Reviewerly/exHarmony huggingface dataset:
Depth Restoration From RGB-D Data via Joint Adaptive Regularization and Thresholding on Manifolds

In this paper, we propose a novel depth restoration algorithm from RGB-D data through combining characteristics of local and non-local manifolds, which provide low-dimensional parameterizations of the local and non-local geometry of depth maps. Specifically, on the one hand, a local manifold model is defined to favor local neighboring relationship of pixels in depth, according to which, manifold regularization is introduced to promote smoothing along the manifold structure. On the other hand, the non-local characteristics of the patch-based manifold can be used to build highly data-adaptive orthogonal bases to extract elongated image patterns, accounting for self-similar structures in the manifold. We further define a manifold thresholding operator in 3D adaptive orthogonal spectral bases-eigenvectors of the discrete Laplacian of local and non-local manifolds-to retain only low graph frequencies for depth maps restoration. Finally, we propose a unified alternating direction method of multipliers optimization framework, which elegantly casts the adaptive manifold regularization and thresholding jointly to regularize the inverse problem of depth maps recovery. Experimental results demonstrate that our method achieves superior performance compared with the state-of-the-art works with respect to both objective and subjective quality evaluations.
A novel variational model for noise robust document image binarization

Binarization is a fundamental problem in document image analysis systems. Different from current thresholding techniques, a novel variational model is proposed for noise robust document image binarization in this work, which is inspired by two classical variational models that have been successfully applied in image segmentation and denoising. In our variational model, the energy functional consists of three terms: data fidelity term, binary classification term and regularization term. As a result, the proposed model is capable of performing image binarization and suppressing noise simultaneously. Concretely, we firstly design a variational model for the original document image, the minimizer of which is our desired binarization result. Secondly, the gradient descent flow equation of our model is derived by means of variational principle. Lastly, a simple finite difference scheme, time forward and space center difference, is employed to solve the gradient descent flow equation. Extensive experiments are conducted on three types of document image datasets to validate our model qualitatively and quantitatively. The experiment results not only demonstrate the effectiveness of our approach, but also verify its noise and illumination robustness.
Cost-Effective Cloud Server Provisioning for Predictable Performance of Big Data Analytics

Cloud datacenters are underutilized due to server over-provisioning. To increase datacenter utilization, cloud providers offer users an option to run workloads such as big data analytics on the underutilized resources, in the form of cheap yet revocable transient servers (e.g., EC2 spot instances, GCE preemptible instances). Though at highly reduced prices, deploying big data analytics on the unstable cloud transient servers can severely degrade the job performance due to instance revocations. To tackle this issue, this paper proposes iSpot, a cost-effective transient server provisioning framework for achieving predictable performance in the cloud, by focusing on Spark as a representative Directed Acyclic Graph (DAG)-style big data analytics workload. It first identifies the stable cloud transient servers during the job execution by devising an accurate Long Short-Term Memory (LSTM)-based price prediction method. Leveraging automatic job profiling and the acquired DAG information of stages, we further build an analytical performance model and present a lightweight critical data checkpointing mechanism for Spark, to enable our design of iSpot provisioning strategy for guaranteeing the job performance on stable transient servers. Extensive prototype experiments on both EC2 spot instances and GCE preemptible instances demonstrate that, iSpot is able to guarantee the performance of big data analytics running on cloud transient servers while reducing the job budget by up to 83.8 percent in comparison to the state-of-the-art server provisioning strategies, yet with acceptable runtime overhead.
Runtime Network Routing for Efficient Image Classification

In this paper, we propose a generic Runtime Network Routing (RNR) framework for efficient image classification, which selects an optimal path inside the network. Unlike existing static neural network acceleration methods, our method preserves the full ability of the original large network and conducts dynamic routing at runtime according to the input image and current feature maps. The routing is performed in a bottom-up, layer-by-layer manner, where we model it as a Markov decision process and use reinforcement learning for training. The agent determines the estimated reward of each sub-path and conducts routing conditioned on different samples, where a faster path is taken when the image is easier for the task. Since the ability of network is fully preserved, the balance point is easily adjustable according to the available resources. We test our method on both multi-path residual networks and incremental convolutional channel pruning, and show that RNR consistently outperforms static methods at the same computation complexity on both the CIFAR and ImageNet datasets. Our method can also be applied to off-the-shelf neural network structures and easily extended to other application scenarios.
Toward Efficient Action Recognition: Principal Backpropagation for Training Two-Stream Networks

In this paper, we propose the novel principal backpropagation networks (PBNets) to revisit the backpropagation algorithms commonly used in training two-stream networks for video action recognition. We content that existing approaches always take all the frames/snippets for the backpropagation not optimal for video recognition since the desired actions only occur in a short period within a video. To remedy these drawbacks, we design a watch-and-choose mechanism. In particular, the watching stage exploits a dense snippet-wise temporal pooling strategy to discover the global characteristic for each input video, while the choosing phase only backpropagates a small number of representative snippets that are selected with two novel strategies, i.e., Max-rule and KL-rule. We prove that with the proposed selection strategies, performing the backpropagation on the selected subset is capable of decreasing the loss of the whole snippets as well. The proposed PBNets are evaluated on two standard video action recognition benchmarks UCF101 and HMDB51, where it surpasses the state of the arts consistently, but requiring less memory and computation to achieve high performance.
Temporal Task Scheduling of Multiple Delay-Constrained Applications in Green Hybrid Cloud

A growing number of global companies select Green Data Centers (GDCs) to manage their delay-constrained applications. The fast growth of users’ tasks dramatically increases the energy consumed by GDCs owned by a company, e.g., Google and Amazon. The random nature of tasks brings a big challenge of scheduling tasks of each application with limited infrastructure resources of GDCs. Therefore, hybrid cloud is widely employed to smartly outsource some tasks to public clouds. However, the temporal variation in many factors including revenue, price of power grid, solar irradiance, wind speed, price of public clouds makes it challenging to schedule all tasks of each application in a cost-effective way while strictly meeting their expected delay constraints. This work proposes a temporal task scheduling algorithm investigating the temporal variation in green hybrid cloud to schedule all tasks within their delay constraints. Besides, it explicitly presents a mathematical equation of service rates and task refusal. The maximization problem is formulated and tackled by the proposed hybrid optimization algorithm called Genetic Simulated-annealing-based particle swarm optimization. Trace-driven experiments demonstrate that larger profit are achieved than several existing scheduling algorithms.
The potential of photovoltaic systems to reduce energy costs for office buildings in time-dependent and peak-load-dependent tariffs

Electrical power generation across the world is facing dramatic changes for a variety of reasons related to reliability, economics and environmental concerns. Over recent years a significant increase has been observed in installed capacity of photovoltaic systems. Due to their typical seasonal and diurnal energy conversion patterns their integration into power systems creates new opportunities as well as threats. This paper intends to show how photovoltaics can contribute to reducing peak load in office buildings and thereby minimise expenditure on electricity during time- and peak-load-dependent energy prices/tariffs. An additional benefit is also provided to the national power system by reducing the need for peaking power stations. The calculations are performed for energy tariffs commonly used for commercial buildings in Poland. The simulation relies on climatic and price data for 2016. The results show significant potential for photovoltaics to reduce the peak load (from almost 60 kW to slightly over 44 kW) whilst simultaneously minimising energy costs to the building (from 1.2% up to 5.8% depending on the selected tariff). This study demonstrates the economic benefits of using PV system for reducing peak loads. A sensitivity analysis with regard to photovoltaics investment costs is carried out showing that the increasing investment costs have different impact on total energy cost depending on the considered energy tariff.
Generating Image Distortion Maps Using Convolutional Autoencoders With Application to No Reference Image Quality Assessment

We present two contributions in this work: 1) a reference-free image distortion map generating algorithm for spatially localizing distortions in a natural scene; and 2) no reference image quality assessment (NRIQA) algorithms derived from the generated distortion map. We use a convolutional autoencoder (CAE) for distortion map generation. We rely on distortion maps generated by the SSIM image quality assessment algorithm as the “ground truth” for training the CAE. We train the CAE on a synthetically generated dataset composed of pristine images and their distorted versions. Specifically, the dataset was created by applying standard distortions such as JPEG compression, JP2K compression, additive white Gaussian noise, and blur to the pristine images. SSIM maps are then generated on a per distorted image basis for each of the distorted images in the dataset and are in turn used for training the CAE. We first qualitatively demonstrate the robustness of the proposed distortion map generation algorithm over several images with both traditional and authentic distortions. We also demonstrate the distortion map's effectiveness quantitatively on both standard distortions and authentic distortions by deriving three different NRIQA algorithms. We show that these NRIQA algorithms deliver competitive performance over traditional databases like LIVE Phase II, CSIQ, TID 2013, LIVE MD, and MDID 2013, and databases with authentic distortions like LIVE Wild and KonIQ-10K. In summary, the proposed method generates high-quality distortion maps that are used to design robust NRIQA algorithms. Furthermore, the CAE-based distortion maps generation method can easily be modified to work with other ground truth distortion maps.
Hyperspectral Image Denoising by Fusing the Selected Related Bands

Hyperspectral images (HSIs) convey more useful information than RGB or gray images, which are widely used in many remote sensing tasks. In real scenarios, HSIs are inevitably corrupted by noise because of sensors' imperfectness or atmospheric influence. Recently, many HSI denoising methods have been proposed to utilize the interband information between different spectral bands. However, these methods regard the HSI as a whole and treat the different spectral bands with the same noise level. In fact, the noise levels in different bands are different. Especially, only few certain bands are corrupted by noise, named the target noised bands. Under this circumstance, an HSI denoising method is proposed by considering the band relationship and different noise levels. The target noised bands are adaptively denoised by fusing some selected bands. Specifically, some related but quality superior bands are selected according to the target noised bands. Then, the target noised bands can be denoised by fusing the selected related bands. Experimental results show that the proposed method achieves considerable performances in comparison with several state-of-the-art hyperspectral denoising methods.
Possibilistic fuzzy clustering with high-density viewpoint

Fuzzy clustering algorithms are usually data-driven. Recently, knowledge has been introduced into these methods to form knowledge-driven and data-driven fuzzy clustering algorithms. However, these algorithms still have the problems of sensitivity to clustering center initialization and a lack of robustness, in general. There is a genuine need for a sound acquisition of viewpoints. In this study, a new fuzzy clustering algorithm driven by data and knowledge named Density Viewpoint-induced Possibilistic Fuzzy C-Means (DVPFCM) is put forward. To begin with, we propose a new method to calculate the density radius, which determines the density range of each data point. Based on this, we establish a Hypersphere Density-based Clustering Center Initialization method (HDCCI), which can obtain the initial clustering centers located in the denser region of the dataset. Furthermore, the high density point obtained by the HDCCI method is taken as a new viewpoint and integrated into the clustering algorithm. The new viewpoint helps to speed up the convergence of the algorithm. It can also guide the clustering algorithm to discover the data structure. Finally, on the basis of the HDCCI method, the idea of high-density viewpoint is introduced, and the advantages of FCM (Fuzzy C-Means) and PFCM (Possibilistic Fuzzy C-Means) are combined, and then the DVPFCM algorithm is proposed. Through experimental studies including some comparative analyses, it is demonstrated that the DVPFCM algorithm is better in several different ways in terms of initializing clustering centers and values of some performance indexes. It also exhibits better performance in determining the distance between the computed clustering centers and the reference centers.
Anonymity for Practical Quantum Networks

Quantum communication networks have the potential to revolutionize information and communication technologies. Here we are interested in a fundamental property and formidable challenge for any communication network, that of guaranteeing the anonymity of a sender and a receiver when a message is transmitted through the network, even in the presence of malicious parties. We provide the first practical protocol for anonymous communication in realistic quantum networks.
A new perspective: Recognizing online handwritten Chinese characters via 1-dimensional CNN

For online handwritten Chinese character recognition (OLHCCR), it has become a popular choice to employ the 2-dimensional convolutional neural network (2-D CNN) in recognizing the extracted feature images or utilize the recurrent neural network (RNN) to classify handwriting trajectories directly. Instead, here we propose to apply the 1-dimensional CNN (1-D CNN), which, to our knowledge, is novel in the context of OLHCCR. Specifically, a 1-D CNN is engaged upon the sequential handwriting trajectories. Each output sequence is then averaged over time to form a fixed-size vector representation, upon which the final classification is made via a softmax operation. Compared with the 2-D CNN architecture, our approach is capable of delivering better results with a more compact model. This is achieved without adopting the computationally demanding techniques that are necessary when working with the 2-D CNN, including data augmentation and feature image extraction. Furthermore, our method attains a faster test time speed compared with the RNN, and this is more pronounced in processing long sequences. Empirically, our method yields the near-state-of-the-art accuracy of 98.11% on ICDAR 2013 competition dataset, and the state-of-the-art accuracy of 97.14% on in-air handwriting dataset IAHCC-UCAS2016, respectively.
Context-aware recommender systems using hierarchical hidden Markov model

Recommender systems often generate recommendations based on user’s prior preferences. Users’ preferences may change over time due to user mode change or context change, identification of such a change is important for generating personalized recommendations. Many earlier methods have been developed under the assumption that each user has a fixed pattern. Regardless of these changes, the recommendation may not match the user’s personal preference and this recommendation will not be useful to the user based on the current context of the user. Context-aware recommender systems deal with this problem by utilizing contextual information that affects user preferences and states. Using contextual information is challenging because it is not always possible to obtain all the contextual information. Also, adding various types of contexts to recommender systems increases its dimensionality and sparsity. This paper presents a novel hierarchical hidden Markov model to identify changes in user’s preferences over time by modeling the latent context of users. Using the user-selected items, the proposed method models the user as a hidden Markov process and considers the current context of the user as a hidden variable. The latent contexts are automatically learned for each user utilizing hidden Markov model on the data collected from the user’s feedback sequences. The results of the experiments, on the benchmark data sets, show that the proposed model has a better performance compared to other methods.
Designing context-aware systems: A method for understanding and analysing context in practice

Context-aware systems are systems that have the ability to sense and adapt to the environment. To operate in large-scale multi-stakeholder environments, systems often require context awareness. The context elements that systems in such environments should take into account are becoming ever more complex and go beyond elements like geographic location. In addition, these environments are themselves so complex that it is hard to determine what parts of them belong to the relevant context of a context-aware system. However, insight into what belongs to this context is needed to establish what the design of a context-aware system should be to meet its goal. The ambiguity of what belongs to context in these complex organizational environments causes the design process to become either inefficient or less effective. In this paper, we provide a method to identify what elements of the environment are relevant context and to then base the design on this insight. The proposed method consists of three steps: 1) getting insight into context, 2) determining what components are needed to sense and adapt to context, and 3) determining the rules for how the system should adapt in different situations. To reduce ambiguity by organizations, the method requires a more specified definition of context than the ones in current literature, which we also provide in this paper. In addition to reducing ambiguity, the highly structured way in which the components and rules are derived from insight into context provides a way to further deal with the high complexity of the context. The method was applied for the development of a context-aware system for business-to-government (B2G) information sharing in the container shipping domain. Information sharing in this domain is highly complex, as legislation, many stakeholders, and a mix of cooperation and competition result in a highly complex environment. The development of this B2G information sharing system thus provides an example of how the method can be used to develop a context-aware system in a highly complex environment.
Questioning the news about economic growth: Sparse forecasting using thousands of news-based sentiment values

The modern calculation of textual sentiment involves a myriad of choices as to the actual calibration. We introduce a general sentiment engineering framework that optimizes the design for forecasting purposes. It includes the use of the elastic net for sparse data-driven selection and the weighting of thousands of sentiment values. These values are obtained by pooling the textual sentiment values across publication venues, article topics, sentiment construction methods, and time. We apply the framework to the investigation of the value added by textual analysis-based sentiment indices for forecasting economic growth in the US. We find that the additional use of optimized news-based sentiment values yields significant accuracy gains for forecasting the nine-month and annual growth rates of the US industrial production, compared to the use of high-dimensional forecasting techniques based on only economic and financial indicators.
Low-Illumination Image Enhancement Algorithm Based on a Physical Lighting Model

Low-illumination images are usually taken in non-uniform environmental light, such as extremely dark or bright light or artificial light. The enhancement results achieved by existing techniques are prone to halo artifacts, color unnaturalness, and information loss. To address these problems, we present a physical lighting model that describes the degradation of poor illumination images, in which the environmental light is a point-wise variable and changes with the local light source. As long as the parameters in the model are properly estimated, the low-illumination images can be directly recovered by solving the model. First, the initial environmental light can be considered as the incident component according to the Retinex theory and estimated via a Gaussian surrounding function. Second, the environmental light and light-scattering attenuation rate are iteratively adjusted with the information loss constraint. Finally, to restrain the halo and block effects, the two parameters are refined by the weighted guide filter. The experimental results indicate that the proposed algorithm can improve the appearance of low-illumination images that are captured in different scenes, reveal the details in textured regions with few halo effects, increase the richness of the visible edges, retain color consistency and reproduce the color quality and naturalness.