Reviewerly/exHarmony huggingface dataset:
Nowcasting Methods For Optimising Building Performance

In meteorology term, nowcasting is weather forecasting for the next few minutes to six hours using all immediately available weather data.It is a relatively new subject, which often involves remote sensing, numerical weather prediction models, and advanced data communication infrastructure.High-quality weather nowcasting is crucial for optimising building performance in the near future.A range of nowcasting techniques has been used for such purposes.It includes statistical, machine learning, Numerical Weather Prediction (NWP), top-down and bottom-up approaches.This paper firstly reviews the advantages and disadvantages of common nowcasting methods with the focus on solar radiation nowcasting.Based on the review, popular methods have been classified into five categories.Authors then investigated further the nowcasting data provided by weather Application Programming Interfaces (APIs) that is backed by Numerical Weather Prediction.This is due to its large-scale application potential and the significances in the most recent update on solar radiation nowcast.Secondly, the paper explores the implications of applying weather nowcasting to dynamic building simulations, most importantly, examining its impact on the accuracy of indoor temperature prediction for free float buildings, heating load prediction and heating energy for heated buildings.The study used three buildings from BESTEST ANSI/ASHRAE Standard 140-2014 as the case studies.The results show that the most recent update of weather API includes meaningful solar radiation prediction.If the building does not have a large south facing glazing, the indoor temperature and heating load predictions from dynamic models are reasonably accurate.
Semi-supervised Text Style Transfer: Cross Projection in Latent Space

Text style transfer task requires the model to transfer a sentence of one style to another style while retaining its original content meaning, which is a challenging problem that has long suffered from the shortage of parallel data. In this paper, we first propose a semi-supervised text style transfer model that combines the small-scale parallel data with the large-scale nonparallel data. With these two types of training data, we introduce a projection function between the latent space of different styles and design two constraints to train it. We also introduce two other simple but effective semi-supervised methods to compare with. To evaluate the performance of the proposed methods, we build and release a novel style transfer dataset that alters sentences between the style of ancient Chinese poem and the modern Chinese.
Scene Matching for Infrared and Visible Images with Compressive Sensing SIFT Feature Representation in Bandelet Domain

Aimed at scene matching problem for taking infrared image as the actual data and the visible image as the referenced data, a multi-resolution matching algorithm which fuses compressive sensing Scale Invariant Feature Transform (SIFT) feature is presented based on Bandelet transform. Two kinds of images are separately transformed into Bandelet domain to compress the feature search space of scene matching based on the best sparse representation of natural images by Bandelet transform. On the basis of adaptive Bayes threshold denoising for infrared image, the concept of sparse feature representation of compressive sensing theory is introduced into SIFT algorithm. For low-frequency image in Bandelet domain, high-dimensional SIFT key point feature description vector is projected on compressive sensing random measurement matrix to achieve dimensionality reduction. Then, the improved Genetic Algorithm (GA) to overcome premature phenomena is used as the search strategy, and the L 1 distance measure of SIFT feature vectors of compressive sensing for two kinds of images is applied to the search similarity criterion to match low-frequency image of high scale in Bandelet domain. The matching result is used as the guidance of the matching process for low-frequency image of low scale, and the matching result of full-resolution image is obtained iteratively. Experimental results show that the proposed method has not only high matching accuracy and fast matching speed, but also better robustness in comparison with some classic matching algorithms, which can resist the geometric distortion of rotation for actual image.
Real-Time Financial Data Prediction Using Meta-cognitive Recurrent Kernel Online Sequential Extreme Learning Machine

This paper proposes a novel algorithm called Meta-cognitive Recurrent Kernel Online Sequential Extreme Learning Machine with a kernel filter and a modified Drift Detector Mechanism (Meta-RKOS-ELM$$_\mathrm{ALD}$$-DDM). The algorithm aims to tackle a well-known concept drift problem in time series prediction by utilising the modified concept drift detector mechanism. Moreover, the new meta-cognitive learning strategy is employed to solve parameter dependency and reduce learning time. The experimental results show that the proposed method can achieve better performance than the conventional algorithm in a set of financial datasets.
Neural Data Server: A Large-Scale Search Engine for Transfer Learning Data

Transfer learning has proven to be a successful technique to train deep learning models in the domains where little training data is available. The dominant approach is to pretrain a model on a large generic dataset such as ImageNet and finetune its weights on the target domain. However, in the new era of an ever-increasing number of massive datasets, selecting the relevant data for pretraining is a critical issue. We introduce Neural Data Server (NDS), a large-scale search engine for finding the most useful transfer learning data to the target domain. NDS consists of a dataserver which indexes several large popular image datasets, and aims to recommend data to a client, an end-user with a target application with its own small labeled dataset. The dataserver represents large datasets with a much more compact mixture-of-experts model, and employs it to perform data search in a series of dataserver-client transactions at a low computational cost. We show the effectiveness of NDS in various transfer learning scenarios, demonstrating state-of-the-art performance on several target datasets and tasks such as image classification, object detection and instance segmentation. Neural Data Server is available as a web-service at http://aidemos.cs.toronto.edu/nds/.
A Laser Modulation Driver in 130-nm CMOS Technology for Quantum Key Distribution System

Laser modulation is a process to modulate the photons to different states in quantum key distribution (QKD) system. A laser modulation driver, which is a pre-driver for the laser modulator, is presented in this paper. The laser modulation driver is implemented in a 130-nm complementary metal oxide semiconductor (CMOS) process. The laser modulation driver can provide a current pulse signal with an adjustable amplitude and an adjustable pulse-width to a distributed power amplifier. The pulse-width of the current signal can be adjusted from 300 ps to 3800 ps, the maximum adjustable amplitude is 14.4 mA, and the data rate can reach 625 Mb/s. According to the test results, the laser modulation driver can meet the requirements of the electro-optic modulator (EOM) in QKD system, and it can be used in the laser modulation circuit in QKD experiments.
Enhanced Security Mechanism in Cloud Based on DNA Excess 3 Codes

Exchanging data over the system has generally utilized quick and solid hot spot for correspondence. Clients from wide devotion utilize this component for exchanging and retrieving the required data. Portability and operability inside cloud framework are achieved through disconnected and online mediums, which are persistently alluring, yet the issue of security emerges amid the transmission process. Security and unwavering quality are the key issues during the exchange process, which requires serious research consideration. Data security is achieved by utilizing the public and private key-enabled block-level DNA-based EX-3 code. The analysis is inferred on the disconnected information as well as on the online information, for example, Google Docs. Redundancy handling mechanism is utilized to guarantee space for the information storage supplier, which remains a minimum utilized characteristic as it considers the capacity utilization in DSP. With the proposed system, the overall space allocation for heavy documents is decreased and online data security has been improved by the utilization of byte-level DNA-based EX-3 code.
Superdense coding in the resource theory of asymmetry

We analyze the task of encoding classical information into a quantum system under the restriction by symmetry. Motivated by an analogy between the resource theories of asymmetry and entanglement, we ask whether an analog of superdense coding is possible in the former. I.e., we investigate whether the classical information capacity of an asymmetric state can be strictly larger than that of any symmetric state whereas the latter is a strictly positive constant. We prove that this is possible if and only if the unitary representation of the symmetry is non-Abelian and reducible. The result provides an information-theoretical classification of symmetries of quantum systems. We also discuss the possibility of superdense coding in other resource theories.
Exploring Deep Convolution Neural Networks with Transfer Learning for Transformation Zone Type Prediction in Cervical Cancer

Cancer of the cervix is one of the most common types of women’s cancer. Cervical malignancy can be reasonably neutralized if distinguished in the initial stage. Classification of medical images is known to be a difficult problem for a number of reasons, but recent advancements in deep learning techniques have shown promise for such. In order to appropriately treat cervical cancer, making a right decision of a right type patient’s cervix type is necessary. The patient cervixes are classified as type 1, type 2, and type 3. We attempt to model a deep neural network by utilizing the fine-tuned transfer learning for classifying the cervical images. The model uses pre-computed activations of ResNet and Inceptionv3 which was pretrained on ImageNet Challenge. The dataset is taken from Kaggle challenge. It comprises 1481 training images, 512 test images, and 6734 additional images that we utilized for training model. Because of the less availability of the dataset, we apply image augmentation techniques. We experiment model with and without augmented data. The results show that model using the pretrained weights of ResNet performs better with top-down augmentations which results in 72% of accuracy on test set.
Image Steganography: Hiding Secrets in Random LSB Pixels

Steganography is the art of secret communication and evolved rapidly in the research community, and hiding data in images is relatively secure against spoofing; buying these point researchers targeted hiding secrets in LSBs of image pixels, and that particular method is known as LSB image steganography and evolved as an emerging method of secure data transmission over Internet. Influenced with this fundamental concept, this research is presenting a secure data hiding approach that ensures to maintain the quality of stego image while concealing more data per image. This research leverages the LZW compression technique to enlarge the text coverage in cover images, and for added security, it hides data in random pixels of the image which deliver the strength of “transposition cryptography.” LSB-based presented approach is influenced by the novel invention of H. B. Kekre and same algorithm would be considered for the comparative analysis of data hiding capabilities of presented method. This research is focused to hide data in LSB bits and will target MSBs only in the cases when LSB bits of all image pixels have conceived the secret bits, and this approach is the heartbeat of this research and ensures the better quality stego image after the hiding process, it could be considered that a bare eye cannot distinguish any difference comparing with original image. Presented approach is fully secured against the possibility of R-S detection attacks. Experimental analysis indicates that presented approach is very secure even having 2 times more data concealing as compared to kekre’s method. Apart from that all digested data was recovered at the destination, this approach is a lossless data hiding approach.
Design and control of a variable structure robot

Because wheeled mobile robots have so many applications in warehouse logistics, improving their ability to adapt to the environment has been the subject of extensive research. To enhance the traffic and obstacle avoidance capabilities of wheeled robots, a wheeled mobile robot with a transformable chassis is proposed in this article for use in an automated warehouse. The robot can not only move in all directions but also change its chassis structure according to the specific warehouse environment. First, the robot structure is designed, and the motion modes of the robot are analyzed. After kinematics analysis is performed, a kinematics model is presented. In addition, with the goal of trajectory tracking and indoor positioning, a sliding mode controller and an extended Kalman filter controller are designed. Then, the effectiveness of the kinematics model and the controller design is verified by computer simulation. Finally, a physical prototype of the robot is built and the effectiveness of the robot is demonstrated through experiments.
Content-Based Retrieval of Multimedia Information Using Multiple Similarity Indexes

Describing images in terms of keywords only does not satisfy the requirement of image retrieval in many domains. Present state of research in image retrieval allows representation of images in the form of their content, i.e., shape, color, texture, etc. Representation of images in their content has provided a significant level of achievement in image retrieval, yet the requirement of a methodology which allows storage and retrieval of image in all its possible representation is still needed. In current manuscript, a content-based image retrieval model has been presented which provides a methodology where image can be retrieved on the basis of similarity on multiple features.
Research on target location of unmanned aerial vehicles in parallel path

This paper mainly studies how to use the stereo vision system that combines the monocular vision with parallel path search to locate the target. When the unmanned aerial vehicle (UAV) searches in the mission area according to the parallel path, the SSD image detection algorithm based on deep learning is adopted to detect and identify the target in the area. The image coordinate information is inversely calculated by using the pixel coordinate information fed back by machine vision. The auxiliary coordinate system is established according to the relationship of angle position between the track line and the basic coordinate system in the parallel path. Combining the position relation and the attitude direction information of UAV, the target position conversion relation between the imaging coordinate system and the auxiliary coordinate system is solved by using the direction cosine matrix. Combined with the coordinate information of UAV, the coordinate position of the target point in the basic coordinate system is finally solved through three coordinate conversion operations. In order to avoid the single calculating error of the target coordinates, the weighted average operation is carried out. On the basis of not changing the search trip of the parallel path, the target location function is preliminarily realized through the reverse solution and the weighted average operation of the target coordinates.
A Course Scheduling Algorithm in Secondary Vocational School Based on Genetic Algorithm

In the secondary vocational school teaching management task, the most important part is the teaching plan. However, with the implementation of the enrollment plan of secondary vocational education, the increase in the number of students and the shortage of teachers bring difficulties to the arrangement of secondary vocational schools. This paper develops and innovates the traditional genetic algorithm, designs the secondary vocational school curriculum scheduling algorithm, and applies it to the curriculum scheduling problem. Firstly, combinational classification method is adopted to reduce the amount of data in sequence in the genetic algorithm, and then three-dimensional coding is adopted to facilitate genetic work. Meanwhile, fitness functions including course requirements, teacher conflicts, course conflicts, time requirements, and interval requirements are designed to ensure the correct optimization direction and evaluation strategy. Finally, experiments show that this algorithm can effectively meet all the requirements of humanized course scheduling.
An Improved Hand Gesture Recognition System Based on Optimized MSVM and SIFT Feature Extraction Algorithm

In today, the robots and machine, translation tasks are performing the main role in hand gestures. Gesture or detection implementation will help humans in several means. Gesture recognition systems are used in various fields such as DNN, ML, and NNs. The applications of this research are sign language translation, music creation, robot remote controlling, etc. In this research work, proposed HGR methods are using feature extraction and optimized MSVM classification. This process gives the applications of edge detection, interference, filters, SIFT-ALO algorithm, and binary during image preprocessing, in which these approaches add to better extraction and selection. In proposed work, implemented an optimized MSVM method are two classes such as training and testing. Optimized M-SVM is performed on the ASL gesture dataset along with existing SURF and SIFT techniques. This research work is used for the simulation tool (MATLAB) and calculated performance metrics like processing time, error rate, and the accuracy rate with the MSVM value at 99.1 percent as compared to existing feature extraction methods.
Alternating Direction Projections onto Convex Sets for Super-Resolution Image Reconstruction

Image reconstruction is important in computer vision and many technologies have been presented to achieve better results. In this paper, gradient information is introduced to define new convex sets. A novel POCS-based model is proposed for super resolution reconstruction. The projection on the convex sets is alternative according to the gray value field and the gradient field. Then the local noise estimation is introduced to determine the threshold adaptively. The efficiency of our proposed model is verified by several numerical experiments. Experimental results show that, the PSNR and the SSIM can be both significantly improved by the proposed model.
Application of Deep Learning in Predicting the Prognosis of Acute Myeloid Leukemia using Cytogenetics, Age, and Mutations

Objective: We explored how Deep Learning can be utilized to predict the prognosis of acute myeloid leukemia. Methods: Out of The Cancer Genome Atlas database, 94 acute myeloid leukemia cases were used in this study. Input data included age, 10 most common cytogenetic and 23 most common mutation results; output was the prognosis (diagnosis to death). In our Deep Learning network, autoencoders were stacked to form a hierarchical Deep Learning model from which raw data were compressed and organized, and high-level features were extracted. The network was written in R language and was designed to predict the prognosis of acute myeloid leukemia for a given case (diagnosis to death of either more or less than 730 days). Results: The Deep Learning network achieved an excellent accuracy of 83% in predicting prognosis. Conclusion: As a proof-of-concept study, our preliminary results demonstrated a practical application of Deep Learning in the future practice of prognostic prediction using next-generation sequencing data.
Unsupervised Learning Towards the Future

We have created so far a basis for unsupervised learningUnsupervised learning in a more general sense. We proposed a set of principles, which we could consider when designing a system that learns in the space-time domain with minimal supervision. We have also demonstrated in practice the usefulness and validity of our key ideas and showed how to create models that learn by themselves for a wide variety of vision tasks. We started with the case of unsupervised learningUnsupervised learning for feature matching in the first two chapters. Then we extended the ideas to the case of feature selection and classifier learning. The theoretical methods were developed and the initial principles were then applied to the case of object discovery and segmentation in video. In the last chapter, we presented a more general approach and showed how we can learn in an unsupervised way several student-teacher generations, through an evolution process that takes advantage of all the key building blocks developed so far. In this final chapter, we will take one step further and show how a more general self-supervised vision system, the Visual Story Graph Neural NetworkVisual Story Graph Neural Network, could be created using the tools presented. We start by first showing how the two main computational models discussed in the book, namely, classical graphs and modern neural networks, could be put together into a single unified recurrent graph neural network that processes data in space and time. We also demonstrate through extensive experiments that our recurrent graph neural net achieves state-of-the-art performance on several higher level problems such as activity recognition, with minimal or no low-level human supervision. Finally, we introduce the more general concept of a visual story graph neural networkVisual story graph neural network (VSN). The VSN concept is based on the ideas and principles presented in this book and could develop into a universal unsupervised learning machineUniversal unsupervised learning machine, which could attain a general and coherent knowledge of its surrounding world in space and time.