Miracle-dz/UniFAR-FTUs huggingface dataset:
AdaNSP: Uncertainty-driven Adaptive Decoding in Neural Semantic Parsing

Neural semantic parsers utilize the encoder-decoder framework to learn an end-to-end model for semantic parsing that transduces a natural language sentence to the formal semantic representation. To keep the model aware of the underlying grammar in target sequences, many constrained decoders were devised in a multi-stage paradigm, which decode to the sketches or abstract syntax trees first, and then decode to target semantic tokens. We instead to propose an adaptive decoding method to avoid such intermediate representations. The decoder is guided by model uncertainty and automatically uses deeper computations when necessary. Thus it can predict tokens adaptively. Our model outperforms the state-of-the-art neural models and does not need any expertise like predefined grammar or sketches in the meantime.
An Improved Video Watermarking Algorithm Based on Relationship

This paper researched an improved digital video watermarking algorithm based on relationship. The algorithm combines the video processing and video coding compression principle which adopts improved Arnold algorithm as well as the technology of the block and carries out a series of replacement process in the DCT coefficients of the video frame YCbCr spatial luminance Y components. Meanwhile, we segment information watermark image, aiming at the information watermark and mark watermark to be double embedded to improve the watermark embedding capacity and computing speed of the algorithm. Simulation results show that this algorithm can keep good invisibility of watermark. It has good robustness to video compression processing and special video attacks which can realize the blind extraction and random watermark detection.
An Evaluation Framework for Communication and Coordination Processes in Offshore Software Development Outsourcing Relationship: Using Fuzzy Methods

Offshore software development outsourcing (OSDO) is a modern business strategy for producing high-quality software at a low cost. The OSDO refers to the practice of contracting to an offshore (extrinsic) organization to perform some or all software development work of a product. For the benefit of the OSDO vendors, this paper aims to develop a “communication and coordination challenges mitigation model” (CCCMM) that provides solutions for unambiguously defined communication and coordination processes in global software development (GSD) environment. Our proposed model is based on the fuzzy multi-attribute decision-making (FMADM) approach incorporating the capability of group decision-making. The FMADM approach is used both in the ranking of survey and assessment of case studies. First, the authors undertook a systematic literature review (SLR) that identified all cited challenges from a set of 101 articles. We identified 18 problem areas faced by the GSD vendors in OSDO relationships. Of these, six were ranked as critical. For the purpose of identifying corrective interventions, a second SLR was conducted that revealed 75 remedial measures extracted from 63 chosen articles. To validate our SLR findings, we surveyed 42 outsourcing experts from six countries. We also categorized six critical challenges and 75 corrective practices into four mitigation levels based on CMMI, SOVRM, and SOPM. In addition, two case studies were conducted to evaluate CCCMM outcomes in OSDO companies. The assessment results of the first case study do not recommend Company-A for the successful implementation of level-2 of the CCCMM, so Company-A stands at level-1. We have observed from the second case study that Company-B has implemented all the critical challenges of the level-2 only; therefore, Company-B is at level-2 “success” of the proposed assessment model.
Individualized Indicator for All: Stock-wise Technical Indicator Optimization with Stock Embedding

As one of the most important investing approaches, technical analysis attempts to forecast stock movement by interpreting the inner rules from historic price and volume data. To address the vital noisy nature of financial market, generic technical analysis develops technical trading indicators, as mathematical summarization of historic price and volume data, to form up the foundation for robust and profitable investment strategies. However, an observation reveals that stocks with different properties have different affinities over technical indicators, which discloses a big challenge for the indicator-oriented stock selection and investment. To address this problem, in this paper, we design a Technical Trading Indicator Optimization(TTIO) framework that manages to optimize the original technical indicator by leveraging stock-wise properties. To obtain effective representations of stock properties, we propose a Skip-gram architecture to learn stock embedding inspired by a valuable knowledge repository formed by fund manager's collective investment behaviors. Based on the learned stock representations, TTIO further learns a re-scaling network to optimize the indicator's performance. Extensive experiments on real-world stock market data demonstrate that our method can obtain the very stock representations that are invaluable for technical indicator optimization since the optimized indicators can result in strong investing signals than original ones.
Game Theory for Next Generation Wireless and Communication Networks

With the advancement of telecommunication technologies, wireless networking has become ubiquitous owing to the great demand for pervasive mobile applications. The convergence of computing, communications, and media will allow users to communicate with each other and access any content at anytime, anywhere. The future, beyond 5G wireless networks, will support various services such as high-speed access, augmented reality, smart transportation, video conferencing, real-time Internet games, Internet of Things services, smart homes, automated highways, and disaster relief. However, many technical challenges remain to be addressed to make this wireless vision a reality. One prominent issue is devising distributed, self-organizing, and dynamic algorithms for optimizing the performance of the network over time-varying channels and within dense and heterogeneous environments. Examples of such environments in future wireless networks are abundant and they range from dense deployments of small cell networks to massive Internet of Things systems. Therefore, to support tomorrow’s wireless services, it is essential to develop efficient mechanisms that provide an optimal cost–resource– performance tradeoff and that constitute the basis for next generation ubiquitous and self-organizing wireless networks. Game theory provides a formal framework with a set of mathematical tools to study the complex interactions among interdependent rational players. For more than a half century, it has led to revolutionary developments in economics and has found important applications in a multitude of disciplines, such as politics, sociology, psychology, and transportation. More recently, there has been a surge in research activities that employ game theory to model and analyze wireless communication systems. Combining game theory with the design of efficient distributed algorithms for emerging wireless networks is desirable and challenging. On the one hand, in a large-scale wireless network, devices will be inherently selfish as they seek to optimize individual qualityof-service (QoS) metrics. For instance, distributed mobile devices tend to maximize their own performance, regardless of how this maximization affects the other users in the network, subsequently giving rise to competing scenarios. Such interdependence and selfishness become more pronounced in large-scale and dense systems such as the Internet of Things or small cell networks. On the other hand, in many practical networking scenarios, cooperation is required among wireless network users for performance enhancement. These situations recently motivated researchers and engineers to adopt
Span-Level Model for Relation Extraction

Relation Extraction is the task of identifying entity mention spans in raw text and then identifying relations between pairs of the entity mentions. Recent approaches for this span-level task have been token-level models which have inherent limitations. They cannot easily define and implement span-level features, cannot model overlapping entity mentions and have cascading errors due to the use of sequential decoding. To address these concerns, we present a model which directly models all possible spans and performs joint entity mention detection and relation extraction. We report a new state-of-the-art performance of 62.83 F1 (prev best was 60.49) on the ACE2005 dataset.
Age Factor Removal Network Based on Transfer Learning and Adversarial Learning for Cross-Age Face Recognition

It is well known that expression, pose variations, and especially age factors always affect the performance of a face recognition system in practical conditions. In this paper, we propose a novel framework called age factor removal network (AFRN) for cross-age face recognition, which combines the concepts of transfer learning and adversarial learning to enhance the performance of a pretrained face recognition network and suppress the influence of attributes, such as aging, expression, and pose variations. Similar to the generative adversarial network (GAN), our model consists of two networks: an identity feature generator G and an age discriminator D. First, the D network is trained to discriminate the age information from the feature extracted by G. Second, the G network simultaneously learns two tasks: extracting the feature through transfer learning from a pretrained face recognition network and suppressing age information through adversarial learning with D. Through the optimization of the two networks, the age factor in feature extraction process of G is removed. Besides, our network only requires the attributes’ label to train and preserves the identity discriminant power through transfer learning, and thus, it does not depend on multi-label face databases. The extensive experiments have been performed on the benchmark cross-age face datasets, including MORPH Album2, CACD-VS, and Cross Age LFW, which verify the effectiveness of our model. What is more, our model can be extended to other practical face recognition tasks and decreases the influence of expressions and poses, which is verified by the experimental results on CMU Multi-PIE database.
An Evolutionary GA-Based Approach for Community Detection in IoT

Identifying traffic congestion and solving them by using predictive models has been ongoing research in intelligent transportation scenarios. However, it is improper that such scenarios can be judged on the basis of mean traffic intensity and mean traffic speed. This paper works on this aspect and uses data mining approaches to derive the aggregation metrics of traffic intensity data from the city of Madrid. This work uses a novel similarity measure by utilizing the results of the Wilcoxon Signed Rank test across 2018 locations to discover similarities. We propose a Genetic Algorithm on the results of the Wilcoxon test for forming communities based on the aggregation metrics. This work also compares and evaluates the performance of the proposed algorithm against standard distance measures and other state-of-the-art approaches. For finding the optimal number of possible communities in the data, we have taken the help of Davies - Bouldin Test. Our experimental results show the effectiveness of the Genetic Algorithm using various parameters, such as number of dissimilar points within a cluster, minimum number of dissimilar data points between clusters and overall based on Modified Silhouette coefficient. Furthermore, we find that our method is able to distribute the data points in a more uniform manner across formed communities in comparison to other approaches considered in this work.
Online Fault Diagnosis of Discrete Event Systems Modeled With Labeled Petri Nets Using an Overall Fault Status

In this paper we present a fault diagnosis approach using labeled Petri nets, where the faults are modeled by unobservable transitions and the unobservable subnet is acyclic. In contrast to detecting the individual faults separately, a new specification called an overall fault status is introduced, which indicates the occurrence of faults from a global system perspective. Due to the introduction of the overall fault status, a more precise and informative diagnosis result can be provided and in some cases, the occurrence of some faults in a system can be detected before the actual faults are isolated, i.e., we are certain about the occurrence of faults but which faults have not been ascertained. An integer linear programming (ILP) problem is built according to the observed word. We prove that all transition sequences determined by solutions to the ILP problem constitute the set of sequences consistent with the observed word. By specifying different objective functions to the ILP problem, the diagnosis results of each individual fault and the overall fault status can be obtained. An online diagnosis algorithm is developed to implement the proposed diagnosis process, which reports the diagnosis results after the occurrence of every observable event.
Using Semantic Modelling to Improve the Processing Efficiency of Big Data in the Internet of Things Domain

A.Ya. GLADUN , PhD (Engineering), Senior Researcher of the Department of Complex Research of Information Technologies and Systems email: glanat@yahoo.com Yu.V. ROGUSHINA , PhD (Phys&Math) Senior Researcher of the Department of Automated Information Systems ladanandraka2010@gmail.com A.A. ANDRUSHEVICH , Researcher at the Faculty of Applied Mathematics and Computer Science email: andrushevich@bsu.by 1 International Research and Training Center for Information Technologies and Systems of the National Academy of Sciences of Ukraine and Ministry of Education and Science of Ukraine 40, Acad. Glushkov av. , 03187, Kyiv, Ukraine 2 Institute of Program Systems of the National Academy of Sciences of Ukraine, 40, Acad. Glushkov av. , 03187, Kyiv, Ukraine 3 Belarusian State University, 4, Nezavisimosti av. , 220030, Minsk, Republic of Belarus.
Backstepping-Based Inverse Optimal Attitude Control of Quadrotor

Abstract Input saturation must be taken into account for applying rapid reorientation in the large angle manoeuvre of a quadrotor. In this paper, a backstepping-based inverse optimal attitude controller (BIOAC) is derived which has the property of a maximum convergence rate in the sense of a control Lyapunov function (CLF) under input torque limitation. In the controller, a backstepping technique is used for handling the complexity introducing by the unit quaternion representation of the attitude of a quadrotor with four parameters. Moreover, the inverse optimal approach is employed to circumvent the difficulty of solving the Hamilton-Jacobi-Bellman (HJB) equation. The performance of BIOAC is compared with a PD controller in which the input torque limitation is not considered under the same unit quaternion representation using numerical simulation while the results show that BIOAC gains faster convergence with less control effort. Next, BIOAC is realized on a test bed and the effectiveness of the control law is verified by experimental studies.
DeepStealth: Game-Based Learning Stealth Assessment With Deep Neural Networks

A distinctive feature of game-based learning environments is their capacity for enabling stealth assessment. Stealth assessment analyzes a stream of fine-grained student interaction data from a game-based learning environment to dynamically draw inferences about students’ competencies through evidence-centered design. In evidence-centered design, evidence models have been traditionally designed using statistical rules authored by domain experts that are encoded using Bayesian networks. This article presents DeepStealth, a deep learning-based stealth assessment framework, that yields significant reductions in the feature engineering labor that has previously been required to create stealth assessments. DeepStealth utilizes end-to-end trainable deep neural network-based evidence models. Using this framework, evidence models are devised using a set of predictive features captured from raw, low-level interaction data to infer evidence for competencies. We investigate two deep learning-based evidence models, long short-term memory networks (LSTMs) and n-gram encoded feedforward neural networks (FFNNs). We compare these models’ predictive performance for inferring students’ knowledge to linear-chain conditional random fields (CRFs) and naïve Bayes models. We perform feature set-level analyses of game trace logs and external pre-learning measures, and we examine the models’ early prediction capacity. The framework is evaluated using data collected from 182 middle school students interacting with a game-based learning environment for middle grade computational thinking. Results indicate that LSTM-based stealth assessors outperform competitive baseline approaches with respect to predictive accuracy and early prediction capacity. We find that LSTMs, FFNNs, and CRFs all benefit from combined feature sets derived from both game trace logs and external pre-learning measures.
The Effectiveness of Simple Hybrid Systems for Hypernym Discovery

Hypernymy modeling has largely been separated according to two paradigms, pattern-based methods and distributional methods. However, recent works utilizing a mix of these strategies have yielded state-of-the-art results. This paper evaluates the contribution of both paradigms to hybrid success by evaluating the benefits of hybrid treatment of baseline models from each paradigm. Even with a simple methodology for each individual system, utilizing a hybrid approach establishes new state-of-the-art results on two domain-specific English hypernym discovery tasks and outperforms all non-hybrid approaches in a general English hypernym discovery task.
A Real-time Pedestrian Detection Model Adopted by High-Performance ANS-DPM

In order to fulfill the real-time pedestrian detection system, it is difficult to balance the higher detection rate and greater detection speed at the same time. Therefore, this paper proposed an Adaptive Neighborhood Selection-Deformable Part Model (ANS-DPM) as a novel pedestrian feature operator to solve this problem. ANS-DPM adopts the adaptive feature zones to extract the pedestrian features based on the relationship between features ex-traction score with the experience threshold to decrease the whole calculation and upgrate. Meanwhile, the inner-layer and inter-layer constraint are introduced into the ANS-DPM to deal with the model of robustness. Finally, ANS-DPM with Latent-SVM constructs the pedestrian detection system and experiment result shows that the ANS-DPM can improve the feature rate, while the pedestrian detection system can detect 30 fps to satisfy the real-time detection system.
Contextual Fact Ranking and Its Applications in Table Synthesis and Compression

Modern search engines increasingly incorporate tabular content, which consists of a set of entities each augmented with a small set of facts. The facts can be obtained from multiple sources: an entity's knowledge base entry, the infobox on its Wikipedia page, or its row within a WebTable. Crucially, the informativeness of a fact depends not only on the entity but also the specific context(e.g., the query). To the best of our knowledge, this paper is the first to study the problem of contextual fact ranking: given some entities and a context (i.e., succinct natural language description), identify the most informative facts for the entities collectively within the context.We propose to contextually rank the facts by exploiting deep learning techniques. In particular, we develop pointwise and pair-wise ranking models, using textual and statistical information for the given entities and context derived from their sources. We enhance the models by incorporating entity type information from an IsA (hypernym) database. We demonstrate that our approaches achieve better performance than state-of-the-art baselines in terms of MAP, NDCG, and recall. We further conduct user studies for two specific applications of contextual fact ranking-table synthesis and table compression-and show that our models can identify more informative facts than the baselines.
Multi‐representational convolutional neural networks for text classification

Various studies have demonstrated that convolutional neural networks (CNNs) can be directly applied to different levels of text embedding, such as character‐, word‐, or document‐levels. However, the effectiveness of different embeddings is limited in the reported result and there is a lack of clear guidance on some aspects of their use, including choosing the proper level of embedding and switching word semantics from one domain to another when appropriate. In this paper, we propose a new architecture of CNN based on multiple representations for text classification, by constructing multiple planes so that more information can be dumped into the networks, such as different parts of text obtained through named entity recognizer or part‐of‐speech tagging tools, different levels of text embedding, or contextual sentences. Various large‐scale, domain‐specific datasets are used to validate the proposed architecture. Tasks analyzed include ontology document classification, biomedical event categorization, and sentiment analysis, showing that multi‐representational CNNs, which learns to focus attention to specific representations of text, can obtain further gains in performance over state‐of‐the‐art deep neural network models.
Coverage and Time-optimal Motion Planning for Autonomous Vehicles

Autonomous vehicles are rapidly advancing with a variety of applications, such as area surveillance, environment mapping, and intelligent transportation. These applications require coverage and/or time-optimal motion planning, where the major challenges include uncertainties in the environment, motion constraints of vehicles, limited energy resources and potential failures. While dealing with these challenges in various capacities, this dissertation addresses three fundamental motion planning problems: (1) single-robot complete coverage in unknown environment, (2) multi-robot resilient and efficient coverage in unknown environment, and (3) time-optimal risk-aware motion planning for curvatureconstrained vehicles. First, the ε? algorithm is developed for online coverage path planning in unknown environment using a single autonomous vehicle. It is computationally efficient, and can generate the desired back-and-forth path with less turns and overlappings. Moreover, the algorithm prevents the local extrema problem, thus can guarantee complete coverage. Second, the CARE algorithm is developed which extends ε? for multi-robot resilient and efficient coverage in unknown environment. In case of vehicle failures, it guarantees complete coverage via proactively filling coverage gaps through dynamic task reallocations for other Junnan Song – University of Connecticut, 2019 vehicles, hence provides resilience. Moreover, CARE reallocates idling vehicles to support others in their tasks, hence reduces coverage time and improves team efficiency. Finally, the T? algorithm is developed to address the time-optimal risk-aware motion planning problem for curvature-constrained variable-speed vehicles. To the best of our knowledge, this problem has not been solved in the presence of obstacles. We present a novel risk function based on the concept of collision time, and seamlessly integrate it with the time cost for optimization. Further, an adaptive state pruning technique is developed that can significantly reduce the computation time, while maintaining the solution quality and the completeness of this algorithm. The above-mentioned algorithms have been validated via simulations in complex scenarios and/or real experiments, and the results have shown clear advantages over existing popular approaches. Coverage and Time-optimal Motion Planning for Autonomous Vehicles