Reviewerly/exHarmony huggingface dataset:
Anomaly detection, classification and CEP with ML methods

A compendium of Machine Learning methods was presented in the previous chapter, highlighting their suitability and potential issues in health applications. This chapter analyzes how Machine Learning methods can be used for anomaly detection, classification, and complex event processing. Fundamental concepts, models and techniques are presented. Then, we look at the design of Machine Learning pipelines for medicine applications based on anomaly detection, classification and complex event processing.
Chapter 11. Neuro-Symbolic Semantic Reasoning

Humans have astounding reasoning capabilities. They can learn from very few examples while providing explanations for their decision-making process. In contrast, deep learning techniques–even though robust to noise and very effective in generalizing across several fields including machine vision, natural language understanding, speech recognition, etc. –require large amounts of data and are mostly unable to provide explanations for their decisions. Attaining human-level robust reasoning requires combining sound symbolic reasoning with robust connectionist learning. However, connectionist learning uses low-level representations–such as embeddings–rather than symbolic representations. This challenge constitutes what is referred to as the Neuro-Symbolic gap. A field of study to bridge this gap between the two paradigms has been called neuro-symbolic integration or neuro-symbolic computing. This chapter aims to present approaches that contribute towards bridging the Neuro-Symbolic gap specifically in the Semantic Web field, RDF Schema (RDFS) and EL+ reasoning and to discuss the benefits and shortcomings of neuro-symbolic reasoning.
Optimization of Fuzzy Logic Controller by Using Heuristic Algorithms

There are many design parameters in the structure of fuzzy logic controllers. Conventional methods that don't have a systematic approach are often used in determining of these parameters. However, setting the controller parameters in this way leads to long experiments and this takes a lot of time. For this reason, design parameters of the fuzzy logic controller are usually determined by using heuristic algorithms. Because, heuristic algorithms can offer solutions that are very close to the optimal solution for the problems where exact solution cannot be obtained. In this study, output membership functions of a fuzzy logic controller are optimized using particle swarm optimization and genetic algorithm. Design and optimization stages are explained in detail and results are compared with each other.
A case study: effect of wrist sensor displacement on HAR performance using LSTM and attention mechanism

Loose wearing or self-placement usually causes sensor displacement, which can deteriorate the performance of classifiers in real use. As a case study, this paper focuses on investigating the effect of wrist-worn sensor displacement on human activity recognition. We construct a new HAR dataset from different positions of the wrist. We create a LSTM model and an multi-stage attention model for the evaluation of our three designed scenarios. Experimental results show that the classification accuracies are affected by sensor positions and the worst performance occurs when test data are from a new position for a model. In addition, the results also indicate the superior performance of the attention model on all the scenarios compared with the LSTM model.
Enhanced Text Classification using Proxy Labels and Knowledge Distillation

Text Classification has a variety of applications in the pickup and delivery services industry where customers require one or more items to be picked up from a location and delivered to a certain destination. Categorizing these customer transactions helps understand the market needs and trends while also assisting in building a personalized experience for each customer segment. In this paper, each transaction is accompanied by a free text description provided by the customer to describe the products to be picked up and delivered. These descriptions tend to be short, incoherent and code-mixed (Hindi-English) text.Here, we focus on a specific use-case where each customer transaction can be mapped to a single product category. We propose a cost-effective transaction classification approach based on proxy-labelling and knowledge distillation using the transaction descriptions provided by the customer. We introduce R-ALBERT, a model trained with RoBERTa as the “teacher” and ALBERT (33x fewer parameters than RoBERTa) as the “student”. Further, we benchmark R-ALBERT on a large internal dataset as well as the 20Newsgroup dataset. We see that our model shows a 2% increase in performance with 33x fewer parameters. The model is currently deployed in production and is helping understand the customer behaviour across product categories and customer segments.
Consensus: Proof of Work, Proof of Stake and Structural Alternatives

This chapter examines the future of distributed ledger technology from the perspective of the evolution of consensus mechanisms, discussing which new consensus mechanisms could replace proof of work. These include directed acyclic graphs, proof of stake and proof of burn. For those more mathematically inclined, this part also explores the dynamics of consensus mechanisms from a mathematical perspective.
Improve Quora Question Pair Dataset for Question Similarity Task

Automatic detection of semantically equivalent questions is a task of the utmost importance in a question answering system. The Quora dataset, which was released in the Quora Question Pairs competition organized by Kaggle, has now been used by many researches to train the system in solving the task of identifying duplicate questions. However, the ground truth labels on this dataset are not 100% accurate and may include incorrect labeling. In this paper, we concentrate on improving the quality of the Quora dataset by combining several strategies, basing on Bert, rules, and reassigning labels by humans.
A Training-free Genetic Neural Architecture Search

The so-called neural architecture search (NAS) provides an alternative way to construct a "good neural architecture," which would normally outperform hand-made architectures, for solving complex problems without domain knowledge. However, a critical issue for most of the NAS techniques is in that it is computationally very expensive because several complete/partial training processes are involved in evaluating the goodness of a neural architecture during the process of NAS. To mitigate this problem for evaluating a single neural architecture found by the search algorithm of NAS, we present an efficient NAS in this study, called genetic algorithm and noise immunity for neural architecture search without training (GA-NINASWOT). The genetic algorithm (GA) in the proposed algorithm is used to search for high potential neural architectures while a modified scoring method based on the neural architecture search without training (NASWOT) is used to replace the training process of each neural architecture found by the GA for measuring its quality. To evaluate the performance of GA-NINASWOT, we compared it with several state-of-the-art NAS techniques, which include weight-sharing methods, non-weight-sharing methods, and NASWOT. Simulation results show that GA-NINASWOT outperforms all the other state-of-the-art weight-sharing methods and NASWOT compared in this study in terms of the accuracy and computational time. Moreover, GA-NINASWOT gives a result that is comparable to those found by the non-weight-sharing methods while reducing 99% of the search time.
AI Tool with Active Learning for Detection of Rural Roadside Safety Features

Roadway safety, especially in rural areas, is one of the most critical components in transportation planning. In collaboration with North Carolina Department of Transportation (NCDOT), UNC Highway Safety Research Center (HSRC), and DOT Volpe National Transportation Systems Center, UNC Renaissance Computing Institute (RENCI) developed a roadside feature detection solution leveraging multiple convolutional neural networks. The solution used an iterative active learning (AL) computer vision model training pipeline integrated into an AI tool to detect safety features such as guardrails and utility poles in geographically distributed NC rural roads. We utilized transfer learning by adopting the Xception neural network architecture [1] as the feature extraction backbone which was then used in an iterative AL process supported by a web-based annotation tool. The annotation tool not only allowed for the collection of annotations through an iterative AL process for multiple safety features, it also enabled visual analysis and assessment of model prediction performance in the geospatial context. AL techniques were used to direct human annotators to label images that would most effectively improve the model aimed at minimizing the number of required training labels while maximizing the model’s performance. The iterative AL process combined with a common feature extraction backbone allowed fast model inference on millions of images in the AL sampling space. This enabled a rapid transition between AL rounds while also reducing the computing requirements for each round. Model feature extraction weights were then fine-tuned in the last round of AL to obtain the best accuracy. Since only about 2.7% of 2.6 million unlabeled images in the AL sampling space contain guardrails, there is a significant class imbalance problem that must be addressed in our AL sampling strategies for the guardrail classification model. In this paper, we present our AI tool processing pipeline and methodology and discuss our AL results and future work. Our AI tool can be used to detect roadside safety features and be extended to also locate them for assessing roadside hazards.
Accurate Latent Factor Analysis via Particle Swarm Optimizers

A stochastic-gradient-descent-based Latent Factor Analysis (LFA) model is highly efficient in representative learning of a High-Dimensional and Sparse (HiDS) matrix. Its learning rate adaptation is vital in ensuring its efficiency. Such adaptation can be realized with an evolutionary computing algorithm. However, a resultant model tends to suffer from two issues: a) the pre-mature convergence of the swarm of learning rates as caused by an adopted evolution algorithm, and b) the pre-mature convergence of the LFA model as caused jointly by evolution-based learning rate adaptation and an optimization algorithm. This paper focuses on the methods to address such issues. A Hierarchical Particle-swarm-optimization-incorporated Latent factor analysis (HPL) model with a two-layered structure is proposed, where the first layer pre-trains desired latent factors with a position-transitional particle-swarm-optimization-based LFA model, and the second layer performs latent factor refining with a newly-proposed mini-batch particle swarm optimizer. With such design, an HPL model can well handle the pre-mature convergence, which is supported by the positive experimental results achieved on HiDS matrices from industrial applications.
Comparing Linear Systems with Gaussian Mixture Model Additive Uncertainties Using Kullback-Leibler Rate

In simulation-based testing of complex systems, it is essential to deal with multiple fidelities of subsystem models, which necessitates comparison of such models. While models with Gaussian additive uncertainties can be compared easily, comparison of models with more general probability density functions (pdfs) is more challenging. In this study, we examine the propagation of additive uncertainty with a general pdf, modeled using Gaussian Mixture Models (GMMs), and subsequent comparison of model uncertainties between two models. The proposed approach is based on Kullback Leibler (K-L) rate pseudo metric. We also propose a method based on K-L divergence for pruning the number of GMM components as they are propagated through a linear system, in order to reduce computational cost. We exemplify the proposed approach using two models of a vehicle platoon.
Design, Implementation, and Validation of a Multi-Site Gaming Streaming Service Over a 5G-Enabled Platform

Multi-Media applications are amongst the most demanding services, requiring high amounts of network capacity and computational resources for synchronous audio-visual streaming with high quality. Gaming streaming is one of the most demanding of these media applications. Recent technological advances in the 5G domain, precisely Network Function Virtualization (NFV) and Multi-access Edge Computing (MEC), unlock media industries’ potential by offering high-quality media services through dynamic and efficient resource allocation with low latency. This work presents a multi-site gaming streaming use case implemented over an end-to-end 5G-enabled platform provided by the EU H2020 5G-PPP 5G EVE project. Furthermore, we present the executed use case experiment scenarios to validate the use case performance following a set of defined Quality of Experience(QoE)-related Key Performance Indicators (KPIs). In particular, this paper discusses the design workflow and orchestration of the multi-site gaming streaming use case across two 5G EVE sites (i.e., Spain and Greece), providing a detailed description of the network function applications and resources utilized for the use case. Subsequently, leveraging the 5G EVE monitoring platform, this paper elaborates on the executed experiment scenarios that provide the defined KPI metrics data. Finally, this paper presents, discusses, and analyzes the obtained KPI metrics data results and provides recommendations on future works for these kinds of use cases.
Improved Fluctuation Derived Block Selection Strategy in Pixel Value Ordering Based Reversible Data Hiding

Block selection strategy is an effective technique to improve the imperceptibility in pixel value ordering (PVO) based reversible data hiding by selecting blocks with higher smoothness to embed data at first, which largely reduces invalid shifting of pixels before the maximum capacity is reached. As a means to evaluate the smoothness of a certain block selected, fluctuation value is defined accordingly. However, in current solutions, the definition of fluctuation value is not always accurate enough to reflect the true smoothness of blocks, which is usually restrained by the number of pixels participating in calculation as well as those invalid ones introduced. In this paper, we improved the definition of fluctuation in order to achieve a better performance. Take the maximum side for example, context pixels participating in fluctuation calculation are determined at first, considering the second largest pixel as well as neighborhood of the largest pixel simultaneously. Then those invalid ones are eliminated to reduce unnecessary uncertainty introduced. Experimental results show that our proposed scheme outperforms the state-of-the art in accuracy of smoothness evaluation, and achieves a better imperceptibility as a result.
Applying Multi-layers Feature Fusion in SSD for Detection of Small-Scale Objects

At present, object detection using convolution neural networks occupies a dominant position. However, the structure of the competent neural network faces hereditary difficulties: high-level networks have large receptive areas and meaning information has high capacity to illustrate, but the solution is diminished, the calculations are weak. In grassroots networks there are relatively small acceptable areas, and it has strong geometric detail information representation capability. SSD (single shot multi-box detection) object prediction of multi-level feature uses mAP, through predicting things with large fields of reception and small fields of prediction of conditions, it also uses high-layer facilities to do. Based on the analysis and introduction of classic SSD algorithms, in this paper, we aim to find small-scale complain at a fast run, through presenting an approach which adapts the SSD detector in relation to trading with precision and speed. An MSSD (Modified single shot multi-box detection) procedure of multistage components combination is offered to supply old knowledge in SSD. The VGGl6 and deep residual networks are used by the MSSD. With Residual network, this article uses the FPN-based network architecture to integrate high and low layers and improves the traditionally sampled structure. The high-layer semantic information is integrated into the low-layer network feature information, and the multi-scale feature mAPs for predicting relapse location case and classification work input are enriched to develop the disclosure efficiency. Experimental results show that these two fusion modules obtain better mAP on PASCAL VOC2007 and Logo datasets than base-line SSD, especially on some small objects categories.
Speeding Up LAT: Generating a Linear Approximation Table Using a Bitsliced Implementation

The substitution box (S-box) is one of the major components of cryptographic algorithms. An important issue for cryptographic algorithm designers in ensuring sufficient security from linear cryptanalysis, one of the most powerful attacks, is finding an S-box with a sufficiently low linear spectrum. However, to the best of our knowledge, most of the published S-box analysis tools cannot generate linear approximation tables for large S-boxes, such as 16-bit S-boxes. Even tools that support the generation of 16-bit linear approximation tables using parallel processing, such as <i>Eval16BitSbox</i>, require a long time. We used bitslice, which can efficiently process bitwise operations in parallel by taking advantage of independent operations, for generating a linear approximation table. In this study, the linear approximation table generation method implemented using the element unit operation of the existing S-box was upgraded to a <inline-formula> <tex-math notation="LaTeX">$vector$ </tex-math></inline-formula> unit operation in a bitslice manner. This improved method enabled the immediate creation of tables, even for 16-bit S-boxes. This approach allows cryptographic algorithm designers to consider a wider variety of S-boxes.
Opinion Mining and Analysing Real-Time Tweets Using RapidMiner

Earlier, the social media was only a medium for communication, but as the technology is improving and advancing, social media has become a mode by various industries, e-commerce sites, companies, institutions, etc. to get an insight from their users about how well their business is doing in a particular area and how can they grow in market. Apart from social media, there are other sources like blogs, review sites, data sets, microblogs which help them to extract data. But as the sources are increasing, manual mining of data is not possible; hence, data mining is used. Opinion mining (also known as sentiment analysis, review analysis) being a part of data mining helps to extract, process and then analyse the emotions hidden in the reviews or the data collected. A microblogging site, Twitter, lets its users to express their thoughts and emotions within a character limit and provide an API which helps in extracting real-time tweets. In this paper, opinion mining is explained along with the approaches used and how RapidMiner is used to analyse real-time tweets using Naïve Bayes. The accuracy of Naive Bayes algorithm for 100 tweets came out to be 64.29% with a kappa result of 0.186.
What Will You Tell Me About the Chart? – Automated Description of Charts

An automatic chart description is a very challenging task. There are many more relationships between objects in a chart compared to general computer vision problems. Furthermore, charts have a different specificity to natural-scene pictures, so commonly used methods do not perform well. To tackle these problems, we propose a process consisting of three sub-tasks: (1) chart classification, (2) detection of a chart’s essential elements, and (3) generation of text description.Due to the lack of plot datasets dedicated to the task of generating text, we prepared a new dataset – ChaTa+ which contains real-made figures. Additionally, we have adjusted publicly available FigureQA and PlotQA datasets to our particular tasks and tested our method on them. We compared our results with those of the Adobe team [], which we treated as a benchmark. Finally, we obtained comparable results of the models’ performance, although we trained them on a more complex dataset (semi-synthetic PlotQA) and built a less resource-intensive infrastructure.
Group Feature Information Distillation Network For Single Image Super-resolution

Single image super-resolution has achieved great success in various methods based on convolutional neural network (CNN). For a long period of time, the deep neural network (DNN) has a strong ability to map from low-resolution (LR) images to high-resolution (HR) images, and increasing the network depth can quickly improve performance, so the network is getting deeper and deeper. This also leads to a sharp increase in the computation cost, which restricts the application in many real scenarios. Therefore, research on fast and lightweight models is becoming more and more significant. One of the most advanced methods is information distillation algorithm (IDA). In this paper, based on the IDA, we apply the group convolution strategy to construct a group feature information distillation network (GFIDN). Specifically, the middle features are expanded and then grouped, and the IDA is applied in each group feature multiple times to extract useful features gradually. On the one hand, the distillation result of the previous group feature is fed to the next information distillation module. On the other hand, all distillation results are merged, then the candidate features are summarized according to their importance. We find that the GFIDN can not only enjoy the advantages of lightweight of group convolution and high efficiency of jump connection, but also maintain high model accuracy due to the application of IDA. Extensive experimental results suggest that the parameter scale and forward inference time are greatly reduced.
Speech Command Recognition System using Deep Recurrent Neural Networks

Speech command recognition has become increasingly relevant in the past few years. This study proposes a new speech command recognition system based on the Mel-frequency cepstrum coefficients (MFCC) feature extraction method, a well-established approach for speech feature extraction and deep recurrent neural networks. In this work, we have compared different signal processing techniques such as wavelet packet decomposition (WPD), continuous wavelet transforms (CWT) and empirical mode decomposition (EMD) to decompose speech signals and extract MFCC features to train long short-term memory (LSTM) recurrent neural networks. Comprehensive studies on the performance of different network parameters and different signal processing strategies are presented in this paper. The proposed neural network model trained with MFCC features extracted from signals preprocessed using wavelet packet decomposition has performed better than raw speech data as well as both CWT and EMD preprocessing.
Deep Learning from History

Historical photos of towns and villages contain a great deal of information about the built environment of the past. However, it is difficult to evaluate the information of images that are not labeled or incorrectly labeled or not organized in repositories or collections. In order to make the sheer volume of images that are not tagged with metadata found on the Internet or in institutional archives accessible for research, an automated recognition of the image content, in this case of buildings, is necessary. Computer vision can help to address this problem and enable the identification of historical image content. This article describes how artificial intelligence and crowdsourcing are used to identify buildings in nearly half a million historical images of the city of Amsterdam. It explains how computer science and humanities disciplines are linked together to accomplish this task.