OpenRaiser/Intern-Atlas huggingface dataset:
SasCsvToolkit -- A versatile parallel 'bag-of-tasks' job submission application on heterogeneous and homogeneous platforms for Big Data Analytics such as for Biomedical Informatics
Venue: arXiv cs.DC 2021
Year: 2021

Background: The need for big data analysis requires being able to process large data which are being held fine-tuned for usage by corporate. It is only very recently that the need for big data has caught attention for low budget corporate groups and academia who typically do not have money and resources to buy expensive licenses of big data analysis platforms such as SAS. The corporate continue to work on SAS data format largely because of systemic organizational history and that the prior codes have been built on them. The data-providers continue to thus provide data in SAS formats. Acute sudden need has arisen because of this gap of data being in SAS format and the coders not having a SAS expertise or training background as the economic and inertial forces acting of having shaped these two class of people have been different. Method: We analyze the differences and thus the need for SasCsvToolkit which helps to generate a CSV file for a SAS format data so that the data scientist can then make use of his skills in other tools that can process CSVs such as R, SPSS, or even Microsoft Excel. At the same time, it also provides conversion of CSV files to SAS format. Apart from this, a SAS database programmer always struggles in finding the right method to do a database search, exact match, substring match, except condition, filters, unique values, table joins and data mining for which the toolbox also provides template scripts to modify and use from command line. Results: The toolkit has been implemented on SLURM scheduler platform as a `bag-of-tasks` algorithm for parallel and distributed workflow though serial version has also been incorporated.
Analyzing the Influence of Dataset Composition for Emotion Recognition
Venue: arXiv cs.LG 2021
Year: 2021

Recognizing emotions from text in multimodal architectures has yielded promising results, surpassing video and audio modalities under certain circumstances. However, the method by which multimodal data is collected can be significant for recognizing emotional features in language. In this paper, we address the influence data collection methodology has on two multimodal emotion recognition datasets, the IEMOCAP dataset and the OMG-Emotion Behavior dataset, by analyzing textual dataset compositions and emotion recognition accuracy. Experiments with the full IEMOCAP dataset indicate that the composition negatively influences generalization performance when compared to the OMG-Emotion Behavior dataset. We conclude by discussing the impact this may have on HRI experiments.
Reconfigurable and approximate computing for video coding
Venue: arXiv cs.AR 2021
Year: 2021

The Chapter begins with a discussion of the constraints and needs of video coding systems. The lack in flexibility of traditional monolithic codec specifications, not suitable to model commonalities among codecs and foster reusability among successive codec generations/updates, was the main trigger for the development of a new standard initiative within the ISO/IEC MPEG committee, called reconfigurable video coding (RVC). The MPEG-RVC framework exploits the dataflow nature behind video coding to foster flexible and reconfigurable codec design, as well as to support dynamic reconfiguration. The Chapter goes on to consider that the inherent resiliency of various functional blocks (like motion estimation in the high-efficiency video coding, HEVC) and the varying levels of user perception make video coding suitable to apply approximate computing techniques. Approximate computing, if properly supported at design time, allows achieving run-time trade-offs, representing a new direction in hardware-software codesign research. The main assumption behind approximate computing, exploited within video coding, is that the degree of accuracy (in this case during codec execution) is not required to be the same all the time. The final part of the Chapter attempts to put together the concepts addressed and remarks on which are, in the authors' opinion, some interesting research directions.
Fine-tuning Pretrained Multilingual BERT Model for Indonesian Aspect-based Sentiment Analysis
Venue: arXiv cs.CL 2021
Year: 2021

Although previous research on Aspect-based Sentiment Analysis (ABSA) for Indonesian reviews in hotel domain has been conducted using CNN and XGBoost, its model did not generalize well in test data and high number of OOV words contributed to misclassification cases. Nowadays, most state-of-the-art results for wide array of NLP tasks are achieved by utilizing pretrained language representation. In this paper, we intend to incorporate one of the foremost language representation model, BERT, to perform ABSA in Indonesian reviews dataset. By combining multilingual BERT (m-BERT) with task transformation method, we manage to achieve significant improvement by 8% on the F1-score compared to the result from our previous study.
Multi-document Summarization using Semantic Role Labeling and Semantic Graph for Indonesian News Article
Venue: arXiv cs.CL 2021
Year: 2021

In this paper, we proposed a multi-document summarization system using semantic role labeling (SRL) and semantic graph for Indonesian news articles. In order to improve existing summarizer, our system modified summarizer that employed subject, predicate, object, and adverbial (SVOA) extraction for predicate argument structure (PAS) extraction. SVOA extraction is replaced with SRL model for Indonesian. We also replace the genetic algorithm to identify important PAS with the decision tree classifier since the summarizer without genetic algorithm gave better performance. The decision tree model is employed to identify important PAS. The decision tree model with 10 features achieved better performance than decision tree with 4 sentence features. Experiments and evaluations are conducted to generate 100 words summary and 200 words summary. The evaluation shows the proposed model get 0.313 average ROUGE-2 recall in 100 words summary and 0.394 average ROUGE-2 recall in 200 words summary.
BOPI: A Programming Interface For Reuse Of Research Data Available On DSpace Repositories
Venue: arXiv cs.HC 2021
Year: 2021

A recent study showed that more than 70% of researchers fail to reproduce their peers's experiments and more than half fail to reproduce their own experiments. Obviously, from a perspective of scientific quality this is a more than unsatisfying numbers. One approach to mitigate this flaw lies in the transparent provision of relevant research data to increase the base of available material to evaluate and possibly reconduct experiments. However, such data needs to be presented and accessed in a findable and purposefully usable way. In this work, we report the development of a programming interface to enhance findability and accessibility of research data (available in DSpace systems) and hence reproducibility of scientific experiments with data. This interface allows researchers to (i) find research data in multiples languages trough automatic translation of metadata; (ii) display a preview of data without download it beforehand; (iii) provide a detailed statistics of the data with interactive graphs for quality assessment; (iv) automatic download of data directly from Python-based experiments. Usability tests revealed that this interface improves the effectiveness, efficiency and satisfaction during the reuse of research data.
Overcoming Poor Word Embeddings with Word Definitions
Venue: arXiv cs.CL 2021
Year: 2021

Modern natural language understanding models depend on pretrained subword embeddings, but applications may need to reason about words that were never or rarely seen during pretraining. We show that examples that depend critically on a rarer word are more challenging for natural language inference models. Then we explore how a model could learn to use definitions, provided in natural text, to overcome this handicap. Our model's understanding of a definition is usually weaker than a well-modeled word embedding, but it recovers most of the performance gap from using a completely untrained word.
SoK: Cryptojacking Malware
Venue: arXiv cs.CR 2021
Year: 2021

Emerging blockchain and cryptocurrency-based technologies are redefining the way we conduct business in cyberspace. Today, a myriad of blockchain and cryptocurrency systems, applications, and technologies are widely available to companies, end-users, and even malicious actors who want to exploit the computational resources of regular users through \textit{cryptojacking} malware. Especially with ready-to-use mining scripts easily provided by service providers (e.g., Coinhive) and untraceable cryptocurrencies (e.g., Monero), cryptojacking malware has become an indispensable tool for attackers. Indeed, the banking industry, major commercial websites, government and military servers (e.g., US Dept. of Defense), online video sharing platforms (e.g., Youtube), gaming platforms (e.g., Nintendo), critical infrastructure resources (e.g., routers), and even recently widely popular remote video conferencing/meeting programs (e.g., Zoom during the Covid-19 pandemic) have all been the victims of powerful cryptojacking malware campaigns. Nonetheless, existing detection methods such as browser extensions that protect users with blacklist methods or antivirus programs with different analysis methods can only provide a partial panacea to this emerging cryptojacking issue as the attackers can easily bypass them by using obfuscation techniques or changing their domains or scripts frequently. Therefore, many studies in the literature proposed cryptojacking malware detection methods using various dynamic/behavioral features.
FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data
Venue: arXiv cs.LG 2021
Year: 2021

Federated learning (FL) has been proposed to allow collaborative training of machine learning (ML) models among multiple parties where each party can keep its data private. In this paradigm, only model updates, such as model weights or gradients, are shared. Many existing approaches have focused on horizontal FL, where each party has the entire feature set and labels in the training data set. However, many real scenarios follow a vertically-partitioned FL setup, where a complete feature set is formed only when all the datasets from the parties are combined, and the labels are only available to a single party. Privacy-preserving vertical FL is challenging because complete sets of labels and features are not owned by one entity. Existing approaches for vertical FL require multiple peer-to-peer communications among parties, leading to lengthy training times, and are restricted to (approximated) linear models and just two parties. To close this gap, we propose FedV, a framework for secure gradient computation in vertical settings for several widely used ML models such as linear models, logistic regression, and support vector machines. FedV removes the need for peer-to-peer communication among parties by using functional encryption schemes; this allows FedV to achieve faster training times. It also works for larger and changing sets of parties. We empirically demonstrate the applicability for multiple types of ML models and show a reduction of 10%-70% of training time and 80% to 90% in data transfer with respect to the state-of-the-art approaches.
An automated approach to mitigate transcription errors in braille texts for the Portuguese language
Venue: arXiv cs.CV 2021
Year: 2021

The quota system in Brazil made it possible to include blind students in higher education. Teachers' lack of knowledge about the braille system can represent a barrier between them and students who use it for writing and reading. Computer-vision-based transcription solutions represent mechanisms for reducing understanding restrictions on this system. However, such tools face nuisances inherent to image processing systems, e.g., illumination, noise, and scale, harming the result. This paper presents an automated approach to mitigate transcription errors in braille texts for the Portuguese language. We propose a selection function, combined with dictionaries, that provides the best correspondence of words based on their braille representation. We validated our proposal on a dataset of synthetic images by submitting them to different noise levels and testing the proposal's robustness. Experimental results confirm the effectiveness of the solution compared to a standard approach. As a contribution of this paper, we expect to provide a method to support robust and adaptable solutions to real use conditions.
Disambiguating Affective Stimulus Associations for Robot Perception and Dialogue
Venue: arXiv cs.RO 2021
Year: 2021

Effectively recognising and applying emotions to interactions is a highly desirable trait for social robots. Implicitly understanding how subjects experience different kinds of actions and objects in the world is crucial for natural HRI interactions, with the possibility to perform positive actions and avoid negative actions. In this paper, we utilize the NICO robot's appearance and capabilities to give the NICO the ability to model a coherent affective association between a perceived auditory stimulus and a temporally asynchronous emotion expression. This is done by combining evaluations of emotional valence from vision and language. NICO uses this information to make decisions about when to extend conversations in order to accrue more affective information if the representation of the association is not coherent. Our primary contribution is providing a NICO robot with the ability to learn the affective associations between a perceived auditory stimulus and an emotional expression. NICO is able to do this for both individual subjects and specific stimuli, with the aid of an emotion-driven dialogue system that rectifies emotional expression incoherences. The robot is then able to use this information to determine a subject's enjoyment of perceived auditory stimuli in a real HRI scenario.
Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles
Venue: arXiv cs.LG 2021
Year: 2021

NASA's Global Ecosystem Dynamics Investigation (GEDI) is a key climate mission whose goal is to advance our understanding of the role of forests in the global carbon cycle. While GEDI is the first space-based LIDAR explicitly optimized to measure vertical forest structure predictive of aboveground biomass, the accurate interpretation of this vast amount of waveform data across the broad range of observational and environmental conditions is challenging. Here, we present a novel supervised machine learning approach to interpret GEDI waveforms and regress canopy top height globally. We propose a probabilistic deep learning approach based on an ensemble of deep convolutional neural networks(CNN) to avoid the explicit modelling of unknown effects, such as atmospheric noise. The model learns to extract robust features that generalize to unseen geographical regions and, in addition, yields reliable estimates of predictive uncertainty. Ultimately, the global canopy top height estimates produced by our model have an expected RMSE of 2.7 m with low bias.
Selective Replay Enhances Learning in Online Continual Analogical Reasoning
Venue: arXiv cs.AI 2021
Year: 2021

In continual learning, a system learns from non-stationary data streams or batches without catastrophic forgetting. While this problem has been heavily studied in supervised image classification and reinforcement learning, continual learning in neural networks designed for abstract reasoning has not yet been studied. Here, we study continual learning of analogical reasoning. Analogical reasoning tests such as Raven's Progressive Matrices (RPMs) are commonly used to measure non-verbal abstract reasoning in humans, and recently offline neural networks for the RPM problem have been proposed. In this paper, we establish experimental baselines, protocols, and forward and backward transfer metrics to evaluate continual learners on RPMs. We employ experience replay to mitigate catastrophic forgetting. Prior work using replay for image classification tasks has found that selectively choosing the samples to replay offers little, if any, benefit over random selection. In contrast, we find that selective replay can significantly outperform random selection for the RPM task.
We'll Fix It in Post: What Do Bug Fixes in Video Game Update Notes Tell Us?
Venue: arXiv cs.SE 2021
Year: 2021

Bugs that persist into releases of video games can have negative impacts on both developers and users, but particular aspects of testing in game development can lead to difficulties in effectively catching these missed bugs. It has become common practice for developers to apply updates to games in order to fix missed bugs. These updates are often accompanied by notes that describe the changes to the game included in the update. However, some bugs reappear even after an update attempts to fix them. In this paper, we develop a taxonomy for bug types in games that is based on prior work. We examine 12,122 bug fixes from 723 updates for 30 popular games on the Steam platform. We label the bug fixes included in these updates to identify the frequency of these different bug types, the rate at which bug types recur over multiple updates, and which bug types are treated as more severe. Additionally, we survey game developers regarding their experience with different bug types and what aspects of game development they most strongly associate with bug appearance. We find that Information bugs appear the most frequently in updates, while Crash bugs recur the most frequently and are often treated as more severe than other bug types. Finally, we find that challenges in testing, code quality, and bug reproduction have a close association with bug persistence. These findings should help developers identify which aspects of game development could benefit from greater attention in order to prevent bugs. Researchers can use our results in devising tools and methods to better identify and address certain bug types.
Learning from Counting: Leveraging Temporal Classification for Weakly Supervised Object Localization and Detection
Venue: arXiv cs.CV 2021
Year: 2021

This paper reports a new solution of leveraging temporal classification to support weakly supervised object detection (WSOD). Specifically, we introduce raster scan-order techniques to serialize 2D images into 1D sequence data, and then leverage a combined LSTM (Long, Short-Term Memory) and CTC (Connectionist Temporal Classification) network to achieve object localization based on a total count (of interested objects). We term our proposed network LSTM-CCTC (Count-based CTC). This "learning from counting" strategy differs from existing WSOD methods in that our approach automatically identifies critical points on or near a target object. This strategy significantly reduces the need of generating a large number of candidate proposals for object localization. Experiments show that our method yields state-of-the-art performance based on an evaluation on PASCAL VOC datasets.
Intelligent Reflecting Surface Enhanced D2D Cooperative Computing
Venue: arXiv cs.IT 2021
Year: 2021

This paper investigates a device-to-device (D2D) cooperative computing system, where an user can offload part of its computation task to nearby idle users with the aid of an intelligent reflecting surface (IRS). We propose to minimize the total computing delay via jointly optimizing the computation task assignment, transmit power, bandwidth allocation, and phase beamforming of the IRS. To solve the formulated problem, we devise an alternating optimization algorithm with guaranteed convergence. In particular, the task assignment strategy is derived in closed-form expression, while the phase beamforming is optimized by exploiting the semi-definite relaxation (SDR) method. Numerical results demonstrate that the IRS enhanced D2D cooperative computing scheme can achieve a much lower computing delay as compared to the conventional D2D cooperative computing strategy.
ReadNet: A Hierarchical Transformer Framework for Web Article Readability Analysis
Venue: arXiv cs.IR 2021
Year: 2021

Analyzing the readability of articles has been an important sociolinguistic task. Addressing this task is necessary to the automatic recommendation of appropriate articles to readers with different comprehension abilities, and it further benefits education systems, web information systems, and digital libraries. Current methods for assessing readability employ empirical measures or statistical learning techniques that are limited by their ability to characterize complex patterns such as article structures and semantic meanings of sentences. In this paper, we propose a new and comprehensive framework which uses a hierarchical self-attention model to analyze document readability. In this model, measurements of sentence-level difficulty are captured along with the semantic meanings of each sentence. Additionally, the sentence-level features are incorporated to characterize the overall readability of an article with consideration of article structures. We evaluate our proposed approach on three widely-used benchmark datasets against several strong baseline approaches. Experimental results show that our proposed method achieves the state-of-the-art performance on estimating the readability for various web articles and literature.
An Energy Efficient Routing Algorithm for Wireless Sensor Networks Using Mobile Sensors
Venue: arXiv cs.NI 2021
Year: 2021

The increasing usage of wireless sensor networks in human life is an indication of the high importance of this technology. Wireless sensor networks have a vast majority of applications in monitoring and care which are known as target tracking. In this application, the moving targets are monitored and tracked in the environment. One of the most important challenges in this area is the limited energy of the sensors. In this paper, we proposed a new algorithm to reduce energy consumption by increasing the load balancing in the network. The proposed algorithm consists of four phases. In the first phase, which is the hole prevention phase, in each cluster, it is checked by the cluster heads that if the energy level in an area of the cluster is less than the threshold, a mobile node is sent to that area. The second phase is the update phase. In this phase, the parameters required to detect a hole are updated. In the third phase, the hole in the cluster is detected, and in the fourth phase, the hole is covered by static or moving nodes. A comparison of simulation results with the well-known and successful routing method in wireless sensor networks show that the proposed method is suitable and working properly.