Reviewerly/exHarmony huggingface dataset:
Object tracking framework with Siamese network and re-detection mechanism

Abstract To improve the deficient tracking ability of fully-convolutional Siamese networks (SiamFC) in complex scenes, an object tracking framework with Siamese network and re-detection mechanism (Siam-RM) is proposed. The mechanism adopts the Siamese instance search tracker (SINT) as the re-detection network. When multiple peaks appear on the response map of SiamFC, a more accurate re-detection network can re-determine the location of the object. Meanwhile, for the sake of adapting to various changes in appearance of the object, this paper employs a generative model to construct the templates of SiamFC. Furthermore, a method of template updating with high confidence is also used to prevent the template from being contaminated. Objective evaluation on the popular online tracking benchmark (OTB) shows that the tracking accuracy and the success rate of the proposed framework can reach 79.8% and 63.8%, respectively. Compared to SiamFC, the results of several representative video sequences demonstrate that our framework has higher accuracy and robustness in scenes with fast motion, occlusion, background clutter, and illumination variations.
Efficient conformal predictor ensembles

In this paper, we study a generalization of a recently developed strategy for generating conformal predictor ensembles: out-of-bag calibration. The ensemble strategy is evaluated, both theoretically and empirically, against a commonly used alternative ensemble strategy, bootstrap conformal prediction, as well as common non-ensemble strategies. A thorough analysis is provided of out-of-bag calibration, with respect to theoretical validity, empirical validity (error rate), efficiency (prediction region size) and p-value stability (the degree of variance observed over multiple predictions for the same object). Empirical results show that out-of-bag calibration displays favorable characteristics with regard to these criteria, and we propose that out-of-bag calibration be adopted as a standard method for constructing conformal predictor ensembles.
Relative Pointing Interface: A gesture interaction method based on the ability to divide space

A new type of 3D gesture interface called Relative Pointing Interface (RelPoInt) is suggested in this study. RelPoInt allows users to select a virtual button by pointing to a relative position in a virtual 3D menu. This approach makes use of the distance or angle between the relative point and the reference point. Users can set the reference point freely and operate all the functions on the menu with a simple gesture. Microsoft Kinect was used to implement the RelPoInt in this study. To suggest a radial menu suitable for the RelPoInt, we conducted an experiment in which 20 participants were asked to point to virtual buttons, which were divided into four to eight regions. Errors and subjective assessment of easiness were measured at each treatment condition. RelPoInt can make users to manipulate functions without memorizing complicated gesture vocabularies, which is among the most serious pain points of using a gesture interface. Furthermore, RelPoInt does not require expensive gesture recognition equipment compared with finger recognition interfaces because the required resolution of interaction recognition is less sophisticated. It can be useful in applying 3D gesture interfaces to a variety of devices for smart home, virtual or augmented reality. The proposed interface can be applied to various environments with low cost and easy to implement. Therefore, this interface is highly worthy of use in areas where gestures are useful to interact, such as IoT (Internet of Things) and VR (Virtual Reality)/AR (Augmented Reality).
Technical Debt Analysis in Parallel Multi-Disciplinary Systems Engineering

Similar to advanced software engineering, multi-disciplinary systems engineering, such as industrial production systems engineering (PSE), has to integrate partial results from workgroups that design in parallel. Due to the heterogeneity of data sources and the divergence of local data models the exchanged engineering artefacts between PSE workgroups are complex, making the automation of the data exchange process in PSE difficult and prone to technical debt (TD). In this paper, we report on a case study at a large PSE company to analyze TD effects, items, and causes in PSE, focusing on the engineering data exchange process. We identified key use cases and TD types, i.e., TD in data models and TD in data instances, which have adverse effects on project effort, cost and duration as well as data quality.
Quantum Image Edge Detection Based on Four Directional Sobel Operator

Sobel operator is used to detect the edge points of an image. Sobel operator is limited to only two directions but our proposed quantum image edge detection based on novel enhanced quantum representation (NEQR) is sensitive in four directions. As a result, it gives the accurate detection and reduces the loss of edge information of digital image. The computational complexity of our proposed algorithm is ~O(n2 + q2), which result into exponential speed up compare to all the conventional edge detection algorithms and existing Sobel quantum image edge detection method, especially when the image data is growing exponentially.
A Novel Software-Defined Convolutional Neural Networks Accelerator

The convolutional neural network (CNN) has been widely applied in computer vision applications. Due to the intensive computation, the general central processing unit (CPU) processors are not efficient to meet the real-time requirement. Various hardware accelerators based on the application specific integrated circuit (ASIC) and field programmable gate array (FPGA) have been designed to accelerate CNN models during the inference phase. The data flow architecture has been extensively adopted because of the high parallelism. However, given the continual development in the computer vision field, CNN models have become increasingly diverse. Thus, CNN accelerators based on data flow architectures face an emerging challenge to maintain high throughput while coping with various CNN models. In this paper, we design a software-defined architecture to solve this. The goal of this study is to make the hardware change as the application changes to achieve high flexibility and high performance. In our proposed architecture, all the parts can be software-defined to cope with different CNN models. A flexible software-defined processing element (PE) array is designed to compute different weight filter sizes. In addition, a software-defined data reuse technique based on two ideal reuse cases is proposed to ensure that all the parameters need to be loaded only once during the computing phase. To support this reuse technique, we also propose the software-defined on-chip buffer so that the weight and image buffers share one dynamic buffer. By using the sparsity property of the input feature map, the full-connected (FC) layer is accelerated. About 88% of the FC weight parameters can be skipped when loading the VGG-16 model. Finally, we implemented this software-defined accelerator on the FPGA. Compared to the other FPGA based accelerators, our proposed accelerator can preserve high performance while maintaining flexibility.
Privacy Preserving Data Publishing with Multiple Sensitive Attributes based on Overlapped Slicing

Investigation into privacy preserving data publishing with multiple sensitive attributes is performed to reduce probability of adversaries to guess the sensitive values. Masking the sensitive values is usually performed by anonymizing data by using generalization and suppression techniques. A successful anonymization technique should reduce information loss due to the generalization and suppression. This research attempts to solve both problems in microdata with multiple sensitive attributes. We propose a novel overlapped slicing method for privacy preserving data publishing with multiple sensitive attributes. We used discernibility metrics to measure information loss. The experiment result shows that our method obtained a lower discernibility value than other methods.
Wasserstein GAN based on Autoencoder with back-translation for cross-lingual embedding mappings

Recent works about learning cross-lingual word mappings (CWMs) focus on relaxing the requirement of bilingual signals through generative adversarial networks (GANs). GANs based models intend to enforce source embedding space to align target embedding space. However, existing GANs based models cannot exploit the underlying information of target-side for an alignment standard in the training, which may lead to some suboptimal results of CWMs. To address this problem, we propose a novel method, named Wasserstein GAN based on autoencoder with back-translation (ABWGAN) that can effectively exploit the target-side information and improve the performance of GANs based models. ABWGAN is an innovative combination of preliminary mappings learning and back-translation with target-side (BT-TS). In the proposed BT-TS, we back-translate target-side embeddings with preliminary CWMs to learn the final cross-lingual mappings, which enables to improve the quality of the preliminary mappings by reusing the target-side samples. Experimental results on three language pairs demonstrate the effectiveness of the proposed ABWGAN.
Regularized Deep Transfer Learning: When CNN Meets kNN

In this brief, we propose a regularized deep transfer learning architecture composed of a softmax classifier and a k-nearest neighbor (kNN) classifier. We aim to generalize the softmax classifier possibly well under the regularization effect of the kNN classifier. That is, given a training sample, the kNN classifier assigns a soft label vector according to its distance to the center of each class. The working mechanism of the kNN classifier is attributed to both the source and target domains. On the one hand, it gradually becomes stronger by backpropagating the cross-entropy classification loss on the source images. On the other hand, for target data, we enforce to minimize the discrepancy between the label vectors produced by the kNN classifier and the softmax classifier. Using the kNN classifier, we are able to reduce the intra-class variations on the source domain and meanwhile pull close the source and target feature distributions, which can better bound the expected error of target domain. In experiment, we demonstrate that our method compares favorably with the state-of-the-arts on benchmarks.
Deep and Structure-Preserving Autoencoders for Clustering Data With Missing Information

Most real-life data suffer from missing values. Here we deal with the problem of exploratory analysis, via clustering, of data with missing values. For this we need an effective mechanism to deal with missing features so that all available information can be used for clustering. We propose two autoencoder-based methods for handling of missing data for clustering. The autoencoder is trained in a two-phase scheme using only part of the given data set which does not have any incomplete instances in such a manner that the autoencoder is better equipped to deal with incomplete data. To cluster the entire data set which has instances with missing values, we generate the latent space representation of the all instances, with or without, missing information. Before the incomplete instances are submitted to the autoencoder, the missing inputs are filled in by a k-nearest neighbor-based rule. The clustering is then done in the latent space using the fuzzy-c-means (FCM) algorithm. In the second method, to preserve the “structure” of the input data in the latent space we extend our method by adding Sammon's stress as a regularizer to the objective function of the autoencoder. We test the effectiveness of the proposed algorithms on several data sets and compare the results with five state-of-the-art techniques. For comparison, we use two performance indicators: Normalized Mutual Information (NMI) and Adjusted Rand index (ARI).
A hybrid approach for stain normalisation in digital histopathological images

Stain in-homogeneity adversely affects segmentation and quantification of tissues in histology images. Stain normalisation techniques have been used to standardise the appearance of images. However, most the available stain normalisation techniques only work on a particular kind of stain images. In addition, some of these techniques fail to utilise both the spatial and textural information in histology images, leading to image tissue distortion. In this paper, a hybrid approach has been developed, based on an octree colour quantisation algorithm combined with the Beer-Lambert law, a modified blind source separation algorithm, and a modified colour transfer approach. The hybrid method consists of two stages the stain separation stage and colour transfer stage. An octree colour quantisation algorithm combined with Beer-Lambert law, and a modified blind source separation algorithm are used during the stain separation stage to computationally estimate the amount of stain in an histology image based on its chromatic and luminous response. A modified colour transfer algorithm is used during the colour transfer stage to minimise the effect of varying staining and illumination. The hybrid method addresses the colour variation problem in both H&DAB (Haemotoxylin and Diaminobenzidine) and H&E (Haemotoxylin and Eosin) stain images. The stain normalisation method is validated against ground truth data. It is widely known that the Beer-Lambert law applies to only stains (such as haematoxylin, eosin) that absorb light. We demonstrate that the Beer-Lambert law applies is applicable to images containing a DAB stain. Better stain normalisation results are obtained in both H&E and H&DAB images.
Software Defect Estimation Using Machine Learning Algorithms

Software Engineering is a comprehensive domain since it requires a tight communication between system stakeholders and delivering the system to be developed within a determinate time and a limited budget. Delivering the customer requirements include procuring high performance by minimizing the system. Thanks to effective prediction of system defects on the front line of the project life cycle, the project's resources and the effort or the software developers can be allocated more efficiently for system development and quality assurance activities. The main aim of this paper is to evaluate the capability of machine learning algorithms in software defect prediction and find the best category while comparing seven machine learning algorithms within the context of four NASA datasets obtained from public PROMISE repository [12]. All in all, the results of ensemble learners category consisting of Random Forests (RF) and Bagging in defect prediction is pretty much its counterparts.
Skeleton-Based Action Recognition With Key-Segment Descriptor and Temporal Step Matrix Model

Human action recognition based on skeleton has played a key role in various computer visionrelated applications, such as smart surveillance, human-computer interaction, and medical rehabilitation.However, due to various viewing angles, diverse body sizes, and occasional noisy data, etc., this remains a challenging task.The existing deep learning-based methods require long time to train the models and may fail to provide an interpretable descriptor to code the temporal-spatial feature of the skeleton sequence.In this paper, a key-segment descriptor and a temporal step matrix model are proposed to semantically present the temporal-spatial skeleton data.First, a skeleton normalization is developed to make the skeleton sequence robust to the absolute body size and initial body orientation.Second, the normalized skeleton data is divided into skeleton segments, which are treated as the action units, combining 3D skeleton pose and the motion.Each skeleton sequence is coded as a meaningful and characteristic key segment sequence based on the key segment dictionary formed by the segments from all the training samples.Third, the temporal structure of the key segment sequence is coded into a step matrix by the proposed temporal step matrix model, and the multiscale temporal information is stored in step matrices with various steps.Experimental results on three challenging datasets demonstrate that the proposed method outperforms all the hand-crafted methods and it is comparable to recent deep learning-based methods.
CCMT 2019 Machine Translation Evaluation Report

This paper introduces the evaluation procedure, evaluation data, participants and evaluation results of 2019 (15th) China Conference on Machine Translation (CCMT 2019) evaluation campaign. Compared with the last evaluation campaign (CWMT 2018), CCMT 2019 MT evaluation is characterized as follows: a new speech translation task is fulfilled; the translation quality estimation task is augmented with a word level track in addition to the sentence level track. Meanwhile, CCMT 2019 receives increases in the number of participants and systems submitted. This paper presents the anonymous evaluation results of all tasks, with a brief summarization of the techniques applied in this evaluation campaign.
Robot Task Planning via Deep Reinforcement Learning: a Tabletop Object Sorting Application

This paper proposes a Deep Reinforcement Learning powered approach for tabletop object sorting. Once perceived the environment, the system creates a semantic representation of the scene, describing the pose and category of each recognized object. This image is then provided as input to the trained Deep Neural Network in charge of choosing the correct action to be performed to successfully achieve the sorting task. Obtained results prove the capability of the proposed system, including its intrinsic robustness to failures and unpredictable interactions with humans or other environmental agents. Moreover, the use of semantic images makes the Deep Neural Network independent from the type of objects to be sorted and from their final placement location. Finally, the system is scalable, being capable of sorting as many known objects as recognized by the perception system. Currently, the system can sort objects belonging to two predefined categories while treating all the others as obstacles. Future works will extend the system making it capable of sorting potentially any type and number of object categories.
Identity Verification and Management of Electronic Health Records with Blockchain Technology

We present a state-of-art design in healthcare industry with blockchain technology. In our design of the systems, an individual's identity can be verified, and the electronic health records are processed through blockchain networks. The goal of creating such kind of systems is to facilitate identity verification, to provide ease of data access and sharing of electronic health records, and to maximize the protection of patients' data.
Attention to Head Locations for Crowd Counting

Occlusions, complex backgrounds, scale variations and non-uniform distributions present great challenges for crowd counting in practical applications. In this paper, we propose a novel method using an attention model to exploit head locations which are the most important cue for crowd counting. The attention model estimates a probability map in which high probabilities indicate locations where heads are likely to be present. The estimated probability map is used to suppress non-head regions in feature maps from several multi-scale feature extraction branches of a convolutional neural network for crowd density estimation, which makes our method robust to complex backgrounds, scale variations and non-uniform distributions. Experiments on ShanghaiTech dataset demonstrate the effectiveness of our method.
Developing Engaging Learning Experiences in Preservice Education

Opportunities to learn how to develop engaging learning experiences are vital for preservice teachers. This article describes an in-class modeling activity, provides assignment guidelines, and includes examples of candidates' completed tasks when developing technology-integrated instruction using Quick Response (QR) codes. The content of participants' (n = 75) tasks were analyzed for complexity following the SAMR model (Pnuentedura 2013 Pnuentedura, R. 2013. SAMR: An EdTech quintet perspective. Retrieved from http://www.hippasus.com/rrpweblog/archives/2013/07/29/SAMRAnETQPerspective.pdf [Google Scholar]). Most candidates enhanced instructional experiences (73%), while others transformed them (27%) by developing engaging collaborative experiences that culminated in students creating a product using technology tools. Observations revealed that candidates appreciated learning how QR codes could be used to make learning experiences more engaging. Candidates showed a willingness to use their own smart devices for learning purposes. Aligning appropriate curriculum content standards with assigned tasks was achieved by all participants.
Performance Analysis of Out-of-Distribution Detection on Various Trained Neural Networks

Several areas have been improved with Deep Learning during the past years. For non-safety related products adoption of AI and ML is not an issue, whereas in safety critical applications, robustness of such approaches is still an issue. A common challenge for Deep Neural Networks (DNN) occur when exposed to out-of-distribution samples that are previously unseen, where DNNs can yield high confidence predictions despite no prior knowledge of the input. In this paper we analyse two supervisors on two well-known DNNs with varied setups of training and find that the outlier detection performance improves with the quality of the training procedure. We analyse the performance of the supervisor after each epoch during the training cycle, to investigate supervisor performance as the accuracy converges. Understanding the relationship between training results and supervisor performance is valuable to improve robustness of the model and indicates where more work has to be done to create generalized models for safety critical applications.
Mixture Matrix Approximation for Collaborative Filtering

Matrix approximation (MA) methods are integral parts of today's recommender systems. In standard MA methods, only one feature vector is learned for each user/item, which may not be accurate enough to characterize the diverse interests of users/items. For instance, users could have different opinions on a given item, so that they may need different feature vectors for the item to represent their unique interests. To this end, this article proposes a mixture matrix approximation (MMA) method, in which we assume that the user-item ratings follow mixture distributions and the user/item feature vectors vary among different stars to better characterize the diverse interests of users/items. Furthermore, we show that the proposed method can tackle both rating prediction and the top-N recommendation problems. Empirical studies on MovieLens, Netflix and Amazon datasets demonstrate that the proposed method can outperform state-of-the-art MA-based collaborative filtering methods in both rating prediction and top-N recommendation tasks.