Reviewerly/exHarmony huggingface dataset:
COVID-19 Cases Prediction in Saudi Arabia Using Tree-based Ensemble Models

COVID-19 pandemic has affected more than 144 million people and spread to over 200 countries. The prediction of COVID-19 behaviour and trend is crucial to prevent its spreading. Kingdom of Saudi Arabia (KSA) is Asia's fifth largest country, and it hosts the two holiest cities of the Islamic world. KSA hosts millions of pilgrims every year, and it is of great importance to predict the COVID-19 spread to organize these religious activities and bring life to normality in KSA. This study proposes four tree-based ensemble methods to predict the COVID-19 daily new cases in KSA. Tree-based ensemble methods are suggested to reduce the variance and/or bias of inconsistent models. The four models utilized in the study are Gradient Tree Boosting (GB), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Voting Regressor (VR). The study is conducted using "Our Data in World" (OWID) COVID-19 dataset from the first confirmed case in KSA, i.e., 2nd March 2020 to 14th April 2021. The results suggest that the tree-based ensemble models provide a good prediction of daily COVID-19 new cases and can follow the trend of COVID-19. Among the models, XGBoost and VR performed better than the other three models with the best evaluation metric scores (MAE:4.41, RMSE:7.11, MAPE:0.95%). The significant prediction power of the tree-based ensemble methods, especially XGBoost can provide the platform for policymakers to put strategic plans for the closure periods of the educational institutions and organize Hajj and Umrah.
Explainable AI methods on a deep reinforcement learning agent for automatic docking

Artifical neural networks (ANNs) have made their way into marine robotics in the last years, where they are used in control and perception systems, to name a few examples. At the same time, the black-box nature of ANNs is responsible for key challenges related to interpretability and trustworthiness, which need to be addressed if ANNs are to be deployed safely in real-life operations. In this paper, we implement three XAI methods to provide explanations to the decisions made by a deep reinforcement learning agent: Kernel SHAP, LIME and Linear Model Trees (LMTs). The agent was trained via Proximal Policy Optimization (PPO) to perform automatic docking on a fully-actuated vessel. We discuss the properties and suitability of the three methods, and juxtapose them with important attributes of the docking agent to provide context to the explanations.
Exercising With Embodied Young Avatars: How Young vs. Older Avatars in Virtual Reality Affect Perceived Exertion and Physical Activity Among Male and Female Elderly Individuals

This study demonstrated that implementation of the Proteus effect via manipulation of avatar age in VR is effective among elderly individuals in the context of exercise. One hundred and four elderly adults aged 60 years and older who did not engage in vigorous physical activities participated in this experiment with a 2 (avatar age: young vs. older) × 2 (sex: male vs. female) design. The results showed that the embodiment of younger avatars (age approximately 20 years) in VR leads to greater perceived exercise exertion regardless of sex after controlling for age and emotion. Older adults with young avatars perceived a greater contribution of efforts to exercise. This study also found that among those who did not engage in vigorous exercise, female older adults who embodied young avatars reported greater self-efficacy for future exercise and greater physical activity during the exercise phase than those who embodied older avatars. This study suggests that females are more likely to be motivated to continue exercising through young avatar embodiment. In contrast, female elderly who embodied old avatars reported significant fewer physical activity than male elderly who embodied old avatars. This indicated that the Proteus effect had stronger effects among females than among older males. Although we found the Proteus effect through VR avatar manipulation, the effect was temporary and limited to the experimental phase. This study is the first to examine the Proteus effect among elderly individuals in the context of exercise. It also contributes to the literature by indicating that avatar age manipulation is an effective means of promoting exercise among elderly individuals and helping them achieve exercise outcomes. This study further demonstrates that female elderly individuals respond to young avatars differently than male elderly individuals, with female elderly individuals showing more positive effects of young avatar embodiment than males. Implications and theoretical contributions are discussed.
The Shapley Value of Classifiers in Ensemble Games

What is the value of an individual model in an ensemble of binary classifiers? We answer this question by introducing a class of transferable utility cooperative games called ensemble games. In machine learning ensembles, pre-trained models cooperate to make classification decisions. To quantify the importance of models in these ensemble games, we define Troupe - an efficient algorithm that allocates payoffs based on approximate Shapley values of the classifiers. We argue that the Shapley value of models in these games is an effective decision metric for choosing a high-performing subset of models from the ensemble. Our analytical findings prove that our Shapley value estimation scheme is precise and scalable; its performance increases with the size of the dataset and ensemble. Empirical results on real-world graph classification tasks demonstrate that our algorithm produces high-quality estimates of the Shapley value. We find that Shapley values can be utilized for ensemble pruning and that adversarial models receive a low valuation. Complex classifiers are frequently found to be responsible for both correct and incorrect classification decisions.
Boosting Instance Segmentation with Synthetic Data: A study to overcome the limits of real world data sets

A major issue related to computer vision for the automotive industry is that real-world perception models require huge amount of well-annotated data to achieve decent performance. While this data is very expensive to collect and annotate, synthetically generated images seem to be an efficient alternative to solve this problem. More and more public data sets, composed of synthetic data, are available in various domains, however, there is too little concrete methodology to use them properly. In this paper, we propose a simple approach combining the use of synthetic and real images to boost instance segmentation. We mention some pre-processing requirements as harmonizing instance labeling and removing non-valuable instances from synthetic images. We present our training strategy based on data set mixing, and show that it overcomes the domain shift between real and synthetic data sets. A comparison study with other training approaches, such as fine-tuning techniques, highlights the benefits of our method, which boosts network performances on both real and synthetic image inferences.
Accelerating distributed deep neural network training with pipelined MPI allreduce

Abstract TensorFlow (TF) is usually combined with the Horovod (HVD) workload distribution package to obtain a parallel tool to train deep neural network on clusters of computers. HVD in turn utilizes a blocking Allreduce primitive to share information among processes, combined with a communication thread to overlap communication with computation. In this work, we perform a thorough experimental analysis to expose (1) the importance of selecting the best algorithm in MPI libraries to realize the Allreduce operation; and (2) the performance acceleration that can be attained when replacing a blocking Allreduce with its non-blocking counterpart (while maintaining the blocking behaviour via the appropriate synchronization mechanism). Furthermore, (3) we explore the benefits of applying pipelining to the communication exchange, demonstrating that these improvements carry over to distributed training via TF+HVD. Finally, (4) we show that pipelining can also boost performance for applications that make heavy use of other collectives, such as Broadcast and Reduce-Scatter.
Student Can Also be a Good Teacher

Astounding results from transformer models with Vision-and Language Pretraining (VLP) on joint vision-and-language downstream tasks have intrigued the multi-modal community. On the one hand, these models are usually so huge that make us more difficult to fine-tune and serve real-time online applications. On the other hand, the compression of the original transformer block will ignore the difference in information between modalities, which leads to the sharp decline of retrieval accuracy.
Non-local channel aggregation network for single image rain removal

Rain streaks showing in images or videos would severely degrade the performance of computer vision applications. Thus, it is of vital importance to remove rain streaks and facilitate our vision systems. While recent convolutional neural network based methods have shown promising results in single image rain removal (SIRR), they fail to effectively capture long-range location dependencies or aggregate convolutional channel information simultaneously. However, as SIRR is a highly ill-posed problem, these spatial and channel information are very important clues to solve SIRR. First, spatial information could help our model to understand the image context by gathering long-range dependency location information hidden in the image. Second, aggregating channels could help our model to concentrate on channels more related to image background instead of rain streaks. In this paper, we propose a non-local channel aggregation network (NCANet) to address the SIRR problem. NCANet models 2D rainy images as sequences of vectors in three directions, namely vertical direction, transverse direction, and channel direction. Recurrently aggregating information from all three directions enables our model to capture the long-range dependencies in both channels and spatial locations. Extensive experiments on both heavy and light rain image data sets demonstrate the effectiveness of the proposed NCANet model.
Single Image Depth Map Estimation for Improving Posture Recognition

Image-based posture recognition is a very challenging problem since it is difficult to acquire rich 3D information from the posture in color image. To address this issue, we present a novel and unified framework for human posture recognition, applying single image depth map estimation from color images. The proposed method includes two stages. The first stage estimates the depth map from the single-color image by an improved Pix2Pix generation module. The generation module is equipped with a hybrid loss function that captures the high-level features and recovers the sharp depth discontinuities, thus improving the depth estimation results. The second stage (the recognition stage) improves the color image-based recognition performance by incorporating the estimated depth map. Thereby, a two-stream CNN architecture that separately processes the color image and its estimated depth image is developed for robust posture recognition. To verify its effectiveness, we first test the proposed method on a novel pose dataset, which contains 13800 samples of paired color-and-depth of 6 subjects with 15 poses. The dataset used in this work is been created and released, is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">http://media.ritsumei.ac.jp/iipl/database/pose/</uri> . Extensive experiments are also performed on the public OUHANDS hand gesture dataset. Experiments demonstrate that the proposed method achieves superior performance on both human pose and hand gesture recognition tasks.
Assorted Attention Network for Cross-Lingual Language-to-Vision Retrieval

In this paper, we tackle the cross-lingual language-to-vision (CLLV) retrieval task. In the CLLV retrieval task, given the text query in one language, it seeks to retrieve the relevant images/videos from the database based on visual content in images/videos and their captions in another language. As the CLLV retrieval bridges the modal gap and the language gap, it makes many international cross-modal applications feasible. To tackle the CLLV retrieval, in this paper, we propose an assorted attention network (A2N) to synchronously overcome the language gap, bridge the modal gap and fuse features of two modals in an elegant and effective manner. It represents each text query as a set of word features and represents each image/video as a set of its caption's word features in another language and a set of its local visual features. In this case, the relevance between the text query and the image/video is obtained by the matching between the set of query's word features and two sets of image/video features. To enhance the effectiveness of the matching, A2N merges the query's word features and the image/video's visual and word features into an assorted set and further conducts the self-attention operation on items of the assorted set. On one hand, benefited from the attentions between the query's word features and the video/image's visual features, some important word features or visual features of the image/video can be emphasized. On the other hand, benefited from the attentions between the video/image's visual features and its caption word features, the image/video's visual content and the text information can be fused in a more effective manner. Systematic experiments conducted on four datasets demonstrate the effectiveness of the proposed A2N in the CLLV retrieval task.
Comparative Study of Adversarial Training Methods for Long-tailed Classification

Adversarial training is originated in image classification to address the problem of adversarial attacks, where an invisible perturbation in an image leads to a significant change in model decision. It recently has been observed to be effective in alleviating the long-tailed classification problem, where an imbalanced size of classes makes the model has much lower performance on small classes. However, existing methods typically focus on the methods to generate perturbations for data, while the contributions of different perturbations to long-tailed classification have not been well analyzed. To this end, this paper presents an investigation on the perturbation generation and incorporation components of existing adversarial training methods and proposes a taxonomy that defines these methods using three levels of components, in terms of information, methodology, and optimization. This taxonomy may serve as a design paradigm where an adversarial training algorithm can be created by combining different components in the taxonomy. A comparative study is conducted to verify the influence of each component in long-tailed classification. Experimental results on two benchmarking datasets show that a combination of statistical perturbations and hybrid optimization achieves a promising performance, and the gradient-based method typically improves the performance of both the head and tail classes. More importantly, it is verified that a reasonable combination of the components in our taxonomy may create an algorithm that outperforms the state-of-the-art.
ProMutator: Detecting Vulnerable Price Oracles in DeFi by Mutated Transactions

This paper presents ProMutator, a scalable security analysis framework that detects price oracle vulnerabilities before attacks occur. ProMutator’s core idea is to simulate price oracle attacks locally by mutating the data needed for price calculation. ProMutator analyzes existing transactions to reconstruct probable DeFi use patterns, thereby reducing the required simulation runs drastically. ProMutator does not require any examined contracts’ high-level source code. Additionally, ProMutator generates a report for each detected vulnerability to facilitate further investigation. In our evaluation, ProMutator successfully discovered five out of six known and 27 new price oracle vulnerabilities in DeFi protocols. Index Terms—DeFi, price oracle, price oracle attack
Improving Thread-Modular Abstract Interpretation

We give thread-modular non-relational value analyses as abstractions of a local trace semantics. The semantics as well as the analyses are formulated by means of global invariants and side-effecting constraint systems. We show that a generalization of the analysis provided by the static analyzer Goblint as well as a natural improvement of Antoine Miné’s approach can be obtained as instances of this general scheme. We show that these two analyses are incomparable w.r.t. precision and provide a refinement which improves on both precision-wise. We also report on a preliminary experimental comparison of the given analyses on a meaningful suite of benchmarks.
User-Generated Video Quality Assessment: A Subjective and Objective Study

Recently, we have observed an exponential increase of user-generated content (UGC) videos. The distinguished characteristic of UGC videos originates from the video production and delivery chain, as they are usually acquired and processed by non-professional users before uploading to the hosting platforms for sharing. As such, these videos usually undergo multiple distortion stages that may affect visual quality before ultimately being viewed. Inspired by the increasing consensus that the optimization of the video coding and processing shall be fully driven by the perceptual quality, in this paper, we propose to study the quality of the UGC videos from both objective and subjective perspectives. We first construct a UGC video quality assessment (VQA) database, aiming to provide useful guidance for the UGC video coding and processing in the hosting platform. The database contains source UGC videos uploaded to the platform and their transcoded versions that are ultimately enjoyed by end-users, along with their subjective scores. Furthermore, we develop an objective quality assessment algorithm that automatically evaluates the quality of the transcoded videos based on the corrupted reference, which is in accordance with the application scenarios of UGC video sharing in the hosting platforms. The information from the corrupted reference is well leveraged and the quality is predicted based on the inferred quality maps with deep neural networks (DNN). Experimental results show that the proposed method yields superior performance. Both subjective and objective evaluations of the UGC videos also shed lights on the design of perceptual UGC video coding.
Secure deduplication for big data with efficient dynamic ownership updates

With the explosive growth of data on the internet, the trend is for users to store these large amounts of data on the cloud. To protect the privacy of data, users need to encrypt their data before uploading them to the cloud. This brings a big challenge for cloud to perform deduplication. In order to tackle this challenge, we design a secure deduplication scheme for big data with efficient dynamic ownership updates. The proposed scheme can efficiently support user enrollment and revocation. We adopt a new locating technique to effectively locate the re-encrypted data block. As a result, only a small proportion of data needs to be re-encrypted. When one user deletes or updates one file, this user will lost the access to the original data. We also provide the security analysis and experimental evaluation results for the proposed scheme.
Nonadiabatic geometric quantum computation with shortened path on superconducting circuits

Recently, nonadiabatic geometric quantum computation has received much attention due to its fast manipulation and intrinsic error-resilience characteristics. However, to obtain universal geometric quantum control, only limited and special evolution paths have been proposed, which usually require longer gate-time and more operational steps, and thus lead to lower quality of the implemented quantum gates. Here, we present an effective scheme to find the shortest geometric path under conventional conditions of geometric quantum computation, where high-fidelity and robust geometric gates can be realized by only single-loop evolution, and the gate performances are better than the corresponding dynamical ones. Furthermore, we can optimize the pulse shapes in our scheme to further shorten the gate-time, which is determined by how fast the path is traveled. In addition, we also present its physical implementation on superconducting circuits, consisting of capacitively coupled transmon qubits, where fidelities of geometric single- and two-qubit gates can be higher than 99.95% and 99.80% within the current state-of-the-art experimental technologies, respectively. These results indicate that our scheme is promising for large-scale fault-tolerant quantum computation.
Nothing Wasted: Full Contribution Enforcement in Federated Edge Learning

The explosive amount of data generated at the network edge makes mobile edge computing an essential technology to support real-time applications, calling for powerful data processing and analysis provided by machine learning (ML) techniques. In particular, federated edge learning (FEL) becomes prominent in securing the privacy of data owners by keeping the data locally used to train ML models. Existing studies on FEL either utilize in-process optimization or remove unqualified participants in advance. In this paper, we enhance the collaboration from all edge devices in FEL to guarantee that the ML model is trained using all available local data to accelerate the learning process. To that aim, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">collective extortion (CE)</i> strategy under the imperfect-information multi-player FEL game, which is proved to be effective in helping the server efficiently elicit the full contribution of all devices without worrying about suffering from any economic loss. Technically, our proposed CE strategy extends the classical extortion strategy in controlling the proportionate share of expected utilities for a single opponent to the swiftly homogeneous control over a group of players, which further presents an attractive trait of being impartial for all participants. Moreover, the CE strategy enriches the game theory hierarchy, facilitating a wider application scope of the extortion strategy. Both theoretical analysis and experimental evaluations validate the effectiveness and fairness of our proposed scheme.
Enabling Verifiable Privacy-Preserving Multi-Type Data Aggregation in Smart Grids

In this article, we analyze the inherent characteristic of smart grid systems, where we observe that a smart meter always generates different types of electricity consumption data for one user, and a control center (CC) always conducts an in-depth statistic analysis on these data for subsequent services. Among these data, some of them are very sensitive and should be prevented for any leakage and modification. Furthermore, due to the large number of users in a smart grid system, it is advantageous for CC to receive and process the data from different users simultaneously. To this end, we propose a verifiable privacy-preserving multi-type data aggregation scheme (VPMDA) for smart grids. VPMDA enables an aggregator gateway (AG) to aggregate encrypted multi-type data and forward the aggregated data to CC, such that CC checks the integrity of aggregated data and obtains the statistic analysis results (e.g., average, variance) on the aggregated data without learning each individual data content. We further extend VPMDA to improve the performance of verifying data integrity on CC significantly. We formally prove the security of VPMDA against various attacks. We also implement a prototype of VPMDA and conduct a comprehensive performance evaluation to demonstrate its feasibility and efficiency.