Reviewerly/exHarmony huggingface dataset:
Distributed load balancing frequent colossal closed itemset mining algorithm for high dimensional dataset

The focus of extracting colossal closed itemsets from high dimensional biological datasets has been great in recent times. A massive set of short and average sized mined itemsets do not confine complete and valuable information for decision making. But, the traditional itemset mining algorithms expend a gigantic measure of time in mining a massive set of short and average sized itemsets. The greater interest of research in the field of bioinformatics and the abundant data across the variety of domains paved the way for the generation of the high dimensional dataset. These datasets are depicted by an extensive number of features and a smaller number of rows. Colossal closed itemsets are very significant for numerous applications including the field of bioinformatics and are influential during the decision making. Extracting a huge amount of information and knowledge from the high dimensional dataset is a nontrivial task. The existing colossal closed itemsets mining algorithms for the high dimensional dataset are sequential and computationally expensive. Distributed and parallel computing is a good strategy to overcome the inefficiency of the existing sequential algorithm. Balanced Distributed Parallel Frequent Colossal Closed Itemset Mining (BDPFCCIM) algorithm is designed for high dimensional datasets. An efficient closeness checking method to check the closeness of the rowset and an efficient pruning strategy to snip the row enumeration mining search space is enclosed with the proposed BDPFCCIM algorithm. The proposed BDPFCCIM algorithm is the first distributed load balancing algorithm to mine frequent colossal closed itemsets from high dimensional biological datasets. The experimental results demonstrate the efficient performance of the proposed BDPFCCIM algorithm in comparison with the state-of-the-art algorithms.
Fuzzy Logic based Hybrid Model for Automatic Extractive Text Summarization

In the contemporary age of information, accessing data becomes easy, but finding knowledge is very difficult. The participation & publishing of information has consequently escalated the suffering of 'Information Glut.' Assisting users' informational searches with reduced reading or surfing time by extracting and evaluating accurate, authentic & relevant information are the primary concerns in the present milieu. Automatic text summarization condenses an original document into a shorter form to create a smaller, compact version from the abundant information that is available, preserving the content & meaning such that it meets the needs of the user. Though many summarization techniques have been proposed, there are no 'silver bullets' to achieve the superlative results as of human-generated summaries. Fuzzy Logic has appeared as a robust theoretical framework for studying human reasoning. A new hybrid model based on fuzzy logic has been proposed using two graph-based techniques named TextRank and LexRank and one semantic-based technique named Latent semantic analysis (LSA). The techniques are evaluated on the Opinosis dataset using 'ROUGE-1' (Recall-Oriented Understudy for Gisting Evaluation-1) and 'time to extract the keywords.' The proposed technique has outperformed the existing techniques when compared with the results given by the original studies.
Supporting Home Quarantine with Smart Speakers

The outbreak of COVID-19 has been lead many countries to ask their citizens to home quarantine. Smart speakers, like the Google Home or Amazon Alexa, that continuously listen for voice commands, can potentially be used to help in monitoring adherence to quarantine rules. This paper proposes a monitoring solution to count the number of occupants in a home. Our technique is sufficiently lightweight to avoid using backend cloud computing facility so as to protect privacy, and is robust against common background noises such as television. To evaluate the performance of our technique, we perform experiments on real-world traces recorded from five different families. Our results show that the proposed algorithm is able to count the family member reliably across different home environments.
Spatial-Temporal Omni-Scale Feature Learning for Person Re-Identification

State-of-the-art person re-identification (ReID) models use Convolutional Neural Networks (CNN) for feature extraction and comparison. Often these models fail to recognize all the intra- and inter-class variations that emerge in person ReID, making it harder to discriminate between data subjects. In this paper we seek to reduce these problems and improve performance by combining two state-of-the-art models. We use the Omni-Scale Network (OSNet) as our CNN to test the Market1501 and DukeMTMC-ReID datasets for person ReID. To fully utilize the potential of these datasets, we apply the spatialtemporal constraint which extracts the camera ID and timestamp from each image to form a distribution. We combine these two methods to create a hybrid model titled Spatial-Temporal OmniScale Network (st-OSNet). Our model attains a Rank-1 (R1) accuracy of 98.2% and mean average precision (mAP) of 92.7% for the Market1501 dataset. For the DukeMTMC-reID dataset our model achieves 94.3% R1 and 86.1% mAP, hereby surpassing the results of OSNet by a large margin for both datasets (94.3%, 86.4%, 88.4%, 76.1%, respectively).
An Enterprise Transformation Guide for the Inevitable Blockchain Disruption

Blockchain technology presents significant potential along with risk. The blockchain technology transformation framework informs decision makers on how blockchain fits in their processes, what data will be in the transactions, and who the participants will be, allowing users to lower risks and increase their chances for a successful blockchain solution.
Object Detection for Unseen Domains while Reducing Response Time using Knowledge Transfer in Multimedia Event Processing

Event recognition is among one of the popular areas of smart cities that has attracted great attention for researchers. Since Internet of Things (IoT) is mainly focused on scalar data events, research is shifting towards the Internet of Multimedia Things (IoMT) and is still in infancy. Presently multimedia event-based solutions provide low response-time, but they are domain-specific and can handle only familiar classes (bounded vocabulary). However multiple applications within smart cities may require processing of numerous familiar as well as unseen concepts (unbounded vocabulary) in the form of subscriptions. Deep neural network-based techniques are popular for image recognition, but have the limitation of training of classifiers for unseen concepts as well as the requirement of annotated bounding boxes with images. In this work, we explore the problem of training of classifiers for unseen/unknown classes while reducing response-time of multimedia event processing (specifically object detection). We proposed two domain adaptation based models while leveraging Transfer Learning (TL) and Large Scale Detection through Adaptation (LSDA). The preliminary results show that proposed framework can achieve 0.5 mAP (mean Average Precision) within 30 min of response-time for unseen concepts. We expect to improve it further using modified LSDA while applying fastest classification (MobileNet) and detection (YOLOv3) network, along with elimination of requirement of annotated bounding boxes.
Research on the construction and optimization of distributed Moodle course platform

Abstract The distributed Moodle course platform, a teaching platform of computer technology, has a positive driving force for the improvement of the teaching efficiency. To promote Chinese teaching in colleges and universities for the application of technology, the paper has summarized the theory of building a Moodle course platform. The platform for the curriculum system was constructed and applied in the process of teaching practice. Teaching effectiveness showed that students' performance was significantly improved after using the Moodle course platform, so the learning mode based on the Moodle course learning platform has strong feasibility. The purpose of this study is to provide reference and theoretical support for the improvement of the Chinese teaching model.
Incorporating Semantic Knowledge for Visual Lifelog Activity Recognition

The advance in wearable technology has made lifelogging more feasible and more popular. Visual lifelogs collected by wearable cameras capture every single detail of individual's life experience, offering a promising data source for deeper lifestyle analysis and better memory recall assistance. However, building a system for organizing and accessing visual lifelogs is a challenging task due to the semantic gap between visual data and semantic descriptions of life events. In this paper, we introduce semantic knowledge to reduce such a semantic gap for daily activity recognition and lifestyle understanding. We incorporate the semantic knowledge derived from external resources to enrich the training data for the proposed supervised learning model. Experimental results show that incorporating external semantic knowledge is beneficial for improving the performance of recognizing life events.
Survey Paper on Sentiment Analysis for Tourist Reviews

Sentiment Analysis is done by gathering the data, analyzing the text and aggregating the words in the text. The main focus of this survey is on the evaluation methods used and their respective core solutions. In this paper we are going to study the aspect based sentiment classification, which is classified into three. Those are Implicit, Explicit and Coreferential aspect based sentiment classification. In this paper we are also going to elaborate the polarity of the reviews into positive and negative sentiments. Aspect Based Classification not only promises to suppress the noisy data but also provides high efficiency in identifying the aspects with an addition of performing classification task with higher accuracy. This accuracy can be achieved by using Neural Network algorithms.
A new digital signature scheme based on the hardness of some expanded root problems

This paper presents a new digital signature scheme which is based on the hardness of solving root problem and some expanded root problems on Zp. The expanded root problem is a new form of difficult problem without efficient solution, also originally proposed and applied to build digital signature algorithms. This proposed scheme enable to build a high-security digital signature platform for practical applications.
Towards Intelligent Control via Genetic Programming

In this paper an initial approach to Intelligent Control (IC) using Genetic Programming (GP) for access to space applications is presented. GP can be employed successfully to design a controller even for complex systems, where classical controllers fail because of the high nonlinearity of the systems. The main property of GP, that is its ability to autonomously create explicit mathematical equations starting from a very poor knowledge of the considered plant, or just data, can be exploited for a vast range of applications. Here, GP has been used to design the control law in an Intelligent Control framework for a modified version of the Goddard Rocket problem in 3 different failure scenarios, where the approach to IC consists in an online re-evaluation of the control law using GP when a considerably big change in the environment or in the plant happens. The presented results are then used to highlight the potential benefits of the method, as well as aspects that will need further developments.
SHAPed Automated Essay Scoring: Explaining Writing Features’ Contributions to English Writing Organization

This study applies the state of the art in explainable AI techniques to shed light on the automated essay scoring (AES) process. By means of linear regression and Shapley values, SHAP (Shapley Additive Explanations) approximates a complex AES predictive model implemented as a deep neural network and an ensemble regression. This study delves into the essentials of the automated assessment of ‘organization’, a key rubric in writing. Specifically, it explores whether the organization and connections between ideas and/or events are clear and logically sequenced. Built on findings from previous work, this paper, in addition to improving the generalizability and interpretability of the AES model, highlights the means to identify important ‘writing features’ (both global and local) and hint at the best ranges of feature values. By associating ‘organization’ with ‘writing features’, it provides a mechanism to hypothesize causal relationships among variables and shape machine-learned formative feedback in human-friendly terms for the consumption of teachers and students. Finally, it offers an in-depth discussion on linguistic aspects implied by the findings.
Towards Verifiable Specifications for Neural Networks in Autonomous Driving

Autonomous driving functions increasingly incorporate machine learning methods for perception, environment modeling and decision making. The ever-present danger of unintended behavior of neural networks has given cause for concern in safety-critical applications and has spurred research towards the establishment of certified properties. In our work we present a general approach for exploring guaranteed properties of neural networks through a specific use case. We first methodically specified several desired properties for an ideal traffic sign classifier. Then we applied a mixed integer linear programming verification method to certify (or conversely, falsify) that our pre-trained rectified linear unit (ReLU) activation network will satisfy given properties under all conditions. We established through relaxations of our initial specification that our model does in fact happen to guarantee certain (but not all) properties at a different threshold. We thus showed that it is possible to establish the degree to which non-trivial and humanly meaningful properties are inherently guaranteed by an arbitrary pixel space ReLU network. We believe this approach can be a helpful addition in comprehensively evaluating the reliability of a network via quantifying its degree of compliance to an ideal specification.
A framework for dependency estimation in heterogeneous data streams

Abstract Estimating dependencies from data is a fundamental task of Knowledge Discovery. Identifying the relevant variables leads to a better understanding of data and improves both the runtime and the outcomes of downstream Data Mining tasks. Dependency estimation from static numerical data has received much attention. However, real-world data often occurs as heterogeneous data streams: On the one hand, data is collected online and is virtually infinite. On the other hand, the various components of a stream may be of different types, e.g., numerical, ordinal or categorical. For this setting, we propose Monte Carlo Dependency Estimation (MCDE), a framework that quantifies multivariate dependency as the average statistical discrepancy between marginal and conditional distributions, via Monte Carlo simulations. MCDE handles heterogeneity by leveraging three statistical tests: the Mann–Whitney U, the Kolmogorov–Smirnov and the Chi-Squared test. We demonstrate that MCDE goes beyond the state of the art regarding dependency estimation by meeting a broad set of requirements. Finally, we show with a real-world use case that MCDE can discover useful patterns in heterogeneous data streams.
The Sibling Neural Estimator: Improving Iterative Image Decoding with Gradient Communication

For lossy image compression, we develop a neural-based system which learns a nonlinear estimator for decoding from quantized representations. The system links two recurrent networks that "help" each other reconstruct the same target image patches using complementary portions of the spatial context, communicating with each other via gradient signals. This dual agent system builds upon prior work that proposed an iterative refinement algorithm for recurrent neural network (RNN) based decoding. Our approach works with any neural or non-neural encoder. Our system progressively reduces image patch reconstruction error over a fixed number of steps. Experiments with variations of RNN memory cells show that our system consistently creates lower distortion images of higher perceptual quality compared to other approaches. Specifically, on the Kodak Lossless True Color Image Suite, we observe gains of 1:64 decibel (dB) over JPEG, a 1:46 dB over JPEG2000, a 1:34 dB over the GOOG neural baseline, 0:36 over E2E (a modern competitive neural compression model), and 0:37 over a single iterative neural decoder.
COMPARATIVE STUDY OF TWO DIVIDE AND CONQUER SORTING ALGORITHMS: QUICKSORT AND MERGESORT

Divide and Conquer is a well-known technique for designing algorithms. Many of the existing algorithms are a product of this popular algorithm design technique. Such include Quick sort and Merge sort sorting algorithms. These two algorithms have been widely employed for sorting, however, determining the most efficient among the two has always been a contentious issue. Most of the existing literature have compared these algorithms using machine dependent factors such as computational complexity but few have employed machine independent factors such as internal/external sorting, algorithm complexity: best, average, and worst cases, memory usage, stability etc. This study intends to contribute to this discuss using both machine dependent and independent factors. The implementation was carried out in MATLAB programming environment and the internal system clock was set to keep track of the time required for sorting. Results obtained revealed that in terms of computational speed using array of small sizes, Quick sort algorithm is faster, though Merge sort algorithm is faster with array of large sizes. Also, both algorithms are of O(nlogn) best case and average case complexity while the worst case for quicksort is O(n2) and that of merge sort remains unchanged. In terms of stability, Quick sort is stable while Merge sort is not. Despite the excellent performance of Merge sort algorithm, the need for an auxiliary memory for sorting makes it less preferable than Quick sort algorithm for applications where a good cache locality is of paramount importance.
Secure Similarity Search Over Encrypted Non-Uniform Datasets

Searchable symmetric encryption (SSE) enables a user to outsource a private dataset to a cloud server in encrypted form while retaining the ability to search over the encrypted outsourced data. The existing SSE schemes improve the search and safety performances from different perspectives. However, almost none of the existing SSE schemes considers the data distribution issues. We find that when the dataset is not distributed uniformly, the search quality based on the conventional methods decreases. Therefore, the existing SSE schemes cannot guarantee high search quality when faced with non-uniform datasets. In addition, most existing SSE solutions cannot hide the distribution of the query set. In this article, we design a S\ecure similarity search over Encrypted Non-uniform and high-dimensional Datasets (SEND) with a novel way to enhance security. The basic idea is to combine SSE with locality-sensitive hashing (LSH). Unlike earlier schemes, SEND uses selective hashing, which has better performance for non-uniform datasets. Also, we present a novel approach to hide the distribution of the query set, which makes SEND more secure. Our experimental results indicate SEND achieves a high search quality of recall and precision, and it is proven secure against adaptively chosen query attacks in the standard model.
Anomaly Detection for Internet of Things Based on Compressed Sensing and Online Extreme Learning Machine Autoencoder

Abstract Abnormal events refer to specific events, such as forest fire, occurring in the wireless sensor networks of the Internet of Things (IoT), whose behaviors are quite different from normal events. By learning the underlying structure of sensor data, users can be helped to learn about the occurrence of these events as soon as possible. Due to the large number of sensors in the IoT and the periodic collections of data, sensor data has the problems of high dimensions and large amount, and the transmission of a large amount of data in the network is not a small challenge for bandwidth. In addition, the sensor data is unlabeled, so it is time-consuming and unrealistic to manually label all the data. Abnormal events in the IoT require low delay, such as gas concentration monitoring, In the IoT, data is generated continuously, so a well-trained model cannot remain unchanged, and features of new data need to be continuously learned. On account of the limitation of hardware, edge nodes cannot undertake the complicated and time-consuming task of model training and detection. None of the existing algorithms can meet the above requirements well, so this paper proposed an algorithm based on Compressed Sensing and Online Extreme Learning Machine Autoencoder named COELMAE. The proposed algorithm can carry out anomaly detection in low delay, unsupervised and online learning, and also has low computational complexity. What’s more, the algorithm can reduce the amount of data transferred about 60%.
Explaining the Neural Network: A Case Study to Model the Incidence of Cervical Cancer

Neural networks are frequently applied to medical data. We describe how complex and imbalanced data can be modelled with simple but accurate neural networks that are transparent to the user. In the case of a data set on cervical cancer with 753 observations excluding, missing values, and 32 covariates, with a prevalence of 73 cases (9.69%), we explain how model selection can be applied to the Multi-Layer Perceptron (MLP) by deriving a representation using a General Additive Neural Network. The model achieves an AUROC of 0.621 CI [0.519,0.721] for predicting positive diagnosis with Schiller’s test. This is comparable with the performance obtained by a deep learning network with an AUROC of 0.667 [1]. Instead of using all covariates, the Partial Response Network (PRN) involves just 2 variables, namely the number of years on Hormonal Contraceptives and the number of years using IUD, in a fully explained model. This is consistent with an additive non-linear statistical approach, the Sparse Additive Model [2] which estimates non-linear components in a logistic regression classifier using the backfitting algorithm applied to an ANOVA functional expansion. This paper shows how the PRN, applied to a challenging classification task, can provide insights into the influential variables, in this case correlated with incidence of cervical cancer, so reducing the number of unnecessary variables to be collected for screening. It does so by exploiting the efficiency of sparse statistical models to select features from an ANOVA decomposition of the MLP, in the process deriving a fully interpretable model.
From Truth Degree Comparison Games to Sequents-of-Relations Calculi for Gödel Logic

We introduce a game for (extended) Gödel logic where the players’ interaction stepwise reduces claims about the relative order of truth degrees of complex formulas to atomic truth comparison claims. Using the concept of disjunctive game states this semantic game is lifted to a provability game, where winning strategies correspond to proofs in a sequents-of-relations calculus.