Reviewerly/exHarmony huggingface dataset:
Learning multi-tasks with inconsistent labels by using auxiliary big task

Multi-task learning is to improve the performance of the model by transferring and exploiting common knowledge among tasks. Existing MTL works mainly focus on the scenario where label sets among multiple tasks (MTs) are usually the same, thus they can be utilized for learning across the tasks. However, the real world has more general scenarios in which each task has only a small number of training samples and their label sets are just partially overlapped or even not. Learning such MTs is more challenging because of less correlation information available among these tasks. For this, we propose a framework to learn these tasks by jointly leveraging both abundant information from a learnt auxiliary big task with sufficiently many classes to cover those of all these tasks and the information shared among those partially-overlapped tasks. In our implementation of using the same neural network architecture of the learnt auxiliary task to learn individual tasks, the key idea is to utilize available label information to adaptively prune the hidden layer neurons of the auxiliary network to construct corresponding network for each task, while accompanying a joint learning across individual tasks. Extensive experimental results demonstrate that our proposed method is significantly competitive compared to state-of-the-art methods.
YOLOCa: Center aware yolo for dense object detection

Abstract In this report, we propose a fully convolutional one-stage object detector (YOLOCa) with a novel decoupled head, an improved label assignment strategy, and a new loss computation method for dense object detection. By comprehensively considering the IoU, score and position connection between the prediction frame and the ground truth in the sample allocation, and by introducing some current mainstream loss function calculation methods such as Focal loss, DIOU nms, etc. Through the above techniques, we have effectively improved the AP. We conduct experiments on the global wheat detection dataset, and as shown in figure 1, our method achieves an AP of 49.9%, which is a 14.5% improvement over the baseline yolov3 (35.4%). This is still 0.8% AP higher than yolox which also uses the advanced label assignment strategy, and we still maintain the speed efficiency of the first-level object detector. We hope our work can provide some useful tips for developers and researchers, the source code is at https://github.com/gaozixiang13/YOLOCa.
Study on an abnormal temperature detection method for CAFe facility

Abstract Beam availability is an important indicator of the operational stability and safety of the accelerator. As the front-end prototype of CiADS (China initiative Accelerator Driven System), the CAFe (Chinese ADS Front-end Demo Linac) accelerator is used to verify the feasibility of the key technologies of CiADS, which plays an important role in the development of nuclear waste safety treatment technology. The accelerator is often affected by uncontrollable factors during operation, triggering false temperature alarms from the BPM (Beam Position Monitor), causing the accelerator machine protection system to cut the beam, significantly reducing the availability of the beam. In addition, the prevailing method of manually setting the machine protection system threshold increases the beam conditioning time and the probability of false beam cuts. Therefore, it is urgent to study and discriminate the anomalies that cause beam cutting in order to reduce the beam cutting times caused by false alarms. In this paper, a BPM temperature anomaly prediction model based on Isolation Forest is developed, and the historical BPM temperature data from the CAFe accelerator is pre-processed using the Kalman filter to eliminate the influence of noise. The experimental results show that the scheme can accurately predict the abnormal state of the accelerator to remove the anomalous values, thereby avoiding false interlocks, and provide an effective interlock reference for the threshold setting of the machine protection system, thereby improving the beam availability.
A Self-attention Guided Multi-scale Gradient GAN for Diversified X-ray Image Synthesis

Abstract Imbalanced image datasets are commonly available in the domain of biomedical image analysis. Biomedical images contain diversified features that are significant in predicting targeted diseases. Generative Adversarial Networks (GANs) are utilized to address the data limitation problem via the generation of synthetic images. Training challenges such as mode collapse, non-convergence, and instability degrade a GAN’s performance in synthesizing diversified and high-quality images. In this work, MSG-SAGAN, an attention-guided multi-scale gradient GAN architecture is proposed to model the relationship between long-range dependencies of biomedical image features and improves the training performance using a flow of multi-scale gradients at multiple resolutions in the layers of generator and discriminator models. The intent is to reduce the impact of mode collapse and stabilize the training of GAN using an attention mechanism with multi-scale gradient learning for diversified X-ray image synthesis. Multi-scale Structural Similarity Index Measure (MS-SSIM) and Frechet Inception Distance (FID) are used to identify the occurrence of mode collapse and evaluate the diversity of synthetic images generated. The proposed architecture is compared with the multi-scale gradient GAN (MSG-GAN) to assess the diversity of generated synthetic images. Results indicate that the MSG-SAGAN outperforms MSG-GAN in synthesizing diversified images as evidenced by the MS-SSIM and FID scores.
Multi-Graph Convolutional Neural Network for Breast Cancer Multi-task Classification

Abstract Mammography is a popular diagnostic imaging procedure for detecting breast cancer at an early stage. Various deep-learning approaches to breast cancer detection incur high costs and are erroneous. Therefore, they are not reliable to be used by medical practitioners. Specifically, these approaches do not exploit complex texture patterns and interactions. These approaches warrant the need for labelled data to enable learning, limiting the scalability of these methods with insufficient labelled datasets. Further, these models lack generalisation capability to new-synthesised patterns/textures. To address these problems, in the first instance, we design a graph model to transform the mammogram images into a highly correlated multigraph that encodes rich structural relations and high-level texture features. Next, we integrate a pre-training self-supervised learning multigraph encoder (SSL-MG) to improve feature presentations, especially under limited labelled data constraints. Then, we design a semi-supervised mammogram multigraph convolution neural network downstream model (MMGCN) to perform multi-classifications of mammogram segments encoded in the multigraph nodes. Our proposed frameworks, SSL-MGCN and MMGCN, reduce the need for annotated data to 40% and 60%, respectively, in contrast to the conventional methods that require more than 80% of data to be labelled. Finally, we evaluate the classification performance of MMGCN independently and with integration with SSL-MG in a model called SSL-MMGCN over multi-training settings. Our evaluation results on DSSM, one of the recent public datasets, demonstrate the efficient learning performance of SSL-MNGCN and MMGCN with 0.97 and 0.98 AUC classification accuracy in contrast to the multitask deep graph (GCN) method Hao Du et al. (2021) with 0.81 AUC accuracy.
Automatic Vehicle Ego Body Extraction for Reducing False Detections in Automated Driving Applications

Abstract Fisheye cameras are extensively employed in autonomous vehicles due to their wider field of view, which produces a complete 360-degree image of the vehicle with a minimum number of sensors. The drawback of having a broader field of view is that it may include undesirable portions of the vehicle’s ego body in its perspective. Due to objects’ reflections on the car body, this may produce false positives in perception systems. Processing ego vehicle pixels also uses up unnecessary computing power. Unexpectedly, there is no literature on this relevant practical problem. To our knowledge, this is the first attempt to discuss the significance of autonomous ego body extraction for automobile applications that are crucial for safety. We also proposed a simple deep learning model for identifying the vehicle’s ego-body. This model would enable us to eliminate any pointless processing of the car’s bodywork, eliminate the potential for pedestrians or other objects to be mistakenly detected in the car’s ego-body reflection, and finally, check to see if the camera is mounted incorrectly. The proposed network is a U-Net model with a Res-Net50 encoder pre-trained on ImageNet and trained for binary semantic segmentation on vehicle ego-body data. Our training data is an internal Valeo dataset with 10K samples collected by three separate car lines across Europe. This proposed network could then be integrated into the vehicles existing perception system by extracting the ego-body contour data and supplying this to the other algorithms which then ignore the area outside the contour coordinates. The proposed network can run at set intervals to save computing power and to check if the camera is misaligned by comparing the new contour data to the previous data.
A Transformer Architecture for Online Gesture Recognition of Mathematical Expressions

Abstract The Transformer architecture is shown to provide a powerful framework as an end-to-end model for building expression trees from online handwritten gestures corresponding to glyph strokes. In particular, the attention mechanism was successfully used to encode, learn and enforce the underlying syntax of expressions creating latent representations that are correctly decoded to the exact mathematical expression tree, providing robustness to ablated inputs and unseen glyphs. For the first time, the encoder is fed with spatio-temporal data tokens potentially forming an infinitely large vocabulary, which finds applications beyond that of online gesture recognition. A new supervised dataset of online handwriting gestures is provided for training models on generic handwriting recognition tasks and a new metric is proposed for the evaluation of the syntactic correctness of the output expression trees. A small Transformer model suitable for edge inference was successfully trained to an average normalised Levenshtein accuracy of 94%, resulting in valid postfix RPN tree representation for 94% of predictions.
Unreliability-Aware Disentangling for Cross-Domain Semi-supervised Pedestrian Detection

The rapid progress of pedestrian detection is supported by the ever-growing labeled training data and elaborate neural-network-based model. However, adequate labeled training data are not always accessible when it comes to a new scene. Semi-supervised learning is promising for the case where a small amount of manually annotated images and a large amount of unannotated images are handy. In the semi-supervised setting, data generation is a powerful technique as a type of data augmentation. Some methods conduct data generation by disentangling pedestrian instances into different codes in latent space and combining codes of different instances to reconstruct new instances. However, these methods either work in a single domain or cannot handle the case where some instances are partially represented in the images. In this work, we propose to solve code-level information transferring from reliable domains to unreliable domains by incorporating a domain classifier that competes with the disentangling module to generate domain-invariant codes. An external classifier is trained on appearance-enhanced instances and sends integrity signals to the generative module, which facilitates the generative module to recognize fully/partially represented pedestrian instances. The resulting classifier ultimately renders high-quality pseudo-annotations for the unannotated data. The pseudo-annotated data, combined with a small amount of manually annotated data, are used to achieve a detector with more generalization and accuracy. We perform extensive experiments on multiple challenging benchmarks to demonstrate the effectiveness of the proposed method.
Neural Residual Flow Fields for Efficient Video Representations

Neural fields have emerged as a powerful paradigm for representing various signals, including videos. However, research on improving the parameter efficiency of neural fields is still in its early stages. Even though neural fields that map coordinates to colors can be used to encode video signals, this scheme does not exploit the spatial and temporal redundancy of video signals. Inspired by standard video compression algorithms, we propose a neural field architecture for representing and compressing videos that deliberately removes data redundancy through the use of motion information across video frames. Maintaining motion information, which is typically smoother and less complex than color signals, requires a far fewer number of parameters. Furthermore, reusing color values through motion information further improves the network parameter efficiency. In addition, we suggest using more than one reference frame for video frame reconstruction and separate networks, one for optical flows and the other for residuals. Experimental results have shown that the proposed method outperforms the baseline methods by a significant margin.
E-Portfolio as a Component of the Information and Analytical System of Scientific Staff Training

In the conditions of extremely dangerous human life (COVID-19 pandemic, martial law, etc.) the urgent tasks of science and education are the preservation of values, the creation of E-communications for adaptive and comprehensive support of scientists. The goal of the research is to intensify the problems of information analytics in the system of scientific staff training (SST) and to develop a methodology for building an information-analytical system (IAS), which will ensure the integrity of the process of intellectual potential formation. This is aimed at the rationality of the IAS, in particular, its component – E-Portfolio. This will make it possible to convert the accumulated information into electronic form and facilitate the transition to the creation of fundamentally new types of information resources. They can be combined into digital collections, which in the projection of the educational space become a system complex of digital scientific and educational resources – part of the IAS. When force-majeure makes radical changes in the education system and causes a certain failure in all spheres of scientific activity, the E-Portfolio itself becomes a reasonable and well-reasoned decision. Its development as an open component of the IAS for SST is using methods of adequate definition of structure based on of systematic, competency, transdisciplinary and diagnostic approaches. The components of the structure are combined into meaningful blocks based – on conceptual, organizational, socio-psychological, and diagnostic. This allows for establishing transdisciplinary relationships between researchers in different fields of science. Thus E-Portfolio is an effective IAS-tool for distance SST and also helps with the adaptive performance of scientific tasks.
Self-supervised Augmented Patches Segmentation for Anomaly Detection

In this paper, our goal is to detect unknown defects in high-resolution images in the absence of anomalous data. Anomaly detection is usually performed at image-level or pixel-level. Considering that pixel-level anomaly classification achieves better representation learning in a finer-grained manner, we regard data augmentation transforms as a self-supervised segmentation task from which to extract the critical and representative information from images. Due to the unpredictability of anomalies in real scenarios, we propose a novel abnormal sample simulation strategy which augmented patches are randomly pasted to original image to create a generalized anomalous pattern. Following the framework of self-supervised, segmenting augmented patches is used as a proxy task in the training phase to extract representation separating normal and abnormal patterns, thus constructing a one-class classifier with a robust decision boundary. During the inference phase, the classifier is used to perform anomaly detection on the test data, while directly determining regions of unknown defects in an end-to-end manner. Our experimental results on MVTec AD dataset and BTAD dataset demonstrate the proposed SSAPS outperforms any other self-supervised based methods in anomaly detection. Code is available at https://github.com/BadSeedX/SSAPS .
Complex Handwriting Trajectory Recovery: Evaluation Metrics and Algorithm

Many important tasks such as forensic signature verification, calligraphy synthesis, etc., rely on handwriting trajectory recovery of which, however, even an appropriate evaluation metric is still missing. Indeed, existing metrics only focus on the writing orders but overlook the fidelity of glyphs. Taking both facets into account, we come up with two new metrics, the adaptive intersection on union (AIoU) which eliminates the influence of various stroke widths, and the length-independent dynamic time warping (LDTW) which solves the trajectory-point alignment problem. After that, we then propose a novel handwriting trajectory recovery model named Parsing-and-tracing ENcoder-decoder Network (PEN-Net), in particular for characters with both complex glyph and long trajectory, which was believed very challenging. In the PEN-Net, a carefully designed double-stream parsing encoder parses the glyph structure, and a global tracing decoder overcomes the memory difficulty of long trajectory prediction. Our experiments demonstrate that the two new metrics AIoU and LDTW together can truly assess the quality of handwriting trajectory recovery and the proposed PEN-Net exhibits satisfactory performance in various complex-glyph languages including Chinese, Japanese and Indic. The source code is available at https://github.com/ChenZhounan/PEN-Net.
Hyper-parameter Optimization Using Continuation Algorithms

Hyper-parameter optimization is a common task in many application areas and a challenging optimization problem. In this paper, we introduce an approach to search for hyper-parameters based on continuation algorithms that can be coupled with existing hyper-parameter optimization methods. Our continuation approach can be seen as a heuristic to obtain lower fidelity surrogates of the fitness function. In our experiments, we conduct hyper-parameter optimization of neural networks trained using a benchmark set of forecasting regression problems, where generalization from unseen data is required. Our results show a small but statistically significant improvement in accuracy with respect to the state-of-the-art without negatively affecting the execution time.
Self-distilled Vision Transformer for Domain Generalization

In the recent past, several domain generalization (DG) methods have been proposed, showing encouraging performance, however, almost all of them build on convolutional neural networks (CNNs). There is little to no progress on studying the DG performance of vision transformers (ViTs), which are challenging the supremacy of CNNs on standard benchmarks, often built on i.i.d assumption. This renders the real-world deployment of ViTs doubtful. In this paper, we attempt to explore ViTs towards addressing the DG problem. Similar to CNNs, ViTs also struggle in out-of-distribution scenarios and the main culprit is overfitting to source domains. Inspired by the modular architecture of ViTs, we propose a simple DG approach for ViTs, coined as self-distillation for ViTs. It reduces the overfitting of source domains by easing the learning of input-output mapping problem through curating non-zero entropy supervisory signals for intermediate transformer blocks. Further, it does not introduce any new parameters and can be seamlessly plugged into the modular composition of different ViTs. We empirically demonstrate notable performance gains with different DG baselines and various ViT backbones in five challenging datasets. Moreover, we report favorable performance against recent state-of-the-art DG methods. Our code along with pre-trained models are publicly available at: https://github.com/maryam089/SDViT .
Some Tips to Use R and RStudio

This chapter presents the essentials of R, with a focus on the manipulation of variables, plotting, and the use of data frames and classes. We present also some useful packages for standard numerical methods, which will be used in the sequel. Examples of use are given.
New Generation of Engineering Students: Do We Know How to Teach Them?

Rapid changes in the activities of engineers under the influence of the Fourth Industrial Revolution make engineering pedagogy to define up-to-date teaching aims, content, environment, aids and methods. At present university teachers use modern methods, technologies and educational aids to make educational process more effective. Nevertheless, the university teachers are not always quite satisfied with the outcomes of the educational process, with the achievements of their students, and sometimes we come to the conclusion that we do not know how to teach our students. The hypothesis is that one of the reasons for educational failure of engineering students may be connected with the fact that the methods and technologies we use in the classroom are not always adequate to the new generation of students. Based on the analysis of works in pedagogy of higher school including engineering pedagogy, the results of the research undertaken by the author and other specialists, the paper describes the main characteristics of the new generation of engineering students that may influence the choice of teaching methods and technologies. Many representatives of this generation are characterized by pragmatism, narcissism, stratification, clip mentality, high speed of searching for information, time limit of attention, good operational memory, problems with long-term memory, cause-effect relationship, analysis, and communication. These characteristics should be taken into consideration by university teachers when choosing teaching methods and technologies.
A Systematic Literature Review on Information Security Leakage: Evaluating Security Threat

Research in the field of cyber security has seen a significant surge in recent years, primarily with the goal of providing support for cyber applications and warding off important security dangers that these applications face. However, cyber threats such as information leakage and piracy are still anticipated, and they are expected to become even more severe in future 5G scenarios. This research investigates the interplay and synergistic linkages that exist between various technologies. Moreover, this study finds relevant interactions and key problems, with privacy and security being the main focus. Also, this research covers a wide range of topics and includes many different ways to protect information assets and the technological resources that are made available in computer-based systems while reducing the risk of threats.