Reviewerly/exHarmony huggingface dataset:
An Analysis of Public REST Web Service APIs

Businesses are increasingly deploying their services on the web, in the form of web applications, SOAP services, message-based services, and, more recently, REST services. Although the movement towards REST is widely recognized, there is not much concrete information regarding the technical features being used in the field, such as typical data formats, how HTTP verbs are being used, or typical URI structures, just to name a few. In this paper, we go through the Alexa.com top 4000 most popular sites to identify precisely 500 websites claiming to provide a REST web service API. We analyze these 500 APIs for key technical features, degree of compliance with REST architectural principles (e.g., resource addressability), and for adherence to best practices (e.g., API versioning). We observed several trends (e.g., widespread JSON support, software-generated documentation), but, at the same time, high diversity in services, including differences in adherence to best practices, with only 0.8 percent of services strictly complying with all REST principles. Our results can help practitioners evolve guidelines and standards for designing higher quality services and also understand deficiencies in currently deployed services. Researchers may also benefit from the identification of key research areas, contributing to the deployment of more reliable services.
Action Parsing-Driven Video Summarization Based on Reinforcement Learning

How to manage, store, and index large numbers of videos is an urgent problem to be solved. Although there are many video summarization models achieving good results, models based on low-level features cannot summarize important semantic information and models based on semantic analysis need related text descriptions that do not exist for most videos. As a consequence, the mining semantic information contained in the video itself is a more feasible way. In this paper, we propose an action parsing-driven video summarization model based on reinforcement learning. The model is mainly divided into two parts, video cut by action parsing and video summarization based on reinforcement learning. In the first part, a sequential multiple instance learning model is trained with weakly annotated data to solve the problem of full annotation&#x2019;s time consuming and weak annotation&#x2019;s ambiguity. In the second part, we design a deep recurrent neural network-based video summarization model that selects the most distinguishable frames comparing with other actions. Meanwhile, the quality of the extracted key frames could be evaluated by the categorization accuracy. Experiments and comparison with state-of-the-art methods demonstrate the advantage of the proposed approach.
A clustering algorithm applied to the binarization of Swarm intelligence continuous metaheuristics

The binarization of Swarm intelligence continuous metaheuristics is an area of great interest in operations research. This interest is mainly due to the application of binarized metaheuristics to combinatorial problems. In this article we propose a general binarization algorithm called K-means Transition Algorithm (KMTA). KMTA uses K-means clustering technique as learning strategy to perform the binarization process. In particular we apply this mechanism to Cuckoo Search and Black Hole metaheuristics to solve the Set Covering Problem (SCP). A methodology is developed to perform the tuning of parameters. We provide necessary experiments to investigate the role of key ingredients of the algorithm. In addition, with the intention of evaluating the behavior of the binarizations while the algorithms are executed, we use the Page's trend test. Finally to demonstrate the efficiency of our proposal, Set Covering benchmark instances of the literature show that KMTA competes clearly with the state-of-the-art algorithms.
Public auditing for shared cloud data with efficient and secure group management

With increasing popularity of collaboration in clouds, shared data auditing has become an important issue in cloud auditing field, and attracted extensive attention from the research community. However, none of the state of the arts can fully achieve all indispensable functional and security requirements. Thus, in this paper, we present a comprehensive public auditing scheme for shared data. Specifically, to preserve users’ identity privacy, signatures on the challenged blocks are converted to the ones signed by the group manager during proof generation; to protect data privacy, a random masking is adopted to blind data proof; a modification record table is designed to record operation information to support identity traceability; we further extend the dynamic hash table to support shared-data dynamics, and present a batch auditing strategy. Moreover, we design a lazy-revocation based group management mechanism to achieve efficient group dynamics, which can resist collusion attacks while significantly reducing computational costs. We formally prove the security of our scheme, and evaluate its performance by comprehensive experiments and comparisons with the state-of-the-art ones. The results demonstrate that our scheme can effectively achieve secure auditing and outperforms the previous ones in computational overhead while maintaining relatively low communication costs.
Learning Converged Propagations With Deep Prior Ensemble for Image Enhancement

Enhancing visual qualities of images plays very important roles in various vision and learning applications. In the past few years, both knowledge-driven maximum a posterior (MAP) with prior modelings and fully data-dependent convolutional neural network (CNN) techniques have been investigated to address specific enhancement tasks. In this paper, by exploiting the advantages of these two types of mechanisms within a complementary propagation perspective, we propose a unified framework, named deep prior ensemble (DPE), for solving various image enhancement tasks. Specifically, we first establish the basic propagation scheme based on the fundamental image modeling cues and then introduce residual CNNs to help predicting the propagation direction at each stage. By designing prior projections to perform feedback control, we theoretically prove that even with experience-inspired CNNs, DPE is definitely converged and the output will always satisfy our fundamental task constraints. The main advantage against conventional optimization-based MAP approaches is that our descent directions are learned from collected training data, thus are much more robust to unwanted local minimums. While, compared with existing CNN type networks, which are often designed in heuristic manners without theoretical guarantees, DPE is able to gain advantages from rich task cues investigated on the bases of domain knowledges. Therefore, DPE actually provides a generic ensemble methodology to integrate both knowledge and data-based cues for different image enhancement tasks. More importantly, our theoretical investigations verify that the feedforward propagations of DPE are properly controlled toward our desired solution. Experimental results demonstrate that the proposed DPE outperforms state-of-the-arts on a variety of image enhancement tasks in terms of both quantitative measure and visual perception quality.
Enhanced Bi-Prediction With Convolutional Neural Network for High-Efficiency Video Coding

In this paper, we propose an enhanced bi-prediction scheme based on the convolutional neural network (CNN) to improve the rate-distortion performance in video compression. In contrast to the traditional bi-prediction strategy which computes the linear superposition as the predictive signals with pixel-to-pixel correspondence, the proposed scheme employs CNN to directly infer the predictive signals in a data-driven manner. As such, the predicted blocks are fused in a nonlinear fashion to improve the coding performance. Moreover, the patch-to-patch inference strategy with CNN also improves the prediction accuracy since the patch-level information for the prediction of each individual pixel can be exploited. The proposed enhanced bi-prediction scheme is further incorporated into the high-efficiency video coding standard, and the experimental results exhibit a significant performance improvement under different coding configurations.
A novel photovoltaic power forecasting model based on echo state network

In this paper, a novel photovoltaic (PV) forecasting model based on multiple reservoirs echo state network (MR-ESN) is proposed to forecast the power output of PV generation system. Firstly, through the unsupervised learning algorithm of restricted Boltzmann machine, the relative feature of input information can be extracted. According to the forecasting performance evaluation criteria of PV forecasting model, principal component analysis is used to extract the main feature, such that the inputs of MR-ESN and the number of reservoirs of MR-ESN can be determined. Secondly, in order to improve the prediction accuracy, an improved parameter optimization method based on Davidon–Fletcher–Powell (DFP) quasi-Newton algorithm is given to optimize the reservoir parameters of MR-ESN. Thirdly, in order to guarantee that the MR-ESN can be stably applied for PV power forecasting, a sufficient condition of the transient stability of PV forecasting model is given. Finally, a PV power generation forecasting example shows that the proposed PV forecasting model can significantly improve the forecasting performance.
Hybrid Keyword-Field Search With Efficient Key Management for Industrial Internet of Things

Equipped with the emerging cloud computing, clients prefer to outsource the increasing number of Industrial Internet of things (IIoT) data to cloud to reduce the high storage and computation burden. However, existing searchable encryption (SE) schemes just apply to IIoT records containing textual keyword fields rather than both digital and textual keyword ones. Besides, the key management issue still impedes the practicality and availability of SE schemes due to high key storage overhead. To this end, we present an outsourced Hybrid Keyword-Field Search over encrypted data with efficient Keys Management (HKFS-KM) scheme by utilizing the relevance score function and keyed hash tree. Formal security analysis proves that the HKFS-KM scheme can achieve keyword privacy and trapdoor unlinkability in both known ciphertexts attack model and known background attack model. Experimental results using real-world dataset show its efficiency and practicality in practice.
Automatic recognition and classification of multi-channel microseismic waveform based on DCNN and SVM

The recognition and classification of the multi-channel microseismic waveform are important for mine hazard prediction. It is widely used to design the corresponding waveform feature for recognition and classification of the microseismic waveform by hand. The process of designing features manually is arduous and the results of recognition and classification are not ideal. In this paper, we propose a method combining Deep Convolutional Neural Networks (DCNN) with Support Vector Machine (SVM) for identifying the microseismic waveform automatically. We constructed a DCNN structure to train the optimal weight model named the DCNN-Model. The DCNN-Model is used as a tool for extracting features from multi-channel waveforms. After combining the extracted features, we used SVM to classify multi-channel waveforms. We compared the outputs of other classifiers, such as Random Forest and k-Nearest Neighbor (KNN). To extend the dataset of DCNN training and extract the essential characteristics of waveform images more accurately, we pre-process the raw data by means of filtering and de-nosing. The experiment shows that the recognition and classification method is of practical value, and the accuracy rate can reach as high as 98.18%.
Modeling multi-aspects within one opinionated sentence simultaneously for aspect-level sentiment analysis

Aspect-level sentiment analysis aims at inferring the sentiment polarity with respect to a specific aspect term in an opinionated text, and has attracted a surge of active research interest in the research community. Years of research have witnessed significant progress made in aspect-level sentiment analysis by exploiting attention mechanism to learn semantically meaningful aspect-specific representations. Although previous attention-based approaches have proven to be successful and effective for aspect-level sentiment classification, there still exist some problems not well handled in the literature. First, the explicit position context is not well explored. Second, different aspects in one opinionated sentence are processed in isolation. In other words, existing attentive methods ignore the disturbance of other aspects in the same sentence when computing the attention vector for the current aspect. Aiming to address the two issues, in this paper, we develop a two stage paradigm which can be accomplished in two steps: (1) the StageI model introduces position attention to model the explicit position context between the aspect and its context words with the goal of dealing with aspects one by one; and (2) the StageII model investigates how to model multi-aspects within one opinionated sentence all at once using the position attention mechanism. We empirically evaluate our proposed method on the SemEval 2014 datasets and encouraging experimental results turn out that the proposed approach yields a significant performance gain compared to other state-of-the-art attention-based methods.
Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement

We address the problem of restoring a high-resolution face image from a blurry low-resolution input. This problem is difficult as super-resolution and deblurring need to be tackled simultaneously. Moreover, existing algorithms cannot handle face images well as low-resolution face images do not have much texture which is especially critical for deblurring. In this paper, we propose an effective algorithm by utilizing the domain-specific knowledge of human faces to recover high-quality faces. We first propose a facial component guided deep Convolutional Neural Network (CNN) to restore a coarse face image, which is denoted as the base image where the facial component is automatically generated from the input face image. However, the CNN based method cannot handle image details well. We further develop a novel exemplar-based detail enhancement algorithm via facial component matching. Extensive experiments show that the proposed method outperforms the state-of-the-art algorithms both quantitatively and qualitatively.
Embedding Attention and Residual Network for Accurate Salient Object Detection

Salient object detection is usually used as a preprocessing step to facilitate a variety of subsequent applications which should take little time cost. With the quick development of deep learning recently, profound progresses have been made to achieve a new state-of-the-art performance. However, the learned features of the existing deep learning-based methods are not accurate enough thus leading to unsatisfactory detection in complex scenes, such as low contrast or very similar between salient object and background region and multiple (small) salient objects with diverse characteristics. In addition, some post-processing techniques are usually needed for refinement, which is time consuming. To address these issues, this paper presents an efficient fully convolutional salient object detection network. Specifically, we first introduce a visual attention mechanism to guide feature learning in side output layers. In detail, attention weight is employed in a top-down manner which can bridge high level semantic information to help shallow layers better locate salient objects and also filter out noisy response in the background region. Second, we propose a residual refinement network to fuse the learned multilevel features gradually. Not to simply add or concatenate them step by step as previous works, we introduce a second-order term into element-wise addition to learn stage-wise residual features for refinement. Such a second-order term not only benefits efficient gradient propagation but also increases network nonlinearity. Extensive experiments on seven standard benchmarks demonstrate that the proposed approach achieves consistently superior performance and performs well on small salient object detection in comparison with the very recent state-of-the-arts, especially in the metric of structure-measure.
Video Saliency Prediction Based on Spatial-Temporal Two-Stream Network

In this paper, we propose a novel two-stream neural network for video saliency prediction. Unlike some traditional methods based on hand-crafted feature extraction and integration, our proposed method automatically learns saliency related spatiotemporal features from human fixations without any pre-processing, post-processing, or manual tuning. Video frames are routed through the spatial stream network to compute static or color saliency maps for each of them. And a new two-stage temporal stream network is proposed, which is composed of a pre-trained 2D-CNN model (SF-Net) to extract saliency related features and a shallow 3D-CNN model (Te-Net) to process these features, for temporal or dynamic saliency maps. It can reduce the requirement of video gaze data, improve training efficiency, and achieve high performance. A fusion network is adopted to combine the outputs of both streams and generate the final saliency maps. Besides, a convolutional Gaussian priors (CGP) layer is proposed to learn the bias phenomenon in viewing behavior to improve the performance of the video saliency prediction. The proposed method is compared with state-of-the-art saliency models on two public video saliency benchmark datasets. The results demonstrate that our model can achieve advanced performance on video saliency prediction.
A General Framework for Auto-Weighted Feature Selection via Global Redundancy Minimization

Most existing feature selection methods rank all the features by a certain criterion, via which the top ranking features are selected for the subsequent classification or clustering tasks. Due to neglecting the feature redundancy, the selected features are frequently correlated with each other such that performance could be compromised. To address this issue, we propose a novel auto-weighted feature selection framework via global redundancy minimization (AGRM) in this paper. Different from other feature selection methods, the proposed method can truly select the representative and non-redundant features, since the redundancy among the features can be largely reduced from the global perspective. In addition, AGRM is extended to a compact (C-AGRM) framework, which is more concise and efficient. Moreover, both of the proposed frameworks are auto-weighted, i.e., parameterfree, so that they are pragmatic in real applications. In general, the proposed frameworks serve as post-processing system, which can be applied to the existing supervised and unsupervised feature selection methods to refine the original feature score for the non-redundant features. Eventually, extensive experiments on nine benchmark datasets are conducted to demonstrate the effectiveness and the superiority of our proposed frameworks.
Detail-Preserving Underexposed Image Enhancement via Optimal Weighted Multi-Exposure Fusion

Photographs taken by mobile device usually suffer from loss of details and low visual attraction due to the poor light condition. The enhancement of the underexposed image can effectively solve this problem. However, previous work may inevitably wash out some weak edges and lose details when handling several underexposed images. To deal with these problems, this paper presents a detail-preserving underexposed image enhancement method based on a new optimal weighted multi-exposure fusion mechanism. Providing an input underexposed image, we propose a novel multi-exposure image enhancement method which can generate a multi-exposure image sequence. However, none of these images are good enough, as images with high exposure have good brightness and color information, whereas sharp details are better preserved in the images with lower exposure. In order to preserve details and enhance the blurred edges, we propose to solve an energy function to compute the optimal weight of the three measurements: 1) local contrast; 2) saturation; and 3) exposedness. Then a weighted multi-exposed fusion method is used to generate the final image. Since the proposed approach is computationally light-weight, it is possible to implement it on mobile devices, such as smart phones and compact cameras. Various experiment results validate our new method.
Image Based Steganography to Facilitate Improving Counting-Based Secret Sharing

The secret sharing scheme is a security tool that provides reliability and robustness for multi-user authentication systems. This work focuses on improving the security and efficiency of counting-based secret sharing by remodeling its phases. The research presents refining the shares generation phase as well as proposing different secret reconstruction models based on new shares distribution method. This work addressed solving published defects in the original counting-based secret sharing scheme providing optimized efficient methodology. The research further proposed utilizing steganography for practicality purposes. We adopted image-based steganography to hide generated shares preserving the improved security of the scheme enhancing the humanized remembrance usability. The study compared several applicable steganography methods for hiding shares analyzing their distortion, security, and capacity. This facilitating work of image-based steganography with the improved counting-based secret sharing is showing promising results opening research direction for future attractive contributions to follow-on.
Efficient decentralized multi-authority attribute based encryption for mobile cloud data storage

Mobile cloud data storage allows mobile users to outsource personal and corporate data to the cloud for flexibility and money savings. However, data outsourcing introduces high confidentiality and privacy risks. To solve the above issues in mobile cloud data storage, attribute-based encryption has been proposed thanks to the flexibility in encryption policy and the fine grained access control. Existing multi-authority attribute-based encryption schemes however still require a trusted central authority to publish system parameters and to generate user secret keys. They give to the trusted central authority enough privileges to access the plaintext information meant for the user, a problem referred to as key escrow issue. They moreover require high computational and communicational overhead on the different operating entities. We propose in this paper an efficient decentralized multi-authority attribute-based scheme for mobile cloud data storage. Our scheme solves the key escrow problem by removing the central authority, without making use of any global user identity. We demonstrate that our scheme is flexible and exhibits less communication and computation overhead on the data user side, both in an online and offline fashion, as well as prove its security under the decisional bilinear diffie-hellman assumption.