Reviewerly/exHarmony huggingface dataset:
Improving DE-based cooperative coevolution for constrained large-scale global optimization problems using an increasing grouping strategy

Abstract Nowadays, high-dimensional constrained «Black-Box» (BB) optimization problems has become more urgent. At the same time, the constrained large-scale global optimization (cLSGO) problems are not well studied and many modern optimization approaches demonstrate low performance when dealing with cLSGO problems. Evolution algorithms (EAs) has proved their efficiency in solving low-dimensional constrained optimization problems and high-dimensional single-objective optimization problems. In this study, we have proposed a new approach based on the cooperative coevolution (CC) framework and an algorithm for increasing size of variables grouping on the decomposition stage (iCC) when solving cLSGO problems. We have proposed a novel EA that combines SHADE, iCC and ɛ-constrained method (ɛ-iCC-SHADE). The proposed optimization algorithm has been investigated using a new cLSGO benchmark, which is based on scalable problems from IEEE CEC 2017 Competition on Constrained Real-Parameter Optimization. The numerical experiments have shown that ɛ-iCC-SHADE outperforms the early proposed ɛ-CC-SHADE algorithm which operates with the fixed number of subcomponents.
Pipelined Parallel Finite Automata Evaluation

Finite automata are key compute models in modern computational theory and important building blocks for digital logic used for regular expression and protocol parsing, filtering, and control. Finite automata evaluation would seem to be a sequential operation, since we need to complete the evaluation of one state to know the next state in which to evaluate the logic. Nonetheless, parallel theory provides strategies for parallel finite automata evaluation. We show how to exploit this parallel evaluation strategy in practice on today's high capacity FPGAs, including a novel formulation for spatially pipelined evaluation. For non-deterministic finite automata (NFA) with S states, we can evaluate N inputs in a single cycle with O(N * S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) BRAMs and O(N*S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) LUTs. This allows us, for example, to consume 64 inputs on a 16 state NFA in a single cycle on the Xilinx XZCU9EG-ffvb1156-2-i SoC FPGA, achieving 47 GB/s (377 Gb/s) single stream throughput for 8b inputs. For a 40 Gb/s network link, we can support 28 state NFAs.
Design Guidelines for Augmented Reality User Interface: A Case Study of Simultaneous Interpretation

Cognitive load theory (CLT) is concerned with the design of interfaces that allow users maximize their working memory when problem solving. This includes eliminating any subjective mental load that may be imposed by the instruction interface. Augmented reality (AR) is fast gaining application in areas of education, military, business and medicine with new AR applications developed daily. Despite its wide application little has been done in the area of design guidelines for AR user interface and the evaluation of its effect. This is necessary so as to ensure that users are not burdened by the format and amount of information presented in the augmented view. This work - in - progress aims to propose a framework for designing AR user interfaces that imposes reduced subjective mental workload on users. Particular attention is given to highly cognitive tasks such as simultaneous interpretation. This research work will concentrate on presenting the novel guidelines for the user interface design.
An Object Attribute Guided Framework for Robot Learning Manipulations from Human Demonstration Videos

Learning manipulations from videos is an inspiriting way for robots to acquire new skills. In this paper, we propose a framework that can generate robotic manipulation plans by observing human demonstration videos without special marks or unnatural demonstrated behaviors. More specifically, the framework contains a video parsing module and a robot execution module. The first module recognizes the demonstrator's actions using two-stream convolution neural networks, and classifies the operated objects by adopting a Mask R-CNN. After that, two XGBoost classifiers are applied to further classify the objects into subject object and patient object respectively, according to the demonstrator's actions. In the second module, a grammar-based parser is used to summarize the videos and generate the common instructions for robot execution. Extensive experiments are conducted on a publicly available video datasets consisting of 273 videos and manifest that our approach is able to learn manipulation plans from demonstration videos with high accuracy (73.36%). Furthermore, we integrate our framework with a humanoid robot Baxter to perform the manipulation learning from demonstration videos, which effectively verifies the performance of our framework.
An Optical Flow Based Approach for Facial Expression Recognition

Facial expressions provide pertinent cues helpful in understanding one's emotional state. Interpreting emotions through facial expressions has garnered overwhelming interest in the last few decades due to its prospective applications in smart environments and automated systems. In this work, a method is presented where facial expressions are identified by analyzing motion pattern of facial features arising when the face transforms from neutral state to emotional state. Motion patterns are categorized into distinct classes with feature descriptors giving the global spatial distribution of the dense horizontal and vertical optical flow between emotional and neutral facial images. Classification scheme based on multi-label support vector machine has been employed for categorization. Promising results are obtained from the comparative analysis of the technique presented on the Extended Cohn Kanade database with other previously proposed methods.
Stereoscopic Image Feature Indexing Based on Hybrid Grid Multiple Suffix Tree and Hierarchical Clustering

In order to achieve content-based binocular stereoscopic image or video retrieval efficiently, a feature indexing algorithm based on hybrid grid multiple suffix tree and hierarchical clustering is proposed. With the RGB-D image model, the shape features of depth map obtained from the matching of binocular stereoscopic left image and right image and the color features of left image are extracted respectively. The features are quantified and hashed, and the optimized underlying features are sorted as leaf nodes of hierarchical indexing. Then the shape and color feature values of leaf nodes are mapped to the two-dimensional coordinates, and the 2D feature points are put into different grid hash areas respectively by clustering and labeled with multiple suffix tree. Furthermore, to construct the global index, a pointer to an array of clustering grid center point is defined according to the computation of grid area feature values. The experimental results show that compared to the double grid suffix tree and typical stereoscopic image feature indexing structure, the proposed algorithm can effectively reduce the indexing construction complexity While maintaining high recall, it can also greatly improve the query efficiency, which can better realize the feature indexing of binocular stereoscopic images or videos.
Extracting Learning Curves From Puzzle Games

Game design is a time-consuming endeavor and, in contrast to other work in game development, has no tools aiding the designer in speeding up the process. In this paper, the initial work towards developing such a tool is presented. It focuses on the extraction of learning curves from single-player, deterministic, turn-based (puzzle) games. Learning curves in such games can be directly associated with their gameplay quality. The extraction is done by training a number of varying size, feed-forward neural networks on the game transition model. The main contribution of the work is the introduction of learning curve based puzzle game design concepts and their practical examples along with a quantitative method for learning curve estimation.
The RGB-D Triathlon: Towards Agile Visual Toolboxes for Robots

Deep networks have brought significant advances in robot perception, enabling to improve the capabilities of robots in several visual tasks, ranging from object detection and recognition to pose estimation, semantic scene segmentation and many others. Still, most approaches typically address visual tasks in isolation, resulting in overspecialized models which achieve strong performances in specific applications but work poorly in other (often related) tasks. This is clearly sub-optimal for a robot which is often required to perform simultaneously multiple visual recognition tasks in order to properly act and interact with the environment. This problem is exacerbated by the limited computational and memory resources typically available onboard to a robotic platform. The problem of learning flexible models which can handle multiple tasks in a lightweight manner has recently gained attention in the computer vision community and benchmarks supporting this research have been proposed. In this work we study this problem in the robot vision context, proposing a new benchmark, the RGB-D Triathlon, and evaluating state of the art algorithms in this novel challenging scenario. We also define a new evaluation protocol, better suited to the robot vision setting. Results shed light on the strengths and weaknesses of existing approaches and on open issues, suggesting directions for future research.
Recurrent and multi-layer neural networks playing Even-Odd”: reflection against regression

Abstract Reflection understood as an internal representation of the external world by the subject is the key property of consciousness. In a refined form this property is manifested in reflective games. To win a reflective game a player has to use reflection of strictly one rank higher than the opponent. So it can be assumed that there are only two game modes - when only one player uses reflection and wins and when both players use reflection but one of them chooses incorrect reflection rank. The option of random move selection is not considered since firstly, starting the game for a draw is strange, and secondly, it is technically impossible to make random moves without a special device. Experiments with recurrent neural networks playing with each other showed that the entire set of game patterns (time series of the game score) is split into two sharply different groups that can be associated with two modes mentioned above. Experiments, in which a multilayer neural network, which is basically incapable of reflection, played against a recurrent neural network, showed that a recurrent neural network has a clear advantage winning confidently in more than 90% of the games. At the same time game patterns demonstrate splitting into two sharply different groups as was observed in experiments with the game of two recurrent neural networks and in the reflexive game of living people.
A Hybrid Data-Driven Method for Online Power System Dynamic Security Assessment with Incomplete PMU Measurements

With widely deployment of phasor measurement units (PMU), data-driven dynamic security assessment (DSA) has been a promising tool to help power system counteract large disturbance and avoid instability. In case of PMU lost events, the DSA performance may be dramatically degraded. This paper designs a new online DSA model based on hybrid ensemble learning to address the missing data problem. In this method, a set of missing data estimators is proposed to restore the system measurement, and potential inaccurately estimated features are detected in feature validation stage to further ensure the model performance. After excluding inaccurate estimations, the filled-up dataset is provided to support accurate DSA under PMU lost situation. A hybrid ensemble of extreme learning machine (ELM) and random vector functional link networks (RVFL) is designed as learning engine for estimators and DSA classifiers. Simulation results show this approach achieves low estimation error as well as high DSA accuracy and strong robustness.
Research on construction method of knowledge graph in the civil aviation security field

The knowledge graph of the civil aviation safety domain can visually display the complex network relationship between civil aviation safety-related factors, and facilitate effective analysis of potential security risks from the relationship between complex and multi-dimensional security factors, providing in-depth risk analysis and decision support to the field. In this paper, the knowledge system of the civil aviation safety field was firstly combed, and the ontology model suitable for the civil aviation security field has been constructed. Then knowledge was extracted from structured, semi-structured and unstructured data sources in the civil aviation security field. On this basis, entity recognition of unstructured text has been done by using a machine-learning correlation model algorithm, and the entity-relationship in the civil aviation security field has been extracted by using a deep convolutional neural network model combined with keyword features. Finally, the map database Neo4j was used to store the knowledge of the civil aviation safety field, and the knowledge map of the civil aviation safety field has been initially constructed.
Driving Assist Control of Wheeled Inverted Pendulum Wheelchair Using an Active Seat-Slider

For wheeled inverted pendulum (WIP) wheelchairs, a rider operates it in a sitting posture and a weight-shifting motion is achieved by only rotating an upper-body around a waist. This causes a drop of acceleration and deceleration ability of the wheelchairs, as more, a rider feels anxious when the wheelchair inclines back and forth while accelerating and decelerating. To cope these issues, in this paper, a WIP wheelchair with an automatic CoG moving device and a braking system is proposed. The proposed WIP wheelchair has priority in reducing its tilt angle while accelerating and decelerating. This can be realized by automatically moving a rider with an implemented electric slider. The automatic-stop function is realized by controlling the slider so that the wheelchair can stop regardless of a joystick operation when a distance to an obstacle is closer than a predefined threshold distance. The effectiveness of the proposed driving assist functions were confirmed through accelerating experiments and an automatic-stop experiment.
CNN Based Reliable Classification of Household Chores Objects for Service Robotics Applications

Household chores service is one of the desirable functions for a service robot. Object classification is the most important function when searching for objects. We consider that the major concern is that the robot should not misclassify the object. If the robot misclassifies household object images, it will then perform the household tasks with the wrong object. This may cause serious damages to a service robot and to the user. This concept gives us the insight that the precision of the classification must be very high by setting a confidence threshold so that it then can claim a reliable service robot. By exploring this concept, we develop a more convincing indicator, Classification Reliability, to reveal the reliability of deep learning model. Moreover, we develop a fine-tune rule base to continuously regenerate more proper training dataset for the CNN model to increase reliability. Experimental results demonstrate that the CNN model fine-tuned by our closed-loop system achieves the reliability which is higher than the other similar effects such as DenseNet on the CIFAR-10 dataset.
Utility of Process Prioritization for Agile Software Testing using Analytical Hierarchical Process

The immense use of software is changing our society profoundly. Customers rely on the accuracy and functionality that is provided by the software. Agile development is a framework that achieves project goals sooner by involving every stakeholder in the decision process from the very beginning. This research paper exhibits Analytical Hierarchical Process (AHP), for improving the software quality in the organization. Thus this research provides a framework to calculate the critical attributes based on survey done by the experts' working on agile project development to improve the quality of the software in the organization. Overall utility is calculated to assess in the perspective of the developers as well as stakeholders described as Two Way Assessment, which further decides the best attribute from both perspective to improve the efficiency of the software in the organization.
Faster STR-EC-LCS Computation

The longest common subsequence (LCS) problem is a central problem in stringology that finds the longest common subsequence of given two strings A and B. More recently, a set of four constrained LCS problems (called generalized constrained LCS problem) were proposed by Chen and Chao [J. Comb. Optim, 2011]. In this paper, we consider the substring-excluding constrained LCS (STR-EC-LCS) problem. A string Z is said to be an STR-EC-LCS of two given strings A and B excluding P if, Z is one of the longest common subsequences of A and B that does not contain P as a substring. Wang et al. proposed a dynamic programming solution which computes an STR-EC-LCS in O(mnr) time and space where $$m = |A|, n = |B|, r = |P|$$ [Inf. Process. Lett., 2013]. In this paper, we show a new solution for the STR-EC-LCS problem. Our algorithm computes an STR-EC-LCS in $$O(n|\varSigma | + (L+1)(m-L+1)r)$$ time where $$|\varSigma | \le \min \{m, n\}$$ denotes the set of distinct characters occurring in both A and B, and L is the length of the STR-EC-LCS. This algorithm is faster than the O(mnr)-time algorithm for short/long STR-EC-LCS (namely, $$L \in O(1)$$ or $$m-L \in O(1)$$), and is at least as efficient as the O(mnr)-time algorithm for all cases.
Detecting Student Engagement in Classrooms for Intelligent Tutoring Systems

Detecting and tracking student engagement in a large classroom can help tutors or automatic learning systems easily control or summarize the situation. To come up with the advanced technique in machine learning, especially deep learning, nowadays, many schools can build an efficient system for supporting teachers or tutoring systems. In this paper, we propose a transfer learning method applying to a small dataset to classify student actions in the classroom. Another contribution is building a lightweight dataset with a limited number of images for each category for classification work. The experiments show the acceptable result in action recognition with high accuracy compared to other researches.
Joint RGB-Pose Based Human Action Recognition for Anomaly Detection Applications

Human Action Recognition (HAR) and Human Behaviour Anomaly Detection (HBAD) systems require intelligent and multimodal features extraction for classification. The RGB deep learning based methods represent the state-of-the-art for HAR and HBAD. On the other hand, human poses extracted by popular RGB-based detectors have shown promising results for posture-level HAR and HBAD. However, both modalities present limitations, e.g. the RGB-based methods are difficult to extract explainable features relevant to generalisation, especially when contextual data is dominant. Furthermore, human poses cannot model complex human actions, i.e. involving objects or with high contextual information. To overcome the above limitations, three Joint RGB-Pose based multimodal networks are proposed. Combinations of CNNs, 3DCNNs, RNNs, MLSTMs, and ResNet-152 pre-trained CNN networks are exploited. The proposed three methods for joint learning are compared with the correspondent RGB-based and Pose-based methods, in the context of HAR for HBAD applications. Experimental results are provided on the challenging datasets UCF101 and MPOSE2019, showing promising results in terms of recognition accuracy and processing time.
The road drainage planning for flood control in Dr. Djunjunan road, Pasteur – Bandung

Abstract Recently, flood is an important phenomenon which often occurs in cities recently. The Case of this paper is the flood in Dr. Junjunan Road which is the one of high density roads in Bandung and the main road to Cipularang Toll Road. The flood’s inundation depth on this road is up to 40-50cm, and the average of flood duration is about 30 minutes. The rainfall used for calculation in this research is a return period 25 years with the rainfall value of 80.1 mm. The solution for reducing the floods is by increasing the drainage capacity as well as increasing the water infiltration to the ground. The research method uses the physical experimental model, using the scale mode of 1:30. The experimental results are tested through validation process using Hec-RAS 5.0.7. The experiment uses two variables, in which changing two drainage streams and diverting a river which leads to Citepus river through Box Culvert. The results of the validating model indicate that increasing the drainage capacity along the Road is the most effective solution in decreasing the potential of floods.
Toward a Generic Event Management System for Academia

With the advancement of civilization organizing different events is one of the core parts of academic life. In this era of ICT, websites are considered one of the most effective way to reach people. As a result most of the events chose websites as a way of communication. However, it costs the organizer a good amount of money and time. Again for any nontechnical person it is haphazard to maintain and create such websites for different kinds of events like seminars, conferences, cultural fests, etc. every year. By considering this drawback, a generic academic event management system is introduced by this paper. A framework for managing different kinds of events and management for schedules, participants, transportation, location, speakers and so on will be offered by the proposed website for any technical or nontechnical person. The introducing system is offering such a platform that can be used by organizers who can adorn their own website within a very short span of time with necessary information. This platform also ensures maintenance of confidential documents, data which only can be manipulated by proper authority. With the help of this generic academic event management system, the manual effort of organizers can be reduced to great extent.
of Meta-heuristic Optimization Approaches: in Virtue of Grey Wolf Optimization

Optimization is essential in every sector such as engineering design, economics, mathematics, physics, even in day to day activities such as holiday planning, internet usage/routing optimization is required. As resources such as money, resources and time are crucial and limited those needs to be used optimally always. This paper explains concept of optimization and classification of various optimization approaches, later it extends to meta-heuristic optimization algorithms, gives brief explanation about different swarm- based optimization algorithms. Optimization algorithms can be classified by many ways based on focus of algorithm utilization and characteristics. Metaheuristic approach mimics natural phenomena where nature is used as source of inspiration. This paper gives brief about all classifications of metaheuristic algorithms such as Evolutionary algorithms, swarm intelligence, differential evolution and hunting search algorithm which is the main focus of this paper. This paper explains Grey Wolf Optimization (GWO) in comparison of all other metaheuristic algorithms.