Abstract

We present a new class of private information retrieval (PIR) schemes that keep the identity of the file requested private in the presence of at most t colluding servers, based on the recent framework developed for such t-PIR schemes using star products of transitive codes. These t-PIR schemes employ the class of Berman codes as the storage-retrieval code pairs. Berman codes, which are binary linear codes of length nm for any n ≥ 2 and m ≥ 1 being positive integers, were recently shown to achieve the capacity of the binary erasure channel. We provide a complete characterization of the star products of the Berman code pairs, enabling us to calculate the PIR rate of the star product-based schemes that employ these codes. The schemes we present have flexibility in the number of servers, the PIR rate, the storage rate, and the collusion parameter t, owing to numerous codes available in the class of Berman codes. Due to space restrictions, the full version of this paper containing proofs of claims, additional examples, and results,

Abstract

This paper focuses on the Multi-Object Navigation (MultiON) task, which requires a robot to localize an instance (each) of multiple object classes. This is a fundamental task for an assistive robot in a home or a factory. Existing methods for this task have viewed this as a direct extension of Object Navigation (ON), the task of localising a single instance of an object class, and are pre-sequenced, i.e., the sequence in which the object classes are to be explored is provided in advance. This is a strong limitation in practical applications characterized by dynamic changes. On the other hand, our deep reinforcement learning framework for sequence-agnostic MultiON is based on an actor-critic architecture and a reward specification. It exploits past experiences and seeks to reward progress towards individual as well as multiple target object classes. We use photo-realistic scenes from Gibson benchmark dataset in the AI Habitat 3D simulation environment to experimentally show that our method performs better than a pre-sequenced approach and a state of the art ON method extended to MultiON.

Abstract

This work proposes the utilization of a self-supervised pretrained network for developing a Language Identification (LID) system catering to low-resource Indian languages. The framework employed is Wav2vec2.0-XLSR-53, pre-trained on 53k hours of unlabeled speech data. The unsupervised training of the model enables it to learn the acoustic patterns specific to a language. Given that languages share phonetic space, multi-lingual pre-training is instrumental in learning crosslingual information and building systems that cater to low-resource languages. The results showcase a relative improvement of 33.2% over the DNN-A (DNN with attention) model and 19.04% over Dense Resnets for the Language Identification task on the IIITH-ILSC database using the proposed features.

Abstract

Sexism, a form of oppression based on one's sex, manifests itself in numerous ways and causes enormous suffering. In view of the growing number of experiences of sexism reported online, automatically classifying these recollections can aid in the battle against sexism by allowing gender studies researchers and government officials involved in policymaking to conduct more effective analyses. This paper investigates the 23-class fine- grained, multi-label classification of accounts (reports) of sexism.

Abstract

Localizing document objects such as tables, figures, and equations is a primary step for extracting information from document images. We propose a novel end-to-end trainable deep network, termed Document Object Localization Network (dolnet), for detecting various objects present in the document images. The proposed network is a multi-stage extension of Mask r-cnn with a dual backbone having deformable convolution for detecting document objects with high detection accuracy at a higher IoU threshold. We also empirically evaluate the proposed dolnet on the publicly available benchmark datasets. The proposed DOLNet achieves state-of-the-art performance for most of the bench-mark datasets under various existing experimental environments. Our solution has three important properties: (i) a single trained model dolnet that performs well across all the popular benchmark datasets, (ii) reports excellent performances across multiple, including with higher IoU thresholds, and (iii) consistently demonstrate the superior quantitative performance by following the same protocol of the recent works for each of the benchmarks.

Abstract

The diverse excitabilities of cells often produce various spiking‑bursting oscillations that are found in the neural system. We establish the ability of a fractional‑order excitable neuron model with Caputo’s fractional derivative to analyze the effects of its dynamics on the spike train features observed in our results. The significance of this generalization relies on a theoretical framework of the model in which memory and hereditary properties are considered. Employing the fractional exponent, we first provide information about the variations in electrical activities. We deal with the 2D class I and class II excitable Morris‑Lecar (M‑L) neuron models that show the alternation of spiking and bursting features including MMOs & MMBOs of an uncoupled fractional‑order neuron. We then extend the study with the 3D slow‑fast M‑L model in the fractional domain. The considered approach establishes a way to describe various characteristics similarities between fractional‑order and classical integer‑order dynamics. Using the stability and bifurcation analysis, we discuss different parameter spaces where the quiescent state emerges in uncoupled neurons. We show the characteristics consistent with the analytical results. Next, the Erdös‑Rényi network of desynchronized mixed neurons (oscillatory and excitable) is constructed that is coupled through membrane voltage. It can generate complex firing activities where quiescent neurons start to fire. Furthermore, we have shown that increasing coupling can create cluster synchronization, and eventually it can enable the network to fire in unison. Based on cluster synchronization, we develop a reduced‑order model which can capture the activities of the entire network. Our results reveal that the effect of fractional‑order depends on the synaptic connectivity and the memory trace of the system. Additionally, the dynamics captures spike frequency adaptation and spike latency that occur over multiple timescales as the effects of fractional derivative, which has been observed in neural computation.

Abstract

Stuttering is a prevalent speech disorder that affects millions of people worldwide. In this Show and Tell presentation, we demonstrate a novel platform that takes speech samples in English and Kannada to detect and analyze stuttering in patients. The user-friendly interface includes demographic details and speech samples, generating comprehensive reports for different stuttering disfluencies. The platform has four different user types, providing full read-only access for admins and full write access for super admins. Our platform provides valuable assistance for speechlanguage pathologists to evaluate speech samples. The proposed platform supports both live and recorded speech samples and presents a flexible approach to stuttering detection and analysis. Our research demonstrates the potential of technology to improve speech-language pathology for stuttering. Used F-score as a metric for evaluating the models for the stutter detection task.

Abstract

Individual differences are known to modulate brain responses to music. Recent neuroscience research suggests that each individual has unique and fundamentally stable functional brain connections irrespective of task. Our study aims to identify individual differences such as gender and musical expertise from brain responses to music. Thirty-six participants’ functional Magnetic Resonance Imaging (fMRI) responses were measured while listening to three 8-min-long musical pieces representing different musical styles. They were analyzed using various temporal and spectral functional connectivity (FC) measures. These FC measures were compared to identify the one that captured the most variation associated with gender and musical expertise. Subsequently, the measures were used to classify distinct population groupings using a binary Support Vector Machine (SVM). Our results revealed that Coherence, a spectral-domain FC measure, captured the maximum variation for musical stimuli. However, in classifying individuals based on gender and musical expertise, a composite measure outperformed any single measure and had abovechance accuracy. This suggests that each FC measure captures different aspects of the relationship between brain regions and is influenced by the level of analysis (group or individual) and the task, such as similarity analysis or classification. Further ramifications of the findings are discussed.

Abstract

The importance of Scene Text Recognition (STR) in today’s increasingly digital world cannot be overstated. Given the significance of STR, data-intensive deep learning approaches that auto-learn feature mappings have primarily driven the development of STR solutions. Several benchmark datasets and substantial work on deep learning models are available for Latin languages to meet this need. On more complex, syntactically and semantically, Indian languages spoken and read by 1.3 billion people, there is less work and datasets available. This paper aims to address the Indian space’s lack of a comprehensive dataset by proposing the largest and most comprehensive real dataset - IndicSTR12 - and benchmarking STR performance on 12 major Indian languages (Assamese, Bengali, Odia, Marathi, Hindi, Kannada, Urdu, Telugu, Malayalam, Tamil, Gujarati, and Punjabi). A few works have addressed the same …

Abstract

The prevalence of oral potentially malignant disorders (OPMDs) and oral cancer is surging in low- and middle-income countries. A lack of resources for population screening in remote locations delays the detection of these lesions in the early stages and contributes to higher mortality and a poor quality of life. Digital imaging and artificial intelligence (AI) are promising tools for cancer screening. This study aimed to evaluate the utility of AI-based techniques for detecting OPMDs in the Indian population using photographic images of oral cavities captured using a smartphone. A dataset comprising 1120 suspicious and 1058 non-suspicious oral cavity photographic images taken by trained front-line healthcare workers (FHWs) was used for evaluating the performance of different deep learning models based on convolution (DenseNets) and Transformer (Swin) architectures. The best- performing model was also tested on an additional independent test set comprising 440 photographic images taken by untrained FHWs (set I). DenseNet201 and Swin Transformer (base) models show high classification performance with an F1-score of 0.84 (CI 0.79–0.89) and 0.83 (CI 0.78–0.88) on the internal test set, respectively. However, the performance of models decreases on test set I, which has considerable variation in the image quality, with the best F1-score of 0.73 (CI 0.67–0.78) obtained using DenseNet201. The proposed AI model has the potential to identify suspicious and non-suspicious oral lesions using photographic images. This simplified image-based AI solution can assist in screening, early detection, and prompt referral for OPMDs.