Reviewerly/exHarmony huggingface dataset:
CFL/Dyck Reachability

CFL/Dyck reachability is a simple graph-theoretic problem: given a CFL/Dyck language L over an alphabet Σ, a graph G = ( V , E ) of Σ-labeled edges, and two distinguished nodes s , t ∈ V , does there exist a path from s to t that spells out a word in L ? This simple notion of language-based graph reachability serves as the algorithmic formulation of a large number of problems in diverse domains, such as graph databases and program static analysis. This paper takes an algorithmic perspective on CFL/Dyck reachability, and overviews several recent advances concerning the decidability and complexity of the problem and some its close variants, as realized in the areas of automata theory and program verification.
Learning Implementation with RUBA Interactive Multimedia-Based to Beginner Reading

Beginning reading is an early stage ability that must be mastered. However, problems are still often found in elementary schools (SD), there are students who have difficulty reading the beginning. For this reason, appropriate solutions are needed so that students can be help improve their initial reading skills. At SD Negeri 02 Koto Tangah Tilatang Kamang, the teacher provides a solution related to the problem of students who have difficulty learning to read at the beginning by using interactive multimedia learning media RUBA (reading room) which has been proven to be effective. For this reason, it is necessary to conduct research to find out how the implementation of these media in learning to read begins. This research was conducted with a qualitative approach and the type of research was descriptive by collecting data through observation and interviews. The results of this study are the implementation of early reading learning based on interactive multimedia by the teacher can increase TPACK competence, especially competence in technology, which impacts the emergence of children’s enthusiasm in learning.
Modeling andness in multilabel classification to recognize mutual information

Recognizing valid (statistical) dependencies in pattern recognition improves quality of learning extremely. In particular, in a multi-label classification scenario identifying such dependencies among labels can increase the performance of such approaches. In real-world applications, we are dealing in many cases with andness, e.g., a heavy and old car, exhibits naturally a high fuel consumption. Many real problems are affected by andness to some extend, therefore, considering such kind of dependencies in a learning process can be beneficial to capture more insight and interactions from data. To this end, this paper presents a novel approach to model andness dependencies in a multi-label classification scenario. In the experimental part, we demonstrate the real gains achieved by applying this approach.
ER-Net: Efficient Recalibration Network for Multi-View Multi-Person 3D Pose Estimation

Multi-view multi-person 3D human pose estimation is a hot topic in the field of human pose estimation due to its wide range of application scenarios.With the introduction of end-to-end direct regression methods, the field has entered a new stage of development.However, the regression results of joints that are more heavily influenced by external factors are not accurate enough even for the optimal method.In this paper, we propose an effective feature recalibration module based on the channel attention mechanism and a relative optimal calibration strategy, which is applied to the multi-view multi-person 3D human pose estimation task to achieve improved detection accuracy for joints that are more severely affected by external factors.Specifically, it achieves relative optimal weight adjustment of joint feature information through the recalibration module and strategy, which enables the model to learn the dependencies between joints and the dependencies between people and their corresponding joints.We call this method as the Efficient Recalibration Network (ER-Net).Finally, experiments were conducted on two benchmark datasets for this task, Campus and Shelf, in which the PCP reached 97.3% and 98.3%, respectively.
A Deep Learning-Based Crowd Counting Method and System Implementation on Neural Processing Unit Platform

In this paper, a deep learning-based method is proposed for crowdcounting problems.Specifically, by utilizing the convolution kernel density map, the ground truth is generated dynamically to enhance the featureextracting ability of the generator model.Meanwhile, the "cross stage partial" module is integrated into congested scene recognition network (CSRNet) to obtain a lightweight network model.In addition, to compensate for the accuracy drop owing to the lightweight model, we take advantage of "structured knowledge transfer" to train the model in an end-to-end manner.It aims to accelerate the fitting speed and enhance the learning ability of the student model.The crowd-counting system solution for edge computing is also proposed and implemented on an embedded device equipped with a neural processing unit.Simulations demonstrate the performance improvement of the proposed solution in terms of model size, processing speed and accuracy.The performance on the Venice dataset shows that the mean absolute error (MAE) and the root mean squared error (RMSE) of our model drop by 32.63% and 39.18% compared with CSRNet.Meanwhile, the performance on the ShanghaiTech PartB dataset reveals that the MAE and the RMSE of our model are close to those of CSRNet.Therefore, we provide a novel embedded platform system scheme for public safety pre-warning applications.
An Efficient EMD-Based Reversible Data Hiding Technique Using Dual Stego Images

Exploiting modification direction (EMD) based data hiding techniques (DHTs) provide moderate data hiding capacity and high-quality stego images.The overflow problem and the cyclic nature of the extraction function essentially hinder their application in several fields in which reversibility is necessary.Thus far, the few EMD reversible DHTs are complex and numerically demanding.This paper presents a novel EMD-based reversible DHT using dual-image.Two novel 2 × 4 modification lookup tables are introduced, replacing the 256 × 256 reference matrix used in similar techniques and eliminating the numerically demanding search step in similar techniques.In the embedding step, one of the modification tables modifies a pixel in odd columns in the first cover image and keeps its image in the second cover image intact.The other modification table modifies a pixel in even columns in the second cover image and keeps its image in the first cover image intact.This embedding strategy enables direct reversibility at almost zero computational cost.This technique embeds one 4ary secret digit into each pixel in the first cover image and its image in the second cover image resulting in one bit per pixel (bpp) data embedding rate.The use of the 4ary numbering system enables direct and numerically efficient conversion of the binary secret message to the 4ary and vice versa.The advantages of the proposed algorithm are straightforward reversibility, simplicity, numerical efficiency, direct conversion of the binary secret message to 4ary and vice versa, and elimination of the need for the 256 × 256 reference matrix by replacing it with two 2 × 4 lookup tables.Simulation results show that the embedding rate of the proposed technique is one bpp.It achieved more than 49 dB average Peak to Signal Noise Ratio (PSNR) over all test images.
Do additional target points speed up evolutionary algorithms?

Most runtime analyses of randomised search heuristics focus on the expected number of function evaluations to find a unique global optimum. We ask a fundamental question: if additional search points are declared optimal, or declared as desirable target points, do these additional optima speed up evolutionary algorithms? More formally, we analyse the expected hitting time of a target set OPT∪S where S is a set of non-optimal search points and OPT is the set of optima and compare it to the expected hitting time of OPT. We show that the answer to our question depends on the number and placement of search points in S. For all black-box algorithms and all fitness functions with polynomial expected optimisation times we show that, if additional optima are placed randomly, even an exponential number of optima has a negligible effect on the expected optimisation time. Considering Hamming balls around all global optima gives an easier target for some algorithms and functions and can shift the phase transition with respect to offspring population sizes in the (1,λ) EA on OneMax. However, for the one-dimensional Ising model the time to reach Hamming balls of radius (1/2−ε)n around optima does not reduce the asymptotic expected optimisation time in the worst case. Finally, on functions where search trajectories typically join in a single search point, turning one search point into an optimum drastically reduces the expected optimisation time.
Chatbots for Business and Customer Support

Artificial intelligence (AI)-driven chatbots have established themselves as standard front-line solutions for companies looking to update consumer experiences while maximizing client engagement. Chatbots have become a crucial component of a company's customer-centric operations because of their fast replies, round-the-clock assistance, and ability to comprehend user inquiries. This chapter will describe the function of chatbots as customer service representatives and some of the benefits they offer to organizations. There will also be a discussion of the present difficulties facing the industry in integrating these conversational bots.
Chatbot Design Challenges and the Effect on User Behavior

Chatbots are efficient artificial intelligence tools employed frequently across various industries such as healthcare, tourism, entertainment, and education, to assist in performing repetitive activities requiring a conversation, thereby facilitating humans to focus on more innovative tasks. This chapter reviews the design challenges of two types of chatbots classified based on their interaction modes: dyadic chatbots interacting with one individual at a time; and polyadic chatbots interacting with multiple individuals simultaneously. Further, the chapter identified the associated challenges in the chatbots, namely: engagement, trust, and human likeliness based on personality types - at a higher level in the chatbot design, together with the impact of each challenge on the user behavior. The authors review the literature in the relevant areas to pinpoint the research gaps requiring more focus within each area of challenge. The research community can address these identified research areas, which will eventually promote a more efficient human-machine collaboration.
Recent Developments in Chatbot Usability and Design Methodologies

Chatbots are rapidly growing and becoming prevalent in many applications, including healthcare, education, and consumer services. As such, it is crucial to design chatbots with usability in mind. Traditional usability heuristics call for error prevention, consistency, and task efficiency. The heuristics are suited for all types of user interfaces, including chatbots. However, chatbots have their unique challenges, and thus, recently, some researchers have developed sets of usability guidelines specifically for chatbots. However, there is a shortage of studies that summarize the latest advances in chatbot usability design and assessment. As such, this chapter covers the existing general and chatbot-specific usability heuristics and examples of usage. Moreover, the chapter presents the recent developments in chatbot design techniques and challenges.
Timed-SAS: Modeling and Analyzing the Time Behaviors of Self-Adaptive Software under Uncertainty

Self-adaptive software (SAS) is gaining in popularity as it can handle dynamic changes in the operational context or in itself. Time behaviors are of vital importance for SAS systems, as the self-adaptation loops bring in additional overhead time. However, early modeling and quantitative analysis of time behaviors for the SAS systems is challenging, especially under uncertainty environments. To tackle this problem, this paper proposed an approach called Timed-SAS to define, describe, analyze, and optimize the time behaviors within the SAS systems. Concretely, Timed-SAS: (1) provides a systematic definition on the deterministic time constraints, the uncertainty delay time constraints, and the time-based evaluation metrics for the SAS systems; (2) creates a set of formal modeling templates for the self-adaptation processes, the time behaviors and the uncertainty environment to consolidate design knowledge for reuse; and (3) provides a set of statistical model checking-based quantitative analysis templates to analyze and verify the self-adaptation properties and the time properties under uncertainty. To validate its effectiveness, we presented an example application and a subject-based experiment. The results demonstrated that the Timed-SAS approach can effectively reduce modeling and verification difficulties of the time behaviors, and can help to optimize the self-adaptation logic.
Efficient Online Engagement Analytics Algorithm Toolkit That Can Run on Edge

The rapid expansion of video conferencing and remote works due to the COVID-19 pandemic has resulted in a massive volume of video data to be analyzed in order to understand the audience engagement. However, analyzing this data efficiently, particularly in real-time, poses a scalability challenge as online events can involve hundreds of people and last for hours. Existing solutions, especially open-sourced contributions, usually require dedicated and expensive hardware, and are designed as centralized cloud systems. Additionally, they may also require users to stream their video to remote servers, which raises privacy concerns. This paper introduces scalable and efficient computer vision algorithms for analyzing face orientation and eye blink in real-time on edge devices, including Android, iOS, and Raspberry Pi. An example solution is presented for proctoring online meetings, workplaces, and exams. It analyzes audiences on their own devices, thus addressing scalability and privacy issues, and runs at up to 30 fps on a Raspberry Pi. The proposed face orientation detection algorithm is extremely simple, efficient, and able to estimate the head pose in two degrees of freedom, horizontal and vertical. The proposed Eye Aspect Ratio (EAR) with simple adaptive threshold demonstrated a significant improvement in terms of false positives and overall accuracy compared to the existing constant threshold method. Additionally, the algorithms are implemented and open sourced as a toolkit with modular, cross-platform MediaPipe Calculators and Graphs so that users can easily create custom solutions for a variety of purposes and devices.
Artificial Intelligence in Disaster Management: A Survey

This paper provides a literature review of cutting-edge artificial intelligence-based methods for disaster management. Most governments are worried about disasters, which, in general, are unbelievable events. Researchers tried to deploy numerous artificial intelligence (AI)-based approaches to eliminate disaster management at different stages. Machine learning (ML) and deep learning (DL) algorithms can manage large and complex datasets emerging intrinsically in disaster management circumstances and are incredibly well suited for crucial tasks such as identifying essential features and classification. The study of existing literature in this paper is related to disaster management, and further, it collects recent development in nature-inspired algorithms (NIA) and their applications in disaster management.
Hierarchical Plan Execution for Cooperative UxV Missions

A generic reasoning approach for autonomous unmanned vehicle (UxV) mission execution is presented. The system distinguishes (a) mission planning and (b) mission execution, treating these as separate but closely interdependent stages. The context of the work is that of tactical military operations, and the focus of the current (simulated) application is on ground-based platforms. The reference behavior for the UxVs is defined by military doctrine. Two operational requirements are met: (1) Mission plan and execution must be constructed such that they can be understood and evaluated (prior to giving the go ahead for the platforms to commence the mission) by a decision maker. (2) Mission plan and execution must account for both observations/information gathered during execution (for example, the spotting of enemy units) and for foreseeable changes in the internal and external situation (e.g., a sub-system failure, or changes in terrain or weather).
Cloud Computing Considering Both Energy and Time Solved by Two-Objective Simplified Swarm Optimization

Cloud computing is an operation carried out via networks to provide resources and information to end users according to their demands. The job scheduling in cloud computing, which is distributed across numerous resources for large-scale calculation and resolves the value, accessibility, reliability, and capability of cloud computing, is important because of the high development of technology and the many layers of application. An extended and revised study was developed in our last work, titled “Multi Objective Scheduling in Cloud Computing Using Multi-Objective Simplified Swarm Optimization MOSSO” in IEEE CEC 2018. More new algorithms, testing, and comparisons have been implemented to solve the bi-objective time-constrained task scheduling problem in a more efficient manner. The job scheduling in cloud computing, with objectives including energy consumption and computing time, is solved by the newer algorithm developed in this study. The developed algorithm, named two-objective simplified swarm optimization (tSSO), revises and improves the errors in the previous MOSSO algorithm, which ignores the fact that the number of temporary nondominated solutions is not always only one in the multi-objective problem, and some temporary nondominated solutions may not be temporary nondominated solutions in the next generation based on simplified swarm optimization (SSO). The experimental results implemented show that the developed tSSO performs better than the best-known algorithms, including nondominated sorting genetic algorithm II (NSGA-II), multi-objective particle swarm optimization (MOPSO), and MOSSO in the convergence, diversity, number of obtained temporary nondominated solutions, and the number of obtained real nondominated solutions. The developed tSSO accomplishes the objective of this study, as proven by the experiments.
Crowd-Aware Mobile Robot Navigation Based on Improved Decentralized Structured RNN via Deep Reinforcement Learning

Efficient navigation in a socially compliant manner is an important and challenging task for robots working in dynamic dense crowd environments. With the development of artificial intelligence, deep reinforcement learning techniques have been widely used in the robot navigation. Previous model-free reinforcement learning methods only considered the interactions between robot and humans, not the interactions between humans and humans. To improve this, we propose a decentralized structured RNN network with coarse-grained local maps (LM-SRNN). It is capable of modeling not only Robot-Human interactions through spatio-temporal graphs, but also Human-Human interactions through coarse-grained local maps. Our model captures current crowd interactions and also records past interactions, which enables robots to plan safer paths. Experimental results show that our model is able to navigate efficiently in dense crowd environments, outperforming state-of-the-art methods.
Automated Analysis of Open-Ended Students’ Feedback Using Sentiment, Emotion, and Cognition Classifications

Students’ feedback is pertinent in measuring the quality of the educational process. For example, by applying lexicon-based sentiment analysis to students’ open-ended course feedback, we can detect not only their sentiment orientation (positive, negative, or neutral) but also their emotional valences, such as anger, anticipation, disgust, fear, joy, sadness, surprise, or trust. However, most currently used assessment tools cannot effectively measure emotional engagement, such as interest level, enjoyment, support, curiosity, and sense of belonging. Moreover, none of those tools utilize Bloom’s taxonomy for students’ learning-level assessment. In this work, we develop a user-friendly application based on NLP to help the teachers understand the students’ perception of their learning by analyzing their open-ended feedback. This allows us to examine the sentiment and the embedded emotions using a customized dictionary of emotions related to education. The application can also classify the students’ emotions according to Bloom’s taxonomy. We believe our application will help teachers improve their course delivery.
Panoramic Digital Image Correlation for 360-Deg Full-Field Displacement Measurement

In full-field 3D displacement measurement, stereo digital image correlation (Stereo-DIC) has strong capabilities. However, as a result of difficulties with stereo camera calibration and surface merging, 360-deg panoramic displacement measurements remain a challenge. This paper proposes a panoramic displacement field measurement method in order to accurately measure the shape and panoramic displacement field of complex shaped objects with natural textures. The proposed method is based on the robust subset-based DIC algorithm and the well-known Zhang’s calibration method to reconstruct the 3D shape and estimate the full-field displacements of a complex surface from multi-view stereo camera pairs. The method is used in the determination of the scale factor of the 3D reconstructed surface and the stitching of multiple 3D reconstructed surfaces with the aid of the laser point cloud data of the object under test. Based on a discussion of the challenges faced by panoramic DIC, this paper details the proposed solution and describes the specific algorithms implemented. The paper tests the performance of the proposed method using an experimental system with a 360-deg six camera setup. The system was evaluated by measuring the rigid body motion of a cylindrical log sample with known 3D point cloud data. The results confirm that the proposed method is able to accurately measure the panoramic shape and full-field displacement of objects with complex morphologies.
Support for the Vulnerability Management Process Using Conversion CVSS Base Score 2.0 to 3.x

COVID-19 forced a number of changes in many areas of life, which resulted in an increase in human activity in cyberspace. Furthermore, the number of cyberattacks has increased. In such circumstances, detection, accurate prioritisation, and timely removal of critical vulnerabilities is of key importance for ensuring the security of various organisations. One of the most-commonly used vulnerability assessment standards is the Common Vulnerability Scoring System (CVSS), which allows for assessing the degree of vulnerability criticality on a scale from 0 to 10. Unfortunately, not all detected vulnerabilities have defined CVSS base scores, or if they do, they are not always expressed using the latest standard (CVSS 3.x). In this work, we propose using machine learning algorithms to convert the CVSS vector from Version 2.0 to 3.x. We discuss in detail the individual steps of the conversion procedure, starting from data acquisition using vulnerability databases and Natural Language Processing (NLP) algorithms, to the vector mapping process based on the optimisation of ML algorithm parameters, and finally, the application of machine learning to calculate the CVSS 3.x vector components. The calculated example results showed the effectiveness of the proposed method for the conversion of the CVSS 2.0 vector to the CVSS 3.x standard.