Reviewerly/exHarmony huggingface dataset:
Research on Continuous Static Coverage of Multiple Heterogeneous Warning Vehicles Considering No-fly Areas

Abstract Through the analysis of the types of tasks and flight routes faced by the warning vehicle, this paper proposes the problem of continuous static coverage of the multi-heterogeneous warning vehicle (referred to as WV) considering No-fly areas to be solved. By analysing things like detection area, no-fly area, WV patrol strategy form, patrol strategy planning mechanism and detection capability of WV, based on the philosophy of model stratification and time segmentation, a multi-layer multi-stage patrol strategy planning method (referred to as MLMPSPM) for determining the patrol strategies of multiple heterogeneous warning vehicles (WVs) during multi-patrol cycles considering no-fly areas is proposed, including the process of determining the planning stage index. In detail, MLMPSPM mainly introduces the patrol strategy planning method of EF and ER . Whether EF or ER includes input section, planning section, stage warning effect of planning stage, and patrol strategy objective planning, etc. EU planning processes is the extension of EF and ER . In the experimental results section, the relevant experimental parameters are designed in detail, the experimental results show that MLMPSPM can not only determine the patrol strategies of all WVs throughout the alert process considering time-varying no-fly areas, but also achieve a large total warning effect.
Mobile Literacy and Digital Competences of Foreign Language Teachers in Higher School

Active use of information and communication technologies (ICT) in the professional activities of educators is a priority in modernizing education around the world. These technologies not only enrich the professional practice of the teacher but also help the student to adapt to the world of digital technologies. The article presents the experience of the Department’s of Foreign Languages staff at a technical university, acquired as a result of a sudden transition to distance education due to the 2020 pandemic. Data on the technical and methodological readiness of foreign language teachers for online work are given. In addition, survey data of undergraduate students regarding their satisfaction with the teaching staff’s level of proficiency in ICT, as well as overall satisfaction with remote work are presented. Spearman’s correlation coefficient indicates the relationship between teachers’ computer and mobile literacy and the students’ overall satisfaction with distance learning.
English Long and Short Sentence Translation and Recognition Method Based on Deep GLR Model

The translation recognition of English long and short sentence information is an important issue to obtain the focus and core of English articles. Based on the deep GLR model, this paper constructs a method framework for English long and short sentence translation and recognition, using different embedding layer parameter initialization methods and using multi-layer computing methods in the sentence decoder. The initial corpus text is segmented and tagged with part-of-speech, then, the part-of-speech tag is appropriately corrected to reduce ambiguity, and then it is manually syntactically tagged. In the simulation process, the English long and short sentence summary and translation components are designed and developed, which can accurately and efficiently obtain the key information of English long and short sentences. The experimental results show that the English long and short sentence translation and recognition method of the deep GLR model improves the accuracy of the model parameters. In terms of model structure, the deep GLR value can be improved by 70.77% by reproducing the multi-layer representation fusion of semantic translation; in terms of data enhancement, the deep GLR value can be increased by 70.35% by means of "back translation," and the improved model is effective. It promotes the translation and recognition generalization ability of English long and short sentences.
Performance Evaluation of Convolutional Neural Network for Lung Cancer Detection

Cancer is the dangerous diseases nowadays among humans. Particularly lung cancer is the very common one among men and woman. The statistics says, there is a malignancy in men out of 5 and 1 out of 9 in women. Even though it's dangerous, due to the advances in medical field, lung cancer can be treated if it's found at the earlier stage through tomography (CT) screening. However, radiologist felt difficult to detect the malignant lung nodule from CT Image which usually take more time. Thus, the Computer-aided diagnosis systems (CAD) developed to address this issue. But the accuracy and performance of CAD system is still need more improvement. Nowadays deep learning techniques has been use by the researchers to improve the performance lung cancer screening with CT images using CAD system. In this work we reviewed the recent deep learning architectures for lung cancer detection and present the wide-ranging study of standard methods and also the best model has been evaluated using LUNA16 dataset.
D-lib magazine pioneered web-based scholarly communication

The web began with a vision of, as stated by Tim Berners-Lee in 1991, "that much academic information should be freely available to anyone". For many years, the development of the web and the development of digital libraries and other scholarly communications infrastructure proceeded in tandem. A milestone occurred in July, 1995, when the first issue of D-Lib Magazine was published as an online, HTML-only, open access magazine, serving as the focal point for the then emerging digital library research community. In 2017 it ceased publication, in part due to the maturity of the community it served as well as the increasing availability of and competition from eprints, institutional repositories, conferences, social media, and online journals - the very ecosystem that D-Lib Magazine nurtured and enabled. As long-time members of the digital library community and frequent contributors to D-Lib Magazine, we reflect on the many innovations that D-Lib Magazine pioneered and were made possible by the web, including: open access, HTML-only publication and embracing the hypermedia opportunities afforded by HTML, persistent identifiers and stable URLs, rapid publication, and community engagement. Although it ceased publication after 22 years and 265 issues, it remains unchanged on the live web and still provides a benchmark for academic serials and web-based publishing.
Litmus Test for Linus’ Law: A Structural Equation Modeling Based Approach

“Many eyeballs make all bugs shallow” - referred to as Linus’ Law was framed by Raymond. The more is the number of developers working together on similar bugs for their resolution, the more easily and quickly will they get resolved. In this paper, we will be analyzing an open source dataset of 1000+ Android bugs, owned by 70 developers. Our results indicate that, for an arbitrary developer, the stronger is the developer network with other developers working in a similar environment, the more likely it is that they come across the same bugs during the development time. If they get access to the solution of the known bugs in the early phase of the development, then less time will be required for bug resolution and hence, in turn the quality of the software can be enhanced at a faster pace. We have done SEM analysis using Lavaan and have provided significant statistical evidence in support of our results.
A Metal Character Enhancement Method based on Conditional Generative Adversarial Networks

Abstract In order to improve the accuracy and stability of metal stamping character (MSC) automatic recognition technology, a metal stamping character enhancement algorithm based on conditional Generative Adversarial Networks (cGAN) is proposed. We identify character regions manually through region labeling and Unsharpen Mask (USM) sharpening algorithm, and make the cGAN learn the most effective loss function in the adversarial training process to guide the generated model and distinguish character features and interference features, so as to achieve contrast enhancement between character and non-character regions. Qualitative and quantitative analyses show that the generated results have satisfactory image quality, and that the maximum character recognition rate of the recognition network ASTER is improved by 11.03%.
Human joints estimation system of rescue robot with occlusion

Abstract The human body is often in a partially visible state during rescue at the disaster site. At the same time, when the human body is grasped, the camera located on the actuator at the end of the robotic arm cannot fully bring the human body into the field of view. The existing human pose estimation algorithms do not perform well in this situation, and a joint detection method that can estimate the complete human pose through partial human body information and can perform data refresh in real time is required. We use the occlusion part classification module to extract the weight vector, re-weight it and the joint features extracted from AlphaPose, and add human body geometric constraints when generating 3D human body joints to obtain more accurate 3D human body joint positions. Finally, the robot is successfully used to complete the human grasping experiment, which verifies the effectiveness of the algorithm.
Development and Research of Health Examination and Nursing Technology Based on Nanotechnology

With the improvement of the socio-economic level and the continuous popularization of health concepts, more and more people have increased their attention to physical health and often go to physical examinations to protect their health. Nanotechnology, also known as nanotechnology, is a technology for studying the properties and applications of materials with structural dimensions in the range of 1 nm to 100 nm. The rapid development of nanotechnology has provided an important guarantee for healthy physical examination and nursing methods. Therefore, it is necessary to conduct research on the development of health examination and nursing technology based on nanotechnology. Nursing safety means that nursing staff should strictly follow the nursing system and operation procedures in carrying out nursing work, execute medical orders accurately, and implement nursing plans to ensure that patients are physically and mentally safe during treatment and rehabilitation. Based on the development and research of health examination nursing technology under nanotechnology, 220 physical examinees in our hospital were selected and divided into experimental group and control group according to whether nanotechnology-based physical examination nursing method was applied, and then, the physical examination time, physical examination efficiency, and the coincidence degree of physical examination results were compared. The results showed that the physical examination time of the experimental group was <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>58.94</mn> <mo>±</mo> <mn>31.40</mn> </math> min, while that of the control group was <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mn>73.74</mn> <mo>±</mo> <mn>38.95</mn> </math> min; the difference between the two groups was <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mn>14.8</mn> <mo>±</mo> <mn>7.55</mn> </math> min, with statistical significance ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>P</mi> <mo>&lt;</mo> <mn>0.05</mn> </math> ). Therefore, it can be seen that the development and research of health examination nursing technology based on nanotechnology can effectively improve the level of medical care service and the satisfaction of physical examinees.
Protected ECC Still Leaks: A Novel Differential-Bit Side-channel Power Attack on ECDH and Countermeasures

Over the past decade, a few side-channel attacks (SCAs) and countermeasures against implementations of Elliptic-Curve Cryptography (ECC), commonly used in embedded systems and Internet-of- Things (IoT) devices, have been presented. This work discovers a new side-channel power leakage of an ECDH hardware implementation protected against existing attacks, where the power leakage is not directly related to the key bits, but related to the differential of two consecutive key bits. We propose an unsupervised differential-bit horizontal clustering attack and implement it against an ECDH FPGA implementation. We also comprehensively analyze the related operations and circuits, and identify the root cause of such leakage is due to the different arrival times of inputs to combinational circuits. Such leakage generally exists in ECC hardware implementations, including FPGA and ASIC. We further propose several effective countermeasures to address this new vulnerability and evaluate the implemetations.
High Assurance in the Twenty-First Century With Roger Schell

Bob Blakley: Welcome, everybody, to episode three of the IEEE Over the Rainbow podcast. I am your host Bob Blakley, and we’re here today with one of the founders of the field of information security, Roger Schell. Roger, back in the early 1980s, was one of the leaders of the production of a U.S. government standard called the Orange Book [formally titled the Trusted Computer System Evaluation Criteria, (TCSEC)], which essentially defined the model for a secure computer system. I have a copy of it here. Roger, you’ll be happy to see that I used it heavily enough that I had to take the staples out. I think it’s probably a good time to be having this conversation because this year is the 50th anniversary of the reference monitor model. Nineteen seventy-two saw the publication of the Anderson report, which introduced the concept, as far as I’m aware, in the public literature—and that’ll be 50 years ago this year. And—as I think you’re aware, Roger—the title of our podcast is a sort of multilayered pun, and one of the aspects of the pun is a reference to the Rainbow Series, which the Orange Book was the inaugural volume of. One of the sly suggestions we were hinting at in the title was that, in some sense, the computer security industry is over the Rainbow Series—over in the sense of sort of wanting to leave it in the past. But I think one of the things that we’re going to talk about today is that it’s not easy to leave the Orange Book in the past and also possibly not wise. So, with that introduction, Lorrie, I’ll hand it to you to start our questions.
CFDB: Machine Learning Model Analysis via Databases of CounterFactuals

Data Scientists often design and train complex Machine Learning models that evolve over time due to re-training on new data, a revised architecture, or both. To assist Data Scientists in this process, many methods for analyzing models have been recently developed. A prominent approach for model analysis is based on the notion of Counterfactuals. A counterfactual (CF) intuitively explains the label assigned by the model to a particular instance by identifying perturbations to the instance that lead to a different predicted label. A large body of recent literature has demonstrated the usefulness of CFs for deriving insights on the model at large. The analyzed CFs come in various flavors and are applied to instances chosen based on various criteria, in the context of different analysis goals.
3D Estimation of Visual Focus of Attention

Humanoid and social robots may provide valuable resources to society in the most diverse and complex activities and challenges, thanks to their increasing mechanical and decisionmaking abilities. However, robots must comprehend and acquire information about their surroundings for proper interaction with humans. The VFOA can be used as the primary conversational cue. To tackle these challenges, we develop a novel approach that estimates and tracks the VFOA. The proposed model stems from the consideration that the eye gaze and head pose carry information about actions and interactions. The proposed formulation leads to a 3D algorithm that considers: (i) A bounding box of every object in the field of view of the robot&#x2019;s camera, and (ii) a ray casting algorithm that considers head and gaze directions. A Kalman filter performs the tracking of the gaze. Finally, the VFOA algorithm estimates the object of attention based on a weighted sum of gaze and head pose information. We study the parameters of 3D VFOA algorithm, running simulated scenarios for a selection of the most adequate parameters. The novel approach is validated, tested and benchmarked on the public MPI Sintel dataset containing animated real-world interactions.
Designing born-digital scholarship: A study of webtext authors’ experience and design conventions

This study curates an annotated portfolio of print scholarship, web design, and webtext features as a basis for interviews with webtext authors to examine their experiences composing webtexts for publication and design rationales within a four-part design space: orientation, movement, multimodality, and contextualization. Findings show that webtext authors draw on conventions of print-based scholarship and web design to define new possibilities for born-digital scholarship.
Study on the Causal Relationship of Cardiovascular Disease Influencing Factors Based on Bayesian Causal Network

The WHO MONICA dataset was used as an example, and a logistic regression model was used to statistically analyze the data, and then a Bayesian causal network model was constructed using the MMHC hybrid algorithm to analyze the causal relationships among cardiovascular disease risk factors, and Bayesian estimation was used to learn the conditional probabilities of each node of the network so as to predict the survival of patients, and to compare the Bayesian causal network model with respect to logistic regression model in the field of chronic diseases. Bayesian causal network model results showed that hospitalization status, age at diagnosis, and angina status were direct causes of cardiovascular mortality, while previous myocardial infarction, sex, and smoking had indirect effects on cardiovascular mortality through other variables. Compared to logistic regression models, Bayesian causal networks based on the MMHC algorithm are more applicable in clinical research because they can intuitively and effectively identify and define the complex causal relationships between survival outcomes and cardiovascular disease and cardiovascular disease with each other. By analyzing this relationship, we are able to implement timely and targeted preventive and therapeutic measures and avoid possible mortality outcomes in high-risk populations.
SensorViz: Visualizing Sensor Data Across Different Stages of Prototyping Interactive Objects

In this paper, we propose SensorViz, a visualization tool that supports novice makers during different stages of prototyping with sensors. SensorViz provides three modes of visualization: (1) visualizing datasheet specifications before buying sensors, (2) visualizing sensor interaction with the environment via AR before building the physical prototype, and (3) visualizing live/recorded sensor data to test the assembled prototype. SensorViz includes a library of visualization primitives for different types of sensor data and a sensor database builder, which once a new sensor is added automatically creates a matching visualization by composing visualization primitives. Our user study with 12 makers shows that users are more effective in selecting sensors and configuring sensor layouts using SensorViz compared to traditional prototyping utilizing datasheets and manual testing on the prototype. Our post hoc interviews indicate that SensorViz reduces trial and error by allowing makers to explore sensor positions on the prototype early in the design process.
Completion of Contingency Ranking Selection (N-1) Using Ant Colony Optimization Algorithm on 500 kV JAMALI System

Indonesia is an archipelago nation with the world's fourth-largest population. With such a large population, the demand for electricity increases proportionately. At the moment, the JAMALI interconnection system serves the majority of Indonesia's electricity consumers in Jawa, Madura, and Bali. On the other hand, improving the electric power system's safety quality is a requirement that an electric power system must meet. To ensure the power system's security, the Ant Colony Optimization (ACO) algorithm was used to run several contingency scenarios (N-1). The ACO algorithm was implemented in this study through the initial parameter initialization stages, the probability value calculation stage, and the 0/1 knapsack problem calculation stage using the maximum cost function. The test is conducted by varying the capacity of the knapsack. According to the results of the voltage performance index on the JAMALI 500 kV system, the highest value was 95.39 on line 35 connecting bus 26 (Bangil) and bus 27 (Paiton), followed by NaN on line 56 connecting the Krian and Gresik buses. While line 59, which connects bus 46 (Grati) and bus 47 (South Surabaya), has the lowest ranking with the lowest value at 95.14.
An Eye Opener on the Use of Machine Learning in Eye Movement Based Authentication

The viability and need for eye movement-based authentication has been well established in light of the recent adoption of Virtual Reality headsets and Augmented Reality glasses. Previous research has demonstrated the practicality of eye movement-based authentication, but there still remains space for improvement in achieving higher identification accuracy. In this study, we focus on incorporating linguistic features in eye movement based authentication, and we compare our approach to authentication based purely on common first-order metrics across 9 machine learning models. Using GazeBase, a large eye movement dataset with 322 participants, and the CELEX lexical database, we show that AdaBoost classifier is the best performing model with an average F1 score of 74.6%. More importantly, we show that the use of linguistic features increased the accuracy of most classification models. Our results provide insights on the use of machine learning models, and motivate more work on incorporating text analysis in eye movement based authentication.
Research on Classroom Teaching Quality Evaluation and Feedback System Based on Big Data Analysis

With the development of information technology, the era of big data has gradually entered classroom teaching, and the evaluation of students’ learning quality and classroom teaching efficiency has been widely concerned. In order to further improve the quality of classroom teaching in colleges and universities, this paper analyzes the classroom teaching quality evaluation and feedback system based on the big data. Principal component analysis is used to analyze the evaluation index of classroom teaching quality and feedback information to apartment. Through the study of education quality estimate and feedback system under big data, the feedback mechanism of classroom teaching quality evaluation is improved to achieve high-quality and efficient teaching.
Truncated Differential Attacks on Contracting Feistel Ciphers

We improve truncated differential attacks on t-branch contracting Feistel ciphers with a domain size of Nt. Based on new truncated differentials, a generic distinguisher for t2 + t − 2 rounds using O(Nt−1) data and time is obtained. In addition, we obtain a key-recovery attack on t2 + 1 rounds with Õ(Nt−2) data and Õ(Nt−1) time. Compared to previous results by Guo et al. (ToSC 2016), our attacks cover more rounds with a lower data-complexity. Applications of the generic truncated differential to concrete ciphers include full-round attacks on some instances of GMiMC-crf, and the best-known key-recovery attack on 17 rounds of the Chinese block cipher standard SM4. In addition, we propose an automated search method for truncated differentials using SMT, which is effective even for trails with probability below the probability of the truncated differential for a random permutation.