Abstract

Software practitioners use various methods in Requirements Engineering (RE) to elicit, analyze and specify the requirements of a enterprise products. The methods impact the final product characteristics and influence product delivery. Ad-hoc usage of the methods by software practitioners can lead to inconsistency and ambiguity in the product. With the notable rise in enterprise products, games, etc. across various domains, Virtual Reality (VR) has become an essential technology for the future. The methods adopted for requirement engineering for developing VR products requires a detailed study. This paper presents a mapping study on requirement engineering methods prescribed and used for developing VR applications including requirements elicitation, requirements analysis, and requirements specification. Our study provides insights into the use of such methods in the VR community and suggests using specific requirement engineering methods in various fields of interest. We also discuss future directions in requirement engineering for VR products.

Abstract

News headline generation is a crucial task in increasing productivity for both the readers and producers of news. This task can easily be aided by automated News headline-generation models. However, the presence of irrelevant headlines in scraped news articles results in sub-optimal performance of generation models. We propose that relevance-based headline classification can greatly aid the task of generating relevant headlines. Relevance-based headline classification involves categorizing news headlines based on their relevance to the corresponding news articles. While this task is well-established in English, it remains under-explored in low-resource languages like Telugu due to a lack of annotated data. To address this gap, we present TeClass, the first-ever human-annotated Telugu news headline classification dataset, containing 78,534 annotations across 26,178 article-headline pairs. We experiment with various baseline models and provide a comprehensive analysis of their results. We further demonstrate the impact of this work by fine-tuning various headline generation models using TeClass dataset. The headlines generated by the models fine-tuned on highly relevant article-headline pairs, showed about a 5 point increment in the ROUGE-L scores. To encourage future research, the annotated dataset as well as the annotation guidelines will be made publicly available.

Abstract

The use of emerging technologies like Virtual Reality (VR) in therapeutic settings has increased in the past few years. By incorporating VR, a mental health condition like depression can be assessed effectively, while also providing personalized motivation and meaningful engagement for treatment purposes. The integration of external sensors further enhances the engagement of the subjects with the VR scenes. This paper presents a comprehensive review of existing literature on the detection and treatment of depression using VR. It explores various types of VR scenes, external hardware, innovative metrics, and targeted user studies conducted by researchers and professionals in the field. The paper also discusses potential requirements for designing VR scenes specifically tailored for depression assessment and treatment, with the aim of guiding future practitioners in this area.

Abstract

Development of software products can often result in generation of Software Development Waste (SDW). For example, developers may possibly need to rework implementations that cater to ambiguous feature stories. COVID-19 pandemic has influenced the way software development teams operate worldwide. We studied the impact of pandemic enforced work-from-home situation (PWS) on generation of SDW. As a part of a multi-year study, we surveyed 615 participants and interviewed 31 from the software industry across eight countries specializing in various domains. We observed a rise in SDWs, and were able to identify new type of SDW, and other wastes that could be reclassified into existing types of wastes.

Abstract

This work introduces Talk2BEV, a large vision-language model (LVLM) interface for bird’s-eye view (BEV) maps in autonomous driving contexts. While existing perception systems for autonomous driving scenarios have largely focused on a pre-defined (closed) set of object categories and driving scenarios, Talk2BEV blends recent advances in general-purpose language and vision models with BEV-structured map representations, eliminating the need for task-specific models. This enables a single system to cater to a variety of autonomous driving tasks encompassing visual and spatial reasoning, predicting the intents of traffic actors, and decision-making based on visual cues. We extensively evaluate Talk2BEV on a large number of scene understanding tasks that rely on both the ability to interpret free-form natural language queries and in grounding these queries to the visual context embedded into the language-enhanced BEV map. To enable further research in LVLMs for autonomous driving scenarios, we develop and release Talk2BEV-Bench, a benchmark encompassing 1000 human-annotated BEV scenarios, with more than 20,000 questions and ground-truth responses from the NuScenes dataset.

Abstract

DNA methylation and gene expression are two critical aspects of the epigenetic landscape that contribute significantly to cancer pathogenesis. Analysis of aberrant genome-wide methylation patterns can provide insights into how these affect the cancer transcriptome and possible clinical implications for cancer diagnosis and treatment. The role of tumor suppressors and oncogenes is well known in tumorigenesis. Epigenetic alterations can significantly impact the expression and function of these critical genes, contributing to the initiation and progression of cancer. The present protocol chapter presents a unified workflow to explore the role of DNA methylation in gene expression regulation in breast cancer by identifying differentially expressed genes whose promoter or gene body regions are differentially methylated using various Bioconductor packages in R environment. Functional enrichment analysis of these genes can help in understanding the mechanisms leading to tumorigenesis due to epigenetic alterations.

Abstract

Quantum states least affected by interactions with environment play a pivotal role in both foundations and applications of quantum mechanics. Known as pointer states, they surprisingly lacked a systematic description. Working within the Born-Markov approximation, we combine methods of group theory and open quantum systems and derive general conditions describing pointer states. Contrary to common expectations, they are in general different from coherent states. Thus the two notions of being “closest to the classical”—one defined by the uncertainty relations and the other by the interaction with the environment—are in general different. As an example, we study spin-spin and spin-boson models with an arbitrary central spin J.

Abstract

Computing binding affinities is of great importance in drug discovery pipeline and its prediction using advanced machine learning methods still remains a major challenge as the existing datasets and models do not consider the dynamic features of protein-ligand interactions. To this end, we have developed PLAS-20k dataset, an extension of previously developed PLAS-5k, with 97,500 independent simulations on a total of 19,500 different protein-ligand complexes. Our results show good correlation with the available experimental values, performing better than docking scores. This holds true even for a subset of ligands that follows Lipinski’s rule, and for diverse clusters of complex structures, thereby highlighting the importance of PLAS-20k dataset in developing new ML models. Along with this, our dataset is also beneficial in classifying strong and weak binders compared to docking. Further, OnionNet model has been retrained on PLAS-20k dataset and is provided as a baseline for the prediction of binding affinities. We believe that large-scale MD-based datasets along with trajectories will form new synergy, paving the way for accelerating drug discovery.

Abstract

Living organisms are required to sense the environment accurately in order to ensure appropriate responses. The accuracy of estimating the environmental input is severely limited by noise stemming from inherent stochasticity of the chemical reactions involved in signaling pathways. Cells employ multiple strategies to improve the accuracy by tuning the reaction rates, for instance amplifying the response, reducing the noise etc.. However, the pathway also consumes energy through incorporating ATP in phosphorylating key signaling proteins involved in the reaction pathways. In many instances, improvements in accuracy elicit extra energetic cost. For example, higher deactivation rate suppresses the basal pathway activity effectively amplifying the dynamic range of the response which leads to improvement in accuracy. Higher deactivation rate also enhances the energy dissipation rate. Here, we employed a theoretical approach based on thermodynamics of information to explore the role of accuracy and energetic cost in the performance of a Mitogen Activated Protein Kinase signaling system. Our study shows that the accuracy-energy trade-off can explain the optimality of the reaction rates of the reaction pathways rather than accuracy alone. Our analysis elucidates the role of interplay between accuracy and energetic cost in evolutionary shaping of the parameter space of signaling pathways.

Abstract

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