pwc-archive/papers-with-abstracts huggingface dataset:
Simulating User Satisfaction for the Evaluation of Task-oriented Dialogue Systems
Authors: Weiwei Sun, Shuo Zhang, Krisztian Balog, Zhaochun Ren, Pengjie Ren, Zhumin Chen, Maarten de Rijke
Date: 2021-05-08 00:00:00
Tasks: Domain Generalization, Movie Recommendation, Task-Oriented Dialogue Systems, User Simulation

Evaluation is crucial in the development process of task-oriented dialogue systems. As an evaluation method, user simulation allows us to tackle issues such as scalability and cost-efficiency, making it a viable choice for large-scale automatic evaluation. To help build a human-like user simulator that can measure the quality of a dialogue, we propose the following task: simulating user satisfaction for the evaluation of task-oriented dialogue systems. The purpose of the task is to increase the evaluation power of user simulations and to make the simulation more human-like. To overcome a lack of annotated data, we propose a user satisfaction annotation dataset, USS, that includes 6,800 dialogues sampled from multiple domains, spanning real-world e-commerce dialogues, task-oriented dialogues constructed through Wizard-of-Oz experiments, and movie recommendation dialogues. All user utterances in those dialogues, as well as the dialogues themselves, have been labeled based on a 5-level satisfaction scale. We also share three baseline methods for user satisfaction prediction and action prediction tasks. Experiments conducted on the USS dataset suggest that distributed representations outperform feature-based methods. A model based on hierarchical GRUs achieves the best performance in in-domain user satisfaction prediction, while a BERT-based model has better cross-domain generalization ability.
All Together Now: Teachers as Research Partners in the Design of Search Technology for the Classroom
Authors: Emiliana Murgia, Monica Landoni, Theo Huibers, Maria Soledad Pera
Date: 2021-05-08 00:00:00
Tasks: All

In the classroom environment, search tools are the means for students to access Web resources. The perspectives of students, researchers, and industry practitioners lead the ongoing research debate in this area. In this article, we argue in favor of incorporating a new voice into this debate: teachers. We showcase the value of involving teachers in all aspects related to the design of search tools for the classroom; from the beginning till the end. Driven by our research experience designing, developing, and evaluating new tools to support children's information discovery in the classroom, we share insights on the role of the experts-in-the-loop, i.e., teachers who provide the connection between search tools and students. And yes, in our case, always involving a teacher as a research partner.
Long Short-Term Temporal Meta-learning in Online Recommendation
Authors: Ruobing Xie, Yalong Wang, Rui Wang, Yuanfu Lu, Yuanhang Zou, Feng Xia, Leyu Lin
Date: 2021-05-08 00:00:00
Tasks: Meta-Learning

An effective online recommendation system should jointly capture users' long-term and short-term preferences in both users' internal behaviors (from the target recommendation task) and external behaviors (from other tasks). However, it is extremely challenging to conduct fast adaptations to real-time new trends while making full use of all historical behaviors in large-scale systems, due to the real-world limitations in real-time training efficiency and external behavior acquisition. To address these practical challenges, we propose a novel Long Short-Term Temporal Meta-learning framework (LSTTM) for online recommendation. It arranges user multi-source behaviors in a global long-term graph and an internal short-term graph, and conducts different GAT-based aggregators and training strategies to learn user short-term and long-term preferences separately. To timely capture users' real-time interests, we propose a temporal meta-learning method based on MAML under an asynchronous optimization strategy for fast adaptation, which regards recommendations at different time periods as different tasks. In experiments, LSTTM achieves significant improvements on both offline and online evaluations. It has been deployed on a widely-used online recommendation system named WeChat Top Stories, affecting millions of users.
CASTing a Net: Supporting Teachers with Search Technology
Authors: Garrett Allen, Katherine Landau Wright, Jerry Alan Fails, Casey Kennington, Maria Soledad Pera
Date: 2021-05-07 00:00:00

Past and current research has typically focused on ensuring that search technology for the classroom serves children. In this paper, we argue for the need to broaden the research focus to include teachers and how search technology can aid them. In particular, we share how furnishing a behind-the-scenes portal for teachers can empower them by providing a window into the spelling, writing, and concept connection skills of their students.
Highly Efficient Memory Failure Prediction using Mcelog-based Data Mining and Machine Learning
Authors: Chengdong Yao
Date: 2021-04-24 00:00:00
Tasks: BIG-bench Machine Learning

In the data center, unexpected downtime caused by memory failures can lead to a decline in the stability of the server and even the entire information technology infrastructure, which harms the business. Therefore, whether the memory failure can be accurately predicted in advance has become one of the most important issues to be studied in the data center. However, for the memory failure prediction in the production system, it is necessary to solve technical problems such as huge data noise and extreme imbalance between positive and negative samples, and at the same time ensure the long-term stability of the algorithm. This paper compares and summarizes some commonly used skills and the improvement they can bring. The single model we proposed won the top 14th in the 2nd Alibaba Cloud AIOps Competition belonging to the 25th PAKDD conference. It takes only 30 minutes to pass the online test, while most of the other contestants' solution need more than 3 hours. Codes has been open source to https://www.github.com/ycd2016/acaioc2.
Comparative study of maturation profiles of neural cells in different species with the help of Computer Vision and Deep Learning
Authors: Hritam Basak, Rohit Kundu
Date: 2021-02-07 00:00:00

Comparative analysis of neural composition in interconnected species may focus on their neuronal differences during metamorphosis and, can be used to analyze the variation in “neural underpinnings of cognition” and the vulnerability of neural indisposition. In this paper, we compared Neuroblast Progenitor Cells (NPC) of humans and chimpanzees (and bonobos) to observe if the loss of Amyloid Precursor Protein during evolution can affect NPC migration because of distinctive migration plots of NPC between human and Non-Human-Primates can suggest how chronic phenotypic changes of human-neurons can be different from those of non-human primates’. By assessing the inter-species comparative study of the mean speed of each cell and the half-time of gap-closure in Wound Healing Assay, differential dendritic maturation timings between species can be obtained. Faster NPC-migration may suggest different cell-autonomic features. Hence, we have analyzed the in-vitro pluripotent stem cell technology to find the maturity and development of NPC. We have utilized deep learning algorithms for the segmentation of the cell body from the background and tracking of cell migration from microscopic video obtained from the experimental setup. Our results show different migration patterns between species suggesting different maturation profiles as well as different maturation rates of neuroblast cells in different species. The work laid the groundwork for further analysis in this domain.
Wyner wiretap-like encoding scheme for cyber-physical systems
Authors: Walter Lucia, Amr Youssef
Date: 2021-05-07 00:00:00

In this study, the authors consider the problem of exchanging secrete messages in cyber-physical systems (CPSs) without resorting to cryptographic solutions. In particular, they consider a CPS where the networked controller wants to send a secrete message to the plant. They show that such a problem can be solved by exploiting a Wyner wiretap-like encoding scheme taking advantage of the closed-loop operations typical of feedback control systems. Specifically, by resorting to the control concept of one-step reachable sets, they first show that a wiretap-like encoding scheme exists whenever there is an asymmetry in the plant model knowledge available to control system (the defender) and to the eavesdropper. The effectiveness of the proposed scheme is confirmed by means of a numerical example. Finally, they conclude the study by presenting open design challenges that can be addressed by the research community to improve, in different directions, the secrete message exchange problem in CPSs
MEGADOCK-GUI: a GUI-based complete cross-docking tool for exploring protein-protein interactions
Authors: Masahito Ohue, Yutaka Akiyama
Date: 2021-05-08 00:00:00
Tasks: Drug Discovery

Information on protein-protein interactions (PPIs) not only advances our understanding of molecular biology but also provides important clues for target selection in drug discovery and the design of PPI inhibitors. One of the techniques used for computational prediction of PPIs is protein-protein docking calculations, and a variety of software has been developed. However, a friendly interface for users who are not sufficiently familiar with the command line interface has not been developed so far. In this study, we have developed a graphical user interface, MEGADOCK-GUI, which enables users to easily predict PPIs and protein complex structures. In addition to the original 3-D molecular viewer and input file preparation functions, MEGADOCK-GUI is software that can automatically perform complete cross-docking of $M$ vs. $N$ proteins. With MEGADOCK-GUI, various applications related to the prediction of PPIs, such as ensemble docking that handles multiple conformations of proteins and screening of binding partner proteins that bind to specific proteins, can now be easily performed.
Validation of image systems simulation technology using a Cornell Box
Authors: Zheng Lyu, Krithin Kripakaran, Max Furth, Eric Tang, Brian Wandell, Joyce Farrell
Date: 2021-05-10 00:00:00

We describe and experimentally validate an end-to-end simulation of a digital camera. The simulation models the spectral radiance of 3D-scenes, formation of the spectral irradiance by multi-element optics, and conversion of the irradiance to digital values by the image sensor. We quantify the accuracy of the simulation by comparing real and simulated images of a precisely constructed, three-dimensional high dynamic range test scene. Validated end-to-end software simulation of a digital camera can accelerate innovation by reducing many of the time-consuming and expensive steps in designing, building and evaluating image systems.
PyPlutchik: visualising and comparing emotion-annotated corpora
Authors: Alfonso Semeraro, Salvatore Vilella, Giancarlo Ruffo
Date: 2021-04-19 00:00:00

The increasing availability of textual corpora and data fetched from social networks is fuelling a huge production of works based on the model proposed by psychologist Robert Plutchik, often referred simply as the ``Plutchik Wheel''. Related researches range from annotation tasks description to emotions detection tools. Visualisation of such emotions is traditionally carried out using the most popular layouts, as bar plots or tables, which are however sub-optimal. The classic representation of the Plutchik's wheel follows the principles of proximity and opposition between pairs of emotions: spatial proximity in this model is also a semantic proximity, as adjacent emotions elicit a complex emotion (a primary dyad) when triggered together; spatial opposition is a semantic opposition as well, as positive emotions are opposite to negative emotions. The most common layouts fail to preserve both features, not to mention the need of visually allowing comparisons between different corpora in a blink of an eye, that is hard with basic design solutions. We introduce PyPlutchik, a Python library specifically designed for the visualisation of Plutchik's emotions in texts or in corpora. PyPlutchik draws the Plutchik's flower with each emotion petal sized after how much that emotion is detected or annotated in the corpus, also representing three degrees of intensity for each of them. Notably, PyPlutchik allows users to display also primary, secondary, tertiary and opposite dyads in a compact, intuitive way. We substantiate our claim that PyPlutchik outperforms other classic visualisations when displaying Plutchik emotions and we showcase a few examples that display our library's most compelling features.
An interactive dashboard for searching and comparing soccer performance scores
Authors: Paolo Cintia, Giovanni Mauro, Luca Pappalardo, Paolo Ferragina
Date: 2021-04-16 00:00:00

The performance of soccer players is one of most discussed aspects by many actors in the soccer industry: from supporters to journalists, from coaches to talent scouts. Unfortunately, the dashboards available online provide no effective way to compare the evolution of the performance of players or to find players behaving similarly on the field. This paper describes the design of a web dashboard that interacts via APIs with a performance evaluation algorithm and provides graphical tools that allow the user to perform many tasks, such as to search or compare players by age, role or trend of growth in their performance, find similar players based on their pitching behavior, change the algorithm's parameters to obtain customized performance scores. We also describe an example of how a talent scout can interact with the dashboard to find young, promising talents.
On the Role of Incentives in Evolutionary Approaches to Organizational Design
Authors: Stephan Leitner
Date: 2021-05-10 00:00:00

This paper introduces a model of a stylized organization that is comprised of several departments that autonomously allocate tasks. To do so, the departments either take short-sighted decisions that immediately maximize their utility or take long-sighted decisions that aim at minimizing the interdependencies between tasks. The organization guides the departments' behavior by either an individualistic, a balanced, or an altruistic linear incentive scheme. Even if tasks are perfectly decomposable, altruistic incentive schemes are preferred over individualistic incentive schemes since they substantially increase the organization's performance. Interestingly, if altruistic incentive schemes are effective, short-sighted decisions appear favorable since they do not only increase performance in the short run but also result in significantly higher performances in the long run.
The Behavior of Internet Traffic for Internet Services during COVID-19 Pandemic Scenario
Authors: Carlos Alexandre Gouvea da Silva, Allan Christian Krainski Ferrari, Cristiano Osinski, Douglas Antonio Firmino Pelacini
Date: 2021-05-10 00:00:00
Tasks: Blocking

Since the end of 2019, the SARS-CoV-2 virus known as COVID-19 has spread rapidly around the world, forcing many governments to impose restrictive blocking or lockdown to combat the pandemic. With locomotion restriction of people in almost of countries of the world, workers and students needed to keep their activities at home. As a result, people's behavior, habits, and the way they started using the Internet changed significantly. Like professionals of offices, the younger played an important role in this behavior, especially in the type of resources used by them. As result, the characterization and traffic of communication networks were affected in some way. In this perspective article, we join from many available studies about the COVID-19 effect at networks and investigate the effects on the Internet traffic of using services such as video streaming, video conferencing, and gaming during 2020's months of the pandemic.
Study on the temporal pooling used in deep neural networks for speaker verification
Authors: Mickael Rouvier, Pierre-Michel Bousquet, Jarod Duret
Date: 2021-05-10 00:00:00
Tasks: Speaker Verification

The x-vector architecture has recently achieved state-of-the-art results on the speaker verification task. This architecture incorporates a central layer, referred to as temporal pooling, which stacks statistical parameters of the acoustic frame distribution. This work proposes to highlight the significant effect of the temporal pooling content on the training dynamics and task performance. An evaluation with different pooling layers is conducted, that is, including different statistical measures of central tendency. Notably, 3rd and 4th moment-based statistics (skewness and kurtosis) are also tested to complete the usual mean and standard-deviation parameters. Our experiments show the influence of the pooling layer content in terms of speaker verification performance, but also for several classification tasks (speaker, channel or text related), and allow to better reveal the presence of external information to the speaker identity depending on the layer content.
Learned Transferable Architectures Can Surpass Hand-Designed Architectures for Large Scale Speech Recognition
Authors: Liqiang He, Dan Su, Dong Yu
Date: 2020-08-25 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), GPU, Neural Architecture Search, speech-recognition, Speech Recognition

In this paper, we explore the neural architecture search (NAS) for automatic speech recognition (ASR) systems. With reference to the previous works in the computer vision field, the transferability of the searched architecture is the main focus of our work. The architecture search is conducted on the small proxy dataset, and then the evaluation network, constructed with the searched architecture, is evaluated on the large dataset. Especially, we propose a revised search space for speech recognition tasks which theoretically facilitates the search algorithm to explore the architectures with low complexity. Extensive experiments show that: (i) the architecture searched on the small proxy dataset can be transferred to the large dataset for the speech recognition tasks. (ii) the architecture learned in the revised search space can greatly reduce the computational overhead and GPU memory usage with mild performance degradation. (iii) the searched architecture can achieve more than 20% and 15% (average on the four test sets) relative improvements respectively on the AISHELL-2 dataset and the large (10k hours) dataset, compared with our best hand-designed DFSMN-SAN architecture. To the best of our knowledge, this is the first report of NAS results with large scale dataset (up to 10K hours), indicating the promising application of NAS to industrial ASR systems.
Latency-Controlled Neural Architecture Search for Streaming Speech Recognition
Authors: Liqiang He, Shulin Feng, Dan Su, Dong Yu
Date: 2021-05-08 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Neural Architecture Search, speech-recognition, Speech Recognition

Neural architecture search (NAS) has attracted much attention and has been explored for automatic speech recognition (ASR). In this work, we focus on streaming ASR scenarios and propose the latency-controlled NAS for acoustic modeling. First, based on the vanilla neural architecture, normal cells are altered to causal cells to control the total latency of the architecture. Second, a revised operation space with a smaller receptive field is proposed to generate the final architecture with low latency. Extensive experiments show that: 1) Based on the proposed neural architecture, the neural networks with a medium latency of 550ms (millisecond) and a low latency of 190ms can be learned in the vanilla and revised operation space respectively. 2) For the low latency setting, the evaluation network can achieve more than 19\% (average on the four test sets) relative improvements compared with the hybrid CLDNN baseline, on a 10k-hour large-scale dataset.
Domestic activities clustering from audio recordings using convolutional capsule autoencoder network
Authors: Ziheng Lin, Yanxiong Li, Zhangjin Huang, WenHao Zhang, Yufeng Tan, YiChun Chen, Qianhua He
Date: 2021-05-08 00:00:00
Tasks: Clustering

Recent efforts have been made on domestic activities classification from audio recordings, especially the works submitted to the challenge of DCASE (Detection and Classification of Acoustic Scenes and Events) since 2018. In contrast, few studies were done on domestic activities clustering, which is a newly emerging problem. Domestic activities clustering from audio recordings aims at merging audio clips which belong to the same class of domestic activity into a single cluster. Domestic activities clustering is an effective way for unsupervised estimation of daily activities performed in home environment. In this study, we propose a method for domestic activities clustering using a convolutional capsule autoencoder network (CCAN). In the method, the deep embeddings are learned by the autoencoder in the CCAN, while the deep embeddings which belong to the same class of domestic activities are merged into a single cluster by a clustering layer in the CCAN. Evaluated on a public dataset adopted in DCASE-2018 Task 5, the results show that the proposed method outperforms state-of-the-art methods in terms of the metrics of clustering accuracy and normalized mutual information.
Modeling and Identification of Low Rank Vector Processes
Authors: Giorgio Picci, Wenqi Cao, Anders Lindquist
Date: 2020-12-09 00:00:00

We study modeling and identification of processes with a spectral density matrix of low rank. Equivalently, we consider processes having an innovation of reduced dimension for which Prediction Error Methods (PEM) algorithms are not directly applicable. We show that these processes admit a special feedback structure with a deterministic feedback channel which can be used to split the identification in two steps, one of which can be based on standard algorithms while the other is based on a deterministic least squares fit.
Interaction Theory of Hazard-Target System
Authors: Ji Ge, Yu-Yuan Zhang, Kai-Li Xu, Ji-Shuo Li, Xi-Wen Yao, Chun-Ying Wu, Shuang-Yuan Li, Fang Yan, Jin-Jia Zhang, Qing-Wei Xu
Date: 2021-05-10 00:00:00
Tasks: Decision Making

Major accidents (e.g., the Space Shuttle Challenger disaster in the USA, the Bhopal Disaster in India, Fukushima nuclear accident in Japan, Tianjin Port fire and explosion accident in China) have occurred all over the world. Safety scientists are always trying to understand why these accidents happened and how to prevent these accidents. Accident models and theories form the basis for many safety research fields and practices such as investigation of accidents, design of a safer system and decision making on safety related field. There is no universally accepted model with useful elements relating to understanding accident causation, although many accident causation models exist. Based on STAMP and RMF, we proposed a new theory named the Interaction Theory of Hazard-Target System (ITHTS) that incorporate human, organisational and technological characteristics in the same framework. Accident analysis methods provide the necessary information to analysis the accident in a specific setting. In order to solve the issues that current accident analysis methods still face, we proposed a new systemic accident analysis method based on ITHTS and STPA. We choose Tianjin Port fire and explosion accident in China as a case study to demonstrate the viability of the Interaction Theory of Hazard-target System and the applicability of the new accident analysis method. It is concluded that ITHTS can explain the phenomena in safety practice and the new accident analysis method can be application in the explanation and analysis of major accident.