Reviewerly/exHarmony huggingface dataset:
Learning to guide task and motion planning using score-space representation

In this paper, we propose a learning algorithm that speeds up the search in task and motion planning problems. Our algorithm proposes solutions to three different challenges that arise in learning to improve planning efficiency: what to predict, how to represent a planning problem instance, and how to transfer knowledge from one problem instance to another. We propose a method that predicts constraints on the search space based on a generic representation of a planning problem instance, called score-space, where we represent a problem instance in terms of the performance of a set of solutions attempted so far. Using this representation, we transfer knowledge, in the form of constraints, from previous problems based on the similarity in score-space. We design a sequential algorithm that efficiently predicts these constraints, and evaluate it in three different challenging task and motion planning problems. Results indicate that our approach performs orders of magnitudes faster than an unguided planner.
Efficient Certificateless Public Key Cryptography With Equality Test for Internet of Vehicles

The fast progression of the Internet of Vehicles (IoV) has resulted in a large number of vehicles connecting to networks. This leads to massive growth in the data collected from vehicles via IoV. Fortunately, cloud computing provides a vast range of services such as operating systems, hardware, software, and resources. Therefore, the massive amount of data cumulated through IoV can be outsourced to the cloud. However, considering the untrusted nature of the cloud, the cumulated data must be encrypted before it is outsourced to the cloud server. Unfortunately, this ensures to difficulty while searching the data. To address this challenge, an efficient certificateless public key cryptography with equality test (CL-PKC-ET) is presented in this paper. In this scheme, the authorized cloud server has the permission to execute the equality test on encrypted data and retrieve the result without knowing any relevant information about the ciphertext. Our CL-PKC-ET scheme is demonstrated under the Bilinear Diffie-Hellman assumption in the random oracle model. Ultimately, we compare the CL-PKE-ET with a state-of-art scheme and the performance evaluation indicates that our scheme accomplishes 96.40%, 32.08%, and 43.98% reduction in computation costs during the encryption, decryption, and test stages, respectively. Therefore, we assert that our scheme is ideal for deployment in both the cloud and IoV environments.
Maximal Quantum Fisher Information for Mixed States

We study quantum metrology for unitary dynamics. Analytic solutions are given for both the optimal unitary state preparation starting from an arbitrary mixed state and the corresponding optimal measurement precision. This represents a rigorous generalization of known results for optimal initial states and upper bounds on measurement precision which can only be saturated if pure states are available. In particular, we provide a generalization to mixed states of an upper bound on measurement precision for time-dependent Hamiltonians that can be saturated with optimal Hamiltonian control. These results make precise and reveal the full potential of mixed states for quantum metrology.
Texture classification using convolutional neural network optimized with whale optimization algorithm

Texture classification is an active area of research in the field of pattern recognition. Convolutional neural networks (CNNs) have a remarkable capability of recognizing patterns and are one of the most efficient deep learning techniques. But, finding the optimal values of the different hyperparameters of the CNN is a major challenge. Nature-inspired algorithms (NIAs) are the meta-heuristic algorithms well-known for their optimizing capability. Whale optimization algorithm (WOA) is a recent nature-inspired algorithm (NIA) that is inspired by the hunting behaviour of the humpback whales. In this paper, we propose a novel deep learning technique for texture recognition using a CNN optimized through WOA. We apply WOA at the two different levels in the CNN: In the convolutional layer (for optimizing the values of the filters), and in the fully-connected layer (for optimizing the values of the weights and biases). For examining the performance of our technique, we apply it to the following three benchmark texture datasets: Kylberg v1.0, Brodatz, and Outex_TC_00012. Our model performs better than the most of the existing methods for the Kylberg and the Outex_TC_00012 datasets and gives competitive results for the Brodatz dataset. It is evident from the results that our model has the potential for application in the field of texture recognition.
Detecting adversarial examples via prediction difference for deep neural networks

Deep neural networks (DNNs) perform effectively in many computer vision tasks. However, DNNs are found to be vulnerable to adversarial examples which are generated by adding imperceptible perturbations to original images. To address this problem, we propose a novel defense method, transferability prediction difference (TPD), to drastically improve the adversarial robustness of DNNs with small sacrificing verified accuracy. We find out that the adversarial examples have lager prediction difference for various DNN models due to their various complicated decision boundaries, which can be used to identify the adversarial examples by converging decision boundaries to a prediction difference threshold. We adopt the K-means clustering algorithm on benign data to determine transferability prediction difference threshold, by which we can detect adversarial examples accurately and efficiently. Furthermore, TPD method neither modifies the target model nor needs to take knowledge of adversarial attacks. We perform four state-of-the-art adversarial attacks (FGSM, BIM, JSMA and C&W) to evaluate TPD models trained on MNIST and CIFAR-10 and the average detection accuracy is 96.74% and 86.61%. The results show that TPD model has high detection ratio on the demonstrably advanced white-box adversarial examples while keeping low false positive rate on benign examples.
Self-Supervised Adaptation of High-Fidelity Face Models for Monocular Performance Tracking

Improvements in data-capture and face modeling techniques have enabled us to create high-fidelity realistic face models. However, driving these realistic face models requires special input data, e.g., 3D meshes and unwrapped textures. Also, these face models expect clean input data taken under controlled lab environments, which is very different from data collected in the wild. All these constraints make it challenging to use the high-fidelity models in tracking for commodity cameras. In this paper, we propose a self-supervised domain adaptation approach to enable the animation of high-fidelity face models from a commodity camera. Our approach first circumvents the requirement for special input data by training a new network that can directly drive a face model just from a single 2D image. Then, we overcome the domain mismatch between lab and uncontrolled environments by performing self-supervised domain adaptation based on ``consecutive frame texture consistency'' based on the assumption that the appearance of the face is consistent over consecutive frames, avoiding the necessity of modeling the new environment such as lighting or background. Experiments show that we are able to drive a high-fidelity face model to perform complex facial motion from a cellphone camera without requiring any labeled data from the new domain.
Baby steps towards few-shot learning with multiple semantics

Learning from one or few visual examples is one of the key capabilities of humans since early infancy, but is still a significant challenge for modern AI systems. While considerable progress has been achieved in few-shot learning from a few image examples, much less attention has been given to the verbal descriptions that are usually provided to infants when they are presented with a new object. In this paper, we focus on the role of additional semantics that can significantly facilitate few-shot visual learning. Building upon recent advances in few-shot learning with additional semantic information, we demonstrate that further improvements are possible by combining multiple and richer semantics (category labels, attributes, and natural language descriptions). Using these ideas, we offer the community new results on the popular miniImageNet and CUB few-shot benchmarks, comparing favorably to the previous state-of-the-art results for both visual only and visual plus semantics-based approaches. We also performed an ablation study investigating the components and design choices of our approach. Code available on github.com/EliSchwartz/mutiple-semantics.
Multi-label classification via incremental clustering on an evolving data stream

With the advancement of storage and processing technology, an enormous amount of data is collected on a daily basis in many applications. Nowadays, advanced data analytics have been used to mine the collected data for useful information and make predictions, contributing to the competitive advantages of companies. The increasing data volume, however, has posed many problems to classical batch learning systems, such as the need to retrain the model completely with the newly arrived samples or the impracticality of storing and accessing a large volume of data. This has prompted interest on incremental learning that operates on data streams. In this study, we develop an incremental online multi-label classification (OMLC) method based on a weighted clustering model. The model is made to adapt to the change of data via the decay mechanism in which each sample's weight dwindles away over time. The clustering model therefore always focuses more on newly arrived samples. In the classification process, only clusters whose weights are greater than a threshold (called mature clusters) are employed to assign labels for the samples. In our method, not only is the clustering model incrementally maintained with the revealed ground truth labels of the arrived samples, the number of predicted labels in a sample are also adjusted based on the Hoeffding inequality and the label cardinality. The experimental results show that our method is competitive compared to several well-known benchmark algorithms on six performance measures in both the stationary and the concept drift settings.
Software developer productivity loss due to technical debt—A replication and extension study examining developers’ development work

Software companies need to deliver customer value continuously, both from a short- and long-term perspective. However, software development can be impeded by technical debt (TD). Although significant theoretical work has been undertaken to describe the negative effects of TD, little empirical evidence exists on how much wasted time and additional activities TD causes. The study aims to explore the consequences of TD in terms of wastage of development time. This study investigates on which activities this wasted time is spent and whether different TD types impact the wasted time differently. This study reports the results of a longitudinal study surveying 43 developers and including16 interviews followed by validation by an additional study using a different and independent dataset and focused on replicating the findings addressing the findings. The analysis of the reported wasted time revealed that developers waste, on average, 23% of their time due to TD and that developers are frequently forced to introduce new TD. The most common activity on which additional time is spent is performing additional testing. The study provides evidence that TD hinders developers by causing an excessive waste of working time, where the wasted time negatively affects productivity.
Privacy Protection in Street-View Panoramas Using Depth and Multi-View Imagery

The current paradigm in privacy protection in street-view images is to detect and blur sensitive information. In this paper, we propose a framework that is an alternative to blurring, which automatically removes and inpaints moving objects (e.g. pedestrians, vehicles) in street-view imagery. We propose a novel moving object segmentation algorithm exploiting consistencies in depth across multiple street-view images that are later combined with the results of a segmentation network. The detected moving objects are removed and inpainted with information from other views, to obtain a realistic output image such that the moving object is not visible anymore. We evaluate our results on a dataset of 1000 images to obtain a peak noise-to-signal ratio (PSNR) and L 1 loss of 27.2 dB and 2.5%, respectively. To assess overall quality, we also report the results of a survey conducted on 35 professionals, asked to visually inspect the images whether object removal and inpainting had taken place. The inpainting dataset will be made publicly available for scientific benchmarking purposes at https://research.cyclomedia.com/.
Face recognition using fusion of feature learning techniques

A method for face recognition system for both challenging frontal and profile faces is proposed in this paper. The proposed system consists of face pre-processing, feature extraction and classification components. During pre-processing, a region-of-interest for face region is extracted based on facial landmark points, obtained by a Tree Structured Part Model. During feature extraction, Scale Invariant Feature Transform descriptors are computed from patches over detected face region. These descriptors undergo to different feature learning techniques to obtain different feature representations for the input image. The performance of these feature representations are obtained using multi-class linear Support Vector Machine classifier during classification. Finally, the scores from different feature learning techniques are fused to take the decision to recognize the subjects. Extensive experimental results have been demonstrated to show the effectiveness of the proposed face recognition system. The comparison with the exiting state-of-art methods for ORL, IITK, CVL, AR, CASIA-Face-V5, FERET and CAS-PEAL face databases, show the superiority of the proposed system.
MicroScope

The popularity of hardware-based Trusted Execution Environments (TEEs) has recently skyrocketed with the introduction of Intel's Software Guard Extensions (SGX). In SGX, the user process is protected from supervisor software, such as the operating system, through an isolated execution environment called an enclave. Despite the isolation guarantees provided by TEEs, numerous microarchitectural side channel attacks have been demonstrated that bypass their defense mechanisms. But, not all hope is lost for defenders: many modern fine-grain, high-resolution side channels---e.g., execution unit port contention---introduce large amounts of noise, complicating the adversary's task to reliably extract secrets.
Oppositional symbiotic organisms search optimization for multilevel thresholding of color image

Selection of optimal threshold is the most crucial issue in threshold-based segmentation. In case of color image, this task is become challenging, because conventional color image segmentation has computational complexity and also it suffers from lack of accuracy. Various techniques such as threshold based, region growing, edge detection, graph cut, pixel classification, neural network, active contour, gray level co-occurrence matrix are proposed so far for image segmentation in the literature. Out of them, threshold-based segmentation is popular for its simplicity. To address the problem of color image segmentation, we propose an enhanced version of metaheuristic optimization algorithm called Opposition based Symbiotic Organisms Search (OSOS) to solve multilevel image thresholding technique for color image segmentation by introducing opposition based learning concepts to accelerate the convergence rate and enhance the performance of standard symbiotic organisms search (SOS). The performance of the proposed OSOS based algorithm is investigated thoroughly and compared with some existing techniques like Cuckoo Search (CS), BAT algorithm (BAT), artificial bee colony (ABC) and particle swarm optimization (PSO). The comparison is made by applying the algorithm to a set of color images taken from a well-known benchmark dataset (Berkeley Segmentation Dataset (BSDS)) and some of the color images collected for the COCO dataset. It is observed from the results that the performance of the OSOS based algorithm is promising with respect to standards SOS and others in terms of the values of objective functions as well as the values of some well-defined quality metrics such as peak signal-to-noise ratio (PSNR), structure similarity index (SSIM) and feature similarity index (FSIM). The results of the proposed algorithm may encourage the scientists and engineers to apply it into pattern recognition problems.
LUTNet: Rethinking Inference in FPGA Soft Logic

Research has shown that deep neural networks contain significant redundancy, and that high classification accuracies can be achieved even when weights and activations are quantised down to binary values. Network binarisation on FPGAs greatly increases area efficiency by replacing resource-hungry multipliers with lightweight XNOR gates. However, an FPGA's fundamental building block, the K-LUT, is capable of implementing far more than an XNOR: it can perform any K-input Boolean operation. Inspired by this observation, we propose LUTNet, an end-to-end hardware-software framework for the construction of area-efficient FPGA-based neural network accelerators using the native LUTs as inference operators. We demonstrate that the exploitation of LUT flexibility allows for far heavier pruning than possible in prior works, resulting in significant area savings while achieving comparable accuracy. Against the state-of-the-art binarised neural network implementation, we achieve twice the area efficiency for several standard network models when inferencing popular datasets. We also demonstrate that even greater energy efficiency improvements are obtainable.
GraphSSD

Graph analytics play a key role in a number of applications such as social networks, drug discovery, and recommendation systems. Given the large size of graphs that may exceed the capacity of the main memory, application performance is bounded by storage access time. Out-of-core graph processing frameworks try to tackle this storage access bottleneck through techniques such as graph sharding, and sub-graph partitioning. Even with these techniques, the need to access data across different graph shards or sub-graphs causes storage systems to become a significant performance hurdle. In this paper, we propose a graph semantic aware solid state drive (SSD) framework, called GraphSSD, which is a full system solution for storing, accessing, and performing graph analytics on SSDs. Rather than treating storage as a collection of blocks, GraphSSD considers graph structure while deciding on graph layout, access, and update mechanisms. GraphSSD replaces the conventional logical to physical page mapping mechanism in an SSD with a novel vertex-to-page mapping scheme and exploits the detailed knowledge of the flash properties to minimize page accesses. GraphSSD also supports efficient graph updates (vertex and edge modifications) by minimizing unnecessary page movement overheads. GraphSSD provides a simple programming interface that enables application developers to access graphs as native data in their applications, thereby simplifying the code development. It also augments the NVMe (non-volatile memory express) interface with a minimal set of changes to map the graph access APIs to appropriate storage access mechanisms.
Knowledge Graphs and Enterprise AI: The Promise of an Enabling Technology

Adopting a mature AI strategy is fundamental for modern knowledge companies to govern the proliferation of smart AI-driven applications and to coordinate them within coherent knowledge workflows. We propose knowledge graphs as the reference technology for the enterprise AI context, i.e., the complex of entities, properties and relationships that shape a business domain and constitute a common backbone for all AI-driven applications. We contribute and discuss principles to design software architectures for AI-driven applications based on knowledge graphs. We focus on the Vadalog system, a successful knowledge graph middleware from the University of Oxford and show knowledge graphs in action in a number of use cases from the financial domain.
Text Classification using Different Feature Extraction Approaches

In this paper, we examine the results of applying three different text feature extraction approaches while classifying short sentences and phrases into categories with a neural network in order to find out which method is best at capturing text features and allows the classifier to achieve highest accuracy. The examined feature extraction methods include a plain Term Frequency Inverse Document Frequency (TF-IDF) approach and its two modifications by applying different dimensionality reduction techniques: Latent Semantic Analysis (LSA) and Linear Discriminant Analysis (LDA). The results show that the TF-IDF feature extraction approach outperforms other methods allowing the classifier to achieve highest accuracy when working with larger datasets. Furthermore, the results show that the TF-IDF in combination with LSA approach allows the classifier to achieve similar accuracy while working with smaller datasets.
Removal of impulse noise in color images based on convolutional neural network

A new denoising framework based on deep convolutional neural network for suppressing impulse noise in color images is proposed in this paper. The proposed framework consists of two modules: noise detection and image reconstruction, both of which are implemented by a deep convolutional neural network. First, a noise classifier network is trained to detect random-valued impulse noise in a color image, which not only can detect the noisy color vector pixels but also can further identify the corrupted channels of each noisy color pixel. Then, a sparse clean color image is computed by replacing the values of noisy channels with 0 and keeping other noise-free channels unchanged. Finally, the sparse clean color image is fed to another denoiser network to reconstruct the denoised image. Experimental results show that the proposed denoiser outperforms other state-of-the-art methods clearly in both performance measure and visual evaluation.
Semi-Supervised Faster RCNN-Based Person Detection and Load Classification for Far Field Video Surveillance

This paper presents a semi-supervised faster region-based convolutional neural network (SF-RCNN) approach to detect persons and to classify the load carried by them in video data captured from distances several miles away via high-power lens video cameras. For detection, a set of computationally efficient image processing steps are considered to identify moving areas that may contain a person. These areas are then passed onto a faster RCNN classifier whose convolutional layers consist of ResNet50 transfer learning. Frame labels are obtained in a semi-supervised manner for the training of the faster RCNN classifier. For load classification, another convolutional neural network classifier whose convolutional layers consist of GoogleNet transfer learning is used to distinguish a person carrying a bundle from a person carrying a long arm. Despite the challenges associated with the video dataset examined in terms of the low resolution of persons, the presence of heat haze, and the shaking of the camera, it is shown that the developed approach outperforms the faster RCNN approach.