Reviewerly/exHarmony huggingface dataset:
Design of an E-Attendance Checker through Facial Recognition using Histogram of Oriented Gradients with Support Vector Machine

The usual way of checking the attendance in a class has its own drawbacks. To be able to resolve it, automated attendance systems were introduced. In this paper, the design and development of an e-attendance checker using a facial recognition system were implemented. It can scan the faces of multiple students in a standard classroom setup. A commonly used approach for face detection called Histogram of Oriented Gradients (HOG) with Support Vector Machine (SVM) was applied to examine the effect of luminance of the surrounding, the facial orientation of the student and so as their distance from the camera in the facial detection and recognition. The obtained attendance will then be uploaded to a database with authentication. It was found that the system has an accuracy of 95.65% and can detect and recognize up to 37 students. It is suggested that the classroom should have a luminance level of about 217.39 lux or higher to achieve a better accuracy performance of the system. As for the analysis of the effect of distance in the system, it is claimed that the distance of the student does not affect the accuracy of the system. Lastly, it is suggested that the face angles of the subject should be directly facing the camera to achieve a more accurate recognition result.
A C++ application programming interface for co-evolutionary biased random-key genetic algorithms for solution and scenario generation

This paper presents a C++ application programming interface for a co-evolutionary algorithm for solution and scenario generation in stochastic problems. Based on a two-space biased random-key genetic algorithm, it involves two types of populations that are mutually impacted by the fitness calculations. In the solution population, high-quality solutions evolve, representing first-stage decisions evaluated by their performance in the face of the scenario population. The scenario population ultimately generates a diverse set of scenarios regarding their impact on the solutions. This application allows the straightforward implementation of this algorithm, where the user needs only to define the problem-dependent decoding procedure and may adjust the risk profile of the decision-maker. This paper presents the co-evolutionary algorithm and structures the interface. We also present some experiments that validate the impact of relevant features of the application.
Buffered Streaming Graph Partitioning

Partitioning graphs into blocks of roughly equal size is a widely used tool when processing large graphs. Currently, there is a gap observed in the space of available partitioning algorithms. On the one hand, there are streaming algorithms that have been adopted to partition massive graph data on small machines. In the streaming model, vertices arrive one at a time including their neighborhood, and then have to be assigned directly to a block. These algorithms can partition huge graphs quickly with little memory, but they produce partitions with low solution quality. On the other hand, there are offline (shared-memory) multilevel algorithms that produce partitions with high-quality but also need a machine with enough memory to partition huge networks. In this work, we make a first step to close this gap by presenting an algorithm that computes significantly improved partitions of huge graphs using a single machine with little memory in a streaming setting. First, we adopt the buffered streaming model which is a more reasonable approach in practice. In this model, a processing element can store a buffer of nodes alongside with their edges before making assignment decisions. When our algorithm receives a batch of nodes, we build a model graph that represents the nodes of the batch and the already present partition structure. This model enables us to apply multilevel algorithms and in turn, on cheap machines, compute much higher quality solutions of huge graphs than previously possible. To partition the model graph, we develop a multilevel algorithm that optimizes an objective function that has previously been shown to be effective for the streaming setting. Surprisingly, this also removes the dependency on the number of blocks k from the running time compared to the previous state-of-the-art. Overall, our algorithm computes, on average, 75.9% better solutions than Fennel [ 35 ] using a very small buffer size. In addition, for large values of k our algorithm becomes faster than Fennel .
Evaluation of Hologram Distances in Reducing Shoulder Stress During Augmented Reality Interactions

This study investigated the effect of different hologram distances on the shoulder postures, muscle activities, and the task performance (speed) during Augmented Reality (AR) interactions. With a repeated- measures design, 20 participants (10 males) performed the 3-D cube task involving gaze, pinching, and dragging gestures by different hologram distances: near (30 cm), middle (60 cm), and far (90 cm) relative to the participant. Meanwhile, muscle activities (upper trapezius, anterior and middle deltoid) and the right shoulder postures were measured. The results showed that hologram distances significantly affected middle deltoid muscle activities and shoulder flexion and abduction angles, and task performance during 3-D cube task ( p’s &lt; 0.001). The far hologram distance resulted in higher middle deltoid muscle activity and shoulder angles compared to the near and middle distances. Task performance was higher when the hologram was displayed in the middle compared to the near and far distances ( p’s &lt; 0.001). These results indicate that a hologram distance may be an important design factor affecting users’ shoulder stress and task performance during AR interactions.
Model-independent detection of new physics signals using interpretable SemiSupervised classifier tests

A central goal in experimental high energy physics is to detect new physics signals that are not explained by known physics. In this paper we aim to search for new signals that appear as deviations from known Standard Model physics in high-dimensional particle physics data. To do this, we determine whether there is any statistically significant difference between the distribution of Standard Model background samples and the distribution of the experimental observations which are a mixture of the background and a potential new signal. Traditionally, one also assumes access to a sample from a model for the hypothesized signal distribution. Here we instead investigate a model-independent method that does not make any assumptions about the signal and uses a semisupervised classifier to detect the presence of the signal in the experimental data. We construct three test statistics using the classifier: an estimated likelihood ratio test (LRT) statistic, a test based on the area under the ROC curve (AUC), and a test based on the misclassification error (MCE). Additionally, we propose a method for estimating the signal strength parameter and explore active subspace methods to interpret the proposed semisupervised classifier in order to understand the properties of the detected signal. We also propose a score test statistic that can be used in the model-dependent setting. We investigate the performance of the methods on a simulated data set related to the search for the Higgs boson at the Large Hadron Collider at CERN. We demonstrate that the semisupervised tests have power competitive with the classical supervised methods for a well-specified signal but much higher power for an unexpected signal which might be entirely missed by the supervised tests.
Simultaneously Optimizing Inertia Weight and Acceleration Coefficients via Introducing New Functions into PSO Algorithm

Abstract Because of the drawbacks of easy premature in initial iteration stages, the low convergence accuracy and slowed-down converging speed in final stages of the particle swarm optimization (PSO)algorithm therefore the simple particle swarm optimization (SPSO) algorithm with dynamic changes of inertia weight and acceleration coefficients (IASPSO) has been put forward. IASPSO algorithm provides a parameter optimization strategy by using exponential decreasing inertia weight and sine function acceleration coefficient to improve global exploration capacity. Simulation tests are carried out with classic Benchmark test functions. The simulation results show that compared with other PSO algorithms, IASPSO algorithm can converge to the better global optimization with a fast converging velocity and high convergence precision, promoting the optimization performance of the algorithm.
Double Propagation Stereo Matching for Urban 3-D Reconstruction From Satellite Imagery

Stereo matching is one of the popular methods to acquire the depth maps in 3-D reconstruction due to its high accuracy and completeness. However, traditional stereo matching methods are only suitable for general scenes or aerial images instead of urban scenes from large-scale satellite imageries. This article presents a novel double propagation stereo matching (DPSM) for urban 3-D reconstruction from satellite images. To make full use of the geometrical properties of man-made buildings, stereo-rectified satellite images, as input, are initialized to multiple superpixels with geometric models. Then, three similarity metrics are combined to calculate the weighted matching cost. A novel double propagation optimization is developed to optimize iteratively the weighted matching cost under the constraints of region boundaries and geometric models, and the disparity maps can be obtained by minimizing the energy function. Consequently, the corresponding dense 3-D point clouds and digital surface models (DSMs) are calculated by triangulation. Qualitative and quantitative experiments on stereo images captured by Pleiades and WorldView-2 satellites show that the proposed algorithm outperforms the most state-of-the-art stereo matching algorithms in terms of preserving depth discontinuous areas and restoring occlusions.
Differential rights consciousness: Teachers’ perceptions of privacy in the surveillance school

This article explores the rights consciousness of teachers as agents having a professional obligation to promote students’ rights. The case study comprises Israeli teachers, whose social status is low, in the context of school CCTV surveillance. Based on 55 interviews, the findings revealed three clusters of perceptions: dismissing students’ privacy as a discrete consideration in assessing school surveillance; acknowledging students’ privacy as a discrete consideration; and merging students’ and teachers’ privacy. Almost all teachers considered their own privacy. Our conclusions focus on teachers who had differential rights consciousness and alluded to privacy justifications only when they concerned their own rights.
ST-VLAD: Video Face Recognition Based on Aggregated Local Spatial-Temporal Descriptors

How to integrate the temporal and spatial continuity information, when designing the video texture description operator, is crucial to realize video face recognition and facilitate video analysis and understanding, however, it has still yet to be properly addressed.In this paper, a novel video face recognition algorithm is proposed based on an aggregated local spatial-temporal descriptor (ST-VLAD), followed by a novel Fisher Criterion-based weight-learning method, which portrays the local information of the video more accurately, therefore largely improving the representation ability of description vectors.The proposed descriptor was tested on two representative databases, Honda/UCSD and YouTube Face database, achieving accuracies of 89.7% and 87.3%, respectively.The proposed method greatly outperformed the other existing state-of-art methods, suggesting a potential broad utility in the field of video face recognition.
Schematic Representation of Biconnected Graphs

Suppose that a biconnected graph is given, consisting of a large component plus several other smaller components, each separated from the main component by a separation pair. We investigate the existence and the computation time of schematic representations of the structure of such a graph where the main component is drawn as a disk, the vertices that take part in separation pairs are points on the boundary of the disk, and the small components are placed outside the disk and are represented as non-intersecting lunes connecting their separation pairs. We consider several drawing conventions for such schematic representations, according to different ways to account for the size of the small components. We map the problem of testing for the existence of such representations to the one of testing for the existence of suitably constrained 1-page book-embeddings and propose several polynomial-time and pseudo-polynomial-time algorithms.
Vascular enhancement with structure preservation from noisy X-ray angiogram images by employing non-local Hessian-based filter

Enhancement and denoising are a pair of inverse problems in image processing, how to enhance vascular as well as reducing noise with structure preservation from X-ray angiographic images is important for clinicians to diagnose vascular diseases. In order to complete the task, this paper has proposed a non-local Hessian-based filter (NLHF) including the following key parts: (1) a non-local operator is incorporated into the Hessian matrix to obtain noiseless eigenvalues in a noisy environment. (2) The relation of local image is carried by an exponent function which is used to suppress non-vascular structures around real vascular structures. The quantitative and qualitative results of experiments on both synthetic and real-world clinical X-ray vascular angiograms demonstrate that the proposed NLHF method performs better than the other two Hessian-based methods on vascular enhancement, noise reduction, and structure preservation.
Detect Geographical Location by Multi-View Scene Matching

With the help of satellite geographical information, we present an innovative framework for localizing precisely while handling the varying views and different sources. We construct a convolutional neural network named Attentive Siamese-like Net (ASN) which can extract the multi-view scene representation. On top of it, a database retrieval system is established to search the realistic geographic coordinates quickly and reliably. For handling a complicated scene, a mass of visual data is collected from google satellite image and Google Earth Software. Experiments are carried out on two datasets of different scales and geographical range, which shows the superiority and effectiveness of our method.
Brain-inspired model for early vocal learning and correspondence matching using free-energy optimization

We propose a developmental model inspired by the cortico-basal system (CX-BG) for vocal learning in babies and for solving the correspondence mismatch problem they face when they hear unfamiliar voices, with different tones and pitches. This model is based on the neural architecture INFERNO standing for Iterative Free-Energy Optimization of Recurrent Neural Networks. Free-energy minimization is used for rapidly exploring, selecting and learning the optimal choices of actions to perform (eg sound production) in order to reproduce and control as accurately as possible the spike trains representing desired perceptions (eg sound categories). We detail in this paper the CX-BG system responsible for linking causally the sound and motor primitives at the order of a few milliseconds. Two experiments performed with a small and a large audio database show the capabilities of exploration, generalization and robustness to noise of our neural architecture in retrieving audio primitives during vocal learning and during acoustic matching with unheared voices (different genders and tones).
In silico multi-compartment detection based on multiplex immunohistochemical staining in renal pathology

With the rapid advancement in multiplex tissue staining, computer hardware, and machine learning, computationally-based tools are becoming indispensable for the evaluation of digital histopathology. Historically, standard histochemical staining methods such as hematoxylin and eosin, periodic acid- Schiff, and trichrome have been the gold standard for microscopic tissue evaluation by pathologists, and therefore brightfield microscopy images derived from such stains are primarily used for developing computational pathology tools. However, these histochemical stains are nonspecific in terms of highlighting structures and cell types. In contrast, immunohistochemical stains use antibodies to specifically detect and quantify proteins, which can be used to specifically highlight structures and cell types of interest. Traditionally, such immunofluorescence-based methods are only able to simultaneously stain a limited number of target proteins/antigens, typically up to three channels. Fluorescence-based multiplex immunohistochemistry (mIHC) is a new technology that enables simultaneous localization and quantification of numerous proteins/antigens, allowing for the possibility to detect a wide range of histologic structures and cell types within tissue. However, this method is limited by cost, specialized equipment, technical expertise, and time. In this study, we implemented a deep learning-based pipeline to synthetically generate in silico mIHC images from brightfield images of tissue slides-stained with routinely used histochemical stains, in particular PAS. Our tool was trained using fluorescence-based mIHC images as the ground-truth. The proposed pipeline offers high contrast detection of structures in brightfield imaged tissue sections stained with standard histochemical stains. We demonstrate the performance of our pipeline by computationally detecting multiple compartments in kidney biopsies, including cell nuclei, collagen/fibrosis, distal tubules, proximal tubules, endothelial cells, and leukocytes, from PAS-stained tissue sections. Our work can be extended for other histologic structures and tissue types and can be used as a basis for future automated annotation of histologic structures and cell types without the added cost of actually generating mIHC slides.
Towards A Transparent and Privacy-preserving Healthcare Platform with Blockchain for Smart Cities

In smart cities, data privacy and security issues of Electronic Health Record(EHR) are grabbing importance day by day as cyber attackers have identified the weaknesses of EHR platforms. Besides, health insurance companies interacting with the EHRs play a vital role in covering the whole or a part of the financial risks of a patient. Insurance companies have specific policies for which patients have to pay them. Sometimes the insurance policies can be altered by fraudulent entities. Another problem that patients face in smart cities is when they interact with a health organization, insurance company, or others, they have to prove their identity to each of the organizations/companies separately. Health organizations or insurance companies have to ensure they know with whom they are interacting. To build a platform where a patient's personal information and insurance policy are handled securely, we introduce an application of blockchain to solve the above-mentioned issues. In this paper, we present a solution for the healthcare system that will provide patient privacy and transparency towards the insurance policies incorporating blockchain. Privacy of the patient information will be provided using cryptographic tools.
WITHDRAWN: Employment and Entrepreneurship Education for University Students Based On Improved ACO Algorithm and Embedded Database

Employment and entrepreneurship are key components in manageable development, improving economic, social and natural dimensions. However, Employment and Entrepreneurship and open information are concentrated in writing from a holistic perspective. The embedded database identifies the normative aspects of past tests and proposes a theoretical model for breaking down employment through entrepreneurship. College Students clear investigation and co-word testing took place action related to tools that reuse data distributed by public companies by experts from various organizations to create new expert activities, especially for new portable applications. Entrepreneurship resources, development and action plans can be read as basic elements to embedded database employment through entrepreneurship. A calculated model is introduced, and emerging factors are proposed for future exploration. It emphasizes the importance of empowering joint efforts between different entrepreneurship environment experts for progress and improvement. Improved Ant Colony Optimization Algorithm has identified an emerging topic in its infancy: the embedded database of auxiliary employment through open information as a value creation activity to address practical twists globally.
A Novel Intelligent Parallel Parking System Based on Fuzzy Logic Without Using Sensor

The applications of intelligent systems have received a growing interest in the automobile industry. The car parking issue is one of the challenges for drivers, which can be solved by these smart systems. In this study, a new method for automatic parallel parking problems is proposed. The suggested method uses fuzzy inference to control, steer, and park the car in a tight space. Two parallel embedded fuzzy controllers have been designed to navigate the vehicle on the desired path. Unlike other studies that have used the error between developed and reference routes, the proposed method only uses the car's position data, similar to an experienced driver. There is no need to use the sensors to track the desired path in this method, but the car's kinematic model's output is the controllers' input. Based on the obtained results, the generated path by controllers is close to the desired track.
Coordinate-free Isoline Tracking in Unknown 2-D Scalar Fields

The isoline tracking of this work is concerned with the control design for a sensing robot to track a given isoline of an unknown 2-D scalar filed. To this end, we propose a coordinate-free controller with a simple PI-like form using only the concentration feedback for a Dubins robot, which is particularly useful in GPS-denied environments. The key idea lies in the novel design of a sliding surface based error term in the standard PI controller. Interestingly, we also prove that the tracking error can be reduced by increasing the proportion gain, and be eliminated for circular fields with a non-zero integral gain. The effectiveness of our controller is validated via simulations by using a fixed-wing UAV on the real dataset of the concentration distribution of PM2.5 in an area of China.
Consistency of permutation tests of independence using distance covariance, HSIC and dHSIC

The Hilbert–Schmidt independence criterion (HSIC) and its d ‐variable extension dHSIC are measures of (joint) dependence between random variables. While combining these statistics with a permutation test has become a popular method of testing the null hypothesis of (joint) independence, it had thus far not been proved that this results in a consistent test. In this work, we provide a simple proof that the permutation test with the test statistic HSIC or dHSIC is indeed consistent when using characteristic kernels. That is, we prove that under each alternative hypothesis, the power of these permutation tests indeed converges to 1 as the sample size converges to infinity. Since the test is consistent for each number of permutations, we further give a brief discussion of how the number of permutations relates to the power of the test and how the number of permutations may be selected in practice.