Abstract

This paper presents a trim-free, low-power, and high-precision voltage/current reference for IoT and biomedical applications. The voltage reference is generated by the proper addition of CTAT and PTAT voltages, whereas, the current reference is generated using a MOSFET in the active region, which acts like a resistor and helps in lowering power consumption without increasing the form factor. Implemented in the 90nm CMOS process, the proposed voltage/current reference outputs a voltage and current of 820mV and 3.23nA, respectively. Without trimming, the process variation of the proposed voltage/current reference is 1.34%(σ/μ) / 1.75%(σ/μ) and the temperature coefficient of the voltage/current reference is 62ppm/oC / 332ppm/oC over the temperature range of -40oC to 100oC. The line sensitivity of the voltage/current reference is 0.296%/V / 0.414%/V for a wide supply range of 1V - 3.5V. The area occupied by the total circuit is 0.0112mm2, while the total power consumption of the design is 37nW at the typical corner of 27oC and 1V supply.

Abstract

The Industrial Internet of Things (IIoT) is able to connect machines, analytics and people with IoT smart devices, gateway nodes and edge devices to create powerful intuitivenesses to drive smarter, faster and effective business agreements. IIoT having interconnected machines along with devices can monitor, gather, exchange, and analyze information. Since the communication among the entities in IIoT environment takes place insecurely (for instance, wireless communications and Internet), an intruder can easily tamper with the data. Moreover, physical theft of IoT smart devices provides an intruder to mount impersonation and other attacks. To handle such critical issues, in this work, we design a new private blockchain-envisioned access control scheme for Pervasive Edge Computing (PEC) in IIoT environment, called PBACS-PECIIoT. We consider the private blockchain consisting of the transactions and registration credentials of the entities related to IIoT, because the information is strictly confidential and private. The security of PBACS-PECIIoT is significantly improved due to usage of blockchain as immutability, transparency and decentralization along with protection of various potential attacks. A meticulous comparative analysis exhibits that PBACS-PECIIoT achieves greater security and more functionality features, and requires low costs for communication and computational as compared to other pertinent schemes.

Abstract

Second language learners often make grammatical mistakes, which can impact the performance of Spoken Language Understanding (SLU) systems. SLU systems consist of two key components: Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU). This work focuses on the effects of grammatical errors which were synthetically generated by manipulating Parts of Speech (POS) tokens on the NLU module. Our analysis comprises three main aspects. Firstly, we assess the impact of grammatical errors on the overall performance of NLU systems, specifically in the domains of Intent Detection and Slot Filling. Secondly, we investigate NLU performance concerning POS tags. Lastly, we utilize an Attention-based NLU model to evaluate the significance of different POS. This study evaluates NLU models on ATIS and SNIPS datasets for Intent Detection and Slot Filling tasks with introduced grammatical errors, leading to a significant performance drop as large as 4.7% for Intent Detection and 5.1% for Slot Filling. However, training on datasets with synthetic errors helps mitigate this drop, and the models are shown to be sensitive to increasing levels of corrupted tokens. Additionally, attention analysis reveals Proper Nouns’ higher weights in determining intent, while specific POS corruption, particularly Auxiliary Verbs and Nouns, is more likely to cause intent misclassification.

Abstract

Machine Learning models face increased concerns regarding the storage of personal user data and adverse impacts of corrupted data like backdoors or systematic bias. Machine Unlearning can address these by allowing post-hoc deletion of affected training data from a learned model. Achieving this task exactly is computationally expensive; consequently, recent works have proposed inexact unlearning algorithms to solve this approximately as well as evaluation methods to test the effectiveness of these algorithms. In this work, we first outline some necessary criteria for evaluation methods and show no existing evaluation satisfies them all. Then, we design a stronger black-box evaluation method called the Interclass Confusion (IC) test which adversarially manipulates data during training to detect the insufficiency of unlearning procedures. We also propose two analytically motivated baseline methods~(EU-k and CF-k) which outperform several popular inexact unlearning methods. Overall, we demonstrate how adversarial evaluation strategies can help in analyzing various unlearning phenomena which can guide the development of stronger unlearning algorithms.

Abstract

Researchers have extensively studied the field of vision and language, discovering that both visual and textual content is crucial for understanding scenes effectively. Particularly, comprehending text in videos holds great significance, requiring both scene text understanding and temporal reasoning. This paper focuses on exploring two recently introduced datasets, NewsVideoQA and M4-ViteVQA, which aim to address video question answering based on textual content. The NewsVideoQA dataset contains question-answer pairs related to the text in news videos, while M4-ViteVQA comprises question-answer pairs from diverse categories like vlogging, traveling, and shopping. We provide an analysis of the formulation of these datasets on various levels, exploring the degree of visual understanding and multi-frame comprehension required for answering the questions. Additionally, the study includes experimentation with BERT-QA, a text-only model, which demonstrates comparable performance to the original methods on both datasets, indicating the shortcomings in the formulation of these datasets. Furthermore, we also look into the domain adaptation aspect by examining the effectiveness of training on M4-ViteVQA and evaluating on NewsVideoQA and vice-versa, thereby shedding light on the challenges and potential benefits of out-of-domain training.

Abstract

This paper presents a star-of-star topology for internet-of-things (IoT) networks using mega low-Earth-orbit constellations. The proposed topology enables IoT users to broadcast their sensed data to multiple satellites simultaneously over a shared channel, which is then relayed to the ground station (GS) using amplify-and-forward relaying. The GS coherently combines the signals from multiple satellites using maximal ratio combining. To analyze the performance of the proposed topology in the presence of interference, a comprehensive outage probabil- ity (OP) analysis is performed, assuming imperfect channel state information at the GS. The paper employs stochastic geometry to model the random locations of satellites, making the analysis gen- eral and independent of any specific constellation. Furthermore, the paper examines successive interference cancellation (SIC) and capture model (CM)-based decoding schemes at the GS to mitigate interference. The average OP for the CM-based scheme and the OP of the best user for the SIC scheme are derived analytically. The paper also presents simplified expressions for the OP under a high signal-to-noise ratio (SNR) assumption, which are utilized to optimize the system parameters for achieving a tar- get OP. The simulation results are consistent with the analytical expressions and provide insights into the impact of various system parameters, such as mask angle, altitude, number of satellites, and decoding order. The findings of this study demonstrate that the proposed topology can effectively leverage the benefits of multiple satellites to achieve the desired OP and enable burst transmissions without coordination among IoT users, making it an attractive choice for satellite-based IoT networks. Index Terms—Amplify-and-forward, LEO satellites, outage probability, satellite-based IoT, stochastic geometry

Abstract

Large-scale deployment of fully autonomous vehicles re- quires a very high degree of robustness to unstructured traf- fic, weather conditions, and should prevent unsafe mispre- dictions. While there are several datasets and benchmarks focusing on segmentation for drive scenes, they are not specifically focused on safety and robustness issues. We introduce the IDD-AW dataset, which provides 5000 pairs of high-quality images with pixel-level annotations, cap- tured under rain, fog, low light, and snow in unstructured driving conditions. As compared to other adverse weather datasets, we provide i.) more annotated images, ii.) paired Near-Infrared (NIR) image for each frame, iii.) larger label set with a 4-level label hierarchy to capture unstructured traffic conditions. We benchmark state-of-the-art models for semantic segmentation in IDD-AW. We also propose a new metric called “Safe mean Intersection over Union (Safe mIoU)” for hierarchical datasets which penalizes danger- ous mispredictions that are not captured in the traditional definition of mean Intersection over Union (mIoU). The re- sults show that IDD-AW is one of the most challenging datasets to date for these tasks. The dataset and code will be available here: http://iddaw.github.io

Abstract

Unconstrained Asian roads often involve poor infrastructure, affecting overall road safety. Missing traffic signs are a regular part of such roads. Missing or non-existing object detection has been studied for locating missing curbs and estimating reasonable regions for pedestrians on road scene images. Such methods involve analyzing task-specific single object cues. In this paper, we present the first and most challenging video dataset for missing objects, with multiple types of traffic signs for which the cues are visible without the signs in the scenes. We refer to it as the Missing Traffic Signs Video Dataset (MTSVD). MTSVD is challenging compared to the previous works in two aspects i) The traffic signs are generally not present in the vicinity of their cues, ii) The traffic signs’ cues are diverse and unique. Also, MTSVD is the first publicly available missing object dataset. To train the models for identifying missing signs, we complement our dataset with 10K traffic sign tracks, with 40% of the traffic signs having cues visible in the scenes. For identifying missing signs, we propose the Cue-driven Contextual Attention units (CueCAn), which we incorporate in our model’s encoder. We first train the encoder to classify the presence of traffic sign cues and then train the entire segmentation model end-to-end to localize missing traffic signs. Quantitative and qualitative analysis shows that CueCAn significantly improves the performance of base models.

Abstract

Counter-intuitive to classical notions, quantum conditional entropy can be negative, playing a pivotal role in information-processing tasks. This article delves deeply into quantum channels, em- phasizing negative conditional entropy breaking channels (NCEB) and introducing negative condi- tional entropy annihilating channels (NCEA). We characterize these channels from both topological and information-theoretic perspectives, examining their properties when combined serially and in parallel. Our exploration extends to complimentary channels associated with NCEB, leading to the introduction of information-leaking channels. Utilizing the parameters of the standard depolarizing channel, we provide tangible examples and further characterization. We demonstrate the relation- ship of NCEB and NCEA with newly introduced channels like coherent information breaking (CIB) and mutual information breaking (MIB), along with standard channels like zero capacity channels. Preservation of quantum resources is an integral constituent of quantum information theory. Rec- ognizing this, we lay prescriptions to detect channels that do not break the negativity of conditional entropy, ensuring the conservation of this quantum resource

Abstract

The global impact of coal mining and associated activities on land use/land cover (LULC) changes is significant. This study used Landsat satellite images from 1990 to 2020 to assess LULC changes and their impact on land surface temperature (LST) in four districts of Chhattisgarh state, India. Over three decades, Korba and Raigarh districts saw expansion in coal mines, built-up areas, and water bodies, while forest areas diminished by 711.3 km2 and 212.87 km2, respectively. Koriya district saw coal mine expansion of 5.68 km2 (1990–2010), later declining to 2.85 km2, alongside growth in built-up regions, and forest cover reduction by 251.31 km2 in 2020. Surguja district experienced coal mine and built-up area expansion (1990–2020), with initial forest decline of 160.21 km2 in 2010 followed by recovery in 2020. LST was determined using the Mono-window algorithm. LST increased during winter and summer, with the most significant rise in summer. Vegetation-rich regions had lower LST, while coal mines had the highest temperatures. There was a positive relationship between mining land patch size and patch temperatures. This study underscores the need for vegetation restoration in mining areas, particularly abandoned sites, and sustainable mining practices to mitigate coal mining's warming effects.