Reviewerly/exHarmony huggingface dataset:
An approach based on the ifcOWL ontology to support indoor navigation

This paper presents an indoor navigation support system based on the Building Information Models (BIM) paradigm. Although BIM is initially defined for the Architecture, Engineering and Construction/Facility Management (AEC/FM) industry, the authors believe that it can provide added value in this context. To this end, the authors will focus on the Industry Foundation Classes (IFC) standard for the formal representation of BIM. The approach followed in this paper will be based on the ifcOWL ontology, which translates the IFC schemas into Ontology Web Language (OWL). Several modifications of this ontology have been proposed, consisting of the inclusion of new items, SWRL rules and SQWRL searches. This way of expressing the elements of a building can be used to code information that is very useful for navigation, such as the location of elements related to the actions desired by the user. It is important to note that this design is intended to be used as a complement to other well-known tools and techniques for indoor navigation. The proposed modifications have been successfully tested in a variety of simulated and real scenarios. The main limitation of the proposal is the immense amount of information contained in the ifcOWL ontology, which causes difficulties involving its processing and the time necessary to perform operations on it. Those elements that are considered important have been selected, removing those that seem secondary to navigation. This procedure will result in a significant reduction in the storage and semantic processing of the information. Thus, for a system with 1000 individuals (in the ontological sense), the processing time is about 90 s. The authors regard this time as acceptable, since in most cases the tasks involved can be considered part of the system initialization, meaning they will only be executed once at the beginning of the process.
Generating Positive Bounding Boxes for Balanced Training of Object Detectors

Two-stage deep object detectors generate a set of regions-of-interest RoIs in the first stage, then, in the second stage, identify objects among the proposed RoIs that sufficiently overlap with a ground truth (GT) box. The second stage is known to suffer from a bias towards RoIs that have low intersection-over-union (IoU) with the associated GT boxes. To address this issue, we first propose a sampling method to generate bounding boxes (BB) that overlap with a given reference box more than a given IoU threshold. Then, we use this BB generation method to develop a positive RoI (pRoI) generator that, for the second stage, produces RoIs following any desired spatial or IoU distribution. We show that our pRoI generator is able to simulate other sampling methods for positive examples such as hard example mining and prime sampling. Using our generator as an analysis tool, we show that (i) IoU imbalance has an adverse effect on performance, (ii) hard positive example mining improves the performance only for certain input IoU distributions, and (iii) the imbalance among the foreground classes has an adverse effect on performance and that it can be alleviated at the batch level. Finally, we train Faster R-CNN using our pRoI generator and, compared to conventional training, obtain better or on-par performance for low IoUs and significant improvements when trained for higher IoUs for Pascal VOC and MS COCO datasets. The code is available at: https://github.com/kemaloksuz/BoundingBoxGenerator.
Offset Calibration for Appearance-Based Gaze Estimation via Gaze Decomposition

Appearance-based gaze estimation provides relatively unconstrained gaze tracking. However, subject-independent models achieve limited accuracy partly due to individual variations. To improve estimation, we propose a gaze decomposition method that enables low complexity calibration, i.e., using calibration data collected when subjects view only one or a few gaze targets and the number of images per gaze target is small. Lowering the complexity of calibration makes it more convenient and less timeconsuming for the user, and more widely applicable. Motivated by our finding that the inter-subject squared bias exceeds the intra-subject variance for a subject-independent estimator, we decompose the gaze estimate into the sum of a subject-independent term estimated from the input image by a deep convolutional network and a subject-dependent bias term. During training, both the weights of the deep network and the bias terms are estimated. During testing, if no calibration data is available, we can set the bias term to zero. Otherwise, the bias term can be estimated from images of the subject gazing at known gaze targets. Experimental results on three datasets show that without calibration, our method outperforms state-of-the-art by at least 6.3%. For low complexity calibration sets, our method outperforms other calibration methods. More complex calibration algorithms do not outperform our method until the size of the calibration set is excessively large. Even then, the gains obtained by alternatives are small, e.g., only 0.1° lower error for 64 gaze targets. Source code is available at https://github.com/czk32611/Gaze-Decomposition.
Long-Short Graph Memory Network for Skeleton-based Action Recognition

Current studies have shown the effectiveness of long short-term memory network (LSTM) for skeleton-based human action recognition in capturing temporal and spatial features of the skeleton sequence. Nevertheless, it still remains challenging for LSTM to extract the latent structural dependency among nodes. In this paper, we introduce a new long-short graph memory network (LSGM) to improve the capability of LSTM to model the skeleton sequence - a type of graph data. Our proposed LSGM can learn high-level temporal-spatial features end-to-end, enabling LSTM to extract the spatial information that is neglected but intrinsic to the skeleton graph data. To improve the discriminative ability of the temporal and spatial module, we use a calibration module termed as graph temporal-spatial calibration (GTSC) to calibrate the learned temporal-spatial features. By integrating the two modules into the same framework, we obtain a stronger generalization capability in processing dynamic graph data and achieve a significant performance improvement on the NTU and SYSU dataset. Experimental results have validated the effectiveness of our proposed LSGM+GTSC model in extracting temporal and spatial information from dynamic graph data. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Blind Image Watermark Decoder in UDTCWT Domain Using Weibull Mixtures-Based Vector HMT

As one promising solution, digital watermarking has been proposed to resolve image copyright protection and content authentication, and has been applied successfully in many fields. Owing to their excellent description capability and invariance property, statistical models have become a popular tool for the image watermarking resulting in favorable trade-offs among imperceptibility, robustness and data payload. By modeling the robust undecimated dual tree complex Wavelet transform (UDTCWT) coefficient magnitudes with the Weibull mixtures based vector hidden Markov trees (HMT) and employing maximum likelihood (ML) test criterion, we propose a new image watermarking approach in UDTCWT domain in this paper. Our image watermarking approach consists of two parts, namely, embedding and extracting. In the embedding process, we compute the robust UDTCWT coefficient magnitudes with UDTCWT domain real/imaginary parts, and insert the digital watermark into the significant UDTCWT coefficient magnitude subband. In the extracting phase, robust UDTCWT coefficient magnitudes are firstly modeled by employing the Weibull mixture-based vector HMT, where the statistical properties of UDTCWT magnitudes are captured accurately. Then the expectation/conditional maximization (ECM) approach is introduced to estimate the statistical model parameters. Finally, an image watermark decoder for multiplicative watermarking is developed using the Weibull mixtures based vector HMT and ML test. The experiments show that the proposed method not only improves the imperceptibility, but also increases the robustness performance and outperforms state-of-the-art methods on a set of standard test images.
Efficient Breast Cancer Prediction Using Ensemble Machine Learning Models

Breast cancer is the second most exposed cancer in the world. When the growth of breast tissues are out of control is called breast cancer. Breast cancer prediction and prognosis are major challenge to medical community. Breast cancer are prominent cause of death for women. Recurrence of cancer is the biggest fears for cancer patient and this can affect their quality of life. The aim of this research is to predict breast cancer from cancer features with high accuracy. The breast cancer Coimbra dataset taken from UCI (University of California Irvine) [1], [5] to build a most efficient ensemble machine learning models. The major steps we follow, here are feature scaling, cross validation and various ensemble machine learning models with bagging technique. Decision tree and KNN gives highest 100 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> accuracy. Decision tree model gives 100 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> accuracy if we split train-test dataset in ratio of 90:10 and also used 300 bags of trees. KNN gives maximum accuracy 100 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , for k= 1 to 7 in seven loops with 90% is train data and 10% is test data. Here k is the nearest neighbors. And we also evaluate its prediction by accuracy, confusion matrix and classification report. Our aim is to build a most accurate and efficient machine learning model. So as prediction result, patient can take treatment on the early stage.
Generative Adversarial Ensemble Learning for Face Forensics

The recent advance of synthetic image generation and manipulation methods allows us to generate synthetic face images close to real images.On the other hand, the importance of identifying the synthetic face images increases more and more to protect personal privacy from those.Although some deep learning-based image forensic methods have been developed recently, it is still challenging to distinguish synthetic images generated by recent image generation and manipulation methods such as the deep fake, face2face, and face swap.To resolve this challenge, we propose a novel generative adversarial ensemble learning method.We train multiple discriminative and generative networks based on the adversarial learning.Compared to the conventional adversarial learning, our method is however more focused on improving the discrimination ability rather than image generation one.To this end, we improve the discriminabilty by ensembling outputs from different two discriminators.In addition, we train two generators in order to generate general and hard synthetic images.By ensemble learning of all the generators and discriminators, we improve the discriminators by using the generated synthetic face images, and improve the generators by passing the combined feedback of the discriminators.On the FaceForensics benchmark challenge, we thoroughly evaluate our methods by comparing the recent methods.We also provide the ablation study to prove the effectiveness and usefulness of our method.
Human-like Planning for Reaching in Cluttered Environments

Humans, in comparison to robots, are remarkably adept at reaching for objects in cluttered environments. The best existing robot planners are based on random sampling of configuration space- which becomes excessively high-dimensional with large number of objects. Consequently, most planners often fail to efficiently find object manipulation plans in such environments. We addressed this problem by identifying high-level manipulation plans in humans, and transferring these skills to robot planners. We used virtual reality to capture human participants reaching for a target object on a tabletop cluttered with obstacles. From this, we devised a qualitative representation of the task space to abstract the decision making, irrespective of the number of obstacles. Based on this representation, human demonstrations were segmented and used to train decision classifiers. Using these classifiers, our planner produced a list of waypoints in task space. These waypoints provided a high-level plan, which could be transferred to an arbitrary robot model and used to initialise a local trajectory optimiser. We evaluated this approach through testing on unseen human VR data, a physics-based robot simulation, and a real robot (dataset and code are publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> ). We found that the human-like planner outperformed a state-of-the-art standard trajectory optimisation algorithm, and was able to generate effective strategies for rapid planning- irrespective of the number of obstacles in the environment.
Action Segmentation with Mixed Temporal Domain Adaptation

The main progress for action segmentation comes from densely-annotated data for fully-supervised learning. Since manual annotation for frame-level actions is time-consuming and challenging, we propose to exploit auxiliary unlabeled videos, which are much easier to obtain, by shaping this problem as a domain adaptation (DA) problem. Although various DA techniques have been proposed in recent years, most of them have been developed only for the spatial direction. Therefore, we propose Mixed Temporal Domain Adaptation (MTDA) to jointly align frame-and video-level embedded feature spaces across domains, and further integrate with the domain attention mechanism to focus on aligning the frame-level features with higher domain discrepancy, leading to more effective domain adaptation. Finally, we evaluate our proposed methods on three challenging datasets (GTEA, 50Salads, and Breakfast), and validate that MTDA outperforms the current state-of-the-art methods on all three datasets by large margins (e.g. 6.4% gain on F1@50 and 6.8% gain on the edit score for GTEA).
LFGAN

We present a deep neural network called the light field generative adversarial network (LFGAN) that synthesizes a 4D light field from a single 2D RGB image. We generate light fields using a single image super-resolution (SISR) technique based on two important observations. First, the small baseline gives rise to the high similarity between the full light field image and each sub-aperture view. Second, the occlusion edge at any spatial coordinate of a sub-aperture view has the same orientation as the occlusion edge at the corresponding angular patch, implying that the occlusion information in the angular domain can be inferred from the sub-aperture local information. We employ the Wasserstein GAN with gradient penalty (WGAN-GP) to learn the color and geometry information from the light field datasets. The network can generate a plausible 4D light field comprising 8×8 angular views from a single sub-aperture 2D image. We propose new loss terms, namely epipolar plane image (EPI) and brightness regularization (BRI) losses, as well as a novel multi-stage training framework to feed the loss terms at different time to generate superior light fields. The EPI loss can reinforce the network to learn the geometric features of the light fields, and the BRI loss can preserve the brightness consistency across different sub-aperture views. Two datasets have been used to evaluate our method: in addition to an existing light field dataset capturing scenes of flowers and plants, we have built a large dataset of toy animals consisting of 2,100 light fields captured with a plenoptic camera. We have performed comprehensive ablation studies to evaluate the effects of individual loss terms and the multi-stage training strategy, and have compared LFGAN to other state-of-the-art techniques. Qualitative and quantitative evaluation demonstrates that LFGAN can effectively estimate complex occlusions and geometry in challenging scenes, and outperform other existing techniques.
Data-Driven Activities Involving Electronic Health Records: An Activity and Task Analysis Framework for Interactive Visualization Tools

Electronic health records (EHRs) can be used to make critical decisions, to study the effects of treatments, and to detect hidden patterns in patient histories. In this paper, we present a framework to identify and analyze EHR-data-driven tasks and activities in the context of interactive visualization tools (IVTs)—that is, all the activities, sub-activities, tasks, and sub-tasks that are and can be supported by EHR-based IVTs. A systematic literature survey was conducted to collect the research papers that describe the design, implementation, and/or evaluation of EHR-based IVTs that support clinical decision-making. Databases included PubMed, the ACM Digital Library, the IEEE Library, and Google Scholar. These sources were supplemented by gray literature searching and reference list reviews. Of the 946 initially identified articles, the survey analyzes 19 IVTs described in 24 articles that met the final selection criteria. The survey includes an overview of the goal of each IVT, a brief description of its visualization, and an analysis of how sub-activities, tasks, and sub-tasks blend and combine to accomplish the tool’s main higher-level activities of interpreting, predicting, and monitoring. Our proposed framework shows the gaps in support of higher-level activities supported by existing IVTs. It appears that almost all existing IVTs focus on the activity of interpreting, while only a few of them support predicting and monitoring—this despite the importance of these activities in assisting users in finding patients that are at high risk and tracking patients’ status after treatment.
Inductive conformal prediction for silent speech recognition

Objective. Silent speech recognition based on surface electromyography has been studied for years. Though some progress in feature selection and classification has been achieved, one major problem remains: how to provide confident or reliable prediction.Approach. Inductive conformal prediction (ICP) is a suitable and effective method to tackle this problem. This paper applies ICP with the underlying algorithm of random forest to provide confidence and reliability. We also propose a method, test time data augmentation, to use ICP as a way to utilize unlabelled data in order to improve prediction performance.Main Results. Using ICP, p-values and confidence regions for individual predictions are obtained with a guaranteed error rate. Test time data augmentation also outputs relatively better conformal predictions as more unlabelled training data accumulated. Additionally, the validity and efficiency of ICP under different significance levels are demonstrated and evaluated on the silent speech recognition dataset obtained by our own device.Significance. These results show the viability and effectiveness of ICP in silent speech recognition. Moreover, ICP has potential to be a powerful method for confidence predictions to ensure reliability, both in data augmentation and online prediction.
PIDS

We propose PIDS, Pattern Inference Decomposed Storage, an innovative storage method for decomposing string attributes in columnar stores. Using an unsupervised approach, PIDS identifies common patterns in string attributes from relational databases, and uses the discovered pattern to split each attribute into sub-attributes. First, by storing and encoding each sub-attribute individually, PIDS can achieve a compression ratio comparable to Snappy and Gzip. Second, by decomposing the attribute, PIDS can push down many query operators to sub-attributes, thereby minimizing I/O and potentially expensive comparison operations, resulting in the faster execution of query operators.
The comparative analysis on the accuracy of k-NN, Naive Bayes, and Decision Tree Algorithms in predicting crimes and criminal actions in Sleman Regency

Abstract Crime is an action which is considered as a violation of law and can harm others. Nowadays, crimes has increased with erratic patterns. Therefore, crime prevention is necessary since it will occur based on the historical data. Data mining is a technique that can be used to predict crimes that will occur. According to the previous researches, data mining techniques have several methods that can be used to predict crimes by utilizing the data of crimes that have occurred. Hence, it is necessary to conduct a comparative analysis of classification algorithms in order to obtain accurate prediction results based on the crime data in Sleman regency. The classification algorithms analyzed in this study were k-NN, Naive Bayes, and Decision Tree. Based on the three algorithms, the accuracy of k-NN with k = 5 was 57.88 percent, with k = 10 was 59.49 percent, with k = 15 is 59.38 percent, with k = 20 was 60.18 percent, and with k = 25 was 61.57 percent. Meanwhile, for the Naive Bayes algorithm, the accuracy reached 65.59 percent, and the Decision Tree algorithm reached 60.23 percent. In conclusion, the algorithm with the highest accuracy was owned by Naive Bayes.
Visual Question Answering over Scene Graph

Visual question answering (VQA) is a task that takes an image and a related natural language question as input, and produces an answer as output. A successful VQA algorithm requires two key components: to obtain a structured representation of an image and to process a natural language question on the structured representation. While traditional VQA tasks work on raw images or image segmentation, recent VQA datasets such as CLEVR and GQA provide scene graphs that capture objects and their relationships expressed inside an image. However, even when the ground-truth scene graph is given, it is non-trivial to get the right answer to a natural language question, as it needs a sophisticated algorithm to process the scene graph and the question together. We propose to encode a scene graph and a question using Graph Network (GN). Then, we feed the encoded graph with the question to the Memory, Attention, and Composition (MAC) model to classify the answer. By including the question as a global vector in GN, we achieved the accuracy of 96.3% in GQA, surpassing 83.5% of the baseline method reported by the authors of GQA, which also used MAC to classify the answer. Our work suggests that a context-based encoding of the scene graph is crucial for graph-based reasoning tasks such as graph-related question answering.
Exploring Branch Predictors for Constructing Transient Execution Trojans

Transient execution is one of the most critical features used in CPUs to achieve high performance. Recent Spectre attacks demonstrated how this feature can be manipulated to force applications to reveal sensitive data. The industry quickly responded with a series of software and hardware mitigations among which microcode patches are the most prevalent and trusted. In this paper, we argue that currently deployed protections still leave room for constructing attacks. We do so by presenting transient trojans, software modules that conceal their malicious activity within transient execution mode. They appear completely benign, pass static and dynamic analysis checks, but reveal sensitive data when triggered. To construct these trojans, we perform a detailed analysis of the attack surface currently present in today's systems with respect to the recommended mitigation techniques. We reverse engineer branch predictors in several recent x86_64 processors which allows us to uncover previously unknown exploitation techniques. Using these techniques, we construct three types of transient trojans and demonstrate their stealthiness and practicality.