Reviewerly/exHarmony huggingface dataset:
An Efficient Method for Detecting Covered Face Scenarios in ATM Surveillance Camera

Covering face with accessories such as mask, scarf and sunglass is a common criminal activity in automated teller machine (ATM) robbery. Therefore, detection of covered face using ATM surveillance camera can be an effective solution to reduce robbery or crime. This paper presents a novel method to detect covered face from ATM surveillance camera images. Specifically, three facial features, i.e., skin color, elliptical face shape and facial width-to-height ratio (fWHR), incorporated with geometrical property of ellipse have been employed to estimate the covered region. In addition, three parameters, i.e., facial area, fWHR and covered area percentage, have been utilized for reliable classification. Experiment results demonstrate that the method can detect full covered, uncovered and partially covered faces at a correct detection rate of 98.3%, 93.3% and 97.78%, respectively. The overall correct detection rate is 96.48%, which is found to be better than previous studies. Also, the proposed method can handle faces covered with few new face hiding objects such as hijab, niqab and robber’s ski mask. Furthermore, processing time of the proposed algorithm is significantly improved while it is compared to the existing methods. The detection time varies between 31 and 67 ms which is equivalent to 15–32 frames per second.
Objective Bayesian Inference in Probit Models with Intrinsic Priors Using Variational Approximations

There is not much literature on objective Bayesian analysis for binary classification problems, especially for intrinsic prior related methods. On the other hand, variational inference methods have been employed to solve classification problems using probit regression and logistic regression with normal priors. In this article, we propose to apply the variational approximation on probit regression models with intrinsic prior. We review the mean-field variational method and the procedure of developing intrinsic prior for the probit regression model. We then present our work on implementing the variational Bayesian probit regression model using intrinsic prior. Publicly available data from the world's largest peer-to-peer lending platform, LendingClub, will be used to illustrate how model output uncertainties are addressed through the framework we proposed. With LendingClub data, the target variable is the final status of a loan, either charged-off or fully paid. Investors may very well be interested in how predictive features like FICO, amount financed, income, etc. may affect the final loan status.
Making Software Measurement Standards Understandable

Every discipline, e.g. medicine and engineering, has its own vocabulary to describe situations and tools. This dedicated language is important, because it allows for being specific, detailed and precise. On the other hand, this language, specific to each discipline, becomes a barrier for communication across disciplines. International software measurement standards are examples of such language. The standards are documents that provide definitions of terms used and describe processes specific to the discipline of software measurement. However, one major problem we have observed is that standards are difficult to read and to understand; even for the stakeholders that they are intended for. In this paper, we present our experience report from introducing standards to the work of a large software development organization at an infrastructure provider company. We describe one of the concepts that we found essential in making the measurement standards understandable, namely the notion of the indicator.
An Improved Decoding Algorithm to Decode Quadratic Residue Codes Based on the Difference of Syndromes

The paper proposes an improved difference-of-syndromes decoding algorithm for decoding quadratic residue codes up to half the minimum distance. Therein, soft-decision information is not essential, but it can be used to speed up the computations. The improvement over the prior art is demonstrated by simulations. Moreover, the proposed algorithm is also compared with the classic Berlekamp-Massey algorithm by taking two BCH codes as examples.
Loose Hand Gesture Recognition Using CNN

A precise hand gesture recognition (HGR) system is an important facility for human-computer interaction (HCI). In this paper, we propose a multi-resolution convolutional neural network (CNN) to recognize the loose hand gesture, where loose means that the gestures can be more varied on the bending degrees of fingers, on the direction of palm, and on the bending angles of wrist.The proposed loose hand gesture recognition (LHGR) system learn the low-level features from both color and depth images and then concatenate the low-level features to learn the RGBD (RGB color and Depth) high-level features. The advantage is that it not only suppresses the problem of the inaccurate alignment pixels between color images and deep images, but also reduce the parameters of the CNN model. In addition, we use multi-resolution features to classify the hand gestures; therefore, the proposed model has stronger ability for smaller, farther, and blurrier images. In the training stage, we trained the proposed CNN model using various loose hand gestures to make the CNN more robust. In the experiments, we compared the proposed CNN model in several different architectures; the mAP (mean average precision) is highly to 0.9973. The proposed method has reliability in the scaling and rotation of hand gestures.
Beyond Bag-of-Concepts: Vectors of Locally Aggregated Concepts

Bag-of-Concepts, a model that counts the frequency of clustered word embeddings (i.e., concepts) in a document, has demonstrated the feasibility of leveraging clustered word embeddings to create features for document representation. However, information is lost as the word embeddings themselves are not used in the resulting feature vector. This paper presents a novel text representation method, Vectors of Locally Aggregated Concepts (VLAC). Like Bag-of-Concepts, it clusters word embeddings for its feature generation. However, instead of counting the frequency of clustered word embeddings, VLAC takes each cluster’s sum of residuals with respect to its centroid and concatenates those to create a feature vector. The resulting feature vectors contain more discriminative information than Bag-of-Concepts due to the additional inclusion of these first order statistics. The proposed method is tested on four different data sets for single-label classification and compared with several baselines, including TF-IDF and Bag-of-Concepts. Results indicate that when combining features of VLAC with TF-IDF significant improvements in performance were found regardless of which word embeddings were used.
Novel textual features for language modeling of intra-sentential code-switching data

Code-switching refers to the frequent use of non-native language words/phrases by speakers while conversating in their native languages. Traditionally, for training a language model (LM) for code-switching data, one is required to tediously collect a large amount of text corpus in the respective code-switching domain. Alternately, we recently proposed a more viable approach that adapts an existing native LM to handle the code-switching data. In this work, we present our efforts for language modeling of code-switching data following both the traditional and the proposed approaches. The salient contributions of this paper includes: (i) creation of the Hindi-English code-switching text corpus, (ii) an improved parts-of-speech (POS) labeling scheme for accurate tagging of non-native words embedded in the code-switching data, and (iii) the proposal of a novel textual feature referred to as the code-switching location (CSL) feature, that allows LMs to predict the code-switching instances. The evaluation of the proposed features has been done on two code-switching datasets: Hindi-English and Mandarin-English. On experimental evaluation, a substantial reduction in the perplexity is achieved with the use of the improvised POS features. It is also observed that the proposed CSL features provide an independent and additive improvement over the POS features in terms of perplexity.
Online Algorithms for Multiclass Classification Using Partial Labels

In this paper, we propose online algorithms for multiclass classification using partial labels. We propose two variants of Perceptron called Avg Perceptron and Max Perceptron to deal with the partially labeled data. We also propose Avg Pegasos and Max Pegasos, which are extensions of the Pegasos algorithm. We also provide mistake bounds for Avg Perceptron and regret bound for Avg Pegasos. We show the effectiveness of the proposed approaches by experimenting on various datasets and comparing them with the standard Perceptron and Pegasos.
Hybrid Online and Offline Reinforcement Learning for Tibetan Jiu Chess

In this study, hybrid state-action-reward-state-action (SARSA<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mfenced open="(" close=")" separators="|"><mml:mrow><mml:mi>λ</mml:mi></mml:mrow></mml:mfenced></mml:mrow></mml:math>) and Q-learning algorithms are applied to different stages of an upper confidence bound applied to tree search for Tibetan Jiu chess. Q-learning is also used to update all the nodes on the search path when each game ends. A learning strategy that uses SARSA<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:mfenced open="(" close=")" separators="|"><mml:mrow><mml:mi>λ</mml:mi></mml:mrow></mml:mfenced></mml:mrow></mml:math> and Q-learning algorithms combining domain knowledge for a feedback function for layout and battle stages is proposed. An improved deep neural network based on ResNet18 is used for self-play training. Experimental results show that hybrid online and offline reinforcement learning with a deep neural network can improve the game program’s learning efficiency and understanding ability for Tibetan Jiu chess.
Mouth breathing impairs the development of temporomandibular joint at a very early stage

Abstract Objectives The study aimed to explore the effects of mouth breathing and hypoxia on the condyle of temporomandibular joint (TMJ) via two animal models. Methods 24 four‐week‐old rats were randomly separated into three groups, consisting of eight control rats, eight intermittent hypoxia (IH) rats, and eight intermittent nasal obstruction (INO) rats. We use the IH model and the INO model to simulate children suffering from hypoxia and mouth breathing. After 16 days, the condyle of TMJ and surrounding white adipose tissue (WAT) and skeletal muscle tissue were obtained for further staining and qRT‐PCR. Finally, RNA‐seq was used to verify the results. Results The intermittent hypoxia cannot significantly change the overall structure in the cause of short‐term hypoxia stimulation, but the intermittent nasal obstruction can alter the condyle, WAT, and muscle, while also introducing noticeable structural changes in tissue hypoxia and macrophage infiltration. Sequencing data verified these findings and also suggested that this process might involve the Hif‐1α/Vegf axis. Conclusions Our findings reveal the very early structural impact of mouth breathing on condyle reconstruction in rat models, and hypoxia does not induce evident alteration on condyle. However, since these results are mainly focused on rats, further studies are needed to understand its effects on humans.
Physically Meaningful Dictionaries for EO Crowdsourcing: A ML for Blockchain Architecture

Due to the complexity and difficult comprehension of remote sensing data, ubiquitous methods for content exploitation are difficult to identify. The main limitation is ascribed to the fact that different perceptions, individual human experts and non-identical remote sensors fabricate a total heterogeneous context. In order to overpass these breaches, an interactive learning model is demanded, where different experts, participating into a validation process, expose and verify shared dictionaries based on users' understanding. We propose a federated system for collaborative labeling of remote sensing datasets that transform blockchain storage into blockchain knowledge. This writing wants to provide insights into a decentralized methodology, capable of building a publicly available, large-scale benchmark data for image classification and hosting a permanently updated machine learning (ML) model.
Optimized Face Detection and Alignment for Low-Cost and Low-Power IoT Systems

Face detection and alignment are challenging operations due to variations in image angles, background lighting conditions and intermediate blocking objects. Recent work has shown that these tasks can be improved through the use of a multi-task cascaded convolutional neural network (MTCNN) architecture. However, it is difficult to implement such an approach in a low-end edge AI system because of its high computational complexity. This paper presents the design of an MTCNN based on a low-cost and low-power processor/FPGA system that can be used in IoT applications. First, we analyze the computational requirements of the algorithm. Based on this analysis, we develop an optimized implementation to achieve real-time processing, taking advantage of the available hardware resources. In order to enhance the throughput and reduce the power consumption for AI edge devices, we store all intermediate results in on-chip block RAM. We achieve a frame rate of 15.2 frames per second, which meets the needs of security cameras that are widely used in IoT systems. Furthermore, our approach has a 2.67 times lower power consumption than for a previous MTCNN implementation.
Application Research of Computer Mooc Teaching Method in Basic Theory Teaching of Traditional Chinese Medicine

Abstract With the successful application of Internet technology in education, the concept of open education has been widely recognized by the society. This following paper analyzes and studies the computer MOOC teaching method in the basic theory teaching of Traditional Chinese medicine, and proposes corresponding solutions to the existing problems.
Frequent Itemsets Mining of SCADA Data Based on FP-Growth Algorithm

When the powergrid fails, a large number of fault data will be transmitted to the dispatching center of SCADA system. Effective and fast processing of these data can minimize the loss of fault and restore the power supply quickly. In this paper, FP growth algorithm is used to mine frequent itemsets, and event folding window is used to discretize the data for easy processing. Through the correlation analysis of actual SCADA data, the results show that the method has a strong practical engineering significance for speeding up the fault analysis after the power grid fault occurs.
TMVP-Friendly Primes for Efficient Elliptic Curve Cryptography

The need for faster and practical cryptography is a research topic for decades. In case of elliptic curve cryptography, which was proposed by Koblitz and Miller in 1985 as a more efficient alternative to RSA, the applications in real life started after 2000s. Today, most of the popular applications and protocols like Whatsapp, Signal, iOS, Android, TLS, SSH, Bitcoin etc. make use of Elliptic curve cryptography. One of the important factor for high performance elliptic curve cryptography is the finite field multiplication. In this paper, we first describe how to choose proper prime fields that makes use of Topelitz-matrices to get faster field multiplication, then we give parameter choice details to select prime fields that supports Toeplitz-matrix vector product operations. Then, we introduce the safe curve selection rationale and discuss about security. We propose new curves, discuss implementation and benchmark results and conclude our work.
Remote Sensing Target Tracking for UAV Aerial Videos Based on Multi-Frequency Feature Enhancement

Remote sensing target tracking in UAV (Unmanned Aerial Vehicle) aerial videos has gradually become a research hotspot of UAV application areas, in this paper, we propose a remote sensing target tracking method for UAV aerial videos based on multi-frequency feature enhancement. The main contributions are as follows: first, We use SiamRPN as the basic tracking network to obtain the natural ability of multi-scale testing, which can adapt to the scale changing of remote sensing targets caused by changes in shooting perspective. Second, in the stage of feature extraction, we introduce a new multi-frequency feature representation method, which effectively improves feature expression ability of highly dynamic targets. Our algorithm is compared quantitatively and qualitatively with some state-of-the-art tracking algorithms by using a test dataset consisting of a UAV123 dataset and a homemade dataset, the results show that our algorithm improves the tracking accuracy while having strong timeliness.
Statistical Models of the Distribution of Chemical Elements in Precambrian Rocks of the Siberian Craton

Natural chemical systems are an excellent object for studying the properties of various elements. The most diverse and informative geological complexes are crystalline rocks of the Precambrian. These rocks are exposed near the northern and southern margins of the Siberian craton. The chemical composition of rocks, the contents of impurity elements, and metals were studied by us using chemical and spectral analysis methods. Microprobe studies were performed. Using regression and multivariate statistical methods of analysis, the regularities of the distribution of chemical elements were found. It is shown that the distribution of precious metals and carbon dioxide in rocks is attributed to their chemical properties and comparable with close in-chemical properties’ rock-forming elements. It is found that the factor analysis reflects the uniform regularities of the distribution of elements in different regions and rocks. These regularities are similar on macro and micro levels. Comparison of the distribution patterns with the results of geochemical and petrological studies of other authors shows the leading role of the redox potential and acidity of the environment in the formation of rocks and minerals. The role of mathematical statistics for solving problems of chemical petrology and chemical systems analysis is underlined.
Simulation detection based on single photon quantum key distribution protocol

Abstract With the continuous development of quantum communication in recent years, quantum key distribution (QKD) has also become a key technology in the field of quantum communication. Due to the limitation of physical resources and experimental conditions, the effectiveness of different communication protocols cannot be accurately analyzed. To solve this problem, a protocol simulation method is proposed, BB84 and B92 protocols are simulated and analyzed on a classic computer by this method. On the Eclipse IDE Developers platform, the simulation program of the quantum communication protocol was written using Java language, the simulation process under the ideal environment and the eavesdropping environment was analyzed with emphasis. The simulation results show that the key distribution efficiency of BB84 is about 50% without eavesdroppers, which is much higher than 25% of B92, and the key distribution efficiency under eavesdropping conditions is about 25% and 18%. The experimental results are consistent with theoretical expectations, verifying the correctness of the protocol simulation, and also proving the feasibility of using computers for quantum communication protocol simulation.
Enhanced use of mixed-mode clock manager for coherent sampling-based true random number generator

A True Random Number Generator (TRNG) using a pair of Phase-locked Loops (PLLs) or Digital Clock Managers (DCMs) has an advantage of minimal usage of logic elements of FPGA. This paper focuses on a new clocking element of Xilinx FPGAs called mixed-mode clock manager (MMCM) and presents its effective use for better randomness and higher throughput. According to an evaluation on an Artix-7 FPGA using 132 sets of parameters, 89 sets passed AIS-31 Procedure B with the proposed method, while only 9 sets passed when simply porting an existing DCM-based method. The average throughput was 1.18 Mbit/s, which was 5x faster than an existing DCM-based method.
A Saliency-Based Technique for Advertisement Layout Optimisation to Predict Customers’ Behaviour

Customer retail environments represent an exciting and challenging context to develop and put in place cutting-edge computer vision techniques for more engaging customer experiences. Visual attention is one of the aspects that play such a critical role in the analysis of customers behaviour on advertising campaigns continuously displayed in shops and retail environments. In this paper, we approach the optimisation of advertisement layout content, aiming to grab the audience’s visual attention more effectively. We propose a fully automatic method for the delivery of the most effective layout content configuration using saliency maps out of each possible set of images with a given grid layout. Visual Saliency deals with the identification of the most critical regions out of pictures from a perceptual viewpoint. We want to assess the feasibility of saliency maps as a tool for the optimisation of advertisements considering all possible permutations of images which compose the advertising campaign itself. We start by analysing advertising campaigns consisting of a given spatial layout and a certain number of images. We run a deep learning-based saliency model over all permutations. Noticeable differences among global and local saliency maps occur over different layout content out of the same images. The latter aspect suggests that each image gives its contribution to the global visual saliency because of its content and location within the given layout. On top of this consideration, we employ some advertising images to set up a graphical campaign with a given design. We extract relative variance values out the local saliency maps of all permutations. We hypothesise that the inverse of relative variance can be used as an Effectiveness Score (ES) to catch those layout content permutations showing the more balanced spatial distribution of salient pixel. A group of 20 participants have run some eye-tracking sessions over the same advertising layouts to validate the proposed method.
Research on Airport Foundation Settlement Deformation Monitoring Based on Gene Expression Programming

Abstract As the carrier of the aviation industry, the airport is developing rapidly with the rapid development of the national economy and the overall progress of the society. With the aviation industry as the first industry in Jiangxi’s strategic emerging industries, Jiangxi is giving full play to the foundation and advantages of the aviation industry to make the aviation industry better, stronger and bigger. Currently, Jiangxi is vigorously constructing the third-phase expansion project of Nanchang Changbei International Airport, Ruijin Airport, Yingtan Airport, Fuzhou Airport, etc. During the construction of these airports, the geological conditions are relatively complex, and a large number of excavation and filling works are required, and the height of the excavation and filling body is relatively high. As a result, it is difficult to control the settlement and differential settlement of the foundation after construction, which brings great difficulty to the construction of the project. The foundation engineering under the complex environmental conditions of the airport is a complex system engineering, and its foundation settlement and stability are affected by many factors. This article will take the third-phase expansion project of Nanchang Changbei International Airport as an example, using gene expression programming technology to predict the settlement and deformation of the airport foundation, and relying on training data to obtain high-precision prediction models with high intelligence and rapidity.