OpenRaiser/Intern-Atlas huggingface dataset:
Optimized conformal classification using gradient descent approximation
Venue: arXiv cs.LG 2021
Year: 2021

Conformal predictors are an important class of algorithms that allow predictions to be made with a user-defined confidence level. They are able to do this by outputting prediction sets, rather than simple point predictions. The conformal predictor is valid in the sense that the accuracy of its predictions is guaranteed to meet the confidence level, only assuming exchangeability in the data. Since accuracy is guaranteed, the performance of a conformal predictor is measured through the efficiency of the prediction sets. Typically, a conformal predictor is built on an underlying machine learning algorithm and hence its predictive power is inherited from this algorithm. However, since the underlying machine learning algorithm is not trained with the objective of minimizing predictive efficiency it means that the resulting conformal predictor may be sub-optimal and not aligned sufficiently to this objective. Hence, in this study we consider an approach to train the conformal predictor directly with maximum predictive efficiency as the optimization objective, and we focus specifically on the inductive conformal predictor for classification. To do this, the conformal predictor is approximated by a differentiable objective function and gradient descent used to optimize it. The resulting parameter estimates are then passed to a proper inductive conformal predictor to give valid prediction sets. We test the method on several real world data sets and find that the method is promising and in most cases gives improved predictive efficiency against a baseline conformal predictor.
DaN+: Danish Nested Named Entities and Lexical Normalization
Venue: arXiv cs.CL 2021
Year: 2021

This paper introduces DaN+, a new multi-domain corpus and annotation guidelines for Danish nested named entities (NEs) and lexical normalization to support research on cross-lingual cross-domain learning for a less-resourced language. We empirically assess three strategies to model the two-layer Named Entity Recognition (NER) task. We compare transfer capabilities from German versus in-language annotation from scratch. We examine language-specific versus multilingual BERT, and study the effect of lexical normalization on NER. Our results show that 1) the most robust strategy is multi-task learning which is rivaled by multi-label decoding, 2) BERT-based NER models are sensitive to domain shifts, and 3) in-language BERT and lexical normalization are the most beneficial on the least canonical data. Our results also show that an out-of-domain setup remains challenging, while performance on news plateaus quickly. This highlights the importance of cross-domain evaluation of cross-lingual transfer.
A General Theory for the Evolution of Application Models -- Full version
Venue: arXiv cs.AI 2021
Year: 2021

In this article we focus on evolving information systems. First a delimitation of the concept of evolution is provided, resulting in a first attempt to a general theory for such evolutions. The theory makes a distinction between the underlying information structure at the conceptual level, its evolution on the one hand, and the description and semantics of operations on the information structure and its population on the other hand. Main issues within this theory are object typing, type relatedness and identification of objects. In terms of these concepts, we propose some axioms on the well-formedness of evolution. In this general theory, the underlying data model is a parameter, making the theory applicable for a wide range of modelling techniques, including object-role modelling and object oriented techniques.
Real-time Human Action Recognition Using Locally Aggregated Kinematic-Guided Skeletonlet and Supervised Hashing-by-Analysis Model
Venue: arXiv cs.CV 2021
Year: 2021

3D action recognition is referred to as the classification of action sequences which consist of 3D skeleton joints. While many research work are devoted to 3D action recognition, it mainly suffers from three problems: highly complicated articulation, a great amount of noise, and a low implementation efficiency. To tackle all these problems, we propose a real-time 3D action recognition framework by integrating the locally aggregated kinematic-guided skeletonlet (LAKS) with a supervised hashing-by-analysis (SHA) model. We first define the skeletonlet as a few combinations of joint offsets grouped in terms of kinematic principle, and then represent an action sequence using LAKS, which consists of a denoising phase and a locally aggregating phase. The denoising phase detects the noisy action data and adjust it by replacing all the features within it with the features of the corresponding previous frame, while the locally aggregating phase sums the difference between an offset feature of the skeletonlet and its cluster center together over all the offset features of the sequence. Finally, the SHA model which combines sparse representation with a hashing model, aiming at promoting the recognition accuracy while maintaining a high efficiency. Experimental results on MSRAction3D, UTKinectAction3D and Florence3DAction datasets demonstrate that the proposed method outperforms state-of-the-art methods in both recognition accuracy and implementation efficiency.
Augmenting Modelers with Semantic Autocompletion of Processes
Venue: arXiv cs.SE 2021
Year: 2021

Business process modelers need to have expertise and knowledge of the domain that may not always be available to them. Therefore, they may benefit from tools that mine collections of existing processes and recommend element(s) to be added to a new process that they are constructing. In this paper, we present a method for process autocompletion at design time, that is based on the semantic similarity of sub-processes. By converting sub-processes to textual paragraphs and encoding them as numerical vectors, we can find semantically similar ones, and thereafter recommend the next element. To achieve this, we leverage a state-of-the-art technique for embedding natural language as vectors. We evaluate our approach on open source and proprietary datasets and show that our technique is accurate for processes in various domains.
VANiLLa : Verbalized Answers in Natural Language at Large Scale
Venue: arXiv cs.CL 2021
Year: 2021

In the last years, there have been significant developments in the area of Question Answering over Knowledge Graphs (KGQA). Despite all the notable advancements, current KGQA datasets only provide the answers as the direct output result of the formal query, rather than full sentences incorporating question context. For achieving coherent answers sentence with the question's vocabulary, template-based verbalization so are usually employed for a better representation of answers, which in turn require extensive expert intervention. Thus, making way for machine learning approaches; however, there is a scarcity of datasets that empower machine learning models in this area. Hence, we provide the VANiLLa dataset which aims at reducing this gap by offering answers in natural language sentences. The answer sentences in this dataset are syntactically and semantically closer to the question than to the triple fact. Our dataset consists of over 100k simple questions adapted from the CSQA and SimpleQuestionsWikidata datasets and generated using a semi-automatic framework. We also present results of training our dataset on multiple baseline models adapted from current state-of-the-art Natural Language Generation (NLG) architectures. We believe that this dataset will allow researchers to focus on finding suitable methodologies and architectures for answer verbalization.
From Base Data To Knowledge Discovery -- A Life Cycle Approach -- Using Multilayer Networks
Venue: arXiv cs.SI 2021
Year: 2021

Any large complex data analysis to infer or discover meaningful information/knowledge involves the following steps (in addition to data collection, cleaning, preparing the data for analysis such as attribute elimination): i) Modeling the data -- an approach for modeling and deriving a data representation for analysis using that approach, ii) translating analysis objectives into computations on the model generated; this can be as simple as a single computation (e.g., community detection) or may involve a sequence of operations (e.g., pair-wise community detection over multiple networks) using expressions based on the model, iii) computation of the expressions generated -- efficiency and scalability come into picture here, and iv) drill-down of results to interpret or understand them clearly. Beyond this, it is also meaningful to visualize results for easier understanding. Covid-19 visualization dashboard presented in this paper is an example of this. This paper covers all of the above steps of data analysis life cycle using a data representation that is gaining importance for multi-entity, multi-feature data sets - Multilayer Networks. We use several data sets to establish the effectiveness of modeling using MLNs and analyze them using the proposed decoupling approach. For coverage, we use different types of MLNs for modeling, and community and centrality computations for analysis. The data sets used - US commercial airlines, IMDb, DBLP, and Covid-19 data set. Our experimental analyses using the identified steps validate modeling, breadth of objectives that can be computed, and overall versatility of the life cycle approach. Correctness of results is verified, where possible, using independently available ground truth. We demonstrate drill-down that is afforded by this approach (due to structure and semantics preservation) for a better understanding and visualization of results.
On the Optimality of the Stationary Solution of Secrecy Rate Maximization for MIMO Wiretap Channel
Venue: arXiv cs.IT 2021
Year: 2021

To achieve perfect secrecy in a multiple-input multiple-output (MIMO) Gaussian wiretap channel (WTC), we need to find its secrecy capacity and optimal signaling, which involves solving a difference of convex functions program known to be non-convex for the non-degraded case. To deal with this, a class of existing solutions have been developed but only local optimality is guaranteed by standard convergence analysis. Interestingly, our extensive numerical experiments have shown that these local optimization methods indeed achieve global optimality. In this paper, we provide an analytical proof for this observation. To achieve this, we show that the Karush-Kuhn-Tucker (KKT) conditions of the secrecy rate maximization problem admit a unique solution for both degraded and non-degraded cases. Motivated by this, we also propose a low-complexity algorithm to find a stationary point. Numerical results are presented to verify the theoretical analysis.
Attention-guided Temporally Coherent Video Object Matting
Venue: arXiv cs.CV 2021
Year: 2021

This paper proposes a novel deep learning-based video object matting method that can achieve temporally coherent matting results. Its key component is an attention-based temporal aggregation module that maximizes image matting networks' strength for video matting networks. This module computes temporal correlations for pixels adjacent to each other along the time axis in feature space, which is robust against motion noises. We also design a novel loss term to train the attention weights, which drastically boosts the video matting performance. Besides, we show how to effectively solve the trimap generation problem by fine-tuning a state-of-the-art video object segmentation network with a sparse set of user-annotated keyframes. To facilitate video matting and trimap generation networks' training, we construct a large-scale video matting dataset with 80 training and 28 validation foreground video clips with ground-truth alpha mattes. Experimental results show that our method can generate high-quality alpha mattes for various videos featuring appearance change, occlusion, and fast motion. Our code and dataset can be found at: https://github.com/yunkezhang/TCVOM
Graph Neural Network Based VC Investment Success Prediction
Venue: arXiv cs.SI 2021
Year: 2021

Predicting the start-ups that will eventually succeed is essentially important for the venture capital business and worldwide policy makers, especially at an early stage such that rewards can possibly be exponential. Though various empirical studies and data-driven modeling work have been done, the predictive power of the complex networks of stakeholders including venture capital investors, start-ups, and start-ups' managing members has not been thoroughly explored. We design an incremental representation learning mechanism and a sequential learning model, utilizing the network structure together with the rich attributes of the nodes. In general, our method achieves the state-of-the-art prediction performance on a comprehensive dataset of global venture capital investments and surpasses human investors by large margins. Specifically, it excels at predicting the outcomes for start-ups in industries such as healthcare and IT. Meanwhile, we shed light on impacts on start-up success from observable factors including gender, education, and networking, which can be of value for practitioners as well as policy makers when they screen ventures of high growth potentials.
Polarimetric Spatio-Temporal Light Transport Probing
Venue: arXiv cs.CV 2021
Year: 2021

Light emitted from a source into a scene can undergo complex interactions with scene surfaces of different material types before being reflected. During this transport, every surface reflection is encoded in the properties of the photons that reach the detector, including time, direction, intensity, wavelength and polarization. Conventional imaging systems capture intensity by integrating over all other dimensions of the light, hiding this rich scene information. Existing methods are capable of untangling these measurements into their spatial and temporal dimensions, fueling geometric scene understanding tasks. However, examining material properties jointly with geometric properties is an open challenge that could enable unprecedented capabilities beyond geometric scene understanding, allowing for material-dependent scene understanding and imaging through complex transport. In this work, we close this gap, and propose a computational light transport imaging method that captures the spatially- and temporally-resolved complete polarimetric response of a scene. Our method hinges on a 7D tensor theory of light transport. We discover low-rank structure in the polarimetric tensor dimension and propose a data-driven rotating ellipsometry method that learns to exploit redundancy of polarimetric structure. We instantiate our theory with two prototypes: spatio-polarimetric imaging and coaxial temporal-polarimetric imaging. This allows us, for the first time, to decompose scene light transport into temporal, spatial, and complete polarimetric dimensions that unveil scene properties hidden to conventional methods. We validate the applicability of our method on diverse tasks, including shape reconstruction with subsurface scattering, seeing through scattering media, untangling multi-bounce light transport, breaking metamerism, and decomposition of crystals.
Minimizing the Number of Wireless Charging PAD for UAV-Based Wireless Rechargeable Sensor Networks
Venue: arXiv cs.NI 2021
Year: 2021

In wireless rechargeable sensor networks (WRSNs), most of researches address energy scarcity by introducing one or multiple ground mobile vehicles to recharge energy-hungry sensor nodes. The charging efficiency is limited by the moving speed of ground chargers and rough environments, especially in large-scale scenarios or challenging scenarios such as separate islands. To address the limitations, some researchers consider replacing ground mobile chargers with lightweight unmanned aerial vehicles (UAVs) to support extremely large-scale scenarios, because of the UAV moving at higher speed without geographical limitation. Moreover, multiple automatic landing wireless charging PADs are deployed in the network to recharge UAVs automatically. In this work, we investigate the problem of introducing the minimal number of PADs in UAV-based WRSNs. We propose a novel and adaptive PAD deployment scheme named CDC & DSC that can adapt to arbitrary locations of the base station, arbitrary geographic distributions of sensor nodes, and arbitrary sizes of network areas. In the proposed scheme, we first obtain an initial PAD deployment solution by clustering nodes in geographic locations. Then, we propose a center shift combining algorithm to optimize this solution by shifting the location of PADs and attempting to merge the adjacent PADs. The simulation results show that compared to existing algorithms, our proposed scheme can use fewer PADs to charge the whole network.
Transfer Learning and Curriculum Learning in Sokoban
Venue: arXiv cs.AI 2021
Year: 2021

Transfer learning can speed up training in machine learning and is regularly used in classification tasks. It reuses prior knowledge from other tasks to pre-train networks for new tasks. In reinforcement learning, learning actions for a behavior policy that can be applied to new environments is still a challenge, especially for tasks that involve much planning. Sokoban is a challenging puzzle game. It has been used widely as a benchmark in planning-based reinforcement learning. In this paper, we show how prior knowledge improves learning in Sokoban tasks. We find that reusing feature representations learned previously can accelerate learning new, more complex, instances. In effect, we show how curriculum learning, from simple to complex tasks, works in Sokoban. Furthermore, feature representations learned in simpler instances are more general, and thus lead to positive transfers towards more complex tasks, but not vice versa. We have also studied which part of the knowledge is most important for transfer to succeed, and identify which layers should be used for pre-training.
Utterance partitioning for speaker recognition: an experimental review and analysis with new findings under GMM-SVM framework
Venue: arXiv cs.LG 2021
Year: 2021

The performance of speaker recognition system is highly dependent on the amount of speech used in enrollment and test. This work presents a detailed experimental review and analysis of the GMM-SVM based speaker recognition system in presence of duration variability. This article also reports a comparison of the performance of GMM-SVM classifier with its precursor technique Gaussian mixture model-universal background model (GMM-UBM) classifier in presence of duration variability. The goal of this research work is not to propose a new algorithm for improving speaker recognition performance in presence of duration variability. However, the main focus of this work is on utterance partitioning (UP), a commonly used strategy to compensate the duration variability issue. We have analysed in detailed the impact of training utterance partitioning in speaker recognition performance under GMM-SVM framework. We further investigate the reason why the utterance partitioning is important for boosting speaker recognition performance. We have also shown in which case the utterance partitioning could be useful and where not. Our study has revealed that utterance partitioning does not reduce the data imbalance problem of the GMM-SVM classifier as claimed in earlier study. Apart from these, we also discuss issues related to the impact of parameters such as number of Gaussians, supervector length, amount of splitting required for obtaining better performance in short and long duration test conditions from speech duration perspective. We have performed the experiments with telephone speech from POLYCOST corpus consisting of 130 speakers.
A unified framework based on graph consensus term for multi-view learning
Venue: arXiv cs.LG 2021
Year: 2021

In recent years, multi-view learning technologies for various applications have attracted a surge of interest. Due to more compatible and complementary information from multiple views, existing multi-view methods could achieve more promising performance than conventional single-view methods in most situations. However, there are still no sufficient researches on the unified framework in existing multi-view works. Meanwhile, how to efficiently integrate multi-view information is still full of challenges. In this paper, we propose a novel multi-view learning framework, which aims to leverage most existing graph embedding works into a unified formula via introducing the graph consensus term. In particular, our method explores the graph structure in each view independently to preserve the diversity property of graph embedding methods. Meanwhile, we choose heterogeneous graphs to construct the graph consensus term to explore the correlations among multiple views jointly. To this end, the diversity and complementary information among different views could be simultaneously considered. Furthermore, the proposed framework is utilized to implement the multi-view extension of Locality Linear Embedding, named Multi-view Locality Linear Embedding (MvLLE), which could be efficiently solved by applying the alternating optimization strategy. Empirical validations conducted on six benchmark datasets can show the effectiveness of our proposed method.
Complete Requirements-based Testing with Finite State Machines
Venue: arXiv cs.SE 2021
Year: 2021

In this paper, new contributions to requirements-based testing with deterministic finite state machines are presented. Elementary requirements are specified as triples consisting of a state in the reference model, an input, and the expected reaction of the system under test defined by a set of admissible outputs, allowing for different implementation variants. Composite requirements are specified as collections of elementary ones. Two requirements-driven test generation strategies are introduced, and their fault coverage guarantees are proven. The first is exhaustive in the sense that it produces test suites guaranteeing requirements satisfaction if the test suite is passed. If the test suite execution fails for a given implementation, however, this does not imply that the requirement has been violated. Instead, the failure may indicate an arbitrary violation of I/O-equivalence, which could be unrelated to the requirement under test. The second strategy is complete in the sense that it produces test suites guaranteeing requirements satisfaction if and only if the suite is passed. Complexity considerations indicate that for practical application, the first strategy should be preferred to the second. Typical application scenarios for this approach are safety-critical systems, where safety requirements should be tested with maximal thoroughness, while user requirements might be checked with lesser effort, using conventional testing heuristics.
Algorithmic properties of first-order modal logics of linear Kripke frames in restricted languages
Venue: arXiv cs.LO 2021
Year: 2021

We study the algorithmic properties of first-order monomodal logics of frames $\langle \mathbb{N}, \leq \rangle$, $\langle \mathbb{N}, < \rangle$, $\langle \mathbb{Q}, \leq \rangle$, $\langle \mathbb{Q}, < \rangle$, $\langle \mathbb{R}, \leq \rangle$, $\langle \mathbb{R}, < \rangle$, as well as some related logics, in languages with restrictions on the number of individual variables as well as the number and arity of predicate letters. We show that the logics of frames based on $\mathbb{N}$ are $Π^1_1$-hard -- thus, not recursively enumerable -- in languages with two individual variables, one monadic predicate letter and one proposition letter. We also show that the logics of frames based on $\mathbb{Q}$ and $\mathbb{R}$ are $Σ^0_1$-hard in languages with the same restrictions. Similar results are obtained for a number of related logics.