Reviewerly/exHarmony huggingface dataset:
Explainable A.I. in Healthcare

Explainable AI in healthcare is an emerging field with enormous potential to change the structure of the modern healthcare industry. The automation of assistive healthcare and its evolution with the help of ML algorithms and Deep neural nets will play an essential role in advancing the current healthcare infrastructure in the future. Explainable AI can help solve the lack of trust in these near blackbox neural networks in an industry like healthcare where the decision making process to achieve the result holds as much importance as the results themselves. Explainable AI models like LIME, SHAM, decision sets, etc plays a vital role in developing in depth understanding of a prediction model and the factors that lead to a predictions made by it, with this constant learning and feedback not only is it possible to create a transparent model but also improve on the results with human feedback to fine tune the model for a higher degree of accuracy than before.
Holistic Weighted Distillation for Semantic Segmentation

Channel-wise distillation for semantic segmentation has proven to be a more effective method than spatial-based distillation. By removing the redundant information from the teacher model, the student can focus on specific channel-related pixels, which can be viewed as a weighting of the pixels. However, the standard channel-wise distillation ignores the fact that such importance difference also exists among channels. In this paper, we propose a novel method called Holistic Weighted Distillation (HWD) to address this issue. We calculate the channel divergences between the teacher and the student, and convert them into distillation weights, making the student focus more on learning channels that are not well mastered, thus improving the final model performance. Besides, our method does not introduce additional network structure or back-propagation process, which improves the training efficiency. Experiments on ADE20K, Cityscapes, and COCO-Stuff demonstrate the superiority of our method. The code is available at https://github.com/zju-SWJ/HWD.
Deep Learning-Assisted Lung Cancer Diagnosis from Histopathology Images

Early detection plays a critical role in enhancing patient survival rates as lung cancer continues to pose a significant global health challenge and remains one of the primary contributors to cancer-related mortality. Deep learning techniques are promising as they assist doctors in disease diagnosis, especially in medical imaging. In this research, we employed a dataset comprising histopathology images of lung cancer and colon cancer from Kaggle. The data encompassed five distinct categories of the tissues of the lung and colon. To classify the images, we used a deep learning methodology that leveraged the pre-trained neural network known as AlexNet. After fine-tuning the proposed model by substituting the last fully-connected layer, we optimized the parameters using the SGDM optimizer. As a result, the overall accuracy of the method reached 99.46%. Across all considered categories, the lung benign group performed best with 100% in terms of accuracy. The overall accuracy of this research surpassed that of three previously published journal papers and six conference papers, effectively proving the remarkable capability of deep learning in accurately classifying lung cancer images. In conclusion, this research result underscores the potential of deep learning in supporting medical professionals to diagnose lung cancer.
Graph for VLSI Physical Design: An Extended Methodology to Explore Design Data and Boost Design Optimization

The artificial abstraction of VLSI design flow introduces gaps and enclosed domain knowledge and perspectives in the design flow. As VLSI design methodology and algorithms both face increasing challenges in the post-Moore era, it is urgently needed to promote the fusion between different design stages and break down the domain boundary to achieve a global design improvement. In this paper and from the perspective of the industry, we extend the applications of graph theory and related algorithms in VLSI physical design flow. After transforming physical design data to graph data, graph theory supplies a new perspective to understand physical design and extracts valuable logic and architecture information from a physical design view to improve the design. This methodology also supplies a new language for physical designers to communicate with digital designers. To explain such an extended physical design methodology, we propose our framework to introduce graph in physical design flow. After that, we show how several critical problems in physical design including positioning high-fanout registers, identifying timing bottlenecks, design planning and semi-customized physical design, are better tackled by graph algorithms. Related opportunities and challenges in future physical design are also discussed. We believe graph theory brings more opportunities in future industries of physical design and related EDA than we previously expected.
ABCM: A Patent Classification Model Based on Pretrained Language Models with Imbalanced Data

The phenomenon of data imbalance in patent classification research is relatively common. When traditional classification algorithms deal with imbalanced data, the classifier prediction tends to the majority class, and the minority class has a large classification error. To solve this problem, we propose the ABCM model. It is a method based on pre-training language model to solve the problem of sample imbalance in patent classification. It first uses unsupervised patent data to pre-train the language model, and then combines BiGRU and capsule network to abstract patent information in higher dimensions. The paper also modifies the loss function to make it more suitable for training on sample imbalanced tasks, and finally uses supervised data to train the classifier. The experimental results show that this method can effectively improve the classification performance of imbalanced data.
Bayesian Sensor Fusion for Joint Vehicle Localization and Road Mapping Using Onboard Sensors

We propose a method for joint estimation of a host vehicle state and a map of the road based on global navigation satellite system (GNSS) and camera measurements. We model the road using a spline representation described by a parameter vector having a Gaussian prior representing the uncertainty of the prior map. Both GNSS and camera measurements, such as lane-mark measurements, have noise characteristics that vary in time. To adapt to the changing noise levels and hence improve positioning performance, we combine the sensor information in an interacting multiple-model (IMM) setting to choose the best combination of the estimators with the vehicle state and the parameter vector of the map as the state vector. In a simulation study, we compare vehicle models with varying complexity, and on a real road segment we show that the proposed method can accurately adjust to changing noise conditions and correct for errors in the prior map.
Lossless Processing and the Limits of Trackability in MHT

Practical multi-hypothesis trackers (MHTs) often entail a number of parameters for track confirmation and extraction logic, gating and pruning, most of which are chosen heuristically to tradeoff performance and computational cost. Conceptually, these parameters are unnecessary with optimal MHT processing, as these decisions will fall out from the optimal solution, though perhaps with an increase in processing cost. We demonstrate, however, with a canonical MHT model and its attendant association assignment problem that many of these parameters can be chosen losslessly, that is, they only remove hypotheses that an optimal association solution is guaranteed to remove anyway, and thus strictly improve computational cost, with no loss in tracking performance. At the other end of the spectrum, the tools developed likewise yield a number of relations that detect when parameters are set such that practical tracking is no longer possible.
Across-scale Local Difference L1-norm Principal Component Analysis Network for Facial Expression Recognition

Facial expression recognition has long been a research hotspot in the fields of artificial intelligence and computer vision due to its various applications. In this paper, we proposed a novel method named across-scale local difference L1-norm principal component analysis network (ALDL1-PCANet) to extract powerful and discriminative expression features. Based on the idea of PCANet model, we construct a multiscale space to calculate across-scale local differences of expression images to obtain the holist and local information. Then, we implement L1-norm PCA to learn the convolution filters of two stages from the across-scale local differences. Afterwards, we encode the output images by binary hash and concatenate all the block-wise histograms to form expression features. Finally, we employ support vector machine (SVM) with linear kernel for classification. Extensive experiments are conducted on both controlled and uncontrolled expression databases, including CK+, JAFFE, ISED and BAUM-2i. Experimental results demonstrate our proposed method outperforms the most of existing methods by effectively extracting powerful and discriminative features from both acted and spontaneous expressions.
Pedestrian Fall Detection Using Improved YOLOv5

An end-to-end fall detection method was developed using the YOLOv5s model to accurately locate a person and monitor their fall behavior in a crowd. We added the SE attention mechanism to the second and fourth CSP1_X structures in the network using feature extraction to locate a target more precisely. The spatial pyramid pooling and fully connected spatial convolution (SPPFCSPC) structure was designed to replace SPP to extract the information of the target in different scales effectively and enhance its feature expression ability and detection accuracy. Compared to the previous model, the precision, mean average precision (mAP), and recall rate of the YOLOv5s-2nd-4th-C3SE-SPPFCSPC model increased by 3., 6.2, and 2.9%, respectively. the mAP of the fall category increased by 7.3%. The developed model showed improved detection ability which surpassed that of the original YOLOv5s model.
Web System for Storing and Visualizing Web Objects in XML

Web Objects in XML (WOX) is a framework for creating object-based distributed applications, it supports the interoperability among different object-oriented programming languages, it uses XML as the format representation for objects, and it uses HTTP as its transport protocol. This paper presents a web system that was developed to complement the functionality of WOX, which allows the storage of distributed objects and the visualization of their state and methods. The web system has a repository, in which objects can be visualized and their methods can be executed through a web interface where the user can provide values for each of their parameters.
Measuring Multi-Source Redundancy in Factor Graphs

Factor graphs are a ubiquitous tool for multisource inference in robotics and multi-sensor networks. They allow for heterogeneous measurements from many sources to be concurrently represented as factors in the state posterior distribution, so that inference can be conducted via sparse graphical methods. Adding measurements from many sources can supply robustness to state estimation, as seen in distributed pose graph optimization. However, adding excessive measurements to a factor graph can also quickly degrade their performance as more cycles are added to the graph. In both situations, the relevant quality is the redundancy of information. Drawing on recent work in information theory on partial information decomposition (PID), we articulate two potential definitions of redundancy in factor graphs, both within a common axiomatic framework for redundancy in factor graphs. This is the first application of PID to factor graphs, and only one of a few quantitative measures of redundancy.
SSIGAN: Semantic Segmentation via Improved Generative Adversarial Network

Nowadays, although conditional convolutional neural networks have applied to semantic segmentation, their loss function needs to be carefully designed. We propose an improved generative adversarial network including a generator network and a discriminator network for semantic segmentation. In some blocks, we substitute 3×1 and 1×3 factorized convolution for 3×3 convolution to make full use of transverse and longitudinal information. We concat the original image with the output of the generator as the input of the discriminator network to improve the discriminant ability. As a result, our model achieves 69.6% mean intersection over union (mIoU) on the Cityscapes test set. Our experiments exhibit that adversarial training approach leads to improved accuracy on the Cityscapes, Camvid, Kitti and Gatech dataset in road scene.
Oriented Object Detection in Remote Sensing Image Based on YOLOV5

In recent years, remote sensing satellites have developed rapidly and accumulated massive high-resolution image data. Using deep learning to solve remote sensing image object detection has become an important research direction. However, due to shooting from a bird's eye view, the object has an oblique Angle. Because of the perspective problem, remote sensing images have boundary problem when the object is near horizontal. When there are a large number of densely tilted object boxes, using horizontal box detection method will cause box occlusion thus missing or mislabeling. Therefore, it is a challenging task to solve remote sensing image object detection. In this paper, we propose the network structure which based on YOLO-V5 and combined with CSL method. Moreover, the loss function is improved to deal with the overlapping problem of tilted object boxes effectively. As revealed by extensive experiments, the average accuracy of this method on DOTA data sets shows a competitive advantage. At the same time, the size of the network is only 16.3m, which is obviously better than the comparison method.
UAV airframe classification based on trajectory data in UTM collaborative environments

UAVs are a cutting-edge technology whose use is currently highly restricted due to their potentially dangerous characteristics, and due to the lack of legislation adopting them and allowing a safe control of these vehicles. Unmanned Air System Traffic Management (UTM) initiatives seek to regularise their use by means of validation and monitoring techniques for the trajectories of these aircraft, both before flight and in real time. For this purpose, in the UTM framework, drones will be collaborative using similar systems to AIS and ADSB for ships or aircraft vehicles. Currently there are no UAV trajectory datasets that allow research in this field, so in this paper a dataset composed of the position and kinematics of the drones over time has been designed. By means of this dataset it is possible to develop and evaluate machine learning methods that help the verification entity to fulfil its functionalities. In this work we propose a first approach to extract useful information by classifying the type of drone based on its movement dynamics. This information would be useful in the identification of the validity of the proposed trajectory for the airframe indicated by the user. The code used for this research is available at https://github.com/DavidSanpedrochez/UAVTrackClassification
Addressing Cloud Computing Security Issues with Solutions

The provision of IT resources on demand as opposed to the acquisition, ownership, and upkeep of physical servers and data centers is known as cloud computing. Recent cloud computing applications involve crucial infrastructure like power plants and distribution facilities. The majority of necessary services are outsourced, which puts data security and privacy at risk. This paper outlines potential security concerns and practical solutions. A brief explanation of cloud computing is provided prior to the analysis and examination of the security issues.
A Resource Sharing Approach for Logic Synthesis Based on Monte Carlo Tree Search

How to reduce the area during multiple logic synthesis algorithms is a key problem. However, it is very hard because there is very small optimization space left for the logic synthesis. To settle this issue, this paper integrates the resource sharing technique into logic synthesis. Resource sharing is a key area-reduction approach in high-level synthesis (HLS), which is an NP-hard problem. Until now, most existing algorithms for resource sharing is greedy approach, which cannot obtain good result especially for the large circuits with numerous function units (FU). This paper proposes a novel algorithm based on Monte Carlo tree search (MCTS) for resource sharing, and uses it to reduce the resources at the early stage of the logic synthesis. The experimental results show the better area with short and stable runtime (0.5 second for MCTS). Comparing to the traditional greedy approach, the proposed algorithm can reduce the number of MUXs' inputs by about 44% and reduce the area up to 20%.
Application of 3-D Path Planning and Obstacle Avoidance Algorithms on Obstacle-Overcoming Robots

This article presents a MMP algorithm that combines a 3-D path planning algorithm with a DWA obstacle avoidance algorithm, to enable obstacle-overcoming robots to navigate complex, unstructured scenes. To achieve this, a novel A-star algorithm is proposed that can switch to a greedy best-first strategy algorithm based on the characteristics of the scene. The path planning algorithm is integrated with the DWA algorithm, allowing for local dynamic obstacle avoidance while following the global planned path. Additionally, the algorithm enables the robot to correct its path after obstacle avoidance and overcoming. The feasibility and robustness of the algorithms are demonstrated through simulation experiments in a factory with several complex environments. The algorithms quickly generate a reasonable 3-D path and perform reliable local obstacle avoidance, while taking into account the characteristics of the scene and motion obstacles.
Robust Face Morphing Attack Detection Using Fusion of Multiple Features and Classification Techniques

The face morphing process will combine two or more facial images to generate a single morphed facial image demonstrating Face Recognition Systems (FRS) vulnerability. The attack potential of the morphing image directly depends on the perceptual image quality, and when generated with no visible artefacts, it can deceive both human observers and automatic FRS. The current softwares for face morphing generates a morphing image with ghosting artefacts, especially in the eye region, nose and mouth area, which may serve as a potential cue to detect morphing attacks. Hence in this work, we introduce a new dataset comprising 10710 facial images before and after manual post-processing to reduce the visual artefacts and to generate high-quality attacks. Further, we propose a novel single image-based Morph Attack Detection (S-MAD) technique based on the ensemble of features and classifiers using the scale-space domain. The novel concept in the proposed method is the multilevel fusion that combines the comparison scores from different features and classifiers. Extensive experiments are carried out on the newly generated high-quality face images with (i) Morphs before post-processing and (ii) Morphs after post-processing. Further, the experiments are also carried out on two different mediums such as (i) Digital and (ii) Print-scan (or re-digitized) with and without compression. Extensive experimental results are performed to benchmark the detection performance with the existing S-MAD techniques. Obtained results indicate the best performance of the proposed method over existing methods.
MCQG: Multitask Approach for Chinese Question Generation and Question Answering

Question generation and question answering are complicate and require us do more reading comprehension. In this work, we finetuned a multilingual T5(mT5) Transformer in a multitask setting for question generation, question answering using Chinese SQuAD datasets. In the multitask setting, we trained the model to perform answer extraction, question generation and question answering tasks simultaneously. We use one model do all the tasks. To our best knowledge, this is the first academic work that performs end-to-end question generation and question answering from Chinese texts. The experiments evaluations shows that the multitask QG and QA approach is effective and has greater performance than single task setting.
Traffic Sign Recognition Based on YOLOv5 Network Structure

Aiming at the problems of low recognition accuracy, slow rate, missed detection and false detection of traditional target detection algorithm in traffic sign recognition system, The deep learning technology can extract the feature information of traffic sign automatically, so a traffic sign target detection algorithm based on YOLOV5 network structure is proposed. The attention mechanism is added to enhance the effective features of the detected objects, suppress the interference features, and improve the performance of traffic sign detection in the complex background. The results show that survey data for training and testing were collected from China TT100K, and the optimized YOLOv5 algorithm has a good detection effect.
Adaptive Mixture Model Reduction based on the Composite Transportation Dissimilarity

Providing efficient yet accurate statistical models is a challenging problem in many applications. When elementary models are not sufficiently descriptive, mixtures of densities can be used. A complexity management issue arises when mixture models are employed: the number of components should be a trade-off between the complexity and the accuracy of the model. However, in general, it is not obvious how to determine the right number of mixture components for a specific application. In a previous work, theoretical foundations to address such a topic have been laid, grounded on the use of the Composite Transportation Dissimilarity between mixtures, and a preliminary criterion to manage the complexity of a mixture model has been proposed. In this paper, additional theoretical insights are provided that allow to formulate a novel adaptive mixture reduction algorithm. Numerical tests show that in most cases the new algorithm constitutes a significant improvement over the previous one.
Single Image Super-Resolution Based on Enhanced Residual Neural Network

Vision is an important way for humans to perceive the world around them. It is an urgent need to improve image quality by using image super resolution. Residual network plays an important role in image super resolution because of its powerful feature extraction ability. Only stacking residual network is not enough to extract a large amount of effective information, and at the same time, more training memory is needed. Therefore, this paper designs a single feature extraction module with better performance to enhance the feature extraction ability of the network. In this paper, the Residual Network is mainly improved, and a Single Image Super-Resolution Based on Enhanced Residual Neural Network (ERNN) is proposed based on improved residual module. Compared with other classical lightweight algorithms, the lightweight network with the new residual module as the basic unit has higher reconstruction ability, and can achieve better image super-resolution reconstruction with a small number of parameters. Moreover, a large number of experiments have verified that the proposed ERNN can achieve the trade-off between the number of parameters and performance.