Reviewerly/exHarmony huggingface dataset:
Generative Information Retrieval

Historically, information retrieval systems have all followed the same paradigm: information seekers frame their needs in the form of a short query, the system selects a small set of relevant results from a corpus of available documents, rank-orders the results by decreasing relevance, possibly excerpts a responsive passage for each result, and returns a list of references and excerpts to the user. Retrieval systems typically did not attempt fusing information from multiple documents into an answer and displaying that answer directly. This was largely due to available technology: at the core of each retrieval system is an index that maps lexical tokens or semantic embeddings to document identifiers. Indices are designed for retrieving responsive documents; they do not support integrating these documents into a holistic answer.
FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation

Retrieval-augmented generation models offer many benefits over standalone language models: besides a textual answer to a given query they provide provenance items retrieved from an updateable knowledge base. However, they are also more complex systems and need to handle long inputs. In this work, we introduce FiD-Light to strongly increase the efficiency of the state-of-the-art retrieval-augmented FiD model, while maintaining the same level of effectiveness. Our FiD-Light model constrains the information flow from the encoder (which encodes passages separately) to the decoder (using concatenated encoded representations). Furthermore, we adapt FiD-Light with re-ranking capabilities through textual source pointers, to improve the top-ranked provenance precision. Our experiments on a diverse set of seven knowledge intensive tasks (KILT) show FiD-Light consistently improves the Pareto frontier between query latency and effectiveness. FiD-Light with source pointing sets substantial new state-of-the-art results on six KILT tasks for combined text generation and provenance retrieval evaluation, while maintaining high efficiency.
A Geometric Framework for Query Performance Prediction in Conversational Search

Thanks to recent advances in IR and NLP, the way users interact with search engines is evolving rapidly, with multi-turn conversations replacing traditional one-shot textual queries. Given its interactive nature, Conversational Search (CS) is one of the scenarios that can benefit the most from Query Performance Prediction (QPP) techniques. QPP for the CS domain is a relatively new field and lacks proper framing. In this study, we address this gap by proposing a framework for the application of QPP in the CS domain and use it to evaluate the performance of predictors. We characterize what it means to predict the performance in the CS scenario, where information needs are not independent queries but a series of closely related utterances. We identify three main ways to use QPP models in the CS domain: as a diagnostic tool, as a way to adjust the system's behaviour during a conversation, or as a way to predict the system's performance on the next utterance. Due to the lack of established evaluation procedures for QPP in the CS domain, we propose a protocol to evaluate QPPs for each of the use cases. Additionally, we introduce a set of spatial-based QPP models designed to work the best in the conversational search domain, where dense neural retrieval models are the most common approaches and query cutoffs are typically small. We show how the proposed QPP approaches improve significantly the predictive performance over the state-of-the-art in different scenarios and collections.
Abnormal event detection for video surveillance using an enhanced two-stream fusion method

Abnormal event detection is a critical component of intelligent surveillance systems, focusing on identifying abnormal objects or unusual human behaviours in video sequences. However, conventional methods struggle due to the scarcity of labelled data. Existing solutions typically train on normal data, establish boundaries for regular events, and identify outliers during testing. These approaches are often inadequate as they do not efficiently leverage the geometry and image texture information, and they lack a specific focus on different types of abnormal events. This paper introduces a novel two-stream fusion algorithm for abnormal event detection to address these diverse abnormal events better. We first extract the object, pose, and optical flow features. Then, the object and pose information is combined early on to eliminate occluded pose graphs. The trusted pose graphs are fed into a Spatio-Temporal Graph Convolutional Network (ST-GCN) to detect abnormal behaviours. Simultaneously, we propose a video prediction framework that identifies abnormal frames by measuring the difference between predicted and ground truth frames. Lastly, we execute a decision-level fusion between the classification and prediction streams to achieve the final results. Our results on the UCSD PED1 dataset indicate the enhanced performance of the fusion model for various abnormal events. Furthermore, experimental results on the UCSD PED2 dataset and the ShanghaiTech campus dataset underscore our approach's effectiveness compared to other related works.
Benchmarking Biologically-Inspired Automatic Machine Learning for Economic Tasks

Data-driven economic tasks have gained significant attention in economics, allowing researchers and policymakers to make better decisions and design efficient policies. Recently, with the advancement of machine learning (ML) and other artificial intelligence (AI) methods, researchers can now solve complex economic tasks with previously unseen performance and ease. However, to use such methods, one is required to have a non-trivial level of expertise in ML or AI, which currently is not standard knowledge in economics. In order to bridge this gap, automatic machine learning (AutoML) models have been developed, allowing non-experts to efficiently use advanced ML models with their data. Nonetheless, not all AutoML models are created equal in general, particularly for the unique properties associated with economic data. In this paper, we present a benchmarking study of biologically inspired and other AutoML techniques for economic tasks. We evaluate four different AutoML models alongside two baseline methods using a set of 50 diverse economic tasks. Our results show that biologically inspired AutoML models (slightly) outperformed non-biological AutoML in economic tasks, while all AutoML models outperformed the traditional methods. Based on our results, we conclude that biologically inspired AutoML has the potential to improve our economic understanding while shifting a large portion of the analysis burden from the economist to a computer.
A Robust Adaptive Hierarchical Learning Crow Search Algorithm for Feature Selection

Feature selection is a multi-objective problem, which can eliminate irrelevant and redundant features and improve the accuracy of classification at the same time. Feature selection is a great challenge to balance the conflict between the two goals of selection accuracy and feature selection ratio. The evolutionary algorithm has been proved to be suitable for feature selection. Recently, a new meta-heuristic algorithm named the crow search algorithm has been applied to the problem of feature selection. This algorithm has the advantages of few parameters and achieved good results. However, due to the lack of diversity in late iterations, the algorithm falls into local optimal problems. To solve this problem, we propose the adaptive hierarchical learning crow search algorithm (AHL-CSA). Firstly, an adaptive hierarchical learning technique was used to adaptive divide the crow population into several layers, with each layer learning from the top layer particles and the topmost layer particles learning from each other. This strategy encourages more exploration by lower individuals and more exploitation by higher individuals, thus improving the diversity of the population. In addition, in order to make full use of the search information of each level in the population and reduce the impact of local optimization on the overall search performance of the algorithm, we introduce an information sharing mechanism to help adjust the search direction of the population and improve the convergence accuracy of the algorithm. Finally, different difference operators are used to update the positions of particles at different levels. The diversity of the population is further improved by using different difference operators. The performance of the method was tested on 18 standard UCI datasets and compared with eight other representative algorithms. The comparison of experimental results shows that the proposed algorithm is superior to other competitive algorithms. Furthermore, the Wilcoxon rank-sum test was used to verify the validity of the results.
Chatbots for active learning: A case of phishing email identification

Chatbots represent a promising approach to provide instructional content and facilitate active learning processes. However, there is a lack of knowledge as how to design chatbot interactions for active learning. In response to this knowledge gap, we conducted an experimental study (n = 164) comparing four modes for providing instructional content in chatbots, with varying demands for cognitive engagement. The four modes – passive, active, constructive, and interactive – were based on the ICAP framework of active learning. The learning content concerned identification of phishing emails and the four modes were distinguished by how the participants were invited to engage with the content during their chatbot interaction. The ICAP modes of higher cognitive engagement required participants to spend more time on the interaction and led to perceptions of higher subjective learning outcome. However, the effects of the different ICAP modes were not found to be significantly different in terms of user engagement, social presence, intention to use, or objective learning outcomes. The study represents an important first step towards understanding the design of chatbots for active learning.
An integrated explicit and implicit offensive language taxonomy

Abstract The current study represents an integrated model of explicit and implicit offensive language taxonomy. First, it focuses on a definitional revision and enrichment of the explicit offensive language taxonomy by reviewing the collection of available corpora and comparing tagging schemas applied there. The study relies mainly on the categories originally proposed by Zampieri et al. (2019) in terms of offensive language categorization schemata. After the explanation of semantic differences between particular concepts used in the tagging systems and the analysis of theoretical frameworks, a finite set of classes is presented, which cover aspects of offensive language representation along with linguistically sound explanations (Lewandowska-Tomaszczyk et al. 2021). In the analytic procedure, offensive from non-offensive discourse is first distinguished, with the question of offence Target and the following categorization levels and sublevels. Based on the relevant data generated from Sketch Engine ( https://www.sketchengine.eu/ententen-english-corpus/ ), we propose the concept of offensive language as a superordinate category in our system with a number of hierarchically arranged 17 subcategories. The categories are taxonomically structured into 4 levels and verified with the use of neural-based (lexical) embeddings. Together with a taxonomy of implicit offensive language and its subcategorization levels which has received little scholarly attention until now, the categorization is exemplified in samples of offensive discourses in selected English social media materials, i.e., publicly available 25 web-based hate speech datasets (consult Appendix 1 for a complete list). The offensive category levels (types of offence, targets, etc.) and aspects (offensive language property clusters) as well as the categories of explicitness and implicitness are discussed in the study and the computationally verified integrated explicit and implicit offensive language taxonomy proposed in the study.
The Dark Side of Explanations: Poisoning Recommender Systems with Counterfactual Examples

Deep learning-based recommender systems have become an integral part of several online platforms. However, their black-box nature emphasizes the need for explainable artificial intelligence (XAI) approaches to provide human-understandable reasons why a specific item gets recommended to a given user. One such method is counterfactual explanation (CF). While CFs can be highly beneficial for users and system designers, malicious actors may also exploit these explanations to undermine the system's security.
ULS-PBFT: An ultra-low storage overhead PBFT consensus for blockchain

Since the Practical Byzantine Fault Tolerance (PBFT) consensus mechanism can avoid the performance bottleneck of blockchain systems caused by Proof of Work (PoW), it has been widely used in many scenarios. However, in the blockchain system, each node is required to back up all transactions and block data of the system, which will waste a lot of storage resources. It is difficult to apply to scenarios with limited storage resources such as unmanned aerial vehicle networks and smart security protection, thus, is often used in small-scale networks. In order to deploy PBFT-based blockchain systems in large-scale network scenarios, we propose an ultra-low storage overhead PBFT consensus (ULS-PBFT), which groups nodes hierarchically to limit the storage overhead within the group. In this paper, we first propose an optimal double-layer PBFT consensus from the perspective of minimizing the storage overhead, and prove that this consensus can significantly reduce the storage overhead. In addition, we also investigate the superiority of ULS-PBFT in terms of communication overhead, while giving the security threshold in the presence of the possibility of Byzantine nodes. Simulation results demonstrate the advantages of ULS-PBFT. Then, we extend such grouping idea to the blockchain system with X-layer PBFT, and analyze its storage and communication overhead. Finally, the node grouping strategy of double-layer PBFT is studied for four application scenarios when the performance of storage overhead, communication overhead, and security are considered comprehensively.
A Mask-Wearing Detection Model in Complex Scenarios Based on YOLOv7-CPCSDSA

With the rapid development of deep learning technology, many algorithms for mask-wearing detection have achieved remarkable results. However, the detection effect still needs to be improved when dealing with mask-wearing in some complex scenes where the targets are too dense or partially occluded. This paper proposes a new mask-wearing detection model: YOLOv7-CPCSDSA. Based on YOLOv7, this model replaces some convolutions of the original model, CatConv, with FasterNet’s partial convolution (PConv) to form a CatPConv (CPC) structure, which can reduce computational redundancy and memory access. In the case of an increase in the network layer, the parameters are reduced instead. The Small Detection (SD) module is added to the model, which includes structures such as upsampling, concat convolution, and MaxPooling to enhance the ability to capture small targets, thereby improving detection accuracy. In addition, the Shuffle Attention (SA) mechanism is introduced, which enables the model to adaptively focus on important local information, thereby improving the accuracy of detecting mask-wearing. This paper uses comparative and ablation experiments in the mask dataset (including many images in complex scenarios) to verify the model’s effectiveness. The results show that the mean average precision@0.5 (mAP@0.5) of YOLOv7-CPCSDSA reaches 88.4%, which is 1.9% higher than that of YOLOv7, and its frames per second (FPS) rate reaches 75.8 f/s, meeting the real-time detection requirements. Therefore, YOLOv7-CPCSDSA is suitable for detecting mask-wearing in complex scenarios.
UNTIE: Clustering analysis with disentanglement in multi-view information fusion

Multi-view clustering focuses on exploring cluster structures among multiple views and is an effective approach to achieve multi-view information fusion without requiring label supervision. However, multiple views’ useful and meaningless information usually is entangled and the latter might cause negative influences in the fusion process. In this paper, we research the interpretability for different information in multiple views and propose a novel method (termed UNTIE) to address their entanglement. Specifically, in UNTIE, discrete view-common variable is introduced to explore all views’ common information, and continuous view-private variables are introduced to learn individual views’ private information. In this way, the learned representations are disentangled and interpretable where each variable represents a single factor among multi-view data. Then, the useful information in disentangled view-common and view-private representations are leveraged to conduct comprehensive multi-view clustering, making UNTIE can explore common and complementary information from multiple views while obtaining the robustness to meaningless information. Finally, UNTIE has the ability to controllably generate samples and extensive experiments demonstrate its superior representation ability and clustering performance.
Extending UTAUT2 Model With Sustainability and Psychological Factors in Adoption of Blockchain Technology for the Digital Transformation of Banks in India

Financial institutions' digital advancements are vital for sustainability, with blockchain having transformative potential. Banking pursues digital transformation due to fintech competition and cybersecurity worries, driven by technology advancements and customer expectations. FinTech start-ups prompt innovation from big banks. Industry 4.0 integrates blockchain, AI, and technology platforms that align with SDG8 and SDG9, promoting transparency, cost reduction, and company expansion. Therefore, the present research seeks to answer: What drives banks to adopt blockchain technology? It aims to identify factors influencing bankers' intention to adopt blockchain for digital transformation in Indian banks. A standardized scale with the addition of two more constructs (sustainability agenda and psychological framework) was used to achieve the objective of the study. The findings of the research enhance understanding of banks' technology usage and blockchain adoption. Findings validate nine factors influencing bankers' blockchain adoption.
Improved YOLO-v5 model for boosting face mask recognition accuracy on heterogeneous IoT computing platforms

The World Health Organization (WHO) claimed that wearing masks can effectively reduce the spread of COVID-19. In this paper, we devise novel YOLO-v5s detectors for improving the accuracy of recognizing face masks under heterogeneous IoT computing platforms. First, a module of coordinate attention is merged with the original YOLO-v5s backbone to enhance the model's attention to key information and filter out redundant information. Second, the original feature pyramid network (FPN) in the neck network is replaced with the bidirectional FPN for fast multiscale feature fusion. Third, the block of adaptive spatial feature fusion is embedded into the head layer to further solve the problem of feature inconsistency with different scales. Last, the SCYLLA-IoU metric serves as the new bounding box loss function to accelerate the model convergence. The training results show that applying the improved YOLO-v5s model to the AIZOO dataset yields an improvement in recognition accuracy of +2.2% and that the improved model has lower false detections and better detection of dense crowds. In addition, we propose a new evaluation standard of the scale limit factor to measure the detection performance of the model with different shooting distances. The proposed model is deployed on three heterogeneous IoT platforms, the Google Colab cloud, a personal computing terminal, and an Nvidia Jetson Nano edge computing device, to validate the inference feasibility, effectiveness, and robustness.
Mapping quantum algorithms to multi-core quantum computing architectures

Current monolithic quantum computer architectures have limited scalability.One promising approach for scaling them up is to use a modular or multi-core architecture, in which different quantum processors (cores) are connected via quantum and classical links.This new architectural design poses new challenges such as the expensive inter-core communication.To reduce these movements when executing a quantum algorithm, an efficient mapping technique is required.In this paper, a detailed critical discussion of the quantum circuit mapping problem for multi-core quantum computing architectures is provided.In addition, we further explore the performance of a mapping method, which is formulated as a partitioning over time graph problem, by performing an architectural scalability analysis.
SPHINCS+C: Compressing SPHINCS+ With (Almost) No Cost

SPHINCS+ [CCS ’19] is one of the selected post-quantum digital signature schemes of NIST’s post-quantum standardization process. The scheme is a hash-based signature and is considered one of the most secure and robust proposals. The proposal includes a fast (but larger) variant and a small (but slower) variant for each security level. The main problem that might hinder its adoption is its large signature size. Although SPHINCS+ supports a trade-off between signature size and the computational cost of signing, further reducing the signature size (below the small variants) results in a prohibitively high computational cost for the signer.This paper presents several novel methods for further compressing the signature size while requiring negligible added computational costs for the signer and further reducing verification time. Moreover, our approach enables a much more efficient trade-off curve between signature size and the computational costs of the signer. In many parameter settings, we achieve small signatures and faster running times simultaneously. For example, for 128-bit (classical) security, the small signature variant of SPHINCS+ is 7856 bytes long, while our variant is only 6304 bytes long: a compression of approximately 20% while still reducing the signer’s running time. However, other trade-offs that focus, e.g., on verification speed, are possible.The main insight behind our scheme is that there are predefined specific subsets of messages for which the WOTS+ and FORS signatures (that SPHINCS+ uses) can be compressed, and generation can be made faster while maintaining the same security guarantees. Although most messages will not come from these subsets, we can search for suitable hashed values to sign. We sign a hash of the message concatenated with a counter that was chosen such that the hashed value is in the subset. The resulting signature is both smaller and faster to sign and verify.Our schemes are simple to describe and implement. We provide an implementation, a theoretical analysis of speed and security, as well as benchmark results.