Reviewerly/exHarmony huggingface dataset:
Event-Triggered Based State Estimation for Autonomous Operation of an Aerial Robotic Vehicle

In this article the problem of event-triggered (ET) state estimation for autonomous navigation of an aerial vehicle is investigated numerically. The aerial vehicle is considered as a general example of a nonlinear non-Gaussian system for state estimation under process and measurement noise. The motivation behind the problem is the conditions that the aerial vehicles are facing in extreme and hazardous environments due to constant exposure of the sensors and actuators to the high frequency process and measurement noises. Here we consider autonomous operation of a quadcopter for mapping of a radioactive environment, where the quadcopter may subject to radiations and non-Gaussian noises. Autonomous operation of the aerial vehicle with a limited available energy and for a longer period of time, demands an efficient management of the energy sources. Therefore, in this study we take the first step towards this goal by studying an event triggering strategy in which the data measured by the sensors is transmitted to the processing unit only if certain events happen. The sensor employed for navigation purpose is the inertial measurement unit, including accelerometers and gyroscopes, used to estimate the quadcopter states only when their measurements are informative. An event-triggered particle filtering (PF) state estimation technique is adopted for this application. The choice of particle filter as state-estimator is inevitable not only because of nonlinear and non-Gaussian nature of the system, but also because of non-Gaussianity of the conditional distribution of the posteriori probability density function resulting from the event triggering. In the proposed method, it is proved that particles are weighted differently in the case of event triggering and no triggering. The numerical results for robust nonlinear attitude stabilization of the quadcopter in the presence of event-triggered particle filter state estimation confirm the efficiency of the proposed method.
Biomedical Mention Disambiguation using a Deep Learning Approach

Automatically locating named entities in natural language text - named entity recognition - is an important task in the biomedical domain. Many named entity mentions are ambiguous between several bioconcept types, however, causing text spans to be annotated as more than one type when simultaneously recognizing multiple entity types. The straightforward solution is a rule-based approach applying a priority order based on the precision of each entity tagger (from highest to lowest). While this method is straightforward and useful, imprecise disambiguation remains a significant source of error. We address this issue by generating a partially labeled corpus of ambiguous concept mentions. We first collect named entity mentions from multiple human-curated databases (e.g. CTDbase, gene2pubmed), then correlate them with the text mined span from PubTator to provide the context where the mention appears. Our corpus contains more than 3 million concept mentions that ambiguous between one or more concept types in PubTator (? 3% of all mentions). We approached this task as a classification problem and developed a deep learning-based method which uses the semantics of the span being classified and the surrounding words to identify the most likely bioconcept type. More specifically, we develop a convolutional neural network (CNN) and along short-term memory (LSTM) network to respectively handle the semantic syntax features, then concatenate these within a fully connected layer for final classification. The priority ordering rule-based approach demonstrated F1-scores of 71.29% (micro-averaged) and 41.19% (macro-averaged), while the new disambiguation method demonstrated F1-scores of 91.94% (micro-averaged) and 85.42% (macro-averaged), a very substantial increase.
Reinforcement Point and Fuzzy Input Design of Fuzzy Q-Learning for Mobile Robot Navigation System

This paper proposes a fuzzy method to minimize state in reinforcement learning for obstacle avoidance mobile robot. The problem using reinforcement learning to solve obstacle avoidance is how to define the number of states. When the various conditions that might not be predicted by the mobile robot occur, the reinforcement learning is difficult to apply because the number of states become unlimited. The greater the number of states will cause the process requires more memory and performance of the processor. One of the popular methods for reinforcement learning is Q-learning. The Q-learning method is simple and feasible for observing surrounding conditions of mobile robot. The fuzzy used to eliminate the number of state problem, generalize the condition and reduce processor performance is needed. In this paper the comparison between reinforcement point using the change of state and the current state ( St) is conducted also the comparison of angle region sensor between 2, 3, 4 and 5. Simulation result show that reinforcement point using the change of state and 5 angle regions of sensor produce the best performance.
Bidirectional controlled remote state preparation in three-dimensional system

We present a novel scheme for bidirectional controlled remote state preparation (BCRSP) in a three-dimensional system with a six-qutrit entangled state as the quantum channel. In this scheme, two distant parties, Alice and Bob, are not only senders but also receivers. Alice wants to remotely prepare a single-qutrit equatorial state at Bob’s site, while at the same time, Bob wishes to help Alice by remotely preparing another single-qutrit equatorial state. It is shown that, only if the two senders and the controller collaborate with each other, the BCRSP can be completed successfully.
Password Guessing via Neural Language Modeling

Passwords are the major part of authentication in current social networks. The state-of-the-art password guessing approaches, such as Markov model and probabilistic context-free grammars (PCFG) model, assign a probability value to each password by a statistic approach without any parameters. These methods require large datasets to accurately estimate probability due to the law of large number. The neural network, approximating target probability distribution through iteratively training its parameters, was used to model passwords by some researches. However, since the network architectures they used are simple and straightforward, there are many ways to improve it. In this paper, we view password guessing as a language modeling task and introduce a deeper, more robust, and faster-converged model with several useful techniques to model passwords. This model shows great ability in modeling passwords while significantly outperforms state-of-the-art approaches. Inspired by the most advanced sequential model named Transformer, we use it to model passwords with bidirectional masked language model which is powerful but unlikely to provide normalized probability estimation. Then we distill Transformer model’s knowledge into our proposed model to further boost its performance. Comparing with the PCFG, Markov and previous neural network models, our models show remarkable improvement in both one-site tests and cross-site tests. Moreover, our models are robust to the password policy by controlling the entropy of output distribution.
A Dempster‐Shafer theory and uninorm‐based framework of reasoning and multiattribute decision‐making for surveillance system

Closed-circuit television and sensor-based intelligent surveillance systems have attracted considerable attentions in the field of public security affairs. To provide real-time reaction in the case of a huge volume of the surveillance data, researchers have proposed event-reasoning frameworks for modeling and inferring events of interest. However, they do not support decision-making, which is very important for surveillance operators. To this end, this paper incorporate a function of decision-making in an event-reasoning framework, so that our model not only can perform event-reasoning but also can predict, rank, and alarm threats according to uncertain information from multiple heterogeneous sources. In particular, we propose a multiattribute decision-making model, in which an object being watched is modeled as a multiattribute event, where each attribute corresponds to a specific source, and the information from each source can be used to elicit a local threat degree of different malicious situations with respect to the corresponding attribute. Moreover, to assess an overall threat degree of an object being observed, we also propose a method to fuse the conflict threat degrees regarding all the relevant attributes. Finally, we demonstrate the effectiveness of our framework by an airport security surveillance scenario.
Multi-Branch Spatial-Temporal Network for Action Recognition

Human action recognition based on deep-learning methods have received increasing attention and developed rapidly. However, current methods suffer from the confusion caused by convolving over time and space independently, processing shorter sequences, restricted to single temporal scale modeling and so on. The key objective of precisely classifying actions is to capture the appearance and motion throughout entire videos. Based on this purpose, a multi-branch spatial-temporal network (MSTN) is proposed. It consists of a multi-branch deep network and a long-term feature (LTF) layer. Benefits of the proposed MSTN include: (a) the multi-branch spatial-temporal network aims at encoding spatial and temporal information simultaneously, and (b) the LTF layer is used to aggregate the video-level representation with multiple temporal scales. Evaluations on two action datasets and comparison with several state-of-the-art approaches demonstrate the effectiveness of the proposed network.
Elastic Net Constraint-Based Tensor Model for High-Order Graph Matching

The procedure of establishing the correspondence between two sets of feature points is important in computer vision applications. In this article, an elastic net constraint-based tensor model is proposed for high-order graph matching. To control the tradeoff between the sparsity and the accuracy of the matching results, an elastic net constraint is introduced into the tensor-based graph matching model. Then, a nonmonotone spectral projected gradient (NSPG) method is derived to solve the proposed matching model. During the optimization of using NSPG, we propose an algorithm to calculate the projection on the feasible convex sets of elastic net constraint. Further, the global convergence of solving the proposed model using the NSPG method was proved. The superiority of the proposed method is verified through experiments on the synthetic data and natural images.
Designated tester certificateless encryption with keyword search

In this paper, we introduce the notion of designated tester certificateless encryption with keyword search (dCLEKS) and define the required security model. In the defined security model, in addition to the security of ciphertexts, we consider the security of trapdoors through which the security against offline keyword guessing attack (oKGA) is ensured. We further propose a concrete dCLEKS scheme and show that it is secure in the defined security model under the intractability of GBDH and CDH problems. To the best of our knowledge, the proposed dCLEKS scheme is the only secure searchable certificateless encryption scheme that provides security against oKGA performed by outside attackers and does not require secure channels for transmission of ciphertexts and trapdoors between users and the server.
Mineral Resources in Egypt (I): Metallic Ores

Mineral resources in Egypt are diverse. This chapter deals with the metallic ores. The chapter starts with the iron ores in Egypt and special emphasis is given to the origin of Egyptian banded iron-formations. In addition to iron, the chapter covers many types of ore deposits in Egypt including orogenic gold, Ti-rich, sulfide and precious metal, chromite, industrial metal oxides (Sn, W, Ta, Nb, and Mo) deposits as well as U occurrences in the basement rocks of Egypt and Egyptian manganese deposits.
Machine Learning for Clinical Predictive Analytics

In this chapter, we provide a brief overview of applying machine learning techniques for clinical prediction tasks. We begin with a quick introduction to the concepts of machine learning, and outline some of the most common machine learning algorithms. Next, we demonstrate how to apply the algorithms with appropriate toolkits to conduct machine learning experiments for clinical prediction tasks. This chapter is composed of five sections. First, we will explain why machine learning techniques are helpful for researchers in solving clinical prediction problems (Sect. 12.1). Understanding the motivations behind machine learning approaches in healthcare are essential, since precision and accuracy are often critical in healthcare problems, and everything from diagnostic decisions to predictive clinical analytics could dramatically benefit from data-based processes with improved efficiency and reliability. In the second section, we will introduce several important concepts in machine learning in a colloquial manner, such as learning scenarios, objective/target function, error and loss function and metrics, optimization and model validation, and finally a summary of model selection methods (Sect. 12.2). These topics will help us utilize machine learning algorithms in an appropriate way. Following that, we will introduce some popular machine learning algorithms for prediction problems (Sect. 12.3), for example, logistic regression, decision tree and support vector machine. Then, we will discuss some limitations and pitfalls of using the machine learning approach (Sect. 12.4). Lastly, we will provide case studies using real intensive care unit (ICU) data from a publicly available dataset, PhysioNet Challenge 2012, as well as the breast tumor data from Breast Cancer Wisconsin (Diagnostic) Database, and summarize what we have presented in this chapter (Sect. 12.5).
A task priority approach to cooperative mobile manipulation: Theory and experiments

Cooperative manipulation and transportation by means of multi-robot systems is a subject that has received an increased interest in the last few years. In this work, a task priority approach is first recalled from the authors previous works as framework for the control of a single mobile manipulator, to manage all its control objectives, including set membership ones and a proper coordination between the manipulator and its supporting vehicle. The approach is then extended, through a novel coordination policy, to execute a cooperative transportation of a common load by means of two (or more) mobile manipulators, via an explicit but limited information exchange, without modifying the individual controllers. Experimental results with two YouBot mobile manipulators are shown to demonstrate the effectiveness of the approach.
A Hybrid Hindi Printed Document Classification System Using SVM and Fuzzy

This article introduces a new advanced tri-layered segmentation and bi-leveled-classifier-based Hindi printed document classification system, which categorizes imaged documents into pre-defined mutually exclusive categories by using SVM and Fuzzy matching at character and document classifications, respectively. During training, the improved and noise-free image is segmented into lines and words by profiling. Then it obtains Shirorekha Less (SL) isolated characters along with upper, left and right modifier components from the SL words. These components use their locations and inter character-modifier component distance to get associate with their corresponding characters only. Further, confidence values of all characters are calculated with SVM training and all characters are mapped into Romanized labels to generate the words. Finally, documents are classified by Fuzzy based matching of Romanized detected words and predefined classes. The average execution times of SL characters are 0.22675 sec. and 0.20375 sec. and classification accuracy are 74.61% and 80.73% for training and testing, respectively.
Residual attention unit for action recognition

3D CNNs are powerful tools for action recognition that can intuitively extract spatio-temporal features from raw videos. However, most of the existing 3D CNNs have not fully considered the disadvantageous effects of the background motion that frequently appears in videos. The background motion is usually misclassified as a part of human action, which may undermine modeling the dynamic pattern of the action. In this paper, we propose the residual attention unit (RAU) to address this problem. RAU aims to suppress the background motion by upweighting the values associated with the foreground region in the feature maps. Specifically, RAU contains two separate submodules in parallel, i.e., spatial attention as well as channel-wise attention. Given an intermediate feature map, the spatial attention works in a bottom-up top-down manner to generate the attention mask, while the channel-wise attention recalibrates the feature responses of all channels automatically. As applying the attention mechanism directly to the input features may lead to the loss of discriminative information, we design a bypass to preserve the integrity of the original features by a shortcut connection between the input and output of the attention module. Notably, our RAU can be embedded into 3D CNNs easily and enables end-to-end training along with the networks. The experimental results on UCF101 and HMDB51 demonstrate the validity of our RAU.
Video Captioning With Adaptive Attention and Mixed Loss Optimization

The attention mechanism and sequence-to-sequence framework have shown promising advancements in the temporal task of video captioning. However, imposing the attention mechanism on non-visual words, such as “of” and “the”, may mislead the decoder and decrease the overall performance of video captioning. Furthermore, the traditional sequence to sequence framework optimizes the model by using word-level cross entropy loss, which results in an exposure bias problem. This problem occurs because, at test time, the model uses the previously generated words to predict the next word, while it maximizes the likelihood of the next ground-truth word with consideration of the true previous one during training. To address these issues, we propose the reinforced adaptive attention model (RAAM), which integrates an adaptive attention mechanism with long short-term memory to flexibly utilize visual signals and language information as needed. Accordingly, the model is trained with both word-level loss and sentence-level loss to take advantage of these two losses and alleviate the exposure bias problem by directly optimizing the sentence-level metric using a reinforcement learning algorithm. Besides, a novel training method is proposed for mixed loss optimization. Experiments on the Microsoft Video Description benchmark corpus (MSVD) and the challenging MPII-MD Movie Description dataset demonstrate that the proposed RAAM method, which uses only a single feature, achieves competitive or even superior results compared to existing state-of-the-art models for video captioning.
Adaptive <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e44763" altimg="si3.svg"><mml:mi>k</mml:mi></mml:math>-tournament mutation scheme for differential evolution

Mutation in differential evolution (DE) is of considerable importance for the performance of the algorithm. It directly impacts exploration and exploitation. Thus, it represents the driving force for discovering unvisited regions of the search space, whilst also enabling the utilisation of promising points in that space. Since mutation performs search around the base vector, its selection plays a prominent role in directing it. In that regard, a low selection pressure contributes to exploration, whereas a high selection pressure contributes to exploitation. However, a balance between the two is paramount for high and consistent performance. This paper proposes a novel mutation scheme that employs k-tournament selection for choosing the base vector. Each population member is associated with a tournament size that is adapted during the search process with the aim of controlling exploration and exploitation. The mechanism mixes adaptation on an individual and population level. Results of the experimental analysis conducted on a wide range of numerical benchmark problem instances affirm its competitive performance and the benefits of the adaptation of tournament sizes, suggesting it to be a viable measure for increasing DE algorithm performance. Finally, the automatic design of radial basis function networks for classification was tackled. The proposed mutation scheme proved to be effective when dealing with that task as the canonical algorithm incorporating it yielded better fit models than competing approaches.
FPGA Implementation of Floating Point Based Cuckoo Search Algorithm

Cuckoo search algorithm (CSA) has been a candidate for numerous recent applications and showed great compatibility in solving optimization problems. It is a metaheuristic algorithm which is based on the odd breeding strategy of the Cuckoo bird spices. It is used to find an optimum or near optimum solution for a certain problem. In this research, we propose an FPGA hardware implementation for the CSA based on single precision IEEE floating point (FP). The FP format provides a wider range and higher precision when compared to fixed point format. To the best of our knowledge, this is the first study to consider implementing FP format-based CSA on FPGA. The proposed design is implemented using pipelined and parallel techniques to get a high throughput and speed. The design is controlled and coordinated using finite state machines (FSMs) modules and is configured on Cyclone IV E FPGA chip from Intel. Three common benchmark functions are used to evaluate the performance of the proposed design. The design has a maximum operating frequency of 99 MHz. It was found out the maximum power consumption for the most complex function is 610.28 mW, mainly due to the use of FP format. In addition, the proposed design is implementation and evaluated for multidimensional operation. Accordingly, the proposed design is suitable for path planning for unmanned aerial vehicles (UAVs), sensor deployments for wireless sensor networks (WSNs) in addition to medical diagnostic and DSP applications.
Italian Text Categorization with Lemmatization and Support Vector Machines

The paper describes an Italian language text categorizer by Lemmatization and support vector machines. Camastra, Francesco The categorizer is composed of six modules. The first module performs the tokenization, removing the punctuation signs; the second and third ones carry out stopping and lemmatization, respectively; the fourth module implements the bag-of-words approach; the fifth one performs feature dimensionality reduction eliminating poor discriminant features; finally, the last module does the classification. Razi, Gennaro The Italian text categorizer has been validated on a database composed of more than 1100 articles, extracted from online edition of three Italian language newspapers, belonging to eight different categories. The work is highly novel, since to the best our knowledge, there are no works in literature on Italian text categorization.