Reviewerly/exHarmony huggingface dataset:
Alzheimer's Disease Brain Network Classification Using Improved Transfer Feature Learning with Joint Distribution Adaptation

Alzheimer's disease significantly affects the quality of life of patients. This paper proposes an approach to identify Alzheimer's disease based on transfer learning using functional MRI images, which is especially useful when the training dataset is small. Transfer learning improves the performance of the classifier with the help of an auxiliary dataset, which may be obtained from a different population group and/or machine. First, we used the joint distribution adaptation method to project the source and target domain samples into a new feature space, and then we built a classifier that works well in both the source and target domains but emphasizes the target domain. In the classifier, we assigned larger weights to the target domain samples and minimized the weighted loss in classifying the samples in both domains. Experimental results verify the effectiveness of our proposed approach and, with the help of the auxiliary samples, the classification accuracy of our target dataset has been greatly improved.
Geometrical X-lets for Image Denoising

There has been a lot of researches allocated to image denoising in recent years. One of the appropriate approaches for image denoising is applying nonlinear thresholding techniques in time-frequency transform domains. These transforms decompose an image to a series of elementary waveforms called basis functions or dictionary atoms. Different directional time-frequency dictionaries provide various geometrical X-let transforms in two or higher dimensions. In this paper, we have a comparative study of geometrical X-let transforms including 2D-Discrete Wavelet (2D-DWT), Dual-Tree Complex Wavelet (DT-CWT), Curvelet, Contourlet, Steerable Pyramid (STP) and Circlet Transform (CT) in application of image denoising. Experimental results show that in synthetic images of Optical Coherence Tomography (OCT), the Steerable Pyramid outperforms other geometrical X-lets in terms of Peak Signal-to-Noise Ratio (PSNR), while DT-CWT is superior in terms of Structural Similarity Index (SSIM). Moreover, in real images of OCT which consist of retinal layers, Curvelet Transform has better results in terms of Contrast-to-Noise Ratio (CNR) and 2D-DWT is better in Edge Preservation (EP) and Texture Preservation (TP) which indicate various X-lets can be effective due to different criteria and different images.
Universal Thesis of Meanders

Proposed is a universal thesis of meanders: In any flow, the main thread of flow that is the locus of the fastest fluid particle in each the cross-section governs the whole meandering of flow.  The main thread of flow plays the critical role in meanders of flow in such a way that if there is any disturbance the main thread of flow, as well as the flow itself, meander under the condition that the system of the rest subordinate surrounding fluid particles including boundaries is unstable. The balance between the disturbing force and the restoring force maintains the meandering of flow.
A Novel Weighted Combination Method for Feature Selection using Fuzzy Sets

In this paper, we propose a novel weighted combination feature selection method using bootstrap and fuzzy sets. The proposed method mainly consists of three processes, including fuzzy sets generation using bootstrap, weighted combination of fuzzy sets and feature ranking based on defuzzification. We implemented the proposed method by combining four state-of-the-art feature selection methods and evaluated the performance based on three publicly available biomedical datasets using fivefold cross validation. Based on the feature selection results, our proposed method produced comparable (if not better) classification accuracies to the best of the individual feature selection methods for all evaluated datasets. More importantly, we also applied standard deviation and Pearson’s correlation to measure the stability of the methods. Remarkably, our combination method achieved significantly higher stability than the four individual methods when variations and size reductions were introduced to the datasets.
Captions with Several Levels of Explanation

For the art appreciation in museums, usually a certain information will be provided as a caption. The visitor usually read the description to help his/her understanding artworks. Thus, such a help will be necessary for ordinal person’s understanding artworks. (Art) museums usually prepare such descriptions for general visitors. The quality and quantity of description sometimes influence the art appreciations. In the paper. we will discuss the effect of information in captions. Especially, we will discuss the amount of information according to visitor’s intelligent/cognitive level. The discussion will be conducted as curation.
A Novel Time Constraint-Based Approach for Knowledge Graph Conflict Resolution

Knowledge graph conflict resolution is a method to solve the knowledge conflict problem in constructing knowledge graphs. The existing methods ignore the time attributes of facts and the dynamic changes of the relationships between entities in knowledge graphs, which is liable to cause high error rates in dynamic knowledge graph construction. In this article, we propose a knowledge graph conflict resolution method, knowledge graph evolution algorithm based on deep learning (Kgedl), which can resolve facts confliction with high precision by combing time attributes, semantic embedding representations, and graph structure features. Kgedl first trains the semantic embedding vector through the relationships between entities. Then, the path embedding vector is trained from the graph structures of knowledge graphs, and the time attributes of entities are combined with the semantic and path embedding vectors. Finally, Kgedl uses a recurrent neural network to make the inconsistent facts appear in the dynamic evolution of the knowledge graph consistent. A large number of experiments on real datasets show that Kgedl outperforms the state-of-the-art methods. Especially, Kgedl achieves 23% higher performance than the classical method numerical Probabilistic Soft Logic (nPSL).in the metric HITS@10. Also, extensive experiments verified that our proposal possess better robustness by adding noise data.
Shading by Overhang PV Collectors

Photovoltaic modules integrated into buildings may provide shading to windows, doors and walls to protect against sun rays and at the same time generate ancillary electrical energy. The study develops the methodology for calculating the shadow variation cast by overhangs on doors, windows, carports, and calculates the annual incident energy (beam, diffuse and global energy) on overhangs made up of conventional and bifacial PV modules. The methodology of the present study is different from published articles including software programs. The study starts with shadows on walls cast by a horizontal pole and follows by shadows on walls cast by horizontal plates, inclined pole, inclined plate, and shaded area. The study deals also with overhangs placed one above the other. The calculation of the diffuse radiation involves the calculation of view factors to sky, to ground and between overhangs. In addition, the present study suggests using bifacial PV modules for overhangs and calculates the contribution of the reflective energy (5% and more) from walls and ground to the rear side of the bifacial PV module.
Extractive Text Summarization via Graph Entropy Çizge Entropi ile Çıkarıcı Metin Özetleme

There is growing interest in automatic summarizing systems. This study focuses on a subtractive, general and unsupervised summarization system. It is provided to represent the texts to be summarized with graphs and then graph entropy is used to interpret the structural stability and structural information content on the graphs representing the text files. The performance of the proposed text summarizing approach for the purpose of summarizing the text on the data set of Document Understanding Conference (DUC-2002) including open access texts and summaries of these texts was calculated using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) evaluation metrics. Experimental processes were repeated for 200 and 400 word abstracts. Experimental results reveale that the proposed text summarizing system performs competitively with competitive methods for different ROUGE metrics.
Legal Practitioners’ Approach to Regulating AI Risks

Many firms, including those that do not regard of themselves as traditional ‘tech’ firms, consider the prospect of artificial intelligence (AI) both an intriguing possibility and a potential new area of risk for their businesses. The application of existing AI technologies raises significant new issues in some of the most fundamental areas of law, including: ownership and property rights; the creation, allocation and sharing of value; misuse, errors, and responsibility for resulting harm; individual liberty and personal privacy; and economic collusion and monopolies. This chapter first examines how businesses are already managing some of these risks through contract. It then examines some of the considerations involved in public regulation of AI-related risks. It concludes by proposing a four-layer model for thinking about AI regulation in the broad sense.
A framework for sentiment analysis in Arabic text

&lt;p&gt;Over the last decade there has been an increase in number of E-mails or comments to a company via social media sites, to satisfy their customers, the company must take in to consideration these messages and comments and know whether the customers are satisfied with what the company offers or not. Several techniques have been proposed to analyze the sentiment of the comment writer. Dealing with the Arabic language is faced with many challenges, such as it is a morphologically rich language and how to return the word to its original root. In this paper the challenges of dealing with the Arabic language were reviewed and a framework was also established to analyze the comments in Arabic and classify it into positive, negative or neutral sentiment. The framework was trained and tested and then the con-clusions were drawn based on its work.&lt;/p&gt;
Study on factors affecting preschool education service quality

Purpose This paper aims to explore the critical factors which affect the quality of preschool education service so that targeted and effective measures to improve service quality can be put forward. Design/methodology/approach Evidential theory is applied to aggregate experts’ knowledge, and a fuzzy cognitive map (FCM) model of preschool education service quality is established to further carry out a simulation for inference, thus figuring out the critical factors to improve service quality. Findings The simulation results show that the main body of supervision and environment of governments and policies are two critical factors affecting the quality of preschool education service. More emphasis should be put on these two aspects, and corresponding measures can be put forward so as to ensure the quality of preschool education service. Originality/value This paper proposes a new model based on FCM and evidential theory to study the factors affecting preschool education service quality.
The Use of Ladder Snake Games to Improve Learning Outcomes in Computer Networking

Educational games enable students to learn and play. The use of educational games as learning media can also encourage users to discover and build their own knowledge of learning. The purpose of this study is to show the impact of using educational games on student learning outcomes. Educational games can be used as an alternative choice as a learning media. The research instruments used were post test and pre test. Data analysis was done by comparing the results of the post test and pre test. This analysis was conducted to see whether there were changes in student learning outcomes. The results shows that students’ learning outcome are increase by implementing educational games.
Question Classification with Untrained Recurrent Embeddings

Recurrent Neural Networks (RNNs) are at the foundation of many state-of-the-art results in text classification. However, to be effective in practical applications, they often require the use of sophisticated architectures and training techniques, such as gating mechanisms and pre-training by autoencoders or language modeling, with typically high computational cost. In this work, we show that such techniques could actually be not always necessary. In fact, our experimental results on a Question Classification task indicate that using state-of-the-art Reservoir Computing approaches for RNN design, it is possible to achieve competitive or comparable accuracy with a considerable advantage in terms of required training times.
Path planning for drones reconnaissance based on ant colony algorithms

Firstly, the traditional ant colony algorithm is introduced. Then it compares the difference between drone reconnaissance path planning problem and TSP. improving the traditional ant colony algorithm, which constrains the state transfer equation of ant colony algorithm so that the algorithm can simultaneously Planning multiple paths. Finally, we conducted an example analysis of road detection in Puerto Rico with the DroneGo disaster response system, and covered the island with a minimum number of drones.
Development of an Intelligent NLP-Based Audit Plan Knowledge Discovery System

ABSTRACT Auditors' discussions in audit plan brainstorming sessions provide valuable knowledge on how audit engagement teams evaluate information, identify and assess risks, and make audit decisions. Collected expertise and experience from experienced auditors can be used as decision support for future audit plan engagements. With the help of Natural Language Processing (NLP) techniques, this paper proposes an intelligent NLP-based audit plan knowledge discovery system (APKDS) that can collect and extract important contents from audit brainstorming discussions and transfer the extracted contents into an audit knowledge base for future use.
Circles: exploring multi-platform accessible, socially scalable VR in the classroom

In this paper, we describe a work-in-progress VR platform for education called Circles, as well as its associated background motivations, and relevant related work. We highlight the platform's experiential learning opportunities, but also its contributions towards developing socially scalable interactions to enhance collaborative learning and increased VR accessibility by supporting multiple virtual reality platforms (desktop, mobile, and HMD). We also briefly discuss some pilot studies around the development of three virtual environments highlighting the story of Canadian civil rights pioneer Viola Desmond through explorative narrative.
NEWCAST: Joint Resource Management and QoE-Driven Optimization for Mobile Video Streaming

Predicting future throughput in mobile networks becomes more and more possible today thanks to the rich contextual information provided by mobile applications and smartphone sensors. It is even likely that such contextual information, which may include traffic, mobility and radio conditions will lead to a novel agile resource management not yet thought of. In this paper, we propose a framework (called NEWCAST) that anticipates the throughput variations to deliver video streaming content. NEWCAST takes advantage of the capacity prediction in order to better distribute the resources allocated by the scheduler among users over the prediction horizon. This has the advantage of leading towards better user engagement for video streaming users without harming other traffics present in the system. We develop an optimization problem that realizes a fundamental trade-off among critical metrics that impact the user’s perceptual quality of experience (QoE) and the cost of system utilization. Both simulated and real-world throughput traces −1.5mm]Per IEEE style, reference citations are not permitted in the abstract. Please cite in text or confirm okay to leave as uncited. were carried out to evaluate the performance of NEWCAST. It is shown from our numerical results that NEWCAST provides the efficiency that the new 5G architectures require in terms of computational complexity and robustness. We also implement a prototype system of NEWCAST and evaluate it in a real environment with a real player to show its efficiency and scalability in a multi-users scenario compared to baseline adaptive bitrate algorithms.
Analysis of Adaptive Training for Learning to Rank in Information Retrieval

Learning to Rank is an important framework used in search engines to optimize the combination of multiple features in a single ranking function. In the existing work on learning to rank, such a ranking function is often trained on a large set of different queries to optimize the overall performance on all of them. However, the optimal parameters to combine those features are generally query-dependent, making such a strategy of "one size fits all" non-optimal. Some previous works have addressed this problem by suggesting a query-level adaptive training for learning to rank with promising results. However, previous work has not analyzed the reasons for the improvement. In this paper, we present a Best-Feature Calibration (BFC) strategy for analyzing learning to rank models and use this strategy to examine the benefit of query-level adaptive training. Our results show that the benefit of adaptive training mainly lies in the improvement of the robustness of learning to rank in cases where it does not perform as well as the best single feature.
Learning Sentence Embeddings for Coherence Modelling and Beyond

We present a novel and effective technique for performing text coherence tasks while facilitating deeper insights into the data.Despite obtaining ever-increasing task performance, modern deep-learning approaches to NLP tasks often only provide users with the final network decision and no additional understanding of the data.In this work, we show that a new type of sentence embedding learned through self-supervision can be applied effectively to text coherence tasks while serving as a window through which deeper understanding of the data can be obtained.To produce these sentence embeddings, we train a recurrent neural network to take individual sentences and predict their location in a document in the form of a distribution over locations.We demonstrate that these embeddings, combined with simple visual heuristics, can be used to achieve performance competitive with state-of-the-art on multiple text coherence tasks, outperforming more complex and specialized approaches.Additionally, we demonstrate that these embeddings can provide insights useful to writers for improving writing quality and informing document structuring, and assisting readers in summarizing and locating information. How coherent is it?Suggest a coherent sentence order Algorithm: If dashed line is close to diagonal, high coherence.If far, low coherence. Algorithm:Take sentences in the order that the black dots appear along the x-axis.Result: Sentences 1 and 2 may be out of order, otherwise it is quite close, with a coherence of 0.73.
Control in a Group of Unmanned Aerial Vehicles Based on Multi-Valued Logic

A new method for constructing fuzzy knowledge bases for controlling a group of unmanned aerial vehicles (UAVs) is presented, in which a multi-valued logic is used instead of fuzzy one. The truth-values scale is not a chain and it generalizes the valuation notion of fuzzy logic.
Are Cloud Modelling Languages Ready for Multi-Cloud?

Multi-cloud computing promises to deliver certain benefits, like performance optimisation and cost reduction. However, most cloud application modelling languages are tight to one cloud platform and do not have the right expressiveness to cover all application lifecycle phases. This survey attempts to review the most important from these languages, which facilitate application provisioning in commercial cloud platforms. The main review goals are to: (a) highlight those languages already or nearly multi-cloud enabled; (b) determine those parts of the remaining languages that must be extended to support multi-cloud application modelling. This review results also lead to drawing some future work directions towards producing an ideal multi-cloud application specification language.
RETRACTED ARTICLE: Application of deep learning algorithm in college English teaching process evaluation

We, the Editors and Publisher of the journal Behaviour & Information Technology, have retracted the following article which was part of the Special Issue on Behaviour Monitoring and Management of Customers, People and Organizations using Deep Learning: Xing Zhao (2019) Application of deep learning algorithm in college English teaching process evaluation, Behaviour & Information Technology, DOI: 10.1080/0144929X.2019.1690579 After publication it came to our attention that the person named as the Guest Editor of the Special Issue was impersonated by a fraudulent entity and the articles were not reviewed fully in line with the journal’s peer review standards and policy. We did not find any evidence of misconduct by the authors. However, in order to ensure full assessment has been conducted, we sought expert advice on the validity and quality of the published articles from independent peer reviewers. Following this post publication peer review, the editor has determined that the articles do not meet the required scholarly standards to remain published in the journal, and therefore has taken the decision to retract the articles. The authors have been informed of this decision.We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions.The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’.
Time Series Prediction Using Deterministic Geometric Semantic Genetic Programming

Predicting time series data is one of the most important challenges in many different application domains. Constructing the prediction models can be regarded as symbolic regressions, and the model can be optimized by Genetic Programming (GP), which is an evolutionary automatic programming method for tree structural programs. In the last decade, semantics-based genetic operators have attracted much attentions for improving search performance in the field of GP. As one of the semantics-based GP, we have previously proposed Deterministic Geometric Semantic GP (D-GSGP). Crossover operations in D-GSGP generate offspring by affine combinations of parents with the optimal combination ratios. We have shown the effectiveness in several benchmark functions in symbolic regression problems. In this research, we apply the method to a time-series forecasting problem, sunspot number series, as more practical application. The experimental results indicate that D-GSGP works effectively and the acquired programs are useful for knowledge acquisition of the application domain.
An Information Extraction Algorithm for Detecting Adverse Events in Neurosurgery Using Documents Written in a Natural Rich-in-Morphology Language.

Rich-in-morphology language, such as Russian, present a challenge for extraction of professional medical information. In this paper, we report on our solution to identify adverse events (complications) in neurosurgery based on natural language processing and professional medical judgment. The algorithm we proposed is easily implemented and feasible in a broad spectrum of clinical studies.