Reviewerly/exHarmony huggingface dataset:
Hierarchical matching and reasoning for Multi-Query Image Retrieval

Authors:
raw_author_name: Zhong Ji
raw_affiliation_string: School of Electrical and Information Engineering, Tianjin Key Laboratory of Brain-inspired Intelligence Technology, Tianjin University, Tianjin, 300072, China; Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China
raw_author_name: Yan Zhang
raw_affiliation_string: School of Electrical and Information Engineering, Tianjin Key Laboratory of Brain-inspired Intelligence Technology, Tianjin University, Tianjin, 300072, China
raw_author_name: Haoran Wang
raw_affiliation_string: Baidu Research, Beijing, 100193, China
raw_author_name: Yanwei Pang
raw_affiliation_string: School of Electrical and Information Engineering, Tianjin Key Laboratory of Brain-inspired Intelligence Technology, Tianjin University, Tianjin, 300072, China; Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China
raw_author_name: Xuelong Li
raw_affiliation_string: Key Laboratory of Intelligent Interaction and Applications, Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi’an, 710072, China; School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), China

Abstract: As a promising field, Multi-Query Image Retrieval (MQIR) aims at searching for the semantically relevant image given multiple region-specific text queries. Existing works mainly focus on a single-level similarity between image regions and text queries, which neglect the hierarchical guidance of multi-level similarities and result in incomplete alignments. Besides, the high-level semantic correlations that intrinsically connect different region-query pairs are rarely considered. To address above limitations, we propose a novel Hierarchical Matching and Reasoning Network (HMRN) for MQIR. It disentangles MQIR into three hierarchical semantic representations, which is responsible to capture fine-grained local details, contextual global scopes, and high-level inherent correlations. HMRN consists of two modules: Scalar-based Matching (SM) module and Vector-based Reasoning (VR) module. Specifically, the SM module characterizes the multi-level alignment similarity, which consists of a fine-grained local-level similarity and a context-aware global-level similarity. Afterwards, the VR module is developed to excavate the potential semantic correlations among multiple region-query pairs, which further explores the high-level reasoning similarity. Finally, these three-level similarities are aggregated into a joint similarity space to form the ultimate similarity. Extensive experiments on the benchmark dataset demonstrate that our HMRN substantially surpasses the current state-of-the-art methods. For instance, compared with the existing best method Drill-down, the metric R@1 in the last round is improved by 23.4%. Our source codes will be released at https://github.com/LZH-053/HMRN.
A Structured Sentiment Analysis Dataset Based on Public Comments from Various Domains

Authors:
raw_author_name: Zhongliang Wei
raw_affiliation_string: School of Computer Science and Engineering, Anhui University of Science & Technology, Huainan, China
raw_author_name: Shunxiang Zhang
raw_affiliation_string: School of Computer Science and Engineering, Anhui University of Science & Technology, Huainan, China

Abstract: A structured sentiment analysis dataset, derived from social media comments, is introduced in this paper. The dataset spans 22 diverse domains and comprises over 200,000 reviews, providing a rich resource for sentiment analysis tasks in the Chinese language context. Each comment within the dataset has been manually annotated with a sentiment label, either positive, negative, or neutral, and grouped by topic. This meticulous annotation process ensures the dataset's reliability for training, validating, and testing sentiment analysis models. The construction of the dataset involved a three-step process. Initially, data was collected from the topics that garnered high attention and discussion rates, thereby reflecting the authentic opinions of users. Following data collection, preprocessing was undertaken to remove extraneous elements, while preserving emoticons that are crucial for sentiment analysis. The final step involved manual annotation by researchers, who assigned sentiment labels to each comment based on various factors. The dataset stands as a valuable contribution to the field of natural language processing, particularly for sentiment analysis tasks in the Chinese language context.
A Generative Method for a Laryngeal Biosignal

Authors:
raw_author_name: Mahdi Darvish
raw_affiliation_string: Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
raw_author_name: Andreas M. Kist
raw_affiliation_string: Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany. Electronic address: andreas.kist@fau.de.

Abstract: The Glottal Area Waveform (GAW) is an important component in quantitative clinical voice assessment, providing valuable insights into vocal fold function. In this study, we introduce a novel method employing Variational Autoencoders (VAEs) to generate synthetic GAWs. Our approach enables the creation of synthetic GAWs that closely replicate real-world data, offering a versatile tool for researchers and clinicians. We elucidate the process of manipulating the VAE latent space using the Glottal Opening Vector (GlOVe). The GlOVe allows precise control over the synthetic closure and opening of the vocal folds. By utilizing the GlOVe, we generate synthetic laryngeal biosignals. These biosignals accurately reflect vocal fold behavior, allowing for the emulation of realistic glottal opening changes. This manipulation extends to the introduction of arbitrary oscillations in the vocal folds, closely resembling real vocal fold oscillations. The range of factor coefficient values enables the generation of diverse biosignals with varying frequencies and amplitudes. Our results demonstrate that this approach yields highly accurate laryngeal biosignals, with the Normalized Mean Absolute Error values for various frequencies ranging from 9.6 ⋅ 10-3 to 1.20 ⋅ 10-2 for different experimented frequencies, alongside a remarkable training effectiveness, reflected in reductions of up to approximately 89.52% in key loss components. This proposed method may have implications for downstream speech synthesis and phonetics research, offering the potential for advanced and natural-sounding speech technologies.
Smart home system using blockchain technology in green lighting environment in rural areas

Authors:
raw_author_name: Ying Huang
raw_affiliation_string: College of Art & Design, Putian University, Fujian, China.

Abstract: Currently, with the rapid development of smart home technology, the demand for establishing efficient and sustainable smart home systems in rural areas is increasing. However, in rural environments, the effective management and intelligent control of green energy face many challenges. To address these issues, this work aims to design a smart home system based on blockchain technology to achieve efficient energy management and intelligent control in a green lighting environment in rural areas. The main goals include improving the performance and safety of the system to meet the lighting needs of rural areas and promote sustainable development. The system comprises two primary components: the home gateway and cloud services. These components encompass functions like data monitoring and transmission, cloud storage, and remote control. The work also introduces the structural interaction, user node interaction, and the data security transmission scheme of the smart home system. Ultimately, the system's effectiveness is confirmed through simulation experiments. The results demonstrate that the system achieves the lowest latency when the transaction arrival rate is 40tps and the block size is 10. Additionally, the access control scheme based on the Hyperledger Fabric consortium chain can efficiently handle access requests for smart home resources and meet the practical application requirements within an appropriate range of security parameters. The main research conclusion is that the designed smart home system based on blockchain technology has achieved significant results in improving performance and security. This not only provides reliable lighting solutions for rural areas, but also provides important theoretical and practical guidance for the future development of smart home systems. The direction of future work includes further optimizing system performance, expanding the scope of application, and exploring more advanced blockchain technology applications in the field of smart homes. This will provide more possibilities and innovative directions for the development of future smart home systems.
Reimagining learning through AI art: the promise of DALL-E and MidJourney for education and libraries

Authors:
raw_author_name: Adebowale Jeremy Adetayo

Abstract: Purpose This paper aims to explore the potential impact of artificial intelligence (AI) image generators, specifically MidJourney and DALL-E, on education and library services. The study aims to understand how these tools can revolutionize learning experiences and library resources while also addressing the ethical considerations surrounding their use. Design/methodology/approach This study investigates the technical foundations of MidJourney and DALL-E, highlighting their neural network architectures. It also traces the iterative refinement of these models and examines cost, accessibility and the unique prompt-guided capabilities of DALL-E 3. Findings MidJourney and DALL-E show remarkable progress in generating high-quality, photorealistic images from text prompts. The iterative refinement of these models demonstrates a trend toward improved creative output and user accessibility. DALL-E 3, in particular, allows users to guide image generation through prompt modifications, offering unprecedented control over the creative process. The study identifies potential applications in personalized learning, visual communication and research support in libraries, while recognizing challenges such as cost and accessibility. Originality/value This research innovatively explores AI's impact on education and libraries, detailing applications in personalized learning and research while addressing legal and ethical considerations.
Usability Testing of Mobile Applications: A Methodological Framework

Authors:
raw_author_name: Paweł Weichbroth
raw_affiliation_string: Department of Software Engineering, Faculty of Electronics, Gdansk University of Technology, 80-233 Gdansk, Poland

Abstract: Less than five percent of all mobile applications have become successful throughout 2023. The success of a new mobile application depends on a variety of factors ranging from business understanding, customer value, and perceived quality of use. In this sense, the topic of usability testing of mobile applications is relevant from the point of view of user satisfaction and acceptance. However, the current knowledge seems to be fragmented, scattered across many papers and reports, and sometimes poorly documented. This paper attempts to fill this gap by investigating the current state of knowledge by reviewing the previous literature relevant to the research topic and developing a unified view. In particular, the methodological framework is outlined and discussed, including the discourse on settings for laboratory and field studies, data collection techniques, experimental designs for mobile usability testing, and a generic research framework. Therefore, the paper contributes to both the theory and practice of human–computer interaction by providing methodological foundations for usability testing of mobile applications, paving the way for further studies in this area. Moreover, the paper provides a better understanding of the related topics, in particular shedding light on methodological foundations, key concepts, challenges, and issues, equipping readers with a comprehensive knowledge base to navigate and contribute to the advancement of the field of mobile usability.
Flexible preparation of the W state via the GRAPE algorithm in a nanoring-quantum-dot system

Authors:
raw_author_name: Zhen Chen
raw_author_name: Zhi-Cheng Shi
raw_author_name: Jie Song
raw_author_name: Bi-Hua Huang
raw_author_name: Yan Xia

Abstract: Abstract In this paper, we put forward a scheme for flexibly preparing W states in the presence of detunings and asymmetric couplings in a nanoring-quantum-dot (nanoring-QD) system. Based on the gradient ascent pulse engineering algorithm, the coupling strength modulation (CSM) and the energy level modulation (ELM) methods are proposed, respectively. In the CSM method, the waveforms of the coupling strengths are properly designed to compensate for detunings, thus the decline on the fidelity is effectively suppressed. For the asymmetric couplings, a high-fidelity W state can be generated by the ELM method, which alters the energy levels of QDs in a desired manner. Finally, we generalize the current scheme to realize the preparation of N -particle W states in nanoring-QD systems.
A quantum blind signature scheme based on dense coding for non-entangled states

Authors:
raw_author_name: Ke Xing
raw_author_name: Ai-Han Yin
raw_author_name: Yong-Qi Xue

Abstract: Abstract Some schemes where quantum blind signatures require the use of difficult-to-prepare multiparticle entangled states. Considering the communication overhead, quantum operation complexity, verification efficiency and other relevant factors in practical situations, this article proposes a non entangled quantum blind signature scheme based on dense encoding.The information owner utilizes dense encoding and hash functions to blind the information while reducing the use of quantum resources.After receiving particles,the signer encrypts the message using a one-way function and performs a Hadamard gate operation on the selected single photon to generate the signature, and the verifier performs a Hadamard gate inverse operation on the signature and combines it with the encoding rules to restore the message and complete the verification. Compared with some typical quantum blind signature protocols, this protocol has strong blindness in privacy protection, higher flexibility in scalability and application. The signer can adjust the signature operation according to the actual situation, which greatly simplifies the complexity of the signature.Simultaneously utilizing the secondary distribution and rearrangement of non entangled quantum states, a non entangled quantum state representation of three bits of classical information was achieved,reducing the use of a large amount of quantum resources and lowering implementation costs. This improves both signature verification efficiency and communication efficiency, while at the same time, this scheme meets the requirements of unforgeability, non repudiation, and prevention of information leakage.
Hybrid deep learning models for time series forecasting of solar power

Authors:
raw_author_name: Cem Direkoglu
raw_author_name: Mehmet Kusaf

Abstract: Abstract Forecasting solar power production accurately is critical for effectively planning and managing renewable energy systems. This paper introduces and investigates novel hybrid deep learning models for solar power forecasting using time series data. The research analyzes the efficacy of various models for capturing the complex patterns present in solar power data. In this study, all of the possible combinations of convolutional neural network (CNN), long short-term memory (LSTM), and transformer (TF) models are experimented. These hybrid models also compared with the single CNN, LSTM and TF models with respect to different kinds of optimizers. Three different evaluation metrics are also employed for performance analysis. Results show that the CNN–LSTM–TF hybrid model outperforms the other models, with a mean absolute error (MAE) of 0.551% when using the Nadam optimizer. However, the TF–LSTM model has relatively low performance, with an MAE of 16.17%, highlighting the difficulties in making reliable predictions of solar power. This result provides valuable insights for optimizing and planning renewable energy systems, highlighting the significance of selecting appropriate models and optimizers for accurate solar power forecasting. This is the first time such a comprehensive work presented that also involves transformer networks in hybrid models for solar power forecasting.
Absolute-separability witnesses for symmetric multiqubit states

Authors:
raw_author_name: Eduardo Serrano-Ensástiga
raw_affiliation_string: Atomique et de Spectroscopie, CESAM, University of Liège B-4000 Liège, Belgium; Institut de Physique Nucléaire,
raw_author_name: Jérôme Denis
raw_affiliation_string: Atomique et de Spectroscopie, CESAM, University of Liège B-4000 Liège, Belgium; Institut de Physique Nucléaire,
raw_author_name: John Martin
raw_affiliation_string: Atomique et de Spectroscopie, CESAM, University of Liège B-4000 Liège, Belgium; Institut de Physique Nucléaire,

Abstract: The persistent separability of certain quantum states, known as symmetric absolutely separable (SAS), under symmetry-preserving global unitary transformations is of key significance in the context of quantum resources for bosonic systems.In this work, we develop criteria for detecting SAS states of any number of qubits.Our approach is based on the Glauber-Sudarshan P representation for finite-dimensional quantum systems.We introduce three families of SAS witnesses, one linear and two nonlinear in the eigenvalues of the state, formulated respectively as an algebraic inequality or a quadratic optimization problem.These witnesses are capable of identifying more SAS states than previously known counterparts.We also explore the geometric properties of the subsets of SAS states detected by our witnesses, shedding light on their distinctions.
Blockchain Traceability Adoption in Low-Carbon Supply Chains: An Evolutionary Game Analysis

Authors:
raw_author_name: Chen Zhang
raw_affiliation_string: School of Management, Harbin University of Commerce, Harbin 150028, China
raw_author_name: Yaoqun Xu
raw_affiliation_string: Institute of Systems Engineering, Harbin University of Commerce, Harbin 150028, China
raw_author_name: Yi Zheng
raw_affiliation_string: School of Computer and Information Engineering, Harbin University of Commerce, Harbin 150028, China

Abstract: Blockchain technology has brought innovation to supply chain management, particularly in managing carbon emissions in the manufacturing sector. However, there is a research gap regarding the policy tools and the role of local governments in implementing blockchain technology to achieve carbon emissions traceability. Additionally, the strategic relationships and policy implications resulting from the implementation of blockchain technology are not examined systematically. An effective method for examining the strategies used in interactions between supply chain stakeholders and governments is evolutionary game theory, or EGT. This paper employs mathematical modelling and MATLAB 2016 software simulation to examine the decision-making process of manufacturing companies when considering implementing blockchain technology traceability. Specifically, the subjects in the model include product manufacturers (PM), product suppliers (PS), and local governments (LGs). The aim is to examine the decision-making behavior of carbon traceability participants in blockchain technology. This paper analyses the most effective blockchain-based traceability strategies for low-carbon supply chain members under a variety of scenarios by modifying the parameters. The findings suggest the following: (1) Manufacturers and suppliers need to manage the cost of blockchain traceability, collaborate to create an environmentally friendly product certification system, and improve brand image. (2) Local governments should set up efficient reward and punishment systems to incentivize supply chain stakeholders to engage in the blockchain traceability system. The aforementioned discoveries furnish policymakers with guidance to encourage the implementation of blockchain-based carbon footprint traceability technology, thereby establishing a transparent carbon footprint traceability framework across the entire supply chain.
Accelerating Quantum Algorithms with Precomputation

Authors:
raw_author_name: William J. Huggins
raw_affiliation_string: Google Quantum AI, Venice, CA, USA
raw_author_name: Jarrod R. McClean
raw_affiliation_string: Google Quantum AI, Venice, CA, USA

Abstract: Real-world applications of computing can be extremely time-sensitive. It would be valuable if we could accelerate such tasks by performing some of the work ahead of time. Motivated by this, we propose a cost model for quantum algorithms that allows quantum precomputation; i.e., for a polynomial amount of ``free&amp;apos;&amp;apos; computation before the input to an algorithm is fully specified, and methods for taking advantage of it. We analyze two families of unitaries that are asymptotically more efficient to implement in this cost model than in the standard one. The first example of quantum precomputation, based on density matrix exponentiation, could offer an exponential advantage under certain conditions. The second example uses a variant of gate teleportation to achieve a quadratic advantage when compared with implementing the unitaries directly. These examples hint that quantum precomputation may offer a new arena in which to seek quantum advantage.
MedLingua: A Conceptual Framework for a Multilingual Medical Conversational Agent

Authors:
raw_author_name: Galib Muhammad Shahriar Himel
raw_affiliation_string: Department of Computer Science, American International University-Bangladesh, Dhaka, Bangladesh.; School of Computer Sciences, Universiti Sains Malaysia, 11800 USM Penang, Malaysia.

Abstract: This study introduces a hybrid model for an advanced medical chatbot addressing crucial healthcare communication challenges. Leveraging a hybrid ML model, the chatbot aims to provide accurate and prompt responses to users' health-related queries. The proposed model will overcome limitations observed in previous medical chatbots by integrating a dual-stemming approach, P-Stemmer and NLTK-Stemmer, accommodating both semitic and non-semitic languages. The system prioritizes the analysis of cognates, identification of symptoms, doctor recommendations, and prescription generation. It integrates an automatic translation module to facilitate a smooth multilingual diagnostic experience. Following the Scrum methodology for agile development, the framework ensures adaptability to evolving research needs and stays current with recent medical discoveries. This groundbreaking idea aims to improve the effectiveness and availability of healthcare services by introducing an intelligent, multilingual chatbot. This technology enables patients to communicate with doctors from diverse linguistic backgrounds through an automated language translation model, eliminating language barriers and extending healthcare access to rural regions worldwide.•A simple but efficient hybrid conceptual model for advancement in smart medical assistance.•This conceptual model can be applied to implement a medical chatbot that can understand multiple languages.•This method can be utilized to address medical chatbot limitations and enhance accuracy in response generation.