Reviewerly/exHarmony huggingface dataset:
Adaptive genetic algorithm for user preference discovery in multi-criteria recommender systems

A Multi-Criteria Recommender System (MCRS) represents users’ preferences on several factors of products and utilizes these preferences while making product recommendations. In recent studies, MCRS has demonstrated the potential of applying Multi-Criteria Decision Making methods to make effective recommendations in several application domains. However, eliciting actual user preferences is still a major challenge in MCRS since we have many criteria for each product. Therefore, this paper proposes a three-phase adaptive genetic algorithm-based approach to discover user preferences in MCRS. Initially, we build a model by assigning weights to multi-criteria features and then learn the preferences on each criteria during similarity computation among users through a genetic algorithm. This allows us to know the actual preference of the user on each criteria and find other like-minded users for decision making. Finally, products are recommended after making predictions. The comparative results demonstrate that the proposed genetic algorithm based approach outperforms both multi-criteria and single criteria based recommender systems on the Yahoo! Movies dataset based on various evaluation measures.
Self-driven Dual-path Learning for Reference-based Line Art Colorization under Limited Data

Synthesizing color images based on line arts while considering the styles of reference photos is a flexible form of artistic creation that has recently attracted public attention. Previous approaches usually require large datasets at training, causing great inconvenience to the application. Besides, the sparsity of line art pictures often leads to a failure in learning valid mappings. To this end, we present SDL, a self-driven dual-path framework for reference-based line art colorization under limited data. Given small training sets containing sketch-image pairs, SDL first utilizes a novel Dynamic Pseudo Sample Generator (DPSG) to produce quantities of fake samples. Then, we introduce a dual-path network to achieve better visual effects, in which the Content-Generation Path reconstructs reliable content features to help establish multi-level correspondence in the Content-Color Aggregation Module (CCAM) of the Color-Transfer Path. Furthermore, we develop a Region-aware Contrastive Scheme (RCS) to focus on fine-grained details and a Style-augmented Contrastive Scheme (SCS) to encourage style consistency. Experiments verify the superiority of our model compared with existing works. We also demonstrate SDL outperforms state-of-the-art self-driven methods even though they adopt much more data than us (30× on CelebA-HQ Dataset and 17× on ASCP Dataset).
How to select and implement a suitable Low-Code Development Platform

Low-code programming allows the creation of software applications using a graphical user interface with minimal classical programming code ("low code") and without requiring extensive programming knowledge. This puts it in contrast to previous generations of programming languages. The advantages of low-code development are manifold, including the increase of software development capacities through a partial decentralization of the development process, speeding up software development through the low-code approach, and designing software with a strong user-centric focus. Using a low-code development platform can help companies adapt their own business processes to changing requirements more quickly and to make complexity resulting, for example, from heterogeneous customer wishes, manageable. Since many low-code development platforms are available, it is not easy for companies to select and successfully introduce a platform that meets their requirements. For this reason, this article presents a procedure model that assists in the process of selecting and implementing a platform.
Style transfer generative adversarial networks to harmonize multisite <scp>MRI</scp> to a single reference image to avoid overcorrection

Abstract Recent work within neuroimaging consortia have aimed to identify reproducible, and often subtle, brain signatures of psychiatric or neurological conditions. To allow for high‐powered brain imaging analyses, it is often necessary to pool MR images that were acquired with different protocols across multiple scanners. Current retrospective harmonization techniques have shown promise in removing site‐related image variation. However, most statistical approaches may over‐correct for technical, scanning‐related, variation as they cannot distinguish between confounded image‐acquisition based variability and site‐related population variability. Such statistical methods often require that datasets contain subjects or patient groups with similar clinical or demographic information to isolate the acquisition‐based variability. To overcome this limitation, we consider site‐related magnetic resonance (MR) imaging harmonization as a style transfer problem rather than a domain transfer problem. Using a fully unsupervised deep‐learning framework based on a generative adversarial network (GAN), we show that MR images can be harmonized by inserting the style information encoded from a single reference image, without knowing their site/scanner labels a priori. We trained our model using data from five large‐scale multisite datasets with varied demographics. Results demonstrated that our style‐encoding model can harmonize MR images, and match intensity profiles, without relying on traveling subjects. This model also avoids the need to control for clinical, diagnostic, or demographic information. We highlight the effectiveness of our method for clinical research by comparing extracted cortical and subcortical features, brain‐age estimates, and case–control effect sizes before and after the harmonization. We showed that our harmonization removed the site‐related variances, while preserving the anatomical information and clinical meaningful patterns. We further demonstrated that with a diverse training set, our method successfully harmonized MR images collected from unseen scanners and protocols, suggesting a promising tool for ongoing collaborative studies. Source code is released in USC‐IGC/style_transfer_harmonization (github.com).
Learning Multi-Task Commonness and Uniqueness for Multi-Modal Sarcasm Detection and Sentiment Analysis in Conversation

Sarcasm is a form of figurative language device to express human inner feelings, where the author writes the positive sentence on surface form, while he/she actually expresses negative sentiment, vice versa. Sentiment thus comes into sight, and is closely related with sarcasm, leading to the recent popularity of multi-modal sarcasm and sentiment joint detection in conversation (dialogue). The key challenges involve multi-modal fusion and multi-task interaction. Most of the existing studies have focused on building multi-modal fused representation, while the commonness and uniqueness across related tasks has not received attention. To fill this gap, we propose a multi-modal multi-task interaction learning framework, termed MIL, for joint detection of sarcasm and sentiment. Specifically, a cross-modal target attention mechanism is proposed to automatically learn the alignment between texts and images/speeches. In addition, a multi-modal interaction learning paradigm consisting of a dual-gating network, three separate fully-connected layers that simultaneously capture the commonness and uniqueness. Comprehensive experiments on two benchmarking datasets (i.e., Memotion and MUStARD) show the effectiveness of the proposed model over state-of-the-art baselines with a significant improvement of 1.9%, 2.4% in terms of F1.
Developing a Commerce Solutions for Convenient Food Ordering System using Mobile Application

The paper presents the development of a commerce solutions for convenient food ordering system using a mobile application, it designed to revolutionize the food ordering experience for customers. The system's comprehensive evaluation resulted in an impressive overall score of 4.4 out of 5, highlighting its strong performance in usability, accuracy, efficiency, security, portability, and maintainability. The user-centric interface and intuitive design ensure a seamless and hassle-free ordering process, while the application's accuracy in processing orders and managing special instructions enhances customer satisfaction. With efficient response times and robust security measures, the system provides a reliable and secure platform for placing food orders conveniently. Its adaptability across different devices and operating systems ensures broad accessibility, while maintainability allows for future enhancements and updates. Overall, the study presents a promising solution for businesses to offer enhanced services and customers to enjoy a convenient and efficient food ordering experience
MagConv: Mask-Guided Convolution for Image Inpainting

Standard convolution applied to image inpainting would lead to color discrepancy and blurriness for treating valid and invalid/hole regions without difference, which was partially amended by partial convolution (PConv). In PConv, a binary/hard mask was maintained as an indicator of valid and invalid pixels, where valid pixels and invalid pixels were treated differently. However, it can not describe validity degree of an impaired pixel. In addition, mask and image paths were separated, without sharing convolution kernel and exchanging information mutually, reducing data utilization efficiency. In this paper, a mask-guided convolution (MagConv) is proposed for image inpainting. In MagConv, mask and image paths share a convolution kernel to interact with each other and form a joint optimization scheme. In addition, a learnable piecewise activation function is raised to replace the reciprocal function of PConv, providing more flexible and adaptable compensation to convolution contaminated by invalid pixels. It also results in a soft mask of floating-point coefficients from 0 to 1 capable of indicating the validity degree of each pixel. Last but not least, MagConv splits the convolution kernel into positive and negative weights so that they can evaluate the validity of each pixel faithfully. Qualitative and quantitative experiments on the CelebA, Paris StreetView and Places2 datasets demonstrate that our method achieves favorable visual quality against state-of-the-art approaches.
Mobile Math Game Prototype on the Base of Templates for Primary School

The paper presents the first steps in the creation of a package with mobile math educational games for primary school. Examples of mobile math learning games in primary school are examined. The role of games and especially mobile games in learning and game specific elements are presented. Classification of game problems with 13 types, suitable for mobile software development, is shown. For each type, the software template is designed and developed, appropriate for the target group. The prototype of an educational game on the base of the templates is created, where learners have to pass levels, solve math problems and receive bonuses and virtual rewards.
Chaos Engineering of Ethereum Blockchain Clients

In this article, we present ChaosETH , a chaos engineering approach for resilience assessment of Ethereum blockchain clients. ChaosETH operates in the following manner: First, it monitors Ethereum clients to determine their normal behavior. Then, it injects system call invocation errors into one single Ethereum client at a time and observes the behavior resulting from perturbation. Finally, ChaosETH compares the behavior recorded before, during, and after perturbation to assess the impact of the injected system call invocation errors. The experiments are performed on the two most popular Ethereum client implementations: GoEthereum and Nethermind. We assess the impact of 22 different system call errors on those Ethereum clients with respect to 15 application-level metrics. Our results reveal a broad spectrum of resilience characteristics of Ethereum clients w.r.t. system call invocation errors, ranging from direct crashes to full resilience. The experiments clearly demonstrate the feasibility of applying chaos engineering principles to blockchain systems.
pyWitness 1.0: A python eyewitness identification analysis toolkit

Abstract pyWitness is a python toolkit for recognition memory experiments, with a focus on eyewitness identification (ID) data analysis and model fitting. The current practice is for researchers to use different statistical packages to analyze a single dataset. pyWitness streamlines the process. In addition to conducting key data analyses (e.g., receiver operating characteristic analysis, confidence accuracy characteristic analysis), statistical comparisons, signal-detection-based model fits, simulated data generation, and power analyses are also possible. We describe the package implementation and provide detailed instructions and tutorials with datasets so that users can follow. There is also an online manual that is regularly updated. We developed pyWitness to be user-friendly, reduce human interaction with pre-processing and processing of data and model fits, and produce publication-ready plots. All pyWitness features align with open science practices, such that the algorithms, fits, and methods are reproducible and documented. While pyWitness is a python toolkit, it can also be used from R for users more accustomed to this environment.
What’s the Meaning of Superhuman Performance in Today’s NLU?

In the last five years, there has been a significant focus in Natural Language Processing (NLP) on developing larger Pretrained Language Models (PLMs) and introducing benchmarks such as SuperGLUE and SQuAD to measure their abilities in language understanding, reasoning, and reading comprehension.These PLMs have achieved impressive results on these benchmarks, even surpassing human performance in some cases.This has led to claims of superhuman capabilities and the provocative idea that certain tasks have been solved.In this position paper, we take a critical look at these claims and ask whether PLMs truly have superhuman abilities and what the current benchmarks are really evaluating.We show that these benchmarks have serious limitations affecting the comparison between humans and PLMs and provide recommendations for fairer and more transparent benchmarks. 4Boolean Questions (BoolQ), Commitment Bank (CB), Choice of Plausible Alternatives (COPA), Winograd Schema Challenge (WSC), Broadcoverage Diagnostics (AX-b), Winogender Schema Diagnostics (AX-g).
LFTK: Handcrafted Features in Computational Linguistics

Past research has identified a rich set of handcrafted linguistic features that can potentially assist various tasks.However, their extensive number makes it difficult to effectively select and utilize existing handcrafted features.Coupled with the problem of inconsistent implementation across research works, there has been no categorization scheme or generallyaccepted feature names.This creates unwanted confusion.Also, most existing handcrafted feature extraction libraries are not open-source or not actively maintained.As a result, a researcher often has to build such an extraction system from the ground up.We collect and categorize more than 220 popular handcrafted features grounded on past literature.Then, we conduct a correlation analysis study on several task-specific datasets and report the potential use cases of each feature.Lastly, we devise a multilingual handcrafted linguistic feature extraction system in a systematically expandable manner.We open-source our system for public access to a rich set of preimplemented handcrafted features.Our system is coined LFTK and is the largest of its kind.Find at github.com/brucewlee/lftk.
MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection System

Multi-modal sarcasm detection has attracted much recent attention.Nevertheless, the existing benchmark (MMSD) has some shortcomings that hinder the development of reliable multi-modal sarcasm detection system: (1) There are some spurious cues in MMSD, leading to the model bias learning; (2) The negative samples in MMSD are not always reasonable.To solve the aforementioned issues, we introduce MMSD2.0, a correction dataset that fixes the shortcomings of MMSD, by removing the spurious cues and re-annotating the unreasonable samples.Meanwhile, we present a novel framework called multi-view CLIP that is capable of leveraging multi-grained cues from multiple perspectives (i.e., text, image, and textimage interaction view) for multi-modal sarcasm detection.Extensive experiments show that MMSD2.0 is a valuable benchmark for building reliable multi-modal sarcasm detection systems and multi-view CLIP can significantly outperform the previous best baselines.
Learning Semantic Textual Similarity via Topic-informed Discrete Latent Variables

Recently, discrete latent variable models have received a surge of interest in both Natural Language Processing (NLP) and Computer Vision (CV), attributed to their comparable performance to the continuous counterparts in representation learning, while being more interpretable in their predictions.In this paper, we develop a topic-informed discrete latent variable model for semantic textual similarity, which learns a shared latent space for sentence-pair representation via vector quantization.Compared with previous models limited to local semantic contexts, our model can explore richer semantic information via topic modeling.We further boost the performance of semantic similarity by injecting the quantized representation into a transformer-based language model with a well-designed semanticdriven attention mechanism.We demonstrate, through extensive experiments across various English language datasets, that our model is able to surpass several strong neural baselines in semantic textual similarity tasks.
DisCup: Discriminator Cooperative Unlikelihood Prompt-tuning for Controllable Text Generation

Prompt learning with immensely large Casual Language Models (CLMs) has been shown promising for attribute-controllable text generation (CTG).However, vanilla prompt tuning tends to imitate training corpus characteristics beyond the control attributes, resulting in a poor generalization ability.Moreover, it is less able to capture the relationship between different attributes, further limiting the control performance.In this paper, we propose a new CTG approach, namely DisCup, which incorporates the attribute knowledge of discriminator to optimize the control-prompts, steering a frozen CLM to produce attribute-specific texts.Specifically, the frozen CLM model, capable of producing multitudinous texts, is first used to generate the next-token candidates based on the context, so as to ensure the diversity of tokens to be predicted.Then, we leverage an attribute-discriminator to select desired/undesired tokens from those candidates, providing the inter-attribute knowledge.Finally, we bridge the above two traits by an unlikelihood objective for prompt-tuning.Extensive experimental results show that DisCup can achieve a new state-of-the-art control performance while maintaining an efficient and high-quality text generation, only relying on around 10 virtual tokens 1 .
Beyond Model Interpretability: On the Faithfulness and Adversarial Robustness of Contrastive Textual Explanations

Contrastive explanation methods go beyond transparency and address the contrastive aspect of explanations.Such explanations are emerging as an attractive option to provide actionable change to scenarios adversely impacted by classifiers' decisions.However, their extension to textual data is under-explored and there is little investigation on their vulnerabilities and limitations.This work motivates textual counterfactuals by laying the ground for a novel evaluation scheme inspired by the faithfulness of explanations.Accordingly, we extend the computation of three metrics, proximity, connectedness and stability, to textual data and we benchmark two successful contrastive methods, POLYJUICE and MiCE, on our suggested metrics.Experiments on sentiment analysis data show that the connectedness of counterfactuals to their original counterparts is not obvious in both models.More interestingly, the generated contrastive texts are more attainable with POLYJUICE which highlights the significance of latent representations in counterfactual search.Finally, we perform the first semantic adversarial attack on textual recourse methods.The results demonstrate the robustness of POLYJUICE and the role that latent input representations play in robustness and reliability.
Think Beyond Words: Exploring Context-Relevant Visual Commonsense for Diverse Dialogue Generation

Commonsense knowledge has been widely considered for building intelligent open-domain dialogue agents, aiming to generate meaningful and diverse responses.Previous works in this field usually lack the ability to effectively obtain and utilize auxiliary commonsense from the external visual world.In this paper, we argue that exploiting logical information in images related to context can be effective to enrich and steer the generation process.In view of this, we propose VICTOR, a context-relevant VIsual Commonsense enhanced dialogue gen-eraTOR for generating coherent and informative responses.To obtain the associated visual commonsense, we devise a novel approach that expands topic words on the knowledge graph and maps them into daily scenarios.During the generation, the model adopts multimodal fusion mechanism to integrate visual and textual information, and adaptively combine their decoding distributions for better response generation.The experimental results on two public datasets show that our proposed method outperforms the latest competitive methods in terms of coherence and diversity.
Conversation Disentanglement with Bi-Level Contrastive Learning

Conversation disentanglement aims to group utterances into detached sessions, which is a fundamental task in processing multi-party conversations.Existing methods have two main drawbacks.First, they overemphasize pairwise utterance relations but pay inadequate attention to the utterance-to-context relation modeling.Second, a huge amount of human annotated data is required for training, which is expensive to obtain in practice.To address these issues, we propose a general disentangle model based on bi-level contrastive learning.It brings closer utterances in the same session while encourages each utterance to be near its clustered session prototypes in the representation space.Unlike existing approaches, our disentangle model works in both supervised settings with labeled data and unsupervised settings when no such data is available.The proposed method achieves new state-of-the-art performance results on both settings across several public datasets.