Reviewerly/exHarmony huggingface dataset:
Don’t go ‘Along’ with corporate schemes to gather up student data

The Along digital SEL product marketed to teachers by Gradient Learning and the Chan Zuckerberg Initiative (CZI) promises to help teachers establish strong relationships with their students via an exchange of personal reflections. In so doing, it collects intimate personal information from students about their lives. Faith Boninger and Alex Molnar explain how the legal documents that govern Gradient’s retention and use of students’ data allow Gradient to retain students’ de-identified data in perpetuity and use it for unknown purposes. They explain the privacy and other threats associated with de-identified data, and warn against allowing education technology providers — particularly Gradient and the Chan Zuckerberg Initiative — unfettered use of it.
Imbalanced data Classification in Credit Card Fraudulent Activities Detection using Multi-Class Neural Network

Credit card fraud detection is an important research in the recent days because each and every day different fraud activities are increasing. The different techniques are introduced to detect and alert the credit card fraudulent activities. The various artificial intelligence, data mining and machine learning techniques are used to classification and prediction of credit card system. The main drawback of the previous methods such as data is imbalanced, accuracy and false positive rate. In this paper, proposed a new deep learning-based algorithm called Multi-Class Neural Network (MCNN) for prediction of credit card detection. The Multi-Class Neural Network techniques address the imbalance data and misclassification issues in face to face and E-commerce transaction. The proposed work consists of three steps such as pre-processing, texture feature extraction and multi-class natural network. The MCNN consists of trainer node, hidden layer, learning rate, learning iteration and random seed for repeating the process. The implementation of proposed work dataset is classified into three ways such as fraud, No Fraud and invalid record. Using the texture feature extraction and MCNN the prediction and classification and accuracy rates are increased in terms of fraud transaction (0), non-fraud transaction (1) and invalid records (2).
An incrementally updatable and scalable system for large-scale sequence search using the Bentley–Saxe transformation

In the past few years, researchers have proposed numerous indexing schemes for searching large datasets of raw sequencing experiments. Most of these proposed indexes are approximate (i.e. with one-sided errors) in order to save space. Recently, researchers have published exact indexes-Mantis, VariMerge and Bifrost-that can serve as colored de Bruijn graph representations in addition to serving as k-mer indexes. This new type of index is promising because it has the potential to support more complex analyses than simple searches. However, in order to be useful as indexes for large and growing repositories of raw sequencing data, they must scale to thousands of experiments and support efficient insertion of new data.In this paper, we show how to build a scalable and updatable exact raw sequence-search index. Specifically, we extend Mantis using the Bentley-Saxe transformation to support efficient updates, called Dynamic Mantis. We demonstrate Dynamic Mantis's scalability by constructing an index of ≈40K samples from SRA by adding samples one at a time to an initial index of 10K samples. Compared to VariMerge and Bifrost, Dynamic Mantis is more efficient in terms of index-construction time and memory, query time and memory and index size. In our benchmarks, VariMerge and Bifrost scaled to only 5K and 80 samples, respectively, while Dynamic Mantis scaled to more than 39K samples. Queries were over 24× faster in Mantis than in Bifrost (VariMerge does not immediately support general search queries we require). Dynamic Mantis indexes were about 2.5× smaller than Bifrost's indexes and about half as big as VariMerge's indexes.Dynamic Mantis implementation is available at https://github.com/splatlab/mantis/tree/mergeMSTs.Supplementary data are available at Bioinformatics online.
Cost Control Analysis of Construction Projects Based on Wireless Communication and Artificial Intelligence Decisions

In decades of development, the country has been vigorously developing infrastructure projects and providing a steady stream of help, so our infrastructure strength has always been world’s top part. But now, because of the vigorous development of environmental protection enterprises, a variety of raw materials and environmental treatment costs are getting higher and higher, in order to maintain the profitability of enterprises, so we need to carry out cost control of construction projects to ensure a certain profit. In recent years, various emerging terms such as artificial intelligence and big data have become more and more popular, so in order to ensure the stability of the industry, it is imperative for the construction industry to introduce new technologies for the technological revolution. Wireless communication is also widely used with artificial intelligence to build systems because of the fast speed of interaction. In order to ensure the steady development of the construction industry in the future, we need to control the cost of the project and the introduction of high-tech. The purpose of this paper is to control the cost of construction projects by means of wireless communication and artificial intelligence decision-making. After reviewing the history of the construction industry in recent decades, as well as the price fluctuations of building materials and the relevant laws and regulations on environmental protection and construction in recent years, this paper uses the emerging artificial intelligence and traditional wireless communication methods to carry out the traditional project cost control experiments. After a period of experiments, people found that the use of wireless communication with artificial intelligence decision-making ability can be very good control of construction project costs in a certain range, to ensure the profitability of the project and the maximum use of materials.
Factors affecting the innovation ecosystem in public universities in Ethiopia

Universities are now at the centre of the knowledge economy, and they do innovate, but Ethiopian universities have not done much of it. The overall goal of this paper is thus to investigate the innovative behaviour of Ethiopian public universities, with a particular focus on determining the driving forces of innovation and research output in Ethiopian public universities. The determinant factors of innovation activities were analyzed by using panel data analytical frames, with innovation and research output measured in patents and publications, respectively. The data were gathered from Ethiopian public universities between the years 2016 and 2020, with a sample of 24 universities chosen purposely. The result reveals that research output is affected by age of university, number of collaborations with international universities, research funding, number of PhD programmes, and number of research projects done. Innovation is also affected by the number of programmes and the number of MSc/MA staff. To improve research output and innovation in Ethiopian public universities, the study recommends increasing research budgets, strengthening collaboration with international universities, and expanding PhD programmes.
Distraction Detection in Automotive Environment using Appearance-based Gaze Estimation

Distraction detection systems in automotive has great importance due to the prime safety of passengers. Earlier approaches confined to use indirect methods of driving performance metrics to detect visual distraction. Recent methods attempted to develop dedicated classification models for gaze zone estimation whose cross-domain performance was not investigated. We adopt a more generic appearance-based gaze estimation approach where no assumption on setting or participant was made. We proposed MAGE-Net with less number of parameters while achieving on par performance with state of the art techniques on MPIIGaze dataset. We utilized the proposed MAGE-Net and performed a cross-domain evaluation in automotive setting with 10 participants. We observed that the gaze region error using MAGE-Net for interior regions of car is 15.61 cm and 15.13 cm in x and y directions respectively. We utilized these results and demonstrated the capability of proposed system to detect visual distraction using a driving simulator.
Information resources estimation for accurate distribution-based concept drift detection

Machine learning applications must continually utilize label information from the data stream to detect concept drift and adapt to the dynamic behavior. Due to the computational expensiveness of label information, it is impractical to assume that the data stream is fully labeled. Therefore, much research focusing on semi-supervised concept drift detection has been proposed. Despite the large research effort in the literature, there is a lack of analysis on the information resources required with the achievable concept drift detection accuracy. Hence, this paper aims to answer the unexplored research question of "How many labeled samples are required to detect concept drift accurately?" by proposing an analytical framework to analyze and estimate the information resources required to detect concept drift accurately. Specifically, this paper disintegrates the distribution-based concept drift detection task into a learning task and a dissimilarity measurement task for independent analyses. The analyses results are then correlated to estimate the required number of labels within a set of data samples to detect concept drift accurately. The proximity of the information resources estimation is evaluated empirically, where the results suggest that the estimation is accurate with high amount of information resources provided. Additionally, estimation results of a state-of-the-art method and a benchmark data set are reported to show the applicability of the estimation by proposed analytical framework within benchmarked environments. In general, the estimation from the proposed analytical framework can serve as guidance in designing systems with limited information resources. This paper also hopes to assist in identifying research gaps and inspiring new research ideas regarding the analysis of the amount of information resources required for accurate concept drift detection.
Look-ahead energy efficient VM allocation approach for data centers

Abstract Energy efficiency is an important issue for reducing environmental dissipation. Energy efficient resource provisioning in cloud environments is a challenging problem because of its dynamic nature and varied application workload characteristics. In the literature, live migration of virtual machines (VMs) among servers is commonly proposed to reduce energy consumption and to optimize resource usage, although it comes with essential drawbacks, such as migration cost and performance degradation. Energy efficient provisioning is addressed at the data center level in this research. A novel efficient resource management algorithm for virtualized data centers that optimizes the number of servers to meet the requirements of dynamic workloads without migration is proposed in this paper. The proposed approach, named Look-ahead Energy Efficient VM Allocation (LAA), contains a Holt Winters-based prediction module. Energy efficiency and performance are inversely proportional. The energy-performance trade-off relies on periodic comparisons of the predicted and active numbers of servers. To evaluate the proposed algorithm, experiments are conducted with real-world workload traces from Google Cluster. LAA is compared with the best approach provided by CloudSim based on VM migration called Local Regression-Minimum Migration Time (LR-MMT). The experimental results show that the proposed algorithm leads to a consumption reduction of up to 45% to complete one workload compared with the LR-MMT.
Kidney and Kidney Tumor Segmentation Using Spatial and Channel Attention Enhanced U-Net

Kidney and Kidney tumor segmentation from CT scans has tremendous potential to help doctors in early diagnosis and localization of tumor, its size and type and for making timely treatment plans. However, considering the nature and volume of data, it is difficult and time consuming to train on such CT scans. In this paper, we propose enhancements to the 3D U-Net model to incorporate Spatial and Channel Attention in order to improve the identification and localization of segmentation structures by learning on spatial context. When compared with Residual U-Net model with greater depth and more feature maps, our Spatial and Channel Attention enhanced U-Net with less depth and feature maps performed significantly better on validation and training set when trained under similar conditions.
Design of Agilev4 and Its Real-time Implementation on an Economic Mobile Platform

AI visual algorithms have obtained obvious improvements in detection performance. However, the resulting extremely high computational complexity hinders the progress of AI hardware design for real-time and portable applications. In this paper, we have developed a dedicated AI detection neural network along with the necessary AI model speedup techniques. For the development goal, we propose our own backbone neural network, which we named it Agilev4. Besides, we introduce a so-called GoP (Group of Pictures) technology to accelerate the computation by removing temporal redundancy. The test with a dedicated hyper-market scenario data illustrates that the presented system can track each object well. Finally, the frame rate can be accelerated to 35.4 frames per second when we deployed Agilev4 along with the GoP mode acceleration on Jetson Nano. Compared with the original existing neural network, the speedup ratio is more than 1200&#x0025;.
Fast Global Collision Detection Method Based on Feature-Point-Set for Robotic Machining of Large Complex Components

This paper presents a fast global collision detection method for robotic machining of large complex components, aiming to quickly determine whether there is a collision between the robot and the surrounding environment during the whole machining process. Geometric analysis shows that there are always some trajectory points on the motion path of the manipulator that are more likely to collide than the surrounding points during machining. These trajectory points with the highest collision probability within a certain range are defined as the feature points of global collision detection, and are used to replace all trajectory points to perform global collision detection, thus greatly improving the efficiency of related operations while ensuring accuracy. Compare to the traditional discrete collision detection method with computational complexity O( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{n}^{2}$ </tex-math></inline-formula> ), the computational complexity of the proposed method is only O(n). Numerical analysis and application experiments verify the effectiveness of the proposed method. Note to Practitioners—Motion planning in robotic machining of large complex components usually needs to perform a lot of global collision detection. Existing methods generally have the problems of large calculation and low efficiency, which seriously affects the efficiency of motion planning. This is mainly because a single global collision detection usually includes no less than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> times of static collision detection, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> is the number of trajectory points. In order to solve this problem, we present a new global collision detection method based on feature-point-set. It does not need to traverse all trajectory points for static collision detection, but only needs to detect a few feature points, that is, the trajectory points most likely to collide within a certain range. On the premise of ensuring the collision detection accuracy, the proposed method greatly reduces the execution times of static collision detection, and significantly improves the computational efficiency of global collision detection. Numerical analysis and experiments show that this method effectively improves the efficiency of motion planning in robotic machining of large complex components.
Intelligent Wearable Devices Enabled Automatic Vehicle Detection and Tracking System with Video-Enabled UAV Networks Using Deep Convolutional Neural Network and IoT Surveillance

The discipline of computer vision is becoming more popular as a research subject. In a surveillance-based computer vision application, item identification and tracking are the core procedures. They consist of segmenting and tracking an object of interest from a sequence of video frames, and they are both performed using computer vision algorithms. In situations when the camera is fixed and the backdrop remains constant, it is possible to detect items in the background using more straightforward methods. Aerial surveillance, on the other hand, is characterized by the fact that the target, as well as the background and video camera, are all constantly moving. It is feasible to recognize targets in the video data captured by an unmanned aerial vehicle (UAV) using the mean shift tracking technique in combination with a deep convolutional neural network (DCNN). It is critical that the target detection algorithm maintains its accuracy even in the presence of changing lighting conditions, dynamic clutter, and changes in the scene environment. Even though there are several approaches for identifying moving objects in the video, background reduction is the one that is most often used. An adaptive background model is used to create a mean shift tracking technique, which is shown and implemented in this work. In this situation, the background model is provided and updated frame-by-frame, and therefore, the problem of occlusion is fully eliminated from the equation. The target tracking algorithm is fed the same video stream that was used for the target identification algorithm to work with. In MATLAB, the works are simulated, and their performance is evaluated using image-based and video-based metrics to establish how well they operate in the real world.
Degree of Accuracy in Credit Card Fraud Detection Using Local Outlier Factor and Isolation Forest Algorithm

In this era of digitalization where everyone prefers online-based transactional activities, this increases the demand for a credit card, the fraudulent cases are increasing day by day which causes tremendous loss to an individual. Our model comprises 2 major algorithms and uses anomaly detection as a method to classify fraudulent transactions. With the help of these two algorithms i. e., local outlier factor and Isolation Forest. We are implementing our Machine Learning (ML) Model Credit Card Fraud Detection (CCFD) to get the highest possible degree of accuracy of fraud, these two algorithms in layman rs terms isolate the transaction or it can be considered as an outlier i.e., deviation from a normal and common order which have a high rate of anomaly or fraud transaction.
Preserving Privacy and Security in Browser Case Study of Mantis Browser

Abstract: The internet was meant to shorten the distance between the people to ground breaking level. It is essential tool for everyday task. But with this, internet service providers are allowed to share and sell their customers web browsing data without their concedence. sometimes, sold data makes security weakened and makes unprecedented way to spammers. With third party trackers, privacy gone under more risk factor, which makes no deterrent for Cyberattacks, terrorism and other phenomena. In today’s hig h tech environment organizations, Government and individual user has to use web browsers and internet for accessing web data. nowadays some Advertisers trying to mislead users. So, we propose a new way of browsing anonymously, untraceable web surfing and s trong firewall using new browser. There will be no bookmarks, Advertisements and no selling of data to marketers.
Privacy and Security in Mixed Reality Learning Environments by Input and User/Bot Interaction Protection

Mixed reality is known as an advanced technology that provides a new approach for learning environments. Such environments allow learners to interact with both virtual and real worlds and bringing in potential enhancements to the learning process at the same time. For example, chatbots can facilitate the learning process. However, security and privacy settings for interacting with chatbots in such mixed reality environments are complex. In this paper, we introduce a mixed reality virtual assistant that is integrated into the collaborative environment of our existing application VIAProMa. This embodied chatbot allows lecturers and students to participate in mixed reality and online classrooms in real-time. The participants can interact with each other via VIAProMa's avatar representations and can communicate with the chatbot that is represented by the mixed reality bot. The bot is realized by connecting a Slack chatbot with the mixed reality learning environment. It is displayed as an intuitive 3D model and is able to communicate with the users in spoken language. In this environment, privacy and security settings are conducted to protect the user input and user interaction with the bot. The evaluation results show that the system works stably with good performance. All the visualizations and features are well designed and were understood by the users. Users preferred the speech interface with the bot over a textual interface. The research has a strong impact on the design of security and privacy features for mixed reality environments in general.
Chinese text summarization based on fine-tuned GPT2

Pre-trained language model has a good performance in text summarization task, thus we present a neural text summarization based on a powerful pre-trained language model GPT-2. In this paper, we propose a Chinese text summarization model by extending into our downstream task to acquire relevant, contentful, and coherent summarization. By extensive experiments, our model achieves absolute improvements of 10.75% on ROUGE-1, 13.85% on ROUGE-2, and 9.73% on ROUGE-L on the LCSTS datasets. Compared with the state-of-the-art summarization model, e.g. BERTSUM based model, our model also achieves an improvement of 25.22% on ROUGE-1.