Reviewerly/exHarmony huggingface dataset:
Generative Aspect-Based Sentiment Analysis with Contrastive Learning and Expressive Structure

Generative models have demonstrated impressive results on Aspect-based Sentiment Analysis (ABSA) tasks, particularly for the emerging task of extracting Aspect-Category-Opinion-Sentiment (ACOS) quadruples.However, these models struggle with implicit sentiment expressions, which are commonly observed in opinionated content such as online reviews.In this work, we introduce GEN-SCL-NAT, which consists of two techniques for improved structured generation for ACOS quadruple extraction.First, we propose GEN-SCL, a supervised contrastive learning objective that aids quadruple prediction by encouraging the model to produce input representations that are discriminable across key input attributes, such as sentiment polarity and the existence of implicit opinions and aspects.Second, we introduce GEN-NAT, a new structured generation format that better adapts pre-trained autoregressive encoder-decoder models to extract quadruples in a generative fashion.Experimental results show that GEN-SCL-NAT achieves top performance across three ACOS datasets, averaging 1.48% F1 improvement, with a maximum 1.73% increase on the LAPTOP-L1 dataset.Additionally, we see significant gains on implicit aspect and opinion splits that have been shown as challenging for existing ACOS approaches.
Deep Learning for Lung Cancer Diagnosis, Prognosis and Prediction Using Histological and Cytological Images: A Systematic Review

Lung cancer is one of the deadliest cancers worldwide, with a high incidence rate, especially in tobacco smokers. Lung cancer accurate diagnosis is based on distinct histological patterns combined with molecular data for personalized treatment. Precise lung cancer classification from a single H&amp;E slide can be challenging for a pathologist, requiring most of the time additional histochemical and special immunohistochemical stains for the final pathology report. According to WHO, small biopsy and cytology specimens are the available materials for about 70% of lung cancer patients with advanced-stage unresectable disease. Thus, the limited available diagnostic material necessitates its optimal management and processing for the completion of diagnosis and predictive testing according to the published guidelines. During the new era of Digital Pathology, Deep Learning offers the potential for lung cancer interpretation to assist pathologists’ routine practice. Herein, we systematically review the current Artificial Intelligence-based approaches using histological and cytological images of lung cancer. Most of the published literature centered on the distinction between lung adenocarcinoma, lung squamous cell carcinoma, and small cell lung carcinoma, reflecting the realistic pathologist’s routine. Furthermore, several studies developed algorithms for lung adenocarcinoma predominant architectural pattern determination, prognosis prediction, mutational status characterization, and PD-L1 expression status estimation.
Visual-quality-driven unsupervised image dehazing

Most of the existing learning-based dehazing methods require a diverse and large collection of paired hazy/clean images, which is intractable to obtain. Therefore, existing dehazing methods resort to training on synthetic images. This may result in a possible domain shift when treating real scenes. In this paper, we propose a novel unsupervised dehazing (lightweight) network without any reference images to directly predict clear images from the original hazy images, which consists of an interactive fusion module (IFM) and an iterative optimization module (IOM). Specifically, IFM interactively fuses multi-level features to make up for the missing information among deep and shallow features while IOM iteratively optimizes dehazed results to obtain pleasing visual effects. Particularly, based on the observation that hazy images usually suffer from quality degradation, four non-reference visual-quality-driven loss functions are designed to enable the network trained in an unsupervised way, including dark channel loss, contrast loss, saturation loss, and edge sharpness loss. Extensive experiments on two synthetic datasets and one real-world dataset demonstrate that our method performs favorably against the state-of-the-art unsupervised dehazing methods and even matches some supervised methods in terms of metrics such as PSNR, SSIM, and UQI.
The Reliability of W-flow Run-off-Rainfall Model in Predicting Rainfall to the Discharge

This research intends to predict the discharge (run-off) from rainfall for which the model is built using W-flow. The research location is in the Gajah Mungkur reservoir (Wonogiri) in Indonesia. The estimation of reservoir inflow has an important role, mainly in the scheme of reservoir operation and management. However, the heterogeneity of complex spatial and temporal patterns of rainfall and also the physiographic context of a watershed cause the development of a model of real-time run-off and rainfall that can accurately predict the reservoir inflow to become a challenge in the development of water resources. In relation to the analysis and prediction of rainfall, the constraint and problem that is still often faced is the minimal availability of observed rainfall data spatially as well as temporally; the time series of rainfall data is not long and complete enough; and the number of rainfall stations is less evenly distributed. The methodology consists of carrying out the literature study, collecting as much rainfall data as possible to build a W flow model, then carrying out the model calibration and analyzing the prediction of real-time reservoir inflow for operation. The result shows that the dependable discharge of the Wonogiri watershed shows that there are two peak discharges, which happened on February II (the second half of February) and December II (the second half of December). However, the discharge is decreasing in July and reaching its lowest level in October II (the second half of October). Doi: 10.28991/CEJ-2023-09-07-015 Full Text: PDF
An empirical study of critical success factors in implementing knowledge management systems (KMS): The moderating role of culture

This research focuses on the moderating effect of culture on the relationships between KMS and other variables affecting KMS in the service industry. The effects of a number of variables on KMS were examined via analysis and hypothesis testing. These variables included culture; people; process; strategy; and technology. The results show that culture and people have a substantial impact on KMS's performance, emphasizing the need of cultivating a supportive company culture and empowering employees. Furthermore, strategy and technology were shown to be critical in allowing effective knowledge management practices in the service industry. The research also investigates the moderating impacts of culture on these linkages, demonstrating that culture modulates the impact of process, technology, and strategy on KMS. However, it was shown that the interplay between culture and people did not substantially alter the link between people and KMS. These results provide useful insights for firms looking to improve their knowledge management methods, underlining the need to take culture into account and aligning it with strategic goals and technology solutions. While the study adds to our understanding of knowledge management in the service industry, further research is needed to investigate other elements and situations. Overall, this research has practical significance for firms looking to enhance their knowledge management activities and overall organizational performance.
Driving sustainable supply chains: Blockchain-enabled eco-efficiency for resilient customs ports

This paper investigates the driving factors behind sustainable supply chains in Jordan, focusing on the implementation of blockchain technology, customs ports practices, and technological infrastructure. The primary data for the study was collected through questionnaires distributed to employees working in the Jordanian customs. A random sampling method was employed to select participants, and a total of 184 valid questionnaires were retrieved for analysis. The collected data was analyzed using the statistical software Smartpls PLS4. The results of quantitative research reveal that the implementation of blockchain technology and technological infrastructure positively affects the driving of sustainable supply chains in Jordan, also customs ports practices also have a positive impact on driving sustainable supply chains, emphasizing the significance of efficient and resilient customs operations for sustainability. Additionally, compliance with environmental regulations enhances the effectiveness of blockchain technology in achieving sustainability objectives. Moreover, underscoring the role of robust technological capabilities in supporting sustainable operations within customs ports. The study contributes to the understanding of the key drivers of sustainable supply chains in Jordan, providing valuable insights for policymakers, supply chain managers, and other stakeholders involved in promoting sustainability within the customs ports industry. The findings can guide decision-making and inform strategies aimed at enhancing eco-efficiency and resilience in supply chain operations.
A Hierarchical Representation Network for Accurate and Detailed Face Reconstruction from In-The-Wild Images

Limited by the nature of the low-dimensional representational capacity of 3DMM, most of the 3DMM-based face reconstruction (FR) methods fail to recover high-frequency facial details, such as wrinkles, dimples, etc. Some attempt to solve the problem by introducing detail maps or nonlinear operations, however, the results are still not vivid. To this end, we in this paper present a novel hierarchical representation network (HRN) to achieve accurate and detailed face reconstruction from a single image. Specifically, we implement the geometry disentanglement and introduce the hierarchical representation to fulfill detailed face modeling. Meanwhile, 3D priors of facial details are incorporated to enhance the accuracy and authenticity of the reconstruction results. We also propose a deretouching module to achieve better decoupling of the geometry and appearance. It is noteworthy that our framework can be extended to a multi-view fashion by considering detail consistency of different views. Extensive experiments on two single-view and two multi-view FR benchmarks demonstrate that our method outperforms the existing methods in both reconstruction accuracy and visual effects. Finally, we introduce a high-quality 3D face dataset FaceHD-100 to boost the research of high-fidelity face reconstruction. The project homepage is at https://younglbw.github.io/HRN-homepage/.
Devil's on the Edges: Selective Quad Attention for Scene Graph Generation

Scene graph generation aims to construct a semantic graph structure from an image such that its nodes and edges respectively represent objects and their relationships. One of the major challenges for the task lies in the presence of distracting objects and relationships in images; contextual reasoning is strongly distracted by irrelevant objects or backgrounds and, more importantly, a vast number of irrelevant candidate relations. To tackle the issue, we propose the Selective Quad Attention Network (SQUAT) that learns to select relevant object pairs and disambiguate them via diverse contextual interactions. SQUAT consists of two main components: edge selection and quad attention. The edge selection module selects relevant object pairs, i.e., edges in the scene graph, which helps contextual reasoning, and the quad attention module then updates the edge features using both edge-to-node and edge-to-edge cross-attentions to capture contextual information between objects and object pairs. Experiments demonstrate the strong performance and robustness of SQUAT, achieving the state of the art on the Visual Genome and Open Images v6 benchmarks.
Underwater image enhancement via multi-scale fusion and adaptive color-gamma correction in low-light conditions

In dark underwater areas, existing single-model underwater image enhancement methods have poor enhancement effects. We propose an underwater image enhancement method based on color correction and multi-scale fusion (CCMF). Specifically, we first design a color correction method with red channel compensation, which compensates for the red channel according to light attenuation and removes color bias. We propose a contrast enhancement method based on guided filtering to enhance edge texture details. The image is decomposed into a base layer and a detail layer in the logarithmic domain, with layered enhancement. Secondly, we propose an adaptive gamma correction method that dynamically adjusts correction parameters based on the gray image values. This approach prevents over-enhancement and effectively enhances the exposure in dark areas. We extract weight maps that represent different features from the input images and employ a multi-scale pyramid fusion technique to integrate the aforementioned feature information. This approach enables the mutual complementarity of various features and enhances the overall visual effect. Experimental results show that our method can effectively integrate the advantages of different enhancement methods, and the objective indicators of UCIQE, UIQM, and EG are better than other related state-of-the-art methods.
EXAIT: Educational eXplainable Artificial Intelligent Tools for personalized learning

As artificial intelligence systems increasingly make high-stakes recommendations and decisions automatically in many facets of our lives, the use of explainable artificial intelligence to inform stakeholders about the reasons behind such systems has been gaining much attention in a wide range of fields, including education. Also, in the field of education there has been a long history of research into self-explanation, where students explain the process of their answers. This has been recognized as a beneficial intervention to promote metacognitive skills, however, there is also unexplored potential to gain insight into the problems that learners experience due to inadequate prerequisite knowledge and skills that are required, or in the process of their application to the task at hand. While this aspect of self-explanation has been of interest to teachers, there is little research into the use of such information to inform educational AI systems. In this paper, we propose a system in which both students and the AI system explain to each other their reasons behind decisions that were made, such as: self-explanation of student cognition during the answering process, and explanation of recommendations based on internal mechanizes and other abstract representations of model algorithms.
In-sensor reservoir computing based on optoelectronic synaptic devices

Artificial neural networks built with optoelectronic synaptic devices have been proven to process visual information effectively. However, it takes great latency time and energy consumption, especially facing dynamic visual information, due to the separated optical sensor, memory, and process unit. Reservoir computing (RC) based on optoelectronic synaptic devices provides an in-sensor RC for processing temporal information efficiently. It achieves efficient computation by sensing and processing optical signals directly with optoelectronic synaptic devices. Optoelectronic synaptic devices shine in visual information processing, whose application in visual sensing and processing will provide a viable hardware solution for in-sensor computing. Therefore, the application of optoelectronic synaptic devices in reservoir computing has prompted increasing attention. Herein, for promoting the application of physical reservoir computing (PRC) with optoelectrical synapses in machine vision, synaptic plasticity will be introduced first in this work and then illustrate the basic functions of optoelectronic synapses as well as their application in reservoir computing further, and provide a perspective on PRC with optoelectronic synapses in the final.
Measuring ancient technological complexity and its cognitive implications using Petri nets

We implement a method from computer sciences to address a challenge in Paleolithic archaeology: how to infer cognition differences from material culture. Archaeological material culture is linked to cognition, and more complex ancient technologies are assumed to have required complex cognition. We present an application of Petri net analysis to compare Neanderthal tar production technologies and tie the results to cognitive requirements. We applied three complexity metrics, each relying on their own unique definitions of complexity, to the modeled production processes. Based on the results, we propose that Neanderthal technical cognition may have been analogous to that of contemporary modern humans. This method also enables us to distinguish the high-order cognitive functions combining traits like planning, inhibitory control, and learning that were likely required by different ancient technological processes. The Petri net approach can contribute to our understanding of technology and cognitive evolution as it can be used on different materials and technologies, across time and species.
Neuromorphic Computing via Fission‐based Broadband Frequency Generation

Abstract The performance limitations of traditional computer architectures have led to the rise of brain‐inspired hardware, with optical solutions gaining popularity due to the energy efficiency, high speed, and scalability of linear operations. However, the use of optics to emulate the synaptic activity of neurons has remained a challenge since the integration of nonlinear nodes is power‐hungry and, thus, hard to scale. Neuromorphic wave computing offers a new paradigm for energy‐efficient information processing, building upon transient and passively nonlinear interactions between optical modes in a waveguide. Here, an implementation of this concept is presented using broadband frequency conversion by coherent higher‐order soliton fission in a single‐mode fiber. It is shown that phase encoding on femtosecond pulses at the input, alongside frequency selection and weighting at the system output, makes transient spectro‐temporal system states interpretable and allows for the energy‐efficient emulation of various digital neural networks. The experiments in a compact, fully fiber‐integrated setup substantiate an anticipated enhancement in computational performance with increasing system nonlinearity. The findings suggest that broadband frequency generation, accessible on‐chip and in‐fiber with off‐the‐shelf components, may challenge the traditional approach to node‐based brain‐inspired hardware design, ultimately leading to energy‐efficient, scalable, and dependable computing with minimal optical hardware requirements.
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The quantum simulation of fermionic gauge field theories is one of the anticipated uses of quantum computers in the noisy intermediate-scale quantum (NISQ) era. Recently work has been done to simulate properties of the fermionic ${\mathbb{Z}}_{2}$ gauge field theory in $(1+1)\mathrm{D}$ and the pure gauge theory in $(2+1)\mathrm{D}$. In this work, we investigate various options for simulating the fermionic ${\mathbb{Z}}_{2}$ gauge field theory in $(2+1)\mathrm{D}$. To simulate the theory on a NISQ device it is vital to minimize both the number of qubits used and the circuit depth. In this work we propose ways to optimize both criteria for simulating time dynamics. In particular, we develop a new way to simulate this theory on a quantum computer, with minimal qubit requirements. We provide a quantum circuit for simulating a single first-order Trotter step that minimizes the number of 2-qubit gates needed and gives comparable results to methods requiring more qubits. Furthermore, we investigate variational Trotterization approaches that allow us to further decrease the circuit depth.
Information Theoretic Learning-Enhanced Dual-Generative Adversarial Networks With Causal Representation for Robust OOD Generalization

Recently, machine/deep learning techniques are achieving remarkable success in a variety of intelligent control and management systems, promising to change the future of artificial intelligence (AI) scenarios. However, they still suffer from some intractable difficulty or limitations for model training, such as the out-of-distribution (OOD) issue, in modern smart manufacturing or intelligent transportation systems (ITSs). In this study, we newly design and introduce a deep generative model framework, which seamlessly incorporates the information theoretic learning (ITL) and causal representation learning (CRL) in a dual-generative adversarial network (Dual-GAN) architecture, aiming to enhance the robust OOD generalization in modern machine learning (ML) paradigms. In particular, an ITL-and CRL-enhanced Dual-GAN (ITCRL-DGAN) model is presented, which includes an autoencoder with CRL (AE-CRL) structure to aid the dual-adversarial training with causality-inspired feature representations and a Dual-GAN structure to improve the data augmentation in both feature and data levels. Following a newly designed feature separation strategy, a causal graph is built and improved based on the information theory, which can enhance the causally related factors among the separated core features and further enrich the feature representation with the counterfactual features via interventions based on the refined causal relationships. The ITL is incorporated to improve the extraction of low-dimensional feature representations and learn the optimized causal representations based on the idea of “information flow.” A dual-adversarial training mechanism is then developed, which not only enables the generator to expand the boundary of feature distribution in accordance with the optimized feature representation from AE-CRL, but also allows the discriminator to further verify and improve the quality of the augmented data for OOD generalization. Experiment and evaluation results based on an open-source dataset demonstrate the outstanding learning efficiency and classification performance of our proposed model for robust OOD generalization in modern smart applications compared with three baseline methods.
The morphology of rival -ion, -age and -ment selected verbal bases

French -ion, -age and -ment suffixations have been extensively studied as rivals in the construction of event nouns from verbs. In order to shed light on some of the constraints that allow them to coexist, we focus on the morphology of the bases they select. Our results show that -ion, -age and ‑ment, the most productive deverbal nominalization schemas in French, occupy distinct morphological niches in that they display strong preferences that generally do not overlap when they select denominal and deadjectival verbs (suffixed bases for -ion, converted bases for -age and prefixed bases for -ment).