Miracle-dz/UniFAR-FTUs huggingface dataset:
Adversarial attacks and defenses on neural network whale classifiers

Surface and underwater ocean vessels have a need to quickly and accurately detect and identify sound patterns for species monitoring, obstacle avoidance, or identification of other vessels. This can be done by transforming audio into spectrogram images and processing them with convolutional neural network (CNN) models, taking advantage of recent advancements in image classification. While CNNs can achieve high classification performance over ideal data, recent work has shown that image classification models are susceptible to adversarial attacks, nearly-imperceptible image perturbations that significantly degrade model accuracy, precision, and recall. This project investigates the capabilities of a publicly available CNN, AlexNet, on a binary classification problem to detect whale presence from single-channel audio. The audio samples are Fourier transformed into a power spectral density array and up-sampled to fit the model dimensions of AlexNet, achieving ~93% accuracy. The trained models were then tested with perturbed images from multiple white-box attacks (with access to model output and model gradients during inference), and black-box attacks (with access to only model output). These attacks severely degraded the performance of the models. To combat this, various data preprocessing-based defenses were proposed and implemented, along with combinations of defenses. It was found that targeted, computationally intensive defenses improved accuracy significantly, while universal, cheaper defenses such as low-pass filtering could generalize better, defending against multiple types of attacks. Future work would involve expanding our models to classify multiple species of marine mammals and generating more realistic attacks on the raw audio.
Bt-GAN: Generating Fair Synthetic Healthdata via Bias-transforming Generative Adversarial Networks

Synthetic data generation offers a promising solution to enhance the usefulness of Electronic Healthcare Records (EHR) by generating realistic de-identified data. However, the existing literature primarily focuses on the quality of synthetic health data, neglecting the crucial aspect of fairness in downstream predictions. Consequently, models trained on synthetic EHR have faced criticism for producing biased outcomes in target tasks. These biases can arise from either spurious correlations between features or the failure of models to accurately represent sub-groups. To address these concerns, we present Bias-transforming Generative Adversarial Networks (Bt-GAN), a GAN-based synthetic data generator specifically designed for the healthcare domain. In order to tackle spurious correlations (i), we propose an information-constrained Data Generation Process (DGP) that enables the generator to learn a fair deterministic transformation based on a well-defined notion of algorithmic fairness. To overcome the challenge of capturing exact sub-group representations (ii), we incentivize the generator to preserve sub-group densities through score-based weighted sampling. This approach compels the generator to learn from underrepresented regions of the data manifold. To evaluate the effectiveness of our proposed method, we conduct extensive experiments using the Medical Information Mart for Intensive Care (MIMIC-III) database. Our results demonstrate that Bt-GAN achieves state-of-the-art accuracy while significantly improving fairness and minimizing bias amplification. Furthermore, we perform an in-depth explainability analysis to provide additional evidence supporting the validity of our study. In conclusion, our research introduces a novel and professional approach to addressing the limitations of synthetic data generation in the healthcare domain. By incorporating fairness considerations and leveraging advanced techniques such as GANs, we pave the way for more reliable and unbiased predictions in healthcare applications
Engaging Ourselves in Library Work: A Narrative Inquiry on Employee Engagement and Performance of LGBTQ+ Library Employees

: This study aims to investigate the professional experiences of LGBTQ+ library employees, focusing on their work engagement and performance. It further examines how job resources, leadership, inclusion, management support, work-life balance, coworker support, recognition, and reward affect the work engagement, disengagement, and performance of LGBTQ+ library employees. The qualitative study employs a narrative approach to data collection and analysis, using in-depth interviews to elicit rich, detailed narratives of LGBTQ+ library employees' experiences. The findings of this study can benefit libraries by enhancing the engagement and performance of their LGBTQ+ employees, thereby adding to their success, productivity, and growth as an institution. This study has a managerial implication for creating a supportive and inclusive work environment that fosters engagement and performance among LGBTQ+ library employees.
A new perspective on variation of voriconazole steady-state valley concentration in Chinese population: CYP2C19 DNA methylation

Background: Voriconazoleis(VRC) often used in complex therapeutic environments for the treatment and prevention of invasive fungal infections. The steady-state valley concentration (Cminss) of VRC not only varies between individuals, but also within individuals, which is diﬃcult to fully explain by pharmacogenomic theory. It is necessary to propose a new perspective to explain the variation of voriconazole steady-state valley concentration. Objectives: Based on the regulation of ADME gene expression by DNA methylation, this study aimed to explore the eﬀect of CYP2C19 DNA methylation level on the VRC Cminss. Methods: In this study, 116 concentration points were divided into low concentration group (Cminss < 1.0mg/L), standard concentration group (Cminss =1.0-5.5mg/L) and high concentration
Improving Eﬀectiveness of Seamless Redundancy in Real Industrial Wi-Fi Networks

Reliability and determinism of Wi-Fi can be tangibly improved by means of seamless redundancy, to the point of making this technology suitable for industrial environments. As pointed out in recent papers, the most beneﬁts can be achieved when no phenomena can simultaneously aﬀect transmissions on all channels of a redundant link. In this paper, several aspects are analyzed which, if not properly counteracted, may worsen seamless redundancy eﬀectiveness. Eﬀects they cause on communication have been experimentally evaluated in real testbeds, which rely on commercial Wi-Fi devices. Then, practical guidelines are provided, which aim at preventing joint interference through a careful system design. Results show that measured communication quality can be made as good as expected in theory.
Dynamic Assembly of Problem Frames

This paper addresses the support of modular, compositional and incremental analysis and design of software systems by the assembly of problem frames. We use coordination-based techniques to put in place an architectural layer in which, for each subproblem, we provide a description of the machine and the way it is interconnected with the components of the problem domain to fulfill given customer requirements. Composition in this architectural layer is dynamic in the sense that it is not constrained to follow pre-established decomposition structures; instead, it allows fully incremental development. The architectural layer provides a basis on which we will be able to support reconfiguration of the system at execution time by the addition or substitution of new machines or new problem domain components resulting from new requirements.
Fast Integration of Poisson Distributions for Dead Sensor Marginalization

Abstract. In dual-phase time-projection chambers there are photosensor arrays arranged to allow for inference of the positions of interactions within the detector. If there is a gap in data left by a broken or saturated photosensors, the inference of the position is less precise and less accurate. As we are unable to repair or replace photosensors once the experiment has begun, we develop methods to estimate the missing signals. Our group is developing a probabilistic graphical model of the correlations between the number of photons detected by adjacent photosensors that represents the probability distribution over photons detected as a Poisson distribution. Determining the posterior probability distribution over a number of photons detected by a sensor then requires integration over a multivariate Poisson distribution, which is computationally intractable for high-dimensions. In this work, we present an approach to quickly calculate and integrate over a multidimensional Poisson distribution. Our approach uses Zarr , a Python array compression package, to manage large multi-dimensional arrays and approximates the log factorial to quickly calculate the Poisson distribution without overflow.
Parameter tuning of new type energy virtual synchronous generator based on physics-informed reinforcement learning

In the backdrop of the new type power system, the prevalence of new energy generation equipment and power electronic devices is rising. However, traditional power system analysis and parameter tuning methods are inadequate for this changing scenario. To address this, the paper proposes a reinforcement learning framework based on physical information guidance to adjust virtual synchronous generator parameters. This novel approach bridges the gap between traditional methods’ applicability and effectiveness in the new power system. It also improves interpretability by combining artificial intelligence with physical models. The method’s feasibility and efficacy are verified through IEEE-39 bus system simulation experiments.
An Online OCV Calibration-Based Adaptive SOC Estimation Approach for Lithium Battery

With the continuous development of electric vehicles and renewable energy, the rapid and accurate estimation of the State of Charge (SOC) of lithium batteries is crucial for energy storage applications. Traditional Open Circuit Voltage (OCV) measurements, which require prolonged battery rest, are not applicable during battery operation. Therefore, Ampere-Hour Integration is commonly adopted to estimate the battery SOC in real-time during charging and discharging processes. However, this method is prone to cumulative errors, making it challenging to accurately estimate SOC during prolonged battery operation. To address this issue, an adaptive SOC estimation method based on online OCV calibration is proposed in this paper. By establishing the equivalent circuit model (ECM) and combining the experimental test data, the functional relationship between the calibration voltage and the battery resting time is obtained to compensate for the terminal voltage measurement error caused by the battery polarization reaction. Then by using the calibrated OCV, combined with the OCV-SOC relationship obtained from the experimental test, the difference fitting method is used to realize the OCV-SOC online calibration. Based on the SOC estimation results after online OCV calibration, the K-value adaptive mathematical model is established to correct the accumulated error during Ampere-hour integration. Experimental validation and simulation analysis demonstrate the effectiveness of the proposed method in compensating for OCV measurement errors caused by battery polarization and reducing cumulative errors in Ampere-hour integration.
Wildforest Fire Detection with ShRe-Xception Network on Aerial Optical and Infrared Images

It is commonly accepted that wildforest fires are one of the major natural disasters worldwide. This destruction has a crucial impact to fire fighters and civilian lives, animal extinction, and forest degradation. This work focuses on preventing potentially extensive disasters by developing a detection system that can classify images of forests in "Fire" and "non-Fire" cases with input from both RGB and Infrared (IR) cameras. Our design uses both an existing small – architecture of Xception network (small-arch Xception) and the proposed Short Recursive (ShRe)-Xception. We first use transfer learning on the small-arch Xception network with input from either RGB or IR images. We also train the proposed ShRe-Xception network by using all images from the very beginning. Datasets used for training and testing purposes contain "Fire" and "non-Fire" images of forests that are captured from UAVs and uploaded in IEEE portal. When testing the above models, the estimated accuracy was RGB: 77.72% / IR: 96.83% after transfer learning on small-arch Xception, RGB: 84.86% / IR: 29.18% when initially training the small-arch Xception, and RGB: 90.10% / IR: 99.31% after initially training the ShRe-Xception model. This makes the latter model the most accurate one for images of both spectrums in our experiments. The ShRe-Xception can potentially play a vital and efficient role on the real-time fire detection during aerial surveillance on wild-forests.
Internet-Based Smartphone System for After-Stroke Hand Rehabilitation

Hand rehabilitation for after-stroke survived patients is crucial to be initiated very soon for good results in the recovery process. Traditional rehabilitation requires clinical supervision by a physiotherapist, so it can be costly, time consuming, and demotivating. This research suggests a low-cost online wearable hand rehabilitation system to support partially disabled patients with their hand rehabilitation. The proposed online system consists of two main parts, namely patient part, and therapist part. The patient part consists of a mobile phone app. and a Myo gesture control armband. The app. generates rehabilitation exercises and all other operations to help the patients do their exercises on their own with motivating graphics. Meanwhile, the Myo armband detects electromyographic (EMG) signals of patient's arm muscles and recognizes the gestures and movements of the hand. The system can classify these gestures and movements for correct and incorrect to track the patient's performance. On the other hand, the therapist part includes local performance and remote online monitoring portal for monitoring, analysis, and assessment of the patient's recovery progress. After completion of a given exercise, a performance report of the patient will be given locally and sent to the monitoring portal for assessment by the physiotherapist. The system was tested by healthy subjects to prove its efficiency with accuracy of 96.9%. The results showed a smooth performance and compatibility between the main two parts of the system.
ZVS Boundary Analysis and Design Guideline of MV Grid-Compliant Solid-State Transformer for DC Fast Charger Applications

A solid-state transformer (SST) comprising a cascaded H-bridge and a dual active bridge (DAB) converter is a promising solution for a megawatt medium-voltage dc fast charger application. The new IEEE Std 1547.9-2022 comprehensively discusses extending the minimum reactive power capability to electric vehicle chargers. This article analyzes the impact of operating the grid compliant single-phase SST on the overall system. The impact of the dc-link voltage due to the single-phase implementation of the H-bridges on the DAB converter zero-voltage switching (ZVS) mode at light-load operation is highlighted. The system’s operational boundary is analyzed, which defines the reactive power capability limit while ensuring the DAB converter ZVS mode operation for the defined operating points. This ZVS mode boundary analysis is then used to develop a design guideline as part of the SST design process. The proposed guideline allows a simultaneous design of a dc-link capacitor and DAB inductance to ensure the ZVS mode for the defined operating points. It leads to dc-link capacitance reduction that offers cost- and footprint-savings. The proposed concept is validated through simulations and experimental results. Furthermore, a potential benefit analysis is provided to emphasize the effectiveness of the proposed concept. A supplementary video is included to showcase the system’s dynamic active and reactive power operation.
2017). Serum C-reactive protein increases the risk of venous thromboembolism: a prospective study and meta-analysis of published prospective evidence. European Journal of Epidemiology

Evolving debate suggests that C-reactive protein (CRP) might be associated with the development of venous thromboembolism (VTE); however, the evidence is con-ﬂicting. We aimed to assess the prospective association of CRP with VTE risk. C-reactive protein was measured in serum samples at baseline from 2420 men aged 42–61 years, from the Kuopio Ischemic Heart Disease study. Within-person variability in CRP levels was corrected for using repeat measurements of CRP taken 11 years after baseline. Incident VTE events (n = 119) were recorded during a median follow-up of 24.7 years. The age-adjusted regression dilution ratio for log e CRP was 0.57 [95% conﬁdence interval (CIs): 0.51–0.64]. In age-adjusted Cox regression analysis, the hazard ratio (95% CIs) for VTE per 1 standard deviation (SD) increase in log e baseline CRP was 1.17 (0.98–1.40). Further adjustment for several established and emerging risk factors did not alter the association. In a meta-analysis of nine population-based studies (including the current study) comprising 81,625 participants and 2225 VTE cases, the fully-adjusted risk estimate for VTE was 1.14 (1.08–1.19) per SD increase in log e baseline CRP. In a pooled dose–response analysis, a linear association between CRP and VTE risk was suggested ( P for nonlinearity = 0.272). The pooled risk estimate for VTE per 5 mg/l increment in CRP levels was 1.23 (1.09–1.38). C-reactive protein was only modestly associated with VTE risk in the primary analysis. Pooled evidence, however, suggests that elevated CRP is associated with greater VTE risk, consistent with a linear dose–response relationship.
Design of Cascade Power Electronic Transformer Based Flexible Interconnection System for Distribution Network

With large number of grid-connected distribution photovoltaics, charging piles, the traditional distribution network faces challenges in power quality and power supply capacity. This paper proposes a flexible interconnection scheme based on cascade power electronic transformer for distribution network. The comparison of traditional power transformer and power electronic transformer is presented firstly. Then, the design of cascade power electronic transformer based flexible interconnection system is illustrated, including topology, control, and system scheme. Finally, experimental validation is provided.
Bucket-Level Elastic Cuckoo Filter Based on Consistent Hashing with High Memory Efficiency

Approximate Membership Query (AMQ) filter is a group of probabilistic data structures, such as Bloom filter and cuckoo filter, which supports approximate set membership queries under constant memory budget and time cost. It has numerous applications in real-world scenarios, such as connection tracking and IP blacklisting for network management and security. However, dynamic datasets are pervasive in practice, with the number of elements fluctuating significantly over time. This requires an AMQ structure to support the run-time memory reallocation for dynamic set representation. Although previous works exist that enhance the cuckoo filter to be memory elastic, most of them only support coarse-grained memory expansion at the filter level or the mini-filter level. We propose a new AMQ filter named BECF (Bucket-level Elastic Cuckoo Filter) that allows fine-grained expansion and shrinkage at the bucket level. BECF combines cuckoo filter with consistent hashing technique, which one-to-one maps the buckets of a cuckoo filter to the segments of a hashring. To allow the number of segments to increase dynamically, BECF splits an arbitrary segment into two child segments, and adopts a segment re-addressing technique that borrows extra bits from element fingerprint to identify the child segment prefix. We also minimize the data movement between the parent and the child segments (or buckets), and preserve all the good properties of cuckoo filter after the expansion. Our experiments show that BECF attains 37% lower memory cost (while holding the same number of elements), 12% higher insertion speed and 20% faster query speed than other designs.
A Study of the Man and Unmanned Teaming System for Improving Efficiency in a Fulfillment Center

In fulfillment centers, efficient inbound transportation and goods storage are crucial factors that impact overall performance and supply chain costs. Traditional processes often involve human workers performing repetitive tasks, leading to increased expenses. This study presents a Manned-Unmanned Teaming (MUM-T) approach that combines unmanned Automated Guided Vehicles (AGVs) with manned forklift vehicles to automate these processes and minimize costs. The primary objectives are to model AGV-based unmanned inbound transportation, design a manned traveling forklift problem (TFP) with a shortest path algorithm, and compare the MUM-T approach to traditional methods in terms of distance, time, and cost. Results from theoretical analysis and simulations show that the MUM-T approach can reduce traveling distance, working hours, and operational costs by up to 32%, 38%, and 51%, respectively. Moreover, the proposed algorithm enables Beginner and Intermediate-level forklift operators to achieve efficiency comparable to that of Professionals. These findings indicate that implementing the MUM-T approach can significantly enhance the efficiency and cost-effectiveness of inbound transportation and forklift processes in fulfillment centers.
Disco: A Framework for Dynamic Selection of Multipath Congestion Control Algorithms

Many mobile devices are usually equipped with multiple interfaces, providing the opportunity of using multipath transport protocols such as Multipath TCP (MPTCP) to boost performance. The multipath congestion control algorithm (CCA) in MPTCP plays a vital role in achieving high performance and multipath fairness in mobile environments where the paths are often heterogeneous and dynamic. Such environments are very challenging for existing one-size-fits-all CCAs to achieve high performance while ensuring multipath fairness. In this paper, we present a novel framework, Disco, to dynamically select the most appropriate CCAs for MPTCP subflows at runtime according to the perceived network condition. Extensive experiments show that compared with existing multipath CCAs, the proposed solution can improve the average throughput by 19% – 25% and reduce the average queuing delay by up to 21 % while it barely does harm to multipath fairness.
Bifrost: Extending RoCE for Long Distance Inter-DC Links

RDMA over Converged Ethernet (RoCEv2) has been widely deployed to data centers (DCs) for its better compatibility with Ethernet/IP than Infiniband (IB). As cross-DC applications emerge, they also demand high throughput, low latency, and lossless network for cross-DC data transmission. However, RoCEv2's underlying lossless mechanism Priority-based Flow Control (PFC) cannot fit into the long-haul transmission scenario and degrades the performance of RoCEv2. PFC is myopic and only considers queue length to pause upstream senders, which leads to large queueing delay. This paper proposes Bifrost, a downstream-driven lossless flow control that supports long distance cross-DC data transmission. Bifrost uses virtual incoming packets, which indicates the upper bound of in-flight packets, together with buffered packets to control the flow rate. It minimizes the buffer space requirement to one-hop bandwidth delay product (BDP) and achieves low one-way latency. Real-world experiments are conducted with prototype switches and 80 kilometers cables. Evaluations demonstrate that compared to PFC, Bifrost reduces average/tail flow completion time (FCT) of inter-DC flows by up to 22.5%/42.0%, respectively. Bifrost is compatible with existing infrastructure and can support distance of thousands of kilometers.