Reviewerly/exHarmony huggingface dataset:
Secure and controllable data management mechanism for multi‐sensor fusion in internet of things

Abstract With the rapid development of Internet of Things (IoT) technology, the number of sensors in IoT and the data collected from them are increasing. If these data are not managed and stored directly, it will occupy a large amount of storage space. Therefore, studying the data management mechanism of multi‐sensor fusion in IoT is of great significance. Multi‐sensor fusion has achieved a series of successes in the field of data management. Based on this, this article proposes a secure and controllable data management mechanism for multi‐sensor fusion in IoT, including a multi‐sensor fusion model, compression of data using linear regression algorithm, and data storage based on sorted string table algorithm. Then this article analyzes the effectiveness of the model proposed for multi‐sensor fusion data management. Through comparative experiments under different data sets, it is verified that the management mechanism has higher data fusion rate, lower data lossy compression rate and higher data lossless compression rate under different data sets. And it has high data storage security, large reconstruction probability, strong management controllability, and can significantly save storage space. It is a superior secure and controllable data management mechanism for multi‐sensor fusion in IoT.
pySODM: Simulating and optimizing dynamical models in Python 3

In this work we present our generic framework to construct, simulate and calibrate dynamical systems in Python 3. Its goal is to reduce the time it takes to implement a dynamical system with n-dimensional states represented by coupled ordinary differential equations (ODEs), simulate the system deterministically or stochastically, and, calibrate the system using n-dimensional data. We demonstrate our code’s capabilities by building three models in the context of two case studies. First, we forecast the yields of the enzymatic esterification reaction of D-glucose and lauric acid, performed in a continuous-flow, packed-bed reactor. The model yields a satisfactory description of the reaction yields under different flow rates and can be applied to design a viable process. Second, we build a stochastic, age-stratified model to make forecasts on the evolution of influenza in Belgium during the 2017–2018 season. Using only limited data, our simple model was able to make a fairly accurate assessment of the future course of the epidemic. By presenting real-world case studies from two scientific disciplines, we demonstrate our code’s applicability across domains.
Examining the correlation between metrics in the Instagram social network to identify fake pages and improve marketing

Considering the increasing role of social networks in daily life and their role in developing businesses, paying close attention to each of them and their different dimensions is particularly important. As one of the social networks, Instagram has a global scope and an essential role in the field of interpersonal communication in various personal and professional dimensions. One of the significant problems in this platform is the existence of fake accounts with incorrect statistics. In this research, by analyzing the pages of Instagram users, the association between the metrics used to measure the performance of these pages has been investigated. For this purpose, the data of 1860 public pages on Instagram were collected as metrics of the number of followers, comments, likes, posts, followers, and the post's topic. The hypotheses test results show a statistically significant correlation between the number of posts and likes. However, there is no statistically significant association between the number of likes and comments, the number of followers and comments, and the number of followers and likes. The analysis of 100 verified Instagram pages shows that the proposed method can distinguish real from fake pages with 94% accuracy and should be considered in business marketing ads on Instagram. The comparison of results with other classification methods revealed the efficiency of the proposed method.
Development of Smart and Lean Pick-and-Place System Using EfficientDet-Lite for Custom Dataset

Object detection for a pick-and-place system has been widely acknowledged as a significant research area in the field of computer vision. The integration of AI and machine vision with pick-and-place operations should be made affordable for Small and Medium Enterprises (SMEs) so they can leverage this technology. Therefore, the aim of this study is to develop a smart and lean pick-and-place solution for custom workpieces, which requires minimal computational resources. In this study, we evaluate the effectiveness of illumination and batch size to improve the Average Precision (AP) and detection score of an EfficientDet-Lite model. The addition of 8% optimized bright Alpha3 images results in an increase of 7.5% in AP and a 6.3% increase in F1-score as compared to the control dataset. Using a training batch size of 4, the AP is significantly improved to 66.8% as compared to a batch size of 16 at 57.4%. The detection scores are improved to 80% with a low variance of 1.65 using a uniform 135-angle lamp and 0 illumination level. The pick-and-place solution is validated using Single-Shot Detector (SSD) MobileNet V2 Feature Pyramid Network (FPN) Lite. Our experimental results clearly show that the proposed method has an increase of 5.19% in AP compared to SSD MobileNet V2 FPNLite.
Validation and application of the human aspects of information security questionnaire for undergraduates: effects of gender, discipline and grade level

ABSTRACTIn recent years, information security has gained significant academic attention. The present research sought to examine shifts in undergraduates' Information Security Awareness (ISA) as they advanced in their academic pursuits. Comprising two distinct phases, the initial phase involved adapting the Human Aspects of Information Security Questionnaire (HAIS-Q) to fit the context of Chinese tertiary institutions, a process overseen by five eminent ISA specialists. The adapted instrument was then piloted with 647 students, utilising snowball and criterion sampling techniques. Construct validity was established through exploratory factor analysis, and the questionnaire's reliability was ascertained via Cronbach's alpha. The subsequent primary survey engaged 5148 undergraduates from three Chinese academic institutions to further validate the instrument and investigate its relevance to student cohorts. Distinctively, participants exhibited pronounced proficiency in the Attitude component, yet lagged in Behavioural aspects. Analytical outcomes highlighted discernible interactions between gender, academic progression, and the Knowledge, Attitude, and Behaviour (KAB) facets. Notably, female respondents consistently outperformed their male counterparts. However, a marked decline in ISA cognisance and comportment was observed as students ascended academically. The HAIS-Q stands as a reliable tool for evaluating ISA in tertiary education, offering fresh insights to the scholarly discourse on educational information security.KEYWORDS: HAIS-Qhigher educationinformation security awarenessknowledge-attitude-behaviour Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis research was financially supported by (1) Zhejiang Provincial Philosophy and Social Science Planning Project “Research on the Integration Model of Industry and Education in Higher Vocational Education in the Yangtze River Delta Region from the Perspective of Industrial Collaborative Agglomeration” [grant no 21NDJC308YBM]; (2) China's National Social Science Later-stage Project [grant no 21FJKB013].
Secure and Robust Demand Response Using Stackelberg Game Model and Energy Blockchain

Demand response (DR) has been studied widely in the smart grid literature, however, there is still a significant gap in approaches that address security, privacy, and robustness of settlement processes simultaneously. The need for security and robustness emerges as a vital property, as Internet of Things (IoT) devices become part of the smart grid; in the form of smart meters, home energy management systems (HEMSs), intelligent transformers, and so on. In this paper, we use energy blockchain to secure energy transactions among customers and the utility. In addition, we formulate a mixed-strategy stochastic game model to address uncertainties in DR contributions of agents and achieve optimal demand response decisions. This model utilizes the processing hardware of customers for block mining, stores customer DR agreements as distributed ledgers, and offers a smart contract and consensus algorithm for energy transaction validation. We use a real dataset of residential demand profiles and photovoltaic (PV) generation to validate the performance of the proposed scheme. The results show the impact of electric vehicle (EV) discharging and customer demand reduction on increasing the probability of successful block mining and improving customer profits. Moreover, the results demonstrate the security and robustness of our consensus algorithm for detecting malicious activities.
Self-supervised deep monocular visual odometry and depth estimation with observation variation

Recent research in deep learning has greatly advanced the development of monocular visual depth estimation and odometry. These learning-based methods predominantly leverage neural networks to extract prevalent high-dimensional features for regression-based estimation. However, the consistency of observations is still regarded as a core challenge that limits the generalization ability. We attribute this challenge to the significant inconsistency in feature levels due to changes in the environment or motion, collectively referred to as observation variation. To this end, we propose a novel monocular depth estimation and a pose estimation network with attention mechanism to recalibrate features for different environment. The depth and ego-motion estimation tasks are formulated by coupling together in an end-to-end reconstruction problem, and attention modules adaptively recalibrate specific feature responses. Furthermore, we propose a simple sliding window optimization without RNN-based architecture, which is introduced to overcome the movement speed variation and error accumulation. Finally, we collect an observation variation dataset with environmental and motion changes. Evaluations of depth and pose estimation demonstrate that our methods effectively overcomes these observation variation challenges and achieves better performance. Our project will be available at https://github.com/alikesierzhao/OV-SfmLearner.
Information Technology and Fintech Innovation and Its Effect on Indian Financial Services

Fintech is a supply of creativity and a quickly expanding financial industry, yet little is understood about how facts era facilitates to fintech innovation. We discovered that records technology performs a critical function in nearly all factors of business fashions, organizing resources and techniques to offer individualized monetary offerings to customers efficaciously. This paper highlights the fintech innovation as well as information technology and its effect on Indian finance services. Fintech enables users to conduct transactions in a digital format that is more secure. Fintech services have the advantage of lowering operating expenses and making the customer experience more pleasant. India's fintech industry is the world's fastest expanding. Fintech services will alter the Indian financial sector's habits and behaviors.
The Task Allocation to Virtual Machines on Dynamic Load Balancing in Cloud Environments

In cloud computing, a number of resources are accessible to handle incoming requests. A number of VM are overloaded, and a number of VM are underloaded or inactive for task processing due to the ad hoc appearance of requests for task execution. By ensuring that all cloud resources are used with the help of an effective load balancing strategy, we can boost performance. Virtual machine allocation has drawn a lot of attention as one of the most important issues in cloud computing. In order to maximise resource use, we aim to multidimensionally load balance all the physical computers in the cloud computing platform described in this chapter. Numerous meta-heuristic techniques have been developed to address the NP-hard problem of cloud load balancing. In this study, Ant Colony Optimisation (ACO), a cloud load balancing strategy inspired by Ant Systems, is presented. We extensively simulate our proposed approach to show that it can successfully perform load balancing in VM allocation and enhance resource utilisation for the cloud computing.
Domain-specific chatbots for science using embeddings

We demonstrate how large language models (LLMs) can be adapted to domain-specific science topics by connecting them to a corpus of trusted documents.
Investigating the Failure of the Blockchain Technology and Suggested Recommendations

The present study endeavors to undertake a comprehensive investigation of the shortcomings of Blockchain technology, while assessing its advantages and drawbacks. Blockchain is a type of Distributed Ledger Technology (DLT) that relies on a network of peers to achieve consensus. Furthermore, other DLTs with similar functions might offer more viable solutions to replace Blockchain. One of these alternatives is Hashgraph, which employs a unique mechanism to manage distributed ledgers, claiming to be faster, more secure, and impartial. As a result, a thorough comparative analysis was conducted between these two technologies to determine which would emerge as the dominant technology in the market. The fundamental features of DLTs include decentralization, security, transparency, and integrity, albeit not all DLTs possess the same structures or functionalities.
Towards reliable multi-person pose estimation using Conditional Random Fields

Multi-person pose estimation is the task of estimating the coordinates of body joints and predicting the body poses for multiple people in an images. This problem has made breakthroughs in recent years, but the solutions still suffer from some shortcomings. A serious weakness of the state-of-the-art models is the number of poses detected by these models which is generally much larger than the actual number of human instances in the input image. This makes the existing models unreliable and thus unusable in real-world tasks. In this paper, we propose a more reliable multi-person pose estimation method consisting of three main blocks: a top-down multi-person pose estimation, a human detection, and a pose selection block. The proposed method incorporates the bounding box of the segmented objects to select the best subset of the initial pose set. We formulate the pose selection problem using Conditional Random Fields. First, we introduce a set of potential functions to form a general probability model. Then an inference algorithm is proposed to select the best poses which maximize the probability function. Finally, the proposed solution is implemented by a neural network. The proposed pose selection model is a model-agnostic method that can be easily used in conjunction with other pose estimation and object detection models. Experiments demonstrate that the reliability and precision of the proposed model are higher than those of the state-of-the-art models.
Speech and language processing with deep learning for dementia diagnosis: A systematic review

Dementia is a progressive neurodegenerative disease that burdens the person living with the disease, their families, and medical and social services. Timely diagnosis of dementia could be followed by introducing interventions that may slow down its progression or reduce its burdens. However, the diagnostic process of dementia is often complex and resource intensive. Access to diagnostic services is also an issue in low and middle-income countries. The abundance and easy accessibility of speech and language data have created new possibilities for utilizing Deep Learning (DL) technologies to be part of the dementia diagnostic process. This systematic review included studies published between 2012-2022 that utilized such technologies to aid in diagnosing dementia. We identified 72 studies using the PRISMA 2020 protocol, extracted and analyzed data from these studies and reported the related DL technologies. We found these technologies effectively differentiated between healthy individuals and those with a dementia diagnosis, highlighting their potential in the diagnosis of dementia. This systematic review provides insights into the contributions of DL-based speech and language techniques to support the dementia diagnostic process. It also offers an understanding of the advancements made in this field thus far and highlights some challenges that still need to be addressed.
Exploring the feasibility of fine-tuning large-scale speech recognition models for domain-specific applications: A case study on Whisper model and KsponSpeech dataset

This study investigates the fine-tuning of large-scale Automatic Speech Recognition (ASR) models, specifically OpenAI's Whisper model, for domain-specific applications using the KsponSpeech dataset. The primary research questions address the effectiveness of targeted lexical item emphasis during fine-tuning, its impact on domain-specific performance, and whether the fine-tuned model can maintain generalization capabilities across different languages and environments. Experiments were conducted using two fine-tuning datasets: Set A, a small subset emphasizing specific lexical items, and Set B, consisting of the entire KsponSpeech dataset. Results showed that fine-tuning with targeted lexical items increased recognition accuracy and improved domain-specific performance, with generalization capabilities maintained when fine-tuned with a smaller dataset. For noisier environments, a trade-off between specificity and generalization capabilities was observed. This study highlights the potential of fine-tuning using minimal domain-specific data to achieve satisfactory results, emphasizing the importance of balancing specialization and generalization for ASR models. Future research could explore different fine-tuning strategies and novel technologies such as prompting to further enhance large-scale ASR models' domain-specific performance.
An Unbounded Fully Homomorphic Encryption Scheme Based on Ideal Lattices and Chinese Remainder Theorem

We propose an unbounded fully homomorphic encryption scheme, i.e. a scheme that allows one to compute on encrypted data for any desired functions without needing to decrypt the data or knowing the decryption keys. This is a rational solution to an old problem proposed by Rivest, Adleman, and Dertouzos [1] in 1978, and to some new problems that appeared in Peikert [2] as open questions 10 and open questions 11 a few years ago. Our scheme is completely different from the breakthrough work [3] of Gentry in 2009. Gentry's bootstrapping technique constructs a fully homomorphic encryption (FHE) scheme from a somewhat homomorphic one that is powerful enough to evaluate its own decryption function. To date, it remains the only known way of obtaining unbounded FHE. Our construction of an unbounded FHE scheme is straightforward and can handle unbounded homomorphic computation on any refreshed ciphertexts without bootstrapping transformation technique.
A dense station-based, long-term and high-accuracy dataset of daily surface solar radiation in China

Abstract. The lack of long-term and high-quality solar radiation data has been an obstacle for scientific and industrial fields. In this study, a dense station-based, long-term and high-accuracy dataset of daily surface solar radiation was developed using two surface radiation models. One is the model developed by Yang et al. (2006) for global radiation estimation, and the other is the model developed by Tang et al. (2018) for direct radiation estimation. The main inputs for the development of the dataset are surface pressure, air temperature, relative humidity, horizontal visibility and sunshine duration, which are the routine meteorological variables observed at the 2743 China Meteorological Administration (CMA) weather stations. Validation against in situ observations and comparisons with two satellite-based radiation products shows that our station-based radiation dataset clearly outperforms the satellite-based radiation products at both daily and monthly scales. In addition, our dataset is available for more than 60 years and includes three radiation components of global, direct and diffuse radiation, which is not possible with satellite products. This station-based radiation dataset will contribute to the climate change research and solar energy engineering applications in the future. The station-based dataset is now available at https://doi.org/10.11888/Atmos.tpdc.300461 (Tang, 2023).
Implementasi Algoritma Apriori untuk Menentukan Pola Transaksi Penjualan Berbasis Web

The Apriori algorithm is an algorithm that is well known for searching frequent itemsets using the association rule technique. The calculation of the Apriori algorithm uses minimal support and minimal confidence to determine the limit for calculating goods. The a priori algorithm functions to determine the pattern of sales of goods that are often purchased together by customers. The history of sales transactions owned by a store can be calculated for its frequent itemset pattern by using an a priori algorithm so that customers can find patterns of items that are often purchased simultaneously by customers. Therefore, the a priori algorithm is very important to be used by shop owners because it can determine sales strategies and the placement of goods that are often purchased simultaneously by customers. In this study, the authors succeeded in calculating a sales transaction by determining a minimum support limit of 10% and a minimum confidence of 10%. With the minimum support and minimum confidence that has been set by the author to see the results of the a priori algorithm for sales, then the results of 2 combinations of itemsets that meet the calculation requirements are obtained.
Correlation between fractal characteristics of fault structures and metallogenic density in the Nanling area, South China

As fault structures exert a significant effect on metallogenic processes and mineralization, their proper characterization can assist in the determination of target areas for exploration. In this study, we apply the fractal theory on the interpretation of remote sensing images of the Nanling area, South China, to determine the relationship between fault structures and regional mineralization. Our results show that the fractal dimension (D) of fault structures in the Nanling area is 1.72, which is much higher than the regional average (1.35) for southern China. This difference reflects a strong geological tectonic activity in this region. Besides, we find that well-developed faults have a complex spatial distribution, and therefore this region is characterized by favorable metallogenic conditions with good prospecting prospects. Our results indicate the presence of a close relationship between the fractal dimension (D) and the spatial distribution of mineral deposits. Specifically, a higher D value generally corresponds to a dense distribution of ore deposits. Finally, based on regional structural and metallogenic characteristics as well as spatial coupling relationships, two target mineralization areas are identified as potential candidates for further exploration.