Software Impacts最新文献

The Quasi oppositional smell agent optimization (QOBL-SAO) and its levy flight variant (LFQOBL-SAO) are two cutting-edge software tools for optimizing PV/wind/battery power systems. They can also be used to solve real-world CEC2020 optimization problems and are as good as top-performing software such as IUDE, $ϵ$ MAgES and the iLSHAD $\varepsilon$. The QOBL-SAO exploits the random mode’s weakness and then adds a number to the initial population. The LFQOBL-SAO, on the other hand, improves the random mode’s weakness in order to solve this problem. The LFQOBL-SAO improves performance and search space by using levy flight instead of random code.

Sahelian transhumance is a seasonal movement of herds based on strategies. These strategies are based on environmental and socio-economic factors. However, it is empirically difficult to establish the influence of each factor on the spatio-temporal distribution of herds. This paper presents a microsimulation software Sahelian transhumance simulator (STS). STS determines the spatio-temporal influence of each factor on herd movements. It also proposes scenarios for developing and securing the Sahelian pastoral space.

This paper presents a framework for automatic creation of an emotions-labeled dataset specifically designed for short texts written in a blend of Roman Urdu and English, and addresses the inherent absence of distinct structure in Roman Urdu language. The software development is carried out in two key phases. During the first phase, cleaning and automatic annotation of raw text is performed and in the second phase, classification of emotions along with prediction is carried out. The developed software significantly simplifies the process of dataset creation by employing natural language processing (NLP) techniques, tailored for the mixed-codes.

In the modern e-commerce landscape, timely package delivery faces hurdles amid fluctuating traffic conditions. This article proposes optimization techniques employing adaptable intelligent systems for dynamic route adjustments. The primary approach used here is an AI-driven optimal path routing system, leveraging Ant Colony Optimization (ACO) and Genetic Algorithm (GA). Integration of Google Maps (G-Map API) with real-time traffic data enhances route accuracy, ensuring efficient vehicle routing. By addressing these challenges, this research aims to streamline delivery processes and contribute to the advancement of vehicle routing methodologies in the dynamic e-commerce domain.

The use of natural language processing (NLP) models, including the more recent large language models (LLM) in real-world applications obtained relevant success in the past years. To measure the performance of these systems, traditional performance metrics such as accuracy, precision, recall, and f1-score are used. Although it is important to measure the performance of the models in those terms, natural language often requires an holistic evaluation that consider other important aspects such as robustness, bias, accuracy, toxicity, fairness, safety, efficiency, clinical relevance, security, representation, disinformation, political orientation, sensitivity, factuality, legal concerns, and vulnerabilities. To address the gap, we introduce LangTest, an open source Python toolkit, aimed at reshaping the evaluation of LLMs and NLP models in real-world applications. The project aims to empower data scientists, enabling them to meet high standards in the ever-evolving landscape of AI model development. Specifically, it provides a comprehensive suite of more than 60 test types, ensuring a more comprehensive understanding of a model’s behavior and responsible AI use. In this experiment, a Named Entity Recognition (NER) clinical model showed significant improvement in its capabilities to identify clinical entities in text after applying data augmentation for robustness.

Melanoma is one of the most aggressive and prevalent types of cancer and the development of novel drugs for its treatment is an ongoing effort. Virtual screening methods may accelerate the discovery of drug candidates by reducing the number of molecules to be tested in vitro and in vivo, using techniques based on properties of the ligand (eg: QSAR, pharmacophore, Lipinski rules) and the receptor/complex (eg: molecular docking, molecular dynamics). QSAR (Quantitative Structure Activity Relationship) allows the estimation of molecule properties and potential activities based on its structure, usually described based on numerical features, using statistical and machine learning methods. Here we describe Caramel, a web-based QSAR tool that provides predictive models for the growth inhibition of different melanoma cell lines, providing a fast and efficient way to select potentially active molecules in silico.

We introduce an open-source package, ROS-Pose, that allows simultaneous object detection and planar pose estimation through the utilization of image feature detectors and descriptors. Specifically, when presented with an image capturing any of the object’s planes, the software is designed to achieve real-time estimation of the object’s location and pose. The software further offers a visualization mechanism for object detection and the corresponding estimated pose by superimposing directional vectors on the objects. Most importantly, this software requires only a single image per object, making it conducive to fast prototyping and development of robotic applications.

This paper presents pDESy, an open-source Python package for modeling and discrete time-event simulation of the general engineering project. It aims to be the fundamental high-level building block for doing practical, real-world engineering project management by project modeling and simulation. The simulation model consists of four models: Product, Workflow, Workplace, and Team. By using these four models, we can perform an discrete time-event simulation using several types of priority rule. The package can be used to evaluate and design various engineering projects and develop detailed execution plans.

In this information age, we notice an increase in the quality of security threats. Organizations are forced to defend themselves against attacks in several steps. To identify the individual steps of attackers, we use several security technologies, among which we can include attack detection systems. Researchers or members of security teams have to deal with a large number of security events and alerts. A tool can help with this, which allows filtering relevant alerts and combining them into larger units without significant loss of information.

Conventional land-based surveys are often cumbersome, labor-intensive, and costly. To address these issues, a free mobile app called “GeoSurvey” has been developed and deployed on the AWS cloud platform for seamless accessibility. The application has the capability to collect polygons and polylines based on geographic coordinates system in real time. The developed application is native, using JAVA programming language and is freely available for Android users. Powered by Google APIs, “GeoSurvey” is extensively used for ground truth data collection. Its popularity is evident in its over 10,000 downloads, 18,000 polygons drawn, and 1200 polylines created worldwide. The application has been extensively used for collection of big data related to agriculture, which is used for training deep learning models for crop classification.