Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Dermoscopy-Based Skin Lesions Diagnosis: A Fusion of Convolutional Neural Networks and Self-Training

Automatic classification of skin lesions from der-moscopic images is a challenging task due to the similarities in visual features across different lesion types, such as size, shape, texture, and colour. Additional challenges include the presence of artefacts, limited data availability, and overfitting issues associated with deep learning architectures. This research aims to address these challenges by proposing a computer-aided diagnosis (CAD) system that accurately classifies skin lesions using both a publicly available ISIC 2019 challenge dataset and a private dataset. The study introduces three main tasks to achieve this objective. Task 1 focuses on improving data quality by addressing imbalanced class distribution, missing values, and der-moscopic multi-resolutions. Task 2 involves developing the CAD system using transfer learning with a pretrained Noisy Student (EfficientNet-L2) architecture, incorporating additional metadata through a dense neural network concatenated with the CNN output. The classifier, consisting of eight units representing skin lesion classes and an unknown class, follows. The proposed NS-EfficientN et- L2 model effectively classifies skin lesions, surpassing state-of-the-art methods, including the winner of the ISIC 2019 challenge for skin lesion classification. This research provides a promising approach for accurately diagnosing skin lesions based on dermoscopic Images.
Research of Public News Headlines’ Bias in the Context of New Media: Using “Weibo” Hot Search Headlines as a Case Study

In recent years, the criteria for creating popular search headlines on Weibo have made it a socially controversial source of information. Using content analysis as the primary research method, this study seeks to systematically dissect the presentation of news topics within Weibo’s popular search headlines. Through a careful examination of news content, trends, and patterns, it aims to reveal the existence and dynamics of biases in news selection and presentation. Furthermore, this paper investigates the dissemination of public news and its complexity while exploring both internal and external factors contributing to deviations in the publication of public news headlines. It also strives to enrich our understanding of the significance of this platform and its potential impact on Chinese society. Insights gained from this research contribute to our understanding of the evolving patterns of news dissemination within the Chinese internet ecosystem. It underscores the importance of critically evaluating the role of digital platforms in shaping public discourse and information consumption, with a focus on bias issues and their broader societal implications.
Sign Language/Gesture Recognition on OOD Target Domains Using UWB Radar

Radar sensors have the advantages of penetrating obstacles to receive echo signals, detecting long distances, being sensitive to micromovements, being compact, lightweight, and portable, and working normally in a variety of lighting and weather situations. More importantly, the radar sensor’s echo signal does not contain any image or video data, thereby protecting the privacy of users and their personal information security. Radar-based gesture/sign language (SL) recognition technology can enable machines to comprehend the meaning of human hand movements and can be applied in a variety of fields, including human–computer interaction, information exchange, home care, and so on, which has significant research value and broad social benefits. Currently, recognition models driven by machine learning require prebuilt datasets. Therefore, the first stage is to design experiments for radar echo signal acquisition for gesture/SL motions in order to obtain sufficient sample data for driving model training. However, the endeavor to capture radar echo signals can only be conducted under specific environments and operating settings. When a gesture/SL recognition model is applied to never-before-seen application scenarios and operating settings, using sample data gathered in specific application scenarios and operating settings may impair generalization performance. Methods for increasing the model’s generalization performance under varied scenarios and operating settings must, thus, be researched, with the goal of enabling the recognition model to have strong generalization performance for scenes or operating settings that were not seen during training.
Investigation and Analysis of Heat Damage on Rice at Blossoming Stage in Anhui Province in 2003

According to the metrological data and cultivation technique,investigation on heat damage was carried out in Jianghuai areas of Anhui Province.It is indicated that continued higher temperature at blossoming stage caused the seed rate low.Furthermore,different degree damages were found at different blossoming stages in the same variety and different ears in the same field or different spikelets.It is concluded that the longer the blossoming period and high temperature overlapped the heavier the damage was,and different degree damages of seed rate might be related to the heat-endurance of different varieties.
Green Dueling Bandits

The dueling bandit model has been acknowledged as an efficient analytic tool for sequential decision-making problems with qualitative pairwise comparison. For example, the comparison of workers' completion quality of assigned tasks in crowdsourcing systems; user's ranking of recommended items in recommender systems. In dueling bandits, an agent uses pairwise comparisons of selected arms to balance the exploitation-exploration trade-off during the online learning of uncertainties. Despite the wide application of dueling bandits, their green implementation should also consider the non-neglectable energy costs for selecting arms, implying the green dueling bandit model. Particularly, it requires online control to optimize energy costs adaptively in the long run for sustainable system deployment. Therefore, we 1) employ online learning methods to learn the uncertainties via qualitative pairwise comparisons; 2) utilize online control techniques to guarantee a within-budget energy cost for the green real-world deployment. Accordingly, we propose a Green Dueling Bandit Learning (GDBL) algorithm to effectively integrate dueling bandit learning for the exploration-exploitation trade-off and online control for the optimization of energy costs. We prove that GDBL achieves a sublinear round-averaged regret while keeping the energy cost under budget. We conduct simulations to demonstrate the outperformance of GDBL over baselines.
Distribution, Habitat Use, and Size of Atlantic Sturgeon Captured during Cooperative Winter Tagging Cruises, 1988-2006

Declines in Atlantic sturgeon Acipenser oxyrinchus abundance in the early 1990s led the Atlantic States Marine Fisheries Commission (ASMFC) to prepare a mandatory fishery management plan. The principal management measures are fishery closure, bycatch assessment, and bycatch reduction in other ASMFC-managed fisher- ies (i.e., American shad Alosa sapidissima). To better understand Atlantic sturgeon geographic distribution and habitat use, as well as risk of bycatch, we examined off- shore distribution of Atlantic sturgeon based on incidental captures in winter tagging cruises conducted off the coasts of Virginia and North Carolina, including in and near extensive sand shoals adjacent to Oregon Inlet and Cape Hatteras. From 1988 to 2006, 146 juvenile Atlantic sturgeon were captured by bottom trawling in depths from 9.1 to 21.3 m. Numbers of Atlantic sturgeon captured and tagged in a given year ranged from 0 (1993, 1995) to 29 (2006). Atlantic sturgeon were encountered in 4.2% of tows, with
Communication-Efficient Privacy-Preserving Federated Learning via Knowledge Distillation for Human Activity Recognition Systems

Emerging Internet of Things (IoT) applications, such as sensor-based Human Activity Recognition (HAR) systems, require efficient machine learning solutions due to their resource-constrained nature which raises the need to design heterogeneous model architectures. Federated Learning (FL) has been used to train distributed deep learning models. However, standard federated learning (fedAvg) does not allow the training of heterogeneous models. Our work addresses the model and statistical heterogeneities of distributed HAR systems. We propose a Federated Learning via Augmented Knowledge Distillation (FedAKD) algorithm for heterogeneous HAR systems and evaluate it on a self-collected sensor-based HAR dataset. Then, Kullback-Leibler (KL) divergence loss is compared with Mean Squared Error (MSE) loss for the Knowledge Distillation (KD) mechanism. Our experiments demonstrate that MSE contributes to a better KD loss than KL. Experiments show that FedAKD is communication-efficient compared with model-dependent FL algorithms and outperforms other KD-based FL methods under the i.i. d. and non-i.i. d. scenarios.
An Evaluation of the Presentation of Acoustic Cues for Shorelining Techniques

The study explores various scenarios for providing shorelining information via spatial-audio auditory sensory augmentation with a view to assisting people who are blind or have low-vision. Shorelining is a strategy often associated with the use of the "white cane" and generally refers to path-following approaches that rely on contours of structures within a built environment. The aim is to support clear, logical paths of travel. In one approach, auditory shorelining cues are provided via a sequence of two auditory earcons rendered spatially along the shoreline and at the participant’s left or right side. In another approach, the auditory cues are rendered spatially as "gate posts" in between which the participant should walk. The rendering of the auditory earcons follows the participant’s path of travel so that at any given time, there is only ever a sequence of two auditory earcons that provides local shorelining information. The auditory earcons are rendered via two methods: (i) using loudspeakers appropriately placed along the path of travel and (ii) using binaural rendering of virtual speakers via smart glasses. We compared the performance of spatial-audio sensory augmentation with non-spatialized spoken language instruction. Participants’ performance was measured in terms of task accuracy, time, and walking behavior. The cognitive workload was measured using a self-reporting questionnaire. Results indicate that sensory augmentation provided via spatial-audio earcons can provide faster and smoother navigational assistance and guidance compared with spoken verbal instructions. The results also suggest that current binaural rendering using simple augmented-reality tool sets is not as robust as real sound in providing navigational guidance. Lastly, the "gate post" shorelining approach seems more effective than the left and right shorelining technique.
Effects of autonomic nervous system disorders on male infertility

The male reproductive functions are largely regulated by the autonomic nervous system. Male sexual behavior and fertility primarily depend on the normal function of the higher neural centers related to the autonomic nervous system, the hypothalamic–pituitary–gonadal axis, the autonomic nervous components within the spinal cord and spinal nerves, and certain somatic nerves in the pelvic floor. In this review article, we will summarize the role of the autonomic nervous system in regulating male reproductive capabilities and fertility, its impact on male infertility under abnormal conditions, including the role of drug-induced autonomic nervous dysfunctions on male infertility. The main purpose of this article was to provide an overview of the effects of autonomic nervous dysfunction on male reproductive function and shed light on the potential therapeutic target for male infertility.
Will DACA Recipients Return to Their Birth Countries If DACA Is Ended?

Executive Summary Will recipients of Deferred Action for Childhood Arrivals (DACA) leave the United States if a legal challenge succeeds in ending this important program? The fate of over 600,000 current DACA recipients may depend on answers to this question in one expert report and one online survey question, which a Texas District Court found to show “a quantifiable percentage” would leave. Had the District Court in its DACA ruling not credited these sources, Texas’ lawsuit could have collapsed, and DACA would persist. The issue of whether DACA recipients will leave if DACA is ended will matter as this case is litigated, most likely to be decided by the Supreme Court. Drawing on research on immigrant return, DACA recipients, survey questions, priming, and intention-behavior gap, this paper argues that this expert report and the single online survey question used by the District Court cannot support the inferences regarding the return of DACA recipients to their birth countries. Rather, relevant research points toward DACA recipients staying in the United States, even if DACA were terminated. The paper recommends offering DACA recipients and long term undocumented residents a short path to legal status and citizenship, reassessing whether these two sources can support the conclusions drawn by the District Court; and analyzing the benefits accruing from DACA to Texas and all Texans, including US citizen children of DACA recipients, in assessing the claimed injury to the state.
The effect of synthesis conditions and process parameters on aerogel properties

Aerogels are remarkable nanoporous materials with unique properties such as low density, high porosity, high specific surface area, and interconnected pore networks. In addition, their ability to be synthesized from various precursors such as inorganics, organics, or hybrid, and the tunability of their properties make them very attractive for many applications such as adsorption, thermal insulation, catalysts, tissue engineering, and drug delivery. The physical and chemical properties and pore structure of aerogels are crucial in determining their application areas. Moreover, it is possible to tailor the aerogel properties to meet the specific requirements of each application. This review presents a comprehensive review of synthesis conditions and process parameters in tailoring aerogel properties. The effective parameters from the dissolution of the precursor step to the supercritical drying step, including the carbonization process for carbon aerogels, are investigated from the studies reported in the literature.
Patterns of the loop current system and regions of sea surface height variability in the eastern Gulf of Mexico revealed by the self‐organizing maps

The Self-Organizing Map (SOM), an unsupervised learning neural network, is employed to extract patterns evinced by the Loop Current (LC) system and to identify regions of sea surface height (SSH) variability in the eastern Gulf of Mexico (GoM) from 23 years (1993–2015) of altimetry data. Spatial patterns are characterized as different LC extensions and different stages in the process of LC eddy shedding. The temporal evolutions and the frequency of occurrences of these patterns are obtained, and the typical trajectories of the LC system progression on the SOM grid are investigated. For an elongated, northwest-extended, or west-positioned LC, it is common for the LC anticyclonic eddy (LCE) to separate and propagate into the western GoM, while an initially separated LCE in close proximity to the west Florida continental slope often reattaches to the LC and develops into an elongated LC, or reduces intensity locally before moving westward as a smaller eddy. Regions of differing SSH variations are also identified using the joint SOM-wavelet analysis. Along the general axis of the LC, SSH exhibits strong variability on time scales of 3 months to 2 years, also with energetic intraseasonal variations, which is consistent with the joint Empirical Orthogonal Function (EOF)-wavelet analysis. In the more peripheral regions, the SSH has a dominant seasonal variation that also projects across the coastal ocean. The SOM, when applied to both space and time domains of the same data, provides a powerful tool for diagnosing ocean processes from such different perspectives.
THz Communications: A Key Enabler for Future Cellular Networks

Future cellular networks are expected to benefit from terahertz (THz) frequency bands, which enable high-speed and low-latency wireless connectivity, but several challenges have arisen in utilizing THz bands. In this paper, we first investigate channel characteristics and their modeling for THz bands, highlighting new opportunities and emerging challenges. In this context, we discuss the channel characteristics that become more prominent at THz frequencies than at lower microwave frequencies, such as atmospheric attenuation, scattering, and frequency selectivity. We also review various path-loss and scattering models for THz communications. We further survey the radio frequency front-end nonlinearities that affect THz communications and their modeling for multiple-input multiple-output (MIMO) systems. We then review massive MIMO and hybrid beamforming techniques, and their applicability for THz communications. We identify the challenges that arise from operating at ultra-high frequencies, such as beam blockage, power consumption of large-scale arrays, and beam squint. Finally, we provide an overview of emerging intelligent reflecting surface (IRS) technologies and their potential for enhancing THz wireless networks. We investigate how IRS and massive MIMO technologies can be integrated into THz wireless networks and discuss possible solutions to overcome the limitations that affect the throughput and efficiency of such systems.
Distribution of Thermokarst Lakes and Ponds at Three Yedoma Sites in Siberia

Thermokarst lake formation in ice-rich yedoma deposits in north Siberia has a major impact on regional landscape morphology, hydrology, and biogeochemistry. Detailed assessment of lake distribution characteristics is critical for understanding spatial and temporal lake dynamics and quantifying their impacts. The distribution of thermokarst ponds and lakes at three different sites with ice-rich permafrost (Bykovsky Peninsula, SW Lena Delta, and Cherskii) in northeast Siberia was analysed using high-resolution remote sensing and geographical information system (GIS) tools. Despite similarities in geocryological characteristics, the distribution of thermokarst lakes differs strongly among the study regions and is heavily influenced by the overall hydrological and geomorphologic situation as a result of past lake-landscape dynamics. By comparing our high-resolution water body dataset with existing lake inventories, we find major discrepancies in lake distribution and total coverage. The use of low-resolution lake inventories for upscaling of thermokarst lake-related environmental processes like methane emissions would result in a strong underestimation of the environmental impacts of thermokarst lakes and ponds in Arctic lowlands.
Long-term effects of erosion and climate interactions on corn yield

ABSTRACT: An accurate assessment of the impacts of soil erosion on corn production is needed for conservation and management programs. Climatic variability produces a range of crop yield responses on eroded and non-eroded soil profiles and often masks the long-term trends and differences. Short-term corn yield data from representative soils across the North Central region were supplemented with calculated yields from the NTRM model to develop long-term yield responses for the historical climate records at each of seven highly-managed rainfed sites across the region. Results showed that long-term average regional corn yields were reduced by 10% for severely eroded versus control plots. Till-derived soils showed yield reductions averaging 12%, while loess soils lost an average of 7%. Sensitivity runs with the NTRM model made after field validation with 119 plot-years of data suggested that changes in soil physical properties, especially plant-available water holding capacities, probably accounted for most of the yield differences. Mean growing season (May-September) precipitation across all sites for the short-term field study was 35.3 cm, compared with 36.3 cm for the long-term historical record. This resulted in a mean short-term corn yield reduction of 9% for severely eroded soils versus a reduction of 10% for the same soils based on long-term climate projections. However, the close agreement between the short- and long-term studies with regard to mean growing season precipitation and corresponding corn yields should not be used to make the general conclusion that 3 to 6 years is an adequate time period for field research on long-term crop response to eroded soil conditions.
Optimized Fog Colony Framework for Efficient Service Placement using Hybrid Genetic Algorithm

The transition from Cloud Computing to Fog Computing is being investigated as the preferred method for rapid data processing in Internet of Things (IoT) applications. Fog computing is a decentralized computing paradigm, harnesses the computational capabilities of IoT devices at the network edge to perform data processing and provide real-time services. However, effective service placement in fog environments remains a challenge. This research paper focuses on the lack of efficient techniques for organizing fog devices into optimal fog colonies that possess self-rule capabilities and require minimal energy for administration. We propose a hierarchical clustering-based fog colony architecture to fill this void, which will allow for in-depth investigation and analysis of various colony configurations. The resulting dendrogram includes all feasible colony configurations, and a genetic algorithm is used for precise colony selection based on fitness and accuracy. The multi-objective hybrid genetic algorithm NSGA-II is used to assess how efficiently the suggested framework performs under a variety of conditions. The goal is to reduce the delay between an app's initial interaction with a user and the initial practice of the app. The results show improvements in reaching predefined goals when the proposed strategy is applied to different contexts.
Federated Learning Based Trajectory Optimization for UAV Enabled MEC

We present a moving mobile edge computing architecture in which unmanned aerial vehicles (UAV) serve as an equipment, providing computational power and allowing task offloading from mobile devices (MD). By improving user association, resource allocation, and UAV trajectory, we optimizing the energy consumption of all MDs. Towards that purpose, we provide a Trajectory optimization technique for making real-time choices while considering all the situation of the environment, followed by a DRL-based Trajectory control approach (RLCT). The RLCT approach may be adapted to any UAV takeoff point and can find the solution faster. The FL is introduced to address the Optimization problem in a Semi-distributed DRL technique to deal with UAV trajectory constraints. The proposed FRL approach enables devices to rapidly train the models locally while communicating with a local server to construct a network globally. The simulation results in the result section shows that the proposed technique RLCT and FRL in the paper outperforms the existing methods” while the FRL performs best among all.