Reviewerly/exHarmony huggingface dataset:
A Novel Label-Guided Attention Method for Multilabel Classification of Multiple Power Quality Disturbances

Multiple power quality disturbance (PQD) contains various single disturbances, so its classification is essentially a multilabel classification task. Due to the complexity of multilabel tasks, the performance of existing multilabel methods for PQD is hard to meet practical needs, which severely restricts the engineering application of multilabel methods. Therefore, in this article, we propose a novel multilabel method named LGAN, which incorporates deep learning and explores the correlations between PQD labels to improve performance. First, 1-D convolutional neural network extracts features automatically from PQD signals. Then, a label-guided attention module is adopted to learn the specific feature representation of each PQD category. Finally, the bidirectional recurrent neural network models the label correlations from the label-related features, and then predicts the final PQD category. Various comparative experiments show that the performance of LGAN is much better than the existing multilabel methods. Additionally, the test using real-time detection platform further verifies the availability of the proposed method.
Semantically Robust Unpaired Image Translation for Data with Unmatched Semantics Statistics

Many applications of unpaired image-to-image translation require the input contents to be preserved semantically during translations. Unaware of the inherently unmatched semantics distributions between source and target domains, existing distribution matching methods (i.e., GAN-based) can give undesired solutions. In particular, although producing visually reasonable outputs, the learned models usually flip the semantics of the inputs. To tackle this without using extra supervisions, we propose to enforce the translated outputs to be semantically invariant w.r.t. small perceptual variations of the inputs, a property we call "semantic robustness". By optimizing a robustness loss w.r.t. multi-scale feature space perturbations of the inputs, our method effectively reduces semantics flipping and produces translations that outperform existing methods both quantitatively and qualitatively.
Wasserstein Coupled Graph Learning for Cross-Modal Retrieval

Graphs play an important role in cross-modal image-text understanding as they characterize the intrinsic structure which is robust and crucial for the measurement of crossmodal similarity. In this work, we propose a Wasserstein Coupled Graph Learning (WCGL) method to deal with the cross-modal retrieval task. First, graphs are constructed according to two input cross-modal samples separately, and passed through the corresponding graph encoders to extract robust features. Then, a Wasserstein coupled dictionary, containing multiple pairs of counterpart graph keys with each key corresponding to one modality, is constructed for further feature learning. Based on this dictionary, the input graphs can be transformed into the dictionary space to facilitate the similarity measurement through a Wasserstein Graph Embedding (WGE) process. The WGE could capture the graph correlation between the input and each corresponding key through optimal transport, and hence well characterize the inter-graph structural relationship. To further achieve discriminant graph learning, we specifically define a Wasserstein discriminant loss on the coupled graph keys to make the intra-class (counterpart) keys more compact and inter-class (non-counterpart) keys more dispersed, which further promotes the final cross-modal retrieval task. Experimental results demonstrate the effectiveness and state-of-the-art performance.
Breaking one barrier at a time: how women developers cope in a men-dominated industry

Context: Participation of women in software development teams is surrounded by several challenges — including gender bias, the difficulty of engagement, and general acceptance of the teams. Objective: We investigated the women perception in relation to interactions, contributions, gender bias, barriers and challenges that they may face in their work. Method: To achieve this goal, we conducted semi-structured interviews with 17 Brazilian women. We analyzed the data using the principles of Grounded Theory and identified three categories: Challenges, Barriers and Gender Issues. Results: Our findings reveal that tasks considered more complex are allocated to men on the team. Our respondents also commonly observe gender bias from men on the team. Conclusions: The findings indicate that most of the interviewed women observe a sexist behavior amongst the software development team members. Moreover, most of the participants stated that few women perform a leadership role in their team. We close by presenting suggestions to more inclusive work environments.
Deep Transformer modeling via grouping skip connection for neural machine translation

Most of the deep neural machine translation (NMT) models are based on a bottom-up feedforward fashion, in which representations in low layers construct or modulate high layers representations. We conjecture that this unidirectional encoding fashion could be a potential issue in building a deep NMT model. In this paper, we propose to build a deeper Transformer encoder by properly organizing encoder layers into multiple groups, which are connected via a grouping skip connection mechanism. Here, each group is further appropriately fed into subsequent groups to build a deep Transformer encoder. In this way, we successfully build a deep Transformer encoder with up to 48 layers. Moreover, we can share the parameters among groups to extend the encoder (virtual) depth even without introducing additional parameters. Detailed experimentation on the large-scale WMT (workshop on machine translation) 2014 English-to-German, English-to-French translation, WMT 2016 English-to-German, and WMT 2017 Chinese-to-English tasks demonstrates that our proposed deep Transformer model significantly outperforms the strong Transformer baseline. Furthermore, we carry out linguistic probing tasks to analyze the problems existing in the original Transformer model and explain how our deep Transformer encoder improves the translation quality. One particularly nice property of our approach is that it is incredibly easy to implement. We make our code available on Github https://github.com/liyc7711/deep-nmt.
An Indoor Crowd Movement Trajectory Benchmark Dataset

In recent years, technologies of indoor crowd positioning and movement data analysis have received widespread attention in the fields of reliability management, indoor navigation, and crowd behavior monitoring. However, only a few indoor crowd movement trajectory datasets are available to the public, thus restricting the development of related research and application. This paper contributes a new benchmark dataset of indoor crowd movement trajectories. This dataset records the movements of over 5000 participants at a three day large academic conference in a two story indoor venue. The conference comprises varied activities, such as academic seminars, business exhibitions, a hacking contest, interviews, tea breaks, and a banquet. The participants are divided into seven types according to participation permission to the activities. Some of them are involved in anomalous events, such as loss of items, unauthorized accesses, and equipment failures, forming a variety of spatial temporal movement patterns. In this paper, we first introduce the scenario design, entity and behavior modeling, and data generator of the dataset. Then, a detailed ground truth of the dataset is presented. Finally, we describe the process and experience of applying the dataset to the contest of ChinaVis Data Challenge 2019. Evaluation results of the 75 contest entries and the feedback from 359 contestants demonstrate that the dataset has satisfactory completeness, and usability, and can effectively identify the performance of methods, technologies, and systems for indoor trajectory analysis.
Fringe pattern normalization algorithm using Kalman filter

A fringe pattern normalization algorithm is proposed using the smoothing Kalman filter. The background intensity and the fringe amplitude are estimated consecutively. These estimates are subsequently utilized to obtain the normalized fringe pattern. The fringe pattern normalization can be performed by scanning it either in a row-column-wise or in a zigzag manner. The simulation and experimental results are provided to substantiate the practical applicability of the proposed method. • Kalman filter is used to estimate background intensity and amplitude of fringe pattern. • Fringe pattern background intensity and amplitude are estimated separately. • Fringe pattern is processed either in a row-column-wise or zigzag manner. • The smoothing Kalman filter performs reliable fringe normalization.
A New Ensemble Reinforcement Learning Strategy for Solar Irradiance Forecasting using Deep Optimized Convolutional Neural Network Models

Solar irradiance forecasting is a major priority for the power transmission systems in order to generate and incorporate the performance of massive photovoltaic plants efficiently. As such, prior forecasting techniques that use classical modelling and single deep learning models that undertake feature extraction procedures manually were unable to meet the output demands in specific situations with dynamic variability. Therefore, in this study, we propose an efficient novel hybrid solar irradiance forecasting based on three steps. In step I, we employ a powerful variable input selection strategy named as partial mutual information (PMI) to calculate the linear and non-linear correlations of the original solar irradiance data. In step II, unlike the traditional deep learning models designing their architectures manually, we utilize several deep convolutional neural network (CNN) models optimized by a novel modified whale optimization algorithm in order to compute the forecasting results of the solar irradiance datasets. Finally in step III, we deploy a deep Q-learning reinforcement learning strategy for selecting the best subsets of the combined deep optimized CNN models. Through analysing the forecasting results over two USA solar irradiance stations, it can be inferred that the proposed optimized deep RL-ensemble framework (ODERLEN) outperforms other powerful benchmarked algorithms in different time-step horizons.
Discovering Human Interactions with Large-Vocabulary Objects via Query and Multi-Scale Detection

In this work, we study the problem of human-object interaction (HOI) detection with large vocabulary object categories. Previous HOI studies are mainly conducted in the regime of limit object categories (e.g., 80 categories). Their solutions may face new difficulties in both object detection and interaction classification due to the increasing diversity of objects (e.g., 1000 categories). Different from previous methods, we formulate the HOI detection as a query problem. We propose a unified model to jointly discover the target objects and predict the corresponding interactions based on the human queries, thereby eliminating the need of using generic object detectors, extra steps to associate human-object instances, and multi-stream interaction recognition. This is achieved by a repurposed Transformer unit and a novel cascade detection over multi-scale feature maps. We observe that such a highly-coupled solution brings benefits for both object detection and interaction classification in a large vocabulary setting. To study the new challenges of the large vocabulary HOI detection, we assemble two datasets from the publicly available SWiG and 100 Days of Hands datasets. Experiments on these datasets validate that our proposed method can achieve a notable mAP improvement on HOI detection with a faster inference speed than existing one-stage HOI detectors. Our code is available at https://github.com/scwangdyd/large_vocabulary_hoi_detection.
Transparency in Measurement Reporting

Measuring theoretical concepts, so-called constructs, is a central challenge of Player Experience research. Building on recent work in HCI and psychology, we conducted a systematic literature review to study the transparency of measurement reporting. We accessed the ACM Digital Library to analyze all 48 full papers published at CHI PLAY 2020, of those, 24 papers used self-report measurements and were included in the full review. We assessed specifically, whether researchers reported What, How and Why they measured. We found that researchers matched their measures to the construct under study and that administrative details, such as number of points on a Likert-type scale, were frequently reported. However, definitions of the constructs to be measured and justifications for selecting a particular scale were sparse. Lack of transparency in these areas threaten the validity of singular studies, but further compromise the building of theories and accumulation of research knowledge in meta-analytic work. This work is limited to only assessing the current transparency of measurement reporting at CHI PLAY 2020, however we argue this constitutes a fair foundation to assess potential pitfalls. To address these pitfalls, we propose a prescriptive model of a measurement selection process, which aids researchers to systematically define their constructs, specify operationalizations, and justify why these measures were chosen. Future research employing this model should contribute to more transparency in measurement reporting. The research was funded through internal resources. All materials are available on https://osf.io/4xz2v/.
Auction design for cross-edge task offloading in heterogeneous mobile edge clouds

Task offloading is a promising technology to exploit the available resources in edge cloud efficiently. Many incentive mechanisms for offloading systems have been proposed. However, most of existing works study the centralized incentive mechanisms under the assumption that all mobile edge infrastructures are operated by a central cloud. In this paper, we aim to design the auction-based truthful incentive mechanisms for heavily loaded task offloading system in heterogeneous MECs. We first study the homogeneous MEC situation and present a global auction executed in the central cloud as a benchmark. For the heterogeneous MEC situation, we model the system as a dual auction framework, which enables the heterogeneous MECs to perform cross-edge task offloading without the participation of central servers. Specifically, we design two dual auction models: secondary auction-based model, which enables the system to offload tasks from a large-scale region in a single auction, and double auction-based model, which is suitable for the time sensitive tasks. Then the auctions for these two dual auction models are proposed. Through rigorous theoretical analysis, we demonstrate that the proposed auctions achieve desirable properties of computational efficiency, individual rationality, budget balance, truthfulness, and guaranteed approximation. The simulation results show that the secondary auction and double auction can obtain 14.5% and 4.2% more social welfare than comparison algorithm on average, respectively. In addition, the double auction has great advantage in terms of computation efficiency.
An action language for multi-agent domains

The goal of this paper is to investigate an action language, called mA⁎, for representing and reasoning about actions and change in multi-agent domains. The language, as designed, can also serve as a specification language for epistemic planning, thereby addressing an important issue in the development of multi-agent epistemic planning systems. The mA⁎ action language is a generalization of the single-agent action languages, extensively studied in the literature, to the case of multi-agent domains. The language allows the representation of different types of actions that an agent can perform in a domain where many other agents might be present—such as world-altering actions, sensing actions, and communication actions. The action language also allows the specification of agents' dynamic awareness of action occurrences—which has implications on what agents' know about the world and other agents' knowledge about the world. These features are embedded in a language that is simple, yet powerful enough to address a large variety of knowledge manipulation scenarios in multi-agent domains. The semantics of mA⁎ relies on the notion of state, which is described by a pointed Kripke model and is used to encode the agents' knowledge1 and the real state of the world. The semantics is defined by a transition function that maps pairs of actions and states into sets of states. The paper presents a number of properties of the action theories and relates mA⁎ to other relevant formalisms in the area of reasoning about actions in multi-agent domains.
Fast Contextual Adaptation with Neural Associative Memory for On-Device Personalized Speech Recognition

Fast contextual adaptation has shown to be effective in improving Automatic Speech Recognition (ASR) of rare words and when combined with an on-device personalized training, it can yield an even better recognition result. However, the traditional re-scoring approaches based on an external language model is prone to diverge during the personalized training. In this work, we introduce a model-based end-to-end contextual adaptation approach that is decoder-agnostic and amenable to on-device personalization. Our on-device simulation experiments demonstrate that the proposed approach outperforms the traditional re-scoring technique by 12% relative WER and 15.7% entity mention specific F1-score in a continuous personalization scenario.
COVID19 Outbreak: A Hierarchical Framework for User Sentiment Analysis

Social networking sites in the most modernized world are flooded with large data volumes. Extracting the sentiment polarity of important aspects is necessary; as it helps to determine people's opinions through what they write. The Coronavirus pandemic has invaded the world and been given a mention in the social media on a large scale. In a very short period of time, tweets indicate unpredicted increase of coronavirus. They reflect people's opinions and thoughts with regard to coronavirus and its impact on society. The research community has been interested in discovering the hidden relationships from short texts such as Twitter and Weiboa; due to their shortness and sparsity. In this paper, a hierarchical twitter sentiment model (HTSM) is proposed to show people's opinions in short texts. The proposed HTSM has two main features as follows: constructing a hierarchical tree of important aspects from short texts without a predefined hierarchy depth and width, as well as analyzing the extracted opinions to discover the sentiment polarity on those important aspects by applying a valence aware dictionary for sentiment reasoner (VADER) sentiment analysis. The tweets for each extracted important aspect can be categorized as follows: strongly positive, positive, neutral, strongly negative, or negative. The quality of the proposed model is validated by applying it to a popular product and a widespread topic. The results show that the proposed model outperforms the state-of-the-art methods used in analyzing people's opinions in short text effectively.
Adapting Hidden Naive Bayes for Text Classification

Due to its simplicity, efficiency, and effectiveness, multinomial naive Bayes (MNB) has been widely used for text classification. As in naive Bayes (NB), its assumption of the conditional independence of features is often violated and, therefore, reduces its classification performance. Of the numerous approaches to alleviating its assumption of the conditional independence of features, structure extension has attracted less attention from researchers. To the best of our knowledge, only structure-extended MNB (SEMNB) has been proposed so far. SEMNB averages all weighted super-parent one-dependence multinomial estimators; therefore, it is an ensemble learning model. In this paper, we propose a single model called hidden MNB (HMNB) by adapting the well-known hidden NB (HNB). HMNB creates a hidden parent for each feature, which synthesizes all the other qualified features’ influences. For HMNB to learn, we propose a simple but effective learning algorithm without incurring a high-computational-complexity structure-learning process. Our improved idea can also be used to improve complement NB (CNB) and the one-versus-all-but-one model (OVA), and the resulting models are simply denoted as HCNB and HOVA, respectively. The extensive experiments on eleven benchmark text classification datasets validate the effectiveness of HMNB, HCNB, and HOVA.
A Systematic Approach and Analysis of Key Mismatch Attacks on Lattice-Based NIST Candidate KEMs

Research on key mismatch attacks against lattice-based KEMs is an important part of the cryptographic assessment of the ongoing NIST standardization of post-quantum cryptography. There have been a number of these attacks to date. However, a unified method to evaluate these KEMs' resilience under key mismatch attacks is still missing. Since the key index of efficiency is the number of queries needed to successfully mount such an attack, in this paper, we propose and develop a systematic approach to find lower bounds on the minimum average number of queries needed for such attacks. Our basic idea is to transform the problem of finding the lower bound of queries into finding an optimal binary recovery tree (BRT), where the computations of the lower bounds become essentially the computations of a certain Shannon entropy. The optimal BRT approach also enables us to understand why, for some lattice-based NIST candidate KEMs, there is a big gap between the theoretical bounds and bounds observed in practical attacks, in terms of the number of queries needed. This further leads us to propose a generic improvement method for these existing attacks, which are confirmed by our experiments. Moreover, our proposed method could be directly used to improve the side-channel attacks against CCA-secure NIST candidate KEMs.
Stochastic Transformer Networks with Linear Competing Units: Application to end-to-end SL Translation

Automating sign language translation (SLT) is a challenging real-world application. Despite its societal importance, though, research progress in the field remains rather poor. Crucially, existing methods that yield viable performance necessitate the availability of laborious to obtain gloss sequence groundtruth. In this paper, we attenuate this need, by introducing an end-to-end SLT model that does not entail explicit use of glosses; the model only needs text groundtruth. This is in stark contrast to existing end-to-end models that use gloss sequence groundtruth, either in the form of a modality that is recognized at an intermediate model stage, or in the form of a parallel output process, jointly trained with the SLT model. Our approach constitutes a Transformer network with a novel type of layers that combines: (i) local winner-takes-all (LWTA) layers with stochastic winner sampling, instead of conventional ReLU layers, (ii) stochastic weights with posterior distributions estimated via variational inference, and (iii) a weight compression technique at inference time that exploits estimated posterior variance to perform massive, almost lossless compression. We demonstrate that our approach can reach the currently best reported BLEU-4 score on the PHOENIX 2014T benchmark, but without making use of glosses for model training, and with a memory footprint reduced by more than 70%.