Reviewerly/exHarmony huggingface dataset:
OSLO: On-the-Sphere Learning for Omnidirectional Images and Its Application to 360-Degree Image Compression

State-of-the-art 2D image compression schemes rely on the power of convolutional neural networks (CNNs). Although CNNs offer promising perspectives for 2D image compression, extending such models to omnidirectional images is not straightforward. First, omnidirectional images have specific spatial and statistical properties that can not be fully captured by current CNN models. Second, basic mathematical operations composing a CNN architecture, e.g., translation and sampling, are not well-defined on the sphere. In this paper, we study the learning of representation models for omnidirectional images and propose to use the properties of HEALPix uniform sampling of the sphere to redefine the mathematical tools used in deep learning models for omnidirectional images. In particular, we: i) propose the definition of a new convolution operation on the sphere that keeps the high expressiveness and the low complexity of a classical 2D convolution; ii) adapt standard CNN techniques such as stride, iterative aggregation, and pixel shuffling to the spherical domain; and then iii) apply our new framework to the task of omnidirectional image compression. Our experiments show that our proposed on-the-sphere solution leads to a better compression gain that can save 13.7% of the bit rate compared to similar learned models applied to equirectangular images. Also, compared to learning models based on graph convolutional networks, our solution supports more expressive filters that can preserve high frequencies and provide a better perceptual quality of the compressed images. Such results demonstrate the efficiency of the proposed framework, which opens new research venues for other omnidirectional vision tasks to be effectively implemented on the sphere manifold.
Changing World: Smart Homes Review and Future

In the present scenario of digitalisation where everything is becoming digitalised, people are looking more for homes which offer them more comfort, security, convenience and luxury. There are many real estate companies which are promising that smart homes will provide significant amount of domestic comfort, security, expediency and relaxation while on the other hand also minimising the energy used due to optimisation of home energy management. Smart homes have potential to achieve multiple goals, but it mainly depends upon how the various facilities are used by the householders; still this topic is still unexplored and needs further attention. Smart home is a universal computing in which ambient intelligence technology is used and which helps in monitoring the home environment. This chapter focuses upon understanding the details of smart homes and the different technologies associated with it. The authors have tried to explore on the building blocks of smart homes and also the linkage between these different blocks. This chapter the describes the combined information about sensors, different systems and communication protocols and multimedia devices which are extensively employed in the implementation of smart homes. There are different algorithms from the various domains, and also their importance is illustrated according to their application in smart homes. This chapter also describes the various future directions for the researchers which can be beneficial for the development of smart homes which are sustainable and practical in nature.
Targeted Lossy Functions and Applications

Lossy trapdoor functions, introduced by Peikert and Waters (STOC '08), can be initialized in one of two indistinguishable modes: in injective mode, the function preserves all information about its input, and can be efficiently inverted given a trapdoor, while in lossy mode, the function loses some information about its input. Such functions have found countless applications in cryptography, and can be constructed from a variety of Cryptomania assumptions. In this work, we introduce targeted lossy functions (TLFs), which relax lossy trapdoor functions along two orthogonal dimensions. Firstly, they do not require an inversion trapdoor in injective mode. Secondly, the lossy mode of the function is initialized with some target input, and the function is only required to lose information about this particular target. The injective and lossy modes should be indistinguishable even given the target. We construct TLFs from Minicrypt assumptions, namely, injective pseudorandom generators, or even one-way functions under a natural relaxation of injectivity. We then generalize TLFs to incorporate branches, and construct all-injective-but-one and all-lossy-but-one variants. We show a wide variety of applications of targeted lossy functions. In several cases, we get the first Minicrypt constructions of primitives that were previously only known under Cryptomania assumptions. Our applications include: We also discuss a fascinating connection to distributed point functions.
Waterdrop Removal From Hot-Rolled Steel Strip Surfaces Based on Progressive Recurrent Generative Adversarial Networks

Automated visual inspection (AVI) instrument of surface defects for hot-rolled steel strips is conventionally installed closely before the terminal crimping machine, where the adjacent upstream process is laminar spray cooling. Waterdrops, spreading more or less over the steel strip surface, often trigger false alarms, which is a quite common problem in AVI. Stimulated by the idea of image rain removal in visual enhancement field, this article considers the surface waterdrops, pseudodefects in essence, as a conceptual “rain-like layer.” A targeted method, namely progressive recurrent generative adversarial network (PReGAN), is designed for active waterdrop tracking and fine-grained image inpainting. Meanwhile, a steel surface database (2400 raw images with the resolution of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1000\times1000$ </tex-math></inline-formula> ) captured from actual hot-rolling line is constructed for the first time for open evaluation of waterdrop removal. The experimental results indicate that images enhanced by the PReGAN perform the most informative and spotless, with 52.2073 peak signal-to-noise ratio (PSNR) and 0.9502 structural similarity (SSIM) index, when compared with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">four</i> prestigious networks. Assisted by the PReGAN, the false alarms are proved to be reduced at least a half during the application tests using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">four</i> traditional simple detection methods.
On user roles and fragmentation of the global network

The paper represents a critical analysis of interrelated processes: transformation of the user's role as direct subject of communication and fragmentation of the environment of interaction. The choice of the topic is conditioned by (1) the emergence of national network segments, which has recently been quite actively discussed in the context of blocking sites and VPN services as a measure for the protection of information security, and (2) users switching from the consumption of large media to isolated communities and platforms. The latter is associated, in addition to the loss of trust in major media, with deeper changes in the nature of information consumption and a change in the user's role: the network becomes a space of dialogue, where the very fact and quality of its continuation depends on the user's choice and his interpretation. According to the results of the study, it is shown that, firstly, due to the heterogeneity of the network in different segments and in different contexts, users may select messages untypical for other segments; secondly, interactive discourse predetermines the users’ choice from the array of information of those messages that seem «close» to users on the basis of their personal experience, psycho-emotional state, socio-cultural characteristics. The changes that a unit of information undergoes in a certain environment are determined by the characteristics of the environment, including sociocultural ones – they fill messages with symbolic content and determine their viability / potential to further dissemination in media space.
Angular and Svelte Frameworks: a Comparative Analysis

Web-based and mobile applications are increasingly used today in all areas of our life. In order to deliver software products as fast, as simple and economically possible, several cross-platform frameworks were created. The challenge is to choose the best framework that produces the desired solution and that integrates best with the existing systems and with higher user experience results. This paper describes and analyses the performance of two frameworks, Angular and Svelte, used for application development based on the implementation of a small applications. Results reveal that an important factor considered to choose the framework is the target device on which the application runs and also the complexity of the application.
Launch Point Estimation With a Single Passive Sensor Without Trajectory State Estimation

The goal of this article is to provide a real-time implementable algorithm to estimate the launch point (LP) of a thrusting object using angle-only measurements. The measurements are obtained in the object’s thrusting stage and from a single passive sensor (fixed or moving with constant velocity). It is assumed that one has a delayed acquisition, which means that the measurements are available only at some point after the launch time and the target’s mass decreases (i.e., its acceleration increases) due to fuel burn/ejection. Previous works on this problem mainly estimate the target’s state, which, for a passive sensor, requires a long batch of measurements, is sensitive to noise, and is ill-conditioned. In the present article, we only use a set of measurements (azimuth and elevation angles) to estimate the LP without motion state estimation. A polynomial fitting with the least-squares method is used to estimate the LP-based zero angular rate (which determines the launch time) and known LP altitude. Statistical analysis is provided to choose the optimal polynomial order, including overfitting and underfitting evaluation. Then, the estimated launch time is obtained by the “weighted average”. Compared with a more complicated state-of-the-art technique that relies on state estimation, the proposed approach provides a much simpler and effective way to implement in a real-time system.
KlimaKarl – A Chatbot to Promote Employees’ Climate-Friendly Behavior in an Office Setting

Environmental protection is a central challenge these days. At the same time, digital technologies have experienced tremendous technical progress in recent years and their potentials to support firms’ sustainability strategies and corporate social responsibility efforts are intensively discussed. In this respect, companies search for efficient ways to trigger a change of employee behavior in terms of climate-friendly practices. We propose chatbots as a promising technology to promote the climate-friendly behavior of employees. Following a Design Science Research (DSR) procedure, we develop a chatbot prototype called KlimaKarl to sensitize the workforce to behave in a more climate-conscious way in the everyday office life. We show that chatbots may be a suitable instrument to promote employees’ climate-friendly behavior.
KinZ an Azure Kinect toolkit for Python and Matlab

This paper introduces KinZ, an Azure Kinect toolkit for Python and Matlab. This toolkit encapsulates the Azure Kinect's main functionality in an easy-to-use class with high-level methods. This software is written mostly in C/C++ with a pybind interface for Python and Mex functions for Matlab. It provides access to color, depth, infrared cameras, a mapping between cameras, colored point clouds, IMU sensor data, body tracking with 32 body joints plus 32 hand keypoints. We showed that KinZ applications could run in real-time at 1080p resolution, with the benefit of a high-level language reducing the learning curve and allowing a significant decrease in development time for prototyping and research.
Document Layout Analysis with an Enhanced Object Detector

Digital images of documents contain a rich set of information. To automate their extraction, computers are programmed to analyse the content of document images. Document layout analysis is vital in that respect and can enhance the optical character recognition. The boundaries of different document regions, i.e. paragraphs, figures, or tables can be estimated using the convolutional neural networks. In this paper, we present a deep neural network that is inspired by natural scene object detectors. The network is trained and tested using the labeled samples from a large public dataset. Results demonstrate the potential of using object detectors for layout analysis. An implementation of the method will be available at: https://github.com/minouei-kl/layout-detection.
Workshop on Overcoming Measurement Barriers to Internet Research (WOMBIR 2021) final report

In January and April 2021 we held the Workshop on Overcoming Measurement Barriers to Internet Research (WOMBIR) with the goal of understanding challenges in network and security data set collection and sharing. Most workshop attendees provided white papers describing their perspectives, and many participated in short-talks and discussion in two virtual workshops over five days. That discussion produced consensus around several points. First, many aspects of the Internet are characterized by decreasing visibility of important network properties, which is in tension with the Internet's role as critical infrastructure. We discussed three specific research areas that illustrate this tension: security, Internet access; and mobile networking. We discussed visibility challenges at all layers of the networking stack, and the challenge of gathering data and validating inferences. Important data sets require longitudinal (long-term, ongoing) data collection and sharing, support for which is more challenging for Internet research than other fields. We discussed why a combination of technical and policy methods are necessary to safeguard privacy when using or sharing measurement data. Workshop participant proposed several opportunities to accelerate progress, some of which require coordination across government, industry, and academia.
Malweb: An Efficient Malicious Websites Detection System using Machine Learning Algorithms

These days, malware is one of the supreme acknowledged cyber threats. As data volumes increase rapidly, the number of malware threats increases. Malware not only increases in quantities but also becomes smarter and more difficult to detect. Detect malware threats on websites caused by high data traffic, becomes a challenging problem, which must be solved. Moreover, billions of dollars are lost annually due to malicious website scams. Applying analytics to discover new information, predict future malware insights, and make control decisions is a critical process that makes online websites secure. In this research, we propose and analyze a machine learning-based system to detect the behavior of malicious websites based on specific features. With these features, we classify websites as either malicious versus non-malicious. This paper employs a variety of machine learning techniques, including Logistic Regression, Decision Tree, and Naïve Bayes to detect malicious and non-malicious websites, based on various feature selection circumstances to improve results. Applying feature selection with a new way depends on the threshold of categories so Reasonable results are reached with 100% accuracy, recall, and precision when applying Logistic Regression and Decision Tree algorithms while 95% when applying a Naïve Bayes algorithm with an acceptable timing slot.
Aspect-Based Sentiment Analysis in Beauty Product Reviews Using TF-IDF and SVM Algorithm

Product reviews are essential in e-commerce as they can help potential buyers make decisions prior to making purchases and help sellers get the measure of their products. A product can have thousands of reviews, making it burdensome for potential buyers and sellers to draw a conclusion from those abundant reviews. This research built a system that applies Aspect-based Sentiment Analysis (ABSA) with a dataset from product reviews on the Female Daily website. The system was built using TF-IDF as its feature extraction method combined with word bigram and word bigram. The Support Vector Machine (SVM) algorithm is used to classify the sentiments. This experiment results indicate that the preprocessing stage, especially the stemming and stopwords removal process are greatly affects the accuracy results. The choice of word N-gram is also crucial, where this research shows that the word unigram gives a higher accuracy than the word bigram. The final results of this research show that TF-IDF combined with word unigram and SVM with a linear kernel brings out the best accuracy, that is to say, 88.35%.
RETRACTED ARTICLE: Urban ecological environment investigation based on a cloud computing platform and optimization of computer neural network algorithm

The rapid development of Internet technology has contributed to the development of the Internet. The rapid expansion of the industrial application scale and the sharp increase of data generated by an industrial application and cloud computing have promoted the birth and development of cloud computing platform technology. Data analysis has become an important support for company decision-making; how to quickly and effectively extract useful information from large data sets is very important. Among them, clustering analysis and computer neural network algorithms are the most important methods of data mining. Due to the limitations of computer performance and programming models, traditional data mining methods cannot meet the efficiency and computing needs of a large amount of information. The development of cloud computing technology has opened up a new research field of cloud mining clustering analysis and neural network algorithm formation. The first research is the deployment of the Hadoop cluster based on Linux, and the novelty lies in the parallelization of the MapReduce cluster algorithm. Because there are many clustering algorithms, this paper starts with the K-means clustering algorithm and optimizes the earlier algorithm according to the MapReduce programming model. Through the text clustering process of wine data set in the UCI database, the parallel algorithm is applied to the Hadoop cloud computing platform. Experiments show that the parallel K-means clustering algorithm in MapReduce can significantly improve the execution speed. This paper applies it to the urban ecological environment survey in China, clarifies the relationship between various factors of urban environmental governance, expands the content of urban environmental governance in China, and improves the ecological environment of Chinese cities.
Transferable Adversarial Defense by Fusing Reconstruction Learning and Denoising Learning

Deep neural networks have been demonstrated fragile to adversarial examples. Some powerful defense methods have been proposed. However, these methods usually involve modification in the process of model training, which often require more computational complexity. In this paper, we propose a novel defense method that can be directly applied to unmodified off-the-shelf models. Our method adopts standard denoiser to maintain the original features. But standard denoiser cannot remove adversarial perturbations effectively, which will be progressively amplified by classifier and lead to incorrect classification. Therefore, we add a denoising module into our method, in which the magnified adversarial perturbations are used to guide our approach's training. The proposed method effectively removes adversarial perturbations while maintaining the original characteristics. Consequently, our method has good transferability, it can be reused easily to protect different models after once training. Extensive experiments show that our method has outstanding performances against both white-box and black-box attacks. Especially in protecting different models, our method outperforms the state-of-the-art defenses by a big margin.
Trajectory Based Prioritized Double Experience Buffer for Sample-Efficient Policy Optimization

Reinforcement learning has recently made great progress in various challenging domains such as board game of Go and MOBA game of StarCraft II. Policy gradient based reinforcement learning method has become the mainstream due to its effectiveness and simplicity both in discrete and continuous scenarios. However, policy gradient methods commonly involve function approximation and work in an on-policy fashion, which leads to high variance and low sample efficiency. This paper introduces a novel policy gradient method to improve the sample efficiency via a pair of trajectory based prioritized replay buffers and reduce the variance in training with a target network whose weights are updated in a “soft” manner. We evaluate our method on the reinforcement learning suit of Open AI Gym tasks, and the results show that the proposed method can learn more steadily and achieve higher performance than existing methods.
Nighttime vehicle detection based on direction attention network and bayes corner localization

Detecting vehicle at night is critical to both assistant driving systems and autonomous driving systems. In this paper, we propose a deep network scheme assisted by light information with good generalization to detect vehicle at night. Our approach is divided into two branches, the object stream and the pixel stream. The object stream generates a batch of bounding boxes, and the pixel stream utilizes the vehicle light information to calibrate the bounding boxes of the object stream. In the object stream, we propose a new structure, Direction Attention Pooling (DAP), to improve the accuracy of the prior boxes. DAP leads into attention mechanism. The feature maps obtained from backbone network is divided into two branches. One branch obtains direction perception information through IRNN layer, and the other branch learns attention weights. The weights are multiplied with the direction perception features in an element-wise manner. In the pixel stream, we propose a corner localization algorithm based on Bayes to get more accurate corners with the vehicle light pixels. The locations of the corners are considered as a discrete random variable. When the mask of the object is known, solving the probability distribution of the corner of the object is the next step. The corners with the highest probability is the correct corner. On the nighttime vehicle detection datasets CHUK and SYSU, our method achieves the accuracy of 97.2% and 96.86%, which outperforms other state-of-the-art methods by at least 0.31% and 0.34%.
Evaluation Methods for an AI-Supported Learning Management System: Quantifying and Qualifying Added Values for Teaching and Learning

Artificial intelligence offers great opportunities for the future, including for teaching and learning. Applications such as personalized recommendations and learning paths based on learning analytics [i.e. 1], the integration of serious games in intelligent tutoring systems [2], intelligent agents in the form of chatbots [3], and other emerging applications promise great benefits for individualized digital learning. However, what value do these applications really add and how can these benefits be measured? With this article, we would like to give a brief overview of AI-supported functionalities for learning management system as well as their possible benefits for future learning environments. Furthermore, we outline methods for a comprehensive evaluation that meets the users' needs and concretizes the actual benefit of an AI-supported LMS.