Reviewerly/exHarmony huggingface dataset:
Memory-Assisted Dynamic Multi-Objective Evolutionary Algorithm for Feature Drift Problem

In this paper, we propose an enhanced feature selection algorithm able to cope with feature drift problem that may occur in data streams, where the set of relevant features change over time. We utilize a dynamic multi-objective evolutionary algorithm to continuously search for the updated set of relevant features after the occurrence of every change in the environment. An artificial neural network is employed to classify the new instances based on the up-to-date obtained set of relevant features efficiently. Our algorithm exploits a detection mechanism for the severity of changes to estimate the severity level of occurred changes and adaptively replies to these changes by introducing diversity to algorithm solutions. Furthermore, a fixed-size memory is used to store the good solutions and reuse them after each change to accelerate the convergence and searching process of the algorithm. The experimental results using three datasets and different environmental parameters show that the combination of our improved feature selection algorithm with the artificial neural network outperforms related work.
Coevolutive clustering algorithm for large datasets

Clustering is a recurrent task in machine learning. The application of traditional heuristics techniques in large sets of data is not easy. They tend to have at least quadratic complexity with respect to the number of points, yielding prohibitive run times or low quality solutions. The most common approach to tackle this problem is to use weaker, more randomized algorithms with lower complexities to solve the clustering problem. This work proposes a novel approach for performing this task, allowing traditional, stronger algorithms to work on a sample of the data, chosen in such a way that the overall clustering is considered good. Preliminary experimental results indicate that the proposed approach is competitive to classical algorithms in large datasets with the advantage of automatically adapting to many different datasets.
Eudaimonia and Behavior Change

Behavior Change is presently an important objective in the multidisciplinary field of healthcare HCI, and Gamification is one of the most satisfactory ways to address it. However, for a Behavior Change adequate Gamification processes to happen, we have to take into account the occurrence of negative feelings and discomfort that can interfere with reaching their goals. In this paper, we present a pertinent way to embody these antagonistic emotions into the design of a game. We started from the known fact that the primary function of games lies not only in providing fun, but also in teaching players how to positively manage difficulties. This idea is consistent with the tenets of Distributed Cognition, which state that external tools and representations afford the perception, the manipulation, and the sharing of inner thoughts, just like successful games for Behavior Change should do. This paradigm in turn, could represent a way to deal with Cognitive Dissonance and, in particular, with Self-Discrepancy, that are usually occurring during the processes of Behavior Change.
Quantitatively Assessing the Cyber-to-Physical Risk of Industrial Cyber-Physical Systems

Industrial cyber-physical systems (ICPSs) are widely used in critical infrastructures. However, they threaten by various cyberattacks which can directly damage the physical processes of ICPSs. Therefore, we proposed a method to quantitatively assess the risk of cyberattacks on the physical systems of ICPSs. This method conduces implement-appropriate security strategies to protect the security of ICPSs. We use an extended Bayesian attack graph to quantify the probabilities of cyberattacks. In addition, we model the cyberattacks as the illegal control signals injected into the physical system and the illegal actions that change the structure of the physical system. With the established model, we compute a new metric: Physical-System-Deviation-Risk (PSDR), which is used to assess the impact of cyberattacks on the physical system. The risk of the physical systems caused by cyberattacks can be quantified by the PSDR and the probabilities of cyberattacks. Moreover, we use a specific case to demonstrate the effectiveness of this assessment method.
Depthwise and Spatial Factorized Network: a Light-weight Network for Real-time Semantic Segmentation

Abstract Real-time semantic segmentation requires high quality output in relatively short computing time. The existing deep neural network model usually has complex hierarchical structure and a large number of parameters. For the application of real-time computing, the network has higher requirements for computing performance. To address the speed and accuracy trade-off, a novel structure, named Depthwise and Spatial Factorized Network (DSFNet) is proposed in this paper. In DSFNet, two important strategies are introduced. Firstly, Depthwise and Spatial Factorized Convolution is used to reduce the number of parameters in encoder network remarkably. After that, residual attention mechanism is utilized in decoder with a fast downsampling strategy to obtain sufficient receptive field. These two strategies make the process of upsampling more precisely and achieve better performance. Comprehensive experiments on Cityscapes dataset verify the effectiveness of the proposed DSFNet. The DSFNet achieves 63.08 mIoU at 58 FPS with residual attention mechanism.
An Automatic Question Answering Method for Small-Scale Corpus

Abstract It is a growing trend for automatic question answering system to be prominent in the development process of society. There are many methods trying to address this problem, but with deficiencies—relatively developed methods based on template matching need a lot of manual work writing templates, and machine learning based methods need plenty of work collecting a large number of corpus, bring huge burden on small-scale scenery. Facing these problems, we propose an automatic question answering methods meeting the needs of small-scale corpus. This method consists of combining an improved text similarity calculation algorithm and an intention recognition method based on slot filling. We conduct experiments on the problem sets of related fields, and it shows a good performance of the proposed automatic question answering method. Our methods make small scene applications for dialog systems more practical.
Non-linear 3D Visual Control for an Unmanned Aerial Vehicle

This document presents the development of a kinematic control with visual feedback based on images, to solve the objective tracking problem in the semi-structured 3D workspace in UAV. The design of the entire controller is based on a total Jacobian that contains the geometric, image, and object Jacobian. These Jacobians allow the calculation of the control errors that feedback the system through the characteristics of the image, allowing the stability and robustness of the system to be determined. The system implementation was developed by calibrating the UAV’s vision sensor against a global reference system, using the Python programming language, which is the means of communication between the PC and the UAV, in the same way with MATLAB mathematical software.
Behavior-based Speciation in Classification with NeuroEvolution

We propose a neuroevolutionary speciation mechanism that is applied on NeuroEvolution of Augmenting Topologies (NEAT) that solely evolves neural networks' topology and weights and its extension HA-NEAT that also evolves activation functions. The new speciation mechanism is defined based on the behavior of the individuals rather than their topological similarity. Focusing on classification tasks we build artificial datasets of high complexity. Performance is described by (i) median classification accuracy, (ii) computational efficiency (number of generations) and (iii) network complexity (number of nodes and connections). The performance metrics are compared using Kruskal-Wallis hypothesis tests with Bonferroni correction. It is found that the proposed behavioral speciation mechanism outperforms the original speciation solving problems that were not solvable before or improving the accuracy and reducing the network complexity.
Energy efficiency assessment of anthropomorphic manipulator control algorithm

Abstract Currently, developers of robotic systems are very interested in intensively developing unmanned technologies, the concept of which is to use robots to perform routine, harmful and dangerous types of work without direct human participation, which is the key to ensuring safety and high efficiency of solving tasks. Active research is conducted in the field of artificial intelligence and the development of anthropomorphic robots (AR). Modern ARS are not yet able to completely replace a person when performing complex tasks in a dynamic environment. The inability of such robots to completely replace a human is due to the short duration of work in offline mode. The reasons for the short duration of the AP are the low capacity of modern batteries and high power consumption of software and hardware components of the robot. The solution to this problem is possible by developing and modifying mathematical methods and algorithms for performing target operations in order to minimize energy consumption. The article assesses the energy efficiency of the algorithm for copying control of an anthropomorphic manipulator.
Scambaiter Narratives of Victims and Offenders and Their Influence on the Policing of Fraud

Abstract Scambaiters have emerged as a proactive means to targeting fraud offenders. Put simply, scambaiters act as an incapacitator, seeking to deliberately engage fraud offenders to waste their time and reduce their communication with genuine victims. For some, this is a deliberate act of vigilantism that seeks to respond in a way that authorities (such as police) cannot. This article uses a well-known scambaiter website, 419eater, to examine how scambaiters present narratives of fraud victims and fraud offenders. It argues that victims are positioned as vulnerable and undeserving of their victimization, while offenders are positioned as rational beings and as a criminal ‘other’. This last narrative is used to justify the behaviour of scambaiters towards alleged fraud offenders. Overall, this article argues that while both narratives are based largely on facts, the interpretation and use of these narratives (particularly the offender narrative) are problematic in terms of a broader discussion around the policing of online fraud and cyber vigilantism.
CAMERA-BASED DETECTORS AS AN ALTERNATIVE TO DETECTING TRAINS IN A LEVEL CROSSING IMPLEMENTATION

Based on data from Indonesia Directorate General of Railways in 2017, it is mentioned that the problems at the level crossing of railroad tracks are mostly caused by human error factors themselves. The current train headway and the crossing system that is still operated manually can increase the potential for accidents. Therefore, the development of alternative camera-based detectors to support the railroad crossing automation system is needed at this time. The development of this camera-based train detector uses the basic program You Only Look Once (YOLO), where YOLOv3 has proven to be accurate enough to detect moving objects. The development results show promising results for several types of alternative trains.
Hybrid PSO Algorithm with Adaptive Step Search in Noisy and Noise-free Environments

Particle Swarm optimizer (PSO) is a population-based algorithm applied to many applications due to its competitive performance. As a pioneering variant, Dual-Environmental Particle Swarm optimizer (DEPSO) solves optimization problem- s both in noisy and noise-free environments. This paper employs Adaptive step search (ASS) as an improvement of DEPSO by enhancing the information utilization. ASS is an efficient scheme to solve the stochastic point location (SPL) problem. It magnifies or shrinks the step size of a learning mechanism (LM) adaptively according to historical success or failure. This method allows each particle to search with an adaptive step size by enhancing the utilization of historical information. Experimental results performed on CEC2013 benchmark functions indicate that DEPSO-ASS outperforms DEPSO and other the state-of-art PSO variants in both noise-free and noisy environments.
Communication Security Design of Brain-Like System Based on Cloud Computing Platform

Abstract This paper mainly studies the communication security problems of brain-like system (or robotic brain) based on cloud computing platform, and designs a more convenient and safer communication mechanism for the robotic brain, which includes password algorithm design, digital signature design before login, data exchange design during login process, and security protection design against network attacks. Only the legitimate users can communicate with the robotic brain to ensure that the control authority of the robotic brain is not illegally stolen. In the authentication process, the key agreement must be carried out first, the user name and password of the legitimate user must be encrypted and sent, and the encryption algorithm must be guaranteed to be unable to be deciphered under the existing technical conditions. For the important communication data, the encrypted transmission is required, the encryption key is determined through negotiation, the encryption algorithm must be safe and reliable, and the encryption and decryption speed must be guaranteed. This process requires ensuring that communication data is not stolen illegally, and the data can be verified to avoid tampering and illegal data insertion. The users may control the robotic brain to work, and ensure its safety during the communication process.
Visualizing the Loss Landscape of Actor Critic Methods with Applications in Inventory Optimization

Continuous control is a widely applicable area of reinforcement learning. Themain players of this area are actor-critic methods that utilize policy gradients ofneural approximators as a common practice. The focus of our study is to showthe characteristics of the actor loss function which is the essential part of theoptimization. We exploit low dimensional visualizations of the loss function andprovide comparisons for loss landscapes of various algorithms. Furthermore, weapply our approach to multi-store dynamic inventory control, a notoriously difficultproblem in supply chain operations, and explore the shape of the loss functionassociated with the optimal policy. We modeled and solved the problem usingreinforcement learning while having a loss landscape in favor of optimality.
Memetic Multi-agent optimization with Problem Reformulation by Coordinate Rotation

Memetic multi-agent system (MeMAS) is recently proposed as an enhanced version that integrates meme concept into multi-agent system (MAS) wherein all meme-inspired agents have an improvement in learning performance via meme evolution independently or social interaction. In the process of solving the black box optimization problem, the potential advantages of MeMAS have not been utilized well, which makes it a fertile area for further exploration. This paper presents a memetic multi-agent optimization paradigm through coordinate rotation (MeMAO-R) to combine MeMAS with evolutionary algorithms (EAs) to improve optimization efficiency. Based on MeMAS, the particular interest of MeMAO-R is placed on assisting original complex optimization task with new tasks generated by coordinate rotation. Further, MeMAO-R constructs the social interaction mechanism which facilitates to improve their convergence speed for solving the target optimization problem by utilizing meaningful information transferred across multiple agents with differing views of the target problem. Besides, MeMAO-R employs one or more classical EAs as the fundamental population based evolutionary solvers for multiple agents to optimize multiple tasks in a multi-agent scenario. Lastly, to testify the efficacy of the proposed MeMAO-R, comprehensive empirical studies on basic optimization problems are provided.
Empirical Study of Intermediate Filters in Order to Improve the Optical Flow Estimation

Most used models for estimating the motion of pixels between two consecutive images are the variational models, which propose an energy to estimate the motion estimation error. The argument that minimizes that energy is the optical flow (OF) estimation of the sequence. These OF estimation models fail when large displacements and illumination changes occur. To tackle large displacements, OF methods compute the estimation in an image pyramidal scheme. In each scale, an intermediate filter is applied to eliminate outliers or noise. In this work, we considered a robust OF model and we increased the robustness of this model to illumination changes and we studied effects in OF estimation of four different intermediate filters: Gaussian(GF), bilateral (BF), bilateral extended to the gradient domain (BFG), and median filter (MF). Our experiments were performed in a subset of the state of the art MPI-Sintel dataset. Using GF, we obtained an endpoint error EPE = 5.63; for BF we obtained EPE = 7.71; for BFG we obtained EPE = 7.67 and, for MF we obtained EPE = 4.77. The best performance was obtained for the MF but the GF performs also well. Considering processing time, the GF could be also suitable for optical OF.
Asynchronous bio-inspired tuning for the DC motor speed controller with simultaneous identification and predictive strategies

One of the main issues in the control system is the online tuning of its gains. The use of bio-inspired algorithms (BA) is gaining more attention in the control tuning task because they are less sensible to system uncertainties. Nevertheless, the computational time of BA must be reduced to be used in practice. In this work, an event condition is stated to reduce the computational cost of the optimization process in the online bio-inspired tuning approach. This condition activates the tuning approach only when it is required, i.e., when the regulation error tends to increase. Also, in this approach, an identification process and a predictive strategy are simultaneously optimized to find the more suitable control parameters that handle more efficient the parametric uncertainties. The proposed online Asynchronous Bio-inspired Tuning Approach with Simultaneous Identification and Prediction (ABioTASIP) is validated in the study case of the velocity regulation of a DC motor considering dynamic parametric uncertainties. The comparative analysis with an approach where the control parameters are periodically tuned indicates that the proposal decreases the tuning process without considerably increase the regulation error.
A Fuzzy Crow Search Algorithm for Solving Data Clustering Problem

The crow is one of the most intelligent bird and infamous for observing other birds so that they can steal their food. The crow search algorithm (CSA), a nature-based optimizer, is inspired by the social behavior of crows. Scholars have applied the CSA to obtain efficient solutions to certain function and combinatorial optimization problems. Another popular and powerful method with several real-world applications (e.g., energy, finance, marketing, and medical imaging) is fuzzy clustering. The fuzzy c-means (FCM) algorithm is a critical fuzzy clustering approach given its efficiency and implementation easily. However, the FCM algorithm can be easily trapped in the local optima. To solve this data clustering problem, this study proposes a hybrid fuzzy clustering algorithm combines the CSA and a fireworks algorithm. The algorithm performance is evaluated using eight well-known UCI benchmarks. The experimental analysis concludes that adding the fireworks algorithm improved the CSA’s performance and offered better solutions than those by other meta-heuristic algorithms.
Italian Knot-tack Sewing: A Reliable Hypothesis on a Late Medieval Technique

The knot-tack is a technique for sewing books on double supports which features a prominent knot settled over the centre of split-strap sewing supports. This method can be found in archive and library bindings, on manuscripts or printed-books, with stiff or limp covers.Up until a few years ago, the knot-tack had only been observed in books from Austria and described by Eleonore Klee. Recent findings at the Biblioteca Nazionale Marciana in Venice (Italy), followed by new sightings in central Italy have opened the way to an expansion of Klee’s observations. Whilst the relationship between the Austrian and the Italian knot-tack sewing structures is not yet clear, this essay aims to describe, with the aid of pictures, the path of the thread in the making of the knot. It presents a number of possible applications for the book conservator, who can make effective use of the knot-tack sewing.
Dilated Denoising U-Net Network Improving the Adversarial Robustness

Abstract The adversarial attack generates adversarial samples by adding subtle perturbations to the image. Such perturbations are usually very small and undetectable, but the neural network will give completely different results from the real sample. Adversarial example completely fools the neural network. Therefore, it is very important to develop effective defending model. Convolutional neural networks have successfully achieved adversarial robustness by removing noise in adversarial samples. However, the convolutional neural network involves multiple layers, and the denoising model contains a large number of parameters. This paper proposes a dilated convolutional denoising U-Net network to remove adversarial sample noise. Compared with the previously proposed denoising U-Net, our model has fewer layers. Dilated convolution is used in the convolutional layer to expand the receptive field. Use zero padding to ensure that the output dimensions are consistent with the input dimensions We conducted experiments on the ImageNet dataset. The experiments show that the extended of the receptive field can enhance the ability of the U-Net network to capture detailed image information, enhance denoising performance, and effectively improve the robustness at a lower computing cost.
Confronting the Limitations of Hardware-Assisted Security

The articles in this special section focus on hardware assisted security systems. During the past two decades, the use of hardware assistance for improving security and privacy has been steadily increasing. In particular, hardware-assisted trusted execution environments (TEEs), such as Arm TrustZone and Intel Software Guard Extensions, are now widely deployed. This has led to many new initiatives in the research community as well as among practitioners, with enterprises, such as Microsoft and Alibaba, offering cloud-hosted TEEs as a service, while standardization initiatives, including the Global Platform TEE Committee and the Confidential Computing Consortium, strive to promote adoption. At the same time, new types of attack vectors against hardware security mechanisms have been discovered. For example, researchers have shown that microarchitectural side channels can be very effective in breaking the apparent security guarantees offered by hardware.5 Consequently, it is widely accepted that a complete reliance on the guarantees provided by hardware security mechanisms is no longer warranted.
The Argmax Constraint

The argmax function returns the index of the (first copy of the) maximum value occuring in a list of values. argmax is important in models where we choose a characteristic value based on a seperate criteria, and for modelling neural networks which make use of argmax in their definition. The argmax constraint has been studied for the special case of its use to find the index of the first true in a list of Booleans, since this is useful for encoding if-then-else chains. Here we examine the general argmax constraint for the first time. We define an efficient domain consistent propagator and a decomposition for integers which implements domain consistency.