Reviewerly/exHarmony huggingface dataset:
Lambda Expressions

Java 8 introduced the lambda expression, which provides a concise way to represent a method using an expression. A lambda expression consists of three parts: an argument list, the arrow operator (->), and a body. The following lambda takes two integer arguments and returns their sum:
Development orientation of the Hanoi National University of Education in the fourth industrial Revolution context

Hanoi National University of Education (HNUE) is a university of long history and has been identified as one of the key pedagogical universities in Vietnam. Confronted with the opportunities and challenges of the fourth industrial revolution, HNUE needs to innovate to continue writing its successful history pages. The article uses theoretical research methods to analyze theoretical and practical bases to assess the current status and recommend development orientations of HNUE in the new revolution. Accordingly, advancing into a smart university to improve the quality of teacher training and increase the university's value is the right direction. The article also illuminates the essential components of a pedagogical smart-university. After parsing the proposal's feasibility and significance for HNUE, the article further mentions some initial solutions to develop HNUE into a smart university.
Comparative Study of the Optimization of the Multi-prime RSA Algorithm

With the continuous development of computer technology, the amount of data shared on the Internet has increased significantly. Increasing demand for big data continues to grow, facing many security challenges. In today’s information era, data on the Internet is vulnerable to various attacks, and everyone wants to protect their privacy. Therefore, maintaining the security of user-class data has become the current research hotspot. This article integrates four methods, the traditional two-prime RSA, two-prime mixed CRT, and three-prime CRT-RSA and traditional tetraprime and optimized quatertraprime methods operate on the same message summaries to record their respective time-consuming behaviors. optimization is of great significance.
Research on Business Model of Energy Complex for Big Data Multi-station Fusion Architecture

In the continuous renewal of China&#x0027;s energy strategy, the construction of the grid as the core of energy Internet framework has become the main direction of energy system integration development. Understanding the comprehensive energy hub during the construction of energy Internet at the present stage, it is found that the energy flow fusion technology under the multi-station fusion mode has unique advantages. Therefore, this article stood more clear for big data fusion architecture research status quo on the basis of the design of a multiple station fusion in the dc bus as the core architecture, and studied the feasibility of the application of existing technology which and the key problem, the final results not only can help the engineers quickly grasp the development trend of multistation fusion, can also provide effective basis for the subsequent construction practice.
A Machine Learning Method for Spam Detection in Twitter using Naive Bayes and ERF Algorithms

In this era of machinery driven, online social media is a vast growing fact. The main social media is Instagram, Facebook and twitter. These are the media which are connecting the global as fast as other sources. It will be increase as tremendous way in future. These online social media users makes the information independently and also they can gobble the information. There are so many domains accepts the vital role of analyzing the social media. This may improves the throughput and also attain the back-and-forth competition. Now a day the people are spending their most of the time in the online social media. The vast increase in the popularity in the social media also makes the hackers to spam, thus causes the conceivable losses. The Cyber criminals are usually hack by produce the external phishing sites or the malware downloads. This became the major issues in the safety consideration of online social network and this makes the user experience as a damaged one. To combat with the issue of spams, there has been a lot of methods available, Yet, there is not a perfect effective solution for detect the Twitter spams with the exactness. In this paper , the collected tweets are classified with the help of NB and Enhanced Random Forest classifiers. The prediction is then assessed on many validation measures such as accuracy,precision and F1 score.
Performance Enhancement of Action Recognition System Using Inception V3 Model

Any kind of action is done by an agent which can be a human, animal, object, etc. So far most of the exploration done in the field of action recognition targets the actions performed by the agents and not the agents themselves. But, one agent cannot perform an action in the same way as another. In this paper, we addressed this action recognition problem between multiple agents using the Actor-Action Dataset. This study focuses on two scenarios: individual-class mapping and grouped-class mapping. We applied two strategies to model these cases: non-transfer approach and transfer-learning approach. It is observed that transfer learning techniques along with image augmentation outperforms the models without transfer learning. The results show that our approaches provide an average accuracy of 92% on individual-class mapping, 87% on grouped-class mapping.
Siamese network tracker with channel attention mechanism

Recently, object trackers based on Siamese deep network have achieved great progress and attract much attention. It can be helpful to improve the performance of a tracker to mining more effective information from feature maps. In this paper, we propose a Siamese network tracker with channel attention mechanism based on the SiamCAR tracker. Firstly, in each residual unit of the ResNet backbone network, each channel feature of the feature map has different importance for discriminating an object. Channel attention mechanism can be used to calculate the importance of each channel feature. Secondly, deep features and lower-level features play different roles for tracking too, and attention mechanisms can be used to fuse the features of each residual stage. On the benchmark datasets, OTB50 and OTB100, the experiments show that our proposed tracker achieves better tracking performance in AUC and Precision and achieves the real-time speed of 46 FPS.
Object Detection Frameworks fo Real-Time, Scale-Invariant Face Mask Detection

As the world experiences a pandemic caused by the spread of COVID-19, following health protocols like the proper wearing of face masks is essential to help prevent the spread of the virus as stressed by the World Health Organization (WHO). In the Philippines, manual monitoring of these measures may not be as efficient when handling large crowds. The authors were able to generate a real-time, scale-invariant face mask recognition model that also handles occlusions. It was trained using images from the wild to simulate real-world conditions. Existing state-of-the-art object detection frameworks such as MobileNet, RetinaNet, and YOLOv4 were implemented to detect and localize the proper use of face masks in public. To evaluate the performance of the models, the mAP, inference time, and precision curve were used. The performance of the model has resulted to as high as .94 mAP with an inference time of 73 ms.
Candid webinar focuses on growing donor base

Nonprofit Business AdvisorVolume 2021, Issue 384 p. 9-9 Resources Candid webinar focuses on growing donor base First published: 13 August 2021 https://doi.org/10.1002/nba.31072Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume2021, Issue384September 2021Pages 9-9 RelatedInformation
Preface

Due to recent pandemic, the 2021 International Conference on Computer Science and Materials Applications (CSMA 2021) was held virtually online during September 3-5, 2021. The decision to hold the virtual conference was made in compliance with many restrictions and regulations that were imposed by countries around the globe. Such restrictions were made to minimize the risk of people contracting or spreading the COVID-19 through physical contact. There were 110 individuals who attended this on-line conference, represented many countries including Malaysia, India, Iran, Ukraine, and China. CSMA 2021 brings together top professionals from industry, government, and academia from around the world. CSMA 2021 included invited talks, oral presentations, and poster presentations of refereed papers. We invited submissions of papers and abstracts on all topics related to computer science and materials applications. The conference provided networking opportunities for participants to share ideas, designs, and experiences on the state-of-the-art and future direction of computer science and materials applications. During the conference, the conference model was divided into three sessions, including oral presentations, keynote speeches, and online Q&amp;A discussion. In the first part, some scholars, whose submissions were selected as the excellent papers, were given about 5-10 minutes to perform their oral presentations one by one. Then in the second part, keynote speakers were each allocated 30-45 minutes to hold their speeches. In the second part, we invited two professors as our keynote speakers. Prof.Yulin Wang, Wuhan University, China. His research interests include image and video processing, digital rights management, information security, intelligent system, e-commerce, IoT, code clone and so on. And then we had Prof. Wei Wei, Xian University of Technology, China. He has been engaged in research on the Internet of Things, artificial intelligence, big data processing and other related aspects. Their insightful speeches had triggered heated discussion in the third session of the conference. Every participant praised this conference for disseminating useful and insightful knowledge. List of Committee are available in this pdf.
UGV obstacle detection of sensor fusion based on point cloud data segmentation

This paper uses the point cloud data extracted by LiDAR as the basis of obstacle detection and proposes an algorithm to reduce the computational complexity of point cloud. Firstly, it removes the points too high or too low from the point cloud data, leaving only the points forming the obstacle interval. Secondly, the point cloud in the interval is projected to the X-Y plane (horizontal plane) and the resolution is reduced by lattice. Finally, the connected component marker clustering method is used to segment and classify obstacles. After identifying the obstacle, the scene information is provided to the path planning algorithm. In the path planning algorithm, Unmanned Ground Vehicle (UGV) moves along the shortest straight line connecting the target point and the starting point. When encountering obstacles, the edge tracking method is used to bypass the obstacle, and then it goes straight again. After planning by the path planning algorithm, UGV can avoid collision and advance to the destination in real time through the motion control system. The experimental result shows that UGV designed by this research has the ability to reach the target point safely, and it can avoid obstacles and move autonomously in real-time conditions and complete outdoor unknown environmental obstacle detection.
DESIGN &amp; DEVELOPMENT OF ROBUST MACHINE LEARNING DESIGNS UTILIZING AI CALCULATIONS TO CONTROL ADVERSIAL SETTINGS

Numerous offices are currently utilizing AI calculations to settle on high-stake choices. Deciding the correct choice unequivocally depends on the accuracy of the info information. This reality gives enticing motivations to lawbreakers to attempt to mislead AI calculations by controlling the information that is encouraged to the calculations. Then, conventional AI calculations are not intended to be protected when going up against startling data sources. In this exposition, we address the issue of antagonistic AI; i.e., we will likely form safe AI calculations that are hearty within the sight of loud or adversarial controlled information. Ill-disposed AI will be additionally testing when the ideal yield has a mind boggling structure. In this paper, a sign cannot concentrate is on antagonistic AI for anticipating organized yields. To start with, we build up another calculation that dependably performs aggregate classification, which is an organized expectation issue. Our learning strategy is efficient and is defined as a raised quadratic program. This procedure verifies the expectation calculation in both the nearness and the nonappearance of an enemy. Next, we explore the issue of parameter learning for hearty, organized forecast models. This strategy builds regularization capacities dependent on the impediments of the foe. In this exposition, we demonstrate that strength to antagonistic control of information is proportionate to some regularization for huge edge organized expectation, and the other way around. A customary enemy consistently either does not have enough computational capacity to structure a definitive ideal assault, or it doesn't have sufficient data about the student's model to do as such. In this manner, it frequently endeavors to apply numerous irregular changes to the contribution to an expectation of making a leap forward. This reality suggests that on the off chance that we limit the normal misfortune work under antagonistic clamor, we will acquire power against unremarkable enemies. Dropout preparing takes after such a commotion infusion situation. We infer a regularization technique for huge edge parameter learning dependent on the dropout system. We stretch out dropout regularization to non-straight parts in a few unique ways. Experimental assessments demonstrate that our procedures reliably beat the baselines on various datasets. This exploration work incorporates recently distributed and unpublished coauthored material.
Application of Different Algorithms to Optimize Abstractive Summarization

Summarization is used to extract most relevant content from a huge content. It can be extractive and abstractive. But different people may have different scope of relevance in different area. Content can be in the form of opinions, news, judgments and ideas etc. Extractive Summarization extracts most focusing content without any change in the original content. Abstractive summarization is a knowledgebase extraction with some modification in original content. In this paper first we discuss various algorithms for abstractive summarization then on the basis of merits of various algorithms we discuss, how various algorithm may help to optimize Abstractive Summarization. Challenges in Abstractive summarization:  Repetition or duplicity of the content  Arrangement of words  Different people have different meaning for the same content  Number of times a content repeated  Different people have different interest  Nosiness in content  Information diversity Methods for Abstractive summarization: 1. integer linear programming-based summarization framework [3] 2. Improved Semantic Graph Approach [4] 3. novel speech act-guided summarization approach [5] 4. Neural Abstractive Summarization with Diverse Decoding(NASDD) [6] 5. Supervised and unsupervised approach [7] 6. Neural network based representation [8] 7. Integer linear optimization [9] 8. Maximum L∞-norm and minimum entropy regularization [10] 9. Opizer-E and Opizer-A [11] 10. LSTM-CNN based ATSDL model [12] 11. novel concept-level approach [13]
Preventing Cryptographic Attacks in Iot using Lightweight P-Box Permutation Encryption

Internet of Things (IoT) is a leading technology, where numerous sensors with the ability to exchange information over the internet are involved. It supports various applications ranging from smart home to smart city including smart grid for calculating electricity tariff, real time monitoring and controlling traffic, Co2 emission level finding etc. Research advancements in IoT facilitates us to control or monitor the things remotely and take actions accordingly. Efficient working of the IoT, involves continuous exchange of information between the nodes which should be executed in trusted environment as this data is confidential and only trusted nodes should handle it. Current research work in IoT suggests the confidentiality mechanisms which are heavyweight and not suitable in IoT scenario. So there is a need for lightweight confidentiality mechanism. In this paper we have shown how lightweight P-Box permutation encryption algorithm can be effective in IoT. We have compared this encryption algorithm with PRESENT protocol and our results show that lightweight P-Box permutation algorithm took less time. To prove that our encryption algorithm is better w.r.t. security, we have also carried out known plaintext attack and chosen plaintext attack and shown that lightweight P-Box permutation encryption algorithm is resistant to both of these attacks. Further we have used this technique to transfer the smart meter data in Smart Grid and shown that the data exchanged between the two nodes in confidential manner.
Combining the Benefits of Tightly and Loosely Coupled GNSS/INS Integration in UAV Applications: A Two-Stage Filter Approach

Autonomous unmanned aerial vehicles (UAV) require three-dimensional state estimation with high accuracy, availability, integrity, and bandwidth. Both loosely and tightly coupled GNSS/INS navigation filters can provide such state estimates in real-time, each of them having practical drawbacks. On one hand, carrier-phase aided methods like Real-Time Kinematic (RTK) can enable loosely coupling with accuracies in the centimeter level. On the other hand, a stable phase lock can usually not be guaranteed due to the dynamic movement of UAVs, forcing a frequent fall back to RTK with floating ambiguities or even to pseudorange-based differential. A change from RTK to differential and vice-versa causes jumps in the estimated position, leading to unsteady flight behavior. Contrarily, tightly coupled filtering using single or double-differenced pseudoranges provides smooth estimates, but at the cost of higher expected mean errors of up to 1 m. In this paper, we present a method to improve the mean accuracy of a tightly coupled GNSS/INS navigation filter by means of a two-stage approach using RTK. Instead of directly performing pseudorange error corrections using differential techniques, we propose a second Kalman filter that estimates per-satellite pseudorange offsets in real-time using an RTK position when available. In short-term absence of RTK, we can maintain the current positional accuracy by means of propagating these errors in time. The method is implemented using Simulink, evaluated using real-time data from a multirotor flight and compared to the results of pure loosely and tightly coupled filtering. We show that our approach avoids sudden changes in position estimates, thus keeping the smoothness of the tightly coupled integration, while drastically reducing the expected mean position error to around 35 cm. The method is well suited to other applications, in which a smooth estimate is more vital than the respective best estimate at each individual time.
End-to-End Speaker Verification for Short Utterances

Speaker verification is the process used to verify a speaker from his/her voice characteristics. Given a speech segment as input and the target speaker data, the system automatically determines whether the target speaker spoke the test segment. There are many methods of bio-metric verification like a fingerprint, iris scanning signatures, etc. In this list, speech specific authentication is not as much reliable as other methods. Hence, we would like to develop a reliable speaker verification model. Recent advances in deep learning have facilitated the design of speaker verification systems that directly input raw waveforms. Though developing a model with raw waveforms is complex in speech processing, it would yield an end-to-end system, which reduces the time and power of feature extraction. To achieve the motive of end-to-end speaker verification, we propose to use Raw Waveforms as input. The development of such a system is possible without much domain knowledge of feature extraction. Moreover, the availability of a large dataset eases the development of the end-to-end system. The later part of the proposed system also includes analyzing the model's performance using a short utterance dataset to make the model more user-friendly and reduce computation power. Hence, we plan to analyze and improvise RawNet (Jung et al. in Proceedings of Interspeech, pp. 3583–3587, 2020 [1]) for short utterances.
Retraction: A study on the Efficacy of Alcohol Detection in the prevention of Drunken Driving (IOP Conf. Ser.: Mater. Sci. Eng. 1145 012084)

This article (and all articles in the proceedings volume relating to the same conference) has been retracted by IOP Publishing following an extensive investigation in line with the COPE guidelines. This investigation has uncovered evidence of systematic manipulation of the publication process and considerable citation manipulation. IOP Publishing respectfully requests that readers consider all work within this volume potentially unreliable, as the volume has not been through a credible peer review process. IOP Publishing regrets that our usual quality checks did not identify these issues before publication, and have since put additional measures in place to try to prevent these issues from reoccurring. IOP Publishing wishes to credit anonymous whistleblowers and the Problematic Paper Screener [1] for bringing some of the above issues to our attention, prompting us to investigate further. [1] Cabanac G, Labbé C and Magazinov A 2021 arXiv: 2107.06751v1 Retraction published: 23 February 2022
THE IMPACT OF CHANGING THE GRANULARITY OF PREFERENCE-INTENSITY INFORMATION ON THE ONLINE COMPENSATION ELECTION

This study investigates the effect of a change in the granularity of preference intensity information available to voters on their voting behavior in an online referendum with options to express preference intensities and receive compensation.We find that a decrease in the granularity of the real preference intensity information about others does not affect voting behavior and the referendum's compensation outcomes We discuss the implications of these results and possible future directions for research.
Assessment of utilization of ict tools for agricultural purposes by the farmers of Banda district of Uttar Pradesh

Information Communication Technology (ICT) plays a vital and crucial role in disseminating the right information to the right person in the right format at the right time. Looking at the unparalleled importance and applicability of ICT tools, a study was conducted in Baberu and Kamasin blocks of Banda district of Uttar Pradesh with 120 farmers to assess the utilization of ICT tools for agriculture purposes. The Ex-Post-Facto research design and multi-stage random sampling technique was used. It was found that cent percent of the respondents are using mobile phone as an ICT tool. Vast majority of the respondents (85.50%) has been using the mobile phones to call Kisan helpline regularly. Also, 41.67 per cent of farmers were found to be using WhatsApp application for agricultural purposes. Majority of the respondents (79.17%) in the selected are getting farm information (seed and sowing) through ICT. Thus, the study revealed a growing acceptance and use of ICT tools among the farmers of Banda district similar to the pattern of many progressive distrICT across Uttar Pradesh.