Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient and Privacy preserving Computation Framework for Tibetan medicine

With the continuous development of Tibetan medicine, using machine learning technology to enhance the value of Tibetan medical data has become very important. However, the concerns of Tibetan medical institutions about data leakage have hindered the sharing of Tibetan medical data. Therefore, in this paper, we propose a privacy preserving computation framework based on dual servers. Our framework can securely store Tibetan medical data on cloud servers. The secure computation (such as machine learning training or machine learning prediction) is performed by cloud servers without compromising data. On the premise of ensuring data security, we combine the multi-key homomorphic encryption and secret sharing to design some secure building blocks. Through the security analysis and performance evaluation, our proposed scheme is efficient and practical.
PharmBERT: a Fine-tuned Model for Pharmaceutical Error Prediction

Every year, billions of prescriptions are dispensed in North America. Shockingly, medication errors result in up to 9,000 deaths annually in the United States alone. However, the current system for tracking service quality during the medication dispensation process is severely limited. It is essential to identify and understand the patterns of these errors to effectively prevent them. In this study, we employ a deep learning model called Bidirectional Encoder Representations from Transformers (BERT) to predict medication errors related to pharmacy services. Our preliminary experimental results demonstrate that our fine-tuned model achieves an impressive accuracy of approximately 88+%, accurately predicting whether a dispensation procedure will result in a near-miss (caught beforehand) or an incident (caught afterward) error. The attention scores generated by the model parameters offer valuable insights into the data features. We believe that the proposed approach can serve as a vital initial step in uncovering error patterns and ultimately contribute to reducing medication errors.
Lung Diseases Detection Based on ResNet and Deep Learning Method

Usually, a doctor can judge whether a patient has lung disease by X-ray. With the spread of the COVID-19 virus, the number of patients continues to increase while manual work increases. Thus, it's a challenge now to detect what kind of lung diseases the patient has with radiography. , in an accurate and fast way. With the development of deep learning and the emergence of CNN (Convolutional Neural Network), the devices equipped with neural network can become more intelligent and automated. In this article, we adopted ResNet to be our network, and used 3 data augmentation methods and data regularization methods to process the data and get higher accuracy. In this article, we reached 93.39% accuracy based on the experiments.
A New Time Series Framework for Forest Fire Risk Forecasting and Classification

There's an increasing concern about the occurrence and spread of forest fires across the globe, as they contribute to greenhouse gas emissions and play a major influential role in economics and public health. Thus, there's a need for accurate methods to predict and classify forest fire risk. The main known forest fire risk indexes have limitations, such as not taking into account the unique characteristics of the biome in study, and not being able to predict forest fire risk for a given number of days in the future. This last aspect, in particular, is of utmost relevance. Addressing it allows for coordinated planning and action by proper authorities with adequate anticipation. Aiming to solve this problem, we present a new framework that applies Machine Learning methods for: (1) climatic variables forecasting; and (2) forest fire risk classification. For the first objective, different time series forecasting algorithms were tested. The forecasted variables are then used as input for the second objective, for which different classification algorithms were also tested. We evaluated our proposal using Brazilian Pantanal regional biome data from 1999 to 2019, where climatic variables were collected from ground meteorological stations, and fire occurrences (hotspots) were obtained from satellite images. The experiments considered 4 climatic variables and 5 forest fire risk classes. The results were evaluated based on the average correlation between (i) the prediction of forest fire risk classes and (ii) the observation of hotspots. Our proposal proved to be better or competitive with the main forest fire risk indexes, with the advantage of predicting fire risk for a given number of days in the future.
Emulating Side Channel Attacks on gem5: lessons learned

Side channel attacks (SCA) have the potential of disrupting the trust of the users on computing platforms and cryptographic algorithms. The main challenge in the design of countermeasures against such threats is that an evaluation of their effectiveness can only be performed after they have been implemented. By that point, significant resources would have been invested in the creation of a prototype. Moreover, the large volume of combinations from all the potential target algorithms and computing systems complicates a systematical analysis. It is necessary to find strategies to simplify and systematize the study of SCAs and their countermeasures. gem5 is a cycle-accurate simulator which offers the possibility to emulate a broad range of computing architectures. Beyond the functional verification, this tool computes multiple physical statistics from the simulated system. In this paper, we discuss the lessons learned from using gem5 to simulate SCAs on an ARM system. Our work shows that while there is a correlation between the data and the reported statistics, there are significant challenges that must be addressed to improve the use of gem5 for the emulation of physical phenomena.
Semantics-guided hierarchical feature encoding generative adversarial network for natural image reconstruction from brain activities

The use of deep learning methods to decode visual perception images from brain activity recorded by fMRI has received a lot of attention. However, limited fMRI data make the task of visual reconstruction challenging. Inspired by hierarchical encoding of the visual cortex and the theory of brain homology with convolutional neural networks (CNNs), we propose a novel neural decoding model called hierarchical semantic generative adversarial network (HS-GAN). Specifically, we use CNN-based image encoder to extract hierarchical and semantic features of visually stimulus images. Then a neural decoder is used to decode hierarchical and semantic features from fMRI. In order to take full advantage of the information from different visual cortexes, we construct a generator with self-attention modules and skip connections to fuse the image features of different layers. In model training, adversarial learning is introduced to realize more natural image reconstruction. Compared to existing advanced methods, our method significantly improves the naturalness and fidelity of reconstructed images.
Innovative Deep Learning Model-based Stock Price Prediction using a Hybrid Approach of CNN and Gradient Recurrent Unit

The fluctuation in stock prices from industry to industry are a major source of concern in the market. As the market attracts more participants and stock prices play a larger role in more transactions, the ability to accurately predict stock price movements becomes more valuable. When making an investment, many people first look at the share price and then try to anticipate whether or not that price will go up or down in the future. The traditional problem of forecasting the stock market using Machine Learning tools and methodologies has been thoroughly studied. Time dependence, volatility, and similar complicated dependencies are interesting aspects that make this modeling non-trivial. To overcome this the proposed method in this work is a deep learning-based hybrid strategy for predicting stock prices. Preprocessing is performed after input is delivered to increase precision. Selecting MPSOA features is the next step. In the end, it's put to use in MC-GRU model training. The proposed method achieves better results than both the CNN and GRU models.
An Efficient Method based on Multi-view Semantic Alignment for Cross-view Geo-localization

Cross-view geo-localization is to retrieve the most relevant images from different views. The biggest challenge is the visual differences between different views and the location shifts in practical applications. Existing methods usually extract fine-grained features of the retrieval target and match them by semantic alignment. The Transformer-based approach can focus on more contextual information than the CNN-based approach and also learn the geometric correspondence between two viewpoint images directly through the location encoding information. However, the existing methods need to fully utilize the information from different viewpoints, and the model needs to understand the context information sufficiently. To address these issues, we propose an efficient method to fully use image information from cross-views and feature fusion, divided into two branches: Aerial-View Local-Feature Cross-Fusion(ALCF) and Multi-View Global-feature Cross-Fusion(MGCF). By observing the characteristics of the aerial and street views, we perform a targeted fusion of global and local features from different viewpoints. In addition, we introduce a multi-view semantic alignment module, which can solve the problem that more noise information is introduced when the aerial view and street view images are semantically aligned. Experiments show that our proposed method achieves excellent performance in both the drone viewpoint target localization and drone navigation tasks on the University-1652 dataset.
Robustness of Deep Learning Methods for Occluded Object Detection - A Study Introducing a Novel Occlusion Dataset

A large number of deep learning based object detection algorithms have been proposed and applied in a wide range of domains such as security, autonomous driving and robotics. In practical usage, objects being occluded are common, and can result in reduced accuracy and reliability. To increase the robustness of object detection algorithms under occlusion scenarios, it is necessary to consider the influence of different types of occlusion on the performance of object detection approaches. Our research revealed a gap in benchmarking datasets that could provide exemplars of occlusion that covered a range of occlusion scenarios. In this paper, we present a new benchmarking dataset that includes a range of exemplars providing coverage of different types of occlusion cases. This dataset is designed for object detection of everyday objects in indoor scenarios, and comprises occlusion in three orthogonal atomic factors, namely, the degree of occlusion, the location of occlusion, and classes of occluded object and those occluding other objects. Our dataset is balanced in terms of classes and degrees of occlusion, with a total of 5970 sample images. The effect of these three atomic factors has been investigated on some classic general object detectors. Using this benchmarking dataset, we also present results on the impact of the distribution of the training dataset, in terms of degree of occlusion, on the robustness of several typical object detection algorithms (e.g. Fast RCNN, Faster RCNN, and FCOS, etc). The benchmark is available at “https://drive.google. com/drive/folders/13VkLgbx6t0-vA3vRWrlvHcjra-8BS4aL? usp=sharing”. This dataset is seen as a key contribution to research investigating the influence of occlusion on the performance of object detectors.
Generating Host-Based Data from Network Traces for Intrusion Detection

Network intrusion detection has been an active research area for the last 20 years. However, there is a limited amount of publicly available datasets, and, most importantly, most of them are either related to network traces or host-based metrics. For this reason, in this paper, a method for the generation of a hybrid dataset encompassing both network data and host-based data is proposed. We specifically focus on a case study based on the File Transfer Protocol (FTP), a well-known protocol already used with several existing datasets. The proposed methodology consists of two phases, namely: (i) the creation of a seed dataset with correlated network and host-based data and (ii) the creation of a model thereof based on a multi-layer perceptron with discretization for the prediction of host-based data given a network trace. The experimental results indicate that the accuracy of the generation is up to 98% and depends on the number of bins used to encode data and the network size. We also investigate the effects of scaling the network size and show that increasing the bin size is necessary to achieve similar results. To the best of our knowledge, this paper is the first to correlate network-based and host-based traffic. The developed source code was published with an open-source license as an additional contribution.
Adapting the PPVO framework for Audio Reversible Data Hiding

Pixel-based pixel value ordering (PPVO) represents an efficient solution for low bit-rate and high-fidelity reversible data hiding in digital images. The main novelty of this approach is the selection between two distinct prediction values for each potential host pixel. This paper maintains and refines this feature and adapts it for audio reversible data hiding. For each audio sample value, three possible prediction values are considered: a simple average of neighboring samples around the current position and two weighted averages. Differences between neighboring samples are used to select between the two weighted predictors from the three possibilities. The PPVO framework is then used to select the final predictor for each individual sample from the two remaining predictors. The data embedding algorithm is also adapted to allow for multi-bit embedding, which better exploits the prediction error histogram of audio sample values. Experimental are provided, the proposed PPVO-based approach outperforms other existing audio reversible data hiding scheme, most notably at medium bit-rates.
Towards Sharper Risk Bounds for Agnostic Multi-objective Learning

Many real-world machine learning tasks have mul-tiple objectives, such as multi-object detection and product recommendation, which can not be optimized directly through a single objective function. Fortunately, multi-objective learning can be used to solve this problem efficiently by some vector-valued algorithms. Recently, researchers find that the performance of multi-objective learning will be impaired when the mixture weights are unknown, where a fixed algorithm is difficult to select the optimal model in the hypothesis space. Thus, agnostic multi-objective learning has been proposed, which provides an effective approach to solve the problem of simultaneously optimizing multiple objectives with unknown mixture weights. In this way, a proper model will be selected because the agnostic multi-objective learning can improve the worst case of the hypothesis space. However, the current generalization error bounds for agnostic multi-objective learning can not converge faster than $\mathcal{O}(1/\sqrt{n})$, which limits the generalization guarantee. In this paper, we provide a sharper excess risk bound for agnostic multi-objective learning with convergence rate of $\mathcal{O}(1/n)$, which is much faster than the existing results and matches the best theoretical results of centralized learning. Based on our theory, we then propose a novel algorithm to improve the generalization performance of agnostic multi-objective learning.
MorphoGear: An UAV with Multi-Limb Morphogenetic Gear for Rough-Terrain Locomotion

Robots able to run, fly, and grasp have a high potential to solve a wide scope of tasks and navigate in complex environments. Several mechatronic designs of such robots with adaptive morphologies are emerging. However, the task of landing on an uneven surface, traversing rough terrain, and manipulating objects still presents high challenges.This paper introduces the design of a novel rotor UAV MorphoGear with morphogenetic gear and includes a description of the robot’s mechanics, electronics, and control architecture, as well as walking behavior and an analysis of experimental results. MorphoGear is able to fly, walk on surfaces with several gaits, and grasp objects with four compatible robotic limbs. Robotic limbs with three degrees of freedom (DoFs) are used by this UAV as pedipulators when walking or flying and as manipulators when performing actions in the environment. We performed a locomotion analysis of the landing gear of the robot. Three types of robot gaits have been developed.The experimental results revealed low crosstrack error of the most accurate gait (mean of 1.9 cm and max of 5.5 cm) and the ability of the drone to move with a 210 mm step length. Another type of robot gait also showed low crosstrack error (mean of 2.3 cm and max of 6.9 cm). The proposed MorphoGear system can potentially achieve a high scope of tasks in environmental surveying, delivery, and high-altitude operations.
PST-Net: Point Cloud Completion Network Based on Local Geometric Feature Reuse and Neighboring Recovery with Taylor Approximation

Transformer has recently been introduced into point cloud completion and achieved inspirational performance on 3D point cloud generation. However, the low-level local geometric features are ignored in the existing feature extraction network, and this leads to the loss of geometry in the recovery results. Meanwhile, FoldingNet models that estimate neighborhood information from predicted center points cannot effectively recover geometric information. In this paper, we propose a point cloud completion network based on local geometric feature reuse and neighboring recovery with Taylor approximation (PST-Net). Specifically, a feature extraction network named Skip-DGCNN is constructed to integrate local and global geometric features to reduce the geometry loss during the feature extraction. In addition, we propose a computational model through Taylor approximation to recover the geometry information in the neighborhood of the prediction center. Moreover, we design the TSMB module corresponding to Taylor's approximation to maintain the end-to-end training mode. The proposed method is extensively evaluated and compared with previous methods on three datasets including PCN, ShapeNet-55 and ShapeNet-34. The proposed model outperforms the state-of-the-art (SOTA) and PoinTr on ShapeNet-55 and ShapeNet-34. The complexity analysis on the PCN dataset shows that the number of FLOPs of our approach is 60.79% lower than that of the SOTA. Visual comparisons demonstrate that the proposed method can effectively and accurately complete the geometry of missing parts.
Comparative Analysis of Machine Learning Algorithms for Early Detection of Chronic Kidney Disease

Chronic kidney disease (CKD) is a significant public health issue with a high morbidity rate. Machine learning (ML) algorithms have shown potential for improving the detection and prediction of CKD. In this study, four popular ML models, namely naive bayes, support vector machine (SVM), KNN, and linear regression, were used for the active prediction of CKD. A CKD dataset from Kaggle was preprocessed to improve performance. The accuracy (Acc%), precision (Prcsn), recall (Rcll), and F1 score (F1 scr) were calculated for each ML model. The SVM model outperformed the other models with an accuracy of 99.04%. This study suggests that ML algorithms have the potential to improve the prediction of CKD, with SVM being the most effective model. The results of this study can potentially lead to the development of improved CKD prediction models, aiding in early diagnosis and treatment of the disease, ultimately improving patient outcomes, and reducing the burden on the healthcare system.
Energy characteristics of the electrical explosion of two intertwined wires made of dissimilar metals

In this article, we present the results of the research into the characteristics of the conditions of heating and explosive destruction of Al-Cu, Fe-Ti, Fe-Cu, and Fe-Pb wires under a pulse of current with the density of 107 A/cm2. It has been shown that the energy that is deposited into the wire may depend on the relation between the thermophysical parameters and specific electric resistivity of the metals. It has been determined that under a pulse of current, the wires may explode synchronously or non-synchronously. During a synchronous explosion of wires, a single voltage pulse is generated. In the case of non-synchronous explosion, the wires explode in a succession, thus generating two voltage pulses. We suggested a dimensionless parameter that allows for predicting whether an electrical explosion of two wires of dissimilar metals is synchronous or non-synchronous. According to the research findings, non-synchronous nature of wire explosion may impact the formation of bimetallic particles through the explosion of two intertwined wires made of dissimilar metals.In this article, we present the results of the research into the characteristics of the conditions of heating and explosive destruction of Al-Cu, Fe-Ti, Fe-Cu, and Fe-Pb wires under a pulse of current with the density of 107 A/cm2. It has been shown that the energy that is deposited into the wire may depend on the relation between the thermophysical parameters and specific electric resistivity of the metals. It has been determined that under a pulse of current, the wires may explode synchronously or non-synchronously. During a synchronous explosion of wires, a single voltage pulse is generated. In the case of non-synchronous explosion, the wires explode in a succession, thus generating two voltage pulses. We suggested a dimensionless parameter that allows for predicting whether an electrical explosion of two wires of dissimilar metals is synchronous or non-synchronous. According to the research findings, non-synchronous nature of wire explosion may impact the formation of bimetallic particles through the .
Privately Evaluating Region Overlaps with Applications to Collaborative Sensor Output Validation

Advances in computer vision have made it possible to accurately map objects as regions in 3-dimensional space using LIDAR point clouds. These systems are key building blocks of several emerging technologies including autonomous vehicles. Comparing and validating the output of sensors at different vantage points observing the same scenery can enable these systems to detect faults, identify common obstacles, and improve decision making. However sharing sensor outputs among mutually untrusting parties can leak unwanted information, e.g., model parameters or relative location of the sensors. This work initiates the study of cryptographic protocols that enable two parties observing regions (or objects) in an arbitrary-dimension Euclidean space to privately detect if the regions overlap and approximate the volume of the overlapping region. The protocols rely only on cheap symmetric-key primitives and feature reasonable communication costs and compute times. As applications, the protocols have been benchmarked on data generated from the CARLA autonomous driving simulator and the ScanNet 3D image dataset; they outperform a 2PC garbled-circuit baseline in communication volume and compute time. For instance it takes roughly 0.5 seconds to approximate the volume of the overlapping region of two 3D boxes with low error probability.