connected over the internet which produces a huge amount of multi-structured data. This paper describes the multi-tenant data analytics over the serverless cloud platform mainly Amazon Web Service (AWS). We perform big data analytics on the two different types of applications. One produces static data and the other produces real-time dynamic data. Then we analyze the performance of the big data under the constraints of time, data traffic and size of the data.
Network Intrusion Detection Based on Selective Ensemble Learning

In order to improve the generalization performance of ensemble learning under imbalanced datasets, a selective ensemble learning algorithm for imbalanced datasets is proposed in this paper. Firstly, the algorithm resample the majority class samples, constructs multiple training subsets. The training process generates multiple base classifiers using Adaboost algorithm, then select some base classifiers according to maximum correlation and minimum redundancy criteria, and forms sub-classifiers according to weighted integration. Then generates multiple sub-classifiers for multiple training subsets, and then select some sub-classifiers according to maximum correlation and minimum redundancy criteria. Then, the weights of the selected sub-classifiers are calculated by F-means or G-means, and the final classifier is obtained by weighted ensemble. Finally, the improved algorithm is applied to the network anomaly detection with imbalanced data.
Language to Code with Open Source Software

With the development of deep learning, it has been applied in various field of computer science. Generating computer executable code from natural language descriptions is an urgent problem in the artificial intelligence. This paper proposed a solution based on deep learning for code generation. Encoder-Decoder model is used in our method to convert natural language description into target code. Because of the rapid development of information technology, all aspects of software resources have been greatly enriched. The deep learning model we designed takes the natural language description as input and generates the corresponding object code by extracting the code from the open source software library. We collected natural language descriptions of 20 problems that undergraduate students often encounter in their daily programming. Experimental results show that our method is practicable. Our approach also provides a good idea to extract useful code from open resource for code generation.
A Standardised Format for Exchanging User Study Instruments

Increasing re-use in Interactive Information Retrieval (IIR) has been an ongoing aim in IIR for a significant amount of time, however progress has been limited and patchy. While re-use of some study aspects can be difficult due to the varied nature of IIR studies, the use of pre- and post-task self-reported measures is widespread and relatively standardised. Nevertheless, re-use of elements in this area is also limited, in part because systems used to implement them are not able to exchange question, instruments, or complete study setups. To address this, this paper presents a standardised, but extendable, format for IIR survey instrument exchange.
Image Classification on NXP i.MX RT1060 using Ultra-thin MobileNet DNN

Deep Neural Networks play a very significant role in computer vision applications like image classification, object recognition and detection. They have achieved great success in this field but the main obstacles for deploying a DNN model into an Autonomous Driver Assisted System (ADAS) platform are limited memory, constrained resources, and limited power. MobileNet is a very efficient and light DNN model which was developed mainly for embedded and computer vision applications, but researchers still faced many constraints and challenges to deploy the model into resource-constrained microprocessor units. Design Space Exploration of such CNN models can make them more memory efficient and less computationally intensive. We have used the Design Space Exploration technique to modify the baseline MobileNet V1 model and develop an improved version of it. This paper proposes seven modifications on the existing baseline architecture to develop a new and more efficient model. We use Separable Convolution layers, the width multiplier hyperparamater, alter the channel depth and eliminate the layers with the same output shape to reduce the size of the model. We achieve a good overall accuracy by using the Swish activation function, Random Erasing technique and a choosing good optimizer. We call the new model as Ultra-thin MobileNet which has a much smaller size, lesser number of parameters, less average computation time per epoch and negligible overfitting, with a little higher accuracy as compared to the baseline MobileNet V1. Generally, when an attempt is made to make an existing model more compact, the accuracy decreases. But here, there is no trade off between the accuracy and the model size. The proposed model is developed with the intent to make it deployable in a realtime autonomous development platform with limited memory and power and, keeping the size of the model within 5 MB. It could be successfully deployed into NXP i.MX RT1060 ADAS platform due to its small model size of 3.9 MB. It classifies images of different classes in real-time, with an accuracy of more than 90% when it is run on the above-mentioned ADAS platform. We have trained and tested the proposed architecture from scratch on the CIFAR-10 dataset.
Unbiased Conversion of Passive Sensor Measurements

Passive sensor target tracking has the challenge to 3-D tracking applications in that the raw angle-only measurements cannot be converted into Cartesian measurements. Multi-sensor methods can be used to convert the raw measurements into Cartesian measurements by finding the intersection of the two lines of sight. Examples include triangulation, law of sines, and maximum likelihood estimation. However, many of these methods can have an inherent bias present in the conversion as a result of its nonlinearity. In long range applications this can cause significant error. This paper investigates the conversion of passive sensor angle-only measurements into Cartesian coordinates and evaluates the conversion bias present in triangulation.
Empirical Algorithms for General Stochastic Systems with Continuous States and Actions

In this paper, we present Randomized Empirical Value Learning (RAEVL) algorithm for MDPs with continuous state and action spaces. This algorithm combines the ideas of random search over action space with randomized function approximation method to generalize the value functions over state space . Our theoretical analysis is done under a random operator framework combined with stochastic dominance argument. This provides finite-time analysis of the proposed algorithm as well as give the sample complexity.
Protecting Sensitive Location Visits Against Inference Attacks in Trajectory Publishing

With an increasing popularity of Location-Based Services (LBSs), people's trajectories are continuously recorded and collected. The trajectory data are often shared or published for improving user experience, such as personalized recommendations and activity mining. However, releasing the trajectory data makes users' sensitive location visits vulnerable to inference attacks. In this paper, we study the problem of protecting sensitive location visits in the publication of trajectory data, assuming an adversary can do inference attacks using association rules derived from the data. We propose a methodology of anonymizing trajectories employing both generalizations and suppressions, to sanitize the trajectory data and protect sensitive location visits against inference attacks. We design a number of techniques to make our trajectory anonymizing algorithm efficient meanwhile maintaining the utility. We have conducted an empirical study to show that our algorithms can efficiently prevent inference attacks for real datasets while preserving the accuracy of aggregate querying on the published data.
A Great Reduction of WER by Syllable Toneme Prediction for Thai Grapheme to Phoneme Conversion

Thai toneme prediction has been one of the greatest difficulties for Thai grapheme to phoneme conversion (G2P). This paper presents an improvement in the prediction of linguistic features in terms of tone rules. Among these, there will always be exceptions, for example, the tones used in loan words and transliterated words, which are usually adopted from the original language. This paper does not concern itself with the transliteration problem, but aims to show the success of a method which uses an automatic toneme predictor based on the tone rules of Thai pronunciation for the development of a machine learning model. The proposed method attaches a predictor to the final stage of converting a grapheme to a phoneme. Furthermore, this work also explores end-to-end prediction using Long Short Term Memories (LSTM) that takes its input sequence from the National Electronic and Computer Technology Center's Pseudo-Syllable segmentation and alignment tool. An evaluation was conducted to show the success of the proposed system, and also to compare the results with our traditional end-to-end sequence-to-sequence G2P. A comparison of the results shows that sequence-to-sequence modeling obtains the lowest Word Error Rate at 1.6%, and the proposed system works well on a 2018 small device (Raspberry Pi).