Reviewerly/exHarmony huggingface dataset:
Mathematical Modeling of Virtual Machine Life Cycle Using Branching Renewal Process

In this paper, we propose the branching renewal process as a model of virtual machine life cycle in cloud node. We assume that virtual machines have several working modes with different resource consumption. Each machine begins with zero phase and continues performing by probabilistic change of other phases. Virtual machines can leave the cloud node after any phase with some probability. For this model, we obtain probability distribution of the number of simultaneously performing virtual machines in each phase and the distribution function of resource consumption in cloud node.
AI-Generated Inventions: Implications for the Patent System

This symposium article discusses issues raised for patent processes and policy created by inventions generated by artificial intelligence (AI). Using insights from economic research on intellectual property rights, it argues in favor of allowing patent protection for AI-generated inventions. Next, the Article examines how the emergence of AI inventions will alter the patentability standards and whether a differentiated patent system that treats AI-generated inventions differently from human-generated inventions is normatively desirable. The Article concludes by considering the larger implications of allowing patents on AI-generated inventions, including changes to the patent examination process, possible increase in the concentration of patent ownership and patent thickets, and potentially unlimited inventions.
ScholarRec: A User Recommendation System for Academic Social Network

With the development of recommendation algorithms, recommendation systems are being widely used for recommendation tasks in different domains. However, most recommendation systems do not make good use of valid information, resulting in unsatisfactory recommendations. At the same time, the overly complex system design leads to slow recommendation speed, which does not meet the speed requirement. To address the above issues, we introduce a new user recommendation system based on academic social website SCHOLAT, including the key modules of the system and the key processes of the system. Then we introduce the solutions proposed to solve the system performance problem and cold start problem. Finally we introduce the social network dataset of SCHOLAT used for training model, and verify the effect of Variational Graph Normalized Auto-Encoders(VGNAE) on SCHOLAT dataset. We compare the effect of old version and new version recommendation system. The experimental results show that the new version of the recommendation system outperform the old one in terms of recommendation accuracy and relevance. And the system performance also meet the practical application requirements.
A Faster, Lighter and Stronger Deep Learning-Based Approach for Place Recognition

Visual Place Recognition is a vital part of image localization and loop closure detection systems, and it has attracted widespread interest in multiple domains such as computer vision, robotics and AR/VR. In this work, we propose a faster, lighter and stronger approach that can generate models with fewer parameters and can spend less time in the inference stage. We designed RepVGG-lite as the backbone network in our architecture, it is more discriminative than other general networks in the Place Recognition task. RepVGG-lite has more speed advantages while achieving higher performance. We extract only one scale patch-level descriptors from global descriptors in the feature extraction stage. Then we design a trainable feature matcher to exploit both the space relationships and the visual appearance of the features, which is based on the attention mechanism. Extensive experiments on difficult datasets show that the proposed approach outperforming previous other advanced learning approaches, and achieving even higher inference speed. Our system has 14 times less params than Patch-NetVLAD, 6.8 times lower theoretical FLOPs, and run faster 21 and 33 times in feature extraction and feature matching. Moreover, the performance of our approach is 0.5% better than Patch-NetVLAD in Recall@1. We used subsets of Mapillary Street Level Sequences dataset to conduct experiments for all other challenging conditions.
Metaverse Technology in Communication Practices: A Case Study of IT Products Retailers in the UAE

Introduction: Retail companies aim to provide their customers with improved customer support and public relations services. For this purpose, metaverse technology is one of the most preferred approaches to improving customers’ buying and post-purchase experiences. Aims: This research also examined metaverse technology acceptance among the IT products and services companies in the United Arab Emirates. Methods: The researchers employed a self-proposed study model and used the structural equation modeling approach. Results: Results revealed that relative advantage significantly affects customer support and public relations. However, transparency does not affect customer service and public relations significantly, while the effect of perceived compatibility on customer support remained insignificant while public relations remained significant. Finally, the effect of public relations on metaverse technological acceptance remained insignificant. Besides, the effect of customer support on metaverse technology acceptance remained significant. Overall, the results supported the role of certain factors proposed by the diffusion of innovation theory in the context of PR and customer support, which is further accelerating the metaverse technology adoption among the IT retailers in the UAE. Conclusion: Thus, this study concludes that the role and adoption of metaverse technology not only highlight its acceptance but also address its importance in improving IT retail products and services. Doi: 10.28991/ESJ-2023-07-03-019 Full Text: PDF
An artificial vocabulary learning assistant

Abstract This article presents an online application developed to improve vocabulary learning through games, quizzes and interaction with a chatbot. The application was tested experimentally on a sample of twenty proficiency-level students divided into two groups and tested on English vocabulary learning in two stages. In the first stage, the first group of students was the control group, who studied a vocabulary section from the book. The second group was the experimental group, which studied using the proposed application. In the second stage, the two groups were reversed and asked to learn the vocabulary of the second unit. According to experiment results, using the particular application improved students' performance, as, in both tests, students of the experimental group outperformed those of the control group. In addition, the analysis of a questionnaire answered by all students confirmed that most prefer this application over traditional learning methods as they consider it quite enjoyable and easy while agreeing that it helped them learn vocabulary. The application is available on the website: http://users.sch.gr/polizipan .
Fingerprinting-based Minimal Perfect Hashing Revisited

In this paper we study a fingerprint-based minimal perfect hash function ( FMPH for short). While FMPH is not as space-efficient as some other minimal perfect hash functions (for example RecSplit, CHD, or PTHash), it has a number of practical advantages that make it worthy of consideration. FMPH is simple and quite fast to evaluate. Its construction requires very little auxiliary memory, takes a short time and, in addition, can be parallelized or carried out without holding keys in memory. In this paper, we propose an effective method (called FMPHGO ) that reduces the size of FMPH, as well as a number of implementation improvements. In addition, we experimentally study FMPHGO performance and find the best values for its parameters. Our benchmarks show that with our method and an efficient structure to support the rank queries on a bit vector, the FMPH size can be reduced to about 2.1 bits/key, which is close to the size achieved by state-of-the-art methods and noticeably larger only compared to RecSplit. FMPHGO preserves most of the FMPH advantages mentioned above, but significantly reduces its construction speed. However, FMPHGO’s construction speed is still competitive with methods of similar space efficiency (like CHD or PTHash), and seems to be good enough for practical applications.
How should voice assistants be heard? The mitigating effect of verbal and vocal warmth in voice assistant service failure

ABSTRACTABSTRACTVoice assistants have become increasingly popular touchpoints in AI-infused service encounters in the hospitality industry. Although we have seen a growing body of research in this area, little attention has been paid to specific service failures involving exclusively voice interactions. Drawing from the Computers As Social Actors (CASA) research paradigm and the Stereotype Content Model, this research explores how warmth can mitigate the negative consequences of service failure involving voice assistants. In two experiments using both physiological (EDA) and psychological measures, we show that the perception of warmth improves consumers' emotional reactions and increases re-patronage intention following a negative service outcome. We also found that the optimal voice to be used for a voice assistant is a dynamic speech style combined with emotionally expressive and warm verbal content. These findings contribute to the knowledge of voice-based smart service interaction and provide insight into how to mitigate the negative consequences of service failure involving voice assistants.摘要在酒店行业, 语音助手已成为一个越来越受欢迎的基于人工智能的客户触点。尽管这一领域的研究日益增长,但很少有学者关注仅涉及语音交互的特定服务失败。借鉴计算机作为社会 参与者 (CASA) 研究范式和刻板印象内容模型,本研究探讨了温暖如何减轻涉及语音助手的 服务失败的负面影响。在两个使用生理 (EDA) 和心理测量的实验中,结果表明,温暖的感 知改善了消费者的情绪反应,并提升了负面服务结果后的再次光顾意愿。我们还发现,用于 语音助手的最佳声音是动态语音风格,结合高度温暖的语言内容。这些发现有助于了解基于 语音的智能服务交互,并针对如何减轻涉及语音助手的服务失败的负面影响提供了新的见 解。KEYWORDS: Artificial Intelligence (AI)voice assistantwarmthservice failurespeech stylere-patronage intention关键词: 人工智能;语音助手; 温暖;服务失败;语音风格;复购意愿 Disclosure statementNo potential conflict of interest was reported by the author(s).
TechPat: Technical Phrase Extraction for Patent Mining

In recent years, due to the explosive growth of patent applications, patent mining has drawn extensive attention and interest. An important issue of patent mining is that of recognizing the technologies contained in patents, which serves as a fundamental preparation for deeper analysis. To this end, in this article, we make a focused study on constructing a technology portrait for each patent, i.e., to recognize technical phrases concerned in it, which can summarize and represent patents from a technical perspective. Along this line, a critical challenge is how to analyze the unique characteristics of technical phrases and illustrate them with definite descriptions. Therefore, we first generate the detailed descriptions about the technical phrases existing in extensive patents based on different criteria, including various previous works, practical experience, and statistical analyses. Then, considering the unique characteristics of technical phrases and the complex structure of patent documents, such as multi-aspect semantics and multi-level relevances, we further propose a novel unsupervised model, namely TechPat, which can not only automatically recognize technical phrases from massive patents but also avoid the need for expensive human labeling. After that, we evaluate the extraction results from various aspects. Specifically, we propose a novel evaluation metric called Information Retrieval Efficiency (IRE) to quantify the performance of extracted technical phrases from a new perspective. Extensive experiments on real-world patent data demonstrate that the TechPat model can effectively discriminate technical phrases in patents and greatly outperform existing methods. We further apply extracted technical phrases to two practical application tasks, namely patent search and patent classification, where the experimental results confirm the wide application prospects of technical phrases. Finally, we discuss the generalization ability of our proposed methods.
A multi-terrain feature-based deep convolutional neural network for constructing super-resolution DEMs

Scale conversion between DEMs is an important issue in geomorphometry. There are many mature studies on the generation of low-resolution(LR) DEMs from high-resolution(HR) DEMs. However, as an important and convenient means of obtaining HR DEMs, traditional super resolution (SR) methods have shown insufficient consideration of the terrain features embedded in DEMs. Therefore, this article investigates the combination of terrain features and the use of convolutional neural networks (CNN) to reconstruct HR DEMs, and proposes a multi-terrain feature-based deep CNN for super-resolution(SR) DEMs (MTF-SR). In the experiments, from the perspective of vector and raster terrain features, we fuse raster terrain features in the input and loss functions, and fuse vector terrain features in the optimization of the output of the model. The results show that the MTF-SR model has a 30–50 % reduction in mean absolute error (MAE) compared with interpolation methods, has the lowest slope and aspect error and has a 10 to 30 % improvement in streamline matching rate (SMR). These results point to the advantages of the method in overall elevation accuracy and the preservation of terrain features.
From compensation to competition: The impact of graphicons on language use in a Chinese context

This study examines the impact of graphicons (emoticons, emojis, and stickers) on the use of sentence-final particles (SFPs) in Chinese based on a 13-year longitudinal corpus of 941,020 comments posted on the popular Chinese social media platform Bilibili. Quantitative analysis shows that graphicon frequencies increase while SFP frequencies decrease over time, and that the correlation between these two trends is statistically significant. However, the more an SFP encodes a grammatical function or has a negative connotation, the less likely it is to be replaced by graphicons. Qualitative analysis suggests that the relationship between graphicons and SFPs is evolving from syntagmatic, where the two co-occur in the same sentence, to paradigmatic, where either can fulfill the function of expressing (positive) attitude or sentiment. This suggests that the functions of graphicons are shifting from compensation to competition with language, as an alternative to SFPs.
Users' Experiences in a Regional Academic Library Makerspace

Makerspaces are still emerging service offerings and establishments within libraries and on university campuses. They provide equipment and expertise for hands-on projects and skill-building, especially around digital fabrication. This paper presents findings from a qualitative case study of a makerspace in a regional university Library at the University of Southern Queensland. It explores users' experiences of participation and value across three usage types in the Makerspace: course curriculum, extracurricular, and research. To understand users' experiences within the space a visual research method was adopted alongside semi-structured interviews. This facilitated a participant-driven dialogue and adding depth to the data collected. A reflexive thematic analysis of interview transcripts was undertaken from which five themes were developed. These were: a tentative start, practical need, skills development, community connection, and influencing outlook. The resulting themes presented a narrative of users' participation that started with tentative curiosity and hesitancy around using the Makerspace, but ultimately saw participants finding value in new perspectives and skills.
Central Bank Digital Currency: research streams – a literature review

Each type of payment - whether it would be coins and notes, electronic bank transfer, or remittance via fractions of bitcoin - could lead to a considerable number of benefits, as well, could possess a substantial number of risks or even threats to the current financial system and stability. The relatively recent concept of payment - cryptocurrencies, especially central bank digital currencies (CBDCs) - from a historical point of view, remains a mystery with only a few central banks to date launching these particular digital currencies. Since fundamental decisions in the development of CBDCs are taking place nowadays, it highlights the importance of a thorough review of already existing theoretical research with a view to determining various assessments and blind spots, before introducing CBDCs in the wider markets and usage. The scientific literature about CBDC was analysed using an extensive and systematic study of available research publications, their categorization, and summarizing. The authors offer a new glance toward a systemization of available scientific literature with a focus on CBDC. This novel suggestion of dividing research areas supports the idea of the end-to-end product life-cycle and comes into four different groups: motivations, design features, demands, and final impact on monetary sovereignty and the financial system as a whole. The unknown level of CBDC’s demand or expression of design features encourages researchers to consider various theoretical implications before CBDC will be creating major shifts in the banking industry or financial ecosystem.
Code‐mixed Hindi‐English text correction using fuzzy graph and word embedding

Abstract Interaction via social media involves frequent code‐mixed text, spelling errors and noisy elements, which creates a bottleneck in the performance of natural language processing applications. This proposed work is the first approach for code‐mixed Hindi‐English social media text that comprises language identification, detection and correction of non‐word (Out of Vocabulary) errors as well as real‐word errors occurring simultaneously. Each identified language (Devanagari Hindi, Roman Hindi, and English) has its own complexities and challenges. Errors are detected individually for each language and a suggestive list of the erroneous words is created. After this, a fuzzy graph between different words of the suggestive lists is generated using various semantic relations in Hindi WordNet. Word embeddings and Fuzzy graph‐based centrality measures are used to find the correct word. Several experiments are performed on different social media datasets taken from Instagram, Twitter, YouTube comments, Blogs, and WhatsApp. The experimental results demonstrate that the proposed system corrects out‐of‐vocabulary words as well as real‐word errors with a maximum recall of 0.90 and 0.67, respectively for Dev_Hindi and 0.87 and 0.66, respectively for Rom_Hindi. The proposed method is also applied for state‐of‐art sentiment analysis approaches where the F1‐score has been visibly improved.
Mitigating Software Integrity Attacks With Trusted Computing in a Time Distribution Network

Time Distribution Networks (TDNs) evolve as new technologies occur to ensure more accurate, reliable, and secure timing information. These networks typically exploit several distributed time servers, organized in a master-slave architecture, that communicate via dedicated timing protocols. From the security perspective, timing data must be protected since its modification or filtering can lead to grave consequences in different time-based contexts, such as health, energy, finance, or transportation. Thus, adequate countermeasures must be employed in all the stages and systems handling timing data from its calculation until it reaches the final users. We consider a TDN offering highly accurate (nanosecond level) time synchronization through specific time unit devices that exploit terrestrial atomic or rubidium clocks and Global Navigation Satellite Systems (GNSS) receivers. Such devices are appealing targets for attackers, who might exploit various attack vectors to compromise their functionality. We individuate three possible software integrity attacks against time devices, and we propose a solution to counter them by exploiting the cryptographic Trusted Platform Module (TPM), defined and supported by the Trusted Computing Group. We used remote attestation software for cloud environments, namely the Keylime framework, to verify (periodically) the software daemons running on the time devices (or their configuration) from a trusted node. Experiments performed on a dedicated testbed set up in the ROOT project with customized time unit devices from Seven Solutions (currently Orolia Spain) allow us to demonstrate that exploiting TPMs and remote attestation in the TDNs is not only helpful but is fundamental for discovering some attacks that would remain otherwise undetected. Our work helps thus TDN operators build more robust, accurate, and secure time synchronization services.
Multi-Tasking for Aspect-Based Sentiment Analysis via Constructing Auxiliary Self-Supervision ACOP Task

Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. In aspect-based sentiment analysis, the semantic understanding of sentences is critical. The previous approach had to design modules to improve the understanding of sentence semantics. However, We adopt a multi-task approach to learn sentence semantics to avoid design modules. Thus we propose the Aspect Word and Context Order Prediction Task (ACOP) as an auxiliary task. We implement the ACOP task with the global and local way for aspect-based sentiment analysis and adopt the self-supervised method to train our model. Our model improves accuracy and F1 values over the best baseline model on the Rest14 dataset by 1.96% and 1.76%, on the Lap14 dataset by 0.24% and 0.24%, and on the Twitter dataset by 1.45% and 2.03%. Our experiments on three public datasets demonstrate that our approach is effective and that using a multi-task approach is a good choice instead of designing corresponding modules to extract semantic features.
Recognition of Human Chef’s Intentions for Incremental Learning of Cookbook by Robotic Salad Chef

Robotic chefs are a promising technology that can bring sizeable health and economic benefits when deployed ubiquitously.This deployment is hindered by the costly process of programming the robots to cook specific dishes while humans learn from observation or freely available videos.In this paper, we propose an algorithm that incrementally adds recipes to the robot's cookbook based on the visual observation of a human chef, enabling the easier and cheaper deployment of robotic chefs.A new recipe is added only if the current observation is substantially different than all recipes in the cookbook, which is decided by computing the similarity between the vectorizations of these two.The algorithm correctly recognizes known recipes in 93% of the demonstrations and successfully learned new recipes when shown, using off-the-shelf neural networks for computer vision.We show that videos and demonstrations are viable sources of data for robotic chef programming when extended to massive publicly available data sources like YouTube.