Reviewerly/exHarmony huggingface dataset:
Dependable Data Outsourcing Scheme Based on Cloud-of-Clouds Approach with Fast Recovery

Cloud computing is increasingly popular today. Cloud services such as data-outsourcing services provide a growing number of users access to cloud storage for large quantities of data, and enterprises are turning to cloud storage for cost-effective remote backup. In 2011, DEPSKY shows and overcomes four limitations hinder the effectiveness of cloud storage: loss of availability, loss and corruption of data, loss of privacy, and vendor lock-in. Unfortunately, DEPSKY lacks an error detection mechanism and comes with heavy computing costs. Therefore, we propose a new data-outsourcing scheme overcoming not only the four limitations, but also the shortcomings of DEPSKY. In this paper, we modify Nyberg's accumulator and apply it to our three proposed error-detection methods. Moreover, we specially design a fast recovery method that is faster than DEPSKY and alternative methods.
Divergence Free Polar Wavelets for the Analysis and Representation of Fluid Flows

We present a Parseval tight wavelet frame for the representation and analysis of velocity vector fields of incompressible fluids. Our wavelets have closed form expressions in the frequency and spatial domains, are divergence free in the ideal, analytic sense, have a multi-resolution structure and fast transforms, and an intuitive correspondence to common flow phenomena. Our construction also allows for well defined directional selectivity, e.g. to model the behavior of divergence free vector fields in the vicinity of boundaries or to represent highly directional features like in a von K\'arm\'an vortex street. We demonstrate the practicality and efficiency of our construction by analyzing the representation of different divergence free vector fields in our wavelets.
Multi-Gbit/s real-time modems for chaotic optical OFDM data encryption and decryption

Real-time high-speed modems are proposed and demonstrated for physical-layer secure encryption and decryption of optical orthogonal frequency division multiplexing (OFDM) signals using field programmable gate array (FPGA). In the modems, the multi-fold data encryption is applied, including chaotic XOR, chaotic permutation and chaotic dynamic mapping, to enhance the physical-layer security for optical OFDM data during transmission. Real-time modems for encrypted 16-QAM OFDM signals at 4.8 Gbit/s are successfully demonstrated, using parallel multiple channels design on FPGA. The security performances of the real-time encrypted OFDM signals are evaluated, while a huge key space of ∼10168 is created to resist the exhaustive attacks.
From coherent state representation to unitary operators via the IWOP technique

Unitary operators have the important role in quantum optics and quantum information. Based on the coherent state representation, we present the integration forms of some unitary operators including single-mode and two-mode squeezing operators and beam splitter operator in complex form, which are beneficial for discussing quantum teleportation. And then we derive the normally ordering forms and title exponential forms by using the IWOP technique. As application, we further consider the two-cascaded beam splitter. Our method is convenient for obtaining the normally ordering without Lie algebra and can be extended to multi-mode unitary evolution cases. It is also helpful for understanding the role of quantum mechanical representation.
SConE: Siamese Constellation Embedding Descriptor for Image Matching

Numerous computer vision applications rely on local feature descriptors, such as SIFT, SURF or FREAK, for image matching. Although their local character makes image matching processes more robust to occlusions, it often leads to geometrically inconsistent keypoint matches that need to be filtered out, e.g. using RANSAC. In this paper we propose a novel, more discriminative, descriptor that includes not only local feature representation, but also information about the geometric layout of neighbouring keypoints. To that end, we use a Siamese architecture that learns a low-dimensional feature embedding of keypoint constellation by maximizing the distances between non-corresponding pairs of matched image patches, while minimizing it for correct matches. The 48-dimensional floating point descriptor that we train is built on top of the state-of-the-art FREAK descriptor achieves significant performance improvement over the competitors on a challenging TUM dataset.
Advanced spatial spectrum fitting algorithm for significantly improving the noise resistance ability of self-calibration phase shifting interferometry

We propose a novel phase demodulation algorithm for phase shifting interferometry (PSI) named as advanced spatial spectrum fitting (ASSF) algorithm. By utilizing dual-operation of singular value decomposition (SVD) and hybrid temporally and spatially estimating the background intensity, the retrieved phase distribution will become more robust to the noise distribution compared with the traditional PSI. At the same time, with no particular temporal phase shifts distribution requirement, it realize stable self-calibration phase retrieval with few interferograms containing less than one fringe. Therefore, even when the interferograms are seriously corrupted by the random noise or the fringe number is small, the high quality phase retrieval is possible. Both simulation and experimental results will be used to demonstrate the outstanding performance of the proposed ASSF algorithm.
Adaptive dual fractional‐order variational optical flow model for motion estimation

Insufficient illumination and illumination variation in image sequences make it challenging for algorithms to obtain clear outlines for objects in motion. This study proposes a high‐performance adaptive dual fractional‐order variational optical flow model which could be used to resolve these issues. The proposed method revitalises the original dual fractional‐order optical flow model and adopts a fractional differential mask in both the data and smoothness terms of the traditional Horn–Schunck model. The main innovation of this work is to fit a flow field regional to a variety of fractional‐order differential masks. The domain of each region is determined adaptively. The order and size of the fractional‐order differential masks for each region are adjusted by image signal to noise ratio while the shape of the fractional‐order differential mask is regulated to prevent interference from surrounding regions. Adjusting the fractional‐order differential mask adaptively enables the proposed method to accurately segment motion objects in poor and variable illumination regions as well. The experimental results show that our algorithm outperforms the current state‐of‐the‐art algorithms on low‐light real scene videos and also achieves competitive results on the Middlebury, KITTI and MPI Sintel public benchmarks.
A High-Throughput Hardware Accelerator for Lossless Compression of a DDR4 Command Trace

In a memory system, understanding how the host is stressing the memory is important to improve memory performance. Accordingly, the need for the analysis of memory command trace, which the memory controller sends to the dynamic random access memory, has increased. However, the size of this trace is very large; consequently, a high-throughput hardware (HW) accelerator that can efficiently compress these data in real time is required. This paper proposes a high-throughput HW accelerator for lossless compression of the command trace. The proposed HW is designed in a pipeline structure to process Huffman tree generation, encoding, and stream merge. To avoid the HW cost increase owing to high-throughput processing, a Huffman tree is efficiently implemented by utilizing static random access memory-based queues and bitmaps. In addition, variable length stream merge is performed at a very low cost by reducing the HW wire width using the mathematical properties of Huffman coding and processing the metadata and the Huffman codeword using FIFO separately. Furthermore, to improve the compression efficiency of the DDR4 memory command, the proposed design includes two preprocessing operations, the “don't care bits override” and the “bits arrange,” which utilize the operating characteristics of DDR4 memory. The proposed compression architecture with such preprocessing operations achieves a high throughput of 8 GB/s with a compression ratio of 40.13% on average. Moreover, the total HW resource per throughput of the proposed architecture is superior to the previous implementations.
Probabilistic eigensolver with a trapped-ion quantum processor

Quantum simulation of complex quantum systems and their properties often requires the ability to prepare initial states in an eigenstate of the Hamiltonian to be simulated. In addition, to compute the eigenvalues of a Hamiltonian is in general a non-trivial problem. Here, we propose a hybrid quantum-classical probabilistic method to compute eigenvalues and prepare eigenstates of Hamiltonians which are simulatable with a trapped-ion quantum processor.
Energy optimization of parallel programs in a heterogeneous system by combining processor core-shutdown and dynamic voltage scaling

Reducing power consumption and improving efficiency are important aspects of the development of supercomputers into large-scale systems. As a result, heterogeneous systems have become an important development trend in high-performance computing. From the perspective of heterogeneous systems, this study establishes a model for energy optimization of parallel programs (EOPP) and puts forward a method of using it. By considering the energy overheads caused by re-synchronization, voltage switching, and operations in critical sections, the model effectively combines processor core-shutdown and dynamic voltage scaling technologies, which can be applied in a heterogeneous system to guide the optimization process. The results show that the proposed model can effectively reduce the energy consumption of parallel programs. Moreover, increasing the proportion of operations in the critical section enhances the optimal frequency of a processor while decreasing the probability of conflicts in the critical section. It can thus provide optimization space for reducing the frequency of a processor which ultimately reduces the energy overhead of the system.
RHKV: An RDMA and HTM friendly key–value store for data-intensive computing

Serving DRAM as the storage through key–value abstraction has proved as an attractive option, which provides fast data access for data-intensive computing. However, due to the drawbacks of network round trips and requesting conflicts, remote data access over traditional commodity networking technology might incur high latency for the key–value data store. The major performance bottleneck lies in client-side request waiting and server-side I/O overhead. Accordingly, this paper proposes RHKV: a novel RDMA and HTM friendly key–value store to provide fast and scalable data management by using the designed G-Cuckoo hashing scheme. Our work expands the idea as follows: (i) An RHKV client transmits data requests to our improved Cuckoo hashing scheme — G-Cuckoo, which constructs a Cuckoo graph as directed pseudoforests in RHKV server. The RHKV server computes the reply for each data request. The server maintains a bucket-to-vertex mapping and pre-determines the possibility of a loop prior to data insertion. Through the use of this Cuckoo graph, the endless kick-out loop of data insertions that can potentially be experienced in the case of generic Cuckoo hashing can be detected. (ii) Despite messaging primitives are slower than RDMA READs for data requests, RHKV adopts RDMA messaging verbs unconventionally. It leverages rich network semantics and makes full use of RDMA’s high bandwidth and low latency for data access over high-performance RDMA interconnects. (iii) Moreover, in order to ensure the data operation’s atomicity, RHKV strives to utilize the advanced HTM technique. Experimental performance evaluation with YCSB workloads shows that, when basic data operations are conducted, RHKV outperforms several state-of-the-art key–value stores with lower latency and higher throughput.
Sparse and Balanced Reed–Solomon and Tamo–Barg Codes

We study the problem of constructing balanced generator matrices for Reed-Solomon and Tamo-Barg codes. More specifically, we are interested in realizing generator matrices, for the full-length cyclic versions of these codes, where all rows have the same weight and the difference in weight between any columns is at most one. The results presented in this paper translate to computationally balanced encoding schemes, which can be appealing in distributed storage applications. Indeed, the balancedness of these generator matrices guarantees that the computation effort exerted by any storage node is essentially the same. In general, the framework presented can accommodate various values for the required row weight. We emphasize the possibility of constructing sparsest and balanced generator matrices for Reed-Solomon codes, i.e., each row is a minimum distance codeword. The number of storage nodes contacted once a message symbol is updated decreases with the row weight, so sparse constructions are appealing in that context. Results of similar flavor are presented for cyclic Tamo-Barg codes. In particular, we show that for a code with minimum distance d and locality r, a construction in which every row is of weight d+r-1 is possible. The constructions presented are deterministic and operate over the codes' original underlying finite field. As a result, efficient decoding from both errors and erasures is possible thanks to the plethora of efficient decoders available for the codes considered.
Deadlock prevention for service orchestration via controlled Petri nets

In multi-party Web service composition, the non-local choice mismatch is one of the most important problems. To solve it, the reachability graph-based method is widely adopted to check deadlock-freeness by generating adaptors. However, this method is inefficient due to the neglect of future deadlock states and requirements of many possible interactions. This work proposes an abnormity prevention strategy and an optimal controller for service collaboration. To overcome drawbacks of previous studies, this work describes service choreography by using service workflow nets. Moreover, this work formulates a novel reachability graph by combining structures and reachability analysis. To present abnormity, a maximally permissive state feedback control policy is then proposed. Furthermore, to avoid deadlocks in service orchestration, this work constructs an optimal controller for administrators of service composition. Finally, experiments demonstrate the advantage of the proposed method via a realistic example.
Scalable multi-label annotation via semi-supervised kernel semantic embedding

In this paper, we present a novel semi-supervised semantic embedding method based on kernel matrix factorization for automatic multi-label annotation. The method called Semi-supervised Online Kernel Semantic Embedding (SS-OKSE) builds a semantic representation modeled by the document features and the associated labels when available, from this final semantic representation another transformation is simultaneously learned to predict the labels for new documents. Thanks to its kernel-based formulation, the proposed method is suitable for modeling non-linear complex relationships among the data samples. A scalable architecture based on a learning-on-a-budget strategy and its formulation as an end-to-end architecture allows the efficient training using online learning based on stochastic gradient descent. Another distinctive feature of the proposed method is that the kernel parameters can be learned directly, avoiding the necessity of an exhaustive exploration to determine their appropriate values. The effectiveness of the SS-OKSE method was evaluated in a multi-label annotation task under a semi-supervised learning setup over two different textual datasets and it was compared against several supervised and semi-supervised techniques. Experimental results show that SS-OKSE exhibits a significant improvement, showing that a better modeling can be achieved with an adequate selection/construction of a kernel input representation.
A genetic algorithm-based metaheuristic approach to customize a computer-aided classification system for enhanced screen film mammograms

Breast cancer ranks second as the major cause of death due to cancer among women. The disease's mortality rate has decreased over the last few years, and this is believed to be due to early detection, greater awareness, and better treatments. This paper deals with the classification of breast mammograms and the effect of different enhancement techniques on classification accuracy. The algorithm proposed in this study to classify mammograms on the basis of normal and abnormal comprises the following five steps: image database, preprocessing using image enhancement techniques, ROI extraction, texture feature extraction using the Gabor filter, and finally, classification using the SVM classifier. The effect of 14 enhancement methods, namely, alpha trimmed mean filter, contrast adjustment, contrast limited adaptive histogram equalization, contra-harmonic mean filter, hybrid median filter, histogram equalization, morphological enhancement, mean filter, median filter, recursive mean separated histogram equalization, unsharp masking, wavelet based subband filter, morphological enhancement followed by contrast adjustment, and a combination of unsharp masking and contrast adjustment is demonstrated in this study. The work is performed on the MIAS database consisting of 322 mammogram images. ROIs of size 32 × 32 pixels are cropped from the enhanced images and a set of Gabor filters with three different scales and seven orientations is applied to extract features from the cropped ROIs. These features are used to classify abnormal ROIs from normal using SVM. A combination of morphological enhancement and contrast stretching results in the best overall classification accuracy of 92.16%, with individual class accuracy (abnormal) at 90.2% and individual class accuracy (normal) at 94.12%. The feature set derived from these images is further subjected to a GA-SVM feature selection procedure. It is observed that only 21 selected features are significant to classify the screen film mammographic images enhanced by morphological enhancement and contrast stretching.
Sigon: A multi-context system framework for intelligent agents

The usage of agents has become one of the most effective ways to deal with complex systems. The agent’s approach allows for the parallel execution of actions and eases the conception of the system. In this paper, we introduce a language, called Sigon, that allows the definition of agents as multi-context systems. Multi-context systems, in turn, allow for a modular description of the agent, and the implementation of bridge rules contributes to the system flexibility. We propose a generic framework to conceive agents, in which the system’s developer defines their internals in terms of contexts and bridge rules. In order to show the way in which the programmer can aggregate other logical contexts, we first present the configurations of a BDI-like agent in our framework. Subsequently, the programmer can add more contexts, such as emotional or negotiating ones, and develop other agent architectures. Thus, our approach can be characterized as a form of generic and extensible architecture. In order to validate our proposal, we introduce some examples that delineate some strengths of the proposal, and also we present an analysis of bridge rules execution to demonstrate the processing overhead.
A High Performance Parallel Ranking SVM with OpenCL on Multi-core and Many-core Platforms

A ranking support vector machine (RSVM) is a typical pairwise method of learning to rank, which is effective in ranking problems. However, the training speed of RSVMs are not satisfactory, especially when solving large-scale data ranking problems. Recent years, many-core processing units (graphics processing unit (GPU), Many Integrated Core (MIC)) and multi-core processing units have exhibited huge superiority in the parallel computing domain. With the support of hardware, parallel programming develops rapidly. Open Computing Language (OpenCL) and Open Multi-Processing (OpenMP) are two of popular parallel programming interfaces. The authors present two high-performance parallel implementations of RSVM, an OpenCL version implemented on multi-core and many-core platforms, and an OpenMP version implemented on multi-core platform. The experimental results show that the OpenCL version parallel RSVM achieved considerable speedup on Intel MIC 7110P, NVIDIA Tesla K20M and Intel Xeon E5-2692v2, and it also shows good portability.
WLDISR: Weighted Local Sparse Representation-Based Depth Image Super-Resolution for 3D Video System

In this paper, we propose a Weighted Local sparse representation based Depth Image Super-Resolution (WLDISR) schemes aiming at improving the Virtual View Image (VVI) quality of 3D video system. Different from color images, depth images are mainly used to provide geometrical information in synthesizing VVI. Due to the view synthesis characteristics difference between textural structures and smooth regions of depth images, we divide the depth images into edge and smooth patches and learn two local dictionaries, respectively. Meanwhile, the weight term is derived and incorporated explicitly in the cost function to denote different importance of edge structures and smooth regions to the VVI quality. Then, local sparse representation and weighted sparse representation are jointly used in both dictionary learning and reconstruction phases in depth image super-resolution. Based on different optimizations on learning and reconstruction modules, three WLDISR schemes, WLDISR-D, WLDISR-R, and WLDISR-ALL, are proposed. Experimental results on 3D sequences demonstrate that the proposed WLDISR-D, WLDISR-R, and WLDISR-ALL schemes can achieve more than 1.9-, 2.03-, and 2.16-dB gains on average, respectively, in terms of the VVIs' quality, as compared with the state-of-the-art schemes. In addition, the visual quality of VVIs is also improved.
Simultaneous State and Parameter Estimation using Robust Receding-horizon Nonlinear Kalman Filter

Use of measurements corrupted with gross errors for state estimation leads to biased state estimates, which, in turn, deteriorates the performances of model based process monitoring and control schemes. In this work, with the aim of minimizing the effects of gross errors on state estimates, robust versions of receding horizon nonlinear Kalman filter (RNK) are developed by integrating M-estimators with RNK. Two M-estimators, viz. Huber’s fair function and Hampel’s redescending estimator, have been considered for incorporating robustness in RNK. The proposed robust RNK formulation is further used to develop a robust simultaneous state and parameter estimation scheme. The efficacies of the proposed estimation schemes have been demonstrated by conducting simulation studies on a benchmark continuous stirred tank reactor (CSTR) system. The simulation studies reveal that, the proposed robust RNK formulations are able to generate estimation performances comparable to that of robust moving horizon estimation (MHE) scheme. Further, the proposed robust RNK based state and parameter estimator is able to estimate drifting model parameter accurately even in the presence of biased temperature measurements.