Reviewerly/exHarmony huggingface dataset:
Fundamental Concepts of Machine Learning

Machine Learning algorithms are different from conventional algorithms as they automatically improve through experience. They traditionally accomplish this using data. This chapter gives an overview of the fundamental concepts, including the data structures, learning types, and the different machine learning tasks. Additionally, the gradient descent method is introduced to illustrate how many machine learning algorithms learn through experience. Finally, over- and underfitting are discussed, and strategies to avoid them, such as regularization, are explained.
Impact of Data Pre-Processing Techniques on Deep Learning Based Power Attacks

Power-based side channel attacks (SCAs) are recognized as a powerful type of hardware attacks. Recently, attacks based on deep learning (DL) neural networks have become popular due to their high efficiency. However, even these attacks face problems when sophisticated countermeasures exist. Pre-processing the input data is an effective way to improve the performance of such neural networks. Currently, only limited research has focused on exploring pre-processing techniques for DL-based attacks. In this paper, we propose to the best of our knowledge for the first time the usage of data transformation, data concatenation and stacked auto-encoder (encoder only) as pre-processing methods. Thereafter, we compare them with the existing techniques, namely data augmentation and stacked auto-encoder techniques. Our results show that the data transformation technique achieves the best results from the evaluated methods; it improves the validation accuracy from 75% to 95% and 23% to 26% for the RSA and AES implementations, respectively.
Multi Spatial Relation Detection in Images

Detecting spatial relationships between objects depicted in an image is an important sub-task in vision and language understanding. Its practical use lies in visual discourse when referring to objects by their relationship in context of others and finds application in higher level tasks such as visual question answering and image description generation. Presumably, the selection of spatial prepositions grounded in an image is straightforward. However, in general, human beings either do not always agree or are not consistent when choosing spatial prepositions. This could be due to various reasons, such as near synonyms, overlapping terms and different frames of reference. For these reasons, the automatic detection of spatial relations is a non-trivial multi-label problem. This paper addresses the automatic multi-selection of prepositions. The study is based on the development of a number of machine learning models, namely Nearest Neighbor (NN), k-Means Clustering (kM-C), Agglomerative Hierarchical Clustering (A-HC) and Multi-label Neural Network (ML-NN). The model performances are compared quantitatively using multi-label metrics as well as human evaluations that are independent of the ground truth labels. Additionally, the classification results are used as a basis to carry out an error and qualitative analysis that sheds light on the relative merits of how each model deals with synonymous and overlapping relations, and groups common errors to inform future directions. Furthermore, to gain insight into the merits of multi-label models, a single-label Random Forest (RF) classifier is developed and its results are included in the analysis. Of all multi-label models, the ML-NN exhibits the best overall performance when evaluated on both the dataset ground truth and the independent human evaluations. It, however, suffers from under-generating prepositions, while the rest of the models often generate more prepositions at the expense of precision. The clustering-based methods are also not quite consistent, although they do better than the other models in less frequent spatial configurations that other models struggle with. The results from the single-label RF classifier highlight the usefulness of having a multi-label model. Finally, the error analysis indicates that the majority of errors is due to lack of features that give cues on object position and orientation (object pose), the fixed frame of reference, and the failure to resolve depth in perspective view.
A Theoretical-Empirical Approach to Estimating Sample Complexity of DNNs

This paper focuses on understanding how the generalization error scales with the amount of the training data for deep neural networks (DNNs). Existing techniques in statistical learning theory require a computation of capacity measures, such as VC dimension, to provably bound this error. It is however unclear how to extend these measures to DNNs and therefore the existing analyses are applicable to simple neural networks, which are not used in practice, e.g., linear or shallow (at most two-layer) ones or otherwise multi-layer perceptrons. Moreover many theoretical error bounds are not empirically verifiable. In this paper we derive estimates of the generalization error that hold for deep networks and do not rely on unattainable capacity measures. The enabling technique in our approach hinges on two major assumptions: i) the network achieves zero training error, ii) the probability of making an error on a test point is proportional to the distance between this point and its nearest training point in the feature space and at certain maximal distance (that we call radius) it saturates. Based on these assumptions we estimate the generalization error of DNNs. The obtained estimate scales as $\mathcal{O}\left( {\frac{1}{{\delta {N^{1/d}}}}} \right)$, where N is the size of the training data, and is parameterized by two quantities, the effective dimensionality of the data as perceived by the network (d) and the aforementioned radius (δ), both of which we find empirically. We show that our estimates match with the experimentally-obtained behavior of the error on multiple learning tasks using benchmark data-sets and realistic models. Estimating training data requirements is essential for deployment of safety critical applications such as autonomous driving, medical diagnostics etc. Furthermore, collecting and annotating training data requires a huge amount of financial, computational and human resources. Our empirical estimates will help to efficiently allocate resources.
Utilizing Topic Modelling To Identify Abusive Comments On YouTube

Online video platforms such as YouTube was once regarded as a haven for entertainment, educational, and promotional purposes. Now they have become a breeding ground for spreading toxic behavior, radicalizing content, and political propaganda. We have used YouTube data API v3 to scrape several data related to youtube videos like videos URL, description, view count, and comment information like commenters, comments, and replies. We investigated some hot topics which are prone to abusive comments. For example, bullies are highly existent in racial, teenage lifestyle and appearance, LGBTQ topics, or targeted mainly towards women and girls. We randomly selected a couple of videos from these YouTube channels and used them to research and analyze this project. We have used LDA (latent Dirichlet allocation) to identify dominant topics in a particular uploader’s comment section. We have also used the TextBlob library of python for determining the polarity and subjectivity of comments for each uploader.
Turbulent-degraded image restoration via improved principal component analysis method

It is challenging to removing atmospheric turbulence-induced geometric distortion and blurry degradation simultaneously. The improved principal component analysis (i-PCA) method is proposed in this paper. The image set selection strategy is implemented by the Euclidean distance between each sub-module image and its corresponding reference image to remove large geometric deformations and blurs directly. The eigenvector representing the original image data is uniquely determined by combining the maximum correlation with the original image and the maximum variance of the corresponding principal component for resolving the direction issue of the eigenvector. The algorithm is tested using synthetic and real turbulence-distorted images. The experimental results show that the i-PCA method proposed can effectively alleviate the atmospheric turbulence effects and significantly enhance visual quality, promising for its application to turbulence-distorted image.
Named-entity recognition method of key population information based on improved BiLSTM-CRF model

Key population control is related to national security and social stability. Aiming at the difficulty of extracting key population information entities, this paper proposes a sequence labeling method based on dynamic word vectors. Replace the Word2vec pre-training model with the BERT model, which strengthens the feature extraction capabilities of the traditional entity extraction model, and more fully describes the multiple semantic and syntactic information of words. The improvement ideas for the BiLSTM-CRF model are as follows: Embed the BERT model upstream of the model, which is responsible for converting the original corpus into a dynamic vectorized representation, and the trained word vector is input into the BiLSTM layer for semantic encoding, and further mining the semantic related features of the entity context, and finally, the CRF layer outputs the sequence label with the maximum probability. After training with key population information as a data set, the F1 value of the model reached 0.90.
Continuity and Changing Characteristics of Traditional Commodity Specific Markets in Varanasi City

Varanasi city, situated in the Middle Ganga plains in India, has been an important trading centre and economic hub of the region since medieval times. A vital element of the economic landscape of the city is the continuing existence of several specialised traditional market centres, locally known as mandis. Some of these mandis are still thriving despite the corporatization of the market systems. Specialised in specific products, such as milk, flower, betel leaves, grains, brass utensils, birds, coconut and marriage items (bangles), idols, iron equipments, religious books, spices, beeds, etc., these mandis mirror the social and cultural specificity of the city and its surroundings. The transaction and trade of the commodities sustain not only the unique skills of people but also the cultural practices of the region. These mandis constitute distinct spatial elements of the city landscape and add to its heritage value. Generally, people belonging to specific castes and communities dominate the mandis offering specialised products and services. Hence, the location and distribution of these traditional commodity specific markets have a robust areal association with social and cultural geographies of the city. The present study maps the spatial distribution of these markets in the city in the context of the changing social and cultural realities. With the help of observation and interviews of key persons, it attempts to locate the forces of continuity and change in these mandis. It contemplates as how these mandis will negotiate and adapt to revolutionary changes in terms of corporatization of market, new marketing strategies and spatial re-organisation of the city.
Making Up the Difference: Using Custom Reporting to Identify Metadata Inaccuracies in Link Resolver Serial Metadata

Accurate title and holding level metadata in a library’s link resolver knowledgebase is essential to providing access to ejournal entitlements. As a result of their complex title histories, publisher submitted ejournal metadata often needs to be manually updated in the link resolver knowledgebase to ensure title changes and coverage data align. This article will demonstrate how a custom report used at the University of Alberta Library identifies date discrepancies between link resolver and catalog data to drive link resolver knowledgebase clean up work for priority ejournal collections. It will end by highlighting how quality metadata produced by the library community might be leveraged by vendors to improve ejournal holding data globally in their link resolver knowledgebases.
Movie Posters’ Classification into Multiple Genres

Our project intends to classify movies into the three most probable genres that they belong to, from a predefined set of 25 genres, based on only one image i.e the movie poster. We have made use of Convolutional Neural Networks (CNN) to realize this project as we believe it would be of help to extract the features and visual information from the image. Instead of a multi-class classification problem in which the input is classified into any one class, this project would be more correctly described as a multilabel classification problem as a movie belongs to more than one genre. In this project we see a comparative study of different architectures and tune them to yield the best result based on the metric of accuracy. We have applied various techniques such as data augmentation and L2 regularization to comparatively deduce the model that performs best from all the tested models.
Reality based skills development approach in welding technology: An overview

One of the important technologies of Industry 4.0 is digitalization. By 2022, the industrial sector may spend 27% on AR/ VR solutions. Augmented Reality (AR) is a fast growing technology and has various applications in the field of academic studies, sports, medical, military etc. The Virtual Reality (VR) simulator is appropriate training programs for welding skills development and created a new tool to achieve the requirement of higher efficient skill training programs for hazardous environments. Application of AR/VR permits the reduction in time, materials, and cost in comparison with traditional training methods. Use of AR/VR already revealed a reduction of 50% in major energy resources. Its application also reduces pollutants and NO2 apart from reduction of noise pollution, AR technology consists of a software for the welding source simulation, positioning system and plate with base materials and a welding helmet called HMD (head mounted displays) coupled with the welding torch
Enhancing the Visual Efficiency for communicating students’ personal relevant information

During instruction, using information that is directly relevant to students within a lesson creates a more personalized lesson that may better resonate with the students. However, in a class, each student has highly varied individual experiences and it is untenable for the teacher to remember all of the experiences. Furthermore, teaching already involves substantial multitasking that may be quite cognitively overwhelming, even with non-personalized lessons. Therefore, providing support to the teacher to incorporate students’ individual experiences is crucial to enable truly personalized teaching. We propose that visualizing students’ individual experiences for the teacher during instruction may help him or her adapt the lesson in real-time to the students in the class. This paper investigates the kind of visualization that would enable a teacher to uptake a student’s experiences the most effectively. We conducted a within-subjects study to assess the impact that three forms of visualization (plain text, graphical mind map, and story illustration) would have on visual efficiency during a hypothetical teaching scenario. Visual efficiency was measured in terms of response accuracy, response time, and mental effort. Results suggested that visual efficiency can be improved by using proper visualization design. We discuss the implications for supporting real-time personalized instruction.
H5OINA: Oxford Instruments’ data exchange file format for microanalysis

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Quad target method: optical simulation to model the step height and projected fringe frequency

High-quality imaging is typically dependent upon well-focused optical systems; however, precise focusing can be difficult due to the non-linearity of defocus. This paper presents further development of the quad target method (QTM), an alignment technique to linearize defocus for structured light illumination (SLI) systems, by creating a method to simulate the alignment sensitivity. QTM’s sensitivity is the slope of the regression that linearizes defocus and is dependent upon the step height of the quad target and the frequency of the projected fringes. The presented simulation method utilizes the native optical design to faithfully model the residual aberrations and is able to predict the best focus location as a well as the linearized slope. The predicted slope and best focus were compared to experiment using a commercially-available SLI system. For a step height of 500 μm and projected fringe frequency of 6.8 cy/mm, the simulation method predicted the slope to within 5.4% and best focus to 17.7 μm of the measured values.
Image Captioning Using Deep Learning

The task of image caption generator is mainly about extracting the features and ongoings of an image and generating human-readable captions that translate the features of the objects in the image. The contents of an image can be described by having knowledge about natural language processing and computer vision. The features can be extracted using convolution neural networks which makes use of transfer learning to implement the exception model. It stands for extreme inception, which has a feature extraction base with 36 convolution layers. This shows accurate results when compared with the other CNNs. Recurrent neural networks are used for describing the image and to generate accurate sentences. The feature vector that is extracted by using the CNN is fed to the LSTM. The Flicker 8k dataset is used to train the network in which the data is labeled properly. The model will be able to generate accurate captions that nearly describe the activities carried in the image when an input image is given to it. Further, the authors use the BLEU scores to validate the model.
Semi-supervised Semantic Segmentation Network based on Knowledge Distillation

Semantic segmentation of complex traffic scenes is a challenging research topic in the field of computer vision. In order to reduce the dependence of the segmentation model on the pixel-level annotation data of traffic scenes, we propose a semi-supervised semantic segmentation algorithm model based on knowledge distillation. The self-correcting module is used to iteratively optimize the weakly labeled data and generate pseudo-labels. The collaborative learning of multiple students enhances the ability of students to accept potential knowledge online. The proposed method uses the knowledge distillation structure of the teacher-student network to transfer semantic structured information. It solves the problem of insufficient fine label samples in the Cityscapes dataset. The network performance obtained by training with the original label data combined with the pseudo label data can be further improved.
A novel navigation system for an autonomous mobile robot in an uncertain environment

Abstract In this paper, we developed a new navigation system, called ATCM, which detects obstacles in a sliding window with an adaptive threshold clustering algorithm, classifies the detected obstacles with a decision tree, heuristically predicts potential collision and finds optimal path with a simplified Morphin algorithm. This system has the merits of optimal free-collision path, small memory size and less computing complexity, compared with the state of the arts in robot navigation. The modular design of 6-steps navigation provides a holistic methodology to implement and verify the performance of a robot’s navigation system. The experiments on simulation and a physical robot for the eight scenarios demonstrate that the robot can effectively and efficiently avoid potential collisions with any static or dynamic obstacles in its surrounding environment. Compared with the particle swarm optimisation, the dynamic window approach and the traditional Morphin algorithm for the autonomous navigation of a mobile robot in a static environment, ATCM achieved the shortest path with higher efficiency.
Inverted Pendulum Control with a Robotic Arm using Deep Reinforcement Learning

Inverted pendulum control is a benchmark control problem that researchers have used to test the new control strategies over the past 50 years. Deep Reinforcement Learning Algorithm is used recently on the inverted pendulum on a straightforward form. The inverted pendulum had only one degree of freedom and was moving on a plane. This paper demonstrates a successful implementation of a deep reinforcement learning algorithm on an inverted pendulum that rotates freely on a spherical joint with an industrial 6 degrees freedom robot arm. This research used the Deep Reinforcement Learning algorithm in Robot Operating System (ROS) and Gazebo Simulation. Experimental results show that the proposed method achieved promising outputs and reaches the control objectives. We were able to control the inverted pendulum upward for 30 and 20 seconds in two case studies. Two other significant novelties in this research are using an inertial measurement unit (IMU) on the tip of the pendulum, that will facilitate implementation on the real robot for future work and different reward functions in comparing to past publications that enable continuous learning and mastering control in a vertical position
gaCNN: Composing CNNs and GAs to Build an Optimized Hybrid Classification Architecture

Convolutional Neural Networks (CNN) are considered the gold standard for Computer Vision Problems. However, finding the best architecture for CNN often requires handcrafted design and domain knowledge. On the other hand, Genetic Algorithms (GAs) have proven to be an efficient technique to optimize a wide range of problems. Thus, in this paper, we propose the gaCNN, a hybrid classification architecture composed of a CNN and a GA. The gaCNN utilizes heterogeneous activation functions to classify images, optimizing its hyperparameters and activation functions automatically, regardless of the analyzed dataset. The results show that gaCNN is able to identify good architectures. For the Fashion MNIST dataset, the gaCNN obtained, as best accuracy, 93.73%, better than 9 of 13 compared classifiers. For the MNIST Handwritten dataset, the gaCNN obtained, as best accuracy, 99.14%, better than 12 of 16 classifiers.
Sustainable society with a touchless solution using UbiMouse under the pandemic of COVID-19

This paper introduces a new artificial intelligence software which is capable of controlling devices using fingers in the air. With Ubimouse, touch-panels, restaurant ordering systems, ATM systems, and etc., which are commonly used by various people in public, can be contact-less devices. These touch-less devices, especially under the harsh conditions of COVID-19, are desired to prevent infections mediated by touch devices. Also, these devices cannot be used while wearing gloves due to their touch sensing fault. Thus, in fields using gloves, there is a demand for non-contact device operation. To satisfy these demands, we have developed "UbiMouse". This is an AI software that allows you to operate the device by moving your fingers toward the device. In the AIs in UbiMouse, a convolution model and a regression model are used to identify fingers' features from camera footage and to estimates the position of a detected finger, respectively. We demonstrate an operation of UbiMouse without contact. Along with this operation in the air, a mouse cursor is guided to the specified location with high accuracy.