Miracle-dz/UniFAR-FTUs huggingface dataset:
“A Buster Keaton of Linguistics”: First Automated Approaches for the Extraction of Vossian Antonomasia

Attributing a particular property to a person by naming another person, who is typically wellknown for the respective property, is called a Vossian Antonomasia (VA). This subtpye of metonymy, which overlaps with metaphor, has a specific syntax and is especially frequent in journalistic texts. While identifying Vossian Antonomasia is of particular interest in the study of stylistics, it is also a source of errors in relation and fact extraction as an explicitly mentioned entity occurs only metaphorically and should not be associated with respective contexts. Despite rather simple syntactic variations, the automatic extraction of VA was never addressed as yet since it requires a deeper semantic understanding of mentioned entities and underlying relations. In this paper, we propose a first method for the extraction of VAs that works completely automatically. Our approaches use named entity recognition, distant supervision based on Wikidata, and a bi-directional LSTM for postprocessing. The evaluation on 1.8 million articles of the New York Times corpus shows that our approach significantly outperforms the only existing semi-automatic approach for VA identification by more than 30 percentage points in precision.
A REVIEW ON FILTERS

Through this review we intend to present a review on the previous works on filters. With the improvement of microwave filter designs in the last two decades, various types of filters are being used these days. Various types of filter designs are being proposed for good performance. In the paper we have shown the working of multiband filter operating on high gigabytes range.
MulCode: A Multiplicative Multi-way Model for Compressing Neural Language Model

It is challenging to deploy deep neural nets on memory-constrained devices due to the explosion of numbers of parameters. Especially, the input embedding layer and Softmax layer usually dominate the memory usage in an RNN-based language model. For example, input embedding and Softmax matrices in IWSLT-2014 German-to-English data set account for more than 80% of the total model parameters. To compress these embedding layers, we propose MulCode, a novel multi-way multiplicative neural compressor. MulCode learns an adaptively created matrix and its multiplicative compositions. Together with a prior weighted loss, Multicode is more effective than the state-of-the-art compression methods. On the IWSLT-2014 machine translation data set, MulCode achieved 17 times compression rate for the embedding and Softmax matrices, and when combined with quantization technique, our method can achieve 41.38 times compression rate with very little loss in performance.
Adaptation of Feed-Forward Equalizer Settings: A Frequency Domain Analysis Approach

: Characteristics of the channel has a major e ﬀ ect on the signal integrity. The channel distortion is increased with high-rate data transmission. Feed-Forward Equalizer (FFE) at transmitter side is one of the methods that can be used to compensate the e ﬀ ect of the channel. PCIe-Gen3, 4, and 5 standards specify some constraints on the FFE settings and assign some preset values that can be used based on the e ﬀ ect of FFE on time-domain. In this paper, detailed analysis for the FFE in frequency domain is presented. New preset values are recommended to achieve enhancement on the behavior over di ﬀ erent channels. Short and long channel models are created based on the PCIe standard speciﬁcations to check the suggested preset values with worst channels. Continuous-Time Linear Equalizer (CTLE) also is modeled to check the e ﬀ ect of FFE with di ﬀ erent CTLE boost values. The eye-measurements based on the new preset values is enhanced by 10% with respect to the default preset values in PCIe speciﬁcations for short and long channel. Mapping the preset value in the frequency domain helps ﬁne tuning process for FFE settings by facilitating more guidance to get the best FFE settings.
Workload-sensitive approaches to improving graph data partitioning online

Many modern applications, from social networks to network security tools, rely upon the graph data model, using it as part of an offline analytics pipeline or, increasingly, for storing and querying data online, e.g. in a graph database management system (GDBMS). Unfortunately, effective horizontal scaling of this graph data reduces to the NP-Hard problem of “k-way balanced graph partitioning”. Owing to the problem’s importance, several practical approaches exist, producing quality graph partitionings. However, these existing systems are unsuitable for partitioning online graphs, either introducing unnecessary network latency during query processing, being unable to efficiently adapt to changing data and query workloads, or both. In this thesis we propose partitioning techniques which are efficient and sensitive to given query workloads, suitable for application to online graphs and query workloads. To incrementally adapt partitionings in response to workload change, we propose TAPER: a graph repartitioner. TAPER uses novel datastructures to compute the probability of expensive inter -partition traversals (ipt) from each vertex, given the current workload of path queries. Subsequently, it iteratively adjusts an initial partitioning by swapping selected vertices amongst partitions, heuristically maintaining low ipt and high partition quality with respect to that workload. Iterations are inexpensive thanks to time and space optimisations in the underlying datastructures. To incrementally create partitionings in response to graph growth, we propose Loom: a streaming graph partitioner. Loom uses another novel datastructure to detect common patterns of edge traversals when executing a given workload of pattern matching queries. Subsequently, it employs a probabilistic graph isomorphism method to incrementally and efficiently compare sub-graphs in the stream of graph updates, to these common patterns. Matches are assigned within individual partitions if possible, thereby also reducing ipt and increasing partitioning quality w.r. t the given workload.
Broadband Direction of Arrival Estimation Based on Convolutional Neural Network

SUMMARY A convolutional neural network (CNN) for broadband direction of arrival (DOA) estimation of far-ﬁeld electromagnetic signals is presented. The proposed algorithm performs a nonlinear inverse mapping from received signal to angle of arrival. The signal model used for al-gorithm is based on the circular antenna array geometry, and the phase component extracted from the spatial covariance matrix is used as the input of the CNN network. A CNN model including three convolutional layers is then established to approximate the nonlinear mapping. The performance of the CNN model is evaluated in a noisy environment for various values of signal-to-noise ratio (SNR). The results demonstrate that the proposed CNN model with the phase component of the spatial covariance matrix as the input is able to achieve fast and accurate broadband DOA estimation and attains perfect performance at lower SNR values.
Iterative Dual Domain Adaptation for Neural Machine Translation

Previous studies on the domain adaptation for neural machine translation (NMT) mainly focus on the one-pass transferring out-of-domain translation knowledge to in-domain NMT model. In this paper, we argue that such a strategy fails to fully extract the domain-shared translation knowledge, and repeatedly utilizing corpora of different domains can lead to better distillation of domain-shared translation knowledge. To this end, we propose an iterative dual domain adaptation framework for NMT. Specifically, we first pretrain in-domain and out-of-domain NMT models using their own training corpora respectively, and then iteratively perform bidirectional translation knowledge transfer (from in-domain to out-of-domain and then vice versa) based on knowledge distillation until the in-domain NMT model convergences. Furthermore, we extend the proposed framework to the scenario of multiple out-of-domain training corpora, where the above-mentioned transfer is performed sequentially between the in-domain and each out-of-domain NMT models in the ascending order of their domain similarities. Empirical results on Chinese-English and English-German translation tasks demonstrate the effectiveness of our framework.
Hybrid Between Ontology and Quantum Particle Swarm Optimization for Segmenting Noisy Plant Disease Image

One of the main risks to food security is plant diseases, but because of the absence of needed infrastructure and actual noise, scientists are faced with a difficult issue. Semantic segmentation of images divides images into non-overlapped regions, with specified semantic labels allocated. In this paper, The QPSO (quantum particle swarm optimization) algorithm has been used in segmentation of an original noisy image and Ontology has been used in classification the segmented image. Input noisy image segmentation is limited to a classification phase in which the object is transferred to Ontology. With 49,563 images from healthy and diseased plant leaves, 12 plant species were identified and 22 diseases, the proposed method is evaluated. The method proposed produces an accuracy of 86.22 percent for a stopped test set, showing that the strategy is appropriate. EPDO (Enhance Plant Disease Ontology) is built with the web ontology language (OWL). The segmented noisy image elements are paired with EPDO with derived features that come from QPSO. Our results show that a classification based on the suggested method is better than the state-of-the-art algorithms. The proposed method also saves time and effort for removing the noise at noise level from the input image σ=70
Recovery of Subspace Structure from High-Rank Data with Missing Entries

We propose a method to reconstruct and cluster incomplete high-dimensional data lying in a union of low-dimensional subspaces. Exploring the sparse representation model, we jointly estimate the missing data while imposing the intrinsic subspace structure. Although we have a non-convex problem, we propose an algorithm robust to initialization. Extensive experiments with synthetic and real data show that our approach leads to significant improvements in the reconstruction and segmentation, outperforming current state of the art for both low and high-rank data.
PyOpenDial: A Python-based Domain-Independent Toolkit for Developing Spoken Dialogue Systems with Probabilistic Rules

We present PyOpenDial, a Python-based domain-independent, open-source toolkit for spoken dialogue systems. Recent advances in core components of dialogue systems, such as speech recognition, language understanding, dialogue management, and language generation, harness deep learning to achieve state-of-the-art performance. The original OpenDial, implemented in Java, provides a plugin architecture to integrate external modules, but lacks Python bindings, making it difficult to interface with popular deep learning frameworks such as Tensorflow or PyTorch. To this end, we re-implemented OpenDial in Python and extended the toolkit with a number of novel functionalities for neural dialogue state tracking and action planning. We describe the overall architecture and its extensions, and illustrate their use on an example where the system response model is implemented with a recurrent neural network.
Guiding the Flowing of Semantics: Interpretable Video Captioning via POS Tag

In the current video captioning models, the video frames are collected in one network and the semantics are mixed into one feature, which not only increase the difficulty of the caption decoding, but also decrease the interpretability of the captioning models. To address these problems, we propose an Adaptive Semantic Guidance Network (ASGN), which instantiates the whole video semantics to different POS-aware semantics with the supervision of part of speech (POS) tag. In the encoding process, the POS tag activates the related neurons and parses the whole semantic information into corresponding encoded video representations. Furthermore, the potential of the model is stimulated by the POS-aware video features. In the decoding process, the related video features of noun and verb are used as the supervision to construct a new adaptive attention model which can decide whether to attend to the video feature or not. With the explicit improving of the interpretability of the network, the learning process is more transparent and the results are more predictable. Extensive experiments demonstrate the effectiveness of our model when compared with state-of-the-art models.
SMILe: Scalable Meta Inverse Reinforcement Learning through Context-Conditional Policies

Imitation Learning (IL) has been successfully applied to complex sequential decision-making problems where standard Reinforcement Learning (RL) algorithms fail. A number of recent methods extend IL to few-shot learning scenarios, where a meta-trained policy learns to quickly master new tasks using limited demonstrations. However, although Inverse Reinforcement Learning (IRL) often outperforms Behavioral Cloning (BC) in terms of imitation quality, most of these approaches build on BC due to its simple optimization objective. In this work, we propose SMILe, a scalable framework for Meta Inverse Reinforcement Learning (Meta-IRL) based on maximum entropy IRL, which can learn high-quality policies from few demonstrations. We examine the efficacy of our method on a variety of high-dimensional simulated continuous control tasks and observe that SMILe significantly outperforms Meta-BC. Furthermore, we observe that SMILe performs comparably or outperforms Meta-DAgger, while being applicable in the state-only setting and not requiring online experts. To our knowledge, our approach is the first efficient method for Meta-IRL that scales to the function approximator setting. For datasets and reproducing results please refer to https://github.com/KamyarGh/rl_swiss/blob/master/reproducing/smile_paper. md .
Neural Network Model of QoE for Estimation Video Streaming over 5G network

With the rapid increasing demand of commercial video streaming, the satisfaction of the end user is becoming more and more important to measure and assure.The quality of experience (QoE) is defined as the measure of the overall level of customer satisfaction with the usage of a service provided by a vendor. Many works have addressed this issue in many different scenarios in cellular networks however most of these works have addressed video streaming over LTE networks (Long Term Evolution network). Up to day, there are few contributions of work that address the QoE over 5G network since there still still some challenges in this later to address. In this paper, we present the specific aspects we consider important in the evolution from 4G to 5G in term of traffic management and a solution to estimate this QoE in this new context. We adopted an approach based on Neural Network (NN) to estimate the QoE parameters. NN have been successfully used in many domains where it was difficult to derive an exact analytical model of the system so is the case of the 5G network.
Resilient Collaborative Intelligence for Adversarial IoT Environments

Many IoT networks, including for battlefield deployments, involve the deployment of resource-constrained sensors with varying degrees of redundancy/overlap (i.e., their data streams possess significant spatiotemporal correlation). Collaborative intelligence, whereby individual nodes adjust their inferencing pipelines to incorporate such correlated observations from other nodes, can improve both inferencing accuracy and performance metrics (such as latency and energy overheads). Using realworld data from a multicamera deployment, we first demonstrate the significant performance gains (up to 14% increase in accuracy) from such collaborative intelligence, achieved through two different approaches: (a) one involving statistical fusion of outputs from different nodes, and (b) another involving the development of new collaborative deep neural networks (DNNs). We then show that these collaboration-driven performance gains are susceptible to adversarial behaviour by one or more nodes, and thus need resilient mechanisms to provide robustness against such malicious behaviour. We also introduce an under-development testbed at Singapore Management University (SMU), specifically designed to enable real-world experimentation with such collaborative IoT intelligence techniques.
Construction and Security Measurement of Cybersecurity Metrics Framework Based on Network Behavior

Cyberspace security involves national security, enterprise security, personal privacy security and so on, which has been paid more and more attention by the state. Cyberspace security measurement is an important step to protect any network. The important premise is to establish a reasonable and universal security measurement framework for network systems. But the network is very complex, and the network security state is changing all the time. Existing information security models lack the description of network behavior. Aiming at this problem, in this paper, the existing information security models at home and abroad are compared and analysed, and their advantages and disadvantages are summarized. Based on PDR (Protection, Detection, Response) model, this paper adds management functions, and optimizes the original concept of PDR model. On this basis, we add characteristic measurement, efficiency measurement and impact measurement to form 12 indicators to dynamically reveal the evolution mechanism of security characteristics measurement indicators. AHP is used to distribute the weight of each dimension, and the network is evaluated quantitatively from four dimensions: detection, protection, response and management. The framework of the proposed metric framework is verified by experiments.
Review on Algorithmic and Non-Algorithmic Software Cost Estimation Techniques

Effective software cost estimation is the most challenging and important activities in software development. Developers want a simple and accurate method of efforts estimation. Estimation of the cost before starting of work is a prediction and prediction always not accurate. Software effort estimation is a very critical task in the software engineering and to control quality and efficiency a suitable estimation technique is crucial. This paper gives a review of various available software effort estimation methods, mainly focus on the algorithmic model and non – algorithmic model. These existing methods for software cost estimation are illustrated and their aspect will be discussed. No single technique is best for all situations, and thus a careful comparison of the results of several approaches is most likely to produce realistic estimation. This paper provides a detailed overview of existing software cost estimation models and techniques. This paper presents the strength and weakness of various cost estimation methods. This paper focuses on some of the relevant reasons that cause inaccurate estimation.
Segmentation of Lesion in Dermoscopy Images Using Dense-Residual Network with Adversarial Learning

In the field of medical images, skin lesion segmentation in dermoscopic images is a challenging task due to the irregular and blurring edges of the lesion and the presence of various artifacts. With the successful application of generative antagonistic network (GAN), a new neural network for skin lesion segmentation is proposed. The encoder-decoder with Dense-Residual block is used in the segmentation network which enables the network to be trained more efficiently. A multi-scale objective loss function is introduced to utilize deep supervision. We combine Jaccard distance and End Point Error which can solve lesion-background imbalance problem in pixel-level classification for skin lesion segmentation and also alleviate the problem of boundary ambiguity. A joint loss function is finally used, which includes a multi-scale objective loss function, End Point Error and Jaccard distance content loss function. Experiment results show that our algorithm is superior to other state-of-the-art algorithms on the ISBI2017.
Multi-hop Selector Network for Multi-turn Response Selection in Retrieval-based Chatbots

Multi-turn retrieval-based conversation is an important task for building intelligent dialogue systems. Existing works mainly focus on matching candidate responses with every context utterance on multiple levels of granularity, which ignore the side effect of using excessive context information. Context utterances provide abundant information for extracting more matching features, but it also brings noise signals and unnecessary information. In this paper, we will analyze the side effect of using too many context utterances and propose a multi-hop selector network (MSN) to alleviate the problem. Specifically, MSN firstly utilizes a multi-hop selector to select the relevant utterances as context. Then, the model matches the filtered context with the candidate response and obtains a matching score. Experimental results show that MSN outperforms some state-of-the-art methods on three public multi-turn dialogue datasets.
Topic-Guided Coherence Modeling for Sentence Ordering by Preserving Global and Local Information

We propose a novel topic-guided coherence modeling (TGCM) for sentence ordering. Our attention based pointer decoder directly utilize sentence vectors in a permutation-invariant manner, without being compressed into a single fixed-length vector as the paragraph representation. Thus, TGCM can improve global dependencies among sentences and preserve relatively informative paragraph-level semantics. Moreover, to predict the next sentence, we capture topic-enhanced sentence-pair interactions between the current predicted sentence and each next-sentence candidate. With the coherent topical context matching, we promote local dependencies that help identify the tight semantic connections for sentence ordering. The experimental results show that TGCM outperforms state-of-the-art models from various perspectives.
Fine-Grained Action Detection and Classification in Table Tennis with Siamese Spatio-Temporal Convolutional Neural Network

Human action recognition in videos is one of the key problems in visual data interpretation. Despite intensive research, the recognition of actions with low inter-class variability remains a challenge. To answer this problem, my thesis focus on fine-grained classification challenge using a Siamese Spatio-Temporal Convolutional Neural Network and apply it to a new dataset we have introduced TTStroke-21. Our model take as input data RGB images and Optical Flow and is able to reach an accuracy of 91.4% against 43.1% for our baseline on temporal segmented videos. Detection and classification in videos using a sliding temporal window leads to a score of 81.3% over the whole dataset.
Blind Utility and Quality Assessment Using a Convolutional Neural Network and a Patch Selection

Image quality is one of the emerging domain, since it impacts the efficiency of computer vision applications. Another key information that can be exploited to analyze an image is the image utility which estimates the useful of an image. Both information have weak relationship and should be exploited as relevant indicators. In this paper, we focused on both concepts by proposing a Convolutional Neural Network-based method that predicts the subjective utility and quality scores of a given image without reference. For that, the well-known pre-trained VGG16 model was used and adapted to our context. A patch selection step based on the saliency information through a scanpath predictor was also introduced. The proposed method was evaluated using the CU-Nantes dataset and the results was compared to some quality and utility estimators. The results highlighted the efficiency of our method with high correlations obtained for both.