Reviewerly/exHarmony huggingface dataset:
On the Byzantine Robustness of Clustered Federated Learning

Federated Learning (FL) is currently the most widely adopted framework for collaborative training of (deep) machine learning models under privacy constraints. Albeit it's popularity, it has been observed that Federated Learning yields suboptimal results if the local clients' data distributions diverge. The recently proposed Clustered Federated Learning Framework addresses this issue, by separating the client population into different groups based on the pairwise cosine similarities between their parameter updates. In this work we investigate the application of CFL to byzantine settings, where a subset of clients behaves unpredictably or tries to disturb the joint training effort in an directed or undirected way. We perform experiments with deep neural networks on common Federated Learning datasets which demonstrate that CFL (without modifications) is able to reliably detect byzantine clients and remove them from training.
PREMA: A Predictive Multi-Task Scheduling Algorithm For Preemptible Neural Processing Units

To amortize cost, cloud vendors providing DNN acceleration as a service to end-users employ consolidation and virtualization to share the underlying resources among multiple DNN service requests. This paper makes a case for a "preemptible" neural processing unit (NPU) and a "predictive" multi-task scheduler to meet the latency demands of high-priority inference while maintaining high throughput. We evaluate both the mechanisms that enable NPUs to be preemptible and the policies that utilize them to meet scheduling objectives. We show that preemptive NPU multi-tasking can achieve an average 7.8×, 1.4×, and 4.8× improvement in latency, throughput, and SLA satisfaction, respectively.
Automatic Modulation Classification Using Gated Recurrent Residual Network

The development of the Internet-of-Things (IoT) security is comparatively slower than the pace of the IoT innovations. The seamless IoT network operates in an untrusted environment and is exposed to many malicious active attacks. As the process of identifying the modulation format of signals is corrupted by noise and fading, automatic modulation classification (AMC) can be viewed as an effective approach to counter physical-layer threats for IoT as it can detect and identify the pilot jamming, deceptive jamming, and Sybil attacks. Nowadays, data-driven deep learning (DL) techniques, which are capable of extracting discriminative features and perform better robustness to channel and noise conditions, have drawn widespread attention. The deep residual network (ResNet) has a strong representative ability, which can learn latent information repeatedly from the received signals and improve the classification accuracy. Meanwhile, the gated recurrent unit (GRU), which is capable of exploiting temporal information of the received signal can expand the dimension of the signal features for satisfactory classification performance. Considering the advantages of the above networks, this article proposes a novel gated recurrent residual neural network (GrrNet) for feature-based AMC, where the amplitude and phase of the received signal are utilized as the inputs of GrrNet. In GrrNet, a ResNet extractor module is first designed to extract the highly representative features and then temporal information is obtained by the subsequent GRU module which is capable of processing the representative features with the arbitrary length for modulation classification. Moreover, extensive simulations are conducted to verify the classification performance and robustness of the proposed GrrNet and it is shown that GrrNet outperforms other recent DL-based AMC methods. Moreover, the influence of the network parameters, symbol length, and frequency offset on performance is also explored.
Computational System to Classify Cyber Crime Offenses using Machine Learning

Particularly in the last decade, Internet usage has been growing rapidly. However, as the Internet becomes a part of the day to day activities, cybercrime is also on the rise. Cybercrime will cost nearly $6 trillion per annum by 2021 as per the cybersecurity ventures report in 2020. For illegal activities, cybercriminals utilize any network computing devices as a primary means of communication with a victims’ devices, so attackers get profit in terms of finance, publicity and others by exploiting the vulnerabilities over the system. Cybercrimes are steadily increasing daily. Evaluating cybercrime attacks and providing protective measures by manual methods using existing technical approaches and also investigations has often failed to control cybercrime attacks. Existing literature in the area of cybercrime offenses suffers from a lack of a computation methods to predict cybercrime, especially on unstructured data. Therefore, this study proposes a flexible computational tool using machine learning techniques to analyze cybercrimes rate at a state wise in a country that helps to classify cybercrimes. Security analytics with the association of data analytic approaches help us for analyzing and classifying offenses from India-based integrated data that may be either structured or unstructured. The main strength of this work is testing analysis reports, which classify the offenses accurately with 99 percent accuracy.
Stacked U-Shape Network With Channel-Wise Attention for Salient Object Detection

This paper addresses the core issue of how to learn powerful features for saliency. We have two major observations. First, feature maps of different layers in convolutional neural networks play different roles in saliency detection. Second, different feature channels in the same layer are not of equal importance to saliency, and they often have different response to foreground or background. To address these problems, a stacked U-shape network with channel-wise attention is presented to effectively utilize these features, which mainly consists of a parallel dilated convolution (PDC) module and a multi-level attention cascaded feedback (MACF) module. More specifically, PDC aims to enlarge the receptive field without increasing the computation and effectively avoid the gridding problem. MACF is innovatively designed to adaptively select the cross-layer complementary information, and the inter-dependencies between different channel maps in the same layer can be depicted well. Finally, we adopt a multi-layer loss function to improve the commonly used binary cross entropy loss which treats all pixels equally. The extensive experiments on five saliency detection datasets demonstrate that the proposed method outperforms the state-of-the-art approaches.
How 3D printing and social media tackles the PPE shortage during Covid – 19 pandemic

During the recent Covid-19 pandemic, additive Technology and Social Media were used to tackle the shortage of Personal Protective Equipment. A literature review and a social media listening software were employed to explore the number of the users referring to specific keywords related to 3D printing and PPE. Additionally, the QALY model was recruited to highlight the importance of the PPE usage. More than 7 billion users used the keyword covid or similar in the web while mainly Twitter and Facebook were used as a world platform for PPE designs distribution through individuals and more than 100 different 3D printable PPE designs were developed.
The good, the bad and the ugly: An overview of the sustainability of blockchain technology

Blockchain is a buzzword describing the current excitement for an innovative technology that could change and disrupt major industries and economic sectors. Blockchain technology has the promise to change all existing business models and make financial services cheaper contributing therefore to a better financial inclusion and, even a better economic wealth distribution. Numerous studies optimistically praise such potential societal benefits by listing all processes that could be optimized through this technology. However, the picture is not necessary all bright. Blockchain can indeed disrupt and significantly improve our societies but there still exist some societal costs in the way this technology is implemented. This paper provides a perspective overviewing of the current trends related to the development of the most widely used implementation of blockchain technology based on the proof-of-work consensus algorithm. These trends will be discussed in three steps: the ‘Good Blockchain’ overviews how this technology can improve our societies; the ‘Bad blockchain’ offers a more nuanced perspective by discussing the potential polluting activities generated by some mining activities. Finally, the ‘Ugly blockchain’ investigates how this technology might generate a risk of concentration in the mining industry affecting therefore the nature and even the existence of the blockchain technology.
DSNAS: Direct Neural Architecture Search Without Parameter Retraining

If NAS methods are solutions, what is the problem? Most existing NAS methods require two-stage parameter optimization. However, performance of the same architecture in the two stages correlates poorly. In this work, we propose a new problem definition for NAS, task-specific end-to-end, based on this observation. We argue that given a computer vision task for which a NAS method is expected, this definition can reduce the vaguely-defined NAS evaluation to i) accuracy of this task and ii) the total computation consumed to finally obtain a model with satisfying accuracy. Seeing that most existing methods do not solve this problem directly, we propose DSNAS, an efficient differentiable NAS framework that simultaneously optimizes architecture and parameters with a low-biased Monte Carlo estimate. Child networks derived from DSNAS can be deployed directly without parameter retraining. Comparing with two-stage methods, DSNAS successfully discovers networks with comparable accuracy (74.4\%) on ImageNet in 420 GPU hours, reducing the total time by more than 34\%.
Weakly-Supervised 3D Human Pose Learning via Multi-View Images in the Wild

One major challenge for monocular 3D human pose estimation in-the-wild is the acquisition of training data that contains unconstrained images annotated with accurate 3D poses. In this paper, we address this challenge by proposing a weakly-supervised approach that does not require 3D annotations and learns to estimate 3D poses from unlabeled multi-view data, which can be acquired easily in in-the-wild environments. We propose a novel end-to-end learning framework that enables weakly-supervised training using multi-view consistency. Since multi-view consistency is prone to degenerated solutions, we adopt a 2.5D pose representation and propose a novel objective function that can only be minimized when the predictions of the trained model are consistent and plausible across all camera views. We evaluate our proposed approach on two large scale datasets (Human3.6M and MPII-INF-3DHP) where it achieves state-of-the-art performance among semi-/weakly-supervised methods.
Sketch Less for More: On-the-Fly Fine-Grained Sketch-Based Image Retrieval

Fine-grained sketch-based image retrieval (FG-SBIR) addresses the problem of retrieving a particular photo instance given a user's query sketch. Its widespread applicability is however hindered by the fact that drawing a sketch takes time, and most people struggle to draw a complete and faithful sketch. In this paper, we reformulate the conventional FG-SBIR framework to tackle these challenges, with the ultimate goal of retrieving the target photo with the least number of strokes possible. We further propose an on-the-fly design that starts retrieving as soon as the user starts drawing. To accomplish this, we devise a reinforcement learning based cross-modal retrieval framework that directly optimizes rank of the ground-truth photo over a complete sketch drawing episode. Additionally, we introduce a novel reward scheme that circumvents the problems related to irrelevant sketch strokes, and thus provides us with a more consistent rank list during the retrieval. We achieve superior early-retrieval efficiency over state-of-the-art methods and alternative baselines on two publicly available fine-grained sketch retrieval datasets.
Improving Action Segmentation via Graph-Based Temporal Reasoning

Temporal relations among multiple action segments play an important role in action segmentation especially when observations are limited (e.g., actions are occluded by other objects or happen outside a field of view). In this paper, we propose a network module called Graph-based Temporal Reasoning Module (GTRM) that can be built on top of existing action segmentation models to learn the relation of multiple action segments in various time spans. We model the relations by using two Graph Convolution Networks (GCNs) where each node represents an action segment. The two graphs have different edge properties to account for boundary regression and classification tasks, respectively. By applying graph convolution, we can update each node's representation based on its relation with neighboring nodes. The updated representation is then used for improved action segmentation. We evaluate our model on the challenging egocentric datasets namely EGTEA and EPIC-Kitchens, where actions may be partially observed due to the viewpoint restriction. The results show that our proposed GTRM outperforms state-of-the-art action segmentation models by a large margin. We also demonstrate the effectiveness of our model on two third-person video datasets, the 50Salads dataset and the Breakfast dataset.
Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation

In this paper, we propose a novel system named Disp R-CNN for 3D object detection from stereo images. Many recent works solve this problem by first recovering a point cloud with disparity estimation and then apply a 3D detector. The disparity map is computed for the entire image, which is costly and fails to leverage category-specific prior. In contrast, we design an instance disparity estimation network (iDispNet) that predicts disparity only for pixels on objects of interest and learns a category-specific shape prior for more accurate disparity estimation. To address the challenge from scarcity of disparity annotation in training, we propose to use a statistical shape model to generate dense disparity pseudo-ground-truth without the need of LiDAR point clouds, which makes our system more widely applicable. Experiments on the KITTI dataset show that, even when LiDAR ground-truth is not available at training time, Disp R-CNN achieves competitive performance and outperforms previous state-of-the-art methods by 20% in terms of average precision. The code will be available at https://github.com/zju3dv/disprcnn.
The future of pathology is digital

Information, archives, and intelligent artificial systems are part of everyday life in modern medicine. They already support medical staff by mapping their workflows with shared availability of cases’ referral information, as needed for example, by the pathologist, and this support will be increased in the future even more. In radiology, established standards define information models, data transmission mechanisms, and workflows. Other disciplines, such as pathology, cardiology, and radiation therapy, now define further demands in addition to these established standards. Pathology may have the highest technical demands on the systems, with very complex workflows, and the digitization of slides generating enormous amounts of data up to Gigabytes per biopsy. This requires enormous amounts of data to be generated per biopsy, up to the gigabyte range. Digital pathology allows a change from classical histopathological diagnosis with microscopes and glass slides to virtual microscopy on the computer, with multiple tools using artificial intelligence and machine learning to support pathologists in their future work.
dm_control: Software and tasks for continuous control

The dm_control software package is a collection of Python libraries and task suites for reinforcement learning agents in an articulated-body simulation. A MuJoCo wrapper provides convenient bindings to functions and data structures. The PyMJCF and Composer libraries enable procedural model manipulation and task authoring. The Control Suite is a fixed set of tasks with standardised structure, intended to serve as performance benchmarks. The Locomotion framework provides high-level abstractions and examples of locomotion tasks. A set of configurable manipulation tasks with a robot arm and snap-together bricks is also included. dm_control is publicly available at https://www.github.com/deepmind/dm_control
Measuring digital literacy across three age cohorts: Exploring test dimensionality and performance differences

Digital literacy (DL) is an important capacity for students’ learning in a rapidly changing world. However, tension exists between the theoretical conceptualizations of DL as a multidimensional construct and empirical studies reporting unidimensional DL scores. Also, little is known about how DL may vary among different age cohorts, and whether and at which age do performance gaps emerge with respect to gender. The focus of this research is to develop a test appropriate for measuring DL performance at different ages and a comprehensive DL assessment framework has been adopted for this purpose. Using data from three age cohorts of students (one from primary schools and two from secondary schools), the dimensionality of DL and performance differences are examined. Comparisons of unidimensional and multidimensional item response models suggest the measured DL to be a unidimensional construct. The results also show that secondary school students obtained higher levels of DL compared to primary school students. A gender gap in DL is found among secondary school students. There is also a need for further research to understand through longitudinal studies the emergence of the gender gap in DL performance.
EdNet: A Large-Scale Hierarchical Dataset in Education

Advances in Artificial Intelligence in Education (AIEd) and the ever-growing scale of Interactive Educational Systems (IESs) have led to the rise of data-driven approaches for knowledge tracing and learning path recommendation. Unfortunately, collecting student interaction data is challenging and costly. As a result, there is no public large-scale benchmark dataset reflecting the wide variety of student behaviors observed in modern IESs. Although several datasets, such as ASSISTments, Junyi Academy, Synthetic and STATICS are publicly available and widely used, they are not large enough to leverage the full potential of state-of-the-art data-driven models. Furthermore, the recorded behavior is limited to question-solving activities. To this end, we introduce EdNet, a large-scale hierarchical dataset of diverse student activities collected by Santa, a multi-platform self-study solution equipped with an artificial intelligence tutoring system. EdNet contains 131,417,236 interactions from 784,309 students collected over more than 2 years, making it the largest public IES dataset released to date. Unlike existing datasets, EdNet records a wide variety of student actions ranging from question-solving to lecture consumption to item purchasing. Also, EdNet has a hierarchical structure which divides the student actions into 4 different levels of abstractions. The features of EdNet are domain-agnostic, allowing EdNet to be easily extended to different domains. The dataset is publicly released for research purposes. We plan to host challenges in multiple AIEd tasks with EdNet to provide a common ground for the fair comparison between different state-of-the-art models and to encourage the development of practical and effective methods.
Attention-based sentiment analysis using convolutional and recurrent neural network

Convolution and recurrent neural network have obtained remarkable performance in natural language processing(NLP). Moreover, from the attention mechanism perspective convolution neural network(CNN) is applied less than recurrent neural network(RNN). Because RNN can learn long-term dependencies and gives better results than CNN. But CNN has its own advantage, can extract high-level features by using its local fix size context at the input level. Thus, this paper proposed a new model based on RNN with CNN-based attention mechanism by using the merits of both architectures together in one model. In the proposed model, first, CNN learns the high-level features of sentence from input representation. Second, we used attention mechanism to get the attention of the model on the features which contribute much in the prediction task by calculating the attention score from features context generated from CNN filters. Finally, these features context from CNN with attention score are commonly used at the RNN to process them sequentially. To validate the model we experiment on three benchmark datasets. Experiment results and their analysis demonstrate the effectiveness of the model.
Thin cloud removal in optical remote sensing images based on generative adversarial networks and physical model of cloud distortion

Cloud contamination is an inevitable problem in optical remote sensing images. Unlike thick clouds, thin clouds do not completely block out background which makes it possible to restore background information. In this paper, we propose a semi-supervised method based on generative adversarial networks (GANs) and a physical model of cloud distortion (CR-GAN-PM) for thin cloud removal with unpaired images from different regions. A physical model of cloud distortion which takes the absorption of cloud into consideration was also defined in this paper. It is worth noting that many state-of-the-art methods based on deep learning require paired cloud and cloud-free images from the same region, which is often unavailable or time-consuming to collect. CR-GAN-PM has two main steps: first, the cloud-free background and cloud distortion layers were decomposed from an input cloudy image based on GANs and the principles of image decomposition; then, the input cloudy image was reconstructed by putting those layers into the redefined physical model of cloud distortion. The decomposition process ensured that the decomposed background layer was cloud-free and the reconstruction process ensured that generated background layer was correlated with the input cloudy image. Experiments were conducted on Sentinel-2A imagery to validate the proposed CR-GAN-PM. Averaged over all testing images, the SSIMs values (structural similarity index measurement) of CR-GAN-PM were 0.72, 0.77, 0.81 and 0.83 for visible and NIR bands respectively. Those results were similar to the end-to-end deep learning-based methods and better than traditional methods. The number of input bands and values of hyper-parameters affected little on the performance of CR-GAN-PM. Experimental results show that CR-GAN-PM is effective and robust for thin cloud removal in different bands.