Reviewerly/exHarmony huggingface dataset:
Scene Summarization via Motion Normalization

When observing the visual world, temporal phenomena are ubiquitous: people walk, cars drive, rivers flow, clouds drift, and shadows elongate. Some of these, like water splashing and cloud motion, occur over time intervals that are either too short or too long for humans to easily observe. High-speed and timelapse videos provide a popular and compelling way to visualize these phenomena, but many real-world scenes exhibit motions occurring at a variety of rates. Once a framerate is chosen, phenomena at other rates are at best invisible, and at worst create distracting artifacts. In this article, we propose to automatically normalize the pixel-space speed of different motions in an input video to produce a seamless output with spatiotemporally varying framerate. To achieve this, we propose to analyze scenes at different timescales to isolate and analyze motions that occur at vastly different rates. Our method optionally allows a user to specify additional constraints according to artistic preferences. The motion normalized output provides a novel way to compactly visualize the changes occurring in a scene over a broad range of timescales.
Extending The Performance of Extractive Text Summarization By Ensemble Techniques

Text summarization techniques help in automatically shortening the length of text data as well as fluently and accurately passing on the intended message. Extractive Text summarization methods have been well-researched but each such algorithm produces a potentially different summary. There is no standalone “best” algorithm using this method. The algorithm proposed in this paper gives a way to combine the strengths of all the existing algorithms to make text summarization more robust. Though ensemble optimization is a popular technique in machine learning, it has not been tried exhaustively in text summarization. Specifically, soft voting has not been attempted so far. In this paper, we extends the concept of voting classifiers in Machine Learning in text domain and propose a novel optimized ensemble based approach to text summarization. The quality of the summary generated is evaluated based on the Recall Oriented Understudy for Gisting Evaluation (ROUGE) metric. As seen in the results, the proposed model outperforms baseline extractive text summarization models such as textrank, lexrank, LSA, Luhn and KL summarizers by more than 15%.
Design of intelligent community security system based on visual tracking and large data natural language processing technology

This article has been retracted, and the online PDF has been watermarked “RETRACTED”. A retraction notice is available at https://doi.org/10.3233/JIFS-219218.
Program extraction for mutable arrays

We present a lightweight method to represent, verify, and extract efficient programs involving mutable arrays in the Coq proof assistant. Our method mainly consists of a library for handling mutable arrays and an improved extraction plugin. Our library provides a monadic domain specific language for modeling computations involving mutable arrays, a simple reasoning method based on the Ssreflect extension and the Mathematical Components library, and an extraction method to efficient OCaml programs using in-place updates. Our extraction plugin improves the performance of our extracted programs, or more appropriately, through the application of simple program transformations for purely functional programs, it reduces both construction and destruction costs of inductive and coinductive objects and function call costs. As concrete applications for our method, we provide efficient implementations, correctness proofs, and benchmarks of the union–find data structure and the quicksort algorithm.
Multinode Collaborative Image Compression Algorithm for Wireless Multimedia Sensor Networks Based on LBT

WMSNs (Wireless Multimedia Sensor Networks) can handle complex tasks, and image compression and transmission energy consumption are evenly distributed. This makes it possible to effectively solve the high energy consumption and high cost problems caused by the complexity of information processing, and prolong the life cycle of the network, and become the key to the practical application of WMSNs. In order to ensure the quality of monitoring while extending the life cycle of the network as much as possible, this paper proposes a two-layer collaborative WMSNs architecture. Through the three-level cooperation between the common node layer and the camera node layer, the image acquisition and compression energy consumption pressure of the camera node layer is effectively decomposed into the common node layer where redundancy is deployed. The multi-node cooperative implementation of WMSNs for JPEG2000 algorithm has the problems of high computational complexity, high energy consumption and poor image quality under low bit rate. A multi-node suitable for WMSNs two-hop cluster structure is proposed. The simulation results show that the algorithm has low energy consumption and low storage requirements. It can eliminate the block effect by using the time domain overlap transform before DCT transform, and the reconstructed image quality is much better than JPEG200 0 in the WMSNs multi-node collaborative implementation environment.
A Detailed Survey On Cyberbullying in Social Networks

In Today’s World, Internet has made everything possible. It has been booming as it is available at low cost with high speed. Social Media has become popular with the use of internet and connecting people all over the world. With the increase of users in the social media platform, even though it has advantages, equally there are many security threats. Its usage is high but the awareness is less which leads a path to serious cyber threats. Voice messages, images, videos and texts are shared with user permission that is the cause of Cyberbullying. The purpose of this survey is to explore the various research works performed for detection and prevention on Cyberbullying. We conclude the survey by mentioning the future scope of this study by observing the drawbacks of the existing research.
Philosophy as a Methodology of Understanding in the Educational Process

In educational process, not only knowledge is important, but also understanding. However, science that stands as the basis for teaching provides only a partial understanding. Many of the scientific provisions cannot be explained by science itself.The purpose of the article is to explore teaching of philosophy in higher education in the context of formation of understanding skills. The hypothesis of the study is the proposition that philosophy in the educational process acts as a methodology of understanding. The study was conducted based on general theoretical methods characteristic of socio-philosophical analysis.As a result of the analysis, it was revealed that the purpose of teaching philosophy is not to assimilate knowledge, but to gain a universal experience of the transmission of thought, mastery of a philosophical culture, which acts as a methodology of understanding. Teaching philosophy in higher education forms not only students’ culture of thinking but understanding skills as well. Therefore, neither science itself nor its teaching are fundamentally inconceivable without philosophy, as it is usually based on foundations having a philosophical character. It is necessary to prepare a specialist who will not only work, but also be creative in their work.
The Monkey, the Ant, and the Elephant: Addressing Safety in Smart Spaces

Smart spaces deliver digital services to optimize space use and enhance user experience. The impact of ill-programmed applications in such spaces goes beyond loss of data or a computer crash; there is the potential risk of physical harm to the space and its users. Ensuring safety in this type of cyberphysical system is critically important.
An Improved U-Net Architecture for Low Light Image Enhancement for Visibility Improvement

Images are a significant medium to speak to important data. It might be troublesome for computer vision applications and people to remove important data from images with low light. Presently, the improvement of low-light images is a difficult undertaking in the space of image processing and graphics. In spite of the fact that there are numerous techniques for image improvement, the current strategies frequently produce imperfect outcomes concerning the bits of the image with extraordinary or ordinary light, and such strategies additionally definitely corrupt certain visual relics of the images. Taking an image in low light is challenging due to low contrast, short-exposure images suffer from noise, while long exposure can induce blur and is often impractical. Hence the model should address for low light image enhancement must play out the accompanying assignments: improving contrast, preserving details, noise suppression, and color correction. This paper a deep architecture has proposed to improve the performance of low light images. The developed deep architecture has trained and tested with publicly available dataset namely seeing in the dark dataset. The proposed architecture achieved performance metrics such as Peak Signal to Noise Ratio, Mean Square Error and Structural Similarity Index of 41.22, 0.02 and 0.92 respectively.
Fuzzy logic aggregation of crisp data partitions as learning analytics in triage decisions

This paper provides an analytical learning system based on Fuzzy Logic AGgregation (FLAG) of crisp data partitions to improve the Triage process in a hospitality emergency department. The method compares patient rankings made by nurses with those made by an Expert to detect points for improvement. Specifically, a normalized concordance index per nurse and the average of them allow for the evaluation of the ability of the nurses of well aggregating the cases in Triage process. The proposed FLAG system is tested through an empirical case study by simulating the patients arriving at two Emergency Departments Triage. The main contribution is the definition of the global performance index combining both the nurse’s partitioning concordance with respect to the Expert’s one and the accuracy in class assignment. The empirical distribution function of the global concordance index is derived through permutation method. In this way, Kolmogorov-Smirnov testing provides the comparison of the performances of the two healthcare units. The pay-off table concordance-accuracy allows to address improvement actions. Another tool of the system is the correspondence analysis to visualize the accuracy of decisions on the class priority as well as the sharing behaviours that influence the nurse’s judgements. All this becomes part of the FLAG learning analytics system which is able to outline critical points by red flags. to improve further assignments and the overall management organization. FLAG system can be adapted in all other situations of risk where cognitive heuristics face an accuracy-effort trade-off such that their simplified decision process leads to reduced accuracy.
Effect of Laplacian Smoothing Stochastic Gradient Descent with Angular Margin Softmax Loss on Face Recognition

An important task in deep learning for face recognition is to use proper loss functions and optimization technique. Several loss functions have been proposed using stochastic gradient descent for this task. The main purpose of this work is to propose the strategy to use the Laplacian smoothing stochastic gradient descent with combination of multiplicative angular margin to enhance the performance of angularly discriminative features of angular margin softmax loss for face recognition. The model is trained on a most popular face recognition dataset CASIA-WebFace and it achieves the state-of-the-art performance on several academic benchmark datasets such as Labeled Face in the Wild (LFW), YouTube Faces (YTF), VGGFace1 and VGGFace2. Our method achieves a new record accuracy of 99.54% on LFW dataset. On YTF dataset it achieves 95.53% accuracy.
SIOT-RIMM

It is very crucial for an organization to encapsulate the requirements in its early stage when they are intending to build a novel system such as the internet of things (IoT), particularly when it comes to capturing privacy and security requirements to gain the public confidence. The proposed research is focused to develop a secure IoT-requirement implementation maturity model (SIOT-RIMM). The proposed model will assist the software development organizations to improve and modify their requirement engineering processes in terms of security and privacy of IoT. The SIOT-RIMM model will be developed based on the existing IoT literature pertaining to security and privacy, industrial empirical study and understanding of the challenges that could negatively influence the implementation of security and privacy in IoT. To develop the maturity levels of SIOT-RIMM, we will consider the concepts of existing maturity models of other software engineering domains. In this preliminary study, 19 challenges were identified using the SLR approach that might have a negative impact on the IoT requirements engineering process. The identified challenges will contribute to the development of SIOT-RIMM maturity levels.
Modelling Genetic Programming as a Simple Sampling Algorithm

This chapter proposes a new model of tree-based Genetic Programming (GP) as a simple sampling algorithm that samples minimal schemata (subsets of the solution space) described by a single concrete node at a single position in the expression tree. We show that GP explores these schemata in the same way across three benchmarks, rapidly converging the population to a specific function at each position throughout the upper layers of the expression tree. This convergence is driven by covariance between membership of a simple schema and rank fitness. We model this process using Price’s theorem and provide empirical evidence to support our model. The chapter closes with an outline of a modification of the standard GP algorithm that reinforces this bias by converging populations to fit schemata in an accelerated way.
Air-to-Air Automatic Landing of Unmanned Aerial Vehicles: A Quasi Time-Optimal Hybrid Strategy

It is well known that small electric Unmanned Aerial Vehicles (UAVs) suffer from low endurance problems. A possibility to extend the range of UAV missions could be to have a carrier drone with several lightweight multirotors aboard, which can take-off from and land on it. In this letter the challenging problem of Air-to-Air Automatic Landing (AAAL) of UAVs is solved by developing a strategy that combines a quasi-time optimal feedback and a hybrid logic to ensure a safe and fast landing. Eventually, the proposed algorithm is validated through experimental activities involving the landing of a small quadcopter on a bigger octocopter used as a carrier.
Design of Restaurant Billing System (E Bill Resto) by Applying Synchronization of Data Billing in Branch Companies to Main Companies Based on Rest API

Culinary business is a business opportunity that is most in-demand, E Bill Resto is a restaurant billing system that was developed by involving several selling places/restaurants with the name of a brand that is connected to the parent company by a database server. With an integrated system, all revenue from restaurant sales can be monitored in realtime. The system design is made by implementing the RESTFul API architecture with security access tokens. The Master Application as a provider of Embedded Data Service Web resources on 3 Restaurant Information Systems, It does the synchronization of 3 Web Service Clients, Data From the Master-Slave Side was obtained by testing 3 data sampling, where both applications are tested QoS (Quality of Service) with 3 new data samples, from the INDOSAT Internet Provider which showed an average test result of Throughput of 170.3 bps, Packet Loss of 18.851% and Delay (latency) of 78.4 ms. Whereas using the TELKOM Internet Provider with an average throughput of 259.5 bps, Packet Loss of 14.28% and delay (latency) of 83.8 ms. Then it can be concluded that testing based on TIPHON Throughput and Delay (latency) from INDOSAT 4G and TELKOM signal Internet Providers showed the category of "Very Good" while the Packet Loss test obtained the "Bad" category.
Book Recommendation Website with Chatbot

The objective of this project is to recommend books, novels, and Japanese cartoons to those who are interested via the website named doyouwannaread.com. The recommended books will be written in Thai translation. The website has a chatbot function to answer a set of predefined questions. Users can review the book by leaving their comments on the books information page through the Facebook log-in account. With the chatbot as a recommendation tool, users can search by book categories, book list, and a list of main characters. Moreover, the chatbot function can collect the users questions for analysis. We knew the most common of users' questions those of that do not get an answer. This is for improving the chatbot function to answer more user questions.
Soundwalking and Algorithmic Listening

Soundwalking is a listening and composition method that focuses on the exploration of the environment.With roots in the 1970s, the artistic practices that sprung from soundwalking engage both with the unmediated soundscape as well as with multiple approaches to its augmentation or the augmentation of the human sensory apparatus.Soundwalking emphasises the listener's active and participatory role in the construction of dynamic compositions, shaped as much by the environment as by their presence and actions.Given the increasing relevance of computation in physical and public environments, the omnipresence of the metainterface, and how hybrid environments emerge from physical and virtual spaces, this paper discusses how principles and methodologies of soundwalking may allow the exploration and, ultimately, the understanding of computational environments that are increasingly sonic.This paper explores how in these contexts soundwalking can be used as a poetic and aesthetic resource, leading to the development of a listening that emphasises computation and procedurality, an algorithmic listening.
Local Neighbourhood Image Properties for Exposure Region Determination Method in Nonuniform Illumination Images

During image acquisition, nonuniform illumination regions are produced due to several factors, such as improper environment lighting and inappropriate capturing device setting. Applying contrast enhancement methods with the same enhancement concept to the whole image can over enhance or under enhance nonuniform illumination image. Thus, different and specific enhancement concepts should be applied to different regions in nonuniform illumination image. This concept requires identification of those different regions. Almost all existing methods that introduced the region determination process can only detect two different regions, namely, dark and bright, which inadequately represent the real exposure condition because the methods only consider intensity criteria to determine the regions. For this problem, a new method used for the accurate detection of nonuniform illumination regions is proposed. Different illumination levels affect not only the intensity but also the details in an image. Thus, three image attributes, namely, intensity, entropy and contrast, which are evaluated locally in detecting the regions, must be considered. For the detection to be on par with that in humans, the three attributes are combined with a rule-based method for the identification of illumination regions. Experimental results involving evaluation from research experts demonstrate that the proposed method qualitatively detects different illumination regions (i.e., over-exposed, well-exposed and under-exposed) in a nonuniform illumination image more accurate than the state-of-the-art methods.
Brain MRI Modality Understanding: A Guide for Image Processing and Segmentation

Medical image processing is a highly challenging research area, thus medical imaging techniques are used to make diagnosis in human body. Moreover, as tumor in the brain is a critical and medical complaint, segmentation of the images has an important role to make segmentation of the brain tumor and it provides suspicious region diagnosis from the medical images. By the help of MRI scanners, signals generated by the human body tissues could be detected and determined spatially. Thus, we in this paper try to propose basics of MRI image modalities as a guide for understanding the processes and methods. Since original brain image is not appropriate for the examination, segmentation of the images could be very useful method for partition of the digital image into similar regions. This research also presents a guide for understanding the brain MRI sequences in other words modalities.
Bayesian transfer learning between Student-t filters

The problem of sequentially transferring a data-predictive probability distribution from a source to a target Bayesian filter is addressed in this paper. In many practical settings, this transfer is incompletely modelled, since the stochastic dependence structure between the filters typically cannot be fully specified. We therefore adopt fully probabilistic design to select the optimal transfer mechanism. We relax the target observation model via a scale-mixing parameter, which proves vital in successfully transferring the first and second moments of the source data predictor. This sensitivity to the transferred second moment ensures that imprecise predictors are rejected, achieving robust transfer. Indeed, Student-t state and observation models are adopted for both learning processes, in order to handle outliers in all hidden and observed variables. A recursive outlier-robust Bayesian transfer learning algorithm is recovered via a local variational Bayes approximation. The outlier rejection and positive transfer properties of the resulting algorithm are clearly demonstrated in a simulated planar position-velocity system, as is the key property of imprecise knowledge rejection (robust transfer), unavailable in current Bayesian transfer algorithms. Performance comparison with particle filter variants demonstrates the successful convergence of our robust variational Bayes transfer learning algorithm in sequential processing.
Identification of Users Feature Based on Facebook Snippets

Is there lying any actual difference in their posts between men and women or people of different religion or age? Today it is very important to know whether a text is written by male or female or is it possible to identify a person's gender by using their writing. Several works described a lot of differences between men and women and people of different religion. Besides, it is proved that there is actually linguistics, psychological and sociological difference between men and women or the difference between people of different religion. In this paper, we have find out the dissimilarities between different users by using several text classification algorithms, analyze the performance of these algorithms and reports their results on Facebook posts and comments.