Miracle-dz/UniFAR-FTUs huggingface dataset:
Human Action Recognition Using 3D Reconstruction Data

In this paper, the problem of human action recognition using 3D reconstruction data is deeply investigated. 3D reconstruction techniques are employed for addressing two of the most challenging issues related to human action recognition in the general case, namely view variance (i.e., when the same action is observed from different viewpoints) and the presence of (self-) occlusions (i.e., when for a given point of view a body part of an individual conceals another body part of the same or another subject). The main contributions of this paper are summarized as follows. The first one is a detailed examination of the use of 3D reconstruction data for performing human action recognition. The latter includes the introduction of appropriate local/global flow/shape descriptors, extensive experiments in challenging publicly available datasets and exhaustive comparisons with state-of-art approaches. The second one is a new local-level 3D flow descriptor, which incorporates spatial and surface information in the flow representation and efficiently handles the problem of defining 3D orientation at every local neighborhood. The third one is a new global-level 3D flow descriptor that efficiently encodes the global motion characteristics in a compact way. The fourth one is a novel global temporal-shape descriptor that extends the notion of 3D shape descriptions for action recognition, by incorporating the temporal dimension. The proposed descriptor efficiently addresses the inherent problems of temporal alignment and compact representation, while also being robust in the presence of noise (compared with similar tracking-based methods of the literature). Overall, this paper significantly improves the state-of-art performance and introduces new research directions in the field of 3D action recognition, following the recent development and wide-spread use of portable, affordable, high-quality and accurate motion capturing devices (e.g., Microsoft Kinect).
Ciphertext-Policy Attribute-Based Signcryption With Verifiable Outsourced Designcryption for Sharing Personal Health Records

Personal health record (PHR) is a patient-centric model of health information exchange, which greatly facilitates the storage, access, and share of personal health information. In order to share the valuable resources and reduce the operational cost, the PHR service providers would like to store the PHR applications and health information data in the cloud. The private health information may be exposed to unauthorized organizations or individuals since the patient lost the physical control of their health information. Ciphertext-policy attribute-based signcryption is a promising solution to design a cloud-assisted PHR secure sharing system. It provides fine-grained access control, confidentiality, authenticity, and sender privacy of PHR data. However, a large number of pairing and modular exponentiation computations bring heavy computational overhead during designcryption process. In order to reconcile the conflict of high computational overhead and low efficiency in the designcryption process, an outsourcing scheme is proposed in this paper. In our scheme, the heavy computations are outsourced to ciphertext transformed server, only leaving a small computational overhead for the PHR user. At the same time, the extra communication overhead in our scheme is actually tolerable. Furthermore, theoretical analysis and the desired securing properties including confidentiality, unforgeability, and verifiability have been proved formally in the random oracle model. Experimental evaluation indicates that the proposed scheme is practical and feasible.
Tracking Control for the Grasping of a Tumbling Satellite With a Free-Floating Robot

In this letter, a novel method is presented for grasping a partially cooperative tumbling satellite with a free-floating robot. A reference trajectory, provided by an offline motion planner and adapted online to account for modeling uncertainties, is tracked with a visual servo for the approach phase and with a joint-space controller for the rigidization phase, which follows the grasp. An extended Kalman filter is implemented for providing robustness, predictions during measurement unavailability and a velocity estimate of the tumbling satellite as a feed-forward for control, throughout the grasping task. The visual servo is in cascade with an impedance controller to achieve tracking while being compliant. The method is successfully validated on the OOS-SIM facility, which is used for simulating gravity-free multibody dynamics with a novel momentum-based control method.
DEEP-Mon: Dynamic and Energy Efficient Power Monitoring for Container-Based Infrastructures

In the last few years energy efficiency of large scale infrastructures gained a lot of attention, as power consumption became one of the most impacting factors of the operative costs of a data-center and of its Total Cost of Ownership (TCO). Power consumption can be observed at different layers of the data-center, from the overall power grid, moving to each rack and arriving to each machine and system. Given the rise of application containers both in the cloud computing and High Performance Computing (HPC) scenarios, it becomes more and more important to measure power consumption also at the application level, where power-aware schedulers and orchestrators can optimize the execution of the workloads not only from a performance perspective, but also considering performance/power trade-offs. In this paper we propose DEEP-mon, a novel monitoring tool able to measure power consumption and attribute it for each thread and application container running in the system. Moreover, we show how the proposed approach has a negligible impact on the monitored system and on the running workloads, overcoming the limitations of the previous works in the field.
Sport and physical activity after ankle arthrodesis with Ilizarov fixation and internal fixation.

BACKGROUND Severe osteoarthritis (OA) of the ankle joint constitutes an important social problem. OBJECTIVES We used (1) the GRIMBY scale, (2) the LOWER LIMB Activity scale, (3) the UCLA (University of California Los Angeles) activity scale, (4) the VAS (visual analogue scale) ACTIVITY scale, and (5) the FAAM (foot and ankle ability measure) SPORT scale to verify whether the type of ankle joint arthrodesis stabilization affected sports and physical activity levels. MATERIAL AND METHODS We carried out a prospective clinical study of 47 patients who had undergone ankle arthrodesis with Ilizarov external fixator stabilization (Group 1, n = 21) or internal stabilization with screws (Group 2, n = 26) at Orthopaedic Clinic at the Wroclaw Medical University, Poland, from 2007 to 2015. Sports and physical activity levels were measured by (1) the GRIMBY scale, (2) the LOWER LIMB Activity scale, (3) the UCLA activity scale, (4) the VAS ACTIVITY scale, and (5) the FAAM SPORT scale. RESULTS A comparison between the average results of Group 1 and Group 2 on the LOWER LIMB Activity scale and the GRIMBY scale before and after surgery revealed no significant differences. In Group 1, the mean scores on the VAS ACTIVITY scale and the UCLA activity scale after treatment were higher than in Group 2. In Group 1, the mean outcome in the SPORT FAAM scale after treatment was 40; in Group 2 it was 30.06. CONCLUSIONS Ilizarov fixation of ankle arthrodesis is associated with better scores on the FAAM SPORT, UCLA activity and VAS ACTIVITY scales after treatment than internal fixation. The scores on the GRIMBY scale and the UCLA activity scale were significantly higher after treatment than before treatment in both groups. In this study, ankle fusion with Ilizarov fixation and internal fixation was found to be effective in the treatment of ankle arthritis. The levels of sport and physical activity were satisfactory in both groups, but the outcomes after fixation with the Ilizarov apparatus were better than after internal stabilization.
BitFlow: Exploiting Vector Parallelism for Binary Neural Networks on CPU

Deep learning has revolutionized computer vision and other fields since its big bang in 2012. However, it is challenging to deploy Deep Neural Networks (DNNs) into real-world applications due to their high computational complexity. Binary Neural Networks (BNNs) dramatically reduce computational complexity by replacing most arithmetic operations with bitwise operations. Existing implementations of BNNs have been focusing on GPU or FPGA, and using the conventional image-to-column method that doesn't perform well for binary convolution due to low arithmetic intensity and unfriendly pattern for bitwise operations. We propose BitFlow, a gemm-operator-network three-level optimization framework for fully exploiting the computing power of BNNs on CPU. BitFlow features a new class of algorithm named PressedConv for efficient binary convolution using locality-aware layout and vector parallelism. We evaluate BitFlow with the VGG network. On a single core of Intel Xeon Phi, BitFlow obtains 1.8x speedup over unoptimized BNN implementations, and 11.5x speedup over counterpart full-precision DNNs. Over 64 cores, BitFlow enables BNNs to run 1.1x faster than counterpart full-precision DNNs on GPU (GTX 1080).
Design and Evaluation of Approximate Logarithmic Multipliers for Low Power Error-Tolerant Applications

In this paper, the designs of both non-iterative and iterative approximate logarithmic multipliers (ALMs) are studied to further reduce power consumption and improve performance. Non-iterative ALMs, that use three inexact mantissa adders, are presented. The proposed iterative ALMs (IALMs) use a set-one adder in both mantissa adders during an iteration; they also use lower-part-or adders and approximate mirror adders for the final addition. Error analysis and simulation results are also provided; it is found that the proposed approximate LMs with an appropriate number of inexact bits achieve higher accuracy and lower power consumption than conventional LMs using exact units. Compared with conventional LMs with exact units, the normalized mean error distance of 16-bit approximate LMs is decreased by up to 18% and the power-delay product has a reduction of up to 37%. The proposed approximate LMs are also compared with previous approximate multipliers; it is found that the proposed approximate LMs are best suitable for applications allowing larger errors, but requiring lower energy consumption. Approximate Booth multipliers fit applications with less stringent power requirements, but also requiring smaller errors. Case studies for error-tolerant computing applications are provided.
A multi-hop moving zone (MMZ) clustering scheme based on cellular-V2X

A clustering scheme based on pure V2V communications has two prominent issues i.e. broadcast storm and network disconnection. The application of the fifth generation (5G) technology to vehicular networks is an optimal choice due to its wide coverage and low latency features. In this paper, a Multi-hop Moving Zone (MMZ) clustering scheme is proposed by combining IEEE 802.11p with the 3rd Generation Partnership Project (3GPP) 5G cellular technology. In MMZ, vehicles are clustered up-to three hops using V2V communications based on IEEE 802.11p aiming to reduce excessive cellular hand-off cost. While the zonal heads (ZHs) i.e. cluster heads (CHs) are selected by cellular-V2X (C-V2X) on the basis of multi-metrics i.e. relative speed, distance and link life time (LLT). The main goal of MMZ is to form stable clusters achieving high packet delivery and low latency. The simulation results using ns3 show that, 5G wide range technology significantly improves the stability of MMZ in term of ZH duration and change rate. The average Data Packet Delivery Ratio (DPDR) and E2E latency are also improved as compared to the existing clustering schemes.
Sequence variants in the EDAR gene causing hypohidrotic ectodermal dysplasia

Hypohidrotic Ectodermal Dysplasia (HED) is a classic triad encompasses deformities of hair (hypotrichosis), teeth (anodontia/hypodontia), and/or sweat glands (anhidrosis/hypohidrosis). Associated clinical hallmarks include hyperthermia, dryness of skin, eyes, airways, and mucous membranes, prominent forehead, prominent lips, dark, hyperkeratinized skin around the eyes and everted nose (Clarke, 1987). This article is protected by copyright. All rights reserved.
Dual-Band Bandpass Double Ground Plane Coaxial Resonators and Filters

This paper proposes a novel double ground plane coaxial resonator that allows to realize two independent coaxial cavity modes in reduced physical volume. This configuration of compact high performance filters is an enabler for multiband transmitter architectures using modern radio frequency data convertors. The proposed approach benefits from simple design and design flexibility. The benefit of the proposed approach is the ease of design and the design flexibility. The proposed filters provide the ability to control the frequency, the bandwidth, and the introduction of transmission zeros separately for each band. A number of designs are presented to demonstrate the operation principle and the performance characteristics of the proposed filters. A $2 \times 4$ pole filter has been manufactured and tested with very good obtained results.
Dual-Band Shared-Aperture UHF/UWB RFID Reader Antenna of Circular Polarization

A dual-port, dual-band, and circularly polarized (CP) radio frequency identification reader antenna is designed to operate at both ultrahigh frequency (UHF) and lower ultra-wideband (UWB) bands for indoor positioning systems. In conjunction with a properly selected quadruple feed network, a shared aperture using the concept of hybrid mode is utilized for broadening the antenna bandwidth. The concept is demonstrated to achieve a high gain, wideband, and CP operation within a low profile. The measured overlapping 3-dB axis ratio (AR) bandwidth and 10-dB impedance bandwidth for the UHF band ranges from 0.918 to 0.926 GHz with a minimum in-band antenna gain of 9 dBic. The measured overlapping 3-dB AR bandwidth and 10-dB impedance bandwidth covers from 3 to 5.2 GHz with a minimum in-band gain of 9 dBic in the lower UWB band (i.e., 3.1–4.8 GHz). The gain suppression near the two wireless local area network bands (i.e., 2.45 and 5.8 GHz) is more than 20 dB. The isolation between two ports is larger than 25 dB over the UHF and lower UWB bands, respectively.
Parametric-Rate Inference for One-Sided Differentiable Parameters

ABSTRACT Suppose one has a collection of parameters indexed by a (possibly infinite dimensional) set. Given data generated from some distribution, the objective is to estimate the maximal parameter in this collection evaluated at the distribution that generated the data. This estimation problem is typically nonregular when the maximizing parameter is nonunique, and as a result standard asymptotic techniques generally fail in this case. We present a technique for developing parametric-rate confidence intervals for the quantity of interest in these nonregular settings. We show that our estimator is asymptotically efficient when the maximizing parameter is unique so that regular estimation is possible. We apply our technique to a recent example from the literature in which one wishes to report the maximal absolute correlation between a prespecified outcome and one of p predictors. The simplicity of our technique enables an analysis of the previously open case where p grows with sample size. Specifically, we only require that log p grows slower than , where n is the sample size. We show that, unlike earlier approaches, our method scales to massive datasets: the point estimate and confidence intervals can be constructed in O(np) time. Supplementary materials for this article are available online.
A Research on Techniques for Data Fusion and Analysis of Cross-platform MOOC Data

Massive open online courses(MOOCs) tends to open up equal educational opportunities for people around the globe, with characters of great scale, zero threshold, any-time access and so on. It has exerted a profound influence on learning environments and changed people’s way of receiving education. In the promotion of MOOC practice, there still remain problems among which, the low course-completion rate is one of the biggest challenges. This paper presents AMMP, an alliance MOOC management platform which aggregates information, resources, services and people across a number of MOOC platforms. Its overarching goal as a whole is to design and develop methods for learning and teaching analytics to facilitate a personal learning environment. It focuses on the techniques for data fusion and analysis. Particularly, we figure out how to link multiple online identities of learners and data transmission; we also show a couple of examples of data mining. AMMP tends to exert a tremendous influence on researching students learning behavior and offering advice and suggestions to improve service quality.
Crowdsensing-Based Consensus Incident Report for Road Traffic Acquisition

Real-time road traffic information brings great convenience for drivers. Various road information acquisitions are enabled by recent mobile crowdsensing paradigm. However, the accuracy of information can not be guaranteed, and appropriate incentive mechanism is still unavailable. In this paper, we study the problem of extracting the actual road traffic information according to the reports from an amount of unknown contributors. To obtain the accurate road traffic result with high probability, we establish a reputation system to evaluate the reliability of each contributor, which takes both location and time deviation factors into account. We also design an incentive mechanism to elicit the truthful report of each qualified contributor. Furthermore, we improve the existing answer inference methods and derive the correct result in an efficient way. Extensive simulations are carried out to evaluate the proposed algorithms.
Antigenic mimicry of ubiquitin by the gut bacterium Bacteroides fragilis: a potential link with autoimmune disease

Ubiquitin is highly conserved across eukaryotes and is essential for normal eukaryotic cell function. The bacterium Bacteroides fragilis is a member of the normal human gut microbiota, and the only bacterium known to encode a homologue of eukaryotic ubiquitin. The B. fragilis gene sequence indicates a past horizontal gene transfer event from a eukaryotic source. It encodes a protein (BfUbb) with 63% identity to human ubiquitin which is exported from the bacterial cell. The aim of this study was (i) to determine if there was antigenic cross‐reactivity between B. fragilis ubiquitin and human ubiquitin and (ii) to determine if humans produced antibodies to BfUbb. Molecular model comparisons of BfUbb and human ubiquitin predicted a high level (99·8% confidence) of structural similarity. Linear epitope mapping identified epitopes in BfUbb and human ubiquitin that cross‐react. BfUbb also has epitope(s) that do not cross‐react with human ubiquitin. The reaction of human serum (n = 474) to BfUbb and human ubiquitin from the following four groups of subjects was compared by enzyme‐linked immunosorbent assay (ELISA): (1) newly autoantibody‐positive patients, (2) allergen‐specific immunoglobulin (Ig)E‐negative patients, (3) ulcerative colitis patients and (4) healthy volunteers. We show that the immune system of some individuals has been exposed to BfUbb which has resulted in the generation of IgG antibodies. Serum from patients referred for first‐time testing to an immunology laboratory for autoimmune disease are more likely to have a high level of antibodies to BfUbb than healthy volunteers. Molecular mimicry of human ubiquitin by BfUbb could be a trigger for autoimmune disease.
Nonlinear Observers for GNSS- and Camera-Aided Inertial Navigation of a Fixed-Wing UAV

In this paper, exponentially stable nonlinear observers for the estimation of position, velocity, specific force, attitude, and gyro bias of a fixed-wing unmanned aerial vehicle (UAV) are proposed. The sensor suite consists of an inertial measurement unit, a global navigation satellite system receiver, a camera, an altimeter, and, possibly, auxiliary roll and pitch measurements. A first observer is designed making use of all the named sensors and is proved to be globally exponentially stable. Subsequently, the auxiliary roll and pitch measurements are removed and replaced by an additional feedback loop from the estimated attitude, and the new observer is analyzed and proved to be uniformly locally exponentially stable. An optical flow algorithm is used to calculate the UAV velocity based on the camera images, which is used as a measurement of the body-fixed velocity in the attitude observer. The performance of the observers is tested offline on simulated and experimental data.
Label-free detection of angiogenesis biomarkers using Bloch surface waves on one dimensional photonic crystals

We describe the design and fabrication of biochips based on one dimensional photonic crystals supporting Bloch surface waves for label-free optical biosensing. The planar stacks of the biochips are composed of silica, tantala and titania that were deposited using plasma ion assisted evaporation under high vacuum conditions. The biochip surfaces were functionalized by silanization and appropriate fluidic cells were designed to operate in an automated platform. An angularly resolved optical sensing apparatus was assembled to carry out the sensing studies. The angular operation is obtained by a focused laser beam at a fixed wavelength and detection of the angular reflectance spectrum by means of an array detector. Practical application of the sensor was demonstrated by detecting a specific glycoprotein, Angiopoietin 2, that is involved in angiogenesis and inflammation processes. The protocol used for the label-free detection of Angiopoietin 2 is described and the results of an exemplary assay are given, confirming that an efficient detection can be achieved.
UBIS: Utilization-Aware Cluster Scheduling

Data center costs are among the major enterprise expenses, and any improvement in data center resource utilization corresponds to significant savings in true dollars. We focus on the problem of scheduling jobs in distributed execution environments to improve resource utilization. Cluster schedulers like YARN and Mesos base their scheduling decisions on resource requirements provided by end users. It is hard for end-users to predict the exact amount of resources required for a task/ job, especially since resource utilization can vary significantly over time and across tasks. In practice, users pick highly conservative estimates of peak utilization across all tasks of a job to ensure job completion, leading to resource fragmentation and severe under utilization in production clusters. We present UBIS, a utilization-aware approach to cluster scheduling, to address resource fragmentation and to improve cluster utilization and job throughput. UBIS considers actual usage of running tasks and schedules opportunistic work on under-utilized nodes. It monitors resource usage on these nodes and preempts opportunistic containers when over-subscription becomes untenable. In doing so, UBIS utilizes wasted resources while minimizing adverse effects on regularly scheduled tasks. Our implementation of UBIS on YARN yields improvements of up to 30% in makespan for representative workloads and 25% in individual job durations.