Abstract

: Gathering requirements for developing virtual reality (VR) software products is a labor-intensive process. It requires detailed elicitation of scene flow, articles in the scene, action responses, custom behaviors, and timeline of events. The slightest change in requirements will escalate the design and development costs. While most VR practitioners depend on conventional software engineering (SE) requirement-gathering techniques, there is a need for novel methods to streamline VR software development. With severe software platform fragmentation and hardware volatility, VR practitioners need assistance specifying non-volatile requirements for a minimum viable VR software product.

Abstract

Artificial intelligence is the new technological buzzword. Everything from camera apps on your mobile phone to medical diagnosis algorithms to expert systems are now claiming to be ‘AI’, and many more facets of our lives are being colonized by the application of AI/ML systems (henceforth, ‘AI’). But what does this entail to designers, users and to society at large? Most of the philosophical discourse in this context has focused on the analysis and clarification of the epistemological claims of intelligence within AI and on the moral implications of AI. Philosophical critiques of the plausibility of artificial intelligence do not have much to say about the real-world repercussions of introducing AI systems into every possible domain in the name of automation and efficiency; similarly, most of the moral misgivings about AI have to do with conceiving them as autonomous agents beyond the control of human actors. These discussions have clarified the debate surrounding AI to a great extent; however, certain crucial questions remain outside the ambit of these debates. Arguments in support of AI often take advantage of this void by emphasizing that AI systems are no different than previously existing ‘unintelligent’ technologies, thereby implying that the economic, existential, and ethical threats posed by these systems are either similar in kind as those posed by any other technology or grossly misplaced and exaggerated. In this chapter, we shall think through this assumption by investigating into the ontology of AI systems vis-à-vis ordinary (non-AI) technical artefacts to see wherein lies the distinction between the two. I shall examine how contemporary ontologies of technical artefacts (e.g., intentionalist and non-intentionalist theories of function) apply to AI. Hence, clarifying the ontology of AI is crucial to understand their normative and moral significance and the implications therefrom.

Abstract

Machine Learning models increasingly face data integrity challenges due to the use of large-scale training datasets drawn from the Internet. We study what model developers can do if they detect that some data was manipulated or incorrect. Such manipulated data can cause adverse effects including vulnerability to backdoored samples, systemic biases, and reduced accuracy on certain input domains. Realistically, all manipulated training samples cannot be identified, and only a small, representative subset of the affected data can be flagged. We formalize ``Corrective Machine Unlearning'' as the problem of mitigating the impact of data affected by unknown manipulations on a trained model, only having identified a subset of the corrupted data. We demonstrate that the problem of corrective unlearning has significantly different requirements from traditional privacy-oriented unlearning. We find most existing unlearning methods, including retraining-from-scratch without the deletion set, require most of the manipulated data to be identified for effective corrective unlearning. However, one approach, Selective Synaptic Dampening, achieves limited success, unlearning adverse effects with just a small portion of the manipulated samples in our setting, which shows encouraging signs for future progress. We hope our work spurs research towards developing better methods for corrective unlearning and offers practitioners a new strategy to handle data integrity challenges arising from web-scale training.

Abstract

Stress has a significant mental health problem of the 21st century. The number of people suffering from stress is increasing rapidly. Thus, easy-to-use, inexpensive, and accurate biomarkers are needed to detect stress during its inception. Early detection of stress-related diseases allows people to access health-care services and leads to the development of new therapies. Thus, for the early detection of stress, a biomarker would be beneficial. We aim to find if there are significant differences between stressed and non-stressed groups of participants and which brain region gets impacted during stress. We conducted experiments to acquire EEG signals to identify the most significant brain region that gets affected due to stress. This research investigates if Higuchi Fractal Dimension (HFD) extracted from EEG signals can act as potential stress biomarkers. A gamified mobile application, known as Color Word and Memory Test (CWMT), was developed, inspired by the well-known Stroop Test, to elicit mental stress at two different difficulty levels. A MUSE headband with four EEG sensors (TP9, AF7, AF8, TP10) was used to collect the EEG signals at the two difficulty levels. The Higuchi Fractal Dimension (HFD) was extracted from EEG signals acquired from 32 participants while they were exposed to stress stimuli (using the proposed CWMT application) and we then performed three experimental analyses, i.e., Analysis I, II and III. Analysis I was performed on all the 32 participants performing low and high difficulty tasks on CWMT application. Analysis II was performed on sub-groups of participants. These sub-groups were made based on score obtained by participants while using CWMT application. Analysis II aimed to find the most significantly impacted brain region during stress. For Analysis III, we used the same sub-groups as Analysis II and it aimed to identify any differences between the left and right hemispheres during stress. We conducted the statistical analysis, and p-values were calculated between the two groups (non-stressed and stressed) to detect EEG channels and brain frequency significantly associated with stress. We performed three experimental analyses (Analysis I and II are intra-hemisphere, and analysis III is inter-hemisphere). In Analysis I, we inferred that beta and alpha frequencies from the AF8 region of the brain are affected during stress. In Analysis II, we inferred that beta waves from the AF8 region are a characteristic indicator of stress. In Analysis III, we identified significant differences between the left and right parts of the brain during stress. We found a significant difference (p<0.05) between HFD value in stress and non-stressed groups in the AF8 region. Our results indicated that stressed patients have a significantly higher value of HFD in the frontal areas.

Abstract

A number of anthropogenic and natural disasters that have impacted the fragile ecosystems of the Baratang group of islands in the Andaman and Nicobar archipelago, resulting in the degradation and depletion of their natural environment and biodiversity, have been identified. It is found that, in the early 20th century, the resources of the archipelago, especially Baratang Island, were exploited for infrastructure and plantations, resulting in a decrease in forest cover and an increase in anthropogenically transformed landscapes. However, the most recent available information on land use (LU) and land cover is only from 2013. This study aims to understand the dynamics of LU and land cover change in the Baratang Island group using multitemporal satellite data for 1976, 1993, 2004, 2014, and 2023. Using visual interpretation techniques, spatial and temporal change maps were created to represent the different LU and land cover features in the study area. An analysis of these maps revealed an interesting trend: there is an increase in forest area with a simultaneous decrease in settlements and agricultural land, which contradicts the findings of previous studies. This work has identified several factors contributing to this trend, namely natural regeneration, a reduction in logging and human activities, government policies and laws, and the implementation of conservation measures by the forest department. However, the evidence on mangrove decline attributed to both human activities and natural disasters is consistent with the results of other studies. This study emphasizes the importance of regularly monitoring the island’s landscape dynamics to understand changes in LU and land cover and to take appropriate conservation measures.

Abstract

Urbanization, driven by technological advancements, has brought about improved connectivity and efficiency, especially with the rise of Internet of Things (IoT) devices. Smart cities use these innovations to manage resources better and enhance resident’s quality of life. However, implementing smart city initiatives comes with challenges like monitoring, maintaining, and testing urban infrastructure. Digital Twin (DT) entails the connection of physical facilities or devices with their digital counterparts, facilitating real-time monitoring, manipulation, and predictive analysis of their behavior. This concept offers a virtual replica of assets, processes, and systems, enabling insights into their real-time performance and predictive behaviors. By simulating real-world scenarios, DT aids in planning maintenance activities and conducting comprehensive testing, thereby enhancing the resilience and efficiency of smart city systems. Particularly in the context of managing water networks, DT technology holds significant promise. Visualization capabilities provide intuitive insights into the system’s behavior, facilitating informed decision-making. This visualization, coupled with actuation capabilities, enables control actions based on predictive analytics and optimization algorithms, allowing for proactive management of water resources and infrastructure. To this end, in this paper, we present the architecture of WaterTwin, a DT developed for water quality networks in smart city systems. We demonstrate our approach through the use of a water quality network at the smart city living lab, IIIT Hyderabad campus.

Abstract

Integration challenges of perovskites-based ferroelectrics increased the focus on usage of non-perovskites for the design of Negative Capacitance (NC) FETs. To meet the sub-60mV/decade subthreshold swing without compromising the on-state performance in advanced nodes, integration of Ferroelectric layer in the gate stack of a lateral Heterojunction Tunnel FET(HTFET) is an optimum choice. Ferroelectric materials when stabilized, exhibit hysteresis-free negative capacitance characteristics that improve the subthreshold swing and thereby the on-state performance TFET. In this work we explored the CMOS compatible non-perovskites based ferroelectrics, such as doped Hafnium oxide (HfO2), and Hafnium Zirconium oxide (HZO2), for designing Staggered gap Heterojunction Ferroelectric TFETs(He-FeTFET), for low power logic applications. Using SILVACO TCAD, a commercial Technology CAD software, an exhaustive simulation study is made tuning the device parameters, to achieve a hysteresis-free steeper switching with sub-60 mV/decade subthreshold swing without compromising the on-state performance and to achieve very low leakage, of the order of sub-fA. The material system, dopings of the device and the thickness of the ferroelectric layer, necessary to achieve a point subthreshold-swing of lesser than 10 mV/decade and large on-to-off current ratio (Ion/Ioff ) ≈ 1010 for the He-FeTFET are presented and performance is compared with a plain HTFET.

Abstract

The language acquisition skills are predominant during childhood. Due to the lack of resources, a gap often arises in the language communication abilities of students. To bridge this gap, it is necessary to consider automatic methods for improving the children language skills. Our IIITH Ucchar e-Sudharak web based tool, deployed in a real environment, helps schoolgoing children to practice English reading skills with a teacher in the loop. The choice of English as the target language is strategic, given its status as a global lingua franca. Within the tool, children are provided with class and chapter-specific English sentences for practice tailored by their teachers. During practice sessions, the tool provides real-time feedback in the form of sentence and word-level scores, which are computed by comparing against teacher (expert) audio recordings. These scores offer insights into word-level pronunciation correctness and sentence-level quality by measuring fluency. Moreover, the tool provides a key feature where teachers can assign specific sentences to individual students and assess their performance. This functionality empowers teachers to personalize their instruction, catering to the specific needs of each student. As far as we know, this is a one-of-a-kind tool specifically designed to meet the needs of school-going children.

Abstract

In this paper, we tackle a new and challenging problem of text-driven generation of 3D garments with high-quality textures. We propose "WordRobe", a novel framework for the generation of unposed & textured 3D garment meshes from user-friendly text prompts. We achieve this by first learning a latent representation of 3D garments using a novel coarse-to-fine training strategy and a loss for latent disentanglement, promoting better latent interpolation. Subsequently, we align the garment latent space to the CLIP embedding space in a weakly supervised manner, enabling text-driven 3D garment generation and editing. For appearance modeling, we leverage the zero-shot generation capability of ControlNet to synthesize view-consistent texture maps in a single feed-forward inference step, thereby drastically decreasing the generation time as compared to existing methods. We demonstrate superior performance over current SOTAs for learning 3D garment latent space, garment interpolation, and text-driven texture synthesis, supported by quantitative evaluation and qualitative user study. The unposed 3D garment meshes generated using WordRobe can be directly fed to standard cloth simulation & animation pipelines without any post-processing.

Abstract

Urban stormwater management involves strategies to mitigate the adverse effects of urban runoff. These strategies aim to reduce flood risk and enhance the environmental values and sustainability of urban water systems. But the effective urban stormwater management is highly dependent on the factors such as spatial variability of urban watershed characteristics as well as the scale of the area under consideration. This insight has prompted increasing utilization of physically based urban watershed models such as the Environmental Protection Agency (EPA) Stormwater Management Model (SWMM). Thus, in this study we utilized SWMM for modeling the dynamic runoff response in a stormwater zone of Hyderabad, India. The study results are validated for a particular observed 48-hour storm event occurred on 3rd and 4th of October 2015. Five years (2010-2014) of hourly rainfall data have been used to estimate the design storms with 2, 5, 10, and 25 years return periods using Gumbel distribution. The results indicate that the existing drainage system is insufficient to withstand the generated runoff by the design storm of 2-year return period and causes notable flooding. The water elevation profile generated from the initial node, representing the beginning of the drainage system, to the outfall indicates that the flooding occurred at the outfall during the peak precipitation event. Also, the hydrographs which were developed through SWMM at different links and outfalls are crucial in designing the sumps or ponds and determining pump capacities, aiding in effective stormwater management practices.