Reviewerly/exHarmony huggingface dataset:
Smart Attendance System Based On Face Recognition Techniques

Most academic facilities and institutions still use the traditional methods of employee attendance recording. This is currently done manually which may be a burden on employees and takes a long time to do. Manipulation may also occur with the manual process; thus, it loses its credibility. But now, with the advent of many deep learning algorithms, we propose in this paper the use of an automated system to implement and manage the attendance recording process automatically by a face recognition technique using convolutional neural networks. Our structure is based on a modern high-precision face detection algorithm using the YOLO v4 (You Only Look Once). The Yolo v4 superior using a single GPU achieving a high speed in detecting objects comparing with other models that require to use many GPUs. Using the Darknet-53 layers for face detection we get an accuracy of 100%. The system was designed using PyQt5; an easy-use and high accuracy system which also displays attendance information in the main interface and stores it automatically in an excel file so that it can be reviewed periodically.
Realistic Augmentation For Effective 2d Human Pose Estimation Under Occlusion

Occlusion is a major challenge for effective human pose estimation, occurring naturally in a high percentage of real-world images. Handling occlusion has been a difficult challenge in literature due to a lack of a proper dataset with an actual focus on occlusion, prompting researchers to create artificial datasets as a means of data augmentation. However, all of these datasets lack the features of a real-world occlusion. In this work, we introduce a new realistic data augmentation approach built on top of a base dataset (here the Human3.6m) that tackles this issue, creating realistic samples similar to those found in the wild. Arguing that CNN models pay higher attention to local as opposed to global features, we define occlusion levels, select many to-occlude objects from different categories, and blend those within the original image from the base dataset. We, then, test top-performing 2D human pose estimation models with and without this occlusion-augmented dataset (called RealOcc) to display the drop in performance under occlusion and then train them on the new dataset to show the increase in the accuracy of the model under occlusion, without any change to the models themselves.
The determination of rescue route based on fuzzy analytic hierarchy process (AHP)

Geological disasters as earthquake, often accompanied with the landslide collapse. Although the earthquake is unpredictable, while the relief can be the most optimal. The earthquake area, often due to earthquake, lead to instability of rock and soil mass, the rescue route is blocked. Time equals life, how to choose a best way to rescue, is extremely important. The manuscript is a kind of measure to determine the rescue route based on fuzzy analytic hierarchy process (AHP), mainly aimed at the geological disaster area, involving the instability of rock and soil mass under seismic action lead to traffic jams, establishing the best rescue route. Considering the stability of rock and soil under earthquake inducement, this manuscript establishes the model of optimal route selection; determines the influence of earthquake on slope stability through analytic hierarchy process, and establishes the corresponding model; combines matlab programming, establishes the influencing factors of earthquake on slope stability, and combines probability model to realize the choice of optimal rescue route.
Concept Expansion Based Answer Summarization

This paper presents a concept expansion based unsupervised method for answer summarization in the integer linear programming framework. The expanded question is linked with its associated a saver sentences by the concepts they have in common. An objective function can be built using their mutual concepts. A readable summary is obtained by maximizing the objective function. The method proposed is tested on the DUC 2007 summarization data and Yahoo! Answer data. Experiments show the method performs better than traditional methods.
Gender Prediction using Deep Learning Algorithms and Model on Images of an Individual

Abstract The classification of gender based on the biometric is an old and traditional approach which are treated as a sub-branch of “Soft Computing”. But in this proposed work, works on the celebrity images dataset obtained from the Kaggle repository. The major focus of this to extract the important features from the images by training the neural network by using an autoencoder and decoder. The system extracts the feature maps to visualize the extracted features and makes some sort of interpretations to analyze the data. Gender classification using iris information is a relatively old technique, and most gender classification methods classify all iris texture features. These features are fed to the classifier, this data might be proper or improper, and if it is improper it leads to more generalization results in less accuracy. The proposed algorithm to create increase the size of the dataset implements the Data Augmentation Technique using basic image operations. Then it applies a convolution neural network for predicting the gender of the user. The model has proved practically that increasing the size of the dataset and reducing the features improves the classifiers learning performance.
English Cloze Test Based on BERT

Cloze test is a common test in language examinations. It is also a research direction of natural language processing, which is an important field of artificial intelligence. In general, some words in a complete article are hidden, and several candidates are given to let the student choose the correct hidden word. To explore whether machine can do cloze test, we have done some research to build down-stream tasks of BERT for cloze test. In this paper, we consider the compound words in articles and make an improvement to help the model handling these kind of words. The experimental results show that our model performs well on questions of compound words and has better accuracy on CLOTH dataset.
Accelerating Homomorphic Encryption using Systolic Arrays with Polynomial Optimization

Internet traffics are growing more dense day by day. Internet traffics also represent the privacy of many people. Other than privately-owned data, IoT devices and automation also send their perceived data in its unencrypted form. Cryptography can deliver integrity and data security that are much needed to secure the private data of many people in this world. However, a problem still exists in using cryptography algorithm. As more secure data is, it is far harder for its algorithm to be decoded in linear time complexity. In this paper, we develop an architecture for accelerating BFV homomorphic encryption that can handle data operation without being decrypted. By using systolic arrays and polynomial optimization, we accelerate the time executions for polynomial degrees 4, 8 and 16. Various experiments on Intel I5, RasPi 3B+, and PYNQ FPGA are also shown. The design is useful for secure communication and computing.
Acquisition of English Translation Examples Incorporating Shallow Syntactic Analysis Based on Network Technology

Abstract The example of English translation is the source of machine translation. The researchers found that the key to improve the quality of English translation is to study the methods of obtaining English translation examples. However, there are still many disadvantages in the acquisition of English translation examples. In recent years, with the extensive popularization and progress of shallow syntax research technology, the progress of syntactic analysis provides sufficient basis for the acquisition of English translation examples. This paper mainly uses the existing English and shallow syntactic analysis methods to deal with the related translation of the language database. In this way, we can find effective ways to obtain English translation examples.
Covid 19 Tracker Using REST API Android App

As we all know that during this tough time everyone is facing the summons because of this scary pandemic Covid19. More than 200 countries are affected due to this pandemic and the populace is uninformed when this drastic period will come to an end. How the rapid growth of this virus is going to be stopped, apart from vaccinations and medicare everyone needs to be much more aware about the dangerous situation and must follow the protocols and guidelines imposed by government in order to be safe or free from the scary virus. Our work, to build an android platform to layout mindfulness about the pandemic and furthermore to help the world population by providing them the useful information regarding the coronavirus. An android app which will be helpful in displaying all the data of Covid19 (such as number of total cases worldwide, number of active cases, covid19 hospitals around the covid19 victims, medical facilities near them and many more things. Getting real time data for anything is very beneficial for all the users, as we are displaying the data for covid19 so without going anywhere they can easily get to know their current place status for corona i.e. total number of covid19 cases in their country or city. This way of collecting data will be helpful as the users will not come in direct contact with anyone and hence, they do not get affected.
Understanding Vqa For Negative Answers Through Visual And Linguistic Inference

In order to make Visual Question Answering (VQA) explainable, previous studies not only visualize the attended region of a VQA model, but also generate textual explanations for its answers. However, when the model’s answer is “no,” existing methods have difficulty in revealing detailed arguments that lead to that answer. In addition, previous methods are insufficient to provide logical bases when the question requires common sense to answer. In this paper, we propose a novel textual explanation method to overcome the aforementioned limitations. First, we extract keywords that are essential to infer an answer from a question. Second, we utilize a novel Variable-Constrained Beam Search (VCBS) algorithm to generate explanations that best describe the circumstances in images. Furthermore, if the answer to the question is “yes” or “no,” we apply Natural Langauge Inference (NLI) to determine if contents of the question can be inferred from the explanation using common sense. Our user study, conducted in Amazon Mechanical Turk (MTurk), shows that our proposed method generates more reliable explanations compared to the previous methods. Moreover, by modifying the VQA model’s answer through the output of the NLI model, we show that VQA performance increases by 1.1% from the original model.
Design of daily-use water monitoring platform for the elderly living alone in intelligent community

Based on the actual needs of the elderly living alone, this paper puts forward the overall design scheme of the daily water monitoring platform for the elderly living alone in the intelligent community, and gives the system framework and function realization flow of the platform. The platform collects the daily water consumption of the users through the lot acquisition equipment, and analyzes the data obtained to obtain the status of the single elderly home. The use of this platform can effectively reduce the safety risk coefficient of the elderly living alone, with the characteristics of low cost, easy to use and so on.
Phase sensitivity of an $\operatorname{SU}(1,1)$ interferometer via product detection

Abstract We theoretically analyze the phase sensitivity of an $\operatorname{SU}(1,1)$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>SU</mml:mo> <mml:mo>(</mml:mo> <mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mn>1</mml:mn> <mml:mo>)</mml:mo> </mml:math> interferometer with various input states by product detection in this paper. This interferometer consists of two parametric amplifiers that play the role of beam splitters in a traditional Mach–Zehnder interferometer. The product of the amplitude quadrature of one output mode and the momentum quadrature of the other output mode is measured via balanced homodyne detection. We show that product detection has the same phase sensitivity as parity detection for most cases, and it is even better in the case with two coherent states at the input ports. The phase sensitivity is also compared with the Heisenberg limit and the quantum Cramér–Rao bound of the $\operatorname{SU}(1,1)$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>SU</mml:mo> <mml:mo>(</mml:mo> <mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mn>1</mml:mn> <mml:mo>)</mml:mo> </mml:math> interferometer. This detection scheme can be easily implemented with current homodyne technology, which makes it highly feasible. It can be widely applied in the field of quantum metrology.
Implementation of Robust Privacy-Preserving Machine Learning with Intrusion Detection and Cybersecurity Protection Mechanism

In this work, data mining, and cloud computing problems such as privacy protection, Intrusion detection, identification system have been realizing. Social-Cybersecurity is an evolving science-focused field for characterizing, interpreting, and predicting improvements in human behavior, social, cultural, political outcomes, and development. The cyber-infrastructure is required to sustain its critical existence cyber-mediated knowledge climate under shifting circumstances and cyber threats that are immediate or imminent. When the data moves from the local cloud to another cloud, some security issues automatically arise. At this stage, an advanced robust security system with advanced encryption or decryption algorithm is necessary. At the same time, intrusion may attack the cloud for hacking or modifying the existing information. There an advanced deep learning algorithm is essential to make the cloud efficient. The Logistic net regression Optimization is proposed for cybersecurity and protection. A final performance measures, estimating accuracy,0.952 sensitivity of 0.39, Recall, F1 score 0.54 and processing time.
Neurally-constrained modeling of human gaze strategies in a change blindness task

Despite possessing the capacity for selective attention, we often fail to notice the obvious. We investigated participants’ (n = 39) failures to detect salient changes in a change blindness experiment. Surprisingly, change detection success varied by over two-fold across participants. These variations could not be readily explained by differences in scan paths or fixated visual features. Yet, two simple gaze metrics–mean duration of fixations and the variance of saccade amplitudes–systematically predicted change detection success. We explored the mechanistic underpinnings of these results with a neurally-constrained model based on the Bayesian framework of sequential probability ratio testing, with a posterior odds-ratio rule for shifting gaze. The model’s gaze strategies and success rates closely mimicked human data. Moreover, the model outperformed a state-of-the-art deep neural network (DeepGaze II) with predicting human gaze patterns in this change blindness task. Our mechanistic model reveals putative rational observer search strategies for change detection during change blindness, with critical real-world implications.
The concept of using computer vision in control of autonomous transport

Abstract The paper discusses technologies of control over unmanned self-propelling transportation vehicles in enclosed industrial areas based on computer vision with active adjustable mapping of the vehicle motion path. The authors describe substitution of LIDAR sensor for a light net mapped on the road surface.
Workpiece image Mosaic technology based on OpenCV image processing

With the development of industrial technology, assembly line production of workpiece has been applied in many industries. The automatic stitching technology of assembly line images can quickly obtain wide field and high resolution images. Based on Python development language, through opencv visual computer library and surf image matching algorithm, this paper focuses on the problems of feature extraction and edge detection of pipeline image, image overlapping region fusion and so on, and realizes the rapid splicing of pipeline image. Experimental verification and analysis prove the feasibility of the algorithm technology.
Fusion of Thepade SBTC and GLCM Features for Recognizing Gender from Facial Images

Due to noise and blurring effect while image capturing, poses, changes in the face due to jewelry, glasses, and age of the person gender classification is quite a difficult task. It is a very crucial topic for research in image processing. Many classification systems are designed by using various methods. In this paper, the classification system is created by machine learning. TSBTC algorithm is used for feature extraction. This paper compares the various feature extraction algorithms like TSBTC and GLCM etc. This experimentation is carried out using Matlab.
Four-rotor aerobatic flight of inverted pendulum based on reinforcement learning

Using the four-rotor inverted pendulum system as the research object, the control method of four-rotor is studied in order to realize the aerobatic flight in the air; According to the different states of the rod in the air, the motion of the four-rotor is analyzed; In view of the complex changes of the path, the deep reinforcement learning algorithm is used to train the quadrotor maneuver strategy, and the path tracking and receiving tasks are completed. The tracking error is improved, and the parameter selection is completed by the algorithm, which reduces the number and difficulty of experiments.
Application of AI in Video Games to Improve Game Building

Video Games Industry has been welcoming AI like any other industry for various tasks, AI in gaming helps to convey a much more realistic gaming experience, amplify player interaction and satisfaction over extensive periods. Additionally, the gaming industry is utilizing Artificial Intelligence to liberate its staff by making game development automated, quicker, and less expensive. In this work an experiment is described using Deep Neural Network and Statistical techniques for forecasting the location of an object in future frames of a video, it focuses on the engineering phase of the game, the proposed model combines future prediction of object location which helps to build the infinite universe in the videogame without any additional videos frames of the input video or hard coding any scenes to build the scenes further.
Automatic Deep Learning Framework for Breast Cancer Detection and Classification from H&amp;E Stained Breast Histopathology Images

About half a million breast cancer patients succumb to the disease, and nearly 1.7 million new cases arise every year. These numeric entities are expected to rise significantly due to the advances in social and medical engineering. Furthermore, the histopathological images are a gold standard for identifying and classifying breast cancer compared with other medical imaging. Evidently, the decision of an optimal therapeutic schedule of breast cancer rests upon early detection.The primal motive to have better breast cancer detection algorithm helps to the doctors who know the molecular sub-types of breast cancer in order to control the metastasis of tumor cells early in the disease prognosis and treatment planing. This paper proposes automatic deep learning framework for breast cancer detection and classification model from hematoxylin and eosin (H&E) stained breast histopathology images with $$\ 80.4\% $$ accuracy for supplementing analysis of medical professionals to prevent false negatives. Experimental results yield that proposed architecture provides better classification results as compared to benchmark methods.
Computational Analysis of Relaxed Unscented Transformation in terms of necessary floating point operations

The paper systematically compares the computational demand of the original and relaxed Unscented Transformation. By deriving the pseudocodes of the methods, the number of necessary floating-point operations is determined depending on the complexity of the considered dynamics. Based on the obtained formulas, the paper shows the computational benefits of the relaxed Unscented Transformation.
User Portrait Technology and its Application Scenario Analysis

With the rapid development of big data,it has become a trend to use user portrait technology to transform user information into specific tags, to embody the image of users, and to provide users with personalized services. This article introduces the creation process of user portrait in detail, explains the analysis method of portrait accuracy, and summarizes and analyzes the typical application scenarios of user portrait technology.
Context-Aware Graph Convolutional Autoencoder

Recommendation problems can be addressed as link prediction tasks in a bipartite graph between user and item nodes, labelled with rating on edges. Existing matrix completion approaches model the user’s opinion on items by ignoring context information that can instead be associated with the edges of the bipartite graph. Context is an important factor to be considered as it heavily affects opinions and preferences. Following this line of research, this paper proposes a graph convolutional auto-encoder approach which considers users’ opinion on items as well as the static node features and context information on edges. Our graph encoder produces a representation of users and items from the perspective of context, static features, and rating opinion. The empirical analysis on three real-world datasets shows that the proposed approach outperforms recent state-of-the-art recommendation systems.
Seeding digital competencies from early childhood: a competence based approach

Education has suffered multiple changes due to technological progress. Even though the current generation of preschoolers (aged 3 to 6 years in our country) is called digital natives, the education in using technology lacks from their current teaching activities. This paper presents our approach in introducing fundamentals of digital systems and supporting the development of digital skills of preschoolers by proposing a curriculum for an optional class in technology and by implementing it and assessing its results.
An improved Harris Hawk algorithm based on Golden Sine mechanism

Harris Hawk algorithm has received a lot of attention due to its few control parameters, simplicity and practicality. However, there are still some shortcomings, such as the problem of slow convergence, and hard to maintain the balance between exploration and exploitation when dealing with complex multimodal problems. Therefore, we introduce the golden sine mechanism into the Harris Hawk algorithm, called GSHHO, to enhance the global search capability. Experiments on the benchmark function show that the improved algorithm can effectively enhance the population diversity and its performance is better than the original algorithm.