Reviewerly/exHarmony huggingface dataset:
BEHAVIORAL BASIS OF CRYPTOCURRENCIES MARKETS: EXAMINING EFFECTS OF PUBLIC SENTIMENT, FEAR, AND UNCERTAINTY ON PRICE FORMATION

In recent years, cryptocurrencies have emerged as an exciting, innovative, and highly unorthodox asset class, primarily used for investment and trading purposes by globally-distributed investors. Although cryptocurrencies have attracted significant academic attention, there are currently no credible universally-accepted methodologies for determining their prices and returns. This study explores the use of sentiment analysis to model the effects of four different categories of sentiments towards the cryptocurrency markets to predict the direction of price: positivity/negativity (towards the underlying technology, development, and price of each cryptocurrency) and fear, uncertainty, and bullishness/bearishness in the financial markets. Investor sentiment is shown to successfully predict the price direction of cryptocurrencies, indicating that there is a potential for herding and anchoring biases among investors in crypto assets. Moreover, our analysis shows that cryptocurrencies can be used as a hedge against the stock market during times of market uncertainty, though not necessarily during times of investor fear.
A Preference-based Method of Updating the Surrogate Model by Broad Learning and Its Application

A surrogate model is normally employed to evaluate an individual instead of time-consuming and expensive simulation. Inaccuracy model learning by the limited samples may mislead the evolution process. Thus, effectively updating the model by learning more typical samples is necessary. In this paper, a preference-based method of updating the surrogate model by broad learning is proposed, with the purpose of improving the accuracy of the model with the least computation complexity. The preferred individuals with the least distances to the Pareto front are chosen as the infilling samples. Following that, the surrogate model is updated based on the infilling samples by broad learning, so as to reduce the learning time. The rationality of the proposed updating method is verified by the instance of constructing a surrogate model for bolt supporting quality. The experimental results show that the preference-based updating method has the best accuracy and the least running time. The obtained optimal solutions meet the actual preference of the decision makers with the best convergence.
The kernel of software engineering

The first course in software engineering is the most critical. Education must start from an understanding of the heart of software development, from familiar ground that is common to all software development endeavors. This book is an in-depth introduction to software engineering that uses a systematic, universal kernel to teach the essential elements of all software engineering methods. This kernel, Essence, is a vocabulary for defining methods and practices. Essence was envisioned and originally created by Ivar Jacobson and his colleagues, developed by Software Engineering Method and Theory (SEMAT) and approved by The Object Management Group (OMG) as a standard in 2014. Essence is a practiceindependent framework for thinking and reasoning about the practices we have and the practices we need. Essence establishes a shared and standard understanding of what is at the heart of software development. Essence is agnostic to any particular method, lifecycle independent, programming language independent, concise, scalable, extensible, and formally specified. Essence frees the practices from their method prisons. The first part of the book describes Essence, the essential elements to work with, the essential things to do and the essential competencies you need when developing software. The other three parts describe more and more advanced use cases of Essence. Using real but manageable examples, it covers the fundamentals of Essence and the innovative use of serious games to support software engineering. It also explains how current practices such as user stories, use cases, Scrum, and microservices can be described using Essence, and illustrates how their activities can be represented using the Essence notions of cards and checklists. The fourth part of the book offers a vision how Essence can be scaled to support large, complex systems engineering. Essence is supported by an ecosystem developed and maintained by a community of experienced people worldwide. From this ecosystem, professors and students can select what they need and create their own way of working, thus learning how to create ONE way of working that matches the particular situation and needs.
Using Mandatory Concepts for Knowledge Discovery and Data Structuring

A data scientist could apply several machine learning approaches in order to discover valuable knowledge from the data. While applying several techniques, he might discover that some pieces of knowledge are invariant, what ever the technique he used. We consider such knowledge as mandatory concepts, i.e., unavoidable knowledge to be discovered. As interesting property, a mandatory concept is characterized by a non-shared isolated point, that relates pieces of data, e.g., an object to a property, a document to specific words, an image to a specific topic, etc. Hence, the isolated points allow to make the distinction between the concepts. In this paper, we present a new approach for mandatory concepts extraction by making a level-based properties composition. Hence, the N-Composites isolated points are identified and constitute a key element for mandatory concept localization. We experiment our new algorithm by considering the coverage quality metrics.
Practical Applications of Investments in Cryptocurrencies: <i>Handle with Care!</i>

<h3>Practical Applications Summary</h3> In <b>Investments in Cryptocurrencies: <i>Handle with Care!</i></b> from the Summer 2019 issue of <b><i>The Journal of Alternative Investments</i></b>, author <b>Tobias N. Glas</b> (of the Department of Finance at the <b>University of Bremen, Germany</b>) uses an extensive data set to analyze the new asset class of cryptocurrencies, such as Bitcoin. He demonstrates that the investment performance of cryptocurrencies has little or no correlation with the performance of traditional markets and investments, or the macroeconomic environment in general. The mean monthly investment returns of cryptocurrencies are basically random, and only a few of the traditional investment styles produce positive results when applied to cryptocurrencies. Therefore, traditional market mechanics cannot yet be applied to cryptocurrency markets—so the author advises investors to handle cryptocurrencies with care. On the other hand, a few individual digital coins dominate the cryptocurrency market, and they have posted high average returns for investors who bought and held them. Also, the investment performance of cryptocurrencies is not influenced by that of stocks or other investments—and so cryptocurrencies will not necessarily go down if the stock market goes down. So adding cryptocurrencies to a portfolio can make it more diversified. <b>TOPICS:</b>Currency, portfolio construction, risk management, performance measurement
Quantitative Automata under Probabilistic Semantics.

Automata with monitor counters, where the transitions do not depend oncounter values, and nested weighted automata are two expressiveautomata-theoretic frameworks for quantitative properties. For a well-studiedand wide class of quantitative functions, we establish that automata withmonitor counters and nested weighted automata are equivalent. We study for thefirst time such quantitative automata under probabilistic semantics. We showthat several problems that are undecidable for the classical questions ofemptiness and universality become decidable under the probabilistic semantics.We present a complete picture of decidability for such automata, and even analmost-complete picture of computational complexity, for the probabilisticquestions we consider.
Gradient-free Algorithms for Graph Embedding

Graph-based data are very ubiquitous in many real-world scenarios, and it is usually difficult to mine valuable information from the large scale graphs because of traditional sparse and high-dimension representations of nodes in the graphs. To address this issue, the graph embedding techniques which aim to map the nodes of the graph into a low-dimension dense vector space are proposed. These vectors can act as the features of nodes for many graph analytics tasks. However, most existing graph embedding algorithms highly rely on gradient information, which largely restricts the flexibility and universality of algorithms and easily reaches local optimum. In this paper, we propose a general and flexible gradient-free (e.g. particle swarm optimization and differential evolution) graph embedding algorithmic framework, which introduces how to apply gradient-free algorithms on graph embedding problems. Furthermore, the experiments on three large scale real-world network datasets for nodes classification and nodes multi-label classification tasks show that the gradient-free graph embedding algorithms can obtain promising results.
Optical Fiber 3D Shape Sensor for Motion Capture

An optical fiber 3D shape sensor for motion capture is reported. The probe is comprised of Bragg grating strain sensors embedded into a 3D -printed polymer fiber substrate, allowing for the continuous assessment of the bending magnitude and direction. Moreover, the device was tested on the measurement of the forearm movements, making it possible to correctly replicate the captured motions in an avatar, providing a simple and minimally-invasive approach for applications in virtual reality.
AI judges make good calls on human rights violations but could be gamed

AI is going into law. It can now predict the outcomes of human rights cases and next year Estonia is planning to use the technology to moderate disputes in small claims courts
Enforcing Methodological Rules During Collaborative Brainstorming to Enhance Results

In order to get better results during brainstorming activities participants must respect some rules when they write notes about ideas and when they consider notes written by somebody else. We argue that the respect of these rules can be verified by a multi-agent system analyzing videos and notes produced by the participants in real time. This system can simplify the role of the meeting facilitator. Feedback is sent individually or is addressed globally to the entire team. This paper presents considerations about the necessity of rules, the structure of a multi-agent system for analyzing the respect of those rules and how experiments could show the level of acceptance by people of such a system. The goal of this prospective research is to add a non intrusive system during brainstorming sessions in order to enhance the quality of results.
DNNViz: Training Evolution Visualization for Deep Neural Network

In this paper, we present novel visualization strategies for inspecting, displaying, browsing, comparing, and visualizing deep neural networks (DNN) and their internal state during training. Despite their broad use across many fields of application, deep learning techniques are still often referred to as black boxes. Trying to get a better understanding of these models and how they work is a thriving field of research. To this end, we contribute with a visualization mechanism designed explicitly to enable simple and efficient introspection for deep neural networks. The mechanism processes, computes, and displays neurons activation during the training of a deep neural network. We furthermore demonstrate the usefulness of this visualization technique through different use cases: class similarity detection, hints for network pruning and adversarial attack detection. We implemented this mechanism in an open source tool called DNNViz, which is integrated into DeepDIVA, a highly-functional PyTorch framework for reproducible experiments.
Alignment strategy for an optomechanical image derotator using a laser Doppler vibrometer

When monitoring the condition of technical components, it is of particular interest to obtain information of the current condition of the component during operation. The main objectives are safety and machine efficiency. A special extension in this area is the derotator, since the derotator can be used to extend measuring methods designed for static components to rotating systems. In this paper, we present a new, to the best of our knowledge, approach to align a derotator using a laser Doppler vibrometer (LDV). When aligning the derotator, the optical axis must be coaxial with the rotating object to be measured. Current conventional approaches only ensure that the optical axis and the rotational axis intersect at the center of the measuring object. In our approach, a LDV is used to determine the angular deviation between the two axes, which cannot be determined by common methods. Our approach is easy to implement because the LDV is often already used in combination with the derotator as a measuring device. Experiments are carried out to show that this approach is feasible. In addition, several investigations are being performed to analyze how different parameters affect the result, creating reproducible conditions and deriving an alignment approach. By combining the approach developed in this work for determining a rotational deviation with common image-based approaches for determining a translational deviation of the axes, the derotator can be calibrated entirely. In this way, the quality of the measurement with the derotator can be further increased, as calibration errors can be reduced.
The language of software engineering

The first course in software engineering is the most critical. Education must start from an understanding of the heart of software development, from familiar ground that is common to all software development endeavors. This book is an in-depth introduction to software engineering that uses a systematic, universal kernel to teach the essential elements of all software engineering methods. This kernel, Essence, is a vocabulary for defining methods and practices. Essence was envisioned and originally created by Ivar Jacobson and his colleagues, developed by Software Engineering Method and Theory (SEMAT) and approved by The Object Management Group (OMG) as a standard in 2014. Essence is a practiceindependent framework for thinking and reasoning about the practices we have and the practices we need. Essence establishes a shared and standard understanding of what is at the heart of software development. Essence is agnostic to any particular method, lifecycle independent, programming language independent, concise, scalable, extensible, and formally specified. Essence frees the practices from their method prisons. The first part of the book describes Essence, the essential elements to work with, the essential things to do and the essential competencies you need when developing software. The other three parts describe more and more advanced use cases of Essence. Using real but manageable examples, it covers the fundamentals of Essence and the innovative use of serious games to support software engineering. It also explains how current practices such as user stories, use cases, Scrum, and microservices can be described using Essence, and illustrates how their activities can be represented using the Essence notions of cards and checklists. The fourth part of the book offers a vision how Essence can be scaled to support large, complex systems engineering. Essence is supported by an ecosystem developed and maintained by a community of experienced people worldwide. From this ecosystem, professors and students can select what they need and create their own way of working, thus learning how to create ONE way of working that matches the particular situation and needs.
A Data Mining Approach for Prediction Modeling Using Association rule

Â Â In the present work, a data mining approach is highlighted, a prediction optimization data mining approach association rule is chosen for performing prediction modeling in a supermarket application, a data mining prediction analysis model is formulated based on association rule is presented in this work. Â The result of the model formulated is then compared with the result produced on the similar set of input on the traditional optimization problems. While comparing the results it was observed that the result produced by the presented model is much closer to the reality.
Multitask and Transfer Learning for Autotuning Exascale Applications.

Multitask learning and transfer learning have proven to be useful in the field of machine learning when additional knowledge is available to help a prediction task. We aim at deriving methods following these paradigms for use in autotuning, where the goal is to find the optimal performance parameters of an application treated as a black-box function. We show comparative results with state-of-the-art autotuning techniques. For instance, we observe an average $1.5x$ improvement of the application runtime compared to the OpenTuner and HpBandSter autotuners. We explain how our approaches can be more suitable than some state-of-the-art autotuners for the tuning of any application in general and of expensive exascale applications in particular.
Hybrid 6D Object Pose Estimation from the RGB Image

In this research, we focus on the 6D pose estimation of known objects from the RGB image. In contrast to state of the art methods, which are based on the end-to-end neural network training, we proposed a hybrid approach. We use separate deep neural networks to: detect the object on the image, estimate the center of the object, and estimate the translation and ”in-place” rotation of the object. Then, we use geometrical relations on the image and the camera model to recover the full 6D object pose. As a result, we avoid the direct estimation of the object orientation defined in SO3 using a neural network. We propose the 4D-NET neural network to estimate translation and ”in-place” rotation of the object. Finally, we show results on the images generated from the Pascal VOC and ShapeNet datasets.
Radar Signal Waveform Recognition Based on Convolutional Denoising Autoencoder

To solve the problem of the low recognition rate of the existing methods at low signal-to-noise ratio (SNR), we propose a novel method of radar signal waveform recognition. In this method, we extract the time-frequency images (TFIs) of radar signals through Cohen class time frequency distribution. Then, we introduce convolutional denoising autoencoder (CDAE) to denoise and repairs the TFIs. Finally, we build a convolutional neural network (CNN) to identify the TFIs of radar signals. Simulation experiment shows that the proposed method can identify 12 kinds of radar signal waveforms, and the overall probability of successful recognition (PSR) is 95.4% when the SNR is −7 dB.
RRF-BD: Ranger Random Forest Algorithm for Big Data Classification

In the current era, data are growing with a faster rate in terms of exponential form where these data create a major challenge for suitable classification to classify the statistical data. The relevance of this topic is extraction of data, insights, mining of information from the dataset with an efficient and faster manner has attracted attention towards the best classification strategy. This paper presents a Ranger Random forest (RRF) algorithm for high-dimensional data classification. Random Forest (RF) has been treated as a most popular ensemble technique of classification due to its measure variable importance, out-of-bag error, proximities, etc. To make the classification constraint possible, in this paper, we use three different datasets in order to accommodate the runtime and memory utilization effectively with the same efficiency as given by the traditional random forest. We also depict the improvements of Random Forest in terms of computational time and memory without affecting the efficiency of the traditional Random Forest. Experimental results show that the proposed RRF outperforms with others in terms of memory utilization and computation time.
Evolving Art: Past, Present and Future

Evolutionary Computation techniques have been applied to several fields in the Arts in recent years. This talk overviews how they have been used to create different types of artifacts, and how past developments relate to current approaches, trends and challenges. The focus is to address a main challenge in the field, fitness assignment, analyzing this from the perspective of the interplay between the evolutionary system and the user, and discussing how Machine Learning, Evolutionary Computation and HCI techniques can be combined to create Computer Aided Creativity systems that allow the users to express their artistic or aesthetic intentions.
The Triumvirate: Effective Communication Across Publisher, Library, and Discovery Channels

In examining data transfer challenges from both librarian and publisher perspectives, this article based on a NC Serials Conference presentation calls for proactive industry communication across publisher, library, and discovery service channels. The authors discuss the importance of collaborative communication across the industry.
Motion-Let Clustering for Skeleton-Based Action Recognition

The representation of 3D skeleton is critical for human action recognition. In this paper, we propose a new approach for skeleton-based action recognition by motion-let clustering. To discover discriminative action features, action sequences are represented by the 3D movements of joints among neighbor frames, called motion-lets. We divide the joints into four groups according to the root-to-end structure, and project the motion-lets to 2D projections. In each 2D projection, the 2D motion-lets of each group are clustered to make primitive representation of actions as histograms. The temporal pyramid is employed to capture the temporal ordering of motion-lets followed by the Random Forest algorithm for classification. The proposed method is validated on four benchmark 3D human action datasets and the experimental results show that the proposed approach outperforms state-of-the-art deep-learning-based methods.