Reviewerly/exHarmony huggingface dataset:
Research on Big data Security privacy Protection based on cloud Computing

Abstract Cloud computing is a new way of computing and storage. Users do not need to master professional skills, but can enjoy convenient network services as long as they pay according to their own needs. When we use cloud services, we need to upload data to cloud servers. As the cloud is an open environment, it is easy for attackers to use cloud computing to conduct excessive computational analysis on big data, which is bound to infringe on others’ privacy. In this process, we inevitably face the challenge of data security. How to ensure data privacy security in the cloud environment has become an urgent problem to be solved. This paper studies the big data security privacy protection based on cloud computing platform. This paper starts from two aspects: implicit security mechanism and display security mechanism (encryption mechanism), so as to protect the security privacy of cloud big data platform in data storage and data computing processing.
Low-complexity bit-serial sequential polynomial basis finite field GF(2<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1137" altimg="si237.svg"><mml:msup><mml:mrow /><mml:mrow><mml:mi>m</mml:mi></mml:mrow></mml:msup></mml:math>) Montgomery multipliers

GF(2m) multiplication is a complex and performance-critical operation in Elliptic curve cryptography algorithms. Many techniques have been proposed in the literature for efficient implementation of GF(2m) multipliers. Montgomery multiplication is a technique used for fast GF(2m) multiplications, which is more efficient when there is a need for computation of many consecutive multiplications. In this paper, we present two modified bit-serial algorithms namely most significant bit (MSB) first algorithm and least significant bit (LSB) first algorithm for Montgomery multiplication where the modification involves employing more efficient logical relations in the formulation of the algorithms. Furthermore, the hardware structures developed for the proposed modified algorithms using bit-serial sequential architectures are also presented in this paper. Comparison of the analytical as well as implementation results of the proposed multipliers with the existing multipliers shows that the proposed multipliers require low area and time complexities. The proposed MSB-first multiplier and the proposed LSB-first multiplier achieve around 17% and 13% reduction in area-delay-product (ADP) complexities for m=409, respectively, when compared with the respective best multipliers available in the literature. The proposed bit-serial sequential multipliers can be used in low-hardware and low-cost applications such as IoT edge devices.
Performance Analysis and Optimization for Federated Learning Applications with PySyft-based Secure Aggregation

To address privacy concerns, federated learning (FL) is becoming a promising machine learning technique which enables multiple decentralized clients to train a shared model collaboratively while preserving their private training data. Although FL may reduce the risks of data leak, it is still possible for hackers to reverse-engineer a trained model and figure out the information in the original training dataset provided by a FL client. In order to avoid such risks, secure aggregation (SA) can be used to privately combine the trained models of the clients to update the shared model. However, SA usually introduces performance overhead as it requires additional computation for encryption operations and even communications when secure multi-party computation (SMPC) is used. In this paper, we analyze the performance of FL with SA using PySyft, an open source framework including FL implementation, and propose an asynchronous FL mechanism to improve the overall performance. It turns out that the performance depends on the computational capabilities of the clients and the characteristics of the communication network, and we propose a performance modeling method to help system designers break down the execution time and decide on suitable trade-offs between privacy, efficiency, and accuracy for a balanced system.
Data-driven Privacy With Domain Regularization

We propose a privacy preserving framework to sanitize data so as to eliminate private information while maximally retaining non-sensitive information. We regularize the domain of the sanitized data to make it compatible with a service provider's learning systems already in place for the raw data. Thus, our privacy preserving framework incurs no additional cost for the service provider. We present a probabilistic sanitizer to privatize the raw data and a variational method to approximate the mutual information between the sanitized data and raw data. We include maximum mean discrepancy and domain adaption as the domain regularization techniques, and average information leakage as the privacy metric. We present a deep learning model as an example of the proposed framework where the input data is an image. Numerical experiments verify the feasibility of our approach.
Locating Core Modules through the Association between Software Source Structure and Execution

To improve software quality, the source code that composes software has to be improved, and improving the important code that largely affects the software quality should be a cost-effective method. Static analysis defines important codes as those that occupy important positions in the source network, while dynamic analysis defines important codes as those with high execution frequency. However, neither method analyzes the association between network structure and execution frequency, and both have their disadvantages. Thus, this study analyzed the association between source network structure and execution frequency to solve their disadvantages. The source function of Notepad++ was analyzed, and the function ranking was derived using the association between network structure and execution frequency. For verification, the Spearman correlation between the newly derived function ranking and the function ranking of the network and execution frequency obtained with the conventional method was measured. By measuring the Spearman correlation, the newly derived function ranking had strong correlations with execution frequency and included the network structure’s characteristics. Moreover, similar to the Pareto principle, the analysis showed that 20% of Notepad++’s functions could be categorized as important functions, largely affecting the software’s quality.
Negacyclic codes of prime power length over the finite non-commutative chain ring 𝔽pm[u,𝜃] 〈u2〉

In this paper, we focus on the algebraic structure of left negacyclic codes of length [Formula: see text] over the finite non-commutative chain ring [Formula: see text] where [Formula: see text] is an automorphism on [Formula: see text]. After that, the number of codewords of all left negacyclic codes is obtained. For each left negacyclic code, we also obtain the structure of its right dual code. In the remaining result, the number of distinct left negacyclic codes is given. Finally, a one-to-one correspondence between left negacyclic and left [Formula: see text]-constacyclic codes of length [Formula: see text] over [Formula: see text] is constructed via ring isomorphism, which carries over the results regarding left negacyclic codes corresponding to left [Formula: see text]-constacyclic codes of length [Formula: see text] over [Formula: see text] where [Formula: see text] is a nonzero element of the field [Formula: see text] such that [Formula: see text].
Disparity Map Estimation From Stereo Image Pair Using Deep Convolutional Network

This paper presents a method to estimate a disparity map from a stereo image pair using deep convolutional neural network (CNN), which is mainly divided into three parts, including: feature extraction for each individual view, feature fusion, and disparity regression. We first design 2-D CNN networks to extract the feature maps from both the left and right-view images. Then, we combine the two feature maps into one 4- D feature volume by concatenating and packing them across distinct disparity levels. Finally, we use a 3-D CNN network (modified UNet++) to learn the disparity information from the 4-D feature volume. The whole CNN model is trained end-to-end without any post-processing. In other words, we let our model learn a mapping from input to output. Experimental results show that our method outperforms most of state-of-the-art methods on the publicly available datasets FlyingThings3D and KITTI Stereo 2015, demonstrating the feasibility of our algorithm.
A case study on integration of vehicle defect and recall information analysis

As the information of vehicle defect, the vehicle defect information and the recall information are disclosed by the website of Japanese Ministry of Land, Infrastructure, Transport and Tourism., Unlike other studies which analyze the text data of each one, we focus on the relation between them and analyze it of both. In order to derive the tendency of dependency pairs which means defect phenomena, dependency parsing and BM 25 are applied for the analysis. From results of calculating occurrence and BM 25 score for pairs of dependency words and modified words, some tendencies are found in each of high score's pairs. For the analysis of recall information, correspondence analysis, co-occurrence network and self-organizing map are conducted to find the relational words between three categories of defect factor. Twelve primary factors of five parts are shown as the factors in fuel device which causes liquid leakage. Finally, factors of important defect phenomena is discussed. Several dependency pairs are shown as important defect phenomena, and primary factors of parts are shown as an example of factors of liquid leakage which is one of them.
Object detection algorithm based on feature enhancement

Abstract Recently, many excellent algorithms have made great progress in object detection, but there are also problems in these algorithms’ performance on targets of different sizes, and in particular in small object detection. Aiming at the problem of insufficient feature representation by the feature extractor, in this paper we propose a lightweight algorithm to improve feature extraction. The algorithm includes three modules. First, considering that the shallow features in feature extraction contain much background noise, in this paper we design a multi-level feedback propagation model based on a Gaussian high-pass filter. The shallow layers are enhanced using the filter and then back-propagated to add the upper shallow layer features and obtain new shallow layer features. This process is performed on the newly generated shallow layer for n iterations, which is beneficial for enhancing targets in the foreground area and suppressing background noise. Second, we form a stacked dilated convolution module with different dilation rates to cover the entire deep feature layer densely, which enlarges the receptive field and enriches the contextual information. Finally, we build a multi-scale fusion module to fuse the above-mentioned enhanced shallow and deep features to obtain output features with powerful representational ability for detection tasks. In addition, the model is easily embedded into existing approaches to enhance their performance. We build the model on the VGG-16 and ResNet-50 backbones and successfully applied it on Darknet-19 and Darknet-53 to verify its effectiveness and stability. The experiments on the COCO dataset prove that the proposed algorithm outperforms the state-of-art methods, with a mean average precision improvement reaching 2% on average. The effect is remarkable on small targets and complex backgrounds. Furthermore, it does not affect the detection speed significantly, so real time detection requirements can still be met.
Validity and Practicality Level of Acid-Base Electronic Module Based on Structured Inquiry Containing Three Levels of Chemical Representation for Senior High School Student

Abstract Acid-base is prerequisite material for studying the hydrolysis of salts and buffer solutions. This material is still difficult to understand for students. This study aims to reveal the level of validity and practicality of electronic module based on structured inquiry containing three levels of chemical representation. This research was Research and Development (R&amp;D) using Plomp’s development model. The research instrument was a validaty and practicality questioannair. The e-module was validated by 4 chemistry lectures and 2 chemistry teachers. The practicality test was carried out on 2 chemistry teachers and 65 students grade XI of Senior High School. The data were analyzed using the Kappa Cohen formula. The result of the study showed the developed e-modul had a very high level of validation ( k = 0.86). The results of practicalities also showed a very high level, k=0.90 response of teachers, and k= 0.88 response of students. The e-module was valid and practically for in acid-base learning in Senior High School.
Probabilistic Logic Reasoning in Multi-agent Based Smart Home Environment

The dynamic nature of the ambient assisted living (AAL) environments coupled with vague inhabitant voice commands may result in ambiguous, incomplete, and inconsistent contextual information about the state of the home and its inhabitants. Ultimately, these lead the AAL system into uncertainty, which is common in smart home environments due to inaccurate sensor readings or due to the existence of unobserved variables for privacy reasons. Aiming at tackling some of these challenges, this paper applies a probabilistic logic based reasoning technique into a multi-agent based smart home architecture. Accordingly, the study shows how the probabilistic reasoning technique enables the agents to reason under uncertainty. Further, it discusses how intelligent agents enhance their decision-making process by exchanging information about missing data or unobservable variables using agent interaction protocols. In addition, the paper presents the proof-of-concept implementation and preliminary experimental evaluation of the proposed smart home system architecture. In general, the study demonstrates that the combination of multi-agent system (MAS) technologies and probabilistic logic programming can help in building a smart home reasoning system, which is capable of performing well under vague inhabitant commands and missing information in partially observable environments.
Application Research and Implementation of Face Recognition in Smart Campus

Face recognition technology can be applied to the examination room, library, canteen, dormitory and other areas of smart campus. However, when the original method is applied to face recognition technology, the image enhancement effect is poor, resulting in the slow speed of face recognition. Therefore, a new application method of face recognition in smart campus is studied. In this study, the metric learning method was used to recognize facial information, and based on the extracted facial features, the recognition effect of facial images was enhanced to realize facial detection for students and teachers. The experiment set a total of three face recognition distances of 0.1m, 0.5m and 1.0m. According to the test results, it can be seen that the application method of this study has a faster face recognition speed at different distances than the original application method.
Weakly-Supervised Learning for Multimodal Human Activity Recognition in Human-Robot Collaboration Scenarios

The ability to synchronize expectations among human-robot teams and understand discrepancies between expectations and reality is essential for human-robot collaboration scenarios. To ensure this, human activities and intentions must be interpreted quickly and reliably by the robot using various modalities. In this paper we propose a multimodal recognition system designed to detect physical interactions as well as nonverbal gestures. Existing approaches feature high post-transfer recognition rates which, however, can only be achieved based on well-prepared and large datasets. Unfortunately, the acquisition and preparation of domain-specific samples especially in industrial context is time consuming and expensive. To reduce this effort we introduce a weakly-supervised classification approach. Therefore, we learn a latent representation of the human activities with a variational autoencoder network. Additional modalities and unlabeled samples are incorporated by a scalable product-of-expert sampling approach. The applicability in industrial context is evaluated by two domain-specific collaborative robot datasets. Our results demonstrate, that we can keep the number of labeled samples constant while increasing the network performance by providing additional unprocessed information.
Vehicle Detection with Bottom Enhanced RetinaNet in Aerial Images

Vehicle detection is one of the hot topics in lane detection and vehicle counting. Many works have been done on it and some data sets with satellite images and aerial images are proposed. However, the ratio of the vehicle targets to the background is small and the detection results are unsatisfied. In this paper, a bottom enhanced RetinaNet model named En-RetinaNet is proposed to get better performance on vehicle detection. The EnRetinaNet includes an enhanced feature pyramid network(FPN) and a bottom-top fusion before the region proposal network. Enhanced feature pyramid network adds a bottom layer to the feature pyramid network to exploit more local features. Bottom-top fusion is utilized to get a better fusion of the bottom layers and top layers. In order to get a high ratio of the objects to the images, we take a sliding window mechanism on the testing images. We discuss the effects of the training input crop size on the final results and choose a moderate size of the training input. With all the above work done, we get an improvement on the UCAS—AOD data set in contrast to the RetinaNet.
Collaborative transition to open access publishing by scholarly societies

For decades, universities, researchers, and libraries have sought a systemwide transition of scholarly publishing to open access (OA), but progress has been slow. There is now a potential for more rapid and impactful change, as new collaborative OA publishing models have taken shape. Cooperative publishing arrangements represent a viable path forward for society publishers to transition to OA as the default standard for disseminating research. The traditional article processing charge OA model has introduced sometimes unnavigable financial roadblocks, but cooperative arrangements premised on collective action principles can help to secure long-term stability and prevent the risk of free riding. Investment in cooperative arrangements does not require that cash-strapped libraries discover a new influx of money as their collection budgets continue to shrink, but rather that they purposefully redirect traditional subscription funds toward publishing support. These cooperative arrangements will require a two-way demonstration of trust: On one hand, libraries working together to provide assurances of sustained financial support, and on the other, societies’ willingness to experiment with discarding subscriptions. Organizations such as Society Publishers Coalition and Transitioning Society Publications to Open Access are committed to education about and further development of scalable and cooperative OA publishing models.
The Implementation of Electronic Voting System for Student Representation Council using reCAPTCHA

Abstract The electronic voting system (E-voting System) is a web-based application that enables voters to record safe and confidential votes electronically. This research aims to develop an E-Voting System by exploiting the ‘reCAPTCHA’ security component for a private international college. The Waterfall Model is deemed the most suitable to be used after data was gathered prior to developing the system and it is also considered simple to be employed. The functional testing was conducted with thirty students and results revealed that 63% of the respondents are in agreement that the traditional voting system should be supplanted with electronic voting system, especially to cut down the time in the voting process. Ultimately, the system developed demonstrated that it efficient, reliable and displayed transparency in the E-voting System.
Gesture Recognition Control System Based on Raspberry PI

Abstract With the development of science and technology, people have been searching for a new way of man-machine interaction to break through the constraints of mouse and keyboard. Gesture recognition technology has been widely studied because of its natural and convenient interpersonal communication. As early as 1993, Thomas proposed to recognize gestures with the help of data gloves or special colored auxiliary marks pasted on the hands. Thus, people began to explore the field of gesture recognition. Through more than ten years of development, especially the rapid development of computer vision, people have gradually stepped forward from pasting special color auxiliary marks to naked hand recognition technology. Based on this, the author combined gesture control board with image recognition method to carry out two-factor authentication of gesture recognition. The proposed gesture recognition system has an accuracy rate of 90% and can be constructed by using raspberry PI as the control center.
Lights and Shadows in the e-Communication of Galician Pre-Teens

This article aims to analyze and understand the ICT-based communication of Galician pre-teens; their knowledge and attitudes in the area of communication and collaboration in the framework of digital competence (DC), especially in relation to interaction, netiquette and digital identity. For this, a mixed methodology is used - a multiple case study and a DC evaluation - taking the DigComp model as reference. The results show that there are competence gaps, difficulty in terms of transferring learnings between contexts, gender-based differences in habits and capacities and an important role played by families in the whole process.
Leveraging library technology resources for internal projects, outreach, and engagement: A case study of Overleaf, LaTeX, and the KiltHub Institutional Repository Service at Carnegie Mellon University Libraries

Academic libraries offer educational outreach surrounding their technology services available to users. When using those same technology services for internal projects, librarians can better understand the mechanisms behind these technologies, allowing for meaningful outreach efforts in which librarians serve as both primary users and facilitators of use. This paper highlights a case study of leveraging an institutional license to Overleaf, a collaborative LaTeX editor, to rebuild documentation on the use of KiltHub, an institutional repository. This requires a complex mix of text, images, tables, and even videos, and presented a tremendous challenge to format in Microsoft Word and Google Docs. In leveraging the library’s own technologies, the KiltHub team gained a greater understanding of how to use and teach with these platforms, ultimately better serving campus users.