OpenRaiser/Intern-Atlas huggingface dataset:
Towards Personalized Answer Generation in E-Commerce via Multi-Perspective Preference Modeling
Venue: arXiv cs.CL 2021
Year: 2021

Recently, Product Question Answering (PQA) on E-Commerce platforms has attracted increasing attention as it can act as an intelligent online shopping assistant and improve the customer shopping experience. Its key function, automatic answer generation for product-related questions, has been studied by aiming to generate content-preserving while question-related answers. However, an important characteristic of PQA, i.e., personalization, is neglected by existing methods. It is insufficient to provide the same "completely summarized" answer to all customers, since many customers are more willing to see personalized answers with customized information only for themselves, by taking into consideration their own preferences towards product aspects or information needs. To tackle this challenge, we propose a novel Personalized Answer GEneration method (PAGE) with multi-perspective preference modeling, which explores historical user-generated contents to model user preference for generating personalized answers in PQA. Specifically, we first retrieve question-related user history as external knowledge to model knowledge-level user preference. Then we leverage Gaussian Softmax distribution model to capture latent aspect-level user preference. Finally, we develop a persona-aware pointer network to generate personalized answers in terms of both content and style by utilizing personal user preference and dynamic user vocabulary. Experimental results on real-world E-Commerce QA datasets demonstrate that the proposed method outperforms existing methods by generating informative and customized answers, and show that answer generation in E-Commerce can benefit from personalization.
Learn Layer-wise Connections in Graph Neural Networks
Venue: arXiv cs.LG 2021
Year: 2021

In recent years, Graph Neural Networks (GNNs) have shown superior performance on diverse applications on real-world datasets. To improve the model capacity and alleviate the over-smoothing problem, several methods proposed to incorporate the intermediate layers by layer-wise connections. However, due to the highly diverse graph types, the performance of existing methods vary on diverse graphs, leading to a need for data-specific layer-wise connection methods. To address this problem, we propose a novel framework LLC (Learn Layer-wise Connections) based on neural architecture search (NAS) to learn adaptive connections among intermediate layers in GNNs. LLC contains one novel search space which consists of 3 types of blocks and learnable connections, and one differentiable search algorithm to enable the efficient search process. Extensive experiments on five real-world datasets are conducted, and the results show that the searched layer-wise connections can not only improve the performance but also alleviate the over-smoothing problem.
Parameter Differentiation based Multilingual Neural Machine Translation
Venue: arXiv cs.CL 2021
Year: 2021

Multilingual neural machine translation (MNMT) aims to translate multiple languages with a single model and has been proved successful thanks to effective knowledge transfer among different languages with shared parameters. However, it is still an open question which parameters should be shared and which ones need to be task-specific. Currently, the common practice is to heuristically design or search language-specific modules, which is difficult to find the optimal configuration. In this paper, we propose a novel parameter differentiation based method that allows the model to determine which parameters should be language-specific during training. Inspired by cellular differentiation, each shared parameter in our method can dynamically differentiate into more specialized types. We further define the differentiation criterion as inter-task gradient similarity. Therefore, parameters with conflicting inter-task gradients are more likely to be language-specific. Extensive experiments on multilingual datasets have demonstrated that our method significantly outperforms various strong baselines with different parameter sharing configurations. Further analyses reveal that the parameter sharing configuration obtained by our method correlates well with the linguistic proximities.
Towards the Verification of Strategic Properties in Multi-Agent Systems with Imperfect Information
Venue: arXiv cs.MA 2021
Year: 2021

In logics for the strategic reasoning the main challenge is represented by their verification in contexts of imperfect information and perfect recall. In this work, we show a technique to approximate the verification of Alternating-time Temporal Logic (ATL*) under imperfect information and perfect recall, which is known to be undecidable. Given a model M and a formula $φ$, we propose a verification procedure that generates sub-models of M in which each sub-model M' satisfies a sub-formula $φ'$ of $φ$ and the verification of $φ'$ in M' is decidable. Then, we use CTL* model checking to provide a verification result of $φ$ on M. We prove that our procedure is in the same class of complexity of ATL* model checking under perfect information and perfect recall, we present a tool that implements our procedure, and provide experimental results.
A Framework for Efficient Memory Utilization in Online Conformance Checking
Venue: arXiv cs.SE 2021
Year: 2021

Conformance checking (CC) techniques of the process mining field gauge the conformance of the sequence of events in a case with respect to a business process model, which simply put is an amalgam of certain behavioral relations or rules. Online conformance checking (OCC) techniques are tailored for assessing such conformance on streaming events. The realistic assumption of having a finite memory for storing the streaming events has largely not been considered by the OCC techniques. We propose three incremental approaches to reduce the memory consumption in prefix-alignment-based OCC techniques along with ensuring that we incur a minimum loss of the conformance insights. Our first proposed approach bounds the number of maximum states that constitute a prefix-alignment to be retained by any case in memory. The second proposed approach bounds the number of cases that are allowed to retain more than a single state, referred to as multi-state cases. Building on top of the two proposed approaches, our third approach further bounds the number of maximum states that the multi-state cases can retain. All these approaches forget the states in excess to their defined limits and retain a meaningful summary of them. Computing prefix-alignments in the future is then resumed for such cases from the current position contained in the summary. We highlight the superiority of all proposed approaches compared to a state of the art prefix-alignment-based OCC technique through experiments using real-life event data under a streaming setting. Our approaches substantially reduce memory consumption by up to 80% on average, while introducing a minor accuracy drop.
Forward Collision Warning Systems: Validating Driving Simulator Results with Field Data
Venue: arXiv cs.RO 2021
Year: 2021

With the advent of Advanced Driver Assistance Systems (ADAS), there is an increasing need to evaluate driver behavior while using such technology. In this unique study, a forward collision warning (FCW) system using connected vehicle technology, was introduced in a driving simulator environment, to evaluate driver braking behavior and then the results are validated using data from field tests. A total of 93 participants were recruited for this study, for which a virtual network of South Baltimore was created. A one sample t-test was conducted, and it was found that the mean reduction in speed of 15.07 mph post FCW, is statistically significant. A random forest, machine learning algorithm was found to be the best fit for ranking the most important variables in the dataset by order of importance. Field data obtained from the University of Michigan Transportation Research Institute (UMTRI), substantiated the FCW findings from this driving simulator study.
An Obfuscating C Compiler for Encrypted Computing
Venue: arXiv cs.CR 2021
Year: 2021

This paper describes an `obfuscating' C compiler for encrypted computing. The context consists of (i) a processor that `works encrypted', taking in encrypted inputs and producing encrypted outputs while the data remains in encrypted form throughout processing, and (ii) machine codes that support arbitrary interpretations of the encrypted input and outputs from each instruction, as far as an adversary who does not know the encryption can tell. The compiler on each recompilation of the same source generates object code of the same form for which the runtime traces have the same form, but the data beneath the encryption may arbitrarily differ from nominal at each point in the trace, independently so far as the laws of computation allow.
Predição de Incidência de Lesão por Pressão em Pacientes de UTI usando Aprendizado de Máquina
Venue: arXiv cs.LG 2021
Year: 2021

Pressure ulcers have high prevalence in ICU patients but are preventable if identified in initial stages. In practice, the Braden scale is used to classify high-risk patients. This paper investigates the use of machine learning in electronic health records data for this task, by using data available in MIMIC-III v1.4. Two main contributions are made: a new approach for evaluating models that considers all predictions made during a stay, and a new training method for the machine learning models. The results show a superior performance in comparison to the state of the art; moreover, all models surpass the Braden scale in every operating point in the precision-recall curve. -- -- Lesões por pressão possuem alta prevalência em pacientes de UTI e são preveníveis ao serem identificadas em estágios iniciais. Na prática utiliza-se a escala de Braden para classificação de pacientes em risco. Este artigo investiga o uso de aprendizado de máquina em dados de registros eletrônicos para este fim, a partir da base de dados MIMIC-III v1.4. São feitas duas contribuições principais: uma nova abordagem para a avaliação dos modelos e da escala de Braden levando em conta todas as predições feitas ao longo das internações, e um novo método de treinamento para os modelos de aprendizado de máquina. Os resultados obtidos superam o estado da arte e verifica-se que os modelos superam significativamente a escala de Braden em todos os pontos de operação da curva de precisão por sensibilidade.
Graph Collaborative Reasoning
Venue: arXiv cs.IR 2021
Year: 2021

Graphs can represent relational information among entities and graph structures are widely used in many intelligent tasks such as search, recommendation, and question answering. However, most of the graph-structured data in practice suffers from incompleteness, and thus link prediction becomes an important research problem. Though many models are proposed for link prediction, the following two problems are still less explored: (1) Most methods model each link independently without making use of the rich information from relevant links, and (2) existing models are mostly designed based on associative learning and do not take reasoning into consideration. With these concerns, in this paper, we propose Graph Collaborative Reasoning (GCR), which can use the neighbor link information for relational reasoning on graphs from logical reasoning perspectives. We provide a simple approach to translate a graph structure into logical expressions, so that the link prediction task can be converted into a neural logic reasoning problem. We apply logical constrained neural modules to build the network architecture according to the logical expression and use back propagation to efficiently learn the model parameters, which bridges differentiable learning and symbolic reasoning in a unified architecture. To show the effectiveness of our work, we conduct experiments on graph-related tasks such as link prediction and recommendation based on commonly used benchmark datasets, and our graph collaborative reasoning approach achieves state-of-the-art performance.
Determinization of One-Counter Nets
Venue: arXiv cs.FL 2021
Year: 2021

One-Counter Nets (OCNs) are finite-state automata equipped with a counter that is not allowed to become negative, but does not have zero tests. Their simplicity and close connection to various other models (e.g., VASS, Counter Machines and Pushdown Automata) make them an attractive model for studying the border of decidability for the classical decision problems. The deterministic fragment of OCNs (DOCNs) typically admits more tractable decision problems, and while these problems and the expressive power of DOCNs have been studied, the determinization problem, namely deciding whether an OCN admits an equivalent DOCN, has not received attention. We introduce four notions of OCN determinizability, which arise naturally due to intricacies in the model, and specifically, the interpretation of the initial counter value. We show that in general, determinizability is undecidable under most notions, but over a singleton alphabet (i.e., 1 dimensional VASS) one definition becomes decidable, and the rest become trivial, in that there is always an equivalent DOCN.
AU Dataset for Visuo-Haptic Object Recognition for Robots
Venue: arXiv cs.RO 2021
Year: 2021

Multimodal object recognition is still an emerging field. Thus, publicly available datasets are still rare and of small size. This dataset was developed to help fill this void and presents multimodal data for 63 objects with some visual and haptic ambiguity. The dataset contains visual, kinesthetic and tactile (audio/vibrations) data. To completely solve sensory ambiguity, sensory integration/fusion would be required. This report describes the creation and structure of the dataset. The first section explains the underlying approach used to capture the visual and haptic properties of the objects. The second section describes the technical aspects (experimental setup) needed for the collection of the data. The third section introduces the objects, while the final section describes the structure and content of the dataset.
"A Passage to India": Pre-trained Word Embeddings for Indian Languages
Venue: arXiv cs.CL 2021
Year: 2021

Dense word vectors or 'word embeddings' which encode semantic properties of words, have now become integral to NLP tasks like Machine Translation (MT), Question Answering (QA), Word Sense Disambiguation (WSD), and Information Retrieval (IR). In this paper, we use various existing approaches to create multiple word embeddings for 14 Indian languages. We place these embeddings for all these languages, viz., Assamese, Bengali, Gujarati, Hindi, Kannada, Konkani, Malayalam, Marathi, Nepali, Odiya, Punjabi, Sanskrit, Tamil, and Telugu in a single repository. Relatively newer approaches that emphasize catering to context (BERT, ELMo, etc.) have shown significant improvements, but require a large amount of resources to generate usable models. We release pre-trained embeddings generated using both contextual and non-contextual approaches. We also use MUSE and XLM to train cross-lingual embeddings for all pairs of the aforementioned languages. To show the efficacy of our embeddings, we evaluate our embedding models on XPOS, UPOS and NER tasks for all these languages. We release a total of 436 models using 8 different approaches. We hope they are useful for the resource-constrained Indian language NLP. The title of this paper refers to the famous novel 'A Passage to India' by E.M. Forster, published initially in 1924.
Algorithm for recognizing the contour of a honeycomb block
Venue: arXiv cs.CV 2021
Year: 2021

The article discusses an algorithm for recognizing the contour of fragments of a honeycomb block. The inapplicability of ready-made functions of the OpenCV library is shown. Two proposed algorithms are considered. The direct scanning algorithm finds the extreme white pixels in the binarized image, it works adequately on convex shapes of products, but does not find a contour on concave areas and in cavities of products. To solve this problem, a scanning algorithm using a sliding matrix is proposed, which works correctly on products of any shape.
Two Sparsities Are Better Than One: Unlocking the Performance Benefits of Sparse-Sparse Networks
Venue: arXiv cs.LG 2021
Year: 2021

In principle, sparse neural networks should be significantly more efficient than traditional dense networks. Neurons in the brain exhibit two types of sparsity; they are sparsely interconnected and sparsely active. These two types of sparsity, called weight sparsity and activation sparsity, when combined, offer the potential to reduce the computational cost of neural networks by two orders of magnitude. Despite this potential, today's neural networks deliver only modest performance benefits using just weight sparsity, because traditional computing hardware cannot efficiently process sparse networks. In this article we introduce Complementary Sparsity, a novel technique that significantly improves the performance of dual sparse networks on existing hardware. We demonstrate that we can achieve high performance running weight-sparse networks, and we can multiply those speedups by incorporating activation sparsity. Using Complementary Sparsity, we show up to 100X improvement in throughput and energy efficiency performing inference on FPGAs. We analyze scalability and resource tradeoffs for a variety of kernels typical of commercial convolutional networks such as ResNet-50 and MobileNetV2. Our results with Complementary Sparsity suggest that weight plus activation sparsity can be a potent combination for efficiently scaling future AI models.
Does CLIP Benefit Visual Question Answering in the Medical Domain as Much as it Does in the General Domain?
Venue: arXiv cs.CV 2021
Year: 2021

Contrastive Language--Image Pre-training (CLIP) has shown remarkable success in learning with cross-modal supervision from extensive amounts of image--text pairs collected online. Thus far, the effectiveness of CLIP has been investigated primarily in general-domain multimodal problems. This work evaluates the effectiveness of CLIP for the task of Medical Visual Question Answering (MedVQA). To this end, we present PubMedCLIP, a fine-tuned version of CLIP for the medical domain based on PubMed articles. Our experiments are conducted on two MedVQA benchmark datasets and investigate two MedVQA methods, MEVF (Mixture of Enhanced Visual Features) and QCR (Question answering via Conditional Reasoning). For each of these, we assess the merits of visual representation learning using PubMedCLIP, the original CLIP, and state-of-the-art MAML (Model-Agnostic Meta-Learning) networks pre-trained only on visual data. We open source the code for our MedVQA pipeline and pre-training PubMedCLIP. CLIP and PubMedCLIP achieve improvements in comparison to MAML's visual encoder. PubMedCLIP achieves the best results with gains in the overall accuracy of up to 3%. Individual examples illustrate the strengths of PubMedCLIP in comparison to the previously widely used MAML networks. Visual representation learning with language supervision in PubMedCLIP leads to noticeable improvements for MedVQA. Our experiments reveal distributional differences in the two MedVQA benchmark datasets that have not been imparted in previous work and cause different back-end visual encoders in PubMedCLIP to exhibit different behavior on these datasets. Moreover, we witness fundamental performance differences of VQA in general versus medical domains.
The University of Texas at Dallas HLTRI's Participation in EPIC-QA: Searching for Entailed Questions Revealing Novel Answer Nuggets
Venue: arXiv cs.CL 2021
Year: 2021

The Epidemic Question Answering (EPIC-QA) track at the Text Analysis Conference (TAC) is an evaluation of methodologies for answering ad-hoc questions about the COVID-19 disease. This paper describes our participation in both tasks of EPIC-QA, targeting: (1) Expert QA and (2) Consumer QA. Our methods used a multi-phase neural Information Retrieval (IR) system based on combining BM25, BERT, and T5 as well as the idea of considering entailment relations between the original question and questions automatically generated from answer candidate sentences. Moreover, because entailment relations were also considered between all generated questions, we were able to re-rank the answer sentences based on the number of novel answer nuggets they contained, as indicated by the processing of a question entailment graph. Our system, called SEaRching for Entailed QUestions revealing NOVel nuggets of Answers (SER4EQUNOVA), produced promising results in both EPIC-QA tasks, excelling in the Expert QA task.
A Preordered RNN Layer Boosts Neural Machine Translation in Low Resource Settings
Venue: arXiv cs.CL 2021
Year: 2021

Neural Machine Translation (NMT) models are strong enough to convey semantic and syntactic information from the source language to the target language. However, these models are suffering from the need for a large amount of data to learn the parameters. As a result, for languages with scarce data, these models are at risk of underperforming. We propose to augment attention based neural network with reordering information to alleviate the lack of data. This augmentation improves the translation quality for both English to Persian and Persian to English by up to 6% BLEU absolute over the baseline models.
HiKonv: High Throughput Quantized Convolution With Novel Bit-wise Management and Computation
Venue: arXiv cs.DC 2021
Year: 2021

Quantization for Convolutional Neural Network (CNN) has shown significant progress with the intention of reducing the cost of computation and storage with low-bitwidth data inputs. There are, however, no systematic studies on how an existing full-bitwidth processing unit, such as CPUs and DSPs, can be better utilized to carry out significantly higher computation throughput for convolution under various quantized bitwidths. In this study, we propose HiKonv, a unified solution that maximizes the compute throughput of a given underlying processing unit to process low-bitwidth quantized data inputs through novel bit-wise parallel computation. We establish theoretical performance bounds using a full-bitwidth multiplier for highly parallelized low-bitwidth convolution, and demonstrate new breakthroughs for high-performance computing in this critical domain. For example, a single 32-bit processing unit can deliver 128 binarized convolution operations (multiplications and additions) under one CPU instruction, and a single 27x18 DSP core can deliver eight convolution operations with 4-bit inputs in one cycle. We demonstrate the effectiveness of HiKonv on CPU and FPGA for both convolutional layers or a complete DNN model. For a convolutional layer quantized to 4-bit, HiKonv achieves a 3.17x latency improvement over the baseline implementation using C++ on CPU. Compared to the DAC-SDC 2020 champion model for FPGA, HiKonv achieves a 2.37x throughput improvement and 2.61x DSP efficiency improvement, respectively.