Reviewerly/exHarmony huggingface dataset:
Crawling The Internal Knowledge-Base of Language Models

Language models are trained on large volumes of text, and as a result their parameters might contain a significant body of factual knowledge. Any downstream task performed by these models implicitly builds on these facts, and thus it is highly desirable to have means for representing this body of knowledge in an interpretable way. However, there is currently no mechanism for such a representation.Here, we propose to address this goal by extracting a knowledge-graph of facts from a given language model. We describe a procedure for “crawling” the internal knowledge-base of a language model. Specifically, given a seed entity, we expand a knowledge-graph around it. The crawling procedure is decomposed into sub-tasks, realized through specially designed prompts that control for both precision (i.e., that no wrong facts are generated) and recall (i.e., the number of facts generated). We evaluate our approach on graphs crawled starting from dozens of seed entities, and show it yields high precision graphs (82-92%), while emitting a reasonable number of facts per entity.
Augmenting Pre-trained Language Models with QA-Memory for Open-Domain Question Answering

Existing state-of-the-art methods for open-domain question-answering (ODQA) use an open book approach in which information is first retrieved from a large text corpus or knowledge base (KB) and then reasoned over to produce an answer. A recent alternative is to retrieve from a collection of previously-generated question-answer pairs; this has several practical advantages including being more memory and compute-efficient. Question-answer pairs are also appealing in that they can be viewed as an intermediate between text and KB triples: like KB triples, they often concisely express a single relationship, but like text, have much higher coverage than traditional KBs. In this work, we describe a new QA system that augments a text-to-text model with a large memory of question-answer pairs, and a new pre-training task for the latent step of question retrieval. The pre-training task substantially simplifies training and greatly improves performance on smaller QA benchmarks. Unlike prior systems of this sort, our QA system can also answer multi-hop questions that do not explicitly appear in the collection of stored question-answer pairs.
RNNtcs: A test case selection method for Recurrent Neural Networks

Recurrent Neural Network (RNN) is a typical feedback neural network, which is particularly effective in processing time-series data tasks such as image description, text generation and classification, etc. However, its safety and quality issues have aroused strong concern in academia and industry. One of the most commonly used methods to ensure RNN-driven software quality is testing and optimising RNN. Its core idea is to introduce human efforts to label test cases so that many labelled test cases can be used to trigger faults in RNNs. But this brings a huge overhead. Therefore, it is of great importance to study the test case selection method for RNN. Current research on test case selection for RNN has achieved some results from the stateful view of RNN, but there are still some problems, such as a low bug detection rate, poor inclusiveness, and lack of diversity. In this paper, according to the structural characteristics of RNN, we propose a method of RNN test case selection, RNNtcs, which combines clustering and uncertainty, in order to select test cases that can touch RNN bugs as much as possible, thus reducing the cost of labeling. In order to evaluate RNNtcs, we conduct experiments on popular image datasets, text datasets and RNN models. The experimental results show that RNNtcs is superior to the state-of-the-art approach DeepState in bug detection effectiveness, bug detection inclusiveness and bug detection diversity. The test cases selected by RNNtcs can effectively improve the robustness of the RNN model.
Continuous Cartesian Genetic Programming based representation for multi-objective neural architecture search

We propose a novel neural architecture search (NAS) approach for the challenge of designing convolutional neural networks (CNNs) that achieve a good tradeoff between complexity and accuracy. We rely on Cartesian genetic programming (CGP) and integrated real-based and block-chained CNN representation, for optimization using multi-objective evolutionary algorithms (MOEAs) in the continuous domain. We introduce two variants, CGP-NASV1 and CGP-NASV2, which differ in the granularity of their respective search spaces. To evaluate the proposed algorithms, we utilized the non-dominated sorting genetic algorithm II (NSGA-II) on the CIFAR-10, CIFAR-100,and SVHN datasets. Additionally, we extended the empirical analysis while maintaining the same solution representation to assess other searching techniques such as differential evolution (DE), the multi-objective evolutionary algorithm based on decomposition (MOEA/D), and the S metric selection evolutionary multi-objective algorithm (SMS-EMOA). The experimental results demonstrate that our approach exhibits competitive classification performance and model complexity compared to state-of-the-art methods.
Adv-BDPM: Adversarial attack based on Boundary Diffusion Probability Model

Deep neural networks have become increasingly significant in our daily lives due to their remarkable performance. The issue of adversarial examples, which are responsible for the vulnerability problem of deep neural networks, has attracted the attention of researchers in the study of robustness of these networks. To address the issues caused by the restricted diversity and precision of adversarial perturbations in neural networks, we introduce a novel technique called Adversarial Boundary Diffusion Probability Modeling (Adv-BDPM). This approach combines boundary analysis and diffusion probability modeling. First, we combined the denoising diffusion probability model with the boundary loss to design the boundary diffusion probability model, which can generate corresponding boundary perturbations for a specific neural network. Then, through the iterative process of boundary perturbations and its corresponding orthogonal perturbations, we proposed a decision boundary search algorithm to generate adversarial samples. The comparison experiments with black-box attacks in ImageNet demonstrate that Adv-BDPM has better attack success rate and perturbation precision. The comparison experiments with white-box attacks in CIFAR-10 and CIFAR-100 demonstrate that Adv-BDPM has better attack success rate, attack diversity for the same sample, and can effectively defend against adversarial training with shorter running time.
Data-Efficient Methods For Improving Hate Speech Detection

Scarcity of large-scale datasets, especially for resource-impoverished languages motivates exploration of data-efficient methods for hate speech detection. Hateful intents are expressed explicitly (use of cuss, swear, abusive words) and implicitly (indirect and contextual). In this work, we progress implicit and explicit hate speech detection using an input-level data augmentation technique, task reformulation using entailment and cross-learning across five languages. Our proposed data augmentation technique EasyMix, improves the performance across all english datasets by ~1% and across multilingual datasets by ~1-9%. We also observe substantial gains of ~2-8% by reformulating hate speech detection as entail problem. We further probe the contextual models and observe that higher layers encode implicit hate while lower layers focus on explicit hate, highlighting the importance of token-level understanding for explicit and context-level for implicit hate speech detection. Code and Dataset splits - https://anonymous.4open.science/r/data_efficient_hatedetect/
F-NIDS — A Network Intrusion Detection System based on federated learning

The rise of IoT networks has presented fresh challenges in terms of scalability and security for distributed Network Intrusion Detection Systems (NIDS) due to privacy concerns. While some progress has been made in addressing these challenges, there are still unanswered questions regarding how to achieve a balance between performance and robustness to ensure privacy in a distributed manner. Additionally, there is a need to develop a reliable and scalable architecture for distributed NIDS that can be effectively deployed in various IoT scenarios. These questions about robustness relied mainly on choosing privacy-secured and distributed Machine Learning techniques. In this work, we propose the F-NIDS, an intrusion detector that utilizes federated artificial intelligence and asynchronous communication techniques between system entities to provide horizontal scalability, along with differential privacy techniques to address data confidentiality concerns. The architecture of F-NIDS is designed to be adaptable for usage in IoT networks, suited to be used in cloud or fog-based environments. Results from our experiments have shown that the confidential detection model employed in F-NIDS – considering multi-class accuracy, binary accuracy, precision, and recall metrics – was capable of predicting and determining the nature of attacks when they occur. In order to determine optimal parameters that strike a balance between data privacy and classification performance, three strategies were employed, each evaluated for its corresponding robustness performance. Firstly, models were trained with varying Gaussian noise values, and subjected to membership inference black box rule-based attacks. Secondly, regular membership inference black box attacks were performed, utilizing different stolen samples with varying sizes to determine the maximum amount of data that could be securely stored on the detection agents for training tasks. Lastly, the robustness of the trained models was evaluated against a model inversion attack, and the results were compared through graphical comparisons. Based on these evaluations, Gaussian noise level and sample size values of 21 were obtained for each detection agent in the system, with sample sizes ranging from 10K to 25K.
qCLEF: A Proposal to Evaluate Quantum Annealing for Information Retrieval and Recommender Systems

Quantum Computing (QC) has been a focus of research for many researchers over the last few years. As a result of technological development, QC resources are also becoming available and usable to solve practical problems in the Information Retrieval (IR) and Recommender Systems (RS) fields. Nowadays IR and RS need to perform complex operations on very large datasets. In this scenario, it could be possible to increase the performance of these systems both in terms of efficiency and effectiveness by employing QC and, especially, Quantum Annealing (QA). The goal of this work is to design a Lab composed of different Shared Tasks that aims to: This Lab uses the QC resources provided by CINECA, one of the most important computing centers worldwide, thanks to an already met agreement. In addition, we also show a possible implementation of the required infrastructure which uses Docker containers and the Kubernetes orchestrator to ensure scalability, fault tolerance and that can be deployed on the cloud.
Navigating a Black Box: Students’ Experiences and Perceptions of Automated Hiring

Automated hiring algorithms are increasingly used in computing job recruitment. Prior work has examined perceptions of algorithmic fairness and established bias in hiring algorithms, but there is limited work on the ability of computer science students, who are applying for their first computing job, to overcome new barriers posed by automated hiring. To investigate what challenges students face, how they work through them, and their perceptions of these systems, we conducted semi-structured interviews with post-secondary students who were first-time computing job applicants. Analyses revealed that participants had diverse knowledge of hiring algorithms; some people knew to use strategies, such as keywords in resumes, online assessment practice, and referrals to circumvent automated processes to progress to in-person interviews, but others were entirely unaware of the automation. Participants also expressed that current systems prevented them from demonstrating the full extent of their skills and attributed job offers to personal contacts within the company. While some deemed automation a "necessary evil" to combat scale, many struggled with the inequity automated hiring processes perpetuated. Understanding student experiences and perspectives with automated hiring has relevance for how current computer science curricula prepares students for the transition to computing jobs post-graduation. Our findings have implications for how to develop new practices to better support students in their transitions amid a changing hiring landscape.
Overview of JOKER – CLEF-2023 Track on Automatic Wordplay Analysis

The goal of the JOKER track series is to bring together linguists, translators, and computer scientists to foster progress on the automatic interpretation, generation, and translation of wordplay. Being clearly important for various applications, these tasks are still extremely challenging despite significant recent progress in AI in information retrieval and natural language processing. Building on the lessons learned from last year's edition, JOKER-2023 held three shared tasks aligned with human approaches to the translation of wordplay, or more specifically of puns in English, French, and Spanish: detection, location and interpretation, and finally translation. In this paper, we define these three tasks and describe our approaches to corpus creation and evaluation. We then present an overview of the participating systems, including the summaries of their approaches and a comparison of their performance. As in JOKER-2022, this year's track also solicited contributions making further use of our data (an "unshared task"), which we also report on.
Optimization of Privacy Budget Allocation In Differential Privacy-Based Public Transit Trajectory Data Publishing for Smart Mobility Applications

Trajectory datasets have been widely used in transportation research, but the risk of privacy breach comes with data sharing. Privacy budget allocation is a key step of the differential privacy (DP)-based privacy-preserving data publishing (PPDP) algorithm development, as it directly impacts the data utility of the released dataset. Most prior research used simple logic to allocate privacy budgets, such as evenly distributing them among different tree levels, without theoretical support to reach optimality. This manuscript presents the development of an optimal privacy budget allocation algorithm for transit smart card data, with the goal of publishing non-interactive sanitized trajectory data under a differential privacy definition. To this end, the smart card trajectory data are first stored in a prefix tree structure, and a query probability model is developed to quantitatively measure the probability of a trajectory location pair being queried. Next, the privacy budget is optimized for each prefix tree node to minimize the query error, while satisfying the differential privacy definition. The Lagrangian relaxation method is adopted to derive the optimal privacy budget values, and several propositions on the solution property are proposed and proved. Real-life metro smart card data from Shenzhen, China that include a total of 2.8 million individual travelers and over 220 million records are used in the case study section. The developed algorithm is demonstrated to output a sanitized dataset with the highest utilities when compared with three benchmark algorithms. Sensitivity analysis shows that the resulting data utility remains stable when the privacy budget changes. The runtime of the proposed algorithm is less than 160 seconds in all experiments, exhibiting good computational efficiency.
A Convergent Neural Network for Non-Blind Image Deblurring

In recent years, algorithm unrolling has emerged as a powerful technique for designing interpretable neural networks based on iterative algorithms. Imaging inverse problems have particularly benefited from unrolling based deep network design since many traditional model-based approaches rely on iterative optimization. Despite exciting progress, typical unrolling approaches heuristically design layer-specific convolution weights to improve performance. Crucially, convergence properties of the underlying iterative algorithm are lost once layer specific parameters are learned from training data. In this paper, we propose a neural network architecture that breaks the trade-off between retaining algorithm properties while simultaneously enhancing performance. We focus on non-blind image deblurring problem and unroll the widely-applied Half-Quadratic Splitting (HQS) algorithm. We develop a new parameterization scheme that enforces the layer-specific parameters to asymptotically approach certain fixed points, a new result that we analytically establish. Experimental results show that our approach outperforms many state of the art non-blind deblurring techniques on benchmark datasets, while enabling convergence and interpretability.
A Semi-Supervised Complementary Joint Training Approach for Low-Resource Speech Recognition

Both unpaired speech and text have shown to be beneficial for low-resource automatic speech recognition (ASR), which, however were either separately used for pre-training, self-training and language model (LM) training, or jointly used for designing hybrid models in literature. In this work, we leverage both unpaired speech and text to train a general ASR model, which are used in the form of data pairs by generating the missing parts in prior to model training. We propose to train a model alternatively using the prepared speech-PseudoLabel and SynthesizedAudio-text pairs and reveal the complementary property in both acoustic and linguistic features. The proposed method is thus called complementary joint training (CJT). Based on the basic CJT, label masking for pseudo-labels and parallel layers for synthesized audio are then proposed for re-training to further cope with the deviations from real data, termed as CJT++. In addition, the proposed CJT is extended to the scenario with zero paired data by considering an iterative CJT for the training of seed ASR model. Experimental results on Libri-light show the efficacy of joint training as well as two second-round training strategies, and the superiority over recent models is validated, particularly in extreme low-resource cases.
The Metaverse Data Deluge: What Can We Do About It?

In the metaverse the physical space and the virtual space co-exist, and interact simultaneously. While the physical space is virtually enhanced with information, the virtual space is continuously refreshed with real-time, real-world information. To allow users to process and manipulate information seamlessly between the real and digital spaces, novel technologies must be developed. These include smart interfaces, new augmented realities, and efficient data storage, management, and dissemination techniques. In this paper, we first discuss some promising co-space applications. These applications offer opportunities that neither of the spaces can realize on its own. We then discuss challenges. Finally, we discuss and envision what are likely to be required from the database and system perspectives.
Breast cancer diagnosis and management guided by data augmentation, utilizing an integrated framework of <scp>SHAP</scp> and random augmentation

Abstract Recent research indicates that early detection of breast cancer (BC) is critical in achieving favorable treatment outcomes and reducing the mortality rate associated with it. With the difficulty in obtaining a balanced dataset that is primarily sourced for the diagnosis of the disease, many researchers have relied on data augmentation techniques, thereby having varying datasets with varying quality and results. The dataset we focused on in this study is crafted from SHapley Additive exPlanations (SHAP)‐augmentation and random augmentation (RA) approaches to dealing with imbalanced data. This was carried out on the Wisconsin BC dataset and the effectiveness of this approach to the diagnosis of BC was checked using six machine‐learning algorithms. RA synthetically generated some parts of the dataset while SHAP helped in assessing the quality of the attributes, which were selected and used for the training of the models. The result from our analysis shows that the performance of the models used generally increased to more than 3% for most of the models using the dataset obtained by the integration of SHAP and RA. Additionally, after diagnosis, it is important to focus on providing quality care to ensure the best possible outcomes for patients. The need for proper management of the disease state is crucial so as to reduce the recurrence of the disease and other associated complications. Thus the interpretability provided by SHAP enlightens the management strategies in this study focusing on the quality of care given to the patient and how timely the care is.
The EU AI Act As Global Artificial Intelligence Regulation

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CLIP-TSA: Clip-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly Detection

Video anomaly detection (VAD) – commonly formulated as a multiple-instance learning problem in a weakly-supervised manner due to its labor-intensive nature – is a challenging problem in video surveillance where the frames of anomaly need to be localized in an untrimmed video. In this paper, we first propose to utilize the ViT-encoded visual features from CLIP, in contrast with the conventional C3D or I3D features in the domain, to efficiently extract discriminative representations in the novel technique. We then model temporal dependencies and nominate the snippets of interest by leveraging our proposed Temporal Self-Attention (TSA). The ablation study confirms the effectiveness of TSA and ViT feature. The extensive experiments show that our proposed CLIP-TSA outperforms the existing state-of-the-art (SOTA) methods by a large margin on three commonly-used benchmark datasets in the VAD problem (UCF-Crime, ShanghaiTech Campus and XD-Violence). Our source code is available at https://github.com/joos2010kj/CLIP-TSA.
Compound Multi-Branch Feature Fusion for Image Deraindrop

Image restoration is a challenging and ill-posed problem which also has been a long-standing issue. In this paper, we proposed a multi-branch restoration model inspired from the Human Visual System (i.e., Retinal Ganglion Cells) for image deraindrop. The experiments show that the proposed multi-branch architecture, called CMFNet, has state-of-the-art performance results. The source code and pretrained models are available at https://github.com/FanChiMao/CMFNet. And the interactive demonstration of the proposed deraindrop model can be accessed at https://reurl.cc/dXaeNg.