Ambiguous software requirements are a significant contributor to software project failure. Ambiguity in software requirements is characterized by the presence of multiple possible interpretations. As requirements documents often rely on natural language, ambiguity is a frequent challenge in industrial software construction, with the potential to result in software that fails to meet customer needs and generates issues for developers. Ambiguities arise from grammatical errors, inappropriate language use, multiple meanings, or a lack of detail. Previous studies have suggested the use of supervised machine learning for ambiguity detection, but limitations in addressing all ambiguity types and a lack of accuracy remain. In this paper, we introduce the fault-prone software requirements specification detection model (FPDM), which involves the ambiguity classification model (ACM). The ACM model identifies and selects the optimal algorithm to classify ambiguity in software requirements by employing the deep learning technique, while the FPDM model utilizes Boosting ensemble learning algorithms to detect fault-prone software requirements specifications. The ACM model achieved an accuracy of 0.9907, while the FPDM model achieved an accuracy of 0.9750. To validate the results, a case study was conducted to detect fault-prone software requirements specifications for 30 edge/cloud applications, as edge/cloud-based applications are becoming crucial and significant in the current digital world.

Workflow management systems (WfMS) are considered as accomplish platforms which can provide structured organization in business process and service architecture design. The systems contain workflow models in foundation, which provide flow control in one or more task sequences in parallel. Manipulation and access of artifacts that occur in or between the task sequences can generate unexpected state of artifacts by inappropriate workflow design. The artifact anomalies in a workflow model are classified into two categories, which are types of continuous and concurrent anomalies. A continuous anomaly occurs while an artifact is written redundantly or accessed before production. On the other hand, a concurrent anomaly can occur while an artifact is conflict written in parallel in a workflow model. There are several methods presented for anomaly analysis, however, these methods cannot detect all anomalies due to their definitions and they are either inefficient or lack of proof for the correctness. In this article, we present improved detection methods with an improved C-tree structure, called SP-tree. Based on an updated anomaly definition, our anomaly detection includes two stages: 1) the transformation algorithm generates an equivalent SP-tree from a given structured workflow model; and 2) based on the generated equivalent SP-tree, a series of methods are applied to detect anomalies.

Workflow models are widely applied in business software design. A workflow model contains a set of systematic ordered tasks to achieve designated business goal(s) under the designed flow control. Analyzing artifact usage during design phase can prevent unexpected artifact result due to abnormal artifact operation(s). A sequential anomaly indicates a pair of activities operating on the same artifact that can result in redundant write or missing production. On the other hand, the iteration of a loop structure in a workflow cannot be statically analyzed, thus, detecting process of artifact anomalies in a loop is costly. In this paper, we present an effective method to detect all anomalies associated with a loop by removing the redundant computation due to the repeated structure of the body and control in the iterations. After the removing, the anomalies can be detected on a single iteration generated instead. Here, the process of anomaly detection is now simplified into two phases: First, a workflow model is transformed into a corresponding C-tree structure and next, the proposed anomaly detection methodology is applied to the C-tree. Compared with current approaches, our method can reduce the space complexity and decrease the execution times of anomaly detection as linear.

A Multilingual Corpus for Panic and Worry in Code-Mixed Tweets by VADER Sentiment Analysis

The library's ancient books are deteriorating, due to faded ink, and damaged paper. It is challenging for librarians to both manage and discover information. Therefore, a technique to overcome this is required, such as implementing text recognition by leveraging many IOT -based OCR apps in mobile phones, such as Text Fairy and Camscanner to find out the results of the effectiveness of OCR mobile apps. Accuracy calculations are needed, in this case, the use of CER and WER is implemented to determine the accuracy. From the results of CER and WER calculations, it was found that in book cover data, Camscanner comes out better than Text Fairy. However, on data that is entirely text, Text Fairy is better when compared to Camscanner. It can be concluded that Text Fairy is superior in recognizing text-only parts, whilst Camscanner is superior at recognizing parts that are not entirely text.

Developing an engineering design is resource-intensive and time-consuming, particularly for the floats of a floatplane design, due to its complexity and limited testing facilities. Intelligent-based computational design (IBCD) techniques, which integrate computational design techniques and machine learning (ML) algorithms, offer a solution to reduce required testing by providing predictions. This paper proposes a deep learning (DL)-based IBCD method for modeling floats' lift-to-drag coefficient ratio (CL/CD), where DL is one of the most powerful ML. The proposed method consists of two phases: hyper-parameter optimization and DL model training and evaluation. A genetic algorithm (GA) is employed in the first phase to explore complex hyper-parameter combinations efficiently. Evaluation of the predicted CL/CD of the floats using the DL model resulted in a satisfactory R-squared of 0.9329 and the lowest mean squared error (MSE) of 0,001536. These results demonstrate the ability of DL model to predict the float's performance accurately and can facilitate further design optimization. Thus, the proposed method can offer a time-efficient and cost-effective solution for predicting float performance, aiding in optimizing floatplane designs and enhancing their functionalities.