Reviewerly/exHarmony huggingface dataset:
Hybrid mineral predictive mapping with self-organizing maps and a multilayer perceptron applied to tin deposits in the Erzgebirge, Germany

&amp;lt;p&amp;gt;Self-organizing maps (SOM) are a useful tool to analyze and interpret gridded datasets like potential field or stream sediment geochemistry data. The data are transformed from geographic space to SOM space where they can be clustered according to overall similarity. By transforming the clusters back to geographic space, geological interpretation of the clusters is facilitated. We present the application of a multilayer perceptron (MLP) in SOM space to produce mineral predictive maps. The reduced number of grid cells in SOM space greatly enhances the performance of the MLP and the tolerance to noise in the input data, compared to an application of the MLP to the original data. The method is applied to tin skarn deposits in the German part of the Erzgebirge. The training and validation data required for the MLP are compiled from mining and exploration records. The input data for the SOM are reprocessed gravimetric, magnetic, stream sediment geochemistry, geologic and tectonic data sets. Potentially ore-controlling spatial relationships, such as the distance to different types of partly covered granite intrusions, are derived from a regional scale 3D geological model. The resulting mineral prediction map allows the definition of exploration zones for detailed studies.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The paper has been compiled in the frame of &amp;quot;NEXT - New EXploration Technologies&amp;quot; project. This project has received funding from the European Union&amp;amp;#8217;s Horizon 2020 research and innovation programme under grant agreement No 776804.&amp;lt;/p&amp;gt;
A study of outline to the advantages and disadvantages of social media

Social media is the new technology of computer that enhances the sharing of thoughts and information in order to building virtual networks and communities. Social media is based on internet and facilitate easy electronic communication about personal information and contents, such as photos and videos. Users can engage with social media by using computer, Smartphone or tablet via internet based software or web application. Social media is a tool that initially used by people to interact with family or friends but later it was adopted by businesses that take advantage of a new popular communication method to reach out to customers. The strength of social media is related to share and connect information with everybody all over the world who is using social media. This article argues on social media"s advantages and disadvantages in present era.
On the Impact of Burst Loss for QoE-Based Performance Evaluations for Video Streaming

In the broad area of performance evaluation of multimedia networking mechanisms, quality of experience (QoE) assessments are often used as a performance metric. Even though intensive efforts have been made to understand the impact of packet loss on QoE for different application scenarios, there is no consensus about how specific packet loss patterns impact the QoE for RTP video streaming. In this paper, we conduct an extensive study on the impact of uniformly distributed random and burst loss on the QoE-metrics Peak-Signal-to-Noise-Ratio (PSNR), Structural Similarity (SSIM), and Video Multi-Method Assessment Fusion (VMAF). Overall, our study indicates that for QoE-based performance evaluations, it is in most cases sufficient to only consider easier to understand and easier to model uniformly distributed random loss.
Data Mining based BIM Technology in Tunnel Construction Simulation Modeling

Application research of the BIM technology in the tunnel construction simulation modeling is conducted in this paper. The BIM model is a total brand-new graphic model that contains information. By analyzing the vulnerabilities of each component, an index system for the vulnerability evaluation is constructed; vulnerability is determined by the combined effect of many factors only by analyzing and also sorting out the interrelationship between factors can grasp the internal laws of the system and construct a hierarchical system of vulnerability indicators. By using the data mining technology and multimedia assisted BIM, the proposed system is constructed. The experiment results have proven the performance of the model.
Hurdles to Progress in Long-form Question Answering

The task of long-form question answering (LFQA) involves retrieving documents relevant to a given question and using them to generate a paragraph-length answer. While many models have recently been proposed for LFQA, we show in this paper that the task formulation raises fundamental challenges regarding evaluation and dataset creation that currently preclude meaningful modeling progress. To demonstrate these challenges, we first design a new system that relies on sparse attention and contrastive retriever learning to achieve state-of-the-art performance on the ELI5 LFQA dataset. While our system tops the public leaderboard, a detailed analysis reveals several troubling trends: (1) our system's generated answers are not actually grounded in the documents that it retrieves; (2) ELI5 contains significant train / validation overlap, as at least 81% of ELI5 validation questions occur in paraphrased form in the training set; (3) ROUGE-L is not an informative metric of generated answer quality and can be easily gamed; and (4) human evaluations used for other text generation tasks are unreliable for LFQA. We offer suggestions to mitigate each of these issues, which we hope will lead to more rigorous LFQA research and meaningful progress in the future.
Research on FPGA-based laser measuring instrument for surface flatness

Strip flatness is an important indicator of steel quality, and its detection technology has always been the focus of research at home and abroad. This paper proposes a non-contact online real-time measurement scheme for hot rolled strip steel. Our research is based on the laser triangulation method, which transforms the flatness detection into the strip surface height detection. In order to prevent errors caused by jitter during strip transportation, we have added a three-point measurement method. The flatness measurement system has carried out the system hardware design with FPGA and linear CCD as the core and the system software design with the laser spot positioning as the core. The basic technology realization process is based on FPGA as the main control and processing chip, collecting the laser reflection light signal of the measured strip steel surface through the optical system through the linear array CCD to obtain the strip surface height information. The collected signals are subjected to photoelectric conversion, filtering and amplification, and A/D conversion, and are transmitted through a network port based on UDP protocol. Finally, the spot center is positioned by comparing the selected threshold method to obtain important data for calculating the flatness information. In this study, a semiconductor laser with a wavelength of 450nm, a TCD1304AP linear CCD and an FPGA chip of the XC7A35T model produced by Xilinx were selected. In the range of 4 mm above and below the reference position, the measurement accuracy of the system can reach 10μm, which meets the measurement requirements of the measured strip elongation accuracy of 1% (when the elongation is 10~200 I) or 1 I (when the elongation is less than 1 I).
On the Role of Depth Predictions for 3d Human Pose Estimation

Following the successful application of deep convolutional neural networks to 2d human pose estimation, the next logical problem to solve is 3d human pose estimation from monocular images. While previous solutions have shown some success, they do not fully utilize the depth information from the 2d inputs. With the goal of addressing this depth ambiguity, we build a system that takes 2d joint locations as input along with their estimated depth value and predicts their 3d positions in camera coordinates. Given the inherent noise and inaccuracy from estimating depth maps from monocular images, we perform an extensive statistical analysis showing that given this noise there is still a statistically significant correlation between the predicted depth values and the third coordinate of camera coordinates. We further explain how the state-of-the-art results we achieve on the H3.6M validation set are due to the additional input of depth. Notably, our results are produced on neural network that accepts a low dimensional input and be integrated into a real-time system. Furthermore, our system can be combined with an off-the-shelf 2d pose detector and a depth map predictor to perform 3d pose estimation in the wild.
Financial Contagion and Volatility Spillover: an exploration into Bitcoin Future and FOREX Future Markets

This paper examines the time-varying conditional correlations between Bitcoin future market and five FOREX future markets. A sixvariate dynamic conditional correlation (DCC) GARCH model is applied in order to capture potential contagion effects between the markets for the period 2017-2019. Empirical results reveal contagion during the under investigation period regarding the one sixvariate model, showing potential volatility transmission channels among the future markets. Findings have crucial implications for policymakers who provide regulations for the above derivative markets.&nbsp
ANTIE: The Activism Chatbot

Nationwide attention towards the Black Lives Matter (BLM) movement has motivated many individuals to seek resources related to racial equity activism education. Heightened awareness of racially motivated police brutality has highlighted the realities of systemic racism in the United States' institutions and policies. It has also spurred a growing interest in online platforms offering relevant educational resources, thus demonstrating a need for applications specializing in education around BLM and its platform of anti-racism, police brutality, and equity. This research's purpose is to develop a tool that specializes in directing users who may be unsure how to begin educating themselves to relevant resources. We developed ANTIE, the activism chatbot, using the IBM Watson Assistant framework due to its capability of simulating human-like conversations. We further increased the potential for high levels of user engagement by including fun language and emojis. Overall, our project presents ANTIE in beta. The chatbot currently promotes BLM education by providing voting information, relevant news articles, terminology definitions, media recommendations, a directory of local Black-owned businesses, and other resources. We conducted pilot testing with six participants; however, we plan to gather more results via deployment to a larger audience. Our next steps are to evaluate and improve ANTIE by surveying users' levels of engagement and knowledge about racial equity activism after interacting with the chatbot.
Design of Experiment Management System for Stability Control System Based on Blockchain Technology

Stability control system (SCS) plays a very important role in the operation of power systems, so it is correspondingly essential to ensure the reliability of the SCS during its experimental and verification process. However, there are numerous problems in the management of the paper form based experiment mode of SCS today, such as long circulation cycle caused by low work efficiency, insecure and diseconomy on storage. More importantly, it is difficult to query and review historical records quickly, and it may even lead to the loss of records for various reasons. In this paper, taking advantage of blockchain technology, all the information of each experimental stage of SCS, including the approval information, experimental environment information, experimental equipment information of each manufacturer, experimental test reports and conclusions, are packaged and distributed managed. And then, a blockchain based experimental management system framework with the unified data form and module interface is proposed, in which the whole process of process record can be traced and cannot be tampered with. The experimental management system can be applied in the whole process of the SCS, including the experiment scheme making, experiment environment constructing, experiment processing, result evaluating, and results storage, etc. The blockchain based mechanism and system framework is expect to take many advantage to experiment management of SCS.
Scientific Design of Tools for Amplifying Intelligence

Bill Clancey is a senior research scientist at the Florida Institute for Human and Machine Cognition. Jim Euchner [JE]: Bill, you started your career by building one of the very earliest tutoring s...
Zero-Emission Vehicles Sonification Strategy Based on Shepard-Risset Glissando

In this paper, we present a sonification strategy developed for electric vehicles aiming to synthetize a new engine sound to enhance the driver’s dynamic perception of the vehicle. We chose to mimic the internal combustion engine (ICE) noise by informing the driver through pitch variations. However, ICE noise pitch variations are correlated to the engine’s rotations per minute (RPM) and its dynamics is covered within a limited vehicle speed range. In order to provide the driver with extended pitch variations throughout the full vehicle speed range, we based our sonification strategy on the Shepard-Risset glissando. Such illusory infinite ascending/descending sounds enable to represent accelerations with significant pitch variations for an unlimited range of speeds. In this way, it is possible to conserve the metaphor of ICE noise with unheard gearshifts. We tested this sonification strategy in a perceptual test in a driving simulator and showed that the mapping of this acoustical feedback affects the drivers’ perception of vehicle dynamics.
IMPROVED CLOUD MODEL FOR STABILITY EVALUATION OF RESERVOIR SLOPES

The stability of reservoir slope is affected by various factors so that it is still difficult to accurately evaluate their stability. This paper overcomes the two existing problems:(1)the eigenvalues of cloud model are not unified and (2)the expected rather than actual membership degrees are used as the traditional normal cloud model method is actually applied. It proposes an improved normal cloud model. A program for model application is developed on MATLAB software platform. Twelve factors that mainly affect the reservoir slope stability are selected as the evaluation indexes. The comprehensive cloud model of each stability level in regard to every evaluation index is generated from the cloud generator. Meanwhile,the weight of every evaluation index are obtained by the analytic hierarchy process on the basis of improved entropy weight. The maximum average membership degree is determined as the stability grade of a slope. The proposed method is employed in the stability evaluation of 34 engineering examples of reservoir slopes. And the evaluation results are compared with the ones by the CSMR method recommended by the current national code as well as the actual state of these slopes. It shows that the accuracy of this method is higher than that of CSMR method,and it has good reliability and practicability. The method can also be referred when evaluating the stability of other rock slopes such as road,railway or building slopes.
Online Dispute Resolution Simulation

Online dispute resolution (ODR) simulation workshops are designed to provide students with a virtual learning environment that empowers our students to gain legal and digital skills for their readiness in future employment. Students are invited to act as complainants, opponents and arbitrators/mediators to resolve a real-life case in a team-based, student-centred and research-informed teaching and learning environment. The ODR simulation workshops have been conducted by the author among both undergraduate and postgraduate law students since 2007 at Brunel University and other places. This ongoing project was initially funded by the Nominet Trust in 2010. Throughout these years, ODR simulation workshops have been well-received by students from different cultures, particularly where English is not their first language. Students were asked to conduct online arbitration or mediation hearings and submit arbitral awards and mediation settlements, as well as delivering technical observation notes and group presentations after the process. This article promotes the use of ODR simulation to effectively enhance students’ learning experience, legal skills (i.e. critical thinking, legal reasoning, problem-solving skills) and digital skills. It puts ODR simulation into the context of the shift in teaching approaches in the digital age and explains how modern legal education can be shaped to prepare for digital lawyering.&#x0D; Keywords: online dispute resolution; online arbitration; digital literacy; digital empowerment; artificial intelligence; digital lawyering; flexible learning; team-based learning; student-centred learning, research-informed teaching.
MosAIc: Finding Artistic Connections across Culture with Conditional Image Retrieval

We introduce MosAIc, an interactive web app that allows users to find pairs of semantically related artworks that span different cultures, media, and millennia. To create this application, we introduce Conditional Image Retrieval (CIR) which combines visual similarity search with user supplied filters or conditions. This technique allows one to find pairs of similar images that span distinct subsets of the image corpus. We provide a generic way to adapt existing image retrieval data-structures to this new domain and provide theoretical bounds on our approach's efficiency. To quantify the performance of CIR systems, we introduce new datasets for evaluating CIR methods and show that CIR performs non-parametric style transfer. Finally, we demonstrate that our CIR data-structures can identify blind spots in Generative Adversarial Networks (GAN) where they fail to properly model the true data distribution.
2D reverse Shack-Hartmann ocular refraction measurement

Traditional refraction measurement equipment is expensive, cumbersome and requires professional training for its operation. Therefore, refraction measurements are usually performed by eye care professionals, requiring an in-office visit. A handheld, low-cost and simple refraction measurement device based on the reverse Shack Hartmann technology is aimed for consumers, telehealth and at-home measurements. The device attaches to a smartphone where patterns on the screen are aligned by the user looking through the optics of the device. The optical train consists of chromatic separation into two channels that then combine in the user’s eye. The measurement consists of repeated alignment of the patterns by the user at different meridians of the pupil to allow for refraction mapping. The formulation of a 2D pattern and alignment scheme is presented for achieving optometric refraction values. In this case, the method maintains a low number of required measurements by the user. On the other hand, higher order aberrations may be measured using this method and the general terms of the measurement formulation is presented using the Zernike polynomials. The derivation is based on a model of the eye with an angular deviation field as the source of refraction. The paraxial approximation is used as the angular deviations are shown to be small for any ophthalmic condition. The model and formulation are then validated by a series of experiments in a calibrated model eye. Results show that two measurements are sufficient for producing accurate optometric refraction measurement values.
Evaluating ML Recommender Systems without Interactions Data

Recommendation systems can greatly enhance the user experience in a variety of applications. When developing a recommender system, it is optimal to have a large dataset of user-item interaction data with which to train, test, and evaluate the model. However, many applications that could benefit from a recommender do not have access to a large dataset of user behavior relevant to that application. The research described here concerns the development of a recommender for a web application for which there does not exist a large, appropriate dataset because it is still in the late prototype phase. The first stage of this research focuses on synthesizing artificial data to use in training and testing. The complex structure of user behavior could not be captured with rule-based randomly generated data, but a strategy was discovered for transforming existing, unrelated, datasets to fit the application's needs. This strategy can be broadly applied when developing recommenders for any application, and potentially to solve sparse data problems in other machine learning (ML) domains. With the presence of an adequate dataset, the new recommender can be effectively trained, evaluated, and compared. The second phase of research is ongoing, but preliminary results are promising.
Layered Drawing of Undirected Graphs with Generalized Port Constraints

The aim of this research is a practical method to draw cable plans of complex machines. Such plans consist of electronic components and cables connecting specific ports of the components. Since the machines are configured for each client individually, cable plans need to be drawn automatically. The drawings must be well readable so that technicians can use them to debug the machines. In order to model plug sockets, we introduce port groups; within a group, ports can change their position (which we use to improve the aesthetics of the layout), but together the ports of a group must form a contiguous block. We approach the problem of drawing such cable plans by extending the well-known Sugiyama framework such that it incorporates ports and port groups. Since the framework assumes directed graphs, we propose several ways to orient the edges of the given undirected graph. We compare these methods experimentally, both on real-world data and synthetic data that carefully simulates real-world data. We measure the aesthetics of the resulting drawings by counting bends and crossings. Using these metrics, we compare our approach to Kieler [JVLC 2014], a library for drawing graphs in the presence of port constraints.
Wilf-collapse in permutation classes having two basis elements of size three.

We consider permutation classes having two basis elements of size three and one further basis element. We completely classify the possible enumeration sequences of such classes and demonstrate that there are far fewer of them than might be expected in principle.
Automated Exam Paper Process Based on Schedule and Authenticity

The assessment of the student will be done by conducting of the examination in different levels. Setting of the question paper is the challenging task for the teacher in educational institutions. Question papers setting must follow certain standard methods. Each question will be specified with cognitive level of the course outcome defined by the expert teacher in an institute. A qualified question paper setting will sound the standardization of the educational institute. This will lead to judge the student performance in a qualitative way. Tools are required to design for atomicity of qualitative question papers generation. Most of the researchers are suggested to considered different parameters like cognitive levels, descriptions, duration etc. Atomicity of question paper generation is based on specified scheduled time along with security, authentication & control of the authorities. The proposed system is the smart question paper generation system based on schedule and authenticity. It will generate the question paper in a pdf format based on authenticated scheduling time. Examination paper automaticity is based on cognitive levels specified in the question bank that will store in data repository. Authentication is achieved by giving permissions through keys to generate & decrypt the unique exam paper by the authorized personnel in a given schedule time. Exam paper generation authenticity certificates are produced by the system based on schedule time of generation.