Miracle-dz/UniFAR-FTUs huggingface dataset:
A Bowel Sound Detection Method Based on a Novel Non-speech Body Sound Sensing Device

Bowel sounds are the sounds produced by intestinal peristalsis and can reflect intestinal activities. There is a long history of using bowel sounds as indicators for intestinal health in clinical practices, making bowel sound detection potentially useful as a key enabling technology for health care applications. Traditionally, bowel sounds are detected manually by medical staff. However, manual inspections are time consuming and prone to personal differences. In modern hospitals, advanced medical devices are used to detect the patients’ intestinal activities with rich sensing modalities. However, these devices are often invasive, expensive and large in size, making them only used in limited, critical scenarios. To facilitate bowel sound detection and further intestinal activity analysis in daily living, this paper presents a wearable system to capture and recognize users’ bowel sounds and other types of body sounds without restricting their daily activities. We propose to use dual-channel, stethoscope augmented microphones to capture users’ body sounds. With the captured audio stream, we extract both time- and frequency-domain features to represent signals in each frame. Beyond traditional audio features such as energy and MFCC, we further extract dual-channel features to augment the information obtained from a frame. We evaluate the system’s performance with four subjects in real-world settings, the results suggest our system is effective for achieving a detection accuracy of 85.7%.
An Improved Dynamic Collaborative Filtering Algorithm Based on LDA

Currently, available collaborative filtering (CF) algorithms often utilize user behavior data to generate recommendations. The similarity calculation between users is mostly based on the scores, without considering the explicit attributes of the users with profiles, as these are difficult to generate, or their preferences over time evolve. This paper proposes a collaborative filtering algorithm named hybrid dynamic collaborative filtering (HDCF), which is based on the topic model. Considering that the user’s evaluation of an item will change over time, we add a time-decay function to the subject model and give its variational inference model. In the collaborative filtering score, we generate a hybrid score for similarity calculation with the topic model. The experimental results show that this algorithm has better performance than currently available algorithms on the MovieLens dataset, Netflix dataset and la.fm dataset.
Integrating Heterogeneous Security Knowledge Sources for Comprehensive Security Analysis

With the fast growth of system complexity, it is increasingly difficult to comprehensively analyze security of such large-scale systems, which is a knowledge-intensive task. Although there are various available security knowledge sources, they are not well-connected with each other due to their heterogeneity and unstructured descriptions. In this paper, we propose a systematic approach to construct a comprehensive and reusable knowledge graph in the field of information security. Specifically, we first investigate heterogeneous security knowledge sources and establish a detailed ontology of information security, integrating various security conceptual models. Then, we train a security entity identifier based on active learning to extract security knowledge from unstructured descriptions. Such extracted knowledge is then fused to establish a comprehensive and reusable security knowledge graph based on the unified ontology. Finally, we illustrate the utility of our established knowledge graph with a set of exemplary queries and reasoning rules in the context of a real security scenario.
Neutral lipid production by the yeast Debaryomyces hansenii NCYC102 under different stress conditions

Oleaginous yeasts are very efficient in the accumulation of triacylglycerol, and are expected to be one of the most important feedstocks for the biofuel industry in the future. Lipid content can be enhanced through physiological stress or genetic manipulation. Debaryomyces hansenii NCYC102 was selected from three different yeast species (also including Yarrowia lipolytica NCYC476 and Cryptococcus curvatus NCYC2904) due to the highest neutral lipid content. The growth rate, osmolytes and neutral lipids were measured in cells grown under different concentrations (0, 0.8, 1.6 M) of NaCl. The maximum content of total osmolytes was found in 0.8 M NaCl YM medium. However, the highest level of glycerol was measured in 1.6 M NaCl grown cells. The main osmolytes identified by 1H NMR spectroscopy were glycerol, arabitol, glucose and trehalose. Debaryomyces. hansenii cells were grown in minimal medium with different carbon/nitrogen ratios using either glucose or glycerol as the sole carbon source along with ammonium sulphate as nitrogen source. Maximal neutral lipid production was observed in 48:0.5 glucose/ammonium sulphate ratio which achieved 1.4-fold increase compared with glycerol-based medium (8 glycerol: 0.25 ammonium sulphate). GC-MS analysis of the transesterified fatty acids showed that palmitic, oleic and stearic acids were the main fatty acids present, under normal and stress conditions (high salt and limited nitrogen source). Deletion of the GUT2 encoding for G3P dehydrogenase increased neutral lipid production up to a 1.4-fold compared to wild type strains. The mutant strains displayed slightly higher cell densities in medium with glucose when compared with wild type strains, while they failed to grow on glycerol as a sole carbon source. Collectively, these results indicate that D. hansenii is a good organism to produce biofuels as it has an intrinsic ability to accumulate neutral lipids and this can be further enhanced by genetic and metabolic engineering.
A Comparative Study of Machine Learning Classifiers for Electric Load Disaggregation based on an extended NILM dataset

The appliance evaluation and the power consumption consciousness are becoming essential for improving demand management and power grid enhancement. Load disaggregation becomes a promising engine for this goal, and some researches efforts have been made in the last years. In this sense, achieving the load characterization is essential to the technique’s success; moreover, the proper feature extraction becomes essential. In this way, this paper presents a comparative study of machine learning classifiers for electric load disaggregation using an enhanced version of a household appliance dataset proposed by Souza et al. of Brazilian appliances (NILMbr). The load characterization is performed through the Conservative Power Theory, a recent power theory that extracts appliance signatures by means of power quantities. Then, it is proposed three machine learning models to validate proper load identification, being: classification algorithms - kNearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest (RF). These algorithms were used to assess computational time and performance metrics. Subsequently, the RF algorithm presented the best performance, with an accuracy of 99.5%.
Massive Figure Extraction and Classification in Electronic Component Datasheets for Accelerating PCB Design Preparation

Before starting printed-circuit-board (PCB) design, it is usually very time-consuming for PCB and system designers to review a large amount of electronic component datasheets in order to determine the best integration of electronic components for the target electronic systems. Each datasheet may contain over hundred figures and tables, while the figures and tables usually present the most important electronic component specifications. This paper categorizes various figures, including tables, in electronic component datasheets, and proposes the ECS-YOLO model for massive figure extraction and classification in order to accelerate PCB design preparation process. The experimental results show that, compared with the state-of-the-art object detection model, the proposed ECS-YOLO can consistently achieve better accuracy for figure extraction and classification in electronic component datasheets.
Controlling structural, chemical and electronic properties of single molecules on surfaces

With the prospects of realizing electronic circuitry and machinery consisting of a single-molecule, the control of the molecular characteristics down to the atomic level is essential. In this thesis, fundamental investigations of structures, electronic properties and diffusion of molecules on surfaces are presented. The experiments are performed by means of scanning tunneling microscopy (STM) and atomic force microscopy (AFM) at low temperature in ultra-high vacuum. The control of elaborated structures is achieved by studying on-surface chemical reactions and spray-deposited molecules by high-resolution AFM imaging with CO terminated tips. To mimic a p-n junction, a donor-acceptor (DA) molecule on a thin insulating film is probed to disentangle its structure, electronic states and charge redistribution. Interestingly, the DA character of the molecule is altered at specific atomic positions, and thus controlled by inducing a change of the adsorption site by tip manipulation. This consists of an example of a molecular switch. In this line, tip manipulation experiments are pushed forward during the NanoCar Race by steering a single molecule over the surface using tunneling electrons. The results of this thesis demonstrate the impact of the molecular design on electronic and adhesion properties influencing the performance of such molecular systems.
Adaptive Event-Based Non-Fragile Output Pinning Synchronization Control for Complex Networks With Random Saturations and Cyber-Attacks

This paper addresses the adaptive event-triggered non-fragile output pinning synchronization control for complex networks subject to random saturations and cyber-attacks. An adaptive event-triggered scheme (AETS) based on the output synchronization error is proposed to save network bandwidth, and a pinning control strategy is employed to reduce the input of control signal. Considering the effect of AETS, randomly occurring saturations and cyber-attacks on the drive and response systems, we investigate a novel output security synchronization error model, and then design an event-triggered non-fragile controller such that the asymptotic stability of the error system can be guaranteed. Meanwhile, we obtain the controller gains and event-triggered matrices in terms of solving the linear matrix inequalities (LMIs). Finally, a simulation example is provided to verify the design method.
Accurate Characterization of Electromagnetic Band-Gap Structures

In this paper, electromagnetic band-gap (EBG) structures are accurately characterized using a free-space method. The field transmission and reflection parameters are exploited to determine the effective material parameters both inside and outside the band-gap regions of the EBG structures. The electromagnetic (EM) properties of the effective refractive index, effective wave impedance, effective permittivity, and effective permeability at multiple band-gap regions are derived from a single scattering computation. The derived phase and attenuation constants correctly define the boundaries of the band-gap region and the level of attenuation within the band gap. Deviations in the incidence angle from the normal direction alter the EM properties; hence, the operating bandwidth of the band-gap region is as expected due to the inherent structure anisotropy of the unit cells.
Traffic Engineering Based on Reinforcement Learning for Service Function Chaining With Delay Guarantee

Network Function Virtualization (NFV) is an approach that provides a network service provider with agility and cost-efficiency in managing 6G network services. Standard traffic engineering rules are known limited in assuring a very stringent delay requirement in NFV when a traffic flow is required to follow a sequence of network functions scattered in data center networks. This paper proposes an innovative model and algorithm of traffic engineering for service function chaining (SFC) to maximize cost-efficiency under a delay-guarantee constraint. We first formulate the problem as a mixed-integer linear programming model for obtaining the optimal solution. We then propose an algorithm based on the reinforcement learning principles for finding an approximation solution in a large-scale problem with the dynamics of service demands. Numerical results under both real-world datasets and synthetic network topologies demonstrate that our proposed model and algorithm allow an NFV service provider (NSP) to place a virtual network function and steer a traffic flow efficiently in terms of energy cost for a delay-guarantee SFC. Importantly, the results provide an insight into the optimal and approximation solutions for an NSP to select a suitable traffic engineering approach with regard to network dynamics.
Output feedback controller design for Quadratic Cost Minimization for Linear Systems with Polytopic Uncertainties

This paper proposes a new robust static output feedback control design considering the linear quadratic regulator (LQR) problem based on Linear Matrix Inequalities (LMIs). The output static feedback controller can be used when all the state variables are not available for feedback. This way, the controller design can solve practical problems in which there are no sensors for all state variables of the plant. Furthermore, the presented robust control methodology minimizes an upper bound of a quadratic index (guaranteed cost) related to the output and the control signal of the uncertain closed-loop linear system. Through the designer’s knowledge of the system to be controlled, it is possible to obtain optimized performances. In order to find the best guaranteed cost related to the performance of the system, an algorithm of differential evolution for global optimization was used. The controller was implemented in a bench scale earthquake simulator and the results illustrate the effectiveness of the proposed methodology. In the implementation, a signal fault is assumed, and even in the presence of fault occurrence, the oscillations are attenuated by the proposed robust control.
Kinesthetic Sensing for Peg-In-Hole Assembly Based on In-Hand Manipulation

This study proposes a new way of estimating the hole direction to reduce the search time deviation in peg-in-hole assembly. To perform a peg-in-hole assembly, the hole position has to be found. In our previous studies, we found the hole using a blind search method. The blind search method moves the peg in a predefined path until it finds the hole. However, using blind search, it is difficult to predict the peg-in-hole assembly completion time because hole position search time is different each time. This is because the peg moves along the same predefined path without considering the hole direction. Furthermore, if the direction of the hole is known, the hole search time deviation can be reduced compared to when the direction of the hole is not known at all. Therefore, we propose a new way of finding the hole direction using in-hand manipulation based on ‘kinesthetic sensing’. Additionally, we introduce the concept of ‘Free-Finger’ for in-hand manipulation. The proposed method uses fingers to find the hole direction and perform the peg-in-hole assembly. Hence, we can implement peg-in-hole assembly regardless of the performance of the manipulator. The feasibility of the proposed method was verified through experiments using a three-fingered gripper.
Positron Image Super-Resolution Using Generative Adversarial Networks

Positron images generated by positron non-destructive testing technology under rapid detection scenes such as low concentration dose, low exposure time and short imaging time, which have some problems like low-resolution and poor definition. These issues cannot be solved for the being time. To solves these problems, this research super-resolves the low-resolution positron images to generate images with high-resolution and clear details. To make the generated super-resolution images more capable of restoring the features of low- resolution images, this research proposed a positron image super-resolution reconstruction method based on generative adversarial networks. In order to improve the input information utilization rate, long skip connections were added into the generator. In addition, the discriminant model, where composed of an image discriminator and a feature discriminator, can stimulate the generator to generate clearer super-resolution images which contain more details. In attempting to solve the problem of dataset matching, a special positron image super-resolution dataset is constructed for network application scenarios. In the adversarial training stage, perceptual similarity loss and adversarial loss are used to replace the traditional mean squared error loss to improve the images perception quality. Experimental results show that the proposed model can reconstruct low-resolution images by four times super-resolution in 0.16 seconds. The super-resolution images obtained are superior to other algorithms in visual effect, which have clearer detail structure and higher objective performance values. Hence this model can meet the requirements of rapid non-destructive testing of industrial parts.
Design and Characterization of ADM-based Dual-band SIW Bandpass Filter

This paper deals with the design and characterization of dual-band substrate integrated waveguide (SIW) bandpass filter (BPF) developed based on artificial dielectric material (ADM). The SIW structure is implemented using dielectric substrates flanked by metal layers on the top and bottom sides whereby these metal layers are connected by array of conductor wires called as vias which resemble sidewalls of a waveguide. While the ADM structure is composed of a dielectric substrate incorporated by array of conductor wires in a specific configuration. Both SIW and ADM structures are designed and developed using an FR4 Epoxy dielectric substrate with the relative permittivity of 4.4. Parametric studies are applied upon both structures to obtain an optimum performance of the filter. The characterization result shows that the dual-band SIW BPF configured by a two-stage ADM in face-centered orthorhombic configuration could achieve the bandwidth responses of 150 MHz and 215 MHz with the center frequencies of 1.55 GHz and 2.375 GHz, respectively.
A Centralized and Dynamic Network Congestion Classification Approach for Heterogeneous Vehicular Networks

Network congestion-related studies consist mainly of two parts: congestion detection and congestion control. Several researchers have proposed different mechanisms to control congestion and used channel loads or other factors to detect congestion. However, the number of studies concerning congestion detection and going beyond into congestion prediction is low. On this basis, we decide to propose a method for congestion prediction using supervised machine learning. In this paper, we propose a Naive Bayesian network congestion warning classification method for Heterogeneous Vehicular Networks (HetVNETs) using simulated data that can be locally applied in a fog device in a HetVNET. In addition, we propose a centralized and dynamic cloud-fog-based architecture for HetVNET. The Naive Bayesian network congestion warning classification method can be applied in this architecture. Support Vector Machine (SVM), K Nearest Neighbor (KNN) and Random Forest classifiers, which are popular methods in classification problems, are considered to generate congestion warning prediction models. Numerical results show that the proposed Naive Bayesian classifier is more reliable and stable and can accurately predict the data flow warning state in HetVNET. Moreover, based on the obtained simulation results, applying the proposed congestion classification approach can improve the network’s performance in terms of the packet loss ratio, average delay and average throughput, especially in the dense vehicular environments of HetVNET.
Particle-Based Simulation of the Differential Detectors for Mobile Molecular Communication

In this work, a particle-based simulation of two differential detectors for a reactive receiver in mobile molecular communication (MMC) is presented. The received signal is filtered and used for detection. On-off keying (OOK) is used as the modulation in both schemes. The detector in first scheme finds two-time instants in the filtered signal to maximize absolute concentration difference within the same bit duration. Then the concentration difference is used as the decision metric. The second scheme uses Manchester coding at the transmitter where the bit-0 and bit-1 are represented as the symbol [0 1] and symbol [1 0] respectively. The difference between the peak values of filtered signals in successive bit durations is used as the decision metric. Correlation between the analytical signal and particle-based signal is also found. Further, the bit error rate (BER) of the proposed detection schemes is calculated using the analytical signal and validated using the particle-based signal. Simulation results show a good agreement between the BER obtained using the analytical signal and the particle-based signal.
External drivers of BOLD signal’s non-stationarity

A fundamental challenge in neuroscience is to uncover the principles governing how the brain interacts with the external environment. However, assumptions about external stimuli fundamentally constrain current computational models. We show in silico that unknown external stimulation can produce error in the estimated linear time-invariant dynamical system. To address these limitations, we propose an approach to retrieve the external (unknown) input parameters and demonstrate that the estimated system parameters during external input quiescence uncover spatiotemporal profiles of external inputs over external stimulation periods more accurately. Finally, we unveil the expected (and unexpected) sensory and task-related extra-cortical input profiles using functional magnetic resonance imaging data acquired from 96 subjects (Human Connectome Project) during the resting-state and task scans. Together, we provide evidence that this embodied brain activity model offers information about the structure and dimensionality of the BOLD signal’s external drivers and shines light on likely external sources contributing to the BOLD signal’s non-stationarity.
The Impact of Fast Radiation on the Phylogeny of Bactrocera Fruit Flies as Revealed by Multiple Evolutionary Models and Mutation Rate-Calibrated Clock

True fruit flies (Tephritidae) include several species that cause extensive damage to agriculture worldwide. Among them, species of the genus Bactrocera are widely studied to understand the traits associated to their invasiveness and ecology. Comparative approaches based on a reliable phylogenetic framework are particularly effective, but, to date, molecular phylogenies of Bactrocera are still controversial. Here, we employed a comprehensive genomic dataset to infer a robust backbone phylogeny of eleven representative Bactrocera species and two outgroups. We further provide the first genome scaled inference of their divergence using calibrated relaxed clock. The results of our analyses support a closer relationship of B. dorsalis to B. latifrons than to B. tryoni, in contrast to all mitochondrial-based phylogenies. By comparing different evolutionary models, we show that this incongruence likely derives from the fast and recent radiation of these species that occurred around 2 million years ago, which may be associated with incomplete lineage sorting and possibly (ongoing) hybridization. These results can serve as basis for future comparative analyses and highlight the utility of using large datasets and efficient phylogenetic approaches to study the evolutionary history of species of economic importance.