Miracle-dz/UniFAR-FTUs huggingface dataset:
Stick: A Harmonious Fusion of Buffer-based and Learning-based Approach for Adaptive Streaming

Off-the-shelf buffer-based approaches leverage a simple yet effective buffer-bound to control the adaptive bitrate (ABR) streaming system. Nevertheless, such approaches in standard parameters fail to always provide high quality of experience (QoE) video streaming services under all considered network conditions. Meanwhile, state-of-the-art learning-based ABR approach Pensieve outperforms existing schemes but is impractical to deploy. Therefore, how to harmoniously fuse the buffer-based and learning-based approach has become a key challenge for further enhancing ABR methods. In this paper, we propose Stick, an ABR algorithm that fuses the deep learning method and traditional buffer-based method. Stick utilizes the deep reinforcement learning (DRL) method to train the neural network, which outputs the buffer-bound to control the buffer-based approach for maximizing the QoE metric with different parameters. Trace-driven emulation illustrates that Stick betters Pensieve by 3.5% - 9.41% with an overhead reduction of 88%. Moreover, aiming to further reduce the computational costs while preserving the performances, we propose Trigger, a light-weighted neural network that determines whether the buffer-bound should be adjusted. Experimental results show that Stick+Trigger rivals or outperforms existing schemes in average QoE by 1.7%-28%, and significantly reduces the Stick’s computational overhead by 24%-61%. Meanwhile, we show that Trigger also helps other ABR schemes mitigate the overhead. Extensive results on real-world evaluation demonstrate the superiority of Stick over existing state-of-the-art approaches.
Detection of abnormal phonocardiograms through the Mel-frequency ceptrum and convolutional neural networks

Heart auscultation is the main method routinely used to diagnose cardiovascular disease. However, even when auscultation is performed by a knowledgeable and experienced physician, the error rate remains high, and accurate computational tools for detecting abnormalities in heart sounds would be of great aid in everyday clinical practice. Still, previous attempts have evidenced how this classification problem is extremely hard, possibly due to the vast heterogeneity and low signal-to–noise ratio of noninvasive heart sound recordings. We present an accurate classification strategy for diagnosing heart sounds based on 1) automatic heart phase segmentation, 2) state-of-the art filters drawn from the filed of speech synthesis (mel-frequency cepstrum rapresentation), and 3) convolutional layers followed by fully connected neuronal ensembles. We achieve an overall area under the receiver operating characteristic curve of 0.77, demonstrating the possibility of designing a machine-learning-based tool for heart sound classification which could serve as a diagnostic as well as screening tool in a variety of situations including telemedicine applications.
Identification of nonstationary parameters of electric arc furnace model using Monte Carlo approach

This paper describes Monte Carlo analysis of an electric arc furnace (EAF) model based on measurements. In this research long-time samples have been analyzed in terms of incidence of occurring of particular values of EAF model coefficients. Their occurrence has been investigated through finding best fitted ones, which was measured by different indicators. These distributions have been approximated by continuous probability density functions that later allowed to develop a probabilistic EAF model.
A Spring-Embedded Planetary-Geared Parallel Elastic Actuator

Parallel elastic actuation utilizing an elastic spring to reduce the required actuator torque is one energy-efficient strategy suitable for various mechatronic applications relating to asymmetric torque requirements. This paper presents a novel concentric drivetrain configuration of a parallel elastic actuator (PEA) embedding a torsion spring in the intermediate gearing stage of a planetary gear mechanism between the sun gear and the carrier, connected to the input shaft and the external load respectively. In addition to the input torque amplified by a gear ratio N, the output torque of the actuator is enhanced by the spring moment amplified by the ratio N-1. An alpha prototype was developed and tested with and without the controlled input torque in order to evaluate the output torque varying against the spring deformation. This proposed concept provides an alternative design solution of PEA using a low-inertia motor in combination with a low-stiffness spring. Connecting the spring arms to the carrier instead of the fixed ring gear not only allows the elastic component to support the load directly but also helps reduce the backlash between the sun gear and the planet gears through the loaded spring.
Toward Vision-based Adaptive Configuring of A Bidirectional Two-Segment Soft Continuum Manipulator

In soft robotics, developing an effective way of robot-environment interaction is a challenging task due to the soft nature of the material that makes the manipulator. This paper demonstrates a vision-based approach to configure a two-segment soft continuum robot manipulator into an user-defined configuration and interact with unknown objects on plane. The soft robot manipulator actuated by cable-driven mechanism, is composed of two cascade continuum segments which are made from poly-dimethyl-siloxane (PDMS). The overall robot configuration can be determined in a point-wise manner on image plane provided by an eye-to-hand system. One can define the end-effectors’ location on the visual system to re-shape the manipulator. The visual servoing fashion allows the robot to optimize its posture to its best fit without developing any complicated model. Experiments on prototype indicate that the proposed model-free approach can be well employed, even when the manipulator is bearing a payload. By adaptively adjusting manipulator’s stiffness to a quasi-deadlock status, the payload capacity is up to nearly 6 times of the manipulator’s mass itself.
Rolling Bearing Fault Diagnosis Based on Convolutional Neural Network and Support Vector Machine

Rolling bearings are one of the essential components in rotating machinery. Efficient bearing fault diagnosis is necessary to ensure the regular operation of the mechanical system. Traditional fault diagnosis methods usually rely on a complex artificial feature extraction process, which requires a lot of human expertise. Emerging deep learning methods can reduce the dependence of the feature extraction process on manual intervention effectively. However, its training requires a large number of fault signals, which is difficult to obtain in actual engineering. In this paper, a rolling bearing fault diagnosis method based on Convolutional Neural Network and Support Vector Machine is proposed to solve the above problems. Firstly, the Continuous Wavelet Transform is used to convert one-dimensional original vibration signals into two-dimensional time-frequency images. Secondly, the obtained time-frequency images are input for training the constructed model. Finally, the diagnosis of the fault location and severity is completed. The method is verified on the CWRU data set and the MFPT data set. The results demonstrate that the proposed method achieves higher diagnostic accuracy and stability than other advanced techniques.
A millimeter-wave textile antenna loaded with EBG structures for 5G and IoT applications

This paper describes the antenna performances made from common clothing fabrics for 5G and Internet of Things (IoT) applications by employing electromagnetic bandgap (EBG) structures as the substrate of the antenna for gain enhancement in millimeter-wave frequency. A suspended transmission line method has been taken into account for the characterization of the EBG structure to obtain the optimum design. Furthermore, the optimum unit cell, which is loaded onto the antenna, is easy to implement and allows obtaining a Peak Gain of 9.79 dBi in 26 GHz band.
SCYLLA: QoE-aware Continuous Mobile Vision with FPGA-based Dynamic Deep Neural Network Reconfiguration

Continuous mobile vision is becoming increasingly important as it finds compelling applications which substantially improve our everyday life. However, meeting the requirements of quality of experience (QoE) diversity, energy efficiency and multi-tenancy simultaneously represents a significant challenge. In this paper, we present SCYLLA, an FPGA-based framework that enables QoE-aware continuous mobile vision with dynamic reconfiguration to effectively address this challenge. SCYLLA pre-generates a pool of FPGA design and DNN models, and dynamically applies the optimal software-hardware configuration to achieve the maximum overall performance on QoE for concurrent tasks. We implement SCYLLA on state-of-the-art FPGA platform and evaluate SCYLLA using drone-based traffic surveillance application on three datasets. Our evaluation shows that SCYLLA provides much better design flexibility and achieves superior QoE trade-offs than status-quo CPU-based solution that existing continuous mobile vision applications are built upon.
Test Data Generation for MC/DC Criterion using Reinforcement Learning

Unit testing focused on MC/DC criterion is essential in development of safety-critical systems. However design of test data that meet the MC/DC criterion needs detailed manual analysis of branching in units under test by test engineers. To deal with this problem we propose a new test data generation approach based on reinforcement learning, which utilize analogy with a game, in which a gamer, the test engineer, plays in an environment, a unit under test, and tries to achieve the highest possible reward, MC/DC coverage. We evaluated our approach for two different granularity levels, test suite and test case, and for two different action types allowed to the gamer, discrete and continuous action spaces. Preliminary results shows that the proposed approach could solve path explosion problem of symbolic approaches and that the proposed approach achieves at least comparable results to the current state-of-the-art search-based test data generation approaches.
Enhancing CNN Based Knowledge Graph Embedding Algorithms Using Auxiliary Vectors: A Case Study of Wordnet Knowledge Graph

Knowledge graphs (KGs) have been utilized by various business fields. One example is Google that stores data in knowledge graphs for searching and retrieval tasks. Even though these graphs have reached an impressive size, they are far from completeness. Missing relations in knowledge graphs is a severe problem for algorithms that operate over knowledge graphs. There are many researchers trying to develop knowledge graph embedding methods so that they can handle different types of relations. ConvKB is one of the knowledge graph embedding methods that utilize convolution neural networks (CNN). However, this method lacks the ability to handle symmetric relations. Being inspired by this limitation, we would like to enhance this method by proposing ConvKB+, which is obtained by modifying ConvKB’s CNN structure and introducing an additional relation vector. Our experiment results show that our method outperforms ConvKB by achieving higher MRR on some symmetric relations of the WN18RR dataset.
Life history evolution in response to changes in metapopulation structure in an arthropod herbivore

The persistence and dynamics of populations largely depends on the way they are configured and integrated into space and the ensuing eco-evolutionary dynamics. We manipulated spatial and temporal variation in patch size in replicated experimental metapopulations of the herbivore mite Tetranychus urticae and followed evolutionary dynamics over approximately 30 generations. A significant divergence in life history traits, physiological endpoints and gene expression was recorded in the spatially and spatiotemporally variable metapopulation, but also a remarkable convergence relative to the stable reference metapopulation in traits related to size and fecundity and in its transcriptional regulation. The observed evolutionary dynamics are tightly linked to demographic changes, more specifically frequent episodes of resource shortage that increased the reproductive performance of mites on tomato, a challenging host plant. This points towards a general, adaptive stress response in stable spatial variable and spatiotemporal variable metapopulations that pre-adapts a herbivore arthropod to novel environmental stressors.
Factor analysis of heart rate variability in healthy people

Well established heart rate variability measures were found for nocturnal heart rate recordings of healthy people at different age. By exploratory factor analysis - the automatic method of data analysis, these measures were found to be determined by five factors. Moreover, each studied measure shown to be driven strongly by one factor only. The resulting five groups of HRV measures may be identified with the physiological phenomena commonly assumed to be involved in the heart rate regulation: (1) activity of vagal nervous system, (2) activity of the sympathetic nervous system, (3) balance in autonomic nervous system, (4) mechanisms of homeostasis - maintaining the system stability, and (5) humoral activity.
Muscle Synergy-Based Control of Human-Manipulator Interactions

One challenge of designing robot-assisted control is to identify and estimate human intention force and motion under dynamic disturbances. We present a robotic control design to help human in manipulator-assisted upperlimb movement applications. The control design uses a muscle synergy-based neural network method to predict human force and intention motion. A disturbance observer-based controller is designed to eliminate the influence of disturbances and allows human operators to achieve task by using their normal efforts. The control design takes advantage of predicted human force and intention motion to provide proper assistance in the human-manipulator interactions. Human subject experiments are presented to demonstrate the control robustness and performance of disturbance rejection. The comparison results with a benchmark controller also confirm that the proposed design provides manipulator-assisted capability to save human effort when there are additional loads and unknown disturbances.
Impacts of Observational Learning and Self-regulated Learning Mechanisms on Online Learning Performance: A Case Study on High School Mathematics Course

The online learning has been recognized as useful learning vehicles. In order to enhance students learning, many strategies were provided to navigate students in the online learning environment. In particular, exposing students in observational learning (OL) or Self-regulated learning (SRL) are both helpful for students. Nevertheless, there is a strong relationship between OL and SRL abilities. The influence of the OL and SRL remains unclear. To deal with this issue, this study aims to identify the effects of the OL and SRL on students’ online learning performance by using a 2X2 factorial design. The results of this study indicated that the influence of OL did not reach the significance level while to lead students to self-regulate their learning plays an important role in improving online learning performance.
BLE2LoRa: Cross-Technology Communication from Bluetooth to LoRa via Chirp Emulation

Wireless Personal Area Network (WPAN) technologies (e.g., Bluetooth, ZigBee) have been widely used in our daily life. However, due to their short transmission distances, the delivery of urgent messages (e.g., emergency alarms) over long distance, suffers from long delay, since the messages have to be transported via multi-hop way. Recent studies show that adding low-power wide-area network (LPWAN) radios such as LoRa onto WPAN devices (e.g., Bluetooth) effectively overcomes the limitation. However, the introduce of heterogeneous communication inevitably incurs extra hardware cost, deployment inconvenience and traffic overhead from gateways. In this paper, we present BLE2LoRa, a novel Bluetooth Low Energy (BLE) to LoRaWAN cross-technology communication (CTC) approach, which leverages the frequency shifting ability of BLE device to emulate LoRa's chirp signal. Such emulation is feasible because a chirp is a signal whose frequency increases or decreases over time, while a BLE device can also construct specific signals with ladder-shaped frequencies which is similar to LoRa chirp by carefully selecting BLE payload bits. Therefore, without any hardware modification at both sender and receiver side, a LoRa device can demodulate BLE frames. Moreover, leveraging the high sensitivity of LoRa base station, a long distant CTC can be achieved. We bulid our BLE2LoRa prototype on USRP B210 (with LoRaWAN PHY) and commodity BLE chips (CC1200). Our evaluation reveals that BLE2LoRa can achieve 4.06kbps throughput from BLE to LoRa with more than 80% frame reception rate, leading to over 600 meters communication distance, which is over 20x range extension over native Bluetooth.
Isolation and differentiation of mouse embryonic stem cells

Background: Recently, embryonic stem (ES) cells have become very important resources in basic medical researches. These cells can differetiate into derivatives of all primary germ layers. Objectives: In order to isolate embryonic stem cells in vitro, the blastocyst were cultured and the morphological aspects, population doubling time, alkalin phosphatse and differentiation properties of the cells were investigated. Materials and Methods: The balstocysts from NMRI mice were cultured for 3 days up to time that inner cell mass (ICM) reach to the outgrowth stage. The cells were disaggregated and trypsinized every 3 days until the appearance of the colonies of ES cells. The colony positive cells were fixed and stained for alkaline phosphatase. The ES cells were cultured in suspension state for 5 days, at the same time Leukaemia Inhibitory Factor (LIF) was removed from media to form embryoid bodies(EBs). The EBs were cultured for 8 - 20 days on collagen coated dish to induce the spontaneouse differentiation. Results: During the 6-9 days after the disaggregation of ICM in the expansion stage, the colony of ES cells appeared as a flat monolayer mass with strike boundaries and nondistinguish cytoplasm including a few nuclei. In colony formation stage, the morphology changed from flat monolayer to round multilayer with strike define boundaries. Undifferentiated cells were seen as intensely small cells attached together compactly with high nucleus/cytoplasm (N/C) ratio. The cells of colonies tend to differetiate by separation from each other and became larger and diffused on substrate by attaching to dish. The positive alkaline phosphatase cells were seen in typical morphology of ES colonies. The EBs cells were seen in culture after 5 days in suspension and began to spontaneously differentiate into various types of cells such as nerve and hematopoitic lineages. Conclusion: Despite strike morphology of ES colonies, it is difficult to distinguish the differentiated from undifferentiated cell colonies in the colony formation stage. New ES cells are capable to give rise into EBs and are susceptible of spontaneously differentiation in various type of cells.
Plating-free Bumping by Cu Nanopaste and Injection Molded Solder (IMS) for Fine Pitch Flip Chip Joining

We have newly developed a plating-free bumping process by Cu nanopaste with injection molded solder (IMS) technology. In the new process, Cu pillars are fabricated by filling photoresist openings with Cu nanopaste followed by pressure-free Cu sintering. The Cu nanopaste in the photoresist opening forms a unique cup-shaped Cu pillar after the sintering process due to the evaporation of solution in the paste and to the aggregation of Cu nanoparticles. Molten solder is injected, by the IMS technology, into the space created by the cup-shaped Cu pillar in the photoresist opening. The photoresist layer is then stripped for solder bump formation. In this paper, we demonstrated the fabrication of 60 μm pitch and 33 μm diameter bumps, and of 80 μm pitch and 46 μm diameter bumps by new bumping process. Mechanical integrity of the bumps was confirmed by bump shear testing. Reliability of the joints was also confirmed through preconditioning test, temperature cycling test, high temperature storage test, or highly accelerated stress test. Thermomechanical analysis of 2D/2.5D packages using the joints with sintered Cu nanoparticles was conducted.
Postoperative Modifications of Cardiovascular Control and Baroreflex Sensitivity in Patients Undergoing Surgical Aortic Valve Replacement

Time and frequency domains indexes derived from spontaneous fluctuations of heart period (HP) and systolic arterial pressure (SAP) and baroreflex sensitivity (BRS) are usually exploited to assess the state of the autonomic control. Surgical aortic valve replacement (SAVR) features postoperative complications, such as atrial fibrillation, that can be favored by an impairment of cardiovascular autonomic control. We computed HP and SAP variability markers in time and frequency domains and BRS via transfer function analysis in seven patients one day before (PRE) and 7 days after (POST) SAVR surgery. Indexes were assessed at rest in supine position (REST) and during active standing (STAND). No autonomic regulatory response to STAND was detected either in PRE or POST sessions. In POST at REST a remarkable depression of BRS indexes was observed compared to PRE, thus suggesting an increased risk for postoperative adverse events and prompting for the search of suitable countermeasures.
Rely/Guarantee Reasoning for Noninterference in Non-Blocking Algorithms

Noninterference characterizes a security property in which an attacker cannot determine the inputs to a system based on outputs of a lower classification. Value-dependent noninterference enables the analysis of systems in which these classifications may depend on the system’s state and evolve throughout execution. Existing approaches to enforcing such a property for concurrent systems are constrained in their capability to express how the concurrent components modify shared variables and, therefore, the value-dependent classifications. Such approaches typically make use of externally verified annotations or coarse locking primitives to express limited constraints on variables, such as read and write permissions. Consequently, these techniques are insufficient for the analysis of programs that feature complex concurrent behaviours or require fine-grained synchronisation, as seen in non-blocking algorithms.This paper presents a compositional logic for enforcing value-dependent noninterference properties for complex concurrent algorithms, including non-blocking algorithms. It uses rely/guarantee reasoning to establish how classifications may be modified by concurrent components. Additionally, the logic allows for the specification of security policies at a component level and ensures their valid composition. These results have been formalised in Isabelle/HOL.
Compact Via-Coupling Fed Monopulse Antenna With Orthogonal Tracking Capability in Radiation Pattern

In this letter, a compact monopulse antenna operating at 10 GHz is presented. The feed network consists of four rat-race couplers, which generate sum and difference patterns in orthogonal radiation planes (azimuth and elevation). The focus is on improving the performance by improving the matching circuit to generate in-phase and out-phase excitations while also maintaining a very high null-depth, which is applicable for tracking purposes. The electromagnetic energy is transmitted from the feed network to the radiators through the via-coupling structure. Using the symmetrical via-fed patch array results in the low-mutual coupling between the antenna ports. A prototype of the proposed antenna is manufactured on a printed circuit board and is tested. A suitable agreement between the measurement and simulation results is obtained. The results show a −10 dB impedance bandwidth of 2.1%, as well as −24 and −20 dB null depths on the elevation and azimuth planes, respectively, in the difference mode of operation.