Reviewerly/exHarmony huggingface dataset:
Nonlinear modelling and optimal control via Takagi-Sugeno fuzzy techniques: A quadrotor stabilization

Abstract Using the principles of Takagi-Sugeno fuzzy modelling allows the integration of flexible fuzzy approaches and rigorous mathematical tools of linear system theory into one common framework. The rule-based T-S fuzzy model splits a nonlinear system into several linear subsystems. Parallel Distributed Compensation (PDC) controller synthesis uses these T-S fuzzy model rules. The resulting fuzzy controller is nonlinear, based on fuzzy aggregation of state controllers of individual linear subsystems. The system is optimized by the linear quadratic control (LQC) method, its stability is analysed using the Lyapunov method. Stability conditions are guaranteed by a system of linear matrix inequalities (LMIs) formulated and solved for the closed loop system with the proposed PDC controller. The additional GA optimization procedure is introduced, and a new type of its fitness function is proposed to improve the closed-loop system performance.
Medical Imbalanced Data Classification Based on Random Forests

This paper studies the problem of imbalance in medical datasets. Today, modern machine learning techniques are becoming increasingly popular for this type of problem, with examples in the areas of health and medical. One of the major difficulties with this technique is that the database handled is highly imbalance. Under-sampling and over-sampling techniques are used to work around this problem. In this paper, we apply random forests, which are combinations of decision trees fitted to subsamples of the data, built using under-sampling and over-sampling. At the end of the work, we compare fit metrics obtained in the various specifications of the models tested and evaluate their results inside and outside the sample. We observed that random forest techniques using imbalanced sub-samples smaller than the original sample presented the best performance among the random forests used and an improvement compared to that practiced in the medical dataset.
Combining Networks Using Cherry Picking Sequences

Phylogenetic networks are important for the study of evolution. The number of methods to find such networks is increasing, but most such methods can only reconstruct small networks. To find bigger networks, one can attempt to combine small networks. In this paper, we study the Network Hybridization problem, a problem of combining networks into another network with low complexity. We characterize this complexity via a restricted problem, Tree-child Network Hybridization, and we present an FPT algorithm to efficiently solve this restricted problem.
Episode-based Prototype Generating Network for Zero-Shot Learning

We introduce a simple yet effective episode-based training framework for zero-shot learning (ZSL), where the learning system requires to recognize unseen classes given only the corresponding class semantics. During training, the model is trained within a collection of episodes, each of which is designed to simulate a zero-shot classification task. Through training multiple episodes, the model progressively accumulates ensemble experiences on predicting the mimetic unseen classes, which will generalize well on the real unseen classes. Based on this training framework, we propose a novel generative model that synthesizes visual prototypes conditioned on the class semantic prototypes. The proposed model aligns the visual-semantic interactions by formulating both the visual prototype generation and the class semantic inference into an adversarial framework paired with a parameter-economic Multi-modal Cross-Entropy Loss to capture the discriminative information. Extensive experiments on four datasets under both traditional ZSL and generalized ZSL tasks show that our model outperforms the state-of-the-art approaches by large margins.
Image Dehaze Method Using Depth Map Estimation Network Based on Atmospheric Scattering Model

The purpose of dehaze is to remove haze effects for image enhancement. In previous typical researches for dehaze using deep learning, the transmission map is estimated directly through the training process. In this paper, we propose to obtain the transmission map indirectly from the depth map, which is estimated using deep learning framework. The haze removal is performed from the transmission map based on the atmospheric scattering model as the existing method. The experimental results show that the proposed method can give improved dehaze results for some images with relatively dark conditions.
A format-preserving encryption FF1, FF3-1 using lightweight block ciphers LEA and, SPECK

Format-preserving encryption has been studied for a long time since its proposal, but the algorithm is yet to be adequately evaluated or verified. The existing standard format-preserving encryption FF1 and FF3-1 use block cipher AES in the internal function. This paper proposes a method to improve the speed of FF1 and FF3-1 whereby the algorithm is implemented by changing the cipher to lightweight block ciphers LEA(Lightweight Encryption Algorithm) and SPECK. The encryption speed is analyzed and compared with that of the existing encryption by dividing it into high-performance computer environments and low-performance Internet of Things environments. The results showed that the encryption speed was improved compared with FF1 and FF3-1. Improving the encryption speed of format-preserving encryption will make it easier to apply format-preserving encryption to various systems.
Average Reward Adjusted Discounted Reinforcement Learning: Near-Blackwell-Optimal Policies for Real-World Applications

Although in recent years reinforcement learning has become very popular the number of successful applications to different kinds of operations research problems is rather scarce. Reinforcement learning is based on the well-studied dynamic programming technique and thus also aims at finding the best stationary policy for a given Markov Decision Process, but in contrast does not require any model knowledge. The policy is assessed solely on consecutive states (or state-action pairs), which are observed while an agent explores the solution space. The contributions of this paper are manifold. First we provide deep theoretical insights to the widely applied standard discounted reinforcement learning framework, which give rise to the understanding of why these algorithms are inappropriate when permanently provided with non-zero rewards, such as costs or profit. Second, we establish a novel near-Blackwell-optimal reinforcement learning algorithm. In contrary to former method it assesses the average reward per step separately and thus prevents the incautious combination of different types of state values. Thereby, the Laurent Series expansion of the discounted state values forms the foundation for this development and also provides the connection between the two approaches. Finally, we prove the viability of our algorithm on a challenging problem set, which includes a well-studied M/M/1 admission control queuing system. In contrast to standard discounted reinforcement learning our algorithm infers the optimal policy on all tested problems. The insights are that in the operations research domain machine learning techniques have to be adapted and advanced to successfully apply these methods in our settings.
Mutation Strategy Selection Based on Fitness Landscape Analysis: A Preliminary Study

Different algorithms and strategies behave disparately for different types of problems. In practical problems, we cannot grasp the nature of the problem in advance, so it is difficult for the engineers to choose a proper method to solve the problem effectively. In this case, the strategy selection task based on fitness landscape analysis comes into being. This paper gives a preliminary study on mutation strategy selection on the basis of fitness landscape analysis for continuous real-parameter optimization based on differential evolution. Some fundamental features of the fitness landscape and the components of standard differential evolution algorithm are described in detail. A mutation strategy selection framework based on fitness landscape analysis is designed. Some different types of classifiers which are applied to the proposed framework are tested and compared.
Helping Preservice Teachers to Reflect

Teacher reflection is a mechanism to evaluate and improve the educational practice. To achieve an efficient reflection, it is convenient to have guidelines that support this activity. In this paper it examines the incidence of studying basic notions of the onto-semiotic approach to develop the ability of future math teachers to reflect on teaching practices. The study was conducted during the second semester of 2018 with seven students, in three phases: first, participants reflected on the classroom practice observed in a video sequence, without intervention; second, training was given on some theoretical notions of the onto-semiotic approach; and third, a new reflective session was conducted on the continuation of a video sequence. Data analysis was focused on identifying and comparing elements referring to indicators of suitability present in both reflections. Results show that participants manage to express clearer ideas, and, mainly make justifications about their value judgments. It has been determined that, with the appropriate use of concepts from a theoretical framework of didactic analysis and guided reflection, there was a change in the nature of the reflection made by future teachers, moving from the description of events to the interpretation and analysis of situations.
Robotic Table Tennis with Model-Free Reinforcement Learning

We propose a model-free algorithm for learning efficient policies capable of returning table tennis balls by controlling robot joints at a rate of 100Hz. We demonstrate that evolutionary search (ES) methods acting on CNN-based policy architectures for non-visual inputs and convolving across time learn compact controllers leading to smooth motions. Furthermore, we show that with appropriately tuned curriculum learning on the task and rewards, policies are capable of developing multi-modal styles, specifically forehand and backhand stroke, whilst achieving 80\% return rate on a wide range of ball throws. We observe that multi-modality does not require any architectural priors, such as multi-head architectures or hierarchical policies.
Simple Implementation of a Fuzzy Logic Speed Controller for a PMDC Motor with a Low Cost Arduino Mega

Control of the permanent magnetic direct current PMDC motor is a common practice, hence the importance of the implementation of the PMDC motor speed controller. The results of a fuzzy logic speed controller for the PMDC motor rely on an appropriate base. As the dimension of the rules increases, its difficulty rises which affects computation time and memory requirements. Fuzzy Logic Controller (FLC) can be carried out by a low-cost Arduino Mega which has a small flash memory and a maximum clock speed of 16MHz. It is realized by three membership functions and each was divided into three memberships. The results of the FLC are satisfactory, revealing superior transient and steady-state performance. In addition, the controller is robust to speed mode variations.
Asymptotic Convergence of Soft-Constrained Neural Networks for Density Estimation

A soft-constrained neural network for density estimation (SC-NN-4pdf) has recently been introduced to tackle the issues arising from the application of neural networks to density estimation problems (in particular, the satisfaction of the second Kolmogorov axiom). Although the SC-NN-4pdf has been shown to outperform parametric and non-parametric approaches (from both the machine learning and the statistics areas) over a variety of univariate and multivariate density estimation tasks, no clear rationale behind its performance has been put forward so far. Neither has there been any analysis of the fundamental theoretical properties of the SC-NN-4pdf. This paper narrows the gaps, delivering a formal statement of the class of density functions that can be modeled to any degree of precision by SC-NN-4pdfs, as well as a proof of asymptotic convergence in probability of the SC-NN-4pdf training algorithm under mild conditions for a popular class of neural architectures. These properties of the SC-NN-4pdf lay the groundwork for understanding the strong estimation capabilities that SC-NN-4pdfs have only exhibited empirically so far.
The Usage of Cohesive Devices by High-Achieving EFL Students in Writing Argumentative Essays

This study aims to investigate types of cohesive devices frequently used in writing argumentative essays by high-achieving college students in Indonesia and to analyse their awareness of usage of these. Participants were asked to write an essay on a pre-determined topic; analysis was then conducted to investigate the most frequent cohesive devices used. Data on the second study question, understanding of the usage of cohesive devices, were gathered though semi-structured interviews. The data indicate that students effectively used various types of grammatical cohesive devices, particularly reference devices. However, some claimed not to know that they were using other cohesive devices. They believed that conjunctions were the only type that functions to connect ideas across sentences and paragraphs, and did not realise that this, that, the, and other reference devices are cohesive devices. In terms of the use of lexical cohesive devices, participants used synonyms more than other sub-types of lexical cohesive devices. The most frequently used device was the, which functions both as a cohesive device and an article. All participants agreed that though links or cohesive devices are necessary to achieve coherence, two claimed that these are not determining factors in producing good writing quality. They argued that high-quality writing needs to contain linguistic features related to sophisticated language and text difficulty. The first suggestion is that focused programs should be adopted to teach the writing of argumentative essays to help lower-achieving students attain the same writing level as high-achieving students. Second, a teaching programme integrating the use of sophisticated grammar, lexical diversity and cohesive devices should be a curriculum priority.
A negative case analysis of visual grounding methods for VQA

Existing Visual Question Answering (VQA) methods tend to exploit dataset biases and spurious statistical correlations, instead of producing right answers for the right reasons. To address this issue, recent bias mitigation methods for VQA propose to incorporate visual cues (e.g., human attention maps) to better ground the VQA models, showcasing impressive gains. However, we show that the performance improvements are not a result of improved visual grounding, but a regularization effect which prevents over-fitting to linguistic priors. For instance, we find that it is not actually necessary to provide proper, human-based cues; random, insensible cues also result in similar improvements. Based on this observation, we propose a simpler regularization scheme that does not require any external annotations and yet achieves near state-of-the-art performance on VQA-CPv2.
EvoDynamic: A Framework for the Evolution of Generally Represented Dynamical Systems and Its Application to Criticality

Dynamical systems possess a computational capacity that may be exploited in a reservoir computing paradigm. This paper presents a general representation of dynamical systems which is based on matrix multiplication. That is similar to how an artificial neural network (ANN) is represented in a deep learning library and its computation can be faster because of the optimized matrix operations that such type of libraries have. Initially, we implement the simplest dynamical system, a cellular automaton. The mathematical fundamentals behind an ANN are maintained, but the weights of the connections and the activation function are adjusted to work as an update rule in the context of cellular automata. The advantages of such implementation are its usage on specialized and optimized deep learning libraries, the capabilities to generalize it to other types of networks and the possibility to evolve cellular automata and other dynamical systems in terms of connectivity, update and learning rules. Our implementation of cellular automata constitutes an initial step towards a more general framework for dynamical systems. Our objective is to evolve such systems to optimize their usage in reservoir computing and to model physical computing substrates. Furthermore, we present promising preliminary results toward the evolution of complex behavior and criticality using genetic algorithm in stochastic elementary cellular automata.
Usability Evaluation Model for Mobile Visually Impaired Applications

The usability of the mobile applications is the most important factor in developing, so the key to develop successful mobile applications is usability, especially for users have specific needs such as visually impaired. However, developers do not focus on visually impaired users. Moreover, there are limited studies and usability evaluation models for mobile applications for visually impaired so developers use just a modified usability evaluation methods which are not enough and useful to evaluate mobile applications for visually impaired, or they use general usability evaluation models. Therefore, using these methods or models is difficult for evaluator and not useful for visually impaired users. This study conducts Systematic Literature Review (SLR) to identify usability dimensions that help mobile applications developers and evaluators to evaluate mobile application for users which have moderate and severe visual impairment. The result shows that, six dimensions that have a significant impact on moderate and severe visually impaired users' satisfaction, who use mobile applications. These dimensions namely efficiency, effectiveness, satisfaction, errors, accessibility and understandability.
Boosting Semantic Human Matting with Coarse Annotations

Semantic human matting aims to estimate the per-pixel opacity of the foreground human regions. It is quite challenging and usually requires user interactive trimaps and plenty of high quality annotated data. Annotating such kind of data is labor intensive and requires great skills beyond normal users, especially considering the very detailed hair part of humans. In contrast, coarse annotated human dataset is much easier to acquire and collect from the public dataset. In this paper, we propose to use coarse annotated data coupled with fine annotated data to boost end-to-end semantic human matting without trimaps as extra input. Specifically, we train a mask prediction network to estimate the coarse semantic mask using the hybrid data, and then propose a quality unification network to unify the quality of the previous coarse mask outputs. A matting refinement network takes in the unified mask and the input image to predict the final alpha matte. The collected coarse annotated dataset enriches our dataset significantly, allows generating high quality alpha matte for real images. Experimental results show that the proposed method performs comparably against state-of-the-art methods. Moreover, the proposed method can be used for refining coarse annotated public dataset, as well as semantic segmentation methods, which reduces the cost of annotating high quality human data to a great extent.
Bitcoin Cryptocurrency : Its Cryptographic Weaknesses and Remedies

Bitcoin (BTC) is a type of cryptocurrency that supports transaction/payment of virtual money between BTC users without the presence of a central authority or any third party like bank. It uses some cryptographic techniques namely public- and private-keys, digital signature and cryptographic-hash functions, and they are used for making secure transactions and maintaining distributed public ledger called blockchain. In BTC system, each transaction signed by sender is broadcasted over the P2P (Peer-to-Peer) Bitcoin network and a set of such transactions collected over a period is hashed together with the previous block/other values to form a block known as candidate block, where the first block known as genesis-block was created independently. Before a candidate block to be the part of existing blockchain (chaining of blocks), a computation-intensive hard problem needs to be solved. A number of miners try to solve it and a winner earns some BTCs as inspiration. The miners have high computing and hardware resources, and they play key roles in BTC for blockchain formation. This paper mainly analyses the underlying cryptographic techniques, identifies some weaknesses and proposes their enhancements. For these, two modifications of BTC are suggested ― (i) All BTC users must use digital certificates for their authentication and (ii) Winning miner must give signature on the compressed data of a block for authentication of public blocks/blockchain.
On optimal weak algebraic manipulation detection codes and weighted external difference families

This paper provides a combinatorial characterization of weak algebraic manipulation detection (AMD) codes via a kind of generalized external difference families called bounded standard weighted external difference families (BSWEDFs). By means of this characterization, we improve a known lower bound on the maximum probability of successful tampering for the adversary’s all possible strategies in weak AMD codes. We clarify the relationship between weak AMD codes and BSWEDFs with various properties. We also propose several explicit constructions for BSWEDFs, some of which can generate new optimal weak AMD codes.