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Sound rendering is the process of determining the sound propagation path from an audio source to a listener and generating 3D sound based on it. This task demands complex calculations, including trigonometric functions. This paper presents hardware-based inverse cosine function calculations using the table method and linear approximation. This approach maintains a high accuracy while limiting hardware size for suitability in sound rendering applications. Consequently, our proposed hardware-based inverse cosine calculation method is a valuable tool for achieving high efficiency and accuracy in 3D sound rendering.

In wireless sensor networks (WSN), flooding increases the reliability in terms of successful transmission of a packet with higher overhead. The flooding consumes the resources of the network quickly, especially in sensor networks, mobile ad-hoc networks, and vehicular ad-hoc networks in terms of the lifetime of the node, lifetime of the network, and battery lifetime, etc. This paper aims to develop an efficient and reliable protocol by using multicasting and unicasting to overcome the issue of higher overhead due to flooding. Unicasting is used when the desired destination is at a minimum distance to avoid an extra overhead and increases the efficiency of the network in terms of overhead and energy because unicasting is favorable where the distance is minimum. Similarly, multicasting is used when the desired destination is at maximum distance and increases the network’s reliability in terms of throughput. The results are implemented in the Department of Computer Science, Bacha Khan University Charsadda (BKUC), Pakistan, as well as in the Network Simulator-2 (NS-2). The results are compared with benchmark schemes such as PUMA and ERASCA, and based on the results, the performance of the proposed approach is improved in terms of overhead, throughput, and packet delivery fraction by avoiding flooding.

Human activity recognition is an emerging field of ubiquitous and pervasive computing. Although recent smartphones have powerful resources, the execution of machine learning algorithms on a large amount of data is still a burden on smartphones. Three major factors including; classification algorithm, data feature, and smartphone position influence the recognition accuracy and time. In this paper, we present a comparative study of six classification algorithms, six data features, and four different positions that are most commonly used in the recognition process using smartphone accelerometer. This analysis can be used to select any specific classification algorithm, data feature, and smartphone position for human activity recognition in terms of accuracy and response time. The methodology we used is composed of two major components; a data collector, and a classifier. A set of eleven activities of daily living, four different positions for data collection and ten volunteers contributed to make it a worth-full comparative study. Results show that K-Nearest Neighbor and J48 algorithms performed well both in terms of time and accuracy irrespective of data features whereas the performance of other algorithms is dependent on the selected data features. Similarly, mean and mode features gave good results in terms of accuracy irrespective of the classification algorithm. A short version of the paper has already been presented at ICIS 2014.

Sentiment analysis is a task in natural language processing that focuses on identifying and categorizing emotions expressed in text. Despite the remarkable predictive performance achieved by deep learning models in this domain, their limited interpretability poses a significant challenge. Moreover, the development of interpretable sentiment analysis models for the Thai language has received insufficient attention. To address this gap, this study proposed a Zero-shot Interpretable Sentiment Analysis Framework, integrating sentiment polarity extraction and leveraging the zero-shot learning with the powerful WangchanBERTa model. Our framework utilized the word selection method from the feeling wheel to represent six primary feelings as sentiment polarities, effectively capturing the subtle emotions expressed in the text. These sentiment polarities played a crucial role as features in training our model, enhancing its interpretability for sentiment analysis tasks. Through the evaluation of three Thai sentiment analysis datasets, we compared the sentiment polarity extraction with two traditional feature extraction methods and ten classification algorithms. The results showed the superiority of the sentiment polarity extraction over Bag of Words and its competitive performance compared to TF-IDF in terms of accuracy. To gain insights into the model’s decision-making process, SHAP (SHapley Additive exPlanations) was employed to analyze feature importance. Our findings highlighted the alignment of influential features with the sentiment polarities of the text, providing a crucial understanding of the model’s functionality. Notably, we uncovered a clear relationship between specific feeling features and their corresponding sentiment classes, deepening our comprehension of the model’s performance in sentiment analysis. This study not only contributed to the advancement of sentiment analysis in the Thai language but also bridged the gap between predictive performance and model transparency, yielding a novel and interpretable approach for sentiment analysis.