Reviewerly/exHarmony huggingface dataset:
Parametric Cepstral Mean Normalization for Robust Speech Recognition

This paper proposes a new channel normalization algorithm called parametric cepstral mean normalization (PCMN) to increase robustness of speech recognition to varying acoustic conditions. Rather than using a simple average of input speech features as channel estimate, as done in the traditional CMN, PCMN weighs the running average of input speech frames in a frequency dependent manner. These weights are jointly optimized together with parameters of the acoustic model training. Experimental results show that, in contrast to traditional CMN, which degrades performance on clean data, PCMN provides 5% relative improvement on clean data, while also providing 11.2% relative improvement on far-field test data. We also propose an adaptive version of PCMN, called aPCMN, where both input speech features and channel estimates have weights. These weights are computed at run time and they change dynamically based on the input speech. aPCMN provides 13.0% relative improvement on far-field test set, while still maintaining 5% relative improvement on clean data.
Identifying individual snow leopards from camera trap images

Conservation biologists use camera traps to study snow leopards. In this research, we introduce a method that streamlines the process of recognizing individual snow leopards in a large camera trap study. The proposed solution is based on an open-source software called HotSpotter, which was originally developed to identify uniquely patterned animals, such as Grevy’s zebras. The legacy HotSpotter involves time-consuming tasks such as manual selection of a region of interest (ROI) within each image, manual querying of each individual image against a database, and manual interpretation of results of each query to arrive at an estimate of a population count in a camera trap study. We introduce autonomous selection of multiple ROIs in motion templates corresponding to camera trap images, automate the query process, and propose a method to build associations between individual ROIs based on clustering of similarity scores using Markov Clustering Algorithm. The proposed technique with its promising results of correctly recognizing individual snow leopards has the potential to save conservation biologists thousands of hours of manual labor.
Efficient Stereo Matching Based on Pervasive Guided Image Filtering

This paper presents an effective cost aggregation strategy for dense stereo matching. Based on the guided image filtering (GIF), we propose a new aggregation scheme called Pervasive Guided Image Filtering (PGIF) to introduce weightings to the energy function of the filter which allows the whole image pair to be taken into account. The filter parameters of PGIF are calculated as two-dimensional convolution using the bright and spatial differences between the corresponding pixels, which can be incrementally calculated for efficient aggregation. The complexity of the proposed algorithm is O( N ), which is linear to the number of image pixels. Furthermore, the algorithm can be further simplified into O( N /4) without significantly sacrificing accuracy if subsampling is applied in the stage of parameter calculation. We also found that a step function to attenuate noise is required in calculating the weights. Experimental evaluation on version 3 of the Middlebury stereo evaluation datasets shows that the proposed method achieves superior disparity accuracy over state-of-the-art aggregation methods with comparable processing speed.
Gender Identification: A Comparative Study of Deep Learning Architectures

Author profiling, dating back to the earliest attempts at of analyzing quantitative text documents, is an extensivel-studied problem among NLP researchers. Because of its utility in crime, marketing and business. In this paper, three deep learning methods were evaluated for author profiling using tweets in Arabic language. The first method is based on a Convolutional Neural Network (CNN) model, while the second and third technique belongs to the family of Recurrent Neural Networks (RNN). The appropriate choice of some parameters, such as the number of amount of filters, training epochs, batch size, dropout and learning rate of Adam optimizer used in a RNN model is crucial in obtaining reliable results. The experimental findings of our comparative evaluation study demonstrate that GRU model outperforms LSTM and CNN models.
Agent-based cloud service negotiation architecture using similarity grouping approach

Challenges and issues in the field of cloud service negotiation framework optimization have been an active area of research. During service level agreement, the probability of negotiation conflict between the service consumers and providers is high. This may arise due to aggressive behavior, selfish misperception, vague preferences and uncertain goals of the negotiating participants. One of the key challenges identified in negotiation framework is optimizing the negotiation conflict among the negotiators. In order to minimize such conflicts, existing frameworks group the negotiation pairs that contain similar and non-aggressive behavioral patterns by exploiting the distance, binary, context dependent and fuzzy similarity approaches. These approaches get better success rate only if the dimensionality of negotiator attributes is low. As emerging real-time cloud service negotiation applications are characterized by negotiation attributes of high dimensionality, the existing approaches are inappropriate for these applications. In addition, the existing approaches group the negotiation pairs using distances based measure in two-dimensional negotiation attribute, whose value will vary for high-dimensional attributes. In this work, an Angle-based Similarity Grouping (ASG) approach is proposed that appropriately groups the highly cooperative negotiation pairs and thereby increases the success rate and decreases communication overhead.
Reducing calibration drift in mobile eye trackers by exploiting mobile phone usage

Automatic saliency-based recalibration is promising for addressing calibration drift in mobile eye trackers but existing bottom-up saliency methods neglect user's goal-directed visual attention in natural behaviour. By inspecting real-life recordings of egocentric eye tracker cameras, we reveal that users are likely to look at their phones once these appear in view. We propose two novel automatic recalibration methods that exploit mobile phone usage: The first builds saliency maps using the phone location in the egocentric view to identify likely gaze locations. The second uses the occurrence of touch events to recalibrate the eye tracker, thereby enabling privacy-preserving recalibration. Through in-depth evaluations on a recent mobile eye tracking dataset (N=17, 65 hours) we show that our approaches outperform a state-of-the-art saliency approach for automatic recalibration. As such, our approach improves mobile eye tracking and gaze-based interaction, particularly for long-term use.
Methodical Design and Trimming of Deep Learning Networks: Enhancing External BP Learning with Internal Omnipresent-supervision Training Paradigm

Back-propagation (BP) is now a classic learning paradigm whose source of supervision is exclusively from the external (input/output) nodes. Consequently, BP is easily vulnerable to curse-of-depth in (very) Deep Learning Networks (DLNs). This prompts us to advocate Internal Neuron’s Learnablility (INL) with (1)internal teacher labels (ITL); and (2)internal optimization metrics (IOM) for evaluating hidden layers/nodes. Conceptually, INL is a step beyond the notion of Internal Neuron’s Explainablility (INE), championed by DARPA’s XAI (or AI3.0). Practically, INL facilitates a structure/parameter NP-iterative learning for (supervised) deep compression/quantization: simultaneously trimming hidden nodes and raising accuracy. Pursuant to our simulations, the NP-iteration appears to outperform several prominent pruning methods in the literature.
PAS3-HSID: a Dynamic Bio-Inspired Approach for Real-Time Hot Spot Identification in Data Streams

Hot spot identification is a very relevant problem in a wide variety of areas such as health care, energy or transportation. A hot spot is defined as a region of high likelihood of occurrence of a particular event. To identify hot spots, location data for those events is required, which is typically collected by telematics devices. These sensors are constantly gathering information, generating very large volumes of data. Current state-of-the-art solutions are capable of identifying hot spots from big static batches of data by means of variations of clustering or instance selection techniques that pre-process the original input data, providing the most relevant locations. However, these approaches neglect to address changes in hot spots over time. This paper presents a dynamic bio-inspired approach to detect hot spots in big data streams. This computational intelligence method is designed and applied to the transportation sector as a case study to identify incidents in the roads caused by heavy goods vehicles. We adapt an immune-based algorithm to account for the temporary aspect of hot spots inspired by the idea of pheromones, which is then subsequently implemented using Apache Spark Streaming. Experimental results on real datasets with up to 4.5 million data points—provided by a telematics company—show that the algorithm is capable of quickly processing large streaming batches of data, as well as successfully adapting over time to detect hot spots. The outcome of this method is twofold, both reducing data storage requirements and demonstrating resilience to sudden changes in the input data (concept drift).
Mobile augmented reality using cloud database for interactive museum guiding system

The use of marker in museum can ruin the aesthetics of a room. In this paper, we will describe ARGuide. ARGuide is a guiding application to help museum visitors, which is implemented on Android smartphone. It uses an unmarked augmented reality method. The application has been developed and tested. The testing is conducted by tracking some objects in a real museum. It shows that ARGuide is able to track different objects at about 63.64% with the tracking speed of between 2-6 seconds. However, ARGuide is not able to work properly if there is either limited or excessive lighting.
An Online Priority Configuration Algorithm for the UAV Swarm in Complex Context

Collision avoidance problems of a swarm of unmanned air vehicles (UAVs) flying in an obstacle-laden environment are investigated in this paper. Given that the UAV swarm needs to adapt to the obstacle distribution in dynamic operation, a priority configuration is designed to guide the UAVs to pass through the obstacles in turn. Based on the collision cone approach and the prediction of the collision time, a collision evaluation model is established to judge the urgency of the imminent collision of each UAV, and the evaluation result is used to assign the priority of each UAV to further instruct them going through the obstacles in descending order. At last, the simulation results provide the promising validation in terms of the efficiency and scalability of the proposed approach.
Investigations of Real-time Gaussian Fftnet and Parallel Wavenet Neural Vocoders with Simple Acoustic Features

This paper examines four approaches to improving real-time neural vocoders with simple acoustic features (SAF) constructed from fundamental frequency and mel-cepstra rather than mel-spectrograms. The investigations are as follows: 1) the effectiveness of single Gaussian (SG) autoregressive (AR) WaveNet and FFTNet vocoders with SAF, 2) the possibility of SG parallel WaveNet vocoder training and synthesis with SAF, 3) the impact of noise shaping on SG AR neural vocoders, and 4) the efficacy of bandwidth extension to synthesize speech waveforms at a sampling frequency of 24 kHz by SG AR neural vocoders from SAF for that of 16 kHz. The results of experiments indicate that SG AR WaveNet and real-time SG AR FFTNet vocoders with noise shaping using SAF can realize sufficient synthesis quality with bandwidth extension effect. Moreover, a real-time SG parallel WaveNet vocoder can also be trained using SAF.
See the World Through Network Cameras

Millions of network cameras have been deployed worldwide. Real-time data from many network cameras can offer instant views of multiple locations for many applications. We describe the real-time data available from these cameras and potential applications.
Review of the application of machine learning to the automatic semantic annotation of images

The massive amount of digital content generated daily in the modern world has created the need for an image retrieval system built on image analysis via image processing and machine learning, therefore this study explains the role of machine learning in bridging the semantic gap in content-based image retrieval, proposes an automatic image annotation framework, in which training images are obtained from social media, and semantic indexing is achieved using a combination of supervised and unsupervised machine learning. Furthermore, the study also highlights the need for continuous vocabulary improvement for optimum system performance and recommends hardware implementation of machine learning algorithms to ensure high overall speed of image retrieval systems.
A Framework for the Evaluation of SUMO-Based Ontologies Using WordNet

This paper offers a new practical approach toward automated commonsense reasoning with first-order logic (FOL) SUMO-based ontologies. We propose a new black-box evaluation framework for SUMO-based ontologies, which exploits the world knowledge encoded in WordNet and its mapping into SUMO. Our proposal consists of both a novel semi-automatic method for the creation of a large set of commonsense problems and a new procedure that enables its automatic evaluation by using automated theorem provers (ATPs). The application of our method enables the creation of a very large benchmark consisting of more than 15 000 problems from a small set of manually built question patterns that exploit the WordNet semantic relations. By means of the resulting benchmark, we successfully evaluate the competency of different translations of SUMO into FOL and the performance of various state-of-the-art FOL ATPs according to several quality criteria. A general analysis of our experimental results demonstrates that the proposed commonsense problems are heterogeneous and non-trivial. Furthermore, a fine-grained analysis of the experimental results obtained for a sample of our benchmark enables the detection of some mapping errors and some discrepancies between the knowledge of WordNet and SUMO. The evaluation benchmark and all the resources that have been used and developed during this work are released in a single package.
Graph cut segmentation of the right ventricle in cardiac MRI using multi-scale feature learning

It is important to segment the right ventricular structure on MR images for the quantitative evaluation of cardiac function. Some factors such as the distinct cavity border and the variable crescent shape bring the challenge to this task. To overcome these difficulties, a segmentation framework integrating graph cut with multi-scale feature learning is presented. Firstly, multi-scale image patches extracted from the training set are used to perform the supervised feature learning by stacked sparse auto-encoder. Secondly, the right ventricular structure is roughly located utilizing the estimated probability map for the testing set. Finally, a regularized energy function based on graph cut is optimized for segmentation refinement. A number of comparative experiments demonstrate the superiority of the proposed method in terms of segmentation accuracy.
Hybrid linear logic, revisited

HyLL (Hybrid Linear Logic) is an extension of intuitionistic linear logic (ILL) that has been used as a framework for specifying systems that exhibit certain modalities. In HyLL, truth judgements are labelled by worlds (having a monoidal structure) and hybrid connectives ( at and ↓) relate worlds with formulas. We start this work by showing that HyLL's axioms and rules can be adequately encoded in linear logic (LL), so that one focused step in LL will correspond to a step of derivation in HyLL. This shows that any proof in HyLL can be exactly mimicked by a LL focused derivation. Another extension of LL that has extensively been used for specifying systems with modalities is Subexponential Linear Logic (SELL). In SELL, the LL exponentials (!, ?) are decorated with labels representing locations , and a pre-order on such labels defines the provability relation. We propose an encoding of HyLL into SELL ⋒ (SELL plus quantification over locations) that gives better insights about the meaning of worlds in HyLL. More precisely, we identify worlds as locations, and show that a flat subexponential structure is sufficient for representing any world structure in HyLL. This shows that HyLL's monoidal structure is not reflected in LL derivations, hence not increasing the expressiveness of LL, from a proof theoretical point of view. We conclude by proposing the notion of fixed points in multiplicative additive HyLL (μHyMALL), which can be encoded into multiplicative additive linear logic with fixed points (μMALL). As an application, we propose encodings of Computational Tree Logic (CTL) into both μMALL and μHyMALL. In the former, states are represented as atoms in the linear context, hence reflecting a more operational view of CTL connectives. In the latter, worlds represent states of the transition system, thus exhibiting a pleasant similarity with the semantics of CTL.
D2PGGAN: Two Discriminators Used in Progressive Growing of GANS

Generative adversarial network (GAN) is a powerful generative model. However, it suffers from two key problems which are convergence instability and mode collapse. Recently, progressive growing of GANs for improving quality, stability and variation (PGGAN) is proposed to better solve these two problems. Although the performance of PGGAN is good on these two problems, it is still not satisfied on mode collapse problem. In this paper, we propose a new architecture based on PGGAN called D2PGGAN to better solve the mode collapse problem. The key idea consists of one generator and two different discriminators in PGGAN. With the fact that GAN is the analogy of a minimax game, the proposed architecture is as follows. The generator (G) aims to produce realistic-looking samples to fool both of two discriminators. The first discriminator (D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ) rewards high scores for samples from the data distribution, while the second one (D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) favors samples from the generator conversely. Specifically, a novel loss function is designed to optimize the proposed D2PGGAN. Extensive experiments on CIFAR-10 and CIFAR-100 datasets demonstrate that the proposed method is effective and obtains the highest inception scores compared with others state-of-the-art GANs.
Linking visual saliency deviation to image quality degradation: A saliency deviation-based image quality index

Advances in image quality research have shown the benefits of modeling functional components of the human visual system in image quality metrics. Recently, visual saliency, an important aspect of the human visual system, is increasingly investigated in relation to visual quality perception. Existing studies have showed that incorporating visual saliency leads to improved performance of image quality metrics. However, current applications of visual saliency in image quality metrics mainly focus on the extension of a specific metric with a specific visual saliency model. Issues regarding the optimal use of visual saliency in image quality metrics remain. Psychophysical experiments conducted in the literature have revealed that visual artifacts occurring in an image can change fixation deployment relative to that of the image without distortion. As such, instead of using saliency models as add-ons to image quality metrics, we explored the approach of directly assessing image quality by measuring the visual saliency deviation triggered by visual artifacts. We first analyzed the relationship between visual saliency deviation and image quality degradation on the basis of a large-scale eye-tracking dataset. A saliency deviation-based image quality index was then devised. Experimental results showed that the proposed metric features high prediction accuracy and relatively low computational cost.
A Novel Framework of Hand Localization and Hand Pose Estimation

In this paper, we propose a novel framework for hand localization and pose estimation from a single depth image. For hand localization, unlike most existing methods that using heuristic strategies, e.g. color segmentation, we propose Hierarchical Hand location Networks (HHLN) to estimate the hand location from coarse to fine in depth images, which is robust to the complex environment and efficient. It first applied at a low-resolution octree of the whole depth image and produced coarse hand region and then constructs the hand region into a high-resolution octree for fine location estimation. For pose estimation, we propose Wide Receptive-filed (WR-OCNN) which is able to capture meaningful hand structure in different scales and estimate the 3D hand pose accurately. Experiments on two widely-used hand datasets(NYU dataset and ICVL dataset) demonstrate the effectiveness and superiority of the proposed framework.
Towards Learned Color Representations for Image Splicing Detection

The detection of images that are spliced from multiple sources is one important goal of image forensics. Several methods have been proposed for this task, but particularly since the rise of social media, it is an ongoing challenge to devise forensic approaches that are highly robust to common processing operations such as strong JPEG recompression and downsampling.In this work, we make a first step towards a novel type of cue for image splicing, which is based on the color formation of an image. We make the assumption that the color formation is a joint result of the camera hardware, the software settings, and the depicted scene, and as such can be used to locate spliced patches that originally stem from different images. To this end, we train a two-stage classifier on the full set of colors from a Macbeth color chart, and compare two patches for their color consistency. Our preliminary results on a challenging dataset on downsampled data of identical scenes indicate that the color distribution can be a useful forensic tool that is highly resistant to JPEG compression.
Mirror Surface Reconstruction Using Polarization Field

Mirror surfaces are notoriously difficult to reconstruct. In this paper, we present a novel computational imaging approach for reconstructing complex mirror surfaces using a dense illumination field with angularly varying polarization states, which we call the polarization field. Specifically, we generate the polarization field using a commercial LCD with the top polarizer removed. We mathematically model the liquid crystals as polarization rotators using Jones calculus and show that the rotated polarization states of outgoing rays encode angular information (e.g., ray directions). To model reflection under the polarization field, we derive a reflection image formation model based on the Fresnel's equations and estimate incident ray positions and directions by coding the polarization field. Finally, we triangulate the incident rays with the camera rays to recover normals/depths of the mirror surface. Comprehensive simulations and real experiments demonstrate the effectiveness of our approach.