Reviewerly/exHarmony huggingface dataset:
LevelUp: A thin-cloud approach to game livestreaming

Game livestreaming is hugely popular and growing. Each month, Twitch hosts over two million unique broadcasters with a collective audience of 140 million unique viewers. Despite its success, livestreaming services are costly to run. AWS and Azure both charge hundreds of dollars to encode 100 hours of multi-bitrate video, and potentially thousands each month to transfer the video data of one gamer to a relatively small audience.In this work, we demonstrate that mobile edge devices are ready to play a more central role in multi-bitrate livestreaming. In particular, we explore a new strategy for game livestreaming that we call a thin-cloud approach. Under a thin-cloud approach, livestreaming services rely on commodity web infrastructure to store and distribute video content and leverage hardware acceleration on edge devices to transcode video and boost the video quality of low-bitrate streams. We have built a prototype system called LevelUp that embodies the thin-cloud approach, and using our prototype we demonstrate that mobile hardware acceleration can support real-time video transcoding and significantly boost the quality of low-bitrate video through a machine-learning technique called super resolution. We show that super-resolution can improve the visual quality of low-resolution game streams by up to 88% while requiring approximately half the bandwidth of higher-bitrate streams. Finally, energy experiments show that LevelUp clients consume only 5% of their battery capacity watching 30 minutes of video.
A New Method for Polygon Detection Based on Hough Parameter Space and USAN Region

This paper propose a new approach that combine Hough Transform (HT) and corner detection to detect polygons, which consider integrated characteristics not the individual characteristics. We establish a Polygon Parameter Space (PPS) to fit and characterize polygons, which consist of angles, coordinates, USAN values and every two lines of intersections. Firstly, canny operator is used to extract edges map, applied HT to detect line along edges of polygon shape and compute PPS. Secondly, corner detection among intersections is realized by comparing USAN value with angle of intersections, an adaptive threshold and adjusted brightness of nucleus of USAN is introduced to obtain accurate vertices from corners. Finally, we propose an algorithm based on Deep First Search (DFS) to fit the set of vertices regardless convex polygons (CVPs) or concave polygons (CCPs) according to parameters in PPS. The experimental results show that the proposed approach can effectively detect polygons with a less running time and higher accuracy, and shows the advantage of detecting the CVP and CCP shapes of broken vertices.
Investigating the Classification of Human Recognition on Heterogeneous Devices Using Recurrent Neural Networks

The wide spread of motion sensors has encouraged the creation of many useful applications. In this chapter, the authors use Human Activity Recognition (HAR) application as a case study to make it more useful and more applicable in real life by tolerating the heterogeneity of the types of devices. This chapter investigates the ability of Recurrent Neural Networks (RNN) to make HAR applications handle the heterogeneity of different smart devices. To this end, statistical features are obtained from a huge dataset of raw sensor data using a nontrivial sliding window technique. And then, an alignment technique is used to synchronize the extracted features of both the accelerometer and gyroscope sensors for each smart device. The results show that the device placement, the size, and the heterogeneity of the data can significantly affect the performance of HAR applications. The results also show that a realistic heterogeneous HAR system performance is far less than the optimistic homogeneous HAR performance. The findings have several implications into practices.
Efficiency Enhancement of Solar Panel Using Photodiode

Solar energy is becoming a very fast and important means of renewable sources of energy. The output of solar panels varies with movement of the sun. In order to track maximum power, maximum power point trackers are used to track maximum power from the solar panel. With the solar tracking, more energy generation is possible, since the solar panel can maintain a perpendicular profile to the rays of the sun. Hence, increasing the overall efficiency of the solar panel.
Security architecture for the protection of digital assets in SMEs

Currently, around 6 billion fake emails are sent around the world, resulting in data leakage losses of approximately $ 3 million per violated company, despite this, SMEs have reduced their budgets for security, thus being exposed to various threats. The security architectures that support companies are aimed at large organizations that have the resources to implement them, the opposite situation occurs with SMEs. This work proposes a security architecture for the protection of digital assets in SMEs, which seeks to increase the levels of effectiveness in defense mechanisms through the deployment of effective controls. The architecture is based on OSA concepts and SABSA methodology, and considers the contextual and conceptual, logical and physical layers adapted to SME. Also, consider a toolkit to support the controls. Finally, a simulation of the use of architecture for a company is carried out, thus achieving an increase in the security level from 34.90% to 67.86%.
Development of a Tool for Modeling Security Threats of an Enterprise Information System

A qualitative risk assessment of information security threats make it possible to solve the problem of information security most effectively. The paper analyzes existing approaches to assessing the security risks of information systems based on existing techniques. The stages and methods of compiling a threat model of an information system are considered. Methodological approaches to improving the methods of assessing information security risks are considered. Existing software solutions for compiling threat models based on building attack trees have been analyzed, and their capabilities have been tested. A conceptual approach is proposed to optimize the process of analyzing information security threats. The paper explains the feasibility of using software modeling approaches to optimize threat assessment procedures. For this purpose, an attack graph is used, which is automatically compiled taking into account the current information assets of an enterprise. The process of assessing information security threats is carried out considering a study of the initial state of the information system security and probabilistic characteristics of an actions of a violator. To automate the process of modeling threats of information security, a method for calculating the probability of the implementation of threats based on the fuzzy logic algorithm is proposed. The proposed software solution uses the threat database FSTEC of Russia. The program database is updated offline.
Hybrid Hyper-parameter Optimization for Collaborative Filtering

Collaborative filtering (CF) became a prevalent technique to filter objects a user might like, based on other users' reactions. The neural network based solutions for CF rely on hyper-parameters to control the learning process. This paper documents a solution for hyper-parameter optimization (HPO). We empirically prove that optimizing the hyperparameters leads to a significant performance gain. Moreover, we show a method to streamline HPO while substantially reducing computation time. Our solution relies on the separation of hyper-parameters into two groups, predetermined and automatically optimizable parameters. By minimizing the later, we can significantly reduce the overall time needed for HPO. After an extensive experimental analysis, the method produced significantly better results than manual HPO in the context of a real-world dataset.
Detecting tampered region in video using LSTM and U‐Net

Abstract In the world today, we can always shoot digital videos with smartphones and share them with family or friends after editing them. It is necessary to check whether these videos have been tampered with to use them as forensic evidence in criminal investigations. This paper proposes a method for detecting tampered regions in a video where a specific subject is removed from a scene. In the proposed method, we use LSTM for the time domain analysis and UNet for the space domain analysis. The detection accuracy achieved 0.98 in terms of F‐measure, even though tampered the region was deformed or moved in the video. The experimental results show superior performance in the detection of tampered regions in digital videos.
A Frequency-Based Approach For Multi-Class Data Classification Problem

Data classification is a process of categorizing data into various types or any other distinctive form. In this study, a new frequency-based method is proposed for data classification. This method splits the each attribute to intervals and calculates the frequency of the number of classes occurring in the interval of each attribute. The proposed method is tested on UCI benchmark test problems by 10-fold cross-validation method. The results have shown that the new method reaches to satisfactory solutions in a shorter time compared to the existing results. The better performance is observed in 9 of 11 datasets.
BreastNet: Breast Cancer Categorization Using Convolutional Neural Networks

Breast cancer is usually classified as either benign or malignant, where the former is not considered hazardous to health. Nonetheless, the benign tumors must be periodically monitored to control their activity and to prevent them from becoming malignant eventually. Several automated techniques have been proposed to aid the diagnosis by indicating potential tumor locations or by providing a broader insight. Although benign and malignant tumors are divided into four categories each, most of the works cope with their classification as just benign and malignant. This work addresses the problem of providing a more detailed classification of the tumors by proposing a deep-based architecture able to distinguish between eight types of tumors (i.e., four benign and four malignant). The proposed approach relies on the fusion of traditional convolution kernels with dilated convolutions before pooling, which can learn better spatial information, thus providing better feature detection prior to classification. Experimental results showed that the proposed approach outperformed the techniques compared in this work.
Research on Machine Learning Based on Multi-label Algorithm

In recent years, multi-label learning has performed well in solving related problems such as a single object may have multiple associated categories, which has attracted widespread attention from researchers. This paper applies data mining to the information and needs of different users. Analyze the correlation between many label features, and use the sample clustering information to adjust the similarity matrix of weakly labelled samples. This paper proposes a measure for classifier credibility, and constructs a similarity based on k-means. Matrix, and introduce different levels of multi-example learning algorithms as our classifier model, and propose two classification distances: the minimum classification distance of labels and the average classification distance of labels. Finally, we apply the model to natural scene image classification and text classification. After comparing the method proposed in this paper with the ordinary sample, it is found that the method in this paper can obtain better classification performance in various evaluation indicators under the same training sample.
Quantum Key Distribution Protocol Based on GHZ Like State and Bell State

In this paper, we propose a multi-party quantum key distribution protocol using Bell state particles and GHZ like particles. The protocol adds Charlie between Alice and Bob. Alice and Bob can establish a secret key with Charlie at the same time. Through security analysis, Alice and Bob can’t eavesdrop on each other’s key. The three parties can not cheat each other. The protocol can also resist eavesdroppers. The advantage of this protocol is that two different keys can be established among three parties at the same time.
Preserving Information Security Using Fractal-Based Cryptosystem

The chapter intends to propose a hybrid cryptosystem based on a chaotic map and a fractal function. The sequential order of process execution provides a computationally less expensive and simple approach that still designed a secure cryptosystem. A one-dimensional Ricker map and its modified form are employed to initially shuffle the image pixels twice, and also a pseudo-random sequence is generated using both maps. The algorithm implemented a sequence of pixel confusion-diffusion steps using the image rotation and a transcendental anti-Mandelbrot fractal function (TAMFF) and its Mann-iterated fractal function (Sup-TAMFF). Finally, the pixel value of an image obtained in the last step and the recent two pixels of the encrypted image is XORed with the corresponding pseudo-random matrix value to get the cipher image. Subsequently, various performance tests are conducted to verify the suitability of the given method to be used in real-world information transmission.
Deep Hashing Regularization Towards Hamming Space Retrieval

Hashing methods have been extensively used so as to resolve image and video retrieval problems due to their computation and storage efficiency. During the recent years, following the impressive performance of deep learning methods in various computer vision tasks, deep hashing methods have powerfully emerged in the field of large scale image and video retrieval, accomplishing superior performance over the previous approaches. In this paper, a novel regularization framework is proposed, on the top of Deep Cauchy Hashing method, for improving the performance of the produced hash codes towards Hamming space retrieval. The proposed framework includes two regularization approaches, namely Class-Agnostic regularizer and Class-Aware regularizer. The experimental evaluation on two retrieval datasets validates the efficiency of both the proposed approaches in improving the retrieval performance, outperforming previous state-of-the-art approaches.
A Comparative Analysis of dot NET-Based and Open Source Platforms for Ontologies Development

Many studies have evaluated and compared existing open-sources semantic web platforms for ontologies development. However, none of these studies has included the dot NET-based semantic web platforms in the empirical investigations. This study conducts a comparative analysis of open-source and dot NET-based semantic web platforms for ontologies development. Two popular dot NET-based semantic web platforms, namely, SemWeb.NET and dotNetRDF are analyzed and compared against open-source environments including Jena API, Protégé and RDF4J also known as Sesame SDK. Various metrics are used to compare the two categories of platforms such as the storage mode, query support, consistency checking, interoperability with other tools, and many more. A number of five ontologies of different sizes are used in the experiments. The experimental results showed that the open-source platforms provides more facilities for creating, storing and processing ontologies compared to the dot NET-based tools. Furthermore, the experiments revealed that Protégé and RDF4J open-source and dotNetRDF platforms provide both graphical user interface (GUI) and command line interface for ontologies processing, whereas, Jena open-source and SemWeb.NET are command line platforms. Moreover, the results showed that the open-source platforms are capable of processing multiple ontologies' files formats including RDF and OWL formats, whereas, the dot NET-based tools only process RDF ontologies. Finally, the experiments enabled to learn that the dot NET-based platforms have limited memories size as they failed to load and query larges ontologies compared to open-source environments.
Fuzzy Interval Modelling based on Joint Supervision

This paper presents a new methodology for Prediction Interval (PI) construction based on a modified Takagi-Sugeno fuzzy system trained with a joint Supervision loss function. Given a desired coverage level, this model is capable of providing predictions of the expected value of the system along with the interval bounds. This methodology is tested by simulation experiments using a dataset containing real temperature data from a rural community in southern Chile. The proposed model was compared with a state-of-the-art Takagi-Sugeno Fuzzy Numbers model. It was shown that the Joint Supervision method manages to obtain slightly superior results to the Fuzzy Numbers approach while greatly reducing the complexity of the training loss function. Additionally, since the proposed model was trained using Particle Swarm Optimization, further performance improvements could be made by employing gradient-based optimization algorithms, since they are compatible with the Joint Supervision loss function.
Virtual Reality vs Pancake Environments: A Comparison of Interaction on Immersive and Traditional Screens

Virtual Reality environments provide an immersive experience for the user. Since humans see the real world in 3D, being placed in a virtual environment allows the brain to perceive the virtual world as a real environment. This paper examines the contrasts between two different user interfaces by presenting test subjects with the same 3D environment through a traditional flat screen (pancake) and an immersive virtual reality (VR) system. The participants (n = 31) are computer literate and familiar with computer generated virtual worlds. We recorded each user’s interactions while they undertook a short-supervised play session with both hardware options to gathering objective data; with a questionnaire to collect subjective data, to gain an understanding of their interaction in a virtual world. The information provided an opportunity to understand how we can influence future interface implementations used in the technology. Analysis of the data has found that people are open to using VR to explore a virtual space with some unconventional interaction abilities such as using the whole body to interact. Due to modern VR being a young platform very few best practice conventions are known in this space compared to the more established flat screen equivalent.
Fuzzy Multi-objective Requirements for NRP Based on Particle Swarm Optimization

In software engineering, the development of software products raises a new set of development requirements each time. Considering the interaction between requirements, how to select an optimal subset of requirements becomes an important problem. In this paper, a fast method of requirements optimization is proposed, which can select an optimal subset from the next release of product development requirements under the limitation of user satisfactions and cost. The multiple requirements in this paper are limited by user satisfaction and cost. We mainly make the following contributions: (1) We define this problem as multi-objective problem for optimization. (2) Then particle swarm optimization (PSO) algorithm is used to adjust the convergence parameters of multiple object to search the optimal solution quickly. (3) Finally, the results of the algorithm are evaluated by using NDS number and time of multi-objective problem through fuzzy simulation data. Experimental results show that the algorithm is efficient and reliable, and can help developers make reasonable decisions.
A Novel IoT-based Framework for Indoor Rescue Operations

Scientists and researchers are in pursuit of finding ways to make precise predictions of disasters. Despite the way that foreseeing a disaster isn't always possible, researchers have developed certain tools that are proving to be massively valuable in forecasting of tremors, landslides & tsunamis, and for post-disaster management. This paper surveys the various state-of-the-art disaster alerting, reporting and management tools installed around the world. This article intends to share the research work currently in progress that proposes a novel IoT-based post-disaster rescue operations management framework for large indoor facilities to send automatic alert signals to rescue managers in case of disaster and to direct the rescue team members to those areas inside the affected facilities where the victims might be waiting to be rescued.
Interocular Distance based Facial Recognition

Facial Recognition has become a key feature when it comes to biometrics. In order to capture a person's face, there are certain vital attributes of the face that we need to understand such as length and width of nose, eyes, chin, eyebrows etc. There are many existing methods (algorithms) for performing facial detection but it is difficult to assess the performance of these methods because of its complexity. In this paper, facial recognition using the property of golden ratio of human face is discussed. The proposed technique is more efficient in terms of run time length and detection process is easy as it only stores the eye data. Hence measuring only the distance between the eyes would help us do facial recognition. A realistic comparison between Haar feature-based cascade classifier and contour mapping based algorithm is presented. Georgia tech university's dataset has been used for the purpose of comparison of different algorithms.
Semi-supervised learning for acoustic model retraining: Handling speech data with noisy transcript

We address the problem of retraining a seed acoustic model from a large corpus which is associated with noisy labeling. We propose a forced-alignment likelihood and fuzzy string matching score based iterative selection of the corpus data to retrain the acoustic model in an order of increasing degree of noise in the transcript, yielding a succession of enhanced acoustic models, offering progressively lower error rates on an held-out test data. We show results in terms of PER (phoneme-error-rate) on a large broadcast news data from a national broadcast network containing multiple languages of transcribed-speech, demonstrating the strong utility of such an approach for training of acoustic models from noisy-transcript.
Advanced Distribution Method for Smart Grids in the Mobility World Using Blockchain Technology

With the integration and arrival of mobility platforms and variety of vehicle types, the smart grid is being projected as a solution for the challenges regarding electricity supply in the future. It is only evident and matter of future time when mobility platforms become an integral part to manage and distribute electricity. However, the complexity and security in the management and consumption of electricity data pose serious challenges in the adoption of the smart grid. To address these challenges, blockchain technology is being researched for applicability in smart grid with respect to mobility methods. Apart from its properties of distribution and high security layer, blockchain can be integrated within demanding mobility platforms to provide viable feedback and promote allocations of energy into proper storages. Energy distribution systems can also use blockchain to remotely control energy flow to a particular area by monitoring the mobility usage and feedback. One use case is when blockchain network manager to utilize mobility users to obtain energy status and deliver hash codes of designated and secured energy resources. Storing energy and matching platforms in the mobility world is a huge challenge to overcome. Finally, all related Smart Grid and mobility challenges for integrating blockchain are discussed.