Reviewerly/exHarmony huggingface dataset:
PosterBot: A System for Generating Posters of Scientific Papers with Neural Models

Posters are broadly used to present the important points of academic papers and can be seen as a special form of document summarization. However, the problem of automatic poster generation is under-investigated. In this paper, we present PosterBot, an automatic poster generation system for academic papers. Given a scholarly paper, PosterBot takes three steps to generate the poster. It first selects the most important sections, and then generates corresponding panels from them. Finally, all panels are integrated to get the complete poster. The demonstration shows the efficacy of our proposed system.
Hibernated Backdoor: A Mutual Information Empowered Backdoor Attack to Deep Neural Networks

We report a new neural backdoor attack, named Hibernated Backdoor, which is stealthy, aggressive and devastating. The backdoor is planted in a hibernated mode to avoid being detected. Once deployed and fine-tuned on end-devices, the hibernated backdoor turns into the active state that can be exploited by the attacker. To the best of our knowledge, this is the first hibernated neural backdoor attack. It is achieved by maximizing the mutual information (MI) between the gradients of regular and malicious data on the model. We introduce a practical algorithm to achieve MI maximization to effectively plant the hibernated backdoor. To evade adaptive defenses, we further develop a targeted hibernated backdoor, which can only be activated by specific data samples and thus achieves a higher degree of stealthiness. We show the hibernated backdoor is robust and cannot be removed by existing backdoor removal schemes. It has been fully tested on four datasets with two neural network architectures, compared to five existing backdoor attacks, and evaluated using seven backdoor detection schemes. The experiments demonstrate the effectiveness of the hibernated backdoor attack under various settings.
Pinpointing Fine-Grained Relationships between Hateful Tweets and Replies

Recent studies in the hate and counter hate domain have provided the grounds for investigating how to detect this pervasive content in social media. These studies mostly work with synthetic replies to hateful content written by annotators on demand rather than replies written by real users. We argue that working with naturally occurring replies to hateful content is key to study the problem. Building on this motivation, we create a corpus of 5,652 hateful tweets and replies. We analyze their fine-grained relationships by indicating whether the reply (a) is hate or counter hate speech, (b) provides a justification, (c) attacks the author of the tweet, and (d) adds additional hate. We also present linguistic insights into the language people use depending on these fine-grained relationships. Experimental results show improvements (a) taking into account the hateful tweet in addition to the reply and (b) pretraining with related tasks.
Compilation of Aggregates in ASP Systems

Answer Set Programming (ASP) is a well-known declarative AI formalism for knowledge representation and reasoning. State-of-the-art ASP implementations employ the ground&amp;solve approach, and they were successfully applied to industrial and academic problems. Nonetheless there are classes of ASP programs whose evaluation is not efficient (sometimes not feasible) due to the combinatorial blow-up of the program produced by the grounding step. Recent researches suggest that compilation-based techniques can mitigate the grounding bottleneck problem. However, no compilation-based technique has been developed for ASP programs that contain aggregates, which are one of the most relevant and commonly-employed constructs of ASP. In this paper, we propose a compilation-based approach for ASP programs with aggregates. We implement it on top of a state-of-the-art ASP system, and evaluate the performance on publicly-available benchmarks. Experiments show our approach is effective on ground-intensive ASP programs.
A Study on the Types of Aviation Tourism Majors’ Perception on Face-to-Face Practical Classes after Non-Face-to-Face Practical Classes due to COVID-19: A Phenomenological Approach

Purpose: The purpose of this study was to explore the types of aviation tourism majors’ perceptions of face-to-face practical classes after non-face-to-face practical classes due to COVID-19.&#x0D; Method: In order to conduct the phenomenological approach of this study, in-depth interviews were conducted with aviation tourism majors. According to the phenomenological approach method procedure, sentence units with similar meanings were classified according to the interview contents.&#x0D; Results: As a result of the analysis, it turned out that their face-to-face classes perceptions are mainly derived from three sub-components(interaction, practice performance, and preparing for employment).&#x0D; Conclusion: Consequently, This research reveals that this study will provide both academic and practical implications to serve as an important indicator for developing practical educational strategies for advising aviation tourism students.
Sentiment Analysis of Twitter Data Using Named Entity Recognition

Social media has been a choice of people for expressing themselves. With the addition of platforms such as Twitter, Instagram, Facebook, and WhatsApp, people have been using these platforms for posting their views on various aspects such as personal, political, travel, tourism, etc. Social media platforms have huge data and a good amount of this data is available for research while it preserves the privacy right of the people. In this work, twitter data is used for the analysis of people’s sentiments. Named entity recognition separates key objects from the tweets data and then analyzes the sentence for the overall sentiment. Flair framework is used in this work for analysis of tweets and results are presented for natural events, Yes bank crisis, and COCID19 related tweets. The results are analyzed for the location, frequency, and tags for the trending topics. This method has commercial aspects in the product design, product placement, and marketing of the products at various locations.
SmartIdx: Reducing Communication Cost in Federated Learning by Exploiting the CNNs Structures

Top-k sparsification method is popular and powerful forreducing the communication cost in Federated Learning(FL). However, according to our experimental observation, it spends most of the total communication cost on the index of the selected parameters (i.e., their position informa-tion), which is inefficient for FL training. To solve this problem, we propose a FL compression algorithm for convolution neural networks (CNNs), called SmartIdx, by extending the traditional Top-k largest variation selection strategy intothe convolution-kernel-based selection, to reduce the proportion of the index in the overall communication cost and thusachieve a high compression ratio. The basic idea of SmartIdx is to improve the 1:1 proportion relationship betweenthe value and index of the parameters to n:1, by regarding the convolution kernel as the basic selecting unit in parameter selection, which can potentially deliver more informationto the parameter server under the limited network traffic. Tothis end, a set of rules are designed for judging which kernel should be selected and the corresponding packaging strategies are also proposed for further improving the compressionratio. Experiments on mainstream CNNs and datasets show that our proposed SmartIdx performs 2.5×−69.2× higher compression ratio than the state-of-the-art FL compression algorithms without degrading model performance.
Noise-Robust Learning from Multiple Unsupervised Sources of Inferred Labels

Deep Neural Networks (DNNs) generally require large-scale datasets for training. Since manually obtaining clean labels for large datasets is extremely expensive, unsupervised models based on domain-specific heuristics can be used to efficiently infer the labels for such datasets. However, the labels from such inferred sources are typically noisy, which could easily mislead and lessen the generalizability of DNNs. Most approaches proposed in the literature to address this problem assume the label noise depends only on the true class of an instance (i.e., class-conditional noise). However, this assumption is not realistic for the inferred labels as they are typically inferred based on the features of the instances. The few recent attempts to model such instance-dependent (i.e., feature-dependent) noise require auxiliary information about the label noise (e.g., noise rates or clean samples). This work proposes a theoretically motivated framework to correct label noise in the presence of multiple labels inferred from unsupervised models. The framework consists of two modules: (1) MULTI-IDNC, a novel approach to correct label noise that is instance-dependent yet not class-conditional; (2) MULTI-CCNC, which extends an existing class-conditional noise-robust approach to yield improved class-conditional noise correction using multiple noisy label sources. We conduct experiments using nine real-world datasets for three different classification tasks (images, text and graph nodes). Our results show that our approach achieves notable improvements (e.g., 6.4% in accuracy) against state-of-the-art baselines while dealing with both instance-dependent and class-conditional noise in inferred label sources.
Semi-supervised Conditional Density Estimation with Wasserstein Laplacian Regularisation

Conditional Density Estimation (CDE) has wide-reaching applicability to various real-world problems, such as spatial density estimation and environmental modelling. CDE estimates the probability density of a random variable rather than a single value and can thus model uncertainty and inverse problems. This task is inherently more complex than regression, and many algorithms suffer from overfitting, particularly when modelled with few labelled data points. For applications where unlabelled data is abundant but labelled data is scarce, we propose Wasserstein Laplacian Regularisation, a semi-supervised learning framework that allows CDE algorithms to leverage these unlabelled data. The framework minimises an objective function which ensures that the learned model is smooth along the manifold of the underlying data, as measured by Wasserstein distance. When applying our framework to Mixture Density Networks, the resulting semi-supervised algorithm can achieve similar performance to a supervised model with up to three times as many labelled data points on baseline datasets. We additionally apply our technique to the problem of remote sensing for chlorophyll-a estimation in inland waters.
Competition of State Powers During the COVID-19 Pandemic

The aim of this article is to analyse what kind of powers deriving from the rule of law can be extended to public authorities in response to the global COVID-19 pandemic, what is the balance of powers between the legislative and the executive whilst imposing measures controlling the virus, and what role is played by the constitutional courts whilst deliberating on constitutional disputes involving public authorities.
Restorable Image Operators with Quasi-Invertible Networks

Image operators have been extensively applied to create visually attractive photos for users to share processed images on social media. However, most image operators often smooth out details or generate textures after the processing, which removes the original content and raises challenges for restoring the original image. To resolve this issue, we propose a quasi-invertible model that learns common image processing operators in a restorable fashion: the learned image operators can generate visually pleasing results with the original content embedded. Our model is trained on input-output pairs that represent an image processing operator's behavior and uses a network that consists of an invertible branch and a non-invertible branch to increase our model's approximation capability. We evaluate the proposed model on ten image operators, including detail enhancement, abstraction, blur, photographic style, and non-photorealistic style. Extensive experiments show that our approach outperforms relevant baselines in the restoration quality, and the learned restorable operator is fast in inference and robust to compression. Furthermore, we demonstrate that the invertible operator can be easily applied to practical applications such as restorable human face retouching and highlight preserved exposure adjustment.
The COVID-19 and Dynamics in Cryptocurrencies: An empirical evidence

The coronavirus pandemic occurred in 2019 and caused an impact on cryptocurrencies. This paper focuses on the relation between the return of cryptocurrencies and the new daily confirmed cases of COVID-19 worldwide, taking Bitcoin, Ethereum, and Tether as three examples. It is shown that the new daily cases have a significant influence on Bitcoin and Ethereum. And the short-term impact of new global confirmed cases on Bitcoin is positive, then remains volatile and eventually disappears. The different phenomena show that different cryptocurrencies have reacted differently to the pandemic impact.
Syntax Controlled Knowledge Graph-to-Text Generation with Order and Semantic Consistency

The knowledge graph (KG) stores a large amount of structural knowledge, while it is not easy for direct human understanding.Knowledge graph-to-text (KG-to-text) generation aims to generate easy-to-understand sentences from the KG, and at the same time, maintains semantic consistency between generated sentences and the KG.Existing KG-to-text generation methods phrase this task as a sequence-tosequence generation task with linearized KG as input and consider the consistency issue of the generated texts and KG through a simple selection between decoded sentence word and KG node word at each time step.However, the linearized KG order is commonly obtained through a heuristic search without data-driven optimization.In this paper, we optimize the knowledge description order prediction under the order supervision extracted from the caption and further enhance the consistency of the generated sentences and KG through syntactic and semantic regularization.We incorporate the Part-of-Speech (POS) syntactic tags to constrain the positions to copy words from the KG and employ a semantic context scoring function to evaluate the semantic fitness for each word in its local context when decoding each word in the generated sentence.Extensive experiments are conducted on two datasets, WebNLG and DART, and achieve state-of-the-art performances.Our code is now public available 1 .
MIA 2022 Shared Task: Evaluating Cross-lingual Open-Retrieval Question Answering for 16 Diverse Languages

We present the results of the Workshop on Multilingual Information Access (MIA) 2022 Shared Task, evaluating cross-lingual openretrieval question answering (QA) systems in 16 typologically diverse languages.In this task, we adapted two large-scale cross-lingual openretrieval QA datasets in 14 typologically diverse languages, and newly annotated openretrieval QA data in 2 underrepresented languages: Tagalog and Tamil.Four teams submitted their systems.The best constrained system uses entity-aware contextualized representations for document retrieval, thereby achieving an average F1 score of 31.6, which is 4.1 F1 absolute higher than the challenging baseline.The best system obtains particularly significant improvements in Tamil (20.8 F1), whereas most of the other systems yield nearly zero scores.The best unconstrained system achieves 32.2 F1, outperforming our baseline by 4.5 points.The official leaderboard 1 and baselines 2 models are publicly available.
Re-weighting Negative Samples for Model-Agnostic Matching

Recommender Systems (RS), as an efficient tool to discover users' interested items from a very large corpus, has attracted more and more attention from academia and industry. As the initial stage of RS, large-scale matching is fundamental yet challenging. A typical recipe is to learn user and item representations with a two-tower architecture and then calculate the similarity score between both representation vectors, which however still struggles in how to properly deal with negative samples. In this paper, we find that the common practice that randomly sampling negative samples from the entire space and treating them equally is not an optimal choice, since the negative samples from different sub-spaces at different stages have different importance to a matching model. To address this issue, we propose a novel method named Unbiased Model-Agnostic Matching Approach (UMA2). It consists of two basic modules including 1) General Matching Model (GMM), which is model-agnostic and can be implemented as any embedding-based two-tower models; and 2) Negative Samples Debias Network (NSDN), which discriminates negative samples by borrowing the idea of Inverse Propensity Weighting (IPW) and re-weighs the loss in GMM. UMA$^2$ seamlessly integrates these two modules in an end-to-end multi-task learning framework. Extensive experiments on both real-world offline dataset and online A/B test demonstrate its superiority over state-of-the-art methods.
Workshop on Explainable User Models and Personalised Systems (ExUM)

Adaptive and personalized systems have become pervasive technologies, gradually playing an increasingly important role in our daily lives. Indeed, we are now used to interacting with algorithms that help us in several scenarios, ranging from services that suggest us music or movies to personal assistants able to proactively support us in complex decision-making tasks. As the importance of such technologies in our everyday lives grows, it is fundamental that the internal mechanisms that guide these algorithms are as clear as possible. The workshop aims to provide a forum for discussing problems, challenges, and innovative research approaches in this area by investigating the role of transparency and explainability in the recent methodologies for building user models or developing personalized and adaptive systems.
Nearly Optimal Protocols for Computing Multi-party Private Set Union

Private Set Operations (PSO) are a hot research topic and one of the most extensive research problems in data mining. In the PSO, Multi-party Private Set Union (MPSU) is one of the fundamental problems. It allows some participants to learn the union of their data sets without leaking any useful information. However, most of the existing works have high communication, computation and round complexities. In this paper, we first propose a novel and efficient protocol to securely compute MPSU under the semi-honest model. In our system model, there exist n participants where each participant has a set of size k (k could be different among participants). There are also up to t (0 ≤ t < n) participants which could collude with each other. We suppose the communication channels among participants are insecure and can easily suffer from eavesdropping attacks. Our first protocol using element computing algorithm and Homomorphic Encryption, i.e., HE-MPSU, only requires O(1) rounds and has O(nNλ) communication complexity which almost matches the communication lower bound Ω(nN/log n) for the MPSU problem, where λ is a security parameter and N (k ≤ N ≤ nk) is the set union cardinality. In addition, we note that for the two-party case, i.e., n = 2, our HE-MPSU protocol has the same complexities as the state-of-the-art work in [1]. For this special case, i.e., two-party Private Set Union (PSU), we further optimize and design a more efficient protocol using oblivious transfer (OT) protocol, i.e., OT-PSU. It only requires O(1) rounds and O(kλ) communication complexity which almost matches the communication lower bound Ω(k). More importantly, it avoids using computationally expensive public-key operations (exponentiations). In other words, the number of exponentiations in this protocol is independent of the size of the data sets. Compared with the existing protocols, our two protocols have the lowest communication, computation and round complexities.
IOT Based Early Flood Detection and Preventive Measurements

When the flood came, the floods affected both individuals and communities, and they had social, economic, and environmental consequences. So we're designing that kind of system that gets floods before the flood comes. The purpose of the flood warning is to detect and predict flood events so that the public can be informed in advance. Thanks to flood warnings, the effects of major floods can be reduced and flood protection can be strengthened. The warning system will monitor nearby dams, rivers and collect data i.e. water level in the river and river flow rate in relation to floods and the surrounding environment from different sensors. There are three main components of this program. The first part of the system is to detect the water level using the ultrasonic sensor and the water flow rate obtained using the flow sensor as well as the temperature and humidity using the DHT11 sensor and the rain detection by Rain Sensor. The second part of the system sends data to the Thing speak cloud and the third part of the system receives the data from the cloud and displays it in the valley again when a flood situation occurs and an announcement is made to this. Part 3. The structure of the system can be expanded to include a fully functional system to inform the public of the impending flood disaster.
A Playful Approach to Learn Scrum through Distant Collaboration using Online Whiteboards

We present an approach for teaching introductory Scrum concepts through a playful online collaborative activity. It was conducted with 1st-semester students who never had any in-person contact and started their course under emergency online teaching during the Covid-19 pandemic. We adapted the Lego4Scrum approach and instead of plastic bricks, we combined an online whiteboard (Miro) with a communication tool (Discord) for teams.
Rapid Deployment of Whole Slide Imaging for Primary Diagnosis in Surgical Pathology at Stanford Medicine: Responding to Challenges of the COVID-19 Pandemic

Stanford Pathology began stepwise subspecialty implementation of whole slide imaging (WSI) in 2018 soon after the first US Food and Drug Administration approval. In 2020, during the COVID-19 pandemic, the Centers for Medicare & Medicaid Services waived the requirement for pathologists to perform diagnostic tests in Clinical Laboratory Improvement Amendments (CLIA)-licensed facilities. This encouraged rapid implementation of WSI across all surgical pathology subspecialties.To present our experience with validation and implementation of WSI at a large academic medical center encompassing a caseload of more than 50 000 cases per year.Validation was performed independently for 3 subspecialty services with a diagnostic concordance threshold above 95%. Analysis of user experience, staffing, infrastructure, and information technology was performed after department-wide expansion.Diagnostic concordance was achieved in 96% of neuropathology cases, 100% of gynecologic pathology cases, and 98% of immunohistochemistry cases. After full implementation, 8 high-capacity scanners were operational, with whole slide images generated on greater than 2000 slides per weekday, accounting for approximately 80% of histologic slides at Stanford Medicine. Multiple modifications in workflow and information technology were needed to improve performance. Within months of full implementation, most attending pathologists and trainees had adopted WSI for primary diagnosis.WSI across all surgical subspecialities is achievable at scale at an academic medical center; however, adoption required flexibility to adjust workflows and develop tailored solutions. WSI at scale supported the health and safety of medical staff while facilitating high-quality patient care and education during COVID-19 restrictions.