Reviewerly/exHarmony huggingface dataset:
Human Facial Emotion Recognition using Deep Learning Techniques

Human beings are social animals; which means that social interactions form a key part of our life. In social interactions, emotions play an important part as well. Our facial expressions play an important part in expressing meaning along with spoken words. It is quite easy for us to recognize facial expressions; but for a computer algorithm, it is a challenging task. Automatic facial recognition using deep learning is one of the technologies with which one can easily recognize human emotions from the interlocutor. As a matter of fact, deep learning architectures such as CNN display extraordinary results for the detection of facial emotions. Presented work discuss development of CNN model for the recognition of facial expressions. Based on existing work, we utilize a dataset named “Face expression recognition” for training and testing the proposed CNN model. Using relevant technologies, we developed a system that is able to detect multiple facial expressions at a given time.
Autonomous Load Carrier Approaching Based on Deep Reinforcement Learning with Compressed Visual Information

In intralogistics, a large number of tasks are already fully automated. This holds true especially for tasks where strictly predefined positions and paths are specified and implementable. Challenges still exist when this is not the case and a more dynamic environment is present. One example of such a dynamic environment is the approaching and lifting of freely positioned pallet-like carriers with forklifts. In this work, we propose a method for approaching and picking up pallet-like carriers with a forklift based on data from an RGB camera. Unlike previous work, our method does not require an estimation of the pose of the load carrier. In order to control the forklift, we use a soft actor critical reinforcement learning agent. The required input consists of the bounding box of the load carrier in combination with the current speed and steering of the forklift. Our simulation experiments show that this compressed visual information is sufficient to successfully approach load carriers while reducing training time and network size. In a next step, we are going to apply the presented result on a real-world scenario and investigate its transferability.
On Symbolic 2-Plithogenic Real Matrices and Their Algebraic Properties

Symbolic n-plithogenic sets came with many generalizations to classical algebraic structures, with many interesting properties and theorems, where the symbolic 2-plithogenic structures are very similar in their algebraic properties to refined neutrosophic algebraic structures. The main goal of this article is to study the algebraic properties of symbolic 2-plithogenic matrices such as the computing on symbolic 2-plithogenic determinants, symbolic 2-plithogenic special values, and symbolic 2-plithogenic representations by linear functions. In addition, many examples will be presented and discussed in terms of theorems to clarify the validity of the content of this paper.
What Cyber Security Can Learn from Local Government

Abstract Dave Richardson MBCS, ICT and Digital Services Business Manager at Newark and Sherwood District Council, explains how local authorities can lead by example in tackling the information security challenge.
Innovative Solutions for a Better World: The Role of ChatGPT in Vegan Agriculture

This research study explores the application of ChatGPT in vegan agriculture, examining its functionality and ability to provide valuable assistance to businesses and farmers in this industry. It reviews how this application can assist in cost management and challenges associated in adopting the same. Additionally, the study highlights the emerging trends and innovations in vegan agriculture, such as precision farming and artificial intelligence, and how chatbots like ChatGPT can play a vital role in driving growth and success in this industry. The study concludes that the integration of chatbots in vegan agriculture represents a significant opportunity for businesses and farmers to leverage technology to optimize operations, reduce costs, and increase profitability, while promoting sustainable and ethical practices.
Learning implicit sentiments in Alzheimer's disease recognition with contextual attention features

Background Alzheimer's disease (AD) is difficult to diagnose on the basis of language because of the implicit emotion of transcripts, which is defined as a supervised fuzzy implicit emotion classification at the document level. Recent neural network-based approaches have not paid attention to the implicit sentiments entailed in AD transcripts. Method A two-level attention mechanism is proposed to detect deep semantic information toward words and sentences, which enables it to attend to more words and fewer sentences differentially when constructing document representation. Specifically, a document vector was built by progressively aggregating important words into sentence vectors and important sentences into document vectors. Results Experimental results showed that our method achieved the best accuracy of 91.6% on annotated public Pitt corpora, which validates its effectiveness in learning implicit sentiment representation for our model. Conclusion The proposed model can qualitatively select informative words and sentences using attention layers, and this method also provides good inspiration for AD diagnosis based on implicit sentiment transcripts.
SiP Architecture For Accelerating Collective Communication in Distributed Deep Learning

We present a silicon photonic architecture for accelerating collective commu nications in distributed deep learning. We demonstrate a 22% job completion time improvement in a small-scale testbed and 1.4 to 5.9x improvement in large-scale simulations.
Towards an Explainable AI-Based Tool to Predict Preterm Birth

Preterm birth (PTB) is defined as delivery occurring before 37 weeks of gestation. In this paper, Artificial Intelligence (AI)-based predictive models are adapted to accurately estimate the probability of PTB. In doing so, pregnant women' objective results and variables extracted from the screening procedure in combination with demographics, medical history, social history, and other medical data are used. A dataset consisting of 375 pregnant women is used and a number of alternative Machine Learning (ML) algorithms are applied to predict PTB. The ensemble voting model produced the best results across all performance metrics with an area under the curve (ROC-AUC) of approximately 0.84 and a precision-recall curve (PR-AUC) of approximately 0.73. An attempt to provide clinicians with an explanation of the prediction is performed to increase trustworthiness.
AI-Based Chatbot for College Management System

Abstract: The days of solely engaging with a service through a keyboard are over. Users interact with systems more and more through chats and chatbots. A chatbot is a computer program that can converse with humans using Artificial Intelligence in messaging platforms. Every time the chatbot gets input from the user, it saves input and response which helps the chatbot with little initial knowledge to evolve using gathered responses. With increased responses, the precision of the chatbot also gets increases. The ultimate goal of this project is to add a chatbot feature and API for College. This project will investigate how advancements in Artificial Intelligence and Machine Learning technology are being used to improve many services.
Transformer-Based Multi-Scale Feature Integration Network for Video Saliency Prediction

Most cutting-edge video saliency prediction models rely on spatiotemporal features extracted by 3D convolutions due to its local contextual cues acquirement ability. However, the shortage of 3D convolutions is that it cannot effectively capture long-term spatiotemporal dependencies in videos. To address this limitation, we propose a novel Transformer-based Multi-scale Feature Integration Network (TMFI-Net) for video saliency prediction, where the proposed TMFI-Net consists of a semantic-guided encoder and a hierarchical decoder. Firstly, embarking on the Transformer-based multi-level spatiotemporal features, the semantic-guided encoder enhances the features by inserting the high-level feature into each level feature via a top-down pathway and a longitudinal connection, which endows the multi-level spatiotemporal features with rich contextual information. In this way, the features are steered to give more concerns to saliency regions. Secondly, the hierarchical decoder employs a multi-dimensional attention (MA) module to elevate features along channel, temporal, and spatial dimensions jointly. Successively, the hierarchical decoder deploys a progressive decoding block to conduct an initial saliency prediction, which provides a coarse localization of saliency regions. Lastly, considering the complementarity of different saliency predictions, we integrate all initial saliency prediction results into the final saliency map. Comprehensive experimental results on four video saliency datasets firmly demonstrate that our model achieves superior performance when compared with the state-of-the-art video saliency models. The code is available at https://github.com/wusonghe/TMFI-Net.
Interoperability of Open Science Metadata: What About the Reality?

Open Science aims at sharing results and data widely between different research domains. Interoperability is one of the keys to enable the exchange and crossing of data between different research communities. In this paper, we assess the state of the interoperability of Open Science datasets from various communities. The diversity of metadata schemata of these datasets from different sources does not allow for native interoperability, highlighting the need for matching tools. The question is whether current metadata schema matching tools are sufficiently efficient to achieve interoperability between existing datasets. In our study, we first define our vision of interoperability by transversally considering the technical and semantic aspects when dealing with metadata schemata coming from various domains. We then evaluate the interoperability of some datasets from the medical domain and Earth system study domain using acknowledged matching tools. We evaluate the performance of the tools, then we investigate the correlation between various metrics characterizing the schemata and the performance related to their mapping. This paper leads to identify complementary ways to improve dataset interoperability: (1) to adapt mapping algorithms to the issues raised by metadata schema matching; (2) to adapt metadata schemata, for instance by sharing a core vocabulary and/or reusing existing standards; (3) to combine various trends in a more complex interoperability approach that would also make available and operational the (RDA) crosswalks between schemata and that would promote good practices in metadata labeling and documentation.
Assessment of Heavy Metal Pollution Using the Geo-accumulation Index (I-Geo), Pollution Load Index (PLI) and Potential Ecological Risk Index (RI) in Paddy Field Soils Adjacent to Ultrabasic Soils

The content of heavy metals was appraised at paddy fields, from a total of 54 soil samples gathered from two locations in Ranau, Sabah and one from the UKM experimental paddy plot in Peninsular Malaysia, during2014 and 2015. Through an appraisal evaluate of contamination using the Index of Geo-accumulation (I geo) it was noted that Chromium and Nickel levels in Ranau field 1 came under grade 6 (I-geo &gt; 5 is considered to be very contaminated), showing that the soil was heavily tainted with Cr and Ni, whilst Cobalt was discovered to be at the grade 3 level (medium to strong contamination). On the other hand, Ranau field 2's I-geo for other metals came under grade 1(which means medium to no contamination), highlighting the fact that the soils in these areas were tainted with the examined metals, while the control field was found to be grade 0 (no contamination). Ranau field 1 showed strong levels of the contamination factor (CF) for Co, Cr, and Ni, and its Pollution Load Index was(PLI) was&gt; 1, denoting a steady degradation in the area, with high levels of pollution compared to those in the other tested areas. The average monomial ecological risk for Ni was found to be between (40 – 80), highlighting that Ni contamination was considered to be of medium danger to the surrounding environment. Through the Potential Ecological Risk Index, it was seen that Ni was a likely hazard and presented a substantial danger. The RI values for the test sites were as follows: season 2015 (2.89) &lt; control for season 2014 (3.18).
Multiple vehicle detection and tracking using improved YOLOv5 and strong SORT

Multiple object tracking (MOT) is an important subject in applications of computer vision As a subtask of object detection and tracking, vehicle tracking has important research significance. This paper proposes a vehicle tracking and detection technology which is based on improved YOLOv5 and Strong SORT. The YOLOv5 combined with the CBAM attention mechanism work as the detector of Strong SORT in the tracking process, this arrangement decreases computational time. Experiments proved that this proposed algorithm can effectively deal with the problems of object occlusion, target loss, and ID switch. The trained model is easy to deploy for an embedded device, which makes it a very good candidate for a real-time surveillance system.
Computing the Many-Body Green’s Function with Adaptive Variational Quantum Dynamics

We present a method to compute the many-body real-time Green's function using an adaptive variational quantum dynamics simulation approach. The real-time Green's function involves the time evolution of a quantum state with one additional electron with respect to the ground state wave function that is first expressed as a linear-linear combination of state vectors. The real-time evolution and the Green's function are obtained by combining the dynamics of the individual state vectors in a linear combination. The use of the adaptive protocol enables us to generate compact ansatzes on-the-fly while running the simulation. In order to improve the convergence of spectral features, Padé approximants are applied to obtain the Fourier transform of the Green's function. We demonstrate the evaluation of the Green's function on an IBM Q quantum computer. As a part of our error mitigation strategy, we develop a resolution-enhancing method that we successfully apply on the noisy data from the real-quantum hardware.
2D Simplified Wildfire Spreading Model in Python: From NumPy to CuPy

Wildfires are a problem of great interest in the world because of the damage they cause every year to forest, wild fauna and flora, an also threatening human lives, among others. There are some computational models for numerical simulations to address this phenomena, and a few of them have an open-source implementation. The goal of this work is to extend a CPU Python’s implementation of wildfire open-source framework developed in NumPy, to a GPU improved version using CuPy. The algorithm used is based on a numerical discretization of a wildfire spreading mathematical model described by a system of partial differential equations. Computational and mathematical components, numerical simulations and applications are described in details in the document. In addition, this work includes includes a brief performance comparison between both implementations, pointing out that we can achieve good execution times using the CuPy GPU implementation without spending enough programming effort.
Enhancing Interoperability and Inferring Evacuation Priorities in Flood Disaster Response

Disaster management is a crucial process that aims at limiting the consequences of a natural disaster. Disaster-related data, that are heterogeneous and multi-source, should be shared among different actors involved in the management process to enhance the interoperability. In addition, they can be used for inferring new information that helps in decision making. The evacuation process of flood victims during a flood disaster is critical and should be simple, rapid and efficient to ensure the victims’ safety. In this paper, we present an ontology that allows integrating and sharing flood-related data to various involved actors and updating these data in real time throughout the flood. Furthermore, we propose using the ontology to infer new information representing evacuation priorities of places impacted by the flood using semantic reasoning to assist in the disaster management process. The evaluation results show that it is efficient for enhancing information interoperability as well as for inferring evacuation priorities.
Simplification of 2D shapes with equivalent rectangles

Real objects in horizontal projection often have a complex geometry.Their irregular shape causes issues during analyses and calculations that consider their geometry.The paper proposes the replacement of real-world objects with equivalent rectangles (ER).The paper also defines the geometric criteria of ER as well as ER parameters and methods for calculating them.The paper also demonstrates the difference in the duration of calculations for different types of rectangles (equivalent rectangle with the same area, surrounding rectangle with the smallest area, inscribed rectangle with the largest area).The presented approach has been illustrated with three case studies.The first one is the application of ER to underground mining cavities to determine post-mining deformations of the ground surface.In the second study, an ER was applied to analyse the geometry of agricultural parcels in a selected part of a rural settlement.ER can help assess whether the spatial layout is faulty and if a planning intervention is necessary.The third example describes a building's geometry with an ER.Regarding the simplification of building's geometry, it is crucial to replace a simplified building with a model that has the same centroid location and the same area.It is the perfect solution for rapid analyses of displaying objects on maps in various scales.
The mathematical characteristic of the Laplace contour filters used in digital image processing. The third order filters

The Laplace operator is a differential operator which is used to detect edges of objects in digital images.This paper presents the properties of the most commonly used third-order 3×3 pixels Laplace contour filters including the difference schemes used to derive them.The authors focused on the mathematical properties of the Laplace filters.The basic reasons of the differences of the properties were studied and indicated using their transfer functions and modified differential equations.The relations between the transfer function for the differential Laplace operator and its difference operators were described and presented graphically.The impact of the corner elements of the masks on the results was discussed.This is a theoretical work.The basic research conducted here refers to a few practical examples which are illustrations of the derived conclusions.We are aware that unambiguous and even categorical final statements as well as indication of areas of the results application always require numerous experiments and frequent dissemination of the results.Therefore, we present only a concise procedure of determination of the mathematical properties of the Laplace contour filters matrices.In the next paper we shall present the spectral characteristic of the fifth order filters of the Laplace type.
Asymmetric learning of dynamic spatial regularities in visual search: Robust facilitation of predictable target locations, fragile suppression of distractor locations.

Static statistical regularities in the placement of targets and salient distractors within the search display can be learned and used to optimize attentional guidance. Whether statistical learning also extends to dynamic regularities governing the placement of targets and distractors on successive trials remains controversial. Here, we applied the same dynamic cross-trial regularity-one-step shift of the critical item in clockwise/counterclockwise direction-to either the target or a distractor. In two experiments, we found and replicated robust learning of the predicted target location: processing of the target at this location was facilitated, compared to random target placement. But we found little evidence of proactive suppression of the predictable distractor location-even in a close replication of Wang et al. (2021), who had reported a dynamic distractor suppression effect. Facilitation of the predictable target location was associated with explicit awareness of the dynamic regularity, whereas participants showed no awareness of the distractor regularity. We propose that this asymmetry arises because, owing to the target's central role in the task set, its location is explicitly encoded in working memory, enabling the learning of dynamic regularities. In contrast, the distractor is not explicitly encoded; so, statistical learning of dynamic distractor locations is more precarious. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
An efficient clustering mechanism towards large scale service composition in IoT

Internet of things (IoT) applications hinder heterogeneity at different levels, such as device level, usage level, and communication level. In this context, service-oriented architecture (SOA) facilitates limited supports for handling heterogeneity in IoT-based applications. This article proposes a clustered hypergraph colouring (CHC) approach for designing large-scale SOA (LSS). Two noble approaches (service clustering and composition) are discussed in LSS for satisfying the service consumers (SCs) requirements. A service clustering approach is presented for the clustering of homogeneous services. This mechanism enables to reduce the search time for the service composition mechanism. A service composition approach is described with some parameters. Further, the proposed approach is validated through an experimental setup by considering the factors such as the number of services, execution time, and memory size. The execution time of LSS is minimised as compared to normal or medium-scale SOA (NSS) due to the service clustering mechanism. The novelty of this work is to minimise the execution time in the large service domain during service composition.
Verifying an Aircraft Collision Avoidance Neural Network with Marabou

In this case study, we have explored the use of a neural network model checker to analyze the safety characteristics of a neural network trained using reinforcement learning to compute collision avoidance flight plans for aircraft. We analyzed specific aircraft encounter geometries (e.g., head-on, overtake) and also examined robustness of the neural network. We verified the minimum horizontal separation property by identifying conditions where the neural network can potentially cause a transition from a safe state to an unsafe state. We show how the property verification problem is mathematically transformed and encoded as linear-constraints that can be analyzed by the Marabou model checker.
REGNUM: Generating Logical Rules with Numerical Predicates in Knowledge Graphs

Mining logical rules from a knowledge graph (KG) can reveal useful patterns for predicting facts, curating the KG, and identifying trends. However, many rule mining systems face challenges when working with numerical data because numerical predicates can take a large number of values, leading to a huge search space. In this work, we present REGNUM, a system that addresses this issue by generating rules with numerical constraints. REGNUM extends the body of rules mined from a KG by using supervised discretization of numerical values with decision trees to increase the confidence of the rules without sacrificing significance. Our experimental results show that the numerical rules have a higher overall quality than the parent rules and are effective at making better predictions.