pwc-archive/papers-with-abstracts huggingface dataset:
MealRec: A Meal Recommendation Dataset
Authors: Ming Li, Lin Li, Qing Xie, Jingling Yuan, Xiaohui Tao
Date: 2022-05-24 00:00:00
Tasks: Recommendation Systems

Bundle recommendation systems aim to recommend a bundle of items for a user to consider as a whole. They have become a norm in modern life and have been applied to many real-world settings, such as product bundle recommendation, music playlist recommendation and travel package recommendation. However, compared to studies of bundle recommendation approaches in areas such as online shopping and digital music services, research on meal recommendations for restaurants in the hospitality industry has made limited progress, due largely to the lack of high-quality benchmark datasets. A publicly available dataset specialising in meal recommendation research for the research community is in urgent demand. In this paper, we introduce a meal recommendation dataset (MealRec) that aims to facilitate future research. MealRec is constructed from the user review records of Allrecipe.com, covering 1,500+ users, 7,200+ recipes and 3,800+ meals. Each recipe is described with rich information, such as ingredients, instructions, pictures, category and tags, etc; and each meal is three-course, consisting of an appetizer, a main dish and a dessert. Furthermore, we propose a category-constrained meal recommendation model that is evaluated through comparative experiments with several state-of-the-art bundle recommendation methods on MealRec. Experimental results confirm the superiority of our model and demonstrate that MealRec is a promising testbed for meal recommendation related research. The MealRec dataset and the source code of our proposed model are available at https://github.com/WUT-IDEA/MealRec for access and reproducibility.
Comprehensive Privacy Analysis on Federated Recommender System against Attribute Inference Attacks
Authors: Shijie Zhang, Wei Yuan, Hongzhi Yin
Date: 2022-05-24 00:00:00
Tasks: Attribute, Inference Attack, Privacy Preserving, Recommendation Systems

In recent years, recommender systems are crucially important for the delivery of personalized services that satisfy users' preferences. With personalized recommendation services, users can enjoy a variety of recommendations such as movies, books, ads, restaurants, and more. Despite the great benefits, personalized recommendations typically require the collection of personal data for user modelling and analysis, which can make users susceptible to attribute inference attacks. Specifically, the vulnerability of existing centralized recommenders under attribute inference attacks leaves malicious attackers a backdoor to infer users' private attributes, as the systems remember information of their training data (i.e., interaction data and side information). An emerging practice is to implement recommender systems in the federated setting, which enables all user devices to collaboratively learn a shared global recommender while keeping all the training data on device. However, the privacy issues in federated recommender systems have been rarely explored. In this paper, we first design a novel attribute inference attacker to perform a comprehensive privacy analysis of the state-of-the-art federated recommender models. The experimental results show that the vulnerability of each model component against attribute inference attack is varied, highlighting the need for new defense approaches. Therefore, we propose a novel adaptive privacy-preserving approach to protect users' sensitive data in the presence of attribute inference attacks and meanwhile maximize the recommendation accuracy. Extensive experimental results on two real-world datasets validate the superior performance of our model on both recommendation effectiveness and resistance to inference attacks.
A Dataset for Sentence Retrieval for Open-Ended Dialogues
Authors: Itay Harel, Hagai Taitelbaum, Idan Szpektor, Oren Kurland
Date: 2022-05-24 00:00:00
Tasks: Conversational Search, Retrieval, Sentence, Sentence Retrieval

We address the task of sentence retrieval for open-ended dialogues. The goal is to retrieve sentences from a document corpus that contain information useful for generating the next turn in a given dialogue. Prior work on dialogue-based retrieval focused on specific types of dialogues: either conversational QA or conversational search. To address a broader scope of this task where any type of dialogue can be used, we constructed a dataset that includes open-ended dialogues from Reddit, candidate sentences from Wikipedia for each dialogue and human annotations for the sentences. We report the performance of several retrieval baselines, including neural retrieval models, over the dataset. To adapt neural models to the types of dialogues in the dataset, we explored an approach to induce a large-scale weakly supervised training data from Reddit. Using this training set significantly improved the performance over training on the MS MARCO dataset.
Categorical Reparametrization with Gumbel-Softmax
Authors: Eric Jang, Shixiang Gu, Ben Poole
Date: 2016-11-01 00:00:00
Proceeding: ICLR 2017 2016 11

Categorical variables are a natural choice for representing discrete structure in the world. However, stochastic neural networks rarely use categorical latent variables due to the inability to backpropagate through samples. In this work, we present an efficient gradient estimator that replaces the non-differentiable sample from a categorical distribution with a differentiable sample from a novel Gumbel-Softmax distribution. This distribution has the essential property that it can be smoothly annealed into a categorical distribution. We show that our Gumbel-Softmax estimator outperforms state-of-the-art gradient estimators on structured output prediction and unsupervised generative modeling tasks with categorical latent variables, and enables large speedups on semi-supervised classification.
Mobile3DRecon: Real-time Monocular 3D Reconstruction on a Mobile Phone
Authors: Xingbin Yang, Liyang Zhou, Hanqing Jiang, Zhongliang Tang, Yuanbo Wang, Hujun Bao, Guofeng Zhang
Date: 2020-07-21 00:00:00
Proceeding: ISMAR 2020 7
Tasks: 3D Reconstruction, Depth Estimation, Monocular Depth Estimation, Pose Tracking

We present a real-time monocular 3D reconstruction system on a mobile phone, called Mobile3DRecon. Using an embedded monocular camera, our system provides an online mesh generation capability on back end together with real-time 6DoF pose tracking on front end for users to achieve realistic AR effects and interactions on mobile phones. Unlike most existing state-of-the-art systems which produce only point cloud based 3D models online or surface mesh offline, we propose a novel online incremental mesh generation approach to achieve fast online dense surface mesh reconstruction to satisfy the demand of real-time AR applications. For each keyframe of 6DoF tracking, we perform a robust monocular depth estimation, with a multi-view semi-global matching method followed by a depth refinement post-processing. The proposed mesh generation module incrementally fuses each estimated keyframe depth map to an online dense surface mesh, which is useful for achieving realistic AR effects such as occlusions and collisions. We verify our real-time reconstruction results on two mid-range mobile platforms. The experiments with quantitative and qualitative evaluation demonstrate the effectiveness of the proposed monocular 3D reconstruction system, which can handle the occlusions and collisions between virtual objects and real scenes to achieve realistic AR effects.
Deep Encoder-Decoder Networks for Classification of Hyperspectral and LiDAR Data
Authors: Danfeng Hong, Lianru Gao, Renlong Hang, Bing Zhang, Jocelyn Chanussot
Date: 2020-08-28 00:00:00
Proceeding: IEEE Geoscience and Remote Sensing Letters 2020 8
Tasks: Classification, Decoder, Diversity

Deep learning (DL) has been garnering increasing attention in remote sensing (RS) due to its powerful data representation ability. In particular, deep models have been proven to be effective for RS data classification based on a single given modality. However, with one single modality, the ability in identifying the materials remains limited due to the lack of feature diversity. To overcome this limitation, we present a simple but effective multimodal DL baseline by following a deep encoder–decoder network architecture, EndNet for short, for the classification of hyperspectral and light detection and ranging (LiDAR) data. EndNet fuses the multimodal information by enforcing the fused features to reconstruct the multimodal input in turn. Such a reconstruction strategy is capable of better activating the neurons across modalities compared with some conventional and widely used fusion strategies, e.g., early fusion, middle fusion, and late fusion. Extensive experiments conducted on two popular hyperspectral and LiDAR data sets demonstrate the superiority and effectiveness of the proposed EndNet in comparison with several state-of-the-art baselines in the hyperspectral-LiDAR classification task. The codes will be available at https://github.com/danfenghong/IEEE_GRSL_EndNet , contributing to the RS community.
Leveraging Long and Short-Term Information in Content-Aware Movie Recommendation via Adversarial Training
Authors: Wei Zhao, Benyou Wang, Min Yang, Jianbo Ye, Zhou Zhao, Xiaojun Chen, and Ying Shen
Date: 2020-01-01 00:00:00
Proceeding: IEEE Transactions on Cybernetics 2020 1
Tasks: Movie Recommendation, Recommendation Systems

Movie recommendation systems provide users with ranked lists of movies based on individual’s preferences and constraints. Two types of models are commonly used to generate ranking results: 1) long-term models and 2) session-based models. The long-term-based models represent the interactions between users and movies that are supposed to change slowly across time, while the session-based models encode the information of users’ interests and changing dynamics of movies’ attributes in short terms. In this paper, we propose the LSIC model, leveraging long and short-term information for content-aware movie recommendation using adversarial training. In the adversarial process, we train a generator as an agent of reinforcement learning which recommends the next movie to a user sequentially. We also train a discriminator which attempts to distinguish the generated list of movies from the real records. The poster information of movies is integrated to further improve the performance of movie recommendation, which is specifically essential when few ratings are available. The experiments demonstrate that the proposed model has robust superiority over competitors and achieves the state-of-the-art results.
Actor-Critic Deep Reinforcement Learning for Solving Job Shop Scheduling Problems
Authors: Chien-Liang Liu, CHUAN-CHIN CHANG, AND CHUN-JAN TSENG
Date: 2020-04-14 00:00:00
Proceeding: IEEE Access 2020 4
Tasks: Decision Making, Deep Reinforcement Learning, Job Shop Scheduling, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling, Sequential Decision Making

In the past decades, many optimization methods have been devised and applied to job shop
scheduling problem (JSSP) to find the optimal solution. Many methods assumed that the scheduling results
were applied to static environments, but the whole environments in the real world are always dynamic.
Moreover, many unexpected events such as machine breakdowns and material problems may be present to
adversely affect the initial job scheduling. This work views JSSP as a sequential decision making problem
and proposes to use deep reinforcement learning to cope with this problem. The combination of deep
learning and reinforcement learning avoids handcraft features as used in traditional reinforcement learning,
and it is expected that the combination will make the whole learning phase more efficient. Our proposed
model comprises actor network and critic network, both including convolution layers and fully connected
layer. Actor network agent learns how to behave in different situations, while critic network helps agent
evaluate the value of statement then return to actor network. This work proposes a parallel training method,
combining asynchronous update as well as deep deterministic policy gradient (DDPG), to train the model.
The whole network is trained with parallel training on a multi-agent environment and different simple
dispatching rules are considered as actions. We evaluate our proposed model on more than ten instances
that are present in a famous benchmark problem library – OR library. The evaluation results indicate that our
method is comparative in static JSSP benchmark problems, and achieves a good balance between makespan
and execution time in dynamic environments. Scheduling score of our method is 91.12% in static JSSP
benchmark problems, and 80.78% in dynamic environments.
Rate-Splitting Multiple Access and its Interplay with Intelligent Reflecting Surfaces
Authors: Arthur S. de Sena, Pedro H. J. Nardelli, Daniel B. da Costa, Petar Popovski, Constantinos B. Papadias
Date: 2022-05-24 00:00:00

Rate-splitting multiple access (RSMA) has recently appeared as a powerful technique for improving the downlink performance of multiple-input multiple-output (MIMO) systems. By flexibly managing interference, RSMA can deliver high spectral and energy efficiency, as well as robustness to imperfect channel state information (CSI). In another development, an intelligent reflecting surface (IRS) has emerged as a method to control the wireless environment through software-configurable, near-passive, sub-wavelength reflecting elements. This article presents the potential of synergy between IRS and RSMA. Three important improvements achievable by IRS-RSMA schemes are identified, supported by insightful numerical examples, and mapped to beyond-5G use cases, along with future research directions.
Edge Semantic Cognitive Intelligence for 6G Networks: Novel Theoretical Models, Enabling Framework, and Typical Applications
Authors: Peihao Dong, Qihui Wu, Xiaofei Zhang, Guoru Ding
Date: 2022-05-24 00:00:00
Tasks: Edge-computing, Semantic Communication

Edge intelligence is anticipated to underlay the pathway to connected intelligence for 6G networks, but the organic confluence of edge computing and artificial intelligence still needs to be carefully treated. To this end, this article discusses the concepts of edge intelligence from the semantic cognitive perspective. Two instructive theoretical models for edge semantic cognitive intelligence (ESCI) are first established. Afterwards, the ESCI framework orchestrating deep learning with semantic communication is discussed. Two representative applications are present to shed light on the prospect of ESCI in 6G networks. Some open problems are finally listed to elicit the future research directions of ESCI.
A Variational Bayesian Perspective on Massive MIMO Detection
Authors: Duy H. N. Nguyen, Italo Atzeni, Antti Tölli, A. Lee Swindlehurst
Date: 2022-05-23 00:00:00

Optimal data detection in massive multiple-input multiple-output (MIMO) systems requires prohibitive computational complexity. A variety of detection algorithms have been proposed in the literature, offering different trade-offs between complexity and detection performance. In this paper, we build upon variational Bayes (VB) inference to design low-complexity multiuser detection algorithms for massive MIMO systems. We first examine the massive MIMO detection problem with perfect channel state information at the receiver (CSIR) and show that a conventional VB method with known noise variance yields poor detection performance. To address this limitation, we devise two new VB algorithms that use the noise variance and covariance matrix postulated by the algorithms themselves. We further develop the VB framework for massive MIMO detection with imperfect CSIR. Simulation results show that the proposed VB methods achieve significantly lower detection errors compared with existing schemes for a wide range of channel models.
Aerosense: A Self-Sustainable And Long-Range Bluetooth Wireless Sensor Node for Aerodynamic and Aeroacoustic Monitoring on Wind Turbines
Authors: Tommaso Polonelli, Hanna Müller, Weikang Kong, Raphael Fischer, Luca Benini, Michele Magno
Date: 2022-05-24 00:00:00
Tasks: Data Compression

This paper presents a low-power, self-sustainable, and modular wireless sensor node for aerodynamic and acoustic measurements on wind turbines and other industrial structures. It includes 40 high-accuracy barometers, 10 microphones, 5 differential pressure sensors, and implements a lossy and a lossless on-board data compression algorithm to decrease the transmission energy cost. The wireless transmitter is based on Bluetooth Low Energy 5.1 tuned for long-range and high throughput while maintaining adequate per-bit energy efficiency (80 nJ). Moreover, we field-assessed the node capability to collect precise and accurate aerodynamic data. Outdoor experimental tests revealed that the system can acquire and sustain a data rate of 850 kbps over 438 m. The power consumption while collecting and streaming all measured data is 120 mW, enabling self-sustainability and long-term in-situ monitoring with a 111 cm^2 photovoltaic panel.
PaddleSpeech: An Easy-to-Use All-in-One Speech Toolkit
Authors: HUI ZHANG, Tian Yuan, Junkun Chen, Xintong Li, Renjie Zheng, Yuxin Huang, Xiaojie Chen, Enlei Gong, Zeyu Chen, Xiaoguang Hu, dianhai yu, Yanjun Ma, Liang Huang
Date: 2022-05-20 00:00:00
Proceeding: NAACL (ACL) 2022 7
Tasks: All, Automatic Speech Recognition (ASR), Environmental Sound Classification, Keyword Spotting, Philosophy, Speaker Diarization, Speaker Identification, Speaker Recognition, Speaker Verification, Speech Recognition, Speech Synthesis, Speech-to-Text, Speech-to-Text Translation, text-to-speech, Text to Speech, Text-To-Speech Synthesis

PaddleSpeech is an open-source all-in-one speech toolkit. It aims at facilitating the development and research of speech processing technologies by providing an easy-to-use command-line interface and a simple code structure. This paper describes the design philosophy and core architecture of PaddleSpeech to support several essential speech-to-text and text-to-speech tasks. PaddleSpeech achieves competitive or state-of-the-art performance on various speech datasets and implements the most popular methods. It also provides recipes and pretrained models to quickly reproduce the experimental results in this paper. PaddleSpeech is publicly avaiable at https://github.com/PaddlePaddle/PaddleSpeech.
A Two-Stage Mechanism for Demand Response Markets
Authors: Bharadwaj Satchidanandan, Mardavij Roozbehani, Munther A. Dahleh
Date: 2022-05-24 00:00:00
Tasks: counterfactual, Vocal Bursts Valence Prediction

Demand response involves system operators using incentives to modulate electricity consumption during peak hours or when faced with an incidental supply shortage. However, system operators typically have imperfect information about their customers' baselines, that is, their consumption had the incentive been absent. The standard approach to estimate the reduction in a customer's electricity consumption then is to estimate their counterfactual baseline. However, this approach is not robust to estimation errors or strategic exploitation by the customers and can potentially lead to overpayments to customers who do not reduce their consumption and underpayments to those who do. Moreover, optimal power consumption reductions of the customers depend on the costs that they incur for curtailing consumption, which in general are private knowledge of the customers, and which they could strategically misreport in an effort to improve their own utilities even if it deteriorates the overall system cost. The two-stage mechanism proposed in this paper circumvents the aforementioned issues. In the day-ahead market, the participating loads are required to submit only a probabilistic description of their next-day consumption and costs to the system operator for day-ahead planning. It is only in real-time, if and when called upon for demand response, that the loads are required to report their baselines and costs. They receive credits for reductions below their reported baselines. The mechanism for calculating the credits guarantees incentive compatibility of truthful reporting of the probability distribution in the day-ahead market and truthful reporting of the baseline and cost in real-time. The mechanism can be viewed as an extension of the celebrated Vickrey-Clarke-Groves mechanism augmented with a carefully crafted second-stage penalty for deviations from the day-ahead bids.
Competitive Prediction-Aware Online Algorithms for Energy Generation Scheduling in Microgrids
Authors: Ali Menati, Sid Chi-Kin Chau, Minghua Chen
Date: 2022-05-24 00:00:00
Tasks: Decision Making, Prediction, Scheduling

Online decision-making in the presence of uncertain future information is abundant in many problem domains. In the critical problem of energy generation scheduling for microgrids, one needs to decide when to switch energy supply between a cheaper local generator with startup cost and the costlier on-demand external grid, considering intermittent renewable generation and fluctuating demands. Without knowledge of future input, competitive online algorithms are appealing as they provide optimality guarantees against the optimal offline solution. In practice, however, future input, e.g., wind generation, is often predictable within a limited time window, and can be exploited to further improve the competitiveness of online algorithms. In this paper, we exploit the structure of information in the prediction window to design a novel prediction-aware online algorithm for energy generation scheduling in microgrids. Our algorithm achieves the best competitive ratio to date for this important problem, which is at most $3-2/(1+\mathcal{O}(\frac{1}{w})),$ where $w$ is the prediction window size. We also characterize a non-trivial lower bound of the competitive ratio and show that the competitive ratio of our algorithm is only $9\%$ away from the lower bound, when a few hours of prediction is available. Simulation results based on real-world traces corroborate our theoretical analysis and highlight the advantage of our new prediction-aware design.
C-AND: Mixed Writing Scheme for Disturb Reduction in 1T Ferroelectric FET Memory
Authors: Mor M. Dahan, Evelyn T. Breyer, Stefan Slesazeck, Thomas Mikolajick, Shahar Kvatinsky
Date: 2022-05-24 00:00:00

Ferroelectric field effect transistor (FeFET) memory has shown the potential to meet the requirements of the growing need for fast, dense, low-power, and non-volatile memories. In this paper, we propose a memory architecture named crossed-AND (C-AND), in which each storage cell consists of a single ferroelectric transistor. The write operation is performed using different write schemes and different absolute voltages, to account for the asymmetric switching voltages of the FeFET. It enables writing an entire wordline in two consecutive cycles and prevents current and power through the channel of the transistor. During the read operation, the current and power are mostly sensed at a single selected device in each column. The read scheme additionally enables reading an entire word without read errors, even along long bitlines. Our Simulations demonstrate that, in comparison to the previously proposed AND architecture, the C-AND architecture diminishes read errors, reduces write disturbs, enables the usage of longer bitlines, and saves up to 2.92X in memory cell area.