Reviewerly/exHarmony huggingface dataset:
Are Cryptocurrencies a Good Investment?

This article identifies the many risk factors an investor should consider before adding cryptocurrencies to their investment portfolio. The returns yielded by these digital assets can in some cases be high, but an investor should seek to understand what risks exist as well. There are numerous factors to consider when investing in any asset. Traditional assets may prompt a fundamental analysis of an industry, company (business plan, financials, leadership) and other considerations. However, cryptocurrencies are not traditional assets. This investment/speculative environment contains: extremely volatile assets, a largely unregulated crowd funding mechanism, chronic theft and loss, money laundering, high transaction processing latency, potential market manipulation, and an overhanging ethical question. This article is not meant as investment advice, but points to many risk factors that are not generally present when investing in traditional assets/companies, and highlights the fact that the data needed to perform due diligence is often lacking. <b>TOPICS:</b>Currency, portfolio construction, wealth management
Make Up Your Mind: The Price of Online Queries in Differential Privacy

We consider the problem of answering queries about a sensitive dataset subject to differential privacy. The queries may be chosen adversarially from a larger set $Q$ of allowable queries in one of three ways, which we list in order from easiest to hardest to answer:Offline: The queries are chosen all at once and the differentially private mechanism answers the queries in a single batch.&#x0D; Online: The queries are chosen all at once, but the mechanism only receives the queries in a streaming fashion and must answer each query before seeing the next query.&#x0D; Adaptive: The queries are chosen one at a time and the mechanism must answer each query before the next query is chosen. In particular, each query may depend on the answers given to previous queries.Many differentially private mechanisms are just as efficient in the adaptive model as they are in the offline model. Meanwhile, most lower bounds for differential privacy hold in the offline setting. This suggests that the three models may be equivalent.&#x0D; We prove that these models are all, in fact, distinct. Specifically, we show that there is a family of statistical queries such that exponentially more queries from this family can be answered in the offline model than in the online model. We also exhibit a family of search queries such that exponentially more queries from this family can be answered in the online model than in the adaptive model. We also investigate whether such separations might hold for simple queries like threshold queries over the real line.
Depth Estimation of a Deformable Object via a Monocular Camera

The depth estimation of the 3D deformable object has become increasingly crucial to various intelligent applications. In this paper, we propose a feature-based approach for accurate depth estimation of a deformable 3D object with a single camera, which reduces the problem of depth estimation to a pose estimation problem. The proposed method needs to reconstruct the target object at the very beginning. With the 3D reconstruction as an a priori model, only one monocular image is required afterwards to estimate the target object’s depth accurately, regardless of pose changes or deformability of the object. Experiments are taken on an NAO robot and a human to evaluate the depth estimation accuracy by the proposed method.
Reflection-mode digital gradient sensing method: measurement accuracy

Surface topography evaluation from measured surface slopes is critical in many engineering applications, including metrology of electronic substrates, optical elements, and small deformation measurement of engineering structures subjected to external thermomechanical stimuli. A recently proposed noncontact full-field optical method called reflection-mode digital gradient sensing (r-DGS) is able to measure small angular deflections of light rays proportional to two orthogonal surface slopes. It has been shown that submicron scale deformations can be detected by processing the slope data using a robust numerical integration scheme called the higher-order finite-difference-based least-squares integration. However, the smallest measurable deformations and the associated accuracy are yet to be determined. In our work, this very issue is addressed by carrying out experiments at temporally different recording frequencies, namely (a) ultrahigh-speed digital photography at 106 frames per second (fps) and (b) slow recording speeds of 101 fps. The results show that r-DGS is able to measure submicron out-of-plane deformations under latter conditions and nanoscale deformations in the former conditions.
An Efficient and Authentication Signcryption Scheme Based on Elliptic Curves

Signcryption schemes are compact and specially suited for efficiency-critical applications such as smart card dependent systems. Several researchers have performed a large number of significant applications of signcryption such as authenticated key recovery and key establishment in one mall data packet, secure ATM networks as well as light weight electronic transaction protocols and multi-casting over the internet. In this paper we have proposed an efficient and efficient scheme of signcryption symmetric key solutions, using elliptic curves by reducing senders computational cost. It needs two elliptic curve point multiplication for sender and comparative study of computational cost for sender and recipient as well as there is no any inverse computation for sender and recipient. This makes it more crucial than others.
Privacy-aware Remote Monitoring System by Skeleton Recognition

As the number of elderly people living alone increases, the need for remote monitoring system is also increasing.The system automatically checks the safety of the elderly and notifies the state to remote areas in case of anomalies.However, how to protect the privacy of the watched person becomes a problem.We propose that skeleton recognition technology is useful to monitor people with high accuracy while protecting the privacy.It can be used not only to investigate his/her posture and motion, but also to selectively analyze the voice emitted by himself/herself.We developed a system that combines skeleton recognition and selective speech recognition by the audio direction.In this paper, we will explain the improvement of our system and report some experiment results.
On the number of k-normal elements over finite fields

In this article we give an explicit formula for the number of $k$-normal elements, hence answering Problem 6.1. of Huczynska et al. (Existence and properties of $k$-normal elements over finite fields, Finite Fields Appl 2013; 24: 170-183). Furthermore, for some cases we provide formulas that require the solutions of some linear Diophantine equations. Our results depend on the explicit factorization of cyclotomic polynomials.
Object Tracking Based on Compressive Features and Extreme Learning Machine

Effective object tracking is a great challenging task in computer vision due to the appearance changes of an object. Extreme learning machine (ELM) can analytically determine the weights of feed-forward neural networks, while compressive features can be extracted via a random sparse matrix. Thus, their common advantages are running fast. Moreover, the strong learning ability of ELM is helpful to make a tracker robust target appearance change. In this paper, we propose an effective tracking method using ELM and compressive features. First, a bank of compressive Haar-like features is extracted with the sparse random matrix. To make the feature invariant to illumination, the mean illumination of each image patch is removed from each Haar-like feature. Second, a bank of ELM-based weak classifiers are combined together to form a strong classifier to complete tracking task. Considering the discrimination performance of features is uneven, the training accuracy of each ELM is employed as the confidence (weight) of the weak classifier. Thus, less discriminative features will be weakened. The proposed tracker has been tested on several representative sequences, and the results show that it achieves the highest tracking success rate and the tracking accuracy among the compared trackers including the FCT, CNT, MIL, KCF, and ESPT.
Pengenalan Pola Tulang Daun Dengan Jaringan Syaraf Tiruan Backpropagation

The development of technology has affected many areas of life. Progress in the field of Computer Science can reach other aspect of science. This research apply the knowledge of Computer Science in Biological Science, the one is the morphology of leaf venation. Leaf venation is an important aspect in the process of identification. Therefore, in this research developed thesystem that classify the type of leaf venation. This application is used as means of research on the performance of pattern recognition on backpropagation neural network. The system designed using the Java programming and socket programming to transfer data from the mobile device into the computer. Data testing is implemented using Android to facilitate process oftaking the picture. While in the process of training data for the optimal weight applied directly on the server computer by using Java Eclipse. In the stage of image processing is implemented by using the library of Canny edge detection. Data consisted of five categories of leaf vein pattern, with a sample of three leaves for each pattern. Training data using two of the three leaves for each pattern, with 10 images each leaf so that there are 20 images for each pattern, with a total of 100 images for all patterns. Data testing use 10 images from the third leaf to count the accuracy. The system managed to getthe best accuracy by using an image size of 200 x 200 with 100 hidden node with the average accuracy of 76%.&#x0D; Index Terms— Android, Canny Edge Detection, Java, Neural Networks Backpropagation, Socket Programming
Synthetic Image Augmentation for Improved Classification using Generative Adversarial Networks

Object detection and recognition has been an ongoing research topic for a long time in the field of computer vision.Even in robotics, detecting the state of an object by a robot still remains a challenging task.Also, collecting data for each possible state is also not feasible.In this literature, we use a deep convolutional neural network with SVM as a classifier to help with recognizing the state of a cooking object.We also study how a generative adversarial network can be used for synthetic data augmentation and improving the classification accuracy.The main motivation behind this work is to estimate how well a robot could recognize the current state of an object.
A unified polynomial selection method for the (tower) number field sieve algorithm

At Eurocrypt 2015, Barbulescu et al. introduced two new methods of polynomial selection, namely the Conjugation and the Generalised Joux-Lercier methods, for the number field sieve (NFS) algorithm as applied to the discrete logarithm problem over finite fields. A sequence of subsequent works have developed and applied these methods to the multiple and the (extended) tower number field sieve algorithms. This line of work has led to new asymptotic complexities for various cases of the discrete logarithm problem over finite fields. The current work presents a unified polynomial selection method which we call Algorithm $ \mathcal{D} $. Starting from the Barbulescu et al. paper, all the subsequent polynomial selection methods can be seen as special cases of Algorithm $ \mathcal{D} $. Moreover, for the extended tower number field sieve (exTNFS) and the multiple extended TNFS (MexTNFS), there are finite fields for which using the polynomials selected by Algorithm $ \mathcal{D} $ provides the best asymptotic complexity. Suppose $ Q = p^n $ for a prime $ p $ and further suppose that $ n = \eta\kappa $ such that there is a $ c_{\theta}&gt;0 $ for which $ p^{\eta} = L_Q(2/3, c_{\theta}) $. For $ c_{\theta}&gt;3.39 $, the complexity of exTNFS-$ \mathcal{D} $ is lower than the complexities of all previous algorithms; for $ c_{\theta}\notin (0, 1.12)\cup[1.45, 3.15] $, the complexity of MexTNFS-$ \mathcal{D} $ is lower than that of all previous methods.
Choosing the right loss function for multi-label Emotion Classification

Natural Language Processing problems has recently been benefited for the advances in Deep Learning. Many of these problems can be addressed as a multi-label classification problem. Usually, the metrics used to evaluate classification models are different from the loss functions used in the learning process. In this paper, we present a strategy to incorporate evaluation metrics in the learning process in order to increase the performance of the classifier according to the measure we are interested to favor. Concretely, we propose soft versions of the Accuracy, micro-F1, and macro-F1 measures that can be used as loss functions in the back-propagation algorithm. In order to experimentally validate our approach, we tested our system in an Emotion Classification task proposed at the International Workshop on Semantic Evaluation, SemEval-2018. Using a Convolutional Neural Network trained with the proposed loss functions we obtained significant improvements both for the English and the Spanish corpora.
Piloting the Purchase of Research Data Sets as Collections: Navigating the Unknowns

In fiscal year 2017, The Ohio State University Libraries in Columbus piloted the purchase of research data sets to explore how to integrate this format into the standard workflows of the collections strategist and electronic resources officer. The pilot project had few restrictions except that one-time money must be used and purchases must be available to the entire campus community. This pilot enabled the libraries to purchase several large-scale data sets while exploring issues around licensing, technical deployment, support, and assessment in an organization without a data librarian. This article details the pilot process and outlines the resulting considerations for future purchases of data sets.
Digital Assets Horizon in Smart Cities: Urban Congestion Management by IoT, Blockchain/DLT and Human Reinforcement

Cities are the future of humanity and for the coming majority, their next great home. This rapid urbanization of the planet presents great challenges and, at the same time, great opportunities. On the one hand, the concentration of people in urban spaces provides numerous economies of scale but, on the other hand, also implies the potential generation of negative externalities that must be managed by a “smarter city” to find a balance. Mobility in cities, the fact that moving in a vehicle (fuel, electric, shared, autonomous), public transport or other means is becoming a common, difficult and complex congestion problem for cities, has negative economic consequences. This enormous pressure for efficiency and the search for solutions are making significant inroads into the incorporation of ICT in smart cities and subsequent large-scale digitization to resolve this issue and other setbacks. This deployment needs to be conducted with a broad perspective incorporating economics and human concepts such as digital assets or citizen incentives in city strategies because in cities, people and resources are key players. Using a forgotten effects methodology, the present exploratory work shows a new model of city congestion management deploying the possibilities offered by some disruptive technologies such as the Internet of Things (IoT), blockchain/distributed ledger technology (DLT) and token economy, combined with a human capital aspect such as a reinforcement theory. The potential possibilities that these concepts have in future mobility and other fields are large in terms of moving towards a more sustainable, decentralized and intelligent management enabling an authentic digital jump.
A Norm Compliance Approach for Open and Goal-Directed Intelligent Systems

The increasing development of autonomous intelligent systems, such as smart vehicles, smart homes, and social robots, poses new challenges to face. Among them, ensuring that such systems behave lawfully is one of the crucial topics to be addressed for improving their employment in real contexts of daily life. In this work, we present an approach for norm compliance in the context of open and goal-directed intelligent systems working in dynamic normative environments where goals, services, and norms may change. Such an approach complements a goal-directed system modifying its goals and the way to achieve them for taking norms into accounts, thus influencing the practical reasoning process that goal-oriented systems implement for figuring out what to do. The conformity to norms is established at the goal level rather than at the action level. The effect of a norm that acts at the goal level spreads out at the lower level of actions, thus also improving system flexibility. Recovery mechanisms are provided to face exceptional situations that could be caused by normative changes. A case study in the field of the business organizations is presented for demonstrating the strengths of the proposed solution.
Creative Cooperation of Subjects of the Educational Process in the Context of Musical Activity

This article discusses the creative cooperation of subjects of the musical-educational process as a practical mechanism for the humanization of music education, which allows establishing “subject-subject” relations between the teacher/lecturer and pupils/students and contributes to the disclosure of the creative potential of all participants in the musical-educational process. The analysis of the scientific literature on the idea of cooperation in the history of education, musical pedagogy in retrospective and modern dimensions is presented. The main contradictions of its solution at the present stage are determined. The purpose of this article is to substantiate the ideas of pedagogy of cooperation in the context of the humanization of music education. The main methods used in the study are comparative, analytical, historical, system-structural. It is concluded that the artistic and pedagogical process in terms of music education allows establishing conditions for the creative cooperation of the teacher and the student.
Failure prediction of e-banking application system using adaptive neuro fuzzy inference system (ANFIS)

Problems often faced by IT operation unit is the difficulty in determining the cause of the failure of an incident such as slowing access to the internet banking url, non-functioning of some features of m-banking or even the cessation of the entire e-banking service. The proposed method to modify ANFIS with Fuzzy C-Means Clustering (FCM) approach is applied to detect four typical kinds of faults that may happen in the e-banking system, which are application response times, transaction per second, server utilization and network performance. Input data is obtained from the e-banking monitoring results throughout 2017 that become data training and data testing. The study shows that an ANFIS modeling with FCM optimized input has a RMSE 0.006 and increased accuracy by 1.27% compared to ANFIS without FCM optimization.
An Online Algorithm for Data Center Demand Response

Data centers often support a range of delay-tolerant workloads with adjustable execution time under a prespecified service level agreement. This potential for workload management has motivated utility companies to deploy demand response programs to encourage data centers toward shifting workload execution away from peak load periods. In this paper, we focus on data centers’ demand response considering the uncertainties in the arrival rates of the workloads, local renewable generation, and time-varying electricity prices. The centralized workload scheduling is shown to be a convex optimization problem. We deploy an online convex optimization framework to solve the centralized problem without any knowledge of the stochastic process that uncertain parameters follow. It also enables us to design a decentralized algorithm to address the high computational complexity of the centralized approach as well as the data centers’ coupled decision making under the real-time pricing scheme. We perform extensive simulations to demonstrate the lower running time of the decentralized algorithm compared to the centralized approach. Data center demand response benefits the utility company by 12.4% reduction in the peak load demand. It also benefits a data center by 12.2% reduction in the average daily cost.
DIGITAL TECHNOLOGY IN HIGHER EDUCATION: SITUATION ANALYSIS AND PROSPECTS ASSESSMENT (ON THE EXAMPLE OF KRASNOYARSK KRAI)

DIGITAL TECHNOLOGY IN HIGHER EDUCATION: SITUATION ANALYSIS AND PROSPECTS ASSESSMENT (ON THE EXAMPLE OF KRASNOYARSK KRAI)
Uploading speaking assignment to YouTube channel as an effort in increasing student’s pronunciation skill

YouTube is an internet channel in which people can freely find, watch, and upload any videos without any particular regulation. Learning motivation can be achieved in many ways. One of those is by uploading speaking assignment to YouTube channel,and a lot of people can watch it. This can be a strong motivation of the students to practice their pronunciation as well as possible. The objective of this study was to investigate student’s motivation in learning pronunciation by uploading their speaking assignment to YouTube. The study employed qualitative study with descriptive analytical approach. Qualitative study was chosen to dig deeper information related to the focus of the study. The instrument of the study was an open questioner with 5 questions related to student’s motivation in learning pronunciation. The study applied random sampling technique with 10 informants who had submitted their speaking assignment to YouTube. The study showed that uploading speaking videos in YouTube could increase student’s motivation. 80 percent of the respondents revealed that before they made their videos, they prepared and rehearsed to have correct pronunciation. In conclusion, uploading video in you tube channel had increased student’s motivation in mastering English pronunciation. DOI: 10.26905/enjourme.v2i2.2741
Prediction of Global Solar Radiation on the Horizontal Area with the Effect of Ambient Temperature Part: II

In the field of thermal conversion of solar energy, several studies have been conducted to model different parameters related to this process.But in all the models encountered, the parameters obtained are more or less close to reality.For us the best is the one that best reflects the variation of the modeled parameter.In fact, the confrontation of the values resulting from the experimental measurements makes it possible to judge the accuracy of the established model.In this context this study was conducted.The aim of the study is to model global solar radiation according to the ambient temperature, geographical location of the site, date and time.The obtained models are compared with the experimental results.In our experimental study we simulated the model developed by comparative study with the model of M-Y Mechraoui, showed that the experimental results are in good agreement with those given by the model developed, which is in agreement with that of M. Capderou,which presents a better estimate of global solar radiation with a small error between the measured values and those calculated.The set of studies was done for the Biskra site, horizontally.