Reviewerly/exHarmony huggingface dataset:
Fall detection system based on real-time pose estimation and SVM

With the rapid growth of the elderly population, fall detection has become a key issue in the medical and health field. Accurately detecting fall behavior in surveillance video and timely feedback can effectively reduce the injury and even death of the elderly due to falls. For the complex scenes in surveillance video and the interference of multiple similar human behaviors, this paper proposes a method based on pose estimation and the auxiliary detection method based on yoloV5. First, extract video frames from different falling video sequences to form a data set; then, input the training sample set into the improved network for training until the network converges; finally, test the category of the target in the video according to the optimized network model and locate the target. Experimental results show that the improved algorithm can effectively detect falls or Activities of Daily Living (ADL) events in each frame of the image and give real-time feedback. The detection of falling behavior in the video further verifies the feasibility and efficiency of the recognition method based on our deep learning methods.
Meta grayscale adaptive network for 3D integrated renal structures segmentation

Three-dimensional (3D) integrated renal structures (IRS) segmentation targets segmenting the kidneys, renal tumors, arteries, and veins in one inference. Clinicians will benefit from the 3D IRS visual model for accurate preoperative planning and intraoperative guidance of laparoscopic partial nephrectomy (LPN). However, no success has been reported in 3D IRS segmentation due to the inherent challenges in grayscale distribution: low contrast caused by the narrow task-dependent distribution range of regions of interest (ROIs), and the networks representation preferences caused by the distribution variation inter-images. In this paper, we propose the Meta Greyscale Adaptive Network (MGANet), the first deep learning framework to simultaneously segment the kidney, renal tumors, arteries and veins on CTA images in one inference. It makes innovations in two collaborate aspects: 1) The Grayscale Interest Search (GIS) adaptively focuses segmentation networks on task-dependent grayscale distributions via scaling the window width and center with two cross-correlated coefficients for the first time, thus learning the fine-grained representation for fine segmentation. 2) The Meta Grayscale Adaptive (MGA) learning makes an image-level meta-learning strategy. It represents diverse robust features from multiple distributions, perceives the distribution characteristic, and generates the model parameters to fuse features dynamically according to image’s distribution, thus adapting the grayscale distribution variation. This study enrolls 123 patients and the average Dice coefficients of the renal structures are up to 87.9%. Fine selection of the task-dependent grayscale distribution ranges and personalized fusion of multiple representations on different distributions will lead to better 3D IRS segmentation quality. Extensive experiments with promising results on renal structures reveal powerful segmentation accuracy and great clinical significance in renal cancer treatment.
A Chatbot for Changing Lifestyle in Education

In this pandemic situation, all are learning education online. There is a lot of drawbacks to these methods, the main drawback of this system is the interaction between students and teachers become low. A chatbot is one of the most convenient ways of studying for students and it also rectifies student doubts at any time without human support. This paper aims to approach a typical way to design a chatbot for MATLAB practical dataset. Students can ask a question in the chatbot in the form of text then, the question is processed with natural language processing and deep learning technology. Finally, the chatbot can answer the students with exact answers. So this kind of chatbot is useful for both the students and teachers.
Leaky Nets: Recovering Embedded Neural Network Models and Inputs Through Simple Power and Timing Side-Channels—Attacks and Defenses

With the recent advancements in machine learning theory, many commercial embedded micro-processors use neural network models for a variety of signal processing applications. However, their associated side-channel security vulnerabilities pose a major concern. There have been several proof-of-concept attacks demonstrating the extraction of their model parameters and input data. But, many of these attacks involve specific assumptions, have limited applicability, or pose huge overheads to the attacker. In this work, we study the side-channel vulnerabilities of embedded neural network implementations by recovering their parameters using timing-based information leakage and simple power analysis side-channel attacks. We demonstrate our attacks on popular micro-controller platforms over networks of different precisions such as floating point, fixed point, binary networks. We are able to successfully recover not only the model parameters but also the inputs for the above networks. Countermeasures against timing-based attacks are implemented and their overheads are analyzed.
Learning deep discriminative representations with pseudo supervision for image clustering

Image clustering is a crucial but challenging task in machine learning and computer vision. Its performance highly depends on the quality of image feature representations. Recently, deep joint clustering which combines representation learning with clustering has presented a promising performance. However, existing joint methods suffer from two severe problems. That is, the learned representations lack discriminability especially for intricate images, and the performance often encounters a bottleneck due to the lack of supervision information. To address these problems, we propose a pseudo-supervised joint method for image clustering, i.e., Discriminative Pseudo Supervision Clustering (DPSC). Our key idea is to discover and utilize the pseudo supervision information to provide supervisory guidance for discriminative representation learning. With the aid of pseudo supervision, the representations can be continuously refined to facilitate inter-cluster separability and intra-cluster compactness, thereby leading to more discriminative representations and correctly separated clusters. To fully benefit from joint learning, we further introduce a self-evolution training algorithm to jointly optimize the DPSC model, in which the learned representations and clustering results boost each other progressively as more reliable pseudo supervision information is discovered during the iteration. Experimental results show that DPSC significantly outperforms state-of-the-art methods on various image datasets. Moreover, the learned feature representations generalize well across various algorithms.
FREDE

Low-dimensional representations, or embeddings , of a graph's nodes facilitate several practical data science and data engineering tasks. As such embeddings rely, explicitly or implicitly, on a similarity measure among nodes, they require the computation of a quadratic similarity matrix, inducing a tradeoff between space complexity and embedding quality. To date, no graph embedding work combines (i) linear space complexity, (ii) a nonlinear transform as its basis, and (iii) nontrivial quality guarantees. In this paper we introduce FREDE ( FREquent Directions Embedding ), a graph embedding based on matrix sketching that combines those three desiderata. Starting out from the observation that embedding methods aim to preserve the covariance among the rows of a similarity matrix, FREDE iteratively improves on quality while individually processing rows of a nonlinearly transformed PPR similarity matrix derived from a state-of-the-art graph embedding method and provides, at any iteration , column-covariance approximation guarantees in due course almost indistinguishable from those of the optimal approximation by SVD. Our experimental evaluation on variably sized networks shows that FREDE performs almost as well as SVD and competitively against state-of-the-art embedding methods in diverse data science tasks, even when it is based on as little as 10% of node similarities.
Prediction of Cancer Disease using Machine learning Approach

Cancer has identified a diverse condition of several various subtypes. The timely screening and course of treatment of a cancer form is now a requirement in early cancer research because it supports the medical treatment of patients. Many research teams studied the application of ML and Deep Learning methods in the field of biomedicine and bioinformatics in the classification of people with cancer across high- or low-risk categories. These techniques have therefore been used as a model for the development and treatment of cancer. As, it is important that ML instruments are capable of detecting key features from complex datasets. Many of these methods are widely used for the development of predictive models for predicating a cure for cancer, some of the methods are artificial neural networks (ANNs), support vector machine (SVMs) and decision trees (DTs). While we can understand cancer progression with the use of ML methods, an adequate validity level is needed to take these methods into consideration in clinical practice every day. In this study, the ML & DL approaches used in cancer progression modeling are reviewed. The predictions addressed are mostly linked to specific ML, input, and data samples supervision.
Optimizing quantum heuristics with meta-learning

Abstract Variational quantum algorithms, a class of quantum heuristics, are promising candidates for the demonstration of useful quantum computation. Finding the best way to amplify the performance of these methods on hardware is an important task. Here, we evaluate the optimization of quantum heuristics with an existing class of techniques called “meta-learners.” We compare the performance of a meta-learner to evolutionary strategies, L-BFGS-B and Nelder-Mead approaches, for two quantum heuristics (quantum alternating operator ansatz and variational quantum eigensolver), on three problems, in three simulation environments. We show that the meta-learner comes near to the global optima more frequently than all other optimizers we tested in a noisy parameter setting environment. We also find that the meta-learner is generally more resistant to noise, for example, seeing a smaller reduction in performance in Noisy and Sampling environments, and performs better on average by a “gain” metric than its closest comparable competitor L-BFGS-B. Finally, we present evidence that indicates the meta-learner trained on small problems will generalize to larger problems. These results are an important indication that meta-learning and associated machine learning methods will be integral to the useful application of noisy near-term quantum computers.
IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation

Natural language generation (NLG) benchmarks provide an important avenue to measure progress and develop better NLG systems.Unfortunately, the lack of publicly available NLG benchmarks for low-resource languages poses a challenging barrier for building NLG systems that work well for languages with limited amounts of data.Here we introduce IndoNLG, the first benchmark to measure natural language generation (NLG) progress in three low-resource-yet widely spokenlanguages of Indonesia: Indonesian, Javanese, and Sundanese.Altogether, these languages are spoken by more than 100 million native speakers, and hence constitute an important use case of NLG systems today.Concretely, IndoNLG covers six tasks: summarization, question answering, chit-chat, and three different pairs of machine translation (MT) tasks.We collate a clean pretraining corpus of Indonesian, Sundanese, and Javanese datasets, Indo4B-Plus, which is used to pretrain our models: IndoBART and IndoGPT.We show that IndoBART and IndoGPT achieve competitive performance on all tasks-despite using only one-fifth the parameters of a larger multilingual model, mBART LARGE (Liu et al., 2020).This finding emphasizes the importance of pretraining on closely related, local languages to achieve more efficient learning and faster inference for very low-resource languages like Javanese and Sundanese. 1 * These authors contributed equally. 1 Beyond the clean pretraining data, we publicly release all pretrained models and tasks at https://github.com/indobenchmark/indonlg to facilitate NLG research in these languages.
Towards Resilient Method: An exhaustive survey of fault tolerance methods in the cloud computing environment

Fault Tolerance (FT) is one of the cloud’s very critical problems for providing security assistance. Due to the diverse service architecture, detailed architectures & multiple interrelationships that occur in the cloud, implementation is complicated. A few other previous studies attempt to integrate the different fault tolerance frameworks and solutions suggested for the cloud environment, but some accounts occur to be constrained. This research article provides a detailed survey of the state-of-the-art research on emerging methods of fault tolerance for Cloud Computing & categorizes techniques of fault tolerance into three categories: Reactive Methods, Proactive Methods & Resilient Methods. Reactive Methods allow the system to reach a defect condition but instead attempt to get the device back up. Proactive Methods help to prevent the device from reaching a defective condition by introducing actions to minimize defects before impacting the device. On either side, newly developed Resilient Methods strive to reduce the amount of time a device takes to find from a malfunction. In the Resilient Methods context, Machine Learning and Artificial Intelligence played an important role in mapping the recovery period to a task to be configured. This survey offers a comprehensive and detailed description of the various faults kinds, factors, & different methods to fault tolerance used in the cloud. In light of their Basic Methods & some other specific characteristics, & also offers a Clear study of different tolerance mechanisms for failures & provides a comparative study of the structures under the article. It is noted that approaches of fault tolerance directed to checkpoint restart and replication are mainly included to address the crash faults in the cloud.
SmartPulse: Automated Checking of Temporal Properties in Smart Contracts

Smart contracts are programs that run on the blockchain and digitally enforce the execution of contracts between parties. Because bugs in smart contracts can have serious monetary consequences, ensuring the correctness of such software is of utmost importance. In this paper, we present a novel technique, and its implementation in a tool called SMARTPULSE, for automatically verifying temporal properties in smart contracts. SMARTPULSE is the first smart contract verification tool that is capable of checking liveness properties, which ensure that "something good" will eventually happen (e.g., "I will eventually receive my refund"). We experimentally evaluate SMARTPULSE on a broad class of smart contracts and properties and show that (a) SMARTPULSE allows automatically verifying important liveness properties, (b) it is competitive with or better than state-of-the-art tools for safety verification, and (c) it can automatically generate attacks for vulnerable contracts.
MSSM: A Multiple-level Sparse Sharing Model for Efficient Multi-Task Learning

Multi-task learning(MTL) is an open and challenging problem in various real-world applications. The typical way of conducting multi-task learning is establishing some global parameter sharing mechanism across all tasks or assigning each task an individual set of parameters with cross-connections between tasks. However, for most existing approaches, all tasks just thoroughly or proportionally share all the features without distinguishing the helpfulness of them. By that, some tasks would be intervened by the unhelpful features that are useful for other tasks, leading to undesired negative transfer between tasks. In this paper, we design a novel architecture named the Multiple-level Sparse Sharing Model (MSSM), which can learn features selectively and share knowledge across all tasks efficiently. MSSM first employs a field-level sparse connection module (FSCM) to enable much more expressive combinations of feature fields to be learned for generalization across tasks while still allowing for task-specific features to be customized for each task. Furthermore, a cell-level sparse sharing module (CSSM) can recognize the sharing pattern through a set of coding variables that selectively choose which cells to route for a given task. Extensive experimental results on several real-world datasets show that MSSM outperforms SOTA models significantly in terms of AUC and LogLoss metrics.
Contour Extraction of Individual Cattle From an Image Using Enhanced Mask R-CNN Instance Segmentation Method

In animal husbandry, the traceability of individual cattle, their health information, and performance records greatly depend on computer vision and image processing-based approaches.However, some of these approaches perform below expectations in obtaining real-time information about individual cattle.No doubt, inaccurate segmentation and incomplete extraction of each cattle object from an image are notable contributory factors.As accurate segmentation is a prerequisite for obtaining real-time information about individual cattle, and since the algorithm of Mask R-CNN relies on the algorithm of simultaneous localization and mapping (SLAM), for the construction of the semantic map, which sometimes exchanges image background for the foreground, there is a need to enhance the available approaches towards achieving precision animal husbandry.To achieve this, an enhanced Mask R-CNN instance segmentation method is proposed to support indistinct boundaries and irregular shapes of cattle bodies.The methods employed in the research are in multiple folds: (1) Pre-enhancement of the image using generalized color Fourier descriptors (GCFD); (2) Provision of optimal filter size that was smaller than ResNet101 (the backbone of Mask R-CNN) for the extraction of smaller and composite features; (3) Utilization of multiscale semantic features using region proposals; (4) A fully connected layer of existing Mask R-CNN integrated with a sub-network for enhanced segmentation and (5) Post-enhancement of the image using Grabcut.Experiments on the datasets of cattle images produced better results when compared to other state-of-the-art methods with 0.93 mAP.
Motif-Preserving Dynamic Attributed Network Embedding

Network embedding has emerged as a new learning paradigm to embed complex network into a low-dimensional vector space while preserving node proximities in both network structures and properties. It advances various network mining tasks, ranging from link prediction to node classification. However, most existing works primarily focus on static networks while many networks in real-life evolve over time with addition/deletion of links and nodes, naturally with associated attribute evolution. In this work, we present Motif-preserving Temporal Shift Network (MTSN), a novel dynamic network embedding framework that simultaneously models the local high-order structures and temporal evolution for dynamic attributed networks. Specifically, MTSN learns node representations by stacking the proposed TIME module to capture both local high-order structural proximities and node attributes by motif-preserving encoder and temporal dynamics by temporal shift operation in a dynamic attributed network. Finally, we perform extensive experiments on four real-world network datasets to demonstrate the superiority of MTSN against state-of-the-art network embedding baselines in terms of both effectiveness and efficiency. The source code of our method is available at: https://github.com/ZhijunLiu95/MTSN.
Effective training of convolutional neural networks for age estimation based on knowledge distillation

Abstract Age estimation from face images can be profitably employed in several applications, ranging from digital signage to social robotics, from business intelligence to access control. Only in recent years, the advent of deep learning allowed for the design of extremely accurate methods based on convolutional neural networks (CNNs) that achieve a remarkable performance in various face analysis tasks. However, these networks are not always applicable in real scenarios, due to both time and resource constraints that the most accurate approaches often do not meet. Moreover, in case of age estimation, there is the lack of a large and reliably annotated dataset for training deep neural networks. Within this context, we propose in this paper an effective training procedure of CNNs for age estimation based on knowledge distillation, able to allow smaller and simpler “student” models to be trained to match the predictions of a larger “teacher” model. We experimentally show that such student models are able to almost reach the performance of the teacher, obtaining high accuracy over the LFW+, LAP 2016 and Adience datasets, but being up to 15 times faster. Furthermore, we evaluate the performance of the student models in the presence of image corruptions, and we demonstrate that some of them are even more resilient to these corruptions than the teacher model.
ValueNet: A Natural Language-to-SQL System that Learns from Database Information

In this paper we propose ValueNet light and ValueNet - two end-to-end Natural Language-to-SQL systems that incorporate values using the challenging Spider dataset. The main idea of our approach is to use not only metadata information from the underlying database but also information on the base data as input for our neural network architecture. In particular, we propose a novel architecture sketch to extract values from a user question and come up with possible value candidates which are not explicitly mentioned in the question. We then use a neural model based on an encoder-decoder architecture to synthesize the SQL query. Finally, we evaluate our model on the Spider challenge using the Execution Accuracy metric, a more difficult metric than used by most participants of the challenge. Our experimental evaluation demonstrates that ValueNet light and ValueNet reach state-of-the-art results of 67% and 62% accuracy, respectively, for translating from NL to SQL whilst incorporating values.
MGSampler: An Explainable Sampling Strategy for Video Action Recognition

Frame sampling is a fundamental problem in video action recognition due to the essential redundancy in time and limited computation resources. The existing sampling strategy often employs a fixed frame selection and lacks the flexibility to deal with complex variations in videos. In this paper, we present a simple, sparse, and explainable frame sampler, termed as Motion-Guided Sampler (MGSampler). Our basic motivation is that motion is an important and universal signal that can drive us to adaptively select frames from videos. Accordingly, we propose two important properties in our MGSampler design: motion sensitive and motion uniform. First, we present two different motion representations to enable us to efficiently distinguish the motion-salient frames from the background. Then, we devise a motion-uniform sampling strategy based on the cumulative motion distribution to ensure the sampled frames evenly cover all the important segments with high motion salience. Our MGSampler yields a new principled and holistic sampling scheme, that could be incorporated into any existing video architecture. Experiments on five benchmarks demonstrate the effectiveness of our MGSampler over the previous fixed sampling strategies, and its generalization power across different backbones, video models, and datasets. The code is available at https://github.com/MCG-NJU/MGSampler.