Reviewerly/exHarmony huggingface dataset:
Anonymous Aggregate Encryption Scheme for Industrial Internet of Things

With the rapid development of information technology and the continuous upgrading of special equipment, such as sensing and control, the application of industrial Internet of Things (IIoTs) has received great attention. Within the IIoTs, a large amount of sensitive data needs to be transmitted in time. Therefore, protecting data security and user privacy has been a key issue. In this article, the concept of public key anonymous aggregation encryption (AAE) is proposed for the first time. The sender of the message encrypts n different messages into one ciphertext using the public keys of n different receivers. After receiving the ciphertext, the receiver decrypts the ciphertext using his private key to get the corresponding message. The messages obtained by different receivers are different, and the identity of the receiver is kept secret to anyone including other receivers. A concrete AAE scheme then is constructed, which is proved to be secure in the standard model based on the decisional Diffie-Hellman problem. It does not require pairing operations, so it is more efficient than previous public encryption schemes and is more suitable for IIoTs.
Recovering Transitive Traceability Links among Various Software Artifacts for Developers

Traceability links between software artifacts can assist in several software development tasks. There are some automatic traceability recovery methods that help with managing the massive number of software artifacts and their relationships, but they do not work well for software artifacts whose descriptions are different in terms of language or abstraction level. To overcome these weakness, we propose the Connecting Links Method (CLM), which recovers transitive traceability links between two artifacts by intermediating a third artifact. In order to apply CLM for general use without limitation in terms of software artifact type, we have designed a standardized method to calculate the relation score of transitive traceability links using the scores of direct traceability links between three artifacts. Furthermore, we propose an improvement of CLM by considering software version. We evaluated CLM by applying it to three software products and found that it is more effective for software artifacts whose language type or vocabulary are different compared to previous methods using textual similarity.
Real-Time Workload Allocation Optimizer for Computing Systems by Using Deep Learning

With the increase of the Internet of Things (IoT) business, the number of edge computing systems are rapidly increasing. Reducing the power consumption of these computing systems has become a social issue. For that purpose, we proposed and demonstrated the power consumption reduction method by the optimal task assignment technology. Specifically, for sequential real-time jobs, we proposed a workload allocation optimizer (WAO) to minimize the power consumption of computing systems. This assignment algorithm achieved a 13% power reduction compared to the worst job assignment.
Shape Matching Based on Rectangularized Curvature Scale-Space Maps

The curvature scale-space (CSS) technique is a shape descriptor in the MPEG-7 standard. To match a shape under query, a common approach is to exploit a simplified CSS map, which is produced by representing each arch-shaped contour of the original CSS map by a vertical line segment with the same height. However, we consider this approach is oversimplified, since some very useful information presented in the original CSS map has been totally ignored. To solve the problem, a new CSS map approximation is proposed in this paper, which is a rectangularized bin circumscribing each arch-shaped contour of the CSS map so that both the height and width characteristics are simultaneously captured and preserved. For conducting shape matching, an efficient method is further proposed, which has a significantly more succinct structure than the existing CSS matching technique used in MPEG-7. Simulation results have shown that the proposed method can yield superior performance.
Non-Planar Inside-Out Dense Light-Field Dataset and Reconstruction Pipeline

Light-field imaging provides full spatio-angular information of the real world by capturing the light rays in various directions. This allows image processing algorithms to result in immersive user experiences such as VR. To evaluate, and develop reconstruction algorithms, a precise and dense light-field dataset of the real world that can be used as ground truth is desirable. In this paper, a non-planar capture is done and a view rendering pipeline is implemented. The acquired dataset includes two scenes that are captured by an accurate industrial robot with an attached color camera such that the camera is looking outward. The arm moves on a cylindrical path for a field of view of 125 degrees with angular step size of 0.01 degrees. Both scenes and their corresponding geometric calibration parameters will be available with the publication of the paper. The images are pre-processed in different steps. The disparity between two adjacent views with resolution of 5168×3448 is less than 1.6 pixels; the parallax between the foreground and the background objects is less than 0.6 pixels. Furthermore, the pre-processed data is used for a view rendering experiment to demonstrate an exemplary use case. In addition, the rendered results are evaluated visually and objectively.
Foxnet: A Multi-Face Alignment Method

Multi-face alignment aims to identify geometry structures of multiple faces in an image, and its performance is essential for the many practical tasks, such as face recognition, face tracking, and face animation. In this work, we present a fast bottom-up multi-face alignment approach, which can simultaneously localize multi-person facial landmarks with high precision. In more detail, our bottom-up architecture maps the landmarks to the high-dimensional space with which landmarks of all faces are represented. By clustering the features belonging to the same face, our approach can align the multi-person facial landmarks synchronously. Extensive experiments show that our method can achieve high performance in the multi-face landmark alignment task while our model is extremely fast. Moreover, we propose a new multi-face dataset to compare the speed and precision of bottom-up face alignment method with top-down methods. Our dataset is publicly available at <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Improving Ranking for Systematic Reviews Using Query Adaptation

Identifying relevant studies for inclusion in systematic reviews requires significant effort from human experts who manually screen large numbers of studies. The problem is made more difficult by the growing volume of medical literature and Information Retrieval techniques have proved to be useful to reduce workload. Reviewers are often interested in particular types of evidence such as Diagnostic Test Accuracy studies. This paper explores the use of query adaption to identify particular types of evidence and thereby reduce the workload placed on reviewers. A simple retrieval system that ranks studies using TF.IDF weighted cosine similarity was implemented. The Log-Likelihood, Chi-Squared and Odds-Ratio lexical statistics and relevance feedback were used to generate sets of terms that indicate evidence relevant to Diagnostic Test Accuracy reviews. Experiments using a set of 80 systematic reviews from the CLEF2017 and CLEF2018 eHealth tasks demonstrate that the approach improves retrieval performance.
Compression Artifact Removal with Stacked Multi-Context Channel-Wise Attention Network

Image compression plays an important role in saving disk storage and transmission bandwidth. Among traditional compression standards, JPEG is one of the commonly used standards in lossy image compression. However, the decompressed JPEG images usually have inevitable artifacts due to the quantization step, especially at low bitrate. Many recent works leverage deep learning networks to remove the JPEG artifacts and have achieved notable progress. In this paper, we propose a stacked multi-context channel-wise attention model. The channel-wise attention adaptively integrates features along the channel dimension given a set of feature maps. We apply multiple context-based channel attentions to enable the network to capture features from different resolutions. The entire architecture is trained progressively from the image space of low quality factor to that of high quality factor. Experiments show that we can achieve the state-of-the-art performance with lower complexity.
Adaptive Hard Example Mining for Image Captioning

Reinforcement Learning (RL) based methods optimize evaluation metric directly in image captioning task. In these methods, metric scores of captions are regard as rewards for examples. However, existing methods suffer from inferior performances on hard examples. In this paper, we propose an adaptive hard example mining method with additional supervised training for image captioning. Beam search algorithm is leveraged to estimate score expectation for each example. Examples whose caption scores are lower than expectation are selected automatically. For the selected hard examples, we propose an additional reward policy for high-scoring captions to force model learning from them. The proposed method is hyper-parameter free without tuning. Experimental results on MSCOCO dataset validate effectiveness of the proposed method.
Exact Incremental and Decremental Learning for LS-SVM

In this paper, we present a novel incremental and decremental learning method for the least-squares support vector machine (LS-SVM). The goal is to adapt a pre-trained model to changes in the training dataset, without retraining the model on all the data, where the changes can include addition and deletion of data samples. We propose a provably exact method where the updated model is exactly the same as a model trained from scratch using the entire (updated) training dataset. Our proposed method only requires access to the updated data samples, the previous model parameters, and a unique, fixed-size matrix that quantifies the effect of the previous training dataset. Our approach can significantly reduce the storage requirement of model updating, preserve the privacy of unchanged training samples without loss of model accuracy, and enhance the computational efficiency. Experiments on real-world image dataset validate the effectiveness of our proposed method.
Evaluation of Detection of Red Blood Cells using Convolutional Neural Networks

Extraction of data from video sequences of biological experiments is invaluable tool for evaluation of computer simulations. For the extraction of data, such as cell velocities, their trajectories, even their deformation, sufficient quality of red blood cell detection is required. In case of incomplete detection, the subsequent tracking algorithm is only able to compensate for some errors. In this work, we will evaluate existing implementations for object detection with Convolutional Neural Networks, in order to provide a baseline and comparison for our task with existing methods.
Fast: Flow-Assisted Shearlet Transform for Densely-Sampled Light Field Reconstruction

Shearlet Transform (ST) is one of the most effective methods for Densely-Sampled Light Field (DSLF) reconstruction from a Sparsely-Sampled Light Field (SSLF). However, ST requires a precise disparity estimation of the SSLF. To this end, in this paper a state-of-the-art optical flow method, i.e. PWC-Net, is employed to estimate bidirectional disparity maps between neighboring views in the SSLF. Moreover, to take full advantage of optical flow and ST for DSLF reconstruction, a novel learning-based method, referred to as Flow-Assisted Shearlet Transform (FAST), is proposed in this paper. Specifically, FAST consists of two deep convolutional neural networks, i.e. disparity refinement network and view synthesis network, which fully leverage the disparity information to synthesize novel views via warping and blending and to improve the novel view synthesis performance of ST. Experimental results demonstrate the superiority of the proposed FAST method over the other state-of-the-art DSLF reconstruction methods on nine challenging real-world SSLF sub-datasets with large disparity ranges (up to 26 pixels).
Standard pairs and existence of symmetric multiscaling functions

Construction of multiwavelets begins with finding a solution to the multiscaling equation. The solution is known as multiscaling function. Then, a multiwavelet basis is constructed from the multiscaling function. Symmetric multiscaling functions make the wavelet basis symmetric. The existence and properties of the multiscaling function depend on the symbol function. Symbol functions are trigonometric matrix polynomials. A trigonometric matrix polynomial can be constructed from a pair of matrices known as the standard pair. The square matrix in the pair and the matrix polynomial have the same spectrum. Our objective is to find necessary and sufficient conditions on standard pairs for the existence of compactly supported, symmetric multiscaling functions. First, necessary as well as sufficient conditions on the standard pairs for the existence of symbol functions corresponding to compactly supported multiscaling functions are found. Then, the necessary and sufficient conditions on the class of standard pairs, which make the multiscaling function symmetric, are derived. A method to construct symbol function corresponding to a compactly supported, symmetric multiscaling function from an appropriate standard pair is developed.
Clustering-Based Ensemble Pruning and Multistage Organization Using Diversity

The purpose of ensemble pruning is to reduce the number of predictive models in order to improve efficiency and predictive performance of the ensemble. In clustering-based approach, we are looking for groups of similar models, and then we prune each of them separately in order to increase overall diversity of the ensemble. In this paper we propose two methods for this purpose using classifier clustering on the basis of a criterion based on diversity measure. In the first method we select from each cluster the model with the best predictive performance to form the final ensemble, while the second one employs the multistage organization, where instead of removing the classifiers from the ensemble each classifier group makes the decision independently. The final answer of the proposed framework is the result of the majority voting of the decisions returned by each group. Experimentation results validated through statistical tests confirmed the usefulness of the proposed approaches.
Text Classification and Topic Modeling for Online Discussion Forums

Online discussion forums play an important role in building and sharing domain knowledge. An extensive amount of information can be found in online forums, covering every aspect of life and professional discourse. This chapter introduces the application of supervised and unsupervised machine learning techniques to analyze forum questions. This chapter starts with supervised machine learning techniques to classify forum posts into pre-defined topic categories. As a supporting technique, web scraping is also discussed to gather data from an online forum. After this, this chapter introduces unsupervised learning techniques to identify latent topics in documents. The combination of supervised and unsupervised machine learning approaches offers us deeper insights of the data obtained from online forums. This chapter demonstrates these techniques through a case study on a very large online discussion forum called LabVIEW from the systems modeling community. In the end, the authors list future trends in applying machine learning to understand the expertise captured in online expert communities.
Introducing the Peabody-Yale Reference Obsidians (PYRO) sets: Open-source calibration and evaluation standards for quantitative X-ray fluorescence analysis

A series of well-characterized specimens, known as the Peabody-Yale Reference Obsidians (PYRO) sets, has been designed to aid with calibrating and assessing X-ray fluorescence analysis (XRF) data, including portable XRF (pXRF) measurements, for obsidian sourcing. Each of these ten matched sets consists of 35 specimens: 20 specimens for calibration and 15 specimens for evaluation. These sets include not only obsidians with common geochemical compositions (i.e., alkaline rhyolites) but also rarer ones (i.e., peralkaline rhyolitic, trachytic, and andesitic specimens, including East African Rift obsidians). When used as described, the PYRO sets are suitable to calibrate and evaluate XRF data for obsidians worldwide. A set can be borrowed following loan policies of the Peabody Museum of Natural History, which will also accession a set. Publishing all source information – from their names and GPS coordinates to the datasets used for the recommended values – not only allows the sets to be replicated by others but also fulfills the demands of scientific transparency. Their main purpose is facilitating collaborations and “big data” projects, and the PYRO sets were designed to complement existing protocols for calibration and evaluation. In short, the sets are intended as a tool for almost anyone (e.g., a student who borrows an instrument to source artifacts) to meet – and even exceed – experts' practices involving transparency, accuracy, and reproducibility.
Improving Imbalanced Students’ Text Feedback Classification Using Re-sampling Based Approach

Class imbalance is a major problem in text classification, the problem happens when the used machine learning algorithm biases towards the majority class, so this makes it incorrectly classifies minority class instances. To get over this problem, investigators use the Synthetic Minority Oversampling Technique (SMOTE), it is pre-processing algorithm which was proven as a very good solution for handling imbalanced data sets. In this paper an empirical study have been executed to handle three imbalanced data sets in text format using SMOTE, the recall of all minority classes significantly improved in addition of significant improvement in all models overall performance. Average classes’ recall was improved significantly, by 0.15, 0.09, 0.10 in classification of ASS, FDS, NASS data sets respectively. While the recall for the minority class has significantly increased, ASS (0.23), FDS (0.08), and NASS (0.15).
Process-Driven Incremental Effort Estimation

Effort estimation has shown its value in process decisions, such as feasibility analysis, resource allocation, risk mitigation, and project planning. In this paper, we propose an incremental effort estimation method that integrates phase-based effort estimation models to solve the concerns about software process compatibility and estimation accuracy, which one may often have when adopting effort estimation methods for project management. We define the process framework for incremental effort estimation in terms of the transition between the phase model that defines the analysis and design activities, the system models that specify a system's behavior and structure at different levels of detail, the sizing model that measures software functional size via transaction analysis, the Bayesian model that statistically models the effects that the size measurements have on project effort, and the phase-based effort estimation models that provide improved effort estimation accuracy over the targeted early phases. The phase-based effort estimation models are calibrated and evaluated based on an empirical dataset of 61 master-level student projects from USC CSSE. The evaluation results show their improvements in out-of-sample estimation accuracy, provide a perspective about how estimation accuracy evolves throughout a software process, and set the practical criteria to decide the investment in analysis and design activities for the return in estimation accuracy.
Practical Experience Report: Engineering Safe Deep Neural Networks for Automated Driving Systems

Deep Neural Networks (DNNs) are one of many supervised machine learning approaches. These data-driven deep learning algorithms are revolutionizing the modern society in domains such as image processing, medicine and automotive. In the field of computer vision, DNNs are outperforming the traditional approaches that use hand-crafted feature extractors. As a result, researchers and developers in the automotive industry are using DNNs for the perception tasks of automated driving. Compared to traditional rule-based approaches, DNNs raise new safety challenges that have to be solved. There are four major building blocks in the development pipeline of DNNs: (1) functionality definition, (2) data set specification, selection and preparation, (3) development and evaluation, and (4) deployment and monitoring. This paper gives an overview of the safety challenges along the whole development pipeline of DNN, proposes potential solutions that are necessary to create safe DNNs and shows first experimental results of DNN performing object detection.
Dense-ACSSD for End-to-end Traffic Scenes Recognition

Traffic scenes recognition is the cornerstone of autonomous driving. However, most of the current algorithms are individually trained for tasks such as object detection and road segmentation. In addition, the training data used are mainly concentrated in small datasets such as KITTI, and the trained models are highly susceptible to weather, lighting and other factors. In order to solve the above problems, we propose an end-to-end CNN model for drivable area segmentation and multiple object detection. The feature extraction part of the network is powerful DenseNet. The atrous convolution and spatial pyramid pooling are used for road segmentation, and single shot detection is used for multiple object detection. According to its characteristics, we named the network Dense-ACSSD. Dense-ACSSD is trained on the current largest autonomous driving dataset, called BDD100K. The final training results show that the mIOU of the drivable area segmentation part is as high as 84.15%, and the mAP of the multiple object detection part reaches 30.82%. In addition, the inference time of Dense-ACSSD can meet real-time requirements.
Attention based English-Bodo Neural Machine Translation System for Tourism Domain

Bodo language is a relatively low resource language. Other than the text-book, novels and some print publication of newspaper, there appears to be very few resources available in the public domain. As the technology becomes affordable there is a growing number of active Bodo internet users. It requires a technology that can bring information in their own language. Machine translation appears to be a promising solution for that purpose. In this work we build an English-Bodo Neural Machine Translation by adopting a two layered bidirectional Long Short Term Memory (LSTM) cells that can capture the long term dependencies. As very few work has been done on English-Bodo NMT, we make our baseline model which produced a BLEU Score of 11.8 . We then gradually overcome the baseline model by introducing several attention mechanism. We achieved a BLEU Score of 16.71 using the approach presented in Bahdanu. Furthermore we got a better BLEU score of 17.9 when we introduced beam search with a beam width of 5. We found that the model performs very well despite the few dataset available.