Reviewerly/exHarmony huggingface dataset:
A Study on Human Face Recognition Techniques

The facial recognition system is a technology that is efficient in recognizing or proving a human by using the image or video. It is a way of identifying human by using technology. The facial recognition system uses biometrics to capture the facial features from the photograph or video. It compares the information from the known database to find a match. There are many facial recognition techniques used nowadays. Among the biometrics, facial recognition plays a major role. The facial recognition uses machine learning to discover, match and recognize the person, and it is widely used in a variety of ways. The facial recognition system is used to differentiate among the users, and it produces an accurate result. In this work, facial recognition and its techniques will be briefly described.
A Methodology for the Specification and Generation of Markov Models*

Modelers wish to specify their models in a symbolic high-level language, while analytic Markov process solution techniques require a low-level, numerical representation. The translation between these description levels is a major problem. We describe a general, but surprisingly powerful methodology for specifying system-level models and generating the associated transition rate matrix. The basic approach will be shown to have significant advantages in that it provides the basis for modular, extensible modeling tools. With this methodology, modeling tools tailored to particular application domains can be quickly and easily constructed. An implementation in Prolog of a system based on this methodology is described and some example applications are given.
Spatial phase-shifting profilometry by use of polarization for measuring 3D shapes of metal objects

In this paper, we present a polarization spatial phase-shifting method for fringe projection profilometry. It enables us to measure the three-dimensional shape of a metal object in a fast way requiring only a single-shot implementation. With this method, a couple of projectors are equipped, in front of their lens, with linear polarization filters having orthogonal polarization directions, so that they can simultaneously cast two sinusoidal fringe patterns having different phase shifts onto the measured metal surfaces without mixture. To register the two projected patterns, we suggest a fringe alignment method based on the epipolar geometry between the projectors. By taking advantage of the property of metal surfaces in maintaining polarization state of incident light, the deformed fringe patterns on the measured surfaces are captured by using two coaxially-arranged polarization cameras. As a result, the fringe phases are calculated by using a two-step phase-shifting algorithm and further the 3D shapes of the measured surfaces are reconstructed. Experimental results demonstrate the proposed method to be valid and efficient in measuring metal objects.
Delving into Deep Imbalanced Regression

Real-world data often exhibit imbalanced distributions, where certain target values have significantly fewer observations. Existing techniques for dealing with imbalanced data focus on targets with categorical indices, i.e., different classes. However, many tasks involve continuous targets, where hard boundaries between classes do not exist. We define Deep Imbalanced Regression (DIR) as learning from such imbalanced data with continuous targets, dealing with potential missing data for certain target values, and generalizing to the entire target range. Motivated by the intrinsic difference between categorical and continuous label space, we propose distribution smoothing for both labels and features, which explicitly acknowledges the effects of nearby targets, and calibrates both label and learned feature distributions. We curate and benchmark large-scale DIR datasets from common real-world tasks in computer vision, natural language processing, and healthcare domains. Extensive experiments verify the superior performance of our strategies. Our work fills the gap in benchmarks and techniques for practical imbalanced regression problems. Code and data are available at https://github.com/YyzHarry/imbalanced-regression.
Investigation of the Static and Dynamic Path Planning of Mobile and Aerial Robots

Autonomous mobile robots are getting more important because they can make decisions on their own in the given environment. The robot path planning is an important function in the autonomous robot. In this review, several path planning techniques are investigated to analyze the performance. The path planning techniques are categorized as static and dynamic. A Q-learning method with flower pollination algorithm and the genetic algorithm has an effective performance on the path planning of mobile robot (MR) in a dynamic environment. The ant colony optimization (ACO) method shows considerable performance in the static environment. The aerial vehicle path planning method also reviewed and the method based on geometric random tree shows the effective performance in path planning. The comparison table shows the latest research in the MR path planning with the advantages, limitations, and performance analysis.
Gamification Based Blockchain Tournaments Between Miners

We have modelled mining resource and cryptocurrency-related relationships into a non-cooperative game. Then we took advantage of the traffic congestion results, set a native convention for the Nash equilibria, and created a short algorithm to find the equilibria. Next, we will make calculations for several system models whose variations follow the existing mining resources and have appropriately allocated according to the details of the mining complexity level that has defeated. In the included resources, the game's result is the allocation of resources as a feature of a normalized Nash equilibrium. In the model that has proposed, we provide a property structure of the type of equilibrium that exists, such as a condition where there are two or more mining infrastructures that will be active and another state that explains that no Miners get results in wanting a specific cryptocurrency, like bitcoin.
Quantum algorithms based on the block-encoding framework for matrix functions by contour integrals

he matrix functions can be defined by Cauchy's integral formula and can be approximated by the linear combination of inverses of shifted matrices using a quadrature formula. In this paper, we propose a quantum algorithm for matrix functions based on a procedure to implement the linear combination of the inverses on quantum computers. Compared with the previous study [S. Takahira, A. Ohashi, T. Sogabe, and T.S. Usuda, Quant. Inf. Comput., \textbf{20}, 1\&amp;2, 14--36, (Feb. 2020)] that proposed a quantum algorithm to compute a quantum state for the matrix function based on the circular contour centered at the origin, the quantum algorithm in the present paper can be applied to a more general contour. Moreover, the algorithm is described by the block-encoding framework. Similarly to the previous study, the algorithm can be applied even if the input matrix is not a Hermitian or normal matrix. This is an advantage compared with quantum singular value transformation.
Research on Teaching Design of Colleges Mathematics Micro-Course Based on the Perspective of Network Teaching Platform

Abstract With the development of science and technology, colleges and universities have gradually begun to combine network teaching platforms with online and offline hybrid teaching models to train students. This article mainly analyzes the influence of online learning platform on mathematics education. It also studied the micro-course design of the college mathematics network learning platform.
Pre-trained Transformer-based Classification and Span Detection Models for Social Media Health Applications

This paper describes our approach for six classification tasks (Tasks 1a, 3a, 3b, 4 and 5) and one span detection task (Task 1b) from the Social Media Mining for Health (SMM4H) 2021 shared tasks.We developed two separate systems for classification and span detection, both based on pre-trained Transformer-based models.In addition, we applied oversampling and classifier ensembling in the classification tasks.The results of our submissions are over the median scores in all tasks except for Task 1a.Furthermore, our model achieved first place in Task 4 and obtained a 7% higher F 1 -score than the median in Task 1b.
Video scene change detection based on histogram analysis for hiding message

Abstract The rapid growth of internet technology has greatly increased the opportunity for secrecy communication. Most of communications implemented compression method in digital applications due to fast transferring data in the limited bandwidth. In addition, the information can be changed by a third party in communication. This paper proposed a new hiding technique by modifying DCT coefficients in the video frames. The scene changes of the video data are identified based on histogram analysis for hiding locations. Scene changes among video frames are detected by measuring the significant difference of histogram analysis. The proposed hiding technique in video is also evaluated against MPEG-4 compression. The experimental results show that the proposed scheme achieved high imperceptibility with minimum visual distortion on the video quality. The proposed hiding scheme also can recover the concealed data from the compressed video. The results show that the extracted hidden message is able to resistant against MPEG compression.
A hybrid level set model for image segmentation

Active contour models driven by local binary fitting energy can segment images with inhomogeneous intensity, while being prone to falling into a local minima. However, the segmentation result largely depends on the location of the initial contour. We propose an active contour model with global and local image information. The local information of the model is obtained by bilateral filters, which can also enhance the edge information while smoothing the image. The local fitting centers are calculated before the contour evolution, which can alleviate the iterative process and achieve fast image segmentation. The global information of the model is obtained by simplifying the C-V model, which can assist contour evolution, thereby increasing accuracy. Experimental results show that our algorithm is insensitive to the initial contour position, and has higher precision and speed.
Seasonal Speciation Study of Heavy Metals Content in Well Water of Some Chicken Farms in Osun State, Southwestern Nigeria

Heavy metals are native to various soils, dusts, air, sediments, water and plant constituent in various means and these relations quantify their mobility and availability. In this manuscript, the seasonal differences in contents of arsenic, cadmium, copper, iron, lead and zinc in well water within the proximity of three main chicken farms in Osun State, were studied to ascertain the ecological impacts of the chicken farming on the well water sources. Water samples taken for eight months were assayed for pH, conductivity, total dissolved solids, alkalinity and speciation of heavy metal contents utilizing standard techniques. A column filled up with Amberlite XAD-16 resin was utilized for the quantification of metals bound to humic substances and free metal ions in the water samples. The samples were assayed for their heavy metal content utilizing Flame Atomic Absorption Spectrophotometer. Quality assurance techniques involved blank determination, recovery analysis and calibration of standards. Descriptive and inferential statistics were used for data interpretations. The content ranges of the metals were: 0.0110â0.0878, 0.0130â0.0900, 0.1124â0.5196, 17.0563-59.4264, 0.021-0.2283 and 3.0420â21.1450 µg/mL for As, Cd, Cu, Fe, Pb and Zn, respectively. With the exception of Cu, the content ranges of all the other metals were present to be exceeded the international standards and acceptable level for drinkable water. The speciation analyzes signified that Fe and Zn were present primarily in the particulate phase, As and Cu were predominant in the humic substances phase, while Cd, Fe, Pb and Zn mainly found in the free metal ions. The free metal ions phase of Cd, Fe, Pb and Zn were found to be bio-available, and thus, could present health risks to humans and poultry bird. The contents found from the control site signified that the chicken farm activities might have impacted the well water with high heavy metal burdens.
Sliced Iterative Normalizing Flows

We develop an iterative (greedy) deep learning (DL) algorithm which is able to transform an arbitrary probability distribution function (PDF) into the target PDF. The model is based on iterative Optimal Transport of a series of 1D slices, matching on each slice the marginal PDF to the target. The axes of the orthogonal slices are chosen to maximize the PDF difference using Wasserstein distance at each iteration, which enables the algorithm to scale well to high dimensions. As special cases of this algorithm, we introduce two sliced iterative Normalizing Flow (SINF) models, which map from the data to the latent space (GIS) and vice versa (SIG). We show that SIG is able to generate high quality samples of image datasets, which match the GAN benchmarks, while GIS obtains competitive results on density estimation tasks compared to the density trained NFs, and is more stable, faster, and achieves higher $p(x)$ when trained on small training sets. SINF approach deviates significantly from the current DL paradigm, as it is greedy and does not use concepts such as mini-batching, stochastic gradient descent and gradient back-propagation through deep layers.
Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment

Reinforcement learning from large-scale offline datasets provides us with the ability to learn policies without potentially unsafe or impractical exploration. Significant progress has been made in the past few years in dealing with the challenge of correcting for differing behavior between the data collection and learned policies. However, little attention has been paid to potentially changing dynamics when transferring a policy to the online setting, where performance can be up to 90% reduced for existing methods. In this paper we address this problem with Augmented World Models (AugWM). We augment a learned dynamics model with simple transformations that seek to capture potential changes in physical properties of the robot, leading to more robust policies. We not only train our policy in this new setting, but also provide it with the sampled augmentation as a context, allowing it to adapt to changes in the environment. At test time we learn the context in a self-supervised fashion by approximating the augmentation which corresponds to the new environment. We rigorously evaluate our approach on over 100 different changed dynamics settings, and show that this simple approach can significantly improve the zero-shot generalization of a recent state-of-the-art baseline, often achieving successful policies where the baseline fails.
Cross-platform binary code similarity detection based on NMT and graph embedding

<abstract> Cross-platform binary code similarity detection is determining whether a pair of binary functions coming from different platforms are similar, and plays an important role in many areas. Traditional methods focus on using platform-independent characteristic strands intersecting or control flow graph (CFG) matching to compute the similarity and have shortages in terms of efficiency and scalability. The existing deep-learning-based methods improve the efficiency but have a low accuracy and still using manually constructed features. Aiming at these problems, a cross-platform binary code similarity detection method based on neural machine translation (NMT) and graph embedding is proposed in this manuscript. We train an NMT model and a graph embedding model to automatically extract two parts of semantics of the binary code and represent it as a high-dimension vector, named an embedding. Then the similarity of two binary functions can be measured by the distance between their corresponding embeddings. We implement a prototype named SimInspector. Our comparative experiment result shows that SimInspector outperforms the state-of-the-art approach, Gemini, by about 6$ \% $ with respect to similarity detection accuracy, and maintains a good efficiency. </abstract>
Bias-Free Scalable Gaussian Processes via Randomized Truncations

Scalable Gaussian Process methods are computationally attractive, yet introduce modeling biases that require rigorous study. This paper analyzes two common techniques: early truncated conjugate gradients (CG) and random Fourier features (RFF). We find that both methods introduce a systematic bias on the learned hyperparameters: CG tends to underfit while RFF tends to overfit. We address these issues using randomized truncation estimators that eliminate bias in exchange for increased variance. In the case of RFF, we show that the bias-to-variance conversion is indeed a trade-off: the additional variance proves detrimental to optimization. However, in the case of CG, our unbiased learning procedure meaningfully outperforms its biased counterpart with minimal additional computation.
High-Speed Camera based Experimental Modal Analysis for Dynamic Testing of an Automotive Coil Spring

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Experimental modal analysis (EMA) is a measurement technique to assess the dynamical properties of mechanical components and systems in various phases of their life cycle, e.g. for design, end-of-line testing and health monitoring. The most common EMA uses accelerometers, which provide high frequency acceleration measurements at a few discrete locations. However, attached accelerometers may alter the systems mass and damping properties and multiple tests are required to obtain spatially dense information. To overcome these issues, in this paper we use high-speed cameras and video processing algorithms. In fact, cameras as contact-less sensors do not modify the dynamics of the system under test. Furthermore, cameras provide full-field displacement data, allowing to obtain spatially dense transfer functions with a single excitation, which reduces the experiment duration. On the downside, camera measurements are suitable for relatively low-frequency applications only and require optical contrast on the component surface. While previous camera based research was focused on flat, plate-like components, we demonstrate the methodology on a 3D automotive coil spring. We use a stereo vision setup to measure the 3D displacement field, employing Lucas-Kanade optical flow as feature tracker. Thereby, we make use of local averaging for noise reduction. As cameras are able to capture static information the geometry of the component is obtained in addition to the modal parameters. This allows for intuitive visualization of the EMA results. For the automotive coil spring under investigation we obtain the displacement field up to 140 &lt;i&gt;Hz&lt;/i&gt; with an estimated displacement accuracy in the range of a few micrometer. The EMA results are compared to an accelerometer based EMA highlighting the advantages of camera based EMA. Furthermore, we investigate the sensitivity of the camera based EMA with respect to excitation and environmental conditions and discuss two alternative markers to enhance image contrast.&lt;/div&gt;&lt;/div&gt;
Virtual Assistant in Native Language

In the modern area of fast-moving technology we can do things that we never thought we couldn't do before and automation has taken over everything in day-to-day life. One such creation is the virtual assistant. It has become a boon for everyone in this new era of the 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century. The improvement has gone up to the capabilities of becoming a personal companion to humans. We can ask questions from machines and can interact with machines using this technology of virtual assistance. This technology spread rapidly in smartphones, laptops, computers, etc. Some iconic virtual assistants are Siri, Google Assistant, Cortana, and Alexa. Voice recognition, speech identification, and the relevant reaction is the basis for virtual assistance. Some of the issues regarding the available virtual assistances are the incompatibility with certain languages. Sinhala is one such language at the moment not included in a virtual assistance environment. The proposed project is designed to associate some Sinhala commands with their corresponding responses to guide a Sinhala user. Furthermore, for global reach, the project will be deployed on a cloud basis for the public to reach the global Sinhala language users.
Teaching a foreign language for professional purposes: peculiarities of legal terms used in teaching the language of a speciality for international specialists

Aim. One of the most important tasks which precedes the setting of goals for teaching a foreign language of a specialty and the selection of methods for achieving them is to identify the competencies that a qualified specialist must master. Therefore, the authors of this article see the need to set the following research tasks: the identification of professional competencies in the professional discourse of international affairs; the allocation of competencies among those that require the knowledge of a foreign language; and determination of the language material necessary for studying the specialty in the language lessons for international affairs. Methods. The empirical methods of our research comprise studying and analysis of the works of Russian and foreign scientists and teachers working in the related fields; and interviewing specialists in the international sphere who have graduated from the faculties and institutes of international relations, international law, world politics and economics in the form of an anonymous questionnaire. Theoretical methods include analysis, synthesis, comparison, generalisation, deduction, and induction. Results. A study based on the analysis of a survey completed by international specialists showed that a large number of professionals in the field of international relations, international law, politics, and economics use legal terminology in their work more often than others. Among the professional skills that are required in their work, the respondents named negotiation, business dialogue, and correspondence. Conclusion. A recent study has shown that disciplines such as international law must be included in the language training program for international specialists as diplomates and lawyers.
Argument Mining Driven Analysis of Peer-Reviews

Peer reviewing is a central process in modern research and essential for ensuring high quality and reliability of published work. At the same time, it is a time-consuming process and increasing interest in emerging fields often results in a high review workload, especially for senior researchers in this area. How to cope with this problem is an open question and it is vividly discussed across all major conferences. In this work, we propose an Argument Mining based approach for the assistance of editors, meta-reviewers, and reviewers. We demonstrate that the decision process in the field of scientific publications is driven by arguments and automatic argument identification is helpful in various use-cases. One of our findings is that arguments used in the peer-review process differ from arguments in other domains making the transfer of pre-trained models difficult. Therefore, we provide the community with a new dataset of peer-reviews from different computer science conferences with annotated arguments. In our extensive empirical evaluation, we show that Argument Mining can be used to efficiently extract the most relevant parts from reviews, which are paramount for the publication decision. Also, the process remains interpretable, since the extracted arguments can be highlighted in a review without detaching them from their context.