Reviewerly/exHarmony huggingface dataset:
Improving Paper Classification Using Forecasting

The number of scientific publications increases tremendously over the past decades. Classifying papers to arXiv style categories makes it easier for individuals to find relevant papers and improves the overall efficiency in the research community. However, the majority of papers are not categorized and manually classifying hundreds of millions of papers is prohibitive. Recent advances in machine learning have paved a way for automatic paper classification. In this paper, we propose an approach to further improve the classification accuracy. Our approach takes advantage of the big-data nature of the problem and uses the publication trend to forecast the number of papers for each category. Then we use the forecasted label distribution to revise the classification results returned by base classifiers. A data-driven heuristic approach is introduced to address the label revision problem. Our experiments with a large publicly available dataset demonstrated that the proposed solution improves the accuracy of all existing approaches.
DACMA: Designing space ordering optimizations to scalably manage aerial images

Aerial images are a special class of remote sensing images, as they are intentionally collected with a high degree of overlap. This high degree of overlap complicates existing index strategies such as R-tree and Space Filling Curve (SFC) based index techniques due to complications in space splitting, granularity of the grid cells and excessive duplication of image object identifiers (IOIs). However, SFC based space ordering can be modified to provide scalable management of overlapping aerial images. This involves overcoming similar IOIs in adjacent grid cells, which would naturally occur in SFC based grids with such data. IOI duplication can be minimized by merging adjacent grid cells through the proposed “Designing Adjacent Cell Merge Algorithm” (DACMA). This work focuses on establishing a proper adjacent cell merge metric and merge percentage value. Using a highly scalable, distributed HBase cluster for both a single aerial mapping project, and multiple aerial mapping projects, experiments evaluated Jaccard Similarity (JS) and Percentage of Overlap (PO) merge metrics. JS had significant advantages: (i) generating smaller merged regions and (ii) obtaining over 21% and 36% improvement in reducing query response times compared to PO. As a result, JS is proposed for the merge metric for DACMA. For the merge percentage two considerations were dominant: (i) substantial storage reductions with respect to both straight forward SFC-based cell space indexing and 4SA based indexing, and (ii) minimal impact on the query response time. The proposed merge percentage value was selected to optimize the storage (i.e. space) needs and response time (i.e. time) herein named the "Space-Time Trade-off Optimization Percentage" value (or STOP value) is presented.
Debiased Learning of Self-Labeled Twitter Data for User Demographic Prediction

Labeling sufficient data for supervised learning remains an open challenge in social network analysis. An alternative is to collect self-labeled data, i.e. the data labeled by their owners. Emmery et al show that standard models can be trained and perform well on self-labeled data, suggesting the effectiveness of this approach. In this paper, we argue self-labeled data may not be representative of the population. Taking Twitter demographic prediction as an example, we show the popular FastText model standardly trained on self-labeled data does not generalize well on random testing samples. We then present a new learner DeFastText that aims to correct data bias using the kernel means matching technique. In experiment, we show it achieves lower generalization errors than FastText. This research raises an attention of the data bias problem when learning from self-labeled data in social network analysis.
A Short Survey on the User Cold Start Problem in Recommender Systems: Metadata and Meta-Learning Methods

In recommender systems, the cold start problem, especially the user cold start one, is quite challenging yet important as it mostly comes with the rapid growth of the product and business. A well-performed new or early user recommender system has the potential to not only offer great onboarding experience, but also largely convert these users into loyal ones with considerable long-term values, which is strategically important from a product perspective. From a machine learning perspective, it is not an easy task as modern recommender systems heavily rely on large amounts of user-item interaction data to train a high quality model, and the data sparsity problem that new and early users bring is a clear challenge in terms of model training quality. In this short survey, among many proposed ideas on this topic, we focused on two general approaches for user cold start recommender system problems: metadata and meta-learning. Metadata methods generally aim at calculating similarities and learning representations by well utilizing limited user data that is collected regardless of users’ ratings for recommended items. Meta-learning methods learn the model in a more general and efficient way that could be adapted to new tasks with decent performance given data sparsity, which aligns the user cold start problem settings well.
An Effect Analysis of ISO/IEC 27001 Certification on Technical Security of Norwegian Grid Operators

Digital vulnerabilities and the risk of cyberattacks against the electric grid are a concern for both governments and businesses. There are several opportunities for authorities to impose security measures on grid operators to reduce risks. German legislation requires grid operators to certify their information security management system according to the ISO/IEC 27001 standard. Some researchers have tried to measure various effects of ISO/IEC 27001 certification, but nobody has so far assessed the effect of certification on technical security performance. This study hypothesizes that ISO/IEC 27001 certification will lead to increased technical security performance for Norwegian grid operators. A Quasi-Experimental methodology based on Difference in Differences logic is applied to test the hypothesis. 11.010 technical security scores from 400 entities were collected through BlackKite’s Technical Cyber Rating tool and Security Scorecard’s Security Rating tool. The effect of ISO/IEC 27001 certification was estimated by taking the difference in technical security performance between uncertified Norwegian grid operators and certified German grid operators, and subtracting the difference between a control group of Norwegian and German banks. The analysis predicts a significant positive effect for small Norwegian grid operators, it is inconclusive for medium-sized grid operators, and it indicates a negative effect for large grid operators. Since the research was limited to externally identifiable security mechanisms only, more research is necessary to fully understand the effect of ISO/IEC 27001 certification on technical security performance.
FedLesScan: Mitigating Stragglers in Serverless Federated Learning

Federated Learning (FL) is a machine learning paradigm that enables the training of a shared global model across distributed clients while keeping the training data local. While most prior work on designing systems for FL has focused on using stateful always running components, recent work has shown that components in an FL system can greatly benefit from the usage of serverless computing and Function-as-a-Service technologies. To this end, distributed training of models with serverless FL systems can be more resource-efficient and cheaper than conventional FL systems. However, serverless FL systems still suffer from the presence of stragglers, i.e., slow clients due to their resource and statistical heterogeneity. While several strategies have been proposed for mitigating stragglers in FL, most methodologies do not account for the particular characteristics of serverless environments, i.e., cold-starts, performance variations, and the ephemeral stateless nature of the function instances. Towards this, we propose FedLesScan, a novel clustering-based semi-asynchronous training strategy, specifically tailored for serverless FL. FedLesScan dynamically adapts to the behaviour of clients and minimizes the effect of stragglers on the overall system. We implement our strategy by extending an open-source serverless FL system called FedLess. Moreover, we comprehensively evaluate our strategy using the 2nd generation Google Cloud Functions with four datasets and varying percentages of stragglers. Results from our experiments show that compared to other approaches FedLesScan reduces training time and cost by an average of 8% and 20% respectively while utilizing clients better with an average increase in the effective update ratio of 17.75%.
An Empirical Study of Domain Adaptation: Are We Really Learning Transferable Representations?

Deep learning often relies on the availability of a large amount of high-quality labeled data, which can be very limited in novel domains. To address such data scarcity, domain adaptation is one promising approach that allows for deep networks to leverage large amounts of available data from a source domain to enhance the model’s efficacy on the target domain of interest. However, while there is a plethora of alternate models for domain adaptation proposed over many years in the literature, there is a dearth of studies that objectively compare the relative effectiveness of these models in a rigorous, empirical study. To fill this gap, we provide a thorough, unbiased, empirical study of five state-of-the-art (SOTA) deep domain adaptation models proposed over the past 6 years whose codes are publicly available. Models are evaluated on the complex and diverse domain adaptation tasks featured in the DomainNet benchmark dataset as well as the popular Office-31 dataset. Our results suggest that (1) all 5 models perform similarly, on average, and do not even significantly beat the oldest model, and (2) counter to their intended purpose, the transfer loss functions in the literature do not contribute significantly to learning transferable representations. Our observations suggest that domain adaptation research needs to more thoroughly compare newly proposed models against existing works, along with assessing their loss functions’ utility thoroughly. Our code and data splits are made public for reproducibility of results by the community.
Word Phrase Analysis of High-frequency Ngrams in the Large-scale Korean Law Corpus

We analyze the legal vocabulary, compound terms, and basic phrases of laws and regulations in the Korean law sentences. It was intended to identify the consistency of terms and phrases. Using a large-scale corpus analysis technique, we analyzed the frequency of n-gram word phrases. Through the frequency characteristics of basic phrases, we tried to find translation related problems in the law sentences. In this study, we found that the frequency results of word n-grams can be usefully used through n-gram and (n-1)-gram inclusion relationship analysis when determining the range of common phrases for constructing a translation memory.
Investigating User Information and Social Media Features in Cyberbullying Detection

As society grows increasingly more online with each passing year, the problem of cyberbullying becomes more and more prominent, with such incidents having the capacity to negatively impact mental health in a major way, especially among children and teenagers. The proposed approach builds on our previous work that established multi-modal detection of cyberbullying on Twitter, and restructures the multi-modal approach by incorporating social media features such as time-related features and social network information. As a result, the new models reach a classification accuracy between 94.4% and 94.6%, from the previous accuracy of 93%. The proposed new approach affirms the use of context-based data in addition to more directly-related features when analyzing cyberbullying and other interactions with promising improvements. We believe that this work contributes significantly to the study of cyberbullying detection, which is an imminent problem with growing importance in the post-COVID society.
AuxPOS: Improving Grammatical Correctness with Big Data Based Text Summarization

In the era of big data, with the rapid increase in the number of texts, we have found ourselves submerged in the sea of texts. With the advent of the text summarization methodology, the time-consuming and energy-draining reading process of manual operations can be avoided by condensing and communicating the primary concept to the people. When generating sentences, the generator should comply with grammatical rules, such as lexical constraints. Leveraging lexical constraints in encoder-decoder models for the text summarization task has been extensively analyzed. Previous research add lexical constraints by enriching word embeddings in the encoding process. The model could be fed with more auxiliary knowledge. However, such lexical constraints cannot control the decoder, resulting in generating ungrammatical phrases. To address this issue, this paper proposes a grammar-aware text summarization method by incorporating Part-of-Speech (POS) constraints to guide the decoding process, along with auxiliary embeddings including POS, Lemmatization and Named Entity Recognition features in encoder. Detailed, the proposed POS-constrained AuxPOS model controls the generated word distribution during decoding according to the exact POS tag. Experimental results demonstrate that the proposed AuxPOS model can generate summaries that are more in concert with grammatical logic with high quality on the CNN/DailyMail, XSum and AESLC summarization datasets.
Profiling-free Configuration Adaptation and Latency-Aware Resource Scheduling for Video Analytics

With increasingly deployed cameras and the rapid advances of Computer Vision, large-scale live video analytics becomes feasible. However, analyzing videos is compute-intensive. In addition, live video analytics needs to be performed in real time. In this paper, we design an edge server system for live video analytics. We propose to perform configuration adaptation without profiling video online. We select configurations with a prediction model based on object movement features. In addition, we reduce the latency through resource orchestration on video analytics servers. The key idea of resource orchestration is to batch inference tasks that use the same CNN model, and schedule tasks based on a priority value that estimates their impact on the total latency. We evaluate our system with two video analytic applications, road traffic monitoring and pose detection. The experimental results show that our profiling-free adaptation reduces the workload by 80% of the state-of-the-art adaptation without lowering the accuracy. The average serving latency is reduced by up to 95% comparing with the profiling-based adaptation.
A Novel Video Copy Sub-sequence Detection and Location Method

With the rapid growth of the internet and multimedia technology, there is an exponential growth of copy video, which causes some certain impact on video retrieval and copyright protection. Therefore, it becomes increasingly important to find copy videos and locate subsequent clips in a large-scale video database. In this paper, an efficient method is proposed to solve the current problem of video copy detection when the test video contains both copy video clips and non-copy video clips. This paper presents a method of judging the video copy sub-sequence based on the distance between the test video keyframe and the reference video keyframe. First, AlexNet is used to extract the features of the keyframe. Second, it judges whether each test video keyframe is a copy frame according to the distance. Then, the location of the copy sub-sequence is determined by finding the continuous copy frame, and then the video location of the copy sub-sequence is determined. Experimental results show that the proposed method can achieve 86.29% in recall and 95.71% in precision.
A Survey of Explainable Graph Neural Networks for Cyber Malware Analysis

Malicious cybersecurity activities have become increasingly worrisome for individuals and companies alike. While machine learning methods like Graph Neural Networks (GNNs) have proven successful on the malware detection task, their output is often difficult to understand. Explainable malware detection methods are needed to automatically identify malicious programs and present results to malware analysts in a way that is human interpretable. In this survey, we outline a number of GNN explainability methods and compare their performance on a real-world malware detection dataset. Specifically, we formulated the detection problem as a graph classification problem on the malware Control Flow Graphs (CFGs). We find that gradient-based methods outperform perturbation-based methods in terms of computational expense and performance on explainer-specific metrics (e.g., Fidelity and Sparsity). Our results provide insights into designing new GNN-based models for cyber malware detection and attribution.
EBEM: An Enhanced Bi-encoder Model for Word Sense Disambiguation

Word Sense Disambiguation (WSD) systems have recently achieved unprecedented high performance and have approached or even exceeded the human level. However, almost all these systems only focus on integrating knowledge and designing the WSD classification layer while having few works related to loss function, which is equally essential for WSD systems. In this paper, we propose Enhanced Bi-encoder Model (EBEM) with a more flexible loss function, Circle loss, and enhanced representations via knowledge from documents and WordNet. EBEM is strong in fine-grained sense discrimination. Our experimental results show that EBEM produces a considerable performance improvement compared with Bi-encoder Model (BEM) and achieves state-of-the-art results on English all-words WSD. More importantly, EBEM has an excellent performance in few-shot learning, which will contribute to the further application and extension of WSD. We also verify the effectiveness of Circle loss in WSD systems and report how it affects the clusters of sense representations.
Towards Robust Graph Neural Networks via Adversarial Contrastive Learning

Graph Neural Network (GNN), as a powerful representation learning model on graph data, attracts much attention across various disciplines. However, recent studies show that GNN is vulnerable to adversarial attacks. How to make GNN more robust? What are the key vulnerabilities in GNN? How to address the vulnerabilities and defend GNN against the adversarial attacks? Adversarial training has shown to be effective in improving the robustness of traditional Deep Neural Networks (DNNs). However, existing adversarial training works mainly focus on the image data, which consists of continuous features, while the features and structures of graph data are often discrete. Moreover, rather than assuming each sample is independent and identically distributed as in DNN, GNN leverages the contextual information across the graph (e.g., neighborhoods of a node). Thus, existing adversarial training techniques cannot be directly applied to defend GNN.In this paper, we propose ContrastNet, an effective adversarial defense framework for GNN. In particular, we propose an adversarial contrastive learning method to train the GNN over the adversarial space. To further improve the robustness of GNN, we investigate the latent vulnerabilities in every component of a GNN encoder and propose corresponding refining strategies. Extensive experiments on three public datasets demonstrate the effectiveness of ContrastNet in improving the robustness of popular GNN variants, such as Graph Convolutional Network and GraphSage, under various types of adversarial attacks.
Prediction of Plasma Discharge Video by Convolutional Long Short-Term Memory

In the National Institute for Fusion Science, high-temperature plasmas have been produced in the Large Helical Device (LHD). A large number of videos having plasma discharge are captured during nuclear fusion operations. An observation of these recorded videos can lead to a new discovery and guarantee the safe operation of the experiments. In order to optimize the operational parameter of the experiment, the future emission states of plasmas should be estimated. Thus, this paper proposes a method to make a prediction of sequential future frames containing plasma light emission using the Convolutional Long Short-Term Memory (ConvLSTM). We confirmed that the future frames of plasma discharge could be well predicted with the satisfactory results of Mean Squared Error as 2.78×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> and Cosine Similarity as 0.9997.
Multi-Behavior Recommendation with Hyperbolic Geometry

Even though users interacted diversely on items (e.g., click, add-to-cart, and buy), traditional recommendations were mostly built using only the user-item interaction data on the target behavior (e.g., buy), making them suffer from the severe data sparsity issue. To alleviate the problem, recent works on multi-behavior recommendation incorporated multiple types of user-item interactions such as click, add-to-cart, and buy. However, the latest approaches are still limited by overlooking early-stage interactions, and have limited expressiveness of Euclidean geometry. To overcome these issues, in this paper, we propose a Multi-behavior Hyperbolic Graph Recommender (MB-HGR) with two novel aspects. First, it uses multiple heterogeneous graphs to learn multiple user behavior types, where each heterogeneous graph represents a user-item interaction type. This will help not only alleviate the serious data sparsity problem, but also allow the model to explicitly weight different behavior types and prevent information loss. Second, it leverages the expressiveness of the hyperbolic geometry over Euclidean geometry, where exponential growth of distances in the hyperbolic geometry matches the exponential growth of nodes in the hierarchical structures and learns better users/items representations. Experimental results on two public benchmark datasets show that on average our proposed model achieves a significant improvement of 28.32% at Recall@10 and 30.14% at NDCG@10 over the best baseline.
EVEAL - Expected Variance Estimation for Active Learning

Regression problems frequently occur in the surrounding world, therefore are unavoidable in real-world applications. However, to obtain a model with desired generalization performance, a vast amount of labels usually is required. In many scenarios, obtaining unlabelled data is relatively inexpensive, therefore active learning approaches may be used to reduce the needed annotation effort. Most uncertainty-based regression active learning algorithms use variance estimation of model predictions to choose informative samples. Those algorithms do not incorporate knowledge about the data distribution for the given task. In this paper, we propose a novel algorithm to incorporate information about data distribution and combine it with variance estimation as an informativeness function. Experiments conducted on four data sets show that the proposed approach outperforms standard variance-based sampling by a margin, and indicate its robustness.