Reviewerly/exHarmony huggingface dataset:
Attendance System Based on Face Recognition Technology

With the continuous advancement of industrialization, various enterprises have also emerged. With the continuous expansion of the scale of enterprises, personnel attendance management has also become an important part of the standardized and healthy development of enterprises. Initially, the company adopted the traditional card punching method. This method can achieve employee attendance to a certain extent, but it has problems such as easy loss of cards and inconvenient attendance. The use of face recognition technology can be very convenient and easy to realize the statistics of employee attendance. Therefore, this graduation project decided to design a get off work attendance system. By using Tensor flow as the back-end Keras to build a convolutional neural network, and then using the neural network to realize the face recognition of employees. When the recognition is over, this graduation project is linked to a SQL database to record the recognition results. In addition, this graduation project also adds the functions of adding, deleting, modifying, and searching to the system to edit the SQL database to facilitate entry, deletion, modification, and retrieval of employee information.
Towards Quantum Resistant Key Agreement Schemes Using Unpredictability

Elliptic curve Diffie Hellman (ECDH) is one of today’s most commonly used key agreement schemes, gaining universal usage amongst secure communication protocols such as transport layer security (TLS) and secure shell (SSH). This popularity is attributed to elliptic curve cryptography’s (ECC) known benefits including offering high-security levels for lesser key sizes in comparison to other known public key counterparts. With public key schemes being under the threat of becoming obsolete due to quantum computing and associated algorithms, we propose an enhancement to ECDH aiming at exponentially increasing the hardness of exhaustive search methods utilizing quantum computing powers. In order to reach a key agreement scheme that would withstand future and post-quantum processing powers, we introduce an enhancement that will be based on a distinct cryptographic property labeled ’Unpredictability’, which offers algorithms the ability to use multiple key pairs (up to 256), in different combinations, all whilst maintaining substantially similar arithmetic operations. The resultant scheme is labelled as unpredictable elliptic curve Diffie Hellman (UP-ECDH)
Three Problems in the Philosophy of Movie Music

This chapter examines three issues. First, the question of the nature of movie music. Second the issue of whether there is an implicit musical organizer with respect to the non-diegetic music track. And third, Peter Kivy’s hypothesis that the music track “fills-out” the otherwise “ghostly” visual track.
Agente Conversacional para Servicio al Cliente en Redes Sociales [Not available in English]

No English translation of this document was provided by the author or conference organizers.
Bidirectional Tracking with Missing Warning in SiamMask

Object detection has proven to be the most successful strategy to solve single object tracking. In single object tracking, the bounding box of the target is given to the tracker in the first frame. The goal of the tracker is to locate the same target in all the other frames. However, the target in the most cases does not appear in the first scene of the film. For this reason, this study proposes a bidirectional tracking method which implements based on SiamMask model to avoid the situation. SiamMask is one of the most popular methods to track target objects and produce its mask in each frame. SiamMask uses the position of user-defined initial bounding box as input to predict where the next object will appear in each frame of the video. The proposed method not only allows SiamMask to be used more widely, but also improves the accuracy of detection. The simulation is tested on the VOT-2016, VOT2017 and VOT2019 datasets and achieves the better performance than that of the original SiamMask. Moreover, the proposed method added a new function to make the application of the system more extensive. When the tracking target is indeed missing, the function alerted user a notification of target missing.
Parents’ Usage of a Kindergarten Smart Campus System: An Exploration of the Continuance Intention Factors

In today's digital age, the innovation of smart campuses can bring convenience to the instruction and management of schools.However, parents' support with continued usage determines the application and implementation of smart campus system (SCS).Thus, it is necessary to explore the parents' continuance intention factors of usage of SCS.Based on the existing studies and theories on continuance intention to use (CIU), a research model composed of ten hypotheses was designed to test what factors could predict the kindergarten parents' CIU and the expectancy-confirmation factors.From the data analysis, we could conclude that satisfaction, perceived usefulness, expectation confirmation, subjective norms, and perceived ease of use were positively correlated.It is hoped that the results can be a reference for SCS system designers and managers to improve the SCS implementation in order to facilitate kindergarten children's learning.
Research on Image Classification Based on Deep Learning

With the continuous exploration and research of researchers in the field of deep learning, deep learning has developed by leaps and bounds. Compared with traditional machine learning technology, deep learning has great advantages. The traditional machine learning algorithm for image classification needs to extract local features by hand. The emergence of deep learning has changed this situation and greatly promoted the development of image classification in the field of computer vision. The main content of this paper is to design a simple convolutional neural network model to classify three different common datasets. Comparative experiments are conducted by changing experimental parameters (such as activation function, pooling mode, size of output size, etc.), and then the influence of different parameters on classification and recognition accuracy is analyzed under different data sets. Experimental results show that rein activation function and maximum pooling are more suitable for the classification of image data sets.
Prediction of photovoltaic power generation based on Bayesian neural network with grey correlation

Abstract Photovoltaic power generation depends on weather conditions, which has the characteristics of randomness, volatility and intermittence. In order to ensure the security and stability of the grid connected process and make full use of the advantages of photovoltaic power generation, this paper proposes Bayesian optimization algorithm based on grey correlation to predict the power of photovoltaic power generation. The gray correlation selects the similar correlation data from the original data set, and normalizes the data sample set to eliminate the influence of data size caused by different data dimensions. Bayesian optimization algorithm can effectively prevent over fitting by adding regularization term and super parameter α, β. Experiments show that the photovoltaic power prediction method based on Bayesian optimization algorithm under grey correlation has higher accuracy and robustness, and can provide more comprehensive information for power grid dispatching and control.
Exploiting Egocentric Cues for Action Recognition for Ambient Assisted Living Applications

Being the elder population in constant growth, governments have to cope with higher expenses for elder care from year to year. Helping the elderly to extend their independent lifestyle is of pivotal importance to minimise those costs. That is the goal of the Ambient Assisted Living research field. Through the use of Information and Communication Technologies, it is possible to provide solutions to help the elderly live independently for as long as possible or to predict mental health issues that could seriously harm their independence. The key enablers for these solutions are the egocentric cameras and the egocentric action recognition techniques for the analysis of egocentric videos. This chapter proposes various of those techniques focused on the exploitation of intrinsic egocentric cues.
A new hierarchical temporal memory based on recurrent learning unit

Hierarchical temporal memory is an emerging machine learning technology that aims to model the structural and algorithmic properties of the neocortex. It is particularly suitable for learning and predicting sequential data. However, when dealing with long time series or complex sequences, the accuracy is relatively lower than desired. In this paper, a novel hierarchical temporal memory based on recurrent learning unit is proposed, where a feedback mechanism is involved into the model. The original cell is extended with a recurrent unit to capture long temporal dependencies of synaptic connections between neurons. The temporal pooler algorithm is then modified to adapt to the recurrent learning unit, and the supervised gradient information is combined with the Hebbian synaptogenesis learning rule in speeding up the training. The prototype of the proposed hierarchical temporal memory is implemented and extensive experiments are carried out on two public datasets under various settings. Experimental results show that the proposed model obtains an accuracy increase by up to 32% and a perplexity drop by up to 14% on sequence prediction and text generation tasks, respectively, which indicates the hierarchical temporal memory with recurrent feedback outperforms the original model on sequence learning.
Histogram of Oriented Gradient-Based Abnormal Weapon Detection and Classification

In the fast-paced age of today, crime and terrorism are amplified at a higher rate, due to which the issues related to safety and security in public places and law enforcement are becoming most significant and crucial. Video surveillance is a method of monitoring events, activities, or objects through CCTV cameras, and that supervision should be done carefully. Due to limited capabilities of human operators, it is highly essential to automate this manual surveillance processes that led to increase in demand of intelligent video surveillance. Defining an object as abnormal is the most important task. If a suspicious object is found near secured area, or if the object is unattended for a certain period, then it may be termed as abnormal. Person moving with arm near public areas, gun inside the ATM or bank, unattended luggage at a railway station or an airport, and packet near secure areas can be considered as abnormal. A novel approach is proposed in this article that can detect and classify weapons from the video sequences. Feature extraction is performed by the combination of histogram of oriented gradient and principal component analysis on the background-subtracted input video frames. Extracted features are classified by support vector machine and neural network.
Implement of CORDIC Algorithm with a Small Capacity ROM Table

In order to optimize the implementation delay and hardware resource consumption of the Coordinate Rotation Digital Computer（CORDIC） in pipeline architecture，a new implementation method based on look-up table was proposed，which completely eliminatesd the iterative computation. This method requires only a lower capacity ROM table，as well as a simple shift operation on the output of the ROM table，so that sine wave or cosine wave output with high accuracy can be obtained. Theoretical simulation and practical verification were carried out in Matlab，Modelsim and XILINX ISE，and the results show that this method of CORDIC only requires two clock cycles of processing delay，and the hardware resource consumption is also reduced when compared with other methods. Additionally，the circuit output accuracy and maximum working frequency are also improved at a certain level. Keywords: coordinate rotation digital calculation（CORDIC),  pipeline architecture,  look-up table,  programmable logic gate array,  digital signal processing
Research on ocean current visualization method based on particle

Abstract NetCDF network general data format is a common data format used to store meteorological data such as ocean current field. However, the ocean current data in NetCDF format can not be read directly on the web for dynamic visualization. To solve this problem, this paper takes the Pearl River Estuary area as the research area, adopts the sparse algorithm based on local Moran’s I index and improved point center buffer to dilute the ocean current data, converts the ocean current point data into particle data by using the vector synthesis calculation method of ocean current data, and designs specific JSON data structure for data storage. After completing the pre-processing of the visualization data of ocean current based on particles, a dynamic visualization display platform of ocean current particles is built on the Web side, and the JSON data of ocean current is read. The space-time dynamic visualization effect of ocean current data in the form of particles in the format of NetCDF is realized, and the static and intuition problem of ocean current visualization is solved.
A Customized Weather Adaptive Blind Crutch Based on Target Recognition Research Using Deep Learning

Object detection problem is one of the important topics in the field of computer vision. This technology can be especially used to help blind people identify the objects with details. In these cases, the preciseness of detection of objects is extremely important for blind people, since no other references can be made for them. In this project, we create a blind crutch that can not only alarm users about presenting obstacles, but also identify objects with clear labels using the convolutional neural network (CNN), a neural network especially designed for image identification, to train the classifications needed. Among the process of creating the detection function, in order to improve the accuracy of real scene detections, the snow and rain scenes preprocessing of the image data is completed by the algorithms of dark channel prior and the generative adversarial network. To make up the insufficiency of the GPS module of detecting exact locations, we also create a function of detecting guideposts on streets and broadcast the current location for the blind people. After testing, the blind crutch can precisely classify objects and complete the broadcast function under different environments caused by different weathers. This electronic document is a “live” template and already defines the components of your paper [title, text, heads, etc.] in its style sheet.
Development of an aerial interface for extraction of the electrodynamic fingerprints of the single‐photon detectors

In this study, a novel structure of semi array of antipodal Vivaldi antenna is designed to extract the RF radiations from the single-photon detectors in a quantum key distribution system. The proposed antenna covers a frequency range from 2 to 12 GHz with an average gain of 12.5 dBi which has improved by a lens structure. In the first step, the design proceeding of the antenna has been discussed, and then, some of the unique futures of the proposed antenna which promoted it to use in quantum attack systems, such as linear momentum, a good Envelope Correlation Coefficient, and Gaussian pulse response, are discussed.
Performance analysis of Low energy and highspeed DA-RNS based FIR filter design for SDR Applications on FPGA

For different applications, the Finite Impulse Response (FIR) filter is widely used in digital signal processing (DSP) applications. We exhibit a significant Residue Number System (RNS)-based FIR filter design for Software Defined Radio (SDR) filtration in this article. Including its underlying concurrency and information clustering process, the RNS provides important statistics over FIR application in specific. According to several residue computing and reverse translation, expanded bit size results in a significant performance trade-off, conversely. Through RNS replication, accompanied by conditional delay optimized reverse processing to minimize the FIR filter trade-off features with filter duration optimized Residue Number System arithmetic is proposed in this study, which involves distributed arithmetic-based residue processing. To execute the task of reverse translation and to store pre-computational properties, the suggested Residue Number System architecture makes use of built-in RAM blocks found in field-programmable gate array (FPGA) devices. The proposed FIR filter with core optimized RNS has the benefit of lowering processing latency delay while rising performance torque. Followed by FPGA hardware synthesis for different input word sizes and FIR lengths verification by the efficiency of the FIR filter core, fetal audio signal detection is performed first. The test results reveal that over the optimization procedure RNS method, a compromise in traditional RNS FIR over filter size is narrowed, as well as a substantial decrease in sophistication.
Out-of-Distribution Detection Using Outlier Detection Methods

Out-of-distribution detection (OOD) deals with anomalous input to neural networks. In the past, specialized methods have been proposed to identify anomalous input. Similarly, it was shown that feature extraction models in combination with outlier detection algorithms are well suited to detect anomalous input. We use outlier detection algorithms to detect anomalous input as reliable as specialized methods from the field of OOD. No neural network adaptation is required; detection is based on the model's softmax score. Our approach works unsupervised using an Isolation Forest and can be further improved by using a supervised learning method such as Gradient Boosting.
Digital Image Correlation using Image Calibration under Restricted Environment

Digital Image Correlation (DIC) is a new method of measuring material properties. DIC requires a fixed camera and a separate instrument to acquire images. The measurement position and orientation for the DIC measurement of a workpiece are severely constrained. Therefore, additional devices are required. In this study, only a marker with a grid pattern was attached, and a photograph of a specimen surface was captured without additional equipment. Specimen images were moved and tilted because the camera would shake during the experiment and the camera and specimen were not parallel. The acquired specimen images might be shifted or deformed. Sample images were acquired form various angles and then calibrated with perspective transformation by a developed algorithm and software. Surface strain (or deformation) field data were obtained using the calibrated specimen images and DIC. Therefore, the developed algorithm was verified by comparing the strain field data of the corrected DIC images.
Benchmarking Quantum Correlations in Scalable Photonic Systems

A benchmark protocol is established to certify quantum correlations, demanding an exponential increase of resources. We handle this scaling and show nonclassicality of up to ten photons distributed over more than sixty modes.
Rec.HTML: Declarative HTML

Interactive user experiences on the web are becoming the norm. Client-side programs are becoming more complicated and have to deal with event handling, reading HTML document state and updating the interface. In this paper we propose a declarative language that supports these three facets of client-side browser development declaratively and provides a programming model where complex interfaces can be written using simple programming techniques such as records, functions and recursion.
Attention-Based Multi-Task Learning For Fine-Grained Image Classification

Fine-Grained Image Classification is an inherently challenging task because of its inter-class similarity and intra-class variance. Most existing studies solve this problem by localization-and-classification strategies, which, however, always causes the problem of information loss or heavy computational expenses. Instead of localization-and-classification strategy, we propose a novel end-to-end optimization procedure named Multi-Task Attention Learning (MTAL), which reinforces the neural network’ correspondence to attention regions. Experimental results on CUB-Birds and Stanford Cars show that our procedure distinctly outperforms the baselines and is comparable with state-of-the-art studies despite its simplicity*.
IRIS

Language learning from an increasingly early age is a challenge that most schools in all countries are taking up. Numerous methods are available to facilitate language teaching at different levels. In the case of children, interactive systems are especially convenient to increase their motivation and engagement. In this paper we present an interactive system, called IRIS, specifically designed to support English language learning for primary school children, applying teaching techniques based on "learning while playing". During the learning process the figure of the teacher is very important, and this system has been implemented to cover both roles, that of students and teachers, so that the latter can design and guide the activities to be carried out by the students and subsequently monitor their progress. The teacher accesses the Control Panel, designed for the management of students and activities, while the students directly access the playground, where they playfully carry out the activities selected by their teacher. The system allows a personalized monitoring of students and is especially useful in contexts where students cannot attend classes in person, or in schools where students are separated in different locations, as it is the case of Gathered Rural Schools.
Semantic Preserving Generative Adversarial Network For Cross-Modal Hashing

Cross-modal hashing has achieved significant progress in recent years. However, how to effectively learn more discriminative hash codes of each modality and simultaneous alleviate the loss of modality information is still a challenging problem. Focusing on this problem, in this paper, we propose a novel cross-modal hashing approach named Semantic Preserving Generative Adversarial Network (SPGAN). The overall network architecture consists of two sub-networks, i.e., a semantic preserving generative adversarial network module and a discriminative hashing module. The generator maps text features into the image feature space. And the discriminator judges whether the feature representations are real image features or generated image features. The adversarial learning process can effectively reduce modality difference and preserve information of the image modality as much as possible. The discriminative hashing module projects the real and generated image features into a Hamming space to obtain hash codes, and explores semantic similarities for enhancing the discriminant ability of hash codes. Experiments on two widely used datasets demonstrate that SPGAN can outperform state-of-the-art related works.