Reviewerly/exHarmony huggingface dataset:
SecGNN: Privacy-Preserving Graph Neural Network Training and Inference as a Cloud Service

Graphs are widely used to model the complex relationships among entities. As a powerful tool for graph analytics, graph neural networks (GNNs) have recently gained wide attention due to its end-to-end processing capabilities. With the proliferation of cloud computing, it is increasingly popular to deploy the services of complex and resource-intensive model training and inference in the cloud due to its prominent benefits. However, GNN training and inference services, if deployed in the cloud, will raise critical privacy concerns about the information-rich and proprietary graph data (and the resulting model). While there has been some work on secure neural network training and inference, they all focus on convolutional neural networks handling images and text rather than complex graph data with rich structural information. In this article, we design, implement, and evaluate SecGNN, the first system supporting privacy-preserving GNN training and inference services in the cloud. SecGNN is built from a synergy of insights on lightweight cryptography and machine learning techniques. We deeply examine the procedure of GNN training and inference, and devise a series of corresponding secure customized protocols to support the holistic computation. Extensive experiments demonstrate that SecGNN achieves comparable plaintext training and inference accuracy, with promising performance.
ConSolid: A federated ecosystem for heterogeneous multi-stakeholder projects

In many industries, multiple parties collaborate on a larger project. At the same time, each of those stakeholders participates in multiple independent projects simultaneously. A double patchwork can thus be identified, with a many-to-many relationship between actors and collaborative projects. One key example is the construction industry, where every project is unique, involving specialists for many subdomains, ranging from the architectural design over technical installations to geospatial information, governmental regulation and sometimes even historical research. A digital representation of this process and its outcomes requires semantic interoperability between these subdomains, which however often work with heterogeneous and unstructured data. In this paper we propose to address this double patchwork via a decentralized ecosystem for multi-stakeholder, multi-industry collaborations dealing with heterogeneous information snippets. At its core, this ecosystem, called ConSolid, builds upon the Solid specifications for Web decentralization, but extends these both on a (meta)data pattern level and on microservice level. To increase the robustness of data allocation and filtering, we identify the need to go beyond Solid’s current LDP-inspired interfaces to a Solid Pod and introduce the concept of metadata-generated ‘virtual views’, to be generated using an access-controlled SPARQL interface to a Pod. A recursive, scalable way to discover multi-vault aggregations is proposed, along with data patterns for connecting and aligning heterogeneous (RDF and non-RDF) resources across vaults in a mediatype-agnostic fashion. We demonstrate the use and benefits of the ecosystem using minimal running examples, concluding with the setup of an example use case from the Architecture, Engineering, Construction and Operations (AECO) industry.
Performance Analysis of Classical and Evolutionary Algorithms for Mining Association Rules

Association rule mining techniques based on support and confidence, generates a large number of rules - many of which are useless. As a result, post analysis is needed. For such an optimization problem evolutionary algorithms can be used as they use objective functions that measures the interestingness of rules better. We apply classical and evolutionary algorithms on different types of datasets. Though none of the algorithms is superior than the others - some of the rules obtained by evolutionary algorithms, could not be obtained by classical algorithms. Time is also a great issue that get affected significantly by support value. To our knowledge, there is no work to compare classical (Apriori and FP-growth) and evolutionary (EARMGA and MOEA) algorithms focusing on their performances in different phases of execution. In this work, detailed information is specified according to some of the major components with various aspects. The whole process is treated from data mining perspective and discussed the issues responsible for affecting the performance of the algorithms.
Formalized Inversion of Geophysical Data Using Neural Network Technologies with Application to the Tasks of Geoelectrics and Gravimetry

Summary The approximation neural network (ANN) method for solving conditionally well-posed nonlinear inverse problems of geophysics is presented. The method is based on the neural network approximation of the inverse operator. The ANN method and its modifications make it possible to find stable approximate solutions of 2D and 3D inverse coefficient problems of geoelectrics in the class of grid models of media on a regularized parameterization grid with a practically acceptable accuracy without setting the first approximation. Estimates of the ambiguity degree (error), which do not depend on the inversion method applied, can be calculated for resulting approximate solutions of the inverse problem. The a posteriori estimates of the degree of ambiguity of the approximated solutions are calculated. The work of the method is illustrated by the example of the 3D inversion of synthesized area data and 2D real geoelectric data by the method MTS. It is shown that it is possible in principle to use the ANN method for solving solving conditionally well-posed nonlinear 3D gravity inverse problems (determining the geometry of the lower boundary).
Experimental Analysis of the Pixel Non Uniformity (PNU) in SEM for Digital Forensics Purposes

Recent years saw an explosion in the number of the counterfeit or stolen images in scientific papers. In particular in the field of biomedical science publication this is becoming a serious problem for the health and economic issues caused by this fraud [1]. In this paper we investigate the possibility to extend a technique commonly used in image forensics to associate a given image with the camera used to take it. The original technique, proposed by Fridrich et al. in [3] uses the PNU, a unique fingerprint present in each photo and generated by natural the imperfection in the silicium slice that composes the Charge-Coupled Device (CCD) sensor. We analyze the quality of the PNU present in the residual noise by evaluating the quality of this noise using its variance. The experimental results shows that some PNU is still present in the residual noise, but is less than the one present in photo from digital cameras. This technique of evaluation is promisingly because is possible to use also to speedup the source camera identification process in videos by excluding the frames that not preserving enough PNU in the residual noise.
Resilient Sensor Management for Dismounted Assured-PNT

Accurate and reliable Position, Navigation, and Timing (PNT) information is vital in military applications for mission success. In this work, the solution to vulnerability of GPS systems for dismounted warfighters is to provide a resilient and assured-PNT capability using low-cost, COTS systems. This article will provide details on the development of an architecture and intelligent small SWaP-C system-based GPS (SAASM)/IMU/ holdover-oscillator /vision multisensory integration to provide assured-PNT for operational domains in naturally and/or intentionally GPS-denied environments. The system allows real-time GPS assurance through multisensory integrity assessment, which enhances situational awareness and jamming/spoofing resiliency. The architecture of the assured PNT system includes: (1) stable holdover oscillator for sensor time synchronization; (2) an intelligent dead-reckoning module; (3) visual-aiding to the dead-reckoning module to constrain motion relative to observed environment; and (4) detection of jamming and RF interference to the GPS receiver order to expand the GPS signals operational envelope. The architecture allows resilient sensor management and multi-sensor fusion, including (a) incorporating measures of reliability into measurement fusion for robust PNT estimates; (b) detecting sensor faults and resilient sensor management; (c) smooth transition from GPS availability to GPS-denied and vice versa; (d) support for civilian GPS receivers to combine both military and civil signals from multiple GNSS’s; (e) using redundant IMUs to increase performance during GPS outages; and (f) using the holdover oscillator (master-clock) over a distributed network for TDOA/TOA positioning. Considering the system design for dismounted warfighters, the dead-reckoning module was developed to treat the human body as a virtual sensor used in estimating the dismount’s locomotion pattern. A closed feedback between the dead-reckoning module and 3D vision modeling is the main advantage of the solution. The solution couples a deep integration of vision-aided personal navigation system and the human body as a navigation sensor to facilitate dead-reckoning navigation in indoor environments.
Neural Architecture Search for Lightweight Non-Local Networks

Non-Local (NL) blocks have been widely studied in various vision tasks. However, it has been rarely explored to embed the NL blocks in mobile neural networks, mainly due to the following challenges: 1) NL blocks generally have heavy computation cost which makes it difficult to be applied in applications where computational resources are limited, and 2) it is an open problem to discover an optimal configuration to embed NL blocks into mobile neural networks. We propose AutoNL to overcome the above two obstacles. Firstly, we propose a Lightweight Non-Local (LightNL) block by squeezing the transformation operations and incorporating compact features. With the novel design choices, the proposed LightNL block is 400x computationally cheaper} than its conventional counterpart without sacrificing the performance. Secondly, by relaxing the structure of the LightNL block to be differentiable during training, we propose an efficient neural architecture search algorithm to learn an optimal configuration of LightNL blocks in an end-to-end manner. Notably, using only 32 GPU hours, the searched AutoNL model achieves 77.7% top-1 accuracy on ImageNet under a typical mobile setting (350M FLOPs), significantly outperforming previous mobile models including MobileNetV2 (+5.7%), FBNet (+2.8%) and MnasNet (+2.1%). Code and models are available at https://github.com/LiYingwei/AutoNL.
DHP Framework: Digital Health Passports Using Blockchain -- Use case on international tourism during the COVID-19 pandemic

In order to contain the COVID-19 pandemic, several countries enforced extended social distancing measures for several weeks, effectively pausing the majority of economic activities. In an effort to resume economic activity safely, several Digital Contact Tracing applications and protocols have been introduced with success. However, DCT is a reactive method, as it aims to break existing chains of disease transmission in a population. Therefore DCT is not suitable for proactively preventing the spread of a disease; an approach that relevant to certain use cases, such as international tourism, where individuals travel across borders. In this work, we first identify the limitations characterising DCT related to privacy issues, unwillingness of the public to use DCT mobile apps due to privacy concerns, lack of interoperability among different DCT applications and protocols, and the assumption that there is limited, local mobility in the population. We then discuss the concept of a Health Passport as a means of verifying that individuals are disease risk-free and how it could be used to resume the international tourism sector. Following, we present the DHP Framework that uses a private blockchain and Proof of Authority for issuing Digital Health Passports. The framework provides a distributed infrastructure supporting the issuance of DHPs by foreign health systems and their verification by relevant stakeholders, such as airline companies and border control authorities. We discuss the attributes of the system in terms of its usability and performance, security and privacy. Finally, we conclude by identifying future extensions of our work on formal security and privacy properties that need to be rigorously guaranteed via appropriate security protocols.
You say Normalizing Flows I see Bayesian Networks

Normalizing flows have emerged as an important family of deep neural networks for modelling complex probability distributions. In this note, we revisit their coupling and autoregressive transformation layers as probabilistic graphical models and show that they reduce to Bayesian networks with a pre-defined topology and a learnable density at each node. From this new perspective, we provide three results. First, we show that stacking multiple transformations in a normalizing flow relaxes independence assumptions and entangles the model distribution. Second, we show that a fundamental leap of capacity emerges when the depth of affine flows exceeds 3 transformation layers. Third, we prove the non-universality of the affine normalizing flow, regardless of its depth.
Never Neutral, Never Alone

Library science is getting harder to teach. The variety in libraries of all types is increasing as more and more mold themselves to their communities rather than field-wide norms. How can library science education change to meet the new variety, and the variety in a post-neutrality world? The author proposes new metrics for evaluating the effectiveness of LIS programs: Agility—What ongoing methods are in place to identify, evaluate, and prepare students for developments in a rapidly changing profession? Connectedness—Who are the partners networked with the program and its faculty to ensure direct connection of the classroom to the field? Embeddedness—What is the program’s ability to deliver authentic field experiences to students that allow them to contextualize theory and research methods? Resilience—How prepared are librarians to face, understand (i.e., analyze), and solve the problems in a community in line with the professional mission and values of librarianship?
A Feature Oriented Framework and Enhanced Assessments for Imaging Compression

This paper offers a new feature-oriented compression algorithm for flexible reduction of data redundancy commonly found in images and videos streams. Using a combination of image segmentation and face detection techniques as a preprocessing step, we derive a compression framework to adaptively treat `feature' and `ground' while balancing the total compression and quality of `feature' regions. We demonstrate the utility of a feature compliant compression algorithm (FC-SVD), a revised peak signal-to-noise ratio PSNR assessment, and a relative quality ratio to control artificial distortion. The goal of this investigation is to provide new contributions to image and video processing research via multi-scale resolution and the block-based adaptive singular value decomposition.
Parameterized Structured Pruning for Deep Neural Networks

As a result of the growing size of Deep Neural Networks (DNNs), the gap to hardware capabilities in terms of memory and compute increases. To effectively compress DNNs, quantization and pruning are usually considered. However, unconstrained pruning usually leads to unstructured parallelism, which maps poorly to massively parallel processors, and substantially reduces the efficiency of general-purpose processors. Similar applies to quantization, which often requires dedicated hardware. We propose Parameterized Structured Pruning (PSP), a novel technique to dynamically learn the shape of DNNs through structured sparsity. PSP parameterizes structures (e.g. channel- or layer-wise) in a weight tensor and leverages weight decay to learn a clear distinction between important and unimportant structures. As a result, PSP maintains prediction performance, creates a substantial amount of sparsity that is structured and, thus, easy and efficient to map to a variety of massively parallel processors, which are mandatory for utmost compute power and energy efficiency.
A Comparative Study of Java and Kotlin for Android Mobile Application Development

There are several programming languages that can be used to develop Android application, such as C++, Java, Kotlin, JavaScript, and many more. Each programming language certainly has some advantages and disadvantages in the development of Android application. Therefore, a comparison to see the values of those programming languages is needed. This research compared two popular programming languages for Android application development, namely Java and Kotlin. The testing was done by building two applications using Java and Kotlin that access data in a remote server. Our comparison includes source code evaluation, testing on the performance of the app performed on two devices, and testing on the data usage. From the test results, it was proved that Kotlin is superior in terms of more concise lines of code and less data usage which will reduce bugs substantially therefore will lead to faster development time. On the other hand, Java is superior in its compiling time (for first time build but on par with Kotlin for incremental build) and APK size albeit not significant. We also compare ecosystem and programming language constructs for both programming languages. Overall, for Android mobile application development, Java should be used if mobile application development priority is the APK size and compilation / build time while Kotlin should be used if mobile application development priority is lesser bugs, concise code, and faster development time.
An Invocation Chain Test and Evaluation Method Based on Knowledge Graph

The paper, on basis of the software vulnerability description specification, works out the vulnerability knowledge base for vulnerability combination, completes the specification modeling of target environment information and vulnerability description, proposes the vulnerability instantiation attack mode, obtains the atomic attack set, improves the process of atomic attack node combination, and finally proposes the automatic construction algorithm of invocation chain to get the vulnerability combination information and invocation chain information of the target system. The experimental results show that the invocation chain test and evaluation method based on knowledge graph can evaluate the system risk quantitatively rather than qualitatively by calculating comprehensive probability on all test sequences.
WITHDRAWN: Secure medical image transmission using modified leading diagonal sorting with probabilistic visual cryptography

Informations are being sent through open channels and the security of those informations has been primary considerations. Aside from numerous regular cryptographic techniques, Visual Cryptographic (VC) methods have likewise been being used for information and data security. VC is a secret sharing method as it breaks the original image into share images in order, when the shares are stacked on each other a hidden secret image is uncovered. The VC technique is a secure technique that encodes a secret file or image by breaking it into share images. Cloud computing is a progression model for large scale systems. Security for medical image transmission has been an ongoing computational problem from image recovery to its processing, storage, and retrieval with the advancement of data in the medical sector and health care systems into the cloud. However, the role of Cloud Computing is applied only if security is assured. Modified Leading Diagonal Sorting with Probabilistic Visual Cryptography (MLDS-PVC) is proposed in this research. Breast cancer mammography images from the MIAS dataset are used in this work for medical images. The proposed model could support the transmission of medical images using cloud storage between medical organizations, diagnostics centers, and other health centers.
U-SDRC: a novel deep learning-based method for lesion enhancement in liver CT images

The contrast enhancement of tumor regions in medical images can improve the performance of tumor detection, segmentation, and diagnosis. However, the main existing enhancement methods aim to enhance the contrast and the resolution on the whole image, instead of highlighting the lesion regions. The blurry edges lead to the difficulty of distinguishing the tumor from the healthy tissues accurately. This issue can be hardly solved by those global enhancement methods. In this paper, we focus on the local enhancement and propose a novel deep learning-based approach called U-SDRC to enhance the contrast between tumor regions and surrounding background tissues to make the tumor regions distinguishable. We introduce a U-net deep network to tackle this problem and present a novel SDRC loss function to achieve the goal of enhancing tumor lesions and simultaneously preserving the original appearance of other regions in the image. We evaluate our approach on a clinical dataset that comprises 1394 liver CT slices. The encouraging experimental results show that the proposed method can lead to a good visual enhancement effect and bring improvements to medical tasks such as tumor segmentation and diagnosis.
Assessing Performance of Multitemporal SAR Image Despeckling Filters via a Benchmarking Tool

In this work we present a novel benchmarking framework for assessing the quality of despeckled multitemporal SAR images on an objective basis. Taking cues from a recent work providing a useful tool for measuring the performance of single-channel despeckling filters, here we extend that analysis to multitemporal filters and datasets. Due to the lack of reference speckle-free real SAR images, the quantitative performance parameters introduced here are evaluated on simulated time series of canonical scenes obtained through a reliable and well-assessed SAR simulator which accounts for both geometrical and electromagnetic properties of the scattering surface. First we analyze image quality over datasets with homogeneous reflectivity in time. Then, for a more realistic performance assessment in practical situations, ad-hoc quality parameters are introduced to measure the effects of time-varying scene characteristics on the despeckled dataset. The consistency of the proposed framework is tested on state-of-the-art multitemporal despeckling algorithms.