Miracle-dz/UniFAR-FTUs huggingface dataset:
EasyChair Preprint No 567 3 D Skeletal Gesture Recognition via Sparse Coding of Time-Warping Invariant Riemannian Trajectories

3D skeleton based human representation for gesture recognition has increasingly attracted attention due to its invariance to camera view and environment dynamics. Existing methods typically utilize absolute coordinate to present human motion features. However, gestures are independent of the performer’s locations, and the features should be invariant to the body size of performer. Moreover, temporal dynamics can significantly distort the distance metric when comparing and identifying gestures. In this paper, we represent each skeleton as a point in the product space of special orthogonal group SO3, which explicitly models the 3D geometric relationships between body parts. Then, a gesture skeletal sequence can be characterized by a trajectory on a Riemannian manifold. Next, we generalize the transported square-root vector field to obtain a re-parametrization invariant metric on the product space of SO(3), therefore, the goal of comparing trajectories in a time-warping invariant manner is realized. Furthermore, we present a sparse coding of skeletal trajectories by explicitly considering the labeling information with each atoms to enforce the discriminant validity of dictionary. Experimental results demonstrate that proposed method has achieved state-of-the-art performance on three challenging benchmarks for gesture recognition.
AN EFFICIENT ATTRIBUTE BASED CRYPTOGRAPHIC ALGORITHM FOR SECURING TRUSTWORTHY STORAGE AND AUDITING FOR HEALTHCARE BIG DATA IN CLOUD

A Medical Information System (MIS) is an emerging patient-driven strategy for medical data platform, which is for the most part circulated to outsource at an outsider, such as cloud suppliers. Be that as it may, there have been a lot of security worries as individual medicinal data should be uncovered to outsider servers and to unapproved parties. To guarantee the patients' entrance to their own Medical Records, it is a most commonplace strategy to scramble the Medical Records before outsourcing. Yet, concerns, dangers of security system, adaptability in key technique, dependable access and productive client renouncement, have dependably been the most imperative objectives toward accomplishing fine grained and cryptographically authorized information access privacy control. This paper, we proposed secured sharing of individual medical reports in cloud computing. Considering reliable cloud servers, to accomplish fine-grained access we perform Attribute Based Encryption (ABE) specifically Rijndael method encrypting the Medical Information System (MIS) data, so that the trustworthy is maintained. We ensure by auditing the process to prove the security. One of a kind from past works in safe keeping information outsourcing, we receive the various information proprietor situation, and split the clients in the Medical Information framework into a few security spaces that extraordinarily diminishes the key system basic for proprietors and clients. A higher positioning of patient trust and security is ensured in the meantime by abusing multi-power ABE. Our plan likewise outlines dynamic adjustment of access benefits or document properties, bolsters proficient on-interest client/characteristic repudiation and break-glass access under crisis situations. Sufficient of exploratory results are spoken to which demonstrate the security, adaptability, reliable and effectiveness of our proposed plan.
Hadoop framework implementation and performance analysis on a cloud

The Hadoop framework uses the MapReduce programming paradigm to process big data by distributing data across a cluster and aggregating. MapReduce is one of the methods used to process big data hosted on large clusters. In this method, jobs are processed by dividing into small pieces and distributing over nodes. Parameters such as distributing method over nodes, the number of jobs held in a parallel fashion, and the number of nodes in the cluster affect the execution time of jobs. The aim of this paper is to determine how the numbers of nodes, maps, and reduces affect the performance of the Hadoop framework in a cloud environment. For this purpose, tests were carried out on a Hadoop cluster with 10 nodes hosted in a cloud environment by running PiEstimator, Grep, Teragen, and Terasort benchmarking tools on it. These benchmarking tools available under the Hadoop framework are classified as CPU-intensive and CPU-light applications as a result of tests. In CPU-light applications, increasing the numbers of nodes, maps, and reduces does not improve the efficiency of these applications; they even cause an increase in time spent on jobs by using system resources unnecessarily. Therefore, in CPU-light applications, selecting the numbers of nodes, maps, and reduces as minimum is found as the optimization of time spent on a process. In CPU-intensive applications, according to the phase that small job pieces is processed, it is found that selecting the number of maps or reduces equal to total number of CPUs on a cluster is the optimization of time spent on a process.
On Intercausal Interactions in Probabilistic Relational Models

Probabilistic relational models (PRMs) extend Bayes-ian networks beyond propositional expressiveness by allowing the representation of multiple interacting classes. For a specific instance of sets of concrete objects per class, a ground Bayesian network is composed by replicating parts of the PRM. The interactions between the objects that are thereby induced, are not always obvious from the PRM. We demonstrate in this paper that the replicative structure of the ground network in fact constrains the space of possible probability distributions and thereby the possible patterns of intercausal interaction.
Single-pixel imaging using balanced detection and a digital micromirror device

Over the past decade, single-pixel imaging (SPI) has established as a viable tool in scenarios where traditional imaging techniques struggle to provide images with acceptable quality in practicable times and reasonable costs. However, SPI still has several limitations inherent to the technique, such as working with spurious light and in real time. Here we present a novel approach, using complementary measurements and a single balanced detector. By using balanced detection, we improve the frame rate of the complementary measurement architectures by a factor of two. Furthermore, the use of a balanced detector provides environmental light immunity to the method.
The Impact of Shareholder Activism On CEO Remuneration Structures

Given the long-running controversy surrounding CEO remuneration and the role remuneration played in the GFC, we examine two research questions (1) how did CEO remuneration structures change after the GFC, and (2) how did external corporate governance mechanisms influence the changes in CEO remuneration after the GFC? Using the CEO remuneration structures for the top 150 Australian listed companies from 2007 to 2009 this study makes several important contributions. First, it provides a comprehensive analysis of CEO remuneration structures in Australia during the GFC. Second, it is the first (known) study to examine the influence of shareholder activism, including both retail and institutional investors, on changes in CEO remuneration structures during the GFC. Our findings indicate that whilst the percentage of short-term remuneration did not significantly change after the GFC, there appears that a reduction in the proportion of bonuses after the GFC was accompanied by an increase in salaries. The Australian Shareholders’ Association (ASA) and institutional shareholders are associated with a change in remuneration structures, but only where total remuneration levels are higher.
On Performance Analysis of Underlay Cognitive Radio-Aware Hybrid OMA/NOMA Networks with Imperfect CSI

This study considers the outage and throughput performance of downlink in the secondary network of cognitive radio assisted non-orthogonal multiple access (NOMA) systems. Both orthogonal multiple access (OMA) mode and NOMA mode are investigated with respect to status of decoding operation of each user. Depending on the transmit signal-to-noise ratio (SNR) at the primary source and interference constraint from the primary network, the closed-form expressions of the outage probability for two users are obtained and compared in terms of performance. To obtain further insights, an asymptotic analysis of the outage probability in the high SNR regime is presented. Optimal throughput also provides insight in the computation of the power allocation factor. Furthermore, power allocation factor, target rates, and transmit SNR are evaluated to obtain reasonable outage performance. Monte Carlo simulations are conducted to confirm the analytical results.
Theories and Empirical Proxies for Corporate Tax Avoidance

This study critically reviews the accounting literature of tax avoidance with an emphasis on theories of corporate tax avoidance as well as empirical proxies for tax avoidance. The agency theory should be one of the relevant analytical bases to improve the understanding of the interactions between managers and shareholders with respect to corporate tax avoidance strategies. A number of empirical proxies for corporate tax avoidance are computed using financial statement variables, but their relevance is limited for firms that engage in conforming tax avoidance that reduce both book and taxable income. Alternatively, tax shelters and uncertain tax benefits can be used as proxies for aggressive tax avoidance.
Resource-Aware Automata and Games for Optimal Synthesis

We consider quantitative notions of parity automaton and parity game aimed at modelling resource aware behaviour, and study (memory-full) strategies for exhibiting accepting runs that require a minimum amount of initial resources, respectively for winning a game with minimum initial resources. We also show how such strategies can be simplified to consist of only two types of moves: the former aimed at increasing resources, the latter aimed at satisfying the acceptance condition.
Gradient Path Labeling Method Used to Refine the Solar Corona Differential Rotation Profile

Coronal Bright Points (CBPs) allow researchers to trace the solar differential rotational profile up to almost polar latitudes detecting and tracking them, over a sufficiently long sequence of images. In this work we discuss the suitability of using an automatic image segmentation algorithm, called Gradient Path Labelling (GPL), for CBP detection and tracking. The GPL algorithm was applied to high-resolution images from the AIA instrument onboard the SDO satellite (19.3 nm channel data with a 10 min cadence and from 3 days interval) to detect and locate CBPs. Their heliographical position was then used for tracking them on a sequence of images. Our test results show that this algorithm is a promising tool to obtain more precise measurements of the solar rotational speed and the meridional motions of CBPs. Moreover, we are developing an interactive website, where the SDO images are continuously downloaded and analysed allowing users to explore selected periods for the corresponding solar rotational profile and some other characteristics of CBPs.
1 DOES SOCIETY BENEFIT FROM VERY LONG RANGE DAY-TO-DAY WEATHER FORECASTS ?

_____________________________________ *Corresponding author address: Dr Harvey Stern, Bureau of Meteorology, Box 1636, Melbourne, 3001, Australia; e-mail: h.stern@bom.gov. au o For Day-8, Quantitative Precipitation Forecasts (QPFs) explained 4.18% of the observed variance, whilst Minimum Temperature Forecasts (MINFs) explained 17.88% of the observed variance and Maximum Temperature Forecasts (MAXFs) explained 17.46% of the observed variance.
Stud Krill Herd Algorithm for optimal sizing and location of DG in a Radial Distribution System

Distributed Generation (DG) plays a vital role in modern power systems to achieve the requirements and satisfaction of end users while transmitting and distributing power from one to another point. Due to technology advancements, DG technologies are gearing-up and are becoming alternatives to conventional energy sources to improve system reliability. Stud Krill Herd Algorithm (SKHA) is one of the new bio-inspired, heuristic techniques which are based on the Krill Herd Algorithm (KHA) with stud operators. The main aim of the paper is to minimize the system power losses and voltage profile improvement. The proposed algorithm is implemented on IEEE 33-bus and IEEE-69 bus test system. The results of the base case are compared with Krill Herd Algorithms and Stud Krill Herd Algorithm. 1 International Journal of Pure and Applied Mathematics Volume 120 No. 6 2018, 12161-12180 ISSN: 1314-3395 (on-line version) url: http://www.acadpubl. eu/hub/ Special Issue http://www.acadpubl. eu/hub/
SPECTROGRAM IMAGE FEATURES

Nearly all automatic speech segmentation approaches rely solely on acoustic features, which differs from the way humans segment speech using phonetic annotation software. In order to get closer to human-level precision in speech segmentation, we adopt a multimodal approach to improve the segmentation accuracy. To this end, we analyze a database of segmentation behavior collected using an eye tracker, obtained from human experts performing a manual segmentation task. This allows us to introduce gaze as an additional modality for automatic segmentation by transforming it into features for image based phoneme segmentation (ISeg). Experiments were conducted for automatic speech segmentation, comparing the image-only, ISeg technique, as well as ISeg combined with hidden Markov model (HMM) based acoustic segmentation, with respective segmentation approaches conditioned on the gaze data. The results show that enhancing the image based segmentation with gaze information improves the accuracy of ISeg, as well as ISeg combined with HMMs.
Supplementary Material to “A Trilateral Weighted Sparse Coding Scheme for for Real-World Image Denoising”

Proof. Since A,B are symmetric matrices, they can be diagonalized as A = UAΣAU > A , B = UBΣBU > B , where ΣA = diag(λ 1 A, ., λ 3p A ) and ΣB = diag(λB , ., λ M B ) are diagonal matrices. Since A,B are positive semi-definite matrices, their corresponding eigenvalues are non-negative, i.e., λA ≥ 0 for ∀i = 1, . , 3p and λjB ≥ 0 for ∀j = 1, . ,M . If at least one of the matrices A,B is positive definite, then λA +λ j B > 0 holds true for ∀i, j and σ(A)∩σ(−B) = ∅. Therefore, the Sylvester equation AC +CB = E has a unique solution.
Enhancement of some culture conditions for optimizing growth and lipid production in the diatom Nitzschia palea

Diatoms are unicellular and colonial microalgae which are currently explored as one of the potential algal species for biofuel production since they are known to accumulate high lipid content under different environmental stresses. This paper focuses on the diatom Nitzschia palea subjected under various culture conditions of light intensity, temperature, and pH, using batch culture experiments for optimizing their growth, lipid content and lipid productivity. Nitzschia palea attained a maximum growth of 3.08106 cells/mL under the optimum conditions of 18 mmol photons m–2 s–1 light intensity, temperature of 30°C, and pH 9.
FOR GRADIENT AIRBORNE GEOPHYSICAL DATASETS

Intrepid Geophysics has adapted its existing software to include a new series of objects that can be used when processing gradient datasets. These have the purpose of hiding the details (abstraction) of exactly what components of a field have been observed in a survey dataset. This avoids the problem of doing a general rewrite of processing software from the ground up for each special case. Historically, codes have mostly been written to filter, level and grid “scalar” line data (e.g. Total Magnetic Intensity), so Intrepid Geophysics latest software update is a dramatic shift to the world of vectors & tensors. The new family of classes in this adapted software is designed to honour all the commonly available airborne geophysical observation packages. Specifically, for magnetic gradiometry systems, the magnetic intensity plus: 1. vertical gradient only, 2. transverse gradient (wing tip sensors), 3. transverse & longitudinal gradient (wing tip & tail stinger), 4. all gradients (full tri-axial system), 5. all components of a field, 6. full 2 order tensor gradients. For moving platform gravity, the vertical component (if available) plus: 1. vertical component plus motion monitors (L&R / ZLS), 2. 2 horizontal curvature tensor (Falcon system), 3. 3 gravity components (Sander), 4. full 2 tensor gradients (Bell), With this approach, each derived class is delegated the task of enforcing any appropriate invariant relationships e.g. tensor symmetry, trace invariance, rotational invariance, boost symmetry, etc. This innate behaviour can be relied upon to carry through in any process involving a manipulation of a measurement with another reading. This greatly assists the development of algorithms that work with all the various systems in a physically consistent way. The basic core functions, namely of database support, vector statistics, visualization mimic of each sample, gridding, filtering and levelling have proven to be viable for large surveys.
Modal Analysis of DFIG-based Wind Farms Considering Converter Controllers

Wind power generations are now being installed widely in the power systems throughout the world. Due to the differences between conventional synchronous generators and doubly-fed induction generator (DFIG) –based wind farms (WFs), the impacts of WFs on power system stability should be investigated. In this paper, the impacts of DFIG-based WF on power system small signal stability are analyzed. The converter controllers are modeled and the effects of controller parameters on system dominant modes are studied in a single machine infinite bus (SMIB) system. The appropriate values for controller parameters are decided according to the eigenvalue analysis results. Besides, the impacts of shaft model and its parameters on dominant modes are evaluated. Keywords— Doubly-fed induction generator, Power system small signal stability, Eigenvalue analysis.
Leveraging Autonomous Segmentation and Grasp Calculation for Programming by Demonstration

We present an Autonomous Grasping Pipeline that uses object segmentation and grasp calculation to make grasping more robust in robots programmed by demonstration. The object segmentation was performed as a pre-processing step before grasping. We leveraged a model-free approach which enables segmenting unknown objects in a scene without prior knowledge. We applied the existing work of Richtsfeld [1], [2] by dividing the point cloud into small surface patches and using Support Vector Machines to evaluate the relationships between patches and form objects. The segmented 3D data can then be passed to an autonomous grasping algorithm. We use a novel hybrid of model-based and model-free approaches to generate grasps. We learn a model for grasp preferences over context provided by object features, formulated using pairwise ranking. We show that this pairwise grasp preference model yields statistically significant grasp rate improvements over contemporary methods for autonomous grasp selection. We then incorporate the object segmentation and autonomous grasping into a Programming by Demonstration system.
Detection of adrenaline based on substrate recycling amplification

An amperometric enzyme biosensor has been applied for the detection of adrenaline. The adrenaline biosensor has been prepared by modification of an oxygen electrode with the enzyme laccase that operates at a broad pH range between pH 3.5 to pH 8. The enzyme molecules were immobilized via cross-linking with glutaraldehyde. The sensitivity of the developed adrenaline biosensor in different pH buffer solutions has been studied. © 2015 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the organizing committee of EUROSENSORS 2015.
Predictors of an increased length of stay following Total Knee Arthroplasty - Survey Report.

OBJECTIVE To find various preoperative, intraoperative and postoperative factors that predict an increased length of stay in patients following total knee arthroplasty. METHODS The retrospective cohort study was conducted at the Aga Khan University Hospital, Karachi, and comprised record of patients regardless of gender, co-morbids and age who had undergone a unilateral or bilateral total knee arthroplasty between January 2007 and December 2015. An increased length of stay was defined as ≥75th centile (≥11 days). RESULTS Of the 577 patients, 448(77.6%) were women. Overall, 311(53.9%) patients were aged 56-70 years. Of the total, 100(17.3%) patients stayed for at least 11 days. There was a weak but positive correlation between increasing body mass index and increased length of stay (p=0.017). Following adjusted-multivariate logistic regression analysis, the most significant predictors contributing to an increased length of stay were bilateral knee surgery, postoperative transfusion and postoperative special care unit stay (p<0.05 each). CONCLUSIONS Bilateral total knee arthroplasty, postoperative transfusion and postoperative special care unit stay were found to be associated with an increased length of stay.
Modular Visual Navigation using Active Neural Mapping

Learning-based navigation algorithms exploit the semantic structure of the world and are effective at handling sensor noise and noisy maps. However, they are sample inefficient, do not transfer across domains, and fail at long-term planning. On the other hand, classical algorithms are effective at long-term planning and need little to no training data but do not exploit the semantic structure and fail with noisy maps/observations. In this work, we present a modular and hierarchical navigation algorithm which leverages the strengths of both classical and learningbased methods. Our model consists of long-term+short-term goal generators and long-term+short-term planners. We use learning to generate long-term goals and a deterministic planner to solve for long-term paths. Short-term goals are generated based on the planned paths but the short-term planner is learned, making it robust to sensor noise. Our modular approach provides efficient, exhaustive exploration; accurate long-term planning; and transferability across domains and tasks. We perform experiments on the Gibson and Matterport real-world reconstruction datasets in the Habitat simulator. We show that the proposed model outperforms prior methods on both exploration (43% relative improvement in coverage) and PointGoal navigation (21% absolute improvement in success rate), while also improving sample efficiency.2
How to Secure a RISC-V Embedded System in Just 30 Minutes

The free and open RISC-V ISA defines many important building blocks of security. Properly implementing them is the system designer responsibility. So, the real question is: How does one properly secure a RISC-V embedded system? This paper offers a practical guide to using these security blocks to build a state-of-the-art Trusted Execution Environment (TEE) with a multitude of isolated security domains Zones, and secure communications between them. The paper also shows how to verify Zone isolation and benchmark overall TEE system performance. Keywords—RISC-V; Embedded Security; Trusted Execution Environment; TEE; Secure boot; Root of Trust; Firmware