Reviewerly/exHarmony huggingface dataset:
Modeling of UAV path planning based on IMM under POMDP framework

In order to enhance the capability of tracking targets autonomously of unmanned aerial vehicle (UAV), the partially observable Markov decision process (POMDP) model for UAV path planning is established based on the POMDP framework. The elements of the POMDP model are analyzed and described. The state transfer law in the model can be described by the method of interactive multiple model (IMM) due to the diversity of the target motion law, which is used to switch the motion model to accommodate target maneuvers, and hence improving the tracking accuracy. The simulation results show that the model can achieve efficient planning for the UAV route, and effective tracking for the target. Furthermore, the path planned by this model is more reasonable and efficient than that by using the single state transition law.
Raising binaries to LLVM IR with MCTOLL (WIP paper)

The need to analyze and execute binaries from legacy ISAs on new or different ISAs has been addressed in a variety of ways over the past few decades. Solutions using complementary static and dynamic binary translation techniques have been deployed in most real-world situations. As new ISAs are designed and legacy ISAs re-examined, the need for binary translation infrastructure re-emerges, and needs to be re- engineered all over again.
Deep Chit-Chat

The tutorial is based on our long-term research on open domain conversation, rich hands-on experience on development of Microsoft XiaoIce, and our previous tutorials on EMNLP 2018 and the Web Conference 2019. It starts from a summary of recent achievement made by both academia and industry on chatbots, and then performs a thorough and systematic introduction to state-of-the-art methods for open domain conversation modeling including both retrieval-based methods and generation-based methods. In addition to these, the tutorial also covers some new progress on both groups of methods, such as transition from model design to model learning, transition from knowledge agnostic conversation to knowledge aware conversation, and transition from single-modal conversation to multi-modal conversation. The tutorial is ended by some promising future directions such as how to combine non-task-oriented dialogue systems with task-oriented dialogue systems and how to enhance language learning with chatbots.
Specific Emitter Identification Using IMF-DNA with a Joint Feature Selection Algorithm

Specific emitter identification (SEI) is a technique to distinguish among different emitters of the same type using weak individual characteristics instead of conventional modulation parameters. The biggest challenge in SEI is to not only distinguish the different emitters with close modulation parameters but also to identify a specific emitter when its modulation parameters change significantly. For this paper, individual differences in pulse envelopes were investigated and four types of pulse envelopes were modeled. To exploit the individual features along with the pulse envelope for the identification of a specific emitter, an intrinsic mode function distinct native attribute (IMF-DNA) feature extraction algorithm and a joint feature selection (JFS) algorithm were proposed, which together constitute the final proposed SEI technique. Compared with four other feature selection methods, the proposed feature selection algorithm performed better for finding the most useful features for classification, which greatly helps in the reduction of feature dimension. Compared with radio frequency DNA (RF-DNA), IMF-DNA had a far superior correct emitter identification rate under similar conditions. A real data verification method was developed to verify the performance of IMF-DNA for specific emitter identification. The method achieved a correct identification rate of 85.3% at a sampling rate of 200 MHz and had an estimated signal-to-noise ratio (SNR) of approximately 10 dB.
Are You for Real? Detecting Identity Fraud via Dialogue Interactions

Identity fraud detection is of great importance in many real-world scenarios such as the financial industry.However, few studies addressed this problem before.In this paper, we focus on identity fraud detection in loan applications and propose to solve this problem with a novel interactive dialogue system which consists of two modules.One is the knowledge graph (KG) constructor organizing the personal information for each loan applicant.The other is structured dialogue management that can dynamically generate a series of questions based on the personal KG to ask the applicants and determine their identity states.We also present a heuristic user simulator based on problem analysis to evaluate our method.Experiments have shown that the trainable dialogue system can effectively detect fraudsters, and achieve higher recognition accuracy compared with rule-based systems.Furthermore, our learned dialogue strategies are interpretable and flexible, which can help promote real-world applications.
Externally Recurrent Neural Network based identification of dynamic systems using Lyapunov stability analysis

This paper proposes an Externally Recurrent Neural Network (ERNN) for approximating the unknown dynamics of complex nonlinear systems and time series prediction. The proposed model utilizes the present as well as delayed values of the system outputs as well as of the external input. The weight update equations are tested for their boundedness by applying the Lyapunov stability method. Further, the error convergence proof is also given. The proposed model is put to test by considering various nonlinear examples and its performance is also compared with other state of the art methods. The results obtained in the present study indicate that the method is efficient and has provided accurate results.
Expediting TTS Synthesis with Adversarial Vocoding

Author(s): Neekhara, Paarth; Donahue, Chris; Puckette, Miller; Dubnov, Shlomo; McAuley, Julian | Abstract: Recent approaches in text-to-speech (TTS) synthesis employ neural network strategies to vocode perceptually-informed spectrogram representations directly into listenable waveforms. Such vocoding procedures create a computational bottleneck in modern TTS pipelines. We propose an alternative approach which utilizes generative adversarial networks (GANs) to learn mappings from perceptually-informed spectrograms to simple magnitude spectrograms which can be heuristically vocoded. Through a user study, we show that our approach significantly outperforms na quot;ive vocoding strategies while being hundreds of times faster than neural network vocoders used in state-of-the-art TTS systems. We also show that our method can be used to achieve state-of-the-art results in unsupervised synthesis of individual words of speech.
Annotary: A Concolic Execution System for Developing Secure Smart Contracts

Ethereum smart contracts are executable programs, deployed on a peer-to-peer network and executed in a consensus-based fashion. Their bytecode is public, immutable and once deployed to the blockchain, cannot be patched anymore. As smart contracts may hold Ether worth of several million dollars, they are attractive targets for attackers and indeed some contracts have successfully been exploited in the recent past, resulting in tremendous financial losses. The correctness of smart contracts is thus of utmost importance. While first approaches on formal verification exist, they demand users to be well-versed in formal methods which are alien to many developers and are only able to analyze individual contracts, without considering their execution environment, i.e., calls to external contracts, sequences of transaction, and values from the actual blockchain storage. In this paper, we present Annotary, a concolic execution framework to analyze smart contracts for vulnerabilities, supported by annotations which developers write directly in the Solidity source code. In contrast to existing work, Annotary supports analysis of inter-transactional, inter-contract control flows and combines symbolic execution of EVM bytecode with a resolution of concrete values from the public Ethereum blockchain. While the analysis of Annotary tends to weight precision higher than soundness, we analyze inter-transactional call chains to eliminate false positives from unreachable states that traditional symbolic execution would not be able to handle. We present the annotation and analysis concepts of Annotary, explain its implementation on top of the Laser symbolic virtual machine, and demonstrate its usage as a plugin for the Sublime Text editor.
Captioning Ultrasound Images Automatically

We describe an automatic natural language processing (NLP)-based image captioning method to describe fetal ultrasound video content by modelling the vocabulary commonly used by sonographers and sonologists. The generated captions are similar to the words spoken by a sonographer when describing the scan experience in terms of visual content and performed scanning actions. Using full-length second-trimester fetal ultrasound videos and text derived from accompanying expert voice-over audio recordings, we train deep learning models consisting of convolutional neural networks and recurrent neural networks in merged configurations to generate captions for ultrasound video frames. We evaluate different model architectures using established general metrics (BLEU, ROUGE-L) and application-specific metrics. Results show that the proposed models can learn joint representations of image and text to generate relevant and descriptive captions for anatomies, such as the spine, the abdomen, the heart, and the head, in clinical fetal ultrasound scans.
Directional Compactly Supported Tensor Product Complex Tight Framelets with Applications to Image Denoising and Inpainting

Compactly supported tight framelets are of great interest and importance in both theory and application. In this paper we discuss how to construct directional compactly supported tensor product complex tight framelets having varied directionality and good performance for applications in image processing. Our construction algorithms employ optimization techniques and put extensive emphasis on frequency response and spatial localization of their underlying one-dimensional tight framelet filter banks. Several concrete examples of directional compactly supported tensor product complex tight framelet filter banks are provided in this paper. Our numerical experiments show that such constructed directional compactly supported tensor product complex tight framelets have good performance for applications such as image denoising and inpainting compared with several other state-of-the-art transform-based methods.
On the Importance of Word Boundaries in Character-level Neural Machine Translation

Neural Machine Translation (NMT) models generally perform translation using a fixedsize lexical vocabulary, which is an important bottleneck on their generalization capability and overall translation quality.The standard approach to overcome this limitation is to segment words into subword units, typically using some external tools with arbitrary heuristics, resulting in vocabulary units not optimized for the translation task.Recent studies have shown that the same approach can be extended to perform NMT directly at the level of characters, which can deliver translation accuracy on-par with subword-based models, on the other hand, this requires relatively deeper networks.In this paper, we propose a more computationally-efficient solution for character-level NMT which implements a hierarchical decoding architecture where translations are subsequently generated at the level of words and characters.We evaluate different methods for open-vocabulary NMT in the machine translation task from English into five languages with distinct morphological typology, and show that the hierarchical decoding model can reach higher translation accuracy than the subword-level NMT model using significantly fewer parameters, while demonstrating better capacity in learning longer-distance contextual and grammatical dependencies than the standard character-level NMT model.
Customizations and Expression Breakdowns in Ecosystems of Communication Apps

The growing adoption of emojis, stickers and GIFs suggests a corresponding demand for rich, personalized expression in messaging apps. Some people customize apps to enable more personal forms of expression, yet we know little about how such customizations shape everyday communication. Since people increasingly communicate via multiple apps side-by-side, we are also interested in how customizing one app influences communication via other apps. We created a taxonomy of customization options based on interviews with 15 "extreme users" of communication apps. We found that participants tailored their apps to express their identities, organizational culture, and intimate bonds with others. They also experienced expression breakdowns: frustrations around barriers to transferring personal forms of expression across apps, which inspired inventive workarounds to maintain cross-app habits of expression, such as briefly switching apps to generate and export content for a particular conversation. We conclude with implications for personalized expression in ecosystems of communication apps.
Simple Yet Effective Way for Improving the Performance of GAN

In adversarial learning, the discriminator often fails to guide the generator successfully since it distinguishes between real and generated images using silly or nonrobust features. To alleviate this problem, this brief presents a simple but effective way that improves the performance of the generative adversarial network (GAN) without imposing the training overhead or modifying the network architectures of existing methods. The proposed method employs a novel cascading rejection (CR) module for discriminator, which extracts multiple nonoverlapped features in an iterative manner using the vector rejection operation. Since the extracted diverse features prevent the discriminator from concentrating on nonmeaningful features, the discriminator can guide the generator effectively to produce images that are more similar to the real images. In addition, since the proposed CR module requires only a few simple vector operations, it can be readily applied to existing frameworks with marginal training overheads. Quantitative evaluations on various data sets, including CIFAR-10, CelebA, CelebA-HQ, LSUN, and tiny-ImageNet, confirm that the proposed method significantly improves the performance of GAN and conditional GAN in terms of the Frechet inception distance (FID), indicating the diversity and visual appearance of the generated images.
Inverse-Transform AutoEncoder for Anomaly Detection

Reconstruction-based methods have recently shown great promise for anomalydetection. We here propose a new transform-based framework for anomalydetection. A selected set of transformations based on human priors is used toerase certain targeted information from input data. An inverse-transformautoencoder is trained with the normal data only to embed corresponding erasedinformation during the restoration of the original data. The normal andanomalous data are thus expected to be differentiable based on restorationerrors. Extensive experiments have demonstrated that the proposed methodsignificantly outperforms several state-of-the-arts on multiple benchmarkdatasets, especially on ImageNet, increasing the AUROC of the top-performingbaseline by 10.1%. We also evaluate our method on a real-world anomalydetection dataset MVTec AD and a video anomaly detection dataset ShanghaiTechto validate the effectiveness of the method in real-world environments.
Generative Models for Effective ML on Private, Decentralized Datasets

To improve real-world applications of machine learning, experienced modelers develop intuition about their datasets, their models, and how the two interact. Manual inspection of raw data—of representative samples, of outliers, of misclassifications—is an essential tool in a) identifying and fixing problems in the data, b) generating new modeling hypotheses, and c) assigning or refining human-provided labels. However, manual data inspection is risky for privacy-sensitive datasets, such as those representing the behavior of real-world individuals. Furthermore, manual data inspection is impossible in the increasingly important setting of federated learning, where raw examples are stored at the edge and the modeler may only access aggregated outputs such as metrics or model parameters. This paper demonstrates that generative models—trained using federated methods and with formal differential privacy guarantees—can be used effectively to debug data issues even when the data cannot be directly inspected. We explore these methods in applications to text with differentially private federated RNNs and to images using a novel algorithm for differentially private federated GANs.
A Novel Shot Detection Approach Based on ORB Fused With Structural Similarity

Shots are the basic units for analyzing and retrieving video, and also the essential elements in creating video datasets. The traditional methods of shot detection exhibit unsatisfactory performance for being too sensitive to motion or too much time-consuming. This paper proposes an automatic shot detection method, by employing the fast feature descriptor of Oriented FAST and Rotated BRIEF (ORB) fused with Structural Similarity (SSIM). Firstly, ORB descriptor is used to preselect candidate segments with a high tolerance for rapidly extracting the features of twenty-frame intervals in video sequences. Then, the cut transition is detected by comparing ORB features, fused with SSIM, of consecutive frames in the candidate segment. Finally, the gradual transition is detected by determining the maximum amount of the continuous increasing/decreasing interframe differences in the candidate segment without cut transition. Experimental result indicates that the proposed method can achieve an F1-Score of 92.5% and five times of real-time speed with one CPU on 106049 test frames from the Open-video project, YouTube, and YOUKU. In addition, the proposed method can outperform the existing shot detection methods, including the rule-based and learning-based methods, by testing on the video sequences from the Open-video project and RAI dataset.
User-Centered Development of Smart Glasses Support for Skills Training in Nursing Education

Abstract An ageing society creates an increasing need for a well-trained nursing staff. In particular, physically demanding motion sequences must be learned correctly to preserve carers’ long-term health. During training, support in practical skills training must also leave the carers’ hands free to allow them to perform the motion sequences unencumbered. Wearables might provide the necessary information “hands-free” and thus support skills training. In this paper, we present and discuss a User-Centered Design approach conducted with nursing students to determine the suitability of smart glasses support for skills training in nursing education. This User-Centered Design process consisted of a survey, two design thinking workshops, and a summative evaluation of a high-fidelity prototype. The developed smart glasses application was well evaluated and is usable for training purposes.
A<scp>lin</scp>: improving interactive ontology matching by interactively revising mapping suggestions

Abstract Ontology matching aims at discovering mappings between the entities of two ontologies. It plays an important role in the integration of heterogeneous data sources that are described by ontologies. Interactive ontology matching involves domain experts in the matching process. In some approaches, the expert provides feedback about mappings between ontology entities, that is, these approaches select mappings to present to the expert who replies which of them should be accepted or rejected, so taking advantage of the knowledge of domain experts towards finding an alignment. In this paper, we present Alin , an interactive ontology matching approach which uses expert feedback not only to approve or reject selected mappings but also to dynamically improve the set of selected mappings, that is, to interactively include and to exclude mappings from it. This additional use for expert answers aims at increasing in the benefit brought by each expert answer. For this purpose, Alin uses four techniques. Two techniques were used in the previous versions of Alin to dynamically select concept and attribute mappings. Two new techniques are introduced in this paper: one to dynamically select relationship mappings and another one to dynamically reject inconsistent selected mappings using anti-patterns. We compared Alin with state-of-the-art tools, showing that it generates alignment of comparable quality.
Cognitive Dwell Time Allocation for Distributed Radar Sensor Networks Tracking via Cone Programming

For a dwell time limited radar network, the achievable tracking performance can be further enhanced by adaptively tuning in the dwell time allocation strategy to accommodate the future operation scenario. In this paper, considering this dynamical resource allocation problem, a cone programming-based dwell time allocation scheme is proposed for achieving the optimal tracking performance. The basic mechanism is to exploit the feedback of its operating environment based on the interrogation of received echoes for informing the decision of future optimal dwell time allocation. To achieve this purpose, the predicted conditional Cramer-Rao lower bound (PC-CRLB) of target state estimates is developed for evaluating the tracking performance of each candidate allocation strategy conditioned on the cognitive knowledge firstly. And, its trace is chosen as a scalar allocation strategy evaluation metric. On this basis, the closed-form expression of the scalar evaluation metric is derived, which enables us to transform the dwell time allocation problem into the second-order cone program (SOCP). Extensive simulations demonstrate the efficiency and superiority of the proposed allocation scheme.
Efficient Keyword Matching for Deep Packet Inspection based Network Traffic Classification

Network traffic classification has a range of applications in network management including QoS and security monitoring. Deep Packet Inspection (DPI) is one of the effective method used for traffic classification. DPI is computationally expensive operation involving string matching between payload and application signatures. Existing traffic classification techniques perform multiple scans of payload to classify the application flows - first scan to extract the words and the second scan to match the words with application signatures. In this paper we propose an approach which can classify network flows with single scan of flow payloads using a heuristic method to achieve a sub-linear search complexity. The idea is to scan few initial bytes of payload and determine potential application signature(s) for subsequent signature matching. We perform experiments with a large dataset containing 171873 network flows and show that it has a good classification accuracy of 98%.