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In recent years, the establishment of a substantial amount of academic groups on scientific social network has brought new opportunities for the collaboration among researchers. In this situation, conducting paper recommendation to these academic groups is of terrific necessity in that it can further facilitate group activities. However, when producing group recommendation, existing methods fail to make full use of the abundant group information, from which a great deal of valuable information can be inferred to facilitate the recommendation performance. In addition, those methods tend to assign an equal weight to each group member when aggregating their recommendations, which is unreasonable in practice. Although some improvements have been made to remedy this problem by assigning different weights to group members, they fail to take into account the reliabilities of group members. Therefore, a group-oriented paper recommendation method based on probabilistic matrix factorization and evidential reasoning (GPMF_ER) is proposed in this article to tackle these problems. More specifically, the group and paper content information are integrated into the probabilistic matrix factorization model to enhance the accuracy of individual recommendation. Afterward, evidential reasoning rule is introduced in the aggregation step to consider both the weights and reliabilities of group members. Extensive experiments have been conducted on the real world CiteULike dataset and the results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Promoting a wider range of contribution types can facilitate healthy growth of the visualization community, while increasing the intellectual diversity of visualization research papers. In this paper, we discuss the importance of contribution types and summarize contribution types that can be meaningful in visualization research. We also propose several concrete next steps we can and should take to ensure a successful launch of the contribution types. [ABSTRACT FROM AUTHOR]

This work analyzes sensemaking frameworks and experiments with an iteratively designed visual analysis tool to identify design implications for facilitating research idea generation using visualizations. Our tool, ThoughtFlow, structures and visualizes literature collections using topic models to bridge the information gap between core activities during research ideation. To help users stay focused on a topic while discovering relevant documents, we designed and analyzed usage patterns for two types of embedded visualization that help determine document relevance while minimizing distraction. We analyzed how research ideation outcomes and processes differ when using ThoughtFlow and conventional search engines by augmenting insight-based evaluation with concept-map analysis. Our results suggest that operations afforded by topic models match well with later ideation stages when coherent topics have emerged, but not with early stages when users are still relying heavily on individual keywords to gather background knowledge. We also present qualitative evidence that citation sparklines encourage more exploration of recommended references, and that a preference for paper thumbnails may depend on the consistency between the evidence and the current mental frame. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) signals are widely utilized in the field of cognitive workload decoding (CWD). However, when the recognition scenario is shifted from subject-dependent to subject-independent or spans a long period, the accuracy of CWD deteriorates significantly. Current solutions are either dependent on extensive training datasets or fail to maintain clear distinctions between categories, additionally lacking a robust feature extraction mechanism. In this paper, we tackle these issues by proposing a Bi-Classifier Joint Domain Adaptation (BCJDA) model for EEG-based cross-time and cross-subject CWD. Specifically, the model consists of a feature extractor, a domain discriminator, and a Bi-Classifier, containing two sets of adversarial processes for domain-wise alignment and class-wise alignment. In the adversarial domain adaptation, the feature extractor is forced to learn the common domain features deliberately. The Bi-Classifier also fosters the feature extractor to retain the category discrepancies of the unlabeled domain, so that its classification boundary is consistent with the labeled domain. Furthermore, different adversarial distance functions of the Bi-Classifier are adopted and evaluated in this model. We conduct classification experiments on a publicly available BCI competition dataset for recognizing low, medium, and high cognitive workload levels. The experimental results demonstrate that our proposed BCJDA model based on cross-gradient difference maximization achieves the best performance.

Smart cyber-physical systems (S-CPSs) are complex engineered systems empowered by cyber-physical computing and equipped with the capability of reasoning, learning, adapting, and evolving. As an outcome of data-driven dynamic computing, reasoning capabilities, and the run-time obtained own knowledge, nonlinear and emergent behavior of S-CPSs whilst in operation is an open issue, not experienced in the case of conventional technical systems. This paper analyzes the technical issues of run-time operation and emergent behavior of S-CPSs, reviews the current understanding and state of advancement in designing S-CPSs for run-time, explores the paradox, and issues of designing for run-time adaptation, and synthesizes some general principles that can be taken into consideration when addressing the challenges, first of all, in the context of advanced manufacturing systems. This paper introduces four levels of CPSs according to reasoning capabilities and adaptation freedom of systems, and recognizes the paradox that a system with a higher level of freedom requires a higher level of self-control and resource management according to the overall objective of operation. Specific and common design principles are presented and critically assessed for each advancement level of CPSs. The principles synthesized by the authors provide only a partial fulfillment of the generic need. The planned future research addresses these issues and proposes (largely implementation and application independent) genuine principles for system developers. [ABSTRACT FROM AUTHOR]

A smart home needs to be human-centric, where it tries to fulfill human needs given the devices it has. Various works are developed to provide homes with reasoning and planning capability to fulfill goals, but most do not support complex sequence of plans or require significant manual effort in devising subplans. This is further aggravated by the need to optimize conflicting personal goals. A solution is to solve the planning problem represented as constraint satisfaction problem (CSP). But CSP uses hard constraints and, thus, cannot handle optimization and partial goal fulfillment efficiently. This paper aims to extend this approach to weighted CSP. Knowledge representation to help in generating planning rules is also proposed, as well as methods to improve performances. Case studies show that the system can provide intelligent and complex plans from activities generated from semantic annotations of the devices, as well as optimization to maximize personal constraints’ fulfillment. Note to Practitioners—Smart home should maximize the fulfillment of personal goals that are often conflicting. For example, it should try to fulfill as much as possible the requests made by both the mother and daughter who wants to watch TV but both having different channel preferences. That said, every person has a set of goals or constraints that they hope the smart home can fulfill. Therefore, human-centric system that automates the loosely coupled devices of the smart home to optimize the goals or constraints of individuals in the home is developed. Automated planning is done using converted services extracted from devices, where conversion is done using existing tools and concepts from Web technologies. Weighted constraint satisfaction that provides the declarative approach to cover large problem domain to realize the automated planner with optimization capability is proposed. Details to speed up planning through search space reduction are also given. Real-time case studies are run in a prototype smart home to demonstrate its applicability and intelligence, where every planning is performed under a maximum of 10 s. The vision of this paper is to be able to implement such system in a community, where devices everywhere can cooperate to ensure the well-being of the community. [ABSTRACT FROM AUTHOR]

The eight papers in this special issue focus on cognitive inspired networked applications. Cognition is emerging as a new and promising methodology with the development of cognitive-inspired computing, ubiquitous networks, and systems, which has the potential to enable a large class of applications. However, recent advances, such as interference alignment, collaborative communications, and cognitive computational theory, make us lack a clear understanding of analytical cognitive-inspired computing, networks, and systems, as well as best practices, to design resource-efficient, utility, pervasive, scalable, and autonomy oriented cognitive systems and applications. The objective of this special Issue is to bring together state-of-the-art research contributions that address these key aspects of cognitive inspired network systems and applications. [ABSTRACT FROM PUBLISHER]

Quality inspection, a widely adopted practice in supply chains, measures whether delivered products conform to prespecified quality requirements. Due to potential economic benefits, suppliers may deliberately manipulate products to falsify test results. However, unqualified products could cause severe problems in supply chains or even tragedies, such as China’s tainted milk scandal. In this paper, we propose a belief-desire-intention modeling approach to predict suppliers’ behavior and provide inspection suggestions to buyers to overcome the problem. Assuming access to the production process, our approach can represent suppliers’ knowledge of production and deception to mimic their reasoning processes and predict their deception intentions. This flexible approach can also adapt to environmental changes and deliver effective results. In this paper, we build a prototype system for supply chain quality inspections based on the proposed method. We conduct laboratory experiments to collect data for computational assessments of the performance of the prototype. It is shown that our proposed approach is more accurate than classic machine learning methods in detecting suppliers’ deceptions. [ABSTRACT FROM AUTHOR]

Contribution: This paper illustrates how to design and implement curricula in terms of the combined use of flipped learning and inquiry-based learning in an engineering course. Background: Elementary courses in engineering schools are conventional and foundational, and involve a considerable amount of knowledge. Throughout such courses, students are also expected to develop insight, which cannot be obtained by only listening to instructors. Having relevant discussions is also difficult for most instructors. Intended Outcomes: The combined use of flipped learning and inquiry-based learning would be beneficial to broaden student achievement. Application Design: Based on an epistemological approach about knowledge and knowing, this paper applies the combined use of flipped learning and inquiry-based learning to enhance student knowledge and advance ways of thinking on a System Modeling and Control course. Findings: The extended learning time and the collective responsibility for learning are discussed as critical issues in applying the combined use of flipped learning and inquiry-based learning in an engineering school. [ABSTRACT FROM AUTHOR]

Even though digital learning games have become common in education, relatively little is known about the usefulness of game-based assessment. This paper aims to explore if a game-based math test can provide added value to math education with respect to cognitive and affective outcomes. We used in-game measures, embedded in the game called Semideus Exam, focusing on conceptual fraction knowledge. In order to validate the game-based assessment approach, we compared the cognitive outcomes of 51 Finnish sixth graders, who completed both paper-based and game-based math tests in a randomized order. In addition, the students’ test anxiety and flow experience were measured to evaluate the affective outcomes. The results indicate that the game-based test scores correlated significantly with the paper-based test scores suggesting that the game-based assessment was successfully implemented and the game provided comparable data with the paper-based test approach. More importantly, the results revealed that game-based assessment lowered test anxiety and increased engagement which is likely to decrease assessment bias caused by test anxiety. In addition, the results show that earlier playing experience and gender did not influence the game-based test score suggesting fairness of the game-based assessment approach. Although we identified several benefits of the game-based assessment approach, more evidence is needed on the usefulness and fairness of game-based assessments. [ABSTRACT FROM AUTHOR]

Emotion Cause Extraction (ECE) aims to reveal the cause clauses behind a given emotion expressed in a text, which has become an emerging topic in broad research communities, such as affective computing and natural language processing. Despite the fact that current methods about the ECE task have made great progress in text semantic understanding from lexicon- and sentence-level, they always ignore the certain causal narratives of emotion text. Significantly, these causal narratives are presented in the form of semantic structure and highly helpful for structure-level emotion cause understanding. Nevertheless, causal narrative is just an abstract narratological concept and its involving semantics is quite different from the common sequential information. Thus, how to properly model and utilize such particular narrative information to boost the ECE performance still remains an unresolved challenge. To this end, in this paper, we propose a novel Causal Narrative Comprehension Model (CNCM) for emotion cause extraction, which learns and leverages causal narrative information smartly to address the above problem. Specifically, we develop a Narrative-aware Causal Association (NCA) unit, which mines the narrative cue about emotional results and uses the semantic correlation between causes and results to model causal narratives of documents. Besides, we design a Result-aware Emotion Attention (REA) unit to make full use of the known result of causal narrative for multiple understanding about emotional causal associations. Through the ingenious combination and collaborative utilization of these two units, we could better identify the emotion cause in the text with causal narrative comprehension. Extensive experiments on the public English and Chinese benchmark datasets of ECE task have validated the effectiveness of CNCM with significant margin by comparing with the state-of-the-art baselines, which demonstrates the potential of narrative information in long text understanding. [ABSTRACT FROM AUTHOR]

Given a natural language expression and an image/video, the goal of referring segmentation is to produce the pixel-level masks of the entities described by the subject of the expression. Previous approaches tackle this problem by implicit feature interaction and fusion between visual and linguistic modalities in a one-stage manner. However, human tends to solve the referring problem in a progressive manner based on informative words in the expression, i.e., first roughly locating candidate entities and then distinguishing the target one. In this paper, we propose a cross-modal progressive comprehension (CMPC) scheme to effectively mimic human behaviors and implement it as a CMPC-I (Image) module and a CMPC-V (Video) module to improve referring image and video segmentation models. For image data, our CMPC-I module first employs entity and attribute words to perceive all the related entities that might be considered by the expression. Then, the relational words are adopted to highlight the target entity as well as suppress other irrelevant ones by spatial graph reasoning. For video data, our CMPC-V module further exploits action words based on CMPC-I to highlight the correct entity matched with the action cues by temporal graph reasoning. In addition to the CMPC, we also introduce a simple yet effective Text-Guided Feature Exchange (TGFE) module to integrate the reasoned multimodal features corresponding to different levels in the visual backbone under the guidance of textual information. In this way, multi-level features can communicate with each other and be mutually refined based on the textual context. Combining CMPC-I or CMPC-V with TGFE can form our image or video version referring segmentation frameworks and our frameworks achieve new state-of-the-art performances on four referring image segmentation benchmarks and three referring video segmentation benchmarks respectively. Our code is available at https://github.com/spyflying/CMPC-Refseg. [ABSTRACT FROM AUTHOR]

Corporate meetings are a crucial part of business activities. While numerous academic papers investigated how to make the scheduling process of meetings faster or even automatic, little work has been done yet to facilitate the retrospective reasoning about how time is spent on meetings. Traditional calendar applications do not allow users to extract actionable statistics although it has been shown that reflection-oriented design can increase the users’ understanding of their habits and can thereby encourage a shift towards better practices. In this paper, we present MineTime Insight, a tool made of multiple coordinated views for the exploration of personal calendar data, with the overarching goal of improving short and long-term scheduling decisions. Despite being focused on the working environment, our work builds upon recent results in the field of Personal Visual Analytics, as it targets users not necessarily expert in visualization and data analysis. We demonstrate the potential of MineTime Insight, when applied to the agenda of an executive manager. Finally, we discuss the results of an informal user study and a field study. Our results suggest that our visual representations are perceived as easy to understand and helpful towards a change in the scheduling habits. [ABSTRACT FROM AUTHOR]

This paper addresses the task of detecting and recognizing human-object interactions (HOI) in images. Considering the intrinsic complexity and structural nature of the task, we introduce a cascaded parsing network (CP-HOI) for a multi-stage, structured HOI understanding. At each cascade stage, an instance detection module progressively refines HOI proposals and feeds them into a structured interaction reasoning module. Each of the two modules is also connected to its predecessor in the previous stage, enabling efficient cross-stage information propagation. The structured interaction reasoning module is built upon a graph parsing neural network (GPNN), which efficiently models potential HOI structures as graphs and mines rich context for comprehensive relation understanding. In particular, GPNN infers a parse graph that i) interprets meaningful HOI structures by a learnable adjacency matrix, and ii) predicts action (edge) labels. Within an end-to-end, message-passing framework, GPNN blends learning and inference, iteratively parsing HOI structures and reasoning HOI representations (i.e., instance and relation features). Further beyond relation detection at a bounding-box level, we make our framework flexible to perform fine-grained pixel-wise relation segmentation; this provides a new glimpse into better relation modeling. A preliminary version of our CP-HOI model reached 1st place in the ICCV2019 Person in Context Challenge, on both relation detection and segmentation. In addition, our CP-HOI shows promising results on two popular HOI recognition benchmarks, i.e., V-COCO and HICO-DET. [ABSTRACT FROM AUTHOR]

In recent years, weakly supervised object detection has attracted great attention in the computer vision community. Although numerous deep learning-based approaches have been proposed in the past few years, such an ill-posed problem is still challenging and the learning performance is still behind the expectation. In fact, most of the existing approaches only consider the visual appearance of each proposal region but ignore to make use of the helpful context information. To this end, this paper introduces two levels of context into the weakly supervised learning framework. The first one is the proposal-level context, i.e., the relationship of the spatially adjacent proposals. The second one is the semantic-level context, i.e., the relationship of the co-occurring object categories. Therefore, the proposed weakly supervised learning framework contains not only the cognition process on the visual appearance but also the reasoning process on the proposal- and semantic-level relationships, which leads to the novel deep multiple instance reasoning framework. Specifically, built upon a conventional CNN-based network architecture, the proposed framework is equipped with two additional graph convolutional network-based reasoning models to implement object location reasoning and multi-label reasoning within an end-to-end network training procedure. Comprehensive experiments on the widely used PASCAL VOC and MS COCO benchmarks have been implemented, which demonstrate the superior capacity of the proposed approach when compared with other state-of-the-art methods and baseline models. [ABSTRACT FROM AUTHOR]

This paper presents an appliance identification algorithm for use with a non-intrusive home energy monitor based on a cogent confabulation neural network. As a cogent confabulation neural network does not require multiplications during the identification phase, it is an effective choice for systems with low-computational capability. A non-intrusive home energy monitor needs to learn not only the energy patterns of individual appliances but also those of combinations of appliances. To relieve the burden of learning power patterns of the combinations, this paper proposes a parameter-building scheme based on the parameters of individual appliances. The proposed algorithm is evaluated on datasets prepared by the reference energy disaggregation dataset and the authors. The average success rate was 83.8% for up to eight appliances and showed better performance than the combinatorial optimization and artificial neural network approaches. [ABSTRACT FROM AUTHOR]

This paper presents track fusion and behavioral reasoning for moving vehicles in close proximity based on the curvilinear coordinates of roadway geometries. The inferred track and behavior of other vehicles can be used to perform safe actions in intelligent vehicle applications and autonomous driving. Vehicle detections from multiple perception sensors are integrated using track-to-track (T2T) fusion based on a cross-covariance method, and this T2T fusion is performed with curvilinear coordinates created using prebuilt roadway geometry on a digital map. The coordinate conversion to curvilinear space has many benefits for behavioral reasoning and tracking, such as constraining problem spaces and dimensions. A machine learning classifier based on a support vector machine is then applied to deduce the behavior of nearby vehicles. The algorithms presented here for track fusion and behavioral reasoning based on curvilinear coordinates have been verified through experiments in various real traffic scenarios. [ABSTRACT FROM AUTHOR]

Fuzzy systems are known to provide not only accurate but also interpretable predictions. However, their explainability may be undermined if non-semantically grounded linguistic terms are used. Additional non-trivial challenges would arise if a prediction were to be explained counterfactually, i.e., in terms of hypothetical, non-predicted outputs. In this paper, we explore how both factual and counterfactual automated explanations can justify the output of fuzzy rule-based classifiers, and thus contribute to making them more trustworthy. Moreover, we demonstrate how end user preferences can be handled by customizing automated explanations, making them interactive, personalized, and therefore, human-centric. The full immersive article at IEEE Xplore provides detailed interactive examples for better understanding. [ABSTRACT FROM AUTHOR]

Tree reconciliation is a general framework for investigating the mutual influence between gene and species trees according to the parsimony principle, that is, to each evolutionary event a cost is assigned and the goal is to find a reconciliation of minimum total cost. The resulting optimization problem is known as the reconciliation problem. Usually, the considered events are: co-divergence, gene Duplication, horizontal gene Transfer, and gene Loss (DTL model), while in a more conservative setting, gene transfers are not allowed (DL model). The reconciliation problem requires, in the DL model, time linear in the dimension of the two trees and at least quadratic time in the DTL model. Hence, it is reasonable to argue that the introduction of horizontal gene transfers increases the complexity of the problem. Instead, we introduce horizontal gene transfers with some constraints and prove that the problem is still linear in the dimension of the trees. Namely, we allow gene transfers of length bounded by $k=2$ k = 2 , on the basis of the observation that transfers are more likely to occur between closely related species than between distantly related ones. Then we extend the same reasonings to the case in which $k>2$ k > 2 under additional constrains. In this paper we study also another problem related to the reconciliation one, that is optimally rooting one of the two trees when it is not, and also for it we prove similar results. The relevance of this contribution lies in showing that, in the transit from the DL to the DTL model, the computational time does not increase suddenly to quadratic but remains linear in the case when gene transfers are very short (i.e., happening between very close genes). [ABSTRACT FROM AUTHOR]

Negation is a new perspective to represent knowledge. The negation of probability distribution has been proposed, and it has a lot of interesting properties, which can reach a maximum entropy. Because of the defects of the classical probability theory in the expression of uncertainty, the basic belief assignment (BBA) in the Dempster–Shafer theory (D–S theory) are widely used in decision theory. Thus, negation provides a new perspective for D–S theory to measure fuzziness. In this paper, a new definition of negation of BBA is presented. In the proposed negation, BBAs are represented as vectors, and negation is realized by matrix operators. This method has a good interpretation of the matrix operators and has the merit of simplifying the problem. With several different definitions of entropy to determinate the uncertainty of BBA, the proposed negation of BBA can reach a maximum belief entropy when the entropies satisfy a certain property. [ABSTRACT FROM AUTHOR]

Bodies of knowledge (BOK) contain relevant knowledge for several areas. Knowledge representation for BOK is essential to understand the true context and the possible application of BOK in science. Knowledge can be symbolically represented in many ways, and in this paper graphs and software design patterns were chosen to develop knowledge representation techniques for BOK. [ABSTRACT FROM AUTHOR]

Intelligent systems are desired in dynamic fault diagnoses for large and complex systems such as nuclear power plants. Dynamic uncertain causality graph (DUCG) is such a system presented previously. This paper extends the DUCG methodology to deal with negative feedbacks, which is one of the most difficult problems in fault diagnosis, and predicts the fault development online. Two methods are presented. One does not involve causality propagation across time slices. Another involves causality propagation through time slices. A nuclear power plant simulator located at Tsinghua University is used to test the DUCG methodology. A typical experiment involving negative feedback is given, which jointly applies the methods separately presented in different papers, such as how to deal with dynamic fault diagnosis and directed cyclic graphs (DCGs), and the methods presented in this paper. Results show that DUCG is powerful in knowledge representation, diagnosing possible faults, and predicting developments of faults. It also demonstrates that DUCG is robust, which means that the DUCG inference does not rely much on the accuracy of probability parameters. The logics of diagnoses and predictions are graphically displayed, so that users know not only inference results, but also why they are correct. [ABSTRACT FROM AUTHOR]

The efficient mitigation of the detrimental effects of a fault in complex systems requires online fault diagnosis techniques that are able to identify the cause of an observable anomaly. However, an individual diagnosis model can only acquire a limited diagnostic effect and may be insufficient for a particular application. In this paper, a Bayesian network-based probabilistic ensemble learning (PEL-BN) strategy is proposed to address the aforementioned issue. First, an ensemble index is proposed to evaluate the candidate diagnosis models in a probabilistic manner so that the diagnosis models with better diagnosis performance can be selected. Then, based on the selected classifiers, the architecture of the Bayesian network can be constructed using the proposed three types of basic topologies. Finally, the advantages of different diagnosis models are integrated using the developed Bayesian network, and thus, the fault causes of the observable anomaly can be accurately inferred. In addition, the proposed method can effectively capture the mixed fault characteristics of multifaults (MFs) by integrating decisions derived from different diagnosis models. Hence, this method can also provide a feasible solution for diagnosing MFs in real industrial processes. A simulation process and a real industrial process are adopted to verify the performance of the proposed method, and the experimental results illustrate that the proposed PEL-BN strategy improves the diagnosis performance of single faults and is a feasible solution for MF diagnosis. Note to Practitioners—The focus of this paper is to develop a probabilistic ensemble learning strategy based on the Bayesian network (PEL-BN) to diagnose different kinds of faults in industrial processes. The PEL-BN strategy can automatically select the base classifiers to establish the architecture of the Bayesian network. In this way, the conclusions of these base classifiers can be effectively integrated to provide better diagnosis performance. In addition, the proposed method is also a feasible technique for diagnosing MFs resulted from the joint effects of multiple faults. [ABSTRACT FROM AUTHOR]

This paper develops a formal framework, founded on description logics, to assist decision making in relation to the manufacturing operation and control in modern enterprises that stand to benefit from the transition to Industry 4.0. The objective is to provide sophisticated support to individuals making decisions in the area of production operations management and in particular, production scheduling and material requirements planning. Using this framework, this paper demonstrates an approach to encode the domain knowledge of human experts managing the production as sets of formal rules. These rules can be implemented in an intelligent system that can assist and empower human experts, reducing difficulty when making decisions in complex manufacturing environments. [ABSTRACT FROM AUTHOR]

This paper presents the results from a one-year study of 12 patients with moderate dementia in an adult day program who played a novel whack-a-mole game-based measurement instrument for cognitive behavior and performance. The ongoing measurement of cognition and changes associated with dementia is a challenge for healthcare providers. Measurement methods based on a tablet-based instrument are proposed. Partnership with the adult day program greatly eased recruitment: all but 1 eligible participant joined our study, compared to one in five, or lower, for previous studies with similar populations. There are three unique aspects to the design of our game: first, it has two distinct targets requiring different actions, which increases the cognitive processing for the users; second, each level is systematically more difficult; third, it records and analyzes player performance. The results show that the patients’ game performance improves over the first few weeks; this indicates that they are learning the game and retaining ability gains from week-to-week, suggesting some procedural learning is still intact. Over the year, 4 participants showed cognitive decline, 4 were stable and 3 improved based on their Minimental State Exam (MMSE) score. Two measures are proposed based on level progression within the sessions and mole hit performance. The level progression measure identifies declining participants with one false negative (FN) and one false positive error. The mole hit performance measure identifies declining participants with one FN error. These results demonstrate the potential for the proposed instrument to provide an ongoing measurement as an alternative for the repeated application of the MMSE. [ABSTRACT FROM AUTHOR]

Usually, the evaluation of the classifiers performance is not an easy task to be performed, mainly when we analyze different criteria (output parameters). In this evaluation process, we can use quantitative measures (accuracy, specificity, among others), however, when the output values are very close and we have several criteria, the results are difficult to be interpreted by users. This paper aims to propose a new linguistic model to evaluate the performance of several classifiers. It is based on the notion of gradual complex numbers (GCN), proposed in [18]. In this paper, we present the theoretical basis of GCNs for classifier evaluator and we assess the performance of the proposed model (GCN) through an empirical study. In addition, the performance of GCN is compared with that of fuzzy complex numbers [6], and it reveals gains. [ABSTRACT FROM PUBLISHER]

Representing, manipulating, and inferring trust from the user point of view certainly is a grand challenge in virtual worlds, including online games. When someone meets an unknown individual, the question is “Can I trust him/her or not?” This requires the user to have access to a representation of trust about others, as well as a set of operators to undertake inference about the trustability of other users/players. In this paper, we employ a trust representation generated from in-world data in order to feed individual trust decisions. To achieve that purpose, we assume that such a representation of trust already exists; in fact, it was proposed in another paper of ours. Thus, the focus here is on the trust mechanisms required to infer trustability of other users/players. More specifically, we use an individual trust representation deployed as a trust network as base to the inference mechanism that employs two subjective logic operators (consensus and discount) to automatically derive trust decisions. The proposed trust inference system has been validated through OpenSimulator scenarios, which has led to a 5% increase on trustability of avatars in relation to the reference scenario (without trust). [ABSTRACT FROM PUBLISHER]

This paper addresses the problem of automatic analytical pole–zero (PZ) extraction for multistage operational amplifiers (opamps) with frequency compensation. Traditional methods mainly rely on numerical reference to derive approximate PZ expressions without incorporating any design knowledge. Such methods suffer from bad interpretability of the auto-generated results. This paper takes a topological approach and attempts to advocate that certain form of design knowledge can be incorporated in the symbolic term selection process for PZ generation. The generation engine selects the dominant terms by a formal inspection on the token patterns that are correlated to gain factors and compensation elements. Since the gain factors and compensation elements of an opamp are pertinent to the topological details of a circuit, the proposed PZ extraction method is closer to design conception than other numerical reference-based methods. Consequently, the generated pole/zero results are better interpretable. Application to a class of multistage opamps with a variety of compensation structures demonstrates that the proposed method is effective and can match human-derived results. [ABSTRACT FROM PUBLISHER]

Grounding natural language in images, such as localizing “the black dog on the left of the tree”, is one of the core problems in artificial intelligence, as it needs to comprehend the fine-grained language compositions. However, existing solutions merely rely on the association between the holistic language features and visual features, while neglect the nature of composite reasoning implied in the language. In this paper, we propose a natural language grounding model that can automatically compose a binary tree structure for parsing the language and then perform visual reasoning along the tree in a bottom-up fashion. We call our model RvG-Tree: Recursive Grounding Tree, which is inspired by the intuition that any language expression can be recursively decomposed into two constituent parts, and the grounding confidence score can be recursively accumulated by calculating their grounding scores returned by the two sub-trees. RvG-Tree can be trained end-to-end by using the Straight-Through Gumbel-Softmax estimator that allows the gradients from the continuous score functions passing through the discrete tree construction. Experiments on several benchmarks show that our model achieves the state-of-the-art performance with more explainable reasoning. [ABSTRACT FROM AUTHOR]

Vision-language navigation (VLN) is the task of navigating an embodied agent to carry out natural language instructions inside real 3D environments. In this paper, we study how to address three critical challenges for this task: the cross-modal grounding, the ill-posed feedback, and the generalization problems. First, we propose a novel Reinforced Cross-Modal Matching (RCM) approach that enforces cross-modal grounding both locally and globally via reinforcement learning (RL). Particularly, a matching critic is used to provide an intrinsic reward to encourage global matching between instructions and trajectories, and a reasoning navigator is employed to perform cross-modal grounding in the local visual scene. Evaluation on a VLN benchmark dataset shows that our RCM model significantly outperforms baseline methods by 10 percent on Success Rate weighted by Path Length (SPL) and achieves the state-of-the-art performance. To improve the generalizability of the learned policy, we further introduce a Self-Supervised Imitation Learning (SIL) method to explore and adapt to unseen environments by imitating its own past, good decisions. We demonstrate that SIL can approximate a better and more efficient policy, which tremendously minimizes the success rate performance gap between seen and unseen environments (from 30.7 to 11.7 percent). [ABSTRACT FROM AUTHOR]

Glaucoma is the leading reason for irreversible blindness. Early detection and timely treatment of glaucoma are essential for preventing visual field loss or even blindness. In clinical practice, Optical Coherence Tomography (OCT) and Visual Field (VF) exams are two widely-used and complementary techniques for diagnosing glaucoma. OCT provides quantitative measurements of the optic nerve head (ONH) structure, while VF test is the functional assessment of peripheral vision. In this paper, we propose a Deep Relation Transformer (DRT) to perform glaucoma diagnosis with OCT and VF information combined. A novel deep reasoning mechanism is proposed to explore implicit pairwise relations between OCT and VF information in global and regional manners. With the pairwise relations, a carefully-designed deep transformer mechanism is developed to enhance the representation with complementary information for each modal. Based on reasoning and transformer mechanisms, three successive modules are designed to extract and collect valuable information for glaucoma diagnosis, the global relation module, the guided regional relation module, and the interaction transformer module, namely. Moreover, we build a large dataset, namely ZOC-OCT&VF dataset, which includes 1395 OCT-VF pairs for developing and evaluating our DRT. We conduct extensive experiments to validate the effectiveness of the proposed method. Experimental results show that our method achieves 88.3% accuracy and outperforms the existing single-modal approaches with a large margin. The codes and dataset will be publicly available in the future. [ABSTRACT FROM AUTHOR]

Traffic flows with different requirements of quality of service (QoS requirements) are aggregated into different QoS classes to provide differentiated services (Diffserv) and better quality of experience (QoE) for users. The existing aggregation approaches/QoS mapping methods are based on quantitative QoS requirements and static QoS classes. However, they are typically qualitative and time-varying at the edge of the beyond fifth generation (B5G) networks. Therefore, the artificial intelligence technology of preference logic is applied in this paper to achieve an intelligent method for edge computing, called the preference logic based aggregation model (PLM), which effectively groups flows with qualitative requirements into dynamic classes. First, PLM uses preferences to describe QoS requirements of flows, and thus can deal with both quantitative and qualitative cases. Next, the potential conflicts in these preferences are eliminated. According to the preferences, traffic flows are finally mapped into dynamic QoS classes by logic reasoning. The experimental results show that PLM presents better performance in terms of QoE satisfaction compared with the existing aggregation methods. Utilizing preference logic to group flows, PLM implements a novel way of edge intelligence to deal with dynamic classes and improves the Diffserv for massive B5G traffic with quantitative and qualitative requirements. [ABSTRACT FROM AUTHOR]

Fault diagnosis is a critical task for operators in the context of e-TOM (enhanced Telecom Operations Map) assurance process. Its purpose is to reduce network maintenance costs and to improve availability, reliability and performance of network services. Although necessary, this operation is complex and requires significant involvement of human expertise. The study of the fundamental properties of fault diagnosis shows that the diagnosis process complexity needs to be addressed using more intelligent and efficient approaches. In this paper, we present a hybrid approach that combines Bayesian networks and case-based reasoning in order to overcome the usual limits of fault diagnosis techniques and to reduce human intervention in this process. The proposed mechanism allows the identification of the root cause with a finer precision and a higher reliability. At the same time, it helps to reduce computation time while taking into account the network dynamicity. Furthermore, a study case is presented to show the feasibility and performance of the proposed approach based on a real-world use case: a virtual private network topology. [ABSTRACT FROM PUBLISHER]

The ability to anticipate the strategic actions of hackers, including where, when, and how they might attack, and their tactics for evading detection, is a valuable skill for cybersecurity. Therefore, developing the strategic reasoning abilities of cybersecurity students is an important cybersecurity education learning objective. This paper proposes that basic game theory concepts should be taught to cybersecurity students in order to improve their strategic reasoning abilities. It details a pretest–posttest educational experiment that demonstrates that 2 h of basic game theory instruction results in a statistically significant improvement in students’ abilities to anticipate the strategic actions of others. It also provides details of the game theory curriculum to help other cybersecurity educators replicate these results. Additionally, this paper suggests that another benefit of teaching game theory in a cybersecurity course is that it may fundamentally alter the way students view the practice of cybersecurity, helping to sensitize them to the human adversary element inherent in cybersecurity in addition to technology-focused best practices. This could result in a more naturally strategic-minded, and therefore better equipped, cybersecurity workforce. [ABSTRACT FROM PUBLISHER]

This two-part paper aims to study lane change behaviors at the tactical level from an on-road perspective. Compared with longitudinal driving tasks, a lane change is more complicated because this task has more interactions with surrounding vehicles; thus, there are more potential risks during this procedure. Based on the results from Part I on an on-road vehicle trajectory collection, this part investigates lane change extraction and scene-based behavior analysis, and it has a particular focus on understanding the interactions between an ego and surrounding vehicles during the procedure. We claim that this paper provides the following novel contributions: 1) an automatic method is proposed for extracting lane change segments from a continuous driving sequence by modeling and recognizing patterns in a steering angle; 2) a lane change database at the trajectory level is generated, which reflects the interactions between an ego and the surrounding vehicles during the procedures; and 3) we present findings from analyzing lane change procedures using real-world data on the axes of both the ego's trajectory and interactions with the scene vehicles. To the authors' knowledge, this is the first lane change behavior study from an on-road perspective that addresses the vehicle interactions in real-world traffic at the trajectory level. [ABSTRACT FROM PUBLISHER]

Foresight is the ability to visualize alternate futures and shape the direction of future events. Generally considered as an organizational capability, foresight can be the basis of sustained competitive advantage, and needs to be fostered by enabling employees to acquire, assimilate, and analyze information with a future orientation. However, research on what constitutes foresight at the employee (i.e., individual) level is sparse. This paper fills this gap by using a five-stage mixed-mode process to propose an extended foresight measure. This paper has implications for research on the individual aspect of microfoundations of capability building, organizational measures development, and foresight literature. [ABSTRACT FROM AUTHOR]

Text-based visual question answering (VQA) requires to read and understand text in an image to correctly answer a given question. However, most current methods simply add optical character recognition (OCR) tokens extracted from the image into the VQA model without considering contextual information of OCR tokens and mining the relationships between OCR tokens and scene objects. In this paper, we propose a novel text-centered method called RUArt (Reading, Understanding and Answering the Related Text) for text-based VQA. Taking an image and a question as input, RUArt first reads the image and obtains text and scene objects. Then, it understands the question, OCRed text and objects in the context of the scene, and further mines the relationships among them. Finally, it answers the related text for the given question through text semantic matching and reasoning. We evaluate our RUArt on two text-based VQA benchmarks (ST-VQA and TextVQA) and conduct extensive ablation studies for exploring the reasons behind RUArt’s effectiveness. Experimental results demonstrate that our method can effectively explore the contextual information of the text and mine the stable relationships between the text and objects. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and machine learning (ML) continue to demonstrate substantial capabilities in solving a wide range of optical-network-related tasks such as fault management, resource allocation, and lightpath quality of transmission (QoT) estimation. However, the focus of the research community has been centered on ML models' predictive capabilities, neglecting aspects related to models' understanding, i.e., to interpret how the model reasons and makes its predictions. This lack of transparency hinders the understanding of a model's behavior and prevents operators from judging, and hence trusting, the model's decisions. To mitigate the lack of transparency and trust in ML, explainable AI (XAI) frameworks can be leveraged to explain how a model correlates input features to its outputs. In this paper, we focus on the application of XAI to lightpath QoT estimation. In particular, we exploit Shapley additive explanations (SHAP) as the XAI framework. Before presenting our analysis, we provide a brief overview of XAI and SHAP, then discuss the benefits of the application of XAI in networking and survey studies that apply XAI to networking tasks. Then, we model the lightpath QoT estimation problem as a supervised binary classification task to predict whether the value of the bit error rate associated with a lightpath is below or above a reference acceptability threshold and train an ML extreme gradient boosting model as the classifier. Finally, we demonstrate how to apply SHAP to extract insights about the model and to inspect misclassifications. [ABSTRACT FROM AUTHOR]

Text based Visual Question Answering (TextVQA) is a recently raised challenge requiring models to read text in images and answer natural language questions by jointly reasoning over the question, textual information and visual content. Introduction of this new modality - Optical Character Recognition (OCR) tokens ushers in demanding reasoning requirements. Most of the state-of-the-art (SoTA) VQA methods fail when answer these questions because of three reasons: (1) poor text reading ability; (2) lack of textual-visual reasoning capacity; and (3) choosing discriminative answering mechanism over generative couterpart (although this has been further addressed by M4C). In this paper, we propose an end-to-end structured multimodal attention (SMA) neural network to mainly solve the first two issues above. SMA first uses a structural graph representation to encode the object-object, object-text and text-text relationships appearing in the image, and then designs a multimodal graph attention network to reason over it. Finally, the outputs from the above modules are processed by a global-local attentional answering module to produce an answer splicing together tokens from both OCR and general vocabulary iteratively by following M4C. Our proposed model outperforms the SoTA models on TextVQA dataset and two tasks of ST-VQA dataset among all models except pre-training based TAP. Demonstrating strong reasoning ability, it also won first place in TextVQA Challenge 2020. We extensively test different OCR methods on several reasoning models and investigate the impact of gradually increased OCR performance on TextVQA benchmark. With better OCR results, different models share dramatic improvement over the VQA accuracy, but our model benefits most blessed by strong textual-visual reasoning ability. To grant our method an upper bound and make a fair testing base available for further works, we also provide human-annotated ground-truth OCR annotations for the TextVQA dataset, which were not given in the original release. The code and ground-truth OCR annotations for the TextVQA dataset are available at https://github.com/ChenyuGAO-CS/SMA. [ABSTRACT FROM AUTHOR]

The algorithm developed in this paper jointly selects the optimal transmitted waveform and the control input so that a radar sensor on a moving platform with linear dynamics can reach a target by minimizing a predefined cost. The cost proposed in this paper accounts for the energy of the transmitted radar signal, the energy of the platform control input, and the relative position error between the platform and the target, which is a function of the waveform design and control input. Similarly to the linear quadratic Gaussian control problem, we demonstrate that the optimal solution satisfies the separation principle between filtering and optimization and, therefore, the optimum can be found analytically. The performance of the proposed solution is assessed with a set of simulations for a pulsed Doppler radar transmitting linearly frequency modulated chirps. Results show the effectiveness of the proposed approach for optimal waveform design and optimal guidance control. [ABSTRACT FROM AUTHOR]

This paper surveys reasons for and against pursuing the field of machine ethics, understood as research aiming to build “ethical machines.” We clarify the nature of this goal, why it is worth pursuing, and the risks involved in its pursuit. First, we survey and clarify some of the philosophical issues surrounding the concept of an “ethical machine” and the aims of machine ethics. Second, we argue that while there are good prima facie reasons for pursuing machine ethics, including the potential to improve the ethical alignment of both humans and machines, there are also potential risks that must be considered. Third, we survey these potential risks and point to where research should be devoted to clarifying and managing potential risks. We conclude by making some recommendations about the questions that future work could address. [ABSTRACT FROM AUTHOR]

Security operations centers (SOCs) employ various cyber defend measures to monitor network events. Apart from these measures, SOCs also have to resort to human analysts to make sense of the collected data for incident detection and response. However, with the oncoming network data collected and accumulated at a rapid speed, analysts are usually overwhelmed by tedious and repeated data triage tasks so that they can hardly concentrate on in-depth analysis to create timely and quality incident reports. This paper aims to reduce the analysts’ workloads by developing data triage automatons. We have developed a computer-aided tracing method for capturing analysts’ operations while they are performing a task. This paper proposes a graph-based trace mining approach for constructing useful patterns for data triage from the operation traces. Finite state machines can be constructed based on the rules to automate data triage. A human-in-the-loop case study is conducted to evaluate our approach, in which 30 professional analysts were recruited and asked to complete a cyber-analysis task with their operations being traced. State machines were constructed based on the traces and then the effectiveness of developing state machines and the performance of state machines are evaluated. The result shows that it is feasible to conduct automated data triage by leveraging analysts’ traces. The state machines are able to complete processing a large amount of data within minutes. Comparing the performance of automated data triage with the ground truth, we found that a satisfactory false positive rate can be achieved. [ABSTRACT FROM AUTHOR]

Since the all-day and all-weather working characteristics, polarimetric synthetic aperture radar (PolSAR) has been widely used in many application for earth observation. PolSAR system can acquire targets scattering information under different polarization patterns and obtain detailed information, such as physical characteristics, spatial distribution and direction of the targets, etc. However, in high-resolution PolSAR images, the scattering characteristics of targets are very sophisticated, and different parts of the same target may present different scattering mechanisms. As a result, traditional interpretation technologies are sometimes not applicable in a high-resolution PolSAR scenario. Recently, many artificial intelligence algorithms that simulate the scheme of human cognition have been developed and they have great significance for efficient, intelligent, and accurate images interpretation. In this paper, based on human cognition theory and taking into account the characteristics of targets in PolSAR images, an algorithm for the interpretation of various targets in high-resolution PolSAR images is presented. In this paper, a “perception–reasoning–decision” cognition model is built following the cognition process of professional PolSAR image interpreters. In addition, with the help of theories such as multilevel image segmentation, fuzzy logic, neural network, and the context semantic characteristics, the mathematical model is established and implemented. The proposed method is performed on several sets of PolSAR images obtained under different imaging conditions and is validated by comparing with existing classification methods. The results show that the proposed method can effectively improve the identification of targets from PolSAR images. [ABSTRACT FROM AUTHOR]

Recurrent neural network(RNN) has achieved remarkable performances in complex reasoning on knowledge bases, which usually takes as inputs vector embeddings of relations along a path between an entity pair. However, it is insufficient to extract local correlations of a path due to RNN is better at capturing global sequential information of a path. In this paper, we take full advantages of convolutional neural network that can effectively extract local features, and propose a convolutional-based RNN architecture denoted as C-RNN to perform reasoning. C-RNN first utilizes CNN to extract local high-level correlation features of a path, and then feeds the correlation features into recurrent neural network to model the path representation. Our C-RNN architecture is adaptable to obtain not only local features but also global sequential features of a path. Based on C-RNN architecture, we devise two models, the unidirectional C-RNN and bidirectional C-RNN. We empirically evaluate them on a large-scale FreeBase+ClueWeb prediction task. Experimental results show that C-RNN models achieve state-of-the-art predictive performance. [ABSTRACT FROM AUTHOR]

The critical challenge of single image inpainting stems from accurate semantic inference via limited information while maintaining image quality. Typical methods for semantic image inpainting train an encoder-decoder network by learning a one-to-one mapping from the corrupted image to the inpainted version. While such methods perform well on images with small corrupted regions, it is challenging for these methods to deal with images with large corrupted area due to two potential limitations. 1) Such one-to-one mapping paradigm tends to overfit each single training pair of images; 2) The inter-image prior knowledge about the general distribution patterns of visual semantics, which can be transferred across images sharing similar semantics, is not explicitly exploited. In this paper, we propose the Generative Memory-guided Semantic Reasoning Model (GM-SRM), which infers the content of corrupted regions based on not only the known regions of the corrupted image, but also the learned inter-image reasoning priors characterizing the generalizable semantic distribution patterns between similar images. In particular, the proposed GM-SRM first pre-learns a generative memory from the whole training data to explicitly learn the distribution of different semantic patterns. Then the learned memory are leveraged to retrieve the matching semantics for the current corrupted image to perform semantic reasoning during image inpainting. While the encoder-decoder network is used for guaranteeing the pixel-level content consistency, our generative priors are favorable for performing high-level semantic reasoning, which is particularly effective for inferring semantic content for large corrupted area. Extensive experiments on Paris Street View, CelebA-HQ, and Places2 benchmarks demonstrate that our GM-SRM outperforms the state-of-the-art methods for image inpainting in terms of both visual quality and quantitative metrics. [ABSTRACT FROM AUTHOR]

During clinical practice, radiologists often use attributes, e.g., morphological and appearance characteristics of a lesion, to aid disease diagnosis. Effectively modeling attributes as well as all relationships involving attributes could boost the generalization ability and verifiability of medical image diagnosis algorithms. In this paper, we introduce a hybrid neuro-probabilistic reasoning algorithm for verifiable attribute-based medical image diagnosis. There are two parallel branches in our hybrid algorithm, a Bayesian network branch performing probabilistic causal relationship reasoning and a graph convolutional network branch performing more generic relational modeling and reasoning using a feature representation. Tight coupling between these two branches is achieved via a cross-network attention mechanism and the fusion of their classification results. We have successfully applied our hybrid reasoning algorithm to two challenging medical image diagnosis tasks. On the LIDC-IDRI benchmark dataset for benign-malignant classification of pulmonary nodules in CT images, our method achieves a new state-of-the-art accuracy of 95.36% and an AUC of 96.54%. Our method also achieves a 3.24% accuracy improvement on an in-house chest X-ray image dataset for tuberculosis diagnosis. Our ablation study indicates that our hybrid algorithm achieves a much better generalization performance than a pure neural network architecture under very limited training data. [ABSTRACT FROM AUTHOR]