Reviewerly/exHarmony huggingface dataset:
Deep learning-based Subtyping of Atypical and Normal Mitoses using a Hierarchical Anchor-Free Object Detector

Mitotic activity is key for the assessment of malignancy in many tumors. Moreover, it has been demonstrated that the proportion of abnormal mitosis to normal mitosis is of prognostic significance. Atypical mitotic figures (MF) can be identified morphologically as having segregation abnormalities of the chromatids. In this work, we perform, for the first time, automatic subtyping of mitotic figures into normal and atypical categories according to characteristic morphological appearances of the different phases of mitosis. Using the publicly available MIDOG21 and TUPAC16 breast cancer mitosis datasets, two experts blindly subtyped mitotic figures into five morphological categories. Further, we set up a state-of-the-art object detection pipeline extending the anchor-free FCOS approach with a gated hierarchical subclassification branch. Our labeling experiment indicated that subtyping of mitotic figures is a challenging task and prone to inter-rater disagreement, which we found in 24.89% of MF. Using the more diverse MIDOG21 dataset for training and TUPAC16 for testing, we reached a mean overall average precision score of 0.552, a ROC AUC score of 0.833 for atypical/normal MF and a mean class-averaged ROC-AUC score of 0.977 for discriminating the different phases of cells undergoing mitosis.
Federated Few-Shot Learning for Mobile NLP

Natural language processing (NLP) sees rich mobile applications. To support various language understanding tasks, a foundation NLP model is often fine-tuned in a federated, privacy-preserving setting (FL). This process currently relies on at least hundreds of thousands of labeled training samples from mobile clients; yet mobile users often lack willingness or knowledge to label their data. Such an inadequacy of data labels is known as a few-shot scenario; it becomes the key blocker for mobile NLP applications.
On Finite Difference Jacobian Computation in Deformable Image Registration

Producing spatial transformations that are diffeomorphic is a key goal in deformable image registration. As a diffeomorphic transformation should have positive Jacobian determinant |J| everywhere, the number of voxels with |J|<0 has been used to test for diffeomorphism and also to measure the irregularity of the transformation. For digital transformations, |J| is commonly approximated using a central difference, but this strategy can yield positive |J|'s for transformations that are clearly not diffeomorphic -- even at the voxel resolution level. To show this, we first investigate the geometric meaning of different finite difference approximations of |J|. We show that to determine if a deformation is diffeomorphic for digital images, the use of any individual finite difference approximation of |J| is insufficient. We further demonstrate that for a 2D transformation, four unique finite difference approximations of |J|'s must be positive to ensure that the entire domain is invertible and free of folding at the pixel level. For a 3D transformation, ten unique finite differences approximations of |J|'s are required to be positive. Our proposed digital diffeomorphism criteria solves several errors inherent in the central difference approximation of |J| and accurately detects non-diffeomorphic digital transformations. The source code of this work is available at https://github.com/yihao6/digital_diffeomorphism.
Safe Reinforcement Learning with Probabilistic Guarantees Satisfying Temporal Logic Specifications in Continuous Action Spaces

Vanilla Reinforcement Learning (RL) can efficiently solve complex tasks but does not provide any guarantees on system behavior. To bridge this gap, we propose a three-step safe RL procedure for continuous action spaces that provides probabilistic guarantees with respect to temporal logic specifications. First, our approach probabilistically verifies a candidate controller with respect to a temporal logic specification while randomizing the control inputs to the system within a bounded set. Second, we improve the performance of this probabilistically verified controller by adding an RL agent that optimizes the verified controller for performance in the same bounded set around the control input. Third, we verify probabilistic safety guarantees with respect to temporal logic specifications for the learned agent. Our approach is efficiently implementable for continuous action and state spaces. The separation of safety verification and performance improvement into two distinct steps realizes both explicit probabilistic safety guarantees and a straightforward RL setup that focuses on performance. We evaluate our approach on an evasion task where a robot has to reach a goal while evading a dynamic obstacle with a specific maneuver. Our results show that our safe RL approach leads to efficient learning while maintaining its probabilistic safety specification.
Tandem clustering with invariant coordinate selection

For multivariate data, tandem clustering is a well-known technique aiming to improve cluster identification through initial dimension reduction. Nevertheless, the usual approach using principal component analysis (PCA) has been criticized for focusing solely on inertia so that the first components do not necessarily retain the structure of interest for clustering. To address this limitation, a new tandem clustering approach based on invariant coordinate selection (ICS) is proposed. By jointly diagonalizing two scatter matrices, ICS is designed to find structure in the data while providing affine invariant components. Certain theoretical results have been previously derived and guarantee that under some elliptical mixture models, the group structure can be highlighted on a subset of the first and/or last components. However, ICS has garnered minimal attention within the context of clustering. Two challenges associated with ICS include choosing the pair of scatter matrices and selecting the components to retain. For effective clustering purposes, it is demonstrated that the best scatter pairs consist of one scatter matrix capturing the within-cluster structure and another capturing the global structure. For the former, local shape or pairwise scatters are of great interest, as is the minimum covariance determinant (MCD) estimator based on a carefully chosen subset size that is smaller than usual. The performance of ICS as a dimension reduction method is evaluated in terms of preserving the cluster structure in the data. In an extensive simulation study and empirical applications with benchmark data sets, various combinations of scatter matrices as well as component selection criteria are compared in situations with and without outliers. Overall, the new approach of tandem clustering with ICS shows promising results and clearly outperforms the PCA-based approach.
A Survey on Reinforcement Learning Security with Application to Autonomous Driving

Reinforcement learning allows machines to learn from their own experience. Nowadays, it is used in safety-critical applications, such as autonomous driving, despite being vulnerable to attacks carefully crafted to either prevent that the reinforcement learning algorithm learns an effective and reliable policy, or to induce the trained agent to make a wrong decision. The literature about the security of reinforcement learning is rapidly growing, and some surveys have been proposed to shed light on this field. However, their categorizations are insufficient for choosing an appropriate defense given the kind of system at hand. In our survey, we do not only overcome this limitation by considering a different perspective, but we also discuss the applicability of state-of-the-art attacks and defenses when reinforcement learning algorithms are used in the context of autonomous driving.
NMS Strikes Back

Detection Transformer (DETR) directly transforms queries to unique objects by using one-to-one bipartite matching during training and enables end-to-end object detection. Recently, these models have surpassed traditional detectors on COCO with undeniable elegance. However, they differ from traditional detectors in multiple designs, including model architecture and training schedules, and thus the effectiveness of one-to-one matching is not fully understood. In this work, we conduct a strict comparison between the one-to-one Hungarian matching in DETRs and the one-to-many label assignments in traditional detectors with non-maximum supervision (NMS). Surprisingly, we observe one-to-many assignments with NMS consistently outperform standard one-to-one matching under the same setting, with a significant gain of up to 2.5 mAP. Our detector that trains Deformable-DETR with traditional IoU-based label assignment achieved 50.2 COCO mAP within 12 epochs (1x schedule) with ResNet50 backbone, outperforming all existing traditional or transformer-based detectors in this setting. On multiple datasets, schedules, and architectures, we consistently show bipartite matching is unnecessary for performant detection transformers. Furthermore, we attribute the success of detection transformers to their expressive transformer architecture. Code is available at https://github.com/jozhang97/DETA.
Decentralized Nation: Solving the Web’s Identity Crisis

The web of today whether you prefer to call it web 2.0, web 3.0, web 5.0 or even the metaverse is at a critical stage of evolution and challenge, largely centered around its crisis of identity. Like teenagers who cannot assess properly their reason for being and do not seem ready to take responsibility for their actions, we are constantly blaming the very system we are trying to get away from. To truly realize the benefits from innovation and technology, this crisis has to be resolved, not just through tactical solutions but through developments that enhance the sustainability of the web and its benefits. Significant strides are being made in the evolution of digital services enabled by technology, regulation, and the sheer pace of societal change. The journey to the decentralized web is mirroring the convergence of the physical and digital worlds across all economies and is increasingly embracing the digital native world. Technology has provided the foundational platform for individuals and entities to create and manage wealth, potentially without the need for big institutions. Ironically, despite all of the advancements, we are still facing an unprecedented and increasing wealth gap. Clearly, the system is broken, not just around the edges but at the very core of the democratic underpinning of our society. In this whitepaper, we propose how artificial intelligence on blockchain can be used to generate a new class of identity through direct human computer interaction. We demonstrate how this, combined with new perspectives for sustaining community and governance embedded within the use of blockchain technology, will underpin a sustainable solution to protect identity, authorship and privacy at the same time while contributing to restore trust amongst members of a future decentralized nation and hence contribute to solving the web most significant identity crisis.
A Novel Neural Network Training Method for Autonomous Driving Using Semi-Pseudo-Labels and 3D Data Augmentations

Training neural networks to perform 3D object detection for autonomous driving requires a large amount of diverse annotated data. However, obtaining training data with sufficient quality and quantity is expensive and sometimes impossible due to human and sensor constraints. Therefore, a novel solution is needed for extending current training methods to overcome this limitation and enable accurate 3D object detection. Our solution for the above-mentioned problem combines semi-pseudo-labeling and novel 3D augmentations. For demonstrating the applicability of the proposed method, we have designed a convolutional neural network for 3D object detection which can significantly increase the detection range in comparison with the training data distribution.
Data Security in Distributed Environments

&lt;p&gt;This article comprehensively studies data security in cloud computing environments and presents ideas for solving everyday computing issues. Cloud Computing (CC) is a shared pool of resources that allocates those computing resources to cloud clients over the internet. CC provides a set of controls that act as controls between cloud users and storage. Storage has played a crucial role in the cloud and has become ubiquitous among organizations and clients. Nevertheless, various issues such as security, information protection, interoperability, and consistent quality should be defined appropriately. However, the underlying problem is data security and how cloud providers ensure it. Safe and secure protection technology is indispensable for exchanging information between clouds. Encryption technology is used to provide data security in cloud environments. Numerous cryptography algorithms exist to transform plain text to cipher text before placing the data in the cloud to ensure security.&lt;/p&gt;
The Effect of Using Formative Assessment in Distance Learning on the Achievement of Public Schools’ Students in Amman

This research aims to know the effect of formative assessment on the achievement of university students in the English language course. The study sample included (196) students intentionally selected from among the first-year students at Al-Isra University in Jordan. These students were divided into two identical groups: one was taken to represent the experimental group, and the other was to represent the control group. The experimental group used formative assessment methods to study the (English Language) course. The students of both groups sat for the final achievement examination. The student&amp;#39;s results in the two groups were placed in tables and then treated statistically. It was found that the performance of the experimental group students was better than that of the control group, which indicates the effectiveness of using the formative assessment method in teaching English. In addition, a questionnaire was used with the study sample of students to monitor the attitudes of students and teachers toward the formative assessment. The advanced statistical analysis of students&amp;#39; responses showed their positive attitudes toward this assessment. Then the study concluded with some recommendations to strengthen this type of evaluation.
Object-oriented Design based Comprehensive Experimental Development of Document Object Model

JavaScript code using Document Object Model (DOM) can realize the dynamic control of Web pages, which is the important content of the Web development technology course. The application of DOM is very flexible and includes many knowledge points, so it is difficult for students to master. In order to help students to understand each knowledge point and improve their engineering ability to solve practical problems, a DOM comprehensive experiment project similar to blind box is designed and implemented. This experimental project integrates knowledge points such as DOM events, DOM operations, and communication between objects. Practice has proved that running and debugging of the project can help students to understand and master relevant knowledge points.
Construction and application of logic model for telecom network fraud case investigation based on big data

Telecom fraud, as a new means of fraud emerging in the new era, has a very deep impact on People's Daily life and social development. Therefore, it is necessary to use big data to carry out telecom fraud crime investigation activities to adapt to the changing social environment faster. According to the analysis of accumulated experience in the investigation of telecom network fraud cases in recent years, it can be seen that the case investigation mechanism is limited, the on-site collection of evidence is more difficult, and the cross-border arrest cooperation is more difficult. All these problems directly affect the investigation efficiency and quality of telecom network fraud cases in China. Therefore, some scholars proposed to use big data technology to improve the forensics process, build a cross-border reconnaissance cooperation platform, improve the efficiency of on-site forensics and investigation, and strengthen the effective crackdown on criminal and illegal behaviors. On the basis of understanding the development status of big data technology platform, this paper clarified the corresponding logic model according to the investigation process of telecom network fraud cases, and put forward the application countermeasures with big data technology as the core.
Decisions that Explain Themselves: A User-Centric Deep Reinforcement Learning Explanation System

With deep reinforcement learning (RL) systems like autonomous driving being wildly deployed but remaining largely opaque, developers frequently use explainable RL (XRL) tools to better understand and work with deep RL agents. However, previous XRL works employ a techno-centric research approach, ignoring how RL developers perceive the generated explanations. Through a pilot study, we identify major goals for RL practitioners to use XRL methods and four pitfalls that widen the gap between existing XRL methods and these goals. The pitfalls include inaccessible reasoning processes, inconsistent or unintelligible explanations, and explanations that cannot be generalized. To fill the discovered gap, we propose a counterfactual-inference-based explanation method that discovers the details of the reasoning process of RL agents and generates natural language explanations. Surrounding this method, we build an interactive XRL system where users can actively explore explanations and influential information. In a user study with 14 participants, we validated that developers identified 20.9% more abnormal behaviors and limitations of RL agents with our system compared to the baseline method, and using our system helped end users improve their performance in actionability tests by 25.1% in an auto-driving task and by 16.9% in a StarCraft II micromanagement task.
Based on Haar-like feature and improved YOLOv4 navigation line detection algorithm in complex environment

In order to improve the detection accuracy of the navigation line by the unmanned automatic marking vehicle (UAMV) in the complex construction environment. Solve the problem of unqualified road markings drawn by the UAMV due to inaccurate detection during construction. A navigation line detection algorithm based on and improved YOLOv4 and improved Haar-like feature named YOLOv4-HR is proposed in this paper. Firstly, an image enhancement algorithm based on improved Haar-like features is proposed. It is used to enhance the images of the training set, make the images contain more semantic information, which improves the generalization ability of the network; Secondly, a multi-scale feature extraction network is added to the YOLOv4 network, which made model has a stronger learning ability for details and improves the accuracy of detection. Finally, a verification experiment is carried out on the self-built data set. The experimental results show that, compared with the original YOLOv4 network, the method proposed in this paper improves the AP value by 14.3% and the recall by 11.89%. The influence of factors such as the environment on the detection effect of the navigation line is reduced, and the effect of the navigation line detection in the visual navigation of the UAMV is effectively improved.
Web-Based Kupang Sales Information System

Kupang is a food that comes from the sea which contains various health benefits. Kupang is a type of shellfish that lives in the sea. This type of shellfish has a fairly high nutritional content, but unfortunately not everyone knows the content contained in mussels. And sometimes it's just made into processed food with soup without any innovation. The obstacle experienced by the seller of the village of Balnggabus is that the customer must come directly to the location to get the desired Kupang. Another problem faced is that customers are often disappointed because the supply of mussels has run out or when the seller is not selling. With a web-based sales information system, Kupang sales can reach a wider range of consumers and make it easier for consumers to make transactions.
Distance Learning: Between Legality and Harmfulness (Lessons from the Pandemic)

The essence of distance learning is considered, including in the context of the realization of the right to education. It is stated that during the pandemic, only individual components of this educational technology were implemented. Its main organizational and content features, as well as the perceptions of full-time students regarding its effectiveness and level of psychological comfort are analyzed. The most significant shortcomings of the distance learning option that was used in a number (if not most) of universities are revealed
A Network of Argumentation Schemes and Critical Questions

In this paper, we devise a network that consists of argumentation schemes and critical questions that participants in debates can use to easily construct arguments that attack or support former arguments. As a prototype, we build a potential network of argumentation schemes and critical questions with a practical reasoning scheme at its center. The usefulness of a NASCQ in constructing and reconstructing complex arguments and in formal argumentation is also explored along with argumentation more broadly.
Taking Care of History: Toward a Politics of Provenance Linked Open Data in Museums

An account of the limitations of museums’ current approaches to presenting provenance data digitally and of the advantages offered by adopting a linked open data (LOD) approach.
Generalized linear tree: a flexible algorithm for predicting continuous variables

Tree-based models are popular among regression methods to predict continuous variables.Also, Generalized Linear Models (GLMs) are pretty standard in many statistical applications and provide a generalization to many of the most commonly applied statistical procedures.However, in most regression tree methods, there is only one theoretical model associated for prediction in the final nodes, like multiple linear regression, logistic regressions, polynomial models, Poisson models, among others.We, therefore, propose a new tree method in which we estimate a GLM in each leaf node of the estimated tree including variable selection, new hyperparameters optimization, and tree pruning.Our method, called Generalized linear tree (GLT), has shown to be competitive compared to other well-known regression methods in real datasets, with the advantages and estimation flexibility provided by GLMs.
A Tree-Adaptation Mechanism for Covariate and Concept Drift

Although Machine Learning algorithms are solving tasks of ever-increasing complexity, gathering data and building training sets remains an error prone, costly, and difficult problem.However, reusing knowledge from related previouslysolved tasks enables reducing the amount of data required to learn a new task.We here propose a method for reusing a tree-based model learned in a source task with abundant data in a target task with scarce data.We perform an empirical evaluation showing that our method is useful, especially in scenarios where the labels are unavailable in the target task.
Numerical Solution of Shrödinger Equations Based on the Meshless Methods

In this work, two-dimensional time-dependent quantum equation problems are studied. We introduce a numerical algorithm for solving the two-dimensional nonlinear complex quantum system with MLS and FDM methods. An eﬃcient and accurate computational algorithm based on both, the moving least squares (MLS) and the ﬁnite diﬀerence (FDM) methods is proposed for solving it. The results demonstrate that the proposed algorithm is a robust algorithm with good accuracy. This is developed on MLS and FDM methods using numerical simulation for solving these kind of problems.