Reviewerly/exHarmony huggingface dataset:
Word and character segmentation in ancient handwritten documents in Devanagari and Maithili scripts using horizontal zoning

The word and character segmentation in ancient handwritten Devanagari (Indian) and Maithili (Indian) documents in order to develop any optical character recognition (OCR) systems for the ancient documents are still considered as challenging tasks in the field of document analysis and recognition. The presence of horizontal lines over each character, variable spacing between two adjacent characters or words, and text skewness in these two scripts make these tasks more challenging. This article proposes a novel method to segment any textline of ancient handwritten Devanagari and Maithili documents into different words and each word into different characters by dividing the textline into three different horizontal zones—upper, middle, and lower. The middle zone of the textline has been extracted by constructing a linear regression curve passing through the middle zone. The gaps between two adjacent characters or words present in the middle zone have then been utilized to segment one textline into various words as well as one word into various characters. The performance of the proposed method has been evaluated on the self-generated datasets of ancient handwritten textline images in Devanagari and Maithili scripts. The proposed method has achieved accuracies of 96.31% and 98.35% for the word segmentation and character segmentation respectively for ancient Devanagari documents. For ancient handwritten documents in Maithili script, accuracies of 97.39% and 98.65% have been obtained from the proposed method for word segmentation and character segmentation respectively. The experimental results demonstrate that the proposed word and character segmentation system outperforms the state-of-the-art methods in this regard.
Investigating the use experience of restaurant service robots: the cognitive–affective–behavioral framework

This study investigates the approach behavior of the respondents by using the cognitive–affective–behavioral framework in the context of restaurant service robots. An online survey was conducted among customers who had patronage experience visiting service robot restaurants in Taiwan. A total of 230 valid samples were employed for the final analysis using the maximum likelihood estimation method. The hypothesized relationships were tested using structural equation modeling. Furthermore, a multigroup analysis based on the visit experience (frequency of visit) and generation was conducted. Cognitive components such as competence and coolness positively influence affective components such as emotions and satisfaction. Approach behavior, the behavioral outcome, is positively influenced by emotions and satisfaction. Likewise, emotions positively influence satisfaction. This study highlights the sequential relationship in terms of cognitive, affective and behavioral perspectives in the decision-making process of customers after their encounter with service robots at restaurants.
When Criminals Abuse the Blockchain: Establishing Personal Jurisdiction in a Decentralised Environment

In August of 2022, the United States Department of Treasury sanctioned the virtual currency mixer Tornado Cash, an open-source and fully decentralised piece of software running on the Ethereum blockchain, subsequently leading to the arrest of one of its developers in the Netherlands. Not only was this the first time the Office of Foreign Assets Control (OFAC) extended its authority to sanction a foreign ‘person’ to software, but the decentralised nature of the software and global usage highlight the challenge of establishing jurisdiction over decentralised software and its global user base. The government claims jurisdiction over citizens, residents, and any assets that pass through the country’s territory. As a global financial center with most large tech companies, this often facilitates the establishment of jurisdiction over global conduct that passes through US servers. However, decentralised programs on blockchains with nodes located around the world challenge this traditional approach as either nearly all countries can claim jurisdiction over users, subjecting users to criminal laws in countries with which they have no true interaction, or they limit jurisdiction, thereby risking abuse by bad actors. This article takes a comparative approach to examine the challenges to establishing criminal jurisdiction on cryptocurrency-related crimes.
Predictive privacy: Collective data protection in the context of artificial intelligence and big data

Big data and artificial intelligence pose a new challenge for data protection as these techniques allow predictions to be made about third parties based on the anonymous data of many people. Examples of predicted information include purchasing power, gender, age, health, sexual orientation, ethnicity, etc. The basis for such applications of “predictive analytics” is the comparison between behavioral data (e.g. usage, tracking, or activity data) of the individual in question and the potentially anonymously processed data of many others using machine learning models or simpler statistical methods. The article starts by noting that predictive analytics has a significant potential to be abused, which manifests itself in the form of social inequality, discrimination, and exclusion. These potentials are not regulated by current data protection law in the EU; indeed, the use of anonymized mass data takes place in a largely unregulated space. Under the term “predictive privacy,” a data protection approach is presented that counters the risks of abuse of predictive analytics. A person's predictive privacy is violated when personal information about them is predicted without their knowledge and against their will based on the data of many other people. Predictive privacy is then formulated as a protected good and improvements to data protection with regard to the regulation of predictive analytics are proposed. Finally, the article points out that the goal of data protection in the context of predictive analytics is the regulation of “prediction power,” which is a new manifestation of informational power asymmetry between platform companies and society.
Context Attention Fusion Network for crowd counting

Context information, which plays a crucial role in many computer vision tasks, can benefit deep networks to construct better cognitive competence from more comprehensive surrounding. However, most existing crowd counting methods often overlook the importance of context information extracted from both global and local views. To address or alleviate this problem, this paper proposes a novel Context Attention Fusion Network, which is abbreviated as CAFNet for crowd counting. The core idea behind CAFNet is the interaction of multiple context information, including local context, cross-level context, and cross-layer context. To explore local context, we design a local context aggregation module to extract hierarchically local semantic information and then integrate them adaptively. To utilize cross-level context, a guidance attention fusion module is designed to fuse low-level feature map as the guidance of high-level context information so that the spatial details can be effectively compensated. To make full use of cross-layer features, a multi-layer context fusion module is developed to exchange the potentiality of multi-layer information to generate a high-resolution density map. Experimental results on four challenging datasets manifest that the newly proposed CAFNet can deliver impressive results compared with other state-of-the-art crowd counting models.
Identifying novelties and anomalies for incremental learning in streaming time series forecasting

Time series data can be defined as a chronological sequence of observations on a variable of interest. A streaming time series is a time series that arrives continuously at high speed and has a data distribution that may change over time. Streaming time series data usually comes from electronic devices such as sensors and many of the applications dealing with streaming data in Industry 4.0 require real-time responses. Performing real-time forecasting offers the possibility to consider new types of patterns in the incoming streaming data, which is not possible when working with batch models. This paper presents a new approach to detect novelties and anomalies in real-time using a nearest-neighbors based forecasting algorithm. The algorithm works with an offline base model that is updated as stream data arrives following an incremental learning approach. It detects unknown patterns called novelties and anomalies. Novelties are included in the model in an online way and anomalies trigger an alarm as they present unexpected behaviors that need to be specifically analyzed. The algorithm has been tested with Spanish electricity demand data. Results show that the prediction errors obtained when the model is updated considering novelties and anomalies are lower than the errors obtained when the model is not updated. Thus, the model adjusts in real-time to the new patterns of data providing accurate errors and real-time predictions.
A systematic review of value-aware recommender systems

Research on recommender systems (RSs) has traditionally focused on the design of systems capable of suggesting items of interest for users. However, often the most important expectation for RSs used in commercial applications is to improve the business performance of the organization. For this reason, alongside the growth of e-business, we have witnessed growing interest in value-aware RSs that, unlike traditional RSs, are designed to optimize the economic value of recommendations by considering the objectives of multiple stakeholders. In this paper, we provide a systematic literature review, following the PRISMA guidelines, specialized in value-aware RSs. We explore key commercial applications, main algorithms, value categories typically optimized, and the most commonly used datasets. Furthermore, we note limitations of the state-of-the-art approaches and identify future research directions.
Prompting Large Language Models With the Socratic Method

This paper presents a systematic approach to using the Socratic method in developing prompt templates that effectively interact with large language models, including GPT-3. Various methods are examined, and those that yield precise answers and justifications while fostering creativity and imagination to enhance creative writing are identified. Techniques such as definition, elenchus, dialectic, maieutics, generalization, and counterfactual reasoning are discussed for their application in engineering prompt templates and their connections to inductive, deductive, and abductive reasoning. Through examples, the effectiveness of these dialogue and reasoning methods is demonstrated. An interesting observation is made that when the task's goal and user intent are conveyed to GPT-3 via ChatGPT before the start of a dialogue, the large language model seems to connect to the external context expressed in the intent and perform more effectively.
Privacy-preserving Pedestrian Tracking using Distributed 3D LiDARs

The growing demand for intelligent environments unleashes an extraordinary cycle of privacy-aware applications that makes individuals' life more comfortable and safe. Examples of these applications include pedestrian tracking systems in large areas. Although the ubiquity of camera-based systems, they are not a preferable solution due to the vulnerability of leaking the privacy of pedestrians. In this paper, we introduce a novel privacy-preserving system for pedestrian tracking in smart environments using multiple distributed LiDARs of non-overlapping views. The system is designed to leverage LiDAR devices to track pedestrians in partially covered areas due to practical constraints, e.g., occlusion or cost. Therefore, the system uses the point cloud captured by different LiDARs to extract discriminative features that are used to train a metric learning model for pedestrian matching purposes. To boost the system's robustness, we leverage a probabilistic approach to model and adapt the dynamic mobility patterns of individuals and thus connect their sub-trajectories. We deployed the system in a large-scale testbed with 70 colorless LiDARs and conducted three different experiments. The evaluation result at the entrance hall confirms the system's ability to accurately track the pedestrians with a 0.98 F-measure even with zero-covered areas. This result highlights the promise of the proposed system as the next generation of privacy-preserving tracking means in smart environments.
Transmission Line Segmentation Solutions for UAV Aerial Photography Based on Improved UNet

The accurate and efficient detection of power lines and towers in aerial drone images with complex backgrounds is crucial for the safety of power grid operations and low-altitude drone flights. In this paper, we propose a new method that enhances the deep learning segmentation model UNet algorithm called TLSUNet. We enhance the UNet algorithm by using a lightweight backbone structure to extract the features and then reconstructing them with contextual information features. In this network model, to reduce its parameters and computational complexity, we adopt DFC-GhostNet (Dubbed Full Connected) as the backbone feature extraction network, which is composed of the DFC-GhostBottleneck structure and uses asymmetric convolution to capture long-distance targets in transmission lines, thus enhancing the model’s extraction capability. Additionally, we design a hybrid feature extraction module based on convolution and a transformer to refine deep semantic features and improve the model’s ability to locate towers and transmission lines in complex environments. Finally, we adopt the up-sampling operator CARAFE (Content-Aware Re-Assembly of FEature) to improve segmentation accuracy by enhancing target restoration using contextual neighborhood pixel information correlation under feature decoding. Our experiments on public aerial photography datasets demonstrate that the improved model requires only 8.3% of the original model’s computational effort and has only 21.4% of the original model’s parameters, while achieving a reduction in inference speed delay by 0.012 s. The segmentation metrics also showed significant improvements, with the mIOU improving from 79.75% to 86.46% and the mDice improving from 87.83% to 92.40%. These results confirm the effectiveness of our proposed method.
Let voice assistants sound like a machine: Voice and task type effects on perceived fluency, competence, and consumer attitude

Voice Assistants (VA) are increasingly penetrating consumers' daily lives. This study aimed to investigate the effects of synthetic vs. human voice on users' perception of the voice, social judgments of the VA, and attitudes towards VAs. Drawing from CASA(Computers-as-socialactors) framework and social perception literature, we developed a theoretical model that explains the psychological underlying mechanism of the voice effects. Through two online experiments, we rejected our initial hypotheses that human voice would increase users' perception and evaluation of the VAs. Instead, our findings support that the VAs were favored when they spoke with a synthetic voice only when the users engaged in functional tasks. There was no difference between the voices for social tasks. A further investigation revealed that participants perceived the synthetic voice to be more fluent when VA responds to functional tasks. This enhanced perception of fluency increased competence perception and attitudes. The findings imply that VAs should not be designed to closely resemble humans. Rather, consideration of usage contexts and consumer expectations should be prioritized in developing most likable VAs.
Secure Internet of Vehicles (IoV) With Decentralized Consensus Blockchain Mechanism

The Internet of vehicles (IoV) is increasingly being used to realize the vision of intelligent transportation systems with the rapid development of computation and communication technologies. However, numerous IoV applications rely on a central unit for storing and processing information and mediators for wireless transmission. This can lead to the leakage of sensitive data and high costs and delays. To address these issues and improve the efficiency of data storage, processing, and sharing in the IoV, we propose a vehicle-based secure blockchain consensus (VBSBC) algorithm. Our VBSBC algorithm overcomes the limitations and drawbacks of state-of-the-art approaches by leveraging blockchain technology and a consensus algorithm to ensure secure communication between vehicles. In addition, the algorithm includes an authentication process and a key distributing and request process, illustrated during vehicles' movement between different zones. In simulation results, our proposed VBSBC algorithm demonstrated high performance compared to existing approaches in terms of authentication delay, key processing time, attack detection rate, throughput, and packet loss rate.
A Diverse Large-Scale Building Dataset and a Novel Plug-and-Play Domain Generalization Method for Building Extraction

In this paper, we introduce a new building dataset and propose a novel domain generalization method to facilitate the development of building extraction from high-resolution remote sensing images. The problem with the current building datasets involves that they lack diversity to train a practical learning model with good generalization ability, and the quality of the labels is unsatisfactory. To address these issues, we built a diverse, large-scale, and high-quality building dataset named the WHU-Mix building dataset, which is more practice-oriented. The WHU-Mix building dataset consists of a training/validation set containing 43,727 diverse images collected from all over the world, and a test set containing 8402 images from five other cities on five continents. In addition, to further improve the generalization ability of a building extraction model, we propose a domain generalization method named batch style mixing (BSM), which can be embedded as an efficient plug-and-play module in the front-end of a building extraction model, providing the model with a progressively larger data distribution to learn data-invariant knowledge. The experiments conducted in this study confirmed the potential of the WHU-Mix building dataset to improve the performance of a building extraction model, resulting in a 6–36% improvement in mIoU, compared to the other existing datasets. The adverse impact of the inaccurate labels in the other datasets can cause about 20% IoU decrease. The experiments also confirmed the high performance of the proposed BSM module in enhancing the generalization ability and robustness of a model, exceeding the baseline model without domain generalization by 13% and the recent domain generalization methods by 4–15% in mIoU.
Automating the Machine Learning Process using PyCaret and Streamlit

Machine learning applications for the industry have seen significant growth and attention in recent years. As a result, there is a significant need for Machine learning engineers across the business, but increasing their productivity is still a major problem. For time-consuming Machine learning pipeline operations such as data pre-processing, feature engineering, model selection, hyperparameter optimization, and prediction result analysis, Automated Machine Learning (AutoML) has arisen as a solution. In this research, we examine the condition of the AutoML application, which aims to automate ML operations. We do multiple evaluations based on numerous datasets, in various data segments, to assess their functionality and compare the outcomes. Using Streamlit, the AutoML application is made to provide a user interface.
Prioritizing the multi-criterial features based on comparative approaches for enhancing security of IoT devices

The Internet of Things (IoT) technologies continuously expand with time due to the advances in automated, connected device technology, mobility, and wide access to information. IoT is the collection of many linked devices due to their unique features, such as scalability, maintainability, fault tolerance, reliability, accuracy, and much more. With the growing number of hybrid devices in large organizations, security and privacy concerns are becoming more challenging. Security is essential for protecting the hardware, network aspects of devices, and information access from unauthorized entities. Most of the security methods and procedures provided by researchers are based on existing Internet security practices. The top-ranking authentication feature categories included the most compatible and common authentication feature for all types of IoT-based devices as an elementary security requirement for protection from unauthorized access. The future challenge is to address the incompatibility of the authentication feature with IoT devices based on appropriate technologies. AI and machine learning are also implementable in order to detect the vulnerability of IoT devices and inform the concerned operator or administration for protection. This research highlights the authentication feature of IoT devices from literature studies and evaluates the significant feature using COPRAS approach to assist organization in enhancing security of IoT devices.
Robust Query Driven Cardinality Estimation under Changing Workloads

Query driven cardinality estimation models learn from a historical log of queries. They are lightweight, having low storage requirements, fast inference and training, and are easily adaptable for any kind of query. Unfortunately, such models can suffer unpredictably bad performance under workload drift, i.e., if the query pattern or data changes. This makes them unreliable and hard to deploy. We analyze the reasons why models become unpredictable due to workload drift, and introduce modifications to the query representation and neural network training techniques to make query-driven models robust to the effects of workload drift. First, we emulate workload drift in queries involving some unseen tables or columns by randomly masking out some table or column features during training. This forces the model to make predictions with missing query information, relying more on robust features based on up-to-date DBMS statistics that are useful even when query or data drift happens. Second, we introduce join bitmaps, which extends sampling-based features to be consistent across joins using ideas from sideways information passing. Finally, we show how both of these ideas can be adapted to handle data updates. We show significantly greater generalization than past works across different workloads and databases. For instance, a model trained with our techniques on a simple workload (JOBLight-train), with 40 k synthetically generated queries of at most 3 tables each, is able to generalize to the much more complex Join Order Benchmark, which include queries with up to 16 tables, and improve query runtimes by 2× over PostgreSQL. We show similar robustness results with data updates, and across other workloads. We discuss the situations where we expect, and see, improvements, as well as more challenging workload drift scenarios where these techniques do not improve much over PostgreSQL. However, even in the most challenging scenarios, our models never perform worse than PostgreSQL, while standard query driven models can get much worse than PostgreSQL.