With the increasing demand of raw materials nowadays, and the decrease in supplies, the industrial sector is suffering. The environment and the society are also indirectly affected. The goal to reach a sustainable development imposes several studies on the economic, environmental and community level. The aim of this paper is to provide an overview of the existing body of literature on automated remanufacturing, and its potential impacts on the three pillars of sustainability. A particular interest is given to the growing use of cobots promoted by the principle of industry 4.0. The investigation that covers each part of the remanufacturing process will help in formalizing an approach about the automation of such processes. It highlights the challenges found and aims to improve the remanufacturing sector towards a more sustainable industry.

Artificial Intelligence (AI) is one of the most promising technologies of the 21. century, with an already noticeable impact on society and the economy. With this work, we provide a short overview of global trends, applications in industry and selected use-cases from our international experience and work in industry and academia. The goal is to present global and regional positive practices and provide an informed opinion on the realistic goals and opportunities for positioning B&H on the global AI scene.

In this meta-ethnography, we explore three different angles of ethical artificial intelligence (AI) design implementation including the philosophical ethical viewpoint, the technical perspective, and framing through a political lens. Our qualitative research includes a literature review which highlights the cross referencing of these angles through discussing the value and drawbacks of contrastive top-down, bottom-up, and hybrid approaches previously published. The novel contribution to this framework is the political angle, which constitutes ethics in AI either being determined by corporations and governments and imposed through policies or law (coming from the top), or ethics being called for by the people (coming from the bottom), as well as top-down, bottom-up, and hybrid technicalities of how AI is developed within a moral construct and in consideration of its users, with expected and unexpected consequences and long-term impact in the world. There is a focus on reinforcement learning as an example of a bottom-up applied technical approach and AI ethics principles as a practical top-down approach. This investigation includes real-world case studies to impart a global perspective, as well as philosophical debate on the ethics of AI and theoretical future thought experimentation based on historical fact, current world circumstances, and possible ensuing realities.

Purpose - To better understand the online reviews and help potential consumers, businessmen, and product manufacturers effectively obtain users' evaluation on product aspects, this paper explores the distribution regularities of user attention and sentiment toward product aspects from the temporal perspective of online reviews. Design/methodology/approach - Temporal characteristics of online reviews (purchase time, review time, and time intervals between purchase time and review time), similar attributes clustering, and attribute-level sentiment computing technologies are employed based on more than 340k smartphone reviews of three products from JD.COM (a famous online shopping platform in China) to explore the distribution regularities of user attention and sentiment toward product aspects in this article. Findings - The empirical results show that a power-law distribution can fit user attention to product aspects, and the reviews posted in short time intervals contain more product aspects. Besides, the results show that the values of user sentiment of product aspects are significantly higher/lower in short time intervals which contribute to judging the advantages and weaknesses of a product. Research limitations - The paper can't acquire online reviews for more products with temporal characteristics to verify the findings because of the restriction on reviews crawling by the shopping platforms. Originality/value -This work reveals the distribution regularities of user attention and sentiment toward product aspects, which is of great significance in assisting decision-making, optimizing review presentation, and improving the shopping experience.

Deep learning models are state-of-the-art in compressive spectral imaging (CSI) recovery. These methods use a deep neural network (DNN) as an image generator to learn non-linear mapping from compressed measurements to the spectral image. For instance, the deep spectral prior approach uses a convolutional autoencoder network (CAE) in the optimization algorithm to recover the spectral image by using a non-linear representation. However, the CAE training is detached from the recovery problem, which does not guarantee optimal representation of the spectral images for the CSI problem. This work proposes a joint non-linear representation and recovery network (JR2net), linking the representation and recovery task into a single optimization problem. JR2net consists of an optimization-inspired network following an ADMM formulation that learns a non-linear low-dimensional representation and simultaneously performs the spectral image recovery, trained via the end-to-end approach. Experimental results show the superiority of the proposed method with improvements up to 2.57 dB in PSNR and performance around 2000 times faster than state-of-the-art methods.

Sequences arise in many real-world scenarios; thus, identifying the mechanisms behind symbol generation is essential to understanding many complex systems. This paper analyzes sequences generated by agents walking on a networked topology. Given that in many real scenarios, the underlying processes generating the sequence is hidden, we investigate whether the reconstruction of the network via the co-occurrence method is useful to recover both the network topology and agent dynamics generating sequences. We found that the characterization of reconstructed networks provides valuable information regarding the process and topology used to create the sequences. In a machine learning approach considering 16 combinations of network topology and agent dynamics as classes, we obtained an accuracy of 87% with sequences generated with less than 40% of nodes visited. More extensive sequences turned out to generate improved machine learning models. Our findings suggest that the proposed methodology could be extended to classify sequences and understand the mechanisms behind sequence generation.

Academic and scientific research activities have a growing economic importance, requiring larger outlays and investments both in advanced and emerging nations. In both cases it is a matter of national prestige but more than that, of strategic relevance, since the presence of large numbers of highly educated citizens contributing to the advance of human knowledge has well-established impacts on technological and industrial capabilities. These, in turn, are highly relevant to ensure the competitiveness and the economic security of nations as well as yielding other benefits to the economy (Dasgupta and David (1994) and Salter and Martin (2001)). The care and promotion of research activities should thus be a focus of public policies aimed at ensuring social and economic development (Stephan (2012) and Etzkowitz (2013)). A particularly pressing issue in this matter is to assess the quality of the production generated by researchers in all fields of knowledge. On one hand, it is of interest to detect areas in which nationals have international impact, as to concentrate resources on them.

Recent research has shown that criminal networks have complex organizational structures, but whether this can be used to predict static and dynamic properties of criminal networks remains little explored. Here, by combining graph representation learning and machine learning methods, we show that structural properties of political corruption, police intelligence, and money laundering networks can be used to recover missing criminal partnerships, distinguish among different types of criminal and legal associations, as well as predict the total amount of money exchanged among criminal agents, all with outstanding accuracy. We also show that our approach can anticipate future criminal associations during the dynamic growth of corruption networks with significant accuracy. Thus, similar to evidence found at crime scenes, we conclude that structural patterns of criminal networks carry crucial information about illegal activities, which allows machine learning methods to predict missing information and even anticipate future criminal behavior.

Electric Vehicle (EV) charging demand and charging station availability forecasting is one of the challenges in the intelligent transportation system. With the accurate EV station situation prediction, suitable charging behaviors could be scheduled in advance to relieve range anxiety. Many existing deep learning methods are proposed to address this issue, however, due to the complex road network structure and comprehensive external factors, such as point of interests (POIs) and weather effects, many commonly used algorithms could just extract the historical usage information without considering comprehensive influence of external factors. To enhance the prediction accuracy and interpretability, the Attribute-Augmented Spatial-Temporal Graph Informer (AST-GIN) structure is proposed in this study by combining the Graph Convolutional Network (GCN) layer and the Informer layer to extract both external and internal spatial-temporal dependence of relevant transportation data. And the external factors are modeled as dynamic attributes by the attribute-augmented encoder for training. AST-GIN model is tested on the data collected in Dundee City and experimental results show the effectiveness of our model considering external factors influence over various horizon settings compared with other baselines.

This is my new AI in Healthcare Highlights & Milestones Report for Summer 2022. This report includes an overview of advances made during the summer across the healthcare spectrum including important studies, regulatory clearances, fundraising, partnerships, and growth in the AI ecosystem worldwide. This summer scientists demonstrated how they successfully used AI in many areas including: to reduce sepsis deaths, to predict cardiac events, to detect breast cancer, to detect lung cancer, to detects osteoporosis, to detect Parkinson's, to monitor diabetic retinopathy, to detect heart disease, to detect bladder cancer, to enable pathology, to detect fractures, and to monitor Parkinson's using the Apple Watch. In July scientists in Germany published a large scale study demonstrating that radiologists working with AI were more accurate detecting breast cancer than radiologists working without AI, and vice versa - the AI was more accurate when working with a radiologist than when working independently. The study was led by Vara, a German company, in collaboration with radiologists at the Essen University Hospital in Germany and the Memorial Sloan Kettering Cancer Center in New York. Vara's AI is has been used by radiologists in German breast screening centers for two years and is used in 30% of Germany's breast cancer screening centers. Vara's AI software is also used to screen for breast cancer in a hospital in Mexico and in a hospital in Greece.