Reviewerly/exHarmony huggingface dataset:
Count-Based Exploration with the Successor Representation

In this paper we introduce a simple approach for exploration in reinforcement learning (RL) that allows us to develop theoretically justified algorithms in the tabular case but that is also extendable to settings where function approximation is required. Our approach is based on the successor representation (SR), which was originally introduced as a representation defining state generalization by the similarity of successor states. Here we show that the norm of the SR, while it is being learned, can be used as a reward bonus to incentivize exploration. In order to better understand this transient behavior of the norm of the SR we introduce the substochastic successor representation (SSR) and we show that it implicitly counts the number of times each state (or feature) has been observed. We use this result to introduce an algorithm that performs as well as some theoretically sample-efficient approaches. Finally, we extend these ideas to a deep RL algorithm and show that it achieves state-of-the-art performance in Atari 2600 games when in a low sample-complexity regime.
HQ-ISNet: High-Quality Instance Segmentation for Remote Sensing Imagery

Instance segmentation in high-resolution (HR) remote sensing imagery is one of the most challenging tasks and is more difficult than object detection and semantic segmentation tasks. It aims to predict class labels and pixel-wise instance masks to locate instances in an image. However, there are rare methods currently suitable for instance segmentation in the HR remote sensing images. Meanwhile, it is more difficult to implement instance segmentation due to the complex background of remote sensing images. In this article, a novel instance segmentation approach of HR remote sensing imagery based on Cascade Mask R-CNN is proposed, which is called a high-quality instance segmentation network (HQ-ISNet). In this scheme, the HQ-ISNet exploits a HR feature pyramid network (HRFPN) to fully utilize multi-level feature maps and maintain HR feature maps for remote sensing images’ instance segmentation. Next, to refine mask information flow between mask branches, the instance segmentation network version 2 (ISNetV2) is proposed to promote further improvements in mask prediction accuracy. Then, we construct a new, more challenging dataset based on the synthetic aperture radar (SAR) ship detection dataset (SSDD) and the Northwestern Polytechnical University very-high-resolution 10-class geospatial object detection dataset (NWPU VHR-10) for remote sensing images instance segmentation which can be used as a benchmark for evaluating instance segmentation algorithms in the high-resolution remote sensing images. Finally, extensive experimental analyses and comparisons on the SSDD and the NWPU VHR-10 dataset show that (1) the HRFPN makes the predicted instance masks more accurate, which can effectively enhance the instance segmentation performance of the high-resolution remote sensing imagery; (2) the ISNetV2 is effective and promotes further improvements in mask prediction accuracy; (3) our proposed framework HQ-ISNet is effective and more accurate for instance segmentation in the remote sensing imagery than the existing algorithms.
Consistency and objectivity of automated embryo assessments using deep neural networks

To evaluate the consistency and objectivity of deep neural networks in embryo scoring and making disposition decisions for biopsy and cryopreservation in comparison to grading by highly trained embryologists.Prospective double-blind study using retrospective data.U.S.-based large academic fertility center.Not applicable.Embryo images (748 recorded at 70 hours postinsemination [hpi]) and 742 at 113 hpi) were used to evaluate embryologists and neural networks in embryo grading. The performance of 10 embryologists and a neural network were also evaluated in disposition decision making using 56 embryos.Coefficients of variation (%CV) and measures of consistencies were compared.Embryologists exhibited a high degree of variability (%CV averages: 82.84% for 70 hpi and 44.98% for 113 hpi) in grading embryo. When selecting blastocysts for biopsy or cryopreservation, embryologists had an average consistency of 52.14% and 57.68%, respectively. The neural network outperformed the embryologists in selecting blastocysts for biopsy and cryopreservation with a consistency of 83.92%. Cronbach's α analysis revealed an α coefficient of 0.60 for the embryologists and 1.00 for the network.The results of our study show a high degree of interembryologist and intraembryologist variability in scoring embryos, likely due to the subjective nature of traditional morphology grading. This may ultimately lead to less precise disposition decisions and discarding of viable embryos. The application of a deep neural network, as shown in our study, can introduce improved reliability and high consistency during the process of embryo selection and disposition, potentially improving outcomes in an embryology laboratory.
Driver Yawning Detection Based on Subtle Facial Action Recognition

Various investigations have shown that driver fatigue is the main cause of traffic accidents. Research on the use of computer vision techniques to detect signs of fatigue from facial actions, such as yawning, has demonstrated good potential. However, accurate and robust detection of yawning is difficult because of the complicated facial actions and expressions of drivers in the real driving environment. Several facial actions and expressions have the same mouth deformation as yawning. Thus, a novel approach to detecting yawning based on subtle facial action recognition is proposed in this study to alleviate the abovementioned problems. A 3D deep learning network with a low time sampling characteristic is proposed for subtle facial action recognition. This network uses 3D convolutional and bidirectional long short-term memory networks for spatiotemporal feature extraction and adopts SoftMax for classification. A keyframe selection algorithm is designed to select the most representative frame sequence from subtle facial actions. This algorithm rapidly eliminates redundant frames using image histograms with low computation cost and detects outliers by median absolute deviation. A series of experiments are also conducted on YawDD benchmark and self-collected datasets. Compared with several state-of-the-art methods, the proposed method has high yawning detection rates and can effectively distinguish yawning from similar facial actions.
Typestate-guided fuzzer for discovering use-after-free vulnerabilities

Existing coverage-based fuzzers usually use the individual control flow graph (CFG) edge coverage to guide the fuzzing process, which has shown great potential in finding vulnerabilities. However, CFG edge coverage is not effective in discovering vulnerabilities such as use-after-free (UaF). This is because, to trigger UaF vulnerabilities, one needs not only to cover individual edges, but also to traverse some (long) sequence of edges in a particular order, which is challenging for existing fuzzers. To this end, we propose to model UaF vulnerabilities as typestate properties, and develop a typestate-guided fuzzer, named UAFL, for discovering vulnerabilities violating typestate properties. Given a typestate property, we first perform a static typestate analysis to find operation sequences potentially violating the property. Our fuzzing process is then guided by the operation sequences in order to progressively generate test cases triggering property violations. In addition, we also employ an information flow analysis to improve the efficiency of the fuzzing process. We have performed a thorough evaluation of UAFL on 14 widely-used real-world programs. The experiment results show that UAFL substantially outperforms the state-of-the-art fuzzers, including AFL, AFLFast, FairFuzz, MOpt, Angora and QSYM, in terms of the time taken to discover vulnerabilities. We have discovered 10 previously unknown vulnerabilities, and received 5 new CVEs.
Hierarchy-Aware Global Model for Hierarchical Text Classification

Hierarchical text classification is an essential yet challenging subtask of multi-label text classification with a taxonomic hierarchy.Existing methods have difficulties in modeling the hierarchical label structure in a global view.Furthermore, they cannot make full use of the mutual interactions between the text feature space and the label space.In this paper, we formulate the hierarchy as a directed graph and introduce hierarchy-aware structure encoders for modeling label dependencies.Based on the hierarchy encoder, we propose a novel end-to-end hierarchy-aware global model (Hi-AGM) with two variants.A multi-label attention variant (HiAGM-LA) learns hierarchyaware label embeddings through the hierarchy encoder and conducts inductive fusion of labelaware text features.A text feature propagation model (HiAGM-TP) is proposed as the deductive variant that directly feeds text features into hierarchy encoders.Compared with previous works, both HiAGM-LA and HiAGM-TP achieve significant and consistent improvements on three benchmark datasets.
A new evolutionary algorithm: Learner performance based behavior algorithm

A novel evolutionary algorithm called learner performance based behavior algorithm (LPB) is proposed in this article. The basic inspiration of LPB originates from the process of accepting graduated learners from high school in different departments at university. In addition, the changes those learners should do in their studying behaviors to improve their study level at university. The most important stages of optimization; exploitation and exploration are outlined by designing the process of accepting graduated learners from high school to university and the procedure of improving the learner’s studying behavior at university to improve the level of their study, respectively. To show the accuracy of the proposed algorithm, it is evaluated against a number of test functions, such as traditional benchmark functions, CEC-C06 2019 test functions, and a real-world case study problem. The results of the proposed algorithm are then compared to the DA, GA, and PSO. The proposed algorithm produced superior results in most of the cases and comparative in some others. It is proved that the algorithm has a great ability to deal with the large optimization problems comparing to the DA, GA, and PSO. The overall results proved the ability of LPB in improving the initial population and converging towards the global optima. Moreover, the results of the proposed work are proved statistically.
Fault-tolerant magic state preparation with flag qubits

Magic state distillation is one of the leading candidates for implementing universal fault-tolerant logical gates. However, the distillation circuits themselves are not fault-tolerant, so there is additional cost to first implement encoded Clifford gates with negligible error. In this paper we present a scheme to fault-tolerantly and directly prepare magic states using flag qubits. One of these schemes requires only three ancilla qubits, even with noisy Clifford gates. We compare the physical qubit and gate cost of our scheme to the magic state distillation protocol of Meier, Eastin, and Knill (MEK), which is efficient and uses a small stabilizer circuit. For low enough noise rates, we show that in some regimes the overhead can be improved by several orders of magnitude compared to the MEK scheme which uses Clifford operations encoded in the codes considered in this work.
Quantum neuromorphic computing

Quantum neuromorphic computing physically implements neural networks in brain-inspired quantum hardware to speed up their computation. In this perspective article, we show that this emerging paradigm could make the best use of the existing and near future intermediate size quantum computers. Some approaches are based on parametrized quantum circuits, and use neural network-inspired algorithms to train them. Other approaches, closer to classical neuromorphic computing, take advantage of the physical properties of quantum oscillator assemblies to mimic neurons and compute. We discuss the different implementations of quantum neuromorphic networks with digital and analog circuits, highlight their respective advantages, and review exciting recent experimental results.
Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations

Stance detection is an important component of understanding hidden influences in everyday life.Since there are thousands of potential topics to take a stance on, most with little to no training data, we focus on zero-shot stance detection: classifying stance from no training examples.In this paper, we present a new dataset for zero-shot stance detection that captures a wider range of topics and lexical variation than in previous datasets.Additionally, we propose a new model for stance detection that implicitly captures relationships between topics using generalized topic representations and show that this model improves performance on a number of challenging linguistic phenomena.
A Critical Review by Teachers on the Online Teaching-Learning during the COVID-19

The world has witnessed a sudden change in the teaching-learning processes due to the ongoing pandemic of COVID-19.The worldwide compulsive lockdown for ensuring the preventive measures to stop the spread of this infection has equally affected education sector as other business sectors.As all of us know that quality education is the only long-term rescue for all the challenges and therefore, the need to find out the alternative solution to the traditional classroom teachinglearning is the concern of all stakeholders and the only option found is online mode of teaching-learning, which was somehow already available and had attracted an intense attention during this period.The aim of the paper is to study the teacher's perspective in India about this mode of learning, challenges and issues faced by them in migration to online platform, experience about online tools/platforms used for instructional delivery and their suggestions to improve the process for effective teaching.This study will help in gaining insight towards the possible improvements in the ongoing mode of online teaching and in future situations also.The results obtained based on sample collection through web based questionnaire clearly gives some information, which could be an eye opener for enhancing the implementation of the online teachinglearning among the learners especially teachers, who can further help in implementation of the large.Although, the online mode was already in place and was utilized in blended form to a substantial level in the developed countries, but in developing countries like India, where teachers are not familiar with online platforms/tools, lack of knowledge and skills to handle the online ICT infrastructure in a challenging situation.The results also give an impression about the need of professional development with special focus on digital literacy skills and awareness among the teacher community about the merits of online platforms for the teaching-learning process.
Mitigating Realistic Noise in Practical Noisy Intermediate-Scale Quantum Devices

Quantum error mitigation (QEM) is vital for noisy intermediate-scale quantum (NISQ) devices. While most conventional QEM schemes assume discrete gate-based circuits with noise appearing either before or after each gate, the assumptions are inappropriate for describing realistic noise that may have strong gate dependence and complicated nonlocal effects, and general computing models such as analog quantum simulators. To address these challenges, we first extend the scenario, where each computation process, being either digital or analog, is described by a continuous time evolution. For noise from imperfections of the engineered Hamiltonian or additional noise operators, we show it can be effectively suppressed by a stochastic QEM method. Since our method assumes only accurate single qubit controls, it is applicable to all digital quantum computers and various analog simulators. Meanwhile, errors in the mitigation procedure can be suppressed by leveraging the Richardson extrapolation method. As we numerically test our method with various Hamiltonians under energy relaxation and dephasing noise and digital quantum circuits with additional two-qubit crosstalk, we show an improvement of simulation accuracy by 2 orders. We assess the resource cost of our scheme and conclude the feasibility of accurate quantum computing with NISQ devices.
A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams

In recent years, with the increasing popularity of “Smart Technology”, the number of Internet of Things (IoT) devices and systems have surged significantly. Various IoT services and functionalities are based on the analytics of IoT streaming data. However, IoT data analytics faces concept drift challenges due to the dynamic nature of IoT systems and the ever-changing patterns of IoT data streams. In this article, we propose an adaptive IoT streaming data analytics framework for anomaly detection use cases based on optimized LightGBM and concept drift adaptation. A novel drift adaptation method named Optimized Adaptive and Sliding Windowing (OASW) is proposed to adapt to the pattern changes of online IoT data streams. Experiments on two public datasets show the high accuracy and efficiency of our proposed adaptive LightGBM model compared against other state-of-the-art approaches. The proposed adaptive LightGBM model can perform continuous learning and drift adaptation on IoT data streams without human intervention.
On the Tractability of SHAP Explanations

&#x0D; &#x0D; &#x0D; SHAP explanations are a popular feature-attribution mechanism for explainable AI. They use game-theoretic notions to measure the influence of individual features on the prediction of a machine learning model. Despite a lot of recent interest from both academia and industry, it is not known whether SHAP explanations of common machine learning models can be computed efficiently. In this paper, we establish the complexity of computing the SHAP explanation in three important settings. First, we consider fully-factorized data distributions, and show that the complexity of computing the SHAP explanation is the same as the complexity of computing the expected value of the model. This fully-factorized setting is often used to simplify the SHAP computation, yet our results show that the computation can be intractable for commonly used models such as logistic regression. Going beyond fully-factorized distributions, we show that computing SHAP explanations is already intractable for a very simple setting: computing SHAP explanations of trivial classifiers over naive Bayes distributions. Finally, we show that even computing SHAP over the empirical distribution is #P-hard.&#x0D; &#x0D; &#x0D;
Multimodal Co-learning: Challenges, applications with datasets, recent advances and future directions

Multimodal deep learning systems that employ multiple modalities like text, image, audio, video, etc., are showing better performance than individual modalities (i.e., unimodal) systems. Multimodal machine learning involves multiple aspects: representation, translation, alignment, fusion, and co-learning. In the current state of multimodal machine learning, the assumptions are that all modalities are present, aligned, and noiseless during training and testing time. However, in real-world tasks, typically, it is observed that one or more modalities are missing, noisy, lacking annotated data, have unreliable labels, and are scarce in training or testing, and or both. This challenge is addressed by a learning paradigm called multimodal co-learning. The modeling of a (resource-poor) modality is aided by exploiting knowledge from another (resource-rich) modality using the transfer of knowledge between modalities, including their representations and predictive models. Co-learning being an emerging area, there are no dedicated reviews explicitly focusing on all challenges addressed by co-learning. To that end, in this work, we provide a comprehensive survey on the emerging area of multimodal co-learning that has not been explored in its entirety yet. We review implementations that overcome one or more co-learning challenges without explicitly considering them as co-learning challenges. We present the comprehensive taxonomy of multimodal co-learning based on the challenges addressed by co-learning and associated implementations. The various techniques, including the latest ones, are reviewed along with some applications and datasets. Additionally, we review techniques that appear to be similar to multimodal co-learning and are being used primarily in unimodal or multi-view learning. The distinction between them is documented. Our final goal is to discuss challenges and perspectives and the important ideas and directions for future work that we hope will benefit for the entire research community focusing on this exciting domain.
A Robust GAN-Generated Face Detection Method Based on Dual-Color Spaces and an Improved Xception

In recent years, generative adversarial networks (GANs) have been widely used to generate realistic fake face images, which can easily deceive human beings. To detect these images, some methods have been proposed. However, their detection performance will be degraded greatly when the testing samples are post-processed. In this paper, some experimental studies on detecting post-processed GAN-generated face images find that (a) both the luminance component and chrominance components play an important role, and (b) the RGB and YCbCr color spaces achieve better performance than the HSV and Lab color spaces. Therefore, to enhance the robustness, both the luminance component and chrominance components of dual-color spaces (RGB and YCbCr) are considered to utilize color information effectively. In addition, the convolutional block attention module and multilayer feature aggregation module are introduced into the Xception model to enhance its feature representation power and aggregate multilayer features, respectively. Finally, a robust dual-stream network is designed by integrating dual-color spaces RGB and YCbCr and using an improved Xception model. Experimental results demonstrate that our method outperforms some existing methods, especially in its robustness against different types of post-processing operations, such as JPEG compression, Gaussian blurring, gamma correction, and median filtering.
The Best Practice of Teach Computer Science Students to Use Paper Prototyping

The importance of understanding the repercussions of effective user interface (UI) design is critical for future Computer Science (CS) professionals, given the ubiquity of interfaces on computer devices. Through a paper prototyping activity, this article explains how to teach rapidly and successfully CS students about "fit," a Human-Computer Interaction (HCI) concept. Despite its simplicity, the concept of "fit" can be difficult to grasp without much practice. In practice, designing "fit" into UIs can be prohibitively expensive because workable prototypes are generally beyond the technical capabilities of students. As a result, we illustrate how to use paper prototyping to demonstrate "fit" in a hands-on class exercise based on active learning concepts. To guide students through the process of "fit" in UI design, we provide extensive step-by-step directions for planning, setting up, and presenting the exercise. Students will be better equipped to apply both theoretical and practical applications of "fit" in UI design and execution as a result of this assignment; this exercise can be used in any course that covers user interface design, such as concepts of human-computer interaction, systems analysis and design, software engineering, and project management.