Reviewerly/exHarmony huggingface dataset:
Learning Reasoning-Decision Networks for Robust Face Alignment

In this paper, we propose an end-to-end reasoning-decision networks (RDN) approach for robust face alignment via policy gradient. Unlike the conventional coarse-to-fine approaches which likely lead to bias prediction due to poor initialization, our approach aims to learn a policy by leveraging raw pixels to reason a subset of shape candidates, sequentially making plausible decisions to remove outliers for robust initialization. To achieve this, we formulate face alignment as a Markov decision process by defining an agent, which typically interacts with a trajectory of states, actions, state transitions and rewards. The agent seeks an optimal shape searching policy over the whole shape space by maximizing a discounted sum of the received values. To further improve the alignment performance, we develop an LSTM-based value function to evaluate the shape quality. During the training procedure, we adjust the gradient of our value function in directions of the policy gradient. This prevents our training goal from being trapped into local optima entangled by both the pose deformations and appearance variations especially in unconstrained environments. Experimental results show that our proposed RDN consistently outperforms most state-of-the-art approaches on four widely-evaluated challenging datasets.
Deep Reinforcement Learning in Strategic Board Game Environments

In this paper we propose a novel Deep Reinforcement Learning (DRL) algorithm that uses the concept of "action-dependent state features", and exploits it to approximate the Q-values locally, employing a deep neural network with parallel Long Short Term Memory (LSTM) components, each one responsible for computing an action-related Q-value. As such, all computations occur simultaneously, and there is no need to employ "target" networks and experience replay, which are techniques regularly used in the DRL literature. Moreover, our algorithm does not require previous training experiences, but trains itself online during game play. We tested our approach in the Settlers Of Catan multi-player strategic board game. Our results confirm the effectiveness of our approach, since it outperforms several competitors, including the state-of-the-art jSettler heuristic algorithm devised for this particular domain.
Visual Methodologies for Networked Images: Designing Visualizations for Collaborative Research, Cross-platform Analysis, and Public Participation

In this paper we present visual methodologies attuned to the networked nature of digital images. First, we describe approaches to image research in which images are not separated from their network, but rather studied en groupe. Here, we contrast approaches that treat images as data, and those that regard images as content. Second, we focus on the production of images for digital research, presenting three of their functions: a) the creation of diagrams that facilitate collaboration in interdisciplinary research teams; b) the use of visualizations for cross-platform image analysis; and c) designing images for public participation. Most importantly, such visualizations are not used to form the esthetic culmination of analytical work, but are rather functional tools for digital research that serve parts of the entire research process, from its formulation and operationalization to the engagement of a broader public.
Anomaly detection in ECG based on trend symbolic aggregate approximation

ECG anomaly detection is a necessary approach to detect disease Electrocardiography( ECG) signals before the detail diagnosis process in medical field to gauge the health of the human heart. Nowadays, there are many anomaly detection methods for ECG detection including supervised learning and unsupervised learning. For supervised learning, it requires the knowledge of expert and different types of Arrhythmia data for training. However, since the anomalies are less and unknown in many cases which are di cult to distinguish and be labeled, unsupervised methods are more suitable to detect the ECG anomalies. Furthermore, the existing unsupervised learning studies do not take ECG shape into account where different diseases have different shapes. In this paper, a novel simple trend aggregate approximation method is proposed, the relative binary trend representation are used to record the shape feature in original time series and to detect the anomaly heart signals by similarity comparison. We use the ECG dataset in UCR Time Series Classification Archive to obtain ECG time series data and the experiment results are assessed by means of sensitivity, specificity, false alarm rate measures which is robust and promising with high accuracy.
Efficient and Secure Image Encryption Algorithm Using a Novel Key-Substitution Architecture

Recently, many chaos-based image encryption algorithms have been proposed. Most of them adopt the traditional confusion-diffusion framework. Multiple encryption rounds or one round of complex encryption is typically executed in these schemes to realize high security. However, these operations will reduce the computational efficiency. To overcome these issues, we propose a novel key-substitution encryption architecture (KSA). Under the proposed KSA, we design a key scheming for updating the initial keys using the plain-image and develop a new substitution method for encrypting various types of images. The simulation results and security analysis demonstrate the superior security and high efficiency of the proposed image encryption algorithm using the KSA (KSA-IEA), which executes only one round of encryption.
Mincut pooling in Graph Neural Networks.

The advance of node pooling operations in Graph Neural Networks (GNNs) has lagged behind the feverish design of new message-passing techniques, and pooling remains an important and challenging endeavor for the design of deep architectures. In this paper, we propose a pooling operation for GNNs that leverages a differentiable unsupervised loss based on the mincut optimization objective. For each node, our method learns a soft cluster assignment vector that depends on the node features, the target inference task (e.g., graph classification), and, thanks to the mincut objective, also on the graph connectivity. Graph pooling is obtained by applying the matrix of assignment vectors to the adjacency matrix and the node features. We validate the effectiveness of the proposed pooling method on a variety of supervised and unsupervised tasks.
Telementoring in gynecologic oncology training: changing lives in Mozambique

More than 85% of cervical cancer cases occur in low and middle income countries where there are not enough medical specialists to provide prevention, screening, and treatment services. Numerous cancer patients around the world are victims of the scarcity of institutional organization structure and
Mind Your Language: Abuse and Offense Detection for Code-Switched Languages

In multilingual societies like the Indian subcontinent, use of code-switched languages is much popular and convenient for the users. In this paper, we study offense and abuse detection in the code-switched pair of Hindi and English (i.e, Hinglish), the pair that is the most spoken. The task is made difficult due to non-fixed grammar, vocabulary, semantics and spellings of Hinglish language. We apply transfer learning and make a LSTM based model for hate speech classification. This model surpasses the performance shown by the current best models to establish itself as the state-of-the-art in the unexplored domain of Hinglish offensive text classification. We also release our model and the embeddings trained for research purposes.
Human Activity Recognition Using Gaussian Mixture Hidden Conditional Random Fields

In healthcare, the analysis of patients’ activities is one of the important factors that offer adequate information to provide better services for managing their illnesses well. Most of the human activity recognition (HAR) systems are completely reliant on recognition module/stage. The inspiration behind the recognition stage is the lack of enhancement in the learning method. In this study, we have proposed the usage of the hidden conditional random fields (HCRFs) for the human activity recognition problem. Moreover, we contend that the existing HCRF model is inadequate by independence assumptions, which may reduce classification accuracy. Therefore, we utilized a new algorithm to relax the assumption, allowing our model to use full-covariance distribution. Also, in this work, we proved that computation wise our method has very much lower complexity against the existing methods. For the experiments, we used four publicly available standard datasets to show the performance. We utilized a 10-fold cross-validation scheme to train, assess, and compare the proposed model with the conditional learning method, hidden Markov model (HMM), and existing HCRF model which can only use diagonal-covariance Gaussian distributions. From the experiments, it is obvious that the proposed model showed a substantial improvement with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>p</mml:mi></mml:math> value ≤0.2 regarding the classification accuracy.
Classification of Volumetric Images Using Multi-Instance Learning and Extreme Value Theorem

Volumetric imaging is an essential diagnostic tool for medical practitioners. The use of popular techniques such as convolutional neural networks (CNN) for analysis of volumetric images is constrained by the availability of detailed (with local annotations) training data and GPU memory. In this paper, the volumetric image classification problem is posed as a multi-instance classification problem and a novel method is proposed to adaptively select positive instances from positive bags during the training phase. This method uses the extreme value theory to model the feature distribution of the images without a pathology and use it to identify positive instances of an imaged pathology. The experimental results, on three separate image classification tasks (i.e. classify retinal OCT images according to the presence or absence of fluid build-ups, emphysema detection in pulmonary 3D-CT images and detection of cancerous regions in 2D histopathology images) show that the proposed method produces classifiers that have similar performance to fully supervised methods and achieves the state of the art performance in all examined test cases.
A Multi-Scale Map Method Based on Bioinspired Neural Network Algorithm for Robot Path Planning

With the wide application of Bioinspired Neural Network in the field of robot path planning, the environmental scale of robot path planning is getting larger, and the environmental resolution requirements are getting higher. However, with the increase of the environment size and resolution requirement, the neuronal activity value calculation cost and the time cost of the Bioinspired Neural Network will increase sharply. Aiming at this problem, this paper proposes an improved Bioinspired Neural Network path planning method based on Scaling Terrain. Using a Multi-Scale Map method and Dijkstra algorithm, the optimal path of a Coarse Scale Map is calculated. The optimal path obtained from the Coarse Scale Map is used to guide the neural network planning weights of the Fine Scale Map from the same terrain. Thus, the optimal path of the Fine Scale Map can be calculated by the improved BNN algorithm. Introducing this Multi-Scale Map Method into the Bioinspired Neural Network can greatly reduce the time cost of the Bioinspired Neural Network path planning algorithm and reduce the mathematical complexity. Simulation results in some computer integrated virtual environments further demonstrate the superiority of this method and the experimental results are encouraging.
Privacy-preserving multi-authority attribute-based encryption with dynamic policy updating in PHR

As a new kind of patient-centred health-records model, the personal health record (PHR) system can support the patient in sharing his/her health information online. Attribute-Based Encryption (ABE), as a new public key cryptosystem that guarantees fine-grained access control of outsourced encrypted data, has been used to design the PHR system. Considering that privacy preservation and policy updating are the key problems in PHR, a privacy-preserving multiauthority attribute-based encryption scheme with dynamic policy updating in PHR was proposed. In the scheme, each of the patient?s attributes is divided into two parts: attribute name and attribute value. The values of the user?s attributes will be hidden to prevent them from being revealed to any third parties. In addition, the Linear Secret-Sharing Scheme (LSSS) access structure and policy-updating algorithms are designed to support all types of policy updating (based on ?and?, ?or?, and ?not? operations). Finally, the scheme is demonstrated to be secure against chosen-plaintext attack under the standard model. Compared to the existing related schemes, the sizes of the user?s secret key and ciphertext are reduced, and the lower computing cost makes it more effective in the PHR system.
Entity Enabled Relation Linking

Relation linking is an important problem for knowledge graph-based Question Answering. Given a natural language question and a knowledge graph, the task is to identify relevant relations from the given knowledge graph. Since existing techniques for entity extraction and linking are more stable compared to relation linking, our idea is to exploit entities extracted from the question to support relation linking. In this paper, we propose a novel approach, based on DBpedia entities, for computing relation candidates. We have empirically evaluated our approach on different standard benchmarks. Our evaluation shows that our approach significantly outperforms existing baseline systems in both recall, precision and runtime.
Robustness Verification of Support Vector Machines

We study the problem of formally verifying the robustness to adversarial examples of support vector machines (SVMs), a major machine learning model for classification and regression tasks. Following a recent stream of works on formal robustness verification of (deep) neural networks, our approach relies on a sound abstract version of a given SVM classifier to be used for checking its robustness. This methodology is parametric on a given numerical abstraction of real values and, analogously to the case of neural networks, needs neither abstract least upper bounds nor widening operators on this abstraction. The standard interval domain provides a simple instantiation of our abstraction technique, which is enhanced with the domain of reduced affine forms, an efficient abstraction of the zonotope abstract domain. This robustness verification technique has been fully implemented and experimentally evaluated on SVMs based on linear and nonlinear (polynomial and radial basis function) kernels, which have been trained on the popular MNIST dataset of images and on the recent and more challenging Fashion-MNIST dataset. The experimental results of our prototype SVM robustness verifier appear to be encouraging: this automated verification is fast, scalable and shows significantly high percentages of provable robustness on the test set of MNIST, in particular compared to the analogous provable robustness of neural networks.
A bilevel optimal motion planning (BOMP) model with application to autonomous parking

Abstract In this paper, we present a bilevel optimal motion planning (BOMP) model for autonomous parking. The BOMP model treats motion planning as an optimal control problem, in which the upper level is designed for vehicle nonlinear dynamics, and the lower level is for geometry collision-free constraints. The significant feature of the BOMP model is that the lower level is a linear programming problem that serves as a constraint for the upper-level problem. That is, an optimal control problem contains an embedded optimization problem as constraints. Traditional optimal control methods cannot solve the BOMP problem directly. Therefore, the modified approximate Karush–Kuhn–Tucker theory is applied to generate a general nonlinear optimal control problem. Then the pseudospectral optimal control method solves the converted problem. Particularly, the lower level is the $$J_2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>J</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math> -function that acts as a distance function between convex polyhedron objects. Polyhedrons can approximate objects in higher precision than spheres or ellipsoids. As a result, a fast high-precision BOMP algorithm for autonomous parking concerning dynamical feasibility and collision-free property is proposed. Simulation results and experiment on Turtlebot3 validate the BOMP model, and demonstrate that the computation speed increases almost two orders of magnitude compared with the area criterion based collision avoidance method.
Towards verified stochastic variational inference for probabilistic programs

Probabilistic programming is the idea of writing models from statistics and machine learning using program notations and reasoning about these models using generic inference engines. Recently its combination with deep learning has been explored intensely, which led to the development of so called deep probabilistic programming languages, such as Pyro, Edward and ProbTorch. At the core of this development lie inference engines based on stochastic variational inference algorithms. When asked to find information about the posterior distribution of a model written in such a language, these algorithms convert this posterior-inference query into an optimisation problem and solve it approximately by a form of gradient ascent or descent. In this paper, we analyse one of the most fundamental and versatile variational inference algorithms, called score estimator or REINFORCE, using tools from denotational semantics and program analysis. We formally express what this algorithm does on models denoted by programs, and expose implicit assumptions made by the algorithm on the models. The violation of these assumptions may lead to an undefined optimisation objective or the loss of convergence guarantee of the optimisation process. We then describe rules for proving these assumptions, which can be automated by static program analyses. Some of our rules use nontrivial facts from continuous mathematics, and let us replace requirements about integrals in the assumptions, such as integrability of functions defined in terms of programs' denotations, by conditions involving differentiation or boundedness, which are much easier to prove automatically (and manually). Following our general methodology, we have developed a static program analysis for the Pyro programming language that aims at discharging the assumption about what we call model-guide support match. Our analysis is applied to the eight representative model-guide pairs from the Pyro webpage, which include sophisticated neural network models such as AIR. It finds a bug in one of these cases, reveals a non-standard use of an inference engine in another, and shows that the assumptions are met in the remaining six cases.
Q-learning with UCB Exploration is Sample Efficient for Infinite-Horizon MDP

A fundamental question in reinforcement learning is whether model-free algorithms are sample efficient. Recently, Jin et al. (2018) proposed a Q-learning algorithm with UCB exploration policy, and proved it has nearly optimal regret bound for finite-horizon episodic MDP. In this paper, we adapt Q-learning with UCB-exploration bonus to infinite-horizon MDP with discounted rewards \emph{without} accessing a generative model. We show that the \textit{sample complexity of exploration} of our algorithm is bounded by $\tilde{O}({\frac{SA}{\epsilon^2(1-\gamma)^7}})$. This improves the previously best known result of $\tilde{O}({\frac{SA}{\epsilon^4(1-\gamma)^8}})$ in this setting achieved by delayed Q-learning (Strehlet al., 2006),, and matches the lower bound in terms of $\epsilon$ as well as $S$ and $A$ up to logarithmic factors.
Person Identification using Autonomous Drone through Resource Constraint Devices

Detecting a specific person from the crowd using drone along with some resource constraint device is a major concern which we are discussing in the paper. Combining the advanced algorithms and some smart hardware material, we will be finding a way to search for a missing individual in a crowd or at some location. We can also search for a person at a specific location by setting our aerial vehicle to fly autonomously and search for the required person. This will help us to cover areas which cannot be reached by humans easily. The flying robot helps to solve real-time problems and come up with some new and more advanced ways to search for the missing ones with more ease, as advanced technological methods are applied, the probability of getting accurate results increases axiomatically. The drone can fly fully autonomously and search or capture videos/photos of the required location. Location commands could be given using PC, mobile and with the help of IoT, using Raspberry Pi.
Blockchain and Anomaly Detection based Monitoring System for Enforcing Wastewater Reuse

Industries, household communities consume a lot of water on regular basis, thereby increasing water crisis. There is continuous increase in water consumption by industries. Reusing wastewater can reduce water withdrawals from local water sources thus increasing water availability, lowering wastewater discharges and their pollutant load, reducing thermal energy consumption and, potentially, processing cost. Various ways have been implemented for recycling the generated wastewater. Wastewater must be reused for the benefit of mankind. In this paper we propose a wastewater recycle control system to efficiently manage the wastewater and coordinate it among the industries and the government. Blockchain technology has been deployed for storing data and developing an incentive model to encourage wastewater reuse. Tokens are provided to industries in proportion to quantity and quality of reused wastewater. Rules for issue and trade of these tokens are written as a smart contract. Unfortunately, providing such incentives also provides a motive for tampering the data on which these tokens are awarded. Anomaly detection algorithms are used to detect the potential frauds which take place in the system upon IoT meter data tampering. The system uses IoT meters that measure volume of wastewater generated and reused, along with quality metrics such as pH, hardness and oil content. Multiple machine learning algorithms are used to detect tampering- polynomial regression, DBSCAN, autoencoders and LSTM networks. Their performance has then been compared. A first implementation of this system and an evaluation of the system's performance are also presented.