OpenRaiser/Intern-Atlas huggingface dataset:
Unsupervised Domain Adaptation with Global and Local Graph Neural Networks in Limited Labeled Data Scenario: Application to Disaster Management
Venue: arXiv cs.CL 2021
Year: 2021

Identification and categorization of social media posts generated during disasters are crucial to reduce the sufferings of the affected people. However, lack of labeled data is a significant bottleneck in learning an effective categorization system for a disaster. This motivates us to study the problem as unsupervised domain adaptation (UDA) between a previous disaster with labeled data (source) and a current disaster (target). However, if the amount of labeled data available is limited, it restricts the learning capabilities of the model. To handle this challenge, we utilize limited labeled data along with abundantly available unlabeled data, generated during a source disaster to propose a novel two-part graph neural network. The first-part extracts domain-agnostic global information by constructing a token level graph across domains and the second-part preserves local instance-level semantics. In our experiments, we show that the proposed method outperforms state-of-the-art techniques by $2.74\%$ weighted F$_1$ score on average on two standard public dataset in the area of disaster management. We also report experimental results for granular actionable multi-label classification datasets in disaster domain for the first time, on which we outperform BERT by $3.00\%$ on average w.r.t weighted F$_1$. Additionally, we show that our approach can retain performance when very limited labeled data is available.
COHORTNEY: Non-Parametric Clustering of Event Sequences
Venue: arXiv cs.LG 2021
Year: 2021

Cohort analysis is a pervasive activity in web analytics. One divides users into groups according to specific criteria and tracks their behavior over time. Despite its extensive use, academic circles do not discuss cohort analysis to evaluate user behavior online. This work introduces an unsupervised non-parametric approach to group Internet users based on their activities. In comparison, canonical methods in marketing and engineering-based techniques underperform. COHORTNEY is the first machine learning-based cohort analysis algorithm with a robust theoretical explanation.
Exploring the Role of BERT Token Representations to Explain Sentence Probing Results
Venue: arXiv cs.CL 2021
Year: 2021

Several studies have been carried out on revealing linguistic features captured by BERT. This is usually achieved by training a diagnostic classifier on the representations obtained from different layers of BERT. The subsequent classification accuracy is then interpreted as the ability of the model in encoding the corresponding linguistic property. Despite providing insights, these studies have left out the potential role of token representations. In this paper, we provide a more in-depth analysis on the representation space of BERT in search for distinct and meaningful subspaces that can explain the reasons behind these probing results. Based on a set of probing tasks and with the help of attribution methods we show that BERT tends to encode meaningful knowledge in specific token representations (which are often ignored in standard classification setups), allowing the model to detect syntactic and semantic abnormalities, and to distinctively separate grammatical number and tense subspaces.
Medical Entity Disambiguation Using Graph Neural Networks
Venue: arXiv cs.IR 2021
Year: 2021

Medical knowledge bases (KBs), distilled from biomedical literature and regulatory actions, are expected to provide high-quality information to facilitate clinical decision making. Entity disambiguation (also referred to as entity linking) is considered as an essential task in unlocking the wealth of such medical KBs. However, existing medical entity disambiguation methods are not adequate due to word discrepancies between the entities in the KB and the text snippets in the source documents. Recently, graph neural networks (GNNs) have proven to be very effective and provide state-of-the-art results for many real-world applications with graph-structured data. In this paper, we introduce ED-GNN based on three representative GNNs (GraphSAGE, R-GCN, and MAGNN) for medical entity disambiguation. We develop two optimization techniques to fine-tune and improve ED-GNN. First, we introduce a novel strategy to represent entities that are mentioned in text snippets as a query graph. Second, we design an effective negative sampling strategy that identifies hard negative samples to improve the model's disambiguation capability. Compared to the best performing state-of-the-art solutions, our ED-GNN offers an average improvement of 7.3% in terms of F1 score on five real-world datasets.
Exponentiated Gradient Reweighting for Robust Training Under Label Noise and Beyond
Venue: arXiv cs.LG 2021
Year: 2021

Many learning tasks in machine learning can be viewed as taking a gradient step towards minimizing the average loss of a batch of examples in each training iteration. When noise is prevalent in the data, this uniform treatment of examples can lead to overfitting to noisy examples with larger loss values and result in poor generalization. Inspired by the expert setting in on-line learning, we present a flexible approach to learning from noisy examples. Specifically, we treat each training example as an expert and maintain a distribution over all examples. We alternate between updating the parameters of the model using gradient descent and updating the example weights using the exponentiated gradient update. Unlike other related methods, our approach handles a general class of loss functions and can be applied to a wide range of noise types and applications. We show the efficacy of our approach for multiple learning settings, namely noisy principal component analysis and a variety of noisy classification problems.
Simple Uncoupled No-Regret Learning Dynamics for Extensive-Form Correlated Equilibrium
Venue: arXiv cs.GT 2021
Year: 2021

The existence of simple uncoupled no-regret learning dynamics that converge to correlated equilibria in normal-form games is a celebrated result in the theory of multi-agent systems. Specifically, it has been known for more than 20 years that when all players seek to minimize their internal regret in a repeated normal-form game, the empirical frequency of play converges to a normal-form correlated equilibrium. Extensive-form games generalize normal-form games by modeling both sequential and simultaneous moves, as well as imperfect information. Because of the sequential nature and presence of private information in the game, correlation in extensive-form games possesses significantly different properties than its counterpart in normal-form games, many of which are still open research directions. Extensive-form correlated equilibrium (EFCE) has been proposed as the natural extensive-form counterpart to the classical notion of correlated equilibrium in normal-form games. Compared to the latter, the constraints that define the set of EFCEs are significantly more complex, as the correlation device must keep into account the evolution of beliefs of each player as they make observations throughout the game. Due to that significant added complexity, the existence of uncoupled learning dynamics leading to an EFCE has remained a challenging open research question for a long time. In this article, we settle that question by giving the first uncoupled no-regret dynamics that converge to the set of EFCEs in n-player general-sum extensive-form games with perfect recall. We show that each iterate can be computed in time polynomial in the size of the game tree, and that, when all players play repeatedly according to our learning dynamics, the empirical frequency of play is proven to be a O(T^-0.5)-approximate EFCE with high probability after T game repetitions, and an EFCE almost surely in the limit.
Golden Tortoise Beetle Optimizer: A Novel Nature-Inspired Meta-heuristic Algorithm for Engineering Problems
Venue: arXiv cs.NE 2021
Year: 2021

This paper proposes a novel nature-inspired meta-heuristic algorithm called the Golden Tortoise Beetle Optimizer (GTBO) to solve optimization problems. It mimics golden tortoise beetle's behavior of changing colors to attract opposite sex for mating and its protective strategy that uses a kind of anal fork to deter predators. The algorithm is modeled based on the beetle's dual attractiveness and survival strategy to generate new solutions for optimization problems. To measure its performance, the proposed GTBO is compared with five other nature-inspired evolutionary algorithms on 24 well-known benchmark functions investigating the trade-off between exploration and exploitation, local optima avoidance, and convergence towards the global optima is statistically significant. We particularly applied GTBO to two well-known engineering problems including the welded beam design problem and the gear train design problem. The results demonstrate that the new algorithm is more efficient than the five baseline algorithms for both problems. A sensitivity analysis is also performed to reveal different impacts of the algorithm's key control parameters and operators on GTBO's performance.
Networking Group Content: RESTful Multiparty Access to a Data-centric Web of Things
Venue: arXiv cs.NI 2021
Year: 2021

Content replication to many destinations is a common use case in the Internet of Things (IoT). The deployment of IP multicast has proven inefficient, though, due to its lack of layer-2 support by common IoT radio technologies and its synchronous end-to-end transmission, which is highly susceptible to interference. Information-centric networking (ICN) introduced hop-wise multi-party dissemination of cacheable content, which has proven valuable in particular for low-power lossy networking regimes. Even NDN, however, the most prominent ICN protocol, suffers from a lack of deployment. In this paper, we explore how multiparty content distribution in an information-centric Web of Things (WoT) can be built on CoAP. We augment the CoAP proxy by request aggregation and response replication functions, which together with proxy caches enable asynchronous group communication. In a further step, we integrate content object security with OSCORE into the CoAP multicast proxy system, which enables ubiquitous caching of certified authentic content. In our evaluation, we compare NDN with different deployment models of CoAP, including our data-centric approach in realistic testbed experiments. Our findings indicate that multiparty content distribution based on CoAP proxies performs equally well as NDN, while remaining fully compatible with the established IoT protocol world of CoAP on the Internet.
Timers and Such: A Practical Benchmark for Spoken Language Understanding with Numbers
Venue: arXiv cs.CL 2021
Year: 2021

This paper introduces Timers and Such, a new open source dataset of spoken English commands for common voice control use cases involving numbers. We describe the gap in existing spoken language understanding datasets that Timers and Such fills, the design and creation of the dataset, and experiments with a number of ASR-based and end-to-end baseline models, the code for which has been made available as part of the SpeechBrain toolkit.
OnTarget: An Electronic Archery Scoring
Venue: arXiv cs.CV 2021
Year: 2021

There are several challenges in creating an electronic archery scoring system using computer vision techniques. Variability of light, reconstruction of the target from several images, variability of target configuration, and filtering noise were significant challenges during the creation of this scoring system. This paper discusses the approach used to determine where an arrow hits a target, for any possible single or set of targets and provides an algorithm that balances the difficulty of robust arrow detection while retaining the required accuracy.
Isconna: Streaming Anomaly Detection with Frequency and Patterns
Venue: arXiv cs.LG 2021
Year: 2021

An edge stream is a common form of presentation of dynamic networks. It can evolve with time, with new types of nodes or edges being continuously added. Existing methods for anomaly detection rely on edge occurrence counts or compare pattern snippets found in historical records. In this work, we propose Isconna, which focuses on both the frequency and the pattern of edge records. The burst detection component targets anomalies between individual timestamps, while the pattern detection component highlights anomalies across segments of timestamps. These two components together produce three intermediate scores, which are aggregated into the final anomaly score. Isconna does not actively explore or maintain pattern snippets; it instead measures the consecutive presence and absence of edge records. Isconna is an online algorithm, it does not keep the original information of edge records; only statistical values are maintained in a few count-min sketches (CMS). Isconna's space complexity $O(rc)$ is determined by two user-specific parameters, the size of CMSs. In worst case, Isconna's time complexity can be up to $O(rc)$, but it can be amortized in practice. Experiments show that Isconna outperforms five state-of-the-art frequency- and/or pattern-based baselines on six real-world datasets with up to 20 million edge records.
A Logical Programming Language as an Instrument for Specifying and Verifying Dynamic Memory
Venue: arXiv cs.LO 2021
Year: 2021

This work proposes a Prolog-dialect for the found and prioritised problems on expressibility and automation. Given some given C-like program, if dynamic memory is allocated, altered and freed on runtime, then a description of desired dynamic memory is a heap specification. The check of calculated memory state against a given specification is dynamic memory verification. This contribution only considers formal specification and verification in a Hoare calculus. Issues found include: invalid assignment, (temporary) unavailable data in memory cells, excessive memory allocation, (accidental) heap alteration in unexpected regions and others. Excessive memory allocation is nowadays successfully resolved by memory analysers like Valgrind. Essentially, papers in those areas did not bring any big breakthrough. Possible reasons may also include the decrease of tension due to more available memory and parallel threads. However, starting with Apt, problems related to variable modes have not yet been resolved -- neither entirely nor in an acceptable way. Research contributions over the last decades show again and again that heap issues remain and remain complex and still important. A significant contribution was reached in 2016 by Peter O'Hearn, who accepted the Gödel prize for his parallel approach on a spatial heap operation.
EKO: Adaptive Sampling of Compressed Video Data
Venue: arXiv cs.DB 2021
Year: 2021

Researchers have presented systems for efficiently analysing video data at scale using sampling algorithms. While these systems effectively leverage the temporal redundancy present in videos, they suffer from three limitations. First, they use traditional video storage formats are tailored for human consumption. Second, they load and decode the entire compressed video in memory before applying the sampling algorithm. Third, the sampling algorithms often require labeled training data obtained using a specific deep learning model. These limitations lead to lower accuracy, higher query execution time, and larger memory footprint. In this paper, we present EKO, a storage engine for efficiently managing video data. EKO relies on two optimizations. First, it uses a novel unsupervised, adaptive sampling algorithm for identifying the key frames in a given video. Second, it stores the identified key frames in a compressed representation that is optimized for machine consumption. We show that EKO improves F1-score by up to 9% compared to the next best performing state-of-the-art unsupervised, sampling algorithms by selecting more representative frames. It reduces query execution time by 3X and memory footprint by 10X in comparison to a widely-used, traditional video storage format.
Analyzing Flight Delay Prediction Under Concept Drift
Venue: arXiv cs.LG 2021
Year: 2021

Flight delays impose challenges that impact any flight transportation system. Predicting when they are going to occur is an important way to mitigate this issue. However, the behavior of the flight delay system varies through time. This phenomenon is known in predictive analytics as concept drift. This paper investigates the prediction performance of different drift handling strategies in aviation under different scales (models trained from flights related to a single airport or the entire flight system). Specifically, two research questions were proposed and answered: (i) How do drift handling strategies influence the prediction performance of delays? (ii) Do different scales change the results of drift handling strategies? In our analysis, drift handling strategies are relevant, and their impacts vary according to scale and machine learning models used.
UDO: Universal Database Optimization using Reinforcement Learning
Venue: arXiv cs.DB 2021
Year: 2021

UDO is a versatile tool for offline tuning of database systems for specific workloads. UDO can consider a variety of tuning choices, reaching from picking transaction code variants over index selections up to database system parameter tuning. UDO uses reinforcement learning to converge to near-optimal configurations, creating and evaluating different configurations via actual query executions (instead of relying on simplifying cost models). To cater to different parameter types, UDO distinguishes heavy parameters (which are expensive to change, e.g. physical design parameters) from light parameters. Specifically for optimizing heavy parameters, UDO uses reinforcement learning algorithms that allow delaying the point at which the reward feedback becomes available. This gives us the freedom to optimize the point in time and the order in which different configurations are created and evaluated (by benchmarking a workload sample). UDO uses a cost-based planner to minimize reconfiguration overheads. For instance, it aims to amortize the creation of expensive data structures by consecutively evaluating configurations using them. We evaluate UDO on Postgres as well as MySQL and on TPC-H as well as TPC-C, optimizing a variety of light and heavy parameters concurrently.
Comparative analysis of the government plans of the Peruvian presidential candidates, SDO(UN) and State Policies of the National Agreement based on NLP
Venue: arXiv cs.CY 2021
Year: 2021

The analysis of government proposal during elections from political parties is vital to choose the next authorities in any city or country. In this paper, we use a text mining approach to analyze the documents and provide an easy visualization to support an easy analysis. Besides, a comparison with a national plan based on sustainable development objectives of UN(United Nations) from 2030 Agenda is perfomed using Natural Language techniques.
WhiteningBERT: An Easy Unsupervised Sentence Embedding Approach
Venue: arXiv cs.CL 2021
Year: 2021

Producing the embedding of a sentence in an unsupervised way is valuable to natural language matching and retrieval problems in practice. In this work, we conduct a thorough examination of pretrained model based unsupervised sentence embeddings. We study on four pretrained models and conduct massive experiments on seven datasets regarding sentence semantics. We have there main findings. First, averaging all tokens is better than only using [CLS] vector. Second, combining both top andbottom layers is better than only using top layers. Lastly, an easy whitening-based vector normalization strategy with less than 10 lines of code consistently boosts the performance.
BTS-Net: Bi-directional Transfer-and-Selection Network For RGB-D Salient Object Detection
Venue: arXiv cs.CV 2021
Year: 2021

Depth information has been proved beneficial in RGB-D salient object detection (SOD). However, depth maps obtained often suffer from low quality and inaccuracy. Most existing RGB-D SOD models have no cross-modal interactions or only have unidirectional interactions from depth to RGB in their encoder stages, which may lead to inaccurate encoder features when facing low quality depth. To address this limitation, we propose to conduct progressive bi-directional interactions as early in the encoder stage, yielding a novel bi-directional transfer-and-selection network named BTS-Net, which adopts a set of bi-directional transfer-and-selection (BTS) modules to purify features during encoding. Based on the resulting robust encoder features, we also design an effective light-weight group decoder to achieve accurate final saliency prediction. Comprehensive experiments on six widely used datasets demonstrate that BTS-Net surpasses 16 latest state-of-the-art approaches in terms of four key metrics.
Indonesian Journal of Applied Linguistics: A Bibliometric Portrait of Ten Publication Years
Venue: arXiv cs.DL 2021
Year: 2021

Bibliometric portraits of a single journal appear to be rarely taken in the field of applied linguistics. Viewed from the angles of publication, citation, and indexation, one of the journals worth a bibliometric portrait is the Indonesian Journal of Applied Linguistics. Casting local and regional concerns on the global applied linguistics, the journal has ranked among the big five Open Access Journals in the Asiatic region since its foundation in 2011.
Frequency Estimation Under Multiparty Differential Privacy: One-shot and Streaming
Venue: arXiv cs.CR 2021
Year: 2021

We study the fundamental problem of frequency estimation under both privacy and communication constraints, where the data is distributed among $k$ parties. We consider two application scenarios: (1) one-shot, where the data is static and the aggregator conducts a one-time computation; and (2) streaming, where each party receives a stream of items over time and the aggregator continuously monitors the frequencies. We adopt the model of multiparty differential privacy (MDP), which is more general than local differential privacy (LDP) and (centralized) differential privacy. Our protocols achieve optimality (up to logarithmic factors) permissible by the more stringent of the two constraints. In particular, when specialized to the $\varepsilon$-LDP model, our protocol achieves an error of $\sqrt{k}/(e^{Θ(\varepsilon)}-1)$ using $O(k\max\{ \varepsilon, \frac{1}{\varepsilon} \})$ bits of communication and $O(k \log u)$ bits of public randomness, where $u$ is the size of the domain.
Modeling Gate-Level Abstraction Hierarchy Using Graph Convolutional Neural Networks to Predict Functional De-Rating Factors
Venue: arXiv cs.AR 2021
Year: 2021

The paper is proposing a methodology for modeling a gate-level netlist using a Graph Convolutional Network (GCN). The model predicts the overall functional de-rating factors of sequential elements of a given circuit. In the preliminary phase of the work, the important goal is making a GCN which able to take a gate-level netlist as input information after transforming it into the Probabilistic Bayesian Graph in the form of Graph Modeling Language (GML). This part enables the GCN to learn the structural information of netlist in graph domains. In the second phase of the work, the modeled GCN trained with the a functional de-rating factor of a very low number of individual sequential elements (flip-flops). The third phase includes understanding of GCN models accuracy to model an arbitrary circuit netlist. The designed model was validated for two circuits. One is the IEEE 754 standard double precision floating point adder and the second one is the 10-Gigabit Ethernet MAC IEEE802.3 standard. The predicted results compared to the standard fault injection campaign results of the error called Single EventUpset (SEU). The validated results are graphically pictured in the form of the histogram and sorted probabilities and evaluated with the Confidence Interval (CI) metric between the predicted and simulated fault injection results.