OpenRaiser/Intern-Atlas huggingface dataset:
Where Does Trust Break Down? A Quantitative Trust Analysis of Deep Neural Networks via Trust Matrix and Conditional Trust Densities
Venue: arXiv cs.LG 2020
Year: 2020

The advances and successes in deep learning in recent years have led to considerable efforts and investments into its widespread ubiquitous adoption for a wide variety of applications, ranging from personal assistants and intelligent navigation to search and product recommendation in e-commerce. With this tremendous rise in deep learning adoption comes questions about the trustworthiness of the deep neural networks that power these applications. Motivated to answer such questions, there has been a very recent interest in trust quantification. In this work, we introduce the concept of trust matrix, a novel trust quantification strategy that leverages the recently introduced question-answer trust metric by Wong et al. to provide deeper, more detailed insights into where trust breaks down for a given deep neural network given a set of questions. More specifically, a trust matrix defines the expected question-answer trust for a given actor-oracle answer scenario, allowing one to quickly spot areas of low trust that needs to be addressed to improve the trustworthiness of a deep neural network. The proposed trust matrix is simple to calculate, humanly interpretable, and to the best of the authors' knowledge is the first to study trust at the actor-oracle answer level. We further extend the concept of trust densities with the notion of conditional trust densities. We experimentally leverage trust matrices to study several well-known deep neural network architectures for image recognition, and further study the trust density and conditional trust densities for an interesting actor-oracle answer scenario. The results illustrate that trust matrices, along with conditional trust densities, can be useful tools in addition to the existing suite of trust quantification metrics for guiding practitioners and regulators in creating and certifying deep learning solutions for trusted operation.
Persistent Data Retention Models
Venue: arXiv cs.PL 2020
Year: 2020

Non-Volatile Memory devices may soon be a part of main memory, and programming models that give programmers direct access to persistent memory through loads and stores are sought to maximize the performance benefits of these new devices. Direct access introduces new challenges. In this work, we identify an important aspect of programming for persistent memory: the persistent data retention model. A Persistent Data Retention Model describes what happens to persistent data when code that uses it is modified. We identify two models present in prior work but not described as such, the Reset and Manual Model, and we propose a new one called the Automatic Model. The Reset model discards all persistent data when a program changes leading to performance overheads and write amplification. In contrast, if data is to be retained, the Manual Model relies on the programmer to implement code that upgrades data from one version of the program to the next. This reduces overheads but places a larger burden on the programmer. We propose the Automatic Model to assist a programmer by automating some or all of the conversion. We describe one such automatic approach, Lazily Extendible Data Structures, that uses language extensions and compiler support to reduce the effort and complexity associated with updating persistent data. We evaluate our PDRMs in the context of the Persistent Memory Development Kit (PMDK) using kernels and the TPC-C application. Manual Model shows an overhead of 2.90% to 4.10% on average, and LEDS shows overhead of 0.45% to 10.27% on average, depending on the workload. LEDS reduces the number of writes by 26.36% compared to Manual Model. Furthermore, LEDS significantly reduces the programming complexity by relying on the compiler to migrate persistent data.
Deep Learning-based Pipeline for Module Power Prediction from EL Measurements
Venue: arXiv cs.CV 2020
Year: 2020

Automated inspection plays an important role in monitoring large-scale photovoltaic power plants. Commonly, electroluminescense measurements are used to identify various types of defects on solar modules but have not been used to determine the power of a module. However, knowledge of the power at maximum power point is important as well, since drops in the power of a single module can affect the performance of an entire string. By now, this is commonly determined by measurements that require to discontact or even dismount the module, rendering a regular inspection of individual modules infeasible. In this work, we bridge the gap between electroluminescense measurements and the power determination of a module. We compile a large dataset of 719 electroluminescense measurementsof modules at various stages of degradation, especially cell cracks and fractures, and the corresponding power at maximum power point. Here,we focus on inactive regions and cracks as the predominant type of defect. We set up a baseline regression model to predict the power from electroluminescense measurements with a mean absolute error of 9.0+/-3.7$W_P$ (4.0+/-8.4%). Then, we show that deep-learning can be used to train a model that performs significantly better (7.3+/-2.7$W_P$ or 3.2+/-6.5%) and propose a variant of class activation maps to obtain the per cell power loss, as predicted by the model. With this work, we aim to open a new research topic. Therefore, we publicly release the dataset, the code and trained models to empower other researchers to compare against our results. Finally, we present a thorough evaluation of certain boundary conditions like the dataset size and an automated preprocessing pipeline for on-site measurements showing multiple modules at once.
Truly Subquadratic Exact Distance Oracles with Constant Query Time for Planar Graphs
Venue: arXiv cs.DS 2020
Year: 2020

Given an undirected, unweighted planar graph $G$ with $n$ vertices, we present a truly subquadratic size distance oracle for reporting exact shortest-path distances between any pair of vertices of $G$ in constant time. For any $\varepsilon > 0$, our distance oracle takes up $O(n^{5/3+\varepsilon})$ space and is capable of answering shortest-path distance queries exactly for any pair of vertices of $G$ in worst-case time $O(\log (1/\varepsilon))$. Previously no truly sub-quadratic size distance oracles with constant query time for answering exact all-pairs shortest paths distance queries existed.
Non-elitist Evolutionary Multi-objective Optimizers Revisited
Venue: arXiv cs.NE 2020
Year: 2020

Since around 2000, it has been considered that elitist evolutionary multi-objective optimization algorithms (EMOAs) always outperform non-elitist EMOAs. This paper revisits the performance of non-elitist EMOAs for bi-objective continuous optimization when using an unbounded external archive. This paper examines the performance of EMOAs with two elitist and one non-elitist environmental selections. The performance of EMOAs is evaluated on the bi-objective BBOB problem suite provided by the COCO platform. In contrast to conventional wisdom, results show that non-elitist EMOAs with particular crossover methods perform significantly well on the bi-objective BBOB problems with many decision variables when using the unbounded external archive. This paper also analyzes the properties of the non-elitist selection.
A Vietnamese Dataset for Evaluating Machine Reading Comprehension
Venue: arXiv cs.CL 2020
Year: 2020

Over 97 million people speak Vietnamese as their native language in the world. However, there are few research studies on machine reading comprehension (MRC) for Vietnamese, the task of understanding a text and answering questions related to it. Due to the lack of benchmark datasets for Vietnamese, we present the Vietnamese Question Answering Dataset (UIT-ViQuAD), a new dataset for the low-resource language as Vietnamese to evaluate MRC models. This dataset comprises over 23,000 human-generated question-answer pairs based on 5,109 passages of 174 Vietnamese articles from Wikipedia. In particular, we propose a new process of dataset creation for Vietnamese MRC. Our in-depth analyses illustrate that our dataset requires abilities beyond simple reasoning like word matching and demands single-sentence and multiple-sentence inferences. Besides, we conduct experiments on state-of-the-art MRC methods for English and Chinese as the first experimental models on UIT-ViQuAD. We also estimate human performance on the dataset and compare it to the experimental results of powerful machine learning models. As a result, the substantial differences between human performance and the best model performance on the dataset indicate that improvements can be made on UIT-ViQuAD in future research. Our dataset is freely available on our website to encourage the research community to overcome challenges in Vietnamese MRC.
Point-of-Interest Type Inference from Social Media Text
Venue: arXiv cs.CL 2020
Year: 2020

Physical places help shape how we perceive the experiences we have there. For the first time, we study the relationship between social media text and the type of the place from where it was posted, whether a park, restaurant, or someplace else. To facilitate this, we introduce a novel data set of $\sim$200,000 English tweets published from 2,761 different points-of-interest in the U.S., enriched with place type information. We train classifiers to predict the type of the location a tweet was sent from that reach a macro F1 of 43.67 across eight classes and uncover the linguistic markers associated with each type of place. The ability to predict semantic place information from a tweet has applications in recommendation systems, personalization services and cultural geography.
A cut-free sequent calculus for the bi-intuitionistic logic 2Int
Venue: arXiv cs.LO 2020
Year: 2020

The purpose of this paper is to introduce a bi-intuitionistic sequent calculus and to give proofs of admissibility for its structural rules. The calculus I will present, called SC2Int, is a sequent calculus for the bi-intuitionistic logic 2Int. Calculi for this logic represent a kind of bilateralist reasoning, since they do not only internalize processes of verification or provability but also the dual processes in terms of falsification or what is called dual provability. A normal form theorem for a natural deduction calculus of 2Int has already been stated, in this paper I want to prove a cut-elimination theorem for SC2Int, i.e. if successful, this would extend the results existing so far.
A Systematic Method for On-The-Fly Denormalization of Relational Databases
Venue: arXiv cs.DB 2020
Year: 2020

Normalized relational databases are a common method for storing data, but pulling out usable denormalized data for consumption generally requires either direct access to the source data or creation of an appropriate view or table by a database administrator. End-users are thus limited in their ability to explore and use data that is stored in this manner. Presented here is a method for performing automated denormalization of relational databases at run-time, without requiring access to source data or ongoing intervention by a database administrator. Furthermore, this method does not require a restructure of the database itself and so it can be flexibly applied as a layer on top of already existing databases.
Pea-KD: Parameter-efficient and Accurate Knowledge Distillation on BERT
Venue: arXiv cs.LG 2020
Year: 2020

How can we efficiently compress a model while maintaining its performance? Knowledge Distillation (KD) is one of the widely known methods for model compression. In essence, KD trains a smaller student model based on a larger teacher model and tries to retain the teacher model's level of performance as much as possible. However, existing KD methods suffer from the following limitations. First, since the student model is smaller in absolute size, it inherently lacks model capacity. Second, the absence of an initial guide for the student model makes it difficult for the student to imitate the teacher model to its fullest. Conventional KD methods yield low performance due to these limitations. In this paper, we propose Pea-KD (Parameter-efficient and accurate Knowledge Distillation), a novel approach to KD. Pea-KD consists of two main parts: Shuffled Parameter Sharing (SPS) and Pretraining with Teacher's Predictions (PTP). Using this combination, we are capable of alleviating the KD's limitations. SPS is a new parameter sharing method that increases the student model capacity. PTP is a KD-specialized initialization method, which can act as a good initial guide for the student. When combined, this method yields a significant increase in student model's performance. Experiments conducted on BERT with different datasets and tasks show that the proposed approach improves the student model's performance by 4.4\% on average in four GLUE tasks, outperforming existing KD baselines by significant margins.
Fast Decomposition of Temporal Logic Specifications for Heterogeneous Teams
Venue: arXiv cs.AI 2020
Year: 2020

In this work, we focus on decomposing large multi-agent path planning problems with global temporal logic goals (common to all agents) into smaller sub-problems that can be solved and executed independently. Crucially, the sub-problems' solutions must jointly satisfy the common global mission specification. The agents' missions are given as Capability Temporal Logic (CaTL) formulas, a fragment of signal temporal logic, that can express properties over tasks involving multiple agent capabilities (sensors, e.g., camera, IR, and effectors, e.g., wheeled, flying, manipulators) under strict timing constraints. The approach we take is to decompose both the temporal logic specification and the team of agents. We jointly reason about the assignment of agents to subteams and the decomposition of formulas using a satisfiability modulo theories (SMT) approach. The output of the SMT is then distributed to subteams and leads to a significant speed up in planning time. We include computational results to evaluate the efficiency of our solution, as well as the trade-offs introduced by the conservative nature of the SMT encoding.
Analyzing the Capacity of Distributed Vector Representations to Encode Spatial Information
Venue: arXiv cs.AI 2020
Year: 2020

Vector Symbolic Architectures belong to a family of related cognitive modeling approaches that encode symbols and structures in high-dimensional vectors. Similar to human subjects, whose capacity to process and store information or concepts in short-term memory is subject to numerical restrictions,the capacity of information that can be encoded in such vector representations is limited and one way of modeling the numerical restrictions to cognition. In this paper, we analyze these limits regarding information capacity of distributed representations. We focus our analysis on simple superposition and more complex, structured representations involving convolutive powers to encode spatial information. In two experiments, we find upper bounds for the number of concepts that can effectively be stored in a single vector.
Simulation of Wheelchair Movements in Crowd Using Fine Grid Cellular Automata
Venue: arXiv cs.MA 2020
Year: 2020

Crowd simulation models are used to assess the performance and safety of crowd systems. In some systems, wheelchairs and other moving objects are present in the crowd. The different size and speed of the wheelchairs could significantly change the behavior and dynamics of the crowd. In order to minimize the risks of overcrowding and other types of accidents, it is important to properly model the wheelchairs and their interactions with pedestrians and the environment. Cellular automata are extensively utilized in crowd modeling because of their simple and fast algorithms. Fine grid cellular automata model uses small cells in which moving entities (pedestrians, wheelchairs, cars and etc.) occupy several cells. The entities could have different sizes, shapes, and speeds. In this article, fine grid cellular automata model has been modified to allow building crowd simulation models with different ratios of wheelchairs that could be of different sizes and speed profiles. A scenario of a walkway has been used to evaluate the model. The slow down effect of the slower wheelchairs has been properly reproduced in the results which also match empirical data. Density-speed graphs are also compared to crowds comprising of only pedestrians.
Machine Learning in Airline Crew Pairing to Construct Initial Clusters for Dynamic Constraint Aggregation
Venue: arXiv cs.AI 2020
Year: 2020

The crew pairing problem (CPP) is generally modelled as a set partitioning problem where the flights have to be partitioned in pairings. A pairing is a sequence of flight legs separated by connection time and rest periods that starts and ends at the same base. Because of the extensive list of complex rules and regulations, determining whether a sequence of flights constitutes a feasible pairing can be quite difficult by itself, making CPP one of the hardest of the airline planning problems. In this paper, we first propose to improve the prototype Baseline solver of Desaulniers et al. (2020) by adding dynamic control strategies to obtain an efficient solver for large-scale CPPs: Commercial-GENCOL-DCA. These solvers are designed to aggregate the flights covering constraints to reduce the size of the problem. Then, we use machine learning (ML) to produce clusters of flights having a high probability of being performed consecutively by the same crew. The solver combines several advanced Operations Research techniques to assemble and modify these clusters, when necessary, to produce a good solution. We show, on monthly CPPs with up to 50 000 flights, that Commercial-GENCOL-DCA with clusters produced by ML-based heuristics outperforms Baseline fed by initial clusters that are pairings of a solution obtained by rolling horizon with GENCOL. The reduction of solution cost averages between 6.8% and 8.52%, which is mainly due to the reduction in the cost of global constraints between 69.79% and 78.11%.
Approximation of the Double Travelling Salesman Problem with Multiple Stacks
Venue: arXiv cs.DM 2020
Year: 2020

The Double Travelling Salesman Problem with Multiple Stacks, DTSPMS, deals with the collect and delivery of n commodities in two distinct cities, where the pickup and the delivery tours are related by LIFO constraints. During the pickup tour, commodities are loaded into a container of k rows, or stacks, with capacity c. This paper focuses on computational aspects of the DTSPMS, which is NP-hard. We first review the complexity of two critical subproblems: deciding whether a given pair of pickup and delivery tours is feasible and, given a loading plan, finding an optimal pair of pickup and delivery tours, are both polynomial under some conditions on k and c. We then prove a (3k)/2 standard approximation for the MinMetrickDTSPMS, where k is a universal constant, and other approximation results for various versions of the problem. We finally present a matching-based heuristic for the 2DTSPMS, which is a special case with k=2 rows, when the distances are symmetric. This yields a 1/2-o(1), 3/4-o(1) and 3/2+o(1) standard approximation for respectively Max2DTSPMS, its restriction Max2DTSPMS-(1,2) with distances 1 and 2, and Min2DTSPMS-(1,2), and a 1/2-o(1) differential approximation for Min2DTSPMS and Max2DTSPMS.
Learning from Mistakes: Combining Ontologies via Self-Training for Dialogue Generation
Venue: arXiv cs.CL 2020
Year: 2020

Natural language generators (NLGs) for task-oriented dialogue typically take a meaning representation (MR) as input. They are trained end-to-end with a corpus of MR/utterance pairs, where the MRs cover a specific set of dialogue acts and domain attributes. Creation of such datasets is labor-intensive and time-consuming. Therefore, dialogue systems for new domain ontologies would benefit from using data for pre-existing ontologies. Here we explore, for the first time, whether it is possible to train an NLG for a new larger ontology using existing training sets for the restaurant domain, where each set is based on a different ontology. We create a new, larger combined ontology, and then train an NLG to produce utterances covering it. For example, if one dataset has attributes for family-friendly and rating information, and the other has attributes for decor and service, our aim is an NLG for the combined ontology that can produce utterances that realize values for family-friendly, rating, decor and service. Initial experiments with a baseline neural sequence-to-sequence model show that this task is surprisingly challenging. We then develop a novel self-training method that identifies (errorful) model outputs, automatically constructs a corrected MR input to form a new (MR, utterance) training pair, and then repeatedly adds these new instances back into the training data. We then test the resulting model on a new test set. The result is a self-trained model whose performance is an absolute 75.4% improvement over the baseline model. We also report a human qualitative evaluation of the final model showing that it achieves high naturalness, semantic coherence and grammaticality
Towards Self-learning Edge Intelligence in 6G
Venue: arXiv cs.NI 2020
Year: 2020

Edge intelligence, also called edge-native artificial intelligence (AI), is an emerging technological framework focusing on seamless integration of AI, communication networks, and mobile edge computing. It has been considered to be one of the key missing components in the existing 5G network and is widely recognized to be one of the most sought-after functions for tomorrow's wireless 6G cellular systems. In this article, we identify the key requirements and challenges of edge-native AI in 6G. A self-learning architecture based on self-supervised Generative Adversarial Nets (GANs) is introduced to \blu{demonstrate the potential performance improvement that can be achieved by automatic data learning and synthesizing at the edge of the network}. We evaluate the performance of our proposed self-learning architecture in a university campus shuttle system connected via a 5G network. Our result shows that the proposed architecture has the potential to identify and classify unknown services that emerge in edge computing networks. Future trends and key research problems for self-learning-enabled 6G edge intelligence are also discussed.
WeChat Neural Machine Translation Systems for WMT20
Venue: arXiv cs.CL 2020
Year: 2020

We participate in the WMT 2020 shared news translation task on Chinese to English. Our system is based on the Transformer (Vaswani et al., 2017a) with effective variants and the DTMT (Meng and Zhang, 2019) architecture. In our experiments, we employ data selection, several synthetic data generation approaches (i.e., back-translation, knowledge distillation, and iterative in-domain knowledge transfer), advanced finetuning approaches and self-bleu based model ensemble. Our constrained Chinese to English system achieves 36.9 case-sensitive BLEU score, which is the highest among all submissions.
"Did you really mean what you said?" : Sarcasm Detection in Hindi-English Code-Mixed Data using Bilingual Word Embeddings
Venue: arXiv cs.CL 2020
Year: 2020

With the increased use of social media platforms by people across the world, many new interesting NLP problems have come into existence. One such being the detection of sarcasm in the social media texts. We present a corpus of tweets for training custom word embeddings and a Hinglish dataset labelled for sarcasm detection. We propose a deep learning based approach to address the issue of sarcasm detection in Hindi-English code mixed tweets using bilingual word embeddings derived from FastText and Word2Vec approaches. We experimented with various deep learning models, including CNNs, LSTMs, Bi-directional LSTMs (with and without attention). We were able to outperform all state-of-the-art performances with our deep learning models, with attention based Bi-directional LSTMs giving the best performance exhibiting an accuracy of 78.49%.