Reviewerly/exHarmony huggingface dataset:
The role of digitalization in business and management: a systematic literature review

Abstract Digitalization is a powerful engine for economic growth in the world. In 2018, digitally transformed firms represented 13.5 billion US dollars of global GDP and, towards the end of 2023, they are expected to represent 53.3 billion US dollars, over half of the general nominal GDP (Statista, Nominal GDP driven by digitally transformed and other enterprises worldwide 2018–2023. https://www.statista.com/statistics/1134766/nominal-gdp-driven-by-digitally-transformed-enterprises/ , 2022). The main objective of this study is to provide information (highlighting principal research topics and research agendas) from the literature on state-of-the-art digitalization within firms through a Systematic Literature Review (SLR). In all, 119 review articles on the most mature functional areas of the firm are analyzed: management, marketing, and finance and accounting, published in the WOS over the period 2018-April 2022. In this study, key relevant tendencies are identified in the most mature areas of the firm, which are the impact of digital technologies on the analysis of consumer behavior; digitalization and green innovation within organizations; and blockchain technology applied to financial services. The main contributions of this work are as follows: (1) to provide the most complete and up-to-date review of digitalization from a global perspective, summarizing the current state of knowledge within an integrated framework; (2) to reduce the complexity of digitalization by offering structure and clarity; and (3) to offer links between digitalization and established points of view in the literature on management, marketing, finance, and accounting. The novelty of this paper is centered on a joint analysis of digitalization, digital transformation, and digital technologies, taking into account the most mature functional areas of the firm.
AAD-Net: Advanced end-to-end signal processing system for human emotion detection &amp; recognition using attention-based deep echo state network

Speech signals are the most convenient way of communication between human beings and the eventual method of Human–Computer Interaction (HCI) to exchange emotions and information. Recognizing emotions from speech signals is a challenging task due to the sparse nature of emotional data and features. In this article, we proposed a Deep Echo-State-Network (DeepESN) system for emotion recognition with a dilated convolution neural network and multi-headed attention mechanism. To reduce the model complexity, we incorporate a DeepESN that combines reservoir computing for higher-dimensional mapping. We also used fine-tuned Sparse Random Projection (SRP) to reduce dimensionality and adopted an early fusion strategy to fuse the extracted cues and passed the joint feature vector via a classification layer to recognize emotions. Our proposed model is evaluated on two public speech corpora, EMO-DB and RAVDESS, and tested for subject/speaker-dependent/independent performance. The results show that our proposed system achieves a high recognition rate, 91.14, 85.57 for EMO-DB, and 82.01, 77.02 for RAVDESS, using speaker-dependent and independent experiments, respectively. Our proposed system outperforms the State-of-The-Art (SOTA) while requiring less computational time.
Blockchain Implementation in Financial Sector and Cyber Security System

Initially, coins used block chain solutions as its open digital ledger. Although, cryptographic protocols has lately been taken into consideration, the wide range of additional applications due to its unique combination of decentralization, reliability, accessibility, and vehemently anti features. Such characteristics are especially helpful for a range of significant problems encountered in the finance industry. Therefore, by changing how various financial services are provided ledger technology has the ability to completely transform the financial sector. In this article, we present five distinct finance sector use cases that the use of block chains is predicted to fundamentally disrupt. this paper investigates financial sector and security system implementations and its implications.
A Sidelobe-Aware Small Ship Detection Network for Synthetic Aperture Radar Imagery

Ship detection from synthetic aperture radar (SAR) remote sensing images is essential for monitoring water traffic and marine safety. Numerous methods for ship detection have been developed; however, the detection of small ships presents unique challenges. SAR image characteristics, such as the sidelobe effect and blurred outline induced by the special imaging mechanism, as well as the small ship size, are the primary factors that lower the detection accuracy. This paper provides a sidelobe-aware small ship detection network for synthetic aperture radar imagery. First, considering the sidelobe effect and blurred outline, dual-pooling, i.e., average pooling and max pooling, was utilized to build a feature extraction module that lowered the effects of strong scattering points outside of the ship body and enhanced the ship body information. Second, as the bipartition process of the average pooling and maximum pooling caused some loss of original data information, different feature maps in the network were concatenated to construct a new network structure to compensate for the information lost and enrich the small ship features. Third, because the traditional loss function based on centroid distance and aspect ratio may result in the same loss function value for different prediction box sizes, a novel loss function based on the dual Euclidean distances of the corner point coordinates between the prediction box and the real box was proposed, which could accurately describe various overlapping box situations. Experiments using the Large-Scale SAR Ship Detection Dataset (LS-SSDD), SAR Ship Detection Dataset (SSDD), and AIR-SARShip dataset validated the efficacy and state-of-the-art performance.
Deep Learning Approach for Hand Drawn Emoji Identification

Object detection is a computer technology which is based on image processing and deep learning. It uses a database to recognize objects by comparing them. Emoticons are small symbols or pictures used to express opinions or emotions in instant messages. They are widely used on various social media platforms like Facebook, Twitter, Instagram, etc. In this paper, we focus on hand-drawn emoticons and classify them into 8 categories. Hand-drawn emoticons are created using advanced technology or drawn by hand with a pen. This paper aims to help users organize these emoticons for use in various social media with minimal confusion. We created a dataset of 4000 hand-drawn emoticon images, with 500 images per category, to train our model. Our framework uses a convolutional neural network model to recognize and classify the emoticons with a precision of 64%.
Short-term photovoltaic power point-interval forecasting based on double-layer decomposition and WOA-BiLSTM-Attention and considering weather classification

A reliable short-term forecast of photovoltaic power (PVPF) is essential to maintaining stable power systems and optimizing power grid dispatch. A hybrid prediction framework of PVPF considering similar day screening, signal decomposition technique, and hybrid deep learning is proposed to realize accurate point-interval prediction. First, a double similar day screening model is constructed to divided weather into the three types, which improves the quality of the training set. Second, a double-layer signal decomposition model based on improved Complementary Ensemble Empirical Mode Decomposition (ICEEMD)-Variational mode decomposition(VMD) is proposed to preprocess the data, eliminate the noise data. Third, Whale optimization algorithm(WOA) is introduced to search the best hyper parameters of BiLSTM, and the attention mechanism(AM) is adopted to focus on the influence of key information. WOA-BiLSTM-AM is constructed to predict intrinsic mode function(IMF). Fourth, Kernel density estimation (KDE) is applied to estimate the PV prediction interval at different confidence levels. Finally, compared with the benchmark models, the prediction errors of cloudy, sunny and rainy days are reduced by 56.09%, 80.10% and 71.98%, respectively. In addition, according to the coverage width criterion, KDE (Gaussian) is superior to other interval prediction models and can achieve more reliable prediction interval.
Distilling Knowledge From Super-Resolution for Efficient Remote Sensing Salient Object Detection

Current state-of-the-art remote sensing salient object detectors always require high-resolution spatial context to ensure excellent performance, which incurs enormous computation costs and hinders real-time efficiency. In this work, we propose a universal super-resolution assisted learning (SRAL) framework to boost performance and accelerate the inference efficiency of existing approaches. To this end, we propose to reduce the spatial resolution of the input remote sensing images (RSIs), which is model-agnostic, and can be applied to existing algorithms without extra computation cost. Specifically, a transposed saliency detection decoder (TSDD) is designed to upsample interim features progressively. On top of it, an auxiliary super-resolution decoder (ASRD) is proposed to build a multitask learning (MTL) framework to investigate an efficient complementary paradigm of saliency detection and super-resolution. Furthermore, a novel task-fusion guidance module (TFGM) is proposed to effectively distill domain knowledge from the super-resolution auxiliary task to the salient object detection task in optical RSIs. The presented ASRD and TFGM can be omitted in the inference phase without any extra computational budget. Extensive experiments on three datasets show that the presented SRAL with 224×224 input is superior to more than 20 algorithms. Moreover, it can be successfully generalized to existing typical networks with significant accuracy improvements in a parameter-free manner. Codes and models are available at https://github.com/lyf0801/SRAL.
Autonomous docking trajectory optimization for unmanned surface vehicle: A hierarchical method

Docking trajectory planning for unmanned surface vehicles is challenging due to complicated obstacle-avoidance in the harbor and highly nonlinear motion equations. In this paper, a two-stage (i.e., searching and optimization) hierarchical method is developed for a safe and efficient solution. In the searching stage, we establish a grid map for the harbor environment. The unmanned surface vehicle is represented by multiple featured discs and an artificially-guided Hybrid A* algorithm is implemented to search a coarse collision-free path. In the optimization stage, we first estimate the total sailing time under time-optimal maneuvering assumptions. Given the discretization scheme, we resample state and control variables along the coarse path by interpolation. Then, we construct a series of safe sailing corridors along the interpolated results. The within-corridor constraints are used to reformulate the optimal control problem, and the interpolated results are used for warm-start. The developed method is numerically validated by two docking scenarios, where high-quality trajectory as well as profiles of control inputs can be obtained with satisfactory computational efficiency. Meanwhile, it shows superiority in robustness over traditional planning techniques in the presence of narrow waterways and concave harbor shores. An animation of the simulation results is available at www.bilibili.com/video/BV14D4y1t7QL/.
Three-dimensional path planning for AUVs in ocean currents environment based on an improved compression factor particle swarm optimization algorithm

Three-dimensional path planning for autonomous underwater vehicles (AUVs) in underwater environments is the key to ensuring safe navigation and reliable mission completion. To obtain a safe and smooth three-dimensional path for an AUV in ocean currents and seabed obstacle environments, an improved compression factor particle swarm optimization algorithm is proposed. First, a three-dimensional seabed environment model and Lamb vortex current environment model are constructed. Second, by considering optimization objectives such as travel distance cost, seabed terrain constraints and ocean current constraints, a three-dimensional path planning mathematical model is constructed. Finally, an improved compression factor particle swarm optimization algorithm is proposed and applied to solve the multi-objective nonlinear optimization problem. To verify the optimization performance of the new algorithm, its optimization results are compared with those of other algorithms by minimizing the fitness value. The experimental results reveal that the improved compressed factor particle swarm optimal algorithm has better planning efficiency, path quality, and shorter planning time, which provides a new effective method for path planning of autonomous underwater vehicle.
GMO: geometric mean optimizer for solving engineering problems

This paper introduces a new meta-heuristic technique, named geometric mean optimizer (GMO) that emulates the unique properties of the geometric mean operator in mathematics. This operator can simultaneously evaluate the fitness and diversity of the search agents in the search space. In GMO, the geometric mean of the scaled objective values of a certain agent’s opposites is assigned to that agent as its weight representing its overall eligibility to guide the other agents in the search process when solving an optimization problem. Furthermore, the GMO has no parameter to tune, contributing its results to be highly reliable. The competence of the GMO in solving optimization problems is verified via implementation on 52 standard benchmark test problems including 23 classical test functions, 29 CEC2017 test functions as well as nine constrained engineering problems. The results presented by the GMO are then compared with those offered by several newly proposed and popular meta-heuristic algorithms. The results demonstrate that the GMO significantly outperforms its competitors on a vast range of the problems. Source codes of GMO are publicly available at https://github.com/farshad-rezaei1/GMO .
Multiplex Graph Representation Learning Via Dual Correlation Reduction

Recently, with the superior capacity for analyzing the multiplex graph data, self-supervised multiplex graph representation learning (SMGRL) has received much interest. However, existing SMGRL methods are still limited by the following issues: (i) they generally ignore the noisy information within each graph and the common information among different graphs, thus weakening the effectiveness of SMGRL, and (ii) they conduct negative sample encoding and complex pretext tasks for contrastive learning, thus weakening the efficiency of SMGRL. To solve these issues, in this work, we propose a new framework to conduct effective and efficient SMGRL. Specifically, the proposed method investigates the intra-graph and inter-graph decorrelation losses, respectively, for reducing the impact of noisy information within each graph and capturing the common information among different graphs, to achieve the effectiveness. Moreover, the proposed method does not need negative samples for the SMGRL and designs a simple pretext task, to achieve the efficiency. We further theoretically justify that our method achieves the maximal mutual information instead of directly conducting contrastive learning and theoretically justify that our method actually minimizes the multiplex graph information bottleneck, which guarantees the effectiveness. In addition, an extension for semi-supervised scenarios is proposed to fit the case that a few labels are provided in reality. Extensive experimental results verify the effectiveness and efficiency of the proposed method with respect to various downstream tasks.
Artificial Intelligence in English Language Teaching: The Good, the Bad and the Ugly

The use of educational technologies in English language teaching (ELT) has become widely accepted in the post-pandemic era, and, for better or worse, some of these technologies rely on artificial intelligence (AI). As an area of technological growth and increasing financial investment, we are likely to see more AI-driven technologies in teaching and learning in the post-pandemic ELT world. We are currently in the stage of ‘weak’ AI, which typically performs restricted tasks within specific domains relatively well. However, ‘strong’ AI, equivalent to human intelligence, is the long-term goal, and although this is no more than a theoretical construct at present, we can expect ‘stronger’ AI to emerge over time. ELT will not be immune to this development, and it behoves us as language teachers to be familiar with AI's current benefits and challenges, so that we can better prepare for that future. This article describes how AI is currently used in ELT, and explores some of the opportunities and challenges that AI can provide for learners, teachers and institutions. Ethical issues such as collecting learner data, surveillance and privacy are considered, as well as learner wellbeing and the digital literacies that teachers and learners will need to develop to co-exist in a brave new world of educational AI. Chatbots are examined as one example of AI-driven technology for language learning. There are of course many more, such as machine translation, intelligent tutoring systems and automated writing evaluation to name just a few; however, a detailed consideration of these is beyond the scope of this article.
Hybrid Quantum Neural Network for Drug Response Prediction

Cancer is one of the leading causes of death worldwide. It is caused by a variety of genetic mutations, which makes every instance of the disease unique. Since chemotherapy can have extremely severe side effects, each patient requires a personalized treatment plan. Finding the dosages that maximize the beneficial effects of the drugs and minimize their adverse side effects is vital. Deep neural networks automate and improve drug selection. However, they require a lot of data to be trained on. Therefore, there is a need for machine-learning approaches that require less data. Hybrid quantum neural networks were shown to provide a potential advantage in problems where training data availability is limited. We propose a novel hybrid quantum neural network for drug response prediction, based on a combination of convolutional, graph convolutional, and deep quantum neural layers of 8 qubits with 363 layers. We test our model on the reduced Genomics of Drug Sensitivity in Cancer dataset and show that the hybrid quantum model outperforms its classical analog by 15% in predicting IC50 drug effectiveness values. The proposed hybrid quantum machine learning model is a step towards deep quantum data-efficient algorithms with thousands of quantum gates for solving problems in personalized medicine, where data collection is a challenge.
Advances in nature-inspired metaheuristic optimization for feature selection problem: A comprehensive survey

The main objective of feature selection is to improve learning performance by selecting concise and informative feature subsets, which presents a challenging task for machine learning or pattern recognition applications due to the large and complex search space involved. This paper provides an in-depth examination of nature-inspired metaheuristic methods for the feature selection problem, with a focus on representation and search algorithms, as they have drawn significant interest from the feature selection community due to their potential for global search and simplicity. An analysis of various advanced approach types, along with their advantages and disadvantages, is presented in this study, with the goal of highlighting important issues and unanswered questions in the literature. The article provides advice for conducting future research more effectively to benefit this field of study, including guidance on identifying appropriate approaches to use in different scenarios.
A novel method for the site selection of large-scale PV farms by using AHP and GIS: A case study in İzmir, Türkiye

Effective and sustainable climate-friendly policies to reduce carbon dioxide emissions in line with the 2020 European Green Deal are necessary. Accordingly, renewable energies assumed a critical role, rendering the site selection of these systems very crucial. The present study proposes a novel approach to the site selection of large-scale photovoltaic (PV) plants using a combination of analytic hierarchy process (AHP) and geographic information system (GIS). In the study, the weights of criteria used for selecting solar PV panels are adjusted according to the installed capacity of the PV plant. The land of cost is included as a criterion in the AHP for the first time in PV plant site selection. Besides, a novel method called optimality-based site growing (OBSG) is introduced to further analyze the suitable sites obtained from GIS simulations and to determine the most suitable locations of PV farms. The proposed method is demonstrated with a case study of Türkiye, and the results show that the method effectively determines the most suitable locations for large-scale PV plants.
Conditional probability based multi-objective cooperative task assignment for heterogeneous UAVs

In actual air combat, there is an inevitable risk that an unmanned aerial vehicle (UAV) will be destroyed. However, this risk is rarely considered in the mission planning phase. In this paper, we focus on cooperative mission assignment for heterogeneous UAVs. We develop a multi-objective optimization model to find a balance between mission gains and UAV losses. The objective function is expressed using conditional probability theory by introducing the probabilities of mission success and UAV loss. Munitions loading capacity, time constraints, and priority constraints are modeled as constraints. To solve this combinatorial problem, an improved multi-objective genetic algorithm, which incorporates a natural chromosome encoding format and specially designed genetic operators, is developed. An efficient unlocking method is constructed to address the unavoidable dead-lock phenomenon meanwhile maintaining the population randomness. Numerical simulations for different problem sizes and ammunition stocks are performed, and the proposed algorithm is compared with the Multi-objective Particle Swarm Optimization and the Multi-objective Grey Wolf Optimization, respectively, using different unlocking approaches. The simulation and comparison results demonstrate the practical value and effectiveness of the developed model and the proposed algorithm.
Introduction to the Sixth Annual Lifelog Search Challenge, LSC’23

For the sixth time since 2018, the Lifelog Search Challenge (LSC) was organized as a comparative benchmarking exercise for various interactive lifelog search systems. The goal of this international competition is to test system capabilities to access large multimodal lifelogs. LSC'23 attracted twelve participanting teams, each of whom had developed a competitive interactive lifelog retrieval system. The benchmark was organized in front of live audience at the LSC workshop at ACM ICMR'23. As in previous editions, this introductory paper presents the LSC workshop and introduces the participating lifelog search systems.