Reviewerly/exHarmony huggingface dataset:
A survey of Semantic Reasoning frameworks for robotic systems

Robots are increasingly transitioning from specialized, single-task machines to general-purpose systems that operate in diverse and dynamic environments. To address the challenges associated with operation in real-world domains, robots must effectively generalize knowledge, learn, and be transparent in their decision making. This survey examines Semantic Reasoning techniques for robotic systems, which enable robots to encode and use semantic knowledge, including concepts, facts, ideas, and beliefs about the world. Continually perceiving, understanding, and generalizing semantic knowledge allows a robot to identify the meaningful patterns shared between problems and environments, and therefore more effectively perform a wide range of real-world tasks. We identify the three common components that make up a computational Semantic Reasoning Framework: knowledge sources, computational frameworks, and world representations. We analyze the existing implementations and the key characteristics of these components, highlight the many interactions that occur between them, and examine their integration for solving robotic tasks related to five aspects of the world, including objects, spaces, agents, tasks, and actions. By analyzing the computational formulation and underlying mechanisms of existing methods, we provide a unified view of the wide range of semantic reasoning techniques and identify open areas for future research.
A systematic analysis on the readiness of Blockchain integration in IoT forensics

Internet of Things (IoT) devices are massively utilized in our daily lives which is exposing them to a wide range of attacks. The heterogeneity of evidence produced by IoT devices is complicating the process of evidence collection and processing. Consequently, it is imperative to maintain admissible evidence collection, preservation, and analysis to be presented in a court of law. The currently used digital forensic tools and methodologies are lagging behind the IoT's heterogeneity and distributive nature. The decentralized, distributed, and transparent nature of Blockchain has encouraged lots of research on utilizing Blockchain to store, process, and investigate digital evidence in IoT forensics across various jurisdictions. Therefore, this research work analyzes proposed frameworks in the literature to review their deployment of Blockchain technology to resolve the various presented challenges in IoT Forensics. It presents a systematic review to investigate the readiness of blockchain integration in IoT forensics. Many factors have been addressed to consider when integrating the Blockchain technology into IoT forensics such as data integrity, distributed storage, authentication, transparency, and security where the literature provides an adequate proof on the crucial need to consider them as essential IoT forensic readiness factors. The research findings highlight challenges and open research opportunities of blockchain utilization to facilitate sound and efficient IoT forensics.
Online Learning During Covid-19 Pandemic: A View of Undergraduate Student Perspective in Malaysia

The advancement of technology changes the mode of operation worldwide education industry, where educational services can be delivered either in face-to-face or online teaching. The outbreak of COVID-19 forced higher education institutions to shift from face-to-face teaching to fully online learning, even though online learning is yet to be fully implemented in many institutions. This trend has prompted us to study this interesting topic and gather information about undergraduate students’ satisfaction with online learning from home due to the limited study focus on Malaysia. The operation of the study is based on the user satisfaction theories. A total of 156 questionnaires were distributed via judgement sampling guidelines. PLS-SEM was used for the data analysis. The results confirmed that the online learning system is useful but not user-friendly. Technical system quality is up to the satisfactory level from students’ perception. Furthermore, the attitude was confirmed significantly impact undergraduate students’ satisfaction with online learning. Discussion of the findings, implications, and direction for future research are also presented in the final section of the study.
Artificial Intelligence Technology in Travel, Tourism and Hospitality: Current and Future Developments

Artificial intelligence (AI) is appearing in almost all field of travel and tourism, present in various types of applications for instance the care of critical business processes, ease human experiences and support important governance features. AI has been object of research in travel, tourism and hospitality has gained tremendous impact on making lives easier for travelers around the globe. Tourism companies have applied AI in projecting tourism arrivals, demands, expenses and hotel occupancy, identification of destination points and evaluation of online reviews. This paper presents an overview on current implementation of AI technology in travel, tourism and hospitality based on operation, facility management, supply chain and marketing. In term of company view, they protected data acquisition, the provision of rich service, support customer engagement and improved employee productivity and efficiency. Furthermore, AI technology could increase the perceived service quality through new attractive and interactive manners of service delivery, connecting and engaging with customers. This paper also stresses on potential AI implementations in tourism industry in the future such as self-driving vehicle, crops and forest monitoring, AI in pest control, decision support system, forest protections, virtual reality and AI dimension travel tourism.
Fine-grained facial expression recognition via relational reasoning and hierarchical relation optimization

Facial expression recognition (FER), aiming to recognize the type of facial expressions, has achieved significant progress. However, most of the existing FER approaches ignore the influence of structure relations among image set and the semantic associations between labels. Recently, some studies turn to explore fine-grained FER which includes hierarchical label structure, but they merely explore the influence of hierarchical relations of labels. Inspired by this, in this paper, we propose a relational reasoning and hierarchical relation optimization network (R3HO-Net) that explores the above three kinds of relations simultaneously. Concretely, we first construct two sub-graphs, i.e., intra-image graph (IIG) and intra-label graph (ILG). Meanwhile, we propose an entropy-based relation adaptive initialization strategy to construct a heterogeneous inter-graph (HIG). Then fine-grained stream of R3HO-Net, including a relational update GCN module, updates the graphs simultaneously in an iterative way and outputs the mapping probabilities between heterogeneous node pairs to infer the final mapping results. Moreover, we also propose a hierarchical label optimization module and a hierarchical optimization loss to optimize fine-grained prediction results. Extensive experiments on serveral benchmarks demonstrate the superiority of the proposed approach.
An Enhanced Navigation Algorithm with an Adaptive Controller for Wheeled Mobile Robot Based on Bidirectional RRT

A navigation algorithm providing motion planning for two-wheeled mobile robots is proposed in this paper. The motion planning integrates path planning, velocity planning and controller design. Bidirectional rapidly-exploring random trees algorithms (RRT) with path pruning and smoothing mechanism are firstly used to obtain a collision-free path to the robot destination with directional continuity. Secondly, velocity planning based on trapezoidal velocity profile is used in both linear and angular velocities, but the position error of the endpoint of the curve appears due to the coupling problem of the nonlinear system. To reduce the error, an approximation method is used to gradually modify several parts of time length of the trapezoidal velocity profile, so the continuity of the path can still be maintained. Thirdly, the controller keeping the robot on the planned path and velocity is designed based on the dynamic model of the robot. The parameters of this controller are estimated by the adaptive low, and the gain of controller is dynamic adjusted by fuzzy logic control to avoid the case that the control value is saturated. The controller stability and the convergence of tracking error is guaranteed by Lyapunov theory. Simulation results are presented to illustrate the effectiveness and efficiency.
Combined AGADESN with DBSCAN Algorithm for Cluster Target Motion Intention Recognition

In this paper, we consider the problem of motion intention recognition for cluster targets with splitting behaviour and lack of motion prior information. This is a challenge to the classical Bayesian inference based intention recognition algorithms because they rely heavily on a priori knowledge. In order to solve these problems, a joint algorithm of deep echo state network optimized by adaptive genetic algorithm (AGADESN) and DBSCAN clustering algorithm is proposed in this paper. We use improved Olfati-Saber model with direction noise to generate cluster motion and use the cluster motion data to drive AGADESN algorithm to predict cluster destination, which achieves higher destination prediction accuracy than DESN algorithm. We innovatively design the motion similarity distance (MSD) and take the destination prediction output as one of the distance inputs, alleviating the lack of differentiation among different cluster targets caused by only relying on speed and position distance at the early stage of cluster motion. Based on the MSD, DBSCAN clustering algorithm is used to identify clusters in the field of view to determine whether splitting behaviour occurs. Simulation results demonstrate the effectiveness of the proposed algorithm in cluster target motion intention recognition and its superiority over DESN algorithm and DBSCAN algorithm only based on speed and position distance.
It's Enactment Time!: High-fidelity Enactment Stage for Accessible Automated Driving System Technology Research

Automated driving system (ADS) technology has been incorporated into critical driving functions (e.g., adaptive cruise control and autonomous braking) for over two decades. Now companies like GM and Google are developing and testing fully autonomous vehicles (AVs). However, the current design of AVs is for individuals who can drive rather than those who cannot, those who would benefit most from these vehicles. As the technology is still primarily experimental, conducting research with AVs and older adults or people with disabilities is infeasible. Therefore, to explore the accessibility of AVs, we conduct user enactment studies, as this method works well with technologies that participants have little to no experience utilizing. This pictorial describes the iterative process of developing a high-fidelity enactment environment, the space and objects with which participants interact. The aim is to encourage the HCI community to utilize high-fidelity enactment environments for conducting accessible future technology research.
Creating Personas for Signing User Populations: An Ability-Based Approach to User Modelling in HCI

Personas allow designers to build empathy towards users, avoiding reliance on stereotypes or assumptions. When it comes to creating personas for people with disabilities, a common procedure is to integrate impairments as dedicated characteristics. However, when considering Deaf and Hard of Hearing (DHH) individuals as target group, we argue that such an approach is incompatible with the latter’s own ability-based identification concepts. Thus, we propose a modification of personas for DHH users by defining them in terms of ability ranges and driving or keeping factors within specific contexts. Starting with assumption personas based on previous research, we conducted focus groups with DHH individuals from which we derived the data and perspectives that formed our modification. With this approach, we want to promote an adequate representation of DHH users among designers while also demonstrating an application of Ability-Based Design to HCI methods.
Influence of measurement errors of the complex coherence factor on reconstructed image quality of integrated optical interferometric imagers

Interferometric imagers need to measure the phase, modulus, and spatial frequency coordinates of the complex coherence factor to reconstruct images. However, these three measurement parameters are accompanied by certain measurement errors in the actual imaging process, thereby affecting the quality of the reconstructed image. Based on the checkerboard integrated optical imaging system, we simulate the influence of measurement errors of the complex coherence factor on the imaging performance of the integrated optical interferometric imager. The simulation results show that, on the premise that measurement errors follow the normal distribution, the modulus measurement error does not significantly reduce the quality of the reconstructed image, whereas the phase and spatial frequency coordinate measurement errors have a great impact. When the mean value of the phase measurement error is 0, the standard deviation must be <0.18π to ensure the high quality of the reconstructed image. For the measurement error of the spatial frequency coordinates, when the standard deviation of the lenslet position errors is <0.075 times the shortest baseline length, its influence on the quality of the reconstructed image is acceptable. The simulation results are of great help to the engineering, manufacturing, and application of integrated optical interferometric imaging technology.
Triangularized Orthogonalization-Free Method for Solving Extreme Eigenvalue Problems

A novel orthogonalization-free method together with two specific algorithms is proposed to address extreme eigenvalue problems. On top of gradient-based algorithms, the proposed algorithms modify the multicolumn gradient such that earlier columns are decoupled from later ones. Locally, both algorithms converge linearly with convergence rates depending on eigengaps. Momentum acceleration, exact linesearch, and column locking are incorporated to accelerate algorithms and reduce their computational costs. We demonstrate the efficiency of both algorithms on random matrices with different spectrum distributions and matrices from computational chemistry.
Gel Biter: food texture discriminator based on physical reservoir computing with multiple soft materials

Abstract The human oral structure contains organs with distinctly different physical properties, such as teeth, gums, and tongues. When food enters the oral cavity, we can recognize the tactile sensation and shape of the object from multiple perspectives through the texture of the teeth and tongue. Therefore, it is possible to regard oral structures as a group of tactile sensors based on these functions. In this study, we developed a soft-matter artificial mouth that can accurately detect subtle differences in texture by creating and combining oral structural organs using polymer materials with different physical properties and mounting them as end-effectors for a robot arm. The same piezoelectric film sensor was embedded inside each organ, making it possible to acquire tactile sensations from the same object as completely different signal waveforms. We tested whether the sensor data obtained from each soft-matter material could be used for excellent object recognition by applying various machine learning methods. In an actual experiment, we learned the waveform data obtained from chewing sweets and snacks, such as rice crackers, and applied machine learning to classify the data, which led to an accuracy rate of over 90%.
Dropout Early Warning System (DEWS) in Malaysia’s Primary and Secondary Education: A Conceptual Paper

School dropout is an issue that plagues almost every nation globally, and Malaysia is not an exception. According to Malaysia’s Education Ministry, dropouts are defined as Malaysian students in the school system who choose to leave before completing their education. Student dropout is a grave issue as it impacts the students and negatively implicates society and policymakers. At present, machine learning is the talk of the town as the world has a wealth of data that can be used freely. Machine learning is a method of data analysis that digitizes the development of analytical models. It is a technique that is based on the insights derived from data, recognize patterns, and make decisions with very little human intervention. From the Malaysian education perspective, no study thus far has looked into primary and secondary public-school dropouts while simultaneously using supervised machine learning. Consequently, this study intends to contribute to the literature, especially from Malaysia’s perspective by proposing a dropout early warning system for primary and secondary students using supervised machine learning algorithms. The predictive model with machine learning has an enormous potential to develop early warning systems to identify and help students who are likely to drop out.
SQL Injection Detection Using Machine Learning with Different TF-IDF Feature Extraction Approaches

The risk of attacks on web systems increased with the reliance of web systems in a wide range of businesses, and attackers invent new techniques to crack these systems. According to OWASP SQL injection stays one of the top 10 web applications security risks. This research use machine learning to detect SQL injection attacks, we used four machine learning models to detect SQL injection attacks. An insight into the data showing that data preparation and feature extraction have influenced the detection accuracy. The used training dataset is a combination of live requests extracted from user requests log file and a training dataset contains records of benign and malicious SQL queries. Then we compared the use of these models in term of detection quality and speed of training, results showed that Support Vector Model achieved highest detection accuracy with .997 accuracy followed by Extreme Gradient Boosting with .995 accuracy. In other hand Naïve Bayes using N-gram level feature extraction model was the fastest model it required 6 ms to train the classifier.
A novel approach to alleviate data sparsity and generate dynamic fruit recommendations from point‐of‐sale data

Summary Recommender systems have become a core part of the retail experience. Retailers often rely on recommender systems to help them drive more conversions through targeted communication and advertisements. However, recommender systems are not one size fits all. Specialized retailers require specialized recommender systems to consider various features, attributes, and dynamics about the product category. In this paper, we have proposed a novel fruit recommender system that generates dynamic recommendations while remediating the problem of data sparsity. We have developed a novel fruit recommender system that considers the temporal dynamics in the fruit market, like price fluctuations, fruit seasonality, and quality variations that occur throughout the year. To perform this task, we have used Recurrent Recommender Network (RRN), which uses the deep learning method Long Short‐Term Memory (LSTM) to implement the system model. To ensure that our work and results obtained are practical, we have worked in a real‐world setting, by tying up with a specialty fruit retailer based in New Delhi to get the real‐world Point‐of‐Sale (POS) data of consumers. The result of the study suggests our algorithm performs better than other benchmark algorithms along NDCG and RMSE metrics.
RegCPython: A Register-based Python Interpreter for Better Performance

Interpreters are widely used in the implementation of many programming languages, such as Python, Perl, and Java. Even though various JIT compilers emerge in an endless stream, interpretation efficiency still plays a critical role in program performance. Does a stack-based interpreter or a register-based interpreter perform better? The pros and cons of the pair of architectures have long been discussed. The stack architecture is attractive for its concise model and compact bytecode, but our study finds that the register-based interpreter can also be implemented easily and that its bytecode size only grows by a small margin. Moreover, the latter turns out to be appreciably faster. Specifically, we implemented an open source Python interpreter named RegCPython based on CPython v3.10.1. The former is register based, while the latter is stack based. Without changes in syntax, Application Programming Interface, and Application Binary Interface, RegCPython is excellently compatible with CPython, as it does not break existing syntax or interfaces. It achieves a speedup of 1.287 on the most favorable benchmark and 0.977 even on the most unfavorable benchmark. For all Python-intensive benchmarks, the average speedup reaches 1.120 on x86 and 1.130 on ARM. Our evaluation work, which also serves as an empirical study, provides a detailed performance survey of both interpreters on modern hardware. It points out that the register-based interpreters are more efficient mainly due to the elimination of machine instructions needed, while changes in branch mispredictions and cache misses have a limited impact on performance. Additionally, it confirms that the register-based implementation is also satisfactory in terms of memory footprint, compilation cost, and implementation complexity.
Geometric Path Plans for Perpendicular/Parallel Reverse Parking in a Narrow Parking Spot with Surrounding Space

This study proposes geometric path plans composed of two stages to automatically perform perpendicular and parallel parking with a reverse path in a narrow space. In perpendicular parking, the minimum width of the available parking spot is computed before the start of the parking operation, and the availability of parking and the optimal stage of the path plan are determined by considering the surrounding space together. In a similar concept, even in parallel parking, it is possible to determine whether to park by calculating the minimum length of the available parking spot and the number of repetitions of motion before starting the parking operation. The theoretical results for the parking spot and surrounding space related to the geometric path plans of perpendicular and parallel parking were confirmed through model car tests. Efficient automatic parking will be enabled by selecting and establishing an appropriate path plan along with the availability of parking in consideration of the parking spot and the surrounding space before starting the parking operation.
Automatic Movie Tag Generation System for Improving the Recommendation System

As the content industry develops, the demand for movie content is increasing. Accordingly, the content industry is actively developing super-personalized recommendation systems that match consumers’ tastes. In this paper, we study automatic generation of movie tags to improve the movie recommendation system. We extracted background sounds from movie trailer videos, analyzed the sounds using STFT (Short-Time Fourier transform) and major audio attribute features, and created a genre prediction model. The experimental results show that the pre-collected dataset and the data extracted via the model are similar when compared. In this research, we suggest the methodology of an automatic genre prediction system for movie information from trailer videos. This will help to reduce the time and effort for metadata generation for a recommendation system.