Reviewerly/exHarmony huggingface dataset:
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This paper proposes a new mutation operator current−to−better/1 to keep the population diversity. Under the control of current−to−better/1, a high-ranking individual with a larger fitness value tend to search its nearby area, while the low-ranking individual is stimulated to move towards high-ranking comity in a not fixed direction. A learning matrix is set to store successful amplification factors with their corresponding individual vectors, and then to update the amplification factors matrix for each generation. To test the effectiveness of current−to−better/1, it is embedded into JADE as a collaborator of original mutation operator DE/current−to−pbest/1. The efficiency of this new algorithm, namely, L-JADE, is proved by the performance comparisons with other already known DE-variant algorithms on 2-D, 3-D, 5-D, 10-D, and 20-D COCO testbed. Experimental results show that L-JADE performs better than most algorithms.
Underwater 3D Object Reconstruction for Fish Length Estimation Using Convolutional Neural Networks

The challenge in stereo matching for underwater 3D object modeling is to compute dense depth data with the minimal smoothness at high frame rate. To address this issue, in this paper we propose an object based stereo matching for underwater 3D fish reconstruction using convolutional neural networks (CNNs). For each image in a stereo frame, an instance segmentation CNN is used to segment fish objects from the background. The set of fish objects in the left image is matched against those in the right to detect the object pairs using the proposed support-weights approach. For each pair, the common disparity value is then computed. Next, fish objects in these images are cropped and matched to do the pixel-wise residual disparity computation using the video interpolation CNN. The computed fish disparities and depth values are finally used to estimate the sizes of fish. Instead of estimating the fish length using a single frame, we track each fish across frames of the input stereo video to compute the fish length frame by frame. The mean fish length is finally computed as the result. An underwater dataset with the fish actual length measured by human is constructed to verify the effectiveness of our approach. Experimental results show that the error rate of the proposed approach is less than 6%.
Modeling Phishing Decision using Instance Based Learning and Natural Language Processing

Phishing is the practice of deceiving humans into disclosing sensitive information or inappropriately granting access to a secure system.Unfortunately, there is a severe lack of theoretical models to adequately explain and predict the cognitive dynamics underlying end-user susceptibility to phishing emails.This paper reports findings from an Instance-Based Learning (IBL) model developed to predict human response to emails obtained from a laboratory experiment.Particularly, this work investigates the effectiveness of using established natural language processing methods, such as LSA, GloVe, and BERT, to represent email text within IBL models.We found that using representations that consider contextual meanings assigned by humans could enable IBL agents to predict human response with high accuracy (80%).In addition, we found that traditional NLP methods that capture semantic meanings in natural language may not be effective at representing how people may encode and recall email messages.We discuss the implications of these findings.
Modeling of the Global Daily Horizontal Solar Radiation Data over Togo

Solar photovoltaic appears to be the most interesting renewable energy in developing countries where its deposit is abundant.Unfortunately, the lack of precise knowledge of solar radiation deposit and its limited data hinder optimal exploitation of solar installations.This study presents a performing model for daily global horizontal solar radiation for the five regional capitals in Togo: Lomé, Atakpamé, Sokodé, Kara and Dapaong.The data used for the study were obtained from the General Directorate of National Meteorology of Togo, for five years.The model developed combines linear and nonlinear methods with harmonic and exponential terms taking into account climatological parameters such as location latitude, daily relative humidity, daily ratio of sunshine duration and daily mean temperature.Statistical errors of the model were compared to those of two previous models elaborated for Togo and Nigeria.The results showed that the model is more efficient to predict global horizontal solar radiation over the five main cities in Togo.The comparison of estimated data and measured ones showed a good agreement between them.
Optical biopsy mapping on endoscopic image mosaics with a marker-free probe

Gastric cancer is the second leading cause of cancer-related deaths worldwide. Early diagnosis significantly increases the chances of survival; therefore, improved assisted exploration and screening techniques are necessary. Previously, we made use of an augmented multi-spectral endoscope by inserting an optical probe into the instrumentation channel. However, the limited field of view and the lack of markings left by optical biopsies on the tissue complicate the navigation and revisit of the suspect areas probed in-vivo. In this contribution two innovative tools are introduced to significantly increase the traceability and monitoring of patients in clinical practice: (i) video mosaicing to build a more comprehensive and panoramic view of large gastric areas; (ii) optical biopsy targeting and registration with the endoscopic images. The proposed optical flow-based mosaicing technique selects images that minimize texture discontinuities and is robust despite the lack of texture and illumination variations. The optical biopsy targeting is based on automatic tracking of a free-marker probe in the endoscopic view using deep learning to dynamically estimate its pose during exploration. The accuracy of pose estimation is sufficient to ensure a precise overlapping of the standard white-light color image and the hyperspectral probe image, assuming that the small target area of the organ is almost flat. This allows the mapping of all spatio-temporally tracked biopsy sites onto the panoramic mosaic. Experimental validations are carried out from videos acquired on patients in hospital. The proposed technique is purely software-based and therefore easily integrable into clinical practice. It is also generic and compatible to any imaging modality that connects to a fiberscope.
Design of a Software Tool Supporting Orientation in the Context of Digital Transformation

The digital transformation is a continuous and complex but indispensable endeavor.Because practice and academia regularly develop new digital technologies, companies face difficulties in selecting appropriate technologies to innovate their business model.Step-by-step instructions may guide companies to identify, evaluate, and select potential digitalization ideas.Software tools can support these activities in a structured way.Thus, the research objective of this paper is to design a software tool supporting the orientation phase of a company's digital transformation.To reach this objective, we conduct an action design research approach in cooperation with three companies.As an evaluation step, a focus group discussion with practitioners and end-users is carried out.The final tool design comprises 18 design principles that serve as the basis for a set of visual mockups and a prototype implementation of the intended tool.
QACE: Asking Questions to Evaluate an Image Caption

In this paper, we propose QACE, a new metric based on Question Answering for Caption Evaluation.QACE generates questions on the evaluated caption and checks its content by asking the questions on either the reference caption or the source image.We first develop QACE Ref that compares the answers of the evaluated caption to its reference, and report competitive results with the state-of-the-art metrics.To go further, we propose QACE Img , which asks the questions directly on the image, instead of reference.A Visual-QA system is necessary for QACE Img .Unfortunately, the standard VQA models are framed as a classification among only a few thousand categories.Instead, we propose Visual-T5, an abstractive VQA system.The resulting metric, QACE Img is multi-modal, reference-less, and explainable.Our experiments show that QACE Img compares favorably w.r.t.other reference-less metrics.We will release the pre-trained models to compute QACE.
Is Information Density Uniform in Task-Oriented Dialogues?

The Uniform Information Density principle states that speakers plan their utterances to reduce fluctuations in the density of the information transmitted.In this paper, we test whether, and within which contextual units this principle holds in task-oriented dialogues.We show that there is evidence supporting the principle in written dialogues where participants play a cooperative reference game as well as in spoken dialogues involving instruction giving and following.Our study underlines the importance of identifying the relevant contextual components, showing that information content increases particularly within topically and referentially related contextual units.
Algorithm NextFit for the Bin Packing Problem

Summary . The bin packing problem is a fundamental and important optimization problem in theoretical computer science [4], [6]. An instance is a sequence of items, each being of positive size at most one. The task is to place all the items into bins so that the total size of items in each bin is at most one and the number of bins that contain at least one item is minimum. Approximation algorithms have been intensively studied. Algorithm NextFit would be the simplest one. The algorithm repeatedly does the following: If the first unprocessed item in the sequence can be placed, in terms of size, additionally to the bin into which the algorithm has placed an item the last time, place the item into that bin; otherwise place the item into an empty bin. Johnson [5] proved that the number of the resulting bins by algorithm NextFit is less than twice the number of the fewest bins that are needed to contain all items. In this article, we formalize in Mizar [1], [2] the bin packing problem as follows: An instance is a sequence of positive real numbers that are each at most one. The task is to find a function that maps the indices of the sequence to positive integers such that the sum of the subsequence for each of the inverse images is at most one and the size of the image is minimum. We then formalize algorithm NextFit, its feasibility, its approximation guarantee, and the tightness of the approximation guarantee.
Software Effort Estimation Using ISBSG Dataset: Multiple Case Studies

Effort estimation is one of the base activities in software project planning. There are two base inputs needed for the effort estimation: software size and effort data. The size of the software can be measured in different stages of the project. However, there are known problems regarding effort data collection in the industry. In these circumstances, effort estimation can be difficult in organizations. International Software Benchmarking Standards Group (ISBSG) dataset includes many projects including the size and effort data provided by different organizations. This dataset can be used in organizations for effort estimation. In this study, we performed case studies with graduate students to explore if the ISBSG dataset is beneficial for the effort estimation in their organizations. Firstly, students measured the size of a project from their organizations using the COSMIC Functional Size Measurement Method. Then, they formed an effort estimation model using ISBSG data and predicted the effort for their project. Our study shows the applicability of using ISBSG for effort estimation when there is no effort data in an organization.
On some unexplored decoherence aspects in the Caldeira–Leggett formalism: arrival time distributions, identical particles and diffraction in time

Some unexplored decoherence aspects within the Caldeira-Leggett master equation are analyzed and discussed. The decoherence process is controlled by the two environment parameters, the relaxation rate or friction and the temperature, leading to a gradual transition from the quantum to classical regime. Arrival time distributions, nonminimum-uncertainty-product or stretching Gaussian wave packets, identical particles and diffraction in time display interesting features during the decoherence process undergone by the time dependent interference patterns. We show that the presence of a constant force field does not affect the decoherence, {\it positive} values of the stretching parameter reduces the rate of decoherence, the symmetry of the wave function for identical particles plays no role when open dynamics are considered; and diffraction in time and space is gradually washed out by increasing the temperature and/or relaxation rate in the zero dissipation limit within the so-called quantum shutter problem.
Experiences of the e-learning environment of students at the University of Danang - University of Foreign Language Studies

E-learning is being widely deployed and gradually becoming a popular form of teaching in universities today. To achieve the quality of e-learning teaching requires the impact of many factors, in which the e-learning environment of students is important. This paper explored experiences on e-learning environment of students of The University of Danang - University of Foreign Language Studies (UD-UFLS) to improve e-learning environment of the university's students. The study refered to most students’ awareness of the importance of e-learning at university. The advantages and disadvantages of e-learning at the university (UD-UFLS) have also been identified. Through the results of the student survey, the content of the teacher's skills, the content of policies and management of e-learning activities, facilities and funding for e-learning implementation, class size, and student e-learning culture in e-learning are rated. The results of a questionnaire survey of 407 students of the UD-UFLS show a picture of students' e-learning environment and are the basis for proposing some measures to improve e-learning environment of the university's students.
Analogical Reasoning: An Algorithm Comparison for Natural Language Processing

There is a continual push to make Artificial Intelligence (AI) as human-like as possible; however, this is a difficult task.A significant limitation is the inability of AI to learn beyond its current comprehension.Analogical reasoning (AR), whereby learning by analogy occurs, has been proposed as one method to achieve this goal.Current AR models have their roots in symbolist, connectionist, or hybrid approaches which indicate how analogies are evaluated.No current studies have compared psychologically-inspired and natural language processing (NLP)-produced algorithms to one another; this study compares seven AR algorithms from both realms on multiple-choice word-based analogy problems.Assessment is based on selection of the correct answer, "correctness," and their similarity score prediction compared to the "ideal" score, which is defined as the "goodness" metric.Psychologically-based models have an advantage based on our metrics; however, there is not a clear one-size-fits-all algorithm for all AR problems.
A Multilayer Steganographic Method Using Improved Exploiting Modification Directions Scheme

The exploiting modification direction scheme is a well-known irreversible steganographic method because of its high embedding efficiency and imperceptibility. The shortcomings of the exploiting modification direction scheme are the use of a (<inline-formula> <tex-math notation="LaTeX">$2n +1$ </tex-math></inline-formula>)-ary notational system secret digit and low payload. Some studies transform secret data into a sequence of secret digits in the 8-ary notational system to overcome the disadvantage of using a non-binary secret data problem, which also increases the payload. Additionally, some studies have proposed a two-layer embedding method to enhance data security, which also increases the payload. Besides, most of the EMD-based scheme has the fall of boundary problems when the pixel group is located in the boundary area, which increase the distortion of the stego-image. In this study, we proposed a multilayer steganographic method using an improved exploiting modification direction scheme, which includes the abovementioned advantages, using binary secret data, increasing the payload, enhance data security and preventing the fall of boundary problem. In addition, we demostrate a three-layer EMD-based data hiding example. The experimental results show that the proposed method achieves a high payload (approximately 4.5 bpp) and the quality of the stego-image satisfying the human vision system sensitivity (PSNR value is greater than 30 dB).
Analysis of Human Resource Allocation Model for Tourism Industry Based on Improved BP Neural Network

This paper analyzes the deficiencies of human resource allocation in the tourism industry by investigating the human resource allocation in the tourism industry, puts forward corresponding improvement measures and suggestions, and strives to provide certain guidance and helpful effects for the construction of tourism resource informatization. In this paper, a modified BP neural network model is proposed by introducing random perturbation terms on the hidden layer in the BP neural network algorithm, and the weight matrix connecting the input values is added with the random perturbation matrix to obtain a new weight matrix so that the convergence effect of the improved BP neural network algorithm is improved. Then, to address the problem that the initial weights of the long and short-term memory neural network and gated BP unit neural network have a large impact on the convergence speed and prediction accuracy of the algorithm after the initial weight selection is determined, this paper introduces the random perturbation term into the gate structure of the long and short-term memory neural network and gated BP unit neural network and proposes and connects an improved long and short-term memory neural network and gated BP unit neural network. The weight matrix of the input values is added with the random perturbation matrix to obtain the new weight matrix so that the convergence effect of the improved long and short-term memory neural network algorithm and the gated BP unit neural network algorithm is improved. Constructing the human resource allocation model of the tourism industry and proposing coping strategies and countermeasures and taking the human resource allocation system of the tourism industry as the core, the human resource allocation model of the tourism industry is established by combining the network image crisis life cycle system of tourism scenic spots and the network public opinion dissemination model. From the perspective of managers, the human resource allocation management policy and management procedures of the tourism industry are proposed. Using the quantifiable and disenable characteristics of online text information, the response strategy of online monitoring and propaganda and offline management and enhancement is proposed, and innovative countermeasures to the human resource allocation of the tourism industry are proposed in three categories: network originated, reality coexisting, and reality originated. Through this paper, we propose a new approach to human resource allocation management and development in the tourism industry and improve the efficiency of human resource allocation in the tourism industry.
Block size optimization for PoW consensus algorithm based blockchain applications by using whale optimization algorithm

Blockchain-based applications come up with cryptocurrencies, especially Bitcoin, introducing a distributed ledger technologies for peer-to-peer networks and essentially records the transactions in blocks containing hash value of the previous blocks. Block generation constitutes the basis of this technology, and the optimization of such systems is among the most crucial concerns. Determining either the block size or the number of transactions in the block brings out a remarkable problem that has been solved by the miners in recent years. First, higher block size results in higher transaction time, on the other hand, smaller block size has many disadvantages such as security, lower transaction fees, lower transaction numbers in a given time interval, which makes it unable to compete with other currency systems due to this bulky structure and higher block generation time. In this study, multiobjective optimization problem (OP) is proposed by minimizing block generation and transmission time. This multiobjective OP is transformed into a single OP by applying weighted sum method. To determine the optimal block size, particle swarm optimization (PSO) algorithm and whale optimization evolutionary algorithm (WOA) are employed. Although both algorithms have capability to reach optimum block size and corresponding time, WOA achieves better performance than PSO in terms of the convergence speed and output fluctuation. Moreover, analysis of the prediction of optimum block size is carried out under different weights which creates many optimization functions. Experimental results indicate that if higher weight is assigned to the transmission time, then block size decreases sharply. Furthermore, the experimental results reveal that design of the blockchain network and number of nodes in network profoundly affect the block size selection due to the time constraints.
An improved Framework for Biometric Database’s privacy

Security and privacy are huge challenges in biometric systems. Biometrics are sensitive data that should be protected from any attacker and especially attackers targeting the confidentiality and integrity of biometric data. In this paper an extensive review of different physiological biometric techniques is provided. A comparative analysis of the various sus mentioned biometrics, including characteristics and properties is conducted. Qualitative and quantitative evaluation of the most relevant physiological biometrics is achieved. Furthermore, we propose a new framework for biometric database privacy. Our approach is based on the use of the promising fully homomorphic encryption technology. As a proof of concept, we establish an initial implementation of our security module using JAVA programming language.
Knowledge synthesis through scientific visualization

One of the most powerful yet little-recognized benefits of visualization is the way it synthesizes our knowledge and externalizes our mental models of the science.