Reviewerly/exHarmony huggingface dataset:
Assessment of effectiveness of data dependent activation method: MyAct

One of the momentous transformation performed by an artificial neural network (ANN), Support Vector Machine (SVM), Radial basis Function (RBF) and many other machine learning method is the application of activation function. MyAct the proposed activation method is used here with various ANN archite ctures for link prediction, classification and general prediction. Statistical properties of data used here to prove the effectiveness of proposed activation function MyAct over other popular activation methods. A data dependent transfer method is developed, which is pioneer in its own way. This proves to be an unified formulation for the robust and generalised learning for the classification, link prediction and regression problem types. Classification is done with Iris dataset using ANN with different activation method and results are compared. Improved results are achieved when MyAct used with Tailored Deep Feed Forward Artificial Neural Network (TDFFANN), simple Artificial Neural Network and Deep Artificial Neural Network. Aim here is to develop a novel activation method which work with positive data, negative data, small size data, big size data, skewed data or corrupt data. An attempt is made to cover complete versatile behaviour of data. Currently not a single activation method can work well on all above mentioned data. Results obtained using MyAct on the datasets used here proves it to be a good choice in comparison to logsig, tansig and other popular activation methods for classification and link prediction. Satisfactory improvement is achieved by using data length as well as negative range values in the prediction done by proposed method. MyAct had 22% better standard deviation than ReLU (Rectified Linear unit) and 36. 28% better standard deviation than ELU (Exponential linear unit). MyAct has 2. 6% better accuracy in regression error than Swiss method and 2. 5% better accuracy in regression error than ELU. Other results are discussed in the paper.
Assessing the variability of expert estimates in the USGS Three-part Undiscovered Mineral Resource Assessment methodology: A call for increased skill diversity and scenario-based training

Future mineral exploration will necessarily be conducted in increasingly challenging and uncertain search spaces as near-surface, high-quality ore deposits are progressively depleted. Faced with this increase in task complexity, an important consideration from an exploration management perspective is the behavioural aspect of information interpretation and decision-making. One such challenging search space is the Sandstone Greenstone Belt, Western Australia, covering an area of approximately 920 km2, that is deemed prospective for the discovery of archean orogenic gold deposits, with mined (historic production), inferred and indicated resources (JORC 2004 and 2012) totalling 54 t Au. Gold endowment estimates made by geoscience experts, during an exploration project evaluation workshop, were compared with estimates from a group of non-geoscientists, made during a separate but identical workshop. Significant differences were identified between the estimates of the expert geoscientists and the non-geoscience expert group, with the latter proving more conservative. However, a portion of the geoscience experts (N = 11) group produced conservative estimates, comparable to a non-geoscience expert group (N = 10), with both suggesting the existence of additional gold deposits of similar size and quality to known resources (with group estimates for median total endowment of 99 t and 120 t Au, respectively). The remaining geoscience experts (N = 11) presented significantly more optimistic, albeit inconsistent, estimates for the gold endowment of the project area, predicting the existence of undiscovered deposits significantly larger than those already defined in the belt (with a group estimate for median total endowment of 350 t Au). Although the true undiscovered gold endowment within the project area remains uncertain, several possible factors can explain the variations in estimates. These include the application of contrasting strategies, with participants opting to apply more empirical or conceptual methods, and to differences in background experience, resulting in distinct skillsets and varying ability to estimate uncertainty. To improve the quality of expert estimates, it is suggested that individual expertise and appropriate assessment strategies can be developed through scenario-based training courses, and that greater skill and experience diversity within exploration teams is desirable, leading to more balanced aggregate estimates. Further research is warranted to determine which, if any, of the proposed factors account for these disparities. This research could be used to adapt the composition of exploration teams and develop training programs to promote the development of expertise in predictive exploration targeting, in order to promote discovery of future mineral resources.
Optimization of Deployment Architecture in SOA systems

Service-Oriented Architecture (SOA) has been a popular foundation in corporate architecture management for years. Numerous forms of integration following this approach are developing to such an extent that they automate entire business processes in the form of service call sequences. The level of complexity of integration is growing and the popularity of methods aimed at improving the efficiency of SOA systems is increasing. This article proposes a new method for improving the efficiency of SOA systems. It is organized in the following manner. The first chapter introduces the most important concepts related to SOA. The second presents results of the literature analysis. The third describes the optimization problem and the fourth shows a potential solution. Finally, some studies of the described method and a summary of thereof are presented.
Device to Device Collaboration Architecture for Real- Time Identification of User and Abnormal Activities in Home

Activities of Daily Living (ADL) are indicators for evaluating individual health, ability of independence and daily living, and degenerative brain disease of old people. Therefore, many researches are actively underway to measure user's ADL data by constructing Internet of Things (IoT) based smart home. However, general smart home solutions for measuring user's ADL only focus on collecting user's activity data, appliance usage and home environment data. Such simple ADL data cannot be used as an indicator for early recognition of the above-mentioned symptoms of the elderly people. Intuitively speaking, the ADL data we want to collect should be to know who the user is, and whether the device has been successfully used or misused. In this paper, we propose device-to-device collaboration architecture to identify the user, device to use, and success or failure of the device usage in real-time. By designing and implementing the proposed architecture, we can record the ADL data on the user's wearable device without any user intervention. In addition, as another advantage of the proposed concept, it is possible to easily check and record the physical moving ability of the user between two fixed spaces. The collected ADL and abnormal behavior may help a user or guardian to determine the user's dementia symptoms, activeness and daily living skills.
Newsgroup topic extraction using term-cluster weighting and Pillar K-Means clustering

Topic extraction is an essential tool to help gathering information from a vast amount of sources. This paper introduces a new approach to extract topics from a collection of text documents. In order to obtain the topics, preprocessing steps are conducted to remove unnecessary parts of the documents. Then, a term frequency-inverse document frequency is built to weight terms in documents. After that, SVD-based feature transformation is involved in building features used for clustering. Prior to clustering process, the Pillar algorithm is run to select initial centroids for K-Means clustering. Finally, weights of terms in clusters are calculated using term-cluster weight as a basis to choose topics from clusters. Based on the experimental result, it is concluded that the framework achieves satisfactory results by attaining the accuracy of 100%, 95.1%, 83,7%, and 68.7% for 4 topics obtained from Binary2, Multi5, Multi7, and Multi10 categories of 20Newsgroup dataset.
PARS: Peers-aware Recommender System

The presence or absence of one item in a recommendation list will affect the demand for other items because customers are often willing to switch to other items if their most preferred items are not available. The cross-item influence, called “peers effect”, has been largely ignored in the literature. In this paper, we develop a peers-aware recommender system, named PARS. We apply a ranking-based choice model to capture the cross-item influence and solve the resultant MaxMin problem with a decomposition algorithm. The MaxMin model solves for the recommendation decision in the meanwhile of estimating users’ preferences towards the items, which yields high-quality recommendations robust to input data variation. Experimental results illustrate that PARS outperforms a few frequently used methods in practice. An online evaluation with a flash sales scenario at Taobao also shows that PARS delivers significant improvements in terms of both conversion rates and user value.
Medical Image Watermarking in Four Levels Decomposition of DWT Using Multiple Wavelets in IoT Emergence

Medical images will be an inseparable part for evaluating medical conditions of a person in real-time. This process will become efficient by exploiting the characteristics that are offered by IoT in the healthcare sectoral issues. This will allow more efficiency in the processing of a medical image. However, the medical images are exposed to the major risks through frequent attacks which may lead up to misinformation the physician in the diagnosis of the disease. Piracy eradication remains the major challenge in the present-day world in IoT platform. Subsequently, the medical watermarked image is significant technique of ensuring the clinical information that exists in the medical images. In this regard, this paper labors on a medical image based on digital watermarking can be utilized to protect health sign embedding in a medical image within an invisible status. The proposed method performance is evaluated by utilizing MSE, PSNR, SSIM, and NC, which are necessary to get the best result for performance metrics. This work is achieved in four levels Discrete Wavelet Transform (DWT). Each level is utilized different wavelet family. These wavelets family are composed of biorthogonal wavelet, reverse biorthogonal wavelet, discrete meyer wavelet, symlet wavelet, and coiflets wavelet transform. The proposed technique is highly robust against numerous sorts of attacks. The results refer that this proposed algorithm permits prevention at a higher level compared with other current structures and algorithms.
ICE-GAN: Identity-aware and Capsule-Enhanced GAN for Micro-Expression Recognition and Synthesis.

Micro-expressions can reflect peoples true feelings and motives, which attracts an increasing number of researchers into the studies of automatic facial micro-expression recognition (MER). The detection window of micro-expressions is too short in duration to be perceived by human eye, while their subtle face muscle movements also make MER a challenging task for pattern recognition. To this end, we propose a novel Identity-aware and Capsule-Enhanced Generative Adversarial Network (ICE-GAN), which is adversarially completed with the micro-expression synthesis (MES) task, where synthetic faces with controllable micro-expressions can be produced by the generator with distinguishable identity information to improve the MER performance. Meanwhile, the capsule-enhanced discriminator is optimized to simultaneously detect the authenticity and micro-expression class labels. Our ICE-GAN was evaluated on the 2nd Micro-Expression Grand Challenge (MEGC2019) and outperformed the winner by a significant margin (7%). To the best of our knowledge, we are the first work generating identity-preserving faces with different micro-expressions based on micro-expression datasets only.
A Swarm based Binary Decision Diagram (BDD) Reordering Optimizer for Reversible Circuit Synthesis

Reversible logic has been considered as a promising direction in the field of low power design and quantum computing. However, synthesizing reversible circuits is totally different from that of irreversible circuits. Therefore, several algorithms have been devoted to optimize the synthesis process of reversible circuits in terms of the Quantum Cost (QC). Binary Decision Diagram (BDD) based synthesis has been found as one of the dominant algorithms in reversible logic synthesis, due to its scalability in synthesizing large-scale circuits within a reasonable synthesis time that is bounded by the BDD size. However, the QC of the synthesized circuit is strongly correlated with the used BDD size in the synthesis process, wherein, each BDD node is substituted by a cascade or reversible gates. As a result, exploiting effective BDD reordering algorithms combined with BDD reduction rules turns out into less QC. In this paper, we propose a BDD reordering methodology wherein recently published swarm based optimization algorithms are integrated and compared in terms of the resultant BDD size. Experimental results show that Moth-Flame optimization (MFO) algorithm outperforms other algorithms on the public benchmarks.
Recognition of Film Type Using HSV Features on Deep-Learning Neural Networks

The number of films is numerous and the film contents are complex over the Internet and multimedia sources. It is time consuming for a viewer to select a favorite film. This paper presents an automatic recognition system of film types. Initially, a film is firstly sampled as frame sequences. The color space, including hue, saturation, and brightness value (HSV), is analyzed for each sampled frame by computing the deviation and mean of HSV for each film. These features are utilized as inputs to a deep-learning neural network (DNN) for the recognition of film types. One hundred films are utilized to train and validate the model parameters of DNN. In the testing phase, a film is recognized as one of the five categories, including action, comedy, horror thriller, romance, and science fiction, by the trained DNN. The experimental results reveal that the film types can be effectively recognized by the proposed approach, enabling the viewer to select an interesting film accurately and quickly.
Classification of Medical Data using Character-level CNN

It is necessary to handle an enormous amount of electronic data in medical practice. Therefore, a support system using information technology is desired to analyze the data. In this paper, we propose a preliminary system using character-level convolutional neural networks in order to improve the classification performance of text data on medical practice. The effectiveness of the proposed system is confirmed by the testbed called the NTCIR-13 MedWeb task [15].
Content and Colour Distillation for Learning Image Translations with the Spatial Profile Loss

Generative adversarial networks has emerged as a defacto standard for image translation problems. To successfully drive such models, one has to rely on additional networks e.g., discriminators and/or perceptual networks. Training these networks with pixel based losses alone are generally not sufficient to learn the target distribution. In this paper, we propose a novel method of computing the loss directly between the source and target images that enable proper distillation of shape/content and colour/style. We show that this is useful in typical image-to-image translations allowing us to successfully drive the generator without relying on additional networks. We demonstrate this on many difficult image translation problems such as image-to-image domain mapping, single image super-resolution and photo realistic makeup transfer. Our extensive evaluation shows the effectiveness of the proposed formulation and its ability to synthesize realistic images. [Code release: this https URL]
Features of the analysis of social sciences as an information process in a digital environment

The problems of the need to modify the methodology of science studies in connection with the digitalization of science, in particular the Sciences of the socio-humanitarian cycle, are discussed. At the same time, for many social Sciences and Humanities, the digital environment, including big data, has become in recent years not only a medium for communication and dissemination of results, but also an independent source and object of scientific research. The results of monitoring information resources of social and humanitarian sciences in general, as well as for linguistics and science of science in particular, are presented. Is a brief overview of the methods of scientometrics. The use of blockchain technology to solve many problems of organizing science as an information process is discussed.
2018-2019, Bibframe en producción

The main advances of the Bibframe project throughout the years 2018 and 2019 are described, specially the activities that show the model in production. A review is made of the status of the data model and the main international projects related to the conversion from MARC records; the tools for cataloging on Bibframe, both institutional and multi-institutional; relations between Bibframe and RDA; and feedback of the model from the results analysis.
AutoSys: The Design and Operation of Learning-Augmented Systems.

Although machine learning (ML) and deep learning (DL) provide new possibilities into optimizing system design and performance, taking advantage of this paradigm shift requires more than implementing existing ML/DL algorithms. This paper reports our years of experience in designing and operating several production learning-augmented systems at Microsoft. AutoSys is a framework that unifies the development process, and it addresses common design considerations including ad-hoc and nondeterministic jobs, learning-induced system failures, and programming extensibility. Furthermore, this paper demonstrates the benefits of adopting AutoSys with measurements from one production system, Web Search. Finally, we share long-term lessons stemmed from unforeseen implications that have surfaced over the years of operating learning-augmented systems.
Applying Conventional Filtering and Picking Approaches to DAS Microseismic Data

Summary Distributed Acoustic Sensing (DAS) is a recently established technology that has considerable potential for monitoring subsurface microseismic activity. Increasing numbers of wells have fibre optic cable installations and with a DAS interrogator these cables can be converted into high-density high-coverage seismic arrays. However, DAS systems can have higher noise floors than that of geophone arrays which make these dense datasets technically difficult to handle and a challenge to accurately pick P- and S-phase arrivals from. Traditional manual approaches are not viable on such sizeable data volumes, and hence automated solutions must be developed for efficient analysis. Here we test how conventional filtering and picking methods can be combined and applied to a DAS microseismic dataset. These approaches are tested on a single hydraulic fracture stage recorded by Silixa Ltd. iDAS system. 302 events were automatically detected and windowed. We manually pick the events to approximate a velocity model and then locate the events. We then test filtering (median, Wiener, bandpass, F-K) and picking methodologies including standard STA/LTA and a guided STA/LTA. The combined approaches that we have developed have produced equivalent results to manual picking but are significantly faster while analysing much higher density of traces.
Water-based methods

This chapter explores the watery and water-based method of (self) interviews at sea. Only a surfer knows the feeling, so the saying goes. In this chapter I document the process of designing and conducting an embodied, immersive (self) interviewing method in an attempt to obtain responses as close to the moment of experience as possible, to better understand this feeling and the motivations it creates. Involving a camera and a question sheet attached to a surfboard, this method draws from work on mobile methodologies and sensory ethnography utilising technology to generate audio and visual data from the perspective of the surfer. While the questions probe participants on specific topics, the place of the sea is used as an active prompt, providing insight into the movements and interactions of surfers as well as a chance for them to articulate thoughts and feelings in that moment. Through engagement in these watery encounters, this method offers a novel contribution to our understanding of human–water relations and offers future approaches for studying everyday relationships with the sea.
Block Rearrangements and TSVs for a Standard Cell 3D IC Placement

Cloud computing is surroundings for imparting the information and resources which might be brought as the service to an end users over Internet on call for. Cloud allowed the users to get way into their stored information from any environmental places at any time. Cloud comprised of the key problems like safety, data confidentiality, network dependency and centralization. When storing the client sensitive information into cloud data storage, security plays an essential part. Providing the security to sensitive information is a key issue in cloud computing. In existing works, numerous methods were introduced for securely storing data into the cloud. But, the security level was not improved, and data accessing time was not reduced. Our research work concentrated on the cryptographic and data structure techniques for solving the existing problems during cloud storage and data access.
An Efficient Shot Boundary Detection Using Data-cube Searching Technique

Background:: In the internet era, there is a prime need to access and manage the huge volume of multimedia data in an effective manner. Shot is a sequence of frames captured by a single camera in an uninterrupted space and time. Shot detection is suitable for various applications such that video browsing, video indexing, content based video retrieval and video summarization. Objective:: To detect the shot transitions in the video within a short duration. It compares the visual features of frames like correlation, histogram and texture features only in the candidate region frames instead of comparing the full frames in the video file. Methods: This paper analyses candidate frames by searching the values of frame features which matches with the abrupt detector followed by the correct cut transition frame with in the datacube recursively until it detects the correct transition frame. If they are matched with the gradual detector, then it will give the gradual transition ranges, otherwise the algorithm will compare the frames within the next datacube to detect shot transition. Results:: The total average detection rates of all transitions computed in the proposed Data-cube Search Based Shot Boundary Detection technique are 92.06 for precision, 96.92 for recall and 93.94 for f1 measure and the maximum accurate detection rate. Conclusion:: Proposed method for shot transitions uses correlation value for searching procedure with less computation time than the existing methods which compares every single frame and uses multi features such as color, edge, motion and texture features in wavelet domain.