Reviewerly/exHarmony huggingface dataset:
Learning What to Learn for Video Object Segmentation

Video object segmentation (VOS) is a highly challenging problem, since the target object is only defined by a first-frame reference mask during inference. The problem of how to capture and utilize this limited information to accurately segment the target remains a fundamental research question. We address this by introducing an end-to-end trainable VOS architecture that integrates a differentiable few-shot learner. Our learner is designed to predict a powerful parametric model of the target by minimizing a segmentation error in the first frame. We further go beyond the standard few-shot learning paradigm by learning what our target model should learn in order to maximize segmentation accuracy. We perform extensive experiments on standard benchmarks. Our approach sets a new state-of-the-art on the large-scale YouTube-VOS 2018 dataset by achieving an overall score of 81.5, corresponding to a $$2.6\%$$ relative improvement over the previous best result. The code and models are available at https://github.com/visionml/pytracking .
Smart-Contract Aware Ethereum and Client-Fog-Cloud Healthcare System

The Internet of Medical Things (IoMT) is increasingly being used for healthcare purposes. IoMT enables many sensors to collect patient data from various locations and send it to a distributed hospital for further study. IoMT provides patients with a variety of paid programmes to help them keep track of their health problems. However, the current system services are expensive, and offloaded data in the healthcare network are insecure. The research develops a new, cost-effective and stable IoMT framework based on a blockchain-enabled fog cloud. The study aims to reduce the cost of healthcare application services as they are processing in the system. The study devises an IoMT system based on different algorithm techniques, such as Blockchain-Enable Smart-Contract Cost-Efficient Scheduling Algorithm Framework (BECSAF) schemes. Smart-Contract Blockchain schemes ensure data consistency and validation with symmetric cryptography. However, due to the different workflow tasks scheduled on other nodes, the heterogeneous, earliest finish, time-based scheduling deals with execution under their deadlines. Simulation results show that the proposed algorithm schemes outperform all existing baseline approaches in terms of the implementation of applications.
Solving Multiobjective Constrained Trajectory Optimization Problem by an Extended Evolutionary Algorithm

Highly constrained trajectory optimization problems are usually difficult to solve. Due to some real-world requirements, a typical trajectory optimization model may need to be formulated containing several objectives. Because of the discontinuity or nonlinearity in the vehicle dynamics and mission objectives, it is challenging to generate a compromised trajectory that can satisfy constraints and optimize objectives. To address the multiobjective trajectory planning problem, this paper applies a specific multiple-shooting discretization technique with the newest NSGA-III optimization algorithm and constructs a new evolutionary optimal control solver. In addition, three constraint handling algorithms are incorporated in this evolutionary optimal control framework. The performance of using different constraint handling strategies is detailed and analyzed. The proposed approach is compared with other well-developed multiobjective techniques. Experimental studies demonstrate that the present method can outperform other evolutionary-based solvers investigated in this paper with respect to convergence ability and distribution of the Pareto-optimal solutions. Therefore, the present evolutionary optimal control solver is more attractive and can offer an alternative for optimizing multiobjective continuous-time trajectory optimization problems.
ASSERT: Anti-Spoofing with Squeeze-Excitation and Residual Networks

We present JHU's system submission to the ASVspoof 2019 Challenge: Anti-Spoofing with Squeeze-Excitation and Residual neTworks (ASSERT). Anti-spoofing has gathered more and more attention since the inauguration of the ASVspoof Challenges, and ASVspoof 2019 dedicates to address attacks from all three major types: text-to-speech, voice conversion, and replay. Built upon previous research work on Deep Neural Network (DNN), ASSERT is a pipeline for DNN-based approach to anti-spoofing. ASSERT has four components: feature engineering, DNN models, network optimization and system combination, where the DNN models are variants of squeeze-excitation and residual networks. We conducted an ablation study of the effectiveness of each component on the ASVspoof 2019 corpus, and experimental results showed that ASSERT obtained more than 93% and 17% relative improvements over the baseline systems in the two sub-challenges in ASVspooof 2019, ranking ASSERT one of the top performing systems. Code and pretrained models will be made publicly available.
Corona Virus (COVID-19) Pandemic and Work from Home: Challenges of Cybercrimes and Cybersecurity

The Coronavirus (COVID-19) pandemic has led to biggest number of employees globally bound to work remotely. The people working from home required awareness and knowledge of phishing scams, the fastest growing type of cybercrime, many of which are now playing on fears of the Coronavirus. Employees from organizations of all sizes and types now have minimal cybersecurity resources, if any, compared to what is normally available to them. Organisations are required to ensure any endpoint that an employee is using are fully protected. As the Absolute 2019 Global Endpoint Security Trend Report showed, 42 per cent of endpoints are unprotected at any given time. As the home-working becomes the new normal, criminals are seeking to capitalise on the widespread panic – and succeeding, alas. New coronavirus-themed phishing scams are leveraging fear, hooking vulnerable people and taking advantage of workplace disruption. Therefore, the people working from home should immediately get educated about their cyber privacy and cybersecurity failing which the global cybercrime damage may costs as much as double by the end of this year.
Jupyter: Thinking and Storytelling With Code and Data

Project Jupyter is an open-source project for interactive computing widely used in data science, machine learning, and scientific computing. We argue that even though Jupyter helps users perform complex, technical work, Jupyter itself solves problems that are fundamentally human in nature. Namely, Jupyter helps humans to think and tell stories with code and data. We illustrate this by describing three dimensions of Jupyter: 1) interactive computing; 2) computational narratives; and 3) the idea that Jupyter is more than software. We illustrate the impact of these dimensions on a community of practice in earth and climate science.
Trust and reputation for Internet of Things: Fundamentals, taxonomy, and open research challenges

Internet of things (IoT) is a contemporary technology, which links a tremendous number of devices with each other to ease the life via many useful services such as information exchange, monitoring, and control. IoT comprises different types of entities such as sensors and RFID tags, which mostly deployed in unattended, sensitive, and hostile territories. Due to rapid scalability and high heterogeneity, traditional security approaches fails to provide adequate security mechanisms for the current IoT infrastructure. The possibility of insecure and unattended deployment make some of IoT's entities subject to be captured physically by the attackers. As a result, the victim device can be exploited as a gateway to compromise the entire network. Furthermore, an entity may not work correctly because of resources constraints or instability of network's link. Recently, trust and reputation (TR) extended in IoT to monitor the behaviors deviation of IoT entities. Many TR models introduced, to incorporate the trust concepts in IoT as a new security paradigm. In this study, we provide thematic taxonomy for trust in IoT, considering several issues such as understanding of trust entity roles, trust properties, trust applications, levels of trust management, trust metrics, trust computation schemes and attacks on TR. Finally, the survey presents advances and open research challenges in the IoT's trust.
Developing RNN-T Models Surpassing High-Performance Hybrid Models with Customization Capability

Because of its streaming nature, recurrent neural network transducer (RNN-T) is a very promising end-to-end (E2E) model that may replace the popular hybrid model for automatic speech recognition. In this paper, we describe our recent development of RNN-T models with reduced GPU memory consumption during training, better initialization strategy, and advanced encoder modeling with future lookahead. When trained with Microsoft's 65 thousand hours of anonymized training data, the developed RNN-T model surpasses a very well trained hybrid model with both better recognition accuracy and lower latency. We further study how to customize RNN-T models to a new domain, which is important for deploying E2E models to practical scenarios. By comparing several methods leveraging text-only data in the new domain, we found that updating RNN-T's prediction and joint networks using text-to-speech generated from domain-specific text is the most effective.
Score-Based Generative Modeling through Stochastic Differential Equations

Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise. Crucially, the reverse-time SDE depends only on the time-dependent gradient field (a.k.a., score) of the perturbed data distribution. By leveraging advances in score-based generative modeling, we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples. We show that this framework encapsulates previous approaches in score-based generative modeling and diffusion probabilistic modeling, allowing for new sampling procedures and new modeling capabilities. In particular, we introduce a predictor-corrector framework to correct errors in the evolution of the discretized reverse-time SDE. We also derive an equivalent neural ODE that samples from the same distribution as the SDE, but additionally enables exact likelihood computation, and improved sampling efficiency. In addition, we provide a new way to solve inverse problems with score-based models, as demonstrated with experiments on class-conditional generation, image inpainting, and colorization. Combined with multiple architectural improvements, we achieve record-breaking performance for unconditional image generation on CIFAR-10 with an Inception score of 9.89 and FID of 2.20, a competitive likelihood of 2.99 bits/dim, and demonstrate high fidelity generation of 1024×1024 images for the first time from a score-based generative model.
FASPell: A Fast, Adaptable, Simple, Powerful Chinese Spell Checker Based On DAE-Decoder Paradigm

We propose a Chinese spell checker -FASPell based on a new paradigm which consists of a denoising autoencoder (DAE) and a decoder.In comparison with previous stateof-the-art models, the new paradigm allows our spell checker to be Faster in computation, readily Adaptable to both simplified and traditional Chinese texts produced by either humans or machines, and to require much Simpler structure to be as much Powerful in both error detection and correction.These four achievements are made possible because the new paradigm circumvents two bottlenecks.First, the DAE curtails the amount of Chinese spell checking data needed for supervised learning (to <10k sentences) by leveraging the power of unsupervisedly pre-trained masked language model as in BERT, XLNet, MASS etc.Second, the decoder helps to eliminate the use of confusion set that is deficient in flexibility and sufficiency of utilizing the salient feature of Chinese character similarity.1. inflexibility to address the issue that confusing characters in one scenario may not be confusing in another.The difference between simplified and traditional Chinese shown in Table 1 is an example.Wang et al. (2018) also suggest that confusing characters for ma-
Dynamic Gesture Recognition Using Surface EMG Signals Based on Multi-Stream Residual Network

Gesture recognition technology is widely used in the flexible and precise control of manipulators in the assisted medical field. Our MResLSTM algorithm can effectively perform dynamic gesture recognition. The result of surface EMG signal decoding is applied to the controller, which can improve the fluency of artificial hand control. Much current gesture recognition research using sEMG has focused on static gestures. In addition, the accuracy of recognition depends on the extraction and selection of features. However, Static gesture research cannot meet the requirements of natural human-computer interaction and dexterous control of manipulators. Therefore, a multi-stream residual network (MResLSTM) is proposed for dynamic hand movement recognition. This study aims to improve the accuracy and stability of dynamic gesture recognition. Simultaneously, it can also advance the research on the smooth control of the Manipulator. We combine the residual model and the convolutional short-term memory model into a unified framework. The architecture extracts spatiotemporal features from two aspects: global and deep, and combines feature fusion to retain essential information. The strategy of pointwise group convolution and channel shuffle is used to reduce the number of network calculations. A dataset is constructed containing six dynamic gestures for model training. The experimental results show that on the same recognition model, the gesture recognition effect of fusion of sEMG signal and acceleration signal is better than that of only using sEMG signal. The proposed approach obtains competitive performance on our dataset with the recognition accuracies of 93.52%, achieving state-of-the-art performance with 89.65% precision on the Ninapro DB1 dataset. Our bionic calculation method is applied to the controller, which can realize the continuity of human-computer interaction and the flexibility of manipulator control.
EXS

Retrieval models in information retrieval are used to rank documents for typically under-specified queries. Today machine learning is used to learn retrieval models from click logs and/or relevance judgments that maximizes an objective correlated with user satisfaction. As these models become increasingly powerful and sophisticated, they also become harder to understand. Consequently, it is hard for to identify artifacts in training, data specific biases and intents from a complex trained model like neural rankers even if trained purely on text features. EXS is a search system designed specifically to provide its users with insight into the following questions: "What is the intent of the query according to the ranker?'', "Why is this document ranked higher than another?'' and "Why is this document relevant to the query?''. EXS uses a version of a popular posthoc explanation method for classifiers -- LIME, adapted specifically to answer these questions. We show how such a system can effectively help a user understand the results of neural rankers and highlight areas of improvement.
A systematic review of the current state of collaborative mixed reality technologies: 2013–2018

Over the last few decades, Mixed Reality (MR) interfaces have received great attention from academia and industry. Although a considerable amount of research has already been done to support collaboration between users in MR, there is still no systematic review to determine the current state of collaborative MR applications. In this paper, collaborative MR studies published from 2013 to 2018 were reviewed. A total of 259 papers have been categorised based on their application areas, types of display devices used, collaboration setups, and user interaction and experience aspects. The primary contribution of this paper is to present a high-level overview of collaborative MR influence across several research disciplines. The achievements from each application area are summarised. In addition, remarkable papers in their respective areas are highlighted. Among other things, our study finds that there are three complementary factors to support and enhance collaboration in MR environments: (i) annotation techniques, which provide non-verbal communication cues to users, (ii) cooperative object manipulation techniques, which divide complex 3D object manipulation process into simpler tasks between different users, and (iii) user perception and cognition studies, which aim to lessen cognitive workload for task understanding and completion, and to increase users’ perceptual awareness and presence. Finally, this paper identifies research gaps and future directions that can be useful for researchers who want to explore ways on how to foster collaboration between users and to develop collaborative applications in MR.
Phishing Website Detection With Semantic Features Based on Machine Learning Classifiers

The phishing attack is one of the main cybersecurity threats in web phishing and spear phishing. Phishing websites continue to be a problem. One of the main contributions to our study was working and extracting the URL &amp; Domain Identity feature, Abnormal Features, HTML and JavaScript Features, and Domain Features as semantic features to detect phishing websites, which makes the process of classification using those semantic features, more controllable and more effective. The current study used machine learning model algorithms to detect phishing websites, and comparisons were made. We have used 16 machine learning models adopted with 10 semantic features that represent the most effective features for the detection of phishing webpages extracted from two datasets. The GradientBoostingClassifier and RandomForestClassifier had the best accuracy based on the comparison results (i.e., about 97%). In contrast, GaussianNB and the stochastic gradient descent (SGD) classifier represent the lowest accuracy results; 84% and 81% respectively, in comparison with other classifiers.
Task-Driven Super Resolution: Object Detection in Low-Resolution Images

We consider how image super-resolution (SR) can contribute to an object detection task in low-resolution images. Intuitively, SR gives a positive impact on the object detection task. While several previous works demonstrated that this intuition is correct, SR and detector are optimized independently in these works. This paper analyze a framework to train a deep neural network where the SR sub-network explicitly incorporates a detection loss in its training objective, via a tradeoff with a traditional detection loss. This end-to-end training procedure allows us to train SR preprocessing for any differentiable detector. We demonstrate extensive experiments that show our task-driven SR consistently and significantly improves the accuracy of an object detector on low-resolution images from COCO and PASCAL VOC data set for a variety of conditions and scaling factors.
Blind Denoising Autoencoder

The term ``blind denoising'' refers to the fact that the basis used for denoising is learned from the noisy sample itself during denoising. Dictionary learning- and transform learning-based formulations for blind denoising are well known. But there has been no autoencoder-based solution for the said blind denoising approach. So far, autoencoder-based denoising formulations have learned the model on a separate training data and have used the learned model to denoise test samples. Such a methodology fails when the test image (to denoise) is not of the same kind as the models learned with. This will be the first work, where we learn the autoencoder from the noisy sample while denoising. Experimental results show that our proposed method performs better than dictionary learning (K-singular value decomposition), transform learning, sparse stacked denoising autoencoder, and the gold standard BM3D algorithm.
FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation

The increase of available large clinical and experimental datasets has contributed to a substantial amount of important contributions in the area of biomedical image analysis. Image segmentation, which is crucial for any quantitative analysis, has especially attracted attention. Recent hardware advancement has led to the success of deep learning approaches. However, although deep learning models are being trained on large datasets, existing methods do not use the information from different learning epochs effectively. In this work, we leverage the information of each training epoch to prune the prediction maps of the subsequent epochs. We propose a novel architecture called feedback attention network (FANet) that unifies the previous epoch mask with the feature map of the current training epoch. The previous epoch mask is then used to provide hard attention to the learned feature maps at different convolutional layers. The network also allows rectifying the predictions in an iterative fashion during the test time. We show that our proposed feedback attention model provides a substantial improvement on most segmentation metrics tested on seven publicly available biomedical imaging datasets demonstrating the effectiveness of FANet. The source code is available at https://github.com/nikhilroxtomar/FANet.
Concept Formation Processes of Area and its Formula with GeoGebra: Case of Rectangle

This research aims to investigate the construction of the area of rectangle with GeoGebra and how students can formulate it. It focuses on describing the cognitive process involved in constructing this concept and how technology mediates such a process. The research was conducted in a classroom with 5th grade students in a secondary school and was designed qualitatively as a case study. A readiness test was prepared to determine the preliminary knowledge of the students about the concept of area. The teaching process was carried out with the activities based on GeoGebra. Considering the test results, researcher observations, and teacher views, five voluntary participants were selected, and we had three interviews with each of them. The obtained data from the teaching process and interviews were analyzed according to APOS theoretical framework. The results of the research demonstrate that one of the participants could not conceptualize the area as an object as the number of unit squares that cover the area without any space, while two of the participants could not conceptualize the formula of area of rectangle as an object. The students generally had difficulty in encapsulating the concept of the area and its formula. Area conservation and unit concept are important for conceptualizing the concept of area by realizing the covering feature of it, and understanding the dimension relationship is significantly effective in conceptualizing its formula.