Reviewerly/exHarmony huggingface dataset:
ORE-MAGMATIC SYSTEM SUKUMALAND (TANZANIA)

The overwhelming part of the gold ore potential of the United Republic of Tanzania is confined to the systems of the Neoarchaean greenstone belts (GSB) of the metallogenic province of Lake Victoria in the northwestern part of the country. Based on the interpretation of space imagery, in the western part of the province the Sukumaland ore-magmatic system of concentric-zonal structure has been distinguished. It is formed by a group of arched greenstone belts and associated gold deposits. They form two arcuate branches: the internal and external branch, differing in their composition and structure. The internal arc is represented by the Rwamagaza and Kahama GSB with gold deposits of Tulawaka, Buckreef, Mawe-Meru, Tembo, and Bulyanhulu. The external arc is represented by Geita and Mabale-Buhungurica GSB with gold deposits of Ridge-8, Nyankanga, Geita, Matandani, Kukuluma, Nyanzaga, Kitongo, Golden Ridge, Nyakafuru, and Miabu. Their huge gold potential (over 50 million ounces of gold) stipulates for the urgency of the study of this gigantic structure. GSB of the inner arc (Rwamagaza and Kahama) are characterized by a predominance of basic volcanic rocks, and GSB of the external arc (Geita and Mabale-Buhungurica) – by sedimentary and volcanic-sedimentary formations, in particular, banded ironstone formations (BIF). Accordingly, gold deposits of the inner arc are mainly associated with basic volcanic rocks (Tulawaka, Buckreef, Mawe-Meru, Bulyanhulu, Tembo) while gold deposits of the external arc – with BIF (Geita, Nyankanga, Kukuluma, Matandani). The nuclear part of the ore-magmatic system is of special interest, although manifestations of volcanism are absent there and gold or other mineral deposits are unknown yet, but a tectonic pattern of its structural elements indicates its significant ore potential. In particular, the presence of numerous spatially close systems of radial-circular faults attests to the presence of weakened zones in the space above the core of a hypothetical magma chamber. Gold deposits of different geological and industrial types, yet undiscovered, as well as manifestations of diatreme magmatism may be associated with these weakened zones. In particular, one cannot exclude the possible presence of diamond-bearing volcanic pipes. It is assumed that the ore-magmatic system has originated and evolved in the course of a complex multi-stage ore-magmatic process under the influence of ascending flows of depth fluids due to the long-term functioning of a zone with anomalously heated mantle in that area. During further geological exploration it is recommended to take into account the predictable availability of a single integrated ore-magmatic system of longdevelopment and its structural features.
Workstations of the Future for Transformational Gains in Solving Complex Problems

This paper describes fundamental concepts of future workstations that will achieve transformational gains in problem solving for end users. The results are based on 7 years of Human-Computer Interaction (HCI) research on the bRIGHT platform. bRIGHT already incorporates solutions for modeling and understanding end-user contexts/intent and employs these to design algorithms for task automation, context filtering, and contextual auto-fill to support end users in complex tasks. bRIGHT also includes solutions for collaborative problem solving. We summarize insights gained from conducting user experiments and describe the technical capabilities required of future workstations to ensure a leap of performance when groups of users can jointly solve complex problems.
Performance Evaluation of Head Motion Input Used for AR-Based Communication Supporting System During Endoscopic Surgery

During endoscopic surgery, communication between the operator and the assistant is an important problem to solve to prevent medical errors and to make the operation more efficient and precise. We have been developing an AR-based application for communication support in endoscopic surgery. This application has a function to enable line and shape drawings on the endoscopic images under the AR environment. The application enables the drawing of these images by head motions. To verify the effectiveness of this application, it was necessary to evaluate how much performance the drawing function by the head motion can provide the operator. In this paper, we investigated whether the drawing function by head motion offers effective performance for communication during endoscopic surgery by comparing the operation accuracy of the linear and circular drawings by direct hand motion and indirect mouse motion and modeled using the steering law. Results of the experiment demonstrated that the relationship between indices of difficulty and movement time was well modeled using steering law with more than 97% of coefficient of determination for all three input methods. In conclusion, the precision of drawing by head motion cannot exceed that of by hand and mouse when it is necessary to draw a circular object. It was suggested that the effectiveness of head motion drawing existed when the drawing lines were not precise, possibly for communication between typical practitioners and assistants, drawing straight lines or relatively rough shapes.
Introduction to Python

As data science is increasingly being adopted in the industry, the demand for data scientists is also growing at an astonishing pace. Data scientists are a rare breed of “unicorns” who are required to be omniscient and according to popular culture, a data scientist is someone who knows more statistics than a programmer and more programming than a statistician. One of the most important tools in a data scientist’s toolkit is the knowledge of a general-purpose programming language that enables a data scientist to perform tasks of data cleaning, data manipulation, and statistical analysis with ease. Such requirements call for programming languages that are easy enough to learn and yet powerful enough to accomplish complex coding tasks. Two such de facto programming languages for data science used in industry and academia are Python and R.
The Assessment of Sencogi: A Visual Complexity Model Predicting Visual Fixations

This paper investigates whether a frequency-domain model of complexity can accurately predict human visual salience maps. The Sencogi model uses the frequency domain to calculate maps of spatial (i.e., static) and temporal (i.e., dynamic) complexity. This study compares the complexity maps generated by Sencogi to human fixation maps obtained during a visual quality assessment task on static images. This work is the first part of an ongoing multi-step study designed to assess whether fixation maps are an accurate representation of saliency for spatio-temporal scenes. A supporting experiment confirmed that top-down factors, such as scene type, task or emotional states, did not affect human fixation maps. Results show that the Sencogi visual complexity model estimates human eye fixations of images with prediction scores that are significantly above a Chance baseline and is able to compete with a Single Observer baseline. We conclude that the Sencogi visual complexity model is able to predict human fixations in the spatial domain. The next studies will focus on the assessment of Sencogi’s performance predicting visual fixations in the spatio-temporal domain.
Evaluation of Face Recognition Techniques Based on Symlet 2 Wavelet and Support Vector Machine

In this study, we analyze and study the feature extraction method based on wavelet transform and Principal Component Analysis (PCA), and propose a method combining symlet 2 wavelet (sym2) and Support Vector Machine (SVM) for face recognition. Firstly, the sym2 is used to wavelet decomposition of the original image, and the decomposed low-frequency information is selected for image reconstruction. Then, the reconstructed data is multi-classified using support vector machine. Experiments were carried out using the internationally published ORL face recognition database, and compared with some classical methods used in ORL face recognition. The experimental results show that the recognition success rate increases with the increase in the training set. When 7 poses are selected for each face (A total of 280 photos are used as training sets) in the data set, the recognition success rate reaches 100%. Therefore, the method proposed in this paper is better than the previous method.
Pattern Recognition Techniques for Non Verbal Human Behavior (NVHB)

The matched subspace detector (MSD) is a classical subspace-based method for hyperspectral subpixel target detection. However, the model assumes that noise has the same variance over different bands, which is usually unrealistic in practice. In this letter, we relax the equal variance assumption and propose a matched subspace detector with heterogeneous noise (MSDH). In essence, the noise variances are different for different bands and they can be estimated by using iteratively reweighted least squares methods. Experiments on two benchmark real hyperspectral datasets demonstrate the superiority of MSDH over MSD for subpixel target detection.
Anti-screenshot Keyboard for Web-Based Application Using Cloaking

Online banking administrators usually try to use sufficiently strong security solutions to encourage individuals to use online banks. They provide virtual keyboards for users to log in with as a main security solution to facilitate their users’ trust in their security solutions. The virtual keyboards can be prone to screenshot capture by various means, such as Trojans, malware, and shoulder surfing attacks. Different proposals for virtual keyboards have been presented by many authors with different means of input. In this paper, we provide a cloaking virtual keyboard (CVK) as a model for a virtual keyboard with cloaked input. The proposed model goes beyond using a virtual keyboard to prevent a screenshot attack on the online banking application system. It provides a means to inform the administrators of the new attack and takes the attacker to a false account.
Show Something: Intelligent Shopping Assistant Supporting Quick Scene Understanding and Immersive Preview

In this paper, we introduce an intelligent shopping assistant system supported by quick scene understanding and augmented-reality 3D preview. By understanding the scene that users are looking at, and using the detected scene information, our system recommends related products that are not in the current scene and which could potentially interest users. With the help of existing speech recognition techniques, our system extracts users’ voice command and keywords, and provides responses in real-time, which allows users to search and filter specific products just by using voice. After finding the potential target products, our system provides users with an augmented-reality preview experience. It automatically brings products to the suitable space in front of the users by using life-size three-dimensional virtual products and spatial understanding. Users can also use two-hand gestural manipulation to operate the virtual products. Through our system, users can obtain products that are strongly related to the current environment, and intuitively preview the products in the current scene by automatic placement and two-hand manipulation to make shopping decisions.
Design of Data Acquisition Software for Steam Turbine Based on Qt/Embedded

This paper puts emphasis on a design of data acquisition software for steam turbine. This software is developed by Qt/Embedded, and it runs on the embedded computer. It aims at improving the safety of steam turbine. Operating state of steam turbine can be indicated by some physical quantities, like temperature, pressure or rotational speed of turbine blade. This software acquires data of these quantities, and analyzes it. According to acquired data, it determines whether the steam turbine is in normal state. When the steam turbine is running in abnormal state, it will be forced to do specific actions by output signals of this software. Besides, this software offers users a friendly graphic interface, on which the data and signal is shown in real time. It has been tested and verified that the design can work normally.
Making Multi-platform VR Development More Accessible: A Platform Agnostic VR Framework

Virtual Reality offers developers an immersive and engaging new medium to create simulation applications, training tools, and educational games. One of the first decisions a VR developer will face is which platform to target. In 2014 consumer choice was limited to Google Cardboard. By 2016 this had expanded to six devices, including popular platforms such as Oculus Rift, HTC Vive, and PSVR. Today there are not only more platforms, but even more variation in platform hardware, input devices, and interaction methods. This overwhelming amount of variance can be daunting for developers who want to create a VR application where the goal is to develop and deliver for multiple platforms, typically to reach the largest audience. With applications in so many fields like medical, educational, and cyber security, it is becoming more important that VR experiences can run on multiple platforms in different environments. Therefore, we present and apply our approach to unify these differences with a platform agnostic framework for the Unity game engine that allows developers to focus on creating their vision, without worrying about individual SDKs and the intricacies of each platform. Unity does a great job natively supporting the popular SDKs such as SteamVR and OculusVR, but these SDKs only apply to their own family of hardware, leaving the developer to re-write or even duplicate and alter code just so they can deploy their experience on multiple platforms. As project scope grows, this becomes more and more complex and can be a time-consuming task.
Using Learning Analytics to Explore the Performance of Chinese Mathematical Intelligent Tutoring System

This study introduced the Chinese dialogue-based mathematical intelligent tutoring system that developed to help students to learn mathematics without teachers. Student interactions with the Chinese dialogue-based mathematical intelligent tutoring system produce a wide range of learning sequences. The present study explored students’ learning performance and the effectiveness of knowledge components by using additive factors model and Bayesian knowledge tracing model. The model fit and the learning patterns of students were discussed.
A Deep Reinforcement Learning Approach for Autonomous Car Racing

In this paper, we introduce a deep reinforcement learning approach for autonomous car racing based on the Deep Deterministic Policy Gradient (DDPG). We start by implementing the approach of DDPG, and then experimenting with various possible alterations to improve performance. In particular, we exploit two strategies: the action punishment and multiple exploration, to optimize actions in the car racing environment. We evaluate the performance of our approach on the Car Racing dataset, the experimental results demonstrate the effectiveness of the proposed approach.
Improved Review Sentiment Analysis with a Syntax-Aware Encoder

Review sentiment analysis has drawn a lot of active research interest because of the explosive growth in the amount of available reviews in our day-to-day activities. The current review sentiment classification work often models each sentence as a sequence of words, thus simply training sequence-structured recurrent neural networks (RNNs) end-to-end and optimizing via stochastic gradient descent (SGD). However, such sequence-structured architectures overlook the syntactic hierarchy among the words in a sentence. As a result, they fail to capture the syntactic properties that would naturally combine words to phrases. In this paper, we propose to model each sentence of a review with an attention-based dependency tree-LSTM, where a sentence embedding is obtained relying on the dependency tree of the sentence as well as the attention mechanism in the tree structure. Then, we feed all the sentence representations into a sequence-structured long short-term memory network (LSTM) and exploit attention mechanism to generate the review embedding for final sentiment classification. We evaluate our attention-based tree-LSTM model on three public datasets, and experimental results turn out that it outperforms the state-of-the-art baselines.
Construction Of an Oral Cancer Auto-Classify system Based On Machine-Learning for Artificial Intelligence

Oral cancer is one of the most widespread tumors of the head and neck region. An earlier diagnosis can help dentist getting a better therapy plan, giving patients a better treatment and the reliable techniques for detecting oral cancer cells are urgently required. This study proposes an optic and automation method using reflection images obtained with scanned laser pico-projection system, and Gray-Level Co-occurrence Matrix for sampling. Moreover, the artificial intelligence technology, Support Vector Machine, was used to classify samples. Normal Oral Keratinocyte and dysplastic oral keratinocyte were simulating the evolvement of cancer to be classified. The accuracy in distinguishing two cells has reached 85.22%. Compared to existing diagnosis methods, the proposed method possesses many advantages, including a lower cost, a larger sample size, an instant, a non-invasive, and a more reliable diagnostic performance. As a result, it provides a highly promising solution for the early diagnosis of oral squamous carcinoma.
Creating Your Own Blockchain

In this chapter, I will cover how to build your very own blockchain P2P network. This is a seven-step process, so in each section I’ll start with a brief introduction followed by an exercise. You can download the code for each of the following exercises from GitHub and follow along:
Unsupervised incremental learning for hand shape and pose estimation

We present an unsupervised incremental learning method for refining hand shape and pose estimation. We propose a refiner network (RefNet) that can augment a state-of-the-art hand tracking system (BaseNet) by refining its estimations on unlabeled data. At each input depth frame, the estimations from the BaseNet are iteratively refined by RefNet using a model-fitting strategy. During this process, the RefNet adapts to the input data characteristics by incremental learning. We show that our method provides more accurate hand shape and pose estimates on both a standard dataset and real data.
Depth from Small Motion using Rank-1 Initialization

Depth from Small Motion (DfSM) (Ha et al., 2016) is particularly interesting for commercial handheld devices because it allows the possibility to get depth information with minimal user effort and cooperation. Due to speed and memory issue on these devices, the self calibration optimization of the method using Bundle Adjustment (BA) need as little as 10-15 images. Therefore, the optimization tends to take many iterations to converge or may not converge at all in some cases. This work propose a robust initialization for the bundle adjustment using the rank-1 factorization method (Tomasi and Kanade, 1992), (Aguiar and Moura, 1999a). We create a constraint matrix that is rank-1 in a noiseless situation, then use SVD to compute the inverse depth values and the camera motion. We only need about quarter fraction of the bundle adjustment iteration to converge. We also propose grided feature extraction technique so that only important and small features are tracked all over the image frames. This also ensure speedup in the full execution time on the mobile device. For the experiments, we have documented the execution time with the proposed Rank-1 initialization on two mobile device platforms using optimized accelerations with CPU-GPU co-processing. The combination of Rank 1-BA generates more robust depth-map and is significantly faster than using BA alone.
PlayGAMI

PlayGAMI is an augmented reality origami creativity platform. It has the fun of designing, folding origami and the magic of AR all in a single experience! Our platform lets a user draw on real origami paper and turn their creation into a virtual origami action figure/game character! Further, we use GANs that interpret certain drawn symbols to interactive game elements. The final customized design can be posted to an online 3D Gallery for viewing and sharing on social media.
Unique Mobile Apps for Medical Learning: How Not to Fall Down Alice’s Rabbit Hole

Libraries and medical libraries should strive to become well-versed in mobile apps because they are dynamic tools that actively engage users in learning and are outstanding reference resources. Mobile apps are one of many resources that health care professionals interested in teaching and learning should use to bolster their understanding of medical material. For new students, residents, physicians, nurses, and other health care professionals, being well-informed about existing and emerging mobile apps for learning is a vital tool for knowledge acquisition.
A Study on the Experience and Attitude of Korean Learners Using Online Machine Translation Tools.

The purpose of this study is to examine the experience and attitude of Korean learners about using online machine translation tools. To accomplish this goal, 99 Korean learners of elementary and intermediate level were surveyed and their results were analyzed by each item. As a result, most Korean learners have experienced using machine translation tools in Korean language learning, and have found that a large part of Korean language learning relies on the use of machine translation tools. However, it was still recognized that the use of machine translation tools was an auxiliary means of Korean language learning. For some items, there was a difference in response outcome by proficiency. There was a significant difference between the types of machine translation tools used, the purpose of using machine translation tools, and the degree of dependence on machine translation tools. The results of this study showed that Korean learners have some experience and awareness about online machine translation tool. Also, we could see how these experiences and attitude differ according to learners’ level. This study has significance in that it is the first attempt as a specific research study on this field.
A Sentiment Aggregation System based on an OWA Operator

User-Generated-Content (UGC) in the form of online reviews can be an invaluable source of information for both customers and businesses. Sentiment analysis and opinion mining tools and techniques have been proposed in the literature to extract knowledge from online reviews. Aspect-based opinion mining which has gained growing attention mainly has two tasks including aspect extraction and sentiment polarity detection. Once an aspect-based opinion mining task has been accomplished; a bag of sentiments will be achieved. In many cases, it is necessary to obtain an overall sentiment about a typical aspect. In this study, we have proposed a sentiment aggregation system based on weighted selective aggregated majority OWA (WSAM-OWA). WSAM-OWA considers both the majority and the degree of importance of information source in the process of aggregation. The proposed system exploits the helpfulness rating of reviews in determining the reliability and credibility of each sentiment. A case study was conducted to illustrates the usefulness of the proposed system. The results of this study demonstrated that the proposed sentiment aggregation system could be incorporated in opinion mining systems.