Reviewerly/exHarmony huggingface dataset:
Preventing Adversarial Attacks on Autonomous Driving Models

Autonomous driving systems are among the exceptional technological developments of recent times. Such systems gather live information about the vehicle and respond with skilled human drivers’ skills. The pervasiveness of computing technologies has also resulted in serious threats to the security and safety of autonomous driving systems. Adversarial attacks are among one the most serious threats to autonomous driving models (ADMs). The purpose of the paper is to determine the behavior of the driving models when confronted with a physical adversarial attack against end-to-end ADMs. We analyze some adversarial attacks and their defense mechanisms for certain autonomous driving models. Five adversarial attacks were applied to three ADMs, and subsequently analyzed the functionality and the effects of these attacks on those ADMs. Afterward, we propose four defense strategies against five adversarial attacks and identify the most resilient defense mechanism against all types of attacks. Support Vector Machine and neural regression were the two machine learning models that were utilized to categorize the challenges for the model’s training. The results show that we have achieved 95% accuracy.
Prediction of temperature distribution in a furnace using the incremental deep extreme learning machine

In this article, a data-driven model based on the incremental deep extreme learning machine (IDELM) algorithm is proposed to predict the temperature distribution in the furnace. To this end, computational fluid dynamics (CFD) simulations are carried out first to get temperature distributions under typical working conditions. Based on the air distribution mode, the simulation results are divided into six subclasses. Then the K-means clustering method is applied to find out the benchmark working condition of each subclass. Moreover, the random sampling method is used to extract samples to reduce computational complexity. Modeling inputs are selected according to the CFD boundary conditions and combustion mechanisms, and data sets are reconstructed based on the increments of each actual working condition from the benchmark working condition. Finally, an IDBN-based prediction model is built in each subclass. The experimental results show that the IDBN-based model has a promising predictive ability with less than 11% symmetric mean absolute percentage error.
Measuring the Adoption of TLS Encrypted Client Hello Extension and Its Forebear in the Wild

Abstract The Transport Layer Security (TLS) protocol was introduced to solve the lack of security and privacy in the early versions of the world wide web. However, even though it has substantially evolved over the years, certain features still present privacy issues. One such feature is the Server Name Indication (SNI) extension, which allows multiple web servers to reside behind a provider hosting multiple domains with the same IP address; at the same time it allows third parties to discover the domains that end users visit. In the last few years, the Encrypted Server Name Indication (ESNI) Internet draft is being developed by the Internet Engineering Task Force (IETF); this encrypted variant of the extension was renamed to Encrypted Client Hello (ECH) in latest versions. In this paper, we measure the adoption of both these versions, given that they have substantial differences. By analyzing the top 1M domains in terms of popularity, we identify that only a small portion, less than 19%, supports the privacy-preserving ESNI extension and practically no domain supports ECH. Overall, these results demonstrate that there is still a long way to go to ensure the privacy of end users visiting TLS-protected domains which are co-located behind a common Internet-facing server.
Personæ: Users’ evaluation of a music technology education project

‘Personæ’ is an interactive large-scale space sound installation designed by the students of the music technology classes of the ‘V. Gambara’ music high school in Brescia (Italy). The activities that led to the conception and realization of the installation were part of ‘The Discovery of Interactive Spaces’ project, a set of extracurricular workshops organized by the authors of this article between spring 2019 and winter 2020 with the support of the Italian National Operation Program (PON). At the end of January 2020, the installation was presented during a public event and an evaluation questionnaire was administered to 79 visitors. The purpose of this research is to assess how the public, students, parents, teachers and classmates received the ‘Personæ’ installation, and the technology integration within regular study curricula. While the majority of visitors have well received the artistic and communicative value of the installation, the questionnaire reveals that there is not a similar uniformity of agreement on some aspects of technology integration in school curricula.
Building a Literacy Culture for English Department Student Through Extensive Reading Program

xtensive reading has many benefits to increase students' literacy skills and increase their ability to acquire foreign languages. Because in Extensive Reading, students have many choices of material and the type of reading they want. They also have more time compared to intensive reading. In addition, several studies have concluded that extensive reading can improve foreign language acquisition abilities. Therefore, this community service aims at improving students’ English literacy by conducting a program namely extensive reading program. This activity uses the Participatory Action Research (PAR) method, especially focusing on problem solving (problem solving) and participation (participatory). While the strategy used in conducting this program is to mobilize human resources and student potential, in this case the empowerment team is the driving force in building student awareness of the importance of reading. Therefore, the type of activity that will be carried out is Focus Group Discussion. Community service partners in this extensive reading program are students of Tadris English, Institut Agama Islam Ma’arif NU (IAIMNU) Metro Lampung. At this early stage, this activity is specifically aimed at students who are actively involved in the management of the English Student Association or ESA. Finally, this program was run very well and improve students’ English productive skill and English literacy among the students, and easy searching some source reference at library online.
Depth-Based Dynamic Sampling of Neural Radiation Fields

Although the NeRF approach can achieve outstanding view synthesis, it is limited in practical use because it requires many views (hundreds) for training. With only a few input views, the Depth-DYN NeRF that we propose can accurately match the shape. First, we adopted the ip_basic depth-completion method, which can recover the complete depth map from sparse radar depth data. Then, we further designed the Depth-DYN MLP network architecture, which uses a dense depth prior to constraining the NeRF optimization and combines the depthloss to supervise the Depth-DYN MLP network. When compared to the color-only supervised-based NeRF, the Depth-DYN MLP network can better recover the geometric structure of the model and reduce the appearance of shadows. To further ensure that the depth depicted along the rays intersecting these 3D points is close to the measured depth, we dynamically modified the sample space based on the depth of each pixel point. Depth-DYN NeRF considerably outperforms depth NeRF and other sparse view versions when there are a few input views. Using only 10–20 photos to render high-quality images on the new view, our strategy was tested and confirmed on a variety of benchmark datasets. Compared with NeRF, we obtained better image quality (NeRF average at 22.47 dB vs. our 27.296 dB).
Hate Speech: Detection, Mitigation and Beyond

Social media sites such as Twitter and Facebook have connected billions of people and given the opportunity to the users to share their ideas and opinions instantly. That being said, there are several negative consequences as well such as online harassment, trolling, cyber-bullying, fake news, and hate speech. Out of these, hate speech presents a unique challenge as it is deeply engraved into our society and is often linked with offline violence. Social media platforms rely on human moderators to identify hate speech and take necessary action. However, with the increase in online hate speech, these platforms are turning toward automated hate speech detection and mitigation systems. This shift brings several challenges to the plate, and hence, is an important avenue to explore for the computation social science community.
Model-based Unbiased Learning to Rank

Unbiased Learning to Rank(ULTR), i.e., learning to rank documents with biased user feedback data, is a well-known challenge in information retrieval. Existing methods in unbiased learning to rank typically rely on click modeling or inverse propensity weighting(IPW). Unfortunately, search engines face the issue of a severe long-tail query distribution, which neither click modeling nor IPW handles well. Click modeling usually requires that the same query-document pair appears multiple times for reliable inference, which makes it fall short for tail queries; IPW suffers from high variance since it is highly sensitive to small propensity score values. Therefore, a general debiasing framework that works well under tail queries is sorely needed. To address this problem, we propose a model-based unbiased learning-to-rank framework. Specifically, we develop a general context-aware user simulator to generate pseudo clicks for unobserved ranked lists to train rankers, which addresses the data sparsity problem. In addition, considering the discrepancy between pseudo clicks and actual clicks, we take the observation of a ranked list as the treatment variable and further incorporate inverse propensity weighting with pseudo labels in a doubly robust way. The derived bias and variance indicate that the proposed model-based method is more robust than existing methods. Extensive experiments on benchmark datasets, including simulated datasets and real click logs, demonstrate that the proposed model-based method consistently outperforms state-of-the-art methods in various scenarios. The code is available at https://github.com/rowedenny/MULTR.
Digital labeling for 3D histology: segmenting blood vessels without a vascular contrast agent using deep learning

Recent advances in optical tissue clearing and three-dimensional (3D) fluorescence microscopy have enabled high resolution in situ imaging of intact tissues. Using simply prepared samples, we demonstrate here “digital labeling,” a method to segment blood vessels in 3D volumes solely based on the autofluorescence signal and a nuclei stain (DAPI). We trained a deep-learning neural network based on the U-net architecture using a regression loss instead of a commonly used segmentation loss to achieve better detection of small vessels. We achieved high vessel detection accuracy and obtained accurate vascular morphometrics such as vessel length density and orientation. In the future, such digital labeling approach could easily be transferred to other biological structures.
Learning to Augment Poses for 3D Human Pose Estimation in Images and Videos

Existing 3D human pose estimation methods often suffer inferior generalization performance to new datasets, largely due to the limited diversity of 2D-3D pose pairs in the training data. To address this problem, we present PoseAug, a novel auto-augmentation framework that learns to augment the available training poses towards greater diversity and thus enhances the generalization power of the trained 2D-to-3D pose estimator. Specifically, PoseAug introduces a novel pose augmentor that learns to adjust various geometry factors of a pose through differentiable operations. With such differentiable capacity, the augmentor can be jointly optimized with the 3D pose estimator and take the estimation error as feedback to generate more diverse and harder poses in an online manner. PoseAug is generic and handy to be applied to various 3D pose estimation models. It is also extendable to aid pose estimation from video frames. To demonstrate this, we introduce PoseAug-V, a simple yet effective method that decomposes video pose augmentation into end pose augmentation and conditioned intermediate pose generation. Extensive experiments demonstrate that PoseAug and its extension PoseAug-V bring clear improvements for frame-based and video-based 3D pose estimation on several out-of-domain 3D human pose benchmarks.
Distributed machine learning framework and algorithm implementation in Ps-Lite

Big data analysis based on artificial intelligence is particularly important in the era of Internet. The data is stored in different regions in industry. Meanwhile, sending data to servers generates huge amount of communication cost for centralized training. The distributed machine learning can resolve the storage of data and decrease the cost of data communication. But different distributed machine learning frameworks are also limited with the problems of low algorithm compatibility and poor expandability. The aim of this paper is building the distributed machine learning framework based on Ps-Lite and implementing algorithms in the framework. The framework is realized with asynchronous communication and computation methods. The algorithm implementation includes gradient-aggregating algorithm (distributed Stochastic Gradient Descent) and three regression algorithms (Logistic Regression, Lasso Regression and Ridge Regression). The algorithm implementation illustrates that common algorithms fit this framework with high compatibility and strong expandability. Finally, the experiment of Logistic Regression implementation proves the performance of the framework. The computation time of unit node is saved 50% with the increase of node number. The accuracy of the training model is maintained above 70% in the framework. The convergence efficiency of Logistic Regression is 3 times higher than that of the traditional one in the multiple-node framework.
Digital Skeletonization for Bio-Medical Images

Skeletonization and thinning techniques are the two techniques to be applied for hospitalized patients’ brain images to find object identification, matching, and tracking, and also for many other purposes. When extracting information from an image (the method is called the binarization process), required information is lost hence the thinning method is applied on grayscale brain image intensity values directly in order to recover the information that is lost. By working on grayscale image intensity values, the proposed method was encouraged to do this proposed work. This will evade distortions in the object’s topology and geometry. In the proposed method, the Skeletonization technique is applied directly to the grayscale image intensity values to extract object information, and applied different algorithms results are analyzed which are shown in the result section.
Formation technologies of professional competence for prospective teachers

The organizational and atavistic block of research competencies is characterized by the criterion of acquiring skills to organize research activities by a teacher. Thus, this study attempts to analyze the formation technologies of professional competence for prospective teachers. To attain the aim of the study, a descriptive method is utilized. The studies of the process of managing the psychological and pedagogical competencies of a higher education teacher are considered to come to a reliable conclusion. Based on the results obtained, it can be concluded that there is a necessity for the proper organization of activities and the active use of technology for working with students and young people, which makes it possible to combine teaching and educational goals as much as possible, both in the educational process and beyond.
Effect of a trans theoretical model in improving behaviors of health care workers related to electronic hookah in Mosul, Iraq

Objective: To assess the effect of a trans theoretical model in improving behaviors in health care workers related Hookah. Methodology: This descriptive cross-sectional study included 58 subjects from Mosul Hospital. They were chosen using a probability simple random sampling. The information was gathered from November 1 and November 15, 2022. Results: All of the Trans Theoretical Model's concepts were found to be (p=0.000) significant, with the exception of the concept of experiential processes. Conclusion: The study found that decision balance, self-efficacy/temptation, processes of change, and behavioral processes were among the many health behaviors that were positively impacted by Trans Theoretical Model.
Leveraging Blockchain Interoperability for Interdependent Networks

With the rapid emergence of technologies, coupling interdependent consumer electronics networks have become imperative to enable information exchange, holistic data analysis, and effective decision-making techniques. While interdependent networks can discover crucial information through intuitive understanding and efficient data-driven algorithms, it is crucial to leverage secure and effective data exchange among the underlying networks of consumer electronics. Blockchain comes into play when it requires ensuring trustless communication and data exchange amongst multiple interdependent networks. Blockchain interoperability is evolving and gaining widespread popularity in industry and research due to its ability to exchange data among multiple blockchains. This paper presents Divide-LeapChain, a next-generation efficient and effective interoperable blockchain framework for interdependent networks of heterogeneous consumer electronics devices. The Divide-LeapChain addresses the critical issues of intra- and inter-blockchain communication, including spatial and temporal uncertainties in data traversal, exchange, validation, and cross-chain swaps. It promises significant drops-off in verification steps without weakening blockchain integrity, which extends the possibilities to apply blockchain applications on heterogeneous embedded consumer Internet-of-Things (IoT) devices.
Semi-Supervised Learning for MIMO Detection

The model-driven deep learning method has been verified to be effective for signal detection in the massive multi-input multi-output (MIMO) system. In previous work, this kind of methods need to be trained with numerous pilots in a supervised manner, which will occupy amount of spectrum resources. In addition, the abundant information in the symbols are not utilized in the training procedure. To solve this issue, Firstly in this paper, a new unsupervised deep learning (DL) network named Un-OAMPNet is proposed, which considers Mixture of Gaussian (MoG) noise model under a maximum a posterior (MAP) framework. Secondly, Un-OAMPNet is extended to the semi-supervised DL network (Semi-OAMPNet) with a few pilots to increase the detection performance. In Semi-OAMPNet, the loss function is combined with the mean square error (MSE) loss in the supervised manner and the MAP loss in the unsupervised manner. In this way, Semi-OAMPNet inherits the advantages of OAMP-Net2 and gains better detection performance with fewer pilots. Simulation results show that the Un-OAMPNet is effective without any pilots in the training procedure and proposed Semi-OAMPNet has better performance compared with other model-driven detectors.
Data Centre Infrastructure: Power Efficiency and Protection

The rapid expansion of e-commerce necessitates expanding the capacity and dependability of data centres in order to provide services with the proper level of quality. A green technology that has a lot of potentials to reduce CO2 emissions is optimization data centre design. However, a large data centre required a large amount of electricity because the capacity of the racks is higher, which required more potent cooling systems, power supplies, protection and security. These will increase the cost of the data centre and render it unusable for services. In this chapter, we provide a design for a tire-four data centre that will be situated in Cyberjaya, one of Malaysia’s best locations. This design’s primary goal is to offer highly functional and high-quality e-commerce services, particularly food delivery. Each component of the data centre has been carefully developed to deliver a range of services, including the administration of IT infrastructure, co-location, cooling system and protection. Additionally, advice and support have been given to guarantee that the suggested design outperforms competing data centres in terms of dependability, power efficiency and storage capacity. The analysis, synthesis, and evaluation of each element of the proposed data centre will be considered in this chapter.
Evaluating Designs for Hyperparameter Tuning in Deep Neural Networks

The performance of a learning technique relies heavily on hyperparameter settings. It calls for hyperparameter tuning for a deep learning technique, which may be too computationally expensive for sophisticated learning techniques. As such, expeditiously exploring the relationship between hyperparameters and the performance of a learning technique controlled by these hyperparameters is desired, and thus it entails the consideration of design strategies to collect informative data efficiently to do so. Various designs can be considered for this purpose. The question as to which design to use then naturally arises. In this paper, we examine the use of different types of designs in efficiently collecting informative data to study the surface of test accuracy, a measure of the performance of a learning technique, over hyperparameters. Under the settings we considered, we find that the strong orthogonal array outperforms all other comparable designs.
ONLINE EDUCATION DURING A TIME OF PANDEMIC: TEACHERS' PERSPECTIVE OF ONLINE EDUCATION, DIGITAL COMPETENCIES AND THE POST-COVID-19 LEGACY

In the recent years, the scholarly debate on the importance and development of digital competences within the teaching profession has become increasingly nuanced. While the dimensions and depth of the concept remain highly appraised, it remains unclear the extent to which teachers nowadays are closer to developing adequate digital competencies and whether online teaching itself is considered a beneficial practice. Studies have shown different levels of prejudices in students' learning and well-being during the pandemic, so the question is: do teachers adjust their online teaching practices so as to increase learning outcomes, while considering the well-being of students? The present paper tries to address these issues accordingly, by means of on online quantitative study that was carried out between January 1st 2020 - January 28th 2021, on a sample of 1267 Romanian respondents. The questionnaire illustrates the teachers' experience of online education during the COVID-19 pandemic and the central dimensions of our analysis consisted of: the teachers' perceived progress on digital skills; trust in own digital competences; the level of trust offered by the courses attended on delivering online lessons; the perceived benefit of using digital skills in teaching from the perspective of students' learning outcomes; a general evaluation of all educational tools offered by online education; and the extent to which the methods and applications of online education will be integrated in the future. The results point to a positive self-assessment of teachers' digital skills, as well as an important willingness to implement the online education tools back to the everyday classrooms.