Reviewerly/exHarmony huggingface dataset:
Calculating average shortest path length using Compute Unified Design Architecture (CUDA)

Large graphs with millions and even billions of vertices are found in many real-life network analysis, the processing of which is challenging. One of the toughest tasks is computing the average shortest path length in a large network, which requires a lot of memory and processing time while calculating many independent paths. Hence, this task becomes a good candidate for parallelizing. The idea of using graphics processing units (GPUs) for general purpose computing is not new, and with recent increases in performances and memory capacity, they make a perfect candidate for working with graphs. We will explore how the Dijkstra's algorithm can be used to calculate the average shortest-path length, and how it can be used in a massively parallelized system and compare the performance gains and drawbacks.
Summarizing News Articles Using Question-and-Answer Pairs via Learning

The launch of the new Google News in 2018 ( https://www.blog.google/products/news/new-google-news-ai-meets-human-intelligence/ .) introduced the Frequently asked questions feature to structurally summarize the news story in its full coverage page. While news summarization has been a research topic for decades, this new feature is poised to usher in a new line of news summarization techniques. There are two fundamental approaches: mining the questions from data associated with the news story and learning the questions from the content of the story directly. This paper provides the first study, to the best of our knowledge, of a learning based approach to generate a structured summary of news articles with question and answer pairs to capture salient and interesting aspects of the news story. Specifically, this learning-based approach reads a news article, predicts its attention map (i.e., important snippets in the article), and generates multiple natural language questions corresponding to each snippet. Furthermore, we describe a mining-based approach as the mechanism to generate weak supervision data for training the learning based approach. We evaluate our approach on the existing SQuAD dataset ( https://rajpurkar.github.io/SQuAD-explorer/ .) and a large dataset with 91K news articles we constructed. We show that our proposed system can achieve an AUC of 0.734 for document attention map prediction, a BLEU-4 score of 12.46 for natural question generation and a BLEU-4 score of 24.4 for question summarization, beating state-of-art baselines.
A Quantitative Approach for Calculating Model Assurance Levels

Model-based engineering (MBE) in industry is on the rise. However, improvements are still needed on the oversight and management of MBE efforts. Frequently, program managers and high level decision makers do not have background in MBE to understand models, the value the models are providing, and if they are successfully achieving their MBE goals. To address these concerns in our previous research we developed a rating scale for models, called Model Assurance Levels (MALs). A MAL level is used to concisely express the assurance the model is providing to the program, as well as, risks associated with the model. However, for MAL level to be effective and meaningful, they need to be consistently determined regardless of who is performing the assessment. Therefore, this paper describes a quantitative and repeatable approach for computing MAL levels for software models.
An Emperical Study on Application of Big Data Analytics to Automate Service Desk Business Process

The maturity of the Big Data analytics allows enterprises to enhance their business processes using a data-driven approach. From retailers, E-commerce, risk analysis to pharmaceuticals, Bioinformatics, healthcare informatics, and others, business owners are thinking about mining business data into information for making better decisions to drive business value. However, the lack of reverse engineers and data analysts makes it harder for enterprises to apply Big Data analytics in their business data with a goal to enhance their operational processes. Ultimately, these processes can be automated to reduce human errors. In this paper, we study an empirical dataset containing real world service desk tickets using machine learning with a goal to automate and improve business processes, which will benefit to enterprises and customers in increasing quality of service.
Bayesian Semiparametric Double Autoregressive Modeling

This paper proposes a Bayesian semiparametric modeling approach for the return distribution in double autoregressive models. Monte Carlo investigation of finite sample properties and an empirical application are presented. The results indicate that the semiparametric model developed in this paper is valuable and competitive.
Physically-Based Bimanual Volumetric Selection for Immersive Visualizations

This paper presents the details of a novel selection technique, Physically-Based Volumetric Selection, to combat challenges in selecting volumes of data using immersive display spaces. We present results on a physical experiment that we used to inform design and discuss the details of our proposed technique. We also present the results of a virtual experiment to evaluate feasibility, utility, and usability. A description of user strategies and design is included. Our results will have implications for applications that use alternative rendering techniques as well as volume selection to assist with selection tasks in a physically-based manner. Our technique can be used in combination with other selection and manipulation techniques to improve the overall desired performance of volumetric selection.
Linear Hensel Lifting for Fp[x,y] and Z[x] with Cubic Cost

Hensel lifting is a key tool that is used to factor polynomials and compute polynomial GCDs in Z[x], Z[x1,...,xn] and Fq[x1,...,xn]. There are two versions of Hensel lifting: Linear Hensel Lifting (LHL) and Quadratic Hensel Lifting (QHL). For polynomials in Z[x], if classical quadratic algorithms for multiplication and division are used, LHL and QHL both have a quartic complexity. If asymptotically fast arithmetic is used, up to logarithmic factors, LHL is cubic and QHL is quadratic. In this work we present cubic algorithms for LHL for Z[x] and Fp[x,y]. We present details of C implementations of our cubic algorithms for Z[x] and Fp[x,y]. We compare both with Magma implementations of QHL using fast arithmetic. For both cases, we find that our our cubic LHL outperforms Magma's fast QHL for a very wide range of input sizes.
Educating I-Shaped Computer Science Students to Become T-Shaped System Engineers

With every passing day, software becomes more and more important to the success of the artifacts that we make, sell, buy, use, and evolve. Software increasingly provides a competitive differentiator for products, ways of tailoring them for various uses and users, and ways of fixing or evolving them without expensive product recalls. Unfortunately, as software becomes more and more ubiquitous and complex, an increasing number of new computer science (CS) courses in web services, big-data analytics, computing security, and machine learning fill up CS students’ schedules, leaving little room for non-CS courses providing skills outside of CS. This paper summarizes our experiences in developing and evolving an MS-level software engineering (MSCS-SE) curriculum that takes I-shaped CS BA graduates and enables them to become sufficiently T-shaped to be able to immediately contribute to overall system definition and development on being hired, and to improve their T-shaped skills along their careers. Section 2 summarizes the primary origins and problems with an I-shaped software workforce. Section 3 describes the origins, development, and evolution of the USC MSCS-Software Engineering program and its foundation-stone, real-client, 2-semester project course. Section 4 elaborates on the team-project course and its mechanisms for strengthening the transition from I-shaped to T-shaped systems thinking. Section 5 provides conclusions.
Single image desmoking using haze image model and human visual system

Visual applications depend on image quality for algorithmic decision-making, and atmospheric conditions such as smoke and haze produce a challenge to artificial systems that rely on identification of people, objects, and obstacles. Smoke is particularly difficult because of its nonhomogeneous characteristics and irregular image coverage. Nighttime images worsen the problem because of low light and artificial light conditions. Our aim was to develop an iterative process that removes smoke from images in daytime and nighttime scenes. The haze image model was used as our baseline model. First, we developed a detection method to find the smoky regions on the image and used the dark channel haze removal process to estimate the transmission map for each color channel. We ran the algorithm iteratively because a one-time process left residual smoke. Blue smoke produced an unbalanced particle density, so the blue color channel had to be corrected more times than red and green channels. Finally, we optimized the image in postprocessing, and the results produced smoke-free images. We believe our algorithm is the first to successfully remove nighttime smoke.
Research Status and Difficulties of Ultra-short-term Prediction of Photovoltaic Power

As photovoltaic power generation capacity continues to increase, the impact of its random and volatility power generation characteristics on the power system cannot be ignored. Studying the ultra-short-term prediction model of photovoltaic power generation can provide strong support for safe and stable operation of power grids and power grid dispatching. In this paper, the research status and difficulties of photovoltaic power ultra-short-term prediction are comprehensively discussed. Firstly, various factors affecting photovoltaic power generation are introduced. Then, the application and technical difficulties of ground-based cloud map and numerical weather prediction in prediction model are summarized. The status quo of power ultra-short-term prediction research, and finally put forward the need for perfect direction of ultra-short-term prediction of photovoltaic power.
Tele-Pathology: A Use Case In Colombia

Colombia is a middle income country with a growing incidence of cancer. There exists an acute shortage of trained pathologists in Colombia, i.e., there are a total of 500 general pathologists for a total population of 44 million people. This lack of pathology expertise has resulted in misdiagnosis of this disease. This misinterpretation can lead patients to undergo unnecessary surgery and other treatments, or to miss out on treatments they do need. There is thus a clear lack of more dedicated cancer expertise and technology to improve and alleviate the disease burden caused by misdiagnosis. In this scenario, tele-pathology, the use of information technology and slide digitization (or digital pathology) for rendering remote diagnosis, may be a solution to improve the access to cancer pathology expertise and address the problem of having concentrated in large cities the cancer pathology expertise. This article presents a tele-pathology project in the context of a developing country and the different adapted solutions to overcome the lack of resources.
Dynamic Adaptive Streaming based on Deep Reinforcement Learning

Abstract Dynamic adaptive streaming over HTTP (DASH) is the dominant technology of multimedia delivery over the Internet. In DASH system, adaptive bitrate (ABR) algorithms running on client-side video player are the key to improve user quality of experience (QoE). However, most existing ABR algorithms employ fixed control rules to make bitrate decisions based on throughput, playback buffer size, or a combination of the two. As a result, their performance in the complicated and fluctuant network environment is incompetent. In this paper, we propose QRL, a bitrate adaptation approach based on deep reinforcement learning. QRL uses double Q-Learning, an enhanced Q-Learning method. After training the neural network model, the algorithm can select proper bitrates for future video segments based on all the information collected by client during the video playback process. Simulation results show that QRL achieves better performance than other algorithms.
Monitoring Leaked Confidential Data

During the first half of 2018 over than 945 data breaches resulted in 4.5 Billion data records been compromised worldwide. Data leak is one of the biggest security issues targeting the industrial and governmental sectors. The data loss hemorrhage is too important and uncontrollable that companies and institutions need to react very quickly to reduce the risk of being targeted by an attack exploiting leaked data. Unfortunately, this in not yet the case, because on average a company spend 196 days to identify a data breach and 69 additional days to contain it. In order to reduce the identifications time, we propose a solution to monitor, in real time, huge streams of leaked data published on hacking sources. These ese data are classified, and confidential information is precisely identified. This classification is per-formed by the combination of inference rules and a Convolutional Neural Network pre-trained model, which recognizes different patterns of confidential data. We also describe our observations from the data that we collected and identified in the context of a company monitoring use case.
Differential Privacy Theory

The problem of learning from data while preserving the privacy of individual observations has a long history and spans over multiple disciplines [, , ]. One way to preserve privacy is to corrupt the learning procedure with noise without destroying the information that we want to extract. Differential Privacy (DP) is one of the most powerful tools in this context [, ].
Research and Improvement of Single Image Super-Resolution Based on Generative Adversarial Network

Abstract This paper proposes a new image super-resolution method based on Generative Adversarial Network (GAN). Firstly, the algorithm model includes generating model and discriminant model, generating model to generate high-resolution image, discriminating model to distinguish the image true or false, the original image is true, and the generated image is false. Using alternate training method, the generated model and discriminant model achieve Nash equilibrium, and finally generate high-quality image. Compared with previous super-resolution method based on generative adversarial network (SRGAN), the following changes have been made: modifying the network structure, removing the unnecessary batch normalization layer in the standard residual block, deepening the network layer number and improving the loss function. The experimental results show that compared with the traditional bicubic interpolation method and compared with SRGAN, the proposed algorithm improves the actual image effect, peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) in varying degrees.
Search Engines and Meta Search Engines Great Search for Knowledge: A Frame Work on Keyword Search for Information Retrieval

In the modern information society knowledge have much higher priority, information is more and more accessible. Information Retrieval (IR) is a highly applied scientific discipline. IR is retrieving high quality pages that are relevant to user's need. IR is concerned with filtering specific information from a set of data where Search Engine (SE) and Meta Search Engine (MSE) play an important role. SE is a web-based tool it searches the information as per the keywords given by the user. MSE sends the search query to multiple search engines at the same time to get the result to the user. The paper explores on IR, IR Algorithms, and Technological Evolution of SE. Further, it discusses about SEs Use Case Diagram, working process, types and its limitations of SEs. It also discuss about MSEs Technological Evolution and working process. Finally, paper presents framework for keyword search Architecture, modules for IR, program output written in java with Graphical User Interface, discussion, conclusion and suggestions for future research.
A Novel Algorithm for Segmentation of Parasites in Thin Blood Smears From Microscopy Using Type II Fuzzy Sets and Inverse Gaussian Gradient

Malaria is the leading health problem globally in the equatorial regions. Even after campaigning and control by the World Health Organization (WHO), malaria is a high mortality-causing infectious disease due to inappropriate and late diagnosis. To prevent getting affected by malaria, the diagnosis should be in the early stage and accurate. This research presents an innovative approach for erythrocyte segmentation and malaria parasite detection from microscopic images. In addition, the author utilizes Einstein t-conorm function to generate new fuzzy membership function which is used to segment the infected regions of the blood cells in microscopic images. Finally, the inverse Gaussian gradient function is used for the final segmentation. This technique is compared with state-of-the-art segmentation techniques such as k-means, fuzzy c-means segmentation, and spatial intuitionistic fuzzy c-means (SIFCM) segmentation approaches. Experimental results are validated qualitatively, and it is deduced that the proposed method is the robust segmentation method for malaria parasite detection.
Adaptive Neuro-Fuzzy Inference System in Agriculture

This chapter emphasizes the use of adaptive fuzzy inference system (ANFIS) in agriculture. An overview of the basic concepts of ANFIS is provided at the beginning, where the underlying architecture of ANFIS is also discussed. The introduction is followed by the second section which highlights the diverse applications of ANFIS in agriculture during recent times. The third section describes how Matlab software can be utilized to build the ANFIS model. The fourth section describes the case study of the application of ANFIS for crop yield prediction. The conclusion follows this case study.
CNN-based Image Denoising for Outdoor Active Stereo

Stereo vision has been the most widely used passive 3D sensing technology for a variety of vision tasks. 3D coordinates are computed by triangulating correspondences found in the stereo image pair. For homogeneous areas where stereo matching fails, a stereo projector system can be employed by actively projecting auxiliary texture onto the scene. However, the applicability of this approach is restricted to indoor scenarios, since in outdoor environment where the sunlight is strong, the projected pattern is almost invisible. A simple increase in contrast of the projection leads to dramatic rise of the noise level, which again has an adverse impact on the matching algorithm. We propose a novel framework to tackle this problem, exploiting adaptive contrast improvement with denoising techniques using convolutional neural networks (CNNs) on the difference images to digitally enhance the projection, which is later added back onto the image pair to assist stereo matching. In order to learn an optimal denoising network dedicated to the projected pattern, a straightforward workflow is devised to allow for convenient acquisition of noisy and noiseless pattern images for the input and ground truth respectively. Extensive evaluation on real-world data compared to the state of the art justifies the effectiveness of not only the presented denoising CNN architecture and training routine, but also the entire pipeline for outdoor active stereo reconstruction.
Applying Universal Design Principles in Emergency Situations

The United Nations Convention on the Rights of Persons with Disabilities (CRPD) obligates States' to take all necessary measures to ensure the protection and safety of persons with disabilities in emergency situations. While these requirements represent one aspect of this article's aims, it also focuses on how another paradigm, universal design, can and should offer a useful approach to emergency situations and management. Referring once more to the CRPD, universal design is defined as the "design of products, environments, programs and services to be usable by all people, to the greatest extent possible, without the need for adaptation or specialized design". Consequently, universal design should provide a valid framework for identifying and removing usability and accessibility barriers in emergency situations. Using a heuristic analysis, this article intends to offer a preliminary reflection on the following question: "To what extent can universal design principles be applied to emergency management situations?".
Towards a Mixed Reality Assistance System for the Inspection After Final Car Assembly

Final manual inspection after vehicle assembly is a task that becomes more and more difficult because of the increasing number of customization options. We investigate how an assistance system based on mixed reality can help with this task. To this end we identify four major components that should help guide the worker and document his findings. We implement each component in three versions with increasing implementation complexity and immersion and investigate which version is most suitable in a user study.
Detecting Unsafe Use of a Four-Legged Walker using IoT and Deep Learning

Four legged walkers are used by many elderly persons to retain mobility. They are also used by patients recovering from leg injuries to facilitate rehabilitation. Unfortunately, these walkers are also associated with many injuries, some of which are caused by incorrect use. In this paper, we describe a walker augmented with IoT sensors which continuously monitors the weight distribution on the legs of the walker. We describe an approach where this data stream is processed by a deep neural network based classifier, which learns to recognize dangerous use patterns that can lead to falls and injury. The classifier is trained by providing examples of unsafe use, thus eliminating the costly engineering necessary to customize the algorithm to the specific user and walker. By alerting the user in real time about unsafe use patterns, the user can learn the correct and safe use of the walker.
A Complexity Metrics Suite for Cascading Style Sheets

We perform a theoretical and empirical analysis of a set of Cascading Style Sheets (CSS) document complexity metrics. The metrics are validated using a practical framework that demonstrates their viability. The theoretical analysis is performed using the Weyuker’s properties−a widely adopted approach to conducting empirical validations of metrics proposals. The empirical analysis is conducted using visual and statistical analysis of distribution of metric values, Cliff’s delta, Chi-square and Liliefors statistical normality tests, and correlation analysis on our own dataset of CSS documents. The results show that five out of the nine metrics (56%) satisfy Weyuker’s properties except for the Number of Attributes Defined per Rule Block (NADRB) metric, which satisfies six out of nine (67%) properties. In addition, the results from the statistical analysis show good statistical distribution characteristics (only the Number of Extended Rule Blocks (NERB) metric exceeds the rule-of-thumb threshold value of the Cliff’s delta). The correlation between the metric values and the size of the CSS documents is insignificant, suggesting that the presented metrics are indeed complexity rather than size metrics. The practical application of the presented CSS complexity metric suite is to assess the risk of CSS documents. The proposed CSS complexity metrics suite allows identification of CSS files that require immediate attention of software maintenance personnel.