Reviewerly/exHarmony huggingface dataset:
Learning Interpretable Models Expressed in Linear Temporal Logic

We examine the problem of learning models that characterize the high-level behavior of a system based on observation traces. Our aim is to develop models that are human interpretable. To this end, we introduce the problem of learning a Linear Temporal Logic (LTL) formula that parsimoniously captures a given set of positive and negative example traces. Our approach to learning LTL exploits a symbolic state representation, searching through a space of labeled skeleton formulae to construct an alternating automaton that models observed behavior, from which the LTL can be read off. Construction of interpretable behavior models is central to a diversity of applications related to planning and plan recognition. We showcase the relevance and significance of our work in the context of behavior description and discrimination: i) active learning of a human-interpretable behavior model that describes observed examples obtained by interaction with an oracle; ii) passive learning of a classifier that discriminates individual agents, based on the human-interpretable signature way in which they perform particular tasks. Experiments demonstrate the effectiveness of our symbolic model learning approach in providing human-interpretable models and classifiers from reduced example sets.
CrowdLearn: A Crowd-AI Hybrid System for Deep Learning-based Damage Assessment Applications

Artificial Intelligence (AI) has been widely adopted in many important application domains such as speech recognition, computer vision, autonomous driving, and AI for social good. In this paper, we focus on the AI-based damage assessment applications where deep neural network approaches are used to automatically identify damage severity of impacted areas from imagery reports in the aftermath of a disaster (e.g., earthquake, hurricane, landslides). While AI algorithms often significantly reduce the detection time and labor cost in such applications, their performance sometimes falls short of the desired accuracy and is considered to be less reliable than domain experts. To exacerbate the problem, the black-box nature of the AI algorithms also makes it difficult to troubleshoot the system when their performance is unsatisfactory. The emergence of crowdsourcing platforms (e.g., Amazon Mechanic Turk, Waze) brings about the opportunity to incorporate human intelligence into AI algorithms. However, the crowdsourcing platform is also black-box in terms of the uncertain response delay and crowd worker quality. In this work, we propose the CrowdLearn, a crowd-AI hybrid system that leverages the crowdsourcing platform to troubleshoot, tune, and eventually improve the black-box AI algorithms by welding crowd intelligence with machine intelligence. The system is specifically designed for deep learning-based damage assessment (DDA) applications where the crowd tend to be more accurate but less responsive than machines. Our evaluation results on a real-world case study on Amazon Mechanic Turk demonstrate that CrowdLearn can provide timely and more accurate assessments to natural disaster events than the state-of-the-art AI-only and human-AI integrated systems.
All-in-one silicon photonic polarization processor

Abstract With the great developments in optical communication technology and large-scale optical integration technology, it is imperative to realize the traditional functions of polarization processing on an integration platform. Most of the existing polarization devices, such as polarization multiplexers/demultiplexers, polarization controllers, polarization analyzers, etc., perform only a single function. Definitely, integrating all these polarization functions on a chip will increase function flexibility and integration density and also cut the cost. In this article, we demonstrate an all-in-one chip-scale polarization processor based on a linear optical network. The polarization functions can be configured by tuning the array of phase shifters on the chip. We demonstrate multiple polarization processing functions, including those of a multiple-input-multiple-output polarization descrambler, polarization controller, and polarization analyzer, which are the basic building blocks of polarization processing. More functions can be realized by using an additional two-dimensional output grating. A numerical gradient descent algorithm is employed to self-configure and self-optimize these functions. Our demonstration suggests great potential for chip-scale, reconfigurable, and fully programmable photonic polarization processors with the artificial intelligence algorithm.
Translating High Hopes Into Tangible Benefits: How Incumbents in Supply Chain and Logistics Approach Blockchain

Blockchain is expected to have a transformational effect on supply chain and logistics due to its promise to improve the information flow between the supply chain partners. However, despite their high hopes, incumbent companies from supply chain and logistics are still struggling to deliver on this promise. In this explorative, qualitative interview study, we identify how incumbent companies try to make use of Blockchain in supply chain and logistics and we also analyze the barriers hampering them. The analysis of twenty-four semi-structured expert interviews and extensive secondary data collates a comprehensive picture of incumbent companies' activities around Blockchain adoption. We find that companies use Blockchain to drive digital transformation, constitute new business models and unify the industry through consortia. The main barriers to such solutions are a lack of technological usability and long-term uncertainties. The results of our study provide evidence for theoretical constructs and guide managerial practice.
Overcoming the Blockchain Oracle Problem in the Traceability of Non-Fungible Products

Blockchain implications within the sustainability domain are rapidly arousing the interest of researchers and institutions. However, despite the avalanche of articles, papers, and recently published books, innovation in the blockchain domain is still heavily influenced by light literature, such as news, articles, opinion posts, and white papers. Lacking a homogeneous literature background, case studies often fall into storytelling, providing mere descriptions of the facts according to the writers’ impressions and opinions. We therefore investigate blockchain adoption for sustainable purposes through a case study while remaining firmly grounded in three main theoretical literature streams: knowledge management, knowledge infrastructure, and trust. Since blockchain interaction with the real world is managed by oracles, addressing the oracle problem is essential in order to evaluate the effectiveness of blockchain for sustainability issues. However, to the best of the authors’ knowledge, no other paper has efficiently addressed this subject or even mentioned it. Recognizing its scarce consideration in the literature, the oracle problem will be analyzed in both theoretical and practical terms, thereby providing a way to solve the issues related to non-fungible products in the supply chain. Choice over the selected case study was made in light of the divergence in motives for the adoption of blockchain (economic over social), which makes the results more inferable at a broader scale and offers an insight into how sustainable innovations can also be economically viable.
Contextualized Point-of-Interest Recommendation

Point-of-interest (POI) recommendation has become an increasingly important sub-field of recommendation system research. Previous methods employ various assumptions to exploit the contextual information for improving the recommendation accuracy. The common property among them is that similar users are more likely to visit similar POIs and similar POIs would like to be visited by the same user. However, none of existing methods utilize similarity explicitly to make recommendations. In this paper, we propose a new framework for POI recommendation, which explicitly utilizes similarity with contextual information. Specifically, we categorize the context information into two groups, i.e., global and local context, and develop different regularization terms to incorporate them for recommendation. A graph Laplacian regularization term is utilized to exploit the global context information. Moreover, we cluster users into different groups, and let the objective function constrain the users in the same group to have similar predicted POI ratings. An alternating optimization method is developed to optimize our model and get the final rating matrix. The results in our experiments show that our algorithm outperforms all the state-of-the-art methods.
Face Recognition Attendance System Based on Real-Time Video Processing

With the advent of the era of big data in the world and the commercial value of face recognition technology, the prospects for face recognition technology are very bright and have great market demand. This article aims to design a face recognition attendance system based on real-time video processing. This article mainly sets four directions to consider the problems: the accuracy rate of the face recognition system in the actual check-in, the stability of the face recognition attendance system with real-time video processing, the truancy rate of the face recognition attendance system with real-time video processing and the interface settings of the face recognition attendance system using real-time video processing. By analyzing the situation of these problems, the concept of attendance system based on face recognition technology is proposed, and the research on face recognition attendance system based on real-time video processing is carried out. Experimental data shows that the accuracy rate of the video face recognition system is up to 82%. Compared with the traditional check-in method, the face recognition attendance system can be reduced by about 60%. The rate of skipping classes has greatly reduced the phenomenon of students leaving early and skipping classes. The face recognition time and attendance system with real-time video processing through the above experimental certification can quickly complete the tasks of students in the time and attendance check-in system, get rid of the complex naming phenomenon, greatly improve the efficiency of class, and play an important role in guiding the development of the time and attendance system.
An Improved Moth-Flame Optimization Algorithm for Engineering Problems

In this paper, an improved moth-flame optimization algorithm (IMFO) is presented to solve engineering problems. Two novel effective strategies composed of Lévy flight and dimension-by-dimension evaluation are synchronously introduced into the moth-flame optimization algorithm (MFO) to maintain a great global exploration ability and effective balance between the global and local search. The search strategy of Lévy flight is used as a regulator of the moth-position update mechanism of global search to maintain a good research population diversity and expand the algorithm’s global search capability, and the dimension-by-dimension evaluation mechanism is added, which can effectively improve the quality of the solution and balance the global search and local development capability. To substantiate the efficacy of the enhanced algorithm, the proposed algorithm is then tested on a set of 23 benchmark test functions. It is also used to solve four classical engineering design problems, with great progress. In terms of test functions, the experimental results and analysis show that the proposed method is effective and better than other well-known nature-inspired algorithms in terms of convergence speed and accuracy. Additionally, the results of the solution of the engineering problems demonstrate the merits of this algorithm in solving challenging problems with constrained and unknown search spaces.
Super-Resolution of Sentinel-2 Imagery Using Generative Adversarial Networks

Sentinel-2 satellites provide multi-spectral optical remote sensing images with four bands at 10 m of spatial resolution. These images, due to the open data distribution policy, are becoming an important resource for several applications. However, for small scale studies, the spatial detail of these images might not be sufficient. On the other hand, WorldView commercial satellites offer multi-spectral images with a very high spatial resolution, typically less than 2 m, but their use can be impractical for large areas or multi-temporal analysis due to their high cost. To exploit the free availability of Sentinel imagery, it is worth considering deep learning techniques for single-image super-resolution tasks, allowing the spatial enhancement of low-resolution (LR) images by recovering high-frequency details to produce high-resolution (HR) super-resolved images. In this work, we implement and train a model based on the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) with pairs of WorldView-Sentinel images to generate a super-resolved multispectral Sentinel-2 output with a scaling factor of 5. Our model, named RS-ESRGAN, removes the upsampling layers of the network to make it feasible to train with co-registered remote sensing images. Results obtained outperform state-of-the-art models using standard metrics like PSNR, SSIM, ERGAS, SAM and CC. Moreover, qualitative visual analysis shows spatial improvements as well as the preservation of the spectral information, allowing the super-resolved Sentinel-2 imagery to be used in studies requiring very high spatial resolution.
Knowledge graph enhanced neural collaborative recommendation

Existing neural collaborative filtering (NCF) recommendation methods suffer from severe sparsity problem. Knowledge Graph (KG), which commonly consists of fruitful connected facts about items, presents an unprecedented opportunity to alleviate the sparsity problem. However, pure NCF models can hardly model the high-order connectivity in KG, and ignores complex pairwise correlations between user/item embedding dimensions. To address these problems, we propose a novel Knowledge graph enhanced Neural Collaborative Recommendation (K-NCR) framework, which effectively combines user–item interaction information and auxiliary knowledge information for recommendation task into three parts: (1) For items, the proposed propagating model learns the representation of item entity. It recursively aggregates information from its multi-hop neighbours in KG, and employs an attention mechanism to discriminate the importance of the relation type to mine users’ potential preferences. (2) For users, another heterogeneous attention weights are leveraged to strengthen the embedding learning of users. (3) The user and item embeddings are then fed into a newly designed two-dimensional interaction map with convolutional hidden layers to model the complex pairwise correlations between their embedding dimensions explicitly. Extensive experimental results on three benchmark datasets demonstrate the effectiveness of our K-NCR framework.
PLACE: Proximity Learning of Articulation and Contact in 3D Environments

The following topics are dealt with: image reconstruction; learning (artificial intelligence); solid modelling; image representation; feature extraction; neural nets; stereo image processing; image colour analysis; cameras; convolutional neural nets.
Multitask Genetic Programming-Based Generative Hyperheuristics: A Case Study in Dynamic Scheduling

Evolutionary multitask learning has achieved great success due to its ability to handle multiple tasks simultaneously. However, it is rarely used in the hyperheuristic domain, which aims at generating a heuristic for a class of problems rather than solving one specific problem. The existing multitask hyperheuristic studies only focus on heuristic selection, which is not applicable to heuristic generation. To fill the gap, we propose a novel multitask generative hyperheuristic approach based on genetic programming (GP) in this article. Specifically, we introduce the idea in evolutionary multitask learning to GP hyperheuristics with a suitable evolutionary framework and individual selection pressure. In addition, an origin-based offspring reservation strategy is developed to maintain the quality of individuals for each task. To verify the effectiveness of the proposed approach, comprehensive empirical studies have been conducted on the homogeneous and heterogeneous multitask dynamic flexible job shop scheduling. The results show that the proposed algorithm can significantly improve the quality of scheduling heuristics for each task in all the examined scenarios. In addition, the evolved scheduling heuristics verify the mutual help among the tasks in a multitask scenario.
LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations

This work aims to tackle the challenging heterogeneous graph encoding problem in the text-to-SQL task.Previous methods are typically node-centric and merely utilize different weight matrices to parameterize edge types, which 1) ignore the rich semantics embedded in the topological structure of edges, and 2) fail to distinguish local and nonlocal relations for each node.To this end, we propose a Line Graph Enhanced Text-to-SQL (LGESQL) model to mine the underlying relational features without constructing metapaths.By virtue of the line graph, messages propagate more efficiently through not only connections between nodes, but also the topology of directed edges.Furthermore, both local and non-local relations are integrated distinctively during the graph iteration.We also design an auxiliary task called graph pruning to improve the discriminative capability of the encoder.Our framework achieves state-of-theart results (62.8% with GLOVE, 72.0% with ELECTRA) on the cross-domain text-to-SQL benchmark Spider at the time of writing.
Robust Deep Co-Saliency Detection with Group Semantic

High-level semantic knowledge in addition to low-level visual cues is essentially crucial for co-saliency detection. This paper proposes a novel end-to-end deep learning approach for robust co-saliency detection by simultaneously learning highlevel group-wise semantic representation as well as deep visual features of a given image group. The inter-image interaction at semantic-level as well as the complementarity between group semantics and visual features are exploited to boost the inferring of co-salient regions. Specifically, the proposed approach consists of a co-category learning branch and a co-saliency detection branch. While the former is proposed to learn group-wise semantic vector using co-category association of an image group as supervision, the latter is to infer precise co-salient maps based on the ensemble of group semantic knowledge and deep visual cues. The group semantic vector is broadcasted to each spatial location of multi-scale visual feature maps and is used as a top-down semantic guidance for boosting the bottom-up inferring of co-saliency. The co-category learning and co-saliency detection branches are jointly optimized in a multi-task learning manner, further improving the robustness of the approach. Moreover, we construct a new large-scale co-saliency dataset COCO-SEG to facilitate research of co-saliency detection. Extensive experimental results on COCO-SEG and a widely used benchmark Cosal2015 have demonstrated the superiority of the proposed approach as compared to the state-of-the-art methods.
What are the limits of deep learning?

What are the limits of deep learning?The much-ballyhooed artificial intelligence approach boasts impressive feats but still falls short of human brainpower.Researchers are determined to figure out what's missing.
Algorithmic Bias in Clinical Populations—Evaluating and Improving Facial Analysis Technology in Older Adults With Dementia

The need for the automated facial expression analysis arises in various clinical settings involving mental and physical health assessment of older adults. However, the effect of age (young versus old) and ability (healthy versus physical or cognitive impairment) on the performance of available methods has not yet been investigated. In this paper, we demonstrate a bias affecting the performance of common facial landmark detection and expression recognition algorithms on the faces of older adults with dementia. We also investigate the ways of mitigating this bias via the addition of representative training examples. Results show that landmark placement is less accurate when tested on the faces of individuals with dementia as compared to older adults who are cognitively healthy. Retraining or fine-tuning the methods with images of older adults’ faces improves the performance significantly, but the gap between older adults with versus without dementia persists. As the interest in using facial analysis methods in clinical applications grows, results of this study: 1) highlight the limitations of the existing models when applied to clinical populations and 2) shed light on methods of addressing these limitations as well as the need to develop algorithms designed to be fair with respect to variables such as age and ability.
Not All Areas Are Equal: Transfer Learning for Semantic Segmentation via Hierarchical Region Selection

The success of deep neural networks for semantic segmentation heavily relies on large-scale and well-labeled datasets, which are hard to collect in practice. Synthetic data offers an alternative to obtain ground-truth labels for free. However, models directly trained on synthetic data often struggle to generalize to real images. In this paper, we consider transfer learning for semantic segmentation that aims to mitigate the gap between abundant synthetic data (source domain) and limited real data (target domain). Unlike previous approaches that either learn mappings to target domain or finetune on target images, our proposed method jointly learn from real images and selectively from realistic pixels in synthetic images to adapt to the target domain. Our key idea is to have weighting networks to score how similar the synthetic pixels are to real ones, and learn such weighting at pixel-, region- and image-levels. We jointly learn these hierarchical weighting networks and segmentation network in an end-to-end manner. Extensive experiments demonstrate that our proposed approach significantly outperforms other existing baselines, and is applicable to scenarios with extremely limited real images.
Recent Trends in User Authentication – A Survey

Recent advancements in technology have led to profusion of personal computing devices, such as smart phone, tablet, watch, glasses, and many more. This has contributed to the realization of a digital world where important daily tasks can be performed over the Internet from any place and at any time and using any device. At the same time, advances in pervasive computing technologies have brought to fruition the concept of smart spaces that target the automated provision of customized services to the inhabitants effortlessly. User authentication, i.e., a procedure to verify the identity of the user, is essential in the digital world so as to protect the user's personal data stored online (e.g., online bank accounts) and on personal devices (e.g., smart phones) and to also enable customized services in smart spaces (e.g., adjusting room temperature and so on). Recently, traditional authentication mechanisms (e.g., passwords or fingerprints) have been repeatedly shown to be vulnerable to subversion. Researchers thus have proposed numerous new mechanisms to authenticate the users in the aforementioned scenarios. This paper presents an overview of these novel systems, so as to guide the future research efforts in these domains.