Reviewerly/exHarmony huggingface dataset:
PXCrypto: A Regulated Privacy-Preserving Cross-Chain Transaction Scheme

The cross-chain technology is used to solve the problem of data islands between blockchains. Existing cross-chain techniques are challenging to resolve the conflict between transaction privacy and transaction regulation. Due to the decentralized nature of the blockchain, data on one chain are hard to get verified on another chain. Users cannot exchange across different heterogeneous chains without exposing transaction information. Also, cross-chain transactions increase the barrier to regulation from governments. To fill the gap, this paper proposes an innovative regulated privacy-preserving cross-chain transaction framework, called PXCrypto, which can achieve cross-chain transactions on any heterogeneous blockchain while regulating transactions and users’ identities. PXCrypto maps heterogeneous tokens to the PXCrypto consortium chain by presenting cross-chain asset proofs, and then uses the corresponding wrapped tokens for trading. The scheme leverages a proxy multi-party computation mechanism to achieve cross-chain confidential transactions using an order matching mode to protect the bid prices on transaction orders. PXCrypto conducts transaction-related calculations through a pre-elected committee to regulate these transactions through the regulatory authority. We implement the scheme and accordingly conduct the evaluations. The results prove its security, efficiency, and practicality.
Visual-inertial lateral velocity estimation for motorcycles using inverse perspective mapping

In this paper, the authors propose a visual-inertial algorithm to estimate the lateral velocity of a motorcycle traveling at high speed along a single-carriageway road. The approach comprises the following steps. First, a monocular camera captures real-time images of the road ahead. Lane markers present in the image are detected and segmented using image processing techniques. Next, a bird's eye view transform is applied, and the dashed center lane markers are isolated. The motion of these markers is computed using an image registration algorithm and is expressed in the motorcycle body frame using orientation estimates from an inertial measurement unit. Finally, this measurement is combined with readings from an accelerometer using a Kalman filter to produce a filtered estimate. The approach was validated using data from simulations of two scenarios created in the BikeSim simulation software suite. In the first scenario, the motorcycle performs a double lane-change across both lanes of a straight road. In the second, the motorcycle navigates an s-shaped bend.
DimCL: Dimensional Contrastive Learning for Improving Self-Supervised Learning

Self-supervised learning (SSL) has gained remarkable success, for which contrastive learning (CL) plays a key role. However, the recent development of new non-CL frameworks has achieved comparable or better performance with high improvement potential, prompting researchers to enhance these frameworks further. Assimilating CL into non-CL frameworks has been thought to be beneficial, but empirical evidence indicates no visible improvements. In view of that, this paper proposes a strategy of performing CL along the dimensional direction instead of along the batch direction as done in conventional contrastive learning, named Dimensional Contrastive Learning (DimCL). DimCL aims to enhance the feature diversity, and it can serve as a regularizer to prior SSL frameworks. DimCL has been found to be effective, and the hardness-aware property is identified as a critical reason for its success. Extensive experimental results reveal that assimilating DimCL into SSL frameworks leads to performance improvement by a non-trivial margin on various datasets and backbone architectures.
NuKit: A deep learning platform for fast nucleus segmentation of histopathological images

Nucleus segmentation represents the initial step for histopathological image analysis pipelines, and it remains a challenge in many quantitative analysis methods in terms of accuracy and speed. Recently, deep learning nucleus segmentation methods have demonstrated to outperform previous intensity- or pattern-based methods. However, the heavy computation of deep learning provides impression of lagging response in real time and hampered the adoptability of these models in routine research. We developed and implemented NuKit a deep learning platform, which accelerates nucleus segmentation and provides prompt results to the users. NuKit platform consists of two deep learning models coupled with an interactive graphical user interface (GUI) to provide fast and automatic nucleus segmentation “on the fly”. Both deep learning models provide complementary tasks in nucleus segmentation. The whole image segmentation model performs whole image nucleus whereas the click segmentation model supplements the nucleus segmentation with user-driven input to edits the segmented nuclei. We trained the NuKit whole image segmentation model on a large public training data set and tested its performance in seven independent public image data sets. The whole image segmentation model achieves average [Formula: see text] and [Formula: see text]. The outputs could be exported into different file formats, as well as provides seamless integration with other image analysis tools such as QuPath. NuKit can be executed on Windows, Mac, and Linux using personal computers.
A Sublinear-Time Quantum Algorithm for Approximating Partition Functions

We present a novel quantum algorithm for estimating Gibbs partition functions in sublinear time with respect to the logarithm of the size of the state space. This is the first speed-up of this type to be obtained over the seminal nearly-linear time algorithm of Štefankovič, Vempala and Vigoda [45]. Our result also preserves the quadratic speed-up in precision and spectral gap achieved in previous work by exploiting the properties of quantum Markov chains. As an application, we obtain new polynomial improvements over the best-known algorithms for computing the partition function of the Ising model, counting the number of k-colorings, matchings or independent sets of a graph, and estimating the volume of a convex body.Our approach relies on developing new variants of the quantum phase and amplitude estimation algorithms that return nearly unbiased estimates with low variance and without destroying their initial quantum state. We extend these subroutines into a nearly unbiased quantum mean estimator that reduces the variance quadratically faster than the classical empirical mean. No such estimator was known to exist prior to our work. These properties, which are of general interest, lead to better convergence guarantees within the paradigm of simulated annealing for computing partition functions.* The full version of the paper can be accessed at http://arxiv.org/abs/2207.08643.
Human Knowledge and Visual Intelligence on SDX<sup>tension</sup>B

The Self-Driving Sweeper Bot (SDSB) was previously reported by incorporated the Self-Driving system with the Sweeper mechanism, after several months and numerous times of running test at a pre-planned close-loop campus route, our observation with the best knowledge indicates that SDSB is extendable to be SDX <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tension</sup> B, such as SD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LM</sup> B incorporated Self-Driving Bot with Lawn Mower mechanism, SDPB integrated Self-Driving Bot with Paver mechanism and SD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SK</sup> B is together the Self-Driving Bot with StacKer etc. To reach SDX <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tension</sup> B with visual intelligence and human knowledge capabilities, the observation further reports that three important issues have to be further inspecting, including, 1) complex environmental noises for machine visual learning, 2) modelling trigger-event for failed event to functionality restoration, and 3) sensors security against hacker remote control. Besides that, the investigation on SDSB visual intelligence in terms of road segmentation, rubbish, pedestrian and vehicle detections by YOLO and U-net extension approaches were validated on its promising accuracy and feasibility to reach SDSB visual intelligence.
Systems Theoretic Process Analysis of a Run Time Assured Neural Network Control System

This research considers the problem of identifying safety constraints and developing Run Time Assurance (RTA) for Deep Reinforcement Learning (RL) Tactical Autopilots that use neural network control systems (NNCS). This research studies a specific use case of an NNCS performing autonomous formation flight while an RTA system provides collision avoidance and geofence assurances. First, Systems Theoretic Accident Models and Processes (STAMP) is applied to identify accidents, hazards, and safety constraints as well as define a functional control system block diagram of the ground station, manned flight lead, and surrogate unmanned wingman. Then, Systems Theoretic Process Analysis (STPA) is applied to the interactions of the the ground station, manned flight lead, surrogate unmanned wingman, and internal elements of the wingman aircraft to identify unsafe control actions, scenarios leading to each, and safety requirements to mitigate risks. This research is the first application of STAMP and STPA to an NNCS bounded by RTA.
SimQN: A Network-Layer Simulator for the Quantum Network Investigation

Along with the rapid development of quantum information technologies, quantum networks that transmit quantum states have gradually shifted from laboratory to reality. However, the high cost of building a quantum network testbed seriously hinders the research progress of quantum networks, and the existing simulators have been developed with less exposure to network models. To overcome those deficiencies, this article proposes a new discrete-event driven network-layer simulation framework named SimQN, available at https://github.com/ertuil/SimQN, to provide full functionalities of the quantum networking simulation. The design goal of SimQN is to make it easy-to-configure while providing high performance and accurate simulation capability. We present multiple simulation physical backends to enable different functionalities and reduce computational overhead. The modularized SimQN also provides reusable network protocols and utilities to release the large-scale quantum networking evaluation burden. We present several demonstrations and analyses to illustrate the protocol design and the performance of SimQN.
Short-lived Zero-Knowledge Proofs and Signatures

We introduce the short-lived proof, a non-interactive proof of knowledge with a novel feature: after a specified period of time, the proof is no longer convincing. This time-delayed loss of soundness happens “naturally” without further involvement from the prover or any third party. We propose definitions for short-lived proofs as well as the special case of short-lived signatures. We show several practical constructions built using verifiable delay functions (VDFs). The key idea in our approach is to allow any party to forge any proof by executing a large sequential computation. Some constructions achieve a stronger property called reusable forgeability in which one sequential computation allows forging an arbitrary number of proofs of different statements. We also introduces two novel types of VDFs, re-randomizable VDFs and zero-knowledge VDFs, which may be of independent interest. Our constructions for short-lived $$\Sigma $$ -protocols and signatures are practically efficient for provers and verifiers, adding a few hundred bytes of overhead and tens to hundreds of milliseconds of proving/verification time.
Teaching English to Aircraft Maintenance Students: Challenges and Needs

Aviation English is a specialized language mainly used in aviation, such as in aircraft maintenance areas. Teaching Aviation English usually faces more challenges compared with teaching general English. It pays more attention to the specific needs of learners and the language in the context. Therefore, this study aimed to discuss the challenges of English teachers in teaching English to aircraft maintenance students. Semi-structured interviews were conducted with the three English teachers from three different aviation schools in Malaysia. The data were analyzed thematically and managed by ATLAS.ti software. Overall, the findings showed that the teachers face challenges in teaching and designing Aviation English materials, activities, and syllabi. It was revealed that there was a shortage of textbooks and materials on Aviation English. Another challenge is related to the student's motivation and language proficiency and teachers' excessive workload. Meanwhile, in terms of needs, they should have support in some aspects like teaching materials sources and Aviation English training. Besides that, there is a need for the teachers to collaborate with people from the industry in order to find out their requirements and revise the English course accordingly. The revealed challenges and needs would help the teachers and learning institutions develop solutions to fulfill the needs of the teachers and aviation students.
Intra-Batch Supervision for Panoptic Segmentation on High-Resolution Images

Unified panoptic segmentation methods are achieving state-of-the-art results on several datasets. To achieve these results on high-resolution datasets, these methods apply crop-based training. In this work, we find that, although crop-based training is advantageous in general, it also has a harmful side-effect. Specifically, it limits the ability of unified networks to discriminate between large object instances, causing them to make predictions that are confused between multiple instances. To solve this, we propose Intra-Batch Supervision (IBS), which improves a network’s ability to discriminate between instances by introducing additional supervision using multiple images from the same batch. We show that, with our IBS, we successfully address the confusion problem and consistently improve the performance of unified networks. For the high-resolution Cityscapes and Mapillary Vistas datasets, we achieve improvements of up to +2.5 on the Panoptic Quality for thing classes, and even more considerable gains of up to +5.8 on both the pixel accuracy and pixel precision, which we identify as better metrics to capture the confusion problem.
Self-Aware SGD: Reliable Incremental Adaptation Framework for Clinical AI Models

Healthcare is dynamic as demographics, diseases, and therapeutics constantly evolve. This dynamic nature induces inevitable distribution shifts in populations targeted by clinical AI models, often rendering them ineffective. Incremental learning provides an effective method of adapting deployed clinical models to accommodate these contemporary distribution shifts. However, since incremental learning involves modifying a deployed or in-use model, it can be considered unreliable as any adverse modification due to maliciously compromised or incorrectly labelled data can make the model unsuitable for the targeted application. This paper introduces self-aware stochastic gradient descent (SGD), an incremental deep learning algorithm that utilises a contextual bandit-like sanity check to only allow reliable modifications to a model. The contextual bandit analyses incremental gradient updates to isolate and filter unreliable gradients. This behaviour allows self-aware SGD to balance incremental training and integrity of a deployed model. Experimental evaluations on the Oxford University Hospital datasets highlight that self-aware SGD can provide reliable incremental updates for overcoming distribution shifts in challenging conditions induced by label noise.
A Comparative study of Lexicon based and Machine learning based Classifications in Sentiment analysis

Internet and WWW are growing a huge amount of data from the users entered the text that is related to the product review, opinions, attitudes, and any other services. This information is processing and analyzing various tools to be used. The NLP and Information retrieval methods are used to analyze the information and understanding these process information. The main problem of Sentiment analysis is classified into two categories that are Positive opinion or negative opinion. This paper focus to compare the lexical based sentiment classification and Machine learning based sentiment classification methods. Various algorithms and approaches and features are classified in this paper. Different methods are taken to solve the sentiment analysis problems.
Unitary partitioning and the contextual subspace variational quantum eigensolver

The contextual subspace variational quantum eigensolver (CS-VQE) is a hybrid quantum-classical algorithm that approximates the ground-state energy of a given qubit Hamiltonian. It achieves this by separating the Hamiltonian into contextual and noncontextual parts. The ground-state energy is approximated by classically solving the noncontextual problem, followed by solving the contextual problem using VQE, constrained by the noncontextual solution. In general, computation of the contextual correction needs fewer qubits and measurements compared with solving the full Hamiltonian via traditional VQE. We simulate CS-VQE on different tapered molecular Hamiltonians and apply the unitary partitioning measurement reduction strategy to further reduce the number of measurements required to obtain the contextual correction. Our results indicate that CS-VQE combined with measurement reduction is a promising approach to allow feasible eigenvalue computations on noisy intermediate-scale quantum devices. We also provide a modification to the CS-VQE algorithm; the CS-VQE algorithm previously could cause an exponential increase in Hamiltonian terms but with this modification now at worst will scale quadratically.
“We’re led by stupid people”: Exploring Trump’s use of denigrating and deprecating speech to promote hatred and violence

Abstract In response to a call for criminologists to consider the impact of former President Donald Trump’s presumed criminality, we analyze verbal-textual hostility (VTH) in Trump’s campaign speeches. Politicians have particular power and reach with their speech and their use of VTH is an important part of the trifecta of violence. Using a framework informed by linguistic theory and previous analysis of hate speech in recorded hate crimes, we present the categories of deprecation and denigration, and discuss their relationship to domination. In context, these forms of VTH enhance and serve as precursors to more violent speech and acts.
A Framework for Storage-Accuracy Optimization of IoT Forensic Analysis

The proliferation of Internet of Things (IoT) devices, coupled with the recent popularity of machine-learning and artificial intelligence has given birth to a new research field named IoT forensics. Such a new field considers network traffic from IoT devices as possible source of evidence for forensic investigations. However, the massive amount of IoT devices and traffic produced makes storage challenging, especially when this is performed on limited-resource edge devices such as e.g., WiFi access points. This paper proposes a framework to optimize the storage-accuracy trade-offs of IoT forensic analysis tasks. The goal of the framework is to find the optimal working point in terms of number of features to extract from network traffic and the number of bits used for quantizing each feature, in order to maximize the IoT forensic task accuracy under storage constraints. After presenting the framework, we validate it over two different IoT forensics tasks: IoT device identification and activity recognition from encrypted traffic of IoT cameras. Results show that with low effort it is possible to find the optimal settings to operate to maximize the analysis accuracy under given storage limitations.
A Study on the Effect of Digital Literacy Competency on Learning Flow Earning Satisfaction and Learning Outcomes of College Students Majoring in Aviation Service

Recently, the acquisition and production of information using digital tools and the creation of new knowledge are emphasized as important educational elements. Therefore, in this study, the effect of learning achievement according to the digital literacy level of college students was analyzed. For the analysis, a questionnaire is conducted with college students majoring in aviation services attending universities in Seoul Capital Area and Chungcheong area. To verify the hypothesis of the study, demographic characteristics are identified based on the questionnaire, reliability and validity of measurement items are verified, and structural equation model analysis is performed to verify the hypothesis. The analysis results are as follows. First, among the sub-factors of digital literacy competency of college students majoring in aviation service, ‘technology use’ is found to have a positive effect on ‘cognitive flow’ and ‘emotional flow’ of learning flow except ‘behavioral flow’. Second, among the sub-factors of digital literacy competency, ‘self-learning’ is found to have a positive effect on ‘cognitive flow’, ‘emotional flow’, and ‘behavioral flow’ in learning flow. Third, the sub-factors of learning flow, ‘cognitive flow’, ‘emotional flow’, and ‘behavioral flow’ have a positive effect on ‘learning satisfaction’. Fourth, ‘learning satisfaction’ is found to have a positive effect on ‘learning outcomes’. Based on the research results, practical support measures and strategies for educational success are presented.
Wavefront lens corrector for studying flat surfaces

The method of manufacturing and the results of studies of a lens corrector that converts a spherical diverging front into a plane one and is intended for studying flat surfaces as part of an interferometer with a diffraction comparison wave is described. A feature of the corrector is the use of an aspherical convex surface with a maximum deviation of ~ 200 μm from the nearest sphere. The first experimental results are presented, indicating the prospects for using ion-beam processing to improve the quality of the wavefront. After the procedure of ion-beam processing, the aberrations over the entire aperture of the corrector decreased by more than 4 times and amounted to the parameter of the height difference PV = 207 nm (~ λ/3) and (RMS) = 19.2 nm (~ λ/33). On an area with a diameter of 80%, the aberrations fell to the nanometer level: PV = 65 nm (~ λ/10) and RMS = 8.3 nm (~ λ/76). Keywords: lens corrector, interferometry, aspherical lens, ion-beam processing.
Breast Cancer Detection: A Survey

This chapter looks at several commonly used breast cancer datasets, whether they are text or images, as well as related works demonstrating previous efforts for breast cancer diagnosis. Wisconsin breast cancer dataset downloaded from the UCI machine learning repository in this part. This dataset is utilized to separate between cancerous and benign tumors. The breast carcinoma subtyping dataset is a vast collection of annotated Hematoxylin and Eosin-stained pictures designed to aid in the diagnosis of breast lesions. The breast cancer semantic segmentation dataset comprises approximately 20,000 tissue region segmentation annotations from the cancer genome Atlas breast cancer pictures. The breast cancer histopathological image classification is made up of 9109 microscopic images of breast tumor tissue collected from 82 people at various magnification levels. PatchCamelyon is a dataset for picture categorization. It is made up of 327.680 color pictures taken from lymph node histopathologic scans.