Reviewerly/exHarmony huggingface dataset:
Hedonic pricing of cryptocurrency tokens

A cryptocurrency token offers a method of incentivizing behavior in a way that supports trusted interaction (through its blockchain-based infrastructure). It also acts as a multipurpose instrument that may fulfill a variety of roles, such as facilitating digital use cases or acting as a store of value. Understanding how to value such an instrument is complicated by these multiple roles because the relative valuation of one role cannot be disentangled from another role—a token is a ‘bundled’ good. In this work a general pricing model for cryptocurrency tokens is derived, based upon and extending the hedonic pricing framework of Rosen (J Polit Econ 82(1):34–55, https://doi.org/10.1086/260169, 1974) in a partial equilibrium framework. It is shown that individual roles (or characteristics) of a token may be priced by inverting in a special way the relationship between the token’s aggregate quantity and its provision of characteristics. Interaction between a monopolistic token seller and a representative buyer results in an equilibrium that clears both the aggregate token market and the characteristic market. Particular attention is given to the case in which a token possesses a security role, as this has been a focus of existing discussions regarding the regulation of the cryptocurrency market.
Survey on Local Binary Pattern Descriptors for Face Recognition

During the two past decades, the Local Binary Patterns descriptor demonstrated remarkable performance and high robustness in extracting distinguishing features from a given image. Therefore, this feature extraction method has been widely applied in diverse challenging computer vision applications including face recognition. The efficiency and usability of the LBP operator and its success in various real world applications has inspired the development of much new powerful LBP variants. Indeed, after the appearance of the LBP operator, several renowned extensions and modifications of LBP have been proposed in the literature to the point that it can be difficult to recognize their respective LBP-related strategies, strengths and weaknesses according to a given application, and there is a need for a complete comparative study in face recognition application. This paper reviews the performance of 30 recent state-of-the-art handcrafted descriptors in face recognition through a comprehensive experimental study using widely used benchmarks. Simulated experiments on ORL, Extended Yale B and FERET databases proved that some evaluated descriptors realize good classification results which outperform many recent state-of-the-art face recognition systems.
Deep Learning Based Pedestrian Trajectory Prediction Considering Location Relationship between Pedestrians

Pedestrian trajectory prediction is a challenging task because of the complex nature of humans. We propose to predict displacement between neighboring frames for each pedestrian sequentially. Specifically, we use an LSTM to model motion information for all pedestrians and use a mlp to map the location of each pedestrian to a high dimensional feature space where the inner product between features is used as a measurement for the positional relationship between two pedestrians. Then we weight the motion features of all pedestrians based on their positional relationship to the target for location displacement prediction. Experiments on publicly available datasets validate the effectiveness of our method for trajectory prediction.
Inner entanglements: Narrowing the search in classical planning by problem reformulation

Abstract In the field of automated planning, the central research focus is on domain‐independent planning engines that accept planning tasks (domain models and problem descriptions) in a description language, such as Planning Domain Definition Language, and return solution plans. The performance of planning engines can be improved by gathering additional knowledge about specific planning domain models/tasks (such as control rules) that can narrow the search for a solution plan. Such knowledge is often learned from training plans and solutions of simple tasks. Using techniques to reformulate the given planning task to incorporate additional knowledge, while keeping to the same input language, allows to exploit off‐the‐shelf planning engines. In this paper, we present inner entanglements that are relations between pairs of operators and predicates that represent the exclusivity of predicate achievement or requirement between the given operators. Inner entanglements can be encoded into a planner's input language by transforming the original planning task; hence, planning engines can exploit them. The contribution of this paper is to provide an in‐depth analysis and evaluation of inner entanglements, covering theoretical aspects such as complexity results, and an extensive empirical study using International Planning Competition benchmarks and state‐of‐the‐art planning engines.
Tuning Genetic Algorithms for Resource Provisioning and Scheduling in Uncertain Cloud Environments: Challenges and Findings

Cloud computing allows users to devise cost-effective solutions for deploying their applications. Nevertheless, the decisions about resource provisioning are very challenging because workloads are seriously affected by the uncertainty of cloud performance and their characteristics vary. In this paper we address these issues by explicitly modeling workload and cloud uncertainty in the decision process. For this purpose, we adopt a probabilistic formulation of the optimization problem aimed at minimizing the expected cost for deploying a parallel application under a deadline constraint. To find a sub-optimal solution of the problem we apply a Genetic Algorithm. By tuning its parameters we are able to assess their role and their impact on the effectiveness and efficiency of the algorithm for provisioning and scheduling in uncertain cloud environments.
Motion Control Analysis of a Spherical Robot as a Surveillance Robot

A robot is designed to be a helping tool for the human to work in the dirty, dull and dangerous environment, such as underwater, underground and disaster area. The most suitable type of robot for surveillance is a spherical robot. This type of robot has the ball-shaped, therefore minimize the contact to the ground, reduce friction, and energy consumption. All the components are shielded inside a shell; therefore it is possible to make this robot to be waterproof. One of the applications of this spherical robot is an industrial piping surveillance robot. The robot considered in this study is semi-autonomous where a user moves the robot through user interface installed in an android. The user can control the robot by examining the monitor. The input analysis of how the signal can move the robot is discussed in this paper. To prove the effectiveness of the proposed method, the robot is moved in an experimental setting including a pipe-like tunnel to test it. The robot moved and controlled smoothly, and it can maintain the position of the camera (robot head) always up to ensure the clear vision for the user.
A New Unsupervised Short-Utterance based Speaker Identification Approach with Parametric t-SNE Dimensionality Reduction

State-of-the-art speaker identification (SI) systems have achieved accuracies of 100% with long-duration utterances which are impractical. Recently, short-utterance based systems have gained attention although identification rates are lower. This paper presents an approach for text-dependent speaker phonemebased (<; 1sec) SI with parametric t-distributed stochastic neighbor embedding (pt-SNE) for dimensionality reduction of features to provide 3D-visualization. The approach employs Gaussian mixture model enhanced by K-means++ and gap statistic methods. As there is no other similar work, a fair comparison is unavailable. The 75% rate achieved is comparable to other works using (i) short-utterances (ii) pt-SNE for recognition of other data types.
Review of the Application of Ontology in the Field of Image Object Recognition

Image object recognition is an important research field in computer vision. It has a wide application prospect and practical significance in the information age. Although the current image recognition technology has achieved high accuracy in some tasks, the computer has many deficiencies in the automatic recognition of images such as fine-grained recognition, recognition of complex scenes. In these tasks, some issues exist like insufficient precision, complex high-level semantics which is difficult to identify and so on. This paper reviews the application of ontology in image object recognition. It is found that combining ontology knowledge model and traditional image recognition technology can improve recognition accuracy, enhance high-level semantic recognition ability, reduce the demand of the large number of training samples, and improve the scalability of the image recognition system. Otherwise, this paper also summarizes the frontier research of ontology applied in the field of image object recognition and the difficulties of deep integration of different technologies and ontology.
Deep learning for breast cancer classification with mammography

Current screening of mammography results in a high recall rate. Furthermore, distinguishing between BI-RADS 3 and BI-RADS 4 is a challenge for radiologists. In order to help radiologists’ diagnosis, researches of CAD system recently have shown that methods of deep learning can significantly improve lesion detection, segmentation, and classification. However, there is not enough evidence to show that deep learning models can reduce the high recall rate because few researches provide the performance of cases in BI-RADS 3 and BI-RADS 4. Moreover, few researches extended the current models to involve images in CC and MLO in a single prediction. Thus, we proposed convolutional neural networks to classify breast cancer. Our model could predict images in four input sizes. Besides, we extended our model to consider images in CC and MLO in a single prediction. To validate our models, we split the data depending on patients rather than images. Our training set was composed of 4255 images, and test set contained 355 images that were proven by biopsy and callback. The overall performance of human experts yielded on an accuracy of 65.3% while our model achieved a better accuracy of 79.6%. Besides, the performance of cases in BI-RADS 3 and 4 by human experts was accuracy of 54.1%, but our model maintained a high accuracy of 75.7%. When we combined images in CC and MLO in the single prediction, we achieved AUC of 0.86.
Automatic localization and extraction of tables from handheld mobile-camera captured handwritten document images

A table is a compact, effective and structured way of representing information in any document. Automatic localization of tables in scanned handwritten document images, and extracting the information are very critical and challenging task for applications like Optical Character Recognition, handwr iting analysis, and auto-evaluation systems. The same task becomes more complex, when the handwritten document images are acquired through handheld mobile-cameras, because the captured images naturally get distorted due to poor illumination, device vibration, camera-angle, camera-orientation, camera-movement, and camera-distance. In this research article, a novel technique of automatic localization and segmentation of tables in handwritten document images which are captured using a handheld mobile-camera is proposed. Generally, ruling lines are used for structuring tables, sketching figures, and scribing scientific equations. In the current research work, tables are detected and extracted based on edge features of the ruling lines subjected to three main stages. Firstly, block– wise mean-computed fuzzy based binarization technique is proposed for analyzing the distortion in the acquired image, and subsequently the background surface that envelops the document area of the image is removed. Secondly, horizontal and vertical granule or strip-based technique is proposed for fast edge-feature extraction from the ruling lines of the table in the binarized image. Finally, entropy quantifiers are employed for segmenting the table in the image. The performance of the proposed technique is evaluated and reported using the proposed composite handwritten benchmark daset. Linear computational benefit 0 (h × w) is observed in the worst-case tolerance.
Image Segmentation Using Contour Models

Image segmentation is an important task in image processing, which is widely used in medical applications such as abnormality detection and after treatment progress monitoring. Conventionally, texture, region, and edge information are used for segmentation. Recently, the majority of image segmentation uses contour-based models. The problem of efficient segmentation in medical images is of great importance in disease diagnosis. Medical images suffer from weak boundaries, and placement of initial contour is a major issue. Level method is an effective method for segmentation of image as it has ability to tackle complex geometries. It helps to detect the precise location of the target region and help to prevent the boundary leakage problem. This chapter presents an overview of the advanced region and edge-based level set segmentation algorithms and their application in the dental x-ray images. Computer-aided diagnosis from x-ray images are of interest to clinicians in detection and accurate decision making. Case studies of multiple region segmentation from dental x-rays are presented.
Comparison of methods for the geochemical determination of rare earth elements: Rock Canyon Creek REE–F–Ba deposit case study, SE British Columbia, Canada

Using the Rock Canyon Creek carbonate-hosted rare earth element (REE)–F–Ba deposit as an example, we demonstrate the need for verifying inherited geochemical data prior to reinterpretation. Inherited La, Ce, Nd and Sm data obtained by pressed pellet X-ray fluorescence (XRF), and La and Y data obtained by aqua regia digestion inductively coupled plasma atomic emission spectroscopy (ICP-AES) for more than 300 drill-core samples were analysed in 2009 and were subsequently compared to sample subsets re-analysed using lithium metaborate-tetraborate (LMB) fusion ICP mass spectroscopy (ICP-MS), Na2O2 fusion ICP-MS, and LMB fusion-XRF. We determine that LMB ICP-MS and Na2O2 ICP-MS accurately determined REE concentrations in control reference materials (CRM) SY-2 and SY-4, and provided precision of about 10%. Fusion-XRF was precise for La, Ce and Nd at concentrations greater than ten times the lower detection limit; however, accuracy of this method was not established because REE concentrations in SY-4 were below the lower detection limit. Analysis of the sample subset revealed substantial discrepancies for Ce concentrations determined by pressed pellet XRF in comparison to those determined by other methods due to Ba spectral interference. Samarium, present in lower concentrations than other REE that were determined, was consistently underestimated by XRF methods relative to ICP-MS methods. This may be the result of Sm concentrations approaching the lower detection limits of XRF methods, elemental interference or inadequate background corrections. Aqua regia dissolution results, reporting only for La and Y, are underestimated relative to the other methods. We highlight the importance of selecting the most appropriate analytical method and reference materials for determining the REE content of mineralized rock which may be several orders of magnitude higher than that of typical host rock.
Development of an application to support in‐service training of anesthesiologists on preoperative evaluation in a public hospital in Brazil

Abstract Rationale, aims and objectives This study investigated the process of construction, use, and field evaluation of a mobile application for teaching in the anesthesiology area. Method The application was built by using the Co‐Design Methodology, which consists of five phases (scoping, shared understanding, brainstorming, refinement and implementation). A one‐week case study was carried out in a hospital involving 20 participants (medical students and residents). Results The results of the test indicated that the developed application presented a high level of usability. Conclusions The students considered it to be a useful tool for training and systematizing the preoperative evaluation.
A Pragmatic View on Code Complexity Management

This article endeavors to underpin complexity understanding by scrutinizing how developers experience code complexity and how certain code characteristics impact complexity. The results provide a distinction between essential and accidental code characteristics and help in evaluating the influence of these characteristics on complexity increase.
Image super-resolution for heterogeneous embedded smart devices and displays in smart global village

The smart city has become the key factor of the progression of the future city, and the smart device sensors are widely used in real-time applications to achieve smart city. Especially, super-resolution techniques have resulted in embedded video surveillance turning into a principal method of protecting public security. In this paper, we present a super-resolution method for embedded system which interpolates unknown pixel components at the zero-padded signals. This work is for a smart global village by applying real-time embedded monitoring system (such as traffic purpose) using visual sensor networks. In general, images are obtained from a number of visual sensor nodes and cameras. The visual sensor node conveys them to the computer devices for processing. To this end, a process called image super-resolution is needed. In this work, a filter design method is researched by using a regression approach. There are two main stages of this work. First, we study filter designing method training dataset. Second, we implement super-resolution process by applying pre-designed filters. Experimental results are given to show the superiority of the proposed method compared to the conventional methods. This paper also demonstrates super-resolution results obtained by designed filter and its implementation.
Topic-Specific Emotion Mining Model for Online Comments

Nowadays, massive texts are generated on the web, which contain a variety of viewpoints, attitudes, and emotions for products and services. Subjective information mining of online comments is vital for enterprises to improve their products or services and for consumers to make purchase decisions. Various effective methods, the mainstream one of which is the topic model, have been put forward to solve this problem. Although most of topic models can mine the topic-level emotion of the product comments, they do not consider interword relations and the number of topics determined adaptively, which leads to poor comprehensibility, high time requirement, and low accuracy. To solve the above problems, this paper proposes an unsupervised Topic-Specific Emotion Mining Model (TSEM), which adds corresponding relationship between aspect words and opinion words to express comments as a bag of aspect–opinion pairs. On one hand, the rich semantic information obtained by adding interword relationship can enhance the comprehensibility of results. On the other hand, text dimensions reduced by adding relationships can cut the computation time. In addition, the number of topics in our model is adaptively determined by calculating perplexity to improve the emotion accuracy of the topic level. Our experiments using Taobao commodity comments achieve better results than baseline models in terms of accuracy, computation time, and comprehensibility. Therefore, our proposed model can be effectively applied to online comment emotion mining tasks.
A Survey of Prototype Side-channel Attacks Based on Machine Learning Algorithms for Cryptographic Chips

Cryptographic chips have been widely used in the field of digital security. With the continuous development of side-channel attacks, the security of cryptographic chips has attracted more and more attention. This paper mainly reviews the most distinguishing template attacks in current side-channel attacks, introduces the basic steps of template attack implementation, analyzes and discusses the existing template attacks, and finally focuses on the research progress of template attacks, especially the current the research status of template attacks based on machine learning and deep learning algorithms lays the foundation for further research.
Leveraging Fine-Grained Labels to Regularize Fine-Grained Visual Classification

Fine-grained visual categorization (FGVC) is challenging mainly due to the large intra-class confusion and small inter-class variance in terms of shape, pose, and appearance. We propose the concept of fine-grained label and that any given label can be further classified into some sub-classes as fine-grained labels, and thus samples of each original label are classifed into several sub-classes in which only more familiar samples are given the same fine-grained label. The samples of fine-grained labels have less intra-class confusion and bigger inter-class variance. Besides, fine-grained labels can be obtained through unsupervised means without any domain knowledge or annotations. Instead of training on the fine-grained labels directly, we utilize these "free" labels as an auxiliary task to regularize the training of the deep learning model. In the test phase, as sub-classes of the original label, the predicted fine-grained labels are used for integration with original labels to get the final classification results. Experiments on the popular CUB-200-2011 dataset demonstrate that employing the proposed fine-grained labels in CNN model improves performance from both training and test phases.
Design of Optical Reversible Hybrid Adder-Subtractor Device Using Mach-Zehnder Interferometers for WDM Applications

Abstract As energy efficiency is one of the prominent issues in the today’s era. Reversible computing may serve as a critical step toward this paramount issue. Optical reversible computing is one of the tactics to serve this aspiration. In this paper, an optical reversible hybrid adder-subtractor device delineated with the help of electro-optic effect inside lithium-niobate based Mach-Zehnder interferometer (MZI) for WDM applications. The proposed device in this work is combinational circuits and can be used as an adder as well as subtractor in advance applications. The perception is established with the help of simulation results of beam propagation method (BPM) and mathematically proven with MATLAB simulation results.
The New Silk Road: a bumpy ride for Sino-European collaborative research under the GDPR?

The Chinese New Silk Road initiative offers unique opportunities for setting up Sino-European research collaborations. Academic cooperation between countries that are rooted in completely different legal, cultural, and academic backgrounds might however also create new challenges. This article investigates the impact of these differences in the field of the protection of personal data, which is a topic that is currently high on the EU’s agenda. Whereas the protection of personal data is engrained in the European Union’s legal framework, this is not the case in China. This might be problematic, given the fact that scientific collaboration often entails the exchange of (sensitive) personal data. We explore to what extent the General Data Protection Regulation still allows the transfer of such data for scientific purposes to China. After having analyzed the Chinese system in light of the European legislation, we conclude that the sharing of personal data with China is challenging at a minimum. Until more stable legal arrangements are set up in order to facilitate such practices, it is important to share only anonymized data or to acquire consent of the data subject.