Reviewerly/exHarmony huggingface dataset:
Hybrid Cross Deep Network for Domain Adaptation and Energy Saving in Visual Internet of Things

Recently, Visual Internet of Things (VIoT) has become a fast-growing field based on various applications. In this paper, we focus on two critical challenges for applications in VIoT, i.e., domain adaptation and energy saving. The images captured by various visual sensors in VIoT appear quite different due to changes in visual sensor locations, visual sensor settings, image resolutions, and illuminations. Meanwhile, VIoT generates a number of images, and transmitting original images would take up much bandwidth. In order to effectively classify such images and save energy, we propose a novel deep model named hybrid cross deep network (HCDN), which could learn domain-invariant and discriminative features for images in VIoT. The proposed HCDN is designed to contain the cross regularization loss and the classification loss. Moreover, it is also trained with images from different visual sensors. Specifically, the cross regularization loss selects the triplet samples from the source domain and the target domain, and adopts the calibration parameter to align the difference between two domains. We employ the vector extracted from the proposed HCDN to represent each image, which requires a smaller storage capacity than the original images. Energy consumption will be reduced when we transmit such vectors to the intelligent visual label system for image classification in VIoT. The proposed HCDN is verified on two domain adaptation datasets, and the experimental results prove its effectiveness.
The Effect of Edge Bundling and Seriation on Sensemaking of Biclusters in Bipartite Graphs

Exploring coordinated relationships (e.g., shared relationships between two sets of entities) is an important analytics task in a variety of real-world applications, such as discovering similarly behaved genes in bioinformatics, detecting malware collusions in cyber security, and identifying products bundles in marketing analysis. Coordinated relationships can be formalized as biclusters. In order to support visual exploration of biclusters, bipartite graphs based visualizations have been proposed, and edge bundling is used to show biclusters. However, it suffers from edge crossings due to possible overlaps of biclusters, and lacks in-depth understanding of its impact on user exploring biclusters in bipartite graphs. To address these, we propose a novel bicluster-based seriation technique that can reduce edge crossings in bipartite graphs drawing and conducted a user experiment to study the effect of edge bundling and this proposed technique on visualizing biclusters in bipartite graphs. We found that they both had impact on reducing entity visits for users exploring biclusters, and edge bundles helped them find more justified answers. Moreover, we identified four key trade-offs that inform the design of future bicluster visualizations. The study results suggest that edge bundling is critical for exploring biclusters in bipartite graphs, which helps to reduce low-level perceptual problems and support high-level inferences.
Hybrid attack free optical cryptosystem based on two random masks and lower upper decomposition with partial pivoting

We propose a novel asymmetric optical image encryption scheme using two random phase masks (RPMs) and lower upper decomposition with partial pivoting (LUDP), in which the encryption process is different from the decryption process and encryption keys are also different from decryption keys. In the proposed algorithm, LUDP is a matrix decomposition operation, which is used to replace the phase-truncated (PT) operation in the encryption path of conventional optical image encryption schemes based on phase-truncated Fourier transform (PTFT). In the proposed decryption process, the original image is completely retrieved by an optical architecture based on the modified 4f system with two private keys generated in the encryption process. Compared to conventional PTFT-based cryptosystems which are vulnerable to special attacks based on the amplitude-phase retrieval technique, our proposed algorithm is immune to the iterative attack and has a higher security level. Numerical simulations are presented to demonstrate the feasibility and robustness of the proposed encryption scheme.
Highly-parallel hardware implementation of optimal ate pairing over Barreto-Naehrig curves

Bilinear pairing over elliptic curves is the key technology to construct identity based encryption schemes. An appropriate hardware design can significantly speed up pairing computation. In this paper, we present a highly-parallel hardware design for optimal ate pairing over Barreto-Naehrig curves. The proposed design exploits parallelism at different levels of the pairing algorithm, including Fp and Fp2 operations as well as the operations based on Fp2. Especially the proposed architecture of dual Fp2 units at the top level makes the pairing computation more efficient. Finally, we implement a system on chip (SoC) that contains Microblaze CPU, AXI-Lite bus and the pairing computation unit. The design is verified on a Virtex-7 FPGA device with the parameters of pairing chosen according to the Identity-Based Cryptographic Algorithms SM9 enacted by China. The results show that our design computes the optimal ate pairing of 128-bit security within 394,806 cycles, which is about 3.4 ms under the working frequency of 115 MHz, and consumes about 28 k Slices and 128 DSPs.
Multi-objective Optimization Training of PLDA for Speaker Verification

Most current state-of-the-art text-independent speaker verifi-cation systems take probabilistic linear discriminant analysis (PLDA) as their backend classifiers. The parameters of PL-DA are often estimated by maximizing the objective function, which focuses on increasing the value of log-likelihood function, but ignoring the distinction between speakers. In order to better distinguish speakers, we propose a multi-objective optimization training for PLDA. Experiment results show that the proposed method has more than 10% relative performance improvement in both EER and MinDCF on the NIST SRE14 i-vector challenge dataset, and about 20% relative performance improvement in EER on the MCE18 dataset.
Multi-goal motion planning using traveling salesman problem in belief space

Abstract In this paper, the multi-goal motion planning problem of an environment with some background information about its map is addressed in detail. The motion planning goal is to find a policy in belief space for the robot to traverse through a number of goal points. This problem is modeled as an asymmetric traveling salesman problem (TSP) in the belief space using Partially Observable Markov Decision Process (POMDP) framework. Then, feedback-based information roadmap (FIRM) algorithm is utilized to reduce the computational burden and complexity. By generating a TSP-FIRM graph, the search policy is obtained and an algorithm is proposed for online execution of the policy. Moreover, approaches to cope with challenges such as map updating, large deviations and high uncertainty in localization , which are more troublesome in a real implementation, are carefully addressed. Finally, in order to evaluate applicability and performance of the proposed algorithms, it is implemented in a simulation environment as well as on a physical robot in which some challenges such as kidnapping and discrepancies between real and computational models and map are examined.
Real-time side-slip angle measurements using digital image correlation

In vehicle dynamics there are many parameters that are desired for vehicle control and modelling. One of the most important parameters for handling and stability is the vehicle side-slip angle. The ability to directly measure the vehicle side-slip in real-time will aid and improve many driver assist systems such as stability control schemes and roll-over mitigation, especially over rough terrain. Commercial side-slip angle solutions are available but they are prohibitively expensive and are only suitable for use during vehicle development and performance evaluation. They are also restricted to small side-slip angles and give unsatisfactory results at low speeds and over uneven terrain. Previous research has proven that digital image correlation can be used to accurately measure vehicle side-slip angle over rough off-road terrain using inexpensive, off-the-shelf cameras. However, side-slip angle calculations were performed in post processing from pre-recorded footage and not implemented in real time due to the large computational times of the novel algorithms developed. This paper describes the improvements made to the algorithms that enable real-time implementation. The side-slip angle is measured using a single camera pointing downwards to the terrain and digital image correlation. The sensor is tested on a flat surface using a rig that allows for validation. The maximum sampling frequency and accuracy are investigated. The system is shown to measure accurately and in real-time up to 100 km/h speeds.
Socio-Sentic framework for sustainable agricultural governance

Livelihood security plays a critical role in strengthening the socio- economic situation of a country. Agriculture is one such sector which is expected to provide a complete array of economic, social, and environmental services. Good governance and management of allied policies at all levels is favourable for long-term sustainability of agricultural sector. The accountability of government is a direct measure of its social responsibility and sustainability. Social media as a powerful online platform reinforces hype and provides opportunities to extract and analyze public opinion about various governmental schemes and policies including the ones related to agriculture. The e-participation platforms such as Twitter offer unparallel means to intelligently gauge the consensus and orientation of people towards an agricultural policy. Motivated by this, the work presented in this research, proffers a Socio-Sentic framework for sustainable agricultural governance which probes the sentiment polarity of user-content on Twitter pertaining to government policies, specifically agricultural policies. In this intelligent analytic framework, supervised machine learning algorithms have been implemented and compared using tweets on an Indian Agricultural Policy launched in 2016, ‘Pradhan Mantri Fasal Bima Yojana’ (PMFBY). The preliminary results indicate that the adoption of the proposed framework for soliciting and probing citizen feedback for government policy evaluation can lead to a sustainable agricultural development.
Developmental Resonance Network

Adaptive resonance theory (ART) networks deal with normalized input data only, which means that they need the normalization process for the raw input data, under the assumption that the upper and lower bounds of the input data are known in advance. Without such an assumption, ART networks cannot be utilized. To solve this problem and improve the learning performance, inspired by the ART networks, we propose a developmental resonance network (DRN) by employing new techniques of a global weight and node connection and grouping processes. The proposed DRN learns the global weight converging to the unknown range of the input data and properly clusters by grouping similar nodes into one. These techniques enable DRN to learn the raw input data without the normalization process while retaining the stability, plasticity, and memory usage efficiency without node proliferation. Simulation results verify that our DRN, applied to the unsupervised clustering problem, can cluster raw data properly without a prior normalization process.
A new imaging model of Lytro light field camera and its calibration

Due to the unique light gathering and post-capture processing, light field cameras with microlens array are being paid more and more attention. Especially, commercial light field cameras, such as Lytro and Raytrix, have been released. But calibrating light field cameras is still an open and challenging problem. In this paper, we present a new imaging model and a calibration method for Lytro camera, which makes its imaging process be easy to understand. Firstly, according to the image formation for pinhole camera and the theory of light propagation, we model the imaging process of Lytro light field camera, and drive a homogenous intrinsic matrix, which represents the relationship between each pixel on the image plane and its corresponding light field. Then, based on the derived intrinsic matrix, a calibration algorithm using the checkerboard grid for Lytro light field camera is proposed. At last, extensive experimental results have demonstrated the correctness of our imaging model of Lytro light field camera and the effectiveness of our calibration algorithm.
Forecast Horizon and Solar Variability Influences on the Performances of Multiscale Hybrid Forecast Model

The tropical insular region is characterized by a large diversity of microclimates and land/sea contrasts, creating a challenging solar forecasting. Therefore, it is necessary to develop and use performant and robustness forecasting techniques. This paper examines the predictive performance of a novel solar forecasting approach, the multiscale hybrid forecast model (MHFM), as a function of several parameters. The MHFM model is a technique recently used for irradiance forecasting based on a hybrid autoregressive (AR) and neural network (NN) model combined with multiscale decomposition methods. This technique presents a relevant performance for 1 h ahead global horizontal irradiance forecast. The goal of this work is to highlight the strength and limits of this model by assessing the influence of different parameters from a metric error analysis. This study illustrates modeling process performance as a function of daily insolation conditions and testifies the influence of learning data and test data time scales. Several forecast horizon strategies and their influence on the MHFM performance were investigated. With the best strategy, a rRMSE value from 4.43 % to 10.24 % was obtained for forecast horizons from 5 min to 6 h. The analysis of intra-day solar resource variability showed that the best performance of MHFM was obtained for clear sky days with a rRMSE of 2.91 % and worst for cloudy sky days with a rRMSE of 6.73 % . These works constitute an additional analysis in agreement with the literature about influence of daily insolation conditions and horizons time scales on modeling process.
Efficient edge‐preserved sonar image enhancement method based on CVT for object recognition

In the field of computer-aided recognition, edge feature is one of the key factors to determine recognition performance. Comparing to an optical image, since sonar image via acoustic wave is easily influenced by underwater environments such as particle density, temperature, and current, edge information should be boosted. Some image preprocessing techniques based on transform domain such as wavelet and curvelet may be good candidates but conventional methods show not only the possibility of enhancing edge features but also the limitation due to the absence of consideration to the edge direction. This study proposes an improved edge enhancement method based on curvelet transform (CVT), which is able to find out edge direction. The proposed method (PM) calculates the maximum value by ridgelet coefficients on each angular line, derived from the sub-step of the CVT, and the real edge direction is determined by local maxima selection after finding the azimuth of this value. In addition, selective sharpening is performed according to the feature information of edge. Experimental results have shown that the PM is comparable with conventional methods in terms of edge intensity, recognition rate, and peak signal-to-noise ratio.
Motion Estimation for Fisheye Video With an Application to Temporal Resolution Enhancement

Surveying wide areas with only one camera is a typical scenario in surveillance and automotive applications. Ultra wide-angle fisheye cameras employed to that end produce video data with characteristics that differ significantly from conventional rectilinear imagery as obtained by perspective pinhole cameras. Those characteristics are not considered in typical image and video processing algorithms such as motion estimation, where translation is assumed to be the predominant kind of motion. This contribution introduces an adapted technique for use in block-based motion estimation that takes into the account the projection function of fisheye cameras and thus compensates for the non-perspective properties of fisheye videos. By including suitable projections, the translational motion model that would otherwise only hold for perspective material is exploited, leading to improved motion estimation results without altering the source material. In addition, we discuss extensions that allow for a better prediction of the peripheral image areas, where motion estimation falters due to spatial constraints, and further include calibration information to account for lens properties deviating from the theoretical function. Simulations and experiments are conducted on synthetic as well as real-world fisheye video sequences that are part of a data set created in the context of this paper. Average synthetic and real-world gains of 1.45 and 1.51 dB in luminance PSNR are achieved compared against conventional block matching. Furthermore, the proposed fisheye motion estimation method is successfully applied to motion compensated temporal resolution enhancement, where average gains amount to 0.79 and 0.76 dB.
A constant-time chunking algorithm for packet-level deduplication

Redundancy elimination or deduplication over network packets requires significant computing resources to find basic units of repeated contents, called chunks, by checking every byte in every packet. In this paper, we present the first constant-time chunking algorithm that divides every packet into a predefined number of chunks, irrespective of the packet size. In addition, we present the best implementation practice for packet-level deduplication by selecting an optimal combination of chunking, fingerprinting, and hash table algorithms. Through experiments with real traffic, we confirm that the throughput is improved by three times, compared with even the state-of-the-art scheme.
Heteroscedastic watermark detector in the contourlet domain

A new contourlet domain image watermark detector is proposed in the present study. As the performance of the detector completely depends on the accuracy of the statistical model, the contourlet coefficients and statistical properties are studied first. The heavy‐tailed distribution and heteroscedasticity of these coefficients are demonstrated in this study. These characteristics cannot be captured simultaneously by the models, which are proposed previously. A two‐dimensional generalised autoregressive conditional heteroscedasticity (2D GARCH) model is suggested to overcome this problem. Dependencies of the contourlet coefficients can be explained by the efficient structure provided by this model. A 2D GARCH model‐based contourlet domain watermark detector is designed and its performance analysed by computing the receiver operating characteristics. The high accuracy of the proposed detector, its robustness under several types of attacks, and its outperformance compared to alternative watermarking methods are verified by the obtained experimental results.
Fuzzy rule-based models with randomized development mechanisms

Fuzzy rule-based models have attracted attention because of their modular architectures, well-developed design methodologies and practices as well as interpretability aspects. Methods exploiting factors of randomness offer significant efficiency and implementation simplicity that are essential in numerous application areas. In this study, we propose an original development of fuzzy rule-based models established with the aid of concepts of randomization algorithms. Several design strategies involving different random prototypes generation and basis functions approximation are studied. We investigate performance aspects of randomized rule-base and look at the performance versus the key components of the models such as the number of rules and the use of the randomized algorithms in the development. Furthermore, a comparative study is offered to quantify the efficiency of randomized algorithms. Experimental studies are reported for a series of publicly available data sets to illustrate the effectiveness of the proposed method and discuss its main features.
A visual management tool for program planning, project management and evaluation in paediatric health care

This article describes the development and implementation of a custom-designed Excel-based visual management tool. The tool's purpose was to support program planning and evaluation by our resource support team within a paediatric health care setting. Our aims in developing it were to 1) establish a streamlined process and supporting tools to efficiently plan and prioritize program directions and activities; 2) track progress; and 3) evaluate and report on our performance, outputs and outcomes. A collaborative approach based on the ADKAR (Awareness, Desire, Knowledge, Ability, Reinforcement) change management model and the LEADS (Lead self, Engage others, Achieve results, Develop coalitions, Systems transformation) leadership framework was used to guide the design and implementation processes. Team members reported high perceived effectiveness and efficiency with respect to the tool's utility in supporting its proposed aims. A graded approach to building knowledge and skills in using the tool, to individual responsibility for data entry, and to accountability by team members facilitated its successful implementation. Administrative support is important for sustainability and continual improvement of the tool to address changing team needs over time.