pwc-archive/papers-with-abstracts huggingface dataset:
Ngram-LSTM Open Rate Prediction Model (NLORP) and Error_accuracy@C metric: Simple effective, and easy to implement approach to predict open rates for marketing email
Authors: Shubham Joshi, Indradumna Banerjee
Date: 2023-01-25 00:00:00
Tasks: Marketing

Our generation has seen an exponential increase in digital tools adoption. One of the unique areas where digital tools have made an exponential foray is in the sphere of digital marketing, where goods and services have been extensively promoted through the use of digital advertisements. Following this growth, multiple companies have leveraged multiple apps and channels to display their brand identities to a significantly larger user base. This has resulted in products, worth billions of dollars to be sold online. Emails and push notifications have become critical channels to publish advertisement content, to proactively engage with their contacts. Several marketing tools provide a user interface for marketers to design Email and Push messages for digital marketing campaigns. Marketers are also given a predicted open rate for the entered subject line. For enabling marketers generate targeted subject lines, multiple machine learning techniques have been used in the recent past. In particular, deep learning techniques that have established good effectiveness and efficiency. However, these techniques require a sizable amount of labelled training data in order to get good results. The creation of such datasets, particularly those with subject lines that have a specific theme, is a challenging and time-consuming task. In this paper, we propose a novel Ngram and LSTM-based modeling approach (NLORPM) to predict open rates of entered subject lines that is easier to implement, has low prediction latency, and performs extremely well for sparse data. To assess the performance of this model, we also devise a new metric called 'Error_accuracy@C' which is simple to grasp and fully comprehensible to marketers.
Task2KB: A Public Task-Oriented Knowledge Base
Authors: Procheta Sen, Xi Wang, Ruiqing Xu, Emine Yilmaz
Date: 2023-01-24 00:00:00
Tasks: Articles, Knowledge Graphs

Search engines and conversational assistants are commonly used to help users complete their every day tasks such as booking travel, cooking, etc. While there are some existing datasets that can be used for this purpose, their coverage is limited to very few domains. In this paper, we propose a novel knowledge base, 'Task2KB', which is constructed using data crawled from WikiHow, an online knowledge resource offering instructional articles on a wide range of tasks. Task2KB encapsulates various types of task-related information and attributes, such as requirements, detailed step description, and available methods to complete tasks. Due to its higher coverage compared to existing related knowledge graphs, Task2KB can be highly useful in the development of general purpose task completion assistants
Results of SemTab 2022
Authors: Nora Abdelmageed, Jiaoyan Chen, Vincenzo Cutrona, Vasilis Efthymiou, Oktie Hassanzadeh, Madelon Hulsebos, Ernesto Jiménez-Ruiz, Juan Sequeda, Kavitha Srinivas
Date: 2022-10-25 00:00:00
Proceeding: SemTab@ISWC 2022 10
Tasks: Cell Entity Annotation, Column Type Annotation, Graph Matching, Ontology Matching

SemTab 2022 was the fourth edition of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, successfully collocated with the 21st International Semantic Web Conference (ISWC) and the 17th Ontology Matching (OM) Workshop. SemTab provides a common framework to conduct a systematic evaluation of state-of-the-art systems. In this paper, we give an overview of the 2022’s edition of the challenge and summarize the results.
Towards an Approach based on Knowledge Graph Refinement for Tabular Data to Knowledge Graph Matching
Authors: Azanzi Jiomekong, Brice Foko
Date: 2022-10-25 00:00:00
Proceeding: SemTab@ISWC 2022 10
Tasks: Cell Entity Annotation, Column Type Annotation, Graph Matching, Position

This paper presents our contribution to the Accuracy Track of Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab). This contribution consists of the proposition of an approach based on knowledge graph refinement for tabular data annotation. Internal methods were used to predict the links between cells in the table and external methods were used to predict missing entities and relations. This approach was applied to the annotation of HardTables and ToughTables using DBpedia and Wikidata; and GitTables and BiodivTab using DBpedia and Schema.org. During Round 3 of the competition, we were ranked third and second position respectively for the annotation of GitTables and BiodivTab.
Probabilistic Search and Track with Multiple Mobile Agents
Authors: Savvas Papaioannou, Panayiotis Kolios, Theocharis Theocharides, Christos G. Panayiotou, Marios M. Polycarpou
Date: 2023-02-01 00:00:00

In this paper we are interested in the task of searching and tracking multiple moving targets in a bounded surveillance area with a group of autonomous mobile agents. More specifically, we assume that targets can appear and disappear at random times inside the surveillance region and their positions are random and unknown. The agents have a limited sensing range, and due to sensor imperfections they receive noisy measurements from the targets. In this work we utilize the theory of random finite sets (RFS) to capture the uncertainty in the time-varying number of targets and their states and we propose a decision and control framework, in which the mode of operation (i.e. search or track) as well as the mobility control action for each agent, at each time instance, are determined so that the collective goal of searching and tracking is achieved. Extensive simulation results demonstrate the effectiveness and performance of the proposed solution.
Embedding neurophysiological signals
Authors: Pierre Guetschel, Théodore Papadopoulo, Michael Tangermann
Date: 2022-10-25 00:00:00
Proceeding: IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence, and Neural Engineering (MetroXRAINE) 2022 10
Tasks: Brain Computer Interface, Domain Adaptation, EEG, Eeg Decoding, Electroencephalogram (EEG), Transfer Learning

Neurophysiological time-series recordings of brain activity like the electroencephalogram (EEG) or local field potentials can be decoded by machine learning models in order to either control an application, e.g., for communication or rehabilitation after stroke, or to passively monitor the ongoing brain state of the subject, e.g., in a demanding work environment. A typical decoding challenge faced by a brain-computer interface (BCI) is the small dataset size compared to other domains of machine learning like computer vision or natural language processing. The possibilities to tackle classification or regression problems in BCI are to either train a regular model on the available small training data sets or through transfer learning, which utilizes data from other sessions, subjects, or even datasets to train a model. Transfer learning is non-trivial because of the non-stationary of EEG signals between subjects but also within subjects. This variability calls for explicit calibration phases at the start of every session, before BCI applications can be used online. In this study, we present arguments to BCI researchers to encourage the use of embeddings for EEG decoding. In particular, we introduce a simple domain adaptation technique involving both deep learning (when learning the embeddings from the source data) and classical machine learning (for fast calibration on the target data). This technique allows us to learn embeddings across subjects, which deliver a generalized data representation. These can then be fed into subject-specific classifiers in order to minimize their need for calibration data. We conducted offline experiments on the 14 subjects of the High Gamma EEG-BCI Dataset [1]. Embedding functions were obtained by training EEGNet [2] using a leave-one-subject-out (LOSO) protocol, and the embedding vectors were classified by the logistic regression algorithm. Our pipeline was compared to two baseline approaches: EEGNet without subject-specific calibration and the standard FBCSP pipeline in a within-subject training. We observed that the representations learned by the embedding functions were indeed non-stationary across subjects, justifying the need for an additional subject-specific calibration. We also observed that the subject-specific calibration indeed improved the score. Finally, our data suggest, that building upon embeddings requires fewer individual calibration data than the FBCSP baseline to reach satisfactory scores.
Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class-Incremental Learning
Authors: Yibo Yang, Haobo Yuan, Xiangtai Li, Zhouchen Lin, Philip Torr, DaCheng Tao
Date: 2023-02-02 00:00:00
Proceeding: ICLR 2023 2
Tasks: class-incremental learning, Class Incremental Learning, Few-Shot Class-Incremental Learning, Incremental Learning

Few-shot class-incremental learning (FSCIL) has been a challenging problem as only a few training samples are accessible for each novel class in the new sessions. Finetuning the backbone or adjusting the classifier prototypes trained in the prior sessions would inevitably cause a misalignment between the feature and classifier of old classes, which explains the well-known catastrophic forgetting problem. In this paper, we deal with this misalignment dilemma in FSCIL inspired by the recently discovered phenomenon named neural collapse, which reveals that the last-layer features of the same class will collapse into a vertex, and the vertices of all classes are aligned with the classifier prototypes, which are formed as a simplex equiangular tight frame (ETF). It corresponds to an optimal geometric structure for classification due to the maximized Fisher Discriminant Ratio. We propose a neural collapse inspired framework for FSCIL. A group of classifier prototypes are pre-assigned as a simplex ETF for the whole label space, including the base session and all the incremental sessions. During training, the classifier prototypes are not learnable, and we adopt a novel loss function that drives the features into their corresponding prototypes. Theoretical analysis shows that our method holds the neural collapse optimality and does not break the feature-classifier alignment in an incremental fashion. Experiments on the miniImageNet, CUB-200, and CIFAR-100 datasets demonstrate that our proposed framework outperforms the state-of-the-art performances. Our code will be publicly available.
SPINE: a scalable log parser with feedback guidance
Authors: Xuheng Wang
Date: 2022-11-01 00:00:00
Proceeding: ACM Conferences 2022 11
Tasks: Log Parsing, Scheduling

Log parsing, which extracts log templates and parameters, is a critical prerequisite step for automated log analysis techniques. Though existing log parsers have achieved promising accuracy on public log datasets, they still face many challenges when applied in the industry. Through studying the characteristics of real-world log data and analyzing the limitations of existing log parsers, we identify two problems. Firstly, it is non-trivial to scale a log parser to a vast number of logs, especially in real-world scenarios where the log data is extremely imbalanced. Secondly, existing log parsers overlook the importance of user feedback, which is imperative for parser fine-tuning under the continuous evolution of log data. To overcome the challenges, we propose SPINE, which is a highly scalable log parser with user feedback guidance. Based on our log parser equipped with initial grouping and progressive clustering,we propose a novel log data scheduling algorithm to improve the efficiency of parallelization under the large-scale imbalanced log data. Besides, we introduce user feedback to make the parser fast adapt to the evolving logs. We evaluated SPINE on 16 public log datasets. SPINE achieves more than 0.90 parsing accuracy on average with the highest parsing efficiency, which outperforms the state-of-the-art log parsers. We also evaluated SPINE in the production environment of Microsoft, in which SPINE can parse 30million logs in less than 8 minutes under 16 executors, achieving near real-time performance. In addition, our evaluations show that SPINE can consistently achieve good accuracy under log evolution with a moderate number of user feedback.
Toward Efficient Transportation Electrification of Heavy-Duty Trucks: Joint Scheduling of Truck Routing and Charging
Authors: Mikhail A. Bragin, Zuzhao Ye, Nanpeng Yu
Date: 2023-02-01 00:00:00
Tasks: Decision Making, Scheduling

The timely transportation of goods to customers is an essential component of economic activities. However, heavy-duty diesel trucks used for goods delivery significantly contribute to greenhouse gas emissions within many large metropolitan areas, including Los Angeles, New York, and San Francisco. To reduce GHG emissions by facilitating freight electrification, this paper proposes Joint Routing and Charging scheduling for electric trucks. The objective of the associated optimization problem is to minimize the cost of transportation, charging, and tardiness. A large number of possible combinations of road segments as well as a large number of combinations of charging decisions and charging durations leads to a combinatorial explosion in the possible decisions electric trucks can make. The resulting mixed-integer linear programming problem is thus extremely challenging because of the combinatorial complexity even in the deterministic case. Therefore, a Surrogate Level-Based Lagrangian Relaxation (SLBLR) method is employed to decompose the overall problem into significantly less complex truck subproblems. In the coordination aspect, each truck subproblem is solved independently of other subproblems based on the values of Lagrangian multipliers. In addition to serving as a means of guiding and coordinating trucks, multipliers can also serve as a basis for transparent and explanatory decision-making by trucks. Testing results demonstrate that even small instances cannot be solved using the off-the-shelf solver CPLEX after several days of solving. The SLBLR method, on the other hand, can obtain near-optimal solutions within a few minutes for small cases, and within 30 minutes for large ones. Furthermore, it has been demonstrated that as battery capacity increases, the total cost decreases significantly; moreover, as the charging power increases, the number of trucks required decreases as well.
The Investment Management Game: Extending the Scope of the Notion of Core
Authors: Vijay V. Vazirani
Date: 2023-02-01 00:00:00
Tasks: Imputation, Management

The core is a dominant solution concept in economics and cooperative game theory; it is predominantly used for profit, equivalently cost or utility, sharing. This paper demonstrates the versatility of this notion by proposing a completely different use: in a so-called investment management game, which is a game against nature rather than a cooperative game. This game has only one agent whose strategy set is all possible ways of distributing her money among investment firms. The agent wants to pick a strategy such that in each of exponentially many future scenarios, sufficient money is available in the right firms so she can buy an optimal investment for that scenario. Such a strategy constitutes a core imputation under a broad interpretation, though traditional formal framework, of the core. Our game is defined on perfect graphs, since the maximum stable set problem can be solved in polynomial time for such graphs. We completely characterize the core of this game, analogous to Shapley and Shubik characterization of the core of the assignment game. A key difference is the following technical novelty: whereas their characterization follows from total unimodularity, ours follows from total dual integrality
Informationally Robust Cheap-Talk
Authors: Itai Arieli, Ronen Gradwohl, Rann Smorodinsky
Date: 2023-02-01 00:00:00

We study the robustness of cheap-talk equilibria to infinitesimal private information of the receiver in a model with a binary state-space and state-independent sender-preferences. We show that the sender-optimal equilibrium is robust if and only if this equilibrium either reveals no information to the receiver or fully reveals one of the states with positive probability. We then characterize the actions that can be played with positive probability in any robust equilibrium. Finally, we fully characterize the optimal sender-utility under binary receiver's private information, and provide bounds for the optimal sender-utility under general private information.
Beam Selection for Energy-Efficient mmWave Network Using Advantage Actor Critic Learning
Authors: Ycaro Dantas, Pedro Enrique Iturria-Rivera, Hao Zhou, Majid Bavand, Medhat Elsayed, Raimundas Gaigalas, Melike Erol-Kantarci
Date: 2023-02-01 00:00:00
Tasks: Management

The growing adoption of mmWave frequency bands to realize the full potential of 5G, turns beamforming into a key enabler for current and next-generation wireless technologies. Many mmWave networks rely on beam selection with Grid-of-Beams (GoB) approach to handle user-beam association. In beam selection with GoB, users select the appropriate beam from a set of pre-defined beams and the overhead during the beam selection process is a common challenge in this area. In this paper, we propose an Advantage Actor Critic (A2C) learning-based framework to improve the GoB and the beam selection process, as well as optimize transmission power in a mmWave network. The proposed beam selection technique allows performance improvement while considering transmission power improves Energy Efficiency (EE) and ensures the coverage is maintained in the network. We further investigate how the proposed algorithm can be deployed in a Service Management and Orchestration (SMO) platform. Our simulations show that A2C-based joint optimization of beam selection and transmission power is more effective than using Equally Spaced Beams (ESB) and fixed power strategy, or optimization of beam selection and transmission power disjointly. Compared to the ESB and fixed transmission power strategy, the proposed approach achieves more than twice the average EE in the scenarios under test and is closer to the maximum theoretical EE.
Accordion: A Communication-Aware Machine Learning Framework for Next Generation Networks
Authors: Fadhel Ayed, Antonio De Domenico, Adrian Garcia-Rodriguez, David Lopez-Perez
Date: 2023-01-12 00:00:00

In this article, we advocate for the design of ad hoc artificial intelligence (AI)/machine learning (ML) models to facilitate their usage in future smart infrastructures based on communication networks. To motivate this, we first review key operations identified by the 3GPP for transferring AI/ML models through 5G networks and the main existing techniques to reduce their communication overheads. We also present a novel communication-aware ML framework, which we refer to as Accordion, that enables an efficient AI/ML model transfer thanks to an overhauled model training and communication protocol. We demonstrate the communication-related benefits of Accordion, analyse key performance trade-offs, and discuss potential research directions within this realm.
Energy-Based Survival Models for Predictive Maintenance
Authors: Olov Holmer, Erik Frisk, Mattias Krysander
Date: 2023-02-01 00:00:00

Predictive maintenance is an effective tool for reducing maintenance costs. Its effectiveness relies heavily on the ability to predict the future state of health of the system, and for this survival models have shown to be very useful. Due to the complex behavior of system degradation, data-driven methods are often preferred, and neural network-based methods have been shown to perform particularly very well. Many neural network-based methods have been proposed and successfully applied to many problems. However, most models rely on assumptions that often are quite restrictive and there is an interest to find more expressive models. Energy-based models are promising candidates for this due to their successful use in other applications, which include natural language processing and computer vision. The focus of this work is therefore to investigate how energy-based models can be used for survival modeling and predictive maintenance. A key step in using energy-based models for survival modeling is the introduction of right-censored data, which, based on a maximum likelihood approach, is shown to be a straightforward process. Another important part of the model is the evaluation of the integral used to normalize the modeled probability density function, and it is shown how this can be done efficiently. The energy-based survival model is evaluated using both simulated data and experimental data in the form of starter battery failures from a fleet of vehicles, and its performance is found to be highly competitive compared to existing models.
Decentralized Search and Track with Multiple Autonomous Agents
Authors: Savvas Papaioannou, Panayiotis Kolios, Theocharis Theocharides, Christos G. Panayiotou, Marios M. Polycarpou
Date: 2023-02-01 00:00:00

In this paper we study the problem of cooperative searching and tracking (SAT) of multiple moving targets with a group of autonomous mobile agents that exhibit limited sensing capabilities. We assume that the actual number of targets is not known a priori and that target births/deaths can occur anywhere inside the surveillance region. For this reason efficient search strategies are required to detect and track as many targets as possible. To address the aforementioned challenges we augment the classical Probability Hypothesis Density (PHD) filter with the ability to propagate in time the search density in addition to the target density. Based on this, we develop decentralized cooperative look-ahead strategies for efficient searching and tracking of an unknown number of targets inside a bounded surveillance area. The performance of the proposed approach is demonstrated through simulation experiments.
Ensuring reliability: what is the optimal time for power plant maintenance in Texas as the climate changes?
Authors: Hugh Daigle, Joshua D. Rhodes, Aidan Pyrcz, Michael E. Webber
Date: 2023-02-01 00:00:00

We analyzed data for the Electric Reliability Council of Texas (ERCOT) to assess shoulder seasons -- that is, the 45 days of lowest total energy use and peak demand in the spring and fall typically used for power plant maintenance -- and whether their occurrence has changed over time. Over the period 1996--2022, the shoulder seasons never started earlier than late March nor later than mid-October, corresponding well with the minimum of total degree days. In the temperature record 1959--2022, the minimum in degree days in the spring moved earlier, from early March to early February, and in the fall moved later, from early to mid-November. Warming temperatures might cause these minima in degree days to merge into a single annual minimum in December or January by the mid-2040s, a time when there is a non-trivial risk of 1-day record energy use and peak demand from winter storms.