Reviewerly/exHarmony huggingface dataset:
ChainSDI: A Software-Defined Infrastructure for Regulation-Compliant Home-Based Healthcare Services Secured by Blockchains

Healthcare networking and computing infrastructure is rapidly changing from closed environments to open environments that incorporate ubiquitous medical devices and novel application scenarios. For example, home-based healthcare involves the collection and analysis of data from pervasive sensors (often in real time) to guide therapy or to perform medical interventions. In this article, we address the challenges of data interoperability and regulatory compliance when designing and deploying healthcare applications in a heterogeneous home-edge-cloud environment. We propose the ChainSDI framework that leverages the blockchain technique along with abundant edge computing resources to manage secure data sharing and computing on sensitive patient data. We implement programmable ChainSDI application programming interfaces to facilitate the specification of home-based healthcare services running on a software-defined infrastructure (SDI). This article is a collaboration between computer scientists, medical researchers, healthcare IT professionals as well as healthcare providers with the goal of increasing the availability of SDI infrastructures while meeting the performance and regulatory requirements of healthcare applications. Our prototype demonstrates the feasibility of the framework and serves as a testbed for novel experimental studies in the context of emerging healthcare applications.
Lower bounds for finding stationary points II: first-order methods

We establish lower bounds on the complexity of finding $$\epsilon $$ -stationary points of smooth, non-convex high-dimensional functions using first-order methods. We prove that deterministic first-order methods, even applied to arbitrarily smooth functions, cannot achieve convergence rates in $$\epsilon $$ better than $$\epsilon ^{-8/5}$$ , which is within $$\epsilon ^{-1/15}\log \frac{1}{\epsilon }$$ of the best known rate for such methods. Moreover, for functions with Lipschitz first and second derivatives, we prove that no deterministic first-order method can achieve convergence rates better than $$\epsilon ^{-12/7}$$ , while $$\epsilon ^{-2}$$ is a lower bound for functions with only Lipschitz gradient. For convex functions with Lipschitz gradient, accelerated gradient descent achieves a better rate, showing that finding stationary points is easier given convexity.
Face recognition approach by subspace extended sparse representation and discriminative feature learning

To address the problem of face recognition where the number of the labeled samples is insufficient and those samples involve pose, illumination and expression variations, etc., this paper proposes a face recognition approach by subspace extended sparse representation and discriminative feature learning, called SESRC & LDF. In SESRC&LDF, each test image is considered to be the image with small pose variation or the image with large pose variation according to its symmetry. For each test image, if it is considered to be the former, it will be recognized by the proposed subspace extended sparse representation classifier (SESRC), otherwise, it will be recognized by the face recognition method based on learning discriminative feature (LDF) proposed in this paper. On eight benchmark face databases, including Yale, AR, LFW, Extended Yale B, FEI, FERET, UMIST and Georgia Tech, empirical results show that SESRC & LDF achieves the highest recognition rates, outperforming many algorithms. Those algorithms include some state-of-the-art ones, such as PLR, MDFR and OPR.
Private Information Retrieval from Non-Replicated Databases

We consider the problem of private information retrieval (PIR) of a single message out of K messages from N non-colluding and non-replicated databases. Different from the majority of the existing literature, here, we consider the case of non-replicated databases under a special non-replication structure where each database stores M out of K messages and each message is stored across R different databases. This generates an R-regular graph structure for the storage system where the vertices of the graph are the messages and the edges are the databases. We derive a general upper bound for M = 2 that depends on the graph structure. We then specialize the problem to storage systems described by two special types of graph structures: cyclic graphs and fully-connected graphs. We prove that the PIR capacity for the case of cyclic graphs is 2/K+1, and the PIR capacity for the case of fully-connected graphs is min{2/K, 1/2}. In both cases, the results show severe degradation in PIR capacity due to non-replication.
People Detection and Finding Attractive Areas by the use of Movement Detection Analysis and Deep Learning Approach

This paper presents a technical approach related to the video computer analysis, to detect people and control the behaviour of people. Control the behaviour of people in public place can be a benefit for understanding the share of overall traffic your area is attracting. Find out what, encourage customers to buy products are most crucial for big companies to increase the sales rate and to improve the quality of customer service. We use surveillance cameras, which located in the museum. We offered two methods, the first method for detecting people in a closed space and second method finding density areas which people more spend time to visit. The YOLO model makes predictions with a single network evaluation. Systems like R-CNN and Faster R-CNN, on the other hand, make multiple assessments for a single image, making YOLO extremely fast, running in real-time with a capable GPU. For detect people used YOLOv3 algorithm which is published by [18] and shows that it has high accuracy to identify people, also we compared the proposed method with other detectors, HOG, SSD and YOLO-tiny which shows the proposed algorithm has better performance in this point. And for finding density areas, We utilized a background subtraction with Gaussian Mixture algorithms and heatmap colour technique to analysis each frame and figure out, where are the density areas which shows people like to spend more time to visit. The experimental results have shown that the accuracy and the performance of both algorithms are quite good.
Timely Coded Computing

In modern distributed computing systems, unpredictable and unreliable infrastructures result in high variability of computing resources. Meanwhile, there is significantly increasing demand for timely and event-driven services with deadline constraints. Motivated by measurements over Amazon EC2 clusters, we consider a two-state Markov model for variability of computing speed in cloud networks. In this model, each worker can be either in a good state or a bad state in terms of the computation speed, and the transition between these states is modeled as a Markov chain which is unknown to the scheduler. We then consider a Coded Computing framework, in which the data is possibly encoded and stored at the worker nodes in order to provide robustness against nodes that may be in a bad state. Our goal is to design the optimal computation-load allocation strategy that maximizes the timely computation throughput (i.e, the average number of computation tasks accomplished before their deadline). Our main result is the development of a dynamic computation strategy called Estimate-and-Allocate (EA) strategy, which achieves the optimal timely computation throughput. Compared with the static allocation strategy, EA improves the timely computation throughput by 1.44 ×4.6 in experiments over Amazon EC2 clusters.
Attentional Information Fusion Networks for Cross-Scene Power Line Detection

The power line is one of the most hazardous obstacles for low-altitude aircrafts. As aircrafts usually encounter scenes like never before during the flight, cross-scene power line detection is the key for their flight safety. However, compared to regular object detection tasks, cross-scene power line detection is extremely challenging due to its weak visual appearance and widespread existence. In this letter, we propose a cross-scene power line detection method based on attentional information fusion networks. Specifically, we construct a fully convolutional network with attention and information fusion mechanism for cross-scene detection. The two main modules make full use of the semantic and location information, which enables the model to focus more on power lines rather than the unexpected scenes. To the best of author knowledge, our method establishes the first end-to-end convolutional architecture for pixelwise power line detection. Experimental results have shown that our method outperforms previous methods by large margins for cross-scene power line detection.
Optical Flow Estimation Using Dual Self-Attention Pyramid Networks

Recently, optical flow estimation benefits greatly from deep learning based techniques. Most approaches use encoder-decoder architecture (U-Net) or spatial pyramid network (SPN) to learn optical flow. Both U-Net and SPN can extract multi-scale features and can predict optical flow directly. However, existing networks ignore to exploit the global information among channel features and inter-spatial relationship of features. In this paper, we propose a dual self-attention pyramid network, which adaptively integrates local features with their global dependencies and focuses on important features and suppresses unimportant features. Specifically, we introduce two types of attention modules into SPN, which emphasizes meaningful features along channel and spatial axes. The channel attention can adaptively re-weight channel-wise features by considering interdependencies among channels. Moreover, the spatial attention can utilize global contextual information to emphasize or suppress features in different spatial locations. In addition, two attention modules are embedded into each pyramidal level, which can refine features at different scale. We evaluate our method on MPI-Sintel and KITTI. The experimental results show that using the dual self-attention module can improve the representation power of network and further increase the accuracy of optical flow estimation.
Internet public informatioan text data mining and intelligence influence analysis for user intent understanding

We propose models based on SVM, Naïve Bayes and deep learning to solve the consumption intention classification problem. Applying consumption intention mining to prediction tasks in social media. This paper discusses consumption intention towards a certain kind of product, i.e. movie, and uses movi e consumption intention as an important feature in box office prediction. We combine consumption intention with traditional features used in the problem of box office prediction, and achieve a outperforms previous work of this problem We build a system based on linear regression which automatically predicts movies' total box office and opening weekend box office one day prior to the movie's release date.
Enriching Non-negative Matrix Factorization with Contextual Embeddings for Recommender Systems

Recommender Systems (RS) are used to give customized recommendations about specific items to users in a variety of applications including social web sites applications, media recommendation and commerce sites etc. Collaborative Filtering (CF) along with the Content Based Filtering (CBF) are two widely used methods which are being efficiently applied in RS implementation. CF suffers with sparseness problem where user-to-item ratings are amply sparse. On the other hand, CBF performance rely on methods of feature extraction for efficient use of items' description. The sparseness of user-to-item ratings and features extraction impede the performance of RS. Quality of rating prediction and item recommendation further degrades due to the negative values present in users/items latent factors. This paper proposes a novel RS that is built upon the semantics based items' content embedding model, enriched with contextual features extracted through Convolutional Neural Network (CNN). Non-negative Matrix Factorization (NMF), supplied with improvised embedding is used as CF technique. Embedding model captures the item details, thus resolving the sparsity, whereas, NMF caters for information loss due to negative values in latent factors. The proposed RS with contextually enriched NMF (Contx-NMF) simultaneously overcomes both the issues of sparseness and loss due to negative values, thus enhancing the rating prediction accuracy. The proposed model is evaluated on three public datasets (MovieLens 1M, MovieLens 10M)and Amazon Instant Video (AIV). The results demonstrate significant improvement in performance of Contx-NMF over state of the art RS models for sparse user-to-item ratings.
Deep Non-Local Kalman Network for Video Compression Artifact Reduction

Video compression algorithms are widely used to reduce the huge size of video data, but they also introduce unpleasant visual artifacts due to the lossy compression. In order to improve the quality of the compressed videos, we proposed a deep non-local Kalman network for compression artifact reduction. Specifically, the video restoration is modeled as a Kalman filtering procedure and the decoded frames can be restored from the proposed deep Kalman model. Instead of using the noisy previous decoded frames as temporal information, the less noisy previous restored frame is employed in a recursive way, which provides the potential to generate high quality restored frames. In the proposed framework, several deep neural networks are utilized to estimate the corresponding states in the Kalman filter and integrated together in the deep Kalman filtering network. More importantly, we also exploit the non-local prior information by incorporating the spatial and temporal non-local networks for better restoration. Our approach takes the advantages of both the model-based methods and learning-based methods, by combining the recursive nature of the Kalman model and powerful representation ability of neural networks. Extensive experimental results on the Vimeo-90k and HEVC benchmark datasets demonstrate the effectiveness of our proposed method.
DAGCN: Dual Attention Graph Convolutional Networks

Graph convolutional networks (GCNs) have recently become one of the most powerful tools for graph analytics tasks in numerous applications, ranging from social networks and natural language processing to bioinformatics and chemoinformatics, thanks to their ability to capture the complex relationships between concepts. At present, the vast majority of GCNs use a neighborhood aggregation framework to learn a continuous and compact vector, then performing a pooling operation to generalize graph embedding for the classification task. These approaches have two disadvantages in the graph classification task: (1)when only the largest sub-graph structure (k-hop neighbor) is used for neighborhood aggregation, a large amount of early-stage information is lost during the graph convolution step; (2) simple average/sum pooling or max pooling utilized, which loses the characteristics of each node and the topology between nodes. In this paper, we propose a novel framework called, dual attention graph convolutional networks (DAGCN) to address these problems. DAGCN automatically learns the importance of neighbors at different hops using a novel attention graph convolution layer, and then employs a second attention component, a self-attention pooling layer, to generalize the graph representation from the various aspects of a matrix graph embedding. The dual attention network is trained in an end-to-end manner for the graph classification task. We compare our model with state-of-the-art graph kernels and other deep learning methods. The experimental results show that our framework not only outperforms other baselines but also achieves a better rate of convergence.
Transparency and Traceability: In Food Supply Chain System using Blockchain Technology with Internet of Things

In the modern society, where the technologies are rapidly flourishing, the contribution of the supply chain is still at its beginning stage. Supply chain is the network of distributing the raw materials, processing it through intermediate and utilized by consumers. The traditional supply chain logistics pattern is more lenient and it faces the consequences like non-transparent in nature, interference etc. this cannot match the demands of the trade. Blockchain technology is a distributed network of data. This technology provides tamper-proof data with strong authentication where the information about all the products are encrypted. The blockchain technology is united with the Internet of Things to provide the increased transparency and efficient supply chain. In this paper it is demonstrated about the blockchain with IoT in the area of supply chain with its pros and cons.
Few-shot learning with adaptively initialized task optimizer: a practical meta-learning approach

Considering the data collection and labeling cost in real-world applications, training a model with limited examples is an essential problem in machine learning, visual recognition, etc. Directly training a model on such few-shot learning (FSL) tasks falls into the over-fitting dilemma, which would turn to an effective task-level inductive bias as a key supervision. By treating the few-shot task as an entirety, extracting task-level pattern, and learning a task-agnostic model initialization, the model-agnostic meta-learning (MAML) framework enables the applications of various models on the FSL tasks. Given a training set with a few examples, MAML optimizes a model via fixed gradient descent steps from an initial point chosen beforehand. Although this general framework possesses empirically satisfactory results, its initialization neglects the task-specific characteristics and aggravates the computational burden as well. In this manuscript, we propose our AdaptiVely InitiAlized Task OptimizeR (Aviator) approach for few-shot learning, which incorporates task context into the determination of the model initialization. This task-specific initialization facilitates the model optimization process so that it obtains high-quality model solutions efficiently. To this end, we decouple the model and apply a set transformation over the training set to determine the initial top-layer classifier. Re-parameterization of the first-order gradient descent approximation promotes the gradient back-propagation. Experiments on synthetic and benchmark data sets validate that our Aviator approach achieves the state-of-the-art performance, and visualization results demonstrate the task-adaptive features of our proposed Aviator method.
Partial Policy-Based Reinforcement Learning for Anatomical Landmark Localization in 3D Medical Images

Utilizing the idea of long-term cumulative return, reinforcement learning (RL) has shown remarkable performance in various fields. We follow the formulation of landmark localization in 3D medical images as an RL problem. Whereas value-based methods have been widely used to solve RL-based localization problems, we adopt an actor-critic based direct policy search method framed in a temporal difference learning approach. In RL problems with large state and/or action spaces, learning the optimal behavior is challenging and requires many trials. To improve the learning, we introduce a partial policy-based reinforcement learning to enable solving the large problem of localization by learning the optimal policy on smaller partial domains. Independent actors efficiently learn the corresponding partial policies, each utilizing their own independent critic. The proposed policy reconstruction from the partial policies ensures a robust and efficient localization, where the sub-agents uniformly contribute to the state-transitions based on their simple partial policies mapping to binary actions. Experiments with three different localization problems in 3D CT and MR images showed that the proposed reinforcement learning requires a significantly smaller number of trials to learn the optimal behavior compared to the original behavior learning scheme in RL. It also ensures a satisfactory performance when trained on fewer images.
Extraction of Octave Spectra Information for Spoofing Attack Detection

This article focuses on extracting information from the octave power spectra of long-term constant-Q transform (CQT) for spoofing attack detection. A novel framework based on multi-level transform (MLT) is proposed that can capture the relevant information from octave power spectra using level by level in a multi-level manner. We then derive a novel feature referred to as constant-Q multi-level coefficient (CMC) based on proposed MLT. The proposed feature is evaluated on synthetic as well as replay speech detection studies on ASVspoof 2015 and ASVspoof 2017 version 2.0 database, respectively. We find the proposed CMC feature outperforms the conventional constant-Q cepstral coefficient based long-term feature obtained from linear power spectrum after uniform resampling. This depicts the usefulness of MLT to extract salient artifacts from octave power spectrum. Further, the proposed CMC feature performs better than the existing the well known other state-of-the-art systems for spoofing attack detection that showcases its importance.
A service recommendation algorithm with the transfer learning based matrix factorization to improve cloud security

Recommendation system (RS) is designed to provide personalized services based on the users’ historical data. It has been applied in various fields and is expected to recommend the suitable services for the different kinds of users. Considering the importance of individual privacy, current users gradually tend not to expose personal information. This means RS may face the highly sparse datasets in the fields of cloud security. In general, the accuracy of recommendation will be improved with the growth of individual data, but the cold start problem is exactly in this contradictory phenomenon: this question evolves to produce sufficiently accurate recommendation result under the data scarcity problem. RS has to recommend services for the rarely historical data users and the latent users might drain along with the production of counter effects. To alleviate data scarcity problem in cloud security environment, this work is to introduce similar domain knowledge based on the transfer learning. Besides, the content and location based methods have been proved that these ideas work under this situation. So, this work also employs latent dirichlet allocation (LDA) to analysis the service descriptions and explore the relationship between the content and location information. In this framework, the suitable combination of LDA and word2vec models will balance the accuracy and speed which benefit service recommendation particularly. The related experiments demonstrate the effectiveness on the real word dataset. It can be found that the transfer learning based word2vec model shows the potentiality to explore the relationship between topic words, and improve the LDA algorithm from the content relationship. This proves that in both cold start environment and warm start environment, the proposed algorithm is more robust than other model-based state-of-art methods.