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关键词： machine eligibility   just-in-time   worker allocation   precedence constraints   Parallel machines scheduling  

摘要： In today's competitive world, scheduling problems are one of the most important and vital issues. In this study, a bi-objective unrelated parallel machine scheduling problem with worker allocation, sequence dependent setup times, precedence constraints, and machine eligibility is presented. The objective functions are to minimize the costs of tardiness and hiring workers. In order to formulate the proposed problem, a mixed-integer quadratic programming model is presented. A strategy called repair is also proposed to implement the precedence constraints. Because the problem is NP-hard, two metaheuristic algorithms, a multi-objective tabu search (MOTS) and a multi objective simulated annealing (MOSA), are presented to tackle the problem. Furthermore, a hybrid metaheuristic algorithm is also developed. Finally, computational experiments are carried out to evaluate different test problems, and analysis of variance is done to compare the performance of the proposed algorithms. The results show that MOTS is doing better in terms of objective values and mean ideal distance (MID) metric, while the proposed hybrid algorithm outperforms in most cases, considering other employed comparison metrics.

关键词： Recommender systems   Task analysis   Predictive models   Graph neural networks   Cybernetics   Computer science   Collaborative filtering   Collaborative filtering (CF)   graph neural network (GNN)   recommender system  

摘要： The recommender system is a popular research topic in the past decades, and various models have been proposed. Among them, collaborative filtering (CF) is one of the most effective approaches. The underlying philosophy of CF is to capture and utilize two types of relationships among users/items, that is, the user-item preferences and the similarities among users/items, to make recommendations. In recent years, graph neural networks (GNNs) have gained popularity in many research fields, and in the recommendation field, GNN-based CF models have also been proposed, which are shown to have impressive performance. However, in our research, we observe a crucial drawback of these models, that is, while they can explicitly model and utilize the user-item preferences, the other necessary type of relationship, that is, the similarities among users/items, can only be implied and then utilized, which seems to hinder the performance of these models. Motivated by this, in this article, we first propose a novel dual-message propagation mechanism (DPM). The DPM can explicitly model and utilize both preferences and similarities to make recommendations;thus, it seems to be a better realization of CF's philosophy. Then, a dual-message graph CF (DGCF) model is proposed. Different from the existing models, in the DGCF, each user's/item's embedding is processed by two GNNs, with one handling the preferences and the other handling the similarities. Extensive experiments conducted on three real-world datasets demonstrate that DGCF substantially outperforms state-of-the-art CF models, and the small amount of sacrifice of time efficiency is tolerable considering the substantial improvement of model performance.

关键词： Information science   Technological innovation   Oral communication   Auditory system   Planning   Cultural differences   Iterative methods   Computer science   Professional aspects   Engineering profession  

摘要： The revitalized interest in ensuring that computer and information science and engineering (CISE) is a fair and equitable professional path is one of our grandest opportunities. As professionals who have championed diversity, equity, and inclusion over decades, we are pleased to offer four actions that you, our colleagues, can take to help. In this article, we spotlight the opportunities that exist within conferences to create fair and equitable participation for all members of the CISE profession. We focus on leveraging committee structures, developing new leaders, revising policies and procedures, and learning from successful innovations. Creating cultural and structural changes becomes possible when we take these steps together.

摘要： In August 2018 paragraph 2267 of the Catechism of the Catholic Church (CCC) underwent its second revision since the first publication of the Catechism in 1992. The first revision took place in 1997 following the release of John Paul Il's encyclical letter Evangelium Vitae in 1995. In its original 1992 version, paragraph 2267, in conjunction with the preceding paragraph 2266, restated the traditional position of the Catholic magisterium that capital punishment is a morally acceptable means of punishment. […] The 1997 revision of paragraph 2267 retained the position that '[t]he traditional teaching of the Church does not exclude … recourse to the death penalty.' It had a statement added to it that capital punishment should only be used when it is 'the only practicable way to defend the lives of human beings effectively against the aggressor.' This addition placed capital punishment more in the field of legitimate means of self-defense than in the field of forms of punishment. It repeated the teaching that states should strive for using bloodless means of protection because these means 'are more in keeping with the concrete conditions of the common good and more in conformity with the dignity of the human person.' Finally, a new sentence was added to the paragraph — it stated that in today's world, states have at their disposal such means of isolating dangerous criminals that instances where death penalty would be necessary for the protection of citizens are 'very rare, if not practically non-existent.'

关键词： Applied Mathematics   Computational Mathematics   Pure Mathematics   Mathematical Sciences   Discrete Mathematics   Mathematical Physics   Theoretical Sciences   Exact Science   Computational Methods   Computational Intelligence   Applied Sciences   Computer Science   Computational Science   Data Science   Engineering   Electrical and Electronics Engineering   Exact Sciences   Natural Sciences   Social Sciences   Nonlinear Dynamics   Nonlinear Systems   Linear Systems   Fractional Calculus AI   Control   Control Systems   Optimization   Complexity   Chaos   Uncertainty   Causality   Symmetry   Oscillations   Bifurcation   Stability   Sensitivity   Quantum   Medicine   Biology   Physics   Chemistry   Mathematical Bioengineering   Mathematical Neuroscience   Mathematical Biology   Artificial Intelligence   Neural Networks   Artificial Neural Networks   Data-Driven Models   Encryption   Soft Computing   Coding Theory   Machine Learning   Deep Learning   Intelligent Systems   Probabilities and Stochastic Processes   Game Theory   Solitons, Fractals, Multifractals   Fractional Calculus   Fractional Dynamics   Fractional Mathematics   Fractional Systems   Fractional Models   Fractals   Multifractals   Waves   Wavelet   Entropy   Algebraic Methods   Computational (-Algorithmic) Complexity   Fractional Order Differential Equations   Fractional Differential Equations   Fuzzy Fractional Differential Equations   Special Functions   Functional Analysis   Error Estimations   Fuzzy Calculus   Differential/Integral Equations, Stochastic Differential Equations, Fractal-Fractional Differential Equations   Fractional-Order Controllers   Fractional-Order Systems   Intelligent Fractional Order Systems   Variational Principles   Image and/or Signal Processing   Mathematical Models   Fractal AI   Fractional AI   Fractals-Fractional AI   Quantum AI   Dynamical systems   Chaotic Systems   Complex Systems  

摘要： Complex systems, as interwoven miscellaneous interacting entities that emerge and evolve through self-organization in a myriad of spiraling contexts, exhibit subtleties on global scale besides steering the way to understand complexity which has been under evolutionary processes with unfolding cumulative nature wherein order is viewed as the unifying framework. Indicating the striking feature of non-separability in components, a complex system cannot be understood in terms of the individual isolated constituents' properties per se, it can rather be comprehended as a way to multilevel approach systems behavior with systems whose emergent behavior and pattern transcend the characteristics of ubiquitous units composing the system itself. This observation specifies a change of scientific paradigm, presenting that a reductionist perspective does not by any means imply a constructionist view;and in that vein, complex systems science, associated with multiscale problems, is regarded as ascendancy of emergence over reductionism and level of mechanistic insight evolving into complex system. While evolvability being related to the species and humans owing their existence to their ancestors' capability with regards to adapting, emerging and evolving besides the relation between complexity of models, designs, visualization and optimality, a horizon that can take into account the subtleties making their own means of solutions applicable is to be entailed by complexity. Such views attach their germane importance to the future science of complexity which may probably be best regarded as a minimal history congruent with observable variations, namely the most parallelizable or symmetric process which can turn random inputs into regular outputs. Interestingly enough, chaos and nonlinear systems come into this picture as cousins of complexity which with tons of its components are involved in a hectic interaction with one another in a nonlinear fashion amongst the other related systems

关键词： Hidden Markov models   Convolution   Informatics   Smart cities   Recurrent neural networks   Recommender systems   Computer science   Aggregator   augmented Intelligence of Things   graph convolution network   self-attention   successive point-of-interest recommendation  

摘要： Extensive user check-in data incorporating user preferences for location is collected through Internet of Things (IoT) devices, including cell phones and other sensing devices in location-based social network. It can help traveling enterprises intelligently predict users' interests and preferences, provide them with scientific tourism paths, and increase the enterprises income. Thus, successive point-of-interest (POI) recommendation has become a hot research topic in augmented Intelligence of Things (AIoT). Presently, various methods have been applied to successive POI recommendations. Among them, the recurrent neural network-based approaches are committed to mining the sequence relationship between POIs, but ignore the high-order relationship between users and POIs. The graph neural network-based methods can capture the high-order connectivity, but it does not take the dynamic timeliness of POIs into account. Therefore, we propose an Interaction-enhanced and Time-aware Graph Convolution Network (ITGCN) for successive POI recommendation. Specifically, we design an improved graph convolution network for learning the dynamic representation of users and POIs. We also designed a self-attention aggregator to embed high-order connectivity into the node representation selectively. The enterprise management systems can predict the preferences of users, which is helpful for future planning and development. Finally, experimental results prove that ITGCN brings better results compared to the existing methods.

关键词： Data ethics   artificial intelligence ethics   responsible artificial intelligence   computing ethics   ethics education   embedded ethics   computer science ethics modules   educational assessment  

摘要： Embedding ethics modules within computer science courses has become a popular response to the growing recognition that computer science programs need to better equip their students to navigate the ethical dimensions of computing technologies such as artificial intelligence, machine learning, and big data analytics. However, the popularity of this approach has outpaced the evidence of its positive outcomes. To help close that gap, this empirical study reports positive results from Northeastern University's program that embeds values analysis modules into computer science courses. The resulting data suggest that such modules have a positive effect on students' moral attitudes and that students leave the modules believing they are more prepared to navigate the ethical dimensions they will likely face in their eventual careers. Importantly, these gains were accomplished at an institution without a philosophy doctoral program, suggesting this strategy can be effectively employed by a wider range of institutions than many have thought.

关键词： Semantics   Feature extraction   Image segmentation   Image color analysis   Task analysis   Image reconstruction   Computer science   Adaptive feature fusion   dehazing   image restoration   semantic aware  

摘要： Despite that convolutional neural networks (CNNs) have shown high-quality reconstruction for single image dehazing, recovering natural and realistic dehazed results remains a challenging problem due to semantic confusion in the hazy scene. In this article, we show that it is possible to recover textures faithfully by incorporating semantic prior into dehazing network since objects in haze-free images tend to show certain shapes, textures, and colors. We propose a semantic-aware dehazing network (SDNet) in which the semantic prior is taken as a color constraint for dehazing, benefiting the acquisition of a reasonable scene configuration. In addition, we design a densely connected block to capture global and local information for dehazing and semantic prior estimation. To eliminate the unnatural appearance of some objects, we propose to fuse the features from shallow and deep layers adaptively. Experimental results demonstrate that our proposed model performs favorably against the state-of-the-art single image dehazing approaches.

关键词： Industrial Internet of Things   Security   Informatics   Computer science   Privacy   Feature extraction   Image edge detection  

关键词： Neural networks   Degradation   Kernel   Computer science   Task analysis   Semantics   Principal component analysis   Discriminability   domain adaptation (DA)   inter-class distance   intra-class distance   maximum mean discrepancy (MMD)  

摘要： Existing domain adaptation approaches often try to reduce distribution difference between source and target domains and respect domain-specific discriminative structures by some distribution [e.g., maximum mean discrepancy (MMD)] and discriminative distances (e.g., intra-class and inter-class distances). However, they usually consider these losses together and trade off their relative importance by estimating parameters empirically. It is still under insufficient exploration so far to deeply study their relationships to each other so that we cannot manipulate them correctly and the model's performance degrades. To this end, this article theoretically proves two essential facts: 1) minimizing MMD equals to jointly minimizing their data variance with some implicit weights but, respectively, maximizing the source and target intra-class distances so that feature discriminability degrades and 2) the relationship between intra-class and inter-class distances is as one falls and another rises. Based on this, we propose a novel discriminative MMD with two parallel strategies to correctly restrain the degradation of feature discriminability or the expansion of intra-class distance;specifically: 1) we directly impose a tradeoff parameter on the intra-class distance that is implicit in the MMD according to 1) and 2) we reformulate the inter-class distance with special weights that are analogical to those implicit ones in the MMD and maximizing it can also lead to the intra-class distance falling according to 2). Notably, we do not consider the two strategies in one model due to 2). The experiments on several benchmark datasets not only prove the validity of our revealed theoretical results but also demonstrate that the proposed approach could perform better than some compared state-of-art methods substantially. Our preliminary MATLAB code will be available at https://***/WWLoveTransfer/.