Reviewerly/exHarmony huggingface dataset:
LD-GAN: Learning perturbations for adversarial defense based on GAN structure

Deep neural networks achieve outstanding performance in many tasks, so they have been widely used in many applications. However, the vulnerability of deep neural networks will produce many security threats, which drives us to provide sufficient attention to adversarial robustness. Many researchers have paid attention to addressing this problem based on the perturbation injection method, which may fail to consider the content of images that correspond to the perturbed feature while only focusing on their classification scores. In general, the existing methods often improve the robustness of the model at the expense of accuracy. In this paper, we propose LD-GAN, a novel framework to improve the adversarial robustness by learning perturbations and guaranteeing classification accuracy. The classic GAN structure is employed in this work. First, we utilize a generative model to reconstruct a training image from the corresponding perturbed feature. Then, the discriminative model is utilized to control the category. The purpose is to control the magnitude of noise addition and ensure that the noise addition does not fundamentally change the feature distribution of the original category. More specifically, we utilize the soft-attention model in the perturbation-injection module, which generates noise according to different layer concerns and improves the flexibility of the noise parameters. Extensive white-box and black-box attack experiments on CIFAR-10 and CIF-100 with state-of-the-art defense methods show the effectiveness of our method.
Integrating remote sensing data, GIS analysis and field studies for mapping alteration zones at Wadi Saqia area, central Eastern Desert, Egypt

Remote sensing data, spectral analyses, Geographic Information System (GIS) tools and field work were integrated for detecting the spatial distribution of the alteration zones in the study area. Remote sensing studies including; spectral signatures analyses and satellite image processing, were used for identifying the alteration minerals setting of the study area then extracting the alteration zones. A field study was carried out to the study area and several tens of samples were collected. The spectral behavior of the collected samples was obtained using the "Advanced Spectral Devices (ASD) TerraSpec Halo mineral identifier" handheld spectro-radiometer. Spectral analyses were carried out on signatures of selected samples to obtain the characterizing absorption features and to reveal the identity of the alteration minerals (end-members) representing the alteration zones in the area. The identified end-members included; Clay minerals, white Mica, Epidote and Iron oxides. Spectral Angle Mapper (SAM), Spectral Feature Fitting (SFF), Constrained Energy Minimization (CEM), and Mixture Tuned Matched Filtering (MTMF) alteration mapping techniques were used to map the abundance of the identified alteration minerals from ASTER satellite image data. Lineaments were extracted automatically using combined hill-shade images obtained at multi-azimuths derived from Allos PALSAR 12.5 m Digital Elevation Model (DEM) data. The alteration and lineament data were integrated together using the GIS "Weighted Overlay Analysis" tool to delineate the most potential areas for alteration zones existence. The results were validated through plotting the field observation points on the resulted final alteration map; the accuracy was estimated by about 80%.
Hatch: Self-distributing systems for data centers

Designing and maintaining distributed systems remains highly challenging: there is a high-dimensional design space of potential ways to distribute a system’s sub-components over a large-scale infrastructure; and the deployment environment for a system tends to change in unforeseen ways over time. For engineers, this is a complex prediction problem to gauge which distributed design may best suit a given environment. We present the concept of self-distributing systems, in which any local system built using our framework can learn, at runtime, the most appropriate distributed design given its perceived operating conditions. Our concept abstracts distribution of a system’s sub-components to a list of simple actions in a reward matrix of distributed design alternatives to be used by reinforcement learning algorithms. By doing this, we enable software to experiment, in a live production environment, with different ways in which to distribute its software modules by placing them in different hosts throughout the system’s infrastructure. We implement this concept in a framework we call Hatch, which has three major elements: (i) a transparent and generalized RPC layer that supports seamless relocation of any local component to a remote host during execution; (ii) a set of primitives, including relocation, replication and sharding, from which to create an action/reward matrix of possible distributed designs of a system; and (iii) a decentralized reinforcement learning approach to converge towards more optimal designs in real time. Using an example of a self-distributing web-serving infrastructure, Hatch is able to autonomously select the most suitable distributed design from among ≈700,000 alternatives in about 5 min.
Weakly Supervised Semantic Segmentation Via Progressive Patch Learning

Most of the existing semantic segmentation approaches with image-level class labels as supervision, highly rely on the initial class activation map (CAM) generated from the standard classification network. In this paper, a novel “Progressive Patch Learning” approach is proposed to improve the local details extraction of the classification, producing the CAM better covering the whole object rather than only the most discriminative regions as in CAMs obtained in conventional classification models. “Patch Learning” destructs the feature maps into patches and independently processes each local patch in parallel before the final aggregation. Such a mechanism enforces the network to find weak information from the scattered discriminative local parts, achieving enhanced local details sensitivity. “Progressive Patch Learning” further extends the feature destruction and patch learning to multi-level granularities in a progressive manner. Cooperating with a multi-stage optimization strategy, such a “Progressive Patch Learning” mechanism implicitly provides the model with the feature extraction ability across different locality-granularities. As an alternative to the implicit multi-granularity progressive fusion approach, we additionally propose an explicit method to simultaneously fuse features from different granularities in a single model, further enhancing the CAM quality on the full object coverage. Our proposed method achieves outstanding performance on the PASCAL VOC 2012 dataset ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , with 69.6 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> mIoU on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">test</i> set), which surpasses most existing weakly supervised semantic segmentation methods.
An efficient implementation of one-dimensional discrete wavelet transform algorithms for GPU architectures

Abstract In this paper, the authors present several self-developed implementation variants of the Discrete Wavelet Transform (DWT) computation algorithms and compare their execution times against the commonly approved ones for representative modern Graphics Processing Units (GPUs) architectures. The proposed solutions avoid the time-consuming modulo divisions and conditional instructions used for DWT filters wrapping by proper expansion of the DWTs input data vectors. The main goal of the research is to improve the computation times for popular DWT algorithms for representative modern GPU architectures while retaining the code’s clarity and simplicity. The relations between algorithms execution time improvements for GPUs are also compared with their counterparts for traditional sequential processors. The experimental study shows that the proposed implementations, in the case of parallel realization on GPUs, are characterized by very simple kernel code and high execution time performance.
Nearly Perfect Transmission and Transformation of Entangled States in Topologically Protected Channels

Abstract Realization of robust transmission and transformation of entangled states with high fidelity is crucial for the applications in quantum information, computing, and communications. However, it is hard to achieve currently because of scattering loss and disorder. Here, an inverse‐design scheme is theoretically proposed and experimentally demonstrated to realize nearly perfect transmission and transformation of entangled photon states. The scheme is based on the determination of the transmission and transformation properties of entangled states, which depends on the overlap integrals among the initial states, eigenmodes of the system and target states. Thus, topologically protected channels are designed according to the requirements for the overlap integrals of these states. As a result, robust transmission and transformation of entangled states are achieved with high fidelity. The proposed scheme has been demonstrated experimentally using the constructed quantum walk platform. This work interconnects topology, quantum physics, and inverse design, and opens up a new avenue for quantum engineering.
An Empirical Study of Fault Triggers in Deep Learning Frameworks

Deep learning frameworks play a key rule to bridge the gap between deep learning theory and practice. With the growing of safety- and security-critical applications built upon deep learning frameworks, their reliability is becoming increasingly important. To ensure the reliability of these frameworks, several efforts have been taken to study the causes and symptoms of bugs in deep learning frameworks, however, relatively little progress has been made in investigating the fault triggering conditions of those bugs. This paper presents the first comprehensive empirical study on fault triggering conditions in three widely-used deep learning frameworks (i.e., TensorFlow, MXNET and PaddlePaddle). We have collected 3,555 bug reports from GitHub repositories of these frameworks. A bug classification is performed based on fault triggering conditions, followed by the analysis of frequency distribution of different bug types and the evolution features. The correlations between bug types and fixing time are investigated. Moreover, we have also studied the root causes of Bohrbugs and Mandelbugs and investigated the important consequences of each bug type. Finally, the analysis of regression bugs in deep learning frameworks is conducted. We have revealed 12 important findings based on our empirical results and have provided 10 implications for developers and users.
AWaRE2-MM: A Meta-Model for Goal-Driven, Contract-Mediated, Team-Centric Autonomous Middleware Frameworks for Antifragility

In this paper, we introduce a new meta-model that captures core concepts for constructing software architectures for general-purpose, autonomous middleware frameworks that realize internalized and externalized self-adaptivity at both a system- and meta-level in order to achieve antifragility. The proposed meta-model builds on, specializes, and complements existing multi-agent meta-models in line with a previously published reference model for antifragile systems in the cyber domain.
A Multi-view Kernel Clustering framework for Categorical sequences

Multi-view clustering, which optimally integrates complementary information from different views to improve clustering performance, has drawn considerable attention in recent years. Despite recent advances, issues remain when dealing with data of high dimensionality and heterogeneity, especially in categorical sequences. These unique challenges and properties have motivated us to develop a novel M ulti-view K ernel C lustering framework for C ategorical sequences (MKCC), where views are expressed in terms of kernel matrices and a weighted combination of the instances is learned in parallel to the partitioning. Concretely, MKCC adaptively constructs the kernel matrix without the need of defining the kernel function. Nonetheless, the computational cost of storing the kernel matrix is O ( N 2 ) . To address this issue, we integrate a simple and efficient method for approximating the kernel matrix. A new multi-view clustering algorithm and a cluster validity index for categorical sequences are also proposed based on the framework. An empirical analysis on synthetic data sets and several commonly used real-world data sets demonstrates the appropriateness of the proposal, with the results showing the method’s outstanding performance. • A multi-view kernel clustering framework for categorical sequences is proposed. • This method constructs the kernel matrix directly from the sequence views. • A cluster validity index is proposed for categorical sequences. • Experimentation on synthetic and real-world data sets.
FalCon: Fine-grained Feature Map Sparsity Computing with Decomposed Convolutions for Inference Optimization

Many works focus on the model&#x2019;s static parameter optimization (e.g., filters and weights) for CNN inference acceleration. Compared to parameter sparsity, feature map sparsity is per-input related which has better adaptability. The practical sparsity patterns are non-structural and randomly located on feature maps with non-identical shapes. However, the existing feature map sparsity works take computing efficiency as the primary goal, thereby they can only remove structural sparsity and fail to match the above characteristics. In this paper, we develop a novel sparsity computing scheme called FalCon, which can well adapt to the practical sparsity patterns while still maintaining efficient computing. Specifically, we first propose a decomposed convolution design that enables a fine-grained computing unit for sparsity. Additionally, a decomposed convolution computing optimization paradigm is proposed to convert the sparse computing units to practical acceleration. Extensive experiments show that FalCon achieves at most 67.30% theoretical computation reduction with a neglected accuracy drop while accelerating CNN inference by 37%.
Fringe analysis for thickness estimation of optical glass plate using Fizeau interferometer

Shift error of modulation frequency generated from the refractive-index dispersion of a transparent glass plate and the nonlinear properties of phase modulation during wavelength tuning need to be suppressed for determining the homogeneity in optical thickness of the glass plate with wavelength tuning. In this study, we develop a new method to eliminate the dispersive error of refractive index and the nonlinear error of the phase modulation simultaneously. Moreover, a new 21-sample phase-extraction algorithm is derived using the proposed method. Characteristics of the newly designed algorithm are visualized in the frequency domain and on the complex plane. Numerical analysis show that the 21-sample algorithm has a greater error-suppression ability than other classical algorithms. The homogeneity in optical thickness of the transparent glass plate is determined by combining the proposed 21-sample algorithm and Fizeau-type interferometer . The standard deviation of thickness profiling measurement is 5.658 nm. This is significantly smaller than the standard deviation of other algorithms. Furthermore, the two-dimensional thickness distributions demonstrated that the proposed 21-sample algorithm can suppress residual ripples more effectively compared with other algorithms. • New method for designing phase-detection algorithm. • Development of two types of 21-sample extraction algorithm. • Numerical analysis of dispersive error of 21-sample algorithm. • Measurement of homogeneity in the optical thickness of glass plate.
Deeply Activated Salient Region for Instance Search

The performance of instance search relies heavily on the ability to locate and describe a wide variety of object instances in a video/image collection. Due to the lack of a proper mechanism for locating instances and deriving feature representation, instance search is generally only effective when the instances are from known object categories. In this article, a simple but effective instance-level feature representation approach is presented. Different from the existing approaches, the issues of class-agnostic instance localization and distinctive feature representation are considered. The former is achieved by detecting salient instance regions from an image by a layer-wise back-propagation process. The back-propagation starts from the last convolution layer of a pre-trained CNNs that is originally used for classification. The back-propagation proceeds layer by layer until it reaches the input layer. This allows the salient instance regions in the input image from both known and unknown categories to be activated. Each activated salient region covers the full or, more usually, a major range of an instance. The distinctive feature representation is produced by average-pooling on the feature map of a certain layer with the detected instance region. Experiments show that this kind of feature representation demonstrates considerably better performance than most of the existing approaches.
Student and Research and Teaching Staff Engagement as an Indicator of Corporate Culture Assessment and a Tool for University’s Personnel Reserve Formation

The article analyzes the results of measuring the parameters of the engagement of students and research and teaching staff of the university. Based on the experience of the leading consulting companies, MEPhI has developed a technology for assessing the engagement parameters used to measure the level of emotional state of personnel. A special feature of the developed methods for assessing the engagement parameters is the possibility of practical application of the results of analytical research for subsequent management decision-making, as well as the use of the proposed approach by the organizations of higher education. The assessment of the parameters of engagement is focused on solving the problems of increasing the competitiveness of the university by improving the key areas affecting the development of corporate culture: performance management, the possibility of personnel professional development and career growth, the creation of adaptive mechanisms of remuneration and recognition, the development of effective communication methods, improvement of decision-making in the field of infrastructure support, the development of a personnel reserve and mechanisms for adaptation and attraction of young employees. The developed methods for assessing the parameters of engagement incorporate the best practices of domestic and foreign high-tech companies, including the experience of Rosatom State Corporation. The results of the engagement parameters assessment are considered as tools for carrying out work to raise the University performance due to improving the University corporate culture and forming high-potential personnel reserve.
Verifiable medical images for E-healthcare: A novel watermarking approach using robust bit-wise association of self-mutating offsprings of pixels

In the current difficult time of COVID-19 pandemic, social distancing is a common practice adopted by healthy and infected people all over the world. Because of this, electronic healthcare and Telemedicine are coming up a big way. One of the most vital challenges in E-healthcare or telemedicine is the authentication and secure transmission of medical images received by an expert(doctor) at a far-off location from the sender(patient). To address the critical authentication issue, this paper proposes a blind pixel-based self-embedding fragile watermarking approach that is effective enough to localize the tampered region with more than 90% accuracy. The self-embedding approach plays a significant role in fragile watermarking for detecting the tampered region at the receiver side. Most of the self-embedding-based fragile watermarking approaches available in the literature are block-based which has a high False Positive Rate(FPR). In the proposed approach, every pixel of the image dynamically generates eight self-mutating offsprings and gets associated with them tightly at bit level. After a systematic set of procedures on that offsprings with a triple layer of security, a single authentication bit is selected using 16 × 1 multiplexer for each pixel intensity of the image. That is why the proposed approach has a high imperceptibility level (more than 60 dB PSNR). Experimental results confirm the efficacy of the proposed approach in terms of imperceptibility and tamper detection rate.
RPO: Receiver-driven Transport Protocol Using Opportunistic Transmission in Data Center

Modern datacenter applications bring fundamental challenges to transport protocols as they simultaneously require low latency and high throughput. Recent receiver-driven trans-port protocols transmit only one data packet once receiving each grant or credit packet from the receiver to achieve ultra-low queueing delay and zero packet loss. However, the round-trip time variation and the highly dynamic background traffic significantly deteriorate the performance of receiver-driven transport protocols, resulting in under-utilized bandwidth. This paper designs a simple yet effective solution called RPO that retains the advantages of receiver-driven transmission while efficiently utilizing the available bandwidth. Specifically, RPO rationally uses low-priority opportunistic packets to ensure high network utilization without increasing the queueing delay of high-priority normal packets. In addition, since RPO only uses Explicit Congestion Notification (ECN) marking function and priority queues, RPO is ready to deploy on switches. We implement RPO in Linux hosts with DPDK. Our small-scale testbed experiments and large-scale simulations show that RPO significantly improves the network utilization by up to 35% under high workload over the state-of-the-art receiver-driven transmission schemes, without introducing additional queueing delay.
SiamMixer: A Lightweight and Hardware-Friendly Visual Object-Tracking Network

Siamese networks have been extensively studied in recent years. Most of the previous research focuses on improving accuracy, while merely a few recognize the necessity of reducing parameter redundancy and computation load. Even less work has been done to optimize the runtime memory cost when designing networks, making the Siamese-network-based tracker difficult to deploy on edge devices. In this paper, we present SiamMixer, a lightweight and hardware-friendly visual object-tracking network. It uses patch-by-patch inference to reduce memory use in shallow layers, where each small image region is processed individually. It merges and globally encodes feature maps in deep layers to enhance accuracy. Benefiting from these techniques, SiamMixer demonstrates a comparable accuracy to other large trackers with only 286 kB parameters and 196 kB extra memory use for feature maps. Additionally, we verify the impact of various activation functions and replace all activation functions with ReLU in SiamMixer. This reduces the cost when deploying on mobile devices.