Reviewerly/exHarmony huggingface dataset:
Research on an Ensemble Classification Algorithm Based on Differential Privacy

In the field of information security, privacy protection based on machine learning is currently a hot topic. Combining differential privacy protection with AdaBoost, a machine learning ensemble classification algorithm, this paper proposes a scheme under differential privacy named CART-DPsAdaBoost (CART-Differential privacy structure of AdaBoost). In the process of boosting, the algorithm combines the idea of bagging, and uses a classification and regression tree (CART) stump as the base learner for ensemble learning. Applying feature perturbation, based on a random subspace algorithm, the exponential mechanism is used to select the splitting point for continuous attributes. We use the Gini index to find the optimal binary partitioning point for discrete attributes and add noise according to the Laplace mechanism. Throughout the process, a privacy budget is allocated in order to meet the appropriate differential privacy protection needs for the current application. Unlike similar algorithms, this method does not require discretization during preprocessing of the data. Experimental results with the Census Income, Digit Recognizer, and Adult Data Set show that while protecting private information, the scheme has little impact on classification accuracy and can effectively address large-scale and high-dimensional data classification problems.
Adversarial Attentive Multi-Modal Embedding Learning for Image-Text Matching

Matching the image and text with deep models has been extensively studied in recent years.Mining the correlation between image and text to learn effective multi-modal features is crucial for image-text matching.However, most existing approaches model the different types of correlation independently.In this work, we propose a novel model named Adversarial Attentive Multi-modal Embedding Learning (AAMEL) for image-text matching.It combines adversarial networks and attention mechanism to learn effective and robust multi-modal embeddings for better matching between the image and text.Adversarial learning is implemented as an interplay between two processes.First, two attention models are proposed to exploit two types of correlation between the image and text for multi-modal embedding learning and to confuse the other process.Then the discriminator tries to distinguish the two types of multi-modal embeddings learned by the two attention models, in which the two attention models are reinforced mutually.Through adversarial learning, it is expected that both the two embeddings from the attention models can well exploit the two types of correlation, and thus they can deceive the discriminator that they are generated from the other attention-based model.By integrating the attention mechanism and adversarial learning, the learned multi-modal embeddings are more effective for image and text matching.Extensive experiments have been conducted on the benchmark datasets of Flickr30K and MSCOCO to demonstrate the superiority of the proposed approaches over the state-of-the-art methods on image-text retrieval.
Behavioral Acoustic Emanations: Attack and Verification of PIN Entry Using Keypress Sounds

This paper explores the security vulnerability of Personal Identification Number (PIN) or numeric passwords. Entry Device (PEDs) that use small strings of data (PINs, keys or passwords) as means of verifying the legitimacy of a user. Today, PEDs are commonly used by personnel in different industrial and consumer electronic applications, such as entry at security checkpoints, ATMs and customer kiosks, etc. In this paper, we propose a side-channel attack on a 4-6 digit random PIN key, and a PIN key user verification method. The intervals between two keystrokes are extracted from the acoustic emanation and used as features to train machine-learning models. The attack model has a 60% chance to recover the PIN key. The verification model has an 88% accuracy on identifying the user. Our attack methods can perform key recovery by using the acoustic side-channel at low cost. As a countermeasure, our verification method can improve the security of PIN entry devices.
Designing Real-time, Continuous Emotion Annotation Techniques for 360° VR Videos

With the increasing availability of head-mounted displays (HMDs) that show immersive 360° VR content, it is important to understand to what extent these immersive experiences can evoke emotions. Typically to collect emotion ground truth labels, users rate videos through post-experience self-reports that are discrete in nature. However, post-stimuli self-reports are temporally imprecise, especially after watching 360° videos. In this work, we design six continuous emotion annotation techniques for the Oculus Rift HMD aimed at minimizing workload and distraction. Based on a co-design session with six experts, we contribute HaloLight and DotSize, two continuous annotation methods deemed unobtrusive and easy to understand. We discuss the next challenges for evaluating the usability of these techniques, and reliability of continuous annotations.
Predicting Clinical Outcomes with Patient Stratification via Deep Mixture Neural Networks.

The increasing availability of electronic health record data offers unprecedented opportunities for predictive modeling in healthcare informatics including outcomes such as mortality and disease diagnosis as well as risk factor identification. Recently, deep neural networks (DNNs) have been successfully applied in healthcare informatics and achieved state-of-art predictive performance. However, existing DNN models either rely on the pre-defined patient subgroups or take the "one-size-fits-all" approach and are built without considering patient stratification. Consequently, those models are not able to discover patient subgroups and the risk factors are thereafter identified for the entire patient population, failing to account for potential group differences. To address this challenge, we propose the use of deep mixture neural networks (DMNN), a unified DNN model for simultaneous patient stratification and predictive modeling. Experimental results on a clinic dataset show that our proposed DMNN can achieve good performance on predicting diagnosis of acute heart failure. With DMNN's ability to incorporate patient stratification, we are able to systematically identify group-specific risk factors for different patient subgroups which could potentially shed light on revealing factors that contribute to outcome differences.
Feature Extraction of Real-Time Image Using SIFT Algorithm

This paper deals with image processing and feature extraction. Feature extraction plays a vital role in the field of image processing. There exist different image pre-processing approaches for feature extraction such as binarization, thresholding, resizing, normalisation so on...Then after these techniques are applied to obtain high clarity images. In Feature extraction object recognition and stereo matching are at the base of many computer vision problems. The descriptor generator module is changed for increasing the performance of algorithm. SIFT algorithm consist of two modules such as key point detection module and descriptor generation module. When compared to recent solution, the descriptor generation module speed is fifteen times faster and the time for feature extraction is also reduced.
A Spatiotemporal Neural Network Modeling Method for Nonlinear Distributed Parameter Systems

Neural network (NN) has been widely used in the field of modeling of lumped parameter systems. However, an NN approach cannot be used to model complex nonlinear distributed parameter systems (DPSs) because it does not account for this type of system's relationship with space. In this article, we propose a novel spatiotemporal NN (SNN) method to model nonlinear DPSs, which considers not only nonlinear dynamics regarding time, but also a nonlinear relationship with space. A temporal NN model was first constructed to represent the nonlinear temporal dynamics of each sensor's position. A spatial distribution function was then developed to represent the nonlinear relationship between spatial points. This strategy results in inherent consideration of any spatial dynamics. Finally, by integrating both the temporal NN model and the spatial distribution function, a novel SNN model was created to represent the spatiotemporal dynamics of the nonlinear DPSs. A two-step solving approach was further developed to learn the model. Additional analysis and proof of concept showed the effectiveness of this proposed method. This proposed method is different from traditional data-driven modeling methods in that it uses full information from all sensors and does not require model reduction technology. Case studies not only demonstrate the effectiveness of this proposed method, but also its superior modeling performance as compared with several commonly used methods.
Cyber Fraud Prediction with Supervised Machine Learning Techniques

With the spread of electronic payments, the number of credit card transactions continues to grow, taking an increasing share of the U.S. payment system. However, this has led to an increasing rate of stolen accounts and losses for Banks. As a result, improved fraud detection has been the goal of reducing Banks' losses. In recent years, with the rapid development of information technology and the progress of machine learning methods, researchers are committed to applying some machine learning methods to financial field. In this paper, we detect credit card fraud transaction from publicly available datasets using Naive Bayes, logical regression, and artificial neural networks on cybersecurity classification problems. The results from our experiments show that for datasets with more balanced for classification, the accuracy of logistic regression was higher than Naive Bayes and neural network algorithms. The Naive Bayes algorithm performed better than logistic regression and neural networks in the dataset with highly imbalanced distribution.
Flange-Based Hand-Eye Calibration Using a 3D Camera With High Resolution, Accuracy, and Frame Rate

Point cloud data provides three-dimensional (3D) measurement of the geometric details in the physical world, which relies heavily on the quality of the machine vision system. In this paper, we explore the potentials of a 3D scanner of high quality (15 million points per second), accuracy (up to 0.150 mm), and frame rate (up to 20 FPS) during static and dynamic measurements of the robot flange for direct hand-eye calibration and trajectory error tracking. With the availability of high-quality point cloud data, we can exploit the standardized geometric features on the robot flange for 3D measurements, which are directly accessible for hand-eye calibration problems. In the meanwhile, we tested the proposed flange-based calibration methods in a dynamic setting to capture point cloud data in a high frame rate. We found that our proposed method works robustly, even in dynamic environments, enabling a versatile hand-eye calibration during motion. Furthermore, capturing high-quality point cloud data in real-time opens new doors for the use of 3D scanners, capable of detecting sensitive anomalies of refined details even in motion trajectories. Codes and sample data of this calibration method is provided at Github (https://github.com/ancorasir/flange_handeye_calibration).
FLAG-PDFe: Features Oriented Metadata Extraction Framework for Scientific Publications

The unprecedented growth of the research publications in diversified domains has overwhelmed the research community.It requires a cumbersome process to extract this enormous information by manually analyzing these research documents.To automatically extract content of a document in a structured way, metadata and content must be annotated.Scientific community has been focusing on automatic extraction of content by forming different heuristics and applying different machine learning techniques.One of the renowned conference organizers, ESWC organizes state-of-the-art challenge to extract metadata like authors, affiliations, countries in affiliations, supplementary material, sections, table, figures, funding agencies, and EU funded projects from PDF files of research articles.We have proposed a feature centric technique that can be used to extract logical layout structure of articles from publishers with diversified composition styles.To extract unique metadata from a research article placed in logical layout structure, we have developed a four-staged novel approach ''FLAG-PDFe''.The approach is built upon distinct and generic features based on the textual and the geometric information from the raw content of research documents.At the first stage, the distinct features are used to identify different physical layout components of an individual article.Since research journals follow their unique publishing styles and layout formats, therefore, we develop generic features to handle these diversified publishing patterns.We employ support vector classification (SVC) in the third stage to extract the logical layout structure (LLS)/ sections of an article, after performing comprehensive evaluation of generic features and machine learning models.Finally, we further apply heuristics on LLS to extract the desired metadata of an article.The outcomes of the study are obtained using the gold standard data set.The results yields 0.877 recall, precision 0.928 and 0.897 F-measure.Our approach has achieved a 16% gain on f-measure when compared to the best approach of the ESWC challenge.
Examining the effects of developer familiarity on bug fixing

Background: In modern software systems' maintenance and evolution, how to fix software bugs efficiently and effectively becomes increasingly more essential. A deep understanding of developers'/assignees' familiarity with bugs could help project managers make a proper allotment of maintenance resources. However, to our knowledge, the effects of developer familiarity on bug fixing have not been studied. Aims: Inspired by the understanding of developers'/assignees' familiarity with bugs, we aim to investigate the effects of familiarity on efficiency and effectiveness of bug fixing. Method: Based on evolution history of buggy code lines, we propose three metrics to evaluate the developers'/assignees' familiarity with bugs. Additionally, we conduct an empirical study on 6 well-known Apache Software Foundation projects with more than 9000 confirmed bugs. Results: We observe that (a) familiarity is one of the common factors in cases of bug fixing: the developers are more likely to be assigned to fix the bugs introduced by themselves; (b) familiarity has complex effects on bug fixing: although the developers fix the bugs introduced by themselves more quickly (with high efficiency), they are more likely to introduce future bugs when fixing the current bugs (with worse effectiveness). Conclusion: We put forward the following suggestions: (a) managers should assign some "outsiders" to participate in bug fixing. (b) when developers deal with his own code, managers should assign more maintenance resource (e.g., more inspection) to developers.
The digitalization of universities from a students’ perspective

The digitalization of higher education institutions is progressing significantly. Though the use of digital assets enhances the students’ learning experience and offers new opportunities for administration, there are no uniform standards for the use of digital media in teaching and student services. As educational service providers, universities are dependent on students being able to cope with the structures offered. Thus it is essential to ascertain students’ attitudes of the technologies used. We asked students from three blended learning courses about their perceptions. We further asked the students what should be done and by whom. Our results show that students see structural changes occurring not only in themselves but also at the level of the university management. Our research contributes to the actual discussion about the digitalization of higher education by offering suggestions for development from a students’ view. The results are valuable for lecturers and faculty managers who want to advance the digitalization of services and learning.
An integrated retrieval framework for similar questions: Word-semantic embedded label clustering – LDA with question life cycle

Question retrieval is an extremely important research field in Community Question Answering (CQA). Most existing question retrieval methods depend on semantic analysis of questions, whose effectiveness suffers from the short texts of the noise words in the question corpus. In order to recommend the questions with more advanced knowledge to users, the influence of the questions’ popularity should be considered during retrieving questions. To make retrieved questions with both similar semantics and high popularity, we propose an Integrated Retrieval Framework for Similar Questions named Word-semantic Embedded Label Clustering – LDA with Question Life Cycle (WELQLC-QR), consisting of Word-semantic Embedded Label Clustering – LDA (WEL) and Question Life Cycle Optimization Similar Question List Strategy (QLC). Firstly, WEL is proposed for question retrieval from the perspective of semantic matching. It not only overcomes the problem of over-generalization of the semantic information extracted by topic models when facing short questions with multi-levels labels, but also avoids the influence of noise vocabularies during semantic extracting of the questions. Then, based on the internal factors (i.e., the number of comments and answers to the question) and external factors (i.e., programming language ranking information) of questions, QLC constructs a popularity-predicted model to optimize the similar question set searched by WEL, making the final retrieval results both semantically similar and popular. Finally, experiments are conducted on CQADupStack dataset, and results show that the [email protected] of WELQLC-QR model has an average increase of 8.99%, 8.3%, 4.74% and 3.56% compared with that of L-LDA, LC-LDA, BM25 and Word2vec, respectively.
Keywords Extraction Based on Word2Vec and TextRank

In order to improve the performance of keyword extraction by enhancing the semantic representations of documents, we propose a method of keyword extraction which exploits the document's internal semantic information and the semantic representations of words pre-trained by massive external documents. Firstly, we utilize the deep learning tool Word2Vec to characterize the external document information, and evaluate the similarity between the words by the cosine distance, thus we obtain the semantic information between words in the external documents. Then, the word-to-word similarity is used to replace the probability transfer matrix in the TextRank of word graph of the target document. At the same time, the information of the title and the abstract of the internal document are exploited to construct the words' semantic graph for keyword extraction. The experiments select the related academic paper data from AMiner as experimental data set. The experimental results show that our method outperforms the TextRank algorithm and the precision, recall and F-score of the five keywords are increased by 28.60%, 10.70% and 12.90% respectively compared to the single TextRank algorithm.
Efficiently Answering Span-Reachability Queries in Large Temporal Graphs

Reachability is a fundamental problem in graph analysis. In applications such as social networks and collaboration networks, edges are always associated with timestamps. Most existing works on reachability queries in temporal graphs assume that two vertices are related if they are connected by a path with non-decreasing timestamps (time-respecting) of edges. This assumption fails to capture the relationship between entities involved in the same group or activity with no time-respecting path connecting them. In this paper, we define a new reachability model, called span-reachability, designed to relax the time order dependency and identify the relationship between entities in a given time period. We adopt the idea of two-hop cover and propose an index-based method to answer span-reachability queries. Several optimizations are also given to improve the efficiency of index construction and query processing. We conduct extensive experiments on 17 real-world datasets to show the efficiency of our proposed solution.
Multiple-Target Homotopic Quasi-Complete Path Planning Method for Mobile Robot Using a Piecewise Linear Approach

The ability to plan a multiple-target path that goes through places considered important is desirable for autonomous mobile robots that perform tasks in industrial environments. This characteristic is necessary for inspection robots that monitor the critical conditions of sectors in thermal, nuclear, and hydropower plants. This ability is also useful for applications such as service at home, victim rescue, museum guidance, land mine detection, and so forth. Multiple-target collision-free path planning is a topic that has not been very studied because of the complexity that it implies. Usually, this issue is left in second place because, commonly, it is solved by segmentation using the point-to-point strategy. Nevertheless, this approach exhibits a poor performance, in terms of path length, due to unnecessary turnings and redundant segments present in the found path. In this paper, a multiple-target method based on homotopy continuation capable to calculate a collision-free path in a single execution for complex environments is presented. This method exhibits a better performance, both in speed and efficiency, and robustness compared to the original Homotopic Path Planning Method (HPPM). Among the new schemes that improve their performance are the Double Spherical Tracking (DST), the dummy obstacle scheme, and a systematic criterion to a selection of repulsion parameter. The case studies show its effectiveness to find a solution path for office-like environments in just a few milliseconds, even if they have narrow corridors and hundreds of obstacles. Additionally, a comparison between the proposed method and sampling-based planning algorithms (SBP) with the best performance is presented. Furthermore, the results of case studies show that the proposed method exhibits a better performance than SBP algorithms for execution time, memory, and in some cases path length metrics. Finally, to validate the feasibility of the paths calculated by the proposed planner; two simulations using the pure-pursuit controlled and differential drive robot model contained in the Robotics System Toolbox of MATLAB are presented.