Reviewerly/exHarmony huggingface dataset:
Multiple MACE Risk Prediction using Multi-Task Recurrent Neural Network with Attention

With the increasing availability of large amounts of Electronic Health Records (EHR), risk prediction from EHR data has attracted considerable research interests in healthcare. In this paper, we propose a multi-task Recurrent Neural Network (RNN) with attention approach for multiple major adverse cardiovascular events (MACE) risk prediction on EHR data. First, we utilize word embedding to learn real-valued vectors to capture the latent representation of medical concepts. We then use RNN to model the sequential patient events. To better capture the correlations of multiple MACE outcomes (e.g. myocardial infarction, stroke and death), we develop a multi-task learning with attention method to predict different outcomes. The experimental results on a real world EHR data show that our multi-task RNN with attention risk prediction model for MACE has good prediction performance.
A transfer learning fault diagnosis model of distribution transformer considering multi‐factor situation evolution

Aiming at the problem of limited fault data and data expiration of distribution transformers, a transfer learning fault diagnosis model of the distribution transformer considering multi‐factor situation evolution is proposed in this paper. First, the state quantities that influence the distribution transformer operation are constructed, and fuzzy binary quantification is used for the state quantities. The association between the state quantities and the fault is explored by the fuzzy Apriori algorithm, and the key state variables that induce the transformer fault are extracted. The Tanimoto coefficient is introduced for the limited fault data of distribution transformers, and the effective auxiliary fault data is transferred to the target distribution transformer. On this basis, the fault diagnosis model based on information transfer is proposed. A health index is introduced to describe the distribution health levels, and the auxiliary fault data in different health levels is transferred because data has expired. On this basis, the fault diagnosis model for data expiration is proposed. The weights of the target and auxiliary fault data in the above model are iteratively solved by using the TrAdaBoost algorithm. Finally, an example analysis is carried on the basis of distribution transformer fault data, and the simulation results show that the fault diagnosis accuracy of the model is high and that it has stronger generalization ability than traditional diagnosis models. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.
Statistical approach to building a model of recognition operators under conditions of high dimensionality of a feature space

Abstract The task of constructing recognition algorithms (RAs) focused on the classification of objects under conditions of high dimensionality of a feature space is considered. As an initial model, a model of RAs based on the calculation of estimates is considered. A distinctive feature of the proposed model is the formation of subsets of interrelated features (SIF) and the selection of a set of representative features (RFs) in the construction of RAs. Moreover, the proximity estimates between the object and the class under consideration are calculated on the basis of the Bayesian approach. The main advantage of the proposed operators is the allocation of preferred dependency models with the subsequent calculation of the assessment of the ownership of objects and ensuring a significant reduction in the number of computational operations when recognition unknown objects. This feature is very important for real-time recognition systems. To test the performance of the proposed model, experimental studies were carried out in solving a model problem. This model can be used in the compilation of various programs aimed at solving problems of forecasting and classifying objects defined in the space of features of large dimension.
Time-efficient path planning using two virtual robots

This article introduces time-efficient path planning algorithms handling both path length and safety within a reasonable computational time. The path is planned considering the robot’s size so that as the robot traverses the constructed path, it doesn’t collide with an obstacle boundary. This article introduces two virtual robots deploying virtual nodes which discretize the obstacle-free space into a topological map. Using the topological map, the planner generates a safe and near-optimal path within a reasonable computational time. It is proved that our planner finds a safe path to the goal in finite time. Using MATLAB simulations, we verify the effectiveness of our path planning algorithms by comparing it with the rapidly-exploring random tree (RRT)-star algorithm in three-dimensional environments.
Digital Evidence Certainty Descriptors (DECDs)

Whilst many other traditional forensic science disciplines are encouraged to describe the weight of their evidence in some form of quantifiable measurement/expression, this is rarely done in digital forensics. There are calls to rectify this situation, suggesting that the field should begin to develop more robust, scientific methods for evaluating the digital evidence presented by its practitioners. Whilst such a recommendation carries a number of potential benefits, caution must be exercised as at present there are no available satisfactory methods for achieving this. This work suggests that attaining such methods may not actually be possible due to the intricacies of digital data and the difficulties involved with the fine-grained interpretation of events. As a result it is argued that attempts to quantify any uncertainty should be abandoned in favour of methods which reliably describe when uncertainty exists and in what capacity. Here, the Digital Evidence Certainty Descriptors (DECDs) framework is offered as a method for conveying when uncertainty exists in a set of digital findings. The DECDs framework is discussed and applied to working examples to demonstrate the difficulties involved with determining the authenticity of a given hypothesis regarding digital evidence.
Coupling Deep Discriminative and Generative Models for Reactive Robot Planning in Human-Robot Collaboration

Human-robot collaboration towards achieving a common goal is most effective when the robot has the capability to estimate the intentions and needs of its human partner, and to plan complementary actions accordingly. To this end, synergistic coupling between inference engines and task-planning algorithms is essential: the earlier the robot can anticipate the actions performed by its partner, the safer and more seamless the interaction between the two parties will be.In this work, we propose a perception-based analytics framework that incorporates discriminative and generative models, which together estimate the current and future class of an action being performed by a human. The analytics leverage a sequence of human skeletal joint locations extracted from a depth map video stream of the human partner. The generative model ingests current and previous joint positions and outputs a sequence of predicted future positions. The discriminative model produces a vector of probabilities indicating the likelihood that the future action belongs to each class within a set of action classes being considered. The information on current and future actions is fed to a task planning module which selects the robot collaborative action that better suits the estimated present and future human states.
Deep IA-BI and Five Actions in Circling

Deep bidirectional Intelligence (BI) via YIng YAng (IA) system, or shortly Deep IA-BI, is featured by circling A-mapping and I-mapping (or shortly AI circling) that sequentially performs each of five actions. A basic foundation of IA-BI is bidirectional learning that makes the cascading of A-mapping and I-mapping (shortly A-I cascading) approximate an identical mapping, with a nature of layered, topology-preserved, and modularised development. One exemplar is Lmser that improves autoencoder by incremental bidirectional layered development of cognition, featured by two dual natures DPN and DCW. Two typical IA-BI scenarios are further addressed. One considers bidirectional cognition and image thinking, together with a proposal that combines theories of Hubel-Wiesel's versus Chen's. The other considers bidirectional integration of cognition, knowledge accumulation, and abstract thinking for improving implementation of searching, optimising, and reasoning. Particularly, an IA-DSM scheme is proposed for solving a doubly stochastic matrix (DSM) featured combinatorial tasks such as travelling salesman problem, and also a Subtree driven reasoning scheme is proposed for improving production rule based reasoning. In addition, some remarks are made on relations of Deep IA-BI to Hubel and Wiesel theory, Sperry theory, and A5 problem solving paradigm.
A filtering problem with uncertainty in observation

Abstract This paper is concerned with a generalized Kalman-Bucy filtering model and corresponding robust problem under model uncertainty. We find that this robust problem is equivalent to considering an estimate problem under some sublinear operator. Therefore, we turn to obtaining the minimum mean square estimator under a sublinear operator. By Girsanov theorem and minimax theorem, we obtain the optimal estimator x ˆ t of the signal process x t for given time t ∈ ( 0 , T ] .
A Densenet Based Robust Face Detection Framework

Face Detection has become important in various real-life applications such as face recognition, kinship verification, video surveillance, sentiment analysis using videos, etc. There has been significant progress in this field in recent years, thanks to the evolution of deep convolutional neural networks (CNNs). Images taken in real-world scenarios vary a lot in various aspects such as lighting, scale, pose, etc. WIDER FACE dataset contains such images, and is hence, quite challenging. In this paper, we propose a solution which takes the DSFD (Dual Shot Face Detector) as a baseline network, and we apply some tweaks to the network to improve performance with lesser memory usage and inference time. Specifically, we use a Densenet backbone, use focal loss function for classification, a function of IoU (Intersection over Union) metric as a regression loss function, and lastly, use the max-out operation before predicting class probabilities. Consequently, the proposed solution achieves state-of-the-art performance on the WIDER FACE Dataset, with added advantages of being more scalable and taking lesser time to infer than its original DSFD baseline. Also, it gives better face detection performance than many other state-of-the-art face detection frameworks.
Self-directed Lifelong Learning for Robot Vision

Efforts towards robust visual scene understanding tend to rely heavily on manual annotations. When human labels are required, collecting a dataset large enough to train a successful robot vision system is almost certain to be prohibitively expensive. However, we argue that a robot with a vision sensor can learn powerful visual representations in a self-directed manner by relying on fundamental physical priors and bootstrapping techniques. For example, it has been shown that basic visual tracking systems can be used to automatically label short-range correspondences in video that allow one to train a system with capabilities analogous to object permanence in humans. An object permanence system can in turn be used to automatically label long-range correspondences, allowing one to train a system able to compare and contrast objects and scenes. In the end, the agent will develop a representation that encodes persistent material properties, state, lighting, etc. of various parts of a visual scene. Starting with a strong visual representation, the agent can then learn to solve traditional vision tasks such as class and/or instance recognition using only a sparse set of labels that can be found on the Internet or solicited at little cost from humans. More importantly, such a representation would also enable truly robust solutions to challenges in robotics such as global localization, loop closure detection, and object pose estimation.
Compacting ciphertext in multi-channel broadcast encryption and attribute-based encryption

Multi-channel broadcast encryption (MCBE) and Attribute-based encryption (ABE) are important primitives used in a broad range of concrete application scenarios such as Pay-TV, e-Health, Cloud Storage, Blockchain and so on. The former allows a sender to efficiently send different messages to arbitrarily chosen sets of users (target sets) at the same time, while the latter supports more sophisticated access control by allowing both the encryption and decryption phases to be based on the user's attributes. Recently, Canard el al. at Theoretical Computer Science'18 proposed a new technique for compacting header size in both MCBE and ABE. However, their technique leads to schemes in secret-key setting which has limited applications in practice. In this paper, we propose a new technique also for compacting header size in both MCBE and ABE, but our technique leads to schemes in public key setting which therefore can overcome the weakness mentioned above of the Canard el al.'s technique. We finally implement our MCBE and ABE schemes to give some concrete benchmarks.
Forward Collision Prediction with Online Visual Tracking

Safety is the key aspect when comes to driving. Self-driving vehicles are equipped with driver-assistive technologies like Adaptive Cruise Control, Forward Collision Warning system (FCW) and Collsion Mitigation by Breaking (CMbB) to ensure safety while driving. This paper proposes a method by following a lean way of multi-target tracking implementation and 3D bounding box detection without processing much visual information. Object Tracking is an integral part of environment sensing, which enables the vehicle to estimate the surrounding object’s trajectories to accomplish motion planning. The advancement in the object detection methods greatly benefits when following the tracking by detection approach. This will lead to less complex tracking methodology and thus decreasing the computational cost. Estimation based on particle filter is added to precisely associate the tracklets with detections. The model estimates and plots bounding box for the objects in its camera range and predict the 3D positions in camera coordinates from monocular camera data using a deep learning combined with geometric constraints using 2D bounding box, then the actual distance from the vehicle camera is calculated. The model is evaluated on the KITTI car dataset.
Scenario Mining for Development of Predictive Safety Functions

The fulfillment of wide-ranging requirements in regard to safety and comfort of drivers is a challenging task for many automotive manufactures. In order to ensure the reliable and efficient testing of advanced driver assistance systems (ADAS), a data-driven catalog consisting of relevant traffic scenarios is of major importance. In this context, this paper presents a two-layer method for the mining of critical scenarios from accident data containing information on various categories. Firstly, the Extended Successive Odds Ratio Analysis is conducted, in which all the extracted combinations of risk- inducing attributes can be taken into account as the foundation of scenario description. Afterwards, the Successive Association Rules Analysis completes the scenario description using further attributes whereby an optimization problem is formulated to find the best associated attribute successively. The relevant traffic scenarios extracted from real-world data by applying this novel approach give the developers an opportunity to test the functionality of ADAS.
Comparative Study of Bio-inspired Algorithms Applied in the Optimization of Fuzzy Systems

In the medical area, it is very important to have accurate results in diagnosis of diseases that people may suffer. This is why, there is a need to perform the optimization of the fuzzy classifier which provides the nocturnal blood pressure profile of patients, and which is important, due that with this diagnosis we may know if the patient is prone to have a cardiovascular event. This fuzzy system is designed using different membership functions, which are trapezoidal and Gaussian membership functions, in order to select the fuzzy system that provides better results when making the classification. Two bioinspired algorithms are used separately to test their performance, which are the Crow Search Algorithm and Chicken Swarm Optimization. Thirty experiments were performed varying the parameters in the algorithms and from which it can be concluded that the CSO provides better results when optimizing fuzzy systems with both types of membership functions.
Impact of English-Language and French-Language Students and Faculty Populations on the Use of E-Reference in a Bilingual Canadian University

This article reports the results of a quantitative analysis comparing usage rates between English- and French-language e-reference titles at the J. N. Desmarais Library of Laurentian University, Canada. e-Reference usage variables examined including full-text use and searches. French-language e-reference titles were separated from English-language e-reference titles, if they contain entirely or primarily French-language information. The majority of e-reference usage was recorded by the English-language e-reference collection. This corresponds to the much larger English-language student and faculty populations. In most cases, the English-language e-reference exhibited very strong to moderately strong linear relationships with student and faculty populations while French-language e-reference usage exhibited weak to very weak linear relationships. In most cases, the English-language e-reference collection demonstrated a greater level of relative use by English-language student and faculty populations.
Minimizing Task Completion Time of Prioritized Motion Planning in Multi-Robot Systems

Prioritized motion planning is an effective approach to decouple the motion coordination in multi-robot systems, in which priority assignment plays an important role on its performance. Previous approaches apply either a randomized search or a simple rule to prioritize robot motions, which are often computationally expensive and sometimes ineffective. In this paper, we present an effective approach to schedule robots movement with the aim of minimizing their task completion time. This approach first plans a time-optimal motion for each robot individually, then roughly prioritizes robots in the descending order of their motion-time, i.e., a robot is prioritized the highest if it has the largest motion-time, and vice versa. Then, we propose a heuristic to fine-tune robot priorities under the principle that the tuned priority shortens the task completion time. We also develop an asynchronous decentralized algorithm to accelerate the priority-tuning process. Compared to previous priority assignment approaches, our approach needs less computation time to generate a shorter task completion time in a multi-robot system.
A Novel Fast Retina Keypoint Extraction Algorithm for Multispectral Images Using Geometric Algebra

The feature extraction for multispectral images plays an important role in many computer vision applications.Recently, geometric algebra (GA) based scale invariant feature transform algorithm (GA-SIFT) and GA based speeded up robust Features algorithm (GA-SURF), have been proposed to extract feature of multispectral image in GA space.However those methods are difficult to be implemented in realtime applications.Now, the challenge is to design a new algorithm to extract the features of multispectral image more efficiently and quickly, so that it can be used in real-time applications.Although the proposed fast retina keypoint (FREAK) algorithm is faster to compute and more robust than SIFT and SURF, it can not be utilized to extract features directly for multispectral images.In this paper, we propose a novel fast retina keypoint extraction algorithm based on GA, named as GA-FREAK, for multispectral images.Firstly, the multispectral images are represented as multivectors in GA space, then the interest points are detected with the procedure of FREAK in GA space.Finally, our experiments demonstrate that the GA-FREAK is faster and more robust than some previous algorithms in multispectral images.It is expected that the proposed GA-FREAK will be a competitive alternative in real-time applications of multispectral images.
Colonoscopic Image Polyp Classification Using Texture Features

Colonoscopic polyps, which may lead to cancer in the later stage has been claiming lots of lives worldwide. So, an early detection and classification system for such polyps has become crucial for timely diagnosis. Many features learning based classification frameworks have been proposed in the literature. Extreme difficulties have been faced in this field are due to lack of sufficient data set, feature similarity among different classes of polyps and so on. In this paper, a two class classification of polyps namely benign and malignant has been devised. The proposed method uses multi directions and multi frequency texture analysis. Gabor filter banks are used in the initial stage followed by discrete cosine transform (DCT) upon the sub-bands. Local binary pattern (LBP) as texture features is also used. Since malignant polyp generally oozes blood on its surface, color features is also considered. The square of the DCT coefficients calculated on all the sub-bands of the Gabor filter are considered as the energy. The DCT co-efficients derived from all the sub-bands are fused to form the feature vector. Along with these features, LBP features and color features are also added to form the final feature vector. SVM is used as the classifier which performs $$k-$$fold validation on the data set. The proposed method gives competitive results with recent state-of-the-art methods of colonoscopic polyp classification.
From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning — Insights from Biological Systems on Adaptive Flexibility

Recent developments in machine-learning algorithms have led to impressive performance increases in many traditional application scenarios of artificial intelligence research. In the area of deep reinforcement learning, deep learning functional architectures are combined with incremental learning schemes for sequential tasks that include interaction-based, but often delayed feedback. Despite their impressive successes, modern machine-learning approaches, including deep reinforcement learning, still perform weakly when compared to flexibly adaptive biological systems in certain naturally occurring scenarios. Such scenarios include transfers to environments different than the ones in which the training took place or environments that dynamically change, both of which are often mastered by biological systems through a capability that we here term “fluid adaptivity” to contrast it from the much slower adaptivity (“crystallized adaptivity”) of the prior learning from which the behavior emerged. In this article, we derive and discuss research strategies, based on analyzes of fluid adaptivity in biological systems and its neuronal modeling, that might aid in equipping future artificially intelligent systems with capabilities of fluid adaptivity more similar to those seen in some biologically intelligent systems. A key component of this research strategy is the dynamization of the problem space itself and the implementation of this dynamization by suitably designed flexibly interacting modules.