OpenRaiser/Intern-Atlas huggingface dataset:
Multi-Objective Pruning for CNNs Using Genetic Algorithm
Venue: arXiv cs.NE 2019
Year: 2019

In this work, we propose a heuristic genetic algorithm (GA) for pruning convolutional neural networks (CNNs) according to the multi-objective trade-off among error, computation and sparsity. In our experiments, we apply our approach to prune pre-trained LeNet across the MNIST dataset, which reduces 95.42% parameter size and achieves 16$\times$ speedups of convolutional layer computation with tiny accuracy loss by laying emphasis on sparsity and computation, respectively. Our empirical study suggests that GA is an alternative pruning approach for obtaining a competitive compression performance. Additionally, compared with state-of-the-art approaches, GA is capable of automatically pruning CNNs based on the multi-objective importance by a pre-defined fitness function.
TechNet: Technology Semantic Network Based on Patent Data
Venue: arXiv cs.IR 2019
Year: 2019

The growing developments in general semantic networks, knowledge graphs and ontology databases have motivated us to build a large-scale comprehensive semantic network of technology-related data for engineering knowledge discovery, technology search and retrieval, and artificial intelligence for engineering design and innovation. Specially, we constructed a technology semantic network (TechNet) that covers the elemental concepts in all domains of technology and their semantic associations by mining the complete U.S. patent database from 1976. To derive the TechNet, natural language processing techniques were utilized to extract terms from massive patent texts and recent word embedding algorithms were employed to vectorize such terms and establish their semantic relationships. We report and evaluate the TechNet for retrieving terms and their pairwise relevance that is meaningful from a technology and engineering design perspective. The TechNet may serve as an infrastructure to support a wide range of applications, e.g., technical text summaries, search query predictions, relational knowledge discovery, and design ideation support, in the context of engineering and technology, and complement or enrich existing semantic databases. To enable such applications, the TechNet is made public via an online interface and APIs for public users to retrieve technology-related terms and their relevancies.
A Survey of Natural Language Generation Techniques with a Focus on Dialogue Systems - Past, Present and Future Directions
Venue: arXiv cs.CL 2019
Year: 2019

One of the hardest problems in the area of Natural Language Processing and Artificial Intelligence is automatically generating language that is coherent and understandable to humans. Teaching machines how to converse as humans do falls under the broad umbrella of Natural Language Generation. Recent years have seen unprecedented growth in the number of research articles published on this subject in conferences and journals both by academic and industry researchers. There have also been several workshops organized alongside top-tier NLP conferences dedicated specifically to this problem. All this activity makes it hard to clearly define the state of the field and reason about its future directions. In this work, we provide an overview of this important and thriving area, covering traditional approaches, statistical approaches and also approaches that use deep neural networks. We provide a comprehensive review towards building open domain dialogue systems, an important application of natural language generation. We find that, predominantly, the approaches for building dialogue systems use seq2seq or language models architecture. Notably, we identify three important areas of further research towards building more effective dialogue systems: 1) incorporating larger context, including conversation context and world knowledge; 2) adding personae or personality in the NLG system; and 3) overcoming dull and generic responses that affect the quality of system-produced responses. We provide pointers on how to tackle these open problems through the use of cognitive architectures that mimic human language understanding and generation capabilities.
HERA: Partial Label Learning by Combining Heterogeneous Loss with Sparse and Low-Rank Regularization
Venue: arXiv cs.LG 2019
Year: 2019

Partial Label Learning (PLL) aims to learn from the data where each training instance is associated with a set of candidate labels, among which only one is correct. Most existing methods deal with such problem by either treating each candidate label equally or identifying the ground-truth label iteratively. In this paper, we propose a novel PLL approach called HERA, which simultaneously incorporates the HeterogEneous Loss and the SpaRse and Low-rAnk procedure to estimate the labeling confidence for each instance while training the model. Specifically, the heterogeneous loss integrates the strengths of both the pairwise ranking loss and the pointwise reconstruction loss to provide informative label ranking and reconstruction information for label identification, while the embedded sparse and low-rank scheme constrains the sparsity of ground-truth label matrix and the low rank of noise label matrix to explore the global label relevance among the whole training data for improving the learning model. Extensive experiments on both artificial and real-world data sets demonstrate that our method can achieve superior or comparable performance against the state-of-the-art methods.
Direct Linear Time Construction of Parameterized Suffix and LCP Arrays for Constant Alphabets
Venue: arXiv cs.DS 2019
Year: 2019

We present the first worst-case linear time algorithm that directly computes the parameterized suffix and LCP arrays for constant sized alphabets. Previous algorithms either required quadratic time or the parameterized suffix tree to be built first. More formally, for a string over static alphabet $Σ$ and parameterized alphabet $Π$, our algorithm runs in $O(nπ)$ time and $O(n)$ words of space, where $π$ is the number of distinct symbols of $Π$ in the string.
Listening while Speaking and Visualizing: Improving ASR through Multimodal Chain
Venue: arXiv cs.CL 2019
Year: 2019

Previously, a machine speech chain, which is based on sequence-to-sequence deep learning, was proposed to mimic speech perception and production behavior. Such chains separately processed listening and speaking by automatic speech recognition (ASR) and text-to-speech synthesis (TTS) and simultaneously enabled them to teach each other in semi-supervised learning when they received unpaired data. Unfortunately, this speech chain study is limited to speech and textual modalities. In fact, natural communication is actually multimodal and involves both auditory and visual sensory systems. Although the said speech chain reduces the requirement of having a full amount of paired data, in this case we still need a large amount of unpaired data. In this research, we take a further step and construct a multimodal chain and design a closely knit chain architecture that combines ASR, TTS, image captioning, and image production models into a single framework. The framework allows the training of each component without requiring a large number of parallel multimodal data. Our experimental results also show that an ASR can be further trained without speech and text data and cross-modal data augmentation remains possible through our proposed chain, which improves the ASR performance.
Deep Face Recognition Model Compression via Knowledge Transfer and Distillation
Venue: arXiv cs.CV 2019
Year: 2019

Fully convolutional networks (FCNs) have become de facto tool to achieve very high-level performance for many vision and non-vision tasks in general and face recognition in particular. Such high-level accuracies are normally obtained by very deep networks or their ensemble. However, deploying such high performing models to resource constraint devices or real-time applications is challenging. In this paper, we present a novel model compression approach based on student-teacher paradigm for face recognition applications. The proposed approach consists of training teacher FCN at bigger image resolution while student FCNs are trained at lower image resolutions than that of teacher FCN. We explored three different approaches to train student FCNs: knowledge transfer (KT), knowledge distillation (KD) and their combination. Experimental evaluation on LFW and IJB-C datasets demonstrate comparable improvements in accuracies with these approaches. Training low-resolution student FCNs from higher resolution teacher offer fourfold advantage of accelerated training, accelerated inference, reduced memory requirements and improved accuracies. We evaluated all models on IJB-C dataset and achieved state-of-the-art results on this benchmark. The teacher network and some student networks even achieved Top-1 performance on IJB-C dataset. The proposed approach is simple and hardware friendly, thus enables the deployment of high performing face recognition deep models to resource constraint devices.
Robust copy-move forgery detection by false alarms control
Venue: arXiv cs.CV 2019
Year: 2019

Detecting reliably copy-move forgeries is difficult because images do contain similar objects. The question is: how to discard natural image self-similarities while still detecting copy-moved parts as being "unnaturally similar"? Copy-move may have been performed after a rotation, a change of scale and followed by JPEG compression or the addition of noise. For this reason, we base our method on SIFT, which provides sparse keypoints with scale, rotation and illumination invariant descriptors. To discriminate natural descriptor matches from artificial ones, we introduce an a contrario method which gives theoretical guarantees on the number of false alarms. We validate our method on several databases. Being fully unsupervised it can be integrated into any generic automated image tampering detection pipeline.
Unsupervised Neural Generative Semantic Hashing
Venue: arXiv cs.IR 2019
Year: 2019

Fast similarity search is a key component in large-scale information retrieval, where semantic hashing has become a popular strategy for representing documents as binary hash codes. Recent advances in this area have been obtained through neural network based models: generative models trained by learning to reconstruct the original documents. We present a novel unsupervised generative semantic hashing approach, \textit{Ranking based Semantic Hashing} (RBSH) that consists of both a variational and a ranking based component. Similarly to variational autoencoders, the variational component is trained to reconstruct the original document conditioned on its generated hash code, and as in prior work, it only considers documents individually. The ranking component solves this limitation by incorporating inter-document similarity into the hash code generation, modelling document ranking through a hinge loss. To circumvent the need for labelled data to compute the hinge loss, we use a weak labeller and thus keep the approach fully unsupervised. Extensive experimental evaluation on four publicly available datasets against traditional baselines and recent state-of-the-art methods for semantic hashing shows that RBSH significantly outperforms all other methods across all evaluated hash code lengths. In fact, RBSH hash codes are able to perform similarly to state-of-the-art hash codes while using 2-4x fewer bits.
Masked Non-Autoregressive Image Captioning
Venue: arXiv cs.CV 2019
Year: 2019

Existing captioning models often adopt the encoder-decoder architecture, where the decoder uses autoregressive decoding to generate captions, such that each token is generated sequentially given the preceding generated tokens. However, autoregressive decoding results in issues such as sequential error accumulation, slow generation, improper semantics and lack of diversity. Non-autoregressive decoding has been proposed to tackle slow generation for neural machine translation but suffers from multimodality problem due to the indirect modeling of the target distribution. In this paper, we propose masked non-autoregressive decoding to tackle the issues of both autoregressive decoding and non-autoregressive decoding. In masked non-autoregressive decoding, we mask several kinds of ratios of the input sequences during training, and generate captions parallelly in several stages from a totally masked sequence to a totally non-masked sequence in a compositional manner during inference. Experimentally our proposed model can preserve semantic content more effectively and can generate more diverse captions.
Proximity Neighbor Selection in Blockchain Networks
Venue: arXiv cs.DC 2019
Year: 2019

Blockchains have attracted a great deal of attention as a technology for the distributed management of register information at multiple nodes without a centralized system. However, they possess the drawbacks of low transaction throughput and long approval time. These problems can be addressed by shortening the block generation interval; however, shortening this interval alone has the effect of increasing the frequency of forks. In this study, we aim to shorten the block generation interval without increasing the fork generation rate by improving the network topology of the nodes and shortening the propagation time. We propose a neighbor node selection method forming a network topology with a short block propagation time. A blockchain simulator is used to demonstrate the effect of the proposed neighbor node selection method on the propagation delay of the network. This result indicates that the proposed method improves block propagation time.
Problem-Agnostic Speech Embeddings for Multi-Speaker Text-to-Speech with SampleRNN
Venue: arXiv cs.SD 2019
Year: 2019

Text-to-speech (TTS) acoustic models map linguistic features into an acoustic representation out of which an audible waveform is generated. The latest and most natural TTS systems build a direct mapping between linguistic and waveform domains, like SampleRNN. This way, possible signal naturalness losses are avoided as intermediate acoustic representations are discarded. Another important dimension of study apart from naturalness is their adaptability to generate voice from new speakers that were unseen during training. In this paper we first propose the use of problem-agnostic speech embeddings in a multi-speaker acoustic model for TTS based on SampleRNN. This way we feed the acoustic model with speaker acoustically dependent representations that enrich the waveform generation more than discrete embeddings unrelated to these factors. Our first results suggest that the proposed embeddings lead to better quality voices than those obtained with discrete embeddings. Furthermore, as we can use any speech segment as an encoded representation during inference, the model is capable to generalize to new speaker identities without retraining the network. We finally show that, with a small increase of speech duration in the embedding extractor, we dramatically reduce the spectral distortion to close the gap towards the target identities.
Separate In Latent Space: Unsupervised Single Image Layer Separation
Venue: arXiv cs.CV 2019
Year: 2019

Many real world vision tasks, such as reflection removal from a transparent surface and intrinsic image decomposition, can be modeled as single image layer separation. However, this problem is highly ill-posed, requiring accurately aligned and hard to collect triplet data to train the CNN models. To address this problem, this paper proposes an unsupervised method that requires no ground truth data triplet in training. At the core of the method are two assumptions about data distributions in the latent spaces of different layers, based on which a novel unsupervised layer separation pipeline can be derived. Then the method can be constructed based on the GANs framework with self-supervision and cycle consistency constraints, etc. Experimental results demonstrate its successfulness in outperforming existing unsupervised methods in both synthetic and real world tasks. The method also shows its ability to solve a more challenging multi-layer separation task.
Achieving Generalizable Robustness of Deep Neural Networks by Stability Training
Venue: arXiv cs.LG 2019
Year: 2019

We study the recently introduced stability training as a general-purpose method to increase the robustness of deep neural networks against input perturbations. In particular, we explore its use as an alternative to data augmentation and validate its performance against a number of distortion types and transformations including adversarial examples. In our image classification experiments using ImageNet data stability training performs on a par or even outperforms data augmentation for specific transformations, while consistently offering improved robustness against a broader range of distortion strengths and types unseen during training, a considerably smaller hyperparameter dependence and less potentially negative side effects compared to data augmentation.
Application Components Migration in NFV-based Hybrid Cloud/Fog Systems
Venue: arXiv cs.NI 2019
Year: 2019

Fog computing extends the cloud to the edge of the network, close to the end-users enabling the deployment of some application component in the fog while others in the cloud. Network Functions Virtualization (NFV) decouples the network functions from the underlying hardware. In NFV settings, application components can be implemented as sets of Virtual Network Functions (VNFs) chained in specific order representing VNF-Forwarding Graphs (VNF-FG). Many studies have been carried out to map the VNF-FGs to cloud systems. However, in hybrid cloud/fog systems, an additional challenge arises. The mobility of fog nodes may cause high latency as the distance between the end-users and the nodes hosting the components increases. This may not be tolerable for some applications. In such cases, a prominent solution is to migrate application components to a closer fog node. This paper focuses on application component migration in NFV-based hybrid cloud/fog systems. The objective is to minimize the aggregated makespan of the applications. The problem is modeled mathematically, and a heuristic is proposed to find the sub-optimal solution in an acceptable time. The heuristic aims at finding the optimal fog node in each time-slot considering a pre-knowledge of the mobility models of the fog nodes. The experiment's results show that our proposed solution improves the makespan and the number of migrations compared to random migration and No-migration.
Effects of Packet Loss and Jitter on VoLTE Call Quality
Venue: arXiv cs.NI 2019
Year: 2019

This work performs a preliminary, comparative analysis of the end-to-end quality guaranteed by Voice over LTE (VoLTE), examining several millions of VoLTE calls that employ two popular speech audio codecs, namely, Adaptive Multi-Rate (AMR) and Adaptive Multi-Rate WideBand (AMR-WB). To assess call quality, VQmon, an enhanced version of the standardized E-Model, is utilized. The study reveals to what extent AMR-WB based calls are more robust against network impairments than their narrowband counterparts; it further shows that the dependence of call quality on the packet loss rate is approximately exponential when the AMR codec is used, whereas it is nearly linear for the AMR-WB codec.
A gender analysis of top scientists' collaboration behavior: evidence from Italy
Venue: arXiv cs.DL 2019
Year: 2019

This work analyzes the differences in collaboration behavior between males and females among a particular type of scholars: top scientists, and as compared to non top scientists. The field of observation consists of the Italian academic system and the co-authorships of scientific publications by 11,145 professors. The results obtained from a cross-sectional analysis covering the five-year period 2006-2010 show that there are no significant differences in the overall propensity to collaborate in the top scientists of the two genders. At the level of single disciplines there are no differences in collaboration behavior, except in the case of: i) international collaborations, for Mathematics and Chemistry - where the propensity for collaboration is greater for males; and ii) extramural domestic collaborations in Physics, in which it is the females that show greater propensity for collaboration. Because international collaboration is positively correlated to research performance, findings can inform science policy aimed at increasing the representation of female top performers.
Gendered Ambiguous Pronouns Shared Task: Boosting Model Confidence by Evidence Pooling
Venue: arXiv cs.CL 2019
Year: 2019

This paper presents a strong set of results for resolving gendered ambiguous pronouns on the Gendered Ambiguous Pronouns shared task. The model presented here draws upon the strengths of state-of-the-art language and coreference resolution models, and introduces a novel evidence-based deep learning architecture. Injecting evidence from the coreference models compliments the base architecture, and analysis shows that the model is not hindered by their weaknesses, specifically gender bias. The modularity and simplicity of the architecture make it very easy to extend for further improvement and applicable to other NLP problems. Evaluation on GAP test data results in a state-of-the-art performance at 92.5% F1 (gender bias of 0.97), edging closer to the human performance of 96.6%. The end-to-end solution presented here placed 1st in the Kaggle competition, winning by a significant lead. The code is available at https://github.com/sattree/gap.
Exploiting nested task-parallelism in the $\mathcal{H}-LU$ factorization
Venue: arXiv cs.MS 2019
Year: 2019

We address the parallelization of the LU factorization of hierarchical matrices ($\mathcal{H}$-matrices) arising from boundary element methods. Our approach exploits task-parallelism via the OmpSs programming model and runtime, which discovers the data-flow parallelism intrinsic to the operation at execution time, via the analysis of data dependencies based on the memory addresses of the tasks' operands. This is especially challenging for $\mathcal{H}$-matrices, as the structures containing the data vary in dimension during the execution. We tackle this issue by decoupling the data structure from that used to detect dependencies. Furthermore, we leverage the support for weak operands and early release of dependencies, recently introduced in OmpSs-2, to accelerate the execution of parallel codes with nested task-parallelism and fine-grain tasks.
Comparing two- and three-view Computer Vision
Venue: arXiv cs.CV 2019
Year: 2019

To reconstruct the points in three dimensional space, we need at least two images. In this paper we compared two different methods: the first uses only two images, the second one uses three. During the research we measured how camera resolution, camera angles and camera distances influence the number of reconstructed points and the dispersion of them. The paper presents that using the two-view method, we can reconstruct significantly more points than using the other one, but the dispersion of points is smaller if we use the three-view method. Taking into consideration the different camera settings, we can say that both the two- and three-view method behaves the same, and the best parameters are also the same for both methods.