Reviewerly/exHarmony huggingface dataset:
The Touché23-ValueEval Dataset for Identifying Human Values behind Arguments

We present the Touch\'e23-ValueEval Dataset for Identifying Human Values behind Arguments. To investigate approaches for the automated detection of human values behind arguments, we collected 9324 arguments from 6 diverse sources, covering religious texts, political discussions, free-text arguments, newspaper editorials, and online democracy platforms. Each argument was annotated by 3 crowdworkers for 54 values. The Touch\'e23-ValueEval dataset extends the Webis-ArgValues-22. In comparison to the previous dataset, the effectiveness of a 1-Baseline decreases, but that of an out-of-the-box BERT model increases. Therefore, though the classification difficulty increased as per the label distribution, the larger dataset allows for training better models.
A Comparative Study of YOLOv5 and YOLOv7 Object Detection Algorithms

This paper presents a comparative analysis of the widely accepted YOLOv5 and the latest version of YOLO which is YOLOv7. Experiments were carried out by training a custom model with both YOLOv5 and YOLOv7 independently in order to consider which one of the two performs better in terms of precision, recall, mAP@0.5 and mAP@0.5:0.95. The dataset used in the experiment is a custom dataset for Remote Weapon Station which consists of 9,779 images containing 21,561 annotations of four classes gotten from Google Open Images Dataset, Roboflow Public Dataset and locally sourced dataset. The four classes are Persons, Handguns, Rifles and Knives. The experimental results of YOLOv7 were precision score of 52.8%, recall value of 56.4%, mAP@0.5 of 51.5% and mAP@0.5:0.95 of 31.5% while that of YOLOv5 were precision score of 62.6%, recall value of 53.4%, mAP@0.5 of 55.3% and mAP@0.5:0.95 of 34.2%. It was observed from the experiment conducted that YOLOv5 gave a better result than YOLOv7 in terms of precision, mAP@0.5 and mAP@0.5:0.95 overall while YOLOv7 has a higher recall value during testing than YOLOv5. YOLOv5 records 4.0% increase in accuracy compared to YOLOv7.
PGFNet: Preference-Guided Filtering Network for Two-View Correspondence Learning

Accurate correspondence selection between two images is of great importance for numerous feature matching based vision tasks. The initial correspondences established by off-the-shelf feature extraction methods usually contain a large number of outliers, and this often leads to the difficulty in accurately and sufficiently capturing contextual information for the correspondence learning task. In this paper, we propose a Preference-Guided Filtering Network (PGFNet) to address this problem. The proposed PGFNet is able to effectively select correct correspondences and simultaneously recover the accurate camera pose of matching images. Specifically, we first design a novel iterative filtering structure to learn the preference scores of correspondences for guiding the correspondence filtering strategy. This structure explicitly alleviates the negative effects of outliers so that our network is able to capture more reliable contextual information encoded by the inliers for network learning. Then, to enhance the reliability of preference scores, we present a simple yet effective Grouped Residual Attention block as our network backbone, by designing a feature grouping strategy, a feature grouping manner, a hierarchical residual-like manner and two grouped attention operations. We evaluate PGFNet by extensive ablation studies and comparative experiments on the tasks of outlier removal and camera pose estimation. The results demonstrate outstanding performance gains over the existing state-of-the-art methods on different challenging scenes. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/guobaoxiao/PGFNet</uri> .
Stable 3D measurement method for high dynamic range surfaces based on fringe projection profilometry

Fringe projection profilometry (FPP) is widely used because of its simple structure and high robustness. Most existing FPP methods consider the surface of a measurement object to be diffuse, and objects with a high dynamic range (HDR) surface usually violate the assumption that the object is a Lambert surface. As a result, the reconstruction results of many shiny objects will have missing 3D point clouds. To solve this problem, this paper proposes a polarization-plus-phase-shift coding strategy that can stably reconstruct 3D geometric objects under bright light conditions. This method replaces the traditional gray code patterns with polarization code patterns to assist the phase-shift patterns for phase unwrapping. Although the measurement object has an HDR surface, the corresponding phase period can be obtained accurately, which effectively suppresses the phase edge jump error caused by the highlight and improves the robustness of the phase unwrapping. Comparative experimental results show that the method can reliably obtain 3D information under the influence of highlights.
An improved hyperparameter optimization framework for AutoML systems using evolutionary algorithms

Abstract For any machine learning model, finding the optimal hyperparameter setting has a direct and significant impact on the model’s performance. In this paper, we discuss different types of hyperparameter optimization techniques. We compare the performance of some of the hyperparameter optimization techniques on image classification datasets with the help of AutoML models. In particular, the paper studies Bayesian optimization in depth and proposes the use of genetic algorithm, differential evolution and covariance matrix adaptation—evolutionary strategy for acquisition function optimization. Moreover, we compare these variants of Bayesian optimization with conventional Bayesian optimization and observe that the use of covariance matrix adaptation—evolutionary strategy and differential evolution improves the performance of standard Bayesian optimization. We also notice that Bayesian optimization tends to perform poorly when genetic algorithm is used for acquisition function optimization.
Distinguishing academic science writing from humans or ChatGPT with over 99% accuracy using off-the-shelf machine learning tools

ChatGPT has enabled access to artificial intelligence (AI)-generated writing for the masses, initiating a culture shift in the way people work, learn, and write. The need to discriminate human writing from AI is now both critical and urgent. Addressing this need, we report a method for discriminating text generated by ChatGPT from (human) academic scientists, relying on prevalent and accessible supervised classification methods. The approach uses new features for discriminating (these) humans from AI; as examples, scientists write long paragraphs and have a penchant for equivocal language, frequently using words like “but,” “however,” and “although.” With a set of 20 features, we built a model that assigns the author, as human or AI, at over 99% accuracy. This strategy could be further adapted and developed by others with basic skills in supervised classification, enabling access to many highly accurate and targeted models for detecting AI usage in academic writing and beyond.
An Algebraic Approach to Maliciously Secure Private Set Intersection

Private set intersection (PSI) is an important area of research and has been the focus of many works over the past decades. It describes the problem of finding an intersection between the input sets of at least two parties without revealing anything about the input sets apart from their intersection. In this paper, we present a new approach to compute the intersection between sets based on a primitive called Oblivious Linear Function Evaluation (OLE). On an abstract level, we use this primitive to efficiently add two polynomials in a randomized way while preserving the roots of the added polynomials. Setting the roots of the input polynomials to be the elements of the input sets, this directly yields an intersection protocol with optimal asymptotic communication complexity $$O(m\kappa )$$ . We highlight that the protocol is information-theoretically secure against a malicious adversary assuming OLE. We also present a natural generalization of the 2-party protocol for the fully malicious multi-party case. Our protocol does away with expensive (homomorphic) threshold encryption and zero-knowledge proofs. Instead, we use simple combinatorial techniques to ensure the security. As a result we get a UC-secure protocol with asymptotically optimal communication complexity $$O((n^2+nm)\kappa )$$ , where n is the number of parties, m is the set size and $$\kappa $$ is the security parameter. Apart from yielding an asymptotic improvement over previous works, our protocols are also conceptually simple and require only simple field arithmetic. Along the way we develop techniques that might be of independent interest.
The Sockeye 2 Neural Machine Translation Toolkit at AMTA 2020

We present Sockeye 2, a modernized and streamlined version of the Sockeye neural machine translation (NMT) toolkit. New features include a simplified code base through the use of MXNet's Gluon API, a focus on state of the art model architectures, distributed mixed precision training, and efficient CPU decoding with 8-bit quantization. These improvements result in faster training and inference, higher automatic metric scores, and a shorter path from research to production.
Generation of non-classical light in a photon-number superposition

The ability to generate light in a pure quantum state is essential for advances in optical quantum technologies. However, obtaining quantum states with control in the photon-number has remained elusive. Optical light fields with zero and one photon can be produced by single atoms, but so far it has been limited to generating incoherent mixtures, or coherent superpositions with a very small one-photon term. Here, we report on the on-demand generation of quantum superpositions of zero, one, and even two photons, via pulsed coherent control of a single artificial atom. Driving the system up to full atomic inversion leads to the generation of quantum superpositions of vacuum and one photon, with their relative populations controlled by the driving laser intensity. A stronger driving of the system, with $2\pi$-pulses, results in a coherent superposition of vacuum, one and two photons, with the two-photon term exceeding the one-photon component, a state allowing phase super-resolving interferometry. Our results open new paths for optical quantum technologies with access to the photon-number degree-of-freedom.
Single-shot 3D shape measurement of discontinuous objects based on a coaxial fringe projection system

Fringe projection profilometry has been widely used in high-speed three-dimensional (3D) shape measurement. To improve the speed without loss of accuracy, we present a novel single-shot 3D shape measuring system that utilizes a coaxial fringe projection system and a 2CCD camera. The coaxial fringe projection system, comprising a visible light (red, green, and blue) projector and an infrared (IR) light projector, can simultaneously project red, green, blue, and IR fringe patterns. The 2CCD camera, as the name suggests, has two CCD chips that can acquire visible and IR fringe patterns at the same time. Combining the two-step phase-shifting algorithm, Fourier transform profilometry, and the optimum three-frequency selection method, 3D shape measurement of complex surfaces such as large slopes or discontinuous objects can be obtained from single-shot acquisition. A virtual fringe projection measurement system has been established to generate pre-deformed fringe patterns to correct positional deviations of the coaxial fringe projection system. This method has been applied to simulations and experiments on static and dynamic objects with promising results.
Application of Gaussian process regression to plasma turbulent transport model validation via integrated modelling

This paper outlines an approach towards improved rigour in tokamak turbulence transport model validation within integrated modelling. Gaussian process regression (GPR) techniques were applied for profile fitting during the preparation of integrated modelling simulations allowing for rigourous sensitivity tests of prescribed initial and boundary conditions as both fit and derivative uncertainties are provided. This was demonstrated by a JETTO integrated modelling simulation of the JET ITER-like-wall H-mode baseline discharge #92436 with the QuaLiKiz quasilinear turbulent transport model, which is the subject of extrapolation towards a deuterium-tritium plasma. The simulation simultaneously evaluates the time evolution of heat, particle, and momentum fluxes over $\sim10$ confinement times, with a simulation boundary condition at $\rho_{tor} = 0.85$. Routine inclusion of momentum transport prediction in multi-channel flux-driven transport modelling is not standard and is facilitated here by recent developments within the QuaLiKiz model. Excellent agreement was achieved between the fitted and simulated profiles for $n_e$, $T_e$, $T_i$, and $\Omega_{tor}$ within $2\sigma$, but the simulation underpredicts the mid-radius $T_i$ and overpredicts the core $n_e$ and $T_e$ profiles for this discharge. Despite this, it was shown that this approach is capable of deriving reasonable inputs, including derivative quantities, to tokamak models from experimental data. Furthermore, multiple figures-of-merit were defined to quantitatively assess the agreement of integrated modelling predictions to experimental data within the GPR profile fitting framework.
Adversarial Attacks on Deep Neural Networks for Time Series Classification

Time Series Classification (TSC) problems are encountered in many real life data mining tasks ranging from medicine and security to human activity recognition and food safety. With the recent success of deep neural networks in various domains such as computer vision and natural language processing, researchers started adopting these techniques for solving time series data mining problems. However, to the best of our knowledge, no previous work has considered the vulnerability of deep learning models to adversarial time series examples, which could potentially make them unreliable in situations where the decision taken by the classifier is crucial such as in medicine and security. For computer vision problems, such attacks have been shown to be very easy to perform by altering the image and adding an imperceptible amount of noise to trick the network into wrongly classifying the input image. Following this line of work, we propose to leverage existing adversarial attack mechanisms to add a special noise to the input time series in order to decrease the network's confidence when classifying instances at test time. Our results reveal that current state-of-the-art deep learning time series classifiers are vulnerable to adversarial attacks which can have major consequences in multiple domains such as food safety and quality assurance.
The Animal-AI Olympics

A new competition presents AI agents with cognition challenges to test their animal intelligence.
A New Algorithm for Medical Color Images Encryption Using Chaotic Systems

In this paper, we present a new algorithm based on chaotic systems to protect medical images against attacks. The proposed algorithm has two main parts: A high-speed permutation process and adaptive diffusion. After the implementation of the algorithm in the MATLAB software, it is observed that the algorithm is effective and appropriate. Also, to quantitatively evaluate the uniformity of the histogram, the chi-square test is done. Key sensitivity analysis demonstrates that images cannot be decrypted whenever a small change happens in the key, which indicates that the algorithm is suitable. Clearly, part of special images is selected to test the selected plain-text, like an all-white image and an all-black image. Entropy results obtained from the implementation of the algorithm on this type of images show that the proposed method is suitable for this particular type of images. In addition, the obtained results from noise and occlusion attacks analysis show that the proposed algorithm can withstand against these types of attacks. Moreover, it can be seen that the images after encryption and decryption are of good quality; the measures such as the correlation coefficients, the entropy, the number of pixel change rate (NPCR), and the uniform average change intensity (UACI) have suitable values; and the method is better than previous methods.
NNE: A Dataset for Nested Named Entity Recognition in English Newswire

Named entity recognition (NER) is widely used in natural language processing applications and downstream tasks.However, most NER tools target flat annotation from popular datasets, eschewing the semantic information available in nested entity mentions.We describe NNE-a fine-grained, nested named entity dataset over the full Wall Street Journal portion of the Penn Treebank (PTB).Our annotation comprises 279,795 mentions of 114 entity types with up to 6 layers of nesting.We hope the public release of this large dataset for English newswire will encourage development of new techniques for nested NER.
ChID: A Large-scale Chinese IDiom Dataset for Cloze Test

Cloze-style reading comprehension in Chinese is still limited due to the lack of various corpora.In this paper we propose a large-scale Chinese cloze test dataset ChID, which studies the comprehension of idiom, a unique language phenomenon in Chinese.In this corpus, the idioms in a passage are replaced by blank symbols and the correct answer needs to be chosen from well-designed candidate idioms.We carefully study how the design of candidate idioms and the representation of idioms affect the performance of state-of-the-art models.Results show that the machine accuracy is substantially worse than that of human, indicating a large space for further research.
Three-dimensional surface codes: Transversal gates and fault-tolerant architectures

One of the leading quantum computing architectures is based on the two-dimensional (2D) surface code. This code has many advantageous properties such as a high error threshold and a planar layout of physical qubits where each physical qubit need only interact with its nearest neighbors. However, the transversal logical gates available in 2D surface codes are limited. This means that an additional (resource-intensive) procedure known as magic state distillation is required to do universal quantum computing with 2D surface codes. Here, we examine three-dimensional (3D) surface codes in the context of quantum computation. We introduce a picture for visualizing 3D surface codes which is useful for analyzing stacks of three 3D surface codes. We use this picture to prove that the CZ and CCZ gates are transversal in 3D surface codes. We also generalize the techniques of 2D surface-code lattice surgery to 3D surface codes. We combine these results and propose two quantum computing architectures based on 3D surface codes. Magic state distillation is not required in either of our architectures. Finally, we show that a stack of three 3D surface codes can be transformed into a single 3D color code (another type of quantum error-correcting code) using code concatenation.
Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting

Addressing catastrophic forgetting is one of the key challenges in continual learning where machine learning systems are trained with sequential or streaming tasks. Despite recent remarkable progress in state-of-the-art deep learning, deep neural networks (DNNs) are still plagued with the catastrophic forgetting problem. This paper presents a conceptually simple yet general and effective framework for handling catastrophic forgetting in continual learning with DNNs. The proposed method consists of two components: a neural structure optimization component and a parameter learning and/or fine-tuning component. By separating the explicit neural structure learning and the parameter estimation, not only is the proposed method capable of evolving neural structures in an intuitively meaningful way, but also shows strong capabilities of alleviating catastrophic forgetting in experiments. Furthermore, the proposed method outperforms all other baselines on the permuted MNIST dataset, the split CIFAR100 dataset and the Visual Domain Decathlon dataset in continual learning setting.
Exploiting Local Feature Patterns for Unsupervised Domain Adaptation

Unsupervised domain adaptation methods aim to alleviate performance degradation caused by domain-shift by learning domain-invariant representations. Existing deep domain adaptation methods focus on holistic feature alignment by matching source and target holistic feature distributions, without considering local features and their multi-mode statistics. We show that the learned local feature patterns are more generic and transferable and a further local feature distribution matching enables fine-grained feature alignment. In this paper, we present a method for learning domain-invariant local feature patterns and jointly aligning holistic and local feature statistics. Comparisons to the state-of-the-art unsupervised domain adaptation methods on two popular benchmark datasets demonstrate the superiority of our approach and its effectiveness on alleviating negative transfer.
Fusion of Finite-Set Distributions: Pointwise Consistency and Global Cardinality

A recent trend in distributed multisensor fusion is to use random finite-set filters at the sensor nodes and fuse the filtered distributions algorithmically using their exponential mixture densities (EMDs). Fusion algorithms that extend covariance intersection and consensus-based approaches are such examples. In this paper, we analyze the variational principle underlying EMDs and show that the EMDs of finite-set distributions do not necessarily lead to consistent fusion of cardinality distributions. Indeed, we demonstrate that these inconsistencies may occur with overwhelming probability in practice, through examples with Bernoulli, Poisson, and independent identically distributed cluster processes. We prove that pointwise consistency of EMDs does not imply consistency in global cardinality and vice versa. Then, we redefine the variational problems underlying fusion and provide iterative solutions thereby establishing a framework that guarantees cardinality consistent fusion.
Nuclei Segmentation via a Deep Panoptic Model with Semantic Feature Fusion

Automated detection and segmentation of individual nuclei in histopathology images is important for cancer diagnosis and prognosis. Due to the high variability of nuclei appearances and numerous overlapping objects, this task still remains challenging. Deep learning based semantic and instance segmentation models have been proposed to address the challenges, but these methods tend to concentrate on either the global or local features and hence still suffer from information loss. In this work, we propose a panoptic segmentation model which incorporates an auxiliary semantic segmentation branch with the instance branch to integrate global and local features. Furthermore, we design a feature map fusion mechanism in the instance branch and a new mask generator to prevent information loss. Experimental results on three different histopathology datasets demonstrate that our method outperforms the state-of-the-art nuclei segmentation methods and popular semantic and instance segmentation models by a large margin.