Reviewerly/exHarmony huggingface dataset:
Improving Phishing Website Detection Using a Hybrid Two-level Framework for Feature Selection and XGBoost Tuning

In the last few decades, the World Wide Web has become a necessity that offers numerous services to end users. The number of online transactions increases daily, as well as that of malicious actors. Machine learning plays a vital role in the majority of modern solutions. To further improve Web security, this paper proposes a hybrid approach based on the eXtreme Gradient Boosting (XGBoost) machine learning model optimized by an improved version of the well-known metaheuristics algorithm. In this research, the improved firefly algorithm is employed in the two-tier framework, which was also developed as part of the research, to perform both the feature selection and adjustment of the XGBoost hyper-parameters. The performance of the introduced hybrid model is evaluated against three instances of well-known publicly available phishing website datasets. The performance of novel introduced algorithms is additionally compared against cutting-edge metaheuristics that are utilized in the same framework. The first two datasets were provided by Mendeley Data, while the third was acquired from the University of California, Irvine machine learning repository. Additionally, the best performing models have been subjected to SHapley Additive exPlanations (SHAP) analysis to determine the impact of each feature on model decisions. The obtained results suggest that the proposed hybrid solution achieves a superior performance level in comparison to other approaches, and that it represents a perspective solution in the domain of web security.
Conditional Boundary Loss for Semantic Segmentation

Improving boundary segmentation results has recently attracted increasing attention in the field of semantic segmentation. Since existing popular methods usually exploit the long-range context, the boundary cues are obscure in the feature space, leading to poor boundary results. In this paper, we propose a novel conditional boundary loss (CBL) for semantic segmentation to improve the performance of the boundaries. The CBL creates a unique optimization goal for each boundary pixel, conditioned on its surrounding neighbors. The conditional optimization of the CBL is easy yet effective. In contrast, most previous boundary-aware methods have difficult optimization goals or may cause potential conflicts with the semantic segmentation task. Specifically, the CBL enhances the intra-class consistency and inter-class difference, by pulling each boundary pixel closer to its unique local class center and pushing it away from its different-class neighbors. Moreover, the CBL filters out noisy and incorrect information to obtain precise boundaries, since only surrounding neighbors that are correctly classified participate in the loss calculation. Our loss is a plug-and-play solution that can be used to improve the boundary segmentation performance of any semantic segmentation network. We conduct extensive experiments on ADE20K, Cityscapes, and Pascal Context, and the results show that applying the CBL to various popular segmentation networks can significantly improve the mIoU and boundary F-score performance.
Joint Answering and Explanation for Visual Commonsense Reasoning

Visual Commonsense Reasoning (VCR), deemed as one challenging extension of Visual Question Answering (VQA), endeavors to pursue a higher-level visual comprehension. VCR includes two complementary processes: question answering over a given image and rationale inference for answering explanation. Over the years, a variety of VCR methods have pushed more advancements on the benchmark dataset. Despite significance of these methods, they often treat the two processes in a separate manner and hence decompose VCR into two irrelevant VQA instances. As a result, the pivotal connection between question answering and rationale inference is broken, rendering existing efforts less faithful to visual reasoning. To empirically study this issue, we perform some in-depth empirical explorations in terms of both language shortcuts and generalization capability. Based on our findings, we then propose a plug-and-play knowledge distillation enhanced framework to couple the question answering and rationale inference processes. The key contribution lies in the introduction of a new branch, which serves as a relay to bridge the two processes. Given that our framework is model-agnostic, we apply it to the existing popular baselines and validate its effectiveness on the benchmark dataset. As demonstrated in the experimental results, when equipped with our method, these baselines all achieve consistent and significant performance improvements, evidently verifying the viability of processes coupling.
CervixFormer: A Multi-scale swin transformer-Based cervical pap-Smear WSI classification framework

Cervical cancer affects around 0.5 million women per year, resulting in over 0.3 million fatalities. Therefore, repetitive screening for cervical cancer is of utmost importance. Computer-assisted diagnosis is key for scaling up cervical cancer screening. Current recognition algorithms, however, perform poorly on the whole-slide image (WSI) analysis, fail to generalize for different staining methods and on uneven distribution for subtype imaging, and provide sub-optimal clinical-level interpretations. Herein, we developed CervixFormer—an end-to-end, multi-scale swin transformer-based adversarial ensemble learning framework to assess pre-cancerous and cancer-specific cervical malignant lesions on WSIs. The proposed framework consists of (1) a self-attention generative adversarial network (SAGAN) for generating synthetic images during patch-level training to address the class imbalanced problems; (2) a multi-scale transformer-based ensemble learning method for cell identification at various stages, including atypical squamous cells (ASC) and atypical squamous cells of undetermined significance (ASCUS), which have not been demonstrated in previous studies; and (3) a fusion model for concatenating ensemble-based results and producing final outcomes. In the evaluation, the proposed method is first evaluated on a private dataset of 717 annotated samples from six classes, obtaining a high recall and precision of 0.940 and 0.934, respectively, in roughly 1.2 minutes. To further examine the generalizability of CervixFormer, we evaluated it on four independent, publicly available datasets, namely, the CRIC cervix, Mendeley LBC, SIPaKMeD Pap Smear, and Cervix93 Extended Depth of Field image datasets. CervixFormer obtained a fairly better performance on two-, three-, four-, and six-class classification of smear- and cell-level datasets. For clinical interpretation, we used GradCAM to visualize a coarse localization map, highlighting important regions in the WSI. Notably, CervixFormer extracts feature mostly from the cell nucleus and partially from the cytoplasm. In comparison with the existing state-of-the-art benchmark methods, the CervixFormer outperforms them in terms of recall, accuracy, and computing time.
LightGT: A Light Graph Transformer for Multimedia Recommendation

Multimedia recommendation methods aim to discover the user preference on the multi-modal information to enhance the collaborative filtering (CF) based recommender system. Nevertheless, they seldom consider the impact of feature extraction on the user preference modeling and prediction of the user-item interaction, as the extracted features contain excessive information irrelevant to the recommendation.
RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses

Pretrained language models such as BERT have been shown to be exceptionally effective for text ranking. However, there are limited studies on how to leverage more powerful sequence-to-sequence models such as T5. Existing attempts usually formulate text ranking as a classification problem and rely on postprocessing to obtain a ranked list. In this paper, we propose RankT5 and study two T5-based ranking model structures, an encoder-decoder and an encoder-only one, so that they not only can directly output ranking scores for each query-document pair, but also can be fine-tuned with pairwise or listwise ranking losses to optimize ranking performance. Our experiments show that the proposed models with ranking losses can achieve substantial ranking performance gains on different public text ranking data sets. Moreover, ranking models fine-tuned with listwise ranking losses have better zero-shot ranking performance on out-of-domain data than models fine-tuned with classification losses.
Application of artificial intelligence in libraries and information centers services: prospects and challenges

Purpose Artificial intelligence (AI) is one of the emerging technologies of this time. AI is a widely used technology in library services that can transform the best services in the age of information technology. This paper aims to highlight the use of AI in library operations. Several research has been undertaken on this subject, but that only address a few applications. AI and libraries have a substantial nexus; nevertheless, the use and awareness of AI in library services are still creating question marks addressed in this paper. This study will help the policy stakeholder, librarians and scholars in the field to address these issues before the deployment of AI in library services. Design/methodology/approach This study is based on a qualitative method using content analysis techniques. An extensive review of literature on “artificial intelligence”, “smart libraries” was carried to ascertain the emerging technologies in the smart library domain. Literature was searched against various keywords like artificial intelligence, smart technologies, Internet of Things, electronic resource management, data mining and ambient intelligence. This study highlights the pros and cons of AI in library services and its possible solutions. Findings The findings of this study show that AI is a vibrant technology that can be used in library services; however, some obstacles like adequate funds, the attitude of librarians and technical skills are a few obstacles that hamper AI in library operations. The findings also reveal that using AI in library operations will accelerate libraries in the right direction. Furthermore, this study highlights various applications that can be deployed without spending costs. Practical implications This paper may be of interest to academic, librarians, policymakers, researchers and the government to have a perspective on initiatives in the country on application of technology in library services. This study can introduce the current status and potential of this technology to bring the technology revolution in library and information center services. Social implications This study will motivate library professionals to take advantage of AI in library services and further accelerate library operations in the right direction. Originality/value This study covers the understanding of AI in library services that will help the librarian’s and information professionals leverage AI in library scenarios. Furthermore, the practical implication of AI in library services will bring positive change in implementing AI.
A Conversation on Cognitive Aids in Strategy

This is a proposal for our fifth annual panel symposium exploring new horizons in managerial and organizational cognition research. In previous years we respectively covered perceived uncertainty, advances in methods, innovation, and business models. This year we propose a conversation to explore the theoretical foundations of cognitive aids in strategy. Strategy work has witnessed a proliferation of frameworks and tools over the past few decades, now including a plethora of 2x2 matrices, canvases, scenario and war games, Lego, doodling and drawing, music… all supposed to aid processes of organizational cognition and planning in the context of strategy work. We aim to take stock of some of the theoretical foundations of why and how such tools impact cognition. This conversation precedes an anthology that will be published later in 2023 under the title Cognitive Aids in Strategy (the latest volume in the Emerald series New Horizons in Managerial and Organizational Cognition).
Programmable <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><mml:mi>N</mml:mi></mml:math> -Body Interactions with Trapped Ions

Trapped atomic ion qubits or effective spins are a powerful quantum platform for quantum computation and simulation, featuring densely connected and efficiently programmable interactions between the spins. While native interactions between trapped-ion spins are typically pairwise, many quantum algorithms and quantum spin models naturally feature couplings between triplets, quartets, or higher orders of spins. Here, we formulate and analyze a mechanism that extends the standard Mølmer-Sørensen pairwise entangling gate and generates a controllable and programmable coupling between N spins of trapped ions. We show that spin-dependent optical parametric drives applied at twice the motional frequency generate a coordinate transformation of the collective ion motion in phase space, rendering displacement forces that are nonlinear in the spin operators. We formulate a simple framework that enables a systematic and faithful construction of high-order spin Hamiltonians and gates, including the effect of multiple modes of motion, and characterize the performance of such operations under realistic conditions.1 MoreReceived 21 July 2022Accepted 8 June 2023DOI:https://doi.org/10.1103/PRXQuantum.4.030311Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum controlQuantum gatesQuantum information processing with continuous variablesQuantum information with trapped ionsQuantum simulationPhysical SystemsTrapped ionsQuantum InformationAtomic, Molecular & Optical
Super-resolution of magnetic resonance images using Generative Adversarial Networks

Magnetic Resonance Imaging (MRI) typically comes at the cost of small spatial coverage, high expenses and long scan times. Accelerating MRI acquisition by taking less measurements yields the potential to relax these inherent forfeits. Recent breakthroughs in the field of Machine Learning have shown high-resolution (HR) images could be recovered from low-resolution (LR) signals via super-resolution (SR). In particular, a novel class of neural networks named Generative Adversarial Networks (GAN) has manifested an alternative way of conceiving models capable of generating data. GANs can learn to infer details based on some prior information, subsequently recovering missing data. Accordingly, they manifest huge potential in MRI reconstruction and acceleration tasks. This paper conducts a review on GAN-based SR methods, exhibiting the immersive ability of GANs on upscaling MRIs by a scale factor of ×4 while at the same time maintaining trustworthy and high-frequency details. Despite quantitative results suggesting SRResCycGAN outperforms other popular deep learning methods in recovering ×4 downgraded images, qualitative results show Beby-GAN holds the best perceptual quality and proves GAN-based methods hold the capacity to reduce medical costs, distress patients and even enable new MRI applications where it is currently too slow or expensive.
A novel Hyperledger blockchain-enabled decentralized application for drug discovery chain management

The aim of the drug discovery process is to create a brand-new drug that is both safe and useful for treating disease in patients. The trustworthy collaboration of the organisations involved in the drug discovery process and the integrity of their contributions are essential for this process. Current drug discovery chains use centralised systems, which are susceptible to lockdown by cyberattacks. Blockchain, with its many characteristics such as accountability, immutability, integrity, privacy, and security, has the potential to be extremely useful in drug discovery chain management. The purpose of this work is to create a drug discovery framework utilizing the strengths of blockchain technology in combination with machine learning (ML). Such a system would provide a secure, efficient, and faster drug development life cycle. This study presents a novel Hyperledger fabric-based drug discovery application that empowers the permissioned organisations to upload, update, view, and verify contributions. ML is used to preprocess data and visualise features. In the proposed work, a unique identifier is assigned to each contribution asset using the secure hash algorithm (SHA-256). The proposed design also enables the regulatory authority to issue certificates proving the ownership of contributions to the contributing organisations. The blockchain ledger has been used to store the meta-data and InChIKey of drug contributions, and actual contributions are in off-chain storage. In this work, we have successfully built an end-to-end decentralised drug discovery application with a front-end interface and demonstrated chaincode algorithms. The end-to-end application is not available in any previous work. The Caliper tool has been used to investigate throughput, latency, and resource statistics. The performance and comparative analysis show that the proposed design is scalable and promising for drug discovery chain management.
RadAdapt: Radiology Report Summarization via Lightweight Domain Adaptation of Large Language Models

We systematically investigate lightweight strategies to adapt large language models (LLMs) for the task of radiology report summarization (RRS).Specifically, we focus on domain adaptation via pretraining (on natural language, biomedical text, or clinical text) and via discrete prompting or parameter-efficient fine-tuning.Our results consistently achieve best performance by maximally adapting to the task via pretraining on clinical text and finetuning on RRS examples.Importantly, this method fine-tunes a mere 0.32% of parameters throughout the model, in contrast to end-to-end fine-tuning (100% of parameters).Additionally, we study the effect of in-context examples and out-of-distribution (OOD) training before concluding with a radiologist reader study and qualitative analysis.Our findings highlight the importance of domain adaptation in RRS and provide valuable insights toward developing effective natural language processing solutions for clinical tasks.
MoSE: Modality Split and Ensemble for Multimodal Knowledge Graph Completion

Multimodal knowledge graph completion (MKGC) aims to predict missing entities in MKGs.Previous works usually share relation representation across modalities.This results in mutual interference between modalities during training, since for a pair of entities, the relation from one modality probably contradicts that from another modality.Furthermore, making a unified prediction based on the shared relation representation treats the input in different modalities equally, while their importance to the MKGC task should be different.In this paper, we propose MoSE, a Modality Split representation learning and Ensemble inference framework for MKGC.Specifically, in the training phase, we learn modality-split relation embeddings for each modality instead of a single modality-shared one, which alleviates the modality interference.Based on these embeddings, in the inference phase, we first make modality-split predictions and then exploit various ensemble methods to combine the predictions with different weights, which models the modality importance dynamically.Experimental results on three KG datasets show that MoSE outperforms state-ofthe-art MKGC methods.Codes are available at https://github.com/OreOZhao/MoSE4MKGC.
Textless Speech Emotion Conversion using Discrete &amp; Decomposed Representations

Speech emotion conversion is the task of modifying the perceived emotion of a speech utterance while preserving the lexical content and speaker identity.In this study, we cast the problem of emotion conversion as a spoken language translation task.We use a decomposition of the speech signal into discrete learned representations, consisting of phonetic-content units, prosodic features, speaker, and emotion.First, we modify the speech content by translating the phoneticcontent units to a target emotion, and then predict the prosodic features based on these units.Finally, the speech waveform is generated by feeding the predicted representations into a neural vocoder.Such a paradigm allows us to go beyond spectral and parametric changes of the signal, and model non-verbal vocalizations, such as laughter insertion, yawning removal, etc.We demonstrate objectively and subjectively that the proposed method is vastly superior to current approaches and even beats text-based systems in terms of perceived emotion and audio quality.We rigorously evaluate all components of such a complex system and conclude with an extensive model analysis and ablation study to better emphasize the architectural choices, strengths and weaknesses of the proposed method.Samples are available under the following link: [samples].
PromptFL: Let Federated Participants Cooperatively Learn Prompts Instead of Models - Federated Learning in Age of Foundation Model

Quick global aggregation of effective distributed parameters is crucial to federated learning (FL), which requires adequate bandwidth for parameters communication and sufficient user data for local training. Otherwise, FL may cost excessive training time for convergence and produce inaccurate models. In this paper, we propose a brand-new FL framework, P <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rompt</small> FL, that replaces the federated model training with the federated prompt training, i.e., let federated participants train prompts instead of a shared model, to simultaneously achieve the efficient global aggregation and local training on insufficient data by exploiting the power of foundation models (FM) in a distributed way. PromptFL ships an off-the-shelf FM, i.e., CLIP, to distributed clients who would cooperatively train shared soft prompts based on very few local data. Since P <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rompt</small> FL only needs to update the prompts instead of the whole model, both the local training and the global aggregation can be significantly accelerated. And FM trained over large scale data can provide strong adaptation capability to distributed users tasks with the trained soft prompts. We empirically analyze the P <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rompt</small> FL via extensive experiments, and show its superiority in terms of system feasibility, user privacy, and performance.
LFNAT 2023 Challenge on Light Field Depth Estimation: Methods and Results

This paper reviews the 1st LFNAT challenge on light field depth estimation, which aims at predicting disparity information of central view image in a light field (i.e., pixel offset between central view image and adjacent view image). Compared to multi-view stereo matching, light field depth estimation emphasizes efficient utilization of the 2D angular information from multiple regularly varying views. This challenge specifies UrbanLF [20] light field dataset as the sole data source. There are two phases in total: submission phase and final evaluation phase, in which 75 registered participants successfully submit their predicted results in the first phase and 7 eligible teams compete in the second phase. The performance of all submissions is carefully reviewed and shown in this paper as a new standard for the current state-of-the-art in light field depth estimation. Moreover, the implementation details of these methods are also provided to stimulate related advanced research.