Reviewerly/exHarmony huggingface dataset:
Decoding Merged Color-Surface Codes and Finding Fault-Tolerant Clifford Circuits Using Solvers for Satisfiability Modulo Theories

Universal fault-tolerant quantum computers will require the use of efficient protocols to implement encoded operations necessary in the execution of algorithms. In this work, we show how solvers for satisfiability modulo theories (SMT solvers) can be used to automate the construction of Clifford circuits with certain fault-tolerance properties and we apply our techniques to a fault-tolerant magic-state-preparation protocol. Part of the protocol requires converting magic states encoded in the color code to magic states encoded in the surface code. Since the teleportation step involves decoding a color code merged with a surface code, we develop a decoding algorithm that is applicable to such codes.
Characterization of Nuclear Pleomorphism and Tubules in Histopathological Images of Breast Cancer

Breast cancer (BC) diagnosis is made by a pathologist who analyzes a portion of the breast tissue under the microscope and performs a histological evaluation. This evaluation aims to determine the grade of cellular differentiation and the aggressiveness of the tumor by the Nottingham Grade Classification System (NGS). Nowadays, digital pathology is an innovative tool for pathologists in diagnosis and acquiring new learning. However, a recurring problem in health services is the excessive workload in all medical services. For this reason, it is required to develop computational tools that assist histological evaluation. This work proposes a methodology for the quantitative analysis of BC tissue that follows NGS. The proposed methodology is based on digital image processing techniques through which the BC tissue can be characterized automatically. Moreover, the proposed nuclei characterization was helpful for grade differentiation in carcinoma images of the BC tissue reaching an 0.84 accuracy. In addition, a metric was proposed to assess the likelihood of a structure in the tissue corresponding to a tubule by considering spatial and geometrical characteristics between lumina and its surrounding nuclei, reaching an accuracy of 0.83. Tests were performed from different databases and under various magnification and staining contrast conditions, showing that the methodology is reliable for histological breast tissue analysis.
Transformation, vectorization, and optimization

Chapter 3 focuses on transformation, vectorization, and optimization.
Refinement of the feedforward network in multi-class classification problems using a hybrid approach combining supervised clustering and a fuzzy classifier

A FeedForward Neural Network (FFNN) with a single hidden layer (Single Feedforward Neural Networks, SFNN) represents a classical discriminative model able to estimate conditional probabilities (Bayesian probabilities) and provides an efficient alternative in multi-class classification problems. This paper presents an approach to improve the accuracy of SFNN in recognizing multiple patterns in static data focusing on classification problems which systematically pose difficulties in obtaining high levels of accuracy (low misclassification rates) using different classification methods. In an innovative way, the method comprises the joint application of supervised clustering and fuzzy classifier, starting from the probability distribution predicted by the SFNN. The proposed approach​ (Refinement based on Supervised Clustering and Fuzzy Classifier, RSCFC) is applied in real datasets widely used as a benchmark for multi-class classification. The results were compared with other methods involving different categories of neuro-fuzzy systems (cooperative and hybrid) and other networks with tailored topologies for the classification of static data. The RSCFC method proved to be viable, able to improve the results obtained with the SFNN using classical settings (e.g., softmax function, crossed entropy) and provided correct classification rates higher or similar to those achieved with existing methods. Additionally, the method is flexible as any classifier capable of estimating conditional probabilities for data classification can be adopted as a starting point (primary classifier) for the refinement and achievement of greater accuracy.
QoS-Based Classical Trust Management System for the Evaluation of the Trustworthiness of a Cloud Resource

Distributed computing innovation has seen wonderful improvement in different spaces during the most recent twenty years. Among many components, protection, security, and trust are the issues of most significant concern. Trust assumes a pivotal part in cloud computing to offer dependable administration to the cloud clients. It is the fundamental justification behind the notoriety of cloud services among clients. Giving trust between the cloud specialist co-op and the cloud customer is a fundamental essential. Executive trust is commonly used in Internet-based administrations, E-business, and informal communities. This paper predominantly centers around the different methodologies in the trust assessment model for administrative choice in cloud computing. The Classical Trust Management System is intended for choosing the most reliable assets dependent on the QoS (Quality of Service) parameters, specifically, accessibility, achievement rate, execution time, turnaround productivity, and universal response. The universal response is the aggregate response from the clients, representatives, companions, and interpersonal organization. The proposed work beats the other existing models, for example, the FIFO and QoS models.
Improvement of geochemical prospectivity mapping using power spectrum–area fractal modelling of the multi-element mineralization factor (SAF-MF)

Geochemical anomaly mapping is a significant method in exploring for Au and associated elements using fine-fraction sediments. The fractal behaviour of geochemical elements has frequently been modelled for geochemical prospectivity mapping. In this study, concentration–area (C-A) and power spectrum–area (S-A) fractal modelling were utilized for Au concentration in stream sediments near the Saqqez mineralization area, NW Iran. A novel integrated approach based on the S-A fractal method and principal component analysis (PCA) was used to improve the geochemical anomaly map. In this approach, the multi-element mineralization factor returned from PCA was modelled by the S-A fractal method, and the obtained results were compared to the C-A and S-A fractal models of the element Au. Six known mineralization zones in the studied area were revisited to validate the fractal models. The elements Au, As, W, and Sb were identified as associated elements based on the log-ratio-transformed data in the shear zone gold mineralization. S-A fractal modelling of the multi-element mineralization factor (SAF-MF) of the Au-associated elements in the mineralization zones enhanced the geochemical halos and increased the prediction rate of mineralization zones using stream sediment geochemical data. The SAF-MF that models the behaviour of frequency signals of associated elements upgraded the geochemical anomaly mapping compared to conventional fractal models. Supplementary material: Supplementary information including figures S1–S5 is available at https://doi.org/10.6084/m9.figshare.c.6123537
Dual collaborative representation based discriminant projection for face recognition

Collaborative representation based techniques have shown promising results for face recognition; however, most of them code the samples by taking the overall samples as a dictionary, which may contain much noise information. To tackle this problem, a new face recognition algorithm, namely dual collaborative representation based discriminant projection (DCRDP), is proposed in this paper. In DCRDP, each training sample is reconstructed via dual collaborative representation to enhance the robustness to noise information: the first collaborative representation is used to choose an appropriate dictionary with respect to the training sample, while the second collaborative representation is used to find collaborative representation relationships between samples. After dual collaborative representation, DCRDP constructs two adjacency graphs to model the similarity and dissimilarity between samples, and then finds the optimal projection matrix for dimension reduction. Experiments on ExtYaleB, AR and CMU PIE face datasets verify the superiority of DCRDP to some other state-of-the-art approaches.
Conceptual Framework of Database Development on Bidong Island: The Case Vietnamese Boat People (VBP) Campsite Facilities for Historical Tourism

The lack of maximizing the technology in today’s historical tourism is the main concern for the success in this area. Previous studies have proven the success rate of using the current innovation and trends in general tourism sector in which they help to increase the visibility of the tourist attractions and number of visitors in one tourist spot. Terengganu, one of the states in Malaysia, is one of the best tourist attractions in the country. It is well-known for its popular islands, namely Redang Island, Lang Tengah Island, Kapas Island and Tenggol Island. Yet, many local and international tourists are not aware of the high potential historical island called Bidong Island. Not many tourists go there. Many come due to the informal communication such as from mouth to mouth with little formal promotions and marketing activities. Bidong Island was used as Vietnamese Boat People (VBP) refugee camp facilities from 1978 to 1991. Currently, this lonely island has started to show her visibility. This is a good sign of the state’s domestic tourism activity that will contribute towards the Gross Domestic Profits in tourism sector. Therefore, the aim of the present study is to provide an overview of database development on the Bidong Island for the Vietnamese Boat People former campsite facilities. A field observation study is proposed for the actual study.
Implementation of Pedestrian Detection Algorithm Based on Improved Yolov3-Tiny in ROS Framework

In order to improve the low detection accuracy and recall rate of Yolov3-Tiny on small or obscured pedestrian target, this paper proposes an improved Yolov3-Tiny pedestrian detection algorithm. On the basis of data set (head and shoulders) making, we add 52×52 feature maps into the network to improve the multi-scale fusion structure, use a data augmentation method to enhance the generalization ability of the model, and adjust the learning-rate dynamically. Based on this algorithm, we implement real-time pedestrian detection experiments for mobile robots in ROS framework, and the results show that the detection accuracy, recall rate and IOU have been improved.
Using the Community of Inquiry framework to analyze emojis as an emerging language in an online educational experience via WhatsApp

ABSTRACT With the ever-growing interest in WhatsApp as a social space to accommodate pedagogical initiatives, the role of emojis as an emergent Internet language in this social space needs to be better understood as mobile technologies have been integrated in education. With that in mind, this paper reports a study that employed an embedded correlational mixed methods design, and aimed to identify: the emojis used to support cognitive, teaching, and social presences in a teacher education course via WhatsApp; participants’ attitudes regarding the use of emojis, and their age and gender relation to this use. The data generated from the interactions of the teachers via WhatsApp were analyzed and discussed based on Garrison et al.’s (1999) Community of Inquiry Model. This conceptual framework identifies crucial elements for successful online educational experiences and assumes learning occurs through the interaction of three core elements: social, cognitive and teaching presences. The findings show that emojis can effectively be used to support teaching, cognitive, and social presences. The high frequency of emojis seem to be both culturally situated and gender related.
Adaptive feature denoising based deep convolutional network for single image super-resolution

Recently, the feature map recalibration (FMR) mechanism has been widely explored in single image super-resolution (SISR) and obtained remarkable performances. However, the existing FMR-based SISR methods directly incorporate the attention module into a deeper network structure (e.g. EDSR), while neglecting the differences between the low-level and high-level vision problems. In this paper, we design a low-level specific FMR mechanism for SISR task based on a new observation by examining current SISR methods, which all demonstrate a solid correlation between the SISR performance and the convolutional feature noise. Inspired by this, we extend the classic soft thresholding technique in the way of deep network, and develop an Adaptive Soft Thresholding (AST) module for feature noise suppression. Comparing to existing attention modules, AST is light-weighted and can be taken as an easy plug-in module in any SISR networks. To this end, we construct a adaptive Feature Denoising Super-Resolution (FDSR) network by combining the baseline EDSR and the proposed AST. Extensive experimental results show that the proposed FDSR network could achieve the state-of-the-art performances on SISR benchmarks, and significantly reduce the parameter (28.8% for EDSR, 74.6% for RCAN,s 76.0% for SAN) with respect to FMR module.
Superconducting Nanowire Single-Photon Detectors and Multi-Photon Detectors

We present our research progress in superconducting nanowire single-photon detectors (SNSPDs) and multi-photon detectors (SNMPDs), including fractal SNSPDs with reduced polarization sensitivity, two mechanisms of device timing jitter, and SNMPDs integrated with current reservoirs.
A Comparative Study on Various Approaches of Sentimental Analysis

On social networking platforms, millions of people express their thoughts in the form of the text and images every day. A tweet or text present on the online social media networking sites is very useful to carry out the sentiments of the users about the products, news etc., but from these briefs and highly unstructured data sets generated with which great insights can be gained. Several organizations now use Twitter to inform the public about their services and products. An analysis is a method to evaluate the meaning of data, which is one of the fields of Natural Language Processing (NLP) and text data mining. The sentiment analysis of the unstructured data has many facets. This paper shows a comparative study of various methods and approaches to find out the sentiments from the unstructured data.
Litener: An Accelerator-Enabled Lightweight Container for Edge Computing

Containers supporting accelerators, such as Docker, include management tasks that require a significant amount of computing resources. While these resources are available in the Cloud, in other scenarios such as the Edge resources are more limited. An accelerator-enabled lightweight container would be desirable in such scenarios. In this paper, we analyse platforms to containerise applications using accelerators, including but not limited to Docker. After this analysis, we present a lightweight container, referred to as Litener, focused on using accelerators in scenarios with limited resources. Although the focus is on accelerators, many of the optimisations described can also be applied to scenarios that do not use accelerators. Experiments show a speedup of up to 7.78X when compared to other platforms.
Low-level Bias discovery and Mitigation for Image Classification

Identification of bias and its mitigation in a classifier is a fundamental sanity check required in trustworthy AI systems. There have been many methods for mitigation of bias in literature that use bias as apriori information. In this work, we propose a system that can detect the low-level bias (e.g., color, texture) and mitigate the same. A novel auto-encoder architecture to explain the predictions made by a deep neural network is built that helps in identification of the bias. The auto-encoder is trained to produce a generalized representation of the input image by decomposing it into a set of latent embeddings. These embeddings are learned by specializing the group of higher dimensional feature maps to learn the disentangled color and shape concepts. The shape embeddings are trained to reconstruct discrete wavelet transform components of an image and the color embeddings are trained to capture the color information. The feature specialization is done by reconstructing the RGB image using the shape embeddings modulated by color embeddings. We have shown that these representations can be used to detect low level bias in a classification task. Post detection of bias, we also propose a method to de-bias the classifier by training it with counterfactual images generated by manipulating the representations learned by the auto-encoder. We have shown that our proposed method of bias discovery and mitigation is able to achieve state-of-the-art results on ColorMNIST and the newly proposed BiasedShape dataset.
Contrastive Learning between Classical and Modern Chinese for Classical Chinese Machine Reading Comprehension

By leveraging self-supervised tasks, pre-trained language model (PLM) has made significant progress in the field of machine reading comprehension (MRC) . However, in classical Chinese MRC (CCMRC) , the passage is typically in classical style, but the question and options are given in modern style. Existing pre-trained methods seldom model the relationship between classical and modern styles, resulting in overall misunderstanding of the passage. In this paper, we propose a contrastive learning method between classical and modern Chinese in order to reach a deep understanding of the two different styles. In particular, a novel pre-training task and an enhanced co-matching network have been defined: (1) The synonym discrimination (SD) task is used to identify whether modern meaning corresponds to classical Chinese. (2) The enhanced dual co-matching (EDCM) network is employed for a more interactive understanding of the classical passage and the modern options. The experimental results show that our proposed method improves language understanding ability and outperforms existing PLMs on the Haihua, CCLUE, and ChID datasets.
Clearly audible room acoustical differences may not reveal where you are in a room

A common aim in virtual reality room acoustics simulation is accurate listener position dependent rendering. However, it is unclear whether a mismatch between the acoustics and visual representation of a room influences the experience or is even noticeable. Here, we ask if listeners without any special experience in echolocation are able to identify their position in a room based on the acoustics alone. In a first test, direct comparison between acoustic recordings from the different positions in the room revealed clearly audible differences, which subjects described with various acoustic attributes. The design of the subsequent experiment allows participants to move around and explore the sound within different zones in this room while switching between visual renderings of the zones in a head-mounted display. The results show that identification was only possible in some special cases. In about 74% of all trials, listeners were not able to determine where they were in the room. The results imply that audible position dependent room acoustic rendering in virtual reality may not be noticeable under certain conditions, which highlights the importance of evaluation paradigm choice when assessing virtual acoustics.
From Garment to Skin: The visuAAL Skin Segmentation Dataset

Human skin detection has been remarkably incorporated in different computer vision and biometric systems. It has been receiving increasing attention in face analysis, human tracking and recognition, and medical image analysis. For many human-related recognition tasks, using skin detection cue could be a proper choice. Despite the vast area of usage and applications for skin detection, not many large or reliable skin detection datasets are available, and many of the existing ones, are originally created for other tasks such as hand tracking or face analysis. In this paper, we propose a methodology for extracting skin pixels from garment segmentation and recognition datasets. This is achieved by using deep learning methods to generate automatic skin label masks from them by exploiting human body and hair segmentation and provided garment masks. Following this approach, a large human skin segmentation dataset is introduced. A validation set is also manually segmented in order to evaluate the accuracy of the output skin masks. Finally, usual methods for skin detection and segmentation are evaluated on this new dataset.
Virtual and Augmented Reality for Quality Control of Aircraft Interiors

Automation is a driving force in manufacturing, enabling to ensure quality and scale during production and assembly. After a slow start, civil aerospace industry efforts in automating production, assembly and testing, are gaining pace and novel technologies are being experimented for enabling automated assembly and inspection phases, which are reliable and safe. In this context, a cluster of research projects were devised for the improvement of assembly and inspection phases of aircraft interiors, with a focus on cabin and cargo lining and hatrack elements of single-aisle aircrafts. This paper is focused on the inspection phase which follows the assembly, where a novel approach involving automated testing and human supervision aim to provide faster, safer and more flexible inspection operations when compared with more traditional ways, using virtual and augmented reality scenarios.
Use of digital technologies to improve the classical methodology of foreign language teaching in professional educational organizations

This chapter is devoted to the study, analysis of the effectiveness of the use of the methodology, as well as the development of a model for improving the classical methodology of teaching a foreign language in the specialties of vocational education of various technical profiles with elements of digitalization. A brief overview of the available training methods is made, an analysis of the regulatory framework is carried out. A model of teaching a foreign language has been developed and experimentally substantiated. The main element of digitalization is the author's developed online course "Foreign language", attached to the educational platform Moodle.