Reviewerly/exHarmony huggingface dataset:
Consumer Risk Preferences Elicitation From Large Language Models

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Optical Character Recognition of 10 Ã— 10 Size Binary Images

Ten binary images of size 10 × 10 for each of the decimal numbers from 0 to 9 are considered for the simulation setup. Applying different processing techniques on these images, information contents were extracted. Initially, no noise was added in these images and character recognition of 100% was obtained. After that, noise is added in these images (i.e., images were distorted) but even then 9 out of 10 images were successfully recognized. In other words, approximately 90% successful character recognition was obtained.
Methodology for Evaluating Statistical Equivalence in Face Recognition Using Live Subjects with Dissimilar Skin Tones

The general purpose of this study is to propose a methodology that can be employed in the application of facial recognition systems (FRS) to determine if a statistically significant difference exists in a facial recognition system's ability to match two dissimilar skin tone populations to their enrolled images.A particular objective is to test the face recognition system's ability to recognize dark or light skin tone subjects.In addition to the direct comparison of results from two different populations, this study uses a Box Behnken Design to examine four factors commonly effecting facial recognition systems.Four factors were tested, the horizontal angle of the camera viewing the subject, both horizontally to the left and right; the vertical angle, both above and below the subject's line of sight, ;the distance the subjects are from the camera, and the intensity of the illumination on the subject.Experimentation was approached from the assumption that subjects are cooperative, following guidelines for proper enrollment and submission for matching.The experimentation of the four factors was conducted using two sets of three subjects.One set was dark skin tone males, and the second set was light skin tone males.The results of the study showed a significance statistical difference at p = 0.05 level between the two skin tones, with greater difficulty identifying the light skin tone test subjects than those with dark skin tone.
Numerals Recognition for Urdu Script

Optical Character Recognition is a difficult task due to complexities of the script and varying location of the character in the image. Characters of Urdu script based languages are even more difficult to recognize but they have not received much attention even when Urdu ranks sixth in t op 10 most-spoken primary languages (181 million). The problem in numeral recognition in Urdu is the fact that curves and cusps of numerals vary from person to person. This paper presents two schemes for the recognition of numerals of Urdu in unconstrained environment. These two approaches are Feature Extraction & Correlation. Keywords: Optical character recognition (OCR), magnetic character recognition (MCR), correlation of images, feature extraction, urdu numerals
On the importance of domain expertise in feature engineering for predictive product quality in production

Machine Learning (ML) offers significant potential for quality management in production with predictive analytics. Key aspects to building ML models are the selection and engineering of features from data. They allow the usage of relevant data for training ML models. Using the right features consequently improves the quality of the ML models. However, feature engineering requires knowledge of the data, data preprocessing techniques, algorithms, the domain, and use case. Hence, automatic feature engineering tools have become popular. In this paper, we investigate how domain experts and automatic tools compare for engineering features based on a time series dataset from production.
Interpretation Framework of Predictive Quality Models for Process- and Product-oriented Decision Support

In the context of predictive quality in production, there is a need to explain and understand the predictive models used, as well as the dependencies present in the underlying data. For this purpose, we develop a framework for model-independent interpretation of predictive models to enable data-driven, process- and product-oriented decisions. This framework combines different model-agnostic interpretation methods and structures them into two modules, one for a process-oriented view and the other for a product-oriented view. In addition, an implementation concept for the two modules in a machine learning pipeline is also provided.
Ensemble of deep learning and machine learning approach for classification of handwritten Hindi numerals

Abstract Given the vast range of factors, including shape, size, skew, and orientation of handwritten numerals, their machine-based recognition is a difficult challenge for researchers in the pattern recognition field. Due to the abundance of curves and resembling shapes of the symbols, the recognition of Devnagari numerals can leverage the difficulty level of the recognition. The suggested low-classification-cost method for obtaining fine features from given numeral images used benchmark deep learning models, VGG-16Net, VGG-19Net, ResNet-50, and Inception-v3, to address these issues. Principal component analysis, a powerful dimensionality reduction method, was used to efficiently reduce the number of dimensions in the information that pre-trained deep convolutional neural network models provided. The method for improving recognition accuracy by fusing features was provided in the scheme. A machine learning algorithm: support vector machine was employed for the recognition task due to its capacity to distinguish between patterns belonging to distinct classes. The system was able to obtain a recognition accuracy of 99.72% and was effective in demonstrating the importance of ensemble machine learning and deep learning approaches.
An enhanced artificial neural network for hand gesture recognition using multi-modal features

The development of automatic application fields enhanced the advancement in Hand Gesture Recognition (HGR), which enhances human-computer interaction. In particular, hand gestures find a valuable application in hand games, virtual reality, and gesture-based signalling systems. Thus, detecting and recognising hand gestures gain technical significance. Although existing models highlight remarkable recognition outcomes, handling dynamic gestures is a challenging task. Hence, in this research, an enhanced flashfly optimisation-based artificial neural network is developed for the effective identification of hand gestures, and to overcome the existing challenges, the enhanced flash fly optimisation-based ANN technique is proposed. The main objective of the optimisation strategy is to effectively tune the parameters involved in the artificial neural network (ANN) by the attained optimal solution. The optimised ANN classifier is well trained by the pre-processed statistical and the Residual neural Networks (ResNet) features for consistently identifying hand movements. The attained performance of the enhanced flash fly optimisation-based ANN classifier during the representation of hand gestures based on the precision, recall, and F1 score is 0.940 %, 0.791 %, and 0.927 % for the training percentages 50 and 90. Hence, it is illustrated that the proposed HGR model exceeds the competent technique, demonstrating its effectiveness.
Quadratic equations in metabelian Baumslag–Solitar groups

For a finitely generated group G, the Diophantine problem over G is the algorithmic problem of deciding whether a given equation [Formula: see text] (perhaps restricted to a fixed subclass of equations) has a solution in G. In this paper, we investigate the algorithmic complexity of the Diophantine problem for the class [Formula: see text] of quadratic equations over the metabelian Baumslag–Solitar groups [Formula: see text]. We prove that this problem is [Formula: see text]-complete whenever [Formula: see text], and determine the algorithmic complexity for various subclasses (orientable, nonorientable, etc.) of [Formula: see text].
Segmentation of human functional tissue units in support of a Human Reference Atlas

The Human BioMolecular Atlas Program (HuBMAP) aims to compile a Human Reference Atlas (HRA) for the healthy adult body at the cellular level. Functional tissue units (FTUs), relevant for HRA construction, are of pathobiological significance. Manual segmentation of FTUs does not scale; highly accurate and performant, open-source machine-learning algorithms are needed. We designed and hosted a Kaggle competition that focused on development of such algorithms and 1200 teams from 60 countries participated. We present the competition outcomes and an expanded analysis of the winning algorithms on additional kidney and colon tissue data, and conduct a pilot study to understand spatial location and density of FTUs across the kidney. The top algorithm from the competition, Tom, outperforms other algorithms in the expanded study, while using fewer computational resources. Tom was added to the HuBMAP infrastructure to run kidney FTU segmentation at scale-showcasing the value of Kaggle competitions for advancing research.
A Short-Text Similarity Model Combining Semantic and Syntactic Information

As one of the prominent research directions in the field of natural language processing (NLP), short-text similarity has been widely used in search recommendation and question-and-answer systems. Most of the existing short textual similarity models focus on considering semantic similarity while overlooking the importance of syntactic similarity. In this paper, we first propose an enhanced knowledge language representation model based on graph convolutional networks (KEBERT-GCN), which effectively uses fine-grained word relations in the knowledge base to assess semantic similarity and model the relationship between knowledge structure and text structure. To fully leverage the syntactic information of sentences, we also propose a computational model of constituency parse trees based on tree kernels (CPT-TK), which combines syntactic information, semantic features, and attentional weighting mechanisms to evaluate syntactic similarity. Finally, we propose a comprehensive model that integrates both semantic and syntactic information to comprehensively evaluate short-text similarity. The experimental results demonstrate that our proposed short-text similarity model outperforms the models proposed in recent years, achieving a Pearson correlation coefficient of 0.8805 on the STS-B dataset.
How do friendship artificial intelligence chatbots (<scp>FAIC</scp>) benefit the continuance using intention and customer engagement?

Abstract Although there have been many AI chatbots in industry service, social media, and e‐commerce platforms, research on AI chatbots such as Replika, neglected the effects of human‐like traits on users' continuance using intention. This article aims to explore the main effects of human‐like traits (perceived warmth vs. perceived competence) of friendship AI chatbots (FAIC) on continuance using intention and customer engagement, and the moderating effects of the need to belong and information sensitivity. Three studies are conducted to collect data ( N total = 1420 ). Our findings of Study 1 demonstrate that perceived warmth and perceived competence can increase the continuance using intention to FAIC and customer engagement, and perceived usefulness plays a mediating role in our conceptual model. Additionally, consumers' need to belong (high vs. low) (Study 2) and information sensitivity (high vs. low) (Study 3) related to chat contents moderate the main effects significantly. This article contributes to the literature on the relationship between FAIC and consumers by presenting the influence of perceived warmth and perceived competence and establishing the underlying process. Analogously, the findings can be beneficial for marketers and firms in designing and developing the coding program of FAIC to promote consumers' continuance using intention and customer engagement.
Implementation of a Sequence-to-Sequence Stacked Sparse Long Short-Term Memory Autoencoder for Anomaly Detection on Multivariate Timeseries Data of Industrial Blower Ball Bearing Units

The advent of Industry 4.0 introduced new ways for businesses to evolve by implementing maintenance policies leading to advancements in terms of productivity, efficiency, and financial performance. In line with the growing emphasis on sustainability, industries implement predictive techniques based on Artificial Intelligence for the purpose of mitigating machine and equipment failures by predicting anomalies during their production process. In this work, a new dataset that was made publicly available, collected from an industrial blower, is presented, analyzed and modeled using a Sequence-to-Sequence Stacked Sparse Long Short-Term Memory Autoencoder. Specifically the right and left mounted ball bearing units were measured during several months of normal operational condition as well as during an encumbered operational state. An anomaly detection model was developed for the purpose of analyzing the operational behavior of the two bearing units. A stacked sparse Long Short-Term Memory Autoencoder was successfully trained on the data obtained from the left unit under normal operating conditions, learning the underlying patterns and statistical connections of the data. The model was evaluated by means of the Mean Squared Error using data from the unit's encumbered state, as well as using data collected from the right unit. The model performed satisfactorily throughout its evaluation on all collected datasets. Also, the model proved its capability for generalization along with adaptability on assessing the behavior of equipment similar to the one it was trained on.
Evaluation of Open-Source Tools for Differential Privacy

Differential privacy (DP) defines privacy protection by promising quantified indistinguishability between individuals who consent to share their privacy-sensitive information and those who do not. DP aims to deliver this promise by including well-crafted elements of random noise in the published data, and thus there is an inherent tradeoff between the degree of privacy protection and the ability to utilize the protected data. Currently, several open-source tools have been proposed for DP provision. To the best of our knowledge, there is no comprehensive study for comparing these open-source tools with respect to their ability to balance DP's inherent tradeoff as well as the use of system resources. This work proposes an open-source evaluation framework for privacy protection solutions and offers evaluation for OpenDP Smartnoise, Google DP, PyTorch Opacus, Tensorflow Privacy, and Diffprivlib. In addition to studying their ability to balance the above tradeoff, we consider discrete and continuous attributes by quantifying their performance under different data sizes. Our results reveal several patterns that developers should have in mind when selecting tools under different application needs and criteria. This evaluation survey can be the basis for an improved selection of open-source DP tools and quicker adaptation of DP.
Survey on blockchain‐based non‐fungible tokens: History, technologies, standards, and open challenges

Abstract This paper presents a survey of non‐fungible tokens (NFTs), including its history, technologies, standards, and challenges in their development. An NFT is a unique digital entity that is created and maintained using blockchain technology. Each NFT is identified using a unique smart contract and a token ID, so the whole history of the NFT can be globally identified by its address and token ID. The blockchain information indelibly identifies the current owner of any asset, previous owners, and original creator. NFTs are used to manage ownership of digital and physical assets and cryptocurrencies. The prices of popular NFTs have become very high, and the market for them has overheated in recent years. NFT technology and its ecosystem have evolved since Quantum, the first NFT, was stored in the Namecoin blockchain. Ethereum has become the main platform for NFT projects because it provides support for smart contracts. Currently, almost all NFT projects are launched on the Ethereum blockchain. NFT has two major standards called ERC‐721 and ERC‐1155, which have had important functions in the development of NFT. Starting with these two standards, other standards for NFT continue to emerge; they expand the functionality of NFT such as by adding utility. However, NFT is a very early technology, and it has not been long after the NFT concept was created and used. So there are several challenges for further improving NFT technology, in terms of usability, interoperability, and evolution. This paper presents a survey of NFT, including its history, technologies, standards, and challenges of NFT.
What Teams Do: Exploring Volunteer Content Moderation Team Labor on Facebook

Social media sites such as Facebook depend on tens of millions of volunteer moderators across the globe to facilitate platform-based discussion forums. While research has revealed much about the work that these moderators do, some fundamental questions remain. For example, why do volunteer moderators commonly work as teams rather than individuals? In this article, I use data gathered through digital ethnography with Facebook Group moderators to explore the benefits and challenges of moderation team work. I develop a three-part framework to articulate how teams facilitate logistical, discursive, and emotional labor. Finally, I argue that this empirical analysis reveals otherwise hidden and unacknowledged dimensions of volunteer moderation work that make platform-hosted discussion groups possible.
ASMEvoNAS: Adaptive segmented multi-objective evolutionary network architecture search

Network architecture search (NAS) has attracted much attention as an automatic design technique of network architecture. In particular, multi-objective evolutionary algorithms (MOEAs) have been a popular kind of optimizer in NAS due to their global optimization capability. However, as a population-based iterative search method, MOEAs are subject to the unbearable computational cost of individual evaluation on multiple objectives at each generation, which affects their generalization ability and transferability of MOEA-based NAS. Therefore, an adaptive segmented multi-objective evolutionary network architecture search (ASMEvoNAS) method is proposed in this paper. Firstly, an adaptive segmented evaluation strategy is designed to adaptively select different but more suitable objectives to efficiently assess the candidate architectures at different evolutionary stages, instead of evaluating them by all the considered objectives simultaneously. Thus, the computational cost and complexity of the search process can be controlled and reduced to some extent. Secondly, a preference-based pre-selection strategy is designed to filter out the initialized architectures with high parameter quantities to reduce the total parameter scale of the whole population and memory consumption. Last, a novel desirable gene reservation-based crossover and a directed connection-based mutation are proposed to produce offspring. Experimental results show that ASMEvoNAS shows promising performance on CIFAR-10, CIFAR-100, and ImageNet with error rates of 2.21%, 15.57%, and 24.43% top-1, respectively. The proposed method reduces the search cost to 0.36 GPU-Days on CIFAR-10 while maintaining competitive classification performance compared to state-of-the-art networks. In addition, ASMEvoNAS presents superior performance when dealing with the considered transfer tasks, as well as the benchmark dataset of NAS.
Networked Signal and Information Processing: Learning by multiagent systems

The article reviews significant advances in networked signal and information processing, which have enabled in the last 25 years extending decision making and inference, optimization, control, and learning to the increasingly ubiquitous environments of distributed agents. As these interacting agents cooperate, new collective behaviors emerge from local decisions and actions. Moreover, and significantly, theory and applications show that networked agents, through cooperation and sharing, are able to match the performance of cloud or federated solutions, while offering the potential for improved privacy, increasing resilience, and saving resources.
Next Generation Financial Services: Role of Blockchain enabled Federated Learning and Metaverse

Due to the COVID-19 pandemic and rapid digital transformation in financial institutions, cyberattacks, and data breaches are spiking and becoming more targeted and prevalent. According to media sources, banks received the most malware in 2019. Ransomware, phishing assaults, credit card fraud, and bank account hacks pose the highest risk to the financial sector. The growing number of ways cyber and physical security risks overlap is worrying. Digitalized operational and security solutions are becoming standard across sectors. This paper presents how blockchain technology and Federated Learning (FL) influence financial services to improve the privacy, security, and trust of integrated cyber-physical systems (CPS) in the financial sector. First, an overview of financial services applications is provided in detail. Then, how federated learning (FL) can incorporate blockchain for financial services is presented. It has also been pointed out how the metaverse and digital twin can help improve the financial services ecosystem. Finally, some future research challenges have been included to provide some insights to the researchers and industries about how to enhance the security, trust, and privacy of CPS integrated into financial services using blockchain, FL, and Metaverse.
Proactive Conversational Agents in the Post-ChatGPT World

ChatGPT and similar large language model (LLM) based conversational agents have brought shock waves to the research world. Although astonished by their human-like performance, we find they share a significant weakness with many other existing conversational agents in that they all take a passive approach in responding to user queries. This limits their capacity to understand the users and the task better and to offer recommendations based on a broader context than a given conversation. Proactiveness is still missing in these agents, including their ability to initiate a conversation, shift topics, or offer recommendations that take into account a more extensive context. To address this limitation, this tutorial reviews methods for equipping conversational agents with proactive interaction abilities.
Decomposing Logits Distillation for Incremental Named Entity Recognition

Incremental Named Entity Recognition (INER) aims to continually train a model with new data, recognizing emerging entity types without forgetting previously learned ones. Prior INER methods have shown that Logits Distillation (LD), which involves preserving predicted logits via knowledge distillation, effectively alleviates this challenging issue. In this paper, we discover that a predicted logit can be decomposed into two terms that measure the likelihood of an input token belonging to a specific entity type or not. However, the traditional LD only preserves the sum of these two terms without considering the change in each component. To explicitly constrain each term, we propose a novel Decomposing Logits Distillation (DLD) method, enhancing the model's ability to retain old knowledge and mitigate catastrophic forgetting. Moreover, DLD is model-agnostic and easy to implement. Extensive experiments show that DLD consistently improves the performance of state-of-the-art INER methods across ten INER settings in three datasets.