Reviewerly/exHarmony huggingface dataset:
Collaborative Modality Fusion for Mitigating Language Bias in Visual Question Answering

Language bias stands as a noteworthy concern in visual question answering (VQA), wherein models tend to rely on spurious correlations between questions and answers for prediction. This prevents the models from effectively generalizing, leading to a decrease in performance. In order to address this bias, we propose a novel modality fusion collaborative de-biasing algorithm (CoD). In our approach, bias is considered as the model’s neglect of information from a particular modality during prediction. We employ a collaborative training approach to facilitate mutual modeling between different modalities, achieving efficient feature fusion and enabling the model to fully leverage multimodal knowledge for prediction. Our experiments on various datasets, including VQA-CP v2, VQA v2, and VQA-VS, using different validation strategies, demonstrate the effectiveness of our approach. Notably, employing a basic baseline model resulted in an accuracy of 60.14% on VQA-CP v2.
The ABC of academic writing: non-native speakers’ perspective

<h2>Abstract</h2> Academic writing is difficult, especially for non-native English speakers. We share a perspective on writing with a set of heuristics called the Writing Alphabet, consisting of Accurate, Brief, Clear, Dynamic, Engaging, Flowing, Goal, Habit, and Investment. These points can help struggling writers identify issues and, importantly, internalise good writing practices.
Using early LLMs for corpus linguistics: Examining ChatGPT's potential and limitations

This study evaluates the extent to which information can be obtained from early Large Language Models (LLMs) for corpus linguistic research. Various tasks were conducted using ChatGPT 3.5, such as generating word frequency lists, collocations, words that fit certain grammatical patterns, and identifying genres. These were then compared with the search results from a large-scale general corpus (COCA). While favorable results were not achieved in identifying the genres of words or paragraphs, there was notable congruence in the frequency lists (75.0%), collocations (42.8%), and grammatical patterns (53.0%) for the top 20 items. Even when the generated items did not perfectly match those from COCA, it was evident that high-frequency items were produced. Although LLMs may not be sufficient for rigorous academic research, the results are adequate for discerning overall trends or assisting learners. In addition, the results of this study show that the ability to search at the phrase level is an advantage of using LLMs for corpus research.
Dataset of controversial news posts in Spanish from the reader's perspective

This paper presents a corpus of Spanish news posts obtained from X with the annotation of controversy made via crowdsourcing. A total of 60 tweets were obtained from 8 different newspapers. For the annotation task, a survey was developed and sent to 31 different participants to answer it with the controversy level they perceived from the news post summary and headline presented on the post. The most frequent selected option was assigned as the initial controversy level of the post. The final annotation of the corpus was made via an analysis of the raw data by computing the Inter Annotator Agreement (IAA). The analysis showed that the binarization of the data was the most convenient way to annotate it. A potential use for this dataset is detailed in further sections.
Criminal expertise and hacking efficiency

Criminal expertise plays a crucial role in the choices offenders make when committing a crime, including their modus operandi. However, our knowledge about criminal decision making online remains limited. Drawing on insights from cyber security, we conceptualize the cybercrime commission process as the sequence of phases of the cyber kill chain that offenders go through. We assume that offenders who follow the sequence consecutively use the most efficient hacking method. Building upon the expertise paradigm, we hypothesize that participants with greater hacking experience and IT skills undertake more efficient hacks. To test this hypothesis, we analyzed data from 69 computer security and software engineering students who were invited to hack a vulnerable website in a computer lab equipped with monitoring software, which allowed to collect objective behavioral measures. Additionally, we collected individual measures regarding hacking expertise through an online questionnaire. After quantitatively measuring efficiency using sequence analysis, a regression model showed that the expertise paradigm may also apply to hackers. We discuss the implications of our novel research for the study of offender decision-making processes more broadly.
Improved monte Carlo ray-tracing algorithm based on importance sampling

Abstract This paper proposes an improved ray-tracing algorithm with importance sampling (IS) to reduce the computational complexity. For multiple receiver situation, we provide improved IS including adaptive and weighted optimization.
Graph-SeTES: A Graph based Search Task Extraction using Siamese Network

Search task extraction is a sub-process for query suggestion/reformulation, personalized recommendation, and advertisement in search engines and e-commerce platforms. However, they face both internal and external challenges. Internal challenges include short and misspelled queries and incomplete keywords. External datasets include an unknown number of clusters and a limited number of labeled datasets. Deep-learning models require a large amount of data for training; however, search task datasets are rare and small. To overcome these limitations, we proposed Graph-SeTES, which integrates feature extraction with a decision network that utilizes both distance metrics and decision networks. The existing graph-clustering algorithms use the similarities between search query pairs. Because the Siamese network (SN) finds similarities between two objects, it fits search task extraction. Compared with existing models, SN requires fewer parameters for training due to the shared weights. However, it yields good results even with less labeled data, overcoming the external challenges. We benefit from both distance metrics and narrowing of the linear layers for decision networks. Graph-SeTES was compared with state-of-the-art models, and it outperformed its counterparts. The results were 6% better than those of the best baseline on the CSTE dataset, which maintained this performance difference on the WSMC12 dataset.
Mode combinability: Exploring convex combinations of permutation aligned models

We explore element-wise convex combinations of two permutation-aligned neural network parameter vectors ΘA and ΘB of size d. We conduct extensive experiments by examining various distributions of such model combinations parametrized by elements of the hypercube [0,1]d and its vicinity. Our findings reveal that broad regions of the hypercube form surfaces of low loss values, indicating that the notion of linear mode connectivity extends to a more general phenomenon which we call mode combinability. We also make several novel observations regarding linear mode connectivity and model re-basin. We demonstrate a transitivity property: two models re-based to a common third model are also linear mode connected, and a robustness property: even with significant perturbations of the neuron matchings the resulting combinations continue to form a working model. Moreover, we analyze the functional and weight similarity of model combinations and show that such combinations are non-vacuous in the sense that there are significant functional differences between the resulting models.
Validation of artificial intelligence contrast mammography in diagnosis of breast cancer: Relationship to histopathological results

Contrast-enhanced mammography (CEM) is used for characterization of breast lesions with increased diagnostic accuracy compared to digital mammography (DM). Artificial intelligence (AI) approaches are emerging with accuracies equal to an average radiologist. However, most studies trained deep learning (DL) models on DM images and there is a paucity in literature for discovering the application of AI using CEM.To develop and test a DL model that classifies CEM images and produces corresponding highlights of lesions detected.Fully annotated 2006 images of 326 females available from the previously published Categorized Digital Database for Contrast Enhanced Mammography images (CDD-CESM) were used for training. We developed a DL multiview contrast mammography model (MVCM) for classification of CEM low energy and recombined images. An external test set of 288 images of 37 females not included in the training was used for validation. Correlation with histopathological results and follow-up was considered the standard reference. The study protocol was approved by the Institutional Review Board and patient informed consent was obtained.Assessment was done on an external test set of 37 females (mean age, 51.31 years ± 11.07 [SD]) with AUC-ROC for AI performance 0.936; (95 % CI: 0.898, 0.973; p < 0.001) and the best cut off value for prediction of malignancy using AI score = 0.28. Findings were then correlated with histopathological results and follow up which revealed a sensitivity of 75 %, specificity 96.3 %, total accuracy of 90.1 %, positive predictive value (PPV) 87.1 %, and negative predictive value (NPV) 92 %, p-value (<0.001). Diagnostic indices of radiologists were sensitivity 88.9 %, specificity 92.6 %, total accuracy 91.7 %, PPV 80 %, and NPV 96.2 %, p-value (<0.001).A deep learning multiview CEM model was developed and evaluated in a cohort of female participants and showed promising results in detecting breast cancer. This warrants further studies, external training, and validation.
A comprehensive evaluation of OPTICS, GMM and K-means clustering methodologies for geochemical anomaly detection connected with sample catchment basins

The process of data-driven clustering to uncover geochemical anomalies linked to sample catchment basins (SCBs) includes a comprehensive framework to discern areas exhibiting unique geochemical attributes within a specified study area. The Ordering Points to Identify the Clustering Structure (OPTICS) method can serve as a robust methodology for detecting geochemical anomalies in SCBs. This is attributed to its capacity to effectively manage varying cluster densities, adaptively identify cluster numbers, exhibit resilience to noise, and display minimum sensitivity to parameters. A comparison was conducted in this research between the outcomes of the OPTICS clustering algorithm and two traditional clustering techniques, namely the Gaussian Mixture Model (GMM) and K-means clustering. In the following, the Expectation-Maximization (EM) technique is employed to train the GMM for clustering. Moreover, the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) as two validate statistical metrics implemented to ascertain the optimal number of components (clusters) belong to the GMM. It should be noted that the effectiveness of the clustering algorithms was further assessed using the Calinski-Harabasz (CH) index and the success-rate curves. OPTICS, a density-based clustering approach, was confirmed to be more effective than K-means and GMM for identifying MVT PbZn anomalies in Varcheh district, western Iran. Furthermore, the specified anomalies show a geo-spatial correspondence with the geological facts, and it has been observed that strong anomalies are more discoverable in close proximity to MVT PbZn occurrences. This work suggests a novel anomaly detection approach based on OPTICS, which exhibits superior performance and data-modeling efficiency. The main emphasis is on effectively distinguishing geochemical anomalies from sample data originating from populations with uncertain distributions.
Self-restrained contrastive enhanced network for graph structure learning

Existing graph neural networks (GNNs) are mostly applied to representation scenes with complete graph structure. However, the graph structures of complex systems from the real world are generally missing or misconnected. In addition, due to the indistinguishability of graph node features, most of the existing GNNs are limited by the over-smoothing problem when performing classification tasks. To address these issues, we propose a model for learning and optimizing graph structure—Self-restrained Graph Contrastive Enhanced Network (GCEN). Firstly, the multiple branch generation structure is adopted to obtain various graph structures, which are updated by multi-order information fusion to obtain a new graph structure. Secondly, the performance of each generated structure branch is improved by self-restrained network, and then the structure information of the graph is continuously optimized to obtain a high-quality graph structure. Finally, the feature contrast enhancement module is utilized to adaptively adjust the features of nodes to obtain distinguishable features, thus alleviating the over-smoothing problem of GNNs in classification tasks. Extensive experiments on several benchmark datasets indicate that our GCEN is superior to several models for learning task-specific graph structure in optimizing the graph structure.
Unveiling ChatGPT in tourism education: exploring perceptions, advantages and recommendations from educators

Purpose Following the remarkable debut of ChatGPT and its rapid adoption by a global audience since its launch in November 2022, this study delves into educators' perceptions of ChatGPT within the specialized domains of tourism and hospitality education. While acknowledging ChatGPT’s swift rise and advanced capabilities, this research aims to comprehensively explore educators' perspectives, advantages and concerns regarding its integration into academic settings. Design/methodology/approach A qualitative approach was utilized to reveal dominant themes from in-depth, semi-structured face-to-face interviews with twenty lecturers in tourism faculties in North Cyprus. Collected responses from respondents were subjected to analysis using Leximancer software. Findings Our analysis unearthed seven significant themes encapsulating educators' perceptions of ChatGPT: 1 – “reliability and accuracy concerns”; 2 – “dependency”; 3 – “student engagement”; 4 – “ethical considerations”; 5 – “time efficiency and productivity”; 6 – “teacher-student interaction” and 7 – “learning flexibility”. Noteworthy positive perceptions emerged regarding “student engagement,” “time efficiency and productivity,” and “learning flexibility.” Originality/value This study contributes to the originality of research by addressing the underexplored aspect of educators' perceptions of ChatGPT within the domains of tourism and hospitality education, shedding light on its potential implications, advantages and drawbacks in a specialized educational context. Furthermore, it aims to offer insights into educators' recommendations for the effective incorporation of ChatGPT technologies into this specific educational setting, filling a crucial gap in understanding the integration of artificial intelligence (AI) in specialized fields of study.
Partially reflecting jamming objects in correlation-enhanced target detection with entangled photons

In the optical detection of distant objects, one can benefit from quantum light properties over those of classical light. Two-beam correlation-enhanced protocols can improve imaging or target detection even under strong background noise and in the low photon flux regime. We demonstrate that a partially reflecting jamming object introduces noise to these protocols, including quantum illumination. In contrast to background noise, the signal radiation reflected from the jammer is correlated with the corresponding idler beam. We analyze the means to mitigate this noise and introduce an indistinguishability parameter μ, showing how efficiently the jammer can be distinguished from the target. We use the quantum Chernoff bound and the density matrix orthogonalization procedure to separate the contributions from the target, the background, and the jammer. We illustrate our findings with an experiment using optical entangled photon pairs at 800 nm. Our results can be applied to the design of target detection protocols and advanced imaging techniques.
BapFL : You can Backdoor Personalized Federated Learning

In federated learning (FL), malicious clients could manipulate the predictions of the trained model through backdoor attacks, posing a significant threat to the security of FL systems. Existing research primarily focuses on backdoor attacks and defenses within the generic federated learning scenario, where all clients collaborate to train a single global model. A recent study conducted by Qin et al. [24] marks the initial exploration of backdoor attacks within the personalized federated learning (pFL) scenario, where each client constructs a personalized model based on its local data. Notably, the study demonstrates that pFL methods with parameter decoupling can significantly enhance robustness against backdoor attacks. However, in this paper, we whistleblow that pFL methods with parameter decoupling are still vulnerable to backdoor attacks. The resistance of pFL methods with parameter decoupling is attributed to the heterogeneous classifiers between malicious clients and benign counterparts. We analyze two direct causes of the heterogeneous classifiers: (1) data heterogeneity inherently exists among clients and (2) poisoning by malicious clients further exacerbates the data heterogeneity. To address these issues, we propose a two-pronged attack method, BapFL , which comprises two simple yet effective strategies: (1) poisoning only the feature encoder while keeping the classifier fixed and (2) diversifying the classifier through noise introduction to simulate that of the benign clients. Extensive experiments on three benchmark datasets under varying conditions demonstrate the effectiveness of our proposed attack. Additionally, we evaluate the effectiveness of six widely used defense methods and find that BapFL still poses a significant threat even in the presence of the best defense, Multi-Krum. We hope to inspire further research on attack and defense strategies in pFL scenarios. The code is available at: https://github.com/BapFL/code.
VertexShuffle-Based Spherical Super-Resolution for 360-Degree Videos

360-degree video is an emerging form of media that encodes information about all directions surrounding a camera, offering an immersive experience to the users. Unlike traditional 2D videos, visual information in 360-degree videos can be naturally represented as pixels on a sphere. Inspired by state-of-the-art deep-learning-based 2D image super-resolution models and spherical CNNs, in this paper, we design a novel spherical super-resolution (SSR) approach for 360-degree videos. To support viewport-adaptive and bandwidth-efficient transmission/streaming of 360-degree video data and save computation, we propose the Focused Icosahedral Mesh to represent a small area on the sphere. We further construct matrices to rotate spherical content over the entire sphere to the focused mesh area, allowing us to use the focused mesh to represent any area on the sphere. Motivated by the PixelShuffle operation for 2D super-resolution, we also propose a novel VertexShuffle operation on the mesh and an improved version VertexShuffle_V2. We compare our SSR approach with state-of-the-art 2D super-resolution models and show that SSR has the potential to achieve significant benefits when applied to spherical signals.
Research on joint model relation extraction method based on entity mapping

Relationship Extraction (RE) is a central task in information extraction. The use of entity mapping to address complex scenarios with overlapping triples, such as CasRel, is gaining traction, yet faces challenges such as inadequate consideration of sentence continuity, sample imbalance and data noise. This research introduces an entity mapping-based method CasRelBLCF building on CasRel. The main contributions include: A joint decoder for the head entity, utilizing Bi-LSTM and CRF, integration of the Focal Loss function to tackle sample imbalance and a reinforcement learning-based noise reduction method for handling dataset noise. Experiments on relation extraction datasets indicate the superiority of the CasRelBLCF model and the enhancement on model's performance of the noise reduction method.
A Procedural Constructive Learning Mechanism with Deep Reinforcement Learning for Cognitive Agents

Abstract Recent advancements in AI and deep learning have created a growing demand for artificial agents capable of performing tasks within increasingly complex environments. To address the challenges associated with continuous learning constraints and knowledge capacity in this context, cognitive architectures inspired by human cognition have gained significance. This study contributes to existing research by introducing a cognitive-attentional system employing a constructive neural network-based learning approach for continuous acquisition of procedural knowledge. We replace an incremental tabular Reinforcement Learning algorithm with a constructive neural network deep reinforcement learning mechanism for continuous sensorimotor knowledge acquisition, thereby enhancing the overall learning capacity. The primary emphasis of this modification centers on optimizing memory utilization and reducing training time. Our study presents a learning strategy that amalgamates deep reinforcement learning with procedural learning, mirroring the incremental learning process observed in human sensorimotor development. This approach is embedded within the CONAIM cognitive-attentional architecture, leveraging the cognitive tools of CST. The proposed learning mechanism allows the model to dynamically create and modify elements in its procedural memory, facilitating the reuse of previously acquired functions and procedures. Additionally, it equips the model with the capability to combine learned elements to effectively adapt to complex scenarios. A constructive neural network was employed, initiating with an initial hidden layer comprising one neuron. However, it possesses the capacity to adapt its internal architecture in response to its performance in procedural and sensorimotor learning tasks, inserting new hidden layers or neurons. Experimentation conducted through simulations involving a humanoid robot demonstrates the successful resolution of tasks that were previously unsolved through incremental knowledge acquisition. Throughout the training phase, the constructive agent achieved a minimum of 40% greater rewards and executed 8% more actions when compared to other agents. In the subsequent testing phase, the constructive agent exhibited a 15% increase in the number of actions performed in contrast to its counterparts.