Reviewerly/exHarmony huggingface dataset:
The Origins, Current State, and Future of Geological Surveys and Societies

Starting with the Enlightenment in Europe and North America (c. 1685–1815), the different paths to the origin and development of geological societies in the United Kingdom and United States are reviewed and their role in the creation of national geological surveys described. These geological societies and geological surveys have changed with time, as has the challenge of remaining relevant to contemporary society. While the motivation for the formation of geological societies was and remains to advance scientific understanding mainly through meetings and publication, the rationale for the creation of geological surveys was primarily economic, to map the location of minerals and materials needed for economic development. In developing countries that requirement remains a priority in contrast to developed countries where geological surveys are increasingly engaged in environmental monitoring. This article explores the relationship between the provision of geological information, the need to do so impartially, and the role of advice in policy making. Finally, the relationship of geological societies and surveys to funding agencies and the role each plays in developing consensus about future research priorities are described.
The Complex and Opaque Cloud Ecosystem

In the complex and opaque cloud business ecosystem, service providers face several challenges. The fastest growing field of IaaS is evolving towards a commodity market, resulting in an increasing price competition. By first examining current challenges for cloud service providers, giving a theoretical background on value facilitation with a focus on the areas of value creation, and describing a state-of-the-art cloud ecosystem model, a sound understanding of the current situation is established. The role of value facilitation and standardization as core capabilities for successful IaaS providers are discussed and identified as being crucial for successful long-term survival in the competitive ecosystem. Additionally, learnings from expert interviews are analyzed, and five concrete recommendations for IaaS providers are derived. These recommendations should serve the management of IaaS providers in order to compare, challenge, and potentially adapt their current business models.
Ultra-short-term PV power forecasting based on LSTM with PeepHoles connections

With the gradual depletion of global traditional energy sources, solar energy has attracted more and more attentions as a new alternative energy source. According to the research statistics, the total installed capacity of solar stations in the world is expected to reach 124GW in 2019. However, at the same time, due to the volatility and intermittence of photovoltaic (PV) power generation, it is inevitable that the output power of the PV arrays will have an impact on the power system operation. Therefore, PV forecasting technology has a great significance for the application of new energy sources. In this paper, an advanced Long Short-Term Memory (LSTM) model with PeepHoles connections is proposed, and the weather information and current PV power are used as the input of the prediction model. On the basis of the standard LSTM neuron, the state of the previous neuron is added in the forget gate and output gate to prevent the abnormal close of the output gate and the forget gate during training process and actual testing process. At the same time, the new model's ability to extract trend information from the input stream is improved, and the ability to track the change of PV power is enhanced, hence the prediction accuracy of the model is improved. The experiment has been carried out based on the data from 150MW solar station in Yueqing, Zhejiang. It shows that the advanced LSTM model has better weather adaptability and prediction accuracy. The prediction accuracy has been improved by 5.734% compared with standard LSTM in MAPE and the RMSE has been reduced by 0.254MW.
A general architecture for maintaining a symbolic representation of the semantic game state

Planners can be used to manage game content such as interactive narratives and agent behaviour. However, planners require a semantic representation of games objects and events to reason about them. This paper describes a framework to create a symbolic representation of the semantic content of a real-time game for use with a story generation planner. Asynchronous events from the game are marshalled by an event handler into messages that are passed to a domain-specific message-handler. This message handler interprets and converts these messages to first-order predicates for use in a domain-independent planner. This approach aims to limit the amount of semantic data stored within the game that is unnecessary for gameplay logic.
Towards the Fixed Length Redundancy Code

In our previous work we have defined an error-detecting code with a fixed length of the redundancy. The analysed code is defined using the algebraic structure quasigroup. In this paper we analyse the case when a linear quasigroup of order 4 is used for coding. We obtain the number of errors that the code surely detects when the length of the redundancy is 8, 12 and 16 bits. At the end, we give some properties and conclusions for the number of errors that the code surely detects.
Listener Movement Prediction based Realistic Real-Time Binaural Rendering

Realistic binaural rendering refers to a process that implies applying the physically correct signals to the eardrums of a listener. In real-time binaural rendering systems, used in VR environments, the accurate signals should be appropriately updated as the listener freely moves in a simulated room. The update process requires room impulse response and head-related transfer function for each new position and pose of the listener. The classical approach uses a filter set, estimated for a particular position. The switching (to a new position-related filter set) is available after the current signal analysis frame is fully processed. In this paper, we propose an approach for immediate switching to the correct filtered binaural output with a rotation resolution of 3 degrees. Our solution is based on pre-filtering applied to the upcoming audio signal frame. The proposed solution does not exceed the latency limit required by VR audio systems and is suitable for quick head movements during the rendering process.
Building a Prototype Netcloudbook for User Activities-centered Social Network based on Booked Open-source and Docker Technology

The paper tackles the issue of building a user activities-centered social network that helps users to have benefit and enjoy activities in real life. The technical solution is based on an open-source and Docker technology on the Google Cloud Platform (GCP). The paper gives a comparison between some existing digital communities and proposes a relationship circles template.
Approximate Computation of States with Minimal Dispersion in Kuryshkin-Wodkiewicz Quantum Mechanics

The transfer of quantum information via quantum communication channels necessarily includes the process of quantum measurements of output. If we ignore the full description of the functioning of such channels, and focus only on the procedure of quantum measurements, we should work with the most mathematically rigorous standard Holevo model of quantum measurements. The Kuryshkin-Wodkiewicz quantum mechanics is a constructive version of the standard model, which includes a description of the phase representation of quantum mechanics. The longest living pure states in the dissipative quantum model under study are states with minimal dispersion. The work is devoted to an approximate numerical solution of the problem of finding such states.
Game Refinement Settings on Idle Games

Idle game is a game that has minimal interaction with the player so it is considered unpleasant. To make the idle game more entertaining it can be done by applying a comfortable setting. The comfortable setting can be analyzed through measure excitement of game using game refinement. Game with game refinement value within zone value 0,07-0,08 is considered as an ideal and fun to played. Testing is done by determining the parameters first by analyzing idle RPG Infinity Heroes. After getting the parameters, testing is done by applying 4 different settings where every setting will be played 50 times. The result showed that game refinement value on the dynamic setting is within zone value 0,08 which indicates this setting is suitable to be applied on idle RPG game that has same environment with Infinity Heroes.
Implementable Humanitarian Technology

In the 21st century, humanitarian crises have become very complex in nature and affect huge portions of the global population, not just the marginalized, discriminated, indigenous, and disaster/war hit communities, but also people in so called developed and relatively rich nations. A Humanitarian Engineer can be a student, academic or professional from multiple disciplinary backgrounds, who harness a concern for global humanitarian crises with their unique expertise and skill. Together they collaboratively research, analyze and engineer holistic, innovative solutions for this issue. The IEEE is recently focusing much needed widespread attention on humanitarian issues. One example is the annual Global Humanitarian Technology Conference, which is followed by many regional conferences such as the IHTC Canada and HTC Sri Lanka. Only three articles were accepted for this Special Section after a very thorough review process.
Association Rule Mining over Medical Image Dataset: A Survey Approach

In present world due to digital revolution a huge amount of digital photographs, satellite imagery, and medical images are being generated every day. Due to the generation of very large number of images on a vast scale; the process of analysing and diagnosing images has become critical. As a consequence, there is a growing need for Image Mining systems, which serve the purpose of reviewing semantically meaningful information by design and extracting knowledge from large amounts of image data. In contrast to Image Mining, Association Rules are often used to represent frequently occurring patterns that occur together in similar type of images and these rules, can be used further for effective mining of images. This paper aims at comparing various techniques used for mining of images and thus the current state of Image Mining using Image Data Association is reviewed, providing directions for future research in the arena of Image Mining.
Radiometric Identification Based on Parameters Estimation of Transmitter Imperfections

Radio signals show small characteristic differences between radio transmitters resulted from their idiosyncratic hardware properties. Based on the parameters estimation of transmitter imperfections, a novel radiometric identification method is presented in this letter. The fingerprint features of the radio are extracted from the mismatches of the modulator and the nonlinearity of the power amplifier, and used to train a support vector machine classifier to identify the class label of a new data. Experiments on real data sets demonstrate the validation of this method.
Aircraft Detection Based on Saliency Map and Convolution Neural Network

Detection of the aircraft from the remote sensing images attracts many attentions. Not only the aircraft taking off and landing but also the aircraft landing in the airport need to be monitored. This paper proposes a saliency-based CNN (convolutional neural network) method for aircraft detection in the remote sensing images. Many researches of aircraft detection access to a method of R-CNN (region convolutional neural network), which generates two thousand or more of proposal regions as candidates of the aircraft. The researches of using a saliency map to extract the objects from the images are still not so many. We adapt a series of preprocessing as transforming to a gray scale image, deleting the noise, binarizing by threshold, the closing operation, and floodfill to generate a saliency map where the contours of all objects are reinforced. Compared to R-CNN, the using of a saliency map greatly reduces the number of proposal regions, thus improving the efficiency. After this, we find the contours of objects and the minimum rectangles enclosing objects to extract proposed regions. Then, we prepare a negative dataset consisting of different types of backgrounds including the land, grass land and concrete. And for a positive dataset, we prepare images of aircraft in different angles and different sizes. To add more variation data into the dataset, we also use methods of augmentation like elastic distortion and perspective transforms. Then, we train a CNN model using the prepared dataset, which can tell whether a proposed region contains aircraft, after training. At last, we use a method named recall to evaluate the performance of the scheme, attaining an average recall of 0.7217, which shows the rate of aircraft successfully detected in all aircraft.
Tracking of Hand using Gabor Texture Descriptors and HOG Features

Many compressed sensing applications like telesurgery, require that only the desired object in a video frame should be tracked and transported over communication channel in real time. In this paper, we attempt to track hand in a given image, using Gabor texture descriptors for recognition and HOG features to track. We compare the results with Haar features using performance measures like latency and accuracy.
Penalized proportion estimation for non parametric mixture of regressions

In this article, we propose a penalized local log-likelihood method to locally select the number of components in non parametric finite mixture of regression models via proportion shrinkage method. Mean functions and variance functions are estimated simultaneously. We show that the number of components can be estimated consistently, and further establish asymptotic normality of functional estimates. We use a modified EM algorithm to estimate the unknown functions. Simulations are conducted to demonstrate the performance of the proposed method. We illustrate our method via an empirical analysis of the housing price index data of United States.
Towards the development of a grammar checker and its utilization in the teaching of Modern Greek as mother tongue

Abstract The goal of the current study is the exploration of the standardization of grammatical errors through templates, a process that ultimately led to the development of a Grammar Checker for Modern Greek, an electronic tool for the automatic recognition and correction of grammatical errors. Moreover, the study investigates the utilization of such a tool in a classroom setting and its contribution to the teaching of mother tongue. The participants of the study were secondary school students and main results showed that errors they made in text production activities could be categorized as errors of mechanics, grammar, and usage through certain templates, which then lead to the development of a Grammar Checker for Modern Greek. Moreover, we also found other errors that could not be analysed and categorized using the theory of taxonomy of errors by Ho, i.e. style: forms—learned forms and semantic. Additionally, the participants highlighted that the specific tool has advantages for the teaching of mother tongue. Implications for the methodology of Grammar Checkers’ development and their use in the teaching of mother tongue, generally, are discussed.
Nonlinear Control System of Mobile Robot for Avoiding Obstacles

The paper describes the problem of creation of a tracked mobile robot nonlinear control system to be able to move in an environment with obstacles. To solve the problem, a method of mobile robots adaptive control is proposed. The method uses attractor and repeller manifolds. The essential feature of the proposed method is the ability to provide stable operations of mobile robots avoiding obstacles to the desire position, therefor keeping to a safety trajectory. The paper presents a mathematical model of a mobile robot taking into account the nonlinearity of characteristics of the control object. To demonstrate the effectiveness of the developed nonlinear control system, a computer simulation was carried out.
Machine Learning in 5G Multimedia Communications

Machine learning (ML) has evolved to the point that this technique enhances communications and enables fifth-generation (5G) wireless networks. ML is great to get insights about complex networks that use large amounts of data, and for predictive and proactive adaptation to dynamic wireless environments. ML has become a crucial technology for mobile broadband communication. Special case goes to deep learning (DL) in immersive media. Through this chapter, the goal is to present open research challenges and applications of ML. An exploration of the potential of ML-based solution approaches in the context of 5G primary eMBB, mMTC, and uHSLLC services is presented, evaluating at the same time open issues for future research, including standardization activities of algorithms and data formats.
Explorable Virtual Diorama of Indonesian Prehistoric Human Life using Apple ARKit based Mixed Reality

Prehistoric human diorama which are being diplayed in archaeology museums in Indonesia are still static and inexplorable. This research aims to design a virtual diorama application about Indonesian prehistoric human habitat and life which applies Apple ARKit based Mixed Reality (MR) technology. Virtual diorama application lets users to explore dynamic virtual diorama from their ARKit supported iOS devices. Virtual diorama application has an ability to 3D scan the environment around user by taking point cloud data through ARKit. Scanning result is used to generate virtual environment which its objects are arranged according with real-world object arrangement by using heightmap from scanning result. This application uses “Magic Door” concept which the user can get in and out virtual diorama through a virtual door. Test results show that virtual environment arranger system has a success rate of 89% with average global translation of 5,04 cm and global rotation chance of 13,33%. Questionnaire testing to 30 persons showed that 43,33% of respondents were helped by virtual environment arrangements which were matched with real world environment arrangement in prevention of collisions with objects in the real world. There are 36,67% of respondents agree that the displayed dynamic prehistoric human helps respondents in understanding prehistoric human life and 46,67% of the respondents agree that Mixed Reality provides a new experience in learning prehistoric human life.
Inquiry Spam Detection via Jointly Exploiting Temporal-Categorical Behavior and Linguistics

Inquiry performs one of the backbones in current E-commerce websites. Detecting spams in inquiries is essential for these platforms but is surprisingly underexplored by current research. In this paper, we propose a system coined ISTBEL to detect spam inquiries. Motivated by the observations on both behavioral and linguistic differences between spammers and benign users, ISTBEL jointly utilizes the temporal-categorical behavioral sequence and text sequence to accomplish its purpose. At its heart, a variant of LSTM equipped interactive attentions is devised to strengthen the coherence between the categorical and temporal behaviors. A character-level CNN is adopted to capture the capricious linguistic patterns. These two modules are integrated and collaboratively contribute to the final predictions. Our system is trained through an end-to-end manner and can directly utilize raw data as input without tedious feature engineering. We have applied the system to inquiry spam detection on Alibaba.com, and results show ISTBEL is promising in detection spammers and outperforms baselines by a large margin. Meanwhile, ISTBEL can be easily applied to other applications which contain both behavioral and linguistic information.
Effect of nonnormality on tests for a mean vector with missing data under an elliptically contoured pattern-mixture model

We derive the asymptotic null distribution of Hotelling’s T2-type and likelihood ratio test statistics using two-step monotone sample under an elliptically contoured pattern-mixture model up to the order N−1 to investigate the robustness of the test statistics with respect to nonnormality, where N denotes the total sample size. Using some matrix algebra, we obtain more simple expression of the test statistic, reducing the amount of the calculation required for obtaining the main result. The main result indicates some remarkable properties with respect to their distribution up to the order N−1: each test statistic obtained using two-step monotone missing data exhibits a lower effect of nonnormality when compared with the statistic obtained using listwise deletion, and the upper percentiles of each test statistic exhibit lower values when the kurtosis parameters are positive and large. The main results are applied to extend the Bartlett-type correction to the test statistics under an elliptically contoured pattern-mixture model. Monte Carlo simulation demonstrates that the test statistics exhibit the aforementioned distributional properties and that our proposed tests outperform those obtained by assuming multivariate normality in aspects of controlling type I error exactly. Furthermore, the application of our proposed tests to an actual dataset is presented.
Challenges in Implementing Transportation Tracking System in Saudi Arabia

The advancement in information technology is transforming the business environment. The logistics industry is a major beneficiary since it has enabled them to better coordinate and integrate their operations while ensuring realtime tracking. However, the implementation of new technology introduces a unique set of challenges. This paper presents a case study with the main objective to understand the challenges an organisation face in implementing a freight tracking system. Adopting qualitative methods, in-depth interviews are conducted with the implementation team, users and major stakeholders. The in-depth analysis suggests that minimising user resistance and change management strategy forms the basis of successful implementation. In addition, establishing new rules and procedure, learning and development environment, accountability and communication are the pillars for the smooth transition to the new system.
A Study on Efficient Clustering Techniques Involved in Dealing With Diverse Attribute Data

The compatibility issues among the characteristics of data involving numerical as well as categorical attributes (mixed) laid many challenges in pattern recognition field. Clustering is often used to group identical elements and to find structures out of data. However, clustering categorical data poses some notable challenges. Particularly clustering diversified (mixed) data constitute bigger challenges because of its range of attributes. Computations on such data are merely too complex to match the scales of numerical and categorical values due to its ranges and conversions. This chapter is intended to cover literature clustering algorithms in the context of mixed attribute unlabelled data. Further, this chapter will cover the types and state of the art methodologies that help in separating data by satisfying inter and intracluster similarity. This chapter further identifies challenges and Future research directions of state-of-the-art clustering algorithms with notable research gaps.