Reviewerly/exHarmony huggingface dataset:
Advanced quantum image representation and compression using a DCT-EFRQI approach

In recent years, quantum image computing draws a lot of attention due to storing and processing image data faster compared to classical computers. A number of approaches have been proposed to represent the quantum image inside a quantum computer. Representing and compressing medium and big-size images inside the quantum computer is still challenging. To address this issue, we have proposed a block-wise DCT-EFRQI (Direct Cosine Transform Efficient Flexible Representation of Quantum Image) approach to represent and compress the gray-scale image efficiently to save computational time and reduce the quantum bits (qubits) for the state preparation. In this work, we have demonstrated the capability of block-wise DCT and DWT transformation inside the quantum domain to investigate their relative performances. The Quirk simulation tool is used to design the corresponding quantum image circuit. In the proposed DCT-EFRQI approach, a total of 17 qubits are used to represent the coefficients, the connection between coefficients and state (i.e., auxiliary), and their position for representing and compressing grayscale images inside a quantum computer. Among those, 8 qubits are used to map the coefficient values and the rest are used to generate the corresponding coefficient XY-coordinate position including one auxiliary qubit. Theoretical analysis and experimental results show that the proposed DCT-EFRQI scheme provides better representation and compression compared to DCT-GQIR, DWT-GQIR, and DWT-EFRQI in terms of rate-distortion performance.
Tight Multi-User Security Bound of DbHtS

In CRYPTO’21, Shen et al. proved that Two-Keyed-DbHtS construction is secure up to 22n/3 queries in the multi-user setting independent of the number of users. Here the underlying double-block hash function H of the construction realized as the concatenation of two independent n-bit keyed hash functions (HKh,1,HKh,2), and the security holds under the assumption that each of the n-bit keyed hash function is universal and regular. The authors have also demonstrated the applicability of their result to the key-reduced variants of DbHtS MACs, including 2K-SUM-ECBC, 2K-PMAC_Plus and 2K-LightMAC_Plus without requiring domain separation technique and proved 2n/3-bit multi-user security of these constructions in the ideal cipher model. Recently, Guo and Wang have invalidated the security claim of Shen et al.’s result by exhibiting three constructions, which are instantiations of the Two-Keyed-DbHtS framework, such that each of their n-bit keyed hash functions are O(2−n) universal and regular, while the constructions themselves are secure only up to the birthday bound. In this work, we show a sufficient condition on the underlying Double-block Hash (DbH) function, under which we prove an improved 3n/4-bit multi-user security of the Two-Keyed-DbHtS construction in the ideal-cipher model. To be more precise, we show that if each of the n-bit keyed hash function is universal, regular, and cross-collision resistant then it achieves the desired security. As an instantiation, we show that two-keyed Polyhash-based DbHtS construction is multi-user secure up to 23n/4 queries in the ideal-cipher model. Furthermore, due to the generic attack on DbHtS constructions by Leurent et al. in CRYPTO’18, our derived bound for the construction is tight.
K8sSim: A Simulation Tool for Kubernetes Schedulers and Its Applications in Scheduling Algorithm Optimization

In recent years, Kubernetes (K8s) has become a dominant resource management and scheduling system in the cloud. In practical scenarios, short-running cloud workloads are usually scheduled through different scheduling algorithms provided by Kubernetes. For example, artificial intelligence (AI) workloads are scheduled through different Volcano scheduling algorithms, such as GANG_MRP, GANG_LRP, and GANG_BRA. One key challenge is that the selection of scheduling algorithms has considerable impacts on job performance results. However, it takes a prohibitively long time to select the optimal algorithm because applying one algorithm in one single job may take a few minutes to complete. This poses the urgent requirement of a simulator that can quickly evaluate the performance impacts of different algorithms, while also considering scheduling-related factors, such as cluster resources, job structures and scheduler configurations. In this paper, we design and implement a Kubernetes simulator called K8sSim, which incorporates typical Kubernetes and Volcano scheduling algorithms for both generic and AI workloads, and provides an accurate simulation of their scheduling process in real clusters. We use real cluster traces from Alibaba to evaluate the effectiveness of K8sSim, and the evaluation results show that (i) compared to the real cluster, K8sSim can accurately evaluate the performance of different scheduling algorithms with similar CloseRate (a novel metric we define to intuitively show the simulation accuracy), and (ii) it can also quickly obtain the scheduling results of different scheduling algorithms by accelerating the scheduling time by an average of 38.56×.
From Deterioration to Acceleration: A Calibration Approach to Rehabilitating Step Asynchronism in Federated Optimization

In the setting of federated optimization, where a global model is aggregated periodically, step asynchronism occurs when participants conduct model training by efficiently utilizing their computational resources. It is well acknowledged that step asynchronism leads to objective inconsistency under non-i.i.d. data, which degrades the model’s accuracy. To address this issue, we propose a new algorithm <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedaGrac</monospace> , which calibrates the local direction to a predictive global orientation. Taking advantage of the estimated orientation, we guarantee that the aggregated model does not excessively deviate from the global optimum while fully utilizing the local updates of faster nodes. We theoretically prove that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedaGrac</monospace> holds an improved order of convergence rate than the state-of-the-art approaches and eliminates the negative effect of step asynchronism. Empirical results show that our algorithm accelerates the training and enhances the final accuracy.
Automatic segmentation of thyroid with the assistance of the devised boundary improvement based on multicomponent small dataset

Deep learning has been widely considered in medical image segmentation. However, the difficulty of acquiring medical images and labels can affect the accuracy of the segmentation results for deep learning methods. In this paper, an automatic segmentation method is proposed by devising a multicomponent neighborhood extreme learning machine to improve the boundary attention region of the preliminary segmentation results. The neighborhood features are acquired by training U-Nets with the multicomponent small dataset, which consists of original thyroid ultrasound images, Sobel edge images and superpixel images. Afterward, the neighborhood features are selected by min-redundancy and max-relevance filter in the designed extreme learning machine, and the selected features are used to train the extreme learning machine to obtain supplementary segmentation results. Finally, the accuracy of the segmentation results is improved by adjusting the boundary attention region of the preliminary segmentation results with the supplementary segmentation results. This method combines the advantages of deep learning and traditional machine learning, boosting the accuracy of thyroid segmentation accuracy with a small dataset in a multigroup test.
Condition-invariant and compact visual place description by convolutional autoencoder

Abstract Visual place recognition (VPR) in condition-varying environments is still an open problem. Popular solutions are convolutional neural network (CNN)-based image descriptors, which have been shown to outperform traditional image descriptors based on hand-crafted visual features. However, there are two drawbacks of current CNN-based descriptors: (a) their high dimension and (b) lack of generalization, leading to low efficiency and poor performance in real robotic applications. In this paper, we propose to use a convolutional autoencoder (CAE) to tackle this problem. We employ a high-level layer of a pre-trained CNN to generate features and train a CAE to map the features to a low-dimensional space to improve the condition invariance property of the descriptor and reduce its dimension at the same time. We verify our method in four challenging real-world datasets involving significant illumination changes, and our method is shown to be superior to the state-of-the-art. The code of our work is publicly available at https://github.com/MedlarTea/CAE-VPR .
Effects of AI and Logic-Style Explanations on Users’ Decisions Under Different Levels of Uncertainty

Existing eXplainable Artificial Intelligence (XAI) techniques support people in interpreting AI advice. However, although previous work evaluates the users’ understanding of explanations, factors influencing the decision support are largely overlooked in the literature. This article addresses this gap by studying the impact of user uncertainty , AI correctness , and the interaction between AI uncertainty and explanation logic-styles for classification tasks. We conducted two separate studies: one requesting participants to recognize handwritten digits and one to classify the sentiment of reviews. To assess the decision making, we analyzed the task performance, agreement with the AI suggestion, and the user’s reliance on the XAI interface elements. Participants make their decision relying on three pieces of information in the XAI interface (image or text instance, AI prediction, and explanation). Participants were shown one explanation style (between-participants design) according to three styles of logical reasoning (inductive, deductive, and abductive). This allowed us to study how different levels of AI uncertainty influence the effectiveness of different explanation styles. The results show that user uncertainty and AI correctness on predictions significantly affected users’ classification decisions considering the analyzed metrics. In both domains (images and text), users relied mainly on the instance to decide. Users were usually overconfident about their choices, and this evidence was more pronounced for text. Furthermore, the inductive style explanations led to overreliance on the AI advice in both domains—it was the most persuasive, even when the AI was incorrect. The abductive and deductive styles have complex effects depending on the domain and the AI uncertainty levels.
Keyword Embeddings for Query Suggestion

Nowadays, search engine users commonly rely on query suggestions to improve their initial inputs. Current systems are very good at recommending lexical adaptations or spelling corrections to users’ queries. However, they often struggle to suggest semantically related keywords given a user’s query. The construction of a detailed query is crucial in some tasks, such as legal retrieval or academic search. In these scenarios, keyword suggestion methods are critical to guide the user during the query formulation. This paper proposes two novel models for the keyword suggestion task trained on scientific literature. Our techniques adapt the architecture of Word2Vec and FastText to generate keyword embeddings by leveraging documents’ keyword co-occurrence. Along with these models, we also present a specially tailored negative sampling approach that exploits how keywords appear in academic publications. We devise a ranking-based evaluation methodology following both known-item and ad-hoc search scenarios. Finally, we evaluate our proposals against the state-of-the-art word and sentence embedding models showing considerable improvements over the baselines for the tasks.
CoSPLADE: Contextualizing SPLADE for Conversational Information Retrieval

Conversational search is a difficult task as it aims at retrieving documents based not only on the current user query but also on the full conversation history. Most of the previous methods have focused on a multi-stage ranking approach relying on query reformulation, a critical intermediate step that might lead to a sub-optimal retrieval. Other approaches have tried to use a fully neural IR first-stage, but are either zero-shot or rely on full learning-to-rank based on a dataset with pseudo-labels. In this work, leveraging the CANARD dataset, we propose an innovative lightweight learning technique to train a first-stage ranker based on SPLADE. By relying on SPLADE sparse representations, we show that, when combined with a second-stage ranker based on T5Mono, the results are competitive on the TREC CAsT 2020 and 2021 tracks. The source code is available at https://github.com/nam685/cosplade.git .
Learning to Solve Minimum Cost Multicuts Efficiently Using Edge-Weighted Graph Convolutional Neural Networks

The minimum cost multicut problem is the NP-hard/APX-hard combinatorial optimization problem of partitioning a real-valued edge-weighted graph such as to minimize the total cost of the partition. While graph convolutional neural networks (GNN) have proven to be promising in the context of combinatorial optimization, most of them are only tailored to or tested on positive-valued edge weights, i.e. they do not comply with the nature of the multicut problem. We therefore adapt various GNN architectures including Graph Convolutional Networks, Signed Graph Convolutional Networks and Graph Isomorphic Networks to facilitate the efficient encoding of real-valued edge costs. Moreover, we employ a reformulation of the multicut ILP constraints to a polynomial program as loss function that allows us to learn feasible multicut solutions in a scalable way. Thus, we provide the first approach towards end-to-end trainable multicuts. Our findings support that GNN approaches can produce good solutions in practice while providing lower computation times and largely improved scalability compared to LP solvers and optimized heuristics, especially when considering large instances. Our code is available at https://github.com/steffen-jung/GCN-Multicut .
Defending Observation Attacks in Deep Reinforcement Learning via Detection and Denoising

Neural network policies trained using Deep Reinforcement Learning (DRL) are well-known to be susceptible to adversarial attacks. In this paper, we consider attacks manifesting as perturbations in the observation space managed by the external environment. These attacks have been shown to downgrade policy performance significantly. We focus our attention on well-trained deterministic and stochastic neural network policies in the context of continuous control benchmarks subject to four well-studied observation space adversarial attacks. To defend against these attacks, we propose a novel defense strategy using a detect-and-denoise schema. Unlike previous adversarial training approaches that sample data in adversarial scenarios, our solution does not require sampling data in an environment under attack, thereby greatly reducing risk during training. Detailed experimental results show that our technique is comparable with state-of-the-art adversarial training approaches.
An Interpretable Knowledge Representation Framework for Natural Language Processing with Cross-Domain Application

Data representation plays a crucial role in natural language processing (NLP), forming the foundation for most NLP tasks. Indeed, NLP performance highly depends upon the effectiveness of the preprocessing pipeline that builds the data representation. Many representation learning frameworks, such as Word2Vec, encode input data based on local contextual information that interconnects words. Such approaches can be computationally intensive, and their encoding is hard to explain. We here propose an interpretable representation learning framework utilizing Tsetlin Machine (TM). The TM is an interpretable logic-based algorithm that has exhibited competitive performance in numerous NLP tasks. We employ the TM clauses to build a sparse propositional (boolean) representation of natural language text. Each clause is a class-specific propositional rule that links words semantically and contextually. Through visualization, we illustrate how the resulting data representation provides semantically more distinct features, better separating the underlying classes. As a result, the following classification task becomes less demanding, benefiting simple machine learning classifiers such as Support Vector Machine (SVM). We evaluate our approach using six NLP classification tasks and twelve domain adaptation tasks. Our main finding is that the accuracy of our proposed technique significantly outperforms the vanilla TM, approaching the competitive accuracy of deep neural network (DNN) baselines. Furthermore, we present a case study showing how the representations derived from our framework are interpretable. (We use an asynchronous and parallel version of Tsetlin Machine: available at https://github.com/cair/PyTsetlinMachineCUDA ).
Federated Adversarial Domain Hallucination for Privacy-preserving Domain Generalization

Domain generalization aims to reduce the vulnerability of deep neural networks in the out-of-domain distribution scenario. With the recent and increasing data privacy concerns, federated domain generalization, where multiple domains are distributed on different local clients, has become an important research problem and brings new challenges for learning domain-invariant information from separated domains. In this paper, we address the problem of federated domain generalization from the perspective of domain hallucination. We propose a novel federated domain hallucination learning framework, with no additional data exchange between clients other than model weights, based on the idea that a domain hallucination with enlarged prediction uncertainty for the global model is more likely to transform the samples into an unseen domain. These types of desired domain hallucinations are achieved by generating samples that maximize the entropy of the global model and minimize the cross-entropy of the local model, where the latter loss is further introduced to maintain the sample semantics. By training the local models with the learned domain hallucinations, the final model is expected to be more robust to unseen domain shifts. We perform extensive experiments on three object classification benchmarks and one medical image segmentation benchmark. The proposed method outperforms state-of-the-art methods on all the benchmarks, demonstrating its effectiveness.
FETNet: Feature erasing and transferring network for scene text removal

The scene text removal (STR) task aims to remove text regions and recover the background smoothly in images for private information protection. Most existing STR methods adopt encoder-decoder-based CNNs, with direct copies of the features in the skip connections. However, the encoded features contain both text texture and structure information. The insufficient utilization of text features hampers the performance of background reconstruction in text removal regions. To tackle these problems, we propose a novel Feature Erasing and Transferring (FET) mechanism to reconfigure the encoded features for STR in this paper. In FET, a Feature Erasing Module (FEM) is designed to erase text features. An attention module is responsible for generating the feature similarity guidance. The Feature Transferring Module (FTM) is introduced to transfer the corresponding features in different layers based on the attention guidance. With this mechanism, a one-stage, end-to-end trainable network called FETNet is constructed for scene text removal. In addition, to facilitate research on both scene text removal and segmentation tasks, we introduce a novel dataset, Flickr-ST, with multi-category annotations. A sufficient number of experiments and ablation studies are conducted on the public datasets and Flickr-ST. Our proposed method achieves state-of-the-art performance using most metrics, with remarkably higher quality scene text removal results. The source code of our work is available at: \href{https://github.com/GuangtaoLyu/FETNet}{https://github.com/GuangtaoLyu/FETNet.
A Multi-View Vision System for Astronaut Postural Reconstruction with Self-Calibration

Space exploration missions involve significant participation from astronauts. Therefore, it is of great practical importance to assess the astronauts’ performance via various parameters in the cramped and weightless space station. In this paper, we proposed a calibration-free multi-view vision system for astronaut performance capture, including two modules: (1) an alternating iterative optimization of the camera pose and human pose is implemented to calibrate the extrinsic camera parameters with detected 2D keypoints. (2) Scale factors are restricted by the limb length to recover the real-world scale and the shape parameters are refined for subsequent postural reconstruction. These two modules can provide effective and efficient motion capture in a weightless space station. Extensive experiments using public datasets and the ground verification test data demonstrated the accuracy of the estimated camera pose and the effectiveness of the reconstructed human pose.
Breast Cancer Detection Using Deep Learning Model

Early detection of breast cancer is a challenging task. It is the major source of death among the leading and underdeveloped countries. The detection of breast cancer at earlier stages helps patients suffering from the disease to get proper treatment. Considering this aspect, the approach using deep learning architectures had been proposed to detect the presence of breast cancer given the cytology images. Transfer learning method is used in terms of building pre-learned model to improve the performance of the proposed approach. Using average pooling classification, the images were divided into malignant and benign cells. Three architectures of CNN combined in the proposed work for better prediction and accuracy.
Research on nonlinear tracking and evaluation of sports 3D vision action

Abstract In order to effectively upgrade the training quality of the athlete, the study of the three-dimensional optical steps of sports, a three-dimensional optical action amplitude tracing process based on improved reverse kinematics is discussed. The method passes through a pinhole camera, the model calibrate the camera imaging plane, the positioning mark appears in the marker point in the apart neighboring single-sequence action amplitude and then the five-point perspective imaging of the same line is captured on the same straight line by the camera on different positions and postures. The matching method’s accuracy and superiority are demonstrated through experimental evaluation and compared with other similar measures. This characteristic is distinguished by the left and right image frames of the same camera. The translational movement is altered in 10–80 mm/s and the rotational movement is adjusted in 10–300 mm/s. During the confirmation, the spinning of the operational target, the longitudinal movement and the spatial location of the rotation point Measurement, the calculation variability of the stereoscopic visual moving calculation system, is given, and the method of tracking the method is high, which lay the foundation for the promotion of the training quality of the athlete. The experimental findings from simulation demonstrate that the implemented approach has great searching accuracy, establishing a solid platform for further improving the quality of athletes’ training.