OpenRaiser/Intern-Atlas huggingface dataset:
Towards the Generation of Synthetic Images of Palm Vein Patterns: A Review
Venue: arXiv cs.CV 2022
Year: 2022

With the recent success of computer vision and deep learning, remarkable progress has been achieved on automatic personal recognition using vein biometrics. However, collecting large-scale real-world training data for palm vein recognition has turned out to be challenging, mainly due to the noise and irregular variations included at the time of acquisition. Meanwhile, existing palm vein recognition datasets are usually collected under near-infrared light, lacking detailed annotations on attributes (e.g., pose), so the influences of different attributes on vein recognition have been poorly investigated. Therefore, this paper examines the suitability of synthetic vein images generated to compensate for the urgent lack of publicly available large-scale datasets. Firstly, we present an overview of recent research progress on palm vein recognition, from the basic background knowledge to vein anatomical structure, data acquisition, public database, and quality assessment procedures. Then, we focus on the state-of-the-art methods that have allowed the generation of vascular structures for biometric purposes and the modeling of biological networks with their respective application domains. In addition, we review the existing research on the generation of style transfer and biological nature-based synthetic palm vein image algorithms. Afterward, we formalize a general flowchart for the creation of a synthetic database comparing real palm vein images and generated synthetic samples to obtain some understanding into the development of the realistic vein imaging system. Ultimately, we conclude by discussing the challenges, insights, and future perspectives in generating synthetic palm vein images for further works.
Schedulability Analysis of WSAN Applications: Outperformance of A Model Checking Approach
Venue: arXiv cs.NI 2022
Year: 2022

Wireless sensor and actuator networks (WSAN) are real-time systems which demand high degrees of reliability requirements. To ensure this level of reliability, different analysis approaches have been proposed for WSAN applications. Among different alternatives, analytical analysis and model checking are two common approaches which are widely used for the formal analysis of WSAN applications. Analytical approaches apply constraint satisfaction methods, whereas model checking generates explicit states of models and analyze them. In this paper, we compare the two approaches in schedulability analysis of WSAN applications using an application for monitoring and control of civil infrastructures, which is implemented on the Imote2 wireless sensor platform. We show how the highest possible data acquisition frequency for this application is computed while meeting the deadlines, and compare the results of the two approaches as well as their scalability, extensibility, and flexibility.
Compression ensembles quantify aesthetic complexity and the evolution of visual art
Venue: arXiv cs.CV 2022
Year: 2022

The quantification of visual aesthetics and complexity have a long history, the latter previously operationalized via the application of compression algorithms. Here we generalize and extend the compression approach beyond simple complexity measures to quantify algorithmic distance in historical and contemporary visual media. The proposed "ensemble" approach works by compressing a large number of transformed versions of a given input image, resulting in a vector of associated compression ratios. This approach is more efficient than other compression-based algorithmic distances, and is particularly suited for the quantitative analysis of visual artifacts, because human creative processes can be understood as algorithms in the broadest sense. Unlike comparable image embedding methods using machine learning, our approach is fully explainable through the transformations. We demonstrate that the method is cognitively plausible and fit for purpose by evaluating it against human complexity judgments, and on automated detection tasks of authorship and style. We show how the approach can be used to reveal and quantify trends in art historical data, both on the scale of centuries and in rapidly evolving contemporary NFT art markets. We further quantify temporal resemblance to disambiguate artists outside the documented mainstream from those who are deeply embedded in Zeitgeist. Finally, we note that compression ensembles constitute a quantitative representation of the concept of visual family resemblance, as distinct sets of dimensions correspond to shared visual characteristics otherwise hard to pin down. Our approach provides a new perspective for the study of visual art, algorithmic image analysis, and quantitative aesthetics more generally.
Linking sanctions to norms in practice
Venue: arXiv cs.LO 2022
Year: 2022

Within social simulation, we often want agents to interact both with larger systems of norms, as well as respond to their own and other agents norm violations. However, there are currently no norm specifications that allow us to interact with all of these components. To address this issue, this paper introduces the concept of violation modalities in CTL. These modalities do not only allow us to keep track of violations, but also allow us to define the usual deontic operators. On top of this, they give us a convenient way of linking together various different norms, and allow us to reason about norms with repeated violations and obligations. We will discuss various properties of the modalities and the deontic operators, and will also discuss some ways in which this formalization can guide an implementation of normative systems.
EXPANSE: A Deep Continual / Progressive Learning System for Deep Transfer Learning
Venue: arXiv cs.LG 2022
Year: 2022

Deep transfer learning techniques try to tackle the limitations of deep learning, the dependency on extensive training data and the training costs, by reusing obtained knowledge. However, the current DTL techniques suffer from either catastrophic forgetting dilemma (losing the previously obtained knowledge) or overly biased pre-trained models (harder to adapt to target data) in finetuning pre-trained models or freezing a part of the pre-trained model, respectively. Progressive learning, a sub-category of DTL, reduces the effect of the overly biased model in the case of freezing earlier layers by adding a new layer to the end of a frozen pre-trained model. Even though it has been successful in many cases, it cannot yet handle distant source and target data. We propose a new continual/progressive learning approach for deep transfer learning to tackle these limitations. To avoid both catastrophic forgetting and overly biased-model problems, we expand the pre-trained model by expanding pre-trained layers (adding new nodes to each layer) in the model instead of only adding new layers. Hence the method is named EXPANSE. Our experimental results confirm that we can tackle distant source and target data using this technique. At the same time, the final model is still valid on the source data, achieving a promising deep continual learning approach. Moreover, we offer a new way of training deep learning models inspired by the human education system. We termed this two-step training: learning basics first, then adding complexities and uncertainties. The evaluation implies that the two-step training extracts more meaningful features and a finer basin on the error surface since it can achieve better accuracy in comparison to regular training. EXPANSE (model expansion and two-step training) is a systematic continual learning approach applicable to different problems and DL models.
FIND:Explainable Framework for Meta-learning
Venue: arXiv cs.LG 2022
Year: 2022

Meta-learning is used to efficiently enable the automatic selection of machine learning models by combining data and prior knowledge. Since the traditional meta-learning technique lacks explainability, as well as shortcomings in terms of transparency and fairness, achieving explainability for meta-learning is crucial. This paper proposes FIND, an interpretable meta-learning framework that not only can explain the recommendation results of meta-learning algorithm selection, but also provide a more complete and accurate explanation of the recommendation algorithm's performance on specific datasets combined with business scenarios. The validity and correctness of this framework have been demonstrated by extensive experiments.
Robust Task-Oriented Dialogue Generation with Contrastive Pre-training and Adversarial Filtering
Venue: arXiv cs.CL 2022
Year: 2022

Data artifacts incentivize machine learning models to learn non-transferable generalizations by taking advantage of shortcuts in the data, and there is growing evidence that data artifacts play a role for the strong results that deep learning models achieve in recent natural language processing benchmarks. In this paper, we focus on task-oriented dialogue and investigate whether popular datasets such as MultiWOZ contain such data artifacts. We found that by only keeping frequent phrases in the training examples, state-of-the-art models perform similarly compared to the variant trained with full data, suggesting they exploit these spurious correlations to solve the task. Motivated by this, we propose a contrastive learning based framework to encourage the model to ignore these cues and focus on learning generalisable patterns. We also experiment with adversarial filtering to remove "easy" training instances so that the model would focus on learning from the "harder" instances. We conduct a number of generalization experiments -- e.g., cross-domain/dataset and adversarial tests -- to assess the robustness of our approach and found that it works exceptionally well.
Forecasting COVID-19 Caseloads Using Unsupervised Embedding Clusters of Social Media Posts
Venue: arXiv cs.CL 2022
Year: 2022

We present a novel approach incorporating transformer-based language models into infectious disease modelling. Text-derived features are quantified by tracking high-density clusters of sentence-level representations of Reddit posts within specific US states' COVID-19 subreddits. We benchmark these clustered embedding features against features extracted from other high-quality datasets. In a threshold-classification task, we show that they outperform all other feature types at predicting upward trend signals, a significant result for infectious disease modelling in areas where epidemiological data is unreliable. Subsequently, in a time-series forecasting task we fully utilise the predictive power of the caseload and compare the relative strengths of using different supplementary datasets as covariate feature sets in a transformer-based time-series model.
Educational Tools for Mapuzugun
Venue: arXiv cs.CL 2022
Year: 2022

Mapuzugun is the language of the Mapuche people. Due to political and historical reasons, its number of speakers has decreased and the language has been excluded from the educational system in Chile and Argentina. For this reason, it is very important to support the revitalization of the Mapuzugun in all spaces and media of society. In this work we present a tool towards supporting educational activities of Mapuzugun, tailored to the characteristics of the language. The tool consists of three parts: design and development of an orthography detector and converter; a morphological analyzer; and an informal translator. We also present a case study with Mapuzugun students showing promising results. Short Abstract in Mapuzuzgun: Tüfachi küzaw pegelfi kiñe zugun küzawpeyüm kelluaetew pu mapuzugun chillkatufe kimal kizu tañi zugun.
ARLO: A Framework for Automated Reinforcement Learning
Venue: arXiv cs.LG 2022
Year: 2022

Automated Reinforcement Learning (AutoRL) is a relatively new area of research that is gaining increasing attention. The objective of AutoRL consists in easing the employment of Reinforcement Learning (RL) techniques for the broader public by alleviating some of its main challenges, including data collection, algorithm selection, and hyper-parameter tuning. In this work, we propose a general and flexible framework, namely ARLO: Automated Reinforcement Learning Optimizer, to construct automated pipelines for AutoRL. Based on this, we propose a pipeline for offline and one for online RL, discussing the components, interaction, and highlighting the difference between the two settings. Furthermore, we provide a Python implementation of such pipelines, released as an open-source library. Our implementation has been tested on an illustrative LQG domain and on classic MuJoCo environments, showing the ability to reach competitive performances requiring limited human intervention. We also showcase the full pipeline on a realistic dam environment, automatically performing the feature selection and the model generation tasks.
E2FL: Equal and Equitable Federated Learning
Venue: arXiv cs.LG 2022
Year: 2022

Federated Learning (FL) enables data owners to train a shared global model without sharing their private data. Unfortunately, FL is susceptible to an intrinsic fairness issue: due to heterogeneity in clients' data distributions, the final trained model can give disproportionate advantages across the participating clients. In this work, we present Equal and Equitable Federated Learning (E2FL) to produce fair federated learning models by preserving two main fairness properties, equity and equality, concurrently. We validate the efficiency and fairness of E2FL in different real-world FL applications, and show that E2FL outperforms existing baselines in terms of the resulting efficiency, fairness of different groups, and fairness among all individual clients.
Multi-stage Resilience Management of Smart Power Distribution Systems: A Stochastic Robust Optimization Model
Venue: arXiv cs.MA 2022
Year: 2022

Significant outages from weather and climate extremes have highlighted the critical need for resilience-centered risk management of the grid. This paper proposes a multi-stage stochastic robust optimization (SRO) model that advances the existing planning frameworks on two main fronts. First, it captures interactions of operational measures with hardening decisions. Second, it properly treats the multitude of uncertainties in planning. The SRO model coordinates hardening and system operational measures for smart power distribution systems equipped with distributed generation units and switches. To capture the uncertainty in the incurred damage by extreme events, an uncertainty set is developed by integrating probabilistic information of hurricanes with the performance of overhead structures. A novel probabilistic model for the repair time of damaged lines is derived to account for the uncertainty in the recovery process. A solution strategy based on the integration of a differential evolution algorithm and a mixed-integer solver is designed to solve the resilience maximization model. The proposed approach is applied to a modified IEEE 33-bus system with 485 utility poles and a 118-bus system with 1841 poles. The systems are mapped on the Harris County, TX, U.S. Results reveal that optimal hardening decisions can be significantly influenced by resilience operational measures.
Social Fragmentation Transitions in Large-Scale Parameter Sweep Simulations of Adaptive Social Networks
Venue: arXiv cs.SI 2022
Year: 2022

Social fragmentation transition is a transition of social states between many disconnected communities with distinct opinions and a well-connected single network with homogeneous opinions. This is a timely research topic with high relevance to various current societal issues. We had previously studied this problem using numerical simulations of adaptive social network models and found that two individual behavioral traits, homophily and attention to novelty, had the most statistically significant impact on the outcomes of social network evolution. However, our previous study was limited in terms of the range of parameter values examined, and possible interactions between multiple behavioral traits were largely ignored. In this study, we conducted a substantially larger-scale parameter sweep numerical experiment of the same model with expanded parameter ranges by an order of magnitude in each parameter dimension, resulting in a total of 116,640 simulation runs. To capture nontrivial interactions among behavioral parameters, we modeled and visualized the dependence of outcome measures on the model parameters using artificial neural networks. Results show that, while the competition between homophily and attention to novelty is still the primary determinant of social fragmentation, another transition plane emerges when individuals have strong social conformity behavior, which was not previously known. This implies that social fragmentation transition can also occur in the homophily-social conformity trade-off, the two behavioral traits that have very similar microscopic individual-level effects but produce very different macroscopic collective-level outcomes, illustrating the nontrivial macroscopic dynamics of complex collective systems.
Named Entity Linking with Entity Representation by Multiple Embeddings
Venue: arXiv cs.CL 2022
Year: 2022

We propose a simple and practical method for named entity linking (NEL), based on entity representation by multiple embeddings. To explore this method, and to review its dependency on parameters, we measure its performance on Namesakes, a highly challenging dataset of ambiguously named entities. Our observations suggest that the minimal number of mentions required to create a knowledge base (KB) entity is very important for NEL performance. The number of embeddings is less important and can be kept small, within as few as 10 or less. We show that our representations of KB entities can be adjusted using only KB data, and the adjustment can improve NEL performance. We also compare NEL performance of embeddings obtained from tuning language model on diverse news texts as opposed to tuning on more uniform texts from public datasets XSum, CNN / Daily Mail. We found that tuning on diverse news provides better embeddings.
Impact of Multiple Fully-Absorbing Receivers in Molecular Communications
Venue: arXiv cs.IT 2022
Year: 2022

Molecular communication is a promising solution to enable intra-body communications among nanomachines. However, malicious and non-cooperative receivers can degrade the performance, compromising these systems' security. Analyzing the communication and security performance of these systems requires accurate channel models. However, such models are not present in the literature. In this work, we develop an analytical framework to derive the hitting probability of a molecule on a fully absorbing receiver (FAR) in the presence of other FARs, which can be either be cooperative or malicious. We first present an approximate hitting probability expression for the 3-FARs case. A simplified expression is obtained for the case when FARs are symmetrically positioned. Using the derived expressions, we study the impact of malicious receivers on the intended receiver and discuss how to minimize this impact to obtain a secure communication channel. We also study the gain that can be obtained by the cooperation of these FARs. We then present an approach to extend the analysis for a system with N FARs. The derived expressions can be used to analyze and design multiple input/output and secure molecular communication systems.
Scenario-based Multi-product Advertising Copywriting Generation for E-Commerce
Venue: arXiv cs.AI 2022
Year: 2022

In this paper, we proposed an automatic Scenario-based Multi-product Advertising Copywriting Generation system (SMPACG) for E-Commerce, which has been deployed on a leading Chinese e-commerce platform. The proposed SMPACG consists of two main components: 1) an automatic multi-product combination selection module, which itself is consisted of a topic prediction model, a pattern and attribute-based selection model and an arbitrator model; and 2) an automatic multi-product advertising copywriting generation module, which combines our proposed domain-specific pretrained language model and knowledge-based data enhancement model. The SMPACG is the first system that realizes automatic scenario-based multi-product advertising contents generation, which achieves significant improvements over other state-of-the-art methods. The SMPACG has been not only developed for directly serving for our e-commerce recommendation system, but also used as a real-time writing assistant tool for merchants.
Deep Learning vs. Gradient Boosting: Benchmarking state-of-the-art machine learning algorithms for credit scoring
Venue: arXiv cs.LG 2022
Year: 2022

Artificial intelligence (AI) and machine learning (ML) have become vital to remain competitive for financial services companies around the globe. The two models currently competing for the pole position in credit risk management are deep learning (DL) and gradient boosting machines (GBM). This paper benchmarked those two algorithms in the context of credit scoring using three distinct datasets with different features to account for the reality that model choice/power is often dependent on the underlying characteristics of the dataset. The experiment has shown that GBM tends to be more powerful than DL and has also the advantage of speed due to lower computational requirements. This makes GBM the winner and choice for credit scoring. However, it was also shown that the outperformance of GBM is not always guaranteed and ultimately the concrete problem scenario or dataset will determine the final model choice. Overall, based on this study both algorithms can be considered state-of-the-art for binary classification tasks on structured datasets, while GBM should be the go-to solution for most problem scenarios due to easier use, significantly faster training time, and superior accuracy.
Few-Shot Natural Language Inference Generation with PDD: Prompt and Dynamic Demonstration
Venue: arXiv cs.CL 2022
Year: 2022

Natural Language Inference Generation task is to generate a text hypothesis given a text premise and a logical relation between the two. This task can be used in data augmentation and controllable text generation in practice. In this paper, we propose language models with prompt and dynamic demonstration (LM-PDD) to tackle this problem in few-shot settings. Our framework outperforms standard fine-tuned models with low resource, achieving an average 8% absolute improvement on SNLI and MNLI datasets, and the results on 13 natural language classification tasks also show that our dynamic demonstration method has good generalizability.
Experiences with task-based programming using cluster nodes as OpenMP devices
Venue: arXiv cs.DC 2022
Year: 2022

Programming a distributed system, such as a cluster, requires extended use of low-level communication libraries and can often become cumbersome and error prone for the average developer. In this work, we consider each node of a cluster as a separate OpenMP device, able to run code with OpenMP directives in parallel. We make use of the OpenMP device model to provide an easy and intuitive way to program available cluster nodes. Based on that, we examine modifications that were necessary to make existing task-based applications able to exploit such an infrastructure. Finally, we evaluate the performance of the system and conclude that one can gain significant speedup, as long as the application tasks do not produce excessive communication overheads.