Reviewerly/exHarmony huggingface dataset:
An Automatic Chinaware Fragments Reassembly Method Framework Based on Linear Feature of Fracture Surface Contour

For Chinaware fragments, it is difficult to assemble them directly without considering the wonderful patterns painted on them. Given the simplicity of the Chinaware designs, each object contains similar textures and patterns. Compared to the oddly diverse appliance modeling, the difference between fragments of different Chinaware is small. The fragments are small and the fracture is flat, and there are many potential matching objects. For the Chinaware fragments’ reassembly, most of the work is still done manually. There is little available fully automatic reassembly work, and these approaches are for the reassembly of a single Chinaware. But what reality demands is the reassembly of the multi-Chinaware. Therefore, this article proposes an automatic method, whose strategy is analogous to the manual assembly, to accomplish this complex task. First, segment the contours of fracture surfaces of the fragments by the geometric feature of fracture edge; then, the contours are matched using our proposed multi-scale linear feature descriptor; given the massive fragments of multiple objects, we use the inaccurate matching strategy to build a matching set for each fracture surface contour and perform contour matching in this set. In this article, the fracture surface is segmented using the 2D slope information on the fragment edge. The descriptor proposed in the article uses distance triangle towers and chained angles to describe the “undulations” of the fracture surface. Moreover, the article uses a strict absolute-advantage-principle to reject false matching. In addition, after the initial reconstruction, we will iteratively adjust fragments according to the gap between the fragments to achieve better multi-fragment matching results. In this article, 18 porcelains, a total of 103 pieces, have been tested. Experiments are also carried out for special cases, including fragment reassembly with missing fragments and fragment reassembly with redundant fragments. The experimental results showed the effectiveness of method. However, the types of experimental data we currently have are relatively simple, and there is no way to reassemble the fragments in minutes. We hope to enable faster reassembly of more fragments in the future.
Semi‐Paired Semi‐Supervised Deep Hashing for cross‐view retrieval

Abstract Due to its fast computational speed and low storage cost, hashing has been effectively applied to large‐scale multimedia retrieval tasks, such as medical video and security video retrieval. Most existing cross‐view hashing methods require good matching information, however, this exact pairing relationship is difficult to fully realise in practice. The association between views is incomplete, as is the label information. This task of missing paired and labelled information is very challenging, but less explored in research. In this study, a semi‐supervised semi‐paired deep hashing for large‐scale data is proposed, named Semi‐Paired Semi‐Supervised Deep Hashing (SPSDH) to solve this challenging task. SPSDH is a novel end‐to‐end deep neural network model with high‐order affinity. A non‐local higher‐order affinity measure that better considers the multimodal neighbourhood structure is proposed. A common representation to associate different modalities is introduced, which combined with the labelled information greatly maintains the consistency within the modalities. SPSDH is evaluated on three benchmark datasets for large‐scale cross‐view approximate nearest neighbour search and compared with several state‐of‐the‐art hashing methods. Extensive experimental results demonstrate the superior performance of our proposed SPSDH in semi‐supervised semi‐paired retrieval tasks.
Medical Image Analysis With Intelligent Techniques

Medical imaging is an integral part of radiology. This technology has its significance in assisting physicians in clinical procedures. Artificial intelligence and machine learning was recently established in diagnosis of lesions using medical imaging modalities in radiology for various medical diagnoses such as tumor in oral cavities, lungs, using histopathology images. The objective of this chapter is to present the workflow of computer-aided medical diagnosis involved in various imaging modalities with case study on ophthalmology and oral cancer. Towards the end, the machine learning workflow is summarized with directions for future research. Towards the end, summary on research challenges in digital pathology is presented. Finally, the chapter concludes with future research directions.
Interactive Learning of a Dual Convolution Neural Network for Multi-Modal Action Recognition

RGB and depth modalities contain more abundant and interactive information, and convolutional neural networks (ConvNets) based on multi-modal data have achieved successful progress in action recognition. Due to the limitation of a single stream, it is difficult to improve recognition performance by learning multi-modal interactive features. Inspired by the multi-stream learning mechanism and spatial-temporal information representation methods, we construct dynamic images by using the rank pooling method and design an interactive learning dual-ConvNet (ILD-ConvNet) with a multiplexer module to improve action recognition performance. Built on the rank pooling method, the constructed visual dynamic images can capture the spatial-temporal information from entire RGB videos. We extend this method to depth sequences to obtain more abundant multi-modal spatial-temporal information as the inputs of the ConvNets. In addition, we design a dual ILD-ConvNet with multiplexer modules to jointly learn the interactive features of two-stream from RGB and depth modalities. The proposed recognition framework has been tested on two benchmark multi-modal datasets—NTU RGB + D 120 and PKU-MMD. The proposed ILD-ConvNet with a temporal segmentation mechanism achieves an accuracy of 86.9% and 89.4% for Cross-Subject (C-Sub) and Cross-Setup (C-Set) on NTU RGB + D 120, 92.0% and 93.1% for Cross-Subject (C-Sub) and Cross-View (C-View) on PKU-MMD, which are comparable with the state of the art. The experimental results shown that our proposed ILD-ConvNet with a multiplexer module can extract interactive features from different modalities to enhance action recognition performance.
A Concise Tutorial on Functional Analysis for Applications to Signal Processing

Functional analysis is a well-developed field in the discipline of Mathematics, which provides unifying frameworks for solving many problems in applied sciences and engineering. In particular, several important topics (e.g., spectrum estimation, linear prediction, and wavelet analysis) in signal processing had been initiated and developed through collaborative efforts of engineers and mathematicians who used results from Hilbert spaces, Hardy spaces, weak topology, and other topics of functional analysis to establish essential analytical structures for many subfields in signal processing. This paper presents a concise tutorial for understanding the theoretical concepts of the essential elements in functional analysis, which form a mathematical framework and backbone for central topics in signal processing, specifically statistical and adaptive signal processing. The applications of these concepts for formulating and analyzing signal processing problems may often be difficult for researchers in applied sciences and engineering, who are not adequately familiar with the terminology and concepts of functional analysis. Moreover, these concepts are not often explained in sufficient details in the signal processing literature; on the other hand, they are well-studied in textbooks on functional analysis, yet without emphasizing the perspectives of signal processing applications. Therefore, the process of assimilating the ensemble of pertinent information on functional analysis and explaining their relevance to signal processing applications should have significant importance and utility to the professional communities of applied sciences and engineering. The information, presented in this paper, is intended to provide an adequate mathematical background with a unifying concept for apparently diverse topics in signal processing. The main objectives of this paper from the above perspectives are summarized below: (1) Assimilation of the essential information from different sources of functional analysis literature, which are relevant to developing the theory and applications of signal processing. (2) Description of the underlying concepts in a way that is accessible to non-specialists in functional analysis (e.g., those with bachelor-level or first-year graduate-level training in signal processing and mathematics). (3) Signal-processing-based interpretation of functional-analytic concepts and their concise presentation in a tutorial format.
Game Development Topics: A Tag-Based Investigation on Game Development Stack Exchange

Video-game development, despite being a multi-billion-dollar industry, has not attracted sustained attention from software engineering researchers and remains understudied from a software engineering perspective. We aim to uncover, from game developers’ perspectives, which video game development topics are the most asked about and which are the most supported, in order to provide insights about technological and conceptual challenges game developers and managers may face on their projects. To do so, we turned to the Game Development Stack Exchange (GDSE), a prominent Question and Answer forum dedicated to game development. On that forum, users ask questions and tag them with keywords recognized as important categories by the community. Our study relies on those tags, which we classify either as technology or concept topics. We then analysed these topics for their levels of community attention (number of questions, views, upvotes, etc.) and community support (whether their questions are answered and how long it takes). Related to community attention, we found that topics with the most questions include concepts such as 2D and collision detection and technologies such as Unity and C#, whereas questions touching on concepts such as video and augmented reality and technologies such as iOS, Unreal-4 and Three.js generally lack satisfactory answers. Moreover, by pairing topics, we uncovered early clues that, from a community support perspective, (i) the pairing of some technologies appear more challenging (e.g., questions mixing HLSL and MonoGame receive a relatively lower level of support); (ii) some concepts may be more difficult to handle conjointly (e.g., rotation and movement); and some technologies may prove more challenging to use to address a given concept (e.g., Java for 3D). Our findings provide insights to video game developers on the topics and challenges they might encounter and highlight tool selection and integration for video game development as a promising research direction.
The Impossible, the Unlikely, and the Probable Nudges: A Classification for the Design of Your Next Nudge

Nudging provides a way to gently influence people to change behavior towards a desired goal, e.g., by moving towards a healthier or more environmentally friendly lifestyle. Personalized and context-aware digital nudging (named smart nudging) can be a powerful tool for efficient nudging by tailoring nudges to the current situation of each individual user. However, designing smart nudges is challenging, as different users may need different supports to improve their behavior. Determining the next nudge for a specific user must be done based on the user’s current situation, abilities, and potential for improvement. In this paper, we focus on the challenge of designing the next nudge by presenting a novel classification of nudges that distinguishes between (i) nudges that are impossible for the user to follow, (ii) nudges that are unlikely to be followed, and (iii) probable nudges that the user can follow. The classification is tailored to individual users based on user profiles, current situations, and knowledge of previous behaviors. This paper describes steps in the nudge design process and a novel set of principles for designing smart nudges.
Natural Language Processing and Cloud Computing in Disease Prevention and Management

Recent studies show the high prevalence of the use of messaging platform and its growth trend, especially in younger populations (Figueroa Jacinto and Arndt 2018). Messaging apps are used not only to communicate and stay in touch with family and friends, but also to access services. Interactions with commercial purposes, such as making purchases, seeking out assistance and customer support, providing feedback or making reservations are already widely used, but legal and healthcare related areas also present prominent growth (Eeuwen 2017). In this chapter it is our objective to explore how technologies such as natural language processing, speech recognition, text-to-speech, machine learning, and cloud computing can be integrated to develop high quality chatbots for healthcare purposes. Additionally, we will cover an application case based on COVID-19 prevention and management.
Parasite Detection From Digital Images Using Deep Learning

Parasitosis is a disease caused by parasites that could infect humans, animals, or plants. The parasites include mites, ascariasis, liver flukes, and malaria. The methods to detect parasites include pathological examination, immunological examination, and imaging examination. In this chapter, parasitic infections are detected from digital images acquired from a microscope, which will look for the possible infection caused by worms or eggs in a sample, such as mites and malaria. Rapid and accurate classification and detection of parasites will be very helpful for fast diagnosis and treatment. In this chapter, a malaria detection method is deployed by using deep learning based on TensorFlow and achieved 0.73 mAP@0.5IOU. Even if it does not seem to be a perfect performance, in the limited time and resources, the results are still valuable. The future work could port the model to mobile phones for image detection, which would bring much more convenience and portability.
Preprocessing Enhancement Method for Spatial Domain Steganalysis

In the field of steganalysis, in recent years, the research focus has mostly been on optimizing the structures of neural networks, while the application of high-pass filters is still limited to the simple selection of filters and simple adjustment of the number of filters. In this paper, we propose a method to enhance the assistance and contribution of high-pass filters to the detection capability of a spatial domain steganalysis model, which mainly contains the preprocessing enhancement of high-pass filters and cross-layer enhancement of high-pass filters, and we construct a preprocessing enhancement model, the HPF-Enhanced Model, for spatial domain steganalysis, based on Yedroudj-Net. In the experimental part, we find the best preprocessing enhancement method through various validations, and we compare the HPF-Enhanced Model with the classical models. The results show that the proposed enhancement method can bring a significant improvement, and they also show that the preprocessing enhancement method can help to reduce the model size, and it thus can be used to construct a lightweight spatial domain steganalysis model with strong performance.
Residual Information in Deep Speaker Embedding Architectures

Speaker embeddings represent a means to extract representative vectorial representations from a speech signal such that the representation pertains to the speaker identity alone. The embeddings are commonly used to classify and discriminate between different speakers. However, there is no objective measure to evaluate the ability of a speaker embedding to disentangle the speaker identity from the other speech characteristics. This means that the embeddings are far from ideal, highly dependent on the training corpus and still include a degree of residual information pertaining to factors such as linguistic content, recording conditions or speaking style of the utterance. This paper introduces an analysis over six sets of speaker embeddings extracted with some of the most recent and high-performing DNN architectures, and in particular, the degree to which they are able to truly disentangle the speaker identity from the speech signal. To correctly evaluate the architectures, a large multi-speaker parallel speech dataset is used. The dataset includes 46 speakers uttering the same set of prompts, recorded in either a professional studio or their home environments. The analysis looks into the intra- and inter-speaker similarity measures computed over the different embedding sets, as well as if simple classification and regression methods are able to extract several residual information factors from the speaker embeddings. The results show that the discriminative power of the analyzed embeddings is very high, yet across all the analyzed architectures, residual information is still present in the representations in the form of a high correlation to the recording conditions, linguistic contents and utterance duration.
An A to Z of enhancements: <i>B is for books</i>

Amy Jackson continues her tour of the alphabet of enhancements to continuous provision, looking at inventive ways to use books to deepen learning
A declarative modelling framework for the deployment and management of blockchain applications

The deployment and management of Blockchain applications require non-trivial efforts given the unique characteristics of their infrastructure (i.e., immutability) and the complexity of the software systems being executed. The operation of Blockchain applications is still based on ad-hoc solutions that are error-prone, difficult to maintain and evolve, and do not manage their interactions with other infrastructures (e.g., a Cloud backend).
System architecture synthesis for performability by logic solvers

In model-based systems engineering, system architectures often have to make compromises to meet hard constraints of functional and extra-functional requirements while optimizing for a target objective. Design space exploration (DSE) techniques have been developed to automatically propose candidate architectures over an extremely large design and configuration space. (1) Meta-heuristic exploration algorithms are often used to provide practical, best-effort solutions for DSE, but they lack any guarantees of completeness or optimality. (2) Logic synthesis based approaches may offer strong theoretical guarantees, but frequently face scalability issues. In the paper, we propose two logic solver-based approaches to evaluate complex design spaces by using partial models in order to find an optimal solution with respect to performability objectives. One approach uses performability analysis as a post-filtering of valid system architecture candidates, while the other approach uses performability analysis for guiding the actual search over partial models. We evaluate both approaches on an interferometry mission architecture case study using view transformations for performability analysis and compare our approach with a well-known DSE framework based on meta-heuristic search.
Correction

This article refers to:Exploring Usability as a Formative Construct through UMUX: A Multi-Language Approach for Turkish Adaptation
Edge-Based Congestion-Aware Datacenter Load Balancing with Smart Probing

Abstract Data center networks employ multipath topologies for delivering high bisection bandwidth. Load balancing through these multiple paths reduces latency and improves throughput. Network fabric-based schemes need customized hardware and cannot be implemented on commodity hardware. The existing end-host-based solutions have shown performance better than equal-cost multi-path (ECMP) but they use active probing and uneven congestion signal (e.g. coarse-grained RTT or ECN) that degrades performance especially in asymmetric topologies. Therefore, most of these end-host solutions cannot accurately sense congestion. End-to-end latency can effectively provide accurate path congestion information and smart probing with the help of congestion window can reduce network overhead. In this paper, we propose an edge-based congestion-aware load-balancing solution with smart probing. We combine ECN and end-to-end latency to get more accurate congestion metric and with probabilistic approach we avoid congestion at the best available path. Furthermore, we employ congestion window trend to perform smart probing that will reduce network overhead by performing probing only when required. The proposed scheme is implemented in network simulator ns-3 and flow completion time is taken as the key performance metric. The results show that the proposed scheme not only outperformed ECMP, Clove and LetFlow but also improved the performance over Hermes and CONGA at least 10–30% by employing (i) an accurate congestion signal that effectively detects congestion and asymmetries, and (ii) smart probing to reduce network overhead.
Variational inference as iterative projection in a Bayesian Hilbert space with application to robotic state estimation

Abstract Variational Bayesian inference is an important machine learning tool that finds application from statistics to robotics. The goal is to find an approximate probability density function (PDF) from a chosen family that is in some sense “closest” to the full Bayesian posterior. Closeness is typically defined through the selection of an appropriate loss functional such as the Kullback-Leibler (KL) divergence. In this paper, we explore a new formulation of variational inference by exploiting the fact that (most) PDFs are members of a Bayesian Hilbert space under careful definitions of vector addition, scalar multiplication, and an inner product. We show that, under the right conditions, variational inference based on KL divergence can amount to iterative projection, in the Euclidean sense, of the Bayesian posterior onto a subspace corresponding to the selected approximation family. We work through the details of this general framework for the specific case of the Gaussian approximation family and show the equivalence to another Gaussian variational inference approach. We furthermore discuss the implications for systems that exhibit sparsity, which is handled naturally in Bayesian space, and give an example of a high-dimensional robotic state estimation problem that can be handled as a result. We provide some preliminary examples of how the approach could be applied to non-Gaussian inference and discuss the limitations of the approach in detail to encourage follow-on work along these lines.