Miracle-dz/UniFAR-FTUs huggingface dataset:
Laser and radiofrequency-induced hyperthermia treatment via gold-coated magnetic nanocomposites

Introduction The current radiofrequency ablation technique requires invasive needle placement. On the other hand, most of the common photothermal therapeutic methods are limited by lack of accuracy of targeting. Gold and magnetic nanoparticles offer the potential to heat tumor tissue selectively at the cellular level by noninvasive interaction with laser and radiofrequency. Methods Gold nanospheres and gold-coated magnetic nanocomposites were used for inducing hyperthermia to treat subcutaneous Ehrlich carcinoma implanted in female mice. Results In mice treated with gold nanospheres, tumors continued to grow but at a slow rate. In contrast, more than 50% of the tumors treated with gold-coated magnetic nanocomposites completely disappeared. Conclusion This simple and noninvasive method shows great promise as a technique for selective magnetic photothermal treatment.
Learning CRF potentials through fully convolutional networks for satellite image semantic segmentation

This paper introduces a method to automatically learn the unary and pairwise potentials of a conditional random field (CRF) from the input data in a non-parametric fashion, within the framework of the semantic segmentation of remote sensing images. The proposed model is based on fully convolutional networks (FCNs) and fully connected neural networks (FCNNs) to extensively exploit the semantic and spatial information contained in the input data and in the intermediate layers of an FCN. The idea of the model is twofold: first to learn the statistics of a CRF via a convolutional layer, whose kernel defines the clique of interest, and, second, to favor the interpretability of the intermediate layers as posterior probabilities through the FCNNs. The method was tested with the ISPRS 2D Semantic Labeling Challenge Vaihingen dataset, after modifying the ground truths to approximate the ones found in realistic remote sensing applications, characterized by scarce and spatially non-exhaustive annotations. The results confirm the effectiveness of the proposed technique for the semantic segmentation of satellite images.
SSH-BA: Safeguarding Smart Homes with Intelligent IoT Behavior Analysis Using Classification Techniques

Smart home technologies automate regular operations like temperatures and lighting reacting to voice commands, making life easier. Smartphones allow homeowners to remotely operate these devices and monitor and manage their homes in real time. Smart home devices use IoT to make life smarter, more effective, and more customized. Smart home IoT devices may be hacked to access security cameras and personal data. Attackers might manipulate smart locks or thermostats, threatening people and their property. These IoT devices are networked, making strong cybersecurity necessary to guard against emerging threats. Early cybersecurity identification enables quick reaction and remediation, limiting damage and averting attack escalation. Early detection of security breaches allows proactive actions to stop unwanted access, limit the effect, and protect critical data. It dramatically minimizes the window of vulnerability, improving cybersecurity resilience and safeguarding systems from protracted exploitation or criminal activity. This research improves smart home security by training Aposemat IoT-23 dataset. XGBoost, Simple Neural Network, GaussianNB and Logistic Regression are evaluated to identify and prevent harmful data injection and illegal access. The results show they protect smart home networks with over 99% accuracy. Smart home IoT device protection is excellent with machine learning techniques like XGBoost and Simple Neural Network. These algorithms identify and block unwanted access and harmful data injection.
Prognosis Model of The Treatment Period of Tuberculosis Patients with Medication Compliance Parameters using The Gradient Boosting Algorithm

According to the National Guidelines for Tuberculosis (TB) Control in Indonesia, patient discovery begins with finding suspected TB, then physical and laboratory examination, diagnosis, and determining the type of TB patient. After the final diagnosis, knowing the prognosis for a patient's treatment length is necessary. The duration of treatment for patients diagnosed with TB is at least six months. If it is known from the start that the prognosis for a patient's length of treatment exceeds six months, then the patient needs more supervision from the family or the Medication Supervisor. By increasing patient medication adherence, the percentage of TB sufferers cured will increase and can reduce cases of Multi-Drug Resistant (MDR) patients and cases of Failure (Dropout). Previous research used imbalanced data on patients declared cured or completed treatment in 2010-2014. The single classifier comparison results showed that the best performance is C4.5. Unfortunately, the performance of the resulting model is still low, namely accuracy of 74% and specificity of 76%. So, this research will add parameters for medication adherence. This study aims to build a prognosis model for the length of treatment for new cases of pulmonary TB patients by adding medication adherence parameters using the Gradient Boosting algorithm. This research was carried out in six stages following the stages in the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology. The results of this research support government policy in focusing on developing Indonesian research in the field of health independence. The prognosis model can be used as an early warning for a patient regarding the length of treatment time. If a patient is predicted to have a treatment duration of more than six months, then the patient will receive stricter monitoring of drug swallowing. In this way, it is hoped that it will help achieve the target of eliminating tuberculosis by 2030 to reduce the death rate by 90% compared to 2019.
Non-rigid structure from locally-rigid motion

We introduce locally-rigid motion, a general framework for solving the M-point, N-view structure-from-motion problem for unknown bodies deforming under orthography. The key idea is to first solve many local 3-point, N-view rigid problems independently, providing a “soup” of specific, plausibly rigid, 3D triangles. The main advantage here is that the extraction of 3D triangles requires only very weak assumptions: (1) deformations can be locally approximated by near-rigid motion of three points (i.e., stretching not dominant) and (2) local motions involve some generic rotation in depth. Triangles from this soup are then grouped into bodies, and their depth flips and instantaneous relative depths are determined. Results on several sequences, both our own and from related work, suggest these conditions apply in diverse settings — including very challenging ones (e.g., multiple deforming bodies). Our starting point is a novel linear solution to 3-point structure from motion, a problem for which no general algorithms currently exist.
Depressive disorders among young adults

Background. We aimed to provide prevalence data on depression and other current mental disorders, impairment, need of psychiatric care and use of mental health services among young adults. Methods. Based on a semi-structured clinical interview, current DSM-IV disorders, impairment, need of psychiatric care and use of mental health services were evaluated in a sample of 20–24-year- old young urban adults (N 245), mean age 21±8, screened from a baseline population of 706. One- month prevalence estimates for disorders were calculated by the double sampling method, using various additional criteria to identify cases. Results. One in four young adults (23±8%) suffered from a current mental disorder, the most prevalent being depressive (10±8%), anxiety (6±9%), substance use (6±2%) and personality disorders (6±0%). Prevalence estimates varied substantially according to the use of additional diagnostic criteria. Impairment (GAF! 61) together with DSM-IV symptom criteria produced an overall disorder prevalence of 10±3%, and 5±5% for depression. Prevalences were higher for females than males, except for alcohol abuse and personality disorders. Current co-morbidity was found in 39% of subjects with any disorder, and in more than half of those with depression. One-third of subjects with a current disorder reported an associated contact with psychiatric services and 16% had an ongoing contact. Conclusions. Our findings support the use of additional criteria to produce clinically relevant prevalence data. Co-morbidity should receive special attention due to its amplification of both need for psychiatric care and severity of impairment. Finally, our results show disturbed young adults to be severely undertreated.
Stable or Shaky? The Semantics of ChatGPT’s Behavior Under Repeated Queries

Large Language Models (LLMs) like ChatGPT have propelled artificial intelligence to new heights, demonstrating remarkable aptitude across various disciplines. Yet, most studies that have investigated the performance of these models in the wild have not fully captured the nuanced performance characteristics of these models, particularly in terms of response stability — a crucial attribute for their application and development. This study zeroes in on this underexplored facet by examining ChatGPT’s consistency when answering identical queries repeatedly. Employing a robust set of 741 questions, each reiterated 15 times, we deploy an array of analytical metrics to dissect the model’s consistency profile. Our investigation provides a novel benchmark for ChatGPT’s accuracy within a repetitive context, marking a departure from the prevalent approach of assessing LLMs across diverse single-instance prompts. The outcomes of this study are instrumental in delineating the robustness of state-of-the-art LLMs in scenarios where reliable consistency is imperative, such as educational contexts and critical decision-making processes. By systematically characterizing ChatGPT’s consistency patterns, our work lays the groundwork for targeted enhancements and offers a set of benchmarks crucial for future work aimed at optimizing the stability and reliability of AI-generated responses.
Tomato Plant Leaf Disease Classification Using Deep Learning

Plant diseases pose a significant threat to global food production and require early identification to ensure global food security and safeguard the economic interests of agriculture stakeholders. Practical implementation in agriculture requires fast, lightweight and accurate models for edge devices. This research presents a tomato leaf disease classification approach using a fast and lightweight pre-trained CNN architecture designed for efficiency. The method incorporates resampling techniques to address the data class imbalance, while pre-processing steps such as resizing, and augmentation enhance model performance. The transfer learning-based NASNetMobile model is trained and evaluated using the plant village dataset, containing nine disease classes and a healthy class. Performance assessment focuses on lightweight characteristics and classification accuracy of the model. Results demonstrate the effectiveness of the approach in balancing lightweight characteristics and accuracy, offering the potential for extending disease classification to other plant species. This research contributes to the development of low-end devices for easy disease identification, benefiting global food security and agricultural stakeholders’ economic well-being.
Detect and Mitigate Cyberattacks Using SIEM

Cybersecurity is crucial to protect network resources and reduce the risk of cyberattacks like denial of service (DOS) and file modification. Security tools like intrusion detection and prevention systems (IDS and IPS), which work independently on network endpoints, struggle to distinguish between normal and anomalous network traffic in real-time, making cyberattack detection and prevention challenging. As a result, security information and event management (SIEM), which offers centralized network management, is utilized to gather and analyze logs from various endpoints on networks in order to identify threats in real time. This paper introduces a method integrating Wazuh and IDS tool capabilities to improve DOS detection and mitigation, and Wazuh file integrity monitoring (FIM) capabilities for real-time file modification detection. Wazuh monitors and evaluates network resource status centrally, while Snort and Suricata tools are used to monitor the network traffic on the monitored endpoint. After that, logs are sent to the Wazuh server for analysis and alerting. In addition, Wazuh is used to detect modifications to critical files, enabling early detection of malicious changes. The effectiveness of the suggested method is tested using a variety of DOS tools that are produced in real-time and by modifying important files. The experimental findings demonstrate that our method is effective in detecting and mitigating cyberattacks.
Improved Droop Voltage Control Strategy Considering the Access of Energy Storage and Distributed Generation

Owing to the difference of capacity and initial state of charge (SOC) for the multi-group energy storage units, as well as the different line resistance, the SOC of the energy storage unit cannot be equalized under the traditional droop control, which may cause the energy storage unit overcharged or overdischarged, and shorten its lifespan. Therefore, the improved droop control is designed based on the operation current and SOC, which can be obtained by taking advantage of the sample keeper and deduced by the consistency algorithm through the information interaction between neighboring agents. So the improved droop control can adjust the droop factor and voltage compensation by period, thus eliminating the influence of the above factors on the current load distribution and reduce the dependence on communication. Therefore, the current and SOC of the energy storage units may be balanced to avoid overcharged and overdischarged. Finally, the effectiveness and correctness of the proposed strategy is verified by RTLAB real-time simulation experiments.
Miniaturized Reconfigurable Antennas for Intelligent Wireless Communication (Invited)

Pattern reconfigurable antennas with multiple switchable beams can effectively improve the multi-path effects and enhance channel quality. Thus, they are considered one of the key technologies for intelligent wireless communication, satellite systems, and IoT applications [1–3]. Most traditional pattern reconfigurable works realize beam switching by loading multiple reflectors or directors around the radiation source. However, this method usually suffers from large dimensions, limited radiation modes, and complex structures. Thus, for space-limited scenarios, designing a compact pattern reconfigurable single antenna with flexible and multifunctional beam-switching capacity is highly demanded.
Wide Bandwidth Rogowski Coil Overvoltage Measurement System Using the Tap of the Transformer Bushing

Bushings are one of the vulnerable parts to overvoltage effects in power transformers. Therefore, it is significant to investigate the wide bandwidth overvoltage measurement system for the transformer bushing. In this paper, a wide bandwidth overvoltage measurement system using the tap of the bushing was developed. Two Rogowski coils were utilized as the main component to address the wide frequency range and large variation in ground current at the tap of the bushing. Rogowski coils with a frequency threshold of 5kHz were designed to measure the low-frequency and high-frequency components of the overvoltage separately. An integrator was used to reconstruct the overvoltage on the top of the bushing. Lightning overvoltage and operational overvoltage produced by a bushing experimental platform were measured, verifying the excellent performance of the proposed sensor.
Empirical Evaluation of Large Language Models in Resume Classification

The study’s primary objective is to investigate the effectiveness of Large Language Models (LLMs) in the specialized field of resume classification, a critical aspect of talent acquisition and human resource management. The research aims to overcome the limitations of conventional approaches that often rely on basic Natural Language Processing (NLP) techniques by introducing a more efficient alternative through the utilization of LLMs. In pursuit of this goal, a comprehensive empirical assessment was undertaken, encompassing multiple LLMs, including various iterations of Text Davinci and GPT models. The research methodology employed was rigorous, incorporating data preprocessing and text normalization techniques to ensure the robustness and credibility of the results. The study’s outcomes include a comparative analysis of the chosen LLMs, with a focus on essential performance metrics such as accuracy, precision, recall, and the F1-Score. The results obtained from this analysis demonstrate a significant enhancement in the performance of LLMs compared to traditional methods in the context of resume classification. In conclusion, this research provides invaluable insights into the applicability and effectiveness of LLMs within the realm of resume classification. It not only addresses existing limitations but also serves as a foundational work that paves the way for future research in this domain. Moreover, it underscores the transformative potential of LLMs in reshaping the landscape of talent acquisition and human resource management processes.
Involvement of Host Defense Mechanisms against Toxoplasma gondii Infection in Anhedonic and Despair-Like Behaviors in Mice

ABSTRACT Toxoplasma gondii is a pathogen relevant to psychiatric disorders. We recently showed that reactivation of chronic T. gondii infection induced depression-like behaviors in mice. Furthermore, it has been hypothesized that depression-like behaviors are mediated via a host defense mechanism against invading pathogens; proximate mechanisms of this behavioral hypothesis remain unclear. In the present study, we investigate the contribution of indoleamine 2,3-dioxygenase (IDO), inflammation, and interferon gamma (IFN-γ) to anhedonic and despair-related behaviors in T. gondii-infected mice by using sucrose preference and forced-swim tests, respectively. First, we confirmed that BALB/c mice exhibited both sickness and depression-like behaviors during acute infection. Treatment of infected wild-type mice with minocycline (anti-inflammatory drug) abated sickness and anhedonic and despair-like behaviors, whereas in T. gondii-infected mice, treatment normalized kynurenine/tryptophan (Kyn/Trp) ratios in both plasma and brain tissue. Additionally, T. gondii infection failed to induce anhedonic and despair-like behaviors or increase the Kyn/Trp ratio in immunocompromised (IFN-γ−/−) mice, whereas sickness behavior was observed in both immunocompetent and IFN-γ−/− mice following infection. Furthermore, treatment with 1-methyl tryptophan (an IDO inhibitor) did not affect locomotor activity, attenuated clinical scores and anhedonic and despair-like behaviors, and resulted in normal Kyn/Trp ratios in T. gondii-infected wild-type mice. Although low levels of serotonin and dopamine were observed in the brain during acute and chronic infections, anhedonic and despair-like behaviors were not detected in the chronic stage of infection. Collectively, our results demonstrated that immune enhancement in response to infection with T. gondii resulted in IFN-γ production, IDO activation, and inflammation associated with anhedonic and despair-like behaviors.
Millimeter Wave Beamforming for Massive MIMO in 6G Communication Systems

Millimeter wave beamforming for large MIMO in 6G verbal exchange systems is a promising era that could dramatically improve the overall performance and spectral efficiency of 6G networks. This generation entails the use of more than one antenna and antenna array to direct and recognize the electromagnetic power in a specific path. One of the core variations of millimeter wave beamforming in comparison with traditional more than one-antenna approaches is using fairly directional beams. This provides massive blessings in the communique link, together with improved bandwidth, improved user enjoyment, and stepped forward protection. Similarly, the use of a couple of antenna factors in this technology allows for beamforming, which could substantially lessen interference and enhance spectrum performance. Ultimately, the deployment of this era additionally calls for effective signal processing techniques, along with coding and modulation that can similarly boost the overall gadget capability. In conclusion, millimeter wave beamforming for huge MIMO in 6G verbal exchange systems can load up a huge range of possibilities and blessings, which could substantially enhance the user experience and warfare congestion in 6G networks.
Semantic Features Extraction for Anomaly Detection from Real-World Videos*

Video-based anomaly detection opens up numerous practical applications such as video surveillance and healthcare. Despite having been studied for an extended period, this problem remains highly challenging because of variations in anomalies caused by people, objects, or contexts. Furthermore, the issue of missing annotated datasets and imbalances in the training data can complicate the development of automatic detection systems. Moreover, many existing systems have attempted to detect anomalies by performing binary classification without providing any explanation of the results. This paper proposes a method to detect anomalies from video while providing a semantic explanation of the cause. We first deploy off-the-shelf techniques to detect people and objects in the scene, then we extract semantic features (motion, appearance, posture) to recognize anomalies. To focus more on posture change, we introduce new postural descriptions and time series descriptions of poses using a spatiotemporal graph convolutional neural network. The proposed method is validated on three public datasets: UCSD Ped2, CUHK Avenue, and ShanghaiTech Campus showing improvements over the baseline methods.
Efficient Convergence Analysis of Stochastic Gradient Descent for Large-Scale Optimization

Stochastic gradient descent (SGD) is an optimization set of rules used to solve huge-scale optimization issues. It works by way of updating an approximate solution, primarily based on the gradient of the goal characteristic with admire to each parameter, in an iterative fashion. SGD is powerful in cases in which the full-gradient updates are not feasible because of the big length of the dataset, on the grounds that the simplest mini-batch of the facts is used for every update. In this paper, a brand new technique for analyzing the convergence homes of SGD is proposed. Particularly, the authors keep in mind the worst-case situation wherein the goal characteristic is composed of parts: a smooth element and a non-clean part, each with its personal Lipschitz constants. The authors then analyze SGD with a hard and fast step size and show that it may converge to the premier answer with a top-quality rate. The proposed approach is efficient in that it requires fewer reviews of the gradient than the prevailing convex optimization strategies. It is predicted that this new theoretical result will facilitate the improvement of extra green and dependable optimization strategies.