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关键词： machine learning   neural networks   ethics   reading   politics   computer science  

摘要： Computer science tends to foreclose the reading of its texts by social science and humanities scholars - via code and scale, mathematics, black box opacities, secret or proprietary models. Yet, when computer science papers are read in order to better understand what machine learning means for societies, a form of reading is brought to bear that is not primarily about excavating the hidden meaning of a text or exposing underlying truths about science. Not strictly reading to make sense or to discern definitive meaning of computer science texts, reading is an engagement with the sense-making and meaning-making that takes place. We propose a strategy for reading computer science that is attentive to the act of reading itself, that stays close to the difficulty involved in all forms of reading, and that works with the text as already properly belonging to the ethico-politics that this difficulty engenders. Addressing a series of three "reading problems" - genre, readability, and meaning - we discuss machine learning textbooks and papers as sites where today's algorithmic models are actively giving accounts of their paradigmatic worldview. Much more than matters of technical definition or proof of concept, texts are sites where concepts are forged and contested. In our times, when the political application of AI and machine learning is so commonly geared to settle or predict difficult societal problems in advance, a reading strategy must open the gaps and difficulties of that which cannot be settled or resolved.

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.

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： computer science   information systems   agile   learning organization   efiS? framework  

摘要： In large organizations, it is not trivial to spread knowledge to all teams. Often, individual teams need to handle similar topics and re-invent the wheel. Another scenario is that a group of people with a common role (for example "guild" in Spotify model) has to distill their practices to make them shareable. Trainings should have empower participants so to apply the learnings easily in their daily businesses. To realize this, the proposed Self-Service Kit (SSK) approach can be used in the context of a holistic methodology that fosters team autonomy while leveraging knowledge spread and sharing throughout a large organization. Such a methodology is presented and instantiated in an enterprise context in facing the mentioned challenges.

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.