Reviewerly/exHarmony huggingface dataset:
Organ-Branched CNN for Robust Face Super-Resolution

In this paper, we present a novel organ-branched CNN method for face super-resolution, named OBC-FSR. It is the first work focusing on facial-part-specific face SR, which consists of a local (facial part) network and a global network. Specifically, local network enhances the five key regions of human faces separately by Wasserstein generative adversarial networks (WGAN). Simultaneously, it also predicts five key regions’ masks, namely, eyes, eyebrows, mouth, nose, and other parts. The output of the local network is obtained by merging super-resolved five key regions. In order to alleviate boundary effects and distortions in the result of local network, our proposed network also includes a global network, which learns the direct mapping between LR and HR human faces. The final HR result of our FSR method is a fusion of the out-puts of local and global networks. Experimental results verify the superior performance of our method compared to the state-of-the-art.
Developing novel low complexity models using received in-phase and quadrature-phase samples for interference detection and classification in Wireless Sensor Network and GPS edge devices

Despite Wireless Sensor Networks (WSNs) significantly developing over the past decade, these networks, like most wireless networks, remain susceptible to malicious interference and spectrum coexistence. Other vulnerabilities arise as WSN applications adopt open standards and typically resource and energy-constrained commercial-off-the-shelf equipment. Deployments include safety-critical applications such as the internet of things, medical, aerospace and space and deep-sea exploration. To manage safety and privacy requirements across such a diverse wireless landscape, security on wireless edge devices needs improvement while maintaining low complexity. This paper improves wireless edge device security by developing a novel intelligent interference diagnostic framework. Received in-phase (I) and quadrature-phase (Q) samples are exclusively utilized to detect modern, subtle and traditional crude jamming attacks. This I/Q sample utilization inherently enables decentralized decision-making, where the low-order features were extracted in a previous study focused on classifying typical 2.4–2.5 GHz wireless signals. The associated optimal intelligent models are leveraged as the foundation for this paper’s work. Initially, Matlab Monte Carlo simulations investigate the ideal case, which incorporates no hardware limitations, identifies the required data type of signal interactions and motivates a hardware investigation. Software-defined radios (SDRs) collect the required live over-the-air I/Q data and transmit matched signal (ZigBee) and continuous-wave interference in developed ZigBee wireless testbeds. Low complexity supervised machine learning models are developed based exclusively on the low-order features and achieve an average accuracy among the developed models above 98%. The designed methodology involves examining ZigBee over-the-air data for artificial jamming and SDR jamming of ZigBee signals transmitted from SDR and commercial (XBee) sources. This approach expands to a legitimate node classification technique and an overall algorithm for wireless edge device interference diagnostic tools. The investigation includes developing Support Vector Machine, XGBoost and Deep Neural Network (DNN) models, where XGBoost is optimal. Adapting the optimized models to global positioning system signals establishes the transferability of the designed methodology. Implementing the designed approaches on a Raspberry Pi embedded device examines a relatively resource-constrained deployment. The primary contribution is the real experimentally validated interference diagnostic framework that enables independent device operation, as no channel assumptions, network-level information or spectral images are required. Developed models exclusively use I/Q data low-order features and achieve high accuracy and generalization to unseen data.
Privacy Preserving Trajectory Data Publishing with Personalized Differential Privacy

With the development of location-based applications, more and more trajectory data are collected and applied. Trajectory data often contains user's sensitive information, and direct release may pose a threat to users' privacy. Differential privacy, as a privacy preserving method with solid mathematical foundation, has been widely used in trajectory data publishing. However, current methods based on differential privacy can not fully realize the personalized trajectory privacy protection. In this paper, an optimal personalized trajectory differential privacy mechanism is proposed to balance the privacy protection and data utility. Firstly, we filter out the templet trajectory based on the semantic similarity of trajectories, and propose a privacy level allocation method based on stay-point and frequent sub-trajectory. Then, the privacy levels of all users' locations are obtained according to the location matching results. Combined with the optimal location differential privacy mechanism, we disturb the location points on the user's trajectory before publishing, where different location privacy levels correspond to different privacy budgets. Experiments on real-world datasets show that compared with traditional differential privacy, our mechanism provides a better tradeoff between privacy protection and data utility.
Real Time Prediction of American Sign Language Using Convolutional Neural Networks

The American Sign Language (ASL) was developed in the early 19th century in the American School for Deaf, United States of America. It is a natural language inspired by the French sign language and is used by around half a million people around the world with a majority in North America. The Deaf Culture views deafness as a difference in human experience rather than a disability, and ASL plays an important role in this experience. In this project, we have used Convolutional Neural Networks to create a robust model that understands 29 ASL characters (26 alphabets and 3 special characters). We further host our model locally over a real-time video interface which provides the predictions in real-time and displays the corresponding English characters on the screen like subtitles. We look at the application as a one-way translator from ASL to English for the alphabet. We conceptualize this whole procedure in our paper and explore some useful applications that can be implemented.
Novel Algorithm for Blind Classification of Space-Time Block Codes in Cognitive Radio

The blind classification of space-time block codes (STBCs) is a key research issue related to cognitive radio. There have been numerous studies attempting to classify STBCs, but the blind classification of STBCs with transmission impairments merits further research. The novel blind classification algorithm proposed in this paper exploits the fourth-order cyclostationarity of STBCs to classify them with a single receiver antenna in the presence of transmission impairments. The proposed algorithm relies on the fourth-order cyclic moment as a discriminating feature and employs the distance between adjacent spectral lines to classify the transmitted STBCs. As opposed to other methods, this algorithm does not require a priori information regarding the modulation scheme, channel coefficients, or noise power; consequently, it is very well-suited to non-cooperative scenarios. According to rough estimation of symbol duration and carrier frequency offset, the distance between adjacent spectral lines remains almost the same with value dependent on the transmitted STBCs. Simulations indicate that the proposed method performs well even at low signal-to-noise ratios (SNRs).
Categorizing User Sessions at Pinterest

There are many different ways for individuals to use a given Internet application. By recording and analyzing detailed logs of user in-application activity, we identify patterns of behavior that reflect distinct use cases. Not only does this lead to a better understanding of user intent, but also generates important insights with actionable product and business outcomes. In this application oriented paper, we study the problem of user session classification, where the goal is to discover categories of user in-application behavior using event logs, and then consistently label user sessions on an ongoing basis. We develop a practical three-step approach which uses clustering to discover categories of sessions, builds classifiers to label new sessions, and finally performs daily classification in a distributed pipeline. An important innovation of our approach is replacing a set of events identified as long-tail features with a new feature that is less sensitive to product experimentation and logging changes. This allows for robust and stable identification of session types even though the underlying application is constantly changing. We apply the approach to Pinterest sessions and verify the results with a user survey. Our solution classifies millions of user sessions daily and leads to actionable insights that guide business decisions relating to product design, monetization, and growth.
Machine Learning in future intensive care—Classification of stochastic Petri Nets via continuous-time Markov chains

The fast growing digitalization of medicine has facilitated the collection of patient data in databases. A smart city must facilitate such databases in its hospitals. However, handling this big data requires new strategies for filtering and processing such high volumes of patient data. In this regard, Machine Learning (ML) algorithms are increasingly applied to support bedside decisions on patient treatment, preprocess medical data and help to choose adequate medications. Consequently, the choice of the ML modeling scheme is imperative. In contrast to conventional classification representation methodologies used in ML such as Artificial Neural Networks (ANNs), we present a novel concept based on stochastic models. Stochastic Petri Nets (SPNs) are a powerful modeling scheme that is used to describe the dynamic behavior of biochemical kinetics. This chapter presents a framework for building stochastic models based on the sampled sequences from observed SPNs by means of the Expectation Maximization (EM) algorithm and Continuous-Time Markov Chains (CTMC). Apart from its theoretical interest, we expect the framework to be useful for the classification of different stochastic processes and identifying the differences between them in big data context. An important application of the newly developed framework is disease diagnoses especially in Intensive Care Units (ICUs). To test the framework, we conducted simulations using SPNs with different structure and using Gillespie algorithm to generate data. After training, classification results show F1 score 95% using linear Support Vector Machines (SVMs) and 89% using Classification and Regression Trees (CARTs) algorithm.
Dehazing for images with sun in the sky

Image dehazing is one of the most important image processing methods and it is widely used in daily application. A large number of dehazing algorithms have been proposed in recent years. In order to solve the problem that the dehazing performances of the classic dark channel prior methods for hazy images with the sun are bad, we propose a dehazing method for removing mist interference in images with the sun in the sky. An atmospheric scattering model is proposed to estimate the scattered light of the sun in the hazy circumstance. We also propose a gradient-based transmission map estimation method to estimate the refined transmission map accurately while reducing the computational complexity. The effectiveness of our method is confirmed by extensive experiments over a wide variety of images.
Combining Long Short Term Memory and Convolutional Neural Network for Cross-Sentence n-ary Relation Extraction

We propose in this paper a combined model of Long Short Term Memory and Convolutional Neural Networks (LSTM_CNN) model that exploits word embeddings and positional embeddings for cross-sentence n-ary relation extraction. The proposed model brings together the properties of both LSTMs and CNNs, to simultaneously exploit long-range sequential information and capture most informative features, essential for cross-sentence n-ary relation extraction. The LSTM_CNN model is evaluated on standard datasets on cross-sentence n-ary relation extraction, where it significantly outperforms baselines such as CNNs, LSTMs and also a combined CNN_LSTM model. The paper also shows that the proposed LSTM_CNN model outperforms the current state-of-the-art methods on cross-sentence n-ary relation extraction.
A light-weight implementation of latch-based true random number generator

A TRNG (True Random Number Generator) is an important component for security in mobile computing and IoT. This paper presents an light-weight FPGA implementation of a previously proposed latch-based TRNG, while keeping the quality of generated random numbers. By accumulating the generated random numbers fifteen times with an XOR operation for each output word, the proposed TRNG with 16 latches passed the NIST SP 800-22 test suite, whereas the original TRNG required 248 latches. An important finding of this work is that the quality of random sequence is greatly improved by XOR-ing temporally interleaved series of bits.
Optimization of tilt angle of a solar collector in Medan city using genetic algorithm

Abstract Solar energy is one of the renewable energy resources that is not limited in its use and it does not cause pollution. One form of solar energy utilization that is often found is to produce electricity with the using of photovoltaics or solar panels. The main problem with solar energy is the instability of power which generated by the solar panels because it depends on the sunlight intensity which accepted by the solar panels. The intensity of sunlight received by the solar panels can be maximized by installing solar panels with a right angle so it will be obtained the maximum output power. In this study, the optimization of the slope angle of solar panels will be analysed using the Genetic Algorithm method. The results obtained are the optimal optimum slope of the panel from January, February, March, April, May, June, July, August, September, October, November and December are 53.839 55, 55.121°, 53.938°, 53.442°, 54.398°, 53.441°, 53.009°, 52.933°, 54.709°, 57.055°, 57.169°, 54, 632°.
Comparation Between Linear and Polynomial Kernel Function For Ovarium Cancer Classification

Abstract Ovarium cancer is a kind of cancer disease which often attacks a woman. Ovarium cancer consists of benign and malignant type. Based on the data set obtained, this research try to classify the data set with support vector machine algorithm. The principle of SVM is how to identify a hyperplanes with maximal margin. Hyperplanes is a border line between the data. Here, we will used two kernel function in SVM, linear and polynomial kernel function. Next We will compare both of them algorithm to reach the hyperplanes with maximal margin. The hyperplanes then used to predict the class of data testing given. Finally, comparation result will be obtained from confussion matrix and accuration calculation for each kernel function while used to classify the ovarium cancer data set. Accuration result with Linear kernel function is 68.98% for first schema and 66.67% for second schema. Then accuration result with Polynomial Kernel Function is 83.79% for both of schema.
Smart Contracts for Container Based Video Conferencing Services: Architecture and Implementation

Today, container-based virtualization is very popular due to the lightweight nature of containers and the ability to use them flexibly in various heterogeneously composed systems. This makes it possible to collaboratively develop services by sharing various types of resources, such as infrastructures, software and digitalized content. In this work, our home made video-conferencing (VC) system is used to study resource usage optimisation in business context. An application like this, does not provide monetization possibilities to all involved stakeholders including end users, cloud providers, software engineers and similar. Blockchain related technologies, such as Smart Contracts (SC) offer a possibility to address some of these needs. We introduce a novel architecture for monetization of added-value according to preferences of the stakeholders that participate in joint software service offers. The developed architecture facilitates use case scenarios of service and resource offers according to fixed and dynamic pricing schemes, fixed usage period, prepaid quota for flexible usage, division of income, consensual decisions among collaborative service providers, and constrained based usage of resources or services. Our container-based VC service, which is based on the Jitsi Meet Open Source software is used to demonstrate the proposed architecture and the benefits of the investigated use cases.
FOVO: A flexible real-time computer graphics rendering process

Real-time rendering processes in computer graphics systems are often designed around the principles of linear projection (LP), that is, simulating linear light rays passing through 3D space and intersecting a two-dimensional surface in order to create an image.
Threshold Auto-Tuning Metric Learning

It has been reported repeatedly that discriminative learning of distance metric boosts the pattern recognition performance. Although the ITML (Information Theoretic Metric Learning)-based methods enjoy an advantage that the Bregman projection framework can be applied for optimization of distance metric, a weak point of ITML-based methods is that the distance threshold for similarity/dissimilarity constraints must be determined manually, onto which the generalization performance is sensitive. In this paper, we present a new formulation of metric learning algorithm in which the distance threshold is optimized together. Since the optimization is still in the Bregman projection framework, the Dykstra algorithm can be applied for optimization. A nonlinear equation has to be solved to project the solution onto a half-space in each iteration. We have developed an efficient technique for projection onto a half-space. We empirically show that although the distance threshold is automatically tuned for the proposed metric learning algorithm, the accuracy of pattern recognition for the proposed algorithm is comparable, if not better, to the existing metric learning methods.
Classification of Graphical User Interfaces Through Gaze-Based Features

Graphical User Interfaces (GUIs) represent a key part for building interactive applications, and they act as a direct interface between users and software. The complexity of the GUIs increases in relation to the number of users interacting with software and performing multiple complex tasks, resulting in software that are mazes of functionalities. The impact of such a GUI system is adverse on the software quality and may result in non-robust software. To measure software quality, one has to measure attributes such as usability, efficiency, and learnability. There is no general approach for measuring software validity subjectively in a non-invasive way, i.e., without directly asking the users to evaluate directly the software through predefined use cases or through questionnaires. In this paper, we investigate this issue by detecting the presence of potential errors in GUIs by using the users gaze information collected during user interactions. Users gaze is analyzed to generate thirteen unique features based on statistical moments based on the time spent on each control, gaze path, and transitions between controls on the different GUI components present on the screen. The performance is evaluated with five state of the art classifiers. The results support the conclusion that incongruent GUI states can be reliably detected at the single trial level through gaze-based features with an accuracy of 76% (one-versus-rest classification) and 73.66 (one-versus-one classification) using a Gaussian Naïve Bayes classifier. These results suggest the possibility of co-development approaches between early stage users and developers through eyetracking.
Some Computational Considerations for Kernel-Based Support Vector Machine

Sometimes healthcare perspectives in communications technologies require data mining, especially classification as a supervised learning. Support vector machines (SVMs) are considered as efficient supervised learning approaches for classification due to their robustness against several types of model misspecifications and outliers. Kernel-based SVMs are known to be more flexible tools for a wide range of supervised learning tasks and can efficiently handle non-linear relationship between input variables and outputs (or labels). They are more robust with respect to the aforementioned model misspecifications, and also more accurate in the sense that the root-mean-square error computed by fitting the kernel-based SVMs is considerably smaller than the one computed by fitting the standard/linear SVMs. However, the choice of kernel type and particularity kernel’s parameters could have significant impact on the classification accuracy and other supervised learning tasks required in network security, Internet of things, cybersecurity, etc. One of the findings of this study is that larger kernel parameter(s) would encourage SVMs with more localities and vice versa. This chapter provides some results on the effect of the kernel parameter on the kernel-based SVM classification. We thus first examine the effect of these parameters on the classification results using the kernel-based SVM, and then specify the optimal value of these parameters using cross-validation (CV) technique.
Learning semantic abstraction of shape via 3D region of interest

In this paper, we focus on the two tasks of 3D shape abstraction and semantic analysis. This is in contrast to current methods, which focus solely on either 3D shape abstraction or semantic analysis. In addition, previous methods have had difficulty producing instance-level semantic results, which has limited their application. We present a novel method for the joint estimation of a 3D shape abstraction and semantic analysis. Our approach first generates a number of 3D semantic candidate regions for a 3D shape; we then employ these candidates to directly predict the semantic categories and refine the parameters of the candidate regions simultaneously using a deep convolutional neural network. Finally, we design an algorithm to fuse the predicted results and obtain the final semantic abstraction, which is shown to be an improvement over a standard non maximum suppression. Experimental results demonstrate that our approach can produce state-of-the-art results. Moreover, we also find that our results can be easily applied to instance-level semantic part segmentation and shape matching.
Distributed Landmark Selection for Lower Bound Estimation of Distances in Large Graphs

Given two vertices in a graph, computing their distance is a fundamental operation over graphs. However, classical exact methods for this problem often cannot scale up to the rapidly evolving graphs in recent applications. Many approximate methods have been proposed, including some landmark-based methods that have been shown to have good scalability and estimate the upper bound of the distance in acceptable accuracy. In this paper, we propose a new landmark-based framework based a new measure called coverage to more accurately estimate the lower bound of the distance. Although we can prove that selecting the optimal set of landmarks is NP-hard, we propose a heuristic algorithm that can guarantee the approximation ratio. Furthermore, we implement our method through the distributed graph processing systems while considering the characteristic of the distributed graph processing systems. Experiments on large real graphs confirm the superiority of our methods.