Reviewerly/exHarmony huggingface dataset:
Isabelle/Cloud: Delivering Isabelle/HOL as a Cloud IDE for Theorem Proving

As online coding technology advances, various related products are emerging, but we observe that there are not many examples of introducing online coding into the field of theorem proving. We introduce Isabelle/Cloud, an online coding platform and user environment for the Isabelle theorem proving assistant. The primary objective of Isabelle/Cloud is to cloudify Isabelle using online coding technology, thereby addressing the issue of loading large projects. Leveraging the understanding of the Isabelle architecture, we have modified, replaced, and added some modules, encapsulated the Isabelle environment using containers, and developed the front-end and back-end. As a cloud platform, Isabelle/Cloud enables users to create a complete Isabelle environment with different versions that are isolated from each other, while providing basic cloud coding and theorem proving services. The current version integrates most of the popular Isabelle libraries with excellent tutorials and cases, enabling users to directly create projects from the tutorial code for practical exercises. Evaluation of the platform shows that Isabelle/Cloud performs better when dealing with large projects. The new platform opens up new possibilities for interaction and presentation, and it is currently in use.
Comparing the Performance of Different Code Representations for Learning-based Vulnerability Detection

Software vulnerabilities can cause severe security threats to cyberspace, and it is of significant importance to conduct automated vulnerability detection research. Considering that the source code contains rich syntax and semantic information, plenty of learning-based techniques and code representation methods have been proposed to detect vulnerabilities automatically. The most popular code representation methods include static code metrics, code token sequences and code graph structures. Although promising results have been reported by recent studies, there is an emerging urgent demand to understand which aspects contributed and affected most to the performance of learning-based techniques. To address this gap, the paper empirically evaluated and compared various learning-based vulnerability detection approaches, including five different code representations and thirteen different learning models. More importantly, the paper extended a large-scale dataset collected from open-source software systems, and the extensive evaluations have revealed novel and interesting findings that shed light on future research on learning-based vulnerability detection.
Structural-semantics Guided Program Simplification for Understanding Neural Code Intelligence Models

Neural code intelligence models are cutting-edge automated code understanding technologies that have achieved remarkable performance in various software engineering tasks. However, the lack of deep learning models’ interpretability hinders the application of deep learning based code intelligence models in real-world scenarios, particularly in security-critical domains. Previous studies use program simplification to understand neural code intelligence models, but they have overlooked the fact that the most significant difference between source code and natural language is the code’s structural semantics.
FAEG: Feature-Driven Automatic Exploit Generation

Buffer overflow vulnerabilities are prevalent in software applications, and their automatic detection and exploitation are of great significance. Modern operating systems implement security mitigation to prevent the exploitation of these vulnerabilities, which in turn become obstacles for automatic exploit generation (AEG). Many current AEG solutions do not fully consider security mitigation bypassing and the exploitation of vulnerabilities in special cases, resulting in an inability to accurately assess the exploitability of vulnerabilities in such scenarios. In this paper, we propose a feature-driven buffer overflow vulnerability automatic exploit generation method - FAEG, which uses optimized symbolic execution to search target software for potential buffer overflow vulnerabilities, constructs complete vulnerability models, and then adaptively selects appropriate exploitation techniques based on vulnerability type and features, bypassing system protection and generating effective exploit program.
Multi-angle Scheimpflug projection 3D microscope: Design, calibration, and three-dimensional reconstruction

3D measurement of complex micro-structures has becoming increasingly important for precision manufacturing. In this paper, a multi-angle Scheimpflug projection 3D microscope (MASP-3DM) is proposed, and the detailed design procedure is given. The system deployed four Scheimpflug projections and one telecentric imaging to overcome the shortcomings of limited depth of field (DOF), occlusion and reflection. Then, an improved and practical system calibration method is proposed, which utilizes pinhole projection model to calibrate the telecentric imaging. Meanwhile, local homography is combined with perspective-n-point (PnP) strategy to improve the calibration accuracy. Further, a point-cloud registration error correction model is established to achieve complete and high-precision measurement. The experimental results indicate that the re-projection error is within ±0.5μm and the registration error is less than 2μm for system calibration, the maximum of mean deviation, measurement bias, standard deviation, and type A uncertainty are respectively less than 3μm, 7μm, 4μm, and 1μm for 3D measurement, within the 10(H)mm×50(W)mm×40(D)mm volume.
UbiCap: A Capability-based Run-time Model for Heterogeneous Sensors Management in Ubiquitous Operating System

The Ubiquitous Operating System(UOS) is a new type of operating system in response to the new patterns and scenarios of future human-cyber-physical ternary ubiquitous computing. Compared with traditional operating systems, one of the fundamental requirements of UOS is to adaptively manage numerous heterogeneous sensors according to dynamic environments and diverse tasks. However, traditional management focuses on the sensors’ parameters and interfaces without highlighting the perception effect that is users’ concern and dynamic changing. It also lacks a unified management approach for heterogeneous sensors. To overcome the limitations, we propose a novel heterogeneous sensors dynamic management model UbiCap, i.e., Ubiquitous Capability, which is based on the capability abstraction and adaptive run-time capability management mechanism. The capability provides a unified abstract for heterogeneous sensors. The adaptive run-time capability management mechanism transfers the management object from low-level hardware sensors to high-level sensing capability. The capability required and the available capability are matched to support run-time adaptive sensors selection. We implement a software prototype iS2ROS(intelligent Sensor Selection Robot Operating System) based on the UbiCap model. We then simulate a forest fire spot monitoring scenario where iS2ROS selects the optimal image sensor during the identification task execution while light or weather condition changes. Experiment results show that the iS2ROS achieves comparative sensing effectiveness through UbiCap with 50% power consumption lower compared to the traditional both-sensors approach.
Move Annual Fund Donors Into the Major Gift Pipeline

The Major Gifts ReportVolume 25, Issue 11 p. 1-1 Capital Campaign Management Move Annual Fund Donors Into the Major Gift Pipeline Kim Pawlak, Kim PawlakSearch for more papers by this author Kim Pawlak, Kim PawlakSearch for more papers by this author First published: 05 October 2023 https://doi.org/10.1002/mgr.32242Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume25, Issue11November 2023Pages 1-1 RelatedInformation
GAMNet: Global attention via multi‐scale context for depth estimation algorithm and application

Abstract Deep neural networks significantly enhance the accuracy of the stereo‐based disparity estimation. Some current methods suffer from inefficient use of the global context information, which will lead to the loss of structural details in ill‐posed areas. To this end, a novel stereo network GAMNet is designed, composed of three core components (GDA, MPF, DCA) for estimating the depth prediction in challenging real‐world environments. First, a lightweight attention module is presented, integrating the global semantic cues for every feature position across the channel and spatial dimensions. Next, the MPF module is constructed to fuse the diverse semantic and contextual information from different levels of the feature pyramid. Finally, cost volume is aggregated with a stacked encoder‐decoder composed of the DCA module and 3D convolutions, filtering the transmission of matching clues and capturing the rich global contexts. Substantial experiments conducted on KITTI 2012, KITTI 2015, SceneFlow, and Middlebury‐v3 datasets manifest that GAMNet surpasses preceding methods with contour‐preserving disparity predictions. In addition, the first 3D scene reconstruction linear evaluation strategy on spatial grasping points for the end‐to‐end stereo networks in an unsupervised mode is proposed, and it is deployed on the designed robot vision‐guided system. In application experiments, the method can produce densely high‐precision 3D reconstructions to implement the grasping task in complex real‐world scenes, and achieve excellent robust performance with competitive inference efficiency.
New wave front phase sensor used for 3D shape measurements of patterned silicon wafers

On product overlay (OPO) is one of the most critical parameters for the continued scaling according to Moore’s law. Without good overlay between the mask and the silicon wafer inside the lithography tool, yield will suffer. As the OPO budget shrinks, non-lithography process induced stress causing in-plane distortions (IPD) becomes a more dominant contributor to the shrinking overlay budget. To estimate the process induced in-plane wafer distortion after cucking the wafer onto the scanner board, a high-resolution measurement of the freeform wafer shape of the unclamped wafer, with the gravity effect removed, is needed. A high-resolution wafer shape map using a feed-forward prediction algorithm, as has been published by ASML, can account for both intra and inter die wafer distortions, minimizing the need for alignment marks on the die and wafer in addition to that it can be performed at any lithography layer. Up until now, the semiconductor industry has been using Coherent Gradient Sensing (CGS) interferometry or Fizeau interferometry to generate the wave front phase from the reflecting wafer surface to measure the free form wafer shape. However, these techniques have only been available for 300mm wafers. In this paper we introduce Wave Front Phase Imaging (WFPI), a new technique that can measure the free form wafer shape of a patterned silicon wafer using only the intensity of the reflected light. In the WFPI system, the wafer is held vertically to avoid the effects of gravity during measurements. The wave front phase is then measured by acquiring only the 2-dimensional intensity distribution of the reflected non-coherent light at two or more distances along the optical path using a standard, low noise, CMOS sensor. This method allows for very high data acquisition speed, equal to the camera’s shutter time, and a high number of data points with the same number of pixels as available in the digital imaging sensor. In the measurements presented in this paper, we acquired 7.3 million data points on a full 200mm patterned silicon wafer with a lateral resolution of 65&mu;m. The same system presented can also acquire data on a 300mm silicon wafer in which case 16.3 million data points with the same 65&mu;m spatial resolution were collected.
A deep learning facilitated approach for SEM image denoising towards improved contour extraction for 1D and 2D structures

Flow from circuit design to manufacturing a mask is a complex process. A mask is used to print a particular design feature. Extraction of accurate CD contour geometries from ADI (after developed inspection) SEM images plays a pivotal role for a qualitative lithographic process as well as to verify device characterization in aggressive pitches. In our previous work, it has been shown how deep learning based de-noising is helping to improve the contour detection accuracy. We analysed and validated our result with noisy/denoised image pair for categorically different geometrical patterns, such as, L/S (linespace), T2T (tip-to-tip), pillars with different scan types etc., by using a programmable tool (Calibre®SEMSuite<sup>TM</sup>) for contour extraction and further analysis metrics. The comparative analysis demonstrated that denoised images have significantly higher confidence analysis metrics, reduced number of missing patterns as well as false bridges against raw noisy images while keeping the same parameter settings for both data inputs. We have demonstrated that our proposed method is capable to extract contours on the body of the noisy SEM images with accuracy in close proximity with design data. By combining these advanced algorithms as options in Calibre®SEMSuite<sup>TM</sup>, users would be able to process large amount of information for data cleaning, classification & further model calibration intelligently.
Post‐quantum secure two‐party computing protocols against malicious adversaries

Summary Secure two‐party computation allows a pair of parties to compute a function together while keeping their inputs private. Ultimately, each party receives only its own correct output. In this paper, a post‐quantum secure two‐party computation protocol is proposed that can be used to effectively block malicious parties. The protocol solves the problems of traditional protocols based on garbled circuits, which are vulnerable to quantum attacks, high communication costs and low computational efficiency. The input garbled keys of the circuit constructor is structured as a Learning with Error (LWE) equation, enabling the circuit constructor to employ a zero‐knowledge proof that demonstrates the uniformity of inputs across all circuits.In the key transfer phase, an LWE‐based batch single‐choice cut‐and‐choose oblivious transfer is proposed to avoid selective failure attacks. In addition, the protocol employs a penalty mechanism to detect if the circuit constructor has generated an incorrect circuit. We have compared the communication overhead of this protocol with three other secure two‐party computation protocols based on Cut‐and‐Choose technology. The analytical results show that this protocol has the best error probability and is resilient to quantum attacks under the malicious adversary model. In addition, with appropriate parameters, the protocol is able to reduce its communication bandwidth by an average of 40.41%.
Focusing performances of high numerical aperture Fresnel zone plates under different immersion media

The focusing performances of high-numerical-aperture (NA) Fresnel zone plates (FZP) under different immersion media are investigated by combining the vectorial angular spectrum (VAS) theory and the finite-difference time-domain method. It has been found that as the refractive index deviation increases, the focus approximately linearly shifts along the positive z-axis. Simultaneously, the transverse size of the focusing spot gradually decreases, and super-resolution focusing is realized. However, the peak intensity declines with an increase in the refractive index. In addition, when the deviation of the refractive index of the actual immersion medium from the design value is less than about 30%, VAS theory can correctly estimate the focusing light field distribution, except when the FZP has only one or two transparent annuli. On the contrary, VAS theory cannot achieve an accurate prediction when the deviation is larger. These findings will benefit the practical application of high-NA FZPs.
Approximate Similarity Joins over Dense Vector Embeddings

We consider the problem of efficiently answering similarity join queries over vector data generated by machine learning models. Owing to the high dimensionality and density of such vectors, approximate solutions are prevalent for dealing with large datasets. In this context, we investigate how to evaluate similarity joins using the Hierarchical Navigable Small World (HNSW), a state-of-the-art, graph-based index designed for approximate knearest neighbor (kNN) queries. We explore the design space of possible solutions, ranging from alternatives on top of HNSW to deeper integration of similarity join processing into this structure. Experimental results show that our proposal achieves substantial speedups with negligible accuracy loss.
Quantile Symbolic Aggregate approXimation: A guaranteed equiprobable SAX

Time series are broadly present in science and industry. In specific scenarios, it is useful to classify series in order to gain knowledge regarding a specific range of values. In such cases, we often use symbolic representation, as it can reduce the data dimensionality creating representative symbols, making the data discrete and allowing specialized algorithms to be applied to the data. One of the most prominent methods of this type of representation is the Symbolic Aggregate approXimation (SAX), which, in addition to generating the symbolic sequence, also reduces the data dimension. However, one of the problems of SAX is that, in order to guarantee the balance of symbols, it assumes the normality of the distribution, which fails in some distributions and causes the class imbalance problem. We propose a unique seamless approach to guarantee the balance among the classes, which may lead to better performance in classification algorithms.
Tubular structures segmentation of pediatric abdominal-visceral ceCT images with renal tumors: Assessment, comparison and improvement

Renal tubular structures, such as ureters, arteries and veins, are very important for building a complete digital 3D anatomical model of a patient. However, they can be challenging to segment from ceCT images due to their elongated shape, diameter variation and intra- and inter-patient contrast heterogeneity. This task is even more difficult in pediatric and pathological subjects, due to high inter-subject anatomical variations, potential presence of tumors, small volume of these structures compared to the surrounding, and small available labeled datasets. Given the limited literature on methods dedicated to children, and in order to find inspirational approaches, a complete assessment of state-of-the-art methods for the segmentation of renal tubular structures on ceCT images on adults is presented. Then, these methods are tested and compared on a private pediatric and pathological dataset of 79 abdominal-visceral ceCT images with arteriovenous phase acquisitions. To the best of our knowledge, both assessment and comparison in this specific case are novel. Eventually, we also propose a new loss function which leverages for the first time the use of vesselness functions on the predicted segmentation. We show that the combination of this loss function with state-of-the-art methods improves the topological coherence of the segmented tubular structures.2.
Generative inverse reinforcement learning for learning 2-opt heuristics without extrinsic rewards in routing problems

Deep reinforcement learning (DRL) has shown promise in solving challenging combinatorial optimization (CO) problems, such as the traveling salesman problem (TSP) and vehicle routing problem (VRP). However, existing DRL methods rely on manually designed reward functions, which may be inaccurate or unrealistic. Moreover, traditional DRL algorithms suffer from unstable training and sparse reward problems. This paper proposes GIRL (Generative Inverse Reinforcement Learning), a method to learn 2-opt heuristics without explicit extrinsic rewards to address these limitations. GIRL combines generative adversarial networks (GANs) and DRL to learn effective policies and reward functions in a reverse end-to-end fashion, improving generalization capabilities. Furthermore, we introduce a self-attentional policy network tailored for 2-opt heuristics and train the framework using a soft actor-critic algorithm along with a discriminator in the GAN. Extensive experiments on various TSP and VRP instances demonstrate superior performance compared to state-of-the-art methods. Moreover, integrating GANs and DRL enables data-driven reward functions, improving accuracy and realism. Using self-attentional networks and the soft actor-critic algorithm enhances training stability and addresses the sparse reward problem. This work advances reinforcement learning techniques in CO, enabling more accurate and practical optimization methods in real-world applications.
Exclusivity and consistency induced NMF for multi-view representation learning

Many unsupervised multi-view representation learning (MRL) techniques have been devised as multi-view data becomes more common in real-world applications. However, most existing MRL approaches solely concentrate on the diversity or the consistency between different views. In this paper, we propose a Non-negative Matrix Factorization based MRL framework, seeking to jointly consider two components. To be specific, the exclusivity term is designed to exploit the intra-view diverse information, while the consistency term is employed to ensure the common representation among multiple views. Meanwhile, the local manifold is added to preserve the local geometric structure of the data. Finally, we present a multiplicative-based alternating optimization algorithm to solve the problem, along with proofs of convergence. In addition, the experiments conducted on five multi-view datasets demonstrate the superiority of the proposed approach against the state-of-the-art MRL methods.
Task parse tree: Learning task policy from videos with task-irrelevant components

A good task policy should explicitly interpret the preconditions of actions and the composition structure of task. We aim to automatically learn such a task policy from videos which remains challenging at present. This issue can be further aggravated when task-irrelevant components are involved in videos, such as unoperated objects and small actions. Task-irrelevant objects may introduce disruptive visual relations, and task-irrelevant actions would lead to misleading and even failed task planning. Solving both issues simultaneously is beyond the scope of existing methods. To this end, we propose Task Parse Tree (TPT) as a novel task policy representation, distinguishing task-relevant actions with definite preconditions and clear execution order. The automatic generation of TPT relies on two core designs, where spatio-temporal graph (STG) seizes the vital changes in visual relations of objects and their attributes both spatially and temporally, and conjugate action graph (CAG) models the execution logic of actions in a graph. We collect a dataset of a real-world task, Make Tea, and experiment results on the dataset show that TPT realizes both accurate and interpretable task planning in two different scenarios.