Reviewerly/exHarmony huggingface dataset:
An Efficient and Precise Remote Sensing Optical Image Matching Technique Using Binary-Based Feature Points

Matching local feature points is an important but crucial step for various optical image processing applications, such as image registration, image mosaicking, and structure-from-motion (SfM). Three significant issues associated with this subject have been the focus for years, including the robustness of the image features detected, the number of matches obtained, and the efficiency of the data processing. This paper proposes a systematic algorithm that incorporates the synthetic-colored enhanced accelerated binary robust invariant scalar keypoints (SC-EABRISK) method and the affine transformation with bounding box (ATBB) procedure to address these three issues. The SC-EABRISK approach selects the most representative feature points from an image and rearranges their descriptors by adding color information for more precise image matching. The ATBB procedure, meanwhile, is an outreach that implements geometric mapping to retrieve more matches from the feature points ignored during SC-EABRISK processing. The experimental results obtained using benchmark imagery datasets, close-range photos (CRPs), and aerial and satellite images indicate that the developed algorithm can perform up to 20 times faster than the previous EABRISK method, achieve thousands of matches, and improve the matching precision by more than 90%. Consequently, SC-EABRISK with the ATBB algorithm can address image matching efficiently and precisely.
A Discriminative Vectorial Framework for Multi-Modal Feature Representation

Due to the rapid advancements of sensory and computing technology, multi-modal data sources that represent the same pattern or phenomenon have attracted growing attention. As a result, finding means to explore useful information from these multi-modal data sources has quickly become a necessity. In this paper, a discriminative vectorial framework is proposed for multi-modal feature representation in knowledge discovery by employing multi-modal hashing (MH) and discriminative correlation maximization (DCM) analysis. Specifically, the proposed framework is capable of minimizing the semantic similarity among different modalities by MH and exacting intrinsic discriminative representations across multiple data sources by DCM analysis jointly, enabling a novel vectorial framework of multi-modal feature representation. Moreover, the proposed feature representation strategy is analyzed and further optimized based on canonical and non-canonical cases, respectively. Consequently, the generated feature representation leads to effective utilization of the input data sources of high quality, producing improved, sometimes quite impressive, results in various applications. The effectiveness and generality of the proposed framework are demonstrated by utilizing classical features and deep neural network (DNN) based features with applications to image and multimedia analysis and recognition tasks, including data visualization, face recognition, object recognition; cross-modal (text-image) recognition and audio emotion recognition. Experimental results show that the proposed solutions are superior to state-of-the-art statistical machine learning (SML) and DNN algorithms.
Construction of Rural Cultural Service System Based on Mobile Information System

At present, the development of rural culture is gradually rising. Under the promotion of the sense of freedom and democracy, mobile information system also plays an important role in rural cultural services. This paper uses edge algorithm to build mobile information system, combined with rural cultural service elements, to carry out information construction for the people’s needs of tourism, information, and belief culture in the development of rural China. In this paper, with the county or township service projects as the research content and 200 residents of a community as the research object, using the way of questionnaire to survey the respondents, online questionnaire feedback results received 186, the effective rate of 93%. This paper investigates the project support of mobile information system for rural cultural services and the satisfaction degree of residents’ demand for various cultural resources. Research shows that the rural cultural service system based on mobile information system can improve the scope of cultural services. From the survey results, 65.53% of the people think that the cultural service system can reduce people's dependence on special cultural services, so as to obtain resources from the diversified information service system. 35.63% of the people said that the efficiency of cultural service system could be improved by more than 50% compared to that of offline cultural service. Others said that the rural cultural service system could basically meet their living needs. Therefore, whether from the perspective of cultural service or information system, the current rural cultural service mode and service process need to be constructed from the perspective of cultural service supply system reform.
Notable Papers and New Directions in Sensors, Signals, and Imaging Informatics

To identify and highlight research papers representing noteworthy developments in signals, sensors, and imaging informatics in 2020.A broad literature search was conducted on PubMed and Scopus databases. We combined Medical Subject Heading (MeSH) terms and keywords to construct particular queries for sensors, signals, and image informatics. We only considered papers that have been published in journals providing at least three articles in the query response. Section editors then independently reviewed the titles and abstracts of preselected papers assessed on a three-point Likert scale. Papers were rated from 1 (do not include) to 3 (should be included) for each topical area (sensors, signals, and imaging informatics) and those with an average score of 2 or above were subsequently read and assessed again by two of the three co-editors. Finally, the top 14 papers with the highest combined scores were considered based on consensus.The search for papers was executed in January 2021. After removing duplicates and conference proceedings, the query returned a set of 101, 193, and 529 papers for sensors, signals, and imaging informatics, respectively. We filtered out journals that had less than three papers in the query results, reducing the number of papers to 41, 117, and 333, respectively. From these, the co-editors identified 22 candidate papers with more than 2 Likert points on average, from which 14 candidate best papers were nominated after intensive discussion. At least five external reviewers then rated the remaining papers. The four finalist papers were found using the composite rating of all external reviewers. These best papers were approved by consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board.Sensors, signals, and imaging informatics is a dynamic field of intense research. The four best papers represent advanced approaches for combining, processing, modeling, and analyzing heterogeneous sensor and imaging data. The selected papers demonstrate the combination and fusion of multiple sensors and sensor networks using electrocardiogram (ECG), electroencephalogram (EEG), or photoplethysmogram (PPG) with advanced data processing, deep and machine learning techniques, and present image processing modalities beyond state-of-the-art that significantly support and further improve medical decision making.
End to end secure e-voting using blockchain &amp; quantum key distribution

A blockchain is a revolutionary computational data structure that enables an open, public, distributed ledger with a wide range of uses. Any new cryptographic application, on the other hand, should take into consideration expected technical improvement during the lifetime of any possibly deployed systems, many of which will be in use for decades. This document addresses the weaknesses of blockchain technology as a result of the development of quantum computers, as well as general recommendations for making blockchain more resistant to such technological breakthroughs. As a result, utilizing a special approach, this work shows many steps of the suggested electronic voting system. The method contains the idea of electronic voting using Quantum key distribution and blockchain security. Explicitly an extended version of the blockchain-based e-voting system to several networks applications. This work also describes the architecture, design, and design constraints of such a voting system in our society with implementation implications.
Efficient Facial Recognition Authentication Using Edge and Density Variant Sketch Generator

Image translation plays a significant role in realistic image synthesis, entertainment tasks such as editing and colorization, and security including personal identification. In Edge GAN, the major contribution is attribute guided vector that enables high visual quality content generation. This research study proposes automatic face image realism from freehand sketches based on Edge GAN. We propose a density variant image synthesis model, allowing the input sketch to encompass face features with minute details. The density level is projected into non-latent space, having a linear controlled function parameter. This assists the user to appropriately devise the variant densities of facial sketches and image synthesis. Composite data set of Large Scale CelebFaces Attributes (ClebA), Labelled Faces in the Wild (LFWH), Chinese University of Hong Kong (CHUK), and self-generated Asian images are used to evaluate the proposed approach. The solution is validated to have the capability for generating realistic face images through quantitative and qualitative results and human evaluation.
Shared Certificates for Neural Network Verification

Abstract Existing neural network verifiers compute a proof that each input is handled correctly under a given perturbation by propagating a symbolic abstraction of reachable values at each layer. This process is repeated from scratch independently for each input (e.g., image) and perturbation (e.g., rotation), leading to an expensive overall proof effort when handling an entire dataset. In this work, we introduce a new method for reducing this verification cost without losing precision based on a key insight that abstractions obtained at intermediate layers for different inputs and perturbations can overlap or contain each other. Leveraging our insight, we introduce the general concept of shared certificates, enabling proof effort reuse across multiple inputs to reduce overall verification costs. We perform an extensive experimental evaluation to demonstrate the effectiveness of shared certificates in reducing the verification cost on a range of datasets and attack specifications on image classifiers including the popular patch and geometric perturbations. We release our implementation at https://github.com/eth-sri/proof-sharing .
Interpretable Fault Diagnosis for Cyberphysical Systems: A Learning Perspective

Cyberphysical systems (CPSs) are vulnerable to catastrophic fault propagation due to the strong connectivity among their subsystems. This article introduces a learning-based method to enable CPSs to explain their faults to human users, facilitating effective and efficient collaborative error diagnosis.
Convex optimization of random dynamic voltage and frequency scaling against power attacks

Convex optimization is explored in this paper to maximize the security against power analysis attacks while minimizing the power and performance overheads for a cryptographic circuit employs a random dynamic voltage and frequency scaling (RDVFS) technique. To realize this optimization goal, firstly, non-convex functions are used for modeling all the critical parameters related to the RDVFS technique to create a non-convex objective function. Subsequently, the non-convex objective function is appropriately converted into a convex objective function in order to grab the global optimum value. By transforming the acquired convex objective function into the Lagrange dual function and using Karush–Kuhn–Tucker (KKT) conditions to solve this optimization problem, the best RDVFS strategy can be uncovered. As shown in the result, after employing convex optimization for a cryptographic circuit with a RDVFS technique, the corresponding figure-of-merit (FOM) is enhanced by 43.4%. Moreover, in comparison with the conventional machine learning technique, the proposed convex optimization technique improves the overall FOM related with RDVFS technique by 17.6% while reducing 85.3% computational time.
Clustering Hidden Markov Models With Variational Bayesian Hierarchical EM

The hidden Markov model (HMM) is a broadly applied generative model for representing time-series data, and clustering HMMs attract increased interest from machine learning researchers. However, the number of clusters ( K ) and the number of hidden states ( S ) for cluster centers are still difficult to determine. In this article, we propose a novel HMM-based clustering algorithm, the variational Bayesian hierarchical EM algorithm, which clusters HMMs through their densities and priors and simultaneously learns posteriors for the novel HMM cluster centers that compactly represent the structure of each cluster. The numbers K and S are automatically determined in two ways. First, we place a prior on the pair (K,S) and approximate their posterior probabilities, from which the values with the maximum posterior are selected. Second, some clusters and states are pruned out implicitly when no data samples are assigned to them, thereby leading to automatic selection of the model complexity. Experiments on synthetic and real data demonstrate that our algorithm performs better than using model selection techniques with maximum likelihood estimation.
Mining high utility patterns in interval-based event sequences

Sequential pattern mining is an interesting research area with broad range of applications. Most prior research on sequential pattern mining has considered point-based data where events occur instantaneously. However, in many application domains, events persist over intervals of time of varying lengths. Furthermore, traditional frameworks for sequential pattern mining assume all events have the same weight or utility. This simplifying assumption neglects the opportunity to find informative patterns in terms of utilities, such as profits. To address these issues, we incorporate the concept of utility into interval-based sequences and define a framework to mine high utility patterns in interval-based sequences i.e., patterns whose utility meets or exceeds a minimum threshold. In the proposed framework, the utility of events is considered while assuming multiple events can occur coincidentally and persist over varying periods of time. An algorithm named High Utility Interval-based Pattern Miner (HUIPMiner) is proposed and applied to real datasets. To achieve an efficient solution, HUIPMiner is augmented with two effective pruning strategies. Experimental results show that HUIPMiner is an effective solution to the problem of mining high utility interval-based sequences. Moreover, it is shown that the execution time of the algorithm is reduced when the proposed pruning strategies are applied.
Implementation of Assistive Technologies and Robotics in Long-Term Care Facilities: A Three-Stage Assessment Based on Acceptance, Ethics, and Emotions

Assistive technologies including assistive robots (AT/AR) appear to be a promising response to the increasing prevalence of older adults in need of care. An increasing number of long-term care facilities (LTCFs) try to implement AT/AR in order to create a stimulating environment for aging well and to reduce workload for professional care staff. The implementation of new technologies in an organization may lead to noticeable cultural changes in terms of social interactions and care practices associated with positive or negative emotions for the employees. This applies especially for LTCFs with high rates of vulnerable residents affected by increasing care needs and specific ethics in nursing and cultural rules within the setting. Thus, systematic consideration in leadership management of emotions and ethical aspects is essential for stakeholders involved in the implementation process. In this article, we explicitly focus on the emotions of the employees and leaders within LTCFs. We relate to direct consequences for the organizational well-being and culture, which is of course (indirectly) affecting patients and residents. While aspects of technology acceptance such as safety and usefulness are frequently discussed in academic literature, the topic of emotion-management and ethical questions during the organizational implementation process in LTCFs received little attention. Emotional culture entails affective values, ethical norms and perceptions of employees and further investigation is needed to address the importance of transformational leadership during implementation process. For this purpose, we developed a three-staged assessment tool for implementation of AT/AR in long-term care institutions. Acceptance (A), ethical acceptability (A) and emotional consequences (E) are considered as comprehensive assessment, in which emotional consequences comprise management aspects of transformational leadership (T), emotion-management (E) and organizational culture (O). Based on AAE and TEO, this paper presents an integrated framework illustrated with a illustrative example and aims to combine established approaches with ethical insights in order to unfold potentials of AT/AR in LTCSs.
Spectral Albedo in Bifacial Photovoltaic Modeling: What can be learned from Onsite Measurements?

This contribution reports on a yearlong spectral albedo measurement campaign performed in Roskilde, Denmark.Four albedo scenarios are monitored using three sensor types.The ground surfaces include green grass, dry grass, gravel, and snow all of which have been monitored with albedometers based on spectroradiometers, silicon-pyranometers, and thermopile pyranometers.Implications of using the various albedo data sources/assumptions in bifacial PV modeling are assessed with the spectrally weighted bifacial energy gain (BEG).We find that BEG differs by as much as 3% with the different albedo sensors and BEG can deviate by as much as 7% from the ground truth when an incorrect static spectral albedo assumption is used.Finally, the spectral mismatch factor (SMM) is calculated to summarize rear plane of array (POA) spectral shifts.Our measurements show midday backside POA spectral shifts as high as 25% for Silicon bifacial PV devices mounted on single axis trackers above grass.
Blockchain Enterprise: Use Cases on Multiple Industries

The chapter is aimed at explaining the application/use cases of blockchain to bring about transparency, transparency, timeliness, and cost-effectiveness in selected sectors of Indian economy and some areas with respect to a few other countries. The blockchain being a new age technology has attracted many parties/stakeholders to take up initiatives in developing a suitable and a sustainable ecosystem for using it to gain maximum benefits in various application areas/sectors/industries. Blockchain-based technological solutions are being used by banks, currency exchanges, and insurance players and in further applications in other areas such as agriculture, supply chain management, and land record maintenance. Many of these are still being explored in India. This chapter discusses use cases of blockchain enterprise in various sectors like financial services, agriculture, insurance, supply chain management, banking and financial services, and fertilizer subsidy distribution. The issues relating to different sectors are explained first, and then, the process of connecting the existing system/process/technology with block chain to improve efficiency and effectiveness is delineated in detail.
VM placement and cache based side channel attack at computational level in cloud computing : A Survey

AbstractAbstractThere are different issues that need to focus in respect of security and privacy. One of the most security issue at computational level implementation of secure virtualization is cloud computing. In this, we explain virtualization security and their existing defense system at computational level. In computational level need to increase security of cloud and categorized the virtual machine level security challenges that are faced by cloud users at computational level in cloud computing. Day by day cloud attribute are introducing new challenges in cloud environment. In this paper, we focus on cloud security at computational level. In the day scenario to secure a cloud data on shared environment is the important task for IT industries. In virtual environment need to focus on secure VM placement on the host machine that is running on cloud. This paper demonstrates lack of VM placement and side channel attack (SCA). Analysis of different type of scheme is done and comparative study of game theory based, graph theory based and bin packing VM placement scheme. Also we discussed virtualization in cloud computing and their security aspects and comparative study of different type side channel attack (SCA).Subject Classification: 91G2068-XX03D7860K30Keywords: Cloud securitySecurity levelSide channel attackCache-based attackPlacement scheme
Glint: Decentralized Federated Graph Learning with Traffic Throttling and Flow Scheduling

Federated learning has been proposed as a promising distributed machine learning paradigm with strong privacy protection on training data. Existing work mainly focuses on training convolutional neural network (CNN) models good at learning on image/voice data. However, many applications generate graph data and graph learning cannot be efficiently supported by existing federated learning techniques. In this paper, we study federated graph learning (FGL) under the cross-silo setting where several servers are connected by a wide-area network, with the objective of improving the Quality-of-Service (QoS) of graph learning tasks. We find that communication becomes the main system bottleneck because of frequent information exchanges among federated severs and limited network bandwidth. To conquer this challenge, we design Glint, a decentralized federated graph learning system with two novel designs: network traffic throttling and priority-based flows scheduling. To evaluate the effectiveness of Glint, we conduct both experiments on a testbed and trace-driven simulations. The results show that Glint can significantly outperform existing federated learning solutions.
Investigation of a fitting phase-shift method for stress analysis using infrared photoelasticity

The acquisition of phase information is critical in the infrared photoelastic method. As silicon-based semiconductor devices become increasingly finer, the demand is rapidly increasing for a phase information deterimination method with a high stress resolution. This work proposes a fitting phase-shift method based on the multistep phase-shift method, which is more applicable to samples with small stress states or low stress-optic coefficients. The solutions of the phase difference and isoclinic angle can be extracted from a sequence of in situ photoelastic images by iteratively fitting the intensity values under a series of combinations of optical parameters. This method was further implemented in a four-point bending experiment with silicon. The phase information and stress distribution results were analysed and compared with those obtained by the six-step phase-shift method, showing that the fitting phase-shift method is reliable and has higher precision. A three-point bending experiment was also carried out to verify the accuracy of the proposed method under a complex stress state.