Reviewerly/exHarmony huggingface dataset:
Generating Transferable 3D Adversarial Point Cloud via Random Perturbation Factorization

Recent studies have demonstrated that existing deep neural networks (DNNs) on 3D point clouds are vulnerable to adversarial examples, especially under the white-box settings where the adversaries have access to model parameters. However, adversarial 3D point clouds generated by existing white-box methods have limited transferability across different DNN architectures. They have only minor threats in real-world scenarios under the black-box settings where the adversaries can only query the deployed victim model. In this paper, we revisit the transferability of adversarial 3D point clouds. We observe that an adversarial perturbation can be randomly factorized into two sub-perturbations, which are also likely to be adversarial perturbations. It motivates us to consider the effects of the perturbation and its sub-perturbations simultaneously to increase the transferability for sub-perturbations also contain helpful information. In this paper, we propose a simple yet effective attack method to generate more transferable adversarial 3D point clouds. Specifically, rather than simply optimizing the loss of perturbation alone, we combine it with its random factorization. We conduct experiments on benchmark dataset, verifying our method's effectiveness in increasing transferability while preserving high efficiency.
RLEKF: An Optimizer for Deep Potential with Ab Initio Accuracy

It is imperative to accelerate the training of neural network force field such as Deep Potential, which usually requires thousands of images based on first-principles calculation and a couple of days to generate an accurate potential energy surface. To this end, we propose a novel optimizer named reorganized layer extended Kalman filtering (RLEKF), an optimized version of global extended Kalman filtering (GEKF) with a strategy of splitting big and gathering small layers to overcome the O(N^2) computational cost of GEKF. This strategy provides an approximation of the dense weights error covariance matrix with a sparse diagonal block matrix for GEKF. We implement both RLEKF and the baseline Adam in our alphaDynamics package and numerical experiments are performed on 13 unbiased datasets. Overall, RLEKF converges faster with slightly better accuracy. For example, a test on a typical system, bulk copper, shows that RLEKF converges faster by both the number of training epochs (x11.67) and wall-clock time (x1.19). Besides, we theoretically prove that the updates of weights converge and thus are against the gradient exploding problem. Experimental results verify that RLEKF is not sensitive to the initialization of weights. The RLEKF sheds light on other AI-for-science applications where training a large neural network (with tons of thousands parameters) is a bottleneck.
The Devil Is in the Frequency: Geminated Gestalt Autoencoder for Self-Supervised Visual Pre-training

The self-supervised Masked Image Modeling (MIM) schema, following "mask-and-reconstruct" pipeline of recovering contents from masked image, has recently captured the increasing interest in the community, owing to the excellent ability of learning visual representation from unlabeled data. Aiming at learning representations with high semantics abstracted, a group of works attempts to reconstruct non-semantic pixels with large-ratio masking strategy, which may suffer from "over-smoothing" problem, while others directly infuse semantics into targets in off-line way requiring extra data. Different from them, we shift the perspective to the Fourier domain which naturally has global perspective and present a new Masked Image Modeling (MIM), termed Geminated Gestalt Autoencoder (Ge^2-AE) for visual pre-training. Specifically, we equip our model with geminated decoders in charge of reconstructing image contents from both pixel and frequency space, where each other serves as not only the complementation but also the reciprocal constraints. Through this way, more robust representations can be learned in the pre-trained encoders, of which the effectiveness is confirmed by the juxtaposing experimental results on downstream recognition tasks. We also conduct several quantitative and qualitative experiments to investigate the learning behavior of our method. To our best knowledge, this is the first MIM work to solve the visual pre-training through the lens of frequency domain.
Towards Inference Efficient Deep Ensemble Learning

Ensemble methods can deliver surprising performance gains but also bring significantly higher computational costs, e.g., can be up to 2048X in large-scale ensemble tasks. However, we found that the majority of computations in ensemble methods are redundant. For instance, over 77% of samples in CIFAR-100 dataset can be correctly classified with only a single ResNet-18 model, which indicates that only around 23% of the samples need an ensemble of extra models. To this end, we propose an inference efficient ensemble learning method, to simultaneously optimize for effectiveness and efficiency in ensemble learning. More specifically, we regard ensemble of models as a sequential inference process and learn the optimal halting event for inference on a specific sample. At each timestep of the inference process, a common selector judges if the current ensemble has reached ensemble effectiveness and halt further inference, otherwise filters this challenging sample for the subsequent models to conduct more powerful ensemble. Both the base models and common selector are jointly optimized to dynamically adjust ensemble inference for different samples with various hardness, through the novel optimization goals including sequential ensemble boosting and computation saving. The experiments with different backbones on real-world datasets illustrate our method can bring up to 56% inference cost reduction while maintaining comparable performance to full ensemble, achieving significantly better ensemble utility than other baselines. Code and supplemental materials are available at https://seqml.github.io/irene.
Resolving Task Confusion in Dynamic Expansion Architectures for Class Incremental Learning

The dynamic expansion architecture is becoming popular in class incremental learning, mainly due to its advantages in alleviating catastrophic forgetting. However, task confu- sion is not well assessed within this framework, e.g., the discrepancy between classes of different tasks is not well learned (i.e., inter-task confusion, ITC), and certain prior- ity is still given to the latest class batch (i.e., old-new con- fusion, ONC). We empirically validate the side effects of the two types of confusion. Meanwhile, a novel solution called Task Correlated Incremental Learning (TCIL) is pro- posed to encourage discriminative and fair feature utilization across tasks. TCIL performs a multi-level knowledge distil- lation to propagate knowledge learned from old tasks to the new one. It establishes information flow paths at both fea- ture and logit levels, enabling the learning to be aware of old classes. Besides, attention mechanism and classifier re- scoring are applied to generate more fair classification scores. We conduct extensive experiments on CIFAR100 and Ima- geNet100 datasets. The results demonstrate that TCIL con- sistently achieves state-of-the-art accuracy. It mitigates both ITC and ONC, while showing advantages in battle with catas- trophic forgetting even no rehearsal memory is reserved. Source code: https://github.com/YellowPancake/TCIL.
Causal Distillation for Alleviating Performance Heterogeneity in Recommender Systems

Recommendation performance usually exhibits a long-tail distribution over users — a small portion of head users enjoy much more accurate recommendation services than the others. We reveal two sources of this performance heterogeneity problem: the uneven distribution of historical interactions (a natural source); and the biased training of recommender models (a model source). As addressing this problem cannot sacrifice the overall performance, a wise choice is to eliminate the model bias while maintaining the natural heterogeneity. The key to debiased training lies in eliminating the effect of confounders that influence both the user's historical behaviors and the next behavior. The emerging causal recommendation methods achieve this by modeling the causal effect between user behaviors, however potentially neglect unobserved confounders (e.g., friend suggestions) that are hard to measure in practice. To address unobserved confounders, we resort to the front-door adjustment (FDA) in causal theory and propose a causal multi-teacher distillation framework (CausalD). FDA requires proper mediators in order to estimate the causal effects of historical behaviors on the next behavior. To achieve this, we equip CausalD with multiple heterogeneous recommendation models to model the mediator distribution. Then, the causal effect estimated by FDA is the expectation of recommendation prediction over the mediator distribution and the prior distribution of historical behaviors, which is technically achieved by multi-teacher ensemble. To pursue efficient inference, CausalD further distills multiple teachers into one student model to directly infer the causal effect for making recommendations. We instantiate CausalD on two representative models, DeepFM and DIN, and conduct extensive experiments on three real-world datasets, which validate the superiority of CausalD over state-of-the-art methods. Through in-depth analysis, we find that CausalD largely improves the performance of tail users, reduces the performance heterogeneity, and enhances the overall performance.
Advances in deep learning: From diagnosis to treatment

Deep learning has brought about a revolution in the field of medical diagnosis and treatment. The use of deep learning in healthcare has grown exponentially in recent years, achieving physician-level accuracy in various diagnostic tasks and supporting applications such as electronic health records and clinical voice assistants. The emergence of medical foundation models, as a new approach to deep learning, has greatly improved the reasoning ability of machines. Characterized by large training datasets, context awareness, and multi-domain applications, medical foundation models can integrate various forms of medical data to provide user-friendly outputs based on a patien's information. Medical foundation models have the potential to integrate current diagnostic and treatment systems, providing the ability to understand multi-modal diagnostic information and real-time reasoning ability in complex surgical scenarios. Future research on foundation model-based deep learning methods will focus more on the collaboration between physicians and machines. On the one hand, developing new deep learning methods will reduce the repetitive labor of physicians and compensate for shortcomings in their diagnostic and treatment capabilities. On the other hand, physicians need to embrace new deep learning technologies, comprehend the principles and technical risks of deep learning methods, and master the procedures for integrating them into clinical practice. Ultimately, the integration of artificial intelligence analysis with human decision-making will facilitate accurate personalized medical care and enhance the efficiency of physicians.
Interoperable E-Health System Using Structural and Semantic Interoperability Approaches in CAREPATH

Technical and semantic interoperability are broadly used components of interoperability technology in healthcare. Technical Interoperability provides interoperability interfaces to enable data exchange within different healthcare systems, despite any underlying heterogeneity. Semantic interoperability make different healthcare systems understand and interpret the meaning of the data that is exchanged, by using and mapping standardized terminologies, coding systems, and data models to describe the concept and structure of data. We propose a solution using Semantic and Structural Mapping techniques within CAREPATH; a research project designed to develop ICT solutions for the care management of elderly multimorbid patients with mild cognitive impairment or mild dementia. Our technical interoperability solution supplies a standard-based data exchange protocol to enable information exchange between local care systems and CAREPATH components. Our semantic interoperability solution supplies programmable interfaces, in order to semantically mediate different clinical data representation formats and incorporating data format and terminology mapping features. The solution offers a more reliable, flexible and resource efficient method across EHRs.
Identity in Higher Computer Education Research: A Systematic Literature Review

The disciplinary identity as a computer science student has recently received increasing attention as a well-developed subject identity can help with increasing retention, interest, and motivation. Besides, identity theory can serve as an analytical lens for issues around diversity. However, identity is also often perceived as a vague, overused concept with a variety of theories to build upon. In addition, connections to other topics, such as computer science conceptions, remain unclear and there seems to be little intra-disciplinary exchange about the concept. This article, therefore, attempts to provide a starting point by presenting a so far missing systematic literature review of identity in Computing Education Research (CER). We analyzed a corpus of 41 articles published since 2005 with a focus on the variety of identity theories that are used, the reasons for using them, and the overall theoretical framing of the concept in the CER literature up to this point. We use content analysis with both inductive and deductive coding to derive categories from the corpus to answer our research questions. The results show that there is less variety in the theories than originally expected, most publications refer to the theory of “Communities of Practice”. The reasons for employing identity theory are also rather canonical, in particular, there is only little theoretical development of the theories within CER and also only little empirical work. Finally, we also present an extended version of a computing identity that can be theoretically derived from the work in our corpus.
Green hydrogen production ensemble forecasting based on hybrid dynamic optimization algorithm

Solar-powered water electrolysis can produce clean hydrogen for sustainable energy systems. Accurate solar energy generation forecasts are necessary for system operation and planning. Al-Biruni Earth Radius (BER) and Particle Swarm Optimization (PSO) are used in this paper to ensemble forecast solar hydrogen generation. The suggested method optimizes the dynamic hyperparameters of the deep learning model of recurrent neural network (RNN) using the BER metaheuristic search optimization algorithm and PSO algorithm. We used data from the HI-SEAS weather station in Hawaii for 4 months (September through December 2016). We will forecast the level of solar energy production next season in our simulations and compare our results to those of other forecasting approaches. Regarding accuracy, resilience, and computational economy, the results show that the BER-PSO-RNN algorithm has great potential as a useful tool for ensemble forecasting of solar hydrogen generation, which has important ramifications for the planning and execution of such systems. The accuracy of the proposed algorithm is confirmed by two statistical analysis tests, such as Wilcoxon’s rank-sum and one-way analysis of variance (ANOVA). With the use of the proposed BER-PSO-RNN algorithm that excels in processing and forecasting time-series data, we discovered that with the proposed algorithm, the Solar System could produce, on average, 0.622 kg/day of hydrogen during the season in comparison with other algorithms.
Research and Practice on the Curriculum Construction of "Shaobei Wushu Culture" Based on the Action Plan of Sports Aesthetic Education Infiltration

Nowadays, with the vigorous development of wushu culture, the course "Shaobei Wushu Culture" is set up in colleges and universities, which is conducive to exploring the multi-cultural value of Shaobei Wushu and serving the development of Chinese Wushu.Sports aesthetic education is an important part of all-round quality education and plays an important role in promoting students' all-round growth.Shaobei Wushu culture is an important content of sports aesthetic education infiltration action plan.It actively responds to the call of the Ministry of Education to carry out the research on this subject, and explores the establishment of a working mechanism for the collaborative development of physical education in primary and secondary schools, which has important construction significance.Based on investigation and research, this research deeply analyzes the problems existing in the curriculum construction of "Shaobei Wushu Culture" based on the action plan of sports aesthetic education.The implementation plan is proposed from the aspects of "reform of teaching objectives, reform of classroom teaching, reform of practical teaching, reform of assessment system, development and construction of teaching materials, construction of teaching resources, construction of campus culture, construction of teachers and students", so as to guide teachers and students in colleges and universities to strengthen their awareness of serving society and enhance their ability to serve society.
Statistical Approach on Perception of Employees Attainments in the Milieu of Non-Public Organisations

Purpose: The aim of this study is to comprehend the various syndromes that influence the performance of individual employees and other factors that have an impact on employee’s performance. Theoretical framework: Employees are encouraged by management to help facilitate the transition to the new market scenario. Only a balance between work life and family will enable an individual to have a peaceful and healthy life. Indeed, the two factors mentioned above are important considerations for living standards. Design/methodology/approach: This paper aims to investigate the factors that influence employee performance in non-public organization’s context. The study involves 145 employees from non-public organizations, 56.55% male and 43.44% female, of ages 27 to 45 years. The participants were selected at random from well-known non-public organizations. This study employed a descriptive-quantitative research design. Findings: The findings show that job-related training, working conditions, family background, workload, and digital knowledge all have a significant impact on employees’ performance. Research, Practical &amp; Social implications: This study considers job-oriented training, working conditions, work load, employee’s family back ground and their digital knowledge to measure employee’s performance. However clear communication,defining roles,trust leadership and and other variables could also be considered by future researchers on this study. Originality/value: Using statistical Modelling in understanding Perceptions on employee’s performance was hardly adopted in these types of studies and other variables could also be considered by future researchers on this study.
Corrigendum to “A comprehensive review on modelling the adsorption process for heavy metal removal from waste water using artificial neural network technique” [Heliyon 9(4) (April 2023) e15455]

In the original published version of this article, one of the author Mohamed Al Omar is currently working with two institutes simultaneously, one is the Department of Civil Engineering, Al-SMaarif University College, Ramadi, Iraq which has been displayed in the published paper. The other one is Natural and medical sciences research centre, University of Nizwa, Oman which is missing in the current published version. The authors request to add 'Natural and medical sciences research centre, University of Nizwa, Oman' in his affiliations.
Does The Foreign Direct Investment Affect Economic Growth in Jordan in Light of Commercial Openness

Purpose: This paper aims to examine the effect of foreign direct investment on economic growth in Jordan in light of the trade openness, during the period from 1999 to 2019. Theoretical Framework: This paper discusses how Jordan, as a developing country, has worked over the years to attract foreign investment by creating an environment of incentives, benefits, and legislation that encourages such investments; taking advantage of low local labor wages, high administrative costs, and great commercial openness. Design/methodology/approach: To achieve the objectives of the study and to construct the statistical model the study used analytical methodology. The following statistical methods were applied; Hausman, Wald Test, F-Test Statistic, Granger, Multicollinearity test, Normality Test, Lagrange Test, Multiplier Test, and Causality Test. Findings: After conducting the analysis tests, the results showed a statistically significant impact of open trade direct investment on economic growth, Moreover, findings showed a positive impact on the degree of economic growth, whenever the degree of trade openness and the flow of foreign investment increase. Research, Practical &amp; Social implications: The independent variables in the study model were measured by the ratio of foreign direct investment from the nominal GDP. The variable (the degree of trade openness) was expressed as the ratio of exports and imports to trade volume. The generalized Method of Moments (GMM) was used in this study. Originality/value: In light of Jordan's extensive commercial openness, This investigation looked at the impact of FDI on economic expansion. The study's findings are in line with theoretical research and other studies that hold that a country's level of economic growth is not adversely affected by significant trade openness because foreign investments are not less effective.
Novelty and Lifted Helpful Actions in Generalized Planning

It has been shown recently that successful techniques in classical planning, such as goal-oriented heuristics and landmarks, can improve the ability to compute planning programs for generalized planning (GP) problems. In this work, we introduce the notion of action novelty rank, which computes novelty with respect to a planning program, and propose novelty-based generalized planning solvers, which prune a newly generated planning program if its most frequent action repetition is greater than a given bound v, implemented by novelty-based best-first search BFS(v) and its progressive variant PGP(v). Besides, we introduce lifted helpful actions in GP derived from action schemes, and propose new evaluation functions and structural program restrictions to scale up the search. Our experiments show that the new algorithms BFS(v) and PGP(v) outperform the state-of-the-art in GP over the standard generalized planning benchmarks. Practical findings on the above-mentioned methods in generalized planning are briefly discussed.
An Image Matting Algorithm Based on Inception-ResNet-V2 Network

In the era of deep neural networks, deep learning has been introduced as a research technology in almost every field. Image matting, as a part of image processing, has been constantly updated and iterated in recent years through deep learning-based matting techniques, and has achieved remarkable results. Image matting is performed by training a neural network model, and the matting effect is improved by deepening the number of layers of the network. In this paper, we adopt a lighter network structure Inception Resnet v2 compared with the DIM encoding network, which can significantly lessen the number of model parameters and save the training and computation time. At the same time, the network incorporates the residual network, which allows for more accurate training results with less computer performance. Compared with the VGG16 network structure, the Inception Resnet v2 network can accelerate the training process of the model while maintaining a better matting effect.