Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating body condition of striped skunks using non-invasive morphometric indices and bioelectrical impedance analysis

Abstract Assessment of the body composition and body condition of animals in the wild is an important aspect of animal ecology. We were interested in understanding the seasonal changes in body condition in order to investigate the effects of adaptive strategies on life histories of animals. We investigated the merit of morphometric condition indices and bioelectrical impedance analysis (BIA) to predict fat reserves in striped skunks (Mephitis mephitis, n = 26). We took BIA and morphometric measurements following chemical immobilization in the field and completed chemical analyses of whole carcasses postmortem. We determined relationships between body lipid and water using multiple linear regression techniques and performed model selection using Akaike's Information Criterion (AIC). Bioelectric volume (L2/Rs) and chest circumference (CC) best predicted total body water (TBW=–1.1+0.058*CC+0.25 * L2/Rs; r2 = 0.91, SEE=0.15, P <0.001). Lean dry mass (LDM) was significantly correlated with total body mass (LDM=0.26+0.12 * TBM; r2=0.63, SEE=0.13, P<0.001). Total body fat (TBF) can then be estimated using total body water and lean dry mass by the equation TBF=TBM–TBW–LDM. We found condition indices to be poor indicators of body condition in striped skunks. Use of BIA on striped skunks has implications for a fast and accurate method to estimate body condition under field conditions.
Physical Implementation of Shift Register with respect to Timing and Dynamic Drop

In this paper, it is shown how partitioning, placement, and routing of an 8-bit shift register circuit using cadence tools done. The functionality of the shift register circuit is verified by writing a Verilog code and simulated using the Cadence NCLaunch tool. The RTL code is synthesized to convert the gate-level netlist using the Genus tool from Cadence. Reporting the power, area & timing of the gate-level netlist. The generated gate-level netlist is used for placement and routing in the physical design process. The placement and routing of the shift register circuit are carried out in the Cadence Innovus tool using the 180nm library file. Reporting power, area, and timing at each stage of Pre-CTS, Post-CTS, and post-routing of the circuit and GDS II file is generated. In Post-CTS the overall Power is abridged to 12.69mW, Area is 119.7504um^2 and timing is 9787ps.
Face Spoofing Detection Using Relativity Representation on Riemannian Manifold

Face recognition and verification systems are susceptible to spoofing attacks using photographs, videos or masks. Most existing methods focus on spoofing detection in Euclidean space, and ignore the features’ manifold structure and interrelationships, thus limiting their capabilities of discrimination and generalization. In this paper, we propose a relativity representation on Riemannian manifold for face spoofing detection. The relativity representation improves generalization capability while ensuring discriminability, at both levels of feature description and classification score. The feature-level relativity representation generalizes information by modeling interrelationships among basic features, and would not depend too much on characteristics of a particular dataset. The score-level relativity representation makes decisions relatively, not absolutely, according to interrelationships (via Riemannian metric) and competitions (via example reweighting) among data samples on Riemannian manifold. The discriminability is ensured by the high-order nature of the feature-level relativity representation as well as Riemannian reweighted discriminative learning of the score-level relativity representation. Moreover, we integrate an attack-sensitive SVM classifier in Euclidean space to improve spoofing detection. Experiments demonstrate the effectiveness of our method on both intra-dataset and cross-dataset testing.
Predicting Link Sign in Online Social Networks based on Social Psychology Theory and PageRank

Recently, the social networks are evolved as the primary platform for internet users to share their ideas and views. Social media interactions can be modelled as links between two users, where the link weight is different for different types of relationships and interactions. Social networks are dynamic in nature where the links are formed and deformed with time. Broadly, two types of connections, i.e. Positive and Negative can exist in most social networks. Positive connections signify trust and friendship, whereas negative connections show distrust and enmity. Reliable prediction of edge signs can greatly influence in enhancing user experience. Prediction of edge signs has been explored previously also. In this paper, the primary aim is to predict the sign of edges based on extracted features of nodes constructed upon theories of social psychology that includes classical balance theory, the status theory and emotional information theory. The ranking of nodes is used to extract features of nodes that quantify the popularity and trust of nodes on the network. These features are combined with the features extracted from socio psychological theories and try to improve the performance of our sign prediction model. Our results show that the proposed methodology achieves a reasonable performance when implemented on two real-life datasets namely Slashdot and Epinions.
Analyzing the FWM at Different Power Levels in the Fiber Optic DWDM System

To improve the capacity of an optical fiber communication system, the dense wavelength division multiplexing (DWDM) is used. “Dense” means it can bring the wavelength closer together than the wavelength division multiplexing system used before. DWDM's function is to combine and transmit the various signals simultaneously on the same fiber at different wavelengths. Within DWDM, the nonlinear effects are caused by the data rate increase, and the channel spacing decrease. FWM is the nonlinearity that affects the transmitted signal and the overall system performance. In this paper, the four wave mixing on the efficiency of DWDM-based optical fiber communication is examined at different power rates. This paper was designed to simulate the 16-channel dense multiplexing wavelength division (DWDM) method using the simulation software OptiSystem7.0. At the receiver side, the variations in the optical spectrum analyzer, BER, and the Quality factor are examined.
Multi-Objective Genetic Algorithm-Based Autonomous Path Planning for Hinged-Tetro Reconfigurable Tiling Robot

Reconfigurable robots have received broad research interest due to the high dexterity they provide and the complex actions they could perform. Robots with reconfigurability are perfect candidates in tasks like exploration or rescue missions in environments with complicated obstacle layout or with dynamic obstacles. However, the automation of reconfigurable robots is more challenging than fix-shaped robots due to the increased possible combinations of robot actions and the navigation difficulty in obstacle-rich environments. This paper develops a systematic strategy to construct a model of hinged-Tetromino (hTetro) reconfigurable robot in the workspace and proposes a genetic algorithm-based method (hTetro-GA) to achieve path planning for hTetro robots. The proposed algorithm considers hTetro path planning as a multi-objective optimization problem and evaluates the performance of the outcome based on four customized fitness objective functions. In this work, the proposed hTetro-GA is tested in six virtual environments with various obstacle layouts and characteristics and with different population sizes. The algorithm generates Pareto-optimal solutions that achieve desire robot configurations in these settings, with O-shaped and I-shaped morphologies being the more efficient configurations selected from the genetic algorithm. The proposed algorithm is implemented and tested on real hTetro platform, and the framework of this work could be adopted to other robot platforms with multiple configurations to perform multi-objective based path planning.
SYMBIOTIC GERMINATION OF SOME AUSTRALIAN TERRESTRIAL ORCHIDS

SUMMARY In symbiosis tests on an agar medium with powdered cellulose as carbon source, isolates of several species of Tulasnella and of Ceratobasidium cornigerum were compared on seven species of Thelymitra and two each of Diuris and Pterostylis. The Pterostylis species were stimulated to germinate only by Ceratobasidium cornigerum, the Diuris species only by Tulasnella calospora but, in general, the Thelymitra species were stimulated by more than one species of Tulasnella, but not markedly by Ceratobasidium cornigerum. These results correlate well with specificities noted in isolations from the roots of mature orchids of these genera in nature. It was found that different isolates of Tulasnella calospora differed markedly in the efficiency with which they stimulated germination of the species of Diuris and Thelymitra. The most efficient isolates were not necessarily those isolated from these orchids.
A Neighbor-Guided Memory-Based Neural Network for Session-Aware Recommendation

Session-aware recommendation is a special form of session-based recommendation, where users’ historical interactions before the current session are available. Among the existing session-aware recommendation studies, recurrent neural network (RNN) is a popular choice to model users’ current intent of the ongoing session as well as their general preference implied in previous sessions. However, these RNN-based methods present limited memory so as to have difficulty in characterizing long-term behaviour. In addition, when modeling a user’s preference, existing studies mainly refer to his (her) own sessions, while ignore the collaborative information from sessions of other users that may share similar tastes. To tackle the above problems, we propose a neighbor-guided memory-based neural network (MNN) for session-aware recommendation task, which comprehensively considers users’ short-term intent, long-term preference and cross-sessions information to yield the final recommendations. Specifically, we design a long-term memory generator to capture users’ general preference from their historical sessions, meanwhile leverage the neighbor sessions of current session to obtain cross-session collaborative information. Furthermore, with the guidance of long-term memory and cross-session information, we employ a short-term memory generator to yield users’ ongoing intent, which serves as the dominating part of recommendation. Extensive experimental results on three real world datasets show the effectiveness of our model, with improvements up to 12.0% on 30MUSIC, 26.5% on NOWPLAYING and 13.5% on TMALL in terms of Recall@20, respectively. Analysis on current session length and the number of historical interacted items comprehensively demonstrates the superiority of our proposal on processing long sessions and modeling users’ long-term behaviour.
Wideband Filtering Dumbbell-Shaped Slot Antenna With Improved Frequency Selectivity for Both Band-Edges

A novel wideband filtering dumbbell-shaped slot antenna with improved frequency selectivity for both band-edges is proposed in this paper. The filtering slot antenna is composed of a dumbbell-shaped slot, two parasitic patches, eight slot-stubs and a feeding line with a pair of step impedance resonator (SIR) stubs. An additional resonance is generated to increase the bandwidth and a gain null is produced at the upper band-edge to improve the frequency selectivity by loading parasitic patches in the dumbbell-shaped slot. Eight slot-stubs adjacent to the dumbbell-shaped slot are etched to enhance the impedance matching. In addition, the bandwidth is further broadened and two gain nulls are realized at both band-edges by adding a pair of SIR stubs. Moreover, the gain nulls can be adjusted individually by the length of parasitic patches or SIR stubs. As a result, the gain curve of the proposed filtering slot antenna has good flatness and the frequency selectivity for both band-edges is significantly improved. A prototype has been manufactured and measured to verify the validity of the design. The measured impedance bandwidth of $\vert \text{S}_{11}\vert < -10$ dB is 62.5% (2.2 GHz - 4.2 GHz). High suppression levels of 26 dB and 30 dB can be achieved in the lower and upper stopbands, respectively.
Optical Fiber Biosensor Based on an Etched High-Scattering Fiber: Towards Reflector-Less Biosensors

We propose an optical fiber biosensor based on a minimalistic architecture, labelled ‘reflector-less biosensor“. The sensing unit is simply a high-scattering fiber (doped with MgO nanoparticles), wet-etched in a hydrofluoric acid bath. The Rayleigh scattering signatures are detected in reflection, achieving a sensitivity up to over 10 nm/RIU (refractive index units). The system has been functionalized for thrombin biosensing, achieving a detection of tens of nanomolar concentrations.
Using a Patch-Wise M-Net Convolutional Neural Network for Tissue Segmentation in Brain MRI Images

Accurate segmentation of brain tissues, such as gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF), in magnetic resonance imaging (MRI) images, is helpful for the diagnosis of neurological disorders, such as schizophrenia, Alzheimer’s disease, and dementia. Studies on MRI-based brain segmentation have received significant attention in recent years based on the non-invasive imaging and good soft-tissue contrast provided by MRI. A number of studies have used conventional machine learning strategies, as well as convolutional neural network approaches. In this paper, we propose a patch-wise M-net architecture for the automatic segmentation of brain MRI images. In the proposed brain segmentation method, slices from a brain MRI scan are divided into non-overlapping patches, which are then fed into an M-net model with corresponding ground-truth patches to train the network, which is composed of two encoder-decoder processes. Dilated convolutional kernels with different sizes are used in the encoder and decoder modules to derive abundant semantic features from brain MRI scans. The proposed patch-wise M-net overcomes the drawbacks of conventional methods and provides greater retention of fine details. The proposed M-net model was trained and tested on the open-access series of imaging studies dataset. The performance was measured quantitatively using the Dice similarity coefficient. Experimental results demonstrate that the proposed method achieves average segmentation accuracies of 94.81% for CSF, 95.44% for GM, and 96.33% for WM, meaning it outperforms state-of-the-art methods.
Deep Neural Network Based Speech Recognition Systems Under Noise Perturbations

Automatic speech recognition, which plays an important role in human-computer interactions, is the cornerstone of communication between human and smart devices. In the past few years, deep neural networks (DNNs) have been deployed in automatic speech recognition with great success. However, recent research has discovered that DNNs are not robust against small perturbations. In this work, we investigate the capability of noise immunity in various neural network models through the speech recognition task. When the noise is introduced into the original speech audio, our experimental results demonstrate that the phoneme error rate (PER) degrades as the signal-to-noise ratio (SNR) reduces across all evaluated neural network models. On the other hand, when the noise is introduced into the Mel-frequency cepstral coefficient (MFCC) features, the multilayer perceptron (MLP) network model outperforms all other recurrent neural network (RNN) models.
Comparative Framework for the Analysis of Thermal and Resource Management Algorithms for Multi-Core Architectures

On-chip multi-core architectures have emerged as a new paradigm for executing highly parallel and resource demanding applications. However, inefficient resource management and thermal issues in these multi-core architectures may lead to performance degradation. To cater for these issues, several resource and thermal management algorithms have been proposed. These algorithms are usually analyzed independently and under different environmental settings, which makes a fair comparison between them very difficult. In order to overcome this problem, this paper presents a generic framework for analyzing various resource and thermal management algorithms under similar settings. The proposed framework is developed using C++ and has been successfully used to compare mDTM and a variant of DsRem.
Isolation and Characterization of Antibiotic / Antitumor Producing Streptomyces.

A total of 500 Streptomycetes strains were isolated from twenty soil samples and screened for their antimicrobial activity. Only 56 isolates exhibited antimicrobial activity, of which two isolates showed a broad spectrum. They were identified phenotypically and genotypically as Streptomyces fluvissimus-FHM275 and Streptomyces pratensis-FHM572. MIC values of their crude extracts confirmed their antimicrobial potencies. Anticancer activities of the crude extracts were measured against human hepatocellular carcinoma cell line (HePG 2), human Caucasian breast adenocarcinoma (MCF 7), colon cell line (HCT 116) and lung carcinoma cell line (A549) as well as normal human epithelial amnion cells. The crude extract of St fluvissimus- FHM275 showed antitumor activity only against HCT116 and A549 with lC50 15.5, 15.3 µg/ ml and SI, 3.6 and 3.7, respectively; however, St. pratensis-FHM572 showed a wide spectrum of cytotoxic activity towards most tested human antitumor cell lines, its extract appears to be both the most potent and most selective against A549 (IC50 = 7.5 μg/ml, SI = 7.9).
Investigation of Basic Optical Properties of Tissue Phantoms Under 635 nm Low-Level Laser Irradiation

In this study, the optical properties of Zerdine® phantom such as transmittance, absorbance, reflectance, refractive index, and optical attenuation coefficient have been determined by using two different methods. One method is based on optical power measurements made with and without phantom with a 635 nm laser in the homogeneous medium, while the other method is based on the determination of basic optical properties with a single integrating sphere system. As a result, it was determined that the data obtained by using a single integrated sphere system were closer to the actual data. Furthermore, similar optical properties were also calculated for Agar phantom with a single integrating sphere system.
Characterization of Heart Rate Estimation Using Piezoelectric Plethysmography in Time- and Frequency-domain

The increasing need to monitor Heart Rate (HR) has led to the development of dedicated devices based on miniaturized sensors and to their consequent pervasiveness. In this paper the aim is to develop a DAQ based system supervised by a LabVIEW Virtual Instrument to estimate the HR by means of an accelerometric plethysmograph. Two different signal processing techniques have been developed to estimate the HR respectively in time- and frequency domain, to study and compare their performances and to monitor the patient's heartbeat at rest by identifying possible pathological conditions or diseases.
A real-time tracking system for tremor and trajectory estimation in Parkinson’s disease affected patients

The paper proposes a first step toward the realization of a low cost and scalable clinical device to monitor the evolution of Parkinson’s disease based on magnetic measurements. The main aim of the overall research project is the realization of an Internet-connected device able to allow the execution of standardized tests and to share results with specialized medical centers. At this stage, the system has been focused on two parameters useful for medical exams: tremor and hands trajectory. This paper describes the realized set-up based on fixed receiving coils and one mobile transmitting coil. It reports preliminary experimental results based on a robotic arm adopted as a reference. Obtained results clearly show the capability of the realized system to accurately estimate both the tremor frequency and the hand trajectory, even if high levels of tremor are applied.
Spatio-temporal Analysis of Retail Customer Behavior based on Clustering and Sequential Pattern Mining

In the era of big data, the availability of large amounts of spatio-temporal data provides a new path for customer analytics. However, the related research in the retail industry is underdeveloped. In this paper, we introduce spatio-temporal data mining into retail customer analytics and conduct experiments on large real-world data sets from a retail company containing millions of customer purchase records. Spatio-temporal clustering and a new hybrid sequential pattern mining method are used to discover the characteristics of customer behavior at the aggregation as well as the individual level. The typical spatial and temporal distribution of customers and main customer clusters are obtained. Some interesting sequential purchase patterns are also found. Our research will provide not only a new analytic framework for academia but also some guidelines for better development of marketing strategies in the retail industry.
A Method for the Estimation of the Timing of Heart Sound Components Through Blind Source Separation in Multi-Source Phonocardiography

Recently, phonocardiography (PCG) has gained importance as a diagnostic tool for cardiovascular diseases. In particular, the measurement of the time of occurrence of heart sounds may be of interest, in the clinical context, for the analysis of the electromechanical coupling of the heart. To date, though, there is no standardization concerning the positioning of the microphone probe over the chest, and this causes low accuracy and consistency in the measured timing values. Multi-source phonocardiography is a promising approach to face the stated issue. In this work, we present a methodology to estimate the latency of the components of the two main heart sounds towards the corresponding R-wave peak based on the Blind Source Separation (BSS) of the contributions of the left and right side of the heart. We tested our algorithm on a sample population of 12 subjects over 10-minute long recordings of three simultaneous PCG signals and one electrocardiographic (ECG) signal for reference. Results show that the approach is robust with respect to the usage of different algorithms to perform BSS (FastICA, JADE). The measured timing values are consistent with what measured by means of a single-source algorithm we previously developed. This methodology looks promising in terms of obtaining accurate measurements of the time of occurrence of heart sound components and may have an impact in the clinical context.
Preliminary experimental identification of a FEM human knee model

A customizable Finite Elements Model of human knee is proposed for improving inter-individual reproducibility in NSAIDs transdermal delivery measurement. The model simulates: (i) the measurement system, based on Bio-Impedance Spectroscopy, and (ii) the system under test, namely the knee by five parallel, homogeneous, and concentric layers: bone, muscle, adipose tissue, wet skin, and dry skin. In this paper, first the equations and the architecture of the model are described. Then, the results of the numerical characterization and the preliminary experimental validation are reported. A sensitivity analysis was realized for reducing computational burden during Model customization. Only five parameters out of the 64 used in the Cole-Cole equation were sufficient for fitting experimental data of different subjects.