Reviewerly/exHarmony huggingface dataset:
AI Frame-Worked Virtual World Application - The Ramification of Virtual World on Real World

This paper contains the detailed information about the increasing technology in field of Virtual Reality (VR). The paper also present ideas for the future application of Virtual Reality and explaining different method to approach it. Virtual Reality until now just created a vision or a prototype that could be viewed in a 360-degree and Three-dimensional structural view, but with our approach towards proposing an idea of combining Virtual Reality and Artificial Intelligence (AI) together. The combination is going to break the limit of virtual world and making the virtual world much likely to the real world, a world with no limits free and full of imagination and creation. The approach to combine these two technologies has a great motto behind it, which will be explained more briefly in the paper.
Statistical Super Resolution for Data Analysis and Visualization of Large Scale Cosmological Simulations

Cosmologists build simulations for the evolution of the universe using different initial parameters. By exploring the datasets from different simulation runs, cosmologists can understand the evolution of our universe and approach its initial conditions. A cosmological simulation nowadays can generate datasets on the order of petabytes. Moving datasets from the supercomputers to post data analysis machines is infeasible. We propose a novel approach called statistical super-resolution to tackle the big data problem for cosmological data analysis and visualization. It uses datasets from a few simulation runs to create a prior knowledge, which captures the relation between low-and high-resolution data. We apply in situ statistical down-sampling to datasets generated from simulation runs to minimize the requirements of I/O bandwidth and storage. High-resolution datasets are reconstructed from the statistical down-sampled data by using the prior knowledge for scientists to perform advanced data analysis and render high-quality visualizations.
The Future of Cryptotokens

In this chapter, the authors study how cryptotoken issuance, also referred to as initial coin offerings and security token offerings, may disrupt funding markets such as venture capital, crowdfunding, and private equity. The authors discuss the necessary infrastructure to support this new asset class. The authors analyze the market evolution in terms of volatility, global reach, news events, and types of industries that are issuing or considering to issue tokens. The authors discuss some specific token offerings to highlight lessons learned. the authors summarize the regulatory landscape and challenges going forward. This market crashed in terms of market capitalization at the end of 2018. However, this new asset class along with the underlying technology holds great promise to disrupt various types of intermediaries if adequate financial and regulatory infrastructures are developed.
End-to-End Panoptic Segmentation with Pixel-Level Non-Overlapping Embedding

Recent panoptic segmentation even instance segmentation methods usually rely on the region-based method or highly-specialized combination with heuristics module, followed by post-processing techniques. While most of the recent methods neglect low-fill rate linear objects and cannot recognize pixels located in bounding box margins. We propose a branched, end-to-end trainable multi-task architecture focusing on pixel-level grouping problems for panoptic segmentation. The embedding branch regress pixels into an embedding space, so that pixels from the same group are at close range while those from different groups have a specified margin. Every pixel can be considered in an image without overlapping. And semantic branch produces best seed scores with labels as clustering center. The further-embedding branch disentangles each pixel in pixel embedding space. Thus, we are able to segment both thing and stuff classes, and explain all the pixels in the image. We obtain state-of-the-art results on Pascal VOC2012 and Cityscapes.
Probability-Based Online Algorithm for Switch Operation of Energy Efficient Data Center

The huge amount of energy consumed by the data centers around the world every year motivates the cloud service providers to operate data centers in a more energy efficient way. A promising solution is to turn off the idle servers, which, however, may be turned on later, incurring a significant startup cost. The problem turns to dynamically provisioning the workload, and cutting down the energy cost which includes the power to support the running of data center and the startup cost. Different from previous studies that usually consider the worst-case performance guarantee when designing online algorithms, this paper considers the average case which is more practical. We propose a simple online algorithm based on the expectation of job interval of workload, which is proven to be optimal for exponential and uniform distributions and achieves tight competitive ratio <inline-formula><tex-math notation="LaTeX">$\frac{e}{e-1}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\frac{4}{3}$</tex-math></inline-formula> , respectively, for them. Simulations using the synthetic data verify our theoretical analysis. Numerical results employing Google’s data center workload trace demonstrate that the proposed algorithm outperforms the worst case-based algorithm in terms of operation cost reduction.
HiperViz

This showcase presents HiperViz, an interactive visual interface for real-time monitoring the health status of high-performance computing systems. The objective of our visualization system is threefold: 1) to provide a visual interface for tracking CPU temperatures in large data centers, 2) to detect abnormal events and their temporal and spatial correlations, and 3) to assist system administrators in troubleshooting and maintenance. The visualization showcase is available at https://idatavisualizationlab.github.io/ HPCC/HiperView/demo.html.
DeepMammo: Deep Transfer Learning for Lesion Classification of Mammographic Images

Recently, impressive results have been provided by pre-trained convolutional neural networks combined with the transfer learning technique. They have quickly become a great option to classify general image datasets. However, to the best of our knowledge, the majority of works do not explore if these pre-trained architectures are well-suited to specific contexts like the medical image domain (e.g. breast lesions). We focus on breast lesions, because it is one of the most common types of cancer affecting women worldwide, and its early diagnosis is crucial to the success of the treatment. In this paper, we propose a methodology capable of analyzing different approaches, regarding description (e.g through handcrafted and deep features) and classification (e.g. through end-to-end networks and traditional classifiers) to automatically answer the best tuning according to a given mammographic image dataset. Our methodology can also apply a data augmentation method to improve the learning of the networks. It increases the number of training samples to cope with unbalancing and overfitting problems that are intrinsic to the breast lesion classification task. We validate our methodology on public image datasets and our results show classification accuracies of up to 94.34%.
Research on the Influence of Crossover Probability and Mutation Probability in GA-SVM Model

SVM(Support vector machine), a common seen algorithm in machine learning, is well known for its good performance in classification. However, support vector machine is quite sensitive to the value of kernel function parameters. As the times require, some methods employed for the selection of heuristic parameter emerge, and they include particle swarm optimization, genetic algorithm, et al. These methods are mainly applied to the selection of SVM kernel parameters and thus promote the good performance of the SVM model. The process of GA (genetic algorithm) is composed of selection, crossover, and mutation. The probability of crossover and mutation is usually fixed, and this model with fixed probability fails to adapt to the changes within the population. Therefore, a proper probability model is put forward to optimize this kind of problem through the employment of comparative study. By using the new probability model, the performance of SVM classification optimized by genetic algorithm is improved, and the parameters of precision rate, recall rate and F1 value of the new model are improved as well to some extent.
Substantiation of Software Defect Prediction using Statistical Learning: An Empirical Study

Defect Prediction plays a very important role in the development of the software. The defect prediction leads to a more focused testing thereby reducing the testing cost and efforts. In this paper an empirical study is done to investigate the predictive performance of the statistical model for defect prediction. Three open source software projects are taken for the experiment. The Select K Best algorithm is used for feature selection. Random Forest, Gradient Boosting, K Nearest Neighbors, Decision Tree and Naïve Bayes are used as the statistical models. The data models are analyzed using various performance measures. The findings of this study infer that Select-K-Best with chi-square score function with the K Nearest Neighbors gives the optimal performance for software defect prediction.
Solving Symmetric and Asymmetric Traveling Salesman Problems Through Probe Machine with Local Search

Combinatorial optimization problems emerge extensively in the different fields and traveling salesman problem (TSP) is certainly one of the most representative and hardest (i.e., NPC) combinatorial optimization problems. In this paper, we propose a dynamical route construction algorithm to solve both symmetric and asymmetric TSP's, which uses probe concept to generate the better routes step by step. Specifically, a reasonable value of filtering proportion is set in each step such that the worst routes are filtered out. On the other hand, a set of local search operators are further implemented on the retained routes for their variations and improvements. Actually, our proposed algorithm is tested on various TSP instances taken from TSPLIB and compared with the best-known results reported by the data library as well as the other four recent state-of-the-art algorithms. It is demonstrated by the experimental results that our proposed algorithm can achieve the best results in certain cases and generally get the results close to the best-known results within a reasonable period of time. In addition, the proposed algorithm outperforms in some cases and obtains superior results than the best-known results, and even performs better than the recent state-of-the-art algorithms.
Deep Learning Approach in Predicting Personal Traits Based on the Way User Type on Touchscreen

The aim of this paper is to explore the possibility of revealing two common personal traits: age group (<18/18+) and gender (male/female) by measuring and analyzing the way of typing a simple daily-used text on the touchscreen. Deep learning method has been used to develop the model and LOUOCV (Leave-one-user-out cross-validation) has been used to evaluate the effectiveness of our model on the dataset created by 92 volunteers through a web application. Our method outperforms the entire model developed so far as per our knowledge. Accuracy more than 98% in identifying age group and more than 88% in identifying gender have been observed. Automatic age group and gender recognition could be used in a large number of possible application areas such as human-computer interaction, digital forensics, age-specific access mechanism, and targeted advertisement. The way user type on touchscreen concerns with the different issues in user identity verification has been studied well in literature but deep learning approach in revealing the age group and gender based on the typing pattern particularly on a touchscreen is an original idea. Analysis of data, results, and inferences here give a primary account toward achievability to the goal with ample scope for further work in this domain.
Multi-agent Model of Semantics of Simple Extended Sentences Describing Static Scenes

This study developed the basic principles and algorithms for presenting the semantics of simple extended sentences describing static scenes based on multi-agent cognitive architectures. It is shown that software agents can represent word patterns and concepts corresponding to these words. And such multi-agent algorithms can form the basis for modeling the semantics of relations between concepts, representing various semantic classes in the composition of the sentence.
Micro-Kernel OS Architecture and its Ecosystem Construction for Ubiquitous Electric Power IoT

The operating system is extremely important for both "Made in China 2025" and ubiquitous electric power Internet of Things. By investigating of five key requirements for ubiquitous electric power Internet of Things at the OS level (performance, ecosystem, information security, functional security, developer framework), this paper introduces the intelligent NARI microkernel Operating System and its innovative schemes. It is implemented with microkernel architecture based on the trusted computing. Some technologies such as process based fine-grained real-time scheduling algorithm, sigma0 efficient message channel and service process binding in multicore are applied to improve system performance. For better ecological expansion, POSIX standard API is compatible, Linux container, embedded virtualization and intelligent interconnection technology are supported. Native process sandbox and mimicry defense are considered for security mechanism design. Multi-level exception handling and multidimensional partition isolation are adopted to provide High Reliability. Theorem-assisted proof tools based on Isabelle/HOL is used to verify the design and implementation of NARI microkernel OS. Developer framework including tools, kit and specification is discussed when developing both system software and user software on this IoT OS.
Emerging Self-Integration through Coordination of Autonomous Adaptive Systems

The self-improving system integration initiative emerged as response to the ever growing complexity in large-scale open constellations of systems. Especially in the context of a set of self-adaptive systems working together in an overall system-of-systems, integration is a very complex task due to the limited predictability of system behaviour. In this position paper, we argue that one approach to tackle the problem can be found in an adaptive coordination of autonomous adaptive systems. Therefore, we characterise existing coordination approaches and identify four research challenges that need to be addressed. We use the scenario of platooning of autonomous vehicles to demonstrate the resulting challenges.
Exploiting Trademark Databases for Robotic Object Fetching

Service robots require the ability to recognize various household objects in order to carry out certain tasks, such as fetching an object for a person. Manually collecting information on all the objects a robot may encounter in a household is tedious and time-consuming; therefore this paper proposes the use of large-scale data from existing trademark databases. These databases contain logo images and a description of the goods and services the logo was registered under. For example, Pepsi is registered under soft drinks. We extend domain randomization in order to generate synthetic data to train a convolutional neural network logo detector, which outperformed previous logo detectors trained on synthetic data. We also provide a practical implementation for object fetching on a robot, which uses a Kinect and the logo detector to identify the object the human user requested. Tests on this robot indicate promising results, despite not using any real world photos for training.
Deep Learning-based Facial Detection and Recognition in Still Images for Digital Forensics

Smartphones and cheap storage have contributed to a deluge of digital photos. Digital forensic analysis often include the need to process large volumes of digital photos found on devices. Sometimes, this is done either to detect or confirm the ownership of the device or to determine whether the owner of the device has some acquaintance of interest in the case. In this paper, we present the Face Detection and Recognition in Images (FDRI) open source software, and its integration as a module for the digital forensic software Autopsy. FDRI aims to semi-automate the detection of faces in digital photos, flagging photos where at least one face is detected. FDRI software also performs face recognition, searching for the existence of given individual(s) in still photos of the forensically examined devices. For both the detection and recognition of faces, FDRI resorts to deep learning-based algorithms available within the dlib machine learning toolkit. In experimental assessments, FDRI yielded an average precision of 99.46% face detection and 98.10% for face recognition, when dealing with the restrained LFW dataset. For unrestrained real world photos, FDRI achieved a precision of 97.67% for face detection and 81.82% for face recognition. Performance-wise, this study confirms the importance of a fast GPU for fast face detection and recognition, with an NVidia GTX 1070 being roughly three times faster than a GTX 750 Ti, and in certain cases, up to 35× faster than the CPU version.
Solution supporting the communication of the built heritage: Semi-automatic production path to transfer semantic LIDAR data to minecraft environment

The use of world-simulation videogames for cultural heritage (CH) communication presents one of the greatest opportunities for engaging people with the safeguarding of cultural resources. Minecraft has been identified as an optimal solution to represent the cultural built environment. However, one of the Minecraft’s main limitation in terms of its use as a CH communication tool is the lack of efficient/automatic solutions to transform large-scale as-built data in the Minecraft environment. In this paper, we propose a semi-automatic production path to transfer sematic LIDAR data of heritage sites into Minecraft 3D format. This solution takes into consideration the built environment characteristics, overcoming the limitation of voxelization exercise by interpreting and refining the semantic properties of heritage scenarios. The path is demonstrated using LIDAR data of the Historical Citadel of Hamina, in Finland.
Improving Person Re-identification by Background Subtraction Using Two-Stream Convolutional Networks

The field of person re-identification is facing problems related to the variation of illumination and background scenes. In order to reduce the impact of those variations, we propose in this work a two-stream re-identification system based on a siamese network (S-CNN). The proposed system takes as input a pair of person images: the original image and the image without background. In the background subtraction step, a segmentation network (SEG-CNN) is used to detect the person body part and capture a complementary information. We experimentally prove that the combination of the two streams (images with and without background) improves the recognition rates. In the rank-1, the improvement is respectively of $$2\%$$ and $$4\%$$ for Market-1501 and DukeMTMC-reID datasets.
Towards Community and Expert Detection in Open Source Global Development

Finding experts in certain technologies or modules inside a project on global software development can be a challenge. There is a large pool of developers, with distinct knowledge, availability and goals. Furthermore, work times, dedication and cooperation expertise impact directly willingness and capacity to collaborate. To address this scenario, we propose a collaborative network to find experts within projects that can be helpful to community members. We adopt a clustering algorithm in order to find experts not only with specific knowledge, but also expertise in collaboration on the topics required. We choose a popular open source project from GitHub to evaluate our network and the cluster detection. We present evidence that the detected clusters and core individuals reflect real life communities and influential developers.
Embedding Non-Local Mean in Squeeze-and-Excitation Network for Single Image Deraining

Images captured in rainy outdoor usually have poor visual quality due to the appearance of raindrops blur or rain streaks in the image. For many practical vision systems, such as autonomous driving and video surveillance, this problem is urgently required to be solved. In this work, a novel network for single image de-raining has been proposed. The proposed network consists of three stages, encoder stage, Dense Non-Local Residual Block (DNLRB) stage, and decoder stage. To better capture spatial contextual information, which has been analyzed to be meaningful for image de-raining, we adopt squeeze-and-excitation enhancing on feature maps in each convolution layer. We also embed non-local mean operations in DNLRB, which effectively leverages spatial contextual information for extracting rain components. Quantitative and qualitative experimental results demonstrate the superiority of the proposed method compared with the state-of-the-art de-raining methods.
Yawn

Idle-state governors partially turn off idle CPUs, allowing them to go to states known as idle-states to save power. Exiting from these idle-sates, however, imposes delays on the execution of tasks and aggravates tail latency. Menu, the default idle-state governor of Linux, predicts periods of idleness based on the historical data and the disk I/O information to choose proper idle-sates. Our experiments show that Menu can save power, but at the cost of sacrificing tail latency, making Menu an inappropriate governor for data centers that host latency-sensitive applications. In this paper, we present the initial design of Yawn, an idle-state governor that aims to mitigate tail latency without sacrificing power. Yawn leverages online machine learning techniques to predict the idle periods based on information gathered from all parameters affecting idleness, including network I/O, resulting in more accurate predictions, which in turn leads to reduced response times. Preliminary benchmarking results demonstrate that Yawn reduces the 99th latency percentile of Memcached requests by up to 40%.
ERATO: Trading Noisy Aggregate Statistics over Private Correlated Data

With the commoditization of personal privacy, pricing private data has become an intriguing problem. In this paper, we study noisy aggregate statistics trading from the perspective of a data broker in data markets. We thus propose ERATO, which enables aggrEgate statistics pRicing over privATe cOrrelated data. On one hand, ERATO guarantees arbitrage freeness against cunning data consumers. On the other hand, ERATO compensates data owners for their privacy losses using both bottom-up and top-down designs. We further apply ERATO to three practical aggregate statistics, namely weighted sum, probability distribution fitting, and degree distribution, and extensively evaluate their performances on MovieLens dataset, 2009 RECS dataset, and two SNAP large social network datasets, respectively. Our analysis and evaluation results reveal that ERATO well balances utility and privacy, achieves arbitrage freeness, and compensates data owners more fairly than differential privacy based approaches.