Miracle-dz/UniFAR-FTUs huggingface dataset:
Brain Tumor Detection Using YOLOv5 and Faster R-CNN

Brain tumors are viewed as quite possibly the most hazardous problem in the world. Brain tumors spread quickly, and if they are not treated promptly, the patient's chances of survival are slim. Cancer cells can be benign or malignant, which is further subdivided into distinct classes such as meningioma, pituitary, and glioma. Machine-diagnosis-based methods have emerged recently and are able to identify brain cancers by utilizing magnetic resonance imaging. Two deep learning-based approaches for tumor recognition and categorization are included in our proposal, one with the YOLO (You Only Look Once) algorithm and the other using the faster R-CNN. In this case, we used YOLOv5, the fifth version of YOLO. Both methods for object detection rely on deep learning and are essentially convolutional neural networks. YOLOv5 does, however, necessarily require less computational architecture than other computing models. This paper includes a study based on the Kaggle dataset in which both models are trained across the entire dataset, and the model with the highest accuracy is used to detect brain tumors. Because YOLOv5 appears to have significantly higher precision, the dataset is trained and tested, and tumors are detected using a bounding box as well as malignancy classification using pre-trained classes. After careful calculation of the metric values, the final outcomes are shown graphically.
The Impacts of Unreliable Autonomy in Human-Robot Collaboration on Shared and Supervisory Control for Remote Manipulation

This work compared human-robot shared and supervisory control of remote robots for dexterous manipulation, and examined how the reliability of robot autonomy affects human operator performance, workload, and preference for robot assistance. Specifically, we implemented two human-robot collaboration (HRC) paradigms for remote manipulation: (1) shared control, where humans controlled gross manipulation and the robot autonomy controlled precise manipulation actions, and (2) supervisory control, where the robot autonomy controlled both gross and precise manipulation actions but relied on humans to detect and correct errors. We conducted two user studies: one to compare the effectiveness of the two HRC paradigms when assistive autonomy is reliable, and the other to examine the impact of error type and frequency on tasks and human operators in the two HRC paradigms when assistive autonomy is unreliable. Our results show that: (1) the interface with a higher level of reliable autonomy yields significantly better performance, lower workload, and higher user preference but lower engagement, and (2) the frequency and type of the error have significant impacts on the task performance and human workload but only partially affects the operator's preference and usage of autonomy.
Modeling and Simulation of the Human Firewall Against Phishing Attacks in Small and Medium-Sized Businesses

Small and medium-sized businesses (SMBs) are increasingly targeted by cybercriminals due to their limited cybersecurity budgets and lack of expertise. This paper proposes an alternative solution to the advanced but expensive security plan by employing a human firewall system. The system comprises a group of enduser agents with a cybersecurity expertise agent who can detect and prevent cyberattacks. We evaluate the proposed system’s effectiveness in mitigating cyber attack damage in SMBs using agent-based modeling and simulation. Our results show that if the end-user agents have above-average cybersecurity expertise and cybersecurity motivation, the proposed human firewall strategy can be a comparable or better defensive tactic than employing an advanced security plan. Our paper provides insights for SMBs on how to allocate their limited cybersecurity budgets to build a human firewall system to mitigate cyber attack damage.
Maximum Cyclic Gini Index Deconvolution for Rolling Bearing Fault Diagnosis

Rolling bearing fault impulses are often submerged in Gaussian and non-Gaussian noise. In addition, multiple faults interfere with each other, making the weak fault not obvious or even difficult to detect. This article proposes a new deconvolution method to solve these two problems—maximum cyclic Gini index deconvolution (MCGID). The method is based on a new index, the cyclic Gini index (CGI), and the classical Gini index (GI) weighting sequence is applied through the fault characteristic frequency and the nearby narrowband range to highlight the amplitude at low order harmonics and weaken the interference components around higher order. This ensures that CGI has the characterization of specific periodic fault impulses and excellent noise robustness. Then, the adaptive filter is designed by maximizing CGI and morphological processing to achieve the detection of the cyclostationary (CS) components associated with the fault impulses. MCGID has a better accuracy than classical methods. Results from simulations and experiments indicate that MCGID performs better at fault impulse detection under severe Gaussian and non-Gaussian interference, and compound fault diagnosis.
Research and Implementation of a License Plate Detection Algorithm for Complex Environments

To address the problems of distant recognition size discrepancy and unclear recognition due to light intensity transformation in license plate detection under high level camera, an improved YOLOv5s license plate detection optimization algorithm is proposed. The optimization algorithm is improved based on YOLOv5s:MobileNetV2 is introduced as the feature extraction network to make the model more lightweight; the CBAM module is added to introduce the attention mechanism to make the model pay more attention to the license plate area; at the same time, the regression loss of four vertices is added to improve the original loss function to improve the network extraction ability and speed up the extraction speed to further improve the detection ability of license plate characters . Three sets of experiments are observed, including the comparison of the original YOLOv5s and the optimized network model, the comparison of the optimized model with and without the attention mechanism, the current mainstream algorithms and the performance of the optimized model on CCPD. The experimental results show that the optimized model achieves 96.58% mAP on CCPD dataset and the number of parameters of the model is only 6.71M, and the overall performance is also better than that of Faster Region-CNN and SSD, which can better meet the accuracy and real-time requirements of license plate detection.
Semantic wireless localization of WiFi terminals in smart buildings

The wireless localization of mobile terminals in indoor scenarios by means of a semantic interpretation of the environment is addressed in this work. A training‐less approach based on the real‐time calibration of a simple path loss model is proposed which combines (i) the received signal strength information measured by the wireless terminal and (ii) the topological features of the localization domain. A customized evolutionary optimization technique has been designed to estimate the optimal target position that fits the complex wireless indoor propagation and the semantic target‐environment relation, as well. The proposed approach is experimentally validated in a real building area where the available WiFi network is opportunistically exploited for data collection. The presented results point out a reduction of the localization error obtained with the introduction of a very simple semantic interpretation of the considered scenario.
Calculation of Qualification Gain for Terahertz Antennas

At present, terahertz (THz) technology is a hot research topic all over the world. As a key link of THz system, THz antenna has also attracted intense attentions. It is meaningful to recognize the qualification of the gain is good or bad for THz antenna in a specific environment. Under the same distance and transmission loss (TL), the requirements for THz antenna gain are calculated and analyzed with the microwave antenna (2.45GHz) as a reference based on the free space transmission loss model. This paper can offer a kind of method to judge the qualification of the gain for the THz antenna. In the wireless communication system, the gain of antennas is required for a definite infimum in a specific environment generally, various types of antennas are selected to meet the certain requirement for gain. This paper emphatically studies the gain of THz antenna, it is not limited to a specific antenna model, it is suitable for any antenna model.
Study on Ballistic Transport Electronics—A Vacuum Field Emission Transistor with Carbon Nanotube Cold Cathode

Vacuum field emission transistors (VFETs) are emerging as promising alternatives to solid state electronic devices under high frequency environments. Here, we propose a VFET with back gate and carbon nanotube (CNT) arrays as cathode. The output characteristics, transfer characteristics and field emission properties of VFET are obtained by particle trajectory simulation method. The simulation results show that CNT arrays achieve high field enhanced factors, and the field emission current is increased by reducing the field shielding effect. The device has 33 nS transconductance at 8 V anode voltage, which shows fast signal response and low operating voltage. Moreover, VFET based on CNT arrays can change the numbers of CNT emitters to control the properties of the devices and even achieving high power gain. Ulteriorly, considering the ballistic transport in nanogap, these devices are expected to realize power amplify applications in high frequency regime.
Numerical Research on Stochastic Resonance Effect in Bistable System

The model of stochastic resonance in bistable system is analyzed based on adiabatic approximate theory.Ingredients of stochastic resonance,including system parameters,noise intensity,signal amplitude and frequency,are studied.It is concluded that there is an optimal noise intensity for the stochastic resonance system,and augmentation of the signal amplitude is favorable to the occurrence of stochastic resonance.However,the increase of signal frequency will cause the peak of signal in output power spectrum to leave the low-frequency area that noise energy concentrates on.It makes the peak of signal decrease rapidly and the stochastic resonance phenomena disappear gradually.For the fact that signal amplitude,frequency,noise intensity often exceed the requirements of small parameters in the adiabatic approximation theory and turn into large parameters,the re-scaling method for realization of stochastic resonance of large parameter signals is discussed.
Sliding Window and Pseudo-labeling techniques for Cellular Segmentation

Cell segmentation is a fundamental task in biomedical image analysis, which involves the identification and separation of individual cells from microscopy images. Large-size images and unannotated data are two canailing problems degrading the performance in cell segmentation. Regarding these issues, we propose sliding window and pseudo-labeling techniques by conducting several experiments on different neural architectures. Following this approach, our method achieves a significant performance improvement and a final result of 0.8097 F1 score on the tuning set and 0.6379 F1 score on the test set of Weakly Supervised Cell Segmentation in Multi-modality Microscopy challenge hosted at NeurIPS 2022.
Six years of wild bee monitoring shows dramatic seasonal shifts in biodiversity and species declines

Wild bees form diverse communities that pollinate plants in both native and agricultural ecosystems making them both ecologically and economically important. The growing evidence of bee declines has sparked increased interest in monitoring bee community and population dynamics using standardized methods. Here, we studied the dynamics of bee biodiversity within and across years by monitoring wild bees adjacent to four apple orchard locations in Southern Pennsylvania, USA. We collected bees using passive Blue Vane traps continuously from April to October for six years amassing over 26,000 bees representing 144 species. We quantified total abundance, richness, diversity, composition, and phylogenetic structure. There were large seasonal changes in all measures of biodiversity with month explaining an average of 72% of the variation in our models. Changes over time were less dramatic with years explaining an average of 44% of the variation in biodiversity metrics. We found declines in all measures of biodiversity especially in the last 3 years. Analyses of population trends over time for the 40 most abundant species indicate that about one third of species showed at least some evidence for declines in abundance. Bee family explained variation in species-level seasonal patterns but we found no consistent family-level patterns in declines, though bumble bees and sweat bees were groups that declined the most. Overall, our results show that season-wide standardized sampling across multiple years can reveal nuanced patterns in bee biodiversity, phenological patterns of bees, and population trends over time of many co-occurring species. These datasets could be used to quantify the relative effects that different aspects of environmental change have on bee communities and to help prioritize conservation efforts.
Dynamic Programming using Tensor Approximation for Contact-rich Manipulation

—Contact-rich manipulation is a challenging task for robot planning and control, due to the varying contact modes involved and the resultant uncertainties that arise from the contact points. Approximate Dynamic programming (ADP) is a promising approach for such problems as it can handle hybrid systems. Most existing methods in ADP that use neural networks for function approximation and gradient-based optimization procedures for policy retrieval struggle to handle the hybrid nature of contacts. In this work, we present a gradient-free, low-rank tensor approximation approach using Tensor Train (TT) to approximate the value function. The associated numerical optimization techniques for functions in TT format further allow performing optimization over both continuous and discrete variables, hence allowing handling hybrid systems. We demonstrate the effectiveness of our approach on a non-prehensile manipulation task with hybrid states and actions in both simulation and the real world. A video describing our work can be found at: https://youtu.be/ZrShvUQAyIs.
Peru: Moving Upstream to Improve Adolescent's Mental Health

Mental health disorders can emerge at any age, with incidence prevalent in childhood and young adulthood. The cost associated with mental health negatively impacts both personally and economically. The consequences of these impacts are long-lasting and spill over into households and communities. Adolescence is a unique and formative time. Physical, emotional, and social changes, including exposure to poverty, abuse, or violence, can make adolescents vulnerable to mental health problems. Protecting adolescents from adversity, promoting socio-emotional learning and psychological well-being, and ensuring access to mental health care are critical for their health and well-being during adolescence and adulthood (World Health Organization (WHO), 2021b). Providing positive skill sets to adolescents by focusing on an upstream approach using evidence-based programs to promote mental health and well-being is crucial. Peru’s revised mental health reforms have shifted from tertiary care to secondary and primary care to address the “treatment gap. ” The reform focuses on bringing services out of psychiatric hospitals and into local settings, where providers can engage with patients, families, and communities as active partners. While the reform focuses primarily on community mental health centers, this paper aims to address mental health by utilizing school settings. Schools have been positioned at the forefront of promoting positive mental health and well-being through implementing evidence-based interventions (O’Reilly et al., 2018).
Enhancing minBERT for Sentence Similarity with Cosine Similarity and Contrastive Learning

This project aims to first implement minBERT model and subsequently, we utilize the completed BERT model to perform sentiment analysis on the Stanford Sentiment Treebank dataset as well as CFIMDB data set which contains highly polar movie reviews. Finally, in the latter half of this project, we fine-tuned and extended the BERT model to create sentence embeddings that can perform well across a wide range of downstream tasks including sentiment analysis, paraphrase detection and semantics textual similarity. We explored different ways of fine-tuning the model and tried out different methods to improve the model training accuracy, including using cosine similarity to predict similarity score and mean square error as the loss function as well as contrastive learning with supervised approach. The main findings of this project are the approaches that can improve the performance of the BERT-based system performing these three tasks: sentiment analysis, paraphrase detection, and semantic textual. We noticed that training the three datasets simultaneously can yield better results then training sequentially. Furthermore, both cosine similarity and contrastive learning improved the performance and accuracy of the model.
GAN-BERT for Automated Essay Scoring

Every year, millions of individuals take English language proficiency exams, such as TOEFL and IELTS, for professional and academic development. These exams are typically graded by human evaluators; automating the evaluation process can improve both efficiency and fairness of the examinations. Our approach to the Automated Essay Scoring (AES) task is to implement three variations of the GAN-BERT architecture: a feed-forward neural network generator; a BERT transformer generator; and a generator composed of a fine-tined GPT2 language model in tandem with a BERT transformer. We use a single pre-trained RoBERTa model, fine-tuned to our task and dataset, for a baseline comparison. All three GAN-BERT architectures outperformed the baseline model on the test set. The GAN-BERT models are also able to better differentiate between Low and Medium score essays, and Medium and High score essays. The GPT2-BERT generator demonstrated the most evidence of taking advantage of the competitive nature of the GAN structure to improve both generator and discriminator. Key
AN OBJECT-ORIENTED FRAMEWORK USING DESIGN PATTERNS FOR NUMERICAL OPTION PRICING

Nowadays most software used for development purposes has an environment that supports object-oriented concepts and there has been a major shift from the functional approach to object-oriented programming. The software industry has grown exponentially, covering all major areas of businesses. Among these areas, we have also the ﬁnancial market. Many ﬁnan-cial institutions and investors make use of option contracts to speculate on trends in the stock market or to keep their level of risks from other investments under control. The price of an option is therefore an important factor. Many researchers related to ﬁnancial options have come up with several algorithms to calculate option values. These algorithms have been implemented in different computer languages or packages. In this paper, we propose an object-oriented framework using design patterns for pricing European options in MATLAB , under the Black-Scholes framework.
RDF Triple-Text-Story: A Integrated Workflow for Controllable Short Story Generation

We develop an integrated RDF triple-text-story workflow to generate coherent and imaginative short stories based on given triples or prompts. It combines RDF triple-to-text close-ended generation and open-ended story generation, which allows for the incorporation of structured information about characters, settings, and events while maintaining the flexibility of free-form storytelling. To implement this approach, we fine-tune a T5 model on RDF triples-to-texts generation (using WebNLG 2020 dataset) and then a GPT-2 model on open-ended text generation (using the ROCStories and WritingPrompt datasets). We evaluate the model performance by using BLEU score and also human evaluation. The use of two fine-tuned models, T5 and GPT-2, in an integrated workflow can help to combine the strengths of these two models. T5 is known for its ability to summarize and generate text from structured data, while GPT-2 is renowned for its ability to generate engaging and creative narratives. The results show that the triple-text-story workflow could potentially produce high-quality short stories that are both structured and engaging by leveraging the strengths of both models. Finally, we further fine-tuning GPT-2 model on the Writing Prompts dataset using various prompt engineering strategies improved the coherence, imagination, and readability of generated short stories.
Fair Online Knapsack with Value Density Predictions

The online knapsack problem is a classic online resource allocation problem. Its basic version studies how to pack online arriving items of different weights and values into a capacity-limited knapsack, so as to maximize the total value of the admitted items. Prior work has shown optimal deterministic and randomized algorithms for this problem – such algorithms have provably good competitive ratios, but they may treat individual items unfairly or inequitably in several different ways. In this work, we identify a natural notion of arrival time fairness previously studied for similar online problems and motivate its applicability to resource allocation. We extend this notion of fairness to the context of online knapsack, and show that existing algorithms perform poorly under this metric. We propose a parameterized deterministic algorithm where the parameter precisely captures the Pareto-optimal trade-off between fairness and competitiveness. We further show that predictions approximating the lowest value density of an item accepted by an optimal solution can improve both fairness and competitiveness. In particular, we derive an algorithm which leverages such predictions for better (competitive) performance, while simultaneously providing better fairness guarantees when the predictions are trust-worthy. Our work shows that even relatively simple predictions can be utilized remarkably effectively to simultaneously improve the fairness and performance of online algorithms.
Investigating the mechanism of the Escherichia coli ATP-binding cassette (ABC) transporter MetNI

x Chapter 1: Introduction . 1 1.1: The Lipid Bilayer and Membrane Proteins . 1 1.2: ATP-Driven Transport . 2 1.3: ABC Transporter Malfunctions and Their Consequences . 3 1.4: Structure and Function of ABC Transporter Components . 4 1.4. 1: General Structure . 4 1.4. 2: Nucleotide Binding Domains . 4 1.4. 3: Transmembrane Domains . 5 1.4. 4: Substrate Binding Protein . 6 1.4. 5: Regulatory Domains. 7 1.4. 6: Subunits Forming Complete Transporters. 7 1.5: ABC Transporter Types . 8 1.5. 1: Type I Importer . 8 1.5. 2: Type II Importer. 9 1.5. 3: Exporters . 9 1.5. 4: Further Classification of Human ABC Transporters. 10 1.6: Prevailing Transport Mechanism: Alternating Access Model . 10 1.7: A Diversity of Mechanisms for ABC Transporters . 11 1.7. 1: Proposed Mechanisms of a Type I Importer (Maltose Transporter MalFGK2) . 11 1.7. 2: Proposed Mechanisms of a Type II Importer (Vitamin B12 Transporter BtuCD-F). 13 1.8: Transporter of Focus: Type I Methionine Importer MetNI . 14 1.8. 1: MetN: The Nucleotide Binding Domain . 15 1.8. 2: MetI: The Transmembrane Domain. 16 1.8. 3: C2 domain: The Inhibitory Domain of MetNI . 16 1.8. 4: MetQ: The Substrate Binding Protein of MetNI . 16 1.9: Possible Mechanisms for Methionine Importer MetNI . 18 1.9. 1 Canonical Model . 18 1.9. 2: Non-canonical Model . 19 1.10: Experimental Approach and Key Questions Addressed in This Work . 20 1.11: Summary . 22