Reviewerly/exHarmony huggingface dataset:
A 2D hyperspectral library of mineral reflectance, from 900 to 2500 nm

Mineral identification using machine learning requires a significant amount of training data. We built a library of 2D hyperspectral images of minerals. The library contains reflectance images of 130 samples, of 76 distinct minerals, with more than 3.9 million data points. In order to produce this dataset, various well-characterized mineral samples were scanned, using a SPECIM Short Wave Infrared (SWIR) camera, which captures wavelengths from 900 to 2500 nm. Minerals were selected to represent all the mineral classes and the most common mineral occurrences. For each sample, the following data are provided: (a) At least one hyperspectral image of the sample, consisting of 256 wavelengths between 900 and 2500 nm. The raw data, the high dynamic range (HDR) image, and the masked HDR image are provided for each scan (each of them in HDF5 format). (b) A text file describing the sample, providing supplementary information for the subsequent analysis (c) RGB images (JPEG files) and automated 3D reconstructions (Stanford Triangle PLY files). These data help the user to visualize and understand specific sample characteristics. 2D hyperspectral images were produced for each mineral, which consist of many different spectra with high diversity. The scans feature similar spectra than the ones in other available spectral libraries. An artificial neural network was trained to demonstrate the high quality of the dataset. This spectral library is mainly aimed at training machine learning algorithms, such as neural networks, but can be also used as validation data for other types of classification algorithms.
Blockchain Technology used in Taxation

Assuring the gathering of incomes is essential for the working of governments, as it is their essential, if not their sole wellspring of pay. Various proposed use instances of blockchain in tax collection can possibly trigger an upheaval in the manner in which governments gather and spend their monies. As much as individuals may abhor paying them, charges are important to empower the customary working of countries and economies. Despite the fact that administrations don't work revenue driven like organizations, they do expect to subsidize to take care of expenses for the administrations they give to general society. Be that as it may, most governments neglect to recoup a noteworthy part of the assessments owed to them and work under money related obligation and shortfall, which, in the long haul may prompt financial stagnation and a decrease in the nature of open administrations. In this way, recuperating charges is the best money related need for governments. To recoup these expenses, some of the time, government organizations end up going to broad lengths, prompting extra costs, making the whole procedure loaded with wasteful aspects. Utilizing blockchain in tax collection can make the procedure progressively productive and powerful, and guarantee that administration offices get paid what they're expected.
A modified crow search algorithm with niching technique for numerical optimization

This paper proposes a modified crow search algorithm with local search and niching technique. The primitive crow search algorithm is a newly developed population-based algorithm which gained attention from the researchers of many fields as it needs only one parameter to be tuned. Despite its easy implementation, crow search algorithm has weakness to find global optima and suffers from slow convergence rate in multi-modal optimization problems. The search agent of the primitive crow search algorithm does not always follow the best solution obtained so far. Another disadvantage is that the search agents updates its location randomly. In order to enhance its searching capacity, a global search operator is introduced. Also, The proposed method modifies the search techniques and incorporates niching method to increase exploration capacity. The proposed technique is tested on 23 benchmark functions. The results of the proposed method demonstrated faster convergence rate and better solution in most cases when compared with the standard crow search algorithm.
QoS Uncertainty Handling for an Efficient Web Service Selection

Web service selection is one of the core tasks of the service-oriented computing paradigm. The QoS variations of services constitute a major obstacle in computing value-added compositions that fulfill the users' requirements. In this paper, we propose a range voting approach that selects the best compositions for a given user's request in a limited time. The key idea consists of choosing the median interval that represents the QoS attributes of each service; after that, the representative intervals are compared to select the best services. The conducted experiments show that the proposed approach is able to provide reliable and effective results.
DP-FT: A Differential Privacy Graph Generation With Field Theory for Social Network Data Release

Many data analysis applications rely on social networks that contain abundant information about individuals. Nevertheless, these applications can leak private information about individuals in social networks. To protect the privacy of individuals in social networks, several approaches involving graph generation models or differential privacy were proposed for publishing a social network in place of the original graph for data analysis applications. However, these techniques can cause a serious loss of data utility, especially regarding the real social links. In this paper, we propose an approach of degree-differential privacy graph generation with field theory. The approach includes two steps for publishing a social network. The degrees of the nodes are first perturbed with the differential privacy by adding noise that follows a Laplacian distribution. Then, the edges of the social network are synthesized with field theory. We propose a field theory model for social networks by simulating the law of gravity in physics and establish the correspondence of the gravitational field in physics to the field theory model. When an edge is formed, the starting node is preferentially chosen with high probability from the nodes with high degrees, and then the ending node is selected with high probability when the interaction force between the starting node and the ending node is large. Extensive experiments over four datasets show that our approach can preserve more real social ties compared with previous approaches and will not incur a loss of structure features over the datasets, such as the degree distribution and clustering coefficients.
Attention Mechanism in Machine Translation

Abstract Machine translation is an important field in machine learning. In recent years, the way to improve the effect of machine translation through attention mechanism has been well introduced and developed, and has become an important research part in machine translation. However, how to use the attention mechanism to ensure the translation speed and accuracy at the same time has also become an important problem for researchers to solve. In this article, we will introduce the development process of attention mechanism in the field of machine translation, and introduce in detail the algorithm changes of four papers on the development of Attention in Machine translation. Through the analysis of their experimental data, the development level of attention mechanism in the field of machine translation is shown.
Copy-Move Forgery Detection Based on Automatic Threshold Estimation

Recently, users and news followers across websites face many fabricated images. Moreover, it goes far beyond that to the point of defaming or imprisoning a person. Hence, image authentication has become a significant issue. One of the most common tampering techniques is copy-move. Keypoint-based methods are considered as an effective method for detecting copy-move forgeries. In such methods, the feature extraction process is followed by applying a clustering technique to group spatially close keypoints. Most clustering techniques highly depend on the existence of a specific threshold to terminate the clustering. Determination of the most suitable threshold requires a huge amount of experiments. In this article, a copy-move forgery detection method is proposed. The proposed method is based on automatic estimation of the clustering threshold. The cutoff threshold of hierarchical clustering is estimated automatically based on clustering evaluation measures. Experimental results tested on various datasets show that the proposed method outperforms other relevant state-of-the-art methods.
Optical-Reflection Type 3D Augmented Reality Mirrors

Augmented Reality (AR) mirrors can show virtual objects overlaid onto the physical world reflected in the mirror. Optical-reflection type AR mirror displays use half-silvered mirrors attached in front of a digital display. However, prior work suffered from visual depth mismatch between the optical reflection of the 3D physical space and 2D images displayed on the surface of the mirror. In this research, we use 3D visualisation to overcome this problem and improve the user experience by providing better depth perception for watching and interacting with the content displayed on an AR mirror. As a proof of concept, we developed two prototype optical-reflection type 3D AR mirror displays, one using glasses-free multi-view 3D display and another using a head tracked 3D stereoscopic display that supports hand gesture interaction.
A name disambiguation module for intelligent robotic consultant in industrial internet of things

Nowadays, the industrial internet of things (IIoT) is becoming more and more popular and necessary in our daily life. Intelligent robotic consultant which is a very important application of IIoT has attracted the attention of researchers. Robotic literature consultant which aims at helping users find related news or articles are becoming more and more important. As far as we know that the name disambiguation problem of robotic literature consultant still remain unsolved. networks (e.g., Utilizing simple topological structure in academic network (e.g., the relationships of co-authors) and feature engineering are the main method. However, it is generally difficult to generate the features considering the information's privacy and availability. In addition, the semantics underlying the real-world academic data cannot be easily captured by the simple relationship data. Therefore, in this paper, a graph embedding based name disambiguation module named Mech-RL is introduced for the robotic literature consultant and a novel meta-path channel based heterogeneous network representation learning method named Mech-RL is proposed. Firstly, given some meaningful meta-paths, the heterogeneous information network and meta-path channels are extracted. Then, by proposing two meta-path based proximity measures, Mech-RL obtains the node embeddings by sampling and jointly updating according to each meta-path channel. Finally, we apply the clustering algorithm on the generated paper embeddings to complete the disambiguation task. The real-world dataset based experimental results demonstrate our approach is effective compared to the related author name disambiguation approaches.
Multiple Time Series Analysis for organizational research

While multiple time-series analysis (MTSA) is a well-established method in economics, marketing, and finance, few studies have applied MTSA in organizational research. With the growing availability of data sources that contain detailed time-series data and the increasing importance of longitudinal designs, we argue that MTSA blends well with organizational research. We exemplify the possible applications of MTSA to the topics of social media, innovation, ambidexterity, and top management teams. We illustrate the state-of-the-art MTSA technique – Vector Autoregressive (VAR) model – by explaining the key methodological steps needed to estimate and interpret the results and providing a software tutorial in R and STATA. In line with the rising popularity of social media data, we employ a dataset that combines public social media data from Facebook with corporate reputation data from a private data source. We conclude with a discussion on the applicability, limitations, and extensions of MTSA for academics and practitioners.
Novel Internet of Things (IoT) for controlling indoor temperature via Gaussian type-2 fuzzy logic

Due to the rapid development in information and communication technologies, the Internet of Things (IoT) is a popular term in the areas of research. In this paper, we propose Gaussian type 2 fuzzy logic controller (GT2FLC) and applied to IoTs to manage indoor temperature concerning the outdoor ambient circumstances. Three input parameters have been controlled by GT2FLC for the purpose of saving energy and to place a more relaxing atmosphere for the users. The improvement of indoor temperature relies on various factors like the various combinations of outdoor temperature and humidity. The validity of the proposed model is made with the help of statistical analysis and multiple linear regressions. The simulation results revealed that the proposed system was more dependable and approachable to manage indoor temperature as compared to the offline monitoring system used outdoor ambient situations.
Geothermal sweetspots identified in a volcanic lake integrating bathymetry and fluid chemistry

Abstract We investigate fluid pathways beneath volcanic lakes using bathymetry and geochemical measurements to locate best-possible drilling sites. Highly permeable structures, such as faults, provide fluid channels that are the most suitable access points to the geothermal resource. Accurate mapping of these structures therefore guides the successful targeting of wells. Lakes, rivers or ocean, can hide surface footprints of these permeable structures, such as in our case beneath Lake Linau. High-resolution bathymetry identifies linear and conical discontinuities, which are linked to offshore tectonic structures as confirmed by surrounding outcrops and hot springs. Geochemical measurements document inflow of hot saline acidic water into the lake verifying bathymetry-located highly permeable structures. Integrating onshore well data, our bathymetry and chemical results locates an ideal drilling site into the geothermal reservoir beneath the western shore line of Lake Linau.
Understanding priority-based scheduling of graph algorithms on a shared-memory platform

Many task-based graph algorithms benefit from executing tasks according to some programmer-specified priority order. To support such algorithms, graph frameworks use Concurrent Priority Schedulers (CPSs), which attempt---but do not guarantee---to execute the tasks according to their priority order. While CPSs are critical to performance, there is insufficient insight on the relative strengths and weaknesses of the different CPS designs in the literature. Such insights would be valuable to design better CPSs for graph processing.
Mathematics mobile learning with TPACK framework

Abstract The mission of twenty-first-century education is to construct a knowledge-based society through the integration of Information and Communication Technology in the learning process. Mathematics teachers are required to be able to integrate technology in learning. Appropriate model and instructional media can encourage students’ critical thinking skills. Currently, the learning process not solely takes location in the classroom, however also makes use of digital media, online, teleconferencing, and cellular technology outside the classroom. Various learning applications in mobile devices can be utilized in mathematics learning. Mobile Learning is a learning model that is performed between places by means of the use of technology that is easy to raise when the learner is in a mobile situation. By using mobile learning, the availability of teaching materials can be accessed at any time and interesting material visualization. Mathematics Mobile learning is the use of mobile devices in mathematics learning. TPACK is a framework that describes the relationship and complexity between the three basic components of knowledge (technology, pedagogy, and content). The TPACK framework is a model used for technology integration in education. The suitability of material characteristics with appropriate instructional media, learning model, and technology selection in Mathematics Mobile Learning can improve learning achievement and motivation of learners.
Hopping-Pong: Changing Trajectory of Moving Object Using Computational Ultrasound Force

Physically moving real objects via a computational force connects computers and the real world and has been applied to tangible interfaces and mid-air display. Many researchers have controlled only a stationary real object by computational force. On the other hand, controlling a moving object can expand the real space that is controllable by the computer. In this paper, we explore the potential of computational force from the viewpoint of changing the trajectory of a moving object. Changing the trajectory is the primitive model to control a moving object, and it is the technological challenge requiring high-speed measurement and non-contact force with high-spatial resolution. As a proof-of-concept, we introduce Hopping-Pong changing the trajectory of a flying Ping-Pong Ball (PPB) using ultrasound force. The result shows that Hopping-Pong changes the trajectory of a PPB 344 mm. We conclude that a computational force is capable of controlling a moving object in the real world. This research contributes to expanding the computationally controlled space with applications for augmented sports, HCI and factory automation.
Agile methods as stress management tools? an empirical study

Project-based work is considered a prototype for modern knowledge work, but is also often linked to stress. Complementing or substituting for conventional project management, agile methods are on the rise, but the implications of these methods for stress on employees still remain largely hidden. This article presents the results from qualitative case studies in software development that show how diverse agile methods are lived in practice and how these variations affect stress levels. The results unveil how agile methods, under certain assumptions, can protect team members against stress and voluntary self-exploitation - offering a safe space that is, however, fragile and requires accompanying organisational backing.
Villanelle: An Authoring Tool for Autonomous Characters in Interactive Fiction

Our goal is to discover tool and language design principles that enable powerful, usable autonomous character authorship for diverse audiences of storytellers. This paper describes the Villanelle project, an approach to interactive narrative authoring that supports seamless integration of autonomous characters into choice-based storytelling. We present our computational model based on behavior trees uniformly for scripting agent interaction, user interaction, and narrative events; our stand-alone authoring tool, which provides an integrated development and testing environment for authoring with this model; and our JavaScript API for web-based development, demonstrating the expressiveness and simplicity of our approach through two case studies.
Assessing the Sharpness of Satellite Images: Study of the Planetscope Constellation

New micro-satellite constellations enable unprecedented systematic monitoring applications thanks to their wide coverage and short revisit capabilities. However, the large volumes of images that they produce have uneven qualities, creating the need for automatic quality assessment methods. In this work, we quantify the sharpness of images from the PlanetScope constellation by estimating the blur kernel from each image. Once the kernel has been estimated, it is possible to compute an absolute measure of sharpness which allows to discard low quality images and deconvolve blurry images before any further processing. The method is fully blind and automatic, and since it does not require the knowledge of any satellite specifications it can be ported to other constellations.
Contrast Enhancement Using Sensitivity Model-Based Sigmoid Function

For indirect contrast enhancement, researchers have proposed various transformation functions based on histogram equalization and gamma correction.However, these transformation functions tend to result in over-enhancement artifacts such as noise amplification, mean brightness change, and detail loss.To overcome the limitations of conventional transformation functions, this paper introduces a novel sigmoid function based on the contrast sensitivity of human brightness perception.In the proposed method, the contrast sensitivity of the human retina is modeled as an exponential function of the log-intensity, and a transformation function is derived using the sensitivity model as the exponent of Steven's power law.We also present a parameter optimization method that maintains the mean brightness of the input image and stretches the image histogram while minimizing information loss.Experimental results demonstrate that the proposed method has low computational complexity and outperforms the state-of-the-art methods in terms of contrast enhancement performance, mean brightness preservation, and detail preservation.
Domain Adaptation for Semantic Segmentation Using Convolutional Neural Networks

Semantic segmentation is an important analysis task for the investigation of aerial imagery. Recently, the arise of convolutional neural networks has increased the performance of computer vision methods considerably. But the success of deep learning applications mostly relies on the availability of sufficiently large training datasets. However, the manual annotation of images is time consuming and needs human effort. To reduce the necessary amount of training data it is possible to fine-tune a model which is pre-trained on a different larger dataset. But usually orthophotos are affected by weather and sensor dependent light conditions. Additionally, such images are composed of imbalanced classes which leads to poor pixel-wise classification results for sparsely represented labels. In this paper we propose a convolutional neural network based domain adaptation method for semantic segmentation. The encoder-decoder structure uses adaptation modules and an alternately training procedure to adapt the network to the target domain. We employ the large ISPRS Potsdam dataset as source domain to train a base model and adapt it using very few samples. We compared our method to the common fine-tuning approach and evaluated the results for a decreasing number of training samples. We observed an improvement of the average overall prediction accuracy but especially for the sparsely represented vehicle class.
ATL

Transferring knowledge across many streaming processes remains an uncharted territory in the existing literature and features unique characteristics: no labelled instance of the target domain, covariate shift of source and target domain, different period of drifts in the source and target domains. Autonomous transfer learning (ATL) is proposed in this paper as a flexible deep learning approach for the online unsupervised transfer learning problem across many streaming processes. ATL offers an online domain adaptation strategy via the generative and discriminative phases coupled with the KL divergence based optimization strategy to produce a domain invariant network while putting forward an elastic network structure. It automatically evolves its network structure from scratch with/without the presence of ground truth to overcome independent concept drifts in the source and target domain. Rigorous numerical evaluation has been conducted along with comparison against recently published works. ATL demonstrates improved performance while showing significantly faster training speed than its counterparts.