pwc-archive/papers-with-abstracts huggingface dataset:
Low-Latency Online Multiplier with Reduced Activities and Minimized Interconnect for Inner Product Arrays
Authors: Muhammad Usman, Milos Ercegovac, Jeong-A Lee
Date: 2023-04-06 00:00:00

Multiplication is indispensable and is one of the core operations in many modern applications including signal processing and neural networks. Conventional right-to-left (RL) multiplier extensively contributes to the power consumption, area utilization and critical path delay in such applications. This paper proposes a low latency multiplier based on online or left-to-right (LR) arithmetic which can increase throughput and reduce latency by digit-level pipelining. Online arithmetic enables overlapping successive operations regardless of data dependency because of the most significant digit first mode of operation. To produce most significant digit first, it uses redundant number system and we can have a carry-free addition, therefore, the delay of the arithmetic operation is independent of operand bit width. The operations are performed digit by digit serially from left to right which allows gradual increase in the slice activities making it suitable for implementation on reconfigurable devices. Serial nature of the online algorithm and gradual increment/decrement of active slices minimize the interconnects and signal activities resulting in overall reduction of area and power consumption. We present online multipliers with; both inputs in serial, and one in serial and one in parallel. Pipelined and non-pipelined designs of the proposed multipliers have been synthesized with GSCL 45nm technology on Synopsys Design Compiler. Thorough comparative analysis has been performed using widely used performance metrics. The results show that the proposed online multipliers outperform the RL multipliers.
Portfolio Optimization using Predictive Auxiliary Classifier Generative Adversarial Networks with Measuring Uncertainty
Authors: Jiwook Kim, Minhyeok Lee
Date: 2023-04-24 00:00:00
Tasks: Portfolio Optimization

In financial engineering, portfolio optimization has been of consistent interest. Portfolio optimization is a process of modulating asset distributions to maximize expected returns and minimize risks. To obtain the expected returns, deep learning models have been explored in recent years. However, due to the deterministic nature of the models, it is difficult to consider the risk of portfolios because conventional deep learning models do not know how reliable their predictions can be. To address this limitation, this paper proposes a probabilistic model, namely predictive auxiliary classifier generative adversarial networks (PredACGAN). The proposed PredACGAN utilizes the characteristic of the ACGAN framework in which the output of the generator forms a distribution. While ACGAN has not been employed for predictive models and is generally utilized for image sample generation, this paper proposes a method to use the ACGAN structure for a probabilistic and predictive model. Additionally, an algorithm to use the risk measurement obtained by PredACGAN is proposed. In the algorithm, the assets that are predicted to be at high risk are eliminated from the investment universe at the rebalancing moment. Therefore, PredACGAN considers both return and risk to optimize portfolios. The proposed algorithm and PredACGAN have been evaluated with daily close price data of S&P 500 from 1990 to 2020. Experimental scenarios are assumed to rebalance the portfolios monthly according to predictions and risk measures with PredACGAN. As a result, a portfolio using PredACGAN exhibits 9.123% yearly returns and a Sharpe ratio of 1.054, while a portfolio without considering risk measures shows 1.024% yearly returns and a Sharpe ratio of 0.236 in the same scenario. Also, the maximum drawdown of the proposed portfolio is lower than the portfolio without PredACGAN.
SplitAMC: Split Learning for Robust Automatic Modulation Classification
Authors: Jihoon Park, Seungeun Oh, Seong-Lyun Kim
Date: 2023-04-17 00:00:00
Tasks: Classification, Federated Learning

Automatic modulation classification (AMC) is a technology that identifies a modulation scheme without prior signal information and plays a vital role in various applications, including cognitive radio and link adaptation. With the development of deep learning (DL), DL-based AMC methods have emerged, while most of them focus on reducing computational complexity in a centralized structure. This centralized learning-based AMC (CentAMC) violates data privacy in the aspect of direct transmission of client-side raw data. Federated learning-based AMC (FedeAMC) can bypass this issue by exchanging model parameters, but causes large resultant latency and client-side computational load. Moreover, both CentAMC and FedeAMC are vulnerable to large-scale noise occured in the wireless channel between the client and the server. To this end, we develop a novel AMC method based on a split learning (SL) framework, coined SplitAMC, that can achieve high accuracy even in poor channel conditions, while guaranteeing data privacy and low latency. In SplitAMC, each client can benefit from data privacy leakage by exchanging smashed data and its gradient instead of raw data, and has robustness to noise with the help of high scale of smashed data. Numerical evaluations validate that SplitAMC outperforms CentAMC and FedeAMC in terms of accuracy for all SNRs as well as latency.
Training Automated Defense Strategies Using Graph-based Cyber Attack Simulations
Authors: Jakob Nyberg, Pontus Johnson
Date: 2023-04-17 00:00:00
Tasks: Intrusion Detection, reinforcement-learning, Reinforcement Learning

We implemented and evaluated an automated cyber defense agent. The agent takes security alerts as input and uses reinforcement learning to learn a policy for executing predefined defensive measures. The defender policies were trained in an environment intended to simulate a cyber attack. In the simulation, an attacking agent attempts to capture targets in the environment, while the defender attempts to protect them by enabling defenses. The environment was modeled using attack graphs based on the Meta Attack Language language. We assumed that defensive measures have downtime costs, meaning that the defender agent was penalized for using them. We also assumed that the environment was equipped with an imperfect intrusion detection system that occasionally produces erroneous alerts based on the environment state. To evaluate the setup, we trained the defensive agent with different volumes of intrusion detection system noise. We also trained agents with different attacker strategies and graph sizes. In experiments, the defensive agent using policies trained with reinforcement learning outperformed agents using heuristic policies. Experiments also demonstrated that the policies could generalize across different attacker strategies. However, the performance of the learned policies decreased as the attack graphs increased in size.
PowerGAN: A Machine Learning Approach for Power Side-Channel Attack on Compute-in-Memory Accelerators
Authors: Ziyu Wang, Yuting Wu, Yongmo Park, Sangmin Yoo, Xinxin Wang, Jason K. Eshraghian, Wei D. Lu
Date: 2023-04-13 00:00:00
Tasks: Generative Adversarial Network

Analog compute-in-memory (CIM) systems are promising for deep neural network (DNN) inference acceleration due to their energy efficiency and high throughput. However, as the use of DNNs expands, protecting user input privacy has become increasingly important. In this paper, we identify a potential security vulnerability wherein an adversary can reconstruct the user's private input data from a power side-channel attack, under proper data acquisition and pre-processing, even without knowledge of the DNN model. We further demonstrate a machine learning-based attack approach using a generative adversarial network (GAN) to enhance the data reconstruction. Our results show that the attack methodology is effective in reconstructing user inputs from analog CIM accelerator power leakage, even at large noise levels and after countermeasures are applied. Specifically, we demonstrate the efficacy of our approach on an example of U-Net inference chip for brain tumor detection, and show the original magnetic resonance imaging (MRI) medical images can be successfully reconstructed even at a noise-level of 20% standard deviation of the maximum power signal value. Our study highlights a potential security vulnerability in analog CIM accelerators and raises awareness of using GAN to breach user privacy in such systems.
Exploring the Use of ChatGPT as a Tool for Learning and Assessment in Undergraduate Computer Science Curriculum: Opportunities and Challenges
Authors: Basit Qureshi
Date: 2023-04-16 00:00:00

The application of Artificial intelligence for teaching and learning in the academic sphere is a trending subject of interest in the computing education. ChatGPT, as an AI-based tool, provides various advantages, such as heightened student involvement, cooperation, accessibility and availability. This paper addresses the prospects and obstacles associated with utilizing ChatGPT as a tool for learning and assessment in undergraduate Computer Science curriculum in particular to teaching and learning fundamental programming courses. Students having completed the course work for a Data Structures and Algorithms (a sophomore level course) participated in this study. Two groups of students were given programming challenges to solve within a short period of time. The control group (group A) had access to text books and notes of programming courses, however no Internet access was provided. Group B students were given access to ChatGPT and were encouraged to use it to help solve the programming challenges. The challenge was conducted in a computer lab environment using PC2 environment. Each team of students address the problem by writing executable code that satisfies certain number of test cases. Student teams were scored based on their performance in terms of number of successful passed testcases. Results show that students using ChatGPT had an advantage in terms of earned scores, however there were inconsistencies and inaccuracies in the submitted code consequently affecting the overall performance. After a thorough analysis, the paper's findings indicate that incorporating AI in higher education brings about various opportunities and challenges.
Positive AI: Key Challenges in Designing Artificial Intelligence for Wellbeing
Authors: Willem van der Maden, Derek Lomas, Malak Sadek, Paul Hekkert
Date: 2023-04-12 00:00:00

Artificial Intelligence (AI) is a double-edged sword: on one hand, AI promises to provide great advances that could benefit humanity, but on the other hand, AI poses substantial (even existential) risks. With advancements happening daily, many people are increasingly worried about AI's impact on their lives. To ensure AI progresses beneficially, some researchers have proposed "wellbeing" as a key objective to govern AI. This article addresses key challenges in designing AI for wellbeing. We group these challenges into issues of modeling wellbeing in context, assessing wellbeing in context, designing interventions to improve wellbeing, and maintaining AI alignment with wellbeing over time. The identification of these challenges provides a scope for efforts to help ensure that AI developments are aligned with human wellbeing.
ChatGPT: More than a Weapon of Mass Deception, Ethical challenges and responses from the Human-Centered Artificial Intelligence (HCAI) perspective
Authors: Alejo Jose G. Sison, Marco Tulio Daza, Roberto Gozalo-Brizuela, Eduardo C. Garrido-Merchán
Date: 2023-04-06 00:00:00
Tasks: Ethics

This article explores the ethical problems arising from the use of ChatGPT as a kind of generative AI and suggests responses based on the Human-Centered Artificial Intelligence (HCAI) framework. The HCAI framework is appropriate because it understands technology above all as a tool to empower, augment, and enhance human agency while referring to human wellbeing as a grand challenge, thus perfectly aligning itself with ethics, the science of human flourishing. Further, HCAI provides objectives, principles, procedures, and structures for reliable, safe, and trustworthy AI which we apply to our ChatGPT assessments. The main danger ChatGPT presents is the propensity to be used as a weapon of mass deception (WMD) and an enabler of criminal activities involving deceit. We review technical specifications to better comprehend its potentials and limitations. We then suggest both technical (watermarking, styleme, detectors, and fact-checkers) and non-technical measures (terms of use, transparency, educator considerations, HITL) to mitigate ChatGPT misuse or abuse and recommend best uses (creative writing, non-creative writing, teaching and learning). We conclude with considerations regarding the role of humans in ensuring the proper use of ChatGPT for individual and social wellbeing.
The Theory of Gene Family Histories
Authors: Marc Hellmuth, Peter F. Stadler
Date: 2023-04-24 00:00:00

Most genes are part of larger families of evolutionary related genes. The history of gene families typically involves duplications and losses of genes as well as horizontal transfers into other organisms. The reconstruction of detailed gene family histories, i.e., the precise dating of evolutionary events relative to phylogenetic tree of the underlying species has remained a challenging topic despite their importance as a basis for detailed investigations into adaptation and functional evolution of individual members of the gene family. The identification of orthologs, moreover, is a particularly important subproblem of the more general setting considered here. In the last few years, an extensive body of mathematical results has appeared that tightly links orthology, a formal notion of best matches among genes, and horizontal gene transfer. The purpose of this chapter is the broadly outline some of the key mathematical insights and to discuss their implication for practical applications. In particular, we focus on tree-free methods, i.e., methods to infer orthology or horizontal gene transfer as well as gene trees, species trees and reconciliations between them without using \emph{a priori} knowledge of the underlying trees or statistical models for the inference of phylogenetic trees. Instead, the initial step aims to extract binary relations among genes.
Rip Current Detection in Nearshore Areas through UAV Video Analysis with Almost Local-Isometric Embedding Techniques on Sphere
Authors: Anchen Sun, Kaiqi Yang
Date: 2023-04-24 00:00:00
Tasks: Optical Flow Estimation

Rip currents pose a significant danger to those who visit beaches, as they can swiftly pull swimmers away from shore. Detecting these currents currently relies on costly equipment and is challenging to implement on a larger scale. The advent of unmanned aerial vehicles (UAVs) and camera technology, however, has made monitoring near-shore regions more accessible and scalable. This paper proposes a new framework for detecting rip currents using video-based methods that leverage optical flow estimation, offshore direction calculation, earth camera projection with almost local-isometric embedding on the sphere, and temporal data fusion techniques. Through the analysis of videos from multiple beaches, including Palm Beach, Haulover, Ocean Reef Park, and South Beach, as well as YouTube footage, we demonstrate the efficacy of our approach, which aligns with human experts' annotations.
Matching markets with farsighted couples
Authors: Ata Atay, Sylvain Funck, Ana Mauleon, Vincent Vannetelbosch
Date: 2023-04-24 00:00:00

We adopt the notion of the farsighted stable set to determine which matchings are stable when agents are farsighted in matching markets with couples. We show that a singleton matching is a farsighted stable set if and only if the matching is stable. Thus, matchings that are stable with myopic agents remain stable when agents become farsighted. Examples of farsighted stable sets containing multiple non-stable matchings are provided for markets with and without stable matchings. For couples markets where the farsighted stable set does not exist, we propose the DEM farsighted stable set to predict the matchings that are stable when agents are farsighted.
Digital Twins in Wind Energy: Emerging Technologies and Industry-Informed Future Directions
Authors: Florian Stadtman, Adil Rasheed, Trond Kvamsdal, Kjetil André Johannessen, Omer San, Konstanze Kölle, John Olav Giæver Tande, Idar Barstad, Alexis Benhamou, Thomas Brathaug, Tore Christiansen, Anouk-Letizia Firle, Alexander Fjeldly, Lars Frøyd, Alexander Gleim, Alexander Høiberget, Catherine Meissner, Guttorm Nygård, Jørgen Olsen, Håvard Paulshus, Tore Rasmussen, Elling Rishoff, Francesco Scibilia, John Olav Skogås
Date: 2023-04-16 00:00:00
Tasks: Descriptive, Diagnostic

This article presents a comprehensive overview of the digital twin technology and its capability levels, with a specific focus on its applications in the wind energy industry. It consolidates the definitions of digital twin and its capability levels on a scale from 0-5; 0-standalone, 1-descriptive, 2-diagnostic, 3-predictive, 4-prescriptive, 5-autonomous. It then, from an industrial perspective, identifies the current state of the art and research needs in the wind energy sector. The article proposes approaches to the identified challenges from the perspective of research institutes and offers a set of recommendations for diverse stakeholders to facilitate the acceptance of the technology. The contribution of this article lies in its synthesis of the current state of knowledge and its identification of future research needs and challenges from an industry perspective, ultimately providing a roadmap for future research and development in the field of digital twin and its applications in the wind energy industry.
Color-based classification of EEG Signals for people with the severe locomotive disorder
Authors: Ankit Shrestha, Bikram Adhikari
Date: 2023-04-12 00:00:00
Tasks: Classification, EEG

The neurons in the brain produces electric signals and a collective firing of these electric signals gives rise to brainwaves. These brainwave signals are captured using EEG (Electroencephalogram) devices as micro voltages. These sequence of signals captured by EEG sensors have embedded features in them that can be used for classification. The signals can be used as an alternative input for people suffering from severe locomotive disorder.Classification of different colors can be mapped for many functions like directional movement. In this paper, raw EEG signals from NeuroSky Mindwave headset (a single electrode EEG sensor) have been classified with an attention based Deep Learning Network. Attention based LSTM Networks have been implemented for classification of two different colors and four different colors. An accuracy of 93.5\% was obtained for classification of two colors and an accuracy of 65.75\% was obtained for classifcation of four signals using the mentioned attention based LSTM network.
Halcyon -- A Pathology Imaging and Feature analysis and Management System
Authors: Erich Bremer, Tammy DiPrima, Joseph Balsamo, Jonas Almeida, Rajarsi Gupta, Joel Saltz
Date: 2023-04-07 00:00:00
Tasks: Management

Halcyon is a new pathology imaging analysis and feature management system based on W3C linked-data open standards and is designed to scale to support the needs for the voluminous production of features from deep-learning feature pipelines. Halcyon can support multiple users with a web-based UX with access to all user data over a standards-based web API allowing for integration with other processes and software systems. Identity management and data security is also provided.
Deep Unfolding for Fast Linear Massive MIMO Precoders under a PA Consumption Model
Authors: Thomas Feys, Xavier Mestre, Emanuele Peschiera, François Rottenberg
Date: 2023-04-25 00:00:00

Massive multiple-input multiple-output (MIMO) precoders are typically designed by minimizing the transmit power subject to a quality-of-service (QoS) constraint. However, current sustainability goals incentivize more energy-efficient solutions and thus it is of paramount importance to minimize the consumed power directly. Minimizing the consumed power of the power amplifier (PA), one of the most consuming components, gives rise to a convex, non-differentiable optimization problem, which has been solved in the past using conventional convex solvers. Additionally, this problem can be solved using a proximal gradient descent (PGD) algorithm, which suffers from slow convergence. In this work, to overcome the slow convergence, a deep unfolded version of the algorithm is proposed, which can achieve close-to-optimal solutions in only 20 iterations compared to the 3500 plus iterations needed by the PGD algorithm. Results indicate that the deep unfolding algorithm is three orders of magnitude faster than a conventional convex solver and four orders of magnitude faster than the PGD.
Unsourced Random Access With Tensor-Based and Coherent Modulations
Authors: Alberto Rech, Alexis Decurninge, Luis G. Ordóñez
Date: 2023-04-24 00:00:00
Tasks: Tensor Decomposition

Unsourced random access (URA) is a particular form of grant-free uncoordinated random access wherein the users' identities are not associated to specific waveforms at the physical layer. Tensor-based modulation (TBM) has been recently advocated as a promising technique for URA due to its ability to support a large number of active users transmitting simultaneously by exploiting tensor decomposition for user separation. We propose a novel URA scheme that builds upon TBM by splitting the transmit message into two sub-messages. This first part is modulated according to a TBM scheme, while the second is encoded using a coherent non-orthogonal multiple access (NOMA) modulation. At the receiver side, we exploit the advantages of forward error correction (FEC) coding and interference cancellation techniques. We compare the performances of the introduced scheme with state-of-the-art URA schemes under a quasi-static Rayleigh fading model, proving the energy efficiency and the fading robustness of the proposed solution.