Reviewerly/exHarmony huggingface dataset:
Devanagari Handwritten Character Recognition using Convolutional Neural Networks

Devanagari is an Indic script and forms a basis for over 100 languages spoken in India and Nepal including Hindi, Marathi, Sanskrit, and Maithili. It consists of 47 primary alphabets, 14 vowels, 33 consonants, and 10 digits. In addition, the letters of the alphabet are modified when a vowel is added to a consonant. There is no capitalization of letters, like Latin languages. The devanagari script consists of consonants and modifiers. This paper presents a system that works on a set of 29 consonants and one modifier. It uses a self-made Devanagari script dataset which comprises of 29 consonants with no header line (Shirorekha) over them. The dataset has 34604 handwritten images. Deep learning techniques are applied to extract features and recognize the characters in an image. Deep Convolutional Neural Network (DCNN) have been incorporated to extract features and classify the input images. Consecutive convolutional layers are used in this process which brings added advantage in the process of extracting higher-level features. The trained model demonstrated an accuracy of 99.65%.
Learning Step-Size Adaptation in CMA-ES

An algorithm’s parameter setting often affects its ability to solve a given problem, e.g., population-size, mutation-rate or crossover-rate of an evolutionary algorithm. Furthermore, some parameters have to be adjusted dynamically, such as lowering the mutation-strength over time. While hand-crafted heuristics offer a way to fine-tune and dynamically configure these parameters, their design is tedious, time-consuming and typically involves analyzing the algorithm’s behavior on simple problems that may not be representative for those that arise in practice. In this paper, we show that formulating dynamic algorithm configuration as a reinforcement learning problem allows us to automatically learn policies that can dynamically configure the mutation step-size parameter of Covariance Matrix Adaptation Evolution Strategy (CMA-ES). We evaluate our approach on a wide range of black-box optimization problems, and show that (i) learning step-size policies has the potential to improve the performance of CMA-ES; (ii) learned step-size policies can outperform the default Cumulative Step-Size Adaptation of CMA-ES; and transferring the policies to (iii) different function classes and to (iv) higher dimensions is also possible.
Enhancing gene expression programming based on space partition and jump for symbolic regression

When solving a symbolic regression problem, the gene expression programming (GEP) algorithm could fall into a premature convergence which terminates the optimization process too early, and may only reach a poor local optimum. To address the premature convergence problem of GEP, we propose a novel algorithm named SPJ-GEP, which can maintain the GEP population diversity and improve the accuracy of the GEP search by allowing the population to jump efficiently between segmented subspaces. SPJ-GEP first divides the space of mathematical expressions into k subspaces that are mutually exclusive. It then creates a subspace selection method that combines the multi-armed bandit and the ∊-greedy strategy to choose a jump subspace. In this way, the analysis is made on the population diversity and the range of the number of subspaces. The analysis results show that SPJ-GEP does not significantly increase the computational complexity of time and space than classical GEP methods. Besides, an evaluation is conducted on a set of standard SR benchmarks. The evaluation results show that the proposed SPJ-GEP keeps a higher population diversity and has an enhanced accuracy compared with three baseline GEP methods.
A cognitive perception on content-based image retrieval using an advanced soft computing paradigm

Abstract The most prominent research area in the field of advanced machine vision paradigm for medical imaging applications is brought by the content-based image retrieval (CBIR) technique. With the help of emerging medical imaging systems, a patient's medical background can be easily obtained in the form of digitized data such as X-rays, magnetic resonance imaging (MRI), computed tomography, ultrasound, nuclear imaging, and so on. In the past, radiologists manually analyzed the patient's health condition. Now, the medical imaging process provides better information and depiction of the different cases. However, the conventional techniques have created some controversies among the various literatures including insufficient feature set, high semantic gap, and computational time complexity. The methods we have used for content retrieval are gray-level cooccurrence matrix, local binary pattern, color cooccurrence matrix, and support vector machine. MRI data were used for the completion of texture and shape-based retrieval. On improvising the previously ordained results, an algorithm is proposed using semantic image retrieval-based CBIR by combining three-dimensional features. This chapter also emphasizes a detailed comparative analysis of various techniques with the proposed method.
Control for cooperative transport of a bar-shaped payload with rotorcraft UAVs including a landing stage on mobile robots

This paper considers the cooperative transport problem of a cable-suspended rigid bar with two rotorcraft UAVs considering collision avoidance, weight distribution, and wind perturbations. The proposal is based on null-space theory and includes a landing stage where the load is settled down on a formation of ground robots. Therefore, a variable formation is considered according to the task phase. In order to simulate the proposal in a realistic environment, very complete dynamic models for UAVs, ground robots, and load are considered. An adaptation stage is incorporated to link the control actions with the inputs required by the dynamic models. Theoretical bounds for the errors are studied under the assumption that perfect velocity tracking is not fulfilled.
Building Explanations for Fuzzy Decision Trees with the ExpliClas Software

Fairness, Accountability, Transparency and Explainability have become strong requirements in most practical applications of Artificial Intelligence (AI). Fuzzy sets and systems are recognized world-wide because of their outstanding contribution to model AI systems with a good interpretability-accuracy trade-off. Accordingly, fuzzy sets and systems are at the core of the so-called Explainable AI. ExpliClas is a software as a service which paves the way for interpretable and self-explainable intelligent systems. Namely, this software provides users with both graphical visualizations and textual explanations associated with intelligent classifiers automatically learned from data. This paper presents the new functionality of ExpliClas regarding the generation, evaluation and explanation of fuzzy decision trees along with fuzzy inference-grams. This new functionality is validated with two well-known classification datasets (i.e., Wine and Pima), but also with a real-world beer-style classifier.
MERLIN a Cognitive Architecture for Service Robots

Many social robots deployed in public spaces hide hybrid cognitive architectures for dealing with daily tasks. Mostly, two main blocks sustain these hybrid architectures for robot behavior generation: deliberative and behavioral-based mechanisms. Robot Operating System offers different solutions for implementing these blocks, however, some issues arise when both are released in the robot. This paper presents a software engineering approach for normalizing the process of integrating them and presenting them as a fully cognitive architecture named MERLIN. Providing implementation details and diagrams for established the architecture, this research tests empirically the proposed solution using a variation from the challenge defined in the SciRoc @home competition. The results validate the usability of our approach and show MERLIN as a hybrid architecture ready for short and long-term tasks, showing better results than using a by default approach, particularly when it is deployed in highly interactive scenarios.
A Sequential Target Existence Statistic for Joint Probabilistic Data Association

A sequential probability ratio test (SPRT) is introduced as a track management (TM) mechanism for the joint probabilistic data association (JPDA) multitarget tracking algorithm. The SPRT is based on an approximate target likelihood ratio that is derived from the JPDA event and association probabilities. SPRT-based TM functions are defined and compared to the joint integrated probabilistic data association (JIPDA) algorithm, including a discussion of parameter tuning for both approaches. Simulation experiments are reported for multiple closely spaced constant-velocity targets over a range of values for detection probability and clutter density. The SPRT-based algorithm achieves performance similar to JIPDA for this data, but with fewer TM parameters and a more straightforward parameter-tuning process.
Application of Crowd Simulations in the Evaluation of Tracking Algorithms

Tracking and action-recognition algorithms are currently widely used in video surveillance, monitoring urban activities and in many other areas. Their development highly relies on benchmarking scenarios, which enable reliable evaluations/improvements of their efficiencies. Presently, benchmarking methods for tracking and action-recognition algorithms rely on manual annotation of video databases, prone to human errors, limited in size and time-consuming. Here, using gained experiences, an alternative benchmarking solution is presented, which employs methods and tools obtained from the computer-game domain to create simulated video data with automatic annotations. Presented approach highly outperforms existing solutions in the size of the data and variety of annotations possible to create. With proposed system, a potential user can generate a sequence of random images involving different times of day, weather conditions, and scenes for use in tracking evaluation. In the design of the proposed tool, the concept of crowd simulation is used and developed. The system is validated by comparisons to existing methods.
Energy Efficient Fixed-point Inference System of Convolutional Neural Network

In this paper, we implemented a high accuracy, high throughput, low bit-width, and low power consumption convolutional neural network (CNN) inference system for handwritten digit recognition onto an FPGA. With reference to the 32-bit floating-point baseline, our 8-bit fixed-point design has zero accuracy loss and maintains 99.55% accuracy. Moreover, our improved 4-bit fixed-point design achieves 99.28% accuracy, which has less than 0.3% accuracy loss. Also, our 4-bit fixed-point LeNet-5 inference accelerator is implemented on FPGA Evaluation Kit and achieves a throughput of 2296 GOP/s with an energy efficiency of 213 GOP/s/W.
Three‐stage RGBD architecture for vehicle and pedestrian detection using convolutional neural networks and stereo vision

With the growth of autonomous vehicles and collision-avoidance systems, several approaches using deep learning and convolutional neural networks (CNNs) continually address accuracy improvement in obstacle detection. The authors introduce a three-stage architecture that adds side channels as low-level features to serve as input to existing CNNs. In a case study, the architecture is used to extract depth from stereo cameras, and then compose RGBD inputs to state-of-the-art CNNs to improve their vehicle and pedestrian detection accuracy. This can be achieved by simple modifications on the first layers of any existing CNN with RGB inputs. To validate the architecture, the state-of-the-art matching cost-CNN, and cascade residual learning, both specialist algorithms to extract depth information combined to the state-of-the-art Faster-region-based CNN, MSCNCN, and Subcategory-aware Convolutional Neural Network (SubCNN) to yield the models to be tested using the KITTI dataset benchmark. In many cases, the accuracy (in terms of average precision) using their proposal outperforms the original scores in various scenarios of detection difficulty, reaching improvements up to +3.96% in the training and +1.50% in the testing KITTI datasets. This proposal also introduces efficient methods to initialise the weights of the depth convolutional filters during transfer learning using net surgery.
Design and Function Realization of Nuclear Power Inspection Robot System

SUMMARY The particularity of nuclear power plant environment requires that the nuclear power inspection robot must be remote control operation. The main purpose of the inspection robot is to carry out inspection, prevention, reporting, and safety emergency operation on the instruments, so as to provide guarantee for the safe operation of the nuclear power plant. Based on the representative configuration of nuclear power robot at home and abroad, this paper develops a small and lightweight nuclear power plant inspection robot, including walking mechanism, lifting mechanism, operating mechanism, image acquisition, information communication and control system, etc., to carry on the statics analysis to the key components of the inspection robot and verify that the stiffness and strength of the mechanical structure meet the requirements of lightweight design. Modal analysis is carried out to verify that the motor does not cause resonance when working. The kinematic model of the robot has been established and can provide the theoretical basis for the controller design. A hierarchical control system based on LabVIEW upper computer monitoring and control operation interface is established, which uses adaptive fuzzy Proportional Integral Derivative (PID) control to simulate the walking control, and then realizes the control of walking mechanism through software programming, and the adaptive fuzzy PID control has better effect than the conventional PID control. The S-type acceleration and deceleration algorithm is used to realize the accurate control of the position location of the lifting mechanism. Finally, combined with the experiment of 5MS robot comprehensive experimental platform, it is proved that the inspection robot can realize remote control function operation.
Hate Speech Detection Using Transformer Ensembles on the HASOC Dataset

With the ubiquity and anonymity of the Internet, the spread of hate speech has been a growing concern for many years now. The language used for the purpose of dehumanizing, defaming or threatening individuals and marginalized groups not only threatens the mental health of its targets, as well as their democratic access to the Internet, but also the fabric of our society. Because of this, much effort has been devoted to manual moderation. The amount of data generated each day, particularly on social media platforms such as Facebook and twitter, however makes this a Sisyphean task. This has led to an increased demand for automatic methods of hate speech detection.
Curriculum Feature Alignment Domain Adaptation for Epithelium-Stroma Classification in Histopathological Images

In recent years, deep learning methods have received more attention in epithelial-stroma (ES) classification tasks. Traditional deep learning methods assume that the training and test data have the same distribution, an assumption that is seldom satisfied in complex imaging procedures. Unsupervised domain adaptation (UDA) transfers knowledge from a labelled source domain to a completely unlabeled target domain, and is more suitable for ES classification tasks to avoid tedious annotation. However, existing UDA methods for this task ignore the semantic alignment across domains. In this paper, we propose a Curriculum Feature Alignment Network (CFAN) to gradually align discriminative features across domains through selecting effective samples from the target domain and minimizing intra-class differences. Specifically, we developed the Curriculum Transfer Strategy (CTS) and Adaptive Centroid Alignment (ACA) steps to train our model iteratively. We validated the method using three independent public ES datasets, and experimental results demonstrate that our method achieves better performance in ES classification compared with commonly used deep learning methods and existing deep domain adaptation methods.
Large Initial Population and Neighborhood Search incorporated in LSHADE to solve CEC2020 Benchmark Problems

The single objective multimodal bound-constrained optimization problems in CEC (IEEE Congress on Evolutionary Computation) competitions pose tremendous challenges to the researchers in finding the global optimum. This paper introduces the orthogonal array-based initialization of population and neighborhood search strategy in LSHADE (linear population reduction technique in success history based adaptive differential evolution) algorithm to solve the CEC2020 competition problems. In the novel algorithm, named as O-LSHADE, a large number of population members have been initialized in the search space using the orthogonal design. Thereafter, a small Euclidean neighborhood is defined for each member in the population. The adaptive mutation and crossover are performed within the neighborhood during first phase, i.e., predominantly exploration phase of the algorithm. The process ensures that the search space is adequately explored by the algorithm as the neighborhoods are not mixed and each neighborhood performs the mutation and crossover operations independently. At a later stage of the algorithm, a step reduction in population is done to perform the operations like an adaptive differential evolution. The proposed algorithm is found efficient, effective and robust when applied to the CEC2020 benchmark problems for single objective bound constrained optimization. In most cases, the algorithm could achieve the best error value of less than 100. This implies that the method could guide the search process towards the global basin.
BSO-AL: Brain Storm Optimization Algorithm with Adaptive Learning Strategy

Swarm intelligence algorithms have been widely and successfully used to optimize many science and engineering problems, the collective behavior of the agents lead to the emergence of intelligence. These interactions among agents can be classified into three categories: exploring, emulating and learning. Brain Storm Optimization (BSO) is a novel swarm intelligence algorithm which is inspired by the human brainstorming process, and generates new ideas by emulating existing ideas. In this paper, a new BSO algorithm with an adaptive learning strategy (BSOAL) is proposed. By considering the evolutionary speed factor of each individual and the aggregation degree of the swarm, the proposed BSO-AL generates new individuals by exploring, emulating or learning adaptively. Comparative experiments were conducted on a set of benchmark functions with different dimensions. The experimental results show that the proposed BSO-AL algorithm outperforms the classic BSO algorithm and the other two state-of-the-art algorithms, which demonstrates the effectiveness of the learning strategy.
Demystifying Stablecoins

Self-sovereign stablecoins are interesting and probably here to stay; however, they face numerous regulatory hurdles from banking, financial tracking, and securities laws. For stablecoins backed by a governmental currency, the ultimate expression would be a CBDC. Since paper currency has been in steady decline (and disproportionately for legitimate transactions), a CBDC could reintroduce cash with technological advantages and efficient settlement while minimizing user fees.
A Tensor Network based Decision Diagram for Representation of Quantum Circuits

Tensor networks have been successfully applied in simulation of quantum physical systems for decades. Recently, they have also been employed in classical simulation of quantum computing, in particular, random quantum circuits. This article proposes a decision diagram style data structure, called Tensor Decision Diagram (TDD), for more principled and convenient applications of tensor networks. This new data structure provides a compact and canonical representation for quantum circuits. By exploiting circuit partition, the TDD of a quantum circuit can be computed efficiently. Furthermore, we show that the operations of tensor networks essential in their applications (e.g., addition and contraction) can also be implemented efficiently in TDDs. A proof-of-concept implementation of TDDs is presented and its efficiency is evaluated on a set of benchmark quantum circuits. It is expected that TDDs will play an important role in various design automation tasks related to quantum circuits, including but not limited to equivalence checking, error detection, synthesis, simulation, and verification.
Building an Italian-Chinese Parallel Corpus for Machine Translation from the Web

In an increasingly globalized world, being able to understand texts in different languages (even more so in different alphabets and charsets) has become a necessity. This can be strategic even while moving and travelling across different countries, characterized by different languages. With this in mind, bilingual corpora become critical resources since they are the basis of every state-of-the-art automatic translation system; moreover, building a parallel corpus is usually a complex and very expensive operation. This paper describes an innovative approach we have defined and adopted to automatically build an Italian-Chinese parallel corpus, with the aim of using it for training an Italian-Chinese Neural Machine Translation. Our main idea is to scrape parallel texts from the Web: we defined a general pipeline, describing each specific step from the selection of the appropriate data sources to the sentence alignment method. A final evaluation was conducted to evaluate the goodness of our approach and its results show that 90% of the sentences were correctly aligned. The corpus we have obtained consists of more than 6,000 sentence pairs (Italian and Chinese), which are the basis for building a Machine Translation system.
Sentiment Analysis of Sinhala News Comments using Sentence-State LSTM Networks

Different types of deep learning based sentiment classification techniques have been introduced for sentiment analysis, and for Natural Language Processing, in general. However, Sinhala, which is a low resourced, yet morphologically rich language, has not experienced these recent advancements. In particular, for sentiment analysis, there is only one research that used deep learning techniques, and that also gave sub-optimal results. In this paper, we present the use of a novel state-of-the-art deep learning technique called sentence state long short-term memory network for Sinhala sentiment classification. On a binary sentiment classification task, this method outperforms both the previously reported statistical machine learning algorithms and deep learning algorithms.
Atomic cross chain swaps via relays and adapters

Blockchain technologies have proven their potential when it comes to store assets and value. However, swapping assets across chains, for example trading ethers for bitcoins is still a challenging problem to solve. Current solutions widely rely on trusted third parties such as exchanges, which is not acceptable for a distributed technology.
Optimal FIR Filter for Discrete-Time LTV Systems and Fast Iterative Algorithm

An a posteriori optimal finite impulse response (OFIR) filter is proposed for linear time-varying systems. The OFIR filter is derived in a discrete convolution-based batch form and represented with a computationally efficient iterative algorithm using recursions. A comparison of the OFIR filter, Kalman filter (KF), and unbiased FIR (UFIR) filter performances is provided using an experimental example of object tracking. It is shown that responding to temporary uncertainties the OFIR filter produces shorter transients and a bit smaller excursions than the KF and smaller excursions than the UFIR filter.