Abstract

Edge server-assisted computation offloading enables vehicles to leverage server compute resources to deliver connected services, overcoming the limitations of onboard resources. Understanding the compute workloads of edge servers is crucial for effective resource management and scheduling, yet this task is challenging due to the complex interplay of factors such as vehicle mobility and computation offloading patterns. To address this, we propose an empirical analysis framework that systematically characterizes the compute workloads of edge servers. We begin by formalizing the relationships among three key aspects: local load (generated by vehicles), composite load (imposed on edge servers), and traffic flow (vehicle mobility patterns). Our framework then uses models of the local load and traffic flow as inputs to generate the composite loads on edge servers. Experiments were conducted by injecting between 600 and 5,000 vehicles per hour in two distinct geographical areas, New York City and Tampa. We provide a quantitative analysis demonstrating how the composite loads on edge servers vary with changes in traffic flows, geographical areas, and offloading patterns.

Abstract

Forensic sketch-to-mugshot matching is a challenging task in face recognition, primarily hindered by the scarcity of annotated forensic sketches and the modality gap between sketches and photographs. To address this, we propose CLIP4Sketch, a novel approach that leverages diffusion models to generate a large and diverse set of sketch images, which helps in enhancing the performance of face recognition systems in sketch-to-mugshot matching. Our method utilizes Denoising Diffusion Probabilistic Models (DDPMs) to generate sketches with explicit control over identity and style. We combine CLIP and Adaface embeddings of a reference mugshot, along with textual descriptions of style, as the conditions to the diffusion model. We demonstrate the efficacy of our approach by generating a comprehensive dataset of sketches corresponding to mugshots and training a face recognition model on our synthetic data. Our results show significant improvements in sketch-to-mugshot matching accuracy over training on an existing, limited amount of real face sketch data, validating the potential of diffusion models in enhancing the performance of face recognition systems across modalities. We also compare our dataset with datasets generated using GAN-based methods to show its superiority.

Abstract

Edge computing allows for reduced latency and operational costs compared to centralized cloud systems. In this context, serverless functions are emerging as a lightweight and effective paradigm for managing computational tasks on edge infrastructures. However, the placement of such functions in constrained edge nodes remains an open challenge. On one hand, it is key to minimize network delays and optimize resource consumption; on the other hand, decisions must be made in a timely manner due to the highly dynamic nature of edge environments. In this paper, we propose POSEIDON, a solution based on Deep Reinforcement Learning for the efficient placement of functions at the edge. POSEIDON leverages Proximal Policy Optimization (PPO) to place functions across a distributed network of nodes under highly dynamic workloads. A comprehensive empirical evaluation demonstrates that POSEIDON significantly reduces execution time, network delay, and resource consumption compared to state-of-the-art methods.

Abstract

In the circuit model of quantum computing, amplitude amplification techniques can be used to find solutions to NP-hard problems defined on n-bits in time poly(n)2^{n/2}. In this work, we investigate whether such general statements can be made for adiabatic quantum optimization, as provable results regarding its performance are mostly unknown. Although a lower bound of Ω(2^{n/2}) has existed in such a setting for over a decade, a purely adiabatic algorithm with this running time has been absent. We show that adiabatic quantum optimization using an unstructured search approach results in a running time that matches this lower bound (up to a polylogarithmic factor) for a broad class of classical local spin Hamiltonians. For this, it is necessary to bound the spectral gap throughout the adiabatic evolution and compute beforehand the position of the avoided crossing with sufficient precision so as to adapt the adiabatic schedule accordingly. However, we show that the position of the avoided crossing is approximately given by a quantity that depends on the degeneracies and inverse gaps of the problem Hamiltonian and is NP-hard to compute even within a low additive precision. Furthermore, computing it exactly (or nearly exactly) is #P-hard. Our work indicates a possible limitation of adiabatic quantum optimization algorithms, leaving open the question of whether provable Grover-like speed-ups can be obtained for any optimization problem using this approach.

Abstract

In the legal domain, research efforts are being made to enhance precedent retrieval by exploiting features based on meta-data, catchphrases, citations, sentences, paragraphs, etc. It is reported in the legal domain that the text surrounding a citation provides information to identify the referenced judgment and supplies additional information about the referenced judgment and its connection to the formulated argument. In this paper, we have exploited the resourcefulness of the text surrounding the citation to improve the document representation of the referenced judgment. Experiments conducted on Indian court judgments show that the proposed Preceding citation Anchor Text (PAT)-based approach captures certain nuances that are not captured by the text present in the referenced judgment, indicating that there is a scope to exploit PAT to improve the performance of precedent retrieval systems.

Abstract

Smart home technology integrates various devices and systems that upgrade the convenience, security, and efficiency of a home. Due to these benefits, this technology is gaining global attention. In this paper, we have explored the perspectives of the residents of Mumbai on smart home technology adoption. A survey of 425 respondents was conducted, over a period of 3 months, covering demographics, awareness, preferences, concerns, and willingness to adopt. The key factors influencing their willingness to adopt such technology have been identified. The findings reveal insights into the perceived benefits, barriers, and demographic influences on smart home technology adoption in Mumbai. Most respondents show a high level of willing-ness to install smart home technology. More than 40% of respondents con-sider security their top priority when thinking about smart home features. Data privacy and security are the top concerns among the respondents, as they are worried about their personal data management and device vulnerabilities. The results are valuable for policymakers, advertisers, and developers working to promote smart home technology in the urban areas of India.

Abstract

The limited generalization of coreference resolution (CR) models has been a major bottleneck in the task’s broad application. Prior work has identified annotation differences, especially for mention detection, as one of the main reasons for the generalization gap and proposed using additional annotated target domain data. Rather than relying on this additional annotation, we propose an alternative referential task, Major Entity Identification (MEI), where we: (a) assume the target entities to be specified in the input, and (b) limit the task to only the frequent entities. Through extensive experiments, we demonstrate that MEI models generalize well across domains on multiple datasets with supervised models and LLM-based few-shot prompting. Additionally, MEI fits the classification framework, which enables the use of robust and intuitive classification-based metrics. Finally, MEI is also of practical use as it allows a user to search for all mentions of a particular entity or a group of entities of interest.

Abstract

This paper introduces an ultra-low-power RC oscillator that utilizes a resistance amplification technique. The resistance amplifier achieves the effect of a large resistance by amplifying the I-V characteristics of a small resistance, resulting in significant area savings. To improve precision in oscillation frequency, a continuous time auto-zeroing technique is incorporated to dynamically calibrate the offset voltage of an integrated amplifier. Fabricated using a 180−nm CMOS process, the designed oscillator produces a 100−Hz clock signal while consuming only 2 nW power under compact silicon area (0.12mm2). Additionally, it achieves a competitive temperature coefficient (TC) of 280ppm/∘C and line sensitivity (LS) of 1.1 % / V without the need for any calibration.

Abstract

Moving Target Defense (MTD) has emerged as a proactive and dynamic framework to counteract evolving cyber threats. Traditional MTD approaches often rely on assumptions about the attackers knowledge and behavior. However, real-world scenarios are inherently more complex, with adaptive attackers and limited prior knowledge of their payoffs and intentions. This paper introduces a novel approach to MTD using a Markov Decision Process (MDP) model that does not rely on predefined attacker payoffs. Our framework integrates the attackers real-time responses into the defenders MDP using a dynamic Bayesian Network. By employing a factored MDP model, we provide a comprehensive and realistic system representation. We also incorporate incremental updates to an attack response predictor as new data emerges. This ensures an adaptive and robust defense mechanism. Additionally, we consider the costs of switching configurations in MTD, integrating them into the reward structure to balance execution and defense costs. We first highlight the challenges of the problem through a theoretical negative result on regret. However, empirical evaluations demonstrate the frameworks effectiveness in scenarios marked by high uncertainty and dynamically changing attack landscapes.

Abstract

Community detection is the problem of identifying natural divisions in networks. Efficient parallel algorithms for identifying such divisions is critical in a number of applications, where the size of datasets have reached significant scales. This paper presents one of the most efficient multicore implementations of the Louvain algorithm, a high quality community detection method. On a server equipped with dual 16-core Intel Xeon Gold 6226R processors, our Louvain, which we term as GVE-Louvain, outperforms Vite, Grappolo, NetworKit Louvain, and cuGraph Louvain (running on NVIDIA A100 GPU) by 50x, 22x, 20x, and 5.8x faster respectively - achieving a processing rate of 560M edges/s on a 3.8B edge graph. In addition, GVE-Louvain improves performance at an average rate of 1.6x for every doubling of threads.