Reviewerly/exHarmony huggingface dataset:
CUI@CSCW: Collaborating through Conversational User Interfaces

This virtual workshop seeks to bring together the burgeoning communities centred on the design, development, application, and study of so-called Conversational User Interfaces (CUIs). CUIs are used in myriad contexts, from online support chatbots through to entertainment devices in the home. In this workshop, we will examine the challenges involved in transforming CUIs into everyday computing devices capable of supporting collaborative activities across space and time. Additionally, this workshop seeks to establish a cohesive CUI community and research agenda within CSCW. We will examine the roles in which CSCW research can contribute insights into understanding how CUIs are or can be used in a variety of settings, from public to private, and how they can be brought into a potentially unlimited number of tasks. This proposed workshop will bring together researchers from academia and practitioners from industry to survey the state-of-the-art in terms of CUI design, use, and understanding, and will map new areas for work including addressing the technical, social, and ethical challenges that lay ahead. By bringing together existing researchers and new ideas in this space, we intend to foster a strong community and enable potential future collaborations.
A 3mm<sup>2</sup> Programmable Bayesian Inference Accelerator for Unsupervised Machine Perception using Parallel Gibbs Sampling in 16nm

This paper describes a 16nm programmable accelerator for unsupervised probabilistic machine perception tasks that performs Bayesian inference on probabilistic models mapped onto a 2D Markov Random Field, using MCMC. Exploiting two degrees of parallelism, it performs Gibbs sampling inference at up to 1380× faster with 1965× less energy than an Arm Cortex-A53 on the same SoC, and 1.5× faster with 6.3× less energy than an embedded FPGA in the same technology. At 0.8V, it runs at 450MHz, producing 44.6 MSamples/s at 0.88 nJ/sample.
IoEV-Chain: A 5G-Based Secure Inter-Connected Mobility Framework for the Internet of Electric Vehicles

Recent advances in 5G-based disruptive technologies have ushered in a new smart era of interconnected mobility. Hence, smart city planners around the globe are facing the challenges of leveraging these new modalities of future mobility solutions. Some key innovations toward disruptive mobility are the next generation of the Internet of electric vehicles and ultra-high-speed 5G communication paradigms. However, adding security to IoEV data remains of great concern. In this study, we offer a novel secure blockchain and off-chainbased 5G mobility framework that enables the security of IoEV data at rest, in transmission, and in the storage medium. We address the 5G security vulnerabilities in the areas of mutual authentication and authorization, remote software updates, confidentiality and integrity protection, secure provisioning of 5G services, secure storage of IoEV and personal data, and location tracking. We present our experience in building the architecture and the implementation of the framework, in addition to the system evaluation results.
Faulty actuator-based control synthesis for interval type-2 fuzzy systems via memory state feedback approach

This study focuses on the memory state feedback control problem for a family of time delayed interval type-2 Takagi-Sugeno fuzzy systems with external disturbance and actuator faulty input. Notably, an actuator fault model is implemented in the control design. In this study, the membership functions, the premise variables of the controller and the considered plant model are chosen freely. By employing the Lyapunov–Krasovskii functional method, the linear matrix inequality approach and the Abel lemma-based finite sum inequality, a set of sufficient conditions that guarantees the stability of the considered system is obtained via the extended passivity performance index. The developed theoretical results are validated with an artificial example and an application-oriented example.
Quantum amplitude estimation algorithms on IBM quantum devices

Since the publication of the Quantum Amplitude Estimation (QAE) algorithm by Brassard et al., 2002, several variations have been proposed, such as Aaronson et al., 2019, Grinko et al., 2019, and Suzuki et al., 2020. The main difference between the original and the variants is the exclusion of Quantum Phase Estimation (QPE) by the latter. This difference is notable given that QPE is the key component of original QAE, but is composed of many operations considered expensive for the current NISQ era devices. We compare two recently proposed variants (Grinko et al., 2019 and Suzuki et al., 2020) by implementing them on the IBM Quantum device using Qiskit, an open source framework for quantum computing. We analyze and discuss advantages of each algorithm from the point of view of their implementation and performance on a quantum computer.
Online fringe pitch selection for defocusing a binary square pattern projection phase-shifting method

A three-dimensional (3D) shape measurement system using defocusing binary fringe projection can perform high-speed and flexible measurements. In this technology, determining the fringe pitch that matches the current projection's defocus amount is of great significance for an accurate measurement. In this paper, we propose an online binary fringe pitch selection framework. First, by analyzing the fringe images captured by the camera, the defocus amount of projection can be obtained. Next, based on analysis of the harmonic error and camera noise, we establish a mathematical model of the normalized phase error. The fringe pitch that minimizes this normalized phase error is then selected as the optimal fringe pitch for subsequent measurements, which can also lead to more accuracy and robust measurement results. Compared with current methods, our method does not require offline defocus-distance calibration. However, it can achieve the same effect as the offline calibration method. It is also more flexible and efficient. Our experiments validate the effectiveness and practicability of the proposed method.
Antidecay LSTM for Siamese Tracking With Adversarial Learning

Visual tracking is one of the fundamental tasks in computer vision with many challenges, and it is mainly due to the changes in the target’s appearance in temporal and spatial domains. Recently, numerous trackers model the appearance of the targets in the spatial domain well by utilizing deep convolutional features. However, most of these CNN-based trackers only take the appearance variations between two consecutive frames in a video sequence into consideration. Besides, some trackers model the appearance of the targets in the long term by applying RNN, but the decay of the target’s features degrades the tracking performance. In this article, we propose the antidecay long short-term memory (AD-LSTM) for the Siamese tracking. Especially, we extend the architecture of the standard LSTM in two aspects for the visual tracking task. First, we replace all of the fully connected layers with convolutional layers to extract the features with spatial structure. Second, we improve the architecture of the cell unit. In this way, the information of the target appearance can flow through the AD-LSTM without decay as long as possible in the temporal domain. Meanwhile, since there is no ground truth for the feature maps generated by the AD-LSTM, we propose an adversarial learning algorithm to optimize the AD-LSTM. With the help of adversarial learning, the Siamese network can generate the response maps more accurately, and the AD-LSTM can generate the feature maps of the target more robustly. The experimental results show that our tracker performs favorably against the state-of-the-art trackers on six challenging benchmarks: OTB-100, TC-128, VOT2016, VOT2017, GOT-10k, and TrackingNet.
Research on Text Classification Based on Automatically Extracted Keywords

Automatic keywords extraction and classification tasks are important research directions in the domains of NLP (natural language processing), information retrieval, and text mining. As the fine granularity abstracted from text data, keywords are also the most important feature of text data, which has great practical and potential value in document classification, topic modeling, information retrieval, and other aspects. The compact representation of documents can be achieved through keywords, which contains massive significant information. Therefore, it may be quite advantageous to realize text classification with high-dimensional feature space. For this reason, this study designed a supervised keyword classification method based on TextRank keyword automatic extraction technology and optimize the model with the genetic algorithm to contribute to modeling the keywords of the topic for text classification.
Jointly Encoding Word Confusion Network and Dialogue Context with BERT for Spoken Language Understanding

Spoken Language Understanding (SLU) converts hypotheses from automatic speech recognizer (ASR) into structured semantic representations. ASR recognition errors can severely degenerate the performance of the subsequent SLU module. To address this issue, word confusion networks (WCNs) have been used to encode the input for SLU, which contain richer information than 1-best or n-best hypotheses list. To further eliminate ambiguity, the last system act of dialogue context is also utilized as additional input. In this paper, a novel BERT based SLU model (WCN-BERT SLU) is proposed to encode WCNs and the dialogue context jointly. It can integrate both structural information and ASR posterior probabilities of WCNs in the BERT architecture. Experiments on DSTC2, a benchmark of SLU, show that the proposed method is effective and can outperform previous state-of-the-art models significantly.
Weak-Attention Suppression for Transformer Based Speech Recognition

Transformers, originally proposed for natural language processing (NLP) tasks, have recently achieved great success in automatic speech recognition (ASR). However, adjacent acoustic units (i.e., frames) are highly correlated, and long-distance dependencies between them are weak, unlike text units. It suggests that ASR will likely benefit from sparse and localized attention. In this paper, we propose Weak-Attention Suppression (WAS), a method that dynamically induces sparsity in attention probabilities. We demonstrate that WAS leads to consistent Word Error Rate (WER) improvement over strong transformer baselines. On the widely used LibriSpeech benchmark, our proposed method reduced WER by 10%$ on test-clean and 5% on test-other for streamable transformers, resulting in a new state-of-the-art among streaming models. Further analysis shows that WAS learns to suppress attention of non-critical and redundant continuous acoustic frames, and is more likely to suppress past frames rather than future ones. It indicates the importance of lookahead in attention-based ASR models.
Visual-Relation Conscious Image Generation from Structured-Text

We propose an end-to-end network for image generation from given structured-text that consists of the visual-relation layout module and stacking-GANs. Our visual-relation layout module uses relations among entities in the structured-text in two ways: comprehensive usage and individual usage. We comprehensively use all relations together to localize initial bounding-boxes (BBs) of all the entities. We use individual relation separately to predict from the initial BBs relation-units for all the relations. We then unify all the relation-units to produce the visual-relation layout, i.e., BBs for all the entities so that each of them uniquely corresponds to each entity while keeping its involved relations. Our visual-relation layout reflects the scene structure given in the input text. The stacking-GANs is the stack of three GANs conditioned on the visual-relation layout and the output of previous GAN, consistently capturing the scene structure. Our network realistically renders entities’ details while keeping the scene structure. Experimental results on two public datasets show the effectiveness of our method.
Explainable Sentiment Analysis Application for Social Media Crisis Management in Retail

Sentiment Analysis techniques enable the automatic extraction of sentiment in social media data, including popular platforms as Twitter.For retailers and marketing analysts, such methods can support the understanding of customers' attitudes towards brands, especially to handle crises that cause behavioural changes in customers, including the COVID-19 pandemic.However, with the increasing adoption of black-box machine learning-based techniques, transparency becomes a need for those stakeholders to understand why a given sentiment is predicted, which is rarely explored for retailers facing social media crises.This study develops an Explainable Sentiment Analysis (XSA) application for Twitter data, and proposes research propositions focused on evaluating such application in a hypothetical crisis management scenario.Particularly, we evaluate, through discussions and a simulated user experiment, the XSA support for understanding customer's needs, as well as if marketing analysts would trust such an application for their decision-making processes.Results illustrate the XSA application can be effective in providing the most important words addressing customers sentiment out of individual tweets, as well as the potential to foster analysts' confidence in such support. INTRODUCTIONCrisis management and monitoring in social media are essential for retailers to understand their customers' needs (Mehta et al., 2020).A crisis in this context is defined as the negative reaction of customers towards particular products or services of a company, which can happen through their comments and messages on social media platforms (Vignal Lambret and Barki, 2018).That adverse reaction can impact organizations' reputation, as customers are increasingly adopting social media to reveal their opinions and sentiment on brands (Cirqueira et al., 2018).
Generating Diverse Translation from Model Distribution with Dropout

Despite the improvement of translation quality, neural machine translation (NMT) often suffers from the lack of diversity in its generation.In this paper, we propose to generate diverse translations by deriving a large number of possible models with Bayesian modelling and sampling models from them for inference.The possible models are obtained by applying concrete dropout to the NMT model and each of them has specific confidence for its prediction, which corresponds to a posterior model distribution under specific training data in the principle of Bayesian modeling.With variational inference, the posterior model distribution can be approximated with a variational distribution, from which the final models for inference are sampled.We conducted experiments on Chinese-English and English-German translation tasks and the results shows that our method makes a better trade-off between diversity and accuracy.
Using Smartphone Accelerometer for Human Physical Activity and Context Recognition in-the-Wild

Adaptation of smart devices is frequently rising, where a new generation of smartphones is growing into an emerging platform for personal computing, monitoring, and private data processing. Smartphone sensing allows collecting data from immediate environments and surroundings to recognize human daily living activities and behavioral contexts. Although smartphone-based activity recognition is universal; however, there is a need for coinciding recognition of in-the-wild human physical activities and the associated contexts. This research work proposes a two-level scheme for in-the-wild recognition of human physical activities and the corresponding contexts based on the smartphone accelerometer data. Different classifiers are used for experimentation purposes, and the achieved results validate the efficiency of the proposed scheme.
Plan ahead: Self-Supervised Text Planning for Paragraph Completion Task

Despite the recent success of contextualized language models on various NLP tasks, language model itself cannot capture textual coherence of a long, multi-sentence document (e.g., a paragraph).Humans often make structural decisions on what and how to say about before making utterances.Guiding surface realization with such high-level decisions and structuring text in a coherent way is essentially called a planning process.Where can the model learn such high-level coherence?A paragraph itself contains various forms of inductive coherence signals called self-supervision in this work, such as sentence orders, topical keywords, rhetorical structures, and so on.Motivated by that, this work proposes a new paragraph completion task PAR-COM; predicting masked sentences in a paragraph.However, the task suffers from predicting and selecting appropriate topical content with respect to the given context.To address that, we propose a self-supervised text planner SSPlanner that predicts what to say first (content prediction), then guides the pretrained language model (surface realization) using the predicted content.SSPlanner outperforms the baseline generation models on the paragraph completion task in both automatic and human evaluation.We also find that a combination of noun and verb types of keywords is the most effective for content selection.As more number of content keywords are provided, overall generation quality also increases.
CATTmew: Defeating Software-Only Physical Kernel Isolation

All the state-of-the-art rowhammer attacks can break the MMU-enforced inter-domain isolation because the physical memory owned by each domain is adjacent to each other. To mitigate these attacks, physical domain isolation, introduced by CATT, physically separates each domain by dividing the physical memory into multiple partitions and keeping each partition occupied by only one domain. CATT implemented physical kernel isolation as the first generic and practical software-only defense to protect kernel from being rowhammered as kernel is one of the most appealing targets. In this paper, we develop a novel exploit that could effectively defeat the CATT implementation and gain both root and kernel privileges, indicating that the physical kernel isolation is not secure in practice. Our exploit can work without exhausting the page cache or the system memory, or relying on the information of the virtual-to-physical address mapping. The exploit is motivated by our key observation that the modern OSes have double-owned kernel buffers (e.g., video buffers and SCSI Generic buffers) owned concurrently by the kernel and user domains. The existence of such buffers invalidates the physical separation enforced by CATT and makes the rowhammer-based attack possible again. Existing conspicuous rowhammer attacks achieving the root/kernel privilege escalation exhaust the page cache or even the whole system memory. Instead, we propose a new technique, named Memory Ambush. It is able to place the hammerable double-owned kernel buffers physically adjacent to the target objects (e.g., page tables) with only a small amount of memory. As a result, our exploit is stealthier and has fewer memory footprints. We also replace the inefficient rowhammer algorithm that blindly picks up addresses to hammer with an efficient one. Our algorithm selects suitable addresses based on an existing timing channel. We implement our exploit on the Linux kernel version 4.10.0. Our experiment results indicate that a successful attack could be done within 1 minute. The occupied memory is as low as 88 MB.
Identification and Classification of Cyberbullying Posts: A Recurrent Neural Network Approach Using Under-Sampling and Class Weighting

With the number of users of social media and web platforms increasing day-by-day in recent years, cyberbullying has become a ubiquitous problem on the internet. Controlling and moderating these social media platforms manually for online abuse and cyberbullying has become a very challenging task. This paper proposes a Recurrent Neural Network (RNN) based approach for the identification and classification of cyberbullying posts. In highly imbalanced input data, a Tomek Links approach does under-sampling to reduce the data imbalance and remove ambiguities in class labelling. Further, the proposed classification model uses Max-Pooling in combination with Bi-directional Long Short-Term Memory (LSTM) network and attention layers. The proposed model is evaluated using Wikipedia datasets to establish the effectiveness of identifying and classifying cyberbullying posts. The extensive experimental results show that our approach performs well in comparison to competing approaches in terms of precision, recall, with F1 score as 0.89, 0.86 and 0.88, respectively.
ProConAR: A Tool Support for Model-Based AR Product Configuration

Mobile shopping apps have been using Augmented Reality (AR) in the last years to place their products in the environment of the customer. While this is possible with atomic 3D objects, there is still a lack in the runtime configuration of 3D object compositions based on user needs and environmental constraints. For this, we previously developed an approach for model-based AR-assisted product configuration based on the concept of Dynamic Software Product Lines. In this demonstration paper, we present the corresponding tool support ProConAR in the form of a Product Modeler and a Product Configurator. While the Product Modeler is an Angular web app that splits products (e.g. table) up into atomic parts (e.g. tabletop, table legs, funnier) and saves it within a configuration model, the Product Configurator is an Android client that uses the configuration model to place different product configurations within the environment of the customer. We show technical details of our ready to use tool-chain ProConAR by describing its implementation and usage as well as pointing out future research directions.
Fast and Automatic Image Segmentation Using Superpixel-Based Graph Clustering

Although automatic fuzzy clustering framework (AFCF) based on improved density peak clustering is able to achieve automatic and efficient image segmentation, the framework suffers from two problems. The first one is that the adaptive morphological reconstruction (AMR) employed by the AFCF is easily influenced by the initial structuring element. The second one is that the improved density peak clustering using a density balance strategy is complex for finding potential clustering centers. To address these two problems, we propose a fast and automatic image segmentation algorithm using superpixel-based graph clustering (FAS-SGC). The proposed algorithm has two major contributions. First, the AMR based on regional minimum removal (AMR-RMR) is presented to improve the superpixel result generated by the AMR. The binary morphological reconstruction is performed on a regional minimum image, which overcomes the problem that the initial structuring element of the AMR is chosen empirically, since the geometrical information of images is effectively explored and utilized. Second, we use an eigenvalue gradient clustering (EGC) instead of improved density peak (DP) algorithms to obtain potential clustering centers, since the EGC is faster and requires fewer parameters than the DP algorithm. Experiments show that the proposed algorithm is able to achieve automatic image segmentation, providing better segmentation results while requiring less execution time than other state-of-the-art algorithms.