pwc-archive/papers-with-abstracts huggingface dataset:
Predicting Corporate Risk by Jointly Modeling Company Networks and Dialogues in Earnings Conference Calls
Authors: Yunxin Sang, Yang Bao
Date: 2022-05-25 00:00:00
Tasks: Graph Attention, Graph Neural Network, Management

Earnings conference calls are significant information events for volatility forecasting, which is essential for financial risk management and asset pricing. Although some recent volatility forecasting models have utilized the textual content of conference calls, the dialogue structures of conference calls and company relationships are almost ignored in extant literature. To bridge this gap, we propose a new model called Temporal Virtual Graph Neural Network (TVGNN) for volatility forecasting by jointly modeling conference call dialogues and company networks. Our model differs from existing models in several important ways. First, we propose to exploit more dialogue structures by encoding position, utterance, speaker role, and Q\&A segments. Second, we propose to encode the market states for volatility forecasting by extending the Gated Recurrent Units (GRU). Third, we propose a new method for constructing temporal company networks in which the messages can only flow from temporally preceding to successive nodes, and extend the Graph Attention Networks (GAT) for modeling company relationships. We collect conference call transcripts of S\&P500 companies from 2008 to 2019, and construct a dataset of conference call dialogues with additional information on dialogue structures and company networks. Empirical results on our dataset demonstrate the superiority of our model over competitive baselines for volatility forecasting. We also conduct supplementary analyses to examine the effectiveness of our model's key components and interpretability.
Towards Target High-Utility Itemsets
Authors: Jinbao Miao, Wensheng Gan, Shicheng Wan, Yongdong Wu, Philippe Fournier-Viger
Date: 2022-06-09 00:00:00
Tasks: Vocal Bursts Intensity Prediction

For applied intelligence, utility-driven pattern discovery algorithms can identify insightful and useful patterns in databases. However, in these techniques for pattern discovery, the number of patterns can be huge, and the user is often only interested in a few of those patterns. Hence, targeted high-utility itemset mining has emerged as a key research topic, where the aim is to find a subset of patterns that meet a targeted pattern constraint instead of all patterns. This is a challenging task because efficiently finding tailored patterns in a very large search space requires a targeted mining algorithm. A first algorithm called TargetUM has been proposed, which adopts an approach similar to post-processing using a tree structure, but the running time and memory consumption are unsatisfactory in many situations. In this paper, we address this issue by proposing a novel list-based algorithm with pattern matching mechanism, named THUIM (Targeted High-Utility Itemset Mining), which can quickly match high-utility itemsets during the mining process to select the targeted patterns. Extensive experiments were conducted on different datasets to compare the performance of the proposed algorithm with state-of-the-art algorithms. Results show that THUIM performs very well in terms of runtime and memory consumption, and has good scalability compared to TargetUM.
Multi-user Co-inference with Batch Processing Capable Edge Server
Authors: Wenqi Shi, Sheng Zhou, Zhisheng Niu, Miao Jiang, Lu Geng
Date: 2022-06-03 00:00:00
Tasks: GPU, Scheduling

Graphics processing units (GPUs) can improve deep neural network inference throughput via batch processing, where multiple tasks are concurrently processed. We focus on novel scenarios that the energy-constrained mobile devices offload inference tasks to an edge server with GPU. The inference task is partitioned into sub-tasks for a finer granularity of offloading and scheduling, and the user energy consumption minimization problem under inference latency constraints is investigated. To deal with the coupled offloading and scheduling introduced by concurrent batch processing, we first consider an offline problem with a constant edge inference latency and the same latency constraint. It is proven that optimizing the offloading policy of each user independently and aggregating all the same sub-tasks in one batch is optimal, and thus the independent partitioning and same sub-task aggregating (IP-SSA) algorithm is inspired. Further, the optimal grouping (OG) algorithm is proposed to optimally group tasks when the latency constraints are different. Finally, when future task arrivals cannot be precisely predicted, a deep deterministic policy gradient (DDPG) agent is trained to call OG. Experiments show that IP-SSA reduces up to 94.9\% user energy consumption in the offline setting, while DDPG-OG outperforms DDPG-IP-SSA by up to 8.92\% in the online setting.
A heuristic method for data allocation and task scheduling on heterogeneous multiprocessor systems under memory constraints
Authors: Junwen Ding, Liangcai Song, Siyuan Li, Chen Wu, Ronghua He, Zhouxing Su, Zhipeng Lü
Date: 2022-05-09 00:00:00
Tasks: Job Shop Scheduling, Scheduling

Computing workflows in heterogeneous multiprocessor systems are frequently modeled as directed acyclic graphs of tasks and data blocks, which represent computational modules and their dependencies in the form of data produced by a task and used by others. However, for some workflows, such as the task schedule in a digital signal processor may run out of memory by exposing too much parallelism. This paper focuses on the data allocation and task scheduling problem under memory constraints, and concentrates on shared memory platforms. We first propose an integer linear programming model to formulate the problem. Then we consider the problem as an extended flexible job shop scheduling problem, while trying to minimize the critical path of the graph. To solve this problem, we propose a tabu search algorithm (TS) which combines several distinguished features such as a greedy initial solution construction method and a mixed neighborhood evaluation strategy based on exact evaluation and approximate evaluation methods. Experimental results on randomly generated instances show that the the proposed TS algorithm can obtain relatively high-quality solutions in a reasonable computational time. In specific, the tabu search method averagely improves the makespan by 5-25\% compared to the classical load balancing algorithm that are widely used in the literature. Besides, some key features of TS are also analyzed to identify its success factors.
CONTINUER: Maintaining Distributed DNN Services During Edge Failures
Authors: Ayesha Abdul Majeed, Peter Kilpatrick, Ivor Spence, Blesson Varghese
Date: 2022-04-25 00:00:00

Partitioning and deploying Deep Neural Networks (DNNs) across edge nodes may be used to meet performance objectives of applications. However, the failure of a single node may result in cascading failures that will adversely impact the delivery of the service and will result in failure to meet specific objectives. The impact of these failures needs to be minimised at runtime. Three techniques are explored in this paper, namely repartitioning, early-exit and skip-connection. When an edge node fails, the repartitioning technique will repartition and redeploy the DNN thus avoiding the failed nodes. The early-exit technique makes provision for a request to exit (early) before the failed node. The skip connection technique dynamically routes the request by skipping the failed nodes. This paper will leverage trade-offs in accuracy, end-to-end latency and downtime for selecting the best technique given user-defined objectives (accuracy, latency and downtime thresholds) when an edge node fails. To this end, CONTINUER is developed. Two key activities of the framework are estimating the accuracy and latency when using the techniques for distributed DNNs and selecting the best technique. It is demonstrated on a lab-based experimental testbed that CONTINUER estimates accuracy and latency when using the techniques with no more than an average error of 0.28% and 13.06%, respectively and selects the suitable technique with a low overhead of no more than 16.82 milliseconds and an accuracy of up to 99.86%.
On Connections between Opacity and Security in Linear Systems
Authors: Varkey M. John, Vaibhav Katewa
Date: 2022-06-13 00:00:00

Opacity and attack detectability are important properties for any system as they allow the states to remain private and malicious attacks to be detected, respectively. In this paper, we show that a fundamental trade-off exists between these properties for a linear dynamical system, in the sense that if an opaque system is subjected to attacks, all attacks cannot be detected. We first characterize the opacity conditions for the system in terms of its weakly unobservable subspace (WUS) and show that the number of opaque states is proportional to the size of the WUS. Further, we establish conditions under which increasing the opaque sets also increases the set of undetectable attacks. This highlights a fundamental trade-off between security and privacy. We demonstrate application of our results on a remotely controlled automotive system.
Geometry-Based Phase and Time Synchronization for Multi-Antenna Channel Measurements
Authors: Florian Euchner, Phillip Stephan, Marc Gauger, Stephan ten Brink
Date: 2022-06-13 00:00:00

Synchronization of transceiver chains is a major challenge in the practical realization of massive MIMO and especially distributed massive MIMO. While frequency synchronization is comparatively easy to achieve, estimating the carrier phase and sampling time offsets of individual transceivers is challenging. However, under the assumption of phase and time offsets that are constant over some duration and knowing the positions of several transmit and receive antennas, it is possible to estimate and compensate for these offsets even in scattering environments with multipath propagation components. The resulting phase and time calibration is a prerequisite for applying classical antenna array processing methods to massive MIMO arrays and for transferring machine learning models either between simulation and deployment or from one radio environment to another. Algorithms for phase and time offset estimation are presented and several investigations on large datasets generated by an over-the-air-synchronized channel sounder are carried out.
IRS for Multi-Access Edge Computing in 6G Networks
Authors: Mobasshir Mahbub, Raed M. Shubair
Date: 2022-06-10 00:00:00
Tasks: Edge-computing

Computation offloading in multi-access edge computing (MEC) is an effective paradigm for enabling resource-intensive smart applications. However, when the wireless channel utilized for offloading computing activities is hostile, the proper advantages of MEC may not be completely realized. Intelligent reflecting surface (IRS) is a new technology that has recently attracted significant interest can optimize the wireless transmission environment in a programmable way and improving the connectivity between user equipment (UE) and base station (BS). In this paper, the performance of MEC architecture is analyzed considering both IRS-assisted and without IRS communication scenarios in the context of the urban micro cellular scenarios. The research obtained that the deployment of IRS can reduce the spectrum and energy consumption significantly.
An Algorithm for Exact Numerical Age-of-Information Evaluation in Multi-Agent Systems
Authors: Richard Schoeffauer, Gerhard Wunder
Date: 2022-06-11 00:00:00

We present an algorithm for the numerical evaluation of the state-space distribution of an Age-of-Information network. Given enough computational resources, the evaluation can be performed to an arbitrary high precision. An Age-of-Information network is described by a vector of natural numbers, that track how outdated status information from various agents is. Our algorithm yields the means to determine any moment of the corresponding stochastic process. This can be extremely valuable for cases in which the network consists of controllers that communicate with one another, as it potentially allows for less conservative control behavior. It also enables the comparison of different policies regarding their performance (minimizing the average Age-of-Information) to a much more accurate degree than was possible before. This is illustrated using the conventional MaxWeight policy and the optimal policy. We also validate and compare the algorithm with Monte-Carlo-Simulations.
Islamic and capitalist economies: Comparison using econophysics models of wealth exchange and redistribution
Authors: Takeshi Kato
Date: 2022-06-11 00:00:00

Islamic and capitalist economies have several differences, the most fundamental being that the Islamic economy is characterized by the prohibition of interest (riba) and speculation (gharar) and the enforcement of Shariah-compliant profit-loss sharing (mudaraba, murabaha, salam, etc.) and wealth redistribution (waqf, sadaqah, and zakat). In this study, I apply new econophysics models of wealth exchange and redistribution to quantitatively compare these characteristics to those of capitalism and evaluate wealth distribution and disparity using a simulation. Specifically, regarding exchange, I propose a loan interest model representing finance capitalism and riba and a joint venture model representing shareholder capitalism and mudaraba; regarding redistribution, I create a transfer model representing inheritance tax and waqf. As exchanges are repeated from an initial uniform distribution of wealth, wealth distribution approaches a power-law distribution more quickly for the loan interest than the joint venture model; and the Gini index, representing disparity, rapidly increases. The joint venture model's Gini index increases more slowly, but eventually, the wealth distribution in both models becomes a delta distribution, and the Gini index gradually approaches 1. Next, when both models are combined with the transfer model to redistribute wealth in every given period, the loan interest model has a larger Gini index than the joint venture model, but both converge to a Gini index of less than 1. These results quantitatively reveal that in the Islamic economy, disparity is restrained by prohibiting riba and promoting reciprocal exchange in mudaraba and redistribution through waqf. Comparing Islamic and capitalist economies provides insights into the benefits of economically embracing the ethical practice of mutual aid and suggests guidelines for an alternative to capitalism.
Social Practices: a Complete Formalization
Authors: Frank Dignum
Date: 2022-05-22 00:00:00

Multi-agent models are a suitable starting point to model complex social interactions. However, as the complexity of the systems increase, we argue that novel modeling approaches are needed that can deal with inter-dependencies at different levels of society, where many heterogeneous parties (software agents, robots, humans) are interacting and reacting to each other. In this paper, we present a formalization of a social framework for agents based on the concept of Social Practices as high level specifications of normal (expected) behavior in a given social context. We argue that social practices facilitate the practical reasoning of agents in standard social interactions. Thus they can support deliberations for complex situations just like conventions and norms. However, they also come with a social context that gives handles for social planning and deliberation in top of the normal functional deliberation. The main goal of this paper is to give a formalization of social practices that can be used as a basis for implementations and defining precise structures within which social learning can take place.
Learning Generalized Wireless MAC Communication Protocols via Abstraction
Authors: Luciano Miuccio, Salvatore Riolo, Sumudu Samarakoony, Daniela Panno, Mehdi Bennis
Date: 2022-06-06 00:00:00
Tasks: Reinforcement Learning (RL)

To tackle the heterogeneous requirements of beyond 5G (B5G) and future 6G wireless networks, conventional medium access control (MAC) procedures need to evolve to enable base stations (BSs) and user equipments (UEs) to automatically learn innovative MAC protocols catering to extremely diverse services. This topic has received significant attention, and several reinforcement learning (RL) algorithms, in which BSs and UEs are cast as agents, are available with the aim of learning a communication policy based on agents' local observations. However, current approaches are typically overfitted to the environment they are trained in, and lack robustness against unseen conditions, failing to generalize in different environments. To overcome this problem, in this work, instead of learning a policy in the high dimensional and redundant observation space, we leverage the concept of observation abstraction (OA) rooted in extracting useful information from the environment. This in turn allows learning communication protocols that are more robust and with much better generalization capabilities than current baselines. To learn the abstracted information from observations, we propose an architecture based on autoencoder (AE) and imbue it into a multi-agent proximal policy optimization (MAPPO) framework. Simulation results corroborate the effectiveness of leveraging abstraction when learning protocols by generalizing across environments, in terms of number of UEs, number of data packets to transmit, and channel conditions.
Discretization and Stabilization of Energy-Based Controller for Period Switching Control and Flexible Scheduling
Authors: Seyed Amir Tafrishi, Xiaotian Dai, Yasuhisa Hirata, Alan Burns
Date: 2022-06-13 00:00:00
Tasks: Scheduling

Emerging advanced control applications, with increased complexity in software but limited computing resources, suggest that real-time controllers should have adaptable designs. These control strategies also should be designed with consideration of the run-time behavior of the system. One of such research attempts is to design the controller along with the task scheduler, known as control-scheduling co-design, for more predictable timing behavior as well as surviving system overloads. Unlike traditional controller designs, which have equal-distance sampling periods, the co-design approach increases the system flexibility and resilience by explicitly considering timing properties, for example using an event-based controller or with multiple sampling times (non-uniform sampling and control). Within this context, we introduce the first work on the discretization of an energy-based controller that can switch arbitrarily between multiple periods and adjust the control parameters accordingly without destabilizing the system. A digital controller design based on this paradigm for a DC motor with an elastic load as an example is introduced and the stability condition is given based on the proposed Lyapunov function. The method is evaluated with various computer-based simulations which demonstrate its effectiveness.
Signal-informed DNN-based DOA Estimation combining an External Microphone and GCC-PHAT Features
Authors: Ulrik Kowalk, Simon Doclo, Joerg Bitzer
Date: 2022-06-11 00:00:00

Aiming at estimating the direction of arrival (DOA) of a desired speaker in a multi-talker environment using a microphone array, in this paper we propose a signal-informed method exploiting the availability of an external microphone attached to the desired speaker. The proposed method applies a binary mask to the GCC-PHAT input features of a convolutional neural network, where the binary mask is computed based on the power distribution of the external microphone signal. Experimental results for a reverberant scenario with up to four interfering speakers demonstrate that the signal-informed masking improves the localization accuracy, without requiring any knowledge about the interfering speakers.
Robust Trajectory Tracking for Underactuated Quadrotors with Prescribed Performance
Authors: Dženan Lapandić, Christos K. Verginis, Dimos V. Dimarogonas, Bo Wahlberg
Date: 2022-06-13 00:00:00

We propose a control protocol based on the prescribed performance control (PPC) methodology for a quadrotor unmanned aerial vehicle (UAV). Quadrotor systems belong to the class of underactuated systems for which the original PPC methodology cannot be directly applied. We introduce the necessary design modifications to stabilize the considered system with prescribed performance. The proposed control protocol does not use any information of dynamic model parameters or exogenous disturbances. Furthermore, the stability analysis guarantees that the tracking errors remain inside of designer-specified time-varying functions, achieving prescribed performance independent from the control gains' selection. Finally, simulation results verify the theoretical results.
Enhancement of Rural Connectivity by Recycling TV Towers with Massive MIMO Techniques
Authors: Ammar El Falou, Mohamed-Slim Alouini
Date: 2022-06-13 00:00:00

Nowadays, the digital divide is one of the major issues facing the global community. Around 3 billion people worldwide are still not-connected or under-connected. In this article, we investigate the use of TV towers with multi user (MU) massive multiple input multiple output (mMIMO) techniques to offer connectivity in rural areas. Specifically, the coverage range is assessed for a MU mMIMO base station (BS) mounted on a high tower as a TV tower, and compared with a legacy mMIMO BS. The obtained results show that one high tower BS can cover an area at least 25 times larger than the area covered by a legacy BS. This is of high interest as recycling TV towers can enhance the rural connectivity with low expenditures. We apply the proposed solution to a realistic case study in an Ethiopian rural area, based on population densities and locations of current BS and TV towers. Our study shows that a high number of people can be covered by existing TV towers. Additional possible solutions to enhance rural connectivity are discussed in the last section.
A Semi Empirical Approach to a Physically Based Aging Model for Home Energy Management Systems
Authors: Cory Miller, Mithun Goutham, Xiaoling Chen, Prasad Dev Hanumalagutti, Rachel Blaser, Stephanie Stockar
Date: 2022-06-13 00:00:00
Tasks: energy management, Management

A growing interest in the study of aging related phenomena in lithium-ion batteries is propelled by the increasing utilization of energy storage systems in electric vehicles and in buildings as stationery energy accumulators paired with renewable energy sources. This paper proposes a mixed-degradation model approach that combines the benefits of a semi-empirical approach with that of a physics-based model. This enables easy calibration for different battery chemistries, the ability to extrapolate when necessary, and is computationally efficient enough to be coupled with real-time running control systems. To demonstrate the effectiveness of the proposed approach, the effect of two different control strategies in a smart home energy management system is demonstrated on the aging of a Lithium iron phosphate (LFP) battery.