Reviewerly/exHarmony huggingface dataset:
Keyword Semantic Extended Top-k Ciphertext Retrieval Scheme over Hybrid Government Cloud Environment

Privacy protection is a key problem that must be solved when building government cloud system to realize data storage and sharing. In order to meet the privacy protection requirements of data security storage and document security sharing between data publishers and authorized data access users over the government cloud platform, this paper proposes a keyword semantic extended Top-k ciphertext retrieval scheme over hybrid government cloud environment. Firstly, the scheme uses the hybrid cloud mode to build the government cloud platform to realize the storage and sharing of government documents. Then, the key technologies such as mechanical segmentation method, term frequency-inverse document frequency statistics method, keyword semantic expansion method, homomorphic matrix encryption method and vector space model are used to retrieve the ciphertext documents in a completely confidential state. According to the correlation score calculated by ciphertext retrieval, the Top-k ciphertext target documents with the highest correlation score are returned. And then the scheme decrypts and restores to obtain the Top-k plaintext target documents. The analysis of security and experimental test results shows that this scheme can not only meet the data storage and sharing requirements over government cloud environment, but also prevent the privacy leakage risk of data over government cloud. It is an effective solution to promote the construction and development of government cloud.
Profit Driven Blockchain Based Platform for Land Registry

Technology in its relationship to the global changes has always been at the forefront to disrupt the status quo and bring in a new fresh perspective to look upon the things. When it comes to the public domain related industries, the technological changes often end up entangled with the policy regulations and are presented with a great deal of difficulty, while scaling up. Property fraud is one of the biggest problems in India. There are many cases related to document forgery being reported throughout India. One of the biggest challenges that grip various states in India is the issue of land ownership, which is partly due to the fact that there is no end-to-end effective land records management system till date. Due to the uncertainties on the ownership claims, it becomes more robust for the financial institutions to function properly. To avoid property fraud cases, the blockchain technology can be applied. The main aim of this paper is to computerize all land records, including mutations, improving transparency in the land record maintenance system, digitize maps and surveys, update all the settlement records and minimization of the scope of land disputes and also to provide clear titles of land ownership that could be monitored easily by government officials, for facilitating quicker transactions. In this paper, Ethereum blockchain platform along with angular JS has been used to implement decentralized application, and result is presented to demonstrate the successful implementation of DApps and its performance.
It is not as simple as that: Playing out password security trainings in order to nudge password changes

The COVID-19 pandemic forced a number of companies to place their staff into home office. In terms of security awareness measures, this means that content or training can only be played out remotely. Within this work, we report about a security awareness campaign focusing on password security that was carried out at a German mid-size company (2000 employees). We compare the effect of remotely played out training content on user behavior, i.e, on getting employees to change their password. The first content was directly embedded into an e-mail, the second was compiled on an intranet web page, and the third content was embedded into a video. Password changes were observed solely within the IT backend on the basis of events and timestamps generated by the company’s Active Directory service. For the campaign four representative samples (140 employees per sample) among the staff were selected and assigned to the different training contents. A fourth group served as a control group. During a period of 6 weeks, the content was played out two times. Unexpectedly, the measured password change rate observed was very low. Further, compared to the control group’s behavior, none of the different content formats played out led to significant more password changes. Clearly, the campaign failed according to its aim. Based on our observations, we provide several possible explanations for which there is some evidence from the literature.
Dynamic Metric Accelerated Method for Fuzzy Clustering

Features in data samples usually need a unified dimension by a standardization process before clustering. However, there still exists a nonstandardized metric in which the distance between samples is greater than 1 after features are standardized. It is difficult to find the optimal search path if the data sample metrics are not standardized. To address this problem, we develop a dynamic-metric accelerated method for fuzzy clustering by introducing a metric matrix, whose diagonal elements consist of infinite norms of the metric matrix into the Fuzzy C-Means (FCM) clustering algorithm and its derived algorithms. More specifically, we focus on constructing a dynamic metric matrix that is used to unify the metric between data samples and updating cluster centers to optimize the search path of the cluster center. In addition, we propose a new evaluation index named the Coefficient of Variation Metric (CVM) to evaluate metric effectiveness. The dynamic metric accelerated method, whose complexity remains unchanged, can effectively accelerate the iteration speed of fuzzy clustering. The comparisons between the algorithm using the dynamic metric accelerated method and the corresponding algorithm on UCI, business district and COVID-19 CT image datasets show the superiority of the dynamic metric accelerated method in accelerating effect and clustering performance.
Deep Convolutional Neural Network Based Extreme Learning Machine Image Classification

In this paper, we introduce a new deep convolutional neural network based extreme learning machine model for the classification task in order to improve the network's performance. The proposed model has two stages: first, the input images are fed into a convolutional neural network layer to extract deep-learned attributes, and then the input is classified using an ELM classifier. The proposed model achieves good recognition accuracy while reducing computational time on both the MNIST and CIFAR-10 benchmark datasets.
FedURR

Urban risk recognition is an essential task for urban public safety. Recent researches indicate that deep learning is playing an increasingly important role in urban risk recognition. This paper proposes FedURR, a federated transfer learning framework for multi-department collaborative urban risk recognition. The framework supports multiple departments to jointly train a neural network on the premise that the data is stored locally. Specifically, we introduce federated learning to ensure collaborative training, and well-designed transfer learning ensures that better performance can be achieved at a lower cost. Experimental results show that the proposed framework is effective and efficient.
Effect of Mobile Devices and Software in Collaborative Learning Smart Classroom on Students’ Learning Motivation

Motivational factors are critical for engaging and sustaining students to use mobile devices for learning purposes. The purpose of this study is to investigate a group of college students’ attitudes towards applying mobile devices and software in a smart classroom and the effect of the facilities on the students’ language learning motivation. Results indicate that the participants demonstrated a positive attitude towards the role the mobile devices and software plays in the smart classroom in general. Besides, utilization of the mobile devices and software can also help facilitate students’ learning motivation. The structure of a well-designed collaborative learning smart classroom (CLSC) for language learning is proposed in this study as well.
Geochemical characteristics of the Toplika manganese- and iron-bearing ore occurrence, central part of the Srednogorie Zone, Bulgaria: Preliminary results

The growing demand on mineral resources stimulated our interest on manganese- and iron-bearing occurrences as a potential source of Mn, Cu, Ni, Co, Mo, REY, etc. Here we focus on the mineralization of the Toplika ore occurrence, which is located in the central part of the Srednogorie Zone, Bulgaria. We present preliminary data on its field relationships, mineralogy and geochemistry and discuss the possible hydrothermal origin of the metalliferous sediments that should be further studies as source of critical elements.
Provision and Allocation of Large Scaled Data in Virtual Environment: A Case Study with Simulation Approach

The mechanism of processing large scaled data especially in virtual environment is something linked with sharing of resources towards obtaining optimality and maximizing the effectiveness of the shared resources. Perhaps, the dynamical allocation of resources in the present situation is most required towards preservation of data security. Therefore, it is highly essential to prioritize on provision of auto esteemed service on demand, parameterization of the service or application, proper scheduling of resources as well as uninterrupted high speed internet services. Of course, all the facilities should be available while accessing the mechanisms through heterogeneous virtual platforms which are flexible as well as scalable. The system associated with the large scaled data in such situation also can control the mechanism possessing similar descriptions associated with meta-data. The subset of these data can also be confined to specific domains prioritizing the application and may not be embedded. The term large scaled data is basically provisioned with large volume having the ability of high range of processing the data as compared with the response time and of course heterogeneity in nature. Somehow, it can be unstructured or semi structured. But, the more important thing is to analyze the data as well as to extract the information towards enhancement of making decisions. The minimization of complexities linked to the system can be obtained provisioning with scalability as well as reliability. This paper is mainly associated with observing the response time by allocating the databases in the virtual machines and cost of query terms by applying specific ant colony optimization techniques.
Formalization of metatheory of the Lambda Calculus in constructive type theory using the Barendregt variable convention

Abstarct We formalize in Constructive Type Theory the Lambda Calculus in its classical first-order syntax, employing only one sort of names for both bound and free variables, and with α -conversion based upon name swapping. As a fundamental part of the formalization, we introduce principles of induction and recursion on terms which provide a framework for reproducing the use of the Barendregt Variable Convention as in pen-and-paper proofs within the rigorous formal setting of a proof assistant. The principles in question are all formally derivable from the simple principle of structural induction/recursion on concrete terms. We work out applications to some fundamental meta-theoretical results, such as the Church–Rosser Theorem and Weak Normalization for the Simply Typed Lambda Calculus. The whole development has been machine checked using the system Agda.
AdapTTA: Adaptive Test-Time Augmentation for Reliable Embedded ConvNets

Convolutional Neural Networks (ConvNets) are trained offline using the few available data and may therefore suffer from substantial accuracy loss when ported on the field, where unseen input patterns received under unpredictable external conditions can mislead the model. Test-Time Augmentation (TTA) techniques aim to alleviate such common side effect at inference-time, first running multiple feed-forward passes on a set of altered versions of the same input sample, and then computing the main outcome through a consensus of the aggregated predictions. Unfortunately, the implementation of TTA on embedded CPUs introduces latency penalties that limit its adoption on edge applications. To tackle this issue, we propose AdapTTA, an adaptive implementation of TTA that controls the number of feed-forward passes dynamically, depending on the complexity of the input. Experimental results on state-of-the-art ConvNets for image classification deployed on a commercial ARM Cortex-A CPU demonstrate AdapTTA reaches remarkable latency savings, from $1.40 \times$ to $2.21 \times$, and hence a higher frame rate compared to static TTA, still preserving the same accuracy gain.
Distributed Framework for Accelerating Training of Deep Learning Models through Prioritization

Machine learning models such as deep neural networks have been shown to be successful in solving a wide range of problems. Training such a model typically requires stochastic gradient descent, and the process is time-consuming and expensive in terms of computing resources. In this paper, we propose a distributed framework that supports the prioritized execution of the gradient computation. Our proposed distributed framework identifies important data points through computing or estimating the priority for each data point. We evaluate the proposed distributed framework with several machine learning models including multi-layer perceptron (MLP) and convolutional neural networks (CNN). Our experimental results show that prioritized SGD accelerates the training of machine learning models by as much as 1.6X over that of the mini-batch SGD. Further, the distributed framework scales linearly with the number of workers.
Analysing the Lottery Ticket Hypothesis on Face Recognition for Structured and Unstructured Pruning

Modern Face Recognition (FR) backbones have been achieving growing success in various computer vision tasks. Nonetheless, this success reflects the prohibitively computational cost and memory intensity which hinders their deployment on edge devices and in applications with challenging latency obligations. It is necessary to promote the backbones with high accuracy but the comparatively low computational cost in real world applications. To this end, assorted methods have been studied to compress deep neural networks (DNNs) or learn more efficient DNN models in order to reduce expensive computational cost and memory intensity in low-resource settings. The Lottery Ticket Hypothesis is an approach to finding sparse subnetwork within a large model. To this end, we examine The Lottery Ticket Hypothesis on Face Recognition for structured pruning and unstructured pruning. The experiments validate that we find the “winning ticket” for unstructured pruning while getting promising results for structured pruning.
A Visual Analytics System for Water Distribution System Optimization

The optimization of water distribution systems (WDSs) is vital to minimize energy costs required for their operations. A principal approach taken by researchers is identifying an optimal scheme for water pump controls through examining computational simulations of WDSs. However, due to a large number of possible control combinations and the complexity of WDS simulations, it remains non-trivial to identify the best pump controls by reviewing the simulation results. To address this problem, we design a visual analytics system that helps understand relationships between simulation inputs and outputs towards better optimization. Our system incorporates interpretable machine learning as well as multiple linked visualizations to capture essential input-output relationships from complex WDS simulations. We demonstrate our system’s effectiveness through a practical case study and evaluate its usability through expert reviews. Our results show that our system can lessen the burden of analysis and assist in determining optimal operating schemes.
Transformation of Higher Education Management Mode and Coping Strategies Based on Big Data

Abstract Using data technology to optimize and reform teaching field is a part of teaching. Some schools have optimized the education mode, but there are many problems due to the lack of experience in this field. Therefore, this paper puts forward the transformation management mode and coping strategies. The main significance of university education management and the main problems existing, investigated and analyzed, and specific and improvement measures can be put forward according to the current deficiencies. The accuracy of data analysis is improved by optimizing factor analysis algorithm and simplifying steps. Puts forward the coping strategies of College Students’ management in the era of big data, uses big data platform, formulates personalized student training plan, and attaches importance to the application of flexible management and other strategies. According to the survey results, since the adoption management mode, have greatly improved in the way of event handling and management information communication.
Analyzing Emergent Complexity in Particle Swarm Optimization using a Rolling Technique for Updating Hyperparameter Coefficients

Particle Swarm Optimization (PSO) provides a metaheuristic optimization technique for real-life functions with complex behavior. Inspired by the metaphor of social behavior as exhibited by swarms operating in natural surroundings, the algorithm captures the Emergent Complexity demonstrated by individual components of large groups which work together using simple rules and create complex, yet efficient, adaptive, and self-organizing systems. The paper seeks to rigorously analyze the hyperparameters of the algorithm to find the relative importance of cognitive(personal) vis-à-vis social (swarm) parameters of the algorithm so as to gain a perfect understanding of the importance of exploitation versus exploration parameters thus enabling us to enhance the application efficiency of the algorithm. The paper further suggests and applies a rolling coefficient updating technique to the algorithm parameters so as to use a formal statistical procedure in the initialization and subsequent iterations for updating coefficients rather than leaving it on trial and error or on a practitioner’s knowledge. Results suggest that the technique employed provides an enhancement in the efficiency of the algorithm by creating a dynamic Emergent Complexity environment.
Recognition of Engineering Vehicles in Aerial Images of Multi Rotor UAV

It is one of the significant tasks of power inspection by multi rotor Unmanned Aerial Vehicle (UAV) to recognize engineering vehicles in aerial images. If there are engineering vehicles working near or below the high-voltage power line, the UAV would give out the important early warning message to avoid the situation that the bucket or boom of the engineering vehicle enters the safe distance from the high-voltage power line, and reduce accidents such as short circuit breakdown. Aiming at the problem of recognition of engineering vehicles in aerial images of UAV inspection, this paper proposed an improved capsule network method. First, the structure of original capsule network is replaced with a multi-layer densely connected capsule network. Next, the dynamic routing algorithm of the capsule network is improved. As the results of experiments have shown, (1) the improved capsule network method gets a mAP of 93.74% for the recognition of engineering vehicles, and its parameter scale is smaller than other methods. (2) The number of network layers influences the recognition precision greatly. Their relationship is non-monotonic and nonlinear. In addition, whether or not to improve the dynamic routing algorithm does not affect the trends of recognition mAP. The overall performance of the improved capsule network method is obviously better than YOLOv5 and other artificial feature extraction methods.
A Semi‐supervised Classification Method of Parasites Using Contrastive Learning

An expected shortfall of supervised learning for medical imaging classification is the insatiable need for human annotation, which is very expensive. Further, for microscopic parasite images, the salient structures are generally fuzzy, and the insignificant textures are noising. This paper proposes a semi‐supervised classification method of three parasites and Erythrocytes microscopic images using a large amount of unlabeled data and a small amount of labeled data for training. It contains a feature extractor trained by contrastive learning and a classifier optimized by Laplacian Support Vector Machine (LapSVM). First, for the deep Convolutional Neural Network feature extractor, we introduce real‐world images with similar and clear semantic information to enhance the structure at the representation level. In addition, we introduce variant appearance transformations to eliminate the texture at the representation level. Second, the gated linear network is adopted as the classifier to realize a piecewise linear separation boundary. The parameters are optimized implicitly by LapSVM using a kernel function composed of the representation and the gate control signals, which are generated from the learned feature extractor. The proposed method shows excellent performance when only 1% of the microscopic images are labeled. As a result, we achieve affordable and accurate diagnostic testing in parasite classification. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
FastDOG: Fast Discrete Optimization on GPU

We present a massively parallel Lagrange decomposition method for solving 0–1 integer linear programs occurring in structured prediction. We propose a new iterative update scheme for solving the Lagrangean dual and a perturbation technique for decoding primal solutions. For representing subproblems we follow [40] and use binary decision diagrams (BDDs). Our primal and dual algorithms require little synchronization between subproblems and optimization over BDDs needs only elementary operations without complicated control flow. This allows us to exploit the parallelism offered by GPUs for all components of our method. We present experimental results on combinatorial problems from MAP inference for Markov Random Fields, quadratic assignment and cell tracking for developmental biology. Our highly parallel GPU implementation improves upon the running times of the algorithms from [40] by up to an order of magnitude. In particular, we come close to or outperform some state-of-the-art specialized heuristics while being problem agnostic. Our implementation is available at https://github.com/LPMP/BDD.
Research on smart car motion planning algorithm based on RRT

Aiming at the RRT algorithm's strong randomness, the obtained path is not the shortest, and the vehicle differential constraints are not satisfied, this paper proposes an improved RRT smart car motion planning algorithm. In a priori information environment where the map is known, a reasonable metric function that satisfies the vehicle kinematics constraints is proposed, so that the path is smoother and facilitates the optimization of the back-end trajectory. In addition, a pruning optimization method that reduces path nodes and shortens the path length is also proposed. The simulation results show that the improved RRT algorithm has a smoother path than the basic RRT algorithm, with fewer path nodes and a shorter path length. Finally, a collision-free, smooth and continuous trajectory that satisfies the vehicle movement is obtained.