Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Decoding of Polar Codes on Permuted Factor Graphs

Polar codes are a channel coding scheme for the next generation of wireless communications standard (5G). The belief propagation (BP) decoder allows for parallel decoding of polar codes, making it suitable for high throughput applications. However, the error-correction performance of polar codes under BP decoding is far from the requirements of 5G. It has been shown that the error-correction performance of BP can be improved if the decoding is performed on multiple permuted factor graphs of polar codes. However, a different BP decoding scheduling is required for each factor graph permutation which results in the design of a different decoder for each permutation. Moreover, the selection of the different factor graph permutations is at random, which prevents the decoder to achieve a desirable error correction performance with a small number of permutations. In this paper, we first show that the permutations on the factor graph can be mapped into suitable permutations on the codeword positions. As a result, we can make use of a single decoder for all the permutations. In addition, we introduce a method to construct a set of predetermined permutations which can provide the correct codeword if the decoding fails on the original permutation. We show that for the 5G polar code of length 1024, the error-correction performance of the proposed decoder is more than 0.25 dB better than that of the BP decoder with the same number of random permutations at the frame error rate of 0.0001.
High-rejection wideband bandstop filter with good frequency selectivity

In this paper, a brand-new wideband bandstop filter with good frequency selectivity is proposed. The wideband bandstop response with five transmission zeros and high stopband rejection is implemented using the modified coupled-line sections. The transmission zeros and poles are derived based on even- and odd-mode analysis. For demonstration, a design prototype operating at 2 GHz with 79.5% relative bandwidth of 35-dB stopband rejection is simulated, fabricated, and measured. Good agreements between simulated and measured results can be observed.
Reducing the latency of OpenFlow rule changes in data centre networks

The number and size of data centres being constructed continues to grow, in response to increasing demand for cloud-based services. In a Data Centre Network (DCN), maximizing performance is essential, and network management tools that leverage the advantages of Software-Defined Networking (SDN) are key to achieving that goal. DCN switches are now expected to support the OpenFlow protocol — however, performance issues have been observed with hardware switch OpenFlow implementations in relation to path establishment for new flows, and path modification for existing flows. In particular, concerns have been raised regarding the delays in effecting changes to hardware flow tables. We report on recent research that seeks to address this issue. Specifically, we investigate the approach of temporarily tunnelling packets for new flows while more efficient non-tunnelled paths are being established, and to which the flows are later migrated. Our early results show the potential and limitations of this approach.
Sex, Age, and Population Density Affect Aggressive Behaviors in Island Lizards Promoting Cannibalism

Island populations may evolve distinct behavioral repertoires as a response to the conditions of insular life. Strong intraspecific competition is typical in insular lizards and may include cannibalism. In this study, we investigated sexual and age patterns of aggression in two populations of the Skyros wall lizard (Podarcis gaigeae), one from the main island of Skyros (Aegean Sea, Greece) and another from the satellite islet Diavates. The latter is terrestrial predator-free biotope, hosting a dense population of large-bodied lizards that have been reported to exert cannibalism. In staged encounters, we examined the aggressive propensities of adult male and female lizards against their age-peers and juveniles. Males from both populations were much more aggressive than females toward juveniles and other adults. Males from Diavates were more frequently aggressive to juveniles and other male lizards than males from Skyros. Diavates cannibals also captured their targets at shorter latency. We ascribe this distinct behavioral pattern to the high population density. Infanticide and intramale aggressiveness confer two great advantages to cannibals: food and elimination of future rivals.
EPICSAVE — Enhancing vocational training for paramedics with multi-user virtual reality

An anaphylactic shock constitutes a representative scenario for critical paramedical cases that happen too rare to eventually occur within a regular curricular term of vocational training. As a possible solution, this paper presents EPICSAVE, a development case that yields novel training tools using multi-user virtual reality (VR) and serious game methodology. The case describes the interdisciplinary setup and iterative workflow of the development of a simulation prototype. Examples show design tools and methodologies, e.g., finding focus in medical treatment. Results from two pilot studies indicate that specifically multi-user VR may enhance paramedic training. The subsequently developed prototype offers collaborative training for two paramedic trainees and one trainer. Results from a user study with paramedic trainees indicate that experiencing a positive VR training outcome depends on high presence effects and is limited by usability issues. We provide a list of open design and usability issues that shall help to improve future integration of multi-user VR in training facilities.
Towards Mixed Optimization for Reinforcement Learning with Program Synthesis

Deep reinforcement learning has led to several recent breakthroughs, though the learned policies are often based on black-box neural networks. This makes them difficult to interpret and to impose desired specification constraints during learning. We present an iterative framework, MORL, for improving the learned policies using program synthesis. Concretely, we propose to use synthesis techniques to obtain a symbolic representation of the learned policy, which can then be debugged manually or automatically using program repair. After the repair step, we use behavior cloning to obtain the policy corresponding to the repaired program, which is then further improved using gradient descent. This process continues until the learned policy satisfies desired constraints. We instantiate MORL for the simple CartPole problem and show that the programmatic representation allows for high-level modifications that in turn lead to improved learning of the policies.
A Pulmonary Nodule Detection Model Based on Progressive Resolution and Hierarchical Saliency

Detection of pulmonary nodules on chest CT is an essential step in the early diagnosis of lung cancer, which is critical for best patient care. Although a number of computer-aided nodule detection methods have been published in the literature, these methods still have two major drawbacks: missing out true nodules during the detection of nodule candidates and less-accurate identification of nodules from non-nodule. In this paper, we propose an automated pulmonary nodule detection algorithm that jointly combines progressive resolution and hierarchical saliency. Specifically, we design a 3D progressive resolution-based densely dilated FCN, namely the progressive resolution network (PRN), to detect nodule candidates inside the lung, and construct a densely dilated 3D CNN with hierarchical saliency, namely the hierarchical saliency network (HSN), to simultaneously identify genuine nodules from those candidates and estimate the diameters of nodules. We evaluated our algorithm on the benchmark LUng Nodule Analysis 2016 (LUNA16) dataset and achieved a state-of-the-art detection score. Our results suggest that the proposed algorithm can effectively detect pulmonary nodules on chest CT and accurately estimate their diameters.
Vision System-Based Design and Assessment of a Novel Shoulder Joint Mechanism for an Enhanced Workspace Upper Limb Exoskeleton

Exoskeletons arise as the common ground between robotics and biomechanics, where rehabilitation is the main field in which these two disciplines find cohesion. One of the most relevant challenges in upper limb exoskeleton design relies in the high complexity of the human shoulder, where current devices implement elaborate systems only to emulate the drifting center of rotation of the shoulder joint. This paper proposes the use of 3D scanning vision technologies to ease the design process and its implementation on a variety of subjects, while a motion tracking system based on vision technologies is applied to assess the exoskeleton reachable workspace compared with an asymptomatic subject. Furthermore, the anatomic fitting index is proposed, which compares the anatomic workspace of the user with the exoskeleton workspace and provides insight into its features. This work proposes an exoskeleton architecture that considers the clavicle motion over the coronal plane whose workspace is determined by substituting the direct kinematics model with the dimensional parameters of the user. Simulations and numerical examples are used to validate the analytical results and to conciliate the experimental results provided by the vision tracking system.
OntoPeFeGe: Ontology-Based Personalized Feedback Generator

Virtual Learning Environments provide teachers with a web-based platform to create different types of feedback. These environments usually follow the ‘one size fits all’ approach and provide students with the same feedback. Several personalized feedback frameworks have been proposed which adapt the different types of feedback based on the student characteristics and/or the assessment question characteristics. The frameworks are intradisciplinary, neglect the characteristics of the assessment question, and either hard-code or auto-generate the types of feedback from a restricted set of solutions created by a domain expert. This paper contributes to research carried out on personalized feedback frameworks by proposing a generic novel system which is called the Ontology-based Personalized Feedback Generator (OntoPeFeGe). OntoPeFeGe addressed the aforementioned drawbacks using an ontology—a knowledge representation of the educational domain. It integrated several generation strategies and templates to traverse the ontology and auto-generate the questions and feedback. The questions have different characteristics, in particular, aiming to assess students at different levels in Bloom’s taxonomy. Each question is associated with different types of feedback that range from verifying student’s answers to giving the student more details related to the answer. The feedback auto-generated in OntoPeFeGe is personalized using a rule-based algorithm which takes into account the student characteristics and the assessment question characteristics. The personalized feedback in OntoPeFeGe was quantitatively evaluated on 88 undergraduate students. The results revealed that the personalized feedback significantly improved the performance of students with low background knowledge. In addition, the feedback was evaluated qualitatively using questionnaires provided to teachers and students. The results showed that teachers and students were satisfied with the feedback.
Temperature-Robust Air-Gapless EE-Type Transformer Rails for Sliding Doors

In this paper, a sliding EE transformer (SEET) rail, which has a strong and independent magnetic coupling to the gap variation between the transmitter (Tx) line and receiver (Rx) coil for powering the sliding door, is newly proposed. In the SEET rail, the Tx line and the Rx coil share a single EE ferrite core, while the winding window of the ferrite core is considered as the gap variation. In the proposed structure, the magnetic coupling can be maintained as strong and constant even though the ferrite core moves along the door rail when the sliding door is closed or opened. Through detailed coil designs based on electric and magnetic circuit analyses, design constraints on voltage, output power, flux density, and zero voltage switching are proposed with a closed-form solution of output voltage and theoretical maximum output power. The SEET rail is successfully designed to provide a maximum load power of 200 W for the sliding door within an ambient temperature range from −40 to 125 °C by following design constraints. Moreover, optimal design parameters such as number of turns, air-gap, and ferrite thickness are provided, which maximize power transfer efficiency of the SEET rail while maintaining its performance in the temperature range. The proof-of-concept prototype shows a strong magnetic coupling, κ = 0.76 at 20 °C and the variation of the magnetizing inductance with respect to the temperature variation was measured as 13.8%. The maximum power transfer efficiency excluding the regulator at a nominal load power of 100 W was 85%, and the efficiency at the maximum load power of 200 W was measured as 82% with a 3%P variation by the temperature variation.
An Intelligent System for Video Surveillance in IoT Environments

Multimedia traffic has drastically grown in the last few years. In addition, some of the last paradigms proposed, like the Internet of Things (IoT), adds new types of traffic and applications. Software-defined networks (SDNs) improve the capability of network management. Combined with SDN, artificial intelligence (AI) can provide solutions to network problems based on classification and estimation techniques. In this paper, we propose an artificial intelligence system for detecting and correcting errors in multimedia transmission in a surveillance IoT environment connected through a SDN. The architecture, algorithm, and messages of the SDN are detailed. The AI system design is described, and the test-bed and the data set are explained. The AI module consists of two different parts. The first one is a classifying part, which detects the type of traffic that is sent through the network. The second part is an estimator that informs the SDN controller on which kind of action should be executed to guarantee the quality of service and quality of experience. Results show that with the actions performed by the network, like jitter can be reduced up to 70% of average and losses can be reduced from 9.07% to nearly 1.16%. Moreover, the presented AI module is able to detect critical traffic with 77% accuracy.
Towards Interactive Progressive Cutting of Deformable Bodies via Phyxel-Associated Surface Mesh Approach for Virtual Surgery

Interactive progressive cutting of deformable body is vital for surgical training/planning systems with resection operations. Unfortunately, it is challenging to robustly simulate this phenomenon with faithful reflection of user’s motion. The underlying reason is that sophisticated boundary conditions should be considered for the consistency between scalpel motion trajectory and incision during progressive cutting. Meanwhile, both geometrical and physical primitives along cutting trajectory need to be updated for each step induced by topological modifications, which is time-consuming for interactive environment. To tackle the above-mentioned issues, we present a phyxel-associated surface mesh approach for interactive progressive cutting of deformable bodies. Our method is based on a hybrid geometry, including a triangular surface mesh and an internal physical point cloud (phyxels), and we employ the moving least squares shape function to achieve the fast bijective mapping. This structure can well decouple the mechanical modeling from the geometry processing, thus allowing efficient handling of topological modifications, mechanical response, and rendering issues. Furthermore, we propose tailored continuous collision detection and constrained bilateral triangulation methods for fast geometrical modification. Besides, a dynamic adaptation scheme is proposed to avoid material slivers along splitting trajectories and guarantee numerical stability of simulation. Experimental results demonstrate that our method can provide effective progressive cutting simulation with realistic mechanical response, and it has a great potential to be an efficient and versatile computational framework for interactive surgical cutting simulation with high fidelity.
cilantro: A Lean, Versatile, and Efficient Library for Point Cloud Data Processing

We introduce Cilantro, an open-source C++ library for geometric and general-purpose point cloud data processing. The library provides functionality that covers low-level point cloud operations, spatial reasoning, various methods for point cloud segmentation and generic data clustering, flexible algorithms for robust or local geometric alignment, model fitting, as well as powerful visualization tools. To accommodate all kinds of workflows, Cilantro is almost fully templated, and most of its generic algorithms operate in arbitrary data dimension. At the same time, the library is easy to use and highly expressive, promoting a clean and concise coding style. Cilantro is highly optimized, has a minimal set of external dependencies, and supports rapid development of performant point cloud processing software in a wide variety of contexts.
Constant Current/Voltage Charging Operation for Series–Series and Series–Parallel Compensated Wireless Power Transfer Systems Employing Primary-Side Controller

This paper proposes a new control technique, which only employs the primary-side controller and load identification approach to adjust charging voltage/current for series–series (SS) and series–parallel (SP) compensated wireless power transfer (WPT) systems. The advantages are that dual-side wireless communication for real-time charging current/voltage adjustment is avoided as well as it is suitable for different charging modes, e.g., constant voltage (CV) and constant current (CC) charging defined by the battery charging profile. The load identification approach, which utilizes reflected impedance theory and quadrature transformation algorithm for calculating the active power, is proposed to estimate the equivalent load resistance of battery. Then, the CV/CC charging for both SS and SP compensation are achieved by the PI-controlled phase-shift H-bridge inverter. The simulation and experimental results validate the feasibility of proposed control method. During the CC charging, 3.01 and 3.03 A for SS and SP compensation with the error of 1.2% and 1.4% are achieved. During the CV charging, 25.8 and 25.7 V for SS and SP compensation with the error of 1.1% and 1.3% are realized. The proposed method improves the performance of both SS- and SP-compensated WPT systems to be more suitable for the applications that require compact and lightweight receiver.
Ambiance in Social Media Venues: Visual Cue Interpretation by Machines and Crowds

We study the perception of ambiance of places captured in social media images by both machines and crowdworkers. This task is challenging due to the subjective nature of the ambiance construct as well as the large variety in layout, style, and visual characteristics of venues. For machine recognition of ambiance, we use state-of-the-art Residual Deep Convolutional Neural Networks (ResNets), followed by gradient-weighted class activation mapping (Grad-CAM) visualizations. This form of visual explanation obtained from the trained ResNet-50 models were assessed by crowdworkers based on a carefully designed crowdsourcing task, in which both visual ambiance cues of venues and subjective assessment of Grad-CAM results were collected and analyzed. The results show that paintings, photos, and decorative items are strong cues for artsy ambiance, whereas type of utensils, type of lamps and presence of flowers may indicate formal ambiance. Layout and design-related cues such as type of chairs, type of tables/tablecloth and type of windows are noted to have impact for both ambiances. Overall, the ambiance visual cue recognition results are promising, and the crowd-based assessment approach may motivate other studies on subjective perception of place attributes.
Prosumer flexibility management in smart grids

Diverse electricity consumption and distributed production present serious challenges in future smart grids. Smart grid end users can have a role of a consumer and a producer at the same time. So-called prosumers are considered to play a major role in the future grid operation, management and maintenance. One of main concepts supporting this novel role is prosumer flexibility, namely an ability of the prosumers to adapt their consumption or production according to the smart grid needs. Understanding, managing and utilizing the prosumers flexibility remains a serious research challenge that needs to be evaluated and proved in practice. In this paper we will present a synergy of two research projects addressing the area of prosumer flexibility management. First one is a horizon project Flex4Grid, which aims at prosumer flexibility management by using advanced information technology solutions, while the second one is a national Critical Peak Tariff project in Slovenia, that enables distribution system operators to test novel prosumer flexibility management incentives in real life operation. The projects' cooperation allows to utilize modern smart grid infrastructure, boost its usage with advanced IT, and evaluate prosumer flexibility management in practice. We introduce the conceptual model and implementation of our technical solution, as well as some preliminary evaluation results of a large-scale pilot.
Effects of moisture on furfural distribution and aging estimation of transformer cellulose insulation

Furfural content in oil is a common chemical marker indicating the aging condition of cellulose insulation paper in power transformers. However, the accuracy of aging estimation is affected by the furfural distribution ratio between oil and paper. Moisture is considered an important factor that influences the furfural distribution behavior in oil-paper system. In this study, oil-paper samples with different furfural and moisture contents were prepared. For samples with different moisture contents, furfural contents in oil and paper were measured and furfural mass fractions were calculated. Results showed that the oil-paper samples with high moisture contents exhibited a high furfural mass fraction in oil. This finding implies that high humidity promoted the furfural diffusion from paper to oil. The effect of moisture on aging assessment of insulation paper was analyzed. Results showed that the increase of moisture content in oil-paper system would significantly affect the accuracy of insulation paper aging assessment, leading to a lower assessment result than the real value.