Abstract

The ability of students to read, understand and explain code is tightly interlinked to their ability to trace. Multiple studies have shown that tracing helps students improve their comprehension of code. Analysis of the traces also provides insight into student misconceptions about program execution. We are interested in exploring pen-and-paper activities related to tracing control-flow. While the literature presents several tracing methodologies, existing pen-and-paper activities prioritise tracking the values of variables as they change throughout the program; control-flow is not adequately addressed. To fill this gap, we propose a comprehensive suite of class activities around control-flow of programs and report our experience deploying it in a first-year university programming course. In these activities, students construct six hand-drawn artefacts representing the static and dynamic aspects of a program's control-flow. We use a simplified subset of Python that includes sequential, conditional, iterative and (first-order) procedural control. We chose this subset to match the syllabus of the introduction to computer programming course of our university. We report the results of a preliminary study conducted to explore the feasibility of constructing the artefacts. Most students correctly understood the structure of the artefacts. We also report what these artefacts revealed about students' misconceptions related to control-flow in programs. Students had difficulty identifying the destination of a procedure call and its return. A significant number of their misconceptions were related to transfer of control when encountering errors.

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

Data science and artificial intelligence (DSAI) based methods can process massive amounts of data to extract useful knowledge and can be employed to build decision support systems in various domains. Like other domains, the legal systems in several countries are being digitized and there is a scope to build DSAI-based frameworks to improve the performance of legal systems. In the literature, research efforts are being made to investigate DSAI-based methods to improve justice delivery. The Indian legal system is currently experiencing a major problem with a substantial backlog of cases. This paper provides an overview of DSAI-based efforts in the legal domain related to India. We also listed potential research issues to be explored. We hope the issues will encourage further research to improve justice delivery performance in India and other countries.

Abstract

Dysarthric speech poses significant challenges in developing as- sistive technologies, primarily due to the limited availability of data. Recent advances in neural speech synthesis, especially zero-shot voice cloning, facilitate synthetic speech generation for data augmentation; however, they may introduce biases to- wards dysarthric speech. In this paper, we investigate the effec- tiveness of state-of-the-art F5-TTS in cloning dysarthric speech using TORGO dataset, focusing on intelligibility, speaker sim- ilarity, and prosody preservation. We also analyze potential biases using fairness metrics like Disparate Impact and Parity Difference to assess disparities across dysarthric severity lev- els. Results show that F5-TTS exhibits a strong bias toward speech intelligibility over speaker and prosody preservation in dysarthric speech synthesis. Insights from this study can help integrate fairness-aware dysarthric speech synthesis, fostering the advancement of more inclusive speech technologies.

Abstract

Recent progress and development of artificial intelligence and machine learning (AI/ML) techniques have enabled addressing complex biomolecular problems. AI/ML models learn the underlying distribution of data they are trained on and when exposed to new inputs, they make predictions based on patterns and relationships previously observed in the training set. Further, generative artificial intelligence (GenAI) can be used to accurately generate protein structure or sequence from specific selected properties. This review specifically focuses on the applications of AI/ML in predicting important functional properties of proteins, and the potential prospects of reverse-engineering in depicting the sequence and structure, from available protein-property information.

Abstract

Cloud-based storage service has emerged as a promising alternative to managing local storage, offering users additional features, such as storage, backup, and restoration of various resources, including software, applications, and sensitive private information, within a virtual database, while ensuring data confidentiality and security. However, the widespread use of cloud services by individuals and organisations raises concerns regarding user accessibility and data security due to increasing social threats and adversarial attacks. Searchable encryption (SE) has been introduced to address these issues, providing a secure environment for a convenient and effective way of data searching and sharing. SE models have been advanced by integrating blockchain functionalities to tackle certain existing vulnerabilities and enhance user accessibility, data privacy, and integrity. This survey work explores various research works on SE techniques leveraging cloud and blockchain functionalities, discussing and categorizing the diverse approaches based on different criteria. This study also discusses the different enhancements made to SE techniques over the years, including the underlying requirements that led to the inclusion of blockchain functionalities. Moreover, this study provides a comparative analysis of existing survey works done in this area, which highlights a lack of recent literature surveys that thoroughly explore blockchain-based public key encryption with keyword search (PEKS) schemes. Particularly, we delve into the technical aspects of PEKS by classifying, analyzing and comparing different functionalities based on various aspects. The survey concludes with a comparative analysis of existing PEKS solutions and a discussion on identified research gaps, aiming to improve future research on PEKS approaches in this emerging field.

Abstract

The problem of recovering from qubit erasures has recently gained attention as erasures occur in many physical systems such as photonic systems, trapped ions, superconducting qubits and circuit quantum electrodynamics. While several linear-time decoders for error correction are known, their errorcorrecting capability is limited to half the minimum distance of the code, whereas erasure correction allows one to go beyond this limit. As in the classical case, stopping sets pose a major challenge in designing efficient erasure decoders for quantum LDPC codes. In this paper, we show through simulation, that an attractive alternative here, is the use of quantum expander codes in conjunction with the peeling decoder that has linear complexity. We also discuss additional techniques including small-set-flip decoding, that can be applied following the peeling operation, to improve decoding performance and their associated complexity.

Abstract

This paper presents the development of a real-time object detection system using Frequency Modulated Continuous Wave(FMCW) millimeter-wave radar signals and the PYNQ-ZU FPGA Board, which is widely used in (advanced driving assistance system)ADAS and robotic applications. A hardware FPGA platform serves as a valid embedded architecture for the purpose of validating object detection and recognition applications. We used our experiment’s point cloud images to apply different machine learning models to detect these objects. Using a top-view (TV) filter to convert 3D point cloud images into 2D representations made object detection more accurate. Following the use of filtration techniques, we extracted features from the filtered 2D image using the VGG 16 model. We then assessed four machine learning models for object detection and found that the Support Vector Machine (SVM) model and Logistic Regression (LR) had better results, obtaining an accuracy of 97%. Our proposed work uses mmWave radar, Top-View Filter, VGG 16 Model, and LR to highly increase object detection accuracy over existing methods.

Abstract

Robotic inspection of transmission lines presents significant challenges due to the complexity of navigating along the wires. Existing systems often rely on either flight modes for visual inspection or articulated crawling mechanisms for contact-based inspection. However, these approaches face limitations in effectively bypassing in-line obstacles or pylons, which are common in transmission line environments. This letter presents WireFlie, a novel hybrid robotic system that integrates rolling and flight modes to overcome these challenges. The system consists of a pair of underactuated arms mounted on a drone platform, designed for secure, collision-free locking and detaching, enabling seamless transitions between locomotion modes. WireFlie supports both single-arm and dual-arm rolling, allowing it to bypass in-line obstacles such as Stockbridge dampers, dual spacers, and sleeves, and to overcome larger obstacles like pylons using flight. Additionally, we propose a high-level controller for autonomous locking, detaching, and obstacle avoidance. Experiments are conducted on a custom-made setup that closely resembles a transmission wire. We evaluate both the design and control aspects of our system, with results including kinematic analysis, wire detection, autonomous locking and the corresponding trajectory, and obstacle detection and avoidance strategies. This research contributes to the field of robotic infrastructure inspection by merging aerial and wire-based locomotion, providing efficient and autonomous monitoring of power lines.

Abstract

We introduce Berman-intersection-dual Berman (BiD) codes. These are abelian codes of length $3^m$ that can be constructed using Kronecker products of a $3 times 3$ kernel matrix. BiD codes offer minimum distance close to that of Reed-Muller (RM) codes at practical blocklengths, and larger distance than RM codes asymptotically in the blocklength. Simulations of BiD codes of length $3^5=243$ in the erasure and Gaussian channels show that their block error rates under maximum-likelihood decoding are similar to, and sometimes better, than RM, RM-Polar, and CRC-aided Polar codes.