Miracle-dz/UniFAR-FTUs huggingface dataset:
View Synthesis for Light Field Coding Using Depth Estimation

Light Field (LF) image captured by plenoptic camera can record richer scenario information from our world. But a huge number of Sub-Aperture Images (SAIs) from LF image results in great challenges for coding LFI. Therefore, we choose a subset of SAIs as multi-view video, and encode them with their corresponding depth maps using Multi-view Video plus Depth (MVD) structure. Due to lack of depth maps for SAIs, we propose a cost function to determine the depth map for each SAI preliminarily based on horizontal and vertical Epipolar Plane Images (EPI), respectively. Then an SAI-guided depth enhancement algorithm is designed to optimize the estimated depth maps. Since those unselected SAIs have not been encoded but have been synthesized using the specific texture image and depth map, our LF image coding method can naturally achieve bitrates reduction dramatically with a good performance and outperform other algorithms significantly.
A new limestone-dwelling species of Micryletta (Amphibia: Anura: Microhylidae) from northern Vietnam

We report on a new species of the genus Micryletta from limestone karst areas in northern Vietnam, which is described on the basis of molecular and morphological evidence. Micryletta nigromaculata sp. nov. is restricted to narrow areas of subtropical forests covering karst massifs in Cat Ba National Park (Hai Phong Province) and Cuc Phuong National Park (Ninh Binh Province) at elevations of 90–150 m a.s. l. In the phylogenetic analyses, the new species is unambiguously positioned as a sister lineage to all remaining species of Micryletta. We also discuss genealogical relationships and taxonomic problems within the genus Micryletta, provide molecular evidence for the validity of M. erythropoda and discuss the taxonomic status of M. steinegeri. We suggest the new species should be considered as Endangered (B1ab(iii), EN) following the IUCN’s Red List categories. A discussion on herpetofaunal diversity and conservation in threatened limestone karst massifs in Southeast Asia is provided.
Dense Reconstruction from Monocular Slam with Fusion of Sparse Map-Points and Cnn-Inferred Depth

Real-time monocular visual SLAM approaches relying on building sparse correspondences between two or multiple views of the scene, are capable of accurately tracking camera pose and inferring structure of the environment. However, these methods have the common problem, i.e., the reconstructed 3D map is extremely sparse. Recently, convolutional neural network (CNN) is widely used for estimating scene depth from monocular color images. As we observe, sparse map-points generated from epipolar geometry are locally accurate, while CNN-inferred depth map contains high-level global context but generates blurry depth boundaries. Therefore, we propose a depth fusion framework to yield a dense monocular reconstruction that fully exploits the sparse depth samples and the CNN-inferred depth. Color key-frames are employed to guide the depth reconstruction process, avoiding smoothing over depth boundaries. Experimental results on benchmark datasets show the robustness and accuracy of our method.
Morphological characteristics of the sigmoid notch of the distal radius affect the stress distribution patterns in the distal radioulnar joint

The aim of this study was to clarify the effects of morphological patterns of the sigmoid notch on the stress distribution across the articular surface of the distal radioulnar joint using a computed tomography osteoabsorptiometry method. Fourteen wrists from 13 patients were classified into two groups according to the type of sigmoid notch, namely the ‘C’ type and ski-slope sigmoids, and the percentages of high-density areas on the articular surface were quantitatively analysed. The percentage of the high-density area of the dorsal region in the ski-slope sigmoid group was significantly greater than that of the ‘C’ type sigmoid group (16% vs 4.1%) and of the volar region of the ski-slope sigmoid group (16% vs 2.4%). The results indicate that bony morphological differences in the radial sigmoid notch affect the stress distribution pattern through the distal radioulnar joint.
A Novel Matching Theory-Based Framework for Computation Offloading in Device-to-Device Communication

The increasing demand for multimedia mobile applications creates challenges for smartphones that have low memory or slow processor. In this paper, we explore how local cooperation among the devices can help alleviate this problem to some extent. Local cooperation can be achieved through device-to-device communication which is expected to be a significant feature of next generation cellular networks. The cooperation can be achieved by having smartphones with insufficient resources offload their jobs to neighboring smartphones with higher computational capability. We model it as a stable matching problem. We also propose a novel flexible framework and simple distributed algorithms to pair mobile devices of heterogeneous capabilities.
WiFED: WiFi Friendly Energy Delivery with Distributed Beamforming

Wireless RF energy transfer for indoor sensors is an emerging paradigm that ensures continuous operation without battery limitations. However, high power radiation within the ISM band interferes with the packet reception for existing WiFi devices. The paper proposes the first effort in merging the RF energy transfer functions within a standards compliant 802.11 protocol to realize practical and WiFi-friendly Energy Delivery (WiFED). The WiFED architecture is composed of a centralized controller that coordinates the actions of multiple distributed energy transmitters (ETs), and a number of deployed sensors that periodically request energy from the ETs. The paper first describes the specific 802.11 supported protocol features that can be exploited by sensors to request energy and for the ETs to participate in the energy delivery process. Second, it devises a controller-driven bipartite matching-based algorithmic solution that assigns the appropriate number of ETs to energy requesting sensors for an efficient energy transfer process. The proposed in-band and protocol supported coexistence in WiFED is validated via simulations and partly in a software defined radio testbed, showing 15% improvement in network lifetime and 31% reduction in the charging delay compared to the classical nearest distance-based charging schemes that do not anticipate future energy needs of the sensors and are not designed to co-exist with wifi systems.
We divide, you conquer: from large-scale ontology alignment to manageable subtasks with a lexical index and neural embeddings

Large ontologies still pose serious challenges to state-of-the-art on-tology alignment systems. In this paper we present an approach that combines alexical index, a neural embedding model and locality modules to effectively di-vide an input ontology matching task into smaller and more tractable matchingsubtasks. We have conducted a comprehensive evaluation using the datasets ofthe Ontology Alignment Evaluation Initiative. The results are encouraging andsuggest that the proposed methods are adequate in practice and can be integratedwithin the workflow of state-of-the-art systems.
A Markov Chain Based Link Lifetime Prediction in Mobile Ad Hoc Networks

A mobile ad hoc network provides communication and network services to internet of things and cyber physical system applications. The failure of a link in mobile ad hoc network during data transmission increases communication and energy consumption cost due to route rediscovery and reselection process. It may also result into a communication failure and therefore cyber physical system application failure. To avoid link failure during data transmission, a link lifetime prediction model is required. This paper proposes two link lifetime prediction models: LLPC and LLPH. LLPC model predicts link lifetime based on current information of nodes such as mobility and residual energy whereas LLPH uses history of link lifetime intervals to predict the link lifetime. Compared to existing models, LLPC model considers node mobility as well as residual energy whereas LLPH relies on history of links rather than user's mobility patterns or social relationships.
FlowCop: Detecting "Stranger" in Network Traffic Classification

As the cornerstone of future network research, network traffic classification plays an important role on network management, cyberspace security and quality of service. Recently, many researches have used Machine Learning technologies for traffic classification. Most of them only focus on classifying the samples into predefined classes but ignoring the "strangers". In this paper, we use stranger to represent the traffic not belonging to any predefined application, and propose a novel scheme named FlowCop to achieve stranger detection in network traffic classification. By constructing multiple one-class classifiers, FlowCop can divide testing traffic into N classes and a stranger class. Since samples of stranger class are not required during the training stage, FlowCop works in an inexperienced way to detect strangers, just like the cops searching the crowd for strangers. Besides, for accurate classification and low overhead, a feature subspace algorithm is proposed to select outstanding features for each one-class classifier. To verify the effectiveness of FlowCop, we make contrast experiments on two real-world datasets. The results show that FlowCop can not only identify the predefined traffic flows but also detect the strangers. It outperforms four state-of-the-art approaches on both precision and recall.
Block-sparse compressed sensing with partially known signal support via non-convex minimisation

The mixed l 2 / l p (0 <; p ≤ 1) norm minimisation method with partially known support for recovering block-sparse signals is studied. The authors mainly extend this work on block-sparse compressed sensing by incorporating some known part of the block support information as a priori and establish sufficient restricted p -isometry property ( p -RIP) conditions for exact and robust recovery. The authors' theoretical results show it is possible to recover the block-sparse signals via l 2 / l p minimisation from reduced number of measurements by applying the partially known support. The authors also derive a lower bound on necessary random Gaussian measurements for the p -RIP conditions to hold with high possibility. Finally, a series of numerical experiments are carried out to illustrate that fewer measurements with smaller p are needed to reconstruct the signal.
Fully Inkjet-Printed Dual-Mode Ring Bandpass Filter Using a Cross-Bridge Structure Embedded With a Metal–Insulator–Metal Capacitor

A fully inkjet-printed ring-resonator bandpass filter on a liquid crystal polymer was realized by using metal–insulator–metal (MIM) structures and via-hole interconnections. Two short stub feeding lines and a ring resonator were coupled using a cross-bridge structure embedded with an MIM capacitor to provide a high-selectivity passband, low insertion loss, and a more compact size. A stepped-impedance line was used as a perturbation to excite the dual mode of the ring resonator. Using the proposed method, a 29% fractional bandwidth was realized with four transmission zeros to improve the selectivity. In the passband, the minimal insertion loss and maximal return loss were −2.2 and −34.2 dB at 12.7 GHz, respectively. Fully inkjet-printed technology is recognized as a method of fabrication that results in a compact and low-cost 3-D integrated circuit package.
Design of an Enhanced Array Based Approximate Arithmetic Computing Model for Multipliers and Squarers

Exact circuits produce accurate results but consume more power and area. Approximation is done to reduce the high consumption of area and power at the cost of accuracy. An array-based approximate arithmetic computing model (AAAC) is used for the reduction of error occurring in approximate binary arithmetic circuits. Although the model provides a trade-off between power, area and accuracy, these parameters can be improved further. The proposed model in this paper uses reduced number of truncated bits and a different compressor architecture for this improvement. An approximate $16 \times 16$ booth multiplier and an approximate 16 bit squarer are used as the applications. The proposed model for squarers reduce the power and area by 8.4% and 21.5% respectively and has an accuracy improvement of 3.4% when compared to the existing model. Similarly the proposed model for booth multiplier reduces the power and area by 4.5% and 3% respectively and has an accuracy improvement of 3.2% when compared with the existing one.
IOT Based Railway Disaster Management System

Now a days train accidents is a major issue in all over the world due to increase in rash driving. Accidents are responsible for a large number of collision, derailment, fire in trains, level crossing accident each year. In some cases deaths are caused due to unavailability of immediate medical facility to the injured passengers which can be avoided with the help of an automated system that will reduce the time consumed in activities such as taking the victim to the nearest hospital completing formalities such as filling forms and also involving police in case of major crashes. So the proposed system is using motion (accelerometer) and smoke/fire detection sensor based on IOT application integrated with android (GSM technology) to send information about train accident location to local citizens and administration using google map/GSM location system.
Super-Resolution Quality Assessment: Subjective Evaluation Database and Quality Index Based on Perceptual Structure Measurement

With the outstanding performance of deep learning based single image super-resolution (SISR) methods, the traditional SISR evaluation metrics (e.g., PSNR and SSIM, which measure the per-pixel differences and simple structure similarities respectively) are facing great challenges. When assessing SISR algorithms, they generally are hardly consistent with the human visual system (HVS). According to the psychological studies, the HVS presents different sensitivities to the plain, edge and texture regions, which are difficult to be accurately identified and measured with the existing quality indexes, especially for SR images. To deal with this problem, we firstly build a SISR subjective assessment database including several major deep learning based SR methods. Then we propose a more accurate perception structure measurement and use their similarity comparisons to evaluate the SR algorithms. Experimental results on the databases demonstrate that the proposed method performs well consistent with the human visual perception.
Scheduling Cofiows of Multi-stage Jobs to Minimize the Total Weighted Job Completion Time

Datacenter networks are critical to Cloud computing. The coflow abstraction is a major leap forward of application-aware network scheduling. In the context of multistage jobs, there are dependencies among coflows. As a result, there is a large divergence between coflow-completion-time (CCT) and job-completion-time (JCT). To our best knowledge, this is the first work that systematically studies: how to schedule dependent coflows of multi-stage jobs, so that the total weighted job completion time can be minimized. We present a formal mathematical formulation. We also prove that this problem is strongly NP-hard. Inspired by the optimal solution of the relaxed linear programming, we design an algorithm that runs in polynomial time to solve this problem with an approximation ratio of (2M + 1), where $M$ is the number of machines. Evaluation results demonstrate that, the largest gap between our algorithm and the lower bound is only 9.14%. We reduce the average JCT by up to 33.48 % compared with Aalo, a heuristic multi-stage coflow scheduler. We reduce the total weighted JCT by up to 83.31 % compared with LP-OV-LS, the state-of-the-art approximation algorithm of coflow scheduling.
An automated, high-throughput method for standardizing image color profiles to improve image-based plant phenotyping

High-throughput phenotyping has emerged as a powerful method for studying plant biology. Large image-based datasets are generated and analyzed with automated image analysis pipelines. A major challenge associated with these analyses is variation in image quality that can inadvertently bias results. Images are made up of tuples of data called pixels, which consist of R, G, and B values, arranged in a grid. Many factors, for example image brightness, can influence the quality of the image that is captured. These factors alter the values of the pixels within images and consequently can bias the data and downstream analyses. Here, we provide an automated method to adjust an image-based dataset so that brightness, contrast, and color profile is standardized. The correction method is a collection of linear models that adjusts pixel tuples based on a reference panel of colors. We apply this technique to a set of images taken in a high-throughput imaging facility and successfully detect variance within the image dataset. In this case, variation resulted from temperature-dependent light intensity throughout the experiment. Using this correction method, we were able to standardize images throughout the dataset, and we show that this correction enhanced our ability to accurately quantify morphological measurements within each image. We implement this technique in a high-throughput pipeline available with this paper, and it is also implemented in PlantCV.
CloudRand: Building Heterogeneous and Moving-Target Network Interfaces

Some fundamental reasons why our networked systems are still vulnerable to network attacks are because (1) they are more open than necessary; (2) they are homogeneous, i.e., the same way to exploit a vulnerability on one machine is easily applicable to many other machines (which is particularly a severe issue in cloud computing environments when virtual machines images are heavily reused/cloned); (3) current networked services are merely static targets, i.e., they are easily predictable and do not change. While network authentication and access control mechanisms such as firewall and VPN can help reduce the openness (mostly at network perimeter level), they do not help much on the latter two factors. To bridge the gap and greatly complement existing network authentication/access control mechanisms, we propose CloudRand, a new framework to make networked systems/services in the cloud heterogeneous (every host has a different networking interface) and moving targets (such interfaces keep changing and they are unpredictable to untrusted entities). Inspired by the previous work on host-level (memory or instruction) Address Space Randomization (ASR), we build a lightweight solution to randomize network service interfaces. Thus, even derived from the same image, each virtual machine can have very different network service interfaces and they keep changing to further reduce the attack surface. CloudRand is an application-independent security service, orthogonal to existing application/network security mechanisms such as authentication, encryption, and access control. To fit into different environments such as clouds or enterprise networks, we provide various prototype systems at different levels for flexible deployment choices, e.g., host level (kernel drivers for both Linux and Windows), network level (based on Click modular router or software-defined networking technology), virtual machine hypervisor level (based on Xen), and application level (browser plugin). Our extensive evaluation shows that this solution has low overhead, and it can it can significantly reduce the network attack surface and successfully defeat malware epidemic attacks.
An Effective Solar PV Fed Modified Vector Control of IMD for Water Pumping

The present paper deals with a speed sensorless technique of vector controlled induction motor drive fed from solar PV (Photovoltaic) array used for water pumping. This sensorless technique is based stator flux oriented in stationary reference frame. The special feature of the proposed topology is the additional feedforward loop added to the existing vector control loop, which makes it decoupled as a key feature of vector control. The mechanical sensorless induction motor drive is powered by solar photovoltaic (SPV) array to be utilized to drive the submersible water pump. The simulation of proposed topology is performed in MATLAB/Simulink environment and its performance is validated on test setup developed in the laboratory.
Making Gait Training Mobile - A Feasibility Analysis

As a part of a feasibility analysis, this paper reports that gait analysis of orthopedic-surgically treated patients is possible by using a Kinect v2 sensor as a low cost depth camera instead of applying a conventional marker-based multi-camera system in a gait laboratory. Being aware of the strengths and weaknesses of this approach, our concept expands the potential of gait analysis from diagnostic use only toward the use for documented and actively corrective self-training of patients. The paper analyzes which gait parameters are needed for synthetic, but effective gait evaluation, and if it is possible to obtain them from the Kinect SDK skeleton in terms of exact and stable values compared to parameters obtained by a multi-camera system in a gait laboratory. The main contributions of this paper are the analysis of the usability of the Kinect v2 for mobile gait analysis.
Metrics and Tool for Evaluating Data Stream Processing Systems

With the Internet of Things and many other data sources continuously generating events, service providers who analyze these data need fast and flexible software. These software, called data stream processing systems, can be created from scratch or, as is more often the case today, use frameworks that already implement many of the functionality required for stream processing. In fact, programmers implement these software as distributed systems today because they need frameworks to deal with huge, fast and high throughput streams. That is the reason why a centralized system would hardly be able to handle data streams without eventually discarding some events. Therefore, it is important that, before putting a data stream processing system in production, we can simulate the scenarios that this system will handle and analyze its behavior. In this sense, this paper presents the general characteristics of the data stream processing systems, as well as some of the main metrics for the analysis of this sort of system. In addition, we present a new tool called B2-4DaSP that facilitates the task of analyzing data stream processing systems. We also illustrate some examples of the B2-4DaSP use for the analysis of two simple data stream applications.
Dynamic tuning of applications using restricted transactional memory

Transactional Synchronization Extensions (TSX) support for hardware Transactional Memory (TM) on Intel 4th generation Core processors. Two programming interfaces, Hardware Lock Elision (HLE) and Restricted Transactional Memory (RTM), are provided to support software development using TSX. HLE is easy to use and maintains backward compatible with processors without TSX support while RTM is more flexible and scalable. Previous researches have shown that critical sections protected by RTM with a well-designed retry mechanism as its fallback code path can often achieve better performance than HLE. More parallel programs may be programmed in HLE, however, using RTM may obtain greater performance. To embrace both productivity and high performance of parallel program with TSX, we present a framework built on QEMU that can dynamically transform HLE instructions in an application binary to fragments of RTM codes with adaptive tuning on the fly. Compared to HLE execution, our prototype achieves 1.56x speedup with 8 threads on average. Due to the scalability of RTM, the speedup will be more significant as the number of threads increases.