Reviewerly/exHarmony huggingface dataset:
Uncertainty in Selective Bagging: A Dynamic Bi-objective Optimization Model

Bagging is a common approach in ensemble learning that generates a group of classifiers through bootstrapping for classification tasks. Despite its wide applications, generating redundant classifiers remains a central challenge in bagging. In recent years, many selective bagging models have been presented to deal with this challenge. These models mostly focused on the accuracy of classifiers and the diversity among them. Despite the importance of uncertainty in the performance of ensemble classifiers, this criterion has been neglected in selective bagging models. In this paper, we propose a two-stage selective bagging model. In the first stage, we formalize the selective bagging problem as a bi-objective optimization model considering both the uncertainty and accuracy of classifiers. We propose an adaptive evolutionary Two-Arch2 algorithm, named Diverse-Two-Arch2, to solve the bi-objective model. The output of this stage is a subset of classifiers that are diverse, certain about correct predictions, and uncertain about incorrect predictions. While most selective bagging models focus on the selection of a fixed subset of classifiers for all test samples (static approach), our proposed model has a dynamic approach to the selection process. So, in the second stage of the model, we select only certain classifiers to make an ensemble prediction for each test sample. Experimental results on twenty data sets and comparing with two ensemble models, and five state-of-the-art dynamic selective bagging models show the outperformance of the proposed model. We also compare the performance of the proposed Diverse-Two-Arch2 to alternative evolutionary computation methods.
Scalable Batch Acquisition for Deep Bayesian Active Learning

In deep active learning, it is especially important to choose multiple examples to markup at each step to work efficiently, especially on large datasets. At the same time, existing solutions to this problem in the Bayesian setup, such as BatchBALD, have significant limitations in selecting a large number of examples, associated with the exponential complexity of computing mutual information for joint random variables. We, therefore, present the Large BatchBALD algorithm, which gives a well-grounded approximation to the BatchBALD method that aims to achieve comparable quality while being more computationally efficient. We provide a complexity analysis of the algorithm, showing a reduction in computation time, especially for large batches. Furthermore, we present an extensive set of experimental results on image and text data, both on toy datasets and larger ones such as CIFAR-100.
MM-FRec: Multi-Modal Enhanced Fashion Item Recommendation

Existing studies on fashion item recommendation mainly focused on incorporating the visual signals of items to boost the user preference learning, while overlooking the semantic attributes (e.g., material and brand) of fashion items that also contain important cues about items’ properties and users’ preference. To bridge this gap, we aim to comprehensively explore the attribute and vision modalities of items to improve the fashion item recommendation performance. However, this is non-trivial due to the latent visual-semantic consistency, various relation types, and unique attributes with insufficient samples. To address these challenges, we propose a Multi-Modal enhanced Fashion item Recommendation scheme (MM-FRec). Specifically, to cope with the multi-modal data, we introduce a relation-oriented graph as well as a vision-oriented graph, and design MM-FRec with three key components: attribute-enhanced latent representation learning, visual representation learning, and multi-modal enhanced preference modeling. To deal with the various relation types, we present a new relation-aware propagation method for adaptively aggregating the information from neighbor nodes to promote the user and item representation learning. To cope with the unique attributes, we introduce the deep multi-task learning strategy in the relation-aware confidence assignment. Extensive experiments on a real-world dataset demonstrate the superiority of our model over state-of-the-art methods.
Multi-view Self-attention for Regression Domain Adaptation with Feature Selection

In this paper, we address the problem of unsupervised domain adaptation in a regression setting considering that source data have different representations (multiple views). In this work, we investigate an original method which takes advantage of different representations using a discrepancy distance while using attention-based neural networks mechanism to estimate feature importance in domain adaptation. For this purpose, we will begin by introducing a novel formulation of the optimization objective. Then, we will develop an adversarial network domain adaptation algorithm adjusting weights given to each feature, ensuring that those related to the target receive higher weights. Finally, we will evaluate our method on public dataset and compare it to other domain adaptation baselines to demonstrate the improvement for regression tasks.
GIS model for geothermal advantageous target selection

Abstract As the particularly popular green energy, geothermal resources are gradually favored by countries around the world, and the development model centered on geothermal dew point cannot meet the increasing geothermal demand. In this paper, a GIS model combining PCA and AHP is proposed, aiming to select the advantages of geothermal resources at the regional scale and analyze the main influencing indicators. Through the combination of the two methods, both data and empirical can be considered, then the geothermal advantage distribution on the area can be displayed through GIS software images. A multi-index evaluation system is established to qualitatively and quantitatively evaluate the mid-high temperature geothermal resources in Jiangxi Province, and carry out the evaluation of the dominant target areas and the analysis of geothermal impact indicators. The results show that it is divided into 7 geothermal resource potential areas and 38 geothermal advantage targets, and the determination of deep fault is the most critical index of geothermal distribution. This method is suitable for large-scale geothermal research, multi-index and multi-data model analysis and precise positioning of high-quality geothermal resource targets, which can meet the needs of geothermal research at the regional scale.
The Conception of Creativity of Rozanov in the Context of Ancient Tradition.

The creativity of Rozanov is realized as the flow of experiences and associations. This experiences are putting together by means of the topic-conception “becoming”, that belongs to ancient categories. This categories are used to collect the dividual intuitions of Rozanov. Doctrine of Rozanov is interpreted as the coordination between the outside and the inside ranges of becoming. The inside rang of becoming includes by Rozanov the movement of people and things from birth to death. The inside ranges are corresponded with concrete things and represents the complex analogues to the outside range. The specific by Rozanov lies in the impossibility of things to stop eternal formation. By Rozanov this formation eventually determines itself. It is by Rozanov the folded type of entropy. Rozanov explicates forms of entropy, when he describes his life und the being of world. His descriptions are the is the evidence. We explain the cosmos by Rozanov with help of ancient ontological categories and determine his creativity as an interaction between cosmos and chaos. To find balance between cosmos and chaos is the main eksistential problem of his doctrine and life. The decision of this problem is by Rozanov imposable, but this impossibility gives the opportunity to understand the world of Rozanov in context of different directions of philosophy.
On the inverse identification of sheet metal mechanical behaviour using a heterogeneous Arcan virtual experiment

Abstract. Modelling and simulation are critical stages of product development in modern industry. Simulation tools in solid mechanics use constitutive models and their parameters to describe the behaviour of materials. Nowadays, with the use of heterogeneous test configurations and full-field measurements, it is possible to measure a combination of multiple strain states, allowing for the identification of multiple parameters from a single test with reduced cost and time. This work aims to investigate the potential for obtaining heterogeneous states of strain\stress with the Arcan test configuration. A finite element model was developed using a specimen with a smooth arc section in which the loading and material directions varied, producing tensile, shear, or mixed mode responses. The most heterogeneous test configuration was selected using a heterogeneous criterion and the numerical results were used to generate synthetic speckle pattern images and further processed by digital image correlation (DIC). The DIC results were used as input for the identification procedure through the virtual fields method (VFM) for the simultaneous calibration of the Swift hardening law and the Hill'48 anisotropic yield criterion. The identified solution was compared with the ground truth material parameters. The results show the potential of combining the Arcan test with the VFM to simultaneously identify material parameters for anisotropic plasticity models of sheet metals.
On non-monomial APcN permutations over finite fields of even characteristic

Recently, a new concept called the c-differential uniformity was proposed by Ellingsen et al. (2020), which generalizes the notion of differential uniformity measuring the resistance against differential cryptanalysis. Since then, finding functions having low c-differential uniformity has attracted the attention of many researchers. However it seems that, at this moment, there are not many non-monomial permutations having low c-differential uniformity. In this paper, we present new classes of (almost) perfect c-nonlinear non-monomial permutations over a binary field.
Adaptive Robust RBF-NN Nonsingular Terminal Sliding Mode Control Scheme for Application to Snake Robot’s Head for Image Stabilization

Image stabilization is important for snake robots to be used as mobile robots. In this paper, we propose an adaptive robust RBF neural network nonsingular terminal sliding mode control to reduce swinging in the snake robot’s head while it is being driven. To avoid complex dynamic problems and reduce interference during driving, we propose a 2-DOF snake robot’s head system. We designed the control system based on the nonsingular terminal sliding mode control, which ensures a fast response and finite time convergence. To reduce chattering, we incorporated an RBF neural network that can compensate for disturbances. Additionally, we included an adaptive robust term to address the disadvantages of neural network-based control. The adaptive robust term generates control inputs based on the error and is used in conjunction with the reverse saturation function to eliminate chattering. The update law of the neural network and the adaptive robust term is designed based on Lyapunov’s theory. We proved the stability of the proposed controller by investigating finite time convergence before and after the reverse saturation function operation section. Finally, we verified the performance of the proposed controller through computer simulation. The simulation evaluates the controllers using a sinusoidal reference signal similar to snake robot movement and a mixed reference signal considering the controller’s waste case. The proposed controller has excellent tracking performance and improved chattering compared with the previous controller.
Multirobot Task Planning Method Based on the Energy Penalty Strategy

In multirobot task planning, the goal is to meet the multi-objective requirements of the optimal and balanced energy consumption of robots. Thus, this paper introduces the energy penalty strategy into the GA (genetic algorithm) to achieve the optimization of the task planning of multiple robots in different operation scenarios. First, the algorithm model is established, after which the objective function is constructed by taking the energy excess of the relative average energy consumption of each robot as the penalty energy, along with the total energy consumption of multiple robots. In the genetic operation, two-segment chromosome coding is used to realize the iterative optimization of the number and task sequences of robots through diversified cross and mutation operators. Then, in the task scenario with obstacles, the A* (A-Star) algorithm and GA are used to plan the optimal obstacle avoidance path and to realize the secondary optimization of the robot task sequence without changing the number of tasks. During optimization, the energy penalty strategy imposes punishment on the objective function through the size of the penalty energy, enabling the robot energy consumption to reach an equilibrium state by maintaining the total energy consumption at the minimum. Finally, the MATLAB platform is used to conduct the simulation experiments to compare with basic genetic algorithms and penalty function algorithms, after which the optimal allocation scheme and energy consumption iteration of the algorithm are analyzed under different robot numbers, task numbers, and task scenarios, and the simulation results include robot task sequences, total energy consumption, average energy consumption, and standard deviation of energy consumption.
ResNet50 Architecture Based Dog Breed Identification Using Deep Learning

Dog breed identification is one of the challenging task in animal husbandry. Knowing what a dog was bred for can assist you in predicting and comprehending its behavior. Most breeds were created with a specific goal in mind: to have the desire to achieve something specific. Dogs retain part of that drive as pets in our homes, and without a good outlet for that energy, it might manifest in undesirable ways. Developing a deep learning model, that can identify the breed of the dog with high accuracy, using this model one can easily identify the dog breed which makes easier for them to train the dog, so that we cant wait for the experts and no need for spending money for identifying the dog breed, using the mobile phones we can easily identify our dog breed simply using its image. This paper deals with an advanced classification model was designed which identifies the breed of the dog. A training dataset consisting of more than 10000 images is used, CNN is used for the classification and the accuracy is achieved using Resnet50 architecture which is highest when compared to other models.
Methods to Enhance BERT in Aspect-Based Sentiment Classification

BERT is a widely used pre-trained model in Natural Language Processing tasks, including Aspect-Based sentiment classification. BERT is equipped with sufficient prior language knowledge in the enormous amount of pre-trained model parameters, for which the fine-tuning of BERT has become a critical issue. Previous works mainly focused on specialized downstream networks or additional knowledge to fine-tune the BERT to the sentiment classification tasks. In this paper, we design experiments to find the fine-tuning techniques that can be used by all models with BERT in the Aspect-Based Sentiment Classification tasks. Through these experiments, we verify different feature extraction, regularization, and continual learning methods, then we summarize 8 universally applicable conclusions to enhance the training and performance of the BERT model.
Effects of Video Prompting with Augmented Reality on Functional Living Skills of Students with Intellectual and Developmental Disabilities

Training functional living skills is crucial for students with intellectual and developmental disabilities (IDDs) because they are directly related to their independence. The aim of this study was to explore the effect of an augmented reality (AR)–based video prompting (VP) teaching model on the learning of functional living skills among elementary school students with intellectual and developmental disabilities. A multiple-probe, across-subjects, single-case research design was adopted to recruit three participants, namely, fifth- and sixth-grade students with IDDs in a special education program in an elementary school in Taichung City. The independent variable was the AR-based VP teaching model, and the dependent variable was the participants’ performance of functional living skills (cooking rice in a rice cooker). The results indicate that the intervention had immediate, maintenance, and generalization effects on all three participants. In addition, the participants noted that the teaching model relaxed and motivated them. The teachers also indicated that the teaching model motivated the students, resulted in positive learning outcomes, and strengthened the students’ ability to learn independently. Through this AR-based VP teaching model, students with developmental disabilities can improve their functional living skills.
Understanding Customer Satisfaction Towards Delivery Services of E-Commerce

This study intends to investigate the effect of the factors, namely price, reliability, responsiveness, assurance, and empathy on customer satisfaction towards the e-commerce delivery services. A total of 311 respondents were recruited using convenience sampling to participate in this study. All the measurements of constructs were adapted from previous studies. The reliability of the variables was assessed using the Cronbach’s alpha and multiple regression analysis was performed to examine the effect of all the factors in the research model on customer satisfaction. The findings have shown that all the variables, namely price, reliability, assurance, and empathy have a positive influence on customer satisfaction with e-commerce delivery services. Responsiveness, however, has no influence on customer satisfaction. Assurance was revealed to be the strongest predictor of customer satisfaction. The second strongest factor was empathy. Understanding factors influencing the delivery services of e-commerce is essential as it is able to provide useful information for the delivery service company which will help them to enhance the quality of their services. The findings of this study would provide an insight of the customer satisfaction towards the delivery services of e-commerce during the COVID-19 from Malaysian’s perspective.
Computer Crimes

This chapter surveys the history and examples of computer crimesComputercrimes, their types, costs to society, and strategies of detection and prevention. In the discussion, it is noted that a great number of computer attacksComputerattacks fall into two categories: penetrationPenetration and denial of serviceDenial of service attacks. And these are discussed in depth. Attack motives are also discussed. Are nations, businesses, and individuals prepared for computer attacksComputerattacks? Are they ready to pay the price? We look for answers to these questions as we ponder the costs and consequences of computer crimes. We note also that although it is difficult to estimate the actual costs of e-attacksE-attacks on physical system resources, progress is being made for better and more accurate estimates. An in-depth discussion of the social and ethical consequences that include psychological effects, moral decayMoraldecay, loss of privacyPrivacy, and loss of trust follows. We end the chapter with recommendations for educating the computing device users in computer ethics. The need to educate the user to be aware of possible sources of computer crimesComputercrimes and what to do when one becomes a victim of these crimes is stressedStress. It is noted that education can go a long way in reducing computer crimesComputercrimes if the users take crime preventive steps every time, they use the computer and computer-related technologies.
A pilot study on the selection of fundamental loanwords

외래어는 국어의 표현을 다양화하고 국제 통용 용어에 대한 지식을 확산한다는 긍정적 기능이 있지만, 그동안 언어정책적으로는 말다듬기 사업을 통해 쓰지 말아야 할 외래어만을 발표해 왔다. 본고에서는 국어 안에 안정된 자리를 잡아 행정, 언론, 교육 등 공적 영역에서 순화할 필요 없이 쓸 수 있는 외래어, 곧 기본외래어의 목록을 정책적으로 마련할 필요가 있다는 관점에서, 기본외래어 선정을 위한 시론으로서 그 방법론을 모색하고자 하였다. 국어에서 널리, 자주 쓰이며 대체가 불가능하거나 불필요하다는 기본외래어의 조건을 고려하여 내국인 · 외국인 · 북한이탈주민을 대상으로 한 (한)국어 교육용 기본어휘에서 외래어의 목록을 추출하였다. 이를『기본외래어 조사 자료집』(국립국어연구원, 1993)의 기본외래어 목록과 대조함으로써 기본외래어 선정에 있어 고려해야 할 점을 고찰하였다. Loanwords have a positive function of diversifying the expression of Korean language and spreading knowledge of internationally used terms, but in terms of Korean language policy, only loanwords that should not be used have been announced through the language refinement project. We emphasized the necessity of a list of fundamental loanwords, which could be used in public areas such as administration, media, and education without the need to refine them, as they had settled down in the Korean language. It is tried to find the methodology as an pilot study for selecting fundamental loanwords. A list of loanwords was extracted from the fundamental vocabulary for Korean language education targeting Koreans, foreigners, and North Korean refugees; considering if the words are widely and frequently used in the Korean language and cannot be or are unnecessary replaced. And then it was compared with the list of fundamental loanwords of the National Institute of the Korean Language(1993), which introduced the concept of fundamental loanwords for the first time and conducted a basic investigation. With this, some consideration points were suggested in selecting fundamental loanwords.
Knowledge Distillation for Optical Flow-Based Video Super-resolution

Recently, deep learning-based super-resolution (SR) models have been used to improve SR performance by equipping preprocessing networks with baseline SR networks. In particular, in video SR, which creates a high-resolution (HR) image with multiple frames, optical flow extraction is accompanied by a preprocessing process. These preprocessing networks work effectively in terms of quality, but at the cost of increased network parameters, which increase the computational complexity and memory consumption for SR tasks with restricted resources. One well-known approach is the knowledge distillation (KD) method, which can transfer the original model’s knowledge to a lightweight model with less performance degradation. Moreover, KD may improve SR quality with reduced model parameters. In this study, we propose an effective KD method that can effectively reduce the original SR model parameters and even improve network performance. The experimental results demonstrated that our method achieved a better PSNR than the original state-of-the-art SR network despite having fewer parameters.