Reviewerly/exHarmony huggingface dataset:
A flexible angle compensation method for freeform surface testing based on tip/tilt mirror

Freeform surfaces with large asphericity are generally difficult to test. We propose a novel interferometric method based on tip/tilt mirror (TTM) suitable for such surfaces. The incident ray directions are dynamically compensated in several dimensions by the TTM, to control the incident angles of the rays at locations where the local surface profile is steep. Therefore, the tilt angle depend on the local gradients of the tested surface. When the incident angles on all sub-zones are controlled, we can obtain a series of lower density interferograms based on which the surface profile can be determined. The compensating range in our method is analyzed, and the largest measurable slope is 2.8°based on 20-mrad tilt angle and 3.3 F-number. Besides, the influence of the compensating precision of the TTM is simulated, and the compensation error can be ignored when its precision is under 40 μrad. The precision and efficiency of this method were proved by a series of tests.
DL-Extractable UC-Commitment Schemes

We define a new UC functionality (DL-extractable commitment scheme) that allows committer to open a commitment to a group element $$g^x$$; however, the simulator will be able to extract its discrete logarithm x. Such functionality is useful in situations where the secrecy of x is important since the knowledge of x enables to break privacy while the simulator needs to know x to be able to simulate the corrupted committer. Based on Fujisaki's UC-secure commitment scheme and the Damgård-Fujisaki integer commitment scheme, we propose an efficient commitment scheme that realizes the new functionality. As another novelty, we construct the new scheme in the weaker RPK (registered public key) model instead of the CRS model used by Fujisaki.
Hdrnet: Person Re-Identification Using Hybrid Sampling in Deep Reconstruction Network

Person re-identification (re-id) is the task of identifying a person across non-overlapping cameras. Most of the current techniques apply deep learning and achieve a significant accuracy. However, learning a deep model that can generalize well against the challenges of pose variation, occlusion, illumination changes, and low resolution is a difficult task. Toward this, we propose a deep reconstruction re-id network, comprising of an encoder and a multi-resolution decoder, which can learn embeddings invariant to pose, occlusion, illumination, and low resolution. In our model, the encoder acts as a conventional deep re-id network and outputs a discriminative feature embedding. The output feature is then used as an input to the multi-resolution decoder to reconstruct the input images of the same identity under different resolutions, such that they are similar in pose and illumination as well as free from occlusion. We further propose a hybrid sampling strategy to boost the effectiveness of the training loss function. In addition, we propose test set augmentation using the reconstructed images to explicitly transform single query to multi-query setting. In our multi-tasking approach, the feature robustness is enhanced by the multi-resolution decoder, and the overall accuracy is further improved by a sampling strategy and test data augmentation. Furthermore, we empirically show that the proposed network is robust to pose variations, occlusion, and low resolution. We perform rigorous qualitative and quantitative analysis in order to demonstrate that we achieve state-of-the-art person re-id accuracy.
PRIN: A Probabilistic Recommender with Item Priors and Neural Models

In this paper, we present PRIN, a probabilistic collaborative filtering approach for top-N recommendation. Our proposal relies on continuous bag-of-words (CBOW) neural model. This fully connected feedforward network takes as input the item profile and produces as output the conditional probabilities of the users given the item. With that information, our model produces item recommendations through Bayesian inversion. The inversion requires the estimation of item priors. We propose different estimates based on centrality measures on a graph that models user-item interactions. An exhaustive evaluation of this proposal shows that our technique outperforms popular state-of-the-art baselines regarding ranking accuracy while showing good values of diversity and novelty.
Automatic Modulation Recognition in Wireless Communication Systems Using Feature-Based Approach

We propose a novel scheme for automatic recognition of four digital modulation types in the domain of wireless communication systems. The presented scheme exploits the distinct features reflected by two dimensional asynchronous sampled In-phase-Quadrature data’ histograms (ASIQ) for the recognition of various digital modulation types. The effect of the medium between the transmitter and receiver is only limited to additive white Gaussian noise (AWGN). The proposed work utilizes support vector machine (SVM) tool for the recognition of various and popular digital modulation types. Simulation results from the proposed technique showed accurate discrimination among four digital signals with various bit rates of 100% total classification accuracy. The simulation was carried out over a broad range of signal-to-noise-ratio (SNR) of 0–35 dB with a step of 0.5. The proposed method exploits the existed structure of coherent receivers to construct two-dimensional histograms without the need for any added hardware devices. Therefore, it provides a promising solution and cost-effective identification technique for modulation types in next wireless communication systems.
Breast Cancer Prediction using SVM with PCA Feature Selection Method

Cancer has been characterized as one of the leading diseases that cause death in humans. Breast cancer, being a subtype of cancer, causes death in one out of every eight women worldwide. The solution to counter this is by conducting early and accurate diagnosis for faster treatment. To achieve such accuracy in a short span of time proves difficult with existing techniques. Also, the medical tests conducted in hospitals for detecting cancer is expensive and is difficult for any common man to afford. To counter these problems, in this paper, we use the concept of applying Support Vector machine a Machine Learning algorithm to predict whether a person is prone to breast cancer. We evaluate the performance of this algorithm by calculating its accuracy and apply a min-max scaling method so as to counter and overcome the problem of overfitting and outliers. After scaling of the dataset, we apply a feature selection method called Principle component analysis to improve the algorithms accuracy by decreasing the number of parameters. The final algorithm has improved accuracy with the absence of overfitting and outliers, thus this algorithm can be used to develop and build systems that can be deployed in clinics, hospitals and medical centers for early and quick diagnosis of breast cancer. The training dataset is from the University of Wisconsin (UCI) Machine Learning Repository which is used to evaluate the performance of the Support vector machine by calculating its accuracy.
Dynamic Ecological Inference for Time-Varying Population Distributions Based on Sparse, Irregular, and Noisy Marginal Data

Social scientists are frequently interested in how populations evolve over time. Creating poststratification weights for surveys, for example, requires information on the weighting variables’ joint distribution in the target population. Typically, however, population data are sparsely available across time periods. Even when population data are observed, the content and structure of the data—which variables are observed and whether their marginal or joint distributions are known—differ across time, in ways that preclude straightforward interpolation. As a consequence, survey weights are often based only on the small subset of auxiliary variables whose joint population distribution is observed regularly over time, and thus fail to take full advantage of auxiliary information. To address this problem, we develop a dynamic Bayesian ecological inference model for estimating multivariate categorical distributions from sparse, irregular, and noisy data on their marginal (or partially joint) distributions. Our approach combines (1) a Dirichlet sampling model for the observed margins conditional on the unobserved cell proportions; (2) a set of equations encoding the logical relationships among different population quantities; and (3) a Dirichlet transition model for the period-specific proportions that pools information across time periods. We illustrate this method by estimating annual U.S. phone-ownership rates by race and region based on population data irregularly available between 1930 and 1960. This approach may be useful in a wide variety of contexts where scholars wish to make dynamic ecological inferences about interior cells from marginal data. A new R package estsubpop implements the method.
The study of data-oriented and ownership-based security architecture in open internet environment

DOSA (Data-Oriented Security Architecture, or Data Ownership-based Security Architecture) is an architecture for data protection and application in an open Internet environment. DOSA combines data with ownership by using digital certification authentication (CA) and public key infrastructure (PKI). The DOSA is simply described as one body with two wings. The one body is that the data must be combined with ownership. The one wing is that the data should be innately registered. Another wing is that the data should be innately encrypted with the data owner's public key. To share data and make data applicable, DOSA also establishes the authorization of data ownership for data sharing, the recording of data operation for data history tracing, the data behaviour analysis for the discovery of illegal use of data, and the data usage statistics for the assessment of data value, etc. Therefore, data can be securely shared and used in an open environment with ownership authorization. At the same time, data ownership is clarified; the interests of the data owner can be guaranteed.
A Survey of Nurses' Need for Care Robots in Children's Hospitals

The purpose of this study was to investigate nurses' need for care robots in children's hospitals and to help develop care robots that can be used by combining robot-care, game-care, and edu-care. This study employed a mixed-methods design; 198 nurses were recruited for quantitative research and 12 for qualitative research. The findings were as follows. Robot-care: Participants had an overwhelming preference for robots made of plastic or steel that could easily be washed and sterilized. Game-care: Among nursing procedures, vital sign measurements were the most common actions that could be implemented using game elements. Edu-care: The educational content that participants felt should be presented to child patients and caregivers through care robots included information about hospitalization, discharge, infection control, falls, and pressure ulcer prevention. Qualitative data divided the role of care robots into five subcategories associated with children and one subcategory associated with guardians. The findings of this study are meaningful in that it clarified the needs of nurses in the development of care robots for use in children's hospitals.
ITWF: A framework to apply term weighting schemes in topic model

Topic models like Latent Dirichlet Allocation (LDA) and its variants is a type of statistical model for discovering latent topics. However, as revealed by the previous research, some topics generated by LDA may be uninterpretable and semantically incoherent due to the occurrence of irrelevant words in these topics. To improve the semantic qualities of automatically discovered topics, we explore the distributional characteristics of words across topics to identify topic-indiscriminate words which are blamed for the low-quality topics. The main contribution of our research reported in this paper is that we develop a novel framework named Iterative Term Weighting Framework (ITWF) which can effectively identify and filter out topic-indiscriminate words from uncovered topics. In particular, the proposed framework first applies an entropy-based term weighting schemes and adopts a novel iterative method to identify topic-indiscriminate words. To the best of our knowledge, our research is among the very few successful work that aims to enhance both the semantic coherence and the interpretability of LDA-based topic modeling methods. The experimental results show that the proposed framework improves the effectiveness of LDA as well as its variants.
Latent semantic analysis of game models using LSTM

We are proposing a method for identifying whether the observed behaviour of a function at an interface is consistent with the typical behaviour of a particular programming language. This is a challenging problem with significant potential applications such as in security (intrusion detection) or compiler optimisation (profiling). To represent behaviour we use game semantics, a powerful method of semantic analysis for programming languages. It gives mathematically accurate models ('fully abstract') for a wide variety of programming languages. Game-semantic models are combinatorial characterisations of all possible interactions between a term and its syntactic context. Because such interactions can be concretely represented as sets of sequences, it is possible to ask whether they can be learned from examples. Concretely, we are using LSTM, a technique which proved effective in learning natural languages for automatic translation and text synthesis, to learn game-semantic models of sequential and concurrent versions of Idealised Algol (IA), which are algorithmically complex yet can be concisely described. We will measure how accurate the learned models are as a function of the degree of the term and the number of free variables involved. Finally, we will show how to use the learned model to perform latent semantic analysis between concurrent and sequential Idealised Algol.
Multivariable based checkpoints to mitigate the long range attack in proof-of-stake based blockchains

Proof-of-Stake (PoS) is getting popularity among low power computing devices as compared to high power and energy demanded proof-of work (PoW). Among the security issues of PoS, Long range attack is declared as one of the major issue by many researchers. Long range attack allows the minority stakeholders to become majority stakeholders over a long time span and then they can control the whole blockchain protocol to produce the valid alternative history. Checkpointing is referred as solution to mitigate the impact of the long range attack but still for the newly joined users as well as the users those will stay offline for long period of time, checkpointing mechanism needs to be re-evaluated. In this paper we studied the checkpointing mechanism and proposed a new strategy to implement the checkpointing inside the blockchain technology. The proposed strategy is designed specifically to mitigate the long range attack or stake bleeding attack.
Authentication Model using the Bundled CAPTCHA OTP Instead of Traditional Password

In this research, we present identity verification using the “Bundled CAPTCHA OTP” instead of using the traditional password. This includes a combination of CAPTCHA and One Time Password (OTP) to reduce processing steps. Moreover, a user does not have to remember any password. The Bundled CAPTCHA OTP which is the unique random parameter for any login will be used instead of a traditional password. We use an e-mail as the way to receive client-side the Bundled CAPTCHA OTP because it is easier to apply without any problems compare to using mobile phones. Since mobile phones may be crashing, lost, change frequently, and easier violent access than e-mail. In this paper, we present a processing model of the proposed system and discuss advantages and disadvantages of the model.
Which Graph Representation to Select for Static Graph-Algorithms on a CUDA-capable GPU

GPUs seem to be ideal for algorithms that work in parallel. A number of ways to represent graphs in GPU memory are known. But so far there are no guidelines to select the representation that is likely to result in the best performance.
Blind quality assessment of gamut-mapped images via local and global statistical analysis

Gamut mapping is a key technology to achieve high-quality cross-media color reproduction. To optimize a gamut mapping algorithm, an important step is to conduct an accurate evaluation of its psycho-visual performance. This paper presents an objective blind image quality assessment (BIQA) metric for gamut-mapped images based on natural scene statistics. Considering both the local and global aspects of distortions in gamut-mapped images, two categories of statistics are analyzed. Specifically, the local statistical features are used to portray structural and color distortions and features extracted from global statistics are utilized to characterize the naturalness of image. The proposed metric does not need ground truth quality scores for training, thus it is ”completely” blind. Experimental results on three gamut mapping databases demonstrate that our method outperforms the state-of-the-art general-purpose BIQA models. To further validate its effectiveness, the proposed metric is applied for benchmarking GMAs as an application and achieves encouraging performance.
Analysis of Factors for Emission Change in the Waste Incineration Sector caused by Change of Guidance in the Greenhouse Gas Emissions Estimate Method

Due to adoption of the “Paris Agreement” in 2015, we must prepare for “Global stock-taking” assessment. To do so, it is necessary to improve the credibility of the GHG inventory and to identify and manage the emission characteristics of each major GHG emission source.BR Korea estimates GHG emissions using the methodology of the Korean MRV G/L (based on IPCC GPG 2000). The methodology of the IPCC GPG 2000 is not as supported as that of the IPCC 2006 G/L.BR Therefore, to improve the credibility of GHG inventory, IPCC 2006 G/L must be applied. The UNFCCC has specified that MRV work should be implemented based on the 2006 IPCC G/L scheme from 2015 to strengthen the reporting and evaluation systems for implementation of reductions among countries.BR This study analyzed the applicability of the 2006 IPCC emission estimating method in the waste incineration sector and the factors affecting the emission. The study focused on the Gyeonggi do sector, which treats the largest amount of waste in Korea.BR The GHG emission of the 2006 IPCC G/L-based method was estimated to be 405 tCO2eq lower than the Korean MRV G/L-based GHG emission. This difference is because the Korean MRV G/L estimates GHG emissions based on total amount of waste incinerated, while the 2006 IPCC G/L estimates the emissions based on the specific characteristics of each waste type.
Human Detection Based on HOG-LBP for Monitoring Sterile Zone

Human detection has enormous application area in autonomous video surveillance and human-computer interaction. Detecting suspicious event has become a very crucial issue in the current circumstance of our society. As a pioneer, a framework is proposed for detecting human in the sterile zone in this paper. Since, in the case of the sterile zone, we have to deal with low-resolution video, that's why initially input video frames are enhanced by using local histogram equalization. Then a background model is created by using the Gaussian Mixture Model (GMM) where each pixel is represented by a mixture of a number of Gaussian based on probabilistic method. This modeled background is then compared with a new frame to detect the foreground object. After that, the morphological operation is performed to remove discontinuities and to get the region of interest (ROI). Then shape and texture features from ROI are extracted for classification. Finally, combined features from Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) are fed into SVM classifier to detect human. In this paper to achieve better performance in the sterile zone, human shape is analyzed with HOG along with enumerating local features by LBP. Moreover, this proposed framework is tested using various video in different conditions and the outcome demonstrates remarkable efficiency comparative to other alternatives.
Size/Accuracy Trade-Off in Convolutional Neural Networks: An Evolutionary Approach

In recent years, the shift from hand-crafted design of Convolutional Neural Networks (CNN’s) to an automatic approach (AutoML) has garnered much attention. However, most of this work has been concentrated on generating state of the art (SOTA) architectures that set new standards of accuracy. In this paper, we use the NSGA-II algorithm for multi-objective optimization to optimize the size/accuracy trade-off in CNN’s. This approach is inspired by the need for simple, effective, and mobile-sized architectures which can easily be re-trained on any datasets. This optimization is carried out using a Grammatical Evolution approach, which, implemented alongside NSGA-II, automatically generates valid network topologies which can best optimize the size/accuracy trade-off. Furthermore, we investigate how the algorithm responds to an increase in the size of the search space, moving from strictly topology optimization (number of layers, size of filter, number of kernels,etc.) and then expanding the search space to include possible variations in other hyper-parameters such as the type of optimizer, dropout rate, batch size, or learning rate, amongst others.
3D Quantum Cuts for automatic segmentation of porous media in tomography images

Binary segmentation of volumetric images of porous media is a crucial step towards gaining a deeper understanding of the factors governing biogeochemical processes at minute scales. Contemporary work primarily revolves around primitive techniques based on global or local adaptive thresholding that have known common drawbacks in image segmentation. Moreover, the absence of a unified benchmark prohibits quantitative evaluation, which further undermines the impact of existing methodologies. In this study, we tackle the issue on both fronts. First, by drawing parallels with natural image segmentation, we propose a novel, and automatic segmentation technique, 3D Quantum Cuts (QCuts-3D) grounded on a state-of-the-art spectral clustering technique. Secondly, we curate and present a publicly available dataset of 68 multiphase volumetric images of porous media with diverse solid geometries, along with voxel-wise ground truth annotations for each constituting phase. We provide comparative evaluations between QCuts-3D and the current state-of-the-art over this dataset across a variety of evaluation metrics. The proposed systematic approach achieves a 26% increase in AUROC (Area Under Receiver Operating Characteristics) while achieving a substantial reduction of the computational complexity over state-of-the-art competitors. Moreover, statistical analysis reveals that the proposed method exhibits significant robustness against the compositional variations of porous media.
Data Poisoning Attacks against MRMR

Many machine learning models lack the consideration that an adversary can alter data at the time of training or testing. Over the past decade, the machine learning models' vulnerability has been a concern and more secure algorithms are needed. Unfortunately, the security of feature selection (FS) remains an under-explored area. There are only a few works that address data poisoning algorithms that are targeted at embedded FS; however, data poisoning techniques targeted at information-theoretic FS do not exist. In this contribution, a novel data poisoning algorithm is proposed that targets failures in minimum Redundancy Maximum Relevance (mRMR) . We demonstrate that mRMR can be easily poisoned to select features that would not normally have been selected.