Miracle-dz/UniFAR-FTUs huggingface dataset:
LGLog: Semi-supervised Graph Representation Learning for Anomaly Detection based on System Logs

Anomaly detection is an important task that improves the maturity and stability of a software during its development. System logs record rich information about the running states of the software and reveal key insights of anomalous behaviors. This paper addresses anomaly detection using system log data and aims to resolve two challenges: First, different from most existing supervised learning-based anomaly detection methods that rely heavily on expensive, manually-curated labels, we aim to design an algorithm to make the most of scarce label information. Second, as a typical software system would contain very few anomalies, we aim to address the data imbalance issue which is often overlooked by existing studies. To address the challenges above, we propose LGLog, a semi-supervised anomaly detection framework that is based on system logs. First, LGLog transforms log sequences into graphs and employs an unsupervised graph learning model for pre-training. Then, LGLog mitigates the data imbalance issue by learning significant latent space representation of log events via reconstruction loss and node invariance loss, and further applies a weight balance method. Experiments indicate that LGLog outperforms compared approaches, and demonstrates the effectiveness of LGLog in the presence of scarce labels and imbalanced log data.
Frequency and Time Domain EEG Analysis for Prognostication of Postanoxic Comatose Patients

As part of the George B. Moody PhysioNet Challenge 2023, our team (am_vision) presents a novel approach to prognosticate the outcomes of postanoxic comatose patients based on frequency domain and time domain features by using electroencephalogram (EEG) recordings. Frequency domain features include spectral entropy, power distribution, and dominant frequency. In the Time domain, zero crossing rate, energy, and entropy were calculated. We also utilized demographic information: Age, Sex, Return of Spontaneous Circulation (ROSC), Out-of-Hospital Cardiac Arrest (OHCA), Shockable Rhythm, and Targeted Temperature Management (TTM) to train XGBoost regressor and classifier to predict both the Cerebral Performance Category (CPC) score and the Outcome respectively. Our proposed method received a challenge score of 0.485 (ranked 18th out of 36), an outcome accuracy of 0.81 (ranked 4th out of 36), and an outcome F-measure of 0.769 (ranked 5th out of 36) on the test set.
Soft-Start with Hiccup Mode Short-Circuit Protection

Aiming at the voltage overshoot and output short-circuit problems of DC_DC switching power supply, a soft start circuit with hiccup mode short circuit protection is designed. Soft-start eliminates voltage overshoot caused by power-on, while short-circuit hiccup protection makes the system work safely and effectively. The structure proposed in this paper additionally realizes the short-circuit protection with “hiccup mode” in the traditional soft-start circuit, which saves the layout area. In addition, the circuit uses MOS capacitors instead of traditional MIM capacitors to further reduce the soft-start layout area. A DC_DC switching power supply chip is designed using 0.4 um BCD process to verify the proposed soft start circuit. The simulation results show that the power chip successfully realizes the soft start and short circuit protection mechanism. The layout area of the soft start is only 0.122 mm × 0.109 mm, and the power consumption is about 0.00612 mW.
Second-Order Analysis of Secret-Key Capacity From a MIMO Channel

This paper presents an analysis of Maurer-Ahlswede-Csiszar (MAC) bounds on secret-key capacity (SKC) based on a MIMO channel probing scheme between two multi-antenna nodes against a multi-antenna eavesdropper (Eve). To reveal useful insights into SKC, we consider the regime of high signal powers or high signal-to-noise ratios (SNR) where SKC consists of a first-order term (FoT) which is proportional to the logarithm of signal power, and a second-order term (SoT) which is invariant to signal power. Applying random-matrix theory for large numbers of antennas on all nodes, this paper shows the FoT and SoT of a lower MAC bound on SKC. While the FoT is consistent with prior work, which vanishes if none of the two nodes has more antennas than Eve and the channel is not perfectly reciprocal, the SoT shows a positive secrecy that increases linearly with the length of random transmissions from one node to another during channel probing even if Eve has many more antennas than the legitimate nodes.
BDGSE: A Symbolic Execution Technique for High MC/DC

Modified Condition/Decision Coverage (MC/DC) is a test coverage standard with excellent fault detection capability, which is widely used in testing safety-critical software. Symbolic execution generates test cases automatically to achieve high code coverage. However, since symbolic execution uses short-circuit evaluation to evaluate decisions, it fails to guarantee the consistency of other conditions in a decision required by the MC/DC criterion. As a result, it is incapable of generating MC/DC test cases to test safety-critical systems adequately. To solve this problem, we propose Branch Dependence Guided Symbolic Execution (BDGSE), a symbolic execution technique for high MC/DC. The approach utilizes static analysis to compute the branch dependencies, then guides symbolic execution to selectively explore paths and simplify test cases, and finally generates a small number of test cases to achieve high MC/DC coverage. Our experimental results show that BDGSE can generate high-quality test cases. Although BDGSE generates fewer test cases, it can achieve higher MC/DC coverage than SPF and random methods, and the fault detection capability of test cases generated by BDGSE is comparable to that of test cases generated by SPF.
Gastrointestinal helminths of semi - domesticated helmeted guinea fowl ( Numida meleagris ) under different management systems in Arua district , Uganda.

1 Department of Wildlife and Aquatic Animal Resources (WAAR), School of Veterinary Medicine and Animal Resources (SVAR), College of Veterinary Medicine, Animal Resources and Biosecurity (COVAB), Makerere University, P.O. Box 7062, Kampala. 2 Department of Biomolecular Resources and Biolab Sciences, School of Biosecurity, Biotechnical and Laboratory Sciences (SBLS), College of Veterinary Medicine, Animal Resources and Biosecurity (COVAB), Makerere University, P.O. Box 7062, Kampala, Uganda. 3 Office of the ViceChancellor, Muni University P.O. Box725Arua, Uganda.
A CNN-Based Automatic Detection Method for Tunnel Lining Internal Defect Using Ground Penetrating Radar

Regular tunnel lining internal damage (TLID) inspection is very crucial for the railroad operation safety. However, traditional manual identification methods using ground penetrating radar (GPR) images suffer from limited accuracy and efficiency. Fortunately, CNN-based automatic inspection methods have the potential to replace manual inspections, offering improved precision and productivity. In this paper, we propose a CNN-based detection model that builds upon the improved YOLACT framework for identifying defects in tunnel linings. To enhance feature extraction, we employ the Res2Net architecture as the backbone of our model. The overall architecture includes the FPN feature pyramid, prediction heads, protonet branch, as well as assembly and crop processes. Through experimentation, we compare the performance of our model when utilizing different backbone networks, namely Res2Net50, Res2Net101, ResNet50, and ResNet101. Our experiments, conducted on a GPR dataset, demonstrate that the improved YOLACT with Res2Net50 achieves a high detection rate and exhibits good adaptation capabilities for identifying internal damage in complex tunnel environments.
Analysis of the Free Vibration of Porous Functionally Graded Beams with Different Boundary Conditions and a Novel Two-Variable Model

This study introduces a new approach to the vibration analysis of porous functionally graded (PFG) beams by utilizing a two-dimensional elasticity solution. Polynomial and trigonometric series are used to enhance the kinematics of a beam with only two variables along the thickness and axial axes, respectively. The beams' characteristic equations are then derived using Lagrange's equations. Material characteristics, porosity, and boundary conditions all have an effect on natural frequencies, which is explored in this parametric study.
Using Consumer Camera and Custom Firmware to Monitor Heart Rate in Terminally Ill Children during Music Therapy

Photoplethysmography Imaging (PPGI) is considered a prospective non-contact replacement for existing methods of heart rate (HR) and HR variability monitoring. However, “in the wild” studies are rare. Music therapy for children with severe disabilities aims to alleviate pain and increase their well-being. As they are not able to communicate via spoken language, changes in facial expressions and vital signs may be used to quantitatively assess effects of music therapy. Here, cameras can be used to capture both, hints of the emotional expressions and, via PPGI, HR. From a technical perspective, the high variability in HR, the interaction with the therapist, as well as natural lighting form an ideal scenario to stress-test existing algorithms. We used a consumer camera with custom firmware to record raw videos of children during music therapy. We employ the often used pipeline of face detection, skin segmentation, pulse signal extraction and heart rate estimation. To enforce temporal coherence, we apply temporal filtering to the skin masks inside a 5-frame window. We evaluate the estimated HR using a reference ECG. For the full 30 minute therapy sessions we achieved a mean absolute / root-mean-squared error of 3.98/7.11 BPM. Finally, PPGI methods show promising results for music therapy and can provide accurate HR over a wide range.
Asymmetric Grouped Convolutions for Logarithmic Scale Efficient Convolutional Neural Networks

The design of convolutional neural networks has been increasingly focused on small and efficient models to meet the modern demands of edge devices. Thus, analyzing the theoretical limits of convolutional layers of previously unexplored complexities is critical. Here, we present a logarithmic-scale efficient convolutional neural network architecture. Our model is based on the well-known depthwise convolution, and on two new layers, which we introduce in this work: an asymmetric grouped convolution and a depthwise fast wavelet transform layer. By applying asymmetry in channel dimensions and applying a provably optimal fast algorithm we shrink the complexity of convolutional blocks by an $\mathrm{O}(\log D/D)$ factor (from $\mathcal{O}(D^{2})$ to $\mathrm{O}(D\log D))$, where $D$ is the number of channels. Experiments on CIFAR-10, CIFAR100 and ImageNet classification show superior/comparable performances of our model to classical strong baseline models such as MobileNetV2 and ShuffleNet. The new convolutional layers that we propose could serve a variety of applications, from designing efficient models by hand to augmenting the search space of AutoML architectures.
The Emergence of Kwahe’e Black-on-white pottery in the Tewa Basin, New Mexico

ABSTRACT Despite the importance of ceramic typologies for defining archaeological cultures and making chronological inferences, the ceramic types comprising these typologies are not always well dated. Here, we present an analysis of radiocarbon dates generated from Accelerator Mass Spectrometry of corn remains found in association with Kwahe’e Black-on-white (Kwahe’e B/w) ceramics from the Tewa Basin of the Northern Rio Grande region of New Mexico in the American Southwest. This autochthonous ceramic type is the first in a cohesive lineage of decorated white wares known as the Rio Grande ceramic series. A Bayesian analysis of these dates yielded a 95% probability range of a.d. 975–1023 for the beginning temporal boundary marking the emergence of Kwahe’e B/w. This estimate for the chronological emergence of Kwahe’e B/w is at least 27 years earlier than previously demonstrated and appears to coincide with the initial population increase in the Tewa Basin during the Late Developmental period (a.d. 900–1200), and with the emergence of the Tewa language that is still spoken in the region.
Ice thickness measurements and volume estimates for glaciers in Norway

Glacier volume and ice thickness distribution are important variables for water resource management in Norway and the assessment of future glacier changes. We present a detailed assessment of thickness distribution and total glacier volume for mainland Norway based on data and modelling. Glacier outlines from a Landsat-derived inventory from 1999 to 2006 covering an area of 2692 ± 81 km 2 were used as input. We compiled a rich set of ice thickness observations collected over the past 30 years. Altogether, interpolated ice thickness measurements were available for 870 km 2 (32%) of the current glacier area of Norway, with a total ice volume of 134 ± 23 km 3 . Results indicate that mean ice thickness is similar for all larger ice caps, and weakly correlates with their total area. Ice thickness data were used to calibrate a physically based distributed model for estimating the ice thickness of unmeasured glaciers. The results were also used to calibrate volume–area scaling relations. The calibrated total volume estimates for all Norwegian glaciers ranged from 257 to 300 km 3 .
Stacking of Novel 3D-Printed Hexagonal-Prism Plant Microbial Fuel Cells Growing Water Hyacinth (Pontederia Crassipes)

Plant microbial fuel cells (PMFCs) are bio-electrochemical systems that show promise in electricity generation via the plant-microorganism reactions occurring at the rhizosphere of the plant roots. The study aims to determine the scalability of this technology by the stacking of fifteen (15) 3D-printed hexagonal multi-anode/cathode cells growing water hyacinth (Pontederia crassipes); the cells are stacked to determine the extent of power amplification when multiple connections and configurations are employed. In a 30-day experiment, a branched series pattern generated the highest power (1.52 µW) and power density (5.34 µW/m2) among all arrangements. Voltage losses were more prevalent in cells stacked in series than in parallel. Power densities were much higher for cells stacked in series. Still, stacking cells in both series and parallel improved the power-generating capacity of PMFCs compared to an individual cell. Combining series and parallel connections in a clumped stack resulted in a much lower power density than the value generated by an individual cell. Differences were observed when cells were rotated to change the orientation of the connections, as switching from series to parallel caused a consistent deterioration in performance. Polarization tests revealed this clustered pattern to have the highest internal resistance, contributing to internal losses and decreased exerted power. It is hoped that the results of this study will have a significant contribution to the efforts of optimizing PMFC performance, particularly by stacking.
Stack Development and Power Generation Efficiency Analysis of 3D-Printed Plant Microbial Fuel Cells Growing Mung Beans (Vigna Radiata)

Plant microbial fuel cell (PMFC), a green energy source, is a bioelectrochemical system that converts rhizodeposits to bioelectricity. This paper investigated the stacking efficiency of PMFCs with USB connectivity using 3D-printed conductive PLA electrodes with Vigna radiata as the model plant. The stacking techniques successfully connected 16 PMFCs in pure and varying combinations of series and parallel. Stacking all cells in pure parallel obtained a maximum voltage of 98.4 mV. However, due to voltage reversal, cumulative stacking of PMFCs in pure series or parallel is inefficient and not an ideal strategy for scale-up. Further evaluation of the overall performance was done by stacking the cells in series-parallel and parallel-series configurations. The results revealed that stacking 6 (3P-S), 8 (4P-S), and 12 (4P-2S) cells in parallel-series correspondingly amplified the power and power densities of the system. Essentially, it was reported that stacking two sets of two parallel cells in series (2P-S) collected the highest power and power density of 0.42 µW and 13.76 µW/m2, respectively. Despite optimizing the power and power density, PMFC still faces a challenge in retaining the power and power density while connecting stacked cells. The scale-up potential of this system, the optimal power density of PMFCs could be improved by design optimization of electrodes and further analysis of the factors influencing the power generation capacity of this environmentally benign technology.
Time-Shift Coding for Uncoordinated MACs

In this work, we discuss time-shift coding and GRAND ZigZag decoding for uncoordinated multiple access channels (MACs). Time-shift coding relies on the delay difference of the received packets to prevent fully overlapped collisions. Users transmit with different time-shifts in designated time slots until the receiver successfully decodes all messages. At the receiver side, ZigZag decoding is employed to separate packets with linear complexity in noiseless scenarios. For cases with noise, a guessing random additive noise decoding (GRAND)-based algorithm is utilized to identify the most probable noise vector in conjunction with ZigZag decoding. Simulation results demonstrate that time-shift coding significantly reduces collision probabilities, thereby enhancing system throughput in noiseless scenarios. In the context of Gaussian MAC, the GRAND ZigZag decoding method outperforms successive interference cancellation (SIC)-based schemes in high signal-to-noise ratio (SNR) regimes.
Electrotonic Coupling Effect on Pharmacological Cardiotoxicity Assessment in Atrial Tissue

In the last decade, considerable efforts have been dedicated to changing the cardiac proarrhythmia safety paradigm. Novel electrophysiological models are developed to accurately describe the pharmacological response, aiming at assessing pharmacological cardiotoxicity in silico. This opens the potentiality of using mathematical models at the early stages of the manufacturing of new pharmacological compounds for an initial and effective cardiotoxic screening. The Courtemanche model has been leveraged to create a population of stable action potential models which later on have been clustered according to the most relevant anatomical atrial regions. Thus, pharmacological cardiotoxicity assessment has been performed going to simulate the electrophysiological behaviour under the pharmacological effect; firstly, ten drugs have been investigated on a model of isolated cells and then the same drugs have been employed in a model of atrial tissue introducing the electrotonic coupling. In this way, by making use of a metric, the aritmic risk score (ARS Risk Score), the pro-arrhythmicity of the chosen drugs has been assessed. Results show that the electrotonic coupling lowers the Aritmic Risk Score of all the drugs investigated, opening a new scenario of the pharmacological cardiotoxicity assessment.
Pioneer Learning: Discovering Blind Spots of Transferred Rich Visual Knowledge for Skin Lesion Recognition

Transfer learning has been widely applied in many tasks. However, due to the significant difference in the visual concepts of natural images and medical images, there exists huge gaps between the commonly used ImageNet dataset and the skin lesion datasets. The visual knowledge learned from the natural images may not always robust for modeling subtle lesion patterns from the lesion images or local regions of the lesion images. In this study, we propose a novel learning paradigm, called pioneer learning, to complement and augment the original transfer learning procedure effectively. In particular, we attempt to focus on the learning regarding the subtle lesion patterns. We tend to term these subtle patterns as lesion knowledge blind spots (LKBSs) of the pre-trained models. Specifically, we formulate a series of fast pioneer networks, each of which integrates the pre-trained model with a shallow convolutional neural network (CNN), to efficiently estimate whether a particular lesion image or a local image region belongs to the LKBSs. Subsequently, we delicately design an efficient LKBSs searching strategy to collect LKBSs from all evaluated images and regions carefully. Besides, to alleviate computational costs, we propose a granularity-based region sampling procedure for identifying the LKBSs. Lastly, by mixing the LKBSs with the target skin lesion dataset appropriately, we enable the pre-trained model to focus more on learning new visual concepts from the subtle lesion patterns: LKBSs. In practice, the proposed pioneer learning could improve the convergence speed and the recognition performance of the transferred pre-trained models promisingly. Our method achieves state-of-the-art performance on different skin lesion datasets without using any extra training data.
3D ISAR Image Reconstruction of Ground Vehicles Using 3D SL0 on Under-Sampled Aperture Data

This paper considers the three-dimensional (3D) reconstruction of inverse synthetic aperture radar (ISAR) imagery from under-sampled apertures using compressive sensing. Previously, we implemented a 2D version of the smoothed $\ell^{0}$ norm algorithm (2D SLO) on 3D data by creating a 2D data phase history matrix of frequency by aspect angle pairs. The advantage in this previous formulation is the ability to sense specific azimuth/elevation pairs. While the previous formulation includes a reduction in computer memory due to its use the the 2D SLO algorithm, here we have implemented a 3D SLO algorithm that greatly increases the computational efficiency by successive application of the ID SLO algorithm on individual dimensions. Results are provided using synthetic data for a scaled Chevrolet CK1500 pickup truck. We see that the 3D SLO algorithm maintained the integrity of the sparse signal reconstruction by successfully reconstructing nearly all of the dominant scattering centers, while being much faster than our previous approach due to significantly fewer matrix operations.
Quantifying the Gap between Open-Source and Vendor FPGA Place and Route Tools

The use of Field Programmable Gate Arrays (FP-GAs) has increased greatly as a result of their flexibility, power efficiency, and hardware acceleration capabilities. CAD tools needed to map hardware description language (HDL) code to the FPGA are complex and challenging to build. Open-source CAD tools for FPGAs have been developed to facilitate restriction-free customization and have more control over the mapping process. A significant milestone in the development of open-source CAD tools was the ability to target real commercial devices. In recent years, multiple academic CAD tools can map circuits to commercial FPGAs. It is essential to identify and quantify the performance gap between academic and vendor tools targeting commercial devices. To this effect, we compare relevant hardware quality metrics after placement and routing for five tool-flows targeting a commercial FPGA. Our results show the divide between academic and commercial place and route tools for device utilization, run time, and maximum circuit speeds.