Reviewerly/exHarmony huggingface dataset:
Binocular vision profilometry for large-sized rough optical elements using binarized band-limited pseudo-random patterns

In this paper, a non-contact binocular vision profilometry method is proposed to measure a rough lens with aperture of around 300mm. A series of binarized band-limited pseudo-random patterns (BBPPs) are projected onto the rough lens, we utilize the temporal encoding method so that each pixel in the captured images has its specific code word. Homologous points could be matched via stereo matching procedure, then the surface of the rough lens will be reconstructed based on triangulation method according to the previous calibration data. Compared with the three coordinate measuring machine (CMM), this method achieves a fast and cheap measurement of the large-sized rough lens, which might be highly interesting for fast and overall measurement of metre-sized rough elements in the future.
Use Your Brain! Arithmetic 3PC for Any Modulus with Active Security

Secure multiparty computation (MPC) allows a set of mutually distrustful parties to compute a public function on their private inputs without revealing anything beyond the output of the computation. This paper focuses on the specific case of actively secure three-party computation with an honest majority. In particular, we are interested in solutions which allow to evaluate arithmetic circuits over real-world CPU word sizes, like 32- and 64-bit words. Our starting point is the novel compiler of Damgard et al. from CRYPTO 2018. First, we present an improved version of it which reduces the online communication complexity by a factor of 2. Next, we replace their preprocessing protocol (with arithmetic modulo a large prime) with a more efficient preprocessing which only performs arithmetic modulo powers of two. Finally, we present a novel postprocessing check which replaces the preprocessing phase. These protocols offer different efficiency tradeoffs and can therefore outperform each other in different deployment settings. We demonstrate this with benchmarks in a LAN and different WAN settings. Concretely, we achieve a throughput of 1 million 64-bit multiplications per second with parties located in different continents and 3 million in one location.
Combining Induction and Saturation-Based Theorem Proving

A method is devised to integrate reasoning by mathematical induction into saturation-based proof procedures based on resolution or superposition. The obtained calculi are capable of handling formulas in which some of the quantified variables range over inductively defined domains (which, as is well-known, cannot be expressed in first-order logic). The procedure is defined as a set of inference rules that generate inductive invariants incrementally and prove their validity. Although the considered logic itself is incomplete, it is shown that the invariant generation rules are complete, in the sense that if an invariant (of some specific form) is deducible from the considered clauses, then it is eventually generated.
Complexity measurement and characterization of 360-degree content

The appropriate characterization of the test material, used for subjective evaluation tests and for benchmarking image and video processing algorithms and quality metrics, can be crucial in order to perform comparative studies that provide useful insights.This paper focuses on the characterisation of 360-degree images.We discuss why it is important to take into account the geometry of the signal and the interactive nature of 360-degree content navigation, for a perceptual characterization of these signals.Particularly, we show that the computation of classical indicators of spatial complexity, commonly used for 2D images, might lead to different conclusions depending on the geometrical domain used to represent the 360-degree signal.Finally, new complexity measures based on the analysis of visual attention and content exploration patterns are proposed.
Uncertain active contour model based on rough and fuzzy sets for auroral oval segmentation

The shape of the earth's aurora serves as a direct monitor of important physical processes in the magnetosphere. Detecting auroral regions is therefore a crucial step in studying auroral activity in the field of space physics. Since auroral ovals are captured as images under undesirable conditions of low illumination, intensity distributions overlapping between auroral oval regions and the background present challenges for auroral oval segmentation methods. Granular computing, including fuzzy sets and rough sets, is an appropriate choice to better handle the uncertainty inherent in observed images. In this paper, we present a novel active contour model unaffected by intensity inhomogeneity for detecting auroral ovals in satellite imagery. By integrating the principles of fuzzy sets and rough sets, we develop a technique to automatically detect class boundaries. The resulting characterization leads to an efficient description of uncertain regions near auroral oval boundaries, as well as uncertainty in class boundaries. The approach by which an image is approximated by regions with piecewise-constant intensities within different local regions is more suitable for auroral images with intensity inhomogeneity. Considering local image information, we therefore use spatially varied thresholding for each pixel rather than constant thresholding. As a result, our proposed method can effectively and efficiently segment auroral ovals whose boundaries are not easily separated from the background. Further, experimental results on auroral oval images demonstrate the effectiveness of our proposed method in terms of human visual perception and segmentation accuracy.
Characterizing Sources of Ineffectual Computations in Deep Learning Networks

Hardware accelerators for inference with neural networks can take advantage of the properties of data they process. Performance gains and reduced memory bandwidth during inference have been demonstrated by using narrower data types [1] [2] and by exploiting the ability to skip and compress values that are zero [3]-[6]. Similarly useful properties have been identified at a lower-level such as varying precision requirements [7] and bit-level sparsity [8] [9]. To date, the analysis of these potential sources of superfluous computation and communication has been constrained to a small number of older Convolutional Neural Networks (CNNs) used for image classification. It is an open question as to whether they exist more broadly. This paper aims to determine whether these properties persist in: (1) more recent and thus more accurate and better performing image classification networks, (2) models for image applications other than classification such as image segmentation and low-level computational imaging, (3) Long-Short-Term-Memory (LSTM) models for non-image applications such as those for natural language processing, and (4) quantized image classification models. We demonstrate that such properties persist and discuss the implications and opportunities for future accelerator designs.
Howel

In-situ exploration of heart rate (HR) zones during cardio training (CT) is important for training efficiency. However, approaches for monitoring HR, either depend on complex visualizations on small screens (i.e., smartwatches) or intrusive modalities (i.e. haptic, auditory) that might force the attention to the information. We developed an early prototype, Howel, a novel wrist-worn soft wearable to display HR zone information during CT. Our concept utilizes mapping information onto dynamic patterns (color changing stripes) as an easy-to-understand ambient display. To preserve non-intrusiveness, it uses non-emissive modality by heating thermochromic paints on its textile surfaces. Early feedback from three participants suggests that soft wearables with non- emissive dynamic patterns have potential (1) to embed information organically on the body, (2) to give easy-to-understand in-situ intensity information and (3) to keep the attention on the exercise instead of performance measures.
Step-by-Step

This paper presents an exploratory study of a social exergame, called Step-by-Step, to help office workers initiate physical movements and social interactions in the work routine. In this project, we developed a mobile system for exploring a new mechanism of office vitality, through which the fitness task can be relayed from one to another co-worker in a workplace. Based on our prototypes, we evaluated the feasibility of Step-by-Step through a user study with five office workers and an expert interview with three senior designers. We discuss implications for the future development of the Step-by-Step system based on our qualitative findings.
Joint demosaicing and denoising by overfitting of bursts of raw images.

Demosaicking and denoising are the first steps of any camera image processing pipeline and are key for obtaining high quality RGB images. A promising current research trend aims at solving these two problems jointly using convolutional neural networks. Due to the unavailability of ground truth data these networks cannot be currently trained using real RAW images. Instead, they resort to simulated data. In this paper we present a method to learn demosaicking directly from mosaicked images, without requiring ground truth RGB data. We apply this to learn joint demosaicking and denoising only from RAW images, thus enabling the use of real data. In addition we show that for this application fine-tuning a network to a specific burst improves the quality of restoration for both demosaicking and denoising.
Phase sensitivity for an unbalanced interferometer without input phase-matching restrictions

The Cram\'er-Rao bound and the quantum Fisher information have been tools used extensively for interferometric phase sensitivity. Most scenarios considering a Mach-Zehnder interferometer with two input sources focused on the phase-matched case, when the Fisher information is maximal. Under this constraint, the best sensitivity is achieved for a balanced (50/50) input beam splitter. In this paper, we take a different approach: we allow the beam splitter transmission coefficient as well as the input phase mismatch to be variable parameters. We then search for a pair of these parameters that maximizes the Fisher information. We find that for the double coherent input the maximum Fisher information can always be reached in the unbalanced case for a carefully chosen input phase mismatch. For the coherent plus squeezed vacuum case we find that under certain circumstances a threshold phase mismatch exists, beyond which the optimum Fisher information is found for the degenerate case. For the squeezed-coherent plus squeezed vacuum case we find that the optimum actually occurs when the squeezing angles of the two inputs are in antiphase.
Big Data Analysis Using Apache Spark MLlib and Hadoop HDFS with Scala and Java

Nowadays with the technology revolution the term of big data is a phenomenon of the decade moreover, it has a significant impact on our applied science trends. Exploring well big data tool is a necessary demand presently. Hadoop is a good big data analyzing technology, but it is slow because the Job result among each phase must be stored before the following phase is started as well as to the replication delays. Apache Spark is another tool that developed and established to be the real model for analyzing big data with its innovative processing framework inside the memory and high-level programming libraries for machine learning, efficient data treating and etc. In this paper, some comparisons are presented about the time performance evaluation among Scala and Java in apache spark MLlib. Many tests have been done in supervised and unsupervised machine learning methods with utilizing big datasets. However, loading the datasets from Hadoop HDFS as well as to the local disk to identify the pros and cons of each manner and discovering perfect reading or loading dataset situation to reach best execution style. The results showed that the performance of Scala about 10% to 20% is better than Java depending on the algorithm type. The aim of the study is to analyze big data with more suitable programming languages and as consequences gaining better performance.
DALocNet: Improving Localization Accuracy for Domain Adaptive Object Detection

Object detection assumes that the training data is identical to the testing data. However, the distributions of training and testing data, in practice, are different, thereby limiting the detection accuracy of objects. To solve this problem, recent works adopt domain adaptation techniques to reduce the domain discrepancy. In this paper, we present a novel deep neural network design for domain adaptive object detection by further improving the localization accuracy of objects. First, we present to refine the pseudo labels generated from the current object detection methods and use these labels with a weighted loss function to train the network on the target domain. Second, we insert several residual blocks into the shallow layers of a convolutional neural network used in the target domain to enhance detailed spatial information, which helps for object localization. We perform various experiments to evaluate our network on three widely-used public benchmark datasets for domain adaptive object detection. The experimental results show that our DALocNet performs favorably against the state-of-the-art methods on all the datasets quantitatively and qualitatively.
IDAct: Towards Unobtrusive Recognition of User Presence and Daily Activities

The Internet of Things (IoT) promises to revolutionize the way people interact with their surrounding environment and the objects within it by creating a ubiquitous network of physical devices. However, recent advancements have been focused on creating battery-powered electronics. There remains a huge gap between the collection of smart devices and the massive number of everyday physical objects. In this work, we bridge this gap by enhancing the sensing capabilities of everyday objects using commercial long-range RFID. We apply signal processing and machine learning techniques towards its communication channel parameters to detect the presence of users and to understand their daily activities. Different from prior work, our system can adapt to different environments and objects types. In a naturalistic user study deployed in a home environment, IDAct detected user presence with an F1 score of 96.7% and recognizes 24 different daily activities with an F1 score of 82.8%.
Improving motion sickness severity classification through multi-modal data fusion

Head mounted displays (HMD) may prove useful for synthetic training and augmentation of military C5ISR decisionmaking. Motion sickness caused by such HMD use is detrimental, resulting in decreased task performance or total user dropout. The genesis of sickness symptoms is often measured using paper surveys, which are difficult to deploy in live scenarios. Here, we demonstrate a new way to track sickness severity using machine learning on data collected from heterogeneous, non-invasive sensors worn by users who navigated a virtual environment while remaining stationary in reality. We discovered that two models, one trained on heterogeneous sensor data and another trained only on electroencephalography (EEG) data, were able to classify sickness severity with over 95% accuracy and were statistically comparable in performance. Greedy feature optimization was used to maximize accuracy while minimizing the feature subspace. We found that across models, the features with the most weight were previously reported in the literature as being related to motion sickness severity. Finally, we discuss how models constructed on heterogeneous vs homogeneous sensor data may be useful in different real-world scenarios.
A Streamlined Method for Sourcing Discourse-level Argumentation Annotations from the Crowd

Tristan Miller, Maria Sukhareva, Iryna Gurevych. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Recaptured Image Forensics Algorithm Based on Image Texture Feature

With the rapid development of digital phones, the digital image forensics system in current times has had a great impact. It will lead to a serious threat for us, and especially the emergence of the recaptured image makes the existing digital image forensics algorithm invalid. So, it needs an effective image detection algorithm for us to identify recaptured images. In this paper, a new detection algorithm of the recaptured image is presented based on gray level co-occurrence matrix by analyzing the differences between the real and recaptured images. In order to analyze the differences, a new image evaluation model was put forward in this paper, which is called image variance ratio. Firstly, the algorithm proposed extracted high-frequency and low-frequency information of images by wavelet transform, based on which we calculated the relative gray level co-occurrence matrices. Secondly, the features of gray level co-occurrence matrix were extracted. At last, the recaptured image was classified by the support vector machine according to the features. The experimental results showed the algorithm proposed can not only effectively identify the recaptured image obtained from different media but also have better identification rate.
Generative Adversarial Radio Spectrum Networks

Simulating and imitating RF communications signals and systems is a core function of jammers, spoofers, and other attacks in wireless radio environments which seek to confuse spectrum users as to what is occurring in the spectrum around them. Replay attacks and "DRFMs" have long been commonly used to deceive and probe radio systems, however generative models introduce an interesting new angle wherein generative replay can now produce examples of signals of similar structure and properties to arbitrary signals which are not verbatim replays and which may be varied in an infinite number of ways. Further, as GANs have demonstrated a strong ability to learn distributions from complex scenes and datasets, we consider the task of full-band spectral generation in addition to single signal generation to validate and demonstrate the feasibility of such an approach, to refine the algorithmic approach, and to quantify and illustrate the capabilities of such an approach on modern day signal sets.
Privacy Exposure Measure: A Privacy-Preserving Technique for Health-Related Web Search

The increasing use of web search engines (WSEs) for searching healthcare information has resulted in a growing number of users posting personal health information online. A recent survey demonstrates that over 80% of patients use WSE to seek health information. However, WSE stores these user's queries to analyze user behavior, result ranking, personalization, targeted advertisements, and other activities. Since health-related queries contain privacy-sensitive information that may infringe user's privacy. Therefore, privacy-preserving web search techniques such as anonymizing networks, profile obfuscation, private information retrieval (PIR) protocols etc. are used to ensure the user's privacy. In this paper, we propose Privacy Exposure Measure (PEM), a technique that facilitates user to control his/her privacy exposure while using the PIR protocols. PEM assesses the similarity between the user's profile and query before posting to WSE and assists the user in avoiding privacy exposure. The experiments demonstrate 37.2% difference between users' profile created through PEM-powered-PIR protocol and other usual users' profile. Moreover, PEM offers more privacy to the user even in case of machine-learning attack.
A Novel Weighted SVM Classifier Based on SCA for Handwritten Marathi Character Recognition

The research on handwritten optical character recognition (OCR) of Marathi script is very challenging due to the complex structural properties of the script that are not observed in most other scripts. This paper gives an OCR framework for handwritten Marathi document classification and recognition system. Due to the large variety of symbols the Marathi characters recognition poses great challenge and their proximity in appearance. The weighted one-against-rest support vector machines (WOAR-SVM) assume a noteworthy part to deal with vast feature measures which are utilized for the classification. Here, a new sine cosine algorithm is proposed for the identification of handwritten Marathi text. By utilizing different morphological operations the preprocessing is finished and the Marathi text is flexibly segmented in three levels; line segmentation, word segmentation and character segmentation with Modified Pihu method. Various features like statistical, global transformation, geometrical and topological features are extracted from the preprocessed image by extraction techniques. Result obtained show that various features with WOAR-SVM classifier perform the best by yielding high accuracy as 95.14%.
Multi-task Pairwise Neural Ranking for Hashtag Segmentation

Hashtags are often employed on social media and beyond to add metadata to a textual utterance with the goal of increasing discoverability, aiding search, or providing additional semantics.However, the semantic content of hashtags is not straightforward to infer as these represent ad-hoc conventions which frequently include multiple words joined together and can include abbreviations and unorthodox spellings.We build a dataset of 12,594 hashtags split into individual segments and propose a set of approaches for hashtag segmentation by framing it as a pairwise ranking problem between candidate segmentations. 1 Our novel neural approaches demonstrate 24.6% error reduction in hashtag segmentation accuracy compared to the current state-of-the-art method.Finally, we demonstrate that a deeper understanding of hashtag semantics obtained through segmentation is useful for downstream applications such as sentiment analysis, for which we achieved a 2.6% increase in average recall on the Se-mEval 2017 sentiment analysis dataset.
3D Surface Matching by a Voxel-Based Buffer-Weighted Binary Descriptor

3D surface matching by local feature descriptors is a fundamental task in 3D object registration, recognition, and retrieval.In view of 2D projected descriptors' information compression and 3D directly voxelized descriptors' sensitivity to density variation and boundary, this paper first proposes a voxel-based buffer-weighted binary descriptor, named VBBD.After local surfaces of detected keypoints are voxelized, each voxel's buffer region is established, and the buffer-weighted Gaussian kernel density is calculated.If the current voxel's buffer-weighted density is larger than its local surface's average density, the voxel is labeled 1, otherwise, it is 0. The proposed descriptor has several merits: 1) direct acquisition of 3D information without projection, the neighbor information is less compressed.2) voxels are labeled and binarized according to the buffer-weighted density, which improves the robustness to boundary effect, noise and density variation.Based on the VBBD, a global optimal surface matching method based on a Kuhn_Munkres (KM) algorithm is adopted.The strategy has several advantages: 1) by calculating the descriptors of large-scaled surfaces which are down sampled, the number of the point cloud is reduced, and meanwhile, large-scaled information is obtained; and 2) KM algorithm is adopted to find matching pairs to achieve final maximum weight sum of all matching pairs, which can efficiently avoid local optimum.The experimental results show that, compared with other state-of-the-art descriptors, the VBBD has better descriptiveness and robustness, and the surface matching strategy by the VBBD can achieve both high recall and precision.