Reviewerly/exHarmony huggingface dataset:
SVM-Lattice: A Recognition and Evaluation Frame for Double-Peaked Profiles

In big data era, the special data with rare characteristics may be of great significations.However, it is very difficult to automatically search these samples from the massive and high-dimensional datasets and systematically evaluate them.The DoPS, our previous work [1], provided a search method of rare spectra with double-peaked profiles from massive and high-dimensional data of LAMOST survey.The identification of the results is mainly depended on visually inspection by astronomers.In this paper, as a follow-up study, a new lattice structure named SVM-Lattice is designed based on SVM(Support Vector Machine) and FCL(Formal Concept Lattice) and particularly applied in the recognition and evaluation of rare spectra with double-peaked profiles.First, each node in the SVM-Lattice structure contains two components: the intents are defined by the support vectors trained by the spectral samples with the specific characteristics, and the relevant extents are all the positive samples classified by the support vectors.The hyperplanes can be extracted from every lattice node and used as classifiers to search targets by categories.A generalization and specialization relationship is expressed between the layers, and higher layers indicate higher confidence of targets.Then, including a SVM-Lattice building algorithm, a pruning algorithm based on association rules, and an evaluation algorithm, the supporting algorithms are provided and analysed.Finally, for the recognition and evaluation of spectra with double-peaked profiles, several data sets from LAMOST survey are used as experimental dataset.The results exhibit good consistency with traditional methods, more detailed and accurate evaluations of classification results, and higher searching efficiency than other similar methods.
A Virtual Dialogue Assistant for Conducting Remote Exams

In this paper, we demonstrate issues and possible solutions to building an Artificial Intelligence Dialogue Assistant for human-machine communication. We specialize it for conducting written exams at online education platforms, talk about the main logical components of the system: knowledge base, question encoder, question generation module, question analysis module. As a knowledge base we consider text fragments representing parts of the course of text fragments representing parts of a course and is a source for a format ontology; and is also sourced for neural network generation of fact-based questions in question generation module and building dependency trees for answer evaluation in question analysis module.
Automatic liver segmentation method based on improved region growing algorithm

It is difficult to select seed points for the region growing algorithm and the problem of irregular liver shape. This paper proposes an automatic liver segmentation method with the centroid as the seed point of the region growing algorithm. The adaptive median filtering in order to noise reduction and binarization are performed on the image, and the largest connected region of the CT image is locked as the initial contour of the liver. The centroid of the largest connected domain was obtained as the seed point location for regional growth, and the image was segmented using the dual-threshold regional growth method. The experimental results show, This method replaces the traditional method of manually selecting seed points, and solves the problem of manually selecting seed points for the region growing algorithm; At the same time, the use of double-threshold segmentation improves the accuracy of liver region segmentation, makes segmentation more accurate, and the edges and texture parts are smoother and sharper.
The Implementation of Static Telecytology for Teleconsultation Purposes During Preoperative Evaluation of Thyroid Fine-Needle Aspiration Specimens

Objective: The objective of this study was to investigate the impact of applying static telecytology for teleconsultation purposes during preoperative evaluation of challenging thyroid fine-needle aspiration specimens. Materials and Methods: The study was performed on 141 cytologically challenging specimens of 125 patients referred to Alpha Prolipsis Cytopathology Department. All cases were finally confirmed histologically. During the study, 10 characteristic images from each case were transferred through file transfer protocol to password-protected accounts for remote review by three independent cytopathologists, with documented experience in the field of thyroid cytology. In addition to diagnosis, reviewers also commented on overall digital image quality. Contributor's and reviewer's diagnoses were collected, recorded, and statistically evaluated. No significant difference in diagnostic accuracy could be detected between the diagnoses proffered based on digitized images and conventional slides. Telecytology is a prompt and valid method for acquisition of a second opinion diagnosis in challenging cases and can be integrated into daily workflow. The use of telecytology for teleconsultation purposes in the laboratory's daily workflow will further ensure the accuracy of preoperative cytological diagnoses and will contribute to cytopathologists continuous education and better understanding of the criteria applied in thyroid gland lesions cytomorphological diagnosis. Results: Statistical evaluation of cytological diagnoses detected no significant difference in diagnostic accuracy between the diagnoses proffered based on digitized images among contributing cytopathologists. The overall interobserver agreement was very good with κ values of 0.73–0.88. Conclusions: The widespread availability of imaging technology and telecommunication enables instant access to global expert cytopathologists. Static telecytology can be used as an efficient method for acquisition of second opinion in challenging thyroid fine-needle aspiration specimens. It can be used for rapid and accurate diagnosis, by diminishing turnaround times and improving small cytology departments' quality indices.
A template-based code generator for web applications

The importance and usage of web applications grow every day. Today from small businesses to large-scale corporations, many institutions prefer web applications for both their internal and external services. Code size and complexity of these kinds of applications grow rapidly. This brings up the question of how to improve the development process of web applications. A solution can be to use code generators. This paper introduces a template-based code generator to improve the development process of web applications. The code generator was developed and integrated into a real-life web application. Today, the web application together with the code generator is actively used in industry. This proves that an effective integration of a template-based code generator into a real-life large-scale web application can be achieved. In addition, the effectiveness of automatic code generation to manual implementation was shown with experimentation. Throughout the experiments, bug-free code generation was observed. Also, 98.95% improvement in average development time, 93.97% improvement in average test run count, and 49.37% improvement in average code size was achieved.
CoolMomentum: a method for stochastic optimization by Langevin dynamics with simulated annealing

Abstract Deep learning applications require global optimization of non-convex objective functions, which have multiple local minima. The same problem is often found in physical simulations and may be resolved by the methods of Langevin dynamics with Simulated Annealing, which is a well-established approach for minimization of many-particle potentials. This analogy provides useful insights for non-convex stochastic optimization in machine learning. Here we find that integration of the discretized Langevin equation gives a coordinate updating rule equivalent to the famous Momentum optimization algorithm. As a main result, we show that a gradual decrease of the momentum coefficient from the initial value close to unity until zero is equivalent to application of Simulated Annealing or slow cooling, in physical terms. Making use of this novel approach, we propose CoolMomentum—a new stochastic optimization method. Applying Coolmomentum to optimization of Resnet-20 on Cifar-10 dataset and Efficientnet-B0 on Imagenet, we demonstrate that it is able to achieve high accuracies.
Introduction to Explainable AI

As Artificial Intelligence (AI) technologies are increasingly used to make important decisions and perform autonomous tasks, providing explanations that allow users to understand the AI has become a ubiquitous concern in human-AI interaction. Recently, a number of open-source toolkits are making the growing collection of of Explainable AI (XAI) techniques accessible for researchers and practitioners to incorporate explanation features in AI systems. This course is open to anyone interested in implementing, designing and researching on the topic of XAI, aiming to provide an overview on the trends and methods of XAI and also help attendees gain hands-on experience of creating different styles of explanation with an XAI toolkit.
Pick-Object-Attack: Type-specific adversarial attack for object detection

Many recent studies have shown that deep neural models are vulnerable to adversarial samples: images with imperceptible perturbations, for example, can fool image classifiers. In this paper, we present the first type-specific approach to generating adversarial examples for object detection, which entails detecting bounding boxes around multiple objects present in the image and classifying them at the same time, making it a harder task than against image classification. We specifically aim to attack the widely used Faster R-CNN by changing the predicted label for a particular object in an image: where prior work has targeted one specific object (a stop sign), we generalise to arbitrary objects, with the key challenge being the need to change the labels of all bounding boxes for all instances of that object type. To do so, we propose a novel method, named Pick-Object-Attack. Pick-Object-Attack successfully adds perturbations only to bounding boxes for the targeted object, preserving the labels of other detected objects in the image. In terms of perceptibility, the perturbations induced by the method are very small. Furthermore, for the first time, we examine the effect of adversarial attacks on object detection in terms of a downstream task, image captioning; we show that where a method that can modify all object types leads to very obvious changes in captions, the changes from our constrained attack are much less apparent.
Fault-tolerant quantum error correction using error weight parities

In quantum error correction using imperfect primitives, errors of high weight arising from a few faults are major concerns since they might not be correctable by the quantum error correcting code. Fortunately, some errors of different weights are logically equivalent and the same correction procedure is applicable to all equivalent errors, thus correcting high-weight errors is sometimes possible. In this work, we introduce a technique called weight parity error correction (WPEC) which can correct Pauli error of any weight in some stabilizer codes provided that the parity of the weight of the error is known. We show that the technique is applicable to concatenated codes constructed from the [[7,1,3]] Steane code or the [[23,1,7]] Golay code. We also provide a fault-tolerant error correction protocol using WPEC for the [[49,1,9]] concatenated Steane code which can correct up to 3 faults and requires only 2 ancillas.
Multi-Action Dialog Policy Learning with Interactive Human Teaching

We present a framework for improving task-oriented dialog systems through online interactive teaching with human trainers. A dialog policy trained with imitation learning on a limited corpus may not generalize well to novel dialog flows often uncovered in live interactions. This issue is magnified in multi-action dialog policies which have a more expressive action space. In our approach, a pre-trained dialog policy model interacts with human trainers, and at each turn the trainers choose the best output among N-best multi-action outputs. We present a novel multi-domain, multi-action dialog policy architecture trained on MultiWOZ, and show that small amounts of online supervision can lead to significant improvement in model performance. We also present transfer learning results which show that interactive learning in one domain improves policy model performance in related domains.
Generating Robust Audio Adversarial Examples with Temporal Dependency

Audio adversarial examples, imperceptible to humans, have been constructed to attack automatic speech recognition (ASR) systems. However, the adversarial examples generated by existing approaches usually incorporate noticeable noises, especially during the periods of silences and pauses. Moreover, the added noises often break temporal dependency property of the original audio, which can be easily detected by state-of-the-art defense mechanisms. In this paper, we propose a new Iterative Proportional Clipping (IPC) algorithm that preserves temporal dependency in audios for generating more robust adversarial examples. We are motivated by an observation that the temporal dependency in audios imposes a significant effect on human perception. Following our observation, we leverage a proportional clipping strategy to reduce noise during the low-intensity periods. Experimental results and user study both suggest that the generated adversarial examples can significantly reduce human-perceptible noises and resist the defenses based on the temporal structure.
Using Fault Injection to Assess Blockchain Systems in Presence of Faulty Smart Contracts

Blockchain has become particularly popular due to its promise to support business-critical services in very different domains (e.g., retail, healthcare). Blockchain systems rely on complex middleware, like Ethereum or Hyperledger Fabric, that allow running smart contracts, which specify business logic in cooperative applications. The presence of software defects in these contracts has notably caused failures, including severe security problems. In this article, we use software-implemented fault injection (SWIFI) to assess the behavior of permissioned blockchain systems in the presence of faulty smart contracts. We emulate the occurrence of general software faults and also blockchain-specific software faults (e.g., missing require on transaction sender) in smart contracts code and observe the impact on the overall system dependability in terms of reliability and integrity. We also analyze the effectiveness of formal verification and runtime protection mechanisms in detecting the injected faults. Results indicate that formal verification and runtime protections have to complement built-in platform checks to guarantee proper dependability of blockchain systems. The work presented in this article allows smart contract developers to become aware of possible faults in smart contracts and to understand the impact of their presence. It also provides valuable information for middleware developers to improve the overall fault tolerance of their systems.
Deep Learning With Functional Inputs

We present a methodology for integrating functional data into deep densely connected feed-forward neural networks. The model is defined for scalar responses with multiple functional and scalar covariates. A by-product of the method is a set of dynamic functional weights that can be visualized during the optimization process. This visualization leads to greater interpretability of the relationship between the covariates and the response relative to conventional neural networks. The model is shown to perform well in a number of contexts including prediction of new data and recovery of the true underlying functional weights; these results were confirmed through real applications and simulation studies. A forthcoming R package is developed on top of a popular deep learning library (Keras) allowing for general use of the approach.
Inference on high-dimensional implicit dynamic models using a guided intermediate resampling filter

We propose a method for inference on moderately high-dimensional, nonlinear, non-Gaussian, partially observed Markov process models for which the transition density is not analytically tractable. Markov processes with intractable transition densities arise in models defined implicitly by simulation algorithms. Widely used particle filter methods are applicable to nonlinear, non-Gaussian models but suffer from the curse of dimensionality. Improved scalability is provided by ensemble Kalman filter methods, but these are inappropriate for highly nonlinear and non-Gaussian models. We propose a particle filter method having improved practical and theoretical scalability with respect to the model dimension. This method is applicable to implicitly defined models having analytically intractable transition densities. Our method is developed based on the assumption that the latent process is defined in continuous time and that a simulator of this latent process is available. In this method, particles are propagated at intermediate time intervals between observations and are resampled based on a forecast likelihood of future observations. We combine this particle filter with parameter estimation methodology to enable likelihood-based inference for highly nonlinear spatiotemporal systems. We demonstrate our methodology on a stochastic Lorenz 96 model and a model for the population dynamics of infectious diseases in a network of linked regions.
Learning Implicit Credit Assignment for Multi-Agent Actor-Critic.

We present a new policy-based multi-agent reinforcement learning algorithm that implicitly addresses the credit assignment problem under fully cooperative settings. Our key motivation is that credit assignment may not require an explicit formulation as long as (1) the policy gradients of a trained, centralized critic carry sufficient information for the decentralized agents to maximize the critic estimate through optimal cooperation and (2) a sustained level of agent exploration is enforced throughout training. In this work, we achieve the former by formulating the centralized critic as a hypernetwork such that the latent state representation is now fused into the policy gradients through its multiplicative association with the agent policies, and we show that this is key to learning optimal joint actions that may otherwise require explicit credit assignment. To achieve the latter, we further propose a practical technique called adaptive entropy regularization where magnitudes of the policy gradients from the entropy term are dynamically rescaled to sustain consistent levels of exploration throughout training. Our final algorithm, which we call LICA, is evaluated on several benchmarks including the multi-agent particle environments and a set of challenging StarCraft II micromanagement tasks, and we show that LICA significantly outperforms previous methods.
Noise Free Fully Homomorphic Encryption Scheme Over Non-Associative Algebra

Among several approaches to privacy-preserving cryptographic schemes, we have concentrated on noise-free homomorphic encryption. It is a symmetric key encryption that supports homomorphic operations on encrypted data. We present a fully homomorphic encryption (FHE) scheme based on sedenion algebra over finite ℤ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> rings. The innovation of the scheme is the compression of a 16-dimensional vector for the application of Frobenius automorphism. For sedenion, we have p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">16</sup> different possibilities that create a significant bijective mapping over the chosen 16-dimensional vector that adds permutation to our scheme. The security of this scheme is based on the assumption of the hardness of solving a multivariate quadratic equation system over finite ℤ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> rings. The scheme results in 256n multivariate polynomial equations with 256+16n unknown variables for n messages. For this reason, the proposed scheme serves as a security basis for potentially post-quantum cryptosystems. Moreover, after sedenion, no newly constructed algebra loses its properties. This scheme would therefore apply as a whole to the following algebras, such as 32-dimensional trigintadunion.
Classical communication through quantum causal structures

Quantum mechanics allows operations to be in indefinite causal order, leading to advantages in computational and communication tasks. However, basic notions such as communication capacity have not yet been formalized. In light of this, through the process matrix formalism, we formulate different classical capacities for a bipartite quantum process. For one-way communication, we find that general quantum processes satisfy a generalization of the Holevo bound, i.e., we can send, at most, one bit per qubit. The result also holds for processes with indefinite causal order and with shared entanglement between the sender and the receiver. We further extend known bounds to classical channel capacities, showing that general processes cannot outperform causally separable ones. Next, we study bidirectional communication through a causally separable process. Our result shows that a bidirectional protocol cannot exceed the limit of a one-way communication protocol. Finally, we generalize this result to a multiparty broadcast communication protocol through a definite ordered process.
VGG-16 Convolutional Neural Network-Oriented Detection of Filling Flow Status of Viscous Food

A method is proposed to detect the filling flow status for automatic filling of thick liquid food. The method is based on a convolutional neural network algorithm and it solves the problem of poor accuracy in traditional flow detection devices. An adaptive threshold segmentation algorithm was first used to extract the region of interest for the acquired level image. Next, normalization and augmentation treatment were performed on the extracted images to construct a flow status dataset. A VGG-16 network trained on an ImageNet dataset was then used for isomorphic data-oriented feature migration and parameter tuning to automatically extract features and train the model. The identification accuracy and error rate of the network were verified and the advantages and disadvantages of the proposed method were compared to those of other methods. The experimental results demonstrated that the algorithm effectively detects multi-category flow status information and complies with the requirements for actual production.
Quantum Differentially Private Sparse Regression Learning

The eligibility of various advanced quantum algorithms will be questioned if they can not guarantee privacy. To fill this knowledge gap, here we devise an efficient quantum differentially private (QDP) Lasso estimator to solve sparse regression tasks. Concretely, given $N$ $d$-dimensional data points with $N\ll d$, we first prove that the optimal classical and quantum non-private Lasso requires $\Omega(N+d)$ and $\Omega(\sqrt{N}+\sqrt{d})$ runtime, respectively. We next prove that the runtime cost of QDP Lasso is \textit{dimension independent}, i.e., $O(N^{5/2})$, which implies that the QDP Lasso can be faster than both the optimal classical and quantum non-private Lasso. Last, we exhibit that the QDP Lasso attains a near-optimal utility bound $\tilde{O}(N^{-2/3})$ with privacy guarantees and discuss the chance to realize it on near-term quantum chips with advantages.