Reviewerly/exHarmony huggingface dataset:
Metasurface-Based Quantum Searcher on a Silicon-On-Insulator Chip

Optical analog computing has natural advantages of parallel computation, high speed and low energy consumption over traditional digital computing. To date, research in the field of on-chip optical analog computing has mainly focused on classical mathematical operations. Despite the advantages of quantum computing, on-chip quantum analog devices based on metasurfaces have not been demonstrated so far. In this work, based on a silicon-on-insulator (SOI) platform, we illustrated an on-chip quantum searcher with a characteristic size of 60 × 20 μm2. We applied classical waves to simulate the quantum search algorithm based on the superposition principle and interference effect, while combining it with an on-chip metasurface to realize modulation capability. The marked items are found when the incident waves are focused on the marked positions, which is precisely the same as the efficiency of the quantum search algorithm. The proposed on-chip quantum searcher facilitates the miniaturization and integration of wave-based signal processing systems.
A Model for Language Annotations on the Web

Several annotation models have been proposed to enable a multilingual Semantic Web. Such models hone in on the word and its morphology and assume the language tag and URI comes from external resources. These resources, such as ISO 639 and Glottolog, have limited coverage of the world’s languages and have a very limited thesaurus-like structure at best, which hampers language annotation, hence constraining research in Digital Humanities and other fields. To resolve this ‘outsourced’ task of the current models, we developed a model for representing information about languages, the Model for Language Annotation (MoLA), such that basic language information can be recorded consistently and therewith queried and analyzed as well. This includes the various types of languages, families, and the relations among them. MoLA is formalized in OWL so that it can integrate with Linguistic Linked Data resources. Sufficient coverage of MoLA is demonstrated with the use case of French.
An IT Project Management Methodology Generator Based on an Agile Project Management Process Framework

Information Technology Project Management and Software Project Management in particular depends heavily on the project’s type and constraints. Quality, financial, technical, schedule, complexity and other constraints affect significantly the management process. Over the last two decades project management methodologies have been developed to support the project management effort. Many methodologies cover generic approaches emphasizing on the planning or estimation activities, others on tracking, others on quality and others on very specific management practices that could support the delivery of very specific projects. This paper introduces an adjustable (agile) project management framework for managing information technology projects of any type. The framework divides the management activities into systems engineering management and systems acquisitions management phases and operates as a methodology generator feed by the project constraints. The project management methodology that derives is a combination of management and engineering phases based on the needs and constraints of each project per case.
Multi-layer relation networks for relational reasoning

Relational Networks (RN) as introduced by Santoro et al. in 2017 have demonstrated strong relational reasoning capabilities with a rather shallow architecture. Its single-layer design, however, only considers pairs of information objects, making it unsuitable for problems requiring reasoning across a higher number of facts. To overcome this limitation, we propose a multi-layer relation network architecture which enables successive refinements of relational information through multiple layers. We show that the increased depth allows for more complex relational reasoning by applying it to the bAbI 20 QA dataset, solving all 20 tasks with joint training and surpassing the state-of-the-art results.
Automated segmentation of abdominal organs from contrast‐enhanced computed tomography using analysis of texture features

Abstract Generation of three‐dimensional personalized geometric models of anatomical structures is an important process for many practical tasks: computer‐aided diagnosis, treatment planning and numerical modeling in biomedical applications. Despite many efforts done by different research groups, automatic segmentation of organs still does not have any general solution. The main difficulties are caused by peculiarities of different medical imaging modalities, image variability (for the same modality) resulting from the wide range of imaging devices, noise and artifacts, large patient anatomical variability and overlapping of intensity ranges of neighboring anatomical structures. In this article, we propose segmentation method based on analysis of texture features and developed specially for segmentation of abdominal organs. Its main advantage is robustness to interpatient gray level and anatomical variability. The proposed method was validated on the patient data. The method implementation was accelerated using graphics processing unit (GPU).
On Building an Automatic Identification of Country-Specific Feature Requests in Mobile App Reviews

Mobile app stores are available in over 150 countries, allowing users from all over the world to leave public reviews of downloaded apps. Previous studies have shown that such reviews can serve as sources of requirements and suggested that users from different countries have different needs and expectations regarding the same app. However, the tremendous quantity of reviews from multiple countries, as well as several other factors, complicates identifying country-specific app feature requests. In this work, we present a simple approach to address this through NLP-based analysis and discuss some of the challenges involved in using the NLP-based analysis for this task.
An Error Analysis of L2 Writing : How Does Task Complexity Affect L2 Errors?

Most task-based research examined the effects of task complexity on various aspects of L2 production, and this study aims to perform an in-depth analysis of task complexity effects on error patterns observed in L2 writing in terms of morphological, lexical, syntactic, and mechanical errors.39 learners of English carried out one written task with three levels of task complexity, operationalized as the number of elements.Results of a series of repeated-measures ANOVAs revealed that greater task complexity led to significant increases in the amount of all four error types and the numbers of morphological, lexical, and local syntactic errors per T-unit.It was also found that learners are more likely to make errors in terms of plurality, word choice, and article use when they need to mention more elements when carrying out complex tasks.
Environmentally Induced Entanglement – Anomalous Behavior in the Adiabatic Regime

Considering two non-interacting qubits in the context of open quantum systems, it is well known that their common environment may act as an entangling agent. In a perturbative regime the influence of the environment on the system dynamics can effectively be described by a unitary and a dissipative contribution. For the two-spin Boson model with (sub-) Ohmic spectral density considered here, the particular unitary contribution (Lamb shift) easily explains the buildup of entanglement between the two qubits. Furthermore it has been argued that in the adiabatic limit, adding the so-called counterterm to the microscopic model compensates the unitary influence of the environment and, thus, inhibits the generation of entanglement. Investigating this assertion is one of the main objectives of the work presented here. Using the hierarchy of pure states (HOPS) method to numerically calculate the exact reduced dynamics, we find and explain that the degree of inhibition crucially depends on the parameter $s$ determining the low frequency power law behavior of the spectral density $J(\omega) \sim \omega^s e^{-\omega/\omega_c}$. Remarkably, we find that for resonant qubits, even in the adiabatic regime (arbitrarily large $\omega_c$), the entanglement dynamics is still influenced by an environmentally induced Hamiltonian interaction. Further, we study the model in detail and present the exact entanglement dynamics for a wide range of coupling strengths, distinguish between resonant and detuned qubits, as well as Ohmic and deep sub-Ohmic environments. Notably, we find that in all cases the asymptotic entanglement does not vanish and conjecture a linear relation between the coupling strength and the asymptotic entanglement measured by means of concurrence. Further we discuss the suitability of various perturbative master equations for obtaining approximate entanglement dynamics.
Keyphrase Generation with GANs in Low-Resources Scenarios

Keyphrase Generation is the task of predicting Keyphrases (KPs), short phrases that summarize the semantic meaning of a given document.Several past studies provided diverse approaches to generate Keyphrases for an input document.However, all of these approaches still need to be trained on very large datasets.In this paper, we introduce BeGan-KP, a new conditional GAN model to address the problem of Keyphrase Generation in a low-resource scenario.Our main contribution relies in the Discriminator's architecture: a new BERT-based module which is able to distinguish between the generated and humancurated KPs reliably.Its characteristics allow us to use it in a low-resource scenario, where only a small amount of training data are available, obtaining an efficient Generator.The resulting architecture achieves, on five public datasets, competitive results with respect to the state-of-the-art approaches, using less than 1% of the training data.
Effective diagnostic model construction based on discriminative breast ultrasound image regions using deep feature extraction

Purpose This research aims to analyze the diagnostic contribution of different discriminative regions of the breast ultrasound image and develop a more effective diagnosis method taking advantage of the discriminative regions' complementarity. Methods First, the discriminative regions of the original breast ultrasound image as the inner region of the lesion, the marginal zone of the lesion, and the posterior echo region of the lesion were defined. The pretrained Inception‐V3 network was used to analyze the diagnostic contribution of these discriminative regions. Then, the network was applied to extract the deep features of the original image and the other three discriminative region images. Since there are many features, principal components analysis (PCA) was used to reduce the dimensionality of the extracted deep features. The selected deep features from different discriminative regions were fused to original image features and sent to the stacking ensemble learning classifier for classification experiments. In this study, 479 cases of breast ultrasound images, including 356 benign lesions and 123 malignant ones, were collected retrospectively and randomly divided into the training and validation set. Results Experimental results show that by using Inception‐V3, the diagnostic performance of each discriminative region is different, and the diagnostic accuracy and the area under the ROC curve (AUC) of the lesion marginal zone image (78.3%, 0.798) are higher than those of the lesion inner region image (73.3%, 0.763) and the posterior echo region image (71.7%, 0.688), but lower than those of the original image (80.0%, 0.817). Furthermore, the best classification performance was obtained when all the four types of deep features (from the original image and three discriminative region images) were fused, and the ensemble learning for classification evaluation was employed. Compared with the original image, the classification accuracy and AUC increased from 80.83%, 0.818 to 85.00%, 0.872, and the classification sensitivity and specificity varied from 0.710, 0.798 to 0.871, 0.787. Conclusions The inner region of the lesion, the marginal zone of the lesion, and the posterior echo region of the lesion play significant roles in the diagnosis of the breast ultrasound image. Deep feature fusion of these three kinds of images and the original image can effectively improve the accuracy of diagnosis.
A novel visibility semantic feature-aided pedestrian detection scheme for autonomous vehicles

Intelligent transportation systems (ITS) have become a popular method for enhancing transportation safety and efficiency. As essential participants of ITS, autonomous vehicles need to detect pedestrians accurately. In this paper, we propose a one-stage anchor-free pedestrian detection model named Bi-Center Network (BCNet), which is aided by the semantic features of pedestrians’ visible parts. We perform an ablation study to discover how visibility features could benefit the detector’s performance, including introducing two hyper-parameters and adopting three different attention mechanisms, respectively. The experimental results indicate that the performance of pedestrian detection could be significantly improved, since the visibility semantic could prompt stronger responses on the heatmap. We compare our BCNet variants with state-of-the-art models on the CityPersons dataset and ETH dataset; results indicate that our detector is effective and achieves a promising performance.
ASLR: An Adaptive Scheduler for Learning Rate

Training a neural network is a complicated and time-consuming task that involves adjusting and testing different combinations of hyperparameters. One of the essential hyperparameters is the learning rate, which balances the magnitude of changes at each training step. We introduce an Adaptive Scheduler for Learning Rate (ASLR) that significantly lowers the tuning effort since it only has a single hyperparameter. ASLR produces competitive results compared to the state-of-the-art for both hand-optimized learning rate schedulers and line search methods while requiring significantly less tuning effort. Our algorithm's computational cost is trivial and can be used to train various network topologies included quantized networks.
A Sentiment Analysis of Amazon Review Data Using Machine Learning Model

Nowadays everything is digitalized in the world. In the digitalization world E-commerce take a unique place for people. People are not going anywhere and buy all the thing at home using this E-commerce platform. For selecting the platform generally used the reviews of the people which are already buy from there. The paper proposes a sentiment analysis of the large amazon real dataset based on the counter vectorizer (CV) and term frequency inverse document frequency (TF-IDF) and logistic regressor. Firstly, take the dataset from the amazon E-commerce into JSON format and load the dataset and split the dataset into train test model. Secondly, take out the features using the counter vectorizer and term frequency inverse document frequency (TF-IDF). Finally, logistic regressor (LR) is used and measure the positive and negative sentiment of the review. simulation result represents the model accuracy score, precision, recall, confusion matrix of the implemented approach.
Privacy-preserving Online AutoML for Domain-Specific Face Detection

Despite the impressive progress of general face detection, the tuning of hyper-parameters and architectures is still critical for the performance of a domain-specific face detector. Though existing AutoML works can speedup such process, they either require tuning from scratch for a new scenario or do not consider data privacy. To scale up, we derive a new AutoML setting from a platform perspective. In such setting, new datasets sequentially arrive at the platform, where an architecture and hyper-parameter configuration is recommended to train the optimal face detector for each dataset. This, however, brings two major challenges: (1) how to predict the best configuration for any given dataset without touching their raw images due to the privacy concern? and (2) how to continuously improve the AutoML algorithm from previous tasks and offer a better warm-up for future ones? We introduce “HyperFD”, a new privacy-preserving online AutoML framework for face detection. At its core part, a novel meta-feature representation of a dataset as well as its learning paradigm is proposed. Thanks to HyperFD, each local task (client) is able to effectively leverage the learning “experience” of previous tasks without uploading raw images to the platform; meanwhile, the meta-feature extractor is continuously learned to better trade off the bias and variance. Extensive experiments demonstrate the effectiveness and efficiency of our design.
A novel neutrosophic AHP-Copeland approach for distance education: towards sustainability

Providing a sustainable distance education is paramouth of importance as a highly complex process requieing the involvement of various stakeholders. The purpose of this study is to investigate the criteria affecting the sustainability of distance education (DE) in higher education institutions, and to understand the differences in several internal stakeholders’ perspectives in DE via a combined group decision making approach of neutrosophic Analytic Hierarchy Process (AHP) and Copeland. The criteria affecting the sustainability of DE are identified via an in-depth literature review and expert opinions. Next, bibliometric analysis is conducted to illustrate the gap in the literature and justify the novelty of the suggested approach. Then, a novel integrated neutrosophic AHP and Copeland approach is developed to rank critical success factors by internal stakeholder’s perspective. The results integrate all internal stakeholder perspectives to evaluate and improve DE. The findings can guide the practitioners to develop DE planning. The contributions of this study stem from (i) discussing DE sustainability in higher education institutions, (ii) proposing a novel neutrosophic AHP-Copeland approach, (iii) presenting all internal stakeholders, (iv) addressing the uncertainty via truth, falsity and, indeterminacy memberships autonomously.
Active Ensemble Learning for Knowledge Graph Error Detection

Knowledge graphs (KGs) could effectively integrate a large number of real-world assertions, and improve the performance of various applications, such as recommendation and search. KG error detection has been intensively studied since real-world KGs inevitably contain erroneous triples. While existing studies focus on developing a novel algorithm dedicated to one or a few data characteristics, we explore advancing KG error detection by assembling a set of state-of-the-art (SOTA) KG error detectors. However, it is nontrivial to develop a practical ensemble learning framework for KG error detection. Existing ensemble learning models heavily rely on labels, while it is expensive to acquire labeled errors in KGs. Also, KG error detection itself is challenging since triples contain rich semantic information and might be false because of various reasons. To this end, we propose to leverage active learning to minimize human efforts. Our proposed framework - KAEL, could effectively assemble a set of off-the-shelf error detection algorithms, by actively using a limited number of manual annotations. It adaptively updates the ensemble learning policy in each iteration based on active queries, i.e., the answers from experts. After all annotation budget is used, KAEL utilizes the trained policy to identify remaining suspicious triples. Experiments on real-world KGs demonstrate that we can achieve significant improvement when applying KAEL to assemble SOTA error detectors. KAEL also outperforms SOTA ensemble learning baselines significantly.
Study of the factors of choosing the university by parents of as an important element of strategic marketing in education

П. Ю. Романов, Л. В. Смирнова, О
Mutation with Local Searching and Elite Inheritance Mechanism in Multi-Objective Optimization Algorithm: A Case Study in Software Product Line

An effective method for addressing the configuration optimization problem (COP) in Software Product Lines (SPLs) is to deploy a multi-objective evolutionary algorithm, for example, the state-of-the-art SATIBEA. In this paper, an improved hybrid algorithm, called SATIBEA-LSSF, is proposed to further improve the algorithm performance of SATIBEA, which is composed of a multi-children generating strategy, an enhanced mutation strategy with local searching and an elite inheritance mechanism. Empirical results on the same case studies demonstrate that our algorithm significantly outperforms the state-of-the-art for four out of five SPLs on a quality Hypervolume indicator and the convergence speed. To verify the effectiveness and robustness of our algorithm, the parameter sensitivity analysis is discussed and three observations are reported in detail.
NERD: Neural Network for Edict of Risky Data Streams

Cyber incidents can have a wide range of cause from a simple connection loss to an insistent attack. Once a potential cyber security incidents and system failures have been identified, deciding how to proceed is often complex. Especially, if the real cause is not directly in detail determinable. Therefore, we developed the concept of a Cyber Incident Handling Support System. The developed system is enriched with information by multiple sources such as intrusion detection systems and monitoring tools. It uses over twenty key attributes like sync-package ratio to identify potential security incidents and to classify the data into different priority categories. Afterwards, the system uses artificial intelligence to support the further decision-making process and to generate corresponding reports to brief the Board of Directors. Originating from this information, appropriate and detailed suggestions are made regarding the causes and troubleshooting measures. Feedback from users regarding the problem solutions are included into future decision-making by using labelled flow data as input for the learning process. The prototype shows that the decision making can be sustainably improved and the Cyber Incident Handling process becomes much more effective.
Emotions in online debates: Tales from <scp>4Forums</scp> and <scp>ConvinceMe</scp>

Abstract It is increasingly common for people to debate over various topics through online debate forums. While it has been shown that participants' emotional states affect debate processes and outcomes, it is unknown how different types of emotions are represented in online debates and what correlations exist between the emotions and other aspects of the debates such as their debate topic. We conduct a large‐scale analysis of the emotions in two online debate forums, namely, 4Forums and ConvinceMe. Specifically, we first develop an emotion recognition algorithm that uses multiple channels BLSTM with a feedforward attention mechanism, which outperforms the state‐of‐the‐art emotion recognition algorithm. Next, we label the emotions of each comment in the selected 4Forums and ConvinceMe discussions and analyze various aspects of the emotion's influence in the online debates. We observe that certain types of emotions are more likely dependent on the debate topic, and the prevalence of different emotions is independent of the individual discussions. We also observe emotion contagion between a comment and the immediately previous comment. We investigate the emotions of different types of respondents are less likely to express joy when they disagree and more likely to express disgust when they attack or disrespect to others.