Reviewerly/exHarmony huggingface dataset:
Decentralized Stochastic Gradient Descent Ascent for Finite-Sum Minimax Problems

Minimax optimization problems have attracted significant attention in recent years due to their widespread application in numerous machine learning models. To solve the minimax optimization problem, a wide variety of stochastic optimization methods have been proposed. However, most of them ignore the distributed setting where the training data is distributed on multiple workers. In this paper, we developed a novel decentralized stochastic gradient descent ascent method for the finite-sum minimax optimization problem. In particular, by employing the variance-reduced gradient, our method can achieve $O(\frac{\sqrt{n}κ^3}{(1-λ)^2ε^2})$ sample complexity and $O(\frac{κ^3}{(1-λ)^2ε^2})$ communication complexity for the nonconvex-strongly-concave minimax optimization problem. As far as we know, our work is the first one to achieve such theoretical complexities for this kind of problem. At last, we apply our method to optimize the AUC maximization problem and the experimental results confirm the effectiveness of our method.
Sources of Noise in Dialogue and How to Deal with Them

Training dialogue systems often entails dealing with noisy training examples and unexpected user inputs. Despite their prevalence, there currently lacks an accurate survey of dialogue noise, nor is there a clear sense of the impact of each noise type on task performance. This paper addresses this gap by first constructing a taxonomy of noise encountered by dialogue systems. In addition, we run a series of experiments to show how different models behave when subjected to varying levels of noise and types of noise. Our results reveal that models are quite robust to label errors commonly tackled by existing denoising algorithms, but that performance suffers from dialogue-specific noise. Driven by these observations, we design a data cleaning algorithm specialized for conversational settings and apply it as a proof-of-concept for targeted dialogue denoising.
A Robust Image Steganographic Scheme against General Scaling Attacks

Conventional covert image communication is assumed to transmit the message, in the securest way possible for a given payload, over lossless channels, and the associated steganographic schemes are generally vulnerable to active attacks, e.g., JPEG re-compression, scaling, as seen on social networks. Although considerable progress has been made on robust steganography against JPEG re-compression, there exist few steganographic schemes capable of resisting to scaling attacks due to the tricky inverse interpolations involved in algorithm design. To tackle this issue, a framework for robust image steganography resisting to scaling with general interpolations either in standard form with fixed interpolation block, or pre-filtering based anti-aliasing implementation with variable block, is proposed in this paper. And the task of robust steganography can be formulated as one of constrained integer programming aiming at perfectly recover the secret message from stego image while minimizing the difference between cover and stego images. By introducing a metric - the degree of pixel involvement (dPI) to identify the modifiable pixels in cover image, the optimization problem above could be effectively solved using branch and bound algorithm (B\&B). In addition, a customized distortion function for scaled stego images is adopted to further boost the security performance. Extensive experiments are carried out which demonstrate that the proposed scheme could not only outperform the prior art in terms of security by a clear margin, but also be applicable to resisting the scaling attacks with various interpolation techniques at arbitrary scaling factors (SFs).
Evidential Deep Learning for Class-Incremental Semantic Segmentation

Class-Incremental Learning is a challenging problem in machine learning that aims to extend previously trained neural networks with new classes. This is especially useful if the system is able to classify new objects despite the original training data being unavailable. While the semantic segmentation problem has received less attention than classification, it poses distinct problems and challenges since previous and future target classes can be unlabeled in the images of a single increment. In this case, the background, past and future classes are correlated and there exist a background-shift. In this paper, we address the problem of how to model unlabeled classes while avoiding spurious feature clustering of future uncorrelated classes. We propose to use Evidential Deep Learning to model the evidence of the classes as a Dirichlet distribution. Our method factorizes the problem into a separate foreground class probability, calculated by the expected value of the Dirichlet distribution, and an unknown class (background) probability corresponding to the uncertainty of the estimate. In our novel formulation, the background probability is implicitly modeled, avoiding the feature space clustering that comes from forcing the model to output a high background score for pixels that are not labeled as objects. Experiments on the incremental Pascal VOC, and ADE20k benchmarks show that our method is superior to state-of-the-art, especially when repeatedly learning new classes with increasing number of increments.
Semi-supervised Deep Large-baseline Homography Estimation with Progressive Equivalence Constraint

Homography estimation is erroneous in the case of large-baseline due to the low image overlay and limited receptive field. To address it, we propose a progressive estimation strategy by converting large-baseline homography into multiple intermediate ones, cumulatively multiplying these intermediate items can reconstruct the initial homography. Meanwhile, a semi-supervised homography identity loss, which consists of two components: a supervised objective and an unsupervised objective, is introduced. The first supervised loss is acting to optimize intermediate homographies, while the second unsupervised one helps to estimate a large-baseline homography without photometric losses. To validate our method, we propose a large-scale dataset that covers regular and challenging scenes. Experiments show that our method achieves state-of-the-art performance in large-baseline scenes while keeping competitive performance in small-baseline scenes. Code and dataset are available at https://github.com/megvii-research/LBHomo.
A Univalent Formalization of Constructive Affine Schemes

We present a formalization of constructive affine schemes in the Cubical Agda proof assistant. This development is not only fully constructive and predicative, it also makes crucial use of univalence. By now schemes have been formalized in various proof assistants. However, most existing formalizations follow the inherently non-constructive approach of Hartshorne's classic "Algebraic Geometry" textbook, for which the construction of the so-called structure sheaf is rather straightforwardly formalizable and works the same with or without univalence. We follow an alternative approach that uses a point-free description of the constructive counterpart of the Zariski spectrum called the Zariski lattice and proceeds by defining the structure sheaf on formal basic opens and then lift it to the whole lattice. This general strategy is used in a plethora of textbooks, but formalizing it has proved tricky. The main result of this paper is that with the help of the univalence principle we can make this "lift from basis" strategy formal and obtain a fully formalized account of constructive affine schemes.
Genetic Sequence compression using Machine Learning and Arithmetic Encoding Decoding Techniques

We live in a period where bio-informatics is rapidly expanding, a significant quantity of genomic data has been produced as a result of the advancement of high-throughput genome sequencing technology, raising concerns about the costs associated with data storage and transmission. The question of how to properly compress data from genomic sequences is still open. Previously many researcher proposed many compression method on this topic DNA Compression without machine learning and with machine learning approach. Extending a previous research, we propose a new architecture like modified DeepDNA and we have propose a new methodology be deploying a double base-ed strategy for compression of DNA sequences. And validated the results by experimenting on three sizes of datasets are 100, 243, 356. The experimental outcomes highlight our improved approach's superiority over existing approaches for analyzing the human mitochondrial genome data, such as DeepDNA.
Learning to Bound Counterfactual Inference from Observational, Biased and Randomised Data

We address the problem of integrating data from multiple, possibly biased, observational and interventional studies, to eventually compute counterfactuals in structural causal models. We start from the case of a single observational dataset affected by a selection bias. We show that the likelihood of the available data has no local maxima. This enables us to use the causal expectation-maximisation scheme to compute approximate bounds for partially identifiable counterfactual queries, which are the focus of this paper. We then show how the same approach can solve the general case of multiple datasets, no matter whether interventional or observational, biased or unbiased, by remapping it into the former one via graphical transformations. Systematic numerical experiments and a case study on palliative care show the effectiveness and accuracy of our approach, while hinting at the benefits of integrating heterogeneous data to get informative bounds in case of partial identifiability.
Alternating Linear Minimization: Revisiting von Neumann's alternating projections

In 1933 von Neumann proved a beautiful result that one can approximate a point in the intersection of two convex sets by alternating projections, i.e., successively projecting on one set and then the other. This algorithm assumes that one has access to projection operators for both sets. In this note, we consider the much weaker setup where we have only access to linear minimization oracles over the convex sets and present an algorithm to find a point in the intersection of two convex sets.
Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library

Yggdrasil Decision Forests is a library for the training, serving and interpretation of decision forest models, targeted both at research and production work, implemented in C++, and available in C++, command line interface, Python (under the name TensorFlow Decision Forests), JavaScript, Go, and Google Sheets (under the name Simple ML for Sheets). The library has been developed organically since 2018 following a set of four design principles applicable to machine learning libraries and frameworks: simplicity of use, safety of use, modularity and high-level abstraction, and integration with other machine learning libraries. In this paper, we describe those principles in detail and present how they have been used to guide the design of the library. We then showcase the use of our library on a set of classical machine learning problems. Finally, we report a benchmark comparing our library to related solutions.
Open World DETR: Transformer based Open World Object Detection

Open world object detection aims at detecting objects that are absent in the object classes of the training data as unknown objects without explicit supervision. Furthermore, the exact classes of the unknown objects must be identified without catastrophic forgetting of the previous known classes when the corresponding annotations of unknown objects are given incrementally. In this paper, we propose a two-stage training approach named Open World DETR for open world object detection based on Deformable DETR. In the first stage, we pre-train a model on the current annotated data to detect objects from the current known classes, and concurrently train an additional binary classifier to classify predictions into foreground or background classes. This helps the model to build an unbiased feature representations that can facilitate the detection of unknown classes in subsequent process. In the second stage, we fine-tune the class-specific components of the model with a multi-view self-labeling strategy and a consistency constraint. Furthermore, we alleviate catastrophic forgetting when the annotations of the unknown classes becomes available incrementally by using knowledge distillation and exemplar replay. Experimental results on PASCAL VOC and MS-COCO show that our proposed method outperforms other state-of-the-art open world object detection methods by a large margin.
ZeroKBC: A Comprehensive Benchmark for Zero-Shot Knowledge Base Completion

Knowledge base completion (KBC) aims to predict the missing links in knowledge graphs. Previous KBC tasks and approaches mainly focus on the setting where all test entities and relations have appeared in the training set. However, there has been limited research on the zero-shot KBC settings, where we need to deal with unseen entities and relations that emerge in a constantly growing knowledge base. In this work, we systematically examine different possible scenarios of zero-shot KBC and develop a comprehensive benchmark, ZeroKBC, that covers these scenarios with diverse types of knowledge sources. Our systematic analysis reveals several missing yet important zero-shot KBC settings. Experimental results show that canonical and state-of-the-art KBC systems cannot achieve satisfactory performance on this challenging benchmark. By analyzing the strength and weaknesses of these systems on solving ZeroKBC, we further present several important observations and promising future directions.
Counterfeits on Darknet Markets: A measurement between Jan-2014 and Sep-2015

Counterfeits harm consumers, governments, and intellectual property holders. They accounted for 3.3% of worldwide trades in 2016, having an estimated value of $509 billion in the same year. While estimations are mostly based on border seizures, we examined openly labeled counterfeits on darknet markets, which allowed us to gather and analyze information from a different perspective. Here, we analyzed data from 11 darknet markets for the period Jan-2014 and Sep-2015. The findings suggest that darknet markets harbor similar counterfeit product types as found in seizures but that the share of watches is higher and lower for electronics, clothes, shoes, and Tobacco on darknet markets. Also, darknet market counterfeits seem to have similar shipping origins as seized goods, with some exceptions, such as a relatively high share (5%) of dark market counterfeits originating from the US. Lastly, counterfeits on dark markets tend to have a relatively low price and sales volume. However, based on preliminary estimations, the original products on the surface web seem to be worth a multiple of the prices of the counterfeit counterparts on darknet markets. Gathering insights about counterfeits from darknet markets can be valuable for businesses and authorities and be cost-effective compared to border seizures. Thus, monitoring darknet markets can help us understand the counterfeit landscape better.
State Space Closure: Revisiting Endless Online Level Generation via Reinforcement Learning

In this paper, we revisit endless online level generation with the recently proposed experience-driven procedural content generation via reinforcement learning (EDRL) framework. Inspired by an observation that EDRL tends to generate recurrent patterns, we formulate a notion of state space closure which makes any stochastic state appeared possibly in an infinite-horizon online generation process can be found within a finite-horizon. Through theoretical analysis, we find that even though state space closure arises a concern about diversity, it generalises EDRL trained with a finite-horizon to the infinite-horizon scenario without deterioration of content quality. Moreover, we verify the quality and the diversity of contents generated by EDRL via empirical studies, on the widely used Super Mario Bros. benchmark. Experimental results reveal that the diversity of levels generated by EDRL is limited due to the state space closure, whereas their quality does not deteriorate in a horizon which is longer than the one specified in the training. Concluding our outcomes and analysis, future work on endless online level generation via reinforcement learning should address the issue of diversity while assuring the occurrence of state space closure and quality.
Generation of perfectly entangled two and three qubits states by classical random interaction

This study examines the possibility of finding perfect entanglers for a Hamiltonian which corresponds to several quantum information platforms of interest at the present time. However, in this study, we use a superconducting circuit that stands out from other quantum-computing devices, especially because Transmon qubits can be coupled via capacitors or microwave cavities, which enable us to combine high coherence, fast gates, and high flexibility in its design parameters. There are currently two factors limiting the performance of superconducting processors: timing mismatch and the limitation of entangling gates to two qubits. In this work, we present a two-qubit SWAP and a three-qubit Fredkin gate, additionally, we also demonstrate a perfect adiabatic entanglement generation between two and three programmable superconducting qubits. Furthermore, in this study, we also demonstrate the impact of random dephasing, emission, and absorption noises on the quantum gates and entanglement. It is demonstrated by numerical simulation that the CSWAP gate and $W$-state generation can be achieved perfectly in one step with high reliability under weak coupling conditions. Hence, our scheme could contribute to quantum teleportation, quantum communication, and some other areas of quantum information processing.
Perspective Fields for Single Image Camera Calibration

Geometric camera calibration is often required for applications that understand the perspective of the image. We propose perspective fields as a representation that models the local perspective properties of an image. Perspective Fields contain per-pixel information about the camera view, parameterized as an up vector and a latitude value. This representation has a number of advantages as it makes minimal assumptions about the camera model and is invariant or equivariant to common image editing operations like cropping, warping, and rotation. It is also more interpretable and aligned with human perception. We train a neural network to predict Perspective Fields and the predicted Perspective Fields can be converted to calibration parameters easily. We demonstrate the robustness of our approach under various scenarios compared with camera calibration-based methods and show example applications in image compositing.
Towards Better User Requirements: How to Involve Human Participants in XAI Research

Human-Center eXplainable AI (HCXAI) literature identifies the need to address user needs. This paper examines how existing XAI research involves human users in designing and developing XAI systems and identifies limitations in current practices, especially regarding how researchers identify user requirements. Finally, we propose several suggestions on how to derive better user requirements.
Switching to Discriminative Image Captioning by Relieving a Bottleneck of Reinforcement Learning

Discriminativeness is a desirable feature of image captions: captions should describe the characteristic details of input images. However, recent high-performing captioning models, which are trained with reinforcement learning (RL), tend to generate overly generic captions despite their high performance in various other criteria. First, we investigate the cause of the unexpectedly low discriminativeness and show that RL has a deeply rooted side effect of limiting the output words to high-frequency words. The limited vocabulary is a severe bottleneck for discriminativeness as it is difficult for a model to describe the details beyond its vocabulary. Then, based on this identification of the bottleneck, we drastically recast discriminative image captioning as a much simpler task of encouraging low-frequency word generation. Hinted by long-tail classification and debiasing methods, we propose methods that easily switch off-the-shelf RL models to discriminativeness-aware models with only a single-epoch fine-tuning on the part of the parameters. Extensive experiments demonstrate that our methods significantly enhance the discriminativeness of off-the-shelf RL models and even outperform previous discriminativeness-aware methods with much smaller computational costs. Detailed analysis and human evaluation also verify that our methods boost the discriminativeness without sacrificing the overall quality of captions.
Comprehensive Statistics of Test Scores Based on the Thought of Relative Criterion

Abstract The comprehensive statistical ranking of exam score is extremely important for tens of millions of Chinese students taking the national college entrance examination every year. With the gradual improvement of people's understanding of the idea of relative criterion, it is found that the traditional statistical method uses the idea of subjective and absolute standard, and there is a large error between its statistical results and the objective reality. This error leads to great problems in talent selection, which should be abandoned. This paper puts forward that the examination results should be like a timing running competition, without the limit of the maximum full score. Under the guidance of the thought of relative criterion, the objective and scientific theory of relative criterion is adopted for the comprehensive statistics of examination results. In this paper, the application of the formula of relative criterion theory to the comprehensive statistics of examination results is expounded step by step from the simple to the profound in combination with the current actual situation; The specific calculation method is given in detail with an example. A simple relative criterion value of similar exchange rate is derived from the comprehensive statistics of ten million scale examinations. And the method of regional statistics based on relative criterion theory is used to solve the problem of disunity of test papers.
Bibliometric Analysis: Trend of ICT and RME Researches

Research in both ICT and RME is expanding at a breakneck pace. The growth of research in engineering can be tracked by one type of analysis known as bibliometric analysis. This analysis can be used to identify the number of scholarly publications on ICT and RME. This study is of a quantitative and descriptive nature. Researchers are interested in examining the development of ICT and RME, as well as the distribution pattern of publications in Scopus, which is based on research affiliations, research themes, and scientific journals. For the goals of data analysis, reduction, visualization, and mapping, respectively, Bibliometrix R-tool and BiblioShiny were utilized throughout the course of this research. Scopus bibliometric study finds that between the years 1999 and 2022, there was a shift in the number of research articles that could be located in the Scopus online database that were associated with ICT and RME. The year 2020 was the year that this publication - Journal on Education and Information Technologies - reached its zenith. In terms of the quantity of documents generated, research in the fields of ICT is dominated by writers from the Czech Republic and the Netherlands.