Reviewerly/exHarmony huggingface dataset:
Incorporating multi-stage spatial visual cues and active localization offset for pancreas segmentation

Accurately segmenting pancreas or pancreatic tumor from limited computed tomography (CT) scans plays an essential role in making a precise diagnosis and planning the surgical procedure for clinicians. Although deep convolutional neural networks (DCNNs) have greatly advanced in automatic organ segmentation, there are still many challenges in solving the pancreas segmentation problem with small region and complex background. Many researchers have developed a coarse-to-fine scheme, which employ prediction from the coarse stage as a smaller input region for the fine stage. Despite this scheme effectiveness, most existing approaches handle two stages individually, and fail to identify the reliability of coarse stage predictions. In this work, we present a novel coarse-to-fine framework based on spatial contextual cues and active localization offset. The novelty lies in carefully designed two modules: Spacial Visual Cues Fusion (SVCF) and Active Localization OffseT (ALOT). The SVCF combines the correlations between all pixels in an image to optimize the rough and uncertain pixel prediction at the coarse stage, while ALOT dynamically adjusts the localization as the coarse stage iteration. These two modules work together to optimize the coarse stage results and provide high-quality input for the fine stage, thereby achieving inspiring target segmentation. Empirical results on NIH pancreas segmentation and MSD pancreatic tumor segmentation dataset show that our framework yields state-of-the-art results. The code will make available at https://github.com/PinkGhost0812/SANet.
Revisit Out-Of-Vocabulary Problem For Slot Filling: A Unified Contrastive Framework With Multi-Level Data Augmentations

In real dialogue scenarios, the existing slot filling model, which tends to memorize entity patterns, has a significantly reduced generalization facing Out-of-Vocabulary (OOV) problems. To address this issue, we propose an OOV robust slot filling model based on multi-level data augmentations to solve the OOV problem from both word and slot perspectives. We present a unified contrastive learning framework, which pull representations of the origin sample and augmentation samples together, to make the model resistant to OOV problems. We evaluate the performance of the model from some specific slots and carefully design test data with OOV word perturbation to further demonstrate the effectiveness of OOV words. Experiments on two datasets show that our approach outperforms the previous sota methods in terms of both OOV slots and words.
Improving Transformer-Based Networks with Locality for Automatic Speaker Verification

Recently, Transformer-based architectures have been explored for speaker embedding extraction. Although the Transformer employs the self-attention mechanism to efficiently model the global interaction between token embeddings, it is inadequate for capturing short-range local context, which is essential for the accurate extraction of speaker information. In this study, we enhance the Transformer with the enhanced locality modeling in two directions. First, we propose the Locality-Enhanced Conformer (LE-Confomer) by introducing depth-wise convolution and channel-wise attention into the Conformer blocks. Second, we present the Speaker Swin Transformer (SST) by adapting the Swin Transformer, originally proposed for vision tasks, into speaker embedding network. We evaluate the proposed approaches on the VoxCeleb datasets and a large-scale Microsoft internal multilingual (MS-internal) dataset. The proposed models achieve 0.75% EER on VoxCeleb 1 test set, outperforming the previously proposed Transformer-based models and CNN-based models, such as ResNet34 and ECAPA-TDNN. When trained on the MS-internal dataset, the proposed models achieve promising results with 14.6% relative reduction in EER over the Res2Net50 model.
PolarDB-IMCI: A Cloud-Native HTAP Database System at Alibaba

Cloud-native databases have become the de-facto choice for mission-critical applications on the cloud due to the need for high availability, resource elasticity, and cost efficiency. Meanwhile, driven by the increasing connectivity between data generation and analysis, users prefer a single database to efficiently process both OLTP and OLAP workloads, which enhances data freshness and reduces the complexity of data synchronization and the overall business cost. In this paper, we summarize five crucial design goals for a cloud-native HTAP database based on our experience and customers' feedback, i.e., transparency, competitive OLAP performance, minimal perturbation on OLTP workloads, high data freshness, and excellent resource elasticity. As our solution to realize these goals, we present PolarDB-IMCI, a cloud-native HTAP database system designed and deployed at Alibaba Cloud. Our evaluation results show that PolarDB-IMCI is able to handle HTAP efficiently on both experimental and production workloads; notably, it speeds up analytical queries up to $\times149$ on TPC-H (100 $GB$). PolarDB-IMCI introduces low visibility delay and little performance perturbation on OLTP workloads (< 5%), and resource elasticity can be achieved by scaling out in tens of seconds.
Sentiment and emotion in financial journalism: a corpus-based, cross-linguistic analysis of the effects of COVID

Abstract Sentiment and emotion play a crucial role in financial journalism, influencing market perceptions and reactions. However, the impact of the COVID-19 crisis on the language used in financial newspapers remains underexplored. The present study addresses this gap by comparing data from specialized financial newspapers in English and Spanish, focusing on the years immediately prior to the COVID-19 crisis (2018–2019) and during the pandemic itself (2020–2021). We aim to explore how the economic upheaval of the latter period was conveyed in these publications and investigate the changes in sentiment and emotion in their language compared to the previous timeframe. To this end, we compiled comparable corpora of news items from two respected financial newspapers ( The Economist and Expansión ), covering both the pre-COVID and pandemic periods. Our corpus-based, contrastive EN-ES analysis of lexically polarized words and emotions allows us to describe the publications’ positioning in the two periods. We further filter lexical items using the CNN Business Fear and Greed Index, as fear and greed are the opposing emotional states most often linked to financial market unpredictability and volatility. This novel analysis is expected to provide a holistic picture of how these specialist periodicals in English and Spanish have emotionally verbalized the economic havoc of the COVID-19 period compared to their previous linguistic behaviour. By doing so, our study contributes to the understanding of sentiment and emotion in financial journalism, shedding light on how crises can reshape the linguistic landscape of the industry.
Design and Application of Intelligent Classroom for English Language and Literature Based on Artificial Intelligence Technology

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Harnessing the Power of User-Centric Artificial Intelligence: Customized Recommendations and Personalization in Hybrid Recommender Systems

Recommender systems are widely used in various fields, such as e-commerce, entertainment, and education, to provide personalized recommendations to users based on their preferences and/or behavior. Τhis paper presents a novel approach to providing customized recommendations with the use of user-centric artificial intelligence. In greater detail, we introduce an enhanced collaborative filtering (CF) approach in order to develop hybrid recommender systems that personalize search results for users. The proposed CF enhancement incorporates user actions beyond explicit ratings to collect data and alleviate the issue of sparse data, resulting in high-quality recommendations. As a testbed for our research, a web-based digital library, incorporating the proposed algorithm, has been developed. Examples of operation of the use of the system are presented using cognitive walkthrough inspection, which demonstrates the effectiveness of the approach in producing personalized recommendations and improving user experience. Thus, the hybrid recommender system, which is incorporated in the digital library, has been evaluated, yielding promising results.
Exposing low-quality deepfake videos of Social Network Service using Spatial Restored Detection Framework

The increasing abuse of facial manipulation methods, such as FaceSwap, Deepfakes etc., seriously threatens the authenticity of digital images/videos on the Internet. Therefore, it is of great importance to identify the facial videos to confirm the contents and avoid fake news or rumors. Many researchers have paid great attention to the detection of deepfakes and put forward a number of deep-learning-based detection models. The existing approaches mostly face the performance degradation in detecting low-quality(LQ) videos, i.e. heavily compressed or low-resolution videos through some SNS (Social Network Service), resulting in the limitation in real applications. To address this issue, in this paper, a novel Spatial Restore Detection Framework(SRDF) is proposed for improving the detection performance for LQ videos by restoring spatial features. We designed a feature extraction-enhancement block and a mapping block inspired by super-resolution methods, to restore and enhance texture features. An attention module was introduced to guide the texture features restoration and enhancement stage attending to different local areas and restoring the texture features. Besides, an improved isolated loss was put forward to prevent the expansion of a single area concerned. Moreover, we adopted a regional data augmentation strategy to prompt feature restore and enhancement in the region attended. Extensive experiments conducted on two deepfake datasets have validated the superiority of the proposed method compared to the state-of-the-art, especially in the scenarios of detecting low-quality deepfake videos.
Assessing the Impact and Effectiveness of Cybersecurity Measures in e-Learning on Students and Educators: A Case Study

As e-learning has become increasingly prevalent, cyber security has become a major concern.e-Learning platforms collect and store large amounts of sensitive information, such as personal data and financial information, making them attractive targets for cybercriminals.To address these challenges and concerns, e-learning platforms must implement a comprehensive cyber security strategy that includes strong access controls, data encryption, regular software updates, and student training to help them identify and prevent insider threats.This research aims at investigating and determine how satisfied students are with e-learning security and privacy, as well as whether these concerns affect the overall standard of education.A sample study is presented to assess both the impact of the security framework on students' academic achievements and the student's satisfaction with the security countermeasures in an e-learning system.Statistical analysis showed that the use of security and cyber security countermeasures had a significant effect on the frequent use and participation of students in the contents of the system.Furthermore, encouraging feedback and communication from students about their e-learning experience to share their concerns, questions, and suggestions can help in addressing any security issues or concerns, as well as increasing students' participation in the e-learning content.
DynamoRep: Trajectory-Based Population Dynamics for Classification of Black-box Optimization Problems

The application of machine learning (ML) models to the analysis of optimization algorithms requires the representation of optimization problems using numerical features. These features can be used as input for ML models that are trained to select or to configure a suitable algorithm for the problem at hand. Since in pure black-box optimization information about the problem instance can only be obtained through function evaluation, a common approach is to dedicate some function evaluations for feature extraction, e.g., using random sampling. This approach has two key downsides: (1) It reduces the budget left for the actual optimization phase, and (2) it neglects valuable information that could be obtained from a problem-solver interaction. In this paper, we propose a feature extraction method that describes the trajectories of optimization algorithms using simple descriptive statistics. We evaluate the generated features for the task of classifying problem classes from the Black Box Optimization Benchmarking (BBOB) suite. We demonstrate that the proposed DynamoRep features capture enough information to identify the problem class on which the optimization algorithm is running, achieving a mean classification accuracy of 95% across all experiments.
Quantum bath suppression in a superconducting circuit by immersion cooling

Quantum circuits interact with the environment via several temperature-dependent degrees of freedom. Yet, multiple experiments to-date have shown that most properties of superconducting devices appear to plateau out at $T\approx 50$ mK -- far above the refrigerator base temperature. This is for example reflected in the thermal state population of qubits, in excess numbers of quasiparticles, and polarisation of surface spins -- factors contributing to reduced coherence. We demonstrate how to remove this thermal constraint by operating a circuit immersed in liquid $^3$He. This allows to efficiently cool the decohering environment of a superconducting resonator, and we see a continuous change in measured physical quantities down to previously unexplored sub-mK temperatures. The $^3$He acts as a heat sink which increases the energy relaxation rate of the quantum bath coupled to the circuit a thousand times, yet the suppressed bath does not introduce additional circuit losses or noise. Such quantum bath suppression can reduce decoherence in quantum circuits and opens a route for both thermal and coherence management in quantum processors.
UC-SFDA: Source-free domain adaptation via uncertainty prediction and evidence-based contrastive learning

Most unsupervised domain adaptation approaches learn domain-invariant features assuming that source and target domain data are available simultaneously. In practice, the availability of source samples is only sometimes possible. This paper establishes a novel source-free domain adaptation (SFDA) framework based on uncertainty prediction and a neighborhood-guided evidence-based contrastive learning scheme. First, we develop an evidence analyzer based on the uncertainty prediction principle of Dempster–Shafer (D–S) evidence theory, which improves the network capability for discriminating different types of samples. The transformer layer with a self-attention module is adopted to capture long-distance feature dependencies such that the proposed network has better generalization ability on multiple domains. Then, we offer a high-confidence target domain sample (HCS) acquisition strategy through evidence theory, entropy criterion, and distance information. A joint confidence enhancement scheme obtains the final HCS that generates pseudo-labels. Finally, we propose an optimization method based on evidence theory, evidence-based comparative learning, and internal neighborhood structure to ensure the separability between classes and compactness within categories. Experimental results show that the proposed framework performs superiorly on two standard datasets on multiple adaptation tasks. The code for this project is available at github.com/oolown/UC-SFDA.
Ultrafast all-optical quantum control of magnetization dynamics

With the development of laser and magneto-optical technology and the discovery of a broad range of magnetic quantum materials exhibiting exotic properties and new physics, ultrafast magnetization dynamics has become increasingly appealing to advanced magnetic information technology. Furthermore, manipulating magnetization via light provides insights into interactions among multiple degrees of freedom in condensed matters and has revealed a wide range of nonequilibrium phenomena. In this minireview, we first present the theoretical considerations of ultrafast magnetization dynamics from both classical and ab initio points of view. We then discuss several aspects of state-of-the-art experimental studies on light-induced magnetization dynamics in various materials, including ultrafast demagnetization and magnetization reversal, as well as coherent-phonon-driven magnetization precession and phase transitions. In particular, we highlight the role of light-induced phonons from some recent work in the latter two aspects, providing a completely new perspective as well as an alternative approach for optical control of magnetization dynamics. As a powerful means of dynamical control and thanks to the progress and advances of experimental techniques, all-optical quantum manipulation of emergent materials is becoming one of the most far-reaching frontier research areas of ultrafast sciences.
Spam Email Detection Using Machine Learning Techniques

This paper focuses on the security of electronic mail, using machine learning algorithms. Spam email is unwanted messages, usually commercial, sent to a large number of recipients. In this work, an algorithm for the detection of spam messages with the aid of machine learning methods is proposed. The algorithm accepts as input text email messages grouped as benevolent (“ham”) and malevolent (spam) and produces a text file in csv format. This file then is used to train a bunch of ten Machine Learning techniques to classify incoming emails into ham or spam. The following Machine Learning techniques have been tested: Support Vector Machines, k-Nearest Neighbour, Naïve Bayes, Neural Networks, Recurrent Neural Networks, Ada Boost, Random Forest, Gradient Boosting, Logistic Regression and Decision Trees. Testing was performed using two popular datasets, as well as a publicly available csv file. Our algorithm is written in Python and produces satisfactory results in terms of accuracy, compared to state-of-the-art implementations. In addition, the proposed system generates three output files: a csv file with the spam email IP addresses (of originating email servers), a map with their geolocation, as well as a csv file with statistics about the countries of origin. These files can be used to update existing organisational filters and blacklists used in other spam filters.
Overview of Software Agent Platforms Available in 2023

Agent-based computing remains an active field of research with the goal of building (semi-)autonomous software for dynamic ecosystems. Today, this task should be realized using dedicated, specialized frameworks. Over almost 40 years, multiple agent platforms have been developed. While many of them have been “abandoned”, others remain active, and new ones are constantly being released. This contribution presents a historical perspective on the domain and an up-to-date review of the existing agent platforms. It aims to serve as a reference point for anyone interested in developing agent systems. Therefore, the main characteristics of the included agent platforms are summarized, and selected links to projects where they have been used are provided. Furthermore, the described platforms are divided into general-purpose platforms and those targeting specific application domains. The focus of the contribution is on platforms that can be judged as being under active development. Information about “historical platforms” and platforms with an unclear status is included in a dedicated website accompanying this work.
Efficient Privacy-Preserving Spatial Range Query Over Outsourced Encrypted Data

With the rapid development of Location-Based Services (LBS), a large number of LBS providers outsource spatial data to cloud servers to reduce their high computational and storage burdens, but meanwhile incur some security issues such as location privacy leakage. Thus, extensive privacy-preserving LBS schemes have been proposed. However, the existing solutions using Bloom filter do not take into account the redundant bits that do not map information in Bloom filter, resulting in high computational overheads, and reveal the inclusion relationship in Bloom filter. To solve these issues, we propose an efficient Privacy-preserving Spatial Range Query (PSRQ) scheme by skillfully combining Geohash algorithm with Circular Shift and Coalesce Bloom Filter (CSC-BF) framework and Symmetric-key Hidden Vector Encryption (SHVE), which not only greatly reduces the computational cost of generating token but also speeds up the query efficiency on large-scale datasets. In addition, we design a Confused Bloom Filter (CBF) to confuse the inclusion relationship by confusing the values of 0 and 1 in the Bloom filter. Base on this, we further propose a more secure and practical enhanced scheme PSRQ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> by using CBF and Geohash algorithm, which can support more query ranges and achieve adaptive security. Finally, formal security analysis proves that our schemes are secure against Indistinguishability under Chosen-Plaintext Attacks (IND-CPA) and PSRQ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> achieves adaptive IND-CPA, and extensive experimental tests demonstrate that our schemes using million-level dataset improve the query efficiency by 100x compared with previous state-of-the-art solutions.
Improving document-level event detection with event relation graph

The correlation between events within the same document plays a crucial role in event detection. Most existing detection models often ignore event correlations, which is not applicable to multi-event detection at the document level. In the real world, it is a common phenomenon that the probability of correlated events occurring simultaneously is much greater than the probability of uncorrelated events occurring simultaneously. Based on this observation, we propose an event correlation-based document-level event detection model (EventCo-ED) to capture the document-level association between events. Specifically, EventCo-ED first constructs a novel event relation graph (ERG) to capture the correlation between events and uses this correlation to extract the topic features of a document. Secondly, DMBERT is employed to get sentence-level contextual representation as the local features. Finally, a gated feature fusion module is used to aggregate topic features and local features, and a correlation suppression module is used to increase the probability that related events are detected simultaneously and suppress the probability that unrelated events are detected simultaneously. Experimental results show that the proposed model can simultaneously improve the precision and recall of multi-event detection and achieve 1.56% and 3.63% F1 improvements on the LEVEN and MAVEN corpuses, respectively.