Reviewerly/exHarmony huggingface dataset:
Subtensor Quantization for Mobilenets

Quantization for deep neural networks (DNN) have enabled developers to deploy models with less memory and more efficient low-power inference. However, not all DNN designs are friendly to quantization. For example, the popular Mobilenet architecture has been tuned to reduce parameter size and computational latency with separable depthwise convolutions, but not all quantization algorithms work well and the accuracy can suffer against its float point versions. In this paper, we analyzed several root causes of quantization loss and proposed alternatives that do not rely on per-channel or training-aware approaches. We evaluate the image classification task on ImageNet dataset, and our post-training quantized 8-bit inference top-1 accuracy in within 0.7% of the floating point version.
Liver Segemtation in CT Image with No-edge-cuting UNet

UNet model performs well in medical image segmentation. In this paper, UNet model is improved by the same padding after each convolution, so that the image scale remains unchanged through convolution, and the edges of the image are no longer cut off. The improved UNet model is trained for semantic segmentation of the liver in the portal vein in CT images, using binary cross entropy as the loss function, and dice value as the performance evaluation index. The average dice value of the test set reaches 0.85. Our work can be used to help for daily work of liver image segmentation.
Performance Improvements in Quantization Aware Training and Appreciation of Low Precision Computation in Deep Learning

World today is exploding with enormous amounts of multimedia data every second and technologies are being developed to understand it and make use of it in a profound way. Deep learning is the best performing branch of artificial intelligence and highly used to solve complex problems. As the demand for deep learning usage is increasing across domains, also from cloud to edge devices, the need to optimize its implementation is a highly targeted area of research where compression of deep learning models is one among them. In this paper we have proposed performance improvements by incorporation of Quantization Aware Training (QAT) of Deep Neural Networks (DNN). Among the compression techniques, this paper proposes quantization aware training in 8-bit low precision setting. Further we will introduce our implementation of fake quantization during training and inference of a deep neural network in 8-bit setting and its performance improvements over the contemporary quantization techniques. We have exhibited our achievement of better and minimized quantization loss, inference time, memory footprint in a LENET on MNIST, CIFAR-10 datasets and MobileNet Architecture on ImageNet dataset. In this method we present inference improvement of up to 11%, accuracy improvement of upto 44.75% and memory footprint reduction of up to 0.5% than post-training quantization (calibration) technique. This paper also attempts at giving a roadmap in designing better deep learning systems by considering reducing memory footprint and latency while deploying DNN in resource constraint devices (edge devices) using QAT.
Reducing Drift in Structure From Motion Using Extended Features

Low-frequency long-range errors (drift) are an endemic problem in 3D structure from motion, and can often hamper reasonable reconstructions of the scene. In this paper, we present a method to dramatically reduce scale and positional drift by using extended structural features such as planes and vanishing points. Unlike traditional feature matches, our extended features are able to span non-overlapping input images, and hence provide long-range constraints on the scale and shape of the reconstruction. We add these features as additional constraints to a state-of the-art global structure from motion algorithm and demonstrate that the added constraints enable the reconstruction of particularly drift-prone sequences such as long, low field-of-view videos without inertial measurements. Additionally, we provide an analysis of the drift-reducing capabilities of these constraints by evaluating on a synthetic dataset. Our structural features are able to significantly reduce drift for scenes that contain long-spanning man-made structures, such as aligned rows of windows or planar building facades.
Feature Fusion Methods in Deep-Learning Generic Object Detection: A Survey

Feature fusion has become one of the most popular orientations in object detection, which has been widely applied to enrich object representation, especially for the small objects. However, there has not been published an integrated survey paper that concentrate on the feature fusion methods in deep-learning object detection. Therefore, we would like to sort out the relevant content. And we believe that a comprehensive survey is necessary and can provide some useful guidance for the follow-up work. In this paper, we first introduce some classical backbone networks which adopt feature fusion methods. Then we analyses the fusion techniques of several typical or state-of-the-art frameworks. Thirdly, we present a synthesize survey of fusion strategies. At last, the future development trends and challenges are summarized. This survey infers that feature fusion methods have acquired some good results especially in recent five years, but further improvements or potential research directions still widely exist.
Democratising the Digital Revolution: The Role of Data Governance

Data is at the heart of today's AI. As AI technologies advance at a rapid pace, action is needed today to develop and implement governance structures to ensure that the benefits of AI are shared across society.
Efficient $k$-Parallel Pipelined NTT Architecture for Post Quantum Cryptography

This paper presents a high efficiency architecture of number theoretic transform (NTT) for ring learning with error (R-LWE) based cryptosystems. A customized multi-path delay feedback (MDF) NTT architecture is proposed with k-parallel pipelined data paths to perform polynomial multiplication effectively. The design was successfully accelerated on Vertex-7 FPGA platform and significantly reduced the cryptosystem's processing time, which achieved approximately 3 times smaller area-time-product (ATP) than previous approaches. The implementation results confirmed the proposed architecture's applicability for high-speed large data security applications.
Vehicle Detection and Localization for Autonomous Traffic Monitoring Systems in Unstructured Crowded Scenes

Image/video processing has been one of the major developments in the recent history with its applications in areas of Road safety, military, medical and agriculture fields. Due to its complexity a generic solution for multiple object detection in extremely crowded scenes remains to be found. Traditional methods of optical flow, connected component analysis and image segmentation have been extensively studied in image processing and video processing material. With recent developments of machine learning and numerical optimization techniques the use of deep neural networks are getting frequent in image processing applications. Among such deep learningbased methods commonly used in this context are RCNN variants, Mask RCNN and YOLOv3. An exhaustive comparison of the traditional methods and deep learning-based methods and also deep learning methods are discussed in this paper. This study will be of use in selection of a method for any extremely crowded scene object detection problem.
Person Re-Identification from different views based on dynamic linear combination of distances

Person re-identification from videos taken by multiple cameras from different views is a very challenging problem that has attracted growing interest in last years. In fact, the same person from significant cross-view has different appearances from clothes change, illumination, and cluttered background. To deal with this issue, we use the skeleton information since it is not affected by appearance and pose variations. The skeleton as an input is projected on the Grassmann manifold in order to model the human motion as a trajectory. Then, we calculate the distance on the Grassmann manifold, in order to guarantee invariance against rotation, as well as local distances allowing to discriminate anthropometric for each person. The two distances are thereafter combined while defining dynamically the optimal combination weight for each person. Indeed, a machine learning process learns to predict the best weight for each person according to the rank metric of its re-identification results. Experimental results, using challenging 3D (IAS-Lab RGBD-ID and BIWI-Lab RGBD-ID) and 2D (Prid-2011 and i-LIDS-VID) benchmarks, show that the proposed method can boost re-identification ranking thanks to its ability to define the optimal weight for each person independently of view and pose changes.
A 12.1 TOPS/W Mixed-precision Quantized Deep Convolutional Neural Network Accelerator for Low Power on Edge / Endpoint Device

This work proposes a Mixed-precision quantized deep Convolutional Neural Network Accelerator which supports mixed precision like Binary, Ternary and Integer 8-bits (INT8) for different layers. There are some layers having precision redundancy and they can be quantized to lower bits like binary or ternary. On the other hand, some layers which are sensitive to bit precision should be quantized to INT8. In case of the object detection with the Tiny-Yolov3, this accelerator realizes 12.1 TOPS/W energy efficiency by quantizing neural network to mixed precision while maintaining recognition accuracy. Furthermore, Deep neural network computation requires the use of many weight data and input data. So, memory access efficiency is very important for limited memory bandwidth on SoC (System-on-Chip). It achieved access efficiency over 90% on the DRAM bandwidth by arranging data placement on the memory to scan them efficiently. Then it achieved both low power consumption and high performance while maintaining recognition accuracy equivalent to Floating Point 32(FP32) in many neural networks.
A High-Speed KECCAK Architecture Resistant to Fault Attacks

The hash KECCAK algorithm has been proposed by the cryptographic architect with the goal to improve the hash security and the design hardware performances. The KECCAK hash algorithm has been implemented in the cryptographic circuits to ensure the hash security. It is become the standard hash algorithm used to determinate the information integrity. To protect the KECCAK hardware implementation against the fault attacks, a few numbers of fault detection schemes have been proposed. The fault attacks consist to create an erroneous KECCAK message to extract the hash secure data. In this paper, a new fault detection scheme based on modifying the KECCAK architecture is presented where the KECCAK round is divided into two blocks. We explain the details implementation of each blocks. The security simulation results demonstrate that our scheme reaches 99.995% fault coverage. In addition, the proposed scheme has been evaluated from viewpoint FPGA hardware implementation. The efficiency, the throughput, the frequency and the area have been evaluated and it is shown that our proposed scheme leads high frequency overhead and minimum area overhead compared to the previous work.
Introduction to the special issue on digital currency

Although money in digital form is not new to modern economics, advances in technology have led to a revolution in digital currencies and have facilitated instantaneous peer-to-peer transfers of val...
Application Developed on Data Hiding Using Cryptography and Steganography

In today’s world, technology is fast-moving. Humans are more dependent on computer networks to communicate with each other. A computer network accessible to all has lead to many unauthorized activities around the globe. These activities are basically controlled by the attackers who are either the envy employees or the people misusing their rights for personal gains. Vulnerabilities and security breaches occurred due to poor layout, misuse by the user or the defects of the system. In recent years, many authors proposed various approaches to enhance the security of secret data over a transmission but every one of them have their own limitations. In this paper, a model is researched and proposed by implementing enhanced tiny encryption algorithm embedding (ETEA), combining both the features of cryptography and steganography together to further improve the level of security to data.
Reconfigurable Hardware Implementation of AES-RSA Hybrid Encryption and Decryption

Based on the structure of hybrid encryption algorithm, this paper proposes an area-saving hybrid AES-RSA encryption module which can be integrated into network processing unit system. In the implementation of the algorithm, combined with the advantages of reconfigurable technology and traditional algorithms, the reconfigurable computing module which is compatible with the two algorithms is designed. At the same time, an application specific control unit is designed in the periphery, which balances the core area and the usability and flexibility of the algorithm core. The synthesis result based on SMIC 0.13um technology library shows the designed circuit can run at 200 MHz clock within the area of 163.7 thousand gates. According to the test report, the throughput of AES encryption and decryption can reach 2.1Gbps, and the throughput of RSA encryption and decryption can reach 107.2Kbps.
Interaction of Two-Level Atom with Squeezed Vacuum Reservoir

In this paper, the quantum properties of a two-level atom interaction with squeezed vacuum reservoir is throughly analyzed. With the aid of the interaction Hamiltonian and the master equation, we obtain the time evolution of the expectation values of the atomic operators. Employing the steady-state solution of these equations, we calculate the power spectrum and the second-order correlation function for the interaction of two-level atom with squeezed vacuum reservoir. It is found that the half width of the power spectrum of the light increases with the squeeze parameter, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>r</mi> </math> . Furthermore, in the absence of decay constant and interaction time, it enhances the probability for the atom to be in the upper level.
What Leads to Generalization of Object Proposals?

Object proposal generation is often the first step in many detection models. It is lucrative to train a good proposal model, that generalizes to unseen classes. Motivated by this, we study how a detection model trained on a small set of source classes can provide proposals that generalize to unseen classes. We systematically study the properties of the dataset – visual diversity and label space granularity – required for good generalization. We show the trade-off between using fine-grained labels and coarse labels. We introduce the idea of prototypical classes: a set of sufficient and necessary classes required to train a detection model to obtain generalized proposals in a more data-efficient way. On the Open Images V4 dataset, we show that only $$25\%$$ of the classes can be selected to form such a prototypical set. The resulting proposals from a model trained with these classes is only $$4.3\%$$ worse than using all the classes, in terms of average recall (AR). We also demonstrate that Faster R-CNN model leads to better generalization of proposals compared to a single-stage network like RetinaNet.
Lost in zero space – An empirical comparison of 0.y.z releases in software package distributions

Distributions of open source software packages dedicated to specific programming languages facilitate software development by allowing software projects to depend on the functionality provided by such reusable packages. The health of a software project can be affected by the maturity of the packages on which it depends. The version numbers of the used package releases provide an indication of their maturity. Packages with a 0.y.z version number are commonly assumed to be under initial development, suggesting that they are likely to be less stable, and depending on them may be considered as less healthy. In this paper, we empirically study, for four open source package distributions (Cargo, npm, Packagist and RubyGems) to which extent 0.y.z package releases and >=1.0.0 package releases behave differently. We quantify the prevalence of 0.y.z releases, we explore how long packages remain in the initial development stage, we compare the update frequency of 0.y.z and >=1.0.0 package releases, we study how often 0.y.z releases are required by other packages, we assess whether semantic versioning is respected for dependencies towards them, and we compare some characteristics of 0.y.z and >=1.0.0 package repositories hosted on GitHub. Among others, we observe that package distributions are more permissive than what semantic versioning dictates for 0.y.z releases, and that many of the 0.y.z releases can actually be regarded as mature packages. As a consequence, the version number does not provide a good indication of the maturity of a package release.
A New Cost-sensitive SVM Algorithm for Imbalanced Dataset

Support Vector Machine(SVM) is a popular machine learning algorithm for its excellent generalization ability. However, similar to most of traditional algorithms, the proposal of SVM is based on an assumption that the dataset is nearly balanced, and when SVM is applied in imbalanced dataset, the result may be bias towards majority class which leads to poor performance. To solve this problem, a new cost-sensitive SVM algorithm based on samples density are proposed in this paper. In the proposed algorithm, samples' weights are depended on sample density estimated from Kernel Density Estimation(KDE) method, and furthermore, the samples' weights are modified to enlarge the weights of border samples and reduce the weights of noise samples based on Support Vector Data Description(SVDD) algorithm. The experiments result shows that the proposed algorithm could achieve satisfactory performance.
An Autonomous Vehicle Motion Planning Method based On Dynamic Programming

In this article, we propose a new approach to solve local motion planning problem in autonomous vehicle field. Instead of generating several primitives and choosing the optimal trajectory from that, we get the future T time step horizon's vehicle state, and fit that into clothoid local trajectory by solving a dynamic programming problem. The trajectory generated is smooth enough and meet both ego vehicle's dynamics and traffic regulation's constraints. Iterate this process, the ego autonomous vehicle can obtain its future trajectory continuously. This method has a good performance in the simulation using Matlab and Prescan. This method has fast computing speed to apply on autonomous vehicle driving in real road online compared to other methods.
Characterizing Marginalization and Incremental Operations on the Bayes Tree

Perception systems for autonomy are most useful if they can operate within limited/predictable computing resources. Existing algorithms in robot navigation—e.g. simultaneous localization and mapping—employ concepts from filtering, fixed-lag, or incremental smoothing to find feasible inference solutions. Using factor graphs as a probabilistic modeling language, we emphasize the importance of marginalization operations on the equivalent Bayes (junction) tree. The objective is to elucidate the connection between simple tree-based message passing rules with the aforementioned state estimation approaches, and their frequently overlooked relation to direct marginalization on the Bayes tree. We characterize the inherent marginalization operation as part of the fundamental Chapman-Kolmogorov transit integrals which unifies many state-of-the-art approaches. The belief propagation model is then used to define five major tree inference strategies, with regard to computation recycling and resource constrained operation. A series of illustrative examples and results show the versatility of the method.
Extraction Dependency Based on Evolutionary Requirement Using Natural Language Processing

Changes in requirements are one of the critical problems that occur during requirement specification. A change in a requirement could trigger changes in other requirements. Thus the identification process requirement to respond and correct the truth, realistic, require, specific, measurable aspects. Previous work has focused on building a model of interdependency between the requirements. This study proposes a method to identify dependencies among requirements. The dependency relations refer to evolutionary requirements. The technique uses natural language processing to extract dependency relations. This research analyzes how to obtain feature extractions by including the following: 1) Gathering requirements statement from the SRS document, 2) Identifying dependencies between requirements, 3) Developing interdependency extraction methods and, 4) Modeling of the interdependency requirement. The expectation of this experiment indicates the interdependency graph model. This graph defines the interdependency in the (Software Requirement Specification) SRS document. This method gathers interdependency between SRS document requirements such as PART OF, AND, OR, & XOR. Therefore, getting the feature extraction to identify the interdependency requirement will be useful for solving specified requirements changing.
Dynamic Programming Assisted Quantization Approaches for Compressing Normal and Robust DNN Models

In this work, we present effective quantization approaches for compressing the deep neural networks (DNNs). A key ingredient is a novel dynamic programming (DP) based algorithm to obtain the optimal solution of scalar K-means clustering. Based on the approaches with regularization and quantization function, two weight quantization approaches called DPR and DPQ for compressing normal DNNs are proposed respectively. Experiments show that they produce models with higher inference accuracy than recently proposed counterparts while achieving same or larger compression. They are also extended for compressing robust DNNs, and the relevant experiments show 16X compression of the robust ResNet-18 model with less than 3% accuracy drop on both natural and adversarial examples.