Reviewerly/exHarmony huggingface dataset:
A Machine Learning Based Sign Language Interpretation System for Communication with Deaf-mute People

An utmost necessity of the deaf-mute people is to communicate with the non-mute people without the knowledge of sign language in different environments including office premises, shopping centers, educational institutions, and the like. Although several systems exist for teaching or learning sign language for the deaf-mute people, little attention has been paid to the development of useful and usable tools for effortless communication between mute and non-mute people. Therefore, the objective of this research is to develop an intelligent sign language interpretation system to connect with deaf-mute community, which will be used as a two way correspondence between speech impaired and regular speaking people. To attain this objective, firstly, a survey on recent vision-based gesture recognition approaches has been carried out. Secondly, an efficient and improved method for recognition of static hand posture and temporal gesture, focusing on finger-spelling method has been proposed which was developed using raspberry pi with the help of machine learning. Finally, the developed system was evaluated with 60 participants including the deaf-mute people and was found to be capable of successfully carrying out its functions with good performance.
PAC: Privacy preserving proxy re-encryption for access control in public cloud

Proxy re-encryption is an essential prerequisite that facilitates a foreign entity (e.g., cloud server) to enforce the access of provisions of a document extended by one user to another user. While performing this task it is essential that the foreign entity does not learn any information about the actual document inside the encrypted envelop. In this paper, we present a scheme on Privacy-preserving Proxy Re-encryption for Access Control in Public Cloud, termed as PAC, that provides the proxy re-encryption of attribute-based encrypted data with receiver privacy. The proposed PAC scheme enables a proxy server to perform the re-encryption operation without learning anything about the data or the access policy. The PAC provides data confidentiality, and preserves receiver privacy. We show the PAC scheme adaptively secure against indistinguishability of ciphertext policy and chosen-plaintext attack (IND-CP-CPA) under the 2-Decisional Bilinear Diffie-Hellman Inversion assumption. The scheme is implemented on Google cloud instance and the experimental results show that the PAC scheme is storage and computation-efficient when compared to other related schemes.
An evaluation of MorphInd's morphological annotation scheme for Indonesian

MorphInd 2 ( Larasati et al., 2011 ) is a state-of-the-art morphological analyser for Indonesian. To date, there has not been any comprehensive evaluation of the morphological annotation scheme which MorphInd implements. My evaluation of this annotation scheme reveals a number of significant drawbacks. Some analytical features encoded in MorphInd's tagset seem not to reflect features actually present in Indonesian morphology, while certain common features in the analysis of Indonesian are absent. Likewise, the Part of Speech (pos) hierarchy in the MorphInd tagset does not reflect the usual pos hierarchy used by Indonesian reference grammars. Moreover, the MorphInd output does not link morphological tags to the corresponding morpheme. Finally, a number of issues which might problematise text/corpus querying in the annotation's layout are observable, particularly relating to affixes, reduplication, and the affix–reduplication interface.
Beyond Flops In Low-Rank Compression Of Neural Networks: Optimizing Device-Specific Inference Runtime

Neural network compression has become an important practical step when deploying trained models. We consider the problem of low-rank compression of the neural networks with the goal of optimizing the measured inference time. Given a neural network and a target device to run it, we want to find the matrix ranks and the weight values of the compressed model so that network runs as fast as possible on the device while having best task performance (e.g., classification accuracy). This is a hard optimization problem involving weights, ranks, and device constraints. To tackle this problem, we first implement a simple yet accurate model of the on-device runtime that requires only a few measurements. Then we give a suitable formulation of the optimization problem involving the proposed runtime model and solve it using alternating optimization. We validate our approach on various neural networks and show that by using our estimated runtime model we achieve better task performance compared to FLOPs based methods for the same runtime budget on the actual device.
Array-Aware Neural Architecture Search

Convolutional Neural Networks (CNNs) have exceeded human accuracy in many Computer Vision tasks, such as Image Classification, Object Detection, Image Segmentation, etc. This advancement is due to the efficient manual design of CNNs in initially, followed by automated design through Neural Architecture Search (NAS). In parallel to neural network design, advances in Accelerator hardware design, such as Google’s Tensor Processing Unit (TPU), Eyeriss, etc., also occurred for efficient processing of CNN forward propagation. The heart of these accelerators is an array processor (Systolic Array) of a fixed dimension, that limits the amount of CNN computation that can be carried out in a single clock cycle. While NAS is able to produce efficient neural architectures, the networks need to be co-designed with respect to the underlying array dimensions to obtain the best performance. In this paper, we introduce "Array Aware Neural Architecture Search" to automatically design efficient CNNs for a fixed array-based neural network accelerator. Previous Hardware Aware NAS methods consider a fixed search space for different hardware platforms and search within its predefined space. We explore the search space based on the underlying hardware array dimensions to design a more efficient CNN architectures for optimal performance. We observe that our proposed NAS methods on the CIFAR-10 dataset produce similar accuracy as the baseline network while saving a substantial number of cycles on the Array.
The CT Liver Image Segmentation Based on RTV and GMM

The accurate medical image segmentation can help doctors to improve disease diagnosis and treatment. How to achieve the accurate segmentation results depends on the image preprocessing and segmentation algorithm. Traditional filtering methods can erase the noise of the image, but the contrast of the boundaries between different tissues is also weakened at the same time. While the Relative Total Variation method can better filter out the noise and keep the contrast of the boundaries, which plays an important role for improving the accuracy of the threshold segmentation. The Gaussian mixture model is used to segment the CT liver images based on the Gaussian filter and the RTV filter, respectively. The experiment results are compared, and it is verified that the segmentation result based on Gaussian filter is much better than that based on Relative Total Variation filter.
Attention-Based Partial Face Recognition

Photos of faces captured in unconstrained environments, such as large crowds, still constitute challenges for current face recognition approaches as often faces are occluded by objects or people in the foreground. However, few studies have addressed the task of recognizing partial faces. In this paper, we propose a novel approach to partial face recognition capable of recognizing faces with different occluded areas. We achieve this by combining attentional pooling of a ResNet’s intermediate feature maps with a separate aggregation module. We further adapt common losses to partial faces in order to ensure that the attention maps are diverse and handle occluded parts. Our thorough analysis demonstrates that we outperform all baselines under multiple benchmark protocols, including naturally and synthetically occluded partial faces. This suggests that our method successfully focuses on the relevant parts of the occluded face.
Developer Operations and Engineering Multi-agent Systems

In this paper, we propose the integration of approaches to Engineering Multi-Agent Systems (EMAS) with the Developer Operations (DevOps) industry best practice. Whilst DevOps facilitates the organizational autonomy of software teams, as well as the technological automation of testing, deployment, and operations pipelines, EMAS and the agent-oriented programming paradigm help instill autonomy into software artifacts. We discuss the benefits of integrating DevOps and EMAS, for example by highlighting the need for agent-oriented abstractions for quality assurance and test automation approaches. More generally, we introduce an agent-oriented perspective on the DevOps life-cycle and list a range of research challenges that are relevant for the integration of the DevOps and EMAS perspectives.
Using Fisheye Camera For Cost-Effective Multi-View People Localization

In advanced multi-view video surveillance systems, people localization is usually a crucial part of the complete system and need to be accomplished in a short time to reserve sufficient processing time for subsequent high-level analysis. As the surveillance area increases, it is required to install a large number of cameras for multi-view people localization. To lower the equipment cost and setup time, we incorporate fisheye (or wide-angle) camera to an efficient vanishing point-based line sampling scheme for people localization, by ensuring the fisheye camera is looking downward so that its principal point becomes the vanishing point of vertical lines. Experimental results show that the utilization of fisheye-camera can (i) achieve localization accuracy comparable or superior to that using ordinary cameras, (ii) reduce the camera count by 75% on the average while covering the same or larger size of a monitored area, and (iii) greatly simplify the camera installation process.
Deriving efficient program transformations from rewrite rules

An efficient optimizing compiler can perform many cascading rewrites in a single pass, using auxiliary data structures such as variable binding maps, delayed substitutions, and occurrence counts. Such optimizers often perform transformations according to relatively simple rewrite rules, but the subtle interactions between the data structures needed for efficiency make them tricky to write and trickier to prove correct. We present a system for semi-automatically deriving both an efficient program transformation and its correctness proof from a list of rewrite rules and specifications of the auxiliary data structures it requires. Dependent types ensure that the holes left behind by our system (for the user to fill in) are filled in correctly, allowing the user low-level control over the implementation without having to worry about getting it wrong. We implemented our system in Coq (though it could be implemented in other logics as well), and used it to write optimization passes that perform uncurrying, inlining, dead code elimination, and static evaluation of case expressions and record projections. The generated implementations are sometimes faster, and at most 40% slower, than hand-written counterparts on a small set of benchmarks; in some cases, they require significantly less code to write and prove correct.
Hallucination In Object Detection — A Study In Visual Part VERIFICATION

We show that object detectors can hallucinate and detect missing objects; potentially even accurately localized at their expected, but non-existing, position. This is particularly problematic for applications that rely on visual part verification: detecting if an object part is present or absent. We show how popular object detectors hallucinate objects in a visual part verification task and introduce the first visual part verification dataset: DelftBikes <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , which has 10,000 bike photographs, with 22 densely annotated parts per image, where some parts may be missing. We explicitly annotated an extra object state label for each part to reflect if a part is missing or intact. We propose to evaluate visual part verification by relying on recall and compare popular object detectors on DelftBikes. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/oskyhn/DelftBikes
Semantic Perturbations with Normalizing Flows for Improved Generalization

Data augmentation is a widely adopted technique for avoiding overfitting when training deep neural networks. However, this approach requires domain-specific knowledge and is often limited to a fixed set of hard-coded transformations. Recently, several works proposed to use generative models for generating semantically meaningful perturbations to train a classifier. However, because accurate encoding and decoding are critical, these methods, which use architectures that approximate the latent-variable inference, remained limited to pilot studies on small datasets.Exploiting the exactly reversible encoder-decoder structure of normalizing flows, we perform on-manifold perturbations in the latent space to define fully unsupervised data augmentations. We demonstrate that such perturbations match the performance of advanced data augmentation techniques—reaching 96.6% test accuracy for CIFAR10 using ResNet-18 and outperform existing methods, particularly in low data regimes—yielding 10–25% relative improvement of test accuracy from classical training. We find that our latent adversarial perturbations adaptive to the classifier throughout its training are most effective, yielding the first test accuracy improvement results on real-world datasets—CIFAR-10/100—via latent-space perturbations.
How to Construct Quantum Random Functions

Pseudorandom functions ( PRFs ) are one of the foundational concepts in theoretical computer science, with numerous applications in complexity theory and cryptography. In this work, we study the security of PRFs when evaluated on quantum superpositions of inputs. The classical techniques for arguing the security of PRFs do not carry over to this setting, even if the underlying building blocks are quantum resistant. We therefore develop a new proof technique to show that many of the classical PRF constructions remain secure when evaluated on superpositions.
Verilog based Implementation of Real Time Digital Video Stabilization

Video stabilization is an instrumental and a very commonly employed image processing operations, yet still a very active area of research. Although lots of work have been done in the field of Video Stabilization, however still not much work has been done in the field of Real time Video Stabilization, especially works dealing with implementation of Technology on prototyping Boards. This work discusses the implementation of a Real time implementation of a Digital Video Stabilization system based on Upright Speeded up Robust Features mechanism. The U-SURF used in the algorithm has been found to be computationally much lighter as compared to the existing proposed mechanisms and has been implemented using Verilog as the preferred HDL Language. The developed architecture synthesized for Xilinx Zynq XC7030 has the significant benefits of being a fully pipelined architecture, making it much faster than the existing systems and at the same time exhibits better PSNR and MSE values. The system has been meticulously tested on 3 different standard Datasets namely StableNet Dataset, StabNet Dataset and the MATLAB Computer Vision Toolbox Dataset.
Variable-grouping-based exponential crossover for differential evolution algorithm

The performance of differential evolution (DE) algorithm largely depends on its crossover operator, whose substantive characteristics are to make the algorithm search in a subspace of the original search space. Different crossover operators use different subspace divisions, and how to choose a suitable crossover operator for a specific optimisation problem is still an open issue. This paper proposes variable-grouping-based exponential crossover (VGExp), where all variables are divided into multiple groups based on interaction information, and the variables that are mutated simultaneously have a high probability of coming from the same group. Moreover, the solutions can improve the accuracy of the variable grouping and provide initial guidance for optimisation. Therefore, the proposed VGExp seamlessly combines variables grouping technique and differential evolution. The experiment results based on 30 CEC2014 test problems show that VGExp can improve the performance of most DE variants, and it is also better than other well-developed crossover operators.
Hybrid Pruning for Convolutional Neural Network Convolution Kernel

In the model compression, to achieve the optimal pruning effect, weighted pruning or filter pruning was used to prune the network in traditional methods, in fact, the network can continue to be compressed. Therefore, a hybrid pruning method of convolution kernel weight pruning and filter pruning are proposed in this paper. First of all, the weight pruning is carried out for the convolutional neural network model, this paper adopts the method of dynamic convolution kernel for weight pruning, and the fluctuation value of each time normalized value in network update process is taken as the pruning criterion for the weight of the convolutional kernel. Because the weight pruning of the convolution kernel is dynamic, the floating-point operation (FLOP) is significantly reduced, and the parameter scale does not decrease significantly. Then, the model was pruning by convolution kernel ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -norm [1] method, which is not only effectively reduce the parameter scale, but also no extra calculations are introduced. In the case of a small loss of network accuracy, the hybrid pruning of the convolution kernel can maximize the compression of the model to meet the conditions for deployment in mobile edge devices, and has significant improvements in floating-point operations and parameter compression. Therefore, mixed pruning in this paper can effectively make up for the shortage of single pruning method. Through VGG-16, ResNet and other networks, it has been proved in practice that the convolution kernel hybrid pruning method proposed in this paper can effectively reduce the model size and maintain a high level of accuracy.
Secure Image Inference Using Pairwise Activation Functions

Amongst most artificial neural networks, there is considerable reliance on (raw) data in their use, thus raising data privacy and security issues.A homomorphic encryption scheme affords the performing of computations on encrypted data though it has some challenges, including the lack of support for performing computations using transcendental functions such as logarithm and exponentiation, which exists in some of the typical activation functions in neural networks.Polynomial approximation, though can be used to derive polynomials as an approximation to these activation functions as homomorphic encryption scheme favours mostly addition and multiplication operations.While the literature has considered forming new activation functions as pairwise multiplication of well-known activation functions, the design space is mostly unexplored.Therefore this paper explores the design space of such pairwise, multiplied activation functions and their application in homomorphic image inferencing or prediction using the widely popular MNIST and CIFAR-10 benchmark datasets.Moreover, it analyses corresponding curve fitting parameters (range and degree), homomorphic-friendly pooling methods, and optimization methods in the ciphertext domain to avoid incurring huge computation costs via bootstrapping but not compromising accuracy.Results show new activation function combinations yielding similar or better results in ciphertext as compared to the ones in plaintext.
Learning panoptic segmentation through feature discriminability

Panoptic segmentation has attracted increasing attention as a joint task of semantic and instance segmentation. However, previous works have not noticed that the different requirements for semantic and instance segmentation can lead to conflict of feature discriminability. Instance segmentation mainly focuses on the central area of each instance in things regions, while semantic segmentation focuses on the whole region of a specific class. To resolve it, we propose: 1) a Dual-FPN framework which separates the shared Feature Pyramid Network (FPN) in previous works to reduce the conflict of receptive field and meet different requirements of the two tasks; 2) a Region Refinement Module which leverages the prediction of semantic segmentation to refine the result of instance segmentation and resolves the conflict between the things regions and the stuff regions. Experimental results on Cityscapes dataset and Mapillary Vistas dataset show that our proposed method can improve the result of both things and stuff and obtain state-of-the-art performance.
Utilizing latent Features for Building Recommender system Based on RBM Neural Network

Deep learning one of the hottest technologies that applied in various field for enhancing the available results which solving specific problems. The huge growth of the data over internet make recommender systems in need as effective techniques to overcome information overloaded, while employing deep learning methods lead to flourishing recommender system in several digital media. All in all, recommender system play best role based on user preferences. In this paper implement and analysis of building recommender system based on deep learning techniques for prediction movie rating as smart way to predict user preferences. Such technique would be useful in advertising management and social networking as well.
Hybrid quantum-classical algorithms: Foundation, design and applications

Quantum computing, as an emerging computing paradigm, is expected to tackle problems such as quantum chemistry, optimization, quantum chemistry, information security, and artificial intelligence, which are intractable with using classical computing. Quantum computing hardware and software continue to develop rapidly, but they are not expected to realize universal quantum computation in the next few years. Therefore, the use of quantum hardware to solve practical problems in the near term has become a hot topic in the field of quantum computing. Exploration of the applications of near-term quantum hardware is of great significance in understanding the capability of quantum hardware and promoting the practical process of quantum computing. Hybrid quantum-classical algorithm (also known as variational quantum algorithm) is an appropriate model for near-term quantum hardware. In the hybrid quantum-classical algorithm, classical computers are used to maximize the power of quantum devices. By combining quantum computing with machine learning, the hybrid quantum-classical algorithm is expected to achieve the first practical application of quantum computation and play an important role in the studying of quantum computing. In this review, we introduce the framework of hybrid quantum-classical algorithm and its applications in quantum chemistry, quantum information, combinatorial optimization, quantum machine learning, and other fields. We further discuss the challenges and future research directions of the hybrid quantum-classical algorithm.