Reviewerly/exHarmony huggingface dataset:
Image Deduplication Based on Hashing and Clustering in Cloud Storage

With the continuous development of cloud storage, plenty of redundant data exists in cloud storage, especially multimedia data such as images and videos.Data deduplication is a data reduction technology that significantly reduces storage requirements and increases bandwidth efficiency.To ensure data security, users typically encrypt data before uploading it.However, there is a contradiction between data encryption and deduplication.Existing deduplication methods for regular files cannot be applied to image deduplication because images need to be detected based on visual content.In this paper, we propose a secure image deduplication scheme based on hashing and clustering, which combines a novel perceptual hash algorithm based on Local Binary Pattern.In this scheme, the hash value of the image is used as the fingerprint to perform deduplication, and the image is transmitted in an encrypted form.Images are clustered to reduce the time complexity of deduplication.The proposed scheme can ensure the security of images and improve deduplication accuracy.The comparison with other image deduplication schemes demonstrates that our scheme has somewhat better performance.
Trading Cryptocurrencies as a Pandemic Pastime: COVID-19 Lockdowns and Bitcoin Volume

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Performance Evaluation and Analysis of CSPM: a Secure cloud Computing Model

Cloud computing offers an innovative organization and user ability share, store and retrieve data anytime and from anywhere. Recently companies have increases to adopting cloud computing.  The main concerns in adopting cloud computing about the security issues. The critical aspect in cloud computing is security due to privacy and sensitive information. As a lot of users in the company share confidential and critical data on a cloud so the important part is how the data be secured. This paper discusses overview of the cloud computing and presents proposes a cloud security performance management (CSPM) model. Presents experimentation and evolution based in performance in different data size. Applying asymmetric encryption by using RSA and ECC algorithm. Show scenarios   between RSA vs ECC in storing and retrieval data inside proposed CSPM model. Presents CSPM Model-Store algorithm and CSPM Model-Retrieve algorithm.
INTERNATIONALIZATION AND INTERDISCIPLINARY EDUCATION IN FOREIGN LANGUAGE CURRICULA OF FUTURE INNOVATICS MANAGERS

INTERNATIONALIZATION AND INTERDISCIPLINARY EDUCATION IN FOREIGN LANGUAGE CURRICULA OF FUTURE INNOVATICS MANAGERS
Methodology for monitoring the quality of provided educational services in higher educational institutions

The article reflects a detailed study of the issue under consideration with the study of criteria and indicators for assessing the quality of educational services that are relevant, in the opinion of the authors, for higher educational institutions. Based on the results of the analysis, it was developed methodology and algorithm for assessing the quality of the provision of educational services in higher educational institutions. At the heart of the study, when considering the data collection toolkit, it is proposed to assess the quality of educational services by examining three target groups of respondents: students, graduates, and faculty. A general algorithm for the approbation of monitoring tools with the use of a consolidated coefficient of the quality of educational services has been developed. The use of the methodology for monitoring the quality of educational services provided by the authors will make it possible to assess their quality and develop recommendations for improving it.
Shapley Residuals: Quantifying the limits of the Shapley value for explanations.

Popular feature importance techniques compute additive approximations to nonlinear models by first defining a cooperative game describing the value of different subsets of the model’s features, then calculating the resulting game’s Shapley values to attribute credit additively between the features. However, the specific modeling settings in which the Shapley values are a poor approximation for the true game have not been well-described. In this paper we utilize an interpretation of Shapley values as the result of an orthogonal projection between vector spaces to calculate a residual representing the kernel component of that projection. We provide an algorithm for computing these residuals, characterize different modeling settings based on the value of the residuals, and demonstrate that they capture information about model predictions that Shapley values cannot.
A mixture autoregressive model based on Gaussian and Student’s <i>t</i>-distributions

Abstract We introduce a new mixture autoregressive model which combines Gaussian and Student’s t mixture components. The model has very attractive properties analogous to the Gaussian and Student’s t mixture autoregressive models, but it is more flexible as it enables to model series which consist of both conditionally homoscedastic Gaussian regimes and conditionally heteroscedastic Student’s t regimes. The usefulness of our model is demonstrated in an empirical application to the monthly U.S. interest rate spread between the 3-month Treasury bill rate and the effective federal funds rate.
MOST: Multi-Source Domain Adaptation via Optimal Transport for Student-Teacher Learning

Multi-source domain adaptation (DA) is more challenging than conventional DA because the knowledge is transferred from several source domains to a target domain. To this end, we propose in this paper a novel model for multi-source DA using the theory of optimal transport and imitation learning. More specifically, our approach consists of two cooperative agents: a teacher classifier and a student classifier. The teacher classifier is a combined expert that leverages knowledge of domain experts that can be theoretically guaranteed to handle perfectly source examples, while the student classifier acting on the target domain tries to imitate the teacher classifier acting on the source domains. Our rigorous theory developed based on optimal transport makes this cross-domain imitation possible and also helps to mitigate not only the data shift but also the label shift, which are inherently thorny issues in DA research. We conduct comprehensive experiments on real-world datasets to demonstrate the merit of our approach and its optimal transport based imitation learning viewpoint. Experimental results show that our proposed method achieves state-of-the-art performance on benchmark datasets for multi-source domain adaptation including Digits-five, Office-Caltech10, and Office-31 to the best of our knowledge.
Effects of Pre-processing on the Performance of Transfer Learning Based Person Detection in Thermal Images

Thermal images have the property of identifying objects even in low light conditions. However, person detection in thermal is tricky, due to varying person representations depending upon the surrounding temperature. Three major polarities are commonly observed in these representations i.e., 1. person warmer than the background, 2. person colder than the background and 3. person’s body temperature is similar to background. In this work, we have studied and analyzed the performance of the detection network by using the data in its original form and by harmonizing the person representation in two ways i.e., dark persons in the light background and light persons in a darker background. The data passed to each testing scenario was first pre-processed using histogram stretching to enhance the contrast. The work also presents the method to separate the three kinds of images from thermal data. The analysis is performed on publicly available outdoor AAUPD-T and OSU-T datasets. Precision, recall, and F1 score is used to evaluate network performance. The results have shown that network performance is not enhanced by performing the mentioned pre-processing. Best results are obtained by using the data in its original form.
PROBLEMS OF LEGAL REGULATION OF TRAINING OF SCIENTIFIC AND SCIENTIFIC-PEDAGOGICAL PERSONNEL IN POSTGRADUATE STUDIES

Introduction: this article discusses the problems of the institute of postgraduate studies (adjunct studies). The discussion on these issues has been dividing the entire scientific expert community into two camps for quite some time. The experts who make up the first of these camps believe that postgraduate studies (adjunct studies) are one of the levels of higher education. The experts who make up the second camp insist that the main task of postgraduate studies (adjunct studies) is to train a scientific researcher, not a teacher; accordingly, graduate students (adjuncts) should not attend courses of lectures and pass exams, but engage in scientific work. The relevance of the issue is explained by the discussion of a number of legislative initiatives aimed at changing the legal regulation of postgraduate studies (adjunct studies). Purpose: to show ways to solve the problems of the institute of postgraduate studies (adjunct studies), including by evaluating the draft changes in regulations. Methods: system analysis, historical method. Results: analyzed documents of a political and legal nature, as well as draft regulations aimed at reforming the institute of postgraduate studies (adjunct studies), formulated directions for its improvement. Conclusions: according to the authors, the institute of postgraduate studies (adjunct studies) is not compatible with the system of higher education because of the difference in goals that are achieved in the educational process and in the preparation of scientific research in postgraduate studies (adjunct studies), so that there is no and can be no other way than complete and decisive withdrawal of the institute of postgraduate studies (adjunct studies) from absolutely alien to him sphere of higher education in the sphere of science; only in this case, you can count on the revival of this truly important form of training scientific and pedagogical personnel of the highest category.
Ensemble Augmentation for Deep Neural Networks Using 1-D Time Series Vibration Data

Deep Neural Networks (DNNs) typically require enormous labeled training samples to achieve optimum performance. Therefore, numerous forms of data augmentation techniques are employed to compensate for the lack of training samples. In this paper, a data augmentation technique named ensemble augmentation is proposed to generate real-like samples. This augmentation method uses the power of white noise added in ensembles to the original samples to generate real-like samples. After averaging the signal with ensembles, a new signal is obtained that contains the characteristics of the original signal. The parameters for the ensemble augmentation are validated using a simulated signal. The proposed method is evaluated by 10 class-bearing vibration data using three Transfer Learning (TL) models, namely, Inception-V3, MobileNet-V2, and ResNet50. The outputs from the proposed method are compared with no augmentation and different augmentation techniques. The results showed that the classifiers with the ensemble augmentation have higher validation and test accuracy than all the other augmentation techniques. The robustness assessment conducted with noisy test samples and test samples from different loads showed that the classifiers could obtain much higher robustness when trained with samples from ensemble augmentation. The proposed data augmentation technique can be applied to 1-D time series data to achieve robust classifiers.
Lessons from $a$ Dragon Fly's Brain: Evolution Built a Small, Fast, Efficient Neural Network in a Dragonfly. Why Not Copy It for Missile Defense?

IN EACH OF OUR BRAINS, 86 billion neurons work in parallel, processing inputs from senses and memories to produce the many feats of human cognition. The brains of other creatures are less broadly capable, but those animals often exhibit innate aptitudes for particular tasks, abilities honed by millions of years of evolution.
Reliable Data Transmission over Energy-Efficient Vehicular Network Based on Blockchain and MEC

Recently, electric vehicles (EVs) have been widely used under the call of green travel and environmental protection, and diverse requirements for charging are also increasing gradually. In order to ensure the authenticity and privacy of charging information interaction, blockchain technology is proposed and applied in charging station billing systems. However, there are some issues in blockchain itself, including lower computing efficiency of the nodes and higher energy consumption in the consensus process. To handle the above issues, in this paper, combining blockchain and mobile edge computing, we develop a reliable billing data transmission scheme to improve the computing capacity of nodes and reduce the energy consumption of the consensus process. By jointly optimizing the primary and replica nodes offloading decisions, block size and block interval, the transaction throughput of the blockchain system is maximized, as well as the consumption costs of latency and energy consumption is minimized. Moreover, we formulate the joint optimization problem as Markov decision process (MDP). To tackle this dynamic and continuity of the system state, the actor–critic reinforcement learning is introduced to solve the MDP problem. Finally, simulation results demonstrate that the performance improvement of the proposed scheme through comparison with other existing schemes.
On-Machine Calibration Method for <i>In Situ</i> Stereo Deflectometry System

Deflectometry is a promising method for the in situ measurement of specular surfaces. However, the accuracy of this method largely depends on the geometrical calibration, which suffers from a low convergence rate and local minima during the optimization. Off-machine calibration results in a change in geometry parameters due to the repeated disassembly of the measurement system and change of ambient temperature, which largely affects the measurement accuracy. This article presents a flexible on-machine calibration method with fewer intermediate parameters to accelerate the optimization for the in situ stereo deflectometry system. An inclined mirror with a fixed angle was mounted on the spindle of an ultraprecision machine tool and used as a reference plane reflecting the fringe displayed on the display screen. The reflected fringe captured by the cameras was encoded by an inclined mirror moving along the $C$ -axis and the $Z$ -axis of the machine tool. The preknowledge of the movement distance of the ultraprecision machine tool along the $Z$ -axis and rotation angle of the spindle with a nanoscale accuracy was used to largely reduce the number of intermediate variables involved in the optimization, thus improving the convergence speed of the global optimization method and calibration reliability. The experiment results show that this calibration method has higher calibration accuracy, measurement accuracy, and reliability compared to the conventional off-machine calibration method.
Diffractive network-based single-pixel machine vision

We report a single-pixel machine vision framework based on deep learning-designed diffractive surfaces to perform a desired machine learning task. The object within the input field-of-view is illuminated with a broadband light source and the subsequent diffractive surfaces are trained to encode the spatial information of the object features onto the power spectrum of the diffracted light that is collected by a single-pixel detector in a single-shot. We experimentally demonstrated the all-optical inference capabilities of this single-pixel machine vision platform by classifying handwritten digits using 3D-printed diffractive layers and a plasmonic nanoantenna-based time-domain spectroscopy setup operating at THz wavelengths.
IDENTIFYING OIL PADS IN HIGH SPATIAL RESOLUTION AERIAL IMAGES USING FASTER R-CNN

Abstract. Deep learning (DL) methods are used for identifying objects in aerial and ground-based images. Detecting vehicles, roads, buildings, and crops are examples of object identification applications using DL methods. Identifying complex natural and man-made features continues to be a challenge. Oil pads are an example of complex built features due to their shape, size, and presence of other structures like sheds. This work applies Faster Region-based Convolutional Neural Network (R-CNN), a DL-based object recognition method, for identifying oil pads in high spatial resolution (1m), true-color aerial images. Faster R-CNN is a region-based object identification method, consisting of Regional Proposal Network (RPN) that helps to find the area where the target can be possibly present in the images. If the target is present in the images, the Faster R-CNN algorithm will identify the area in an image as foreground and the rest as background. The algorithm was trained with oil pad locations that were manually annotated from orthorectified imagery acquired in 2017. Eighty percent of the annotated images were used for training and the number of epochs was increased from 100 to 1000 in increments of 100 with a fixed length of 1000. After determining the optimal number of epochs the performance of the algorithm was evaluated with an independent set of validation images consisting of frames with and without oil pads. Results indicate that the Faster R-CNN algorithm can be used for identifying oil pads in aerial images.
A heuristics pulse algorithm with relaxation pruning strategy for resources re-initialized UAV path planing

Path planning is the basis and prerequisite for unmanned aerial vehicle (UAV) to perform tasks, and it is important to achieve precise location in path planning. This paper focuses on solving the UAV path planning problem under the constraint of system positioning error. Some nodes can re-initiate the accumulated flight error to zero and this type of scenario can be modeled as the resource-constrained shortest path problem with re-initialization (RCSPP-R). The additional re-initiation conditions expand the set of viable paths for the original constrained shortest path problem and increasing the search cost. To solve the problem, an effective preprocessing method is proposed to reduce the network nodes. At the same time, a relaxed pruning strategy is introduced into the traditional Pulse algorithm to reduce the search space and avoid more redundant calculations on unfavorable scalable nodes by the proposed heuristic search strategy. To evaluate the accuracy and effectiveness of the proposed algorithm, some numerical experiments were carried out. The results indicate that the three strategies can reduce the search space by 99%, 97% and 80%, respectively, and in the case of a large network, the heuristic algorithm combining the three strategies can improve the efficiency by an average of 80% compared to some classical solution.
Computing-aware Proactive Network Reconfiguration for Optical Networks Interconnected Edge Computing System

This paper demonstrates a proactively reconfigured edge network that can adaptively configure IP topology for dynamic service demand. Simulation result show the average utilization has improved from 32.50% to 68.98% compared traditional static network.
Analysis of Regions of Interest and Distractor Regions in Breast Biopsy Images

This paper studies why pathologists can misdiagnose diagnostically challenging breast biopsy cases, using a data set of 240 whole slide images (WSIs). Three experienced pathologists agreed on a consensus reference ground-truth diagnosis for each slide and also a consensus region of interest (ROI) from which the diagnosis could best be made. A study group of 87 other pathologists then diagnosed test sets (60 slides each) and marked their own regions of interest. Diagnoses and ROIs were categorized such that if on a given slide, their ROI differed from the consensus ROI and their diagnosis was incorrect, that ROI was called a distractor. We used the HATNet transformer-based deep learning classifier to evaluate the visual similarities and differences between the true (consensus) ROIs and the distractors. Results showed high accuracy for both the similarity and difference networks, showcasing the challenging nature of feature classification with breast biopsy images. This study is important in the potential use of its results for teaching pathologists how to diagnose breast biopsy slides.
Fair and Adequate Explanations

Recent efforts have uncovered various methods for providing explanations that can help interpret the behavior of machine learning programs. Exact explanations with a rigorous logical foundation provide valid and complete explanations, but they have an epistemological problem: they may be too complex for humans to understand and too expensive to compute even with automated reasoning methods. Interpretability requires good explanations that humans can grasp and can compute. We take an important step toward specifying what good explanations are by analyzing the epistemically accessible and pragmatic aspects of explanations. We characterize sufficiently good, or fair and adequate, explanations in terms of counterfactuals and what we call the conundra of the explainee, the agent that requested the explanation. We provide a correspondence between logical and mathematical formulations for counterfactuals to examine the partiality of counterfactual explanations that can hide biases; we define fair and adequate explanations in such a setting. We then provide formal results about the algorithmic complexity of fair and adequate explanations.
Navigation Simulation of Autonomous Mobile Robot Based on TEB Path Planner

The TEB (timed elastic band) algorithm optimizes the robot trajectory by modifying the initial trajectory generated by the global path plan, and obtains a trajectory that satisfies the robot kinematics and dynamics constraints, avoids obstacles, and has a better time. This paper improves the original TEB algorithm, constructs specific acceleration constraints for the car model during the trajectory optimization process, and implements an automatic navigation and obstacle avoidance system based on the Move_base package in ROS; the system provides positioning information by the AMCL method, and the rf2o algorithm provides radar Odometer information, the A algorithm is used to obtain the global path planning, and the TEB path planner completes the local path planning and movement control. The simulation was then carried out in the Gazebo environment. The experimental results show that the obtained trajectory satisfies the kinematics and dynamics, and can achieve the requirements of avoiding obstacles.
An IoT Based Rectification of Posture using Biofeedback

Nowadays a lot of people are facing back problems due to wrong posture which are due to spine related diseases. So Posture corrector will play a vital role in maintaining people's health. The device will help the user to correct its posture if in wrong position by sensing the user vibrations and fetching the coordinates to the device which in turn will give the result. a very lightweight device which is very easy to use which will consider the parameters such as age, gender, height and weight according to which the value of the optimum posture may change as per the different user. In this paper we propose a posture detection and corrector model which work both on Hardware as well as Software part. The device consist of power sensor and motor circuit, vibration showing to the user the indicating that where the optimal posture lies. As compared to other posture device it is feasible to build as compared to other counterparts made up from expensive devices such as FRDM sensor and high variant accelerometer and gyroscope, Where device will correctly classify its as correct and incorrect posture.