pwc-archive/papers-with-abstracts huggingface dataset:
MOF: A Modular Framework for Rapid Application of Optimization Methodologies to General Engineering Design Problems
Authors: Brian Andersen, Gregory Delipei, David Kropaczek, Jason Hou
Date: 2022-04-01 00:00:00

A variety of optimization algorithms have been developed to solve engineering design problems in which the solution space is too large to manually determine the optimal solution. The Modular Optimization Framework (MOF) was developed to facilitate the development and application of these optimization algorithms. MOF is written in Python 3, and it used object-oriented programming to create a modular design that allows users to easily incorporate new optimization algorithms, methods, or engineering design problems into the framework. Additionally, a common input file allows users to easily specify design problems, update the optimization parameters, and perform comparisons between various optimization methods and algorithms. In the current MOF version, genetic algorithm (GA) and simulated annealing (SA) approaches are implemented. Applications in different nuclear engineering optimization problems are included as examples. The effectiveness of the GA and SA optimization algorithms within MOF are demonstrated through an unconstrained nuclear fuel assembly pin lattice optimization, a first cycle fuel loading constrained optimization of a three-loop pressurized water reactor (PWR), and a third cycle constrained optimization of a four-loop PWR. In all cases, the algorithms efficiently searched the solution spaces and found optimized solutions to the given problems that satisfied the imposed constraints. These results demonstrate the capabilities of the existing optimization tools within MOF, and they also provide a set of benchmark cases that can be used to evaluate the progress of future optimization methodologies with MOF.
A Semismooth Newton Stochastic Proximal Point Algorithm with Variance Reduction
Authors: Andre Milzarek, Fabian Schaipp, Michael Ulbrich
Date: 2022-04-01 00:00:00

We develop an implementable stochastic proximal point (SPP) method for a class of weakly convex, composite optimization problems. The proposed stochastic proximal point algorithm incorporates a variance reduction mechanism and the resulting SPP updates are solved using an inexact semismooth Newton framework. We establish detailed convergence results that take the inexactness of the SPP steps into account and that are in accordance with existing convergence guarantees of (proximal) stochastic variance-reduced gradient methods. Numerical experiments show that the proposed algorithm competes favorably with other state-of-the-art methods and achieves higher robustness with respect to the step size selection.
LoCoV: low dimension covariance voting algorithm for portfolio optimization
Authors: Juntao Duan, Ionel Popescu
Date: 2022-04-01 00:00:00
Tasks: Portfolio Optimization

Minimum-variance portfolio optimizations rely on accurate covariance estimator to obtain optimal portfolios. However, it usually suffers from large error from sample covariance matrix when the sample size $n$ is not significantly larger than the number of assets $p$. We analyze the random matrix aspects of portfolio optimization and identify the order of errors in sample optimal portfolio weight and show portfolio risk are underestimated when using samples. We also provide LoCoV (low dimension covariance voting) algorithm to reduce error inherited from random samples. From various experiments, LoCoV is shown to outperform the classical method by a large margin.
A Statistical Decision-Theoretical Perspective on the Two-Stage Approach to Parameter Estimation
Authors: Braghadeesh Lakshminarayanan, Cristian R. Rojas
Date: 2022-03-31 00:00:00
Tasks: Model Optimization, parameter estimation

One of the most important problems in system identification and statistics is how to estimate the unknown parameters of a given model. Optimization methods and specialized procedures, such as Empirical Minimization (EM) can be used in case the likelihood function can be computed. For situations where one can only simulate from a parametric model, but the likelihood is difficult or impossible to evaluate, a technique known as the Two-Stage (TS) Approach can be applied to obtain reliable parametric estimates. Unfortunately, there is currently a lack of theoretical justification for TS. In this paper, we propose a statistical decision-theoretical derivation of TS, which leads to Bayesian and Minimax estimators. We also show how to apply the TS approach on models for independent and identically distributed samples, by computing quantiles of the data as a first step, and using a linear function as the second stage. The proposed method is illustrated via numerical simulations.
FairRank: Fairness-aware Single-tower Ranking Framework for News Recommendation
Authors: Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang
Date: 2022-04-01 00:00:00
Tasks: Attribute, Fairness, News Recommendation

Single-tower models are widely used in the ranking stage of news recommendation to accurately rank candidate news according to their fine-grained relatedness with user interest indicated by user behaviors. However, these models can easily inherit the biases related to users' sensitive attributes (e.g., demographics) encoded in training click data, and may generate recommendation results that are unfair to users with certain attributes. In this paper, we propose FairRank, which is a fairness-aware single-tower ranking framework for news recommendation. Since candidate news selection can be biased, we propose to use a shared candidate-aware user model to match user interest with a real displayed candidate news and a random news, respectively, to learn a candidate-aware user embedding that reflects user interest in candidate news and a candidate-invariant user embedding that indicates intrinsic user interest. We apply adversarial learning to both of them to reduce the biases brought by sensitive user attributes. In addition, we use a KL loss to regularize the attribute labels inferred from the two user embeddings to be similar, which can make the model capture less candidate-aware bias information. Extensive experiments on two datasets show that FairRank can improve the fairness of various single-tower news ranking models with minor performance losses.
End-to-end Learnable Diversity-aware News Recommendation
Authors: Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang
Date: 2022-04-01 00:00:00
Tasks: Diversity, News Recommendation, Reranking

Diversity is an important factor in providing high-quality personalized news recommendations. However, most existing news recommendation methods only aim to optimize recommendation accuracy while ignoring diversity. Reranking is a widely used post-processing technique to promote the diversity of top recommendation results. However, the recommendation model is not perfect and errors may be propagated and amplified in a cascaded recommendation algorithm. In addition, the recommendation model itself is not diversity-aware, making it difficult to achieve a good tradeoff between recommendation accuracy and diversity. In this paper, we propose a news recommendation approach named LeaDivRec, which is a fully learnable model that can generate diversity-aware news recommendations in an end-to-end manner. Different from existing news recommendation methods that are usually based on point- or pair-wise ranking, in LeaDivRec we propose a more effective list-wise news recommendation model. More specifically, we propose a permutation Transformer to consider the relatedness between candidate news and meanwhile can learn different representations for similar candidate news to help improve recommendation diversity. We also propose an effective list-wise training method to learn accurate ranking models. In addition, we propose a diversity-aware regularization method to further encourage the model to make controllable diversity-aware recommendations. Extensive experiments on two real-world datasets validate the effectiveness of our approach in balancing recommendation accuracy and diversity.
Model-agnostic Counterfactual Synthesis Policy for Interactive Recommendation
Authors: Siyu Wang, Xiaocong Chen, Lina Yao
Date: 2022-04-01 00:00:00
Tasks: counterfactual, Interactive Recommendation, model, reinforcement-learning, Reinforcement Learning (RL)

Interactive recommendation is able to learn from the interactive processes between users and systems to confront the dynamic interests of users. Recent advances have convinced that the ability of reinforcement learning to handle the dynamic process can be effectively applied in the interactive recommendation. However, the sparsity of interactive data may hamper the performance of the system. We propose to train a Model-agnostic Counterfactual Synthesis Policy to generate counterfactual data and address the data sparsity problem by modelling from observation and counterfactual distribution. The proposed policy can identify and replace the trivial components for any state in the training process with other agents, which can be deployed in any RL-based algorithm. The experimental results demonstrate the effectiveness and generality of our proposed policy.
A Versatile Framework for Evaluating Ranked Lists in terms of Group Fairness and Relevance
Authors: Tetsuya Sakai, Jin Young Kim, Inho Kang
Date: 2022-04-01 00:00:00
Tasks: Attribute, Fairness

We present a simple and versatile framework for evaluating ranked lists in terms of group fairness and relevance, where the groups (i.e., possible attribute values) can be either nominal or ordinal in nature. First, we demonstrate that, if the attribute set is binary, our framework can easily quantify the overall polarity of each ranked list. Second, by utilising an existing diversified search test collection and treating each intent as an attribute value, we demonstrate that our framework can handle soft group membership, and that our group fairness measures are highly correlated with both adhoc IR and diversified IR measures under this setting. Third, we demonstrate how our framework can quantify intersectional group fairness based on multiple attribute sets. We also show that the similarity function for comparing the achieved and target distributions over the attribute values should be chosen carefully.
Studying the Impact of Data Disclosure Mechanism in Recommender Systems via Simulation
Authors: Ziqian Chen, Fei Sun, Yifan Tang, Haokun Chen, Jinyang Gao, Bolin Ding
Date: 2022-04-01 00:00:00
Tasks: Decision Making, Federated Learning, Privacy Preserving, Recommendation Systems

Recently, privacy issues in web services that rely on users' personal data have raised great attention. Unlike existing privacy-preserving technologies such as federated learning and differential privacy, we explore another way to mitigate users' privacy concerns, giving them control over their own data. For this goal, we propose a privacy aware recommendation framework that gives users delicate control over their personal data, including implicit behaviors, e.g., clicks and watches. In this new framework, users can proactively control which data to disclose based on the trade-off between anticipated privacy risks and potential utilities. Then we study users' privacy decision making under different data disclosure mechanisms and recommendation models, and how their data disclosure decisions affect the recommender system's performance. To avoid the high cost of real-world experiments, we apply simulations to study the effects of our proposed framework. Specifically, we propose a reinforcement learning algorithm to simulate users' decisions (with various sensitivities) under three proposed platform mechanisms on two datasets with three representative recommendation models. The simulation results show that the platform mechanisms with finer split granularity and more unrestrained disclosure strategy can bring better results for both end users and platforms than the "all or nothing" binary mechanism adopted by most real-world applications. It also shows that our proposed framework can effectively protect users' privacy since they can obtain comparable or even better results with much less disclosed data.
FaiRIR: Mitigating Exposure Bias from Related Item Recommendations in Two-Sided Platforms
Authors: Abhisek Dash, Abhijnan Chakraborty, Saptarshi Ghosh, Animesh Mukherjee, Krishna P. Gummadi
Date: 2022-04-01 00:00:00

Related Item Recommendations (RIRs) are ubiquitous in most online platforms today, including e-commerce and content streaming sites. These recommendations not only help users compare items related to a given item, but also play a major role in bringing traffic to individual items, thus deciding the exposure that different items receive. With a growing number of people depending on such platforms to earn their livelihood, it is important to understand whether different items are receiving their desired exposure. To this end, our experiments on multiple real-world RIR datasets reveal that the existing RIR algorithms often result in very skewed exposure distribution of items, and the quality of items is not a plausible explanation for such skew in exposure. To mitigate this exposure bias, we introduce multiple flexible interventions (FaiRIR) in the RIR pipeline. We instantiate these mechanisms with two well-known algorithms for constructing related item recommendations -- rating-SVD and item2vec -- and show on real-world data that our mechanisms allow for a fine-grained control on the exposure distribution, often at a small or no cost in terms of recommendation quality, measured in terms of relatedness and user satisfaction.
Sequential Recommendation with Causal Behavior Discovery
Authors: Zhenlei Wang, Xu Chen, Rui Zhou, Quanyu Dai, Zhenhua Dong, Ji-Rong Wen
Date: 2022-04-01 00:00:00
Tasks: Causal Discovery, Recommendation Systems, Sequential Recommendation

The key of sequential recommendation lies in the accurate item correlation modeling. Previous models infer such information based on item co-occurrences, which may fail to capture the real causal relations, and impact the recommendation performance and explainability. In this paper, we equip sequential recommendation with a novel causal discovery module to capture causalities among user behaviors. Our general idea is firstly assuming a causal graph underlying item correlations, and then we learn the causal graph jointly with the sequential recommender model by fitting the real user behavior data. More specifically, in order to satisfy the causality requirement, the causal graph is regularized by a differentiable directed acyclic constraint. Considering that the number of items in recommender systems can be very large, we represent different items with a unified set of latent clusters, and the causal graph is defined on the cluster level, which enhances the model scalability and robustness. In addition, we provide theoretical analysis on the identifiability of the learned causal graph. To the best of our knowledge, this paper makes a first step towards combining sequential recommendation with causal discovery. For evaluating the recommendation performance, we implement our framework with different neural sequential architectures, and compare them with many state-of-the-art methods based on real-world datasets. Empirical studies manifest that our model can on average improve the performance by about 7% and 11% on f1 and NDCG, respectively. To evaluate the model explainability, we build a new dataset with human labeled explanations for both quantitative and qualitative analysis.
Deep Reinforcement Learning-Based Joint Satellite Scheduling and Resource Allocation in Satellite-Terrestrial Integrated Networks
Authors: Yabo Yin, Chuanhe Huang, Dong-Fang Wu, Shidong Huang, M. Wasim Abbas Ashraf, Qianqian Guo, Lin Zhang
Date: 2022-02-24 00:00:00
Proceeding: Hindawi Wireless Communications and Mobile Computing 2022 2
Tasks: Deep Reinforcement Learning, Scheduling

atellite-terrestrial integrated networks (STINs) are considered to be a new paradigm for the next generation of global communication because of its distinctive merits, such as wide coverage, high reliability, and flexibility. When the satellite associates with different base stations (BSs) and adopts different channels for communication, the utility of offloading data to BSs is different. In our work, we study how to jointly associate satellites with appropriate BSs and allocate channels to satellites. Our purpose is to maximize the utility of the data offloaded from satellites to BSs while considering the load balance of BSs. However, some satellites are often unable to connect to BSs because of their periodic flight characteristic, which makes the joint satellite-BS association and channel allocation more challenging. To solve the problem that satellites sometimes cannot connect to BSs, we abstract the communication model between satellites and BSs into a bipartite graph and add a virtual BS to ensure that all satellites can connect to at least one BS. Then, in the constructed joint optimization problem, we solve the assignment of satellites and channels simultaneously. Considering that the joint optimization problem is nonconvex, we use double deep Q-Network (DDQN) for achieving the optimal strategy of satellite association and channel allocation. Furthermore, the reward value in most state transition information generated by satellites is 0, which leads to the low learning efficiency of DDQN. Aiming at enhancing the learning efficiency of DDQN, the priority sampling-based DDQN (PSDDQN) algorithm is proposed. Experimental results demonstrate that PSDDQN gets better utility and achieves the load balance of BSs compared with other algorithms.
AAA-WSN: Anonymous access authentication scheme for wireless sensor networks in big data environment
Authors: Shadi Nashwan
Date: 2020-03-02 00:00:00
Proceeding: Jouf University 2020 3

The data capturing and access process is an important stage in the big data applications. Most of these applications are exploited the wireless sensor networks (WSNs) to accomplish this process through the sensor nodes that are deployed in unsecure and unattended environments. Therefore, these applications are suffered from numerous security weaknesses, an adversary may exploit such weaknesses to break user's privacy, confidentiality of sensor nodes, and control the communication channel between the network components. Consequently, the security issues for this process have attracted much interest of the researchers with the increasing spread of use such applications. The majority of the proposed authentication schemes fail to solve all existing security weaknesses simultaneously. Thus, the authentication scheme is a critical issue in the WSNs. This paper proposes anonymous access authentication scheme for wireless sensor networks in big data environments (AAA-WSN) to achieve appealing security services. Comparing with the recent WSNs authentication schemes, the AAA-WSN scheme cannot only achieve strong security services such as user anonymity and mutual authentication, but also performs the perfect forward secrecy feature with high level of efficiency. The security analysis shows that the AAA-WSN scheme is resistant to the current known attacks. Moreover, the performance analysis in terms of the storage, computations and communications costs demonstrates that the AAA-WSN scheme achieves high level of security with desirable level of efficiency comparing the recent WSNs authentication schemes.
Universal Adaptor: Converting Mel-Spectrograms Between Different Configurations for Speech Synthesis
Authors: Fan-Lin Wang, Po-chun Hsu, Da-Rong Liu, Hung-Yi Lee
Date: 2022-04-01 00:00:00
Tasks: Speech Synthesis, Voice Conversion

Most recent speech synthesis systems are composed of a synthesizer and a vocoder. However, the existing synthesizers and vocoders can only be matched to acoustic features extracted with a specific configuration. Hence, we can't combine arbitrary synthesizers and vocoders together to form a complete system, not to mention apply to a newly developed model. In this paper, we proposed Universal Adaptor, which takes a Mel-spectrogram parametrized by the source configuration and converts it into a Mel-spectrogram parametrized by the target configuration, as long as we feed in the source and the target configurations. Experiments show that the quality of speeches synthesized from our output of Universal Adaptor is comparable to those synthesized from ground truth Mel-spectrogram no matter in single-speaker or multi-speaker scenarios. Moreover, Universal Adaptor can be applied in the recent TTS systems and voice conversion systems without dropping quality.
An Arduino based heartbeat detection device (ArdMob-ECG) for real-time ECG analysis
Authors: Tim Julian Möller, Martin Voss, Laura Kaltwasser
Date: 2022-04-01 00:00:00
Tasks: Specificity, Unity

This technical paper provides a tutorial to build a low-cost (10-100 USD) and easy to assemble ECG device (ArdMob-ECG) that can be easily used for a variety of different scientific studies. The advantage of this device is that it automatically stores the data and has a built-in detection algorithm for heartbeats. Compared to a clinical ECG, this device entails a serial interface that can send triggers via USB directly to a computer and software (e.g. Unity, Matlab) with minimal delay due to its architecture. Its software and hardware is open-source and publicly available. The performance of the device regarding sensitivity and specificity is comparable to a professional clinical ECG and is assessed in this paper. Due to the open-source software, a variety of different research questions and individual alterations can be adapted using this ECG. The code as well as the circuit is publicly available and accessible for everyone to promote a better health system in remote areas, Open Science, and to boost scientific progress and the development of new paradigms that ultimately foster innovation.
Evaluating the Text-to-SQL Capabilities of Large Language Models
Authors: Nitarshan Rajkumar, Raymond Li, Dzmitry Bahdanau
Date: 2022-03-15 00:00:00
Tasks: Language Modeling, Language Modelling, Text to SQL, Text-To-SQL

We perform an empirical evaluation of Text-to-SQL capabilities of the Codex language model. We find that, without any finetuning, Codex is a strong baseline on the Spider benchmark; we also analyze the failure modes of Codex in this setting. Furthermore, we demonstrate on the GeoQuery and Scholar benchmarks that a small number of in-domain examples provided in the prompt enables Codex to perform better than state-of-the-art models finetuned on such few-shot examples.
Green Routing Game: Strategic Logistical Planning using Mixed Fleets of ICEVs and EVs
Authors: Hampei Sasahara, György Dán, Saurabh Amin, Henrik Sandberg
Date: 2022-04-01 00:00:00

This paper introduces a "green" routing game between multiple logistic operators (players), each owning a mixed fleet of internal combustion engine vehicle (ICEV) and electric vehicle (EV) trucks. Each player faces the cost of delayed delivery (due to charging requirements of EVs) and a pollution cost levied on the ICEVs. This cost structure models: 1) limited battery capacity of EVs and their charging requirement; 2) shared nature of charging facilities; 3) pollution cost levied by regulatory agency on the use of ICEVs. We characterize Nash equilibria of this game and derive a condition for its uniqueness. We also use the gradient projection method to compute this equilibrium in a distributed manner. Our equilibrium analysis is useful to analyze the trade-off faced by players in incurring higher delay due to congestion at charging locations when the share of EVs increases versus a higher pollution cost when the share of ICEVs increases. A numerical example suggests that to increase marginal pollution cost can dramatically reduce inefficiency of equilibria.