Reviewerly/exHarmony huggingface dataset:
Yottixel – An Image Search Engine for Large Archives of Histopathology Whole Slide Images

With the emergence of digital pathology, searching for similar images in large archives has gained considerable attention. Image retrieval can provide pathologists with unprecedented access to the evidence embodied in already diagnosed and treated cases from the past. This paper proposes a search engine specialized for digital pathology, called Yottixel, a portmanteau for “one yotta pixel,” alluding to the big-data nature of histopathology images. The most impressive characteristic of Yottixel is its ability to represent whole slide images (WSIs) in a compact manner. Yottixel can perform millions of searches in real-time with a high search accuracy and low storage profile. Yottixel uses an intelligent indexing algorithm capable of representing WSIs with a mosaic of patches which are then converted into barcodes, called “Bunch of Barcodes” (BoB), the most prominent performance enabler of Yottixel. The performance of the prototype platform is qualitatively tested using 300 WSIs from the University of Pittsburgh Medical Center (UPMC) and 2,020 WSIs from The Cancer Genome Atlas Program (TCGA) provided by the National Cancer Institute. Both datasets amount to more than 4,000,000 patches of 1000 × 1000 pixels. We report three sets of experiments that show that Yottixel can accurately retrieve organs and malignancies, and its semantic ordering shows good agreement with the subjective evaluation of human observers.
An efficient equilibrium optimizer with mutation strategy for numerical optimization

To alleviate the shortcomings of the standard Equilibrium Optimizer, a new improved algorithm called Modified Equilibrium Optimizer is proposed in this work. This algorithm utilizes the Gaussian mutation and an additional exploratory search mechanism based on the concept of population division and reconstruction. The population in each iteration of the proposed algorithm is constructed using these mechanisms and standard search procedure of the Equilibrium Optimizer. These strategies attempt to maintain the diversity of solutions during the search, so that the tendency of stagnation towards the sub-optimal solutions can be avoided and the convergence rate can be boosted to obtain more accurate optimal solutions. To validate and analyze the performance of the Modified Equilibrium Optimizer, a collection of 33 benchmark problems and four engineering design problems are adopted. Later, in the paper, the Modified Equilibrium Optimizer has been used to train multilayer perceptrons. The experimental results and comparison based on several metrics such as statistical analysis, scalability test, diversity analysis, performance index analysis and convergence analysis demonstrate that the proposed algorithm can be considered a better metaheuristic optimization approach than other compared algorithms.
Blockchain and Smart Healthcare Security: A Survey

The Internet of Things (IoT) has penetrated its roots in almost every domain of life. Smart healthcare is one of the major domains that extensively uses IoT infrastructures and solutions. IoT-based smart healthcare systems have immensely added value to the healthcare domain with the use of wearable and mobile devices. This leads to a substantial use of health data sharing for the improved, accurate, and timely diagnosis. However, smart healthcare systems are highly vulnerable to several security breaches and various malignant attacks, such as privacy leakage, tempering, forgery, etc. Recently, the blockchain technology emerged as a propitious solution against such breaches and challenges. This paper presents an up-to-date survey on different challenges and open issues faced in smart healthcare due to the traditional security measures along with the security requirements of such domains. It also amalgamates the potentials of blockchain technology as a promising security measure, highlights potential challenges in the healthcare domain, and provides an analysis of different blockchain-based security solutions.
Deep Learning for Wireless Communications: An Emerging Interdisciplinary Paradigm

Wireless communications are envisioned to bring about dramatic changes in the future, with a variety of emerging applications, such as virtual reality (VR), Internet of Things (IoT), etc., becoming a reality.However, these compelling applications have imposed many new challenges, including unknown channel models, low-latency requirement in large-scale superdense networks, etc.The amazing success of deep learning (DL) in various fields, particularly in computer science, has recently stimulated increasing interest in applying it to address those challenges.Hence, in this review, a pair of dominant methodologies of using DL for wireless communications are investigated.The first one is DL-based architecture design, which breaks the classical model-based block design rule of wireless communications in the past decades.The second one is DL-based algorithm design, which will be illustrated by several examples in a series of typical techniques conceived for 5G and beyond.Their principles, key features, and performance gains will be discussed.Furthermore, open problems and future research opportunities will also be pointed out, highlighting the interplay between DL and wireless communications.We expect that this review can stimulate more novel ideas and exciting contributions for intelligent wireless communications.
Developing cybersecurity culture to influence employee behavior: A practice perspective

This paper identifies and explains five key initiatives that three Australian organizations have implemented to improve their respective cyber security cultures. The five key initiatives are: identifying key cyber security behaviors, establishing a 'cyber security champion' network, developing a brand for the cyber team, building a cyber security hub, and aligning security awareness activities with internal and external campaigns. These key initiatives have helped organizations exceed minimal standards-compliance to create functional cyber security cultures. This paper discusses why these initiatives have been effective and provides practical guidance on their integration into organizational security program
Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research Directions

Artificial intelligence (AI) techniques have grown rapidly in recent years in the context of computing with smart mobile phones that typically allows the devices to function in an intelligent manner. Popular AI techniques include machine learning and deep learning methods, natural language processing, as well as knowledge representation and expert systems, can be used to make the target mobile applications intelligent and more effective. In this paper, we present a comprehensive view on “mobile data science and intelligent apps” in terms of concepts and AI-based modeling that can be used to design and develop intelligent mobile applications for the betterment of human life in their diverse day-to-day situation. This study also includes the concepts and insights of various AI-powered intelligent apps in several application domains, ranging from personalized recommendation to healthcare services, including COVID-19 pandemic management in recent days. Finally, we highlight several research issues and future directions relevant to our analysis in the area of mobile data science and intelligent apps. Overall, this paper aims to serve as a reference point and guidelines for the mobile application developers as well as the researchers in this domain, particularly from the technical point of view.
The Rocketbox Library and the Utility of Freely Available Rigged Avatars

As part of the open sourcing of the Microsoft Rocketbox avatar library for research and academic purposes, here we discuss the importance of rigged avatars for the Virtual and Augmented Reality (VR, AR) research community. Avatars, virtual representations of humans, are widely used in VR applications. Furthermore many research areas ranging from crowd simulation to neuroscience, psychology or sociology have used avatars to investigate new theories or to demonstrate how they influence human performance and interactions. We divide this paper in two main parts: the first one gives an overview of the different methods available to create and animate avatars. We cover the current main alternatives for face and body animation as well introduce upcoming capture methods. The second part presents the scientific evidence of the utility of using rigged avatars for embodiment but also for applications such as crowd simulation and entertainment. All in all this paper attempts to convey why rigged avatars will be key to the future of VR and its wide adoption.
A Survey on Supply Chain Security: Application Areas, Security Threats, and Solution Architectures

The rapid improvement in the global connectivity standards has escalated the level of trade taking place among different parties. Advanced communication standards are allowing the trade of all types of commodities and services. Furthermore, the goods and services developed in a particular region are transcending boundaries to enter into foreign markets. Supply chains play an essential role in the trade of these goods. To be able to realize a connected world with no boundary restrictions in terms of goods and services, it is imperative to keep the associated supply chains transparent, secure, and trustworthy. Therefore, some fundamental changes in the current supply chain architecture are essential to achieve a secure trade environment. This article discusses the supply chain's security-critical application areas and presents a detailed survey of the security issues in the existing supply chain architecture. Various emerging technologies, such as blockchain, machine learning (ML), and physically unclonable functions (PUFs) as solutions to the vulnerabilities in the existing infrastructure of the supply chain have also been discussed. Recent studies reviewed in this work reveal a growing sentiment in the industry toward new and emerging technologies, such as Internet of Things (IoT), blockchain, and ML. While many organizations have already adopted IoT applications and artificial intelligence systems in their businesses, widespread adoption of blockchain remains distant. It has also been found that over the past decade, PUF-based authentication systems have gained much ground. However, a proper reference model for their implementation in complex supply chains is still missing.
Knowledge-Guided Multi-Label Few-Shot Learning for General Image Recognition

Recognizing multiple labels of an image is a practical yet challenging task, and remarkable progress has been achieved by searching for semantic regions and exploiting label dependencies. However, current works utilize RNN/LSTM to implicitly capture sequential region/label dependencies, which cannot fully explore mutual interactions among the semantic regions/labels and do not explicitly integrate label co-occurrences. In addition, these works require large amounts of training samples for each category, and they are unable to generalize to novel categories with limited samples. To address these issues, we propose a knowledge-guided graph routing (KGGR) framework, which unifies prior knowledge of statistical label correlations with deep neural networks. The framework exploits prior knowledge to guide adaptive information propagation among different categories to facilitate multi-label analysis and reduce the dependency of training samples. Specifically, it first builds a structured knowledge graph to correlate different labels based on statistical label co-occurrence. Then, it introduces the label semantics to guide learning semantic-specific features to initialize the graph, and it exploits a graph propagation network to explore graph node interactions, enabling learning contextualized image feature representations. Moreover, we initialize each graph node with the classifier weights for the corresponding label and apply another propagation network to transfer node messages through the graph. In this way, it can facilitate exploiting the information of correlated labels to help train better classifiers, especially for labels with limited training samples. We conduct extensive experiments on the traditional multi-label image recognition (MLR) and multi-label few-shot learning (ML-FSL) tasks and show that our KGGR framework outperforms the current state-of-the-art methods by sizable margins on the public benchmarks.
Nested Named Entity Recognition via Second-best Sequence Learning and Decoding

When an entity name contains other names within it, the identification of all combinations of names can become difficult and expensive. We propose a new method to recognize not only outermost named entities but also inner nested ones. We design an objective function for training a neural model that treats the tag sequence for nested entities as the second best path within the span of their parent entity. In addition, we provide the decoding method for inference that extracts entities iteratively from outermost ones to inner ones in an outside-to-inside way. Our method has no additional hyperparameters to the conditional random field based model widely used for flat named entity recognition tasks. Experiments demonstrate that our method performs better than or at least as well as existing methods capable of handling nested entities, achieving F1-scores of 85.82%, 84.34%, and 77.36% on ACE-2004, ACE-2005, and GENIA datasets, respectively.
Repairing deep neural networks

Significant interest in applying Deep Neural Network (DNN) has fueled the need to support engineering of software that uses DNNs. Repairing software that uses DNNs is one such unmistakable SE need where automated tools could be beneficial; however, we do not fully understand challenges to repairing and patterns that are utilized when manually repairing DNNs. What challenges should automated repair tools address? What are the repair patterns whose automation could help developers? Which repair patterns should be assigned a higher priority for building automated bug repair tools? This work presents a comprehensive study of bug fix patterns to address these questions. We have studied 415 repairs from Stack Overflow and 555 repairs from GitHub for five popular deep learning libraries Caffe, Keras, Tensorflow, Theano, and Torch to understand challenges in repairs and bug repair patterns. Our key findings reveal that DNN bug fix patterns are distinctive compared to traditional bug fix patterns; the most common bug fix patterns are fixing data dimension and neural network connectivity; DNN bug fixes have the potential to introduce adversarial vulnerabilities; DNN bug fixes frequently introduce new bugs; and DNN bug localization, reuse of trained model, and coping with frequent releases are major challenges faced by developers when fixing bugs. We also contribute a benchmark of 667 DNN (bug, repair) instances.
"Talking without a Voice"

Exploring communication dynamics in digital social spaces such as massively multiplayer online games and 2D/3D virtual worlds has been a long standing concern in HCI and CSCW. As online social spaces evolve towards more natural embodied interaction, it is important to explore how non-verbal communication can be supported in more nuanced ways in these spaces and introduce new social interaction consequences. In this paper we especially focus on understanding novel non-verbal communication in social virtual reality (VR). We report findings of two empirical studies. Study 1 collected observational data to explore the types of non-verbal interactions being used naturally in social VR. Study 2 was an interview study (N=30) that investigated people's perceptions of non-verbal communication in social VR as well as the resulting interaction outcomes. This study helps address the limitations in prior literature on non-verbal communication dynamics in online social spaces. Our findings on what makes non-verbal communication in social VR unique and socially desirable extend our current understandings of the role of non-verbal communication in social interaction. We also highlight potential design implications that aim at better supporting non-verbal communication in social VR.
A Survey on Transfer Learning

To facilitate learning in a target domain, transfer learning borrows knowledge from a source domain. What and how to transfer are two main issues that need to be addressed in transferring learning. Different transfer learning algorithms result in different knowledge transferred between them for a couple of domains. To find the optimal transfer learning algorithm that maximizes learning efficiency in the target domain, scientists need to investigate all current computationally intractable transfer learning algorithms exhaustively. A sub-optimal algorithm is selected as a trade-off, which in an ad hoc way requires considerable expertise. In instructional psychology, meanwhile, it is commonly recognized that people enhance the transfer of teaching abilities to decide what to transfer. This paper discusses what is transfer learning, the different transfer learning techniques, future scope, and applications of it.
Network traffic classification using deep convolutional recurrent autoencoder neural networks for spatial–temporal features extraction

The right choice of features to be extracted from individual or aggregated observations is an extremely critical factor for the success of modern network traffic classification approaches based on machine learning. Such activity, usually in charge of the designers of the classification scheme is strongly related to their experience and skills, and definitely characterizes the whole approach, implementation strategy as well as its performance. The main aim of this work is supporting this process by mining new and more expressive, meaningful and discriminating features from the basic ones without human intervention. For this purpose, a novel autoencoder-based deep neural network architecture is proposed where multiple autoencoders are embedded with convolutional and recurrent neural networks to elicit relevant knowledge about the relations existing among the basic features (spatial-features) and their evolution over time (temporal-features). Such knowledge, consisting in new properties that are not immediately evident and better represent the most hidden and representative traffic dynamics can be successfully exploited by machine learning-based classifiers. Different network combinations are analyzed both from a theoretical perspective, and through specific performance evaluation experiments on a real network traffic dataset. We show that the traffic classifier obtained by stacking the autoencoder with a fully-connected neural network, achieves up to a 28% improvement in average accuracy over state-of-the-art machine learning-based approaches, up to a 10% over pure convolutional and recurrent stacked neural networks, and 18% over pure feed-forward networks. It is also able to maintain high accuracy even in the presence of unbalanced training datasets.
Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding

For many fundamental scene understanding tasks, it is difficult or impossible to obtain per-pixel ground truth labels from real images. We address this challenge by introducing Hypersim, a photorealistic synthetic dataset for holistic indoor scene understanding. To create our dataset, we leverage a large repository of synthetic scenes created by professional artists, and we generate 77,400 images of 461 indoor scenes with detailed per-pixel labels and corresponding ground truth geometry. Our dataset: (1) relies exclusively on publicly available 3D assets; (2) includes complete scene geometry, material information, and lighting information for every scene; (3) includes dense per-pixel semantic instance segmentations and complete camera information for every image; and (4) factors every image into diffuse reflectance, diffuse illumination, and a non-diffuse residual term that captures view-dependent lighting effects.We analyze our dataset at the level of scenes, objects, and pixels, and we analyze costs in terms of money, computation time, and annotation effort. Remarkably, we find that it is possible to generate our entire dataset from scratch, for roughly half the cost of training a popular open-source natural language processing model. We also evaluate sim-to-real transfer performance on two real-world scene understanding tasks – semantic segmentation and 3D shape prediction – where we find that pre-training on our dataset significantly improves performance on both tasks, and achieves state-of-the-art performance on the most challenging Pix3D test set. All of our rendered image data, as well as all the code we used to generate our dataset and perform our experiments, is available online.
Social cybersecurity: an emerging science

With the rise of online platforms where individuals could gather and spread information came the rise of online cybercrimes aimed at taking advantage of not just single individuals but collectives. In response, researchers and practitioners began trying to understand this digital playground and the way in which individuals who were socially and digitally embedded could be manipulated. What is emerging is a new scientific and engineering discipline-social cybersecurity. This paper defines this emerging area, provides case examples of the research issues and types of tools needed, and lays out a program of research in this area.
Hate Speech: A Systematized Review

This review focuses on papers on Hate Speech, particularly in legal and communication studies indexed in Web of Science. It analyzes output published in English and in Spanish as well as surveys the predominant disciplines in which these studies are written, their trend over time, by country, and type of document. This research is extended to determine the debates, lines of work of greatest interest, and the theories elaborated. The legal literature is intended to define hate speech and hate crime for the purposes of applying criminal sanctions. From the communication standpoint, the analysis of hate speech in the media is key to understanding the type of message used, its emitter, the way in which the message rallies supporters, and how they interpret the message. Spanish studies mostly fall within the legal area, in which they focus on cases of insult directed at the Catholic religion. We discuss the importance of interdisciplinarity and transversality and propose a mapping of hate speech that lends itself to comparisons between countries to assess measures to counteract their effects.
Programmable four-photon graph states on a silicon chip

Future quantum computers require a scalable architecture on a scalable technology-one that supports millions of high-performance components. Measurement-based protocols, using graph states, represent the state of the art in architectures for optical quantum computing. Silicon photonics technology offers enormous scale and proven quantum optical functionality. Here we produce and encode photonic graph states on a mass-manufactured chip, using four on-chip-generated photons. We programmably generate all types of four-photon graph state, implementing a basic measurement-based protocol, and measure high-visibility heralded interference of the chip's four photons. We develop a model of the device and bound the dominant sources of error using Bayesian inference. The combination of measurement-based quantum computation, silicon photonics technology, and on-chip multi-pair sources will be a useful one for future scalable quantum information processing with photons.