Reviewerly/exHarmony huggingface dataset:
BestCyte® primary screening of 500 ThinPrep Pap Test thin-layers: 3 Cytologists’ Interobserver diagnostic concordance with predicate manual microscopy relative to Truth Reference diagnoses defining NILM, ASCUS+, LSIL+, and ASCH+ thresholds for specificity, sensitivity, and equivalency grading

The BestCyte® Cell Sorter Imaging System (BestCyte) facilitates algorithmic discrimination of clinically relevant cells in Pap test cytopathology by classifying and projecting images of cells in galleries based on cytomorphology. Warranted is awareness of potential BestCyte advantages as measured through 3 cytologists' interobserver diagnostic concordance, specificity and sensitivity differentials, and equivalency grading relative to manual microscopy (MM).Using 500 MM-reported ThinPrep thin-layers, analyze: (1) cytologists' blinded BestCyte screening to raise Bethesda diagnoses; (2) correlate BestCyte and MM diagnoses (i.e., predicate) to establish Truth Reference Diagnoses (TRDx) from concordance between 4 possible diagnoses; (3) analyze cytologists' and MM predicate diagnoses through 4 diagnostic thresholds defined by TRDx: NILM (Negative) for specificity, and ASCUS+, LSIL+, and ASCH+ (Positive) for graded sensitivity (with abnormal cells decreasing in size with increasing dysplasia); and, (4) statistically determine cytologists' equivalency grading to MM using 95% Confidence Interval (CI) ranges.500 TRDx breakdown (n/%): NILM (241/48.2), ASCUS (79/15.8), ASCH (9/1.80), AGUS (2/0.40), LSIL (86/17.2), HSIL (68/13.6), CA (2/0.40), UNSAT (13/2.60). TRDx breakdown (n/%) per 4 of 4, 3 of 4, 2 of 4 diagnostic concordances: 264 (52.8%), 182 (36.4%), 54 (10.8%), respectively. No cases of discordant diagnoses were recorded. HSIL TRDx were established from 66.2% of 4 of 4 concordances, followed by NILM (59.3%), LSIL (46.5%), ASCUS (41.8%); antithetically, from 4.40% of 2 of 4 concordances. Specificity for MM predicate (NILM): 67.08%; for Cytologists 1, 2, and 3: 89.71%, 82.30%, 97.53%, respectively. For NILM threshold, cytologists revealed Significantly Superior equivalency to MM. Sensitivity for ASCUS+, LSIL+, and ASCH+ thresholds: MM (91.36%, 86.67%, 74.36%); Cytologist 1 (95.88%, 96.97%, 94.87%); Cytologist 2 (95.47%, 95.76%, 93.59%), Cytologist 3 (94.65%, 95.15%, 98.72%), respectively. Cytologists revealed Significantly Superior equivalency to MM for graded Positive thresholds; with Cytologist 3 for ASCUS+ being: Superior.BestCyte detects and efficiently displays abnormal cells in strategic galleries standardizing objectivity by systematizing mosaics of cell-types for cytologists' consideration. BestCyte fosters consistent, enhanced cytologists' sensitivity values for the ASCUS+, LSIL+, and ASCH+ Positive thresholds relative to MM. Also, BestCyte facilitates improved specificity and superior equivalency grading to MM reflecting efficient screening, and reduced labor. Confident interpretations of small dysplastic epithelial cells characteristic of HSIL led to exceptional interobserver diagnostic concordance inferring BestCyte is primed for effective cervical cancer screening practice.
Concept drift detection with quadtree-based spatial mapping of streaming data

Online learning is a complex task, especially when the data stream changes its distribution over time. It’s challenging to monitor and detect these changes to maintain the performance of the learning algorithm. In this work, we present a novel detection method built from a different perspective of other preexisting detectors from literature. It analyzes the space occupied by the data, assuming that it would be immutable unless changes in this space occur among data of different classes. The data is mapped into a quadtree-based memory structure that provides knowledge about which class (label) is dominant in a given region of the feature space. Drifts are detected by checking whether data assigned to a given class occupy spaces considered relevant to the other class. The proposed method was evaluated on benchmark binary classification problems. The results show that our method can compete with well-known drift detectors from the literature on synthetic and real-world datasets.
Next-generation blockchain enabled smart grid: Conceptual framework, key technologies and industry practices review

Technological advancements in smart grid energy systems (SGESs) are introducing sustainable frameworks to meet the demand for the fourth industrial energy revolution. These frameworks are planned to be used in the forthcoming future to maintain the energy network operation with optimization, energy trading, grid automation, and so on. Blockchain (BCn), developing after passing a diverse period of the research journey, comes to the mind of researchers and its integration in SGES paves the way to reach the goal of energy demand. However, still of interest is ongoing in the improvement of BCn features which can be regarded as the next-generation blockchain framework. This paper exhibits the technical framework of the next-generation BCn framework and explores its benefits and challenges in performing the emerging aspects of SGES. This framework enables some advanced features for the sustainable operation of SGES like smart metering, peer-to-peer (P2P) energy trading, self-operation, and transparency. The technical explanation of this BCn technology established on essential features and requisites is also presented in this paper from various points of view which include smart mechanism, intelligent storage system, and interoperability. We also highlight the recent progress and limitations of the current BCn framework in SGES. Finally, some challenges towards integrating the next-generation BCn technology in SGES are reported. This work can provide extended support for the practitioner and researcher in the context of BCn technology and SGES.
Spatial coupling relationship between faults and gold deposits in the Jiaodong ore concentration area and the effect of thermal doming-extension on mineralisation

As the third largest gold ore-forming province in the world, the deep metallogenic potential of and exploration strategies for Jiaodong gold deposits have attracted substantial attention. By applying three-dimensional (3D) geological modelling to these super-giant and giant gold deposits, this study reveals the 3D morphological characteristics of the ore-bearing faults and the ore-related granites, analyses the relationships between ore body locations and slope dip angle changes in the faults, and discusses the coupling relationships among gold mineralisation, ore-controlling faults, and magmatic activities. In the 3D model, the dip angles of ore-controlling faults gradually decrease downwards and alternate between steep and gentle along the fault dip direction. The ore bodies mainly occur in areas with relatively high fault-dip-angle change rates. Hence, positions with fault dip angles of 20°–50° and change rates of ≥0.91 should be considered as target areas for prospecting deep gold ore bodies. In-depth analysis suggests that the ore-hosting faults, granites, and gold mineralisation resulted from regional thermal doming and extensional movements. Gold ore bodies in the Jiaodong Peninsula occur inside or on the periphery of Mesozoic granite. Strong magmatic activities during the transition from enriched to depleted mantle provided the necessary metallogenic and thermal dynamic environment for gold mineralisation. In particular, the magmatic evolution provided abundant ore-forming materials. The detachment faults with varied dip angles provided favourable space for gold mineralisation, as the fluctuations of ore-forming fluid pressure caused by sudden changes in these dip angles served as the primary mechanism underlying the step-like gold mineralisation pattern.
Privacy-Preserving Network Analytics

We develop a new privacy-preserving framework for a general class of financial network models, leveraging cryptographic principles from secure multiparty computation and decentralized systems. We show how aggregate-level network statistics required for stability assessment and stress testing can be derived from real data without any individual node revealing its private information to any outside party, be it other nodes in the network, or even a central agent. Our work bridges the gap between established theories of financial network contagion and systemic risk that assume agents have full network information and the real world where information sharing is hindered by privacy and security concerns. This paper was accepted by Agostino Capponi, finance. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2022.4582 .
Homotopy Method for Optimal Motion Planning With Homotopy Class Constraints

Optimal motion planning is an essential task within the field of control theory. Therein, the key task is to synthesize optimal system trajectories that pass through cluttered environments while respecting given homotopy class constraints, which is critical in many topology-restricted applications such as search and rescue. In this letter, we introduce a novel optimal motion planning technique with 2-dimensional homotopy class constraints for general dynamical systems. We first initialize an optimal system trajectory regardless of obstacles and homotopy class constraints, and design an auxiliary obstacle trajectory for each obstacle such that the system trajectory belongs to the desired homotopy class regarding these auxiliary obstacle trajectories. During the procedure of deforming the auxiliary obstacle trajectory to the original counterparts, we propose a homotopy method based on nonlinear programming (NLP) such that the synthesized optimal system trajectories fulfill the aforementioned homotopy class constraints. The proposed method is validated with numerical results on two classic nonlinear systems with planar static and moving obstacles.
An Enriched e-Learning Model to Teach Kids in Arab Countries How to Write Code

Even for children, e-learning is essential today. Future generations will need to learn how to code. Because codes are complex, teaching them can be difficult. In this research, we present a model for teaching children programming. This study is an attempt to prove that teaching students to write code in Arab schools is feasible. The paper offers several insights and suggestions that would improve the mechanism of teaching young children to create code segments. Additionally, it highlights the significance of utilizing HCI to promote online education for children.
Combination of ROP Defense Mechanisms for Better Safety and Security in Embedded Systems

Control flow integrity (CFI) checks are used in desktop systems, in order to protect them from various forms of attacks, but they are rarely investigated for embedded systems, due to their introduced overhead. The contribution of this paper is an efficient software implementation of a CFI-check for ARM-and Xtensa processors. Moreover, we propose the combination of this CFI-check with another defense mechanism against return-oriented-programming (ROP). We show that by this combination the security is significantly improved. Moreover, it will also in-crease the safety of the system, since the combination can detect a failed ROP-attack and bring the system in a safe state, which is not possible when using each technique separately. We will also report on the introduced overhead in code size and run time.
SmartDelta: Automated Quality Assurance and Optimization in Incremental Industrial Software Systems Development

A common phenomenon in software development is that as a system is being built and incremented with new features, certain quality aspects of the system begin to deteriorate. Therefore, it is important to be able to accurately analyze and determine the quality implications of each change and increment to a system. To address this topic, the multinational SmartDelta project develops automated solutions for quality assessment of product deltas in a continuous engineering environment. The project will provide smart analytics from development artifacts and system executions, offering insights into quality degradation or improvements across different product versions, and providing recommendations for next builds.
Task-Agnostic Vision Transformer for Distributed Learning of Image Processing

Recently, distributed learning approaches have been studied for using data from multiple sources without sharing them, but they are not usually suitable in applications where each client carries out different tasks. Meanwhile, Transformer has been widely explored in computer vision area due to its capability to learn the common representation through global attention. By leveraging the advantages of Transformer, here we present a new distributed learning framework for multiple image processing tasks, allowing clients to learn distinct tasks with their local data. This arises from a disentangled representation of local and non-local features using a task-specific head/tail and a task-agnostic Vision Transformer. Each client learns a translation from its own task to a common representation using the task-specific networks, while the Transformer body on the server learns global attention between the features embedded in the representation. To enable decomposition between the task-specific and common representations, we propose an alternating training strategy between clients and server. Experimental results on distributed learning for various tasks show that our method synergistically improves the performance of each client with its own data.
Inversion of the Main Trends of Digitalization in the Educational Space

The modern social space is increasingly determined by the trends of digitalization. Today we can talk about such consequences of digitalization as the appearance of scattered identity and distancing – a new kind of socialization in the conditions of spatial separation and overcoming social deprivation and virtual transgression. These trends manifest themselves in all spheres – cultural, economic, political, etc. But at the same time, processes of a different kind are also taking place in the field of education: processes that represent a kind of inversion of the digitalization influence on our perception of the world, self-perception and the sphere of our everyday life. The article examines three such major trends and their inversions in the educational space of our time. 1. The tendency to eliminate the intermediary in the educational space leads to an opposite effect of multiplying intermediaries and awareness of teacher’s unavoidability as one of the main participants in education, both due to the complexity and inaccessibility for direct study of educational material and the fact that education is not based on transmission (in which the intermediary could theoretically be eliminated), but creation based on the phenomenon of meeting. 2. The transgressive tendency as “going beyond” and even erasing boundaries is transformed in the educational space into a tendency of distancing as a double movement of alienation (delineation) and inclusion (communion), in which the blurring of the boundary between the public and intimate, the space of self-organization in the order of the norm and the space of spontaneity, real and virtual, etc. is accompanied by the establishment of a new type of socialization in conditions of spatial disunity and social deprivation. 3. The appearance of a diffuse identity reinforced by the tendency to glocalization in the socio-political sphere and the transition to a transmedia narrative in the cultural and entertainment sphere, in the digital space of education is transformed into the possibility of self-actualization, self-expression, turning the modern educational space into a mythopoetic “world of all kinds” and anthropological singularity.
Reference-frame-independent quantum key distribution with advantage distillation

Advantage distillation (AD) provides a means of separating highly correlated raw key bits from weakly correlated information in quantum key distribution (QKD). In this Letter, we apply the AD method to improve the performance of reference-frame-independent QKD (RFI-QKD). Simulation results show that, compared with RFI-QKD without AD, RFI-QKD with AD can tolerate higher system errors and obtain better performance on the secret key rate and transmission distance. Furthermore, we extend the AD method to RFI measurement-device-independent QKD (RFI-MDI-QKD) and demonstrate that the AD method can improve the performance of RFI-MDI-QKD more significantly.
A state response measurement model for problem-solving process data

In computer simulation-based interactive tasks, different people make different response processes to the same tasks, resulting in various action sequences. These sequences contain rich information, not only about respondents, but also about tasks. In this study, we propose a state response (SR) measurement model with a Bayesian approach for analyzing the process sequences, which assumes that each action made is determined by the individual's problem-solving ability and the easiness of the current problem state. This model is closer to reality compared with the action sub-model (referred to as DC model) of Chen's (2020) continuous-time dynamic choice (CTDC) measurement model that defines the easiness parameter only at the task level and ignores the task's process characteristics. The simulation study showed that the SR model performed well in parameter estimation. Moreover, the estimation accuracy of the SR model was quite similar to that of the DC model when state easiness parameters were equal within the task, but was much higher when within-task state easiness parameters were unequal. For the empirical data from the Program for International Student Assessment 2012, the SR model showed better model fit than the DC model. The estimates for state easiness parameters within each task were obviously different and made sense for characterizing task steps, further demonstrating the rationality of the proposed SR model.
Incentive-Aware Blockchain-Assisted Intelligent Edge Caching and Computation Offloading for IoT

The rapid development of artificial intelligence has pushed the Internet of Things (IoT) into a new stage. Facing with the explosive growth of data and the higher quality of service required by users, edge computing and caching are regarded as promising solutions. However, the resources in edge nodes (ENs) are not inexhaustible. In this paper, we propose an incentive-aware blockchain-assisted intelligent edge caching and computation offloading scheme for IoT, which is dedicated to providing a secure and intelligent solution for collaborative ENs in resource optimization and controls. Specifically, we jointly optimize offloading and caching decisions as well as computing and communication resources allocation to minimize the total cost for tasks completion in the EN. Furthermore, a blockchain incentive and contribution co-aware federated deep reinforcement learning algorithm is designed to solve this optimization problem. In this algorithm, we construct an incentive-aware blockchain-assisted collaboration mechanism which operates during local training, with the aim to strengthen the willingness of ENs to participate in collaboration with security guarantee. Meanwhile, a contribution-based federated aggregation method is developed, in which the aggregation weights of EN gradients are based on their contributions, thereby improving the training effect. Finally, compared with other baseline schemes, the numerical results prove that our scheme has an efficient optimization utility of resources with significant advantages in total cost reduction and caching performance.
Enhancing Mask Transformer with Auxiliary Convolution Layers for Semantic Segmentation

Transformer-based semantic segmentation methods have achieved excellent performance in recent years. Mask2Former is one of the well-known transformer-based methods which unifies common image segmentation into a universal model. However, it performs relatively poorly in obtaining local features and segmenting small objects due to relying heavily on transformers. To this end, we propose a simple yet effective architecture that introduces auxiliary branches to Mask2Former during training to capture dense local features on the encoder side. The obtained features help improve the performance of learning local information and segmenting small objects. Since the proposed auxiliary convolution layers are required only for training and can be removed during inference, the performance gain can be obtained without additional computation at inference. Experimental results show that our model can achieve state-of-the-art performance (57.6% mIoU) on the ADE20K and (84.8% mIoU) on the Cityscapes datasets.
The learnability of Pauli noise

Recently, several quantum benchmarking algorithms have been developed to characterize noisy quantum gates on today's quantum devices. A fundamental issue in benchmarking is that not everything about quantum noise is learnable due to the existence of gauge freedom, leaving open the question what information is learnable and what is not, which is unclear even for a single CNOT gate. Here we give a precise characterization of the learnability of Pauli noise channels attached to Clifford gates using graph theoretical tools. Our results reveal the optimality of cycle benchmarking in the sense that it can extract all learnable information about Pauli noise. We experimentally demonstrate noise characterization of IBM's CNOT gate up to 2 unlearnable degrees of freedom, for which we obtain bounds using physical constraints. In addition, we show that an attempt to extract unlearnable information by ignoring state preparation noise yields unphysical estimates, which is used to lower bound the state preparation noise.
Multi-Task Learning Using BERT With Soft Parameter Sharing Between Layers

The ability to learn from one task and pass the knowledge to another task is a remarkable job that is possible because of multi-task learning (MTL). It is helpful in the scenario where data is scarce. In this paper, we study the use of MTL in natural language processing (NLP) using BERT for sentiment classification, category classification, and aspect-opinion sequence classification. The Restaurant-ACOS dataset is split into two subsets, namely the sentiment-category data subset, for the first two tasks and the sequence classification data subset, for the third task. The BERT layers are grouped into top, middle, and bottom layers. These layers are either frozen, shared, or kept unshared for individual tasks. Experiments are conducted to find the best combination for the overall layers of BERT. The experiments demonstrate that MTL can improve the performance on these tasks when compared as a baseline method that uses single task learning. One of the best configurations was based on freezing on the bottom layers, sharing on the middle layers, and keeping on the top layers, respectively. In addition, we investigate the role of each layer via the size of layers. From the result, the individual layers capture the task-specific patterns whereas shared layers capture the domain knowledge.