Reviewerly/exHarmony huggingface dataset:
Hotel customers’ behavioral intentions toward service robots: the role of utilitarian and hedonic values

Purpose This study aims to investigate the effects of hotel customers’ perceived utilitarian and hedonic values on their intention to use service robots. In addition, the influences of innovativeness, ease of use and compatibility on hotel customers’ perceived utilitarian and hedonic values were examined. Design/methodology/approach The data of the current study was collected from 11 countries including the USA, UK, Turkey, Spain, Romania, Japan, Israel, India, Greece, Canada and Brazil. A structural equation modeling was used to test the study hypotheses. Findings The results indicated that hotel customers’ intention to use service robots was positively influenced by their utilitarian and hedonic value perceptions. In addition, customers’ perceptions of robots’ ease of use and compatibility had a positive impact on their perceived utilitarian and hedonic values. Originality/value The findings of the current study provide unique contributions in the context of hospitality robotics technology adoption literature. In addition, this study provides valuable insights and novel opportunities for hospitality decision-makers to capitalize on, as they strive to strategize the integration of robot-based services into their operations.
Representation Learning for Tablet and Paper Domain Adaptation in Favor of Online Handwriting Recognition

The performance of a machine learning model degrades when it is applied to data from a similar but different domain than the data it has initially been trained on. The goal of domain adaptation (DA) is to mitigate this domain shift problem by searching for an optimal feature transformation to learn a domain-invariant representation. Such a domain shift can appear in handwriting recognition (HWR) applications where the motion pattern of the hand and with that the motion pattern of the pen is different for writing on paper and on tablet. This becomes visible in the sensor data for online handwriting (OnHW) from pens with integrated inertial measurement units. This paper proposes a supervised DA approach to enhance learning for OnHW recognition between tablet and paper data. Our method exploits loss functions such as maximum mean discrepancy and correlation alignment to learn a domain-invariant feature representation (i.e., similar covariances between tablet and paper features). We use a triplet loss that takes negative samples of the auxiliary domain (i.e., paper samples) to increase the amount of samples of the tablet dataset. We conduct an evaluation on novel sequence-based OnHW datasets (i.e., words) and show an improvement on the paper domain with an early fusion strategy by using pairwise learning.
Performance Comparison of YOLOv5 and YOLOv8 Architectures in Human Detection using Aerial Images

The development of UAV technology has reached the stage of implementing artificial intelligence, control, and sensing. Cameras as UAV data inputs are employed to ensure flight safety, search for missing persons, and disaster evacuation. Human detection using cameras while flying is the focus of this article. The application of human detection in pedestrian areas using aerial image data is used as the dataset in the deep learning input process. The architectures discussed in this study are YOLOv5 and YOLOv8. The precision, recall, and F1-score values are used as comparisons to evaluate the performance of these architectures. When both architecture performances are applied, YOLOv8 outperforms YOLOv5. The achieved performance of YOLOv8 is a precision of 84.62%, recall of 75.93%, and F1-score of 79.98%.
The blockchain internet of things: review, opportunities, challenges, and recommendations

A new technology known as the internet of things (IoT) allows both physical and virtual items to be linked and communicated with one another, creating new digital services that enhance our fineness of sustenance. The IoT system has a number of benefits, but because of its present centralized architecture, there are several problems with regard to data integrity, security, privacy, and single points of failure. The future development of IoT applications is hampered by these difficulties. To tackle these problems, it might be best to integrate the Internet of Things with one of the distributed ledger solutions. The blockchain is one of the most frequent and well-liked varieties of distributed ledger technologies. Numerous advantages can result from integrating blockchain technology with the IoT called blockchain internet of things (BIoT). In this paper, we show a brief overview of blockchain, its components of blockchain, and its features of blockchain. Meanwhile, we describe the architecture of BIoT, Issues of BIoT, and BIoT applications. Additionally, this paper provides a future research challenge and open issues.
Artificial Intelligence in Finance

Over the past decade, decentralized cryptocurrencies have gotten a lot of attention. With the creation of bitcoin in end of 2000s, bitcoin has created a disruption that has changed the power dynamic in money markets, that its results are still unknown to us, as their consciences are still to be seen in the future. The advancement in artificial intelligence is hard to mess, especially with a lot of our decisions are left for an algorithm to decide for us what to watch (YouTube, Netflix etc.), what to buy (Amazon, Ali baba etc.), and what to eat (Talabat, UberEATS etc.). We took raw data from the internet about the close prices of 600 cryptocurrencies and then feature engineered the data to suit our research better and to help us find the expected results we want. The Artificial intelligence application used is provided by azure Machine Learning, utilizing both Machine Learning and deep learning, Virtual Machines and the data analysis applications that are built in azure. The final results have met and exceeded our expectations reaching almost perfect predication in multiple experiments on different coins.
Where's your head at? Detecting the orientation and position of pigs with rotated bounding boxes

Pig detection in real production environments is a challenging task due to the variations of housing system and dynamic background. Though considerable progress has been made, for practical settings, there still existing challenges for densely housed pigs as they are often arbitrarily arranged at varying orientations in presence of lens distortion, overlap, occlusion, and motion blur. In this paper, we propose a rotated and oriented bounding box detector for fast and accurate predict the location and orientation of each animal. The key point is to parameterize pigs’ geometric parameters (body centre, body length, body width, orientation) with an orientated bounding box (box centre, long edge, short edge and direction vector). To further improve the performance on video object detection, a fast sequential non-Maximum Suppression (FastSeq-NMS) method is proposed by making used of the orientation and temporal information. To quantitative evaluate the proposed method, 3123 images from 27 different pens were selected as training and validation sets, video clips from three new environments were selected as test set. Our lightweight model (1.7 M) achieves 99.21 Average Precision ([email protected]) on validation set, and 96.54 [email protected] on test set, further improved to 97.41 [email protected] with the proposed NMS method. The experiments show the effectiveness of the proposed method. More information available online: https://gitlab.kuleuven.be/m3-biores/public/m3pig.
Image Steganalysis Against Adversarial Steganography by Combining Confidence and Pixel Artifacts

Convolutional Neural Networks (CNNs) have made remarkable progress in steganalysis. However, they struggle to detect adversarial steganography accurately which merges adversarial samples and steganography. While handcrafted models show limited vulnerability to adversarial steganography, their accuracy pales in comparison to that of CNN analyzers. To address these limitations head-on, we propose TStegNet, an innovative two-stream CNN steganalyzer designed to detect adversarial steganography. TStegNet leverages confidence artifacts and pixel artifacts, enabling a comprehensive analysis of hidden information. Specifically, we design a confidence loss function and apply backpropagation to amplify the confidence artifacts, which enhances the performance of our model. Additionally, we use the feature similarity function to minimize the impact of adversarial perturbation. Extensive experiments reveal that proposed TStegNet outperforms existing state-of-the-art methods, representing a significant milestone in the fight against adversarial steganography.
Scaling Up Customer Support Using Artificial Intelligence and Machine Learning Techniques

Cutting-edge technological advancements in artificial intelligence (AI) and its associated technologies such as machine learning (ML) and deep learning (DL) have garnered immense attention from academia and the industry due to their ability to automate tasks previously executed by human beings in various sectors of the economy. In the business sector, there's an increased interest to explore how these emerging technologies can be deployed to enhance engagements between businesses and clients by automating their interactions. Fueled by the prevalence of a plethora of digital marketing data, these data-driven techniques can help businesses to provide faster and more efficient customer support, while also freeing up human staff to focus on more complex business-related issues. Accordingly, this chapter seeks to provide a compact review of the different AI-powered techniques and their applications in scaling up customer support, highlight existing challenges in their usage in the marketing domain along with proposing future research directions.
Save Soil Through Machine Learning

Acknowledging the ability of the soil is the primary step in improving its quality. The system is designed to collect all the required data using various IoT-based technologies. This chapter has an intensive survey of research papers in both national and the global space. And the collected data are trained and prepared by machine learning algorithms to be able to predict the ways to improve the quality. It also gives the current status of the soil.
Postdigital Student Bodies – Mapping the Flesh-Electric

How institutions and researchers understand, explain, and predict student success and attrition, and design interventions to support students, flows from, inter alia, their understanding and assumptions regarding education, and more specifically, of success or attrition in the nexus of teaching and learning. Research is also shaped by how institutions and researchers define and understand data which influences their methodologies, samples, analyses, and findings. Institutions have always, in one form or another, measured, collected, analysed, and used student data. The digitization and datafication of higher education provide institutions access to greater volumes, varieties, velocity of the flow and processing of data, and granularity of students’ digital data, resulting in learning analytics – a research focus and practice of measuring, collecting, analyzing, and using students’ digital data to, inter alia, (re)design pedagogy, student support strategies and the allocation of resources. Among the concerns regarding learning analytics are, for example, students’ privacy, bias, the non-transparency of data collection and analysis processes, and an understanding of digital data as representative of students’ learning without considering the entanglement of students’ digital and analog worlds. This chapter tentatively outlines the contours of postdigital learning analytics as space for reconfiguring understanding student learning.
ZeroPrompt: Scaling Prompt-Based Pretraining to 1,000 Tasks Improves Zero-Shot Generalization

We propose a multitask pretraining approach ZeroPrompt for zero-shot generalization, focusing on task scaling and zero-shot prompting.While previous models are trained on only a few dozen tasks, we scale to 1,000 tasks for the first time using real-world data. This leads to a crucial discovery that task scaling can be an efficient alternative to model scaling; i.e., the model size has less impact on performance with an extremely large number of tasks. Our results show that task scaling can improve training efficiency by 30 times in FLOPs.Empirically, ZeroPrompt substantially improves both the efficiency and the performance of zero-shot learning across a variety of academic and production datasets.
Don’t Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environments

A key missing capacity of current language models (LMs) is grounding to real-world environments.Most existing work for grounded language understanding uses LMs to directly generate plans that can be executed in the environment to achieve the desired effects.It thereby casts the burden of ensuring grammaticality, faithfulness, and controllability all on the LMs.We propose Pangu, a generic framework for grounded language understanding that capitalizes on the discriminative ability of LMs instead of their generative ability.Pangu consists of a symbolic agent and a neural LM working in a concerted fashion: The agent explores the environment to incrementally construct valid plans, and the LM evaluates the plausibility of the candidate plans to guide the search process.A case study on the challenging problem of knowledge base question answering (KBQA), which features a massive environment, demonstrates the remarkable effectiveness and flexibility of Pangu: A BERT-base LM is sufficient for setting a new record on standard KBQA datasets, and larger LMs further bring substantial gains.Pangu also enables, for the first time, effective few-shot in-context learning for KBQA with large LMs such as Codex.
NusaCrowd: Open Source Initiative for Indonesian NLP Resources

We present NusaCrowd, a collaborative initiative to collect and unify existing resources for Indonesian languages, including opening access to previously non-public resources.Through this initiative, we have brought together 137 datasets and 118 standardized data loaders.The quality of the datasets has been assessed manually and automatically, and their value is demonstrated through multiple experiments.NusaCrowd's data collection enables the creation of the first zero-shot benchmarks for natural language understanding and generation in Indonesian and the local languages of Indonesia.Furthermore, NusaCrowd brings the creation of the first multilingual automatic speech recognition benchmark in Indonesian and the local languages of Indonesia.
Generating Visual Spatial Description via Holistic 3D Scene Understanding

Visual spatial description (VSD) aims to generate texts that describe the spatial relations of the given objects within images.Existing VSD work merely models the 2D geometrical vision features, thus inevitably falling prey to the problem of skewed spatial understanding of target objects.In this work, we investigate the incorporation of 3D scene features for VSD.With an external 3D scene extractor, we obtain the 3D objects and scene features for input images, based on which we construct a target object-centered 3D spatial scene graph (GO3D-S 2 G), such that we model the spatial semantics of target objects within the holistic 3D scenes.Besides, we propose a scene subgraph selecting mechanism, sampling topologically-diverse subgraphs from GO3D-S 2 G, where the diverse local structure features are navigated to yield spatially-diversified text generation.Experimental results on two VSD datasets demonstrate that our framework outperforms the baselines significantly, especially improving on the cases with complex visual spatial relations.Meanwhile, our method can produce more spatially-diversified generation.
Learning Latent Relations for Temporal Knowledge Graph Reasoning

Temporal Knowledge Graph (TKG) reasoning aims to predict future facts based on historical data.However, due to the limitations in construction tools and data sources, many important associations between entities may be omitted in TKG.We refer to these missing associations as latent relations.Most of the existing methods have some drawbacks in explicitly capturing intra-time latent relations between co-occurring entities and inter-time latent relations between entities that appear at different times.To tackle these problems, we propose a novel Latent relations Learning method for TKG reasoning, namely L 2 TKG.Specifically, we first utilize a Structural Encoder (SE) to obtain representations of entities at each timestamp.We then design a Latent Relations Learning (LRL) module to mine and exploit the intraand inter-time latent relations.Finally, we extract the temporal representations from the output of SE and LRL for entity prediction.Extensive experiments on four datasets demonstrate the effectiveness of L 2 TKG.
Towards Unifying Multi-Lingual and Cross-Lingual Summarization

To adapt text summarization to the multilingual world, previous work proposes multi-lingual summarization (MLS) and cross-lingual summarization (CLS).However, these two tasks have been studied separately due to the different definitions, which limits the compatible and systematic research on both of them.In this paper, we aim to unify MLS and CLS into a more general setting, i.e., many-to-many summarization (M2MS), where a single model could process documents in any language and generate their summaries also in any language.As the first step towards M2MS, we conduct preliminary studies to show that M2MS can better transfer task knowledge across different languages than MLS and CLS.Furthermore, we propose PISCES, a pre-trained M2MS model that learns language modeling, cross-lingual ability and summarization ability via threestage pre-training.Experimental results indicate that our PISCES significantly outperforms the state-of-the-art baselines, especially in the zero-shot directions, where there is no training data from the source-language documents to the target-language summaries.
Towards a Unified Multi-Dimensional Evaluator for Text Generation

Multi-dimensional evaluation is the dominant paradigm for human evaluation in Natural Language Generation (NLG), i.e., evaluating the generated text from multiple explainable dimensions, such as coherence and fluency.However, automatic evaluation in NLG is still dominated by similarity-based metrics, and we lack a reliable framework for a more comprehensive evaluation of advanced models.In this paper, we propose a unified multi-dimensional evaluator UNIEVAL for NLG.We re-frame NLG evaluation as a Boolean Question Answering (QA) task, and by guiding the model with different questions, we can use one evaluator to evaluate from multiple dimensions.Furthermore, thanks to the unified Boolean QA format, we are able to introduce an intermediate learning phase that enables UNIEVAL to incorporate external knowledge from multiple related tasks and gain further improvement.Experiments on three typical NLG tasks show that UNIEVAL correlates substantially better with human judgments than existing metrics.Specifically, compared to the top-performing unified evaluators, UNIEVAL achieves a 23% higher correlation on text summarization, and over 43% on dialogue response generation.Also, UNIEVAL demonstrates a strong zero-shot learning ability for unseen evaluation dimensions and tasks.Source code, data and all pre-trained evaluators are available on our GitHub repository 1 .Generated Summary: Harry Kane is nominated for both the PFA player and young player of the season.The Spurs striker has been released from the awards ceremony on Sunday.The Tottenham striker features in a new animation.
Exploiting domain-slot related keywords description for Few-Shot Cross-Domain Dialogue State Tracking

Collecting dialogue data with domain-slotvalue labels for dialogue state tracking (DST) could be a costly process.In this paper, we propose a novel framework based on domain-slot related description to tackle the challenge of few-shot cross-domain DST.Specifically, we design an extraction module to extract domainslot related verbs and nouns in the dialogue.Then, we integrates them into the description, which aims to prompt the model to identify the slot information.Furthermore, we introduce a random sampling strategy to improve the domain generalization ability of the model.We utilize a pre-trained model to encode contexts and description and generates answers with an auto-regressive manner.Experimental results show that our approaches substantially outperform the existing few-shot DST methods on MultiWOZ and gain strong improvements on the slot accuracy comparing to existing slot description methods.
Multilingual Relation Classification via Efficient and Effective Prompting

Prompting pre-trained language models has achieved impressive performance on various NLP tasks, especially in low data regimes.Despite the success of prompting in monolingual settings, applying prompt-based methods in multilingual scenarios has been limited to a narrow set of tasks, due to the high cost of handcrafting multilingual prompts.In this paper, we present the first work on prompt-based multilingual relation classification (RC), by introducing an efficient and effective method that constructs prompts from relation triples and involves only minimal translation for the class labels.We evaluate its performance in fully supervised, few-shot and zero-shot scenarios, and analyze its effectiveness across 14 languages, prompt variants, and English-task training in cross-lingual settings.We find that in both fully supervised and few-shot scenarios, our prompt method beats competitive baselines: fine-tuning XLM-R EM and null prompts.It also outperforms the random baseline by a large margin in zero-shot experiments.Our method requires little in-language knowledge and can be used as a strong baseline for similar multilingual classification tasks.
Improving Sharpness-Aware Minimization with Fisher Mask for Better Generalization on Language Models

Fine-tuning large pretrained language models on a limited training corpus usually suffers from poor generalization.Prior works show that the recently-proposed sharpness-aware minimization (SAM) optimization method can improve the model generalization.However, SAM adds a perturbation to each model parameter equally (but not all parameters contribute equally to the optimization of training), which we argue is sub-optimal and will lead to excessive computation.In this paper, we propose a novel optimization procedure, namely FSAM 1 , which introduces a Fisher mask to improve the efficiency and performance of SAM.In short, instead of adding perturbation to all parameters, FSAM uses the Fisher information to identity the important parameters and formulates a Fisher mask to obtain the sparse perturbation, i.e., making the optimizer focus on these important parameters.Experiments on various tasks in GLUE and SuperGLUE benchmarks show that FSAM consistently outperforms the vanilla SAM by 0.67∼1.98average score among four different pretrained models.We also empirically show that FSAM works well in other complex scenarios, e.g., fine-tuning on generation tasks or limited training data.Encouragingly, when training data is limited, FSAM improves the SAM by a large margin, i.e., up to 15.1.