Reviewerly/exHarmony huggingface dataset:
A Systematic Literature Review on the Application of Automation in Logistics

Background: in recent years, automation has emerged as a hot topic, showcasing its capacity to perform tasks independently, without constant supervision. While automation has witnessed substantial growth in various sectors like engineering and medicine, the logistics industry has yet to witness an equivalent surge in research and implementation. Therefore, it becomes imperative to explore the application of automation in logistics. Methods: this article aims to provide a systematic analysis of the scientific literature concerning artificial intelligence (AI) and automation in logistics, laying the groundwork for robust and relevant advancements in the field. Results: the foundation of automation lies in cutting-edge technologies such as AI, machine learning, and deep learning, enabling self-problem resolution and autonomous task execution, reducing the reliance on human labor. Consequently, the implementation of smart logistics through automation has the potential to enhance competitiveness and minimize the margin of error. The impact of AI and robot-driven logistics on automation in logistics is profound. Through collaborative efforts in human–robot integration (HRI), there emerges an opportunity to develop social service robots that coexist harmoniously with humans. This integration can lead to a revolutionary transformation in logistics operations. By exploring the scientific literature on AI and automation in logistics, this article seeks to unravel critical insights into the practical application of automation, thus bridging the existing research gap in the logistics industry. Conclusions: the findings underscore the impact of artificial intelligence and robot-driven logistics on improving operational efficiency, reducing errors, and enhancing competitiveness. The research also provided valuable insights into the applications of various automation techniques, including machine learning and deep learning, in the logistics domain. Hence, the study’s insights can guide practitioners and decision makers in implementing effective automation strategies, thereby improving overall performance and adaptability in the dynamic logistics landscape. Understanding these foundations can pave the way for a future where automation and human expertise work hand in hand to drive logistics toward unparalleled efficiency and success.
Decentralized News-Retrieval Architecture Using Blockchain Technology

Trust is a critical element when it comes to news articles, and an important problem is how to ensure trust in the published information on news websites. First, this paper describes the inner workings of a proposed news-retrieval and aggregation architecture employed by a blockchain-based solution for fighting disinformation; this includes a comparison between existing information retrieval solutions. The decentralized nature of the solution is achieved by separating the crawling (i.e., extracting the web page links) from the scraping (i.e., extracting the article information) and having third-party actors extract the data. A majority-rule mechanism is used to determine the correctness of the information, and the blockchain network is used for traceability. Second, the steps needed to deploy the distributed components in a cloud environment seamlessly are discussed in detail, with a special focus on the open-source OpenStack cloud solution. Lastly, novel methods for achieving a truly decentralized architecture based on community input and blockchain technology are presented, thus ensuring maximum trust and transparency in the system. The results obtained by testing the proposed news-retrieval system are presented, and the optimizations that can be made are discussed based on the crawling and scraping test results.
IoT device type identification using training deep quantum neural networks optimized with a chimp optimization algorithm for enhancing IoT security

IoT networks can be defined as groups of physically connected things and devices that can connect to the Internet and exchange data with one another. Since enabling an increasing number of internets of things devices to connect with their networks, organizations have become more vulnerable to safety issues and attacks. A major drawback of previous research is that it can find out prior seen types only, also any new device types are considered anomalous. In this manuscript, IoT device type detection utilizing Training deep quantum neural networks optimized with a Chimp optimization algorithm for enhancing IOT security (IOT-DTI-TDQNN-COA-ES) is proposed. The proposed method entails three phases namely data collection, feature extraction and detection. For Data collection phase, real network traffic dataset from different IoT device types are collected. For feature mining phase, the internet traffic features are extracted through automated building extraction (ABE) method. IoT device type identification phase, Training deep quantum neural networks (TDQNN) optimized with Chimp optimization algorithm (COA) is utilized to detect the category of IoT devices as known and unknown device. IoT network is implemented in Python. Then the simulation performance of the proposed IOT-DTI-TDQNN-COA-ES method attains higher accuracy as26.82% and 23.48% respectively, when compared with the existing methods.
Multi-Agent Reinforcement Learning for Online Food Delivery with Location Privacy Preservation

Online food delivery services today are considered an essential service that gets significant attention worldwide. Many companies and individuals are involved in this field as it offers good income and numerous jobs to the community. In this research, we consider the problem of online food delivery services and how we can increase the number of received orders by couriers and thereby increase their income. Multi-agent reinforcement learning (MARL) is employed to guide the couriers to areas with high demand for food delivery requests. A map of the city is divided into small grids, and each grid represents a small area of the city that has different demand for online food delivery orders. The MARL agent trains and learns which grid has the highest demand and then selects it. Thus, couriers can get more food delivery orders and thereby increase long-term income. While increasing the number of received orders is important, protecting customer location is also essential. Therefore, the Protect User Location Method (PULM) is proposed in this research in order to protect customer location information. The PULM injects differential privacy (DP) Laplace noise based on two parameters: city area size and customer frequency of online food delivery orders. We use two datasets—Shenzhen, China, and Iowa, USA—to demonstrate the results of our experiments. The results show an increase in the number of received orders in the Shenzhen and Iowa City datasets. We also show the similarity and data utility of courier trajectories after we use our obfuscation (PULM) method.
Temporal Convolutional Networks and BERT-Based Multi-Label Emotion Analysis for Financial Forecasting

The use of deep learning in conjunction with models that extract emotion-related information from texts to predict financial time series is based on the assumption that what is said about a stock is correlated with the way that stock fluctuates. Given the above, in this work, a multivariate forecasting methodology incorporating temporal convolutional networks in combination with a BERT-based multi-label emotion classification procedure and correlation feature selection is proposed. The results from an extensive set of experiments, which included predictions of three different time frames and various multivariate ensemble schemes that capture 28 different types of emotion-relative information, are presented. It is shown that the proposed methodology exhibits universal predominance regarding aggregate performance over six different metrics, outperforming all the compared schemes, including a multitude of individual and ensemble methods, both in terms of aggregate average scores and Friedman rankings. Moreover, the results strongly indicate that the use of emotion-related features has beneficial effects on the derived forecasts.
BFE-Net: Object Detection with Bidirectional Feature Enhancement

In realistic scenarios, existing object detection models still face challenges in resisting interference and detecting small objects due to complex environmental factors such as light and noise. For this reason, a novel scheme termed BFE-Net based on bidirectional feature enhancement is proposed. Firstly, a new multi-scale feature extraction module is constructed, which uses a self-attention mechanism to simulate human visual perception. It is used to capture global information and long-range dependencies between pixels, thereby optimizing the extraction of multi-scale features from input images. Secondly, a feature enhancement and denoising module is designed, based on bidirectional information flow. In the top-down, the impact of noise on the feature map is weakened to further enhance the feature extraction. In the bottom-up, multi-scale features are fused to improve the accuracy of small object feature extraction. Lastly, a generalized intersection over union regression loss function is employed to optimize the movement direction of predicted bounding boxes, improving the efficiency and accuracy of object localization. Experimental results using the public dataset PASCAL VOC2007test show that our scheme achieves a mean average precision (mAP) of 85% for object detection, which is 2.3% to 8.6% higher than classical methods such as RetinaNet and YOLOv5. Particularly, the anti-interference capability and the performance in detecting small objects show a significant enhancement.
Rooftop PV Segmenter: A Size-Aware Network for Segmenting Rooftop Photovoltaic Systems from High-Resolution Imagery

The photovoltaic (PV) industry boom has accelerated the need for accurately understanding the spatial distribution of PV energy systems. The synergy of remote sensing and artificial intelligence presents significant prospects for PV energy monitoring. Currently, numerous studies have focused on extracting rooftop PV systems from airborne or satellite imagery, but their small-scale and size-varying characteristics make the segmentation results suffer from PV internal incompleteness and small PV omission. To address these issues, this study proposed a size-aware deep learning network called Rooftop PV Segmenter (RPS) for segmenting small-scale rooftop PV systems from high-resolution imagery. In detail, the RPS network introduced a Semantic Refinement Module (SRM) to sense size variations of PV panels and reconstruct high-resolution deep semantic features. Moreover, a Feature Aggregation Module (FAM) enhanced the representation of robust features by continuously aggregating deeper features into shallower ones. In the output stage, a Deep Supervised Fusion Module (DSFM) was employed to constrain and fuse the outputs at different scales to achieve more refined segmentation. The proposed RPS network was tested and shown to outperform other models in producing segmentation results closer to the ground truth, with the F1 score and IoU reaching 0.9186 and 0.8495 on the publicly available California Distributed Solar PV Array Dataset (C-DSPV Dataset), and 0.9608 and 0.9246 on the self-annotated Heilbronn Rooftop PV System Dataset (H-RPVS Dataset). This study has provided an effective solution for obtaining a refined small-scale energy distribution database.
Short-Term Photovoltaic Power Forecasting Based on a Feature Rise-Dimensional Two-Layer Ensemble Learning Model

Photovoltaic (PV) power generation has brought about enormous economic and environmental benefits, promoting sustainable development. However, due to the intermittency and volatility of PV power, the high penetration rate of PV power generation may pose challenges to the planning and operation of power systems. Accurate PV power forecasting is crucial for the safe and stable operation of the power grid. This paper proposes a short-term PV power forecasting method using K-means clustering, ensemble learning (EL), a feature rise-dimensional (FRD) approach, and quantile regression (QR) to improve the accuracy of deterministic and probabilistic forecasting of PV power. The K-means clustering algorithm was used to construct weather categories. The EL method was used to construct a two-layer ensemble learning (TLEL) model based on the eXtreme gradient boosting (XGBoost), random forest (RF), CatBoost, and long short-term memory (LSTM) models. The FRD approach was used to optimize the TLEL model, construct the FRD-XGBoost-LSTM (R-XGBL), FRD-RF-LSTM (R-RFL), and FRD-CatBoost-LSTM (R-CatBL) models, and combine them with the results of the TLEL model using the reciprocal error method, in order to obtain the deterministic forecasting results of the FRD-TLEL model. The QR was used to obtain probability forecasting results with different confidence intervals. The experiments were conducted with data at a time level of 15 min from the Desert Knowledge Australia Solar Center (DKASC) to forecast the PV power of a certain day. Compared to other models, the proposed FRD-TLEL model has the lowest root mean square error (RMSE) and mean absolute percentage error (MAPE) in different seasons and weather types. In probability interval forecasting, the 95%, 75%, and 50% confidence intervals all have good forecasting intervals. The results indicate that the proposed PV power forecasting method exhibits a superior performance in forecasting accuracy compared to other methods.
Using random coherent states to mimic quantum illumination

Quantum illumination uses quantum correlations to provide an enhanced detection of an object in the presence of background noise. This advantage has been shown to exist even if one uses nonoptimal direct measurements on the two correlated modes. Here we present a protocol that mimics the behavior of quantum illumination, but does not use correlated or entangled modes. Instead, the protocol uses coherent (or phase-randomized coherent) pulses with randomly chosen intensities. The intensities are drawn from a distribution such that the average state looks thermal. Under appropriate conditions, the mimic protocol can perform similarly to quantum illumination schemes that use direct measurements. This holds even for a reflectance as low as ${10}^{\ensuremath{-}7}$. We also present an analytic condition which allows one to determine the sets of parameters in which each protocol works best.
Modal Information Logics: Axiomatizations and Decidability

Abstract The present paper studies formal properties of so-called modal information logics (MILs)—modal logics first proposed in (van Benthem 1996) as a way of using possible-worlds semantics to model a theory of information. They do so by extending the language of propositional logic with a binary modality defined in terms of being the supremum of two states. First proposed in 1996, MILs have been around for some time, yet not much is known: (van Benthem 2017, 2019) pose two central open problems, namely (1) axiomatizing the two basic MILs of suprema on preorders and posets, respectively, and (2) proving (un)decidability. The main results of the first part of this paper are solving these two problems: (1) by providing an axiomatization [with a completeness proof entailing the two logics to be the same], and (2) by proving decidability. In the proof of the latter, an emphasis is put on the method applied as a heuristic for proving decidability ‘via completeness’ for semantically introduced logics; the logics lack the FMP w.r.t. their classes of definition, but not w.r.t. a generalized class. These results are build upon to axiomatize and prove decidable the MILs attained by endowing the language with an ‘informational implication’—in doing so a link is also made to the work of (Buszkowski 2021) on the Lambek Calculus.
A 3D visualization‐based augmented reality application for brain tumor segmentation

Summary Every year on June 8th, the globe observes World Brain Tumor Day to raise awareness and educate people about brain cancer, encompassing both noncancerous (benign) and cancerous (malignant) growths. Research in the field of brain cancer plays a vital role in supporting medical professionals. In this context, augmented reality (AR) technology has emerged as a valuable tool, enabling surgeons to visualize underlying structures and offering a cost‐effective and time‐efficient alternative. Our study focuses on the efficient segmentation of brain tumor classes using Magnetic Resonance Imaging (MRI) and incorporates a three‐stage approach: preprocessing, segmentation, and 3D reconstruction &amp; AR display. In the preprocessing stage, a Gaussian filter is applied to mitigate intensity heterogeneity. Segmentation and detection are achieved using active geometric contour models, complemented by morphological operations. To establish 3D brain tumor reconstruction, a genuine scene is virtually integrated using 3D Slicer software. The proposed methodology was validated using a genuine patient dataset comprising 496 MRI scans obtained from the local Bab El Oued university hospital center. The results demonstrate the effectiveness of our approach in achieving accurate 3D brain tumor reconstruction, efficient tumor extraction, and augmented reality visualization. The obtained segmentation results showcased an impressive accuracy of 98.61%, outperforming existing state‐of‐the‐art methods and affirming the efficacy of our proposed strategy.
Artificial Intelligence and Archaeological Illustration

Abstract The reconstruction and representation of ancient artifacts and scenes through illustration is a cornerstone in the communication of archaeological findings. Sketches of the past have transformed over time, incorporating broader technological changes, from photography to the digital tools that have become prevalent through the twenty-first century. Most recently, developments in generative artificial intelligence (AI) promise to reshape the way we represent the past to professional and public audiences. This article shows how to use an accessible and inexpensive artificial intelligence platform to generate complex archaeological illustrations. As a case study, we create multiple scenes representing competing hypotheses about Neanderthal behavior. Using the images to visually communicate alternative hypotheses, we demonstrate how archaeological illustration using artificial intelligence promises to democratize the production of visual representations of the past.
Gender gaps in the impact of the pandemic on the use of <scp>ICT</scp> in higher education

Abstract The COVID‐19 pandemic has led to a process of digitalisation of higher education that has forced a change in the habits of use of information and communication technologies (ICT) among professors. The literature analyses this impact from different perspectives, but the gender gaps that may have occurred in this regard have not been studied so far. The main purpose is to analyse the gender gap in the self‐concept of digital competence of Latin American professors and the impact that the pandemic has had on the habits of use of ICT among them. For this purpose, this paper conducts quantitative research. A questionnaire designed by the authors has been answered by a sample of 1062 professors, and the responses have been statistically analysed. The results prove that the self‐concept of digital competence influences males more than females in terms of their assessment of the didactic use of ICT. The pandemic has caused the frequencies of ICT use in different teaching activities to homogenise. Females have increased their use of ICT more than males—a certain correction of the digital gender gap occurs in the region after the pandemic, within faculty. The use of ICT among faculty has increased after the pandemic more among females than males, but insufficient digital competence hinders full digitalisation in both cases. It is recommended that universities develop digital training plans aimed at facilitating an egalitarian process of ICT integration.
Designing New Metaheuristics: Manual Versus Automatic Approaches

A metaheuristic is a collection of algorithmic concepts that can be used to define heuristic methods applicable to a wide set of optimization problems for which exact/analytical approaches are either limited or impractical. In other words, a metaheuristic can be considered a general algorithmic framework that can be easily adapted to different optimization problems. In this article, we discuss the two main approaches used to create new metaheuristics: manual design, which is based on the designer’s “intuition” and often involves looking for inspiration in other fields of knowledge, and automatic design, which seeks to reduce human involvement in the design process by harnessing recent advances in automatic algorithm configuration methods. In this context, we discuss the trend of manually designed “novel” metaphor-based metaheuristics inspired by natural, artificial, and even supernatural behaviors. In recent years, this trend has been strongly criticized due to the uselessness of new metaphors in devising truly novel algorithms and the confusion such metaheuristics have created in the literature. We then present automatic design as a powerful alternative to manual design that has the potential to render the “novel” metaphor-based metaheuristics trend obsolete. Finally, we examine several fundamental aspects of the field of metaheuristics and offer suggestions for improving them.
Exploring Factors Affecting Elementary School Teachers’ Adoption of 3D Printers In Teaching

Abstract Owing to its advantages such as producing durable models and easy accessibility, 3D modeling and printing (3DMP) has become increasingly popular in educational practice and research. However, the results of many studies have shown that the adoption rate of 3DMP among teachers is still low, especially in elementary schools. Therefore, research is needed to expand current knowledge about what influences teachers' decisions to start and continue using 3DMP in elementary schools. To investigate factors that affect elementary school teachers' decisions to use 3D printing in teaching, this study uses a mixed methods research approach combining binary logistic regression with a qualitative thematic analysis approach. Both approaches assembled predictive constructs from a range of theories on (1) technology acceptance and (2) intentions to continue or abandon 3DMP use. Using a sample of 225 elementary teachers from Montenegro, this study empirically concluded that intentions to discontinue 3DMP was slightly more strongly correlated with the predictors (i.e., performance expectancy, effort expectancy, perceived pedagogical impact, personal innovativeness, management support, user interface quality, technology compatibility, social influence, student expectations) than was intentions to continue using 3DMP . Performance expectancy was a significant determinant of teachers’ continued use of a 3DMP approach. The remaining seven factors (constructs) were found to be insignificant predictors. Perceived pedagogical impact and technology compatibility were significantly associated with teachers' decisions to discontinue using 3DMP. Our results also suggest that the time required to use 3DMP during instruction, the impact of 3DMP on the implementation of curriculum activities, and the availability of 3D printers are all relevant factors that influence teachers’' decision to use or abandon 3DMP.