Reviewerly/exHarmony huggingface dataset:
BRACS: A Dataset for BReAst Carcinoma Subtyping in H&amp;E Histology Images

Breast cancer is the most commonly diagnosed cancer and registers the highest number of deaths for women. Advances in diagnostic activities combined with large-scale screening policies have significantly lowered the mortality rates for breast cancer patients. However, the manual inspection of tissue slides by pathologists is cumbersome, time-consuming and is subject to significant inter- and intra-observer variability. Recently, the advent of whole-slide scanning systems has empowered the rapid digitization of pathology slides and enabled the development of Artificial Intelligence (AI)-assisted digital workflows. However, AI techniques, especially Deep Learning, require a large amount of high-quality annotated data to learn from. Constructing such task-specific datasets poses several challenges, such as data-acquisition level constraints, time-consuming and expensive annotations and anonymization of patient information. In this paper, we introduce the BReAst Carcinoma Subtyping (BRACS) dataset, a large cohort of annotated Hematoxylin and Eosin (H&amp;E)-stained images to advance AI development in the automatic characterization of breast lesions. BRACS contains 547 Whole-Slide Images (WSIs) and 4539 Regions Of Interest (ROIs) extracted from the WSIs. Each WSI and respective ROIs are annotated by the consensus of three board-certified pathologists into different lesion categories. Specifically, BRACS includes three lesion types, i.e., benign, malignant and atypical, which are further subtyped into seven categories. It is, to the best of our knowledge, the largest annotated dataset for breast cancer subtyping both at WSI and ROI levels. Furthermore, by including the understudied atypical lesions, BRACS offers a unique opportunity for leveraging AI to better understand their characteristics. We encourage AI practitioners to develop and evaluate novel algorithms on the BRACS dataset to further breast cancer diagnosis and patient care. Database URL: https://www.bracs.icar.cnr.it/.
Non-IID data and Continual Learning processes in Federated Learning: A long road ahead

Federated Learning is a novel framework that allows multiple devices or institutions to train a machine learning model collaboratively while preserving their data private. This decentralized approach is prone to suffer the consequences of data statistical heterogeneity, both across the different entities and over time, which may lead to a lack of convergence. To avoid such issues, different methods have been proposed in the past few years. However, data may be heterogeneous in lots of different ways, and current proposals do not always determine the kind of heterogeneity they are considering. In this work, we formally classify data statistical heterogeneity and review the most remarkable learning Federated Learning strategies that are able to face it. At the same time, we introduce approaches from other machine learning frameworks. In particular, Continual Learning strategies are worthy of special attention, since they are able to handle habitual kinds of data heterogeneity. Throughout this paper, we present many methods that could be easily adapted to the Federated Learning settings to improve its performance. Apart from theoretically discussing the negative impact of data heterogeneity, we examine it and show some empirical results using different types of non-IID data.
Inside-Out Instrument Tracking for Surgical Navigation in Augmented Reality

Surgical navigation requires tracking of instruments with respect to the patient. Conventionally, tracking is done with stationary cameras, and the navigation information is displayed on a stationary display. In contrast, an augmented reality (AR) headset can superimpose surgical navigation information directly in the surgeon’s view. However, AR needs to track the headset, the instruments and the patient, often by relying on stationary infrastructure. We show that 6DOF tracking can be obtained without any stationary, external system by purely utilizing the on-board stereo cameras of a HoloLens 2 to track the same retro-reflective marker spheres used by current optical navigation systems. Our implementation is based on two tracking pipelines complementing each other, one using conventional stereo vision techniques, the other relying on a single-constraint-at-a-time extended Kalman filter. In a technical evaluation of our tracking approach, we show that clinically relevant accuracy of 1.70 mm/1.11° and real-time performance is achievable. We further describe an example application of our system for untethered end-to-end surgical navigation.
An efficient derivative-free optimization algorithm inspired by avian life-saving manoeuvres

A novel meta-heuristic algorithm is introduced that is inspired by aerial escaping strategies of a bird to avoid being captured by the hunter. The escaping bird chooses its life-saving maneuver based on the mass and velocity of it and of the predator bird. These flights are simulated in the developed algorithm as movements of artificial search agents. Hunting flight of the artificial predator, plays the role of search intensification; while the escaping prey explores new places in the space by flying in the opposite direction or making a turn. The method adaptively tunes the flight lengths in each pair of the prey/predator agents to avoid use of extra control parameters. It takes advantage of swarm intelligence and opposition-based learning. Twelve test functions as well as seven engineering problems in discrete, continuous and mixed discrete-continuous types are successfully solved by the proposed Escaping Bird Search; exhibiting its high convergence rate and competitive performance in comparison with the other treated meta-heuristics.
OntoKnowNHS: Ontology Driven Knowledge Centric Novel Hybridised Semantic Scheme for Image Recommendation Using Knowledge Graph

Multimedia content is increasing immensely as there are various websites available to upload images. Image retrieval is a method of searching for, viewing, and retrieving images from a database. Close text choices are often accepted by image search engines. It is difficult for search engines to comprehend users’ search intent merely through terms, resulting in unclear and quavering search results that are far from satisfying. To solve the dilemma in text-based picture retrieval, it is important to employ content-first search. The proposed OntoKnowNHS model is composed of Domain Ontology based query term enrichment and Knowledge enrichment of the images with the help of Google’s Knowledge Base API and Wikidata, incorporated with the Knowledge Graph of images which is compared using Convolutional Neural Networks and, the semantic similarity is computed using Kullback Leibler Divergence, Concept Similarity, and Normalised Compression Distance which recommends images from both Knowledge Graphs and Redefined Image Tag set. All of these factors work together to improve accuracy. The Flickr30k dataset is used to integrate user preferences for picture suggestions, which are then categorized using Convolutional Neural Networks with the aid of extracted query terms from the user information using domain ontology. The developed approach has an accuracy of 96.41% and outperforms the alternative reference models by weakening the robustness of conventional image Recommender Systems.
Understanding the impact of control levels over emotion-aware chatbots

Emotion-aware chatbots that can sense human emotions are becoming increasingly prevalent. However, the exposition of emotions by emotion-aware chatbots undermines human autonomy and users' trust. One way to ensure autonomy is through the provision of control. Offering too much control, in turn, may increase users’ cognitive effort. To investigate the impact of control over emotion-aware chatbots on autonomy, trust, and cognitive effort, as well as user behavior, we carried out an experimental study with 176 participants. The participants interacted with a chatbot that provided emotional feedback and were additionally able to control different chatbot dimensions (e.g., timing, appearance, and behavior). Our findings show, first, that higher control levels increase autonomy and trust in emotion-aware chatbots. Second, higher control levels do not significantly increase cognitive effort. Third, in our post hoc behavioral analysis, we identify four behavioral control strategies based on control feature usage timing, quantity, and cognitive effort. These findings shed light on the individual preferences of user control over emotion-aware chatbots. Overall, our study contributes to the literature by showing the positive effect of control over emotion-aware chatbots and by identifying four behavioral control strategies. With our findings, we also provide practical implications for future design of emotion-aware chatbots.
Integrative multiomics-histopathology analysis for breast cancer classification

Histopathologic evaluation of biopsy slides is a critical step in diagnosing and subtyping breast cancers. However, the connections between histology and multi-omics status have never been systematically explored or interpreted. We developed weakly supervised deep learning models over hematoxylin-and-eosin-stained slides to examine the relations between visual morphological signal, clinical subtyping, gene expression, and mutation status in breast cancer. We first designed fully automated models for tumor detection and pathology subtype classification, with the results validated in independent cohorts (area under the receiver operating characteristic curve ≥ 0.950). Using only visual information, our models achieved strong predictive performance in estrogen/progesterone/HER2 receptor status, PAM50 status, and TP53 mutation status. We demonstrated that these models learned lymphocyte-specific morphological signals to identify estrogen receptor status. Examination of the PAM50 cohort revealed a subset of PAM50 genes whose expression reflects cancer morphology. This work demonstrates the utility of deep learning-based image models in both clinical and research regimes, through its ability to uncover connections between visual morphology and genetic statuses.
Free electrons can induce entanglement between photons

Abstract Entanglement of photons is a fundamental feature of quantum mechanics, which stands at the core of quantum technologies such as photonic quantum computing, communication, and sensing. An ongoing challenge in all these is finding an efficient and controllable mechanism to entangle photons. Recent experimental developments in electron microscopy enable to control the quantum interaction between free electrons and light. Here, we show that free electrons can create entanglement and bunching of light. Free electrons can control the second-order coherence of initially independent photonic states, even in spatially separated cavities that cannot directly interact. Free electrons thus provide a type of optical nonlinearity that acts in a nonlocal manner, offering a way of heralding the creation of entanglement. Intriguingly, pre-shaping the electron’s wavefunction provides the knob for tuning the photonic quantum correlations. The concept can be generalized to entangle not only photons but also photonic quasiparticles such as plasmon-polaritons and phonons.
Concept drift type identification based on multi-sliding windows

Concept drift is a common and important issue in streaming data analysis and mining. Thus far, many concept drift detection methods have been proposed but may not be able to identify the type of concept drift, which will result in some difficulties, such as extracting the wrong key information, inadequate model learning and poor detection efficiency. To solve these problems, a concept drift type identification method is proposed based on multi-sliding windows (CDT_MSW). This method consists of three processes. During the first detection process, the drift position is detected by sliding the basic window forward. Then, in the growth process, the drift length is detected using the growth of the adjoint window, and the drift category is identified according to the drift length. Finally, during tracking process, the drift subcategory can be accurately identified according to the different tracking flow ratio curves generated during window tracking. Experimental results show that the proposed method can effectively identify the type of concept drift, accurately analyze the key information during online learning and improve the efficiency and generalization performance of streaming data analysis and mining.
Smooth and collision-free trajectory generation in cluttered environments using cubic B-spline form

This paper presents an effective trajectory generation algorithm to shorten and smooth the jerky paths obtained from the sampling-based planners in cluttered environments. We utilize the cubic smoothing B-spline curves to represent the final trajectories that are collision-free, strict C2 continuity and respect velocity and acceleration constraints. Our algorithm mainly contains two core parts: a two-layer local adjustment strategy to avoid collisions and a fast trajectory pruning algorithm by introducing the Douglas-Peucker algorithm into the Path pruning technique to remove unnecessary and jerky motions. The two-layer local adjustment strategy takes advantage of B-splines’ local support properties and geometry operations, adaptively selecting the trajectory adjustment method according to the complexity of the environment. We emphasize that our trajectory adjustment strategy only needs to adjust the segments around the colliding sections, and it is even feasible for environments with narrow passages. Furthermore, the pruning algorithm decimates the initial path with fewer dominant points, significantly accelerating the pruning speed. Simulation and actual experimental results show that our algorithm can rapidly generate smooth, collision-free, and natural-looking trajectories.
A Novel Adaptive Sparrow Search Algorithm Based on Chaotic Mapping and T-Distribution Mutation

Aiming at the problems of the basic sparrow search algorithm (SSA) in terms of slow convergence speed and the ease of falling into the local optimum, the chaotic mapping strategy, adaptive weighting strategy and t-distribution mutation strategy are introduced to develop a novel adaptive sparrow search algorithm, namely the CWTSSA in this paper. In the proposed CWTSSA, the chaotic mapping strategy is employed to initialize the population in order to enhance the population diversity. The adaptive weighting strategy is applied to balance the capabilities of local mining and global exploration, and improve the convergence speed. An adaptive t-distribution mutation operator is designed, which uses the iteration number t as the degree of freedom parameter of the t-distribution to improve the characteristic of global exploration and local exploration abilities, so as to avoid falling into the local optimum. In order to prove the effectiveness of the CWTSSA, 15 standard test functions and other improved SSAs, differential evolution (DE), particle swarm optimization (PSO), gray wolf optimization (GWO) are selected here. The compared experiment results indicate that the proposed CWTSSA can obtain higher convergence accuracy, faster convergence speed, better diversity and exploration abilities. It provides a new optimization algorithm for solving complex optimization problems.
Unbounded and Efficient Revocable Attribute-Based Encryption With Adaptive Security for Cloud-Assisted Internet of Things

Existing attribute-based encryption (ABE) schemes with revocation to secure the cloud-assisted Internet of Things (IoTs) raise challenges, such as eliminating the need for predefined public parameters in system initialization, performing the encryption and decryption operations efficiently, and achieving adaptive security under standard security assumption. In this article, we address these challenges by proposing an unbounded and efficient revocable ABE scheme with adaptive security for cloud-assisted IoTs. Distinct from the previous approaches in this field, our scheme not only efficiently realizes access control over encrypted data in a fine-grained and revocable way but also is proved to be adaptively secure under standard decision linear assumption. Meanwhile, the parameters do not need to be predefined in the system initialization and thus, our scheme satisfies the unbounded property. Moreover, the monotonic span program (MSP) is elegantly utilized as the access structure to reduce the number of bilinear pairing and exponentiation operations for encryption and decryption. Theoretical performance analysis and experiment evaluation disclose that our proposed scheme owns outstanding feasibility, efficiency, and effectiveness.
The Impact of Augmented Reality Techniques on Cartographic Visualization

Abstract Augmented reality (AR) is playing an increasingly important role in a variety of everyday application scenarios. Users are not completely disconnected from the current sensory influences of reality. They are merely confronted with additional virtual objects that are projected into reality. This allows users to obtain additional spatial information, which makes this technology interesting for cartographic applications (e.g. navigation). The dynamic positioning of the superimposed image in the scene being viewed is crucial for the generation of AR elements displayed correctly in terms of perspective. Understanding these technical basics is an important prerequisite for the cartographic use of augmented reality. The different techniques influence the visualization and the perception of AR elements in 3D space. This article highlights important visualization properties of current augmented reality techniques.
ESPnet-SLU: Advancing Spoken Language Understanding Through ESPnet

As Automatic Speech Processing (ASR) systems are getting better, there is an increasing interest of using the ASR output to do downstream Natural Language Processing (NLP) tasks. However, there are few open source toolkits that can be used to generate reproducible results on different Spoken Language Understanding (SLU) benchmarks. Hence, there is a need to build an open source standard that can be used to have a faster start into SLU research. We present ESPnet-SLU, which is designed for quick development of spoken language understanding in a single framework. ESPnet-SLU is a project inside end-to-end speech processing toolkit, ESPnet, which is a widely used open-source standard for various speech processing tasks like ASR, Text to Speech (TTS) and Speech Translation (ST). We enhance the toolkit to provide implementations for various SLU benchmarks that enable researchers to seamlessly mix-and-match different ASR and NLU models. We also provide pretrained models with intensively tuned hyper-parameters that can match or even outperform the current state-of-the-art performances. The toolkit is publicly available at https://github.com/espnet/espnet.
Exploring key factors influencing customer behavioral intention in robot restaurants

Purpose The purpose of this study is to explore the impact of the robot restaurant servicescape and robot service competence (RSC) on customers’ behavioral intentions and to analyze the mediating role of hedonic value (HV) and utilitarian value (UV) in these relationships and the moderating role of individual characteristics. Design/methodology/approach This research involves seven constructs to be measured, namely, servicescape, RSC, negative attitude toward robots (NAR), openness to change (OC), HV, UV and behavioral intention. This research selects Foodom robot restaurants, located in Shunde and Guangzhou of China, as the research site, and the research objects are customers having dinner in the restaurant. A total of 485 valid data was collected. Structural equation modeling was used to analyze the data, verify the proposed research model, and test the research hypotheses. Findings The study finds that servicescape and RSC improve customer behavioral intention. Additionally, HV and UV mediate the influence of servicescape and RSC on customer behavioral intention. Moreover, OC negatively moderates the influence of servicescape on UV and that a NAR negatively moderates the influence of RSC on HV. Originality/value Through carefully design of servicescape and the improvement in service capabilities of robots, the original service delivery dominated by frontline service personnel can be transformed into service delivery dominated by service robots, which is conductive to providing a pleasant and unforgettable experience for customers.
Data association in multiple object tracking: A survey of recent techniques

The advances of Visual object tracking tasks in computer vision have enabled a growing value in its application to video surveillance, particularly in a traffic scenario. In recent years, significant attention has been made for the improvement of multiple object tracking frameworks to be effective in real-time while maintaining accuracy and generality. By breaking down the tasks involved in a Multiple Object Tracking framework based on the Tracking-By-Detection approach — an extension of simply detecting and identifying objects, further involved solving a filtering problem by defining a similarity function to associate objects. Hence, this paper focuses on the task of data association via uniquely defined similarity functions and filters only where we review current literature about these techniques which have been used to advance the performance in MOT for vehicle and pedestrian scenarios. While there is difficulty in classifying the quantitative results for the association task only within a proposed MOT framework, our study tries to outline the fundamental ideas put forward by researchers and compare results in a theoretically qualitative approach. Tracking methods are reviewed by categories based on legacy techniques like Probabilistic and Hierarchical methods, followed by an analysis of new approaches and hybrid models. The models identified in each category are further analysed based on performance in stability, accuracy, robustness, speed and computational complexity to derive an understanding of which direction the research within the data association level is strong and which is lacking. Our review further aims to identify the successful models applied to recognize the weaknesses for future improvement.
An Empirical Study of the GraphSAGE and Word2vec Algorithms for Graph Multiclass Classification

A fundamental task in machine learning involves node classification. However, when considering the context of large graph data, this problem becomes much more challenging. In this paper, we use the Wikipedia hyperlink dataset to evaluate our semi-supervised node classification model. Given a small set of labeled nodes, we develop a multiclass classifier that utilizes the network structure as well as textual descriptions of nodes to predict the most probable category(label) for each test node in a semi-supervised setting. Our experiment shows promising results for graph multiclass classification using directed graphSAGE and word2vec algorithms together. We also visualize the node embeddings in 2D using the t-SNE method.