Reviewerly/exHarmony huggingface dataset:
Characterizing the multipartite continuous-variable entanglement structure from squeezing coefficients and the Fisher information

Abstract Understanding the distribution of quantum entanglement over many parties is a fundamental challenge of quantum physics and is of practical relevance for several applications in the field of quantum information. The Fisher information is widely used in quantum metrology since it is related to the quantum gain in metrology measurements. Here, we use methods from quantum metrology to microscopically characterize the entanglement structure of multimode continuous-variable states in all possible multi-partitions and in all reduced distributions. From experimentally measured covariance matrices of Gaussian states with 2, 3, and 4 photonic modes with controllable losses, we extract the metrological sensitivity as well as an upper separability bound for each partition. An entanglement witness is constructed by comparing the two quantities. Our analysis demonstrates the usefulness of these methods for continuous-variable systems and provides a detailed geometric understanding of the robustness of cluster-state entanglement under photon losses.
Conversational Semantic Parsing

Armen Aghajanyan, Jean Maillard, Akshat Shrivastava, Keith Diedrick, Michael Haeger, Haoran Li, Yashar Mehdad, Veselin Stoyanov, Anuj Kumar, Mike Lewis, Sonal Gupta. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020.
How Effective is Task-Agnostic Data Augmentation for Pretrained Transformers?

Task-agnostic forms of data augmentation have proven widely effective in computer vision, even on pretrained models.In NLP similar results are reported most commonly for low data regimes, non-pretrained models, or situationally for pretrained models.In this paper we ask how effective these techniques really are when applied to pretrained transformers.Using two popular varieties of task-agnostic data augmentation (not tailored to any particular task), Easy Data Augmentation (Wei and Zou, 2019) and Back-Translation (Sennrich et al., 2015), we conduct a systematic examination of their effects across 5 classification tasks, 6 datasets, and 3 variants of modern pretrained transformers, including BERT, XL-NET, and ROBERTA.We observe a negative result, finding that techniques which previously reported strong improvements for nonpretrained models fail to consistently improve performance for pretrained transformers, even when training data is limited.We hope this empirical analysis helps inform practitioners where data augmentation techniques may confer improvements.
Controlling Style and Semantics in Weakly-Supervised Image Generation

We propose a weakly-supervised approach for conditional image generation of complex scenes where a user has fine control over objects appearing in the scene. We exploit sparse semantic maps to control object shapes and classes, as well as textual descriptions or attributes to control both local and global style. In order to condition our model on textual descriptions, we introduce a semantic attention module whose computational cost is independent of the image resolution. To further augment the controllability of the scene, we propose a two-step generation scheme that decomposes background and foreground. The label maps used to train our model are produced by a large-vocabulary object detector, which enables access to unlabeled data and provides structured instance information. In such a setting, we report better FID scores compared to fully-supervised settings where the model is trained on ground-truth semantic maps. We also showcase the ability of our model to manipulate a scene on complex datasets such as COCO and Visual Genome.
GDPNet: Refining Latent Multi-View Graph for Relation Extraction

Relation Extraction (RE) is to predict the relation type of two entities that are mentioned in a piece of text, e.g., a sentence or a dialogue. When the given text is long, it is challenging to identify indicative words for the relation prediction. Recent advances on RE task are from BERT-based sequence modeling and graph-based modeling of relationships among the tokens in the sequence. In this paper, we propose to construct a latent multi-view graph to capture various possible relationships among tokens. We then refine this graph to select important words for relation prediction. Finally, the representation of the refined graph and the BERT-based sequence representation are concatenated for relation extraction. Specifically, in our proposed GDPNet (Gaussian Dynamic Time Warping Pooling Net), we utilize Gaussian Graph Generator (GGG) to generate edges of the multi-view graph. The graph is then refined by Dynamic Time Warping Pooling (DTWPool). On DialogRE and TACRED, we show that GDPNet achieves the best performance on dialogue-level RE, and comparable performance with the state-of-the-arts on sentence-level RE.
Do Neural Language Models Overcome Reporting Bias?

Mining commonsense knowledge from corpora suffers from reporting bias, over-representing the rare at the expense of the trivial (Gordon and Van Durme, 2013).We study to what extent pre-trained language models overcome this issue.We find that while their generalization capacity allows them to better estimate the plausibility of frequent but unspoken of actions, outcomes, and properties, they also tend to overestimate that of the very rare, amplifying the bias that already exists in their training corpus.
Improving Grammatical Error Correction with Data Augmentation by Editing Latent Representation

The incorporation of data augmentation method in grammatical error correction task has attracted much attention.However, existing data augmentation methods mainly apply noise to tokens, which leads to the lack of diversity of generated errors.In view of this, we propose a new data augmentation method that can apply noise to the latent representation of a sentence.By editing the latent representations of grammatical sentences, we can generate synthetic samples with various error types.Combining with some pre-defined rules, our method can greatly improve the performance and robustness of existing grammatical error correction models.We evaluate our method on public benchmarks of GEC task and it achieves the state-of-the-art performance on CoNLL-2014 and FCE benchmarks.
Run-Time Monitoring of Machine Learning for Robotic Perception: A Survey of Emerging Trends

As deep learning continues to dominate all state-of-the-art computer vision tasks, it is increasingly becoming an essential building block for robotic perception.This raises important questions concerning the safety and reliability of learning-based perception systems.There is an established field that studies safety certification and convergence guarantees of complex software systems at design-time.However, the unknown future deployment environments of an autonomous system and the complexity of learning-based perception make the generalization of design-time verification to run-time problematic.In the face of this challenge, more attention is starting to focus on run-time monitoring of performance and reliability of perception systems with several trends emerging in the literature in the face of this challenge.This paper attempts to identify these trends and summarize the various approaches to the topic.
Multi-window back-projection residual networks for reconstructing COVID-19 CT super-resolution images

With the increasing problem of coronavirus disease 2019 (COVID-19) in the world, improving the image resolution of COVID-19 computed tomography (CT) becomes a very important task. At present, single-image super-resolution (SISR) models based on convolutional neural networks (CNN) generally have problems such as the loss of high-frequency information and the large size of the model due to the deep network structure. In this work, we propose an optimization model based on multi-window back-projection residual network (MWSR), which outperforms most of the state-of-the-art methods. Firstly, we use multi-window to refine the same feature map at the same time to obtain richer high/low frequency information, and fuse and filter out the features needed by the deep network. Then, we develop a back-projection network based on the dilated convolution, using up-projection and down-projection modules to extract image features. Finally, we merge several repeated and continuous residual modules with global features, merge the information flow through the network, and input them to the reconstruction module. The proposed method shows the superiority over the state-of-the-art methods on the benchmark dataset, and generates clear COVID-19 CT super-resolution images. Both subjective visual effects and objective evaluation indicators are improved, and the model specifications are optimized. Therefore, the MWSR method can improve the clarity of CT images of COVID-19 and effectively assist the diagnosis and quantitative assessment of COVID-19.
Intention-Aware Sequential Recommendation With Structured Intent Transition

Human behaviors in recommendation systems are driven by many high-level, complex, and evolving intentions behind their decision making processes. In order to achieve better performance, it is important for recommendation systems to be aware of user intentions besides considering the historical interaction behaviors. However, user intentions are seldom fully or easily observed in practice, so that the existing works are incapable of fully tracking and modeling user intentions, not to mention using them effectively into recommendation. In this paper, we present the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</b> ntention-Aware <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> equential <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Rec</b> ommendation ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ISRec</b> ) method, for capturing the underlying intentions of each user that may lead to her next consumption behavior and improving recommendation performance. Specifically, we first extract the intentions of the target user from sequential contexts, then take complex intent transition into account through the message-passing mechanism on an intention graph, and finally obtain the future intentions of this target user from inference on the intention graph. The sequential recommendation for a user will be made based on the predicted user intentions, offering more transparent and explainable intermediate results for each recommendation. Extensive experiments on various real-world datasets demonstrate the superiority of our method against several state-of-the-art baselines in sequential recommendation in terms of different metrics.
Pemanfaatan Teknologi Media Pembelajaran di Masa Pandemi Covid-19

In everyday life we are used to dealing with technology, but it is different today. Since the covid-19 virus came to the world, especially the Indonesian state, Indonesian education has been carried out differently. During this pandemic, education was carried out by utilizing learning platforms that actually existed but were rarely used, but for now of course it is a new thing for some education circles because they are not familiar with technology in education. There are so many platforms that can be used for online learning that can be used by all educational institutions including children in elementary schools, namely there is a google classroom which is a form of e-learning that uses WebCT, YouTube, WAG, Edmodo, Zoom, Googlemeet and there are many other supporting applications. This study aims to determine the use of online learning technology during the Covid-19 pandemic using the Google classroom, YouTube, WAG, Edmodo, Zoom, and Googlemeet platforms. And the results of research using the interview method, literature and literature study show that technology has many roles and benefits in the world of education, especially when learning in the midst of the Covid-19 pandemic. By conducting interviews with teachers and students, it can be concluded that the learning media technology that is mostly used by elementary schools is WAG and also google classroom.Keywords: covid-19, platform google classroom, online learning, WAG.
The Effects of Human Aspects on the Requirements Engineering Process: A Systematic Literature Review

Requirements Engineering (RE) requires the collaboration of various roles in SE, such as requirements engineers, stakeholders and other developers, and it is thus a highly human dependent process in software engineering (SE). Identifying how human aspects such as personality, motivation, emotions, communication, gender, culture and geographic distribution might impact RE would assist us in better supporting successful RE. The main objective of this paper is to systematically review primary studies that have investigated the effects of various human aspects on RE. A systematic literature review (SLR) was conducted and identified 474 initial primary research studies. These were eventually filtered down to 74 relevant, high-quality primary studies. Among the studies, the effects of communication have been considered in many RE studies. Other human aspects such as personality, motivation and gender have mainly been investigated to date related to SE studies including RE as one phase. Findings show that studying more than one human aspect together is beneficial, as this reveals relationships between various human aspects and how they together impact the RE process. However, the majority of these studied combinations of human aspects are unique. From 56.8% of studies that identified the effects of human aspects on RE, 40.5% identified the positive impact, 30.9% negative, 26.2% identified both impacts whereas 2.3% mentioned that there was no impact. This implies that a variety of human aspects positively or negatively affects the RE process and a well-defined theoretical analysis on the effects of different human aspects on RE remains to be defined and practically evaluated. Findings of this SLR help researchers who are investigating the impact of various human aspects on RE by identifying well-studied research areas, and highlight new areas that should be focused on in future research.
The Effect of Class Imbalance on Precision-Recall Curves

In this note, I study how the precision of a binary classifier depends on the ratio r of positive to negative cases in the test set, as well as the classifier's true and false-positive rates. This relationship allows prediction of how the precision-recall curve will change with r, which seems not to be well known. It also allows prediction of how Fβ and the precision gain and recall gain measures of Flach and Kull (2015) vary with r.
Digital Communication Tools and Knowledge Creation Processes for Enriched Intellectual Outcome—Experience of Short-Term E-Learning Courses during Pandemic

Social isolation during the pandemic contributed to the transition of educational processes to e-learning. A short-term e-marketing education program for a variety of students was introduced in May 2020 and is taught entirely online. A survey was conducted regularly in the last week of training using Google Forms, and three cohorts were surveyed in July, September, and December 2020. A high level of satisfaction indicates an interest in the content and a positive assessment of the level of comfort of an organization adapted to the needs of students; this positive result contrasted with the negative opinion of the remote learning in Russia since March 2020, and this surprising satisfaction of students has motivated the study to try to explain its reasons. This result was compared with the short-term course taught through the educational pedagogical platform of a university. The students of traditional short- and long-term university programs were asked to assess their satisfaction with different digital communication tools used for e-learning. They showed low satisfaction with the pedagogical platform and a positive reaction to the e-communication tools (messengers, social media, short surveys, video conferences, etc.). The qualitative responses helped to better understand the real problems of the cognitive process and the triple structure of intellectual production during e-learning, including interest in the intellectual outcome, the need for emotional and motivational elements of cooperation and competition between students, and smooth behavioral enrichment, which requires special efforts from students and their leading from teachers. The main conclusion concerns a practical decision to continue the implementation of the educational program in the form of an online course with the use of the mixed digital communication tools of social media, messengers, and video conferences, which most likely meets the expectations and capabilities of students.
Association of Individual Factors with Simulator Sickness and Sense of Presence in Virtual Reality Mediated by Head-Mounted Displays (HMDs)

Many studies have attempted to understand which individual differences may be related to the symptoms of discomfort during the virtual experience (simulator sickness) and the generally considered positive sense of being inside the simulated scene (sense of presence). Nevertheless, a very limited number of studies have employed modern consumer-oriented head-mounted displays (HMDs). These systems aim to produce a high the sense of the presence of the user, remove stimuli from the external environment, and provide high definition, photo-realistic, three-dimensional images. Our results showed that motion sickness susceptibility and simulator sickness are related, and neuroticism may be associated and predict simulator sickness. Furthermore, the results showed that people who are more used to playing videogames are less susceptible to simulator sickness; female participants reported more simulator sickness compared to males (but only for nausea-related symptoms). Female participants also experienced a higher sense of presence compared to males. We suggest that published findings on simulator sickness and the sense of presence in virtual reality environments need to be replicated with the use of modern HMDs.
Psychological and System-Related Barriers to Adopting Blockchain for Operations Management: An Artificial Neural Network Approach

This article aims to identify the barriers to adopting blockchain for operations management (BCOM). The barriers discussed include psychological barriers [information barrier (IB), usage barrier (UB), and functional risk barrier (FRB)] and system-related barriers [security and privacy barrier (SPB), compatibility and interoperability barrier (CIB), and system quality barrier (SQB)]. Data collected from 178 responses from manufacturing firms were analyzed using artificial neural network (ANN) to capture both linear and nonlinear relationships in the proposed model that comprises of psychological and systems-related barriers and resistance to BCOM. Nonlinear relationships of the barriers toward BCOM resistance were found. This is a significant contribution as previous studies only found linear relationships. Results also revealed that FRB has the highest level of importance toward blockchain resistance, followed by UB, SPB, and IB. CIB and SQB were found to have the least importance to BCOM. In addition, the negligible values of root-mean square of error concluded that the ANN model exhibits a good model fit. This article contributes to the understanding of BCOM by integrating psychological barriers and systems-related barriers to the current conceptual model. The study also offers practical guidelines and suggestions for future research.
Multi-objective workflow optimization strategy (MOWOS) for cloud computing

Abstract Workflow scheduling involves mapping large tasks onto cloud resources to improve scheduling efficiency. This has attracted the interest of many researchers, who devoted their time and resources to improve the performance of scheduling in cloud computing. However, scientific workflows are big data applications, hence the executions are expensive and time consuming. In order to address this issue, we have extended our previous work ”Cost Optimised Heuristic Algorithm (COHA)” and presented a novel workflow scheduling algorithm named Multi-Objective Workflow Optimization Strategy (MOWOS) to jointly reduce execution cost and execution makespan. MOWOS employs tasks splitting mechanism to split large tasks into sub-tasks to reduce their scheduling length. Moreover, two new algorithms called MaxVM selection and MinVM selection are presented in MOWOS for task allocations. The design purpose of MOWOS is to enable all tasks to successfully meet their deadlines at a reduced time and budget. We have carefully tested the performance of MOWOS with a list of workflow inputs. The simulation results have demonstrated that MOWOS can effectively perform VM allocation and deployment, and well handle incoming streaming tasks with a random arriving rate. The performance of the proposed algorithm increases significantly in large and extra-large workflow tasks than in small and medium workflow tasks when compared to the state-of-art work. It can greatly reduce cost by 8%, minimize makespan by 10% and improve resource utilization by 53%, while also allowing all tasks to meet their deadlines.
Frame-Wise Cross-Modal Matching for Video Moment Retrieval

Video moment retrieval targets at retrieving a golden moment in a video for a given natural language query. The main challenges of this task include 1) the requirement of accurately localizing (i.e., the start time and the end time of) the relevant moment in an untrimmed video stream, and 2) bridging the semantic gap between textual query and video contents. To tackle those problems, early approaches adopt the sliding window or uniform sampling to collect video clips first and then match each clip with the query to identify relevant clips. Obviously, these strategies are time-consuming and often lead to unsatisfied accuracy in localization due to the unpredictable length of the golden moment. To avoid the limitations, researchers recently attempt to directly predict the relevant moment boundaries without the requirement to generate video clips first. One mainstream approach is to generate a multimodal feature vector for the target query and video frames (e.g., concatenation) and then use a regression approach upon the multimodal feature vector for boundary detection. Although some progress has been achieved by this approach, we argue that those methods have not well captured the cross-modal interactions between the query and video frames. In this paper, we propose an Attentive Cross-modal Relevance Matching (ACRM) model which predicts the temporal boundaries based on an interaction modeling between two modalities. In addition, an attention module is introduced to automatically assign higher weights to query words with richer semantic cues, which are considered to be more important for finding relevant video contents. Another contribution is that we propose an additional predictor to utilize the internal frames in the model training to improve the localization accuracy. Extensive experiments on two public datasets TACoS and Charades-STA demonstrate the superiority of our method over several state-of-the-art methods. Ablation studies have been also conducted to examine the effectiveness of different modules in our ACRM model.