Reviewerly/exHarmony huggingface dataset:
Voice-Controlled Intelligent Personal Assistant

Virtual intelligent personal assistants perform various tasks and services by taking text, voice, and gesture inputs from different individuals. At present, personal assistants such as Google Assistant, Amazon's Alexa, and Apple's Siri are commonly used because they are embedded with our smartphones by default. This paper proposes an Android application-based intelligent personal assistant that differs from the common services provided by the default assistants. The application was made using React Native and Rasa chatbot development tool, which comes with a transformer-based pre-trained language model. This model was then trained to understand various tasks that the users want to be performed and then execute the code that corresponds to those tasks. Survey results confirm that the personal assistant application successfully executes different commands and provides services for its users by interpreting their voice commands. The provided features may find similarities with existing works, but the number of available features can be easily extended by developers using the Python language and React Native framework.
Automatic Annotation Performance of TextBlob and VADER on Covid Vaccination Dataset

With the recent boom in the corpus size of sentiment analysis tasks, automatic annotation is poised to be a necessary alternative to manual annotation for generating ground truth dataset labels.This article aims to investigate and validate the performance of two widely used lexicon-based automatic annotation approaches, TextBlob and Valence Aware Dictionary and Sentiment Reasoner (VADER), by comparing them with manual annotation.The dataset of 5402 Arabic tweets was annotated manually, containing 3124 positive tweets, 1463 negative tweets, and 815 neutral tweets.The tweets were translated into English so that TextBlob and VADER could be used for their annotation.TextBlob and VADER automatically classified the tweets to positive, negative, and neutral sentiments and compared them with manual annotation.This study shows that automatic annotation cannot be trusted as the gold standard for annotation.In addition, the study discussed many drawbacks and limitations of automatic annotation using lexicon-based algorithms.The highest level of accuracies of 75% and 70% were achieved by TextBlob and VADER, respectively.
Hybrid Pelican Komodo Algorithm

In this work, a new metaheuristic algorithm, namely the hybrid pelican Komodo algorithm (HPKA), has been proposed.This algorithm is developed by hybridizing two shortcoming metaheuristic algorithms: the Pelican Optimization Algorithm (POA) and Komodo Mlipir Algorithm (KMA).Through hybridization, the proposed algorithm is designed to adapt the advantages of both POA and KMA.Several improvisations regarding this proposed algorithm are as follows.First, this proposed algorithm replaces the randomized target with the preferred target in the first phase.Second, four possible movements are selected stochastically in the first phase.Third, in the second phase, the proposed algorithm replaces the agent's current location with the problem space width to control the local problem space.This proposed algorithm is then challenged to tackle theoretical and real-world optimization problems.The result shows that the proposed algorithm is better than grey wolf optimizer (GWO), marine predator algorithm (MPA), KMA, and POA in solving 14, 12, 14, and 18 functions.Meanwhile, the proposed algorithm creates 109%, 46%, 47%, and 1% better total capital gain rather than GWO, MPA, KMA, and POA, respectively in solving the portfolio optimization problem.
Causal AI-Powered Event Interpretation: A Cause-and-Effect Discovery for Indoor Thermal Comfort Measurements

Unexplainable indoor thermal comfort events from black-box models influence people to distrust suggestions from decision-support systems and ask for help from engineers and practitioners that are labor intensive and time consuming. These problems come from unknown cause and effect in the environments that cause the system not to produce explainable outcomes. This study proposes the cause-and-effect discovery for indoor thermal comfort events that help systems make human-like explanations to overcome these issues. The research contributions consist of three essential points. The first is perceptions based on the Internet of Things technologies that imitate human perception organs, which could sense signals as a system input component. The second is qualitative knowledge representation using random variable systems and graphs as the ground truth—the representation stores in the manner of human-like intelligence that people and systems can understand. The third is causal discovery algorithms that automatically determine the cause and effect in machine learning (ML) models from observational data. The results showed that models could discover cause-and-effect relationships close to the human-like intelligent-based model blueprint given observational data. They produce reasonable explanations for indoor thermal comfort events that help people trust such information and utilize it to make decisions.
Using Evolving Ensembles to Deal with Concept Drift in Streaming Scenarios

In a time in which streaming data becomes the new normal in Machine Learning problems, to the detriment of batch data, new challenges arise. In the past, a data source would be static in the sense that all data were known at the moment of the training of the model. A model would be trained and it would be in use for relatively long periods of time. Nowadays, data arrive in real-time and their statistical properties may also change over time, rendering trained models outdated. In this paper we propose an approach to deal with the concept drift problem with minimal computational effort. Specifically, we continuously update an ensemble with new weak learners and adjust their weights according to their performance. This approach is suitable to be used in real-time in the form of an ever-evolving model that adapts to change in the data.
Semantic Segmentation and Depth Estimation with RGB and DVS Sensor Fusion for Multi-view Driving Perception

In this research, we present a novel deep multi-task learning model to handle the perception stage of an autonomous driving system. The model leverages the fusion of RGB and dynamic vision sensor (DVS) images to perform semantic segmentation and depth estimation in four different perspectives of view simultaneously. As for the experiment, CARLA simulator is used to generate thousands of simulation data for training, validation, and testing processes. A dynamically changing environment with various weather conditions, daytime, maps, and non-player characters (NPC) is also considered to simulate a more realistic condition with expecting a better generalization of the model. An ablation study is conducted by modifying the network architecture to evaluate the influence of the sensor fusion technique. Based on the test result on 2 different datasets, the model that leverages feature maps sharing from RGB and DVS encoders is performing better. Furthermore, we show that our model can inference faster and have a comparable performance against another recent model. Official implementation code is shared at https://github.com/oskarnatan/RGBDVS-fusion .
On the Theory of Deduction, Part II. The Definitions of Classical and Intuitionist Negation (1948)

This article is a corrected reprint of K. R. Popper (1948c). On the Theory of Deduction, Part II. The Definitions of Classical and Intuitionist Negation. In: Koninklijke Nederlandse Akademie van Wetenschappen, Proceedings of the Section of Sciences 51, pp. 322–331.
The Use of Blockchain Technology in Electronic Health Record Management: An Analysis of State of the Art and Practice

Driven by the need to offer digital solutions to the population, the healthcare sector requires computational solutions with features of security, immutability and traceability for data transactions on the Electronic Health Record (EHR). An EHR is defined as a repository of healthcare information stored and transmitted in a secure and accessible way by authorized users. To address this important area of research, this work investigated state of the art practice and studies that addressed the development and validation of computational solutions with Blockchain technology applied to the following areas of an EHR lifecycle: (i) modeling and standardization (ii) data storage techniques, (iii) standards for data interoperability, and (iv) data retrieval and visualization solutions. Based on the results found, this study presents an analysis of the main advances and opportunities identified in the use of Blockchain technology in the development of healthcare applications.
An Effective VM Consolidation Mechanism by Using the Hybridization of PSO and Cuckoo Search Algorithms

VM Consolidation is one of the prodigious challenges in Cloud Computing as VMs have to be automatically placed into a physical machine based on the load running on the corresponding physical machine i.e., host is in overloaded condition or it may be in underloaded condition. VM consolidation is enacted based on the condition i.e., either overloading or underloading of VMs into a physical host. Energy consumption in data centers is one of the huge challenges because when we consolidate the VMs into a single physical machine based on the conditions it reduces energy consumption in the data centers which is a huge advantage for the cloud provider. Many of the authors proposed VM Consolidation algorithms by addressing energy consumption as a parameter but those algorithms not meeting the standards in terms of energy consumption. In this paper, we have proposed a new hybridized Meta-heuristic approach by combining Particle swarm optimization (PSO) and Cuckoo Search (CS) algorithms for consolidation of VMs based on the status Index of VMs and thereby addressing the energy consumption as a parameter. This work is simulated on Cloudsim and the workload is generated randomly in clouds and is given as input to the algorithm. To evaluate the efficiency of the algorithm in the view of energy consumption we have compared the proposed approach against existing algorithms such as PSO and CS. Simulation results revealed that our proposed approach is improved significantly over compared algorithms with mentioned parameters.
ISBRNM: Integrative Approach for Semantically Driven Blog Recommendation Using Novel Measures

In the present-day time, Blog recommendation is of great importance as the Internet grows, individuals face immense amounts of information. Due to the rise of microblogs, it has become a vital tool to provide accurate and timely information. Thereby, in recent times of Semantic Web, blog recommendation is one of the crucial aspects to search and shortlist the user preferred relevant content which is of extreme value. This paper proposes an integrative approach for Semantically driven Blog recommendation using Latent Dirichlet Allocation for the Natural Language Processing, Gated Recurrent Units as a classifier and semantic similarities are computed using Normalized Pointwise Mutual Index, Normalized Google Distance and Theil index which is classified using the Moth Flame Optimization algorithm. The experiments have been conducted for the Blog Authorship corpus and the accuracy percentage of 95.85% is achieved.
Dynamic Local Path Planning for Intelligent Vehicles Based on Sampling Area Point Discrete and Quadratic Programming

This paper proposes a dynamic path planning method based on discrete optimization applied to suburban highways or expressways. We optimize the generated candidate points according to the cost function and then generates a local path through the quintic spline curve. The kinematics and obstacle boundary conditions are set up to improve the reliability of the planned path. Meanwhile, the two-dimensional normal distribution obstacle cost function, comfort cost function and acceleration cost function are designed to evaluate candidate points and speed. Various types of roads scenes, including dynamic and static obstacles, such as straight roads, S- bend, are established to verify the method&#x2019;s feasibility. The simulation shows that the method can efficiently avoid various obstacles and plan an ideal path that complies with traffic laws.
On Graph Construction for Classification of Clinical Trials Protocols Using Graph Neural Networks

A recent trend in health-related machine learning proposes the use of Graph Neural Networks (GNN’s) to model biomedical data. This is justified due to the complexity of healthcare data and the modelling power of graph abstractions. Thus, GNN’s emerge as the natural choice to learn from increasing amounts of healthcare data. While formulating the problem, however, there are usually multiple design choices and decisions that can affect the final performance. In this work, we focus on Clinical Trial (CT) protocols consisting of hierarchical documents, containing free text as well as medical codes and terms, and design a classifier to predict each CT protocol termination risk as “low” or “high”. We show that while using GNN’s to solve this classification task is very successful, the way the graph is constructed is also of importance and one can benefit from making a priori useful information more explicit. While a natural choice is to consider each CT protocol as an independent graph and pose the problem as a graph classification, consistent performance improvements can be achieved by considering them as super-nodes in one unified graph and connecting them according to some metadata, like similar medical condition or intervention, and finally approaching the problem as a node classification task rather than graph classification. We validate this hypothesis experimentally on a large-scale manually labeled CT database. This provides useful insights on the flexibility of graph-based modeling for machine learning in the healthcare domain.
Multimodal Semantics for Affordances and Actions

In this paper, we argue that, as HCI becomes more multimodal with the integration of gesture, gaze, posture, and other nonverbal behavior, it is important to understand the role played by affordances and their associated actions in human-object interactions (HOI), so as to facilitate reasoning in HCI and HRI environments. We outline the requirements and challenges involved in developing a multimodal semantics for human-computer and human-robot interactions. Unlike unimodal interactive agents (e.g., text-based chatbots or voice-based personal digital assistants), multimodal HCI and HRI inherently require a notion of embodiment, or an understanding of the agent’s placement within the environment and that of its interlocutor. We present a dynamic semantics of the language, VoxML, to model human-computer, human-robot, and human-human interactions by creating multimodal simulations of both the communicative content and the agents’ common ground, and show the utility of VoxML information that is reified within the environment on computational understanding of objects for HOI.
A Decision-Making Tool for Creating and Identifying Face Sketches

A criminal can be quickly identified and prosecuted using a face sketch based on an eyewitness description . Several applications for converting hand-drawn face drawings and using them to automatically identify and recognize the suspect from the police database have been proposed in the past, but the existing system dealt with some drawbacks. It featured a lot of flaws, including as a limited facial features kit and a cartoonish feel to the constructed suspect face, which made it much more difficult to use these apps and acquire the results and efficiency that were required. In this paper, we present a stand- alone tool that allows users to create composite face sketches of suspects without the need for forensic artists. The application offers a drag-and-drop feature that can match the produced composite facial sketch with the criminal database in real time. This can be done considerably more rapidly and efficiently using deep learning and cloud infrastructure.
Predicting Human Psychometric Properties Using Computational Language Models

Transformer-based language models (LMs) continue to achieve state-of-the-art performance on natural language processing (NLP) benchmarks, including tasks designed to mimic human-inspired “commonsense” competencies. To better understand the degree to which LMs can be said to have certain linguistic reasoning skills, researchers are beginning to adapt the tools and concepts from psychometrics. But to what extent can benefits flow in the other direction? In other words, can LMs be of use in predicting the psychometric properties of test items, when those items are given to human participants? If so, the benefit for psychometric practitioners is enormous, as it can reduce the need for multiple rounds of empirical testing. We gather responses from numerous human participants and LMs (transformer- and non-transformer-based) on a broad diagnostic test of linguistic competencies. We then use the human responses to calculate standard psychometric properties of the items in the diagnostic test, using the human responses and the LM responses separately. We then determine how well these two sets of predictions correlate. We find that transformer-based LMs predict the human psychometric data consistently well across most categories, suggesting that they can be used to gather human-like psychometric data without the need for extensive human trials.
The Eyes as a Window to the Brain and Mind

This chapter will provide an overview of the past and current topics in eye movement research, which will be more widely addressed within the proceeding chapters in this book. A greater understanding of eye movements and how eye tracking technology works, as well as what populations it is used with, will allow its application and adoption within professional settings.
Zero-Shot Aspect-Based Scientific Document Summarization using Self-Supervised Pre-training

We study the zero-shot setting for the aspectbased scientific document summarization task.Summarizing scientific documents with respect to an aspect can remarkably improve document assistance systems and readers experience.However, existing large-scale datasets contain a limited variety of aspects, causing summarization models to over-fit to a small set of aspects and a specific domain.We establish baseline results in zero-shot performance (over unseen aspects and the presence of domain shift), paraphrasing, leave-one-out, and limited supervised samples experimental setups.We propose a self-supervised pre-training approach to enhance the zero-shot performance.We leverage the PubMed structured abstracts to create a biomedical aspect-based summarization dataset.Experimental results on the PubMed and FacetSum aspect-based datasets show promising performance when the model is pre-trained using unlabelled in-domain data.
Single-Image Rain Removal Network Based on an Attention Mechanism and a Residual Structure

Image rain removal involves eliminating the impact of rain on an image to increase its visual quality.In this paper, we propose a single-image rain removal network based on an attention mechanism and a residual structure.The proposed method extracts high-level semantic information and low-level detail information via a feature aggregation module and a residual channel attention mechanism, respectively, and fuses the two feature parts through a fusion tail to learn a mapping to the rain removal image.The attention mechanism unequally processes different features and pixels to improve the generalization of the image deraining procedure.A continuous residual structure is used to prevent problems such as overfitting and gradient disappearance during network training.In an ablation study, we show that both the feature aggregation module and the residual channel attention mechanism significantly improve the resulting image quality in terms of the PSNR and SSIM index measure.Moreover, the obtained experimental results demonstrate that our method quantitatively and qualitatively outperforms the current state-of-the-art approaches on the Rain100L, Rain100H and real-world image datasets.
Lessons Learned from Creating, Implementing and Evaluating Assisted E-Learning Incorporating Adaptivity, Recommendations and Learning Analytics

Applications of adaptive e-learning, recommender systems and learning analytics are typically presented individually, however, their combination poses several challenging requirements ranging from organizational to technical issues. This article presents a technical study from a holistic application of a variety of e-learning assistance technologies, including recommender systems, chatbots, adaptivity, and learning analytics. At its core we operationalize interoperability standards such as the Experience API (xAPI) and Learning Tools Interoperability (LTI), and control the data flow via a standard-encapsulating middleware approach. We report on the challenges regarding organization, methodology, content, didactics, and technology. A systematic evaluation with the target group discusses the users' expectations with the measured interactions.
Segmentation-Based Background-Inference and Small-Person Pose Estimation

Despite encouraging results have been achieved in human pose estimation in recent years, it remains challenging problems. When the background is similar to the human body parts, and there are small persons with low-resolution in the image, the performance may degrade dramatically. This paper addresses problems in background-inference and small-person pose estimation. To achieve this, a novel pose estimation algorithm is proposed on the basis of person semantic segmentation deep neural network. Different from most previous methods with a single pose estimation model, we generate mixture models with pose estimation and semantic segmentation. We introduce novel generative adversarial model and auxiliary model to realize the semantic segmentation network, which can handle the confusion of the similar regions in the background. In addition, to address the problem of the scale differences between big and small persons&#x2019; keypoints, we add additional position and channel attention modules to the first two stages of OpenPose. We conduct extensive experiments on COCO and VOC datasets. And we compare the proposed method with the most popular state-of-the-art human pose estimation and semantic segmentation frameworks, including MultiPoseNet, Deterton2 and DeepLab V3. Our experimental results show that the proposed method is more accurate than the state-of-the-art algorithms and performs effectively in tackling the complex situations.
Skew Angle Detection and Correction in Text Images Using RGB Gradient

Detecting and correcting skew angles is critical to success in document layout analysis and optical character recognition tasks, as they are more susceptible to failure when on uneven skews. In automation such as postal systems, library management, office business, and banking data entry, skew angle estimation is crucial to improve procedures response. Although different works have addressed this subject, due to the variability in the input data, many solutions are restricted to a specific language, texts whose contents are within a controlled scope, and entries that differentiate printed from handwritten texts. This paper introduces a new method based on RGB gradient capable of detecting and correcting skew angles in different types of documents. We evaluate the proposed method using two public databases and compare our results with other techniques cited in the literature. In general, our proposal achieved results superior to the approaches compared in all groups of documents in the database. Furthermore, we show that our method can work accurately in various text orientations, and it can work efficiently against documents containing short and sparse text lines, non-textual objects, and image noises caused by imperfect scanning.