Reviewerly/exHarmony huggingface dataset:
Initial Coin Offerings, Blockchain Technology, and White Paper Disclosures

We examine voluntary disclosures made by issuers of initial coin offerings (ICOs) in their “white papers” and use them to rate the blockchain technology being developed by the ICO issuers. First, we find that most ICOs receive a low rating. In fact, over 80% of the ICOs do not need to use a blockchain. Second, ICO projects with a higher rating raised more funds than their counterparts, after controlling for other project-, issuer-, and token-related features. Third, ICOs with a high rating are more likely to have their digital coins/tokens listed on a cryptocurrency exchange within 180 days after their ICO end dates. Finally, results indicate that providing technical details in the white paper can be an effective way to signal the quality of an ICO project. Overall, these results are consistent with ICO token buyers/investors taking into consideration the underlying blockchain technology of the ICO projects when making their purchasing/investing decisions. Since the white paper is the main source of ICO information, our findings imply that the credibility of this information is important to protect the interests of ICO buyers/investors and to ensure the long-term viability of the ICO as a capital-raising tool for blockchain-based ventures.
Ensemble classification for imbalanced data based on feature space partitioning and hybrid metaheuristics

One of the most challenging issues when facing a classification problem is to deal with imbalanced datasets. Recently, ensemble classification techniques have proven to be very successful in addressing this problem. We present an ensemble classification approach based on feature space partitioning for imbalanced classification. A hybrid metaheuristic called GACE is used to optimize the different parameters related to the feature space partitioning. To assess the performance of the proposal, an extensive experimentation over imbalanced and real-world datasets compares different configurations and base classifiers. Its performance is competitive with that of reference techniques in the literature.
Designing for Digital Wellbeing

Traditionally, many consumer-focused technologies have been designed to maximize user engagement with their products and services. More recently, many technology companies have begun to introduce digital wellbeing features, such as for managing time spent and for encouraging breaks in use. These are in the context of, and likely in response to, renewed concerns in the media about technology dependency and even addiction. The promotion of technology abstinence is also increasingly widespread, e.g., via digital detoxes. Given that digital technologies are an important and valuable feature of many people's lives, digital wellbeing features are arguably preferable to abstinence. However, how these are defined and designed is something that needs to be explored further. In this one-day workshop we welcome both industry and academic participants to discuss what digital wellbeing means, who is responsible for it, and whether and how we should design for it going forward.
Deterministic Entanglement Swapping in a Superconducting Circuit

Entanglement swapping, the process to entangle two particles without coupling them in any way, is one of the most striking manifestations of the quantum-mechanical nonlocal characteristic. Besides fundamental interest, this process has applications in complex entanglement manipulation and quantum communication. Here we report a high-fidelity, unconditional entanglement swapping experiment in a superconducting circuit. The measured concurrence characterizing the qubit-qubit entanglement produced by swapping is above 0.75, confirming most of the entanglement of one qubit with its partner is deterministically transferred to another qubit that has never interacted with it. We further realize delayed-choice entanglement swapping, showing whether two qubits previously behaved as in an entangled state or as in a separable state is determined by a later choice of the type of measurement on their partners. This is the first demonstration of entanglement-separability duality in a deterministic way.
In situ sulfur isotope analysis by laser ablation MC-ICPMS and a case study of the Erlihe Zn-Pb ore deposit, Qinling orogenic belt, Central China

Sulfur isotopic composition is useful for tracing sources of ore-forming materials. In situ sulfur isotope analysis by laser ablation coupled with multi-collector inductively coupled plasma mass spectrometry (LA-MC-ICPMS) has an advantage over traditional bulk analysis in tracing sources of sulfur in an ore deposit that has complex mineral types and multiple ore-forming processes. However, in using this method, matrix effects exist widely for sulfides from ore deposits. In this work, matrix effects of different sulfides were examined by matrix-unmatched tests, in which the δ34S values of different types of sulfides were calibrated against a laboratory pyrite standard. The results show that the δ34S values of pyrrhotite, chalcopyrite, molybdenite, sphalerite, and arsenopyrite are in agreement with the reference values, suggesting that the matrix effect was negligible. However, a significant matrix effect occurred in the analyses of Ag2S and galena, leading to significant deviations in δ34S values of up to 1.2‰ and 3.2‰, respectively. The matrix effect occurred not only during the laser ablation stage but also during the transition of ions into the mass spectrometer, resulting in significant space charge effects caused by heavy-mass elements. Thus, pyrite can be used as an external bracketing standard for sulfides that do not have a heavy-mass matrix. Based on our matrix-effect study, the following available external standards were selected for the in situ sulfur isotope analysis of complex-type minerals from the Erlihe Zn-Pb deposit. The coarse-grained sphalerite grains (Sph1) and euhedral to subhedral pyrite grains (Py1) from massive ores have δ34S values ranging from 9.2‰ to 18.1‰. Such heavy sulfur isotopic compositions indicate the derivation of sulfur from the deposits of seawater sulfate, related to a thermochemical reducing process. Fine-grained sphalerite (Sph2) and pyrite (Py2) from vein and disseminated ores coexist with pyrrhotite and chalcopyrite, and have relatively light sulfur isotopic compositions, with δ34S values of about 7‰. The slightly positive δ34S values are similar to those of sulfides from sulfide-rich quartz veins, which contain δ34S values of about 4‰, and granodiorite rocks, with δ34S values of about 6‰. Based on previous studies, the light sulfur of the late-stage sulfides may have resulted from a metamorphic or magmatic hydrothermal event during the Indosinian orogeny. In situ sulfur analysis by LA-MC-ICPMS provides new evidence for sources of sulfur from each stage of mineralization in the Erlihe deposit, suggesting that this is a promising method for studying complex ore deposits.
Experimental study of Shor's factoring algorithm using the IBM Q Experience

We study the results of a compiled version of Shor's factoring algorithm on the ibmqx5 superconducting chip, for the particular case of $N=15$, 21, and 35. The semiclassical quantum Fourier transform is used to implement the algorithm with only a small number of physical qubits, and the circuits are designed to reduce the number of gates to the minimum. We use the square of the statistical overlap to give a quantitative measure of the similarity between the experimentally obtained distribution of phases and the predicted theoretical distribution of phases for different values of the period. This allows us to assign a period to the experimental data without the use of the continued fraction algorithm. A quantitative estimate of the error in our assignment of the period is then given by the overlap coefficient.
FineLocator: A novel approach to method-level fine-grained bug localization by query expansion

Bug localization, namely, to locate suspicious snippets from source code files for developers to fix the bug, is crucial for software quality assurance and software maintenance. Effective bug localization technique is desirable for software developers to reduce the effort involved in bug resolution. State-of-the-art bug localization techniques concentrate on file-level coarse-grained localization by lexical matching bug reports and source code files. However, this would bring about a heavy burden for developers to locate feasible code snippets to make change with the goal of fixing the bug. This paper proposes a novel approach called FineLocator to method-level fine-grained bug localization by using semantic similarity, temporal proximity and call dependency for method expansion. Firstly, the bug reports and the methods of source code are represented by numeric vectors using word embedding (word2vec) and the TF-IDF method. Secondly, we propose three query expansion scores as semantic similarity score, temporal proximity score and call dependency score to address the representation sparseness problem caused by the short lengths of methods in the source code. Then, the representation of a method with short length is augmented by elements of its neighboring methods with query expansion. Thirdly, when a new bug report is incoming, FineLocator will retrieve the methods in source code by similarity ranking on the bug report and the augmented methods for bug localization. We collect bug repositories of ArgoUML, Maven, Kylin, Ant and AspectJ projects to investigate the performance of the proposed FineLocator approach. Experimental results demonstrate that the proposed FineLocator approach can improve the performances of method-level bug localization at average by 20%, 21% and 17% measured by Top-N indicator, MAP and MRR respectively, in comparison with state-of-the-art techniques. This is the first paper to demonstrate how to make use of method expansion to address the representation sparseness problem for method-level fine-grained bug localization.
The Impact of Mobile Game-Based Language Learning Apps on EFL Learners’ Motivation

This study examines the effect of integrating mobile-game based language learning applications (MGBLLAs) on Saudi female English as a Foreign Language (EFL) students&amp;rsquo; motivation to learn English. It explores the perceptions of students regarding the pedagogical value of the following free MGBLLAs: Game books: Great Reader, Game to learn English - EnglishTracker, and Learn English Vocabulary Pop Quiz. A group of thirty Saudi female beginner level students, aged from 18-20 years old and enrolled for their foundation year at King Abdulaziz University (KAU) participated in the study. The study was carried out over a seven week period. Data were collected using two questionnaires. A pre-MGBLLAs integration questionnaire was modified to determine students&amp;rsquo; motivations for learning English. A post-MGBLLAs integration questionnaire designed by the author was also issued. It was utilized to explore the perceptions of students regarding the use of the three mobile game-based language learning apps, and to discover any impact on learner motivation. The results of the pre-MGBLLAs integration revealed that the EFL students were motivated to learn English. However, their motivation was high instrumental motivation, because it is taught as a compulsory course in their foundation year and they must achieve high scores to be able to start studying their preferred major. Significantly, the findings of the post-MGBLLAs integration questionnaire revealed that students perceived the three apps as beneficial for learning and improving motivation. These results contribute to the literature regarding mobile game based learning, and EFL students&amp;rsquo; motivation.
Improved PSO_AdaBoost Ensemble Algorithm for Imbalanced Data

The Adaptive Boosting (AdaBoost) algorithm is a widely used ensemble learning framework, and it can get good classification results on general datasets. However, it is challenging to apply the AdaBoost algorithm directly to imbalanced data since it is designed mainly for processing misclassified samples rather than samples of minority classes. To better process imbalanced data, this paper introduces the indicator Area Under Curve (AUC) which can reflect the comprehensive performance of the model, and proposes an improved AdaBoost algorithm based on AUC (AdaBoost-A) which improves the error calculation performance of the AdaBoost algorithm by comprehensively considering the effects of misclassification probability and AUC. To prevent redundant or useless weak classifiers the traditional AdaBoost algorithm generated from consuming too much system resources, this paper proposes an ensemble algorithm, PSOPD-AdaBoost-A, which can re-initialize parameters to avoid falling into local optimum, and optimize the coefficients of AdaBoost weak classifiers. Experiment results show that the proposed algorithm is effective for processing imbalanced data, especially the data with relatively high imbalances.
Interpretable deep learning for two-prong jet classification with jet spectra

A bstract Classification of jets with deep learning has gained significant attention in recent times. However, the performance of deep neural networks is often achieved at the cost of interpretability. Here we propose an interpretable network trained on the jet spectrum S 2 ( R ) which is a two-point correlation function of the jet constituents. The spectrum can be derived from a functional Taylor series of an arbitrary jet classifier function of energy flows. An interpretable network can be obtained by truncating the series. The intermediate feature of the network is an infrared and collinear safe C-correlator which allows us to estimate the importance of an S 2 ( R ) deposit at an angular scale R in the classification. The performance of the architecture is comparable to that of a convolutional neural network (CNN) trained on jet images, although the number of inputs and complexity of the architecture is significantly simpler than the CNN classifier. We consider two examples: one is the classification of two-prong jets which differ in color charge of the mother particle, and the other is a comparison between and generated jets.
Automatic Convolutional Neural Architecture Search for Image Classification Under Different Scenes

The recent advances in convolutional neural networks (CNNs) have used for image classification to achieve remarkable results.Different fields of image datasets will need different CNN architectures to achieve exceptional performance.However, designing a good CNN architecture is a computationally expensive task and requires expert knowledge.In this paper, we propose an effective framework to solve different image classification tasks using a convolutional neural architecture search (CNAS).The framework is inspired by current research on NAS, which automatically learns the best architecture for a specific training dataset, such as MNIST and CIFAR-10.Many search algorithms have been proposed for implementing NAS; however, insufficient attention has been paid to the selection of primitive operations (POs) in the search space.We propose a more efficient search space for learning the CNN architecture.Our search algorithm is based on Darts (a differential architecture search method), but it considers different numbers of intermediate nodes and replaces some unused POs by channel shuffle operation and squeeze-and-excitation operation.We achieve a better performance than Darts on both the CIFAR10/CIFA100 and Tiny-ImageNet datasets.We retain the none operation in deriving the architecture.The performance of the model has slightly decreased, but the number of architecture parameters has been reduced by approximately 40%.To balance the performance and the number of architecture parameters, the framework can learn a dense architecture for high-performance machines, such as servers, but a sparse architecture for resource-constrained devices, such as embedded systems or mobile devices.
Sentiment Classification by Leveraging the Shared Knowledge from a Sequence of Domains

This paper studies sentiment classification in a setting where a sequence of classification tasks is performed over time. The goal is to leverage the knowledge gained from previous tasks to do better on the new task than without using the previous knowledge. This is a lifelong learning setting. This paper proposes a novel deep learning model for lifelong sentiment classification. The key novelty of the proposed model is that it uses two networks: a knowledge retention network for retaining domain-specific knowledge learned in the past, and a feature learning network for classification feature learning. The two networks work together to perform the classification task. Our experimental results show that the proposed deep learning model outperforms the state-of-the-art baselines.
Boundary objects and their use in agile systems engineering

Summary Agile methods are increasingly introduced in automotive companies in the attempt to become more efficient and flexible in the system development. The adoption of agile practices influences communication between stakeholders and makes companies rethink the management of artifacts and documentation like requirements, safety compliance documents, and architecture models. Practitioners aim to reduce irrelevant documentation but face a lack of guidance to determine what artifacts are needed and how they should be managed. This paper presents artifacts, challenges, guidelines, and practices for the continuous management of systems engineering artifacts in automotive based on a theoretical and empirical understanding of the topic. In collaboration with 53 practitioners from six automotive companies, we conducted a design‐science study involving interviews, a questionnaire, focus groups, and practical data analysis of a systems engineering tool. The guidelines suggest the distinction between artifacts that are shared among different actors in a company (boundary objects) and those that are used within a team (locally relevant artifacts). We propose an analysis approach to identify boundary objects and three practices to manage systems engineering artifacts in industry.
Unknown-Length Handwritten Numeral String Recognition Using Cascade of PCA-SVMNet Classifiers

Automatic recognition of handwritten digit string with unknown length has many potential real applications. The most challenging step in this problem is how to efficiently segment connected and/or overlapped digits exhibited in the input image. Most existing numeral string segmentation approaches combine several segmentation hypotheses to handle various types of connected digits. This paper proposes a new handwritten digit string recognition without applying any explicit segmentation techniques. The proposed method uses a new cascade of hybrid principal component analysis network (PCANet) and support vector machine (SVM) classifier called PCA-SVMNet. PCANet is an emerging unsupervised simple deep neural network typically with only two convolutional layers. The proposed PCA-SVMNet model adds a new fully connected layer trained separately using SVM optimization method. Cascaded stages of PCA-SVMNet classifiers are constructed and trained to recognize various types of isolated and connected digits. Every PCA-SVMNet classifier is trained separately using combinations of real and synthetic touching digits. The first 1D-PCA-SVMNet stage is trained to recognize isolated handwritten digits (0...9) while forwarding non-isolated digits to the next stages. Each of the following stages is designed to recognize a class of connected digits and forwards the higher class to its successor. Multiple stages can be added accordingly to classify more complex touching digits. The experimental results using NIST SD19 real dataset show that the cascade of PCA-SVMNet classifier efficiently recognizes unknown handwritten digit string without applying any sophisticated segmentation methods. The proposed method achieves state-of-the-art recognition accuracy compared to other segmentation-free techniques.
Classification-Based and Energy-Efficient Dynamic Task Scheduling Scheme for Virtualized Cloud Data Center

The size and number of cloud data centers (CDCs) have grown rapidly with the increasing popularity of cloud computing and high-performance computing. This has the unintended consequences of creating new challenges due to inefficient use of resources and high energy consumption. Hence, this necessitates the need to maximize resource utilization and ensure energy efficiency in CDCs. One viable approach to achieve energy efficiency and resource utilization in CDC is task scheduling. While several task scheduling approaches have been proposed in the literature, there appears to be a lack of classification-based merging concept for real-time tasks in these existing approaches. Thus, an energy-efficient dynamic scheduling scheme (EDS) of real-time tasks for virtualized CDC is presented in this paper. In the scheduling scheme, the heterogeneous tasks and virtual machines are first classified based on a historical scheduling record. Then, similar type of tasks are merged and scheduled to maximally utilize an operational state of the host. In addition, energy efficiencies and optimal operating frequencies of heterogeneous physical hosts are employed to attain energy preservation while creating and deleting the virtual machines. Experimental results show that, in comparison with existing techniques, EDS significantly improves overall scheduling performance, achieves a higher CDC resource utilization, increases task guarantee ratio, minimizes the mean response time, and reduces energy consumption.
Are Girls Neko or Shōjo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization

Cross-lingual word embeddings (CLWE) underlie many multilingual natural language processing systems, often through orthogonal transformations of pre-trained monolingual embeddings.However, orthogonal mapping only works on language pairs whose embeddings are naturally isomorphic.For nonisomorphic pairs, our method (Iterative Normalization) transforms monolingual embeddings to make orthogonal alignment easier by simultaneously enforcing that (1) individual word vectors are unit length, and (2) each language's average vector is zero.Iterative Normalization consistently improves word translation accuracy of three CLWE methods, with the largest improvement observed on English-Japanese (from 2% to 44% test accuracy).
Denoising of MR images with Rician noise using a wider neural network and noise range division

Magnetic resonance (MR) images denoising is important in medical image analysis. Denoising methods based on deep learning have shown great promise and outperform all of the other conventional methods. However, deep-learning methods are limited by the number of training samples. In this article, using a small sample size, we applied a wider denoising neural network to MR images with Rician noise and trained several denoising models. The first model is specific to a certain noise, while the other applies to a wide range of noise levels. We considered the noise range as one interval, two sub-intervals, three sub-intervals, or even more sub-intervals to train the corresponding models. Experimental results demonstrate that for MR images, the proposed deep-learning models are efficient in terms of peak-signal-to-noise ratio, structure-similarity-index metrics and normalized mutual information. In addition, for blind noise, the effect of the three sub-intervals is better than that of the other sub-intervals.