Reviewerly/exHarmony huggingface dataset:
Deep Neural Networks in Fully Connected CRF for Image Labeling with Social Network Metadata

We propose a novel method for predicting image labels by fusing image content descriptors with the social media context of each image. An image uploaded to a social media site such as Flickr often has meaningful, associated information, such as comments and other images the user has uploaded, that is complementary to pixel content and helpful in predicting labels. Prediction challenges such as ImageNet [6]and MSCOCO [19] use only pixels, while other methods make predictions purely from social media context [21]. Our method is based on a novel fully connected Conditional Random Field (CRF) framework, where each node is an image, and consists of two deep Convolutional Neural Networks (CNN) and one Recurrent Neural Network (RNN) that model both textual and visual node/image information. The edge weights of the CRF graph represent textual similarity and link-based metadata such as user sets and image groups. We model the CRF as an RNN for both learning and inference, and incorporate the weighted ranking loss and cross entropy loss into the CRF parameter optimization to handle the training data imbalance issue. Our proposed approach is evaluated on the MIR-9K dataset and experimentally outperforms current state-of-the-art approaches.
Object detection and object tracking in front of the vehicle using front view camera

Modern vehicles are equipped with the different systems that help driver in the driving process ensuring safer and more comfortable driving. These systems are called Advanced Driver Assistance Systems (ADAS) and are step toward fully autonomous driving. The integral part of autonomous driving is an object detection and tracking by using front view camera which provides necessary information for emergency braking, collision avoidance, path planning, etc. In this paper, one possible approach to object detection and tracking in autonomous driving is presented. Two object detection methods are implemented and tested: Viola-Jones algorithm and YOLOv3. The Viola-Jones algorithm is used to create object detectors which detections are tracked in a video sequence. Nine object detectors were trained and they are divided into four groups (vehicle detectors, pedestrian detector, traffic light detector and traffic sign detectors). In second case, the YOLOv3 model was used for object detection. Both methods are evaluated in terms of accuracy and processing speed. For the purpose of object tracking, Median Flow tracking method and correlation tracking method are implemented and evaluated.
Evasion Attacks Against Watermarking Techniques found in MLaaS Systems

Deep neural networks have had enormous impact on various domains of computer science applications, considerably outperforming previous state-of-the-art machine learning techniques. To achieve this performance, neural networks need large quantities of data and huge computational resources, which heavily increase their costs. The increased cost of building a good deep neural network model gives rise to a need for protecting this investment from potential copyright infringements. Legitimate owners of a machine learning model want to be able to reliably track and detect a malicious adversary that tries to steal the intellectual property related to the model. This threat is very relevant to Machine Learning as a Service (MLaaS) systems, where a provider supplies APIs to clients, allowing them to interact with their trained proprietary deep learning models. Recently, this problem was tackled by introducing in deep neural networks the concept of watermarking, which allows a legitimate owner to embed some secret information (watermark) in a given model. Through the use of this watermark, the legitimate owners, remotely interacting with a model through input queries, are able to detect a copyright infringement, and prove the ownership of their models that were stolen/copied illegally. In this paper, we focus on assessing the robustness and reliability of state-of-the-art deep neural network watermarking schemes. In particular we show that, a malicious adversary, even in scenarios where the watermark is difficult to remove, can still evade the verification of copyright infringements from the legitimate owners, thus avoiding the detection of the model theft.
BBQ: Using AES in Picnic Signatures

This works studies the use of the AES block-cipher for Picnic-style signatures, which work in the multiparty-computation-in-the-head model. It applies advancements to arithmetic circuits for the computation of the AES S-box over multiparty computation in the preprocessing model to obtain an improvement of signature sizes of 40% on average compared to using binary circuits for AES-128, AES-192 and AES-256 in combination with previous techniques. This work also discusses other methods for the computation of the S-box and provides insights into the reaches and limits of the multiparty-computation-in-the-head paradigm.
Benefiting from multitask learning to improve single image super-resolution

Despite significant progress toward super resolving more realistic images by deeper convolutional neural networks (CNNs), reconstructing fine and natural textures still remains a challenging problem. Recent works on single image super resolution (SISR) are mostly based on optimizing pixel and content wise similarity between recovered and high-resolution (HR) images and do not benefit from recognizability of semantic classes. In this paper, we introduce a novel approach using categorical information to tackle the SISR problem; we present an encoder architecture able to extract and use semantic information to super-resolve a given image by using multitask learning, simultaneously for image super-resolution and semantic segmentation. To explore categorical information during training, the proposed decoder only employs one shared deep network for two task-specific output layers. At run-time only layers resulting HR image are used and no segmentation label is required. Extensive perceptual experiments and a user study on images randomly selected from COCO-Stuff dataset demonstrate the effectiveness of our proposed method and it outperforms the state-of-the-art methods.
The usefulness of software metric thresholds for detection of bad smells and fault prediction

Software metrics may be an effective tool to assess the quality of software, but to guide their use it is important to define their thresholds. Bad smells and fault also impact the quality of software. Extracting metrics from software systems is relatively low cost since there are tools widely used for this purpose, which makes feasible applying software metrics to identify bad smells and to predict faults. To inspect whether thresholds of object-oriented metrics may be used to aid bad smells detection and fault predictions. To direct this research, we have defined three research questions (RQ), two related to identification of bad smells, and one for identifying fault in software systems. To answer these RQs, we have proposed detection strategies for the bad smells: Large Class, Long Method, Data Class, Feature Envy, and Refused Bequest, based on metrics and their thresholds. To assess the quality of the derived thresholds, we have made two studies. The first one was conducted to evaluate their efficacy on detecting these bad smells on 12 systems. A second study was conducted to investigate for each of the class level software metrics: DIT, LCOM, NOF, NOM, NORM, NSC, NSF, NSM, SIX, and WMC, if the ranges of values determined by thresholds are useful to identify fault in software systems. Both studies confirm that metric thresholds may support the prediction of faults in software and are significantly and effective in the detection of bad smells. The results of this work suggest practical applications of metric thresholds to identify bad smells and predict faults and hence, support software quality assurance activities.Their use may help developers to focus their efforts on classes that tend to fail, thereby minimizing the occurrence of future problems.
Recognition of Relevance of Web Resource Content Based on Analysis of Semantic Components

System that automatically distinguishes structure and builds ontological model of a web resource content is considered in the paper. Models of system functioning are proposed. Architecture for realization of software system for structuring and building of web resource ontological model is developed. An example of developed system application for structuring, recognition and revealing of incorrect and non-reliable information on a website of an organization.
A clustering‐based feature selection framework for handwritten Indic script classification

Abstract In India, which has numerous officially recognized scripts, there is a primary need for categorizing the documents on the basis of the scripts used therein. Identification of script used in a document is essential for its effective handling both manually and digitally. Identification of script in a document image is an important research problem in the pattern recognition field, which, at times, suffers from the issue of growing dimensionality of the feature vector and requires an efficient feature selection technique. Keeping this fact in mind, in this paper, we propose a clustering‐based filter feature selection framework in order to extract an optimal and effective feature subset from the original feature vector. The present feature selection methodology is evaluated on a script classification problem involving handwritten documents in 12 major Indic scripts. Experiments are done at word‐level, text‐line‐level, and block‐level. Experiments demonstrate that a reasonable increment in classification accuracy has been realized using comparatively lesser number of features. The proposed framework for feature selection is computationally inexpensive and can be applied to other pattern recognition problems as well.
Multi-Stream Deep Convolutional Network Using High-Level Features Applied to Fall Detection in Video Sequences

Sporadic falls, due to the lack of balance and other factors, are some of the complications that elderly people might experience more frequently than others. Accordingly, as there is a high probability of these events causing major health casualties, such as bone breaking or head clots, studies have been monitoring these falls to rapidly assist the victim. In this work, we propose and evaluate a multi-stream learning model based on convolutional neural networks using high-level handcrafted features as input in order to cope with this situation. Therefore, our approach consists of extracting high-level handcrafted features, for instance, human pose estimation and optical flow, and using each one as an input for a distinct VGG-16 classifier. In addition, these experiments are able to showcase what features can be used in fall detection. The results have shown that by assembling our directed input learners, our approach outperforms, in terms of accuracy and sensitivity rates, to other similar tested methods found in literature.
Cryptocurrencies, Monetary Policy, and New Forms of Monetary Sovereignty

The article aims to bring to light the limits and contradictions of cryptocurrencies, as well as to investigate possible alternative uses of them. Particularly focusing on Bitcoin, understood as a benchmark for the entire sector, the authors seek to answer the following questions: Should Bitcoin be considered a currency, an investment vehicle, or a speculative asset? On which factors does Bitcoin volatility depend? Do Central Banks effectively have no power to influence/stabilize Bitcoin volatility? Following the empirical strategy proposed by Baek and Elbeck, the article shows that Bitcoin returns merely depend on financial conventions and that the cryptocurrency is acting as a highly speculative asset. Sociotechnical innovations introduced by Bitcoin, the authors argue, have concretely opened the possibility of deeply rethinking money. However, several factors are currently negatively affecting the possibility of the cryptocurrency to function as an effective means of payment. Whether this experience can pave the way for the birth of new and more democratic monetary instruments, as the article discusses, is an issue that calls into question a whole combination of political, technical and social elements.
Fuzzy Space Partitioning Based on Hyperplanes Defined by Eigenvectors for Takagi-Sugeno Fuzzy Model Identification

This article presents a novel method for fuzzy space partitioning and the identification of Takagi-Sugeno fuzzy models. The novelty is in its region-splitting mechanism and membership function definition, which is based on hyperplanes. The proposed algorithm introduces a concept of principal component analysis to define the hyperplanes that split the problem space and uses the distances to these hyperplanes as metrics instead of center-oriented clusters. In contrast with many other methods, the presented method delivers reproducible results and has an easy tuning procedure. The performance is illustrated with analytical examples, benchmark problems from the literature, and real-process data. The obtained results are very promising; however, as with most learning methods, the results depend on the data distribution and input variable selection.
Graph Convolutional LSTM Model for Skeleton-Based Action Recognition

Skeleton-based action recognition has made impressive progress these years. Yet few methods consider spatial configuration of joints and temporal correlation meanwhile as a unity. To model action sequences in a way which regard both two dimensions, a Graph Convolutional Long Short Term Memory Networks (GC-LSTM) model is proposed in this paper, which automatically learns spatiotemporal features to model the action. Our model introduces the GCN operation into conventional RNN unit including graph convolution at each time step for input-to-state and state-to-state transition. Plenty of experiment analyses show that the proposed GC-LSTM model strives (1) to focus more on discriminative parts at discriminative frames and (2) to be insensitive to the redundant parts which are irrelevant for recognition. Moreover, several methods are compared with ours on two publicly available datasets and experimental results demonstrate that our model achieves the state-of-the-art performance.
Stereoscopic Image Stitching via Disparity-Constrained Warping and Blending

As a significant branch of virtual reality, stereoscopic image stitching aims to generating wide perspectives and natural-looking scenes. Existing 2D image stitching methods cannot be successfully applied to the stereoscopic images without considering the disparity consistency of stereoscopic images. To address this issue, this paper presents a stereoscopic image stitching method based on disparity-constrained warping and blending, which could avoid visual distortion and preserve disparity consistency. First, a point-line-driven homography based disparity minimization method is designed to pre-align the left and right images and reduce vertical disparity. Afterwards, a multi-constraint warping is proposed to further align the left and right images, where the initial disparity map is introduced to control the consistency of disparities. Finally, a disparity consistency seam-cutting and blending method is presented to determine the optimal seam and conduct stereoscopic image stitching. Experimental results demonstrate that the proposed method achieves competitive performance compared with other state-of-the-art methods.
Joint local constraint and fisher discrimination based dictionary learning for image classification

Dictionary learning has achieved outstanding results in the field of pattern recognition. The locality as an important structure characteristic of the data has been widely used in various learning task including dictionary learning, and Fisher discriminant has also been widely used. In this paper, to improve the performance of dictionary learning, we propose a joint local-constraint and fisher discrimination based dictionary learning method (JLCFDDL) for image classification. Our method uses the Laplacian regularized constraint of atoms rather than that of training samples to preserve the local information of the data. Meanwhile, fisher discriminative constraint is imposed on the atoms to maintain the differences between the atoms of different classes and to reduce the differences of atoms of the same class. The joint constraints make the obtained dictionary with powerful image classification performance. We also provide mathematical analysis of the proposed objective function. A large number of experiments prove that our method achieves better performance than existing state-of-the-art dictionary learning method.
Semi-supervised representation learning via dual autoencoders for domain adaptation

Domain adaptation aims to exploit the knowledge in source domain to promote the learning tasks in target domain, which plays a critical role in real-world applications. Recently, lots of deep learning approaches based on autoencoders have achieved a significance performance in domain adaptation. However, most existing methods focus on minimizing the distribution divergence by putting the source and target data together to learn global feature representations, while they do not consider the local relationship between instances in the same category from different domains. To address this problem, we propose a novel Semi-Supervised Representation Learning framework via Dual Autoencoders for domain adaptation, named SSRLDA. More specifically, we extract richer feature representations by learning the global and local feature representations simultaneously using two novel autoencoders, which are referred to as marginalized denoising autoencoder with adaptation distribution (MDAad) and multi-class marginalized denoising autoencoder (MMDA) respectively. Meanwhile, we make full use of label information to optimize feature representations. Experimental results show that our proposed approach outperforms several state-of-the-art baseline methods.
Attention-based active visual search for mobile robots

We present an active visual search model for finding objects in unknown environments. The proposed algorithm guides the robot towards the sought object using the relevant stimuli provided by the visual sensors. Existing search strategies are either purely reactive or use simplified sensor models that do not exploit all the visual information available. In this paper, we propose a new model that actively extracts visual information via visual attention techniques and, in conjunction with a non-myopic decision-making algorithm, leads the robot to search more relevant areas of the environment. The attention module couples both top-down and bottom-up attention models enabling the robot to search regions with higher importance first. The proposed algorithm is evaluated on a mobile robot platform in a 3D simulated environment. The results indicate that the use of visual attention significantly improves search, but the degree of improvement depends on the nature of the task and the complexity of the environment. In our experiments, we found that performance enhancements of up to 42% in structured and 38% in highly unstructured cluttered environments can be achieved using visual attention mechanisms.
Performance Analysis of Human Face Recognition Techniques

Face recognition has been thought-provoking task in object detection, digital image processing and pattern recognition. Face recognition plays a significant role in realtime surveillance systems. In this paper, we present a comprehensive overview of the state of the art research work on face detection and recognition. Furthermore, the performance analysis of various face recognition approaches are discussed and conclude the paper with future research direction.
A high-resolution tongue-based joystick to enable robot control for individuals with severe disabilities

Assistive robotic arms have shown the potential to improve the quality of life of people with severe disabilities. However, a high performance and intuitive control interface for robots with 6-7 DOFs is still missing for these individuals. An inductive tongue computer interface (ITCI) was recently tested for control of robots and the study illustrated potential in this field. The paper describes the investigation of the possibility of developing a high performance tongue-based joystick-like controller for robots through two studies. The first compared different methods for mapping the 18 sensor signals to a 2D coordinate, as a touchpad. The second evaluated the performance of a novel approach for emulating an analog joystick by the ITCI based on the ISO9241-411 standard. Two subjects performed a multi-directional tapping test using a standard analog joystick, the ITCI system held in hand and operated by the other hand, and finally by tongue when mounted inside the mouth. Throughput was measured as the evaluation parameter. The results show that the contact on the touchpads can be localized by almost 1 mm accuracy. The effective throughput of ITCI system for the multi-directional tapping test was 2.03 bps while keeping it in the hand and 1.31 bps when using it inside the mouth.
Improved Gaussian–Bernoulli restricted Boltzmann machine for learning discriminative representations

Restricted Boltzmann machines (RBMs) have received considerable research interest in recent years because of their capability to discover latent representations in an unsupervised manner. The standard RBM is only suitable for processing binary-valued data. To address this limitation, the Gaussian–Bernoulli RBM (GRBM) has been designed to model real-valued data, particularly images. A GRBM that seeks to map real-valued data nonlinearly into a latent representation space is typically trained by contrastive divergence learning. However, most existing GRBM-based models neglect the inherent interpoint affinity information from the original data that can be used to enhance the expression ability of the model. In this study, a novel interpoint-affinity-based GRBM (abGRBM) is proposed to learn discriminative representations in the hidden layer. By incorporating the interpoint affinity information into the training process of the GRBM, the proposed model can not only utilize the GRBM’s powerful latent representation learning capabilities for real-valued data, it can also transform the original data into another space with improved separability. We prove the availability of our model using several image datasets of Microsoft Research Asia Multimedia for unsupervised clustering and supervised classification tasks. The experimental results show the superior performance of the GRBM in discovering discriminative representations and demonstrate the effectiveness of the affinity information.
Performance Modeling and Analysis of the Bitcoin Inventory Protocol

Blockchains are currently gaining attention as a newly emerging technology in both academia and industry, capable of impacting a variety of domains beyond cryptocurrencies. Performance modeling can be used to provide us with a deeper understanding of the behavior and dynamics within blockchain peer-to-peer networks. Blockchain system architects can leverage network models to properly tune their system and to reduce design costs significantly. In this paper, we focus on the original and well-established Bitcoin blockchain network. In particular, we propose a random graph model for performance modeling and analysis of the inventory-based protocol for block dissemination. This model addresses the impact of key blockchain parameters on the overall performance of Bitcoin. We derive some explicit and closed-form equations for block propagation delay and traffic overhead. We implement our model using the popular network simulator OMNet++. We validate the accuracy of our theoretical model and its implementation with our dataset mined from the Bitcoin network. Our results show the trade-off between the default number of connections per node, network bandwidth, and block size in order to compute the optimal block propagation delay over the network.