Reviewerly/exHarmony huggingface dataset:
Image Processing Based Real Time Motion Detection System Using GSM and Embedded System

In modern era, the security concerns have grown enormously. The security of restricted areas such as secret offices or buffer zones is of extreme importance. Monitoring such areas rely currently on technology and man power, however automatic monitoring has been advancing in order to avoid potential human errors that can be caused by different reasons. The system presented in this paper detects motion during live video acquisition from a camera interfaced with PC in Matlab. Technique of Consecutive Frame Difference algorithm is applied for detection. Afterwards, filtering is applied and based on a specific threshold, owner is intimated through a text message sent through a GSM (Global System for Mobile Communication) module. An alarm system is activated by PIC microcontroller and motor of a hidden door can be closed through a relay interfaced with Microcontroller. An image of that intruder can also be sent to the user's email id through SMTP (Simple Mail Transfer Protocol) protocol in Matlab. An interactive GUI (Graphical User Interface) is designed for the proposed system. Experimental results showed the superiority of the proposed method as it is faster and advanced as compared to previous ones as it incorporates a large number of features.
Aspect Based Sentiment Analysis of Student Housing Reviews

According to a 2016 report by the Indian Ministry of Human Resource Development, there were 39,658 student hostels across India. In recent years, owing to the growing number of students residing in such hostels, there has been an interest in helping students know more about these hostels by providing them with information and reviews from residing students. We aim to categorize these based on various aspects and give greater insights about them using applications of aspect based sentiment analysis. We have used a neural network based approach to pre-process the texts and propose two models, one for aspect extraction and classification and the other for sentiment polarity analysis. Further, we have presented an extensive evaluation of our models and have achieved an accuracy of more than 75% on both the models.
Object Recognition Using Image Segmentation

The aim of the paper is to develop a system that can recognize various targeted objects in real-time by incorporating some computer vision techniques and advanced boosted Machine Learning algorithms to solve the problem of object recognition with high accuracy. The task of object detection is a combination of both, image classification and image localization. Image classification identifies the class of target object whereas localization identifies its position inside the input image. Viola-Jones is an algorithm that can work effectively for recognizing a large number of categories such as airplanes, cars, faces, etc. It works on feature extraction and uses Haar-Like features to achieve the objective. So, Viola-J ones is the algorithm that will be used for the development purpose of this paper. Also, instead of using a normal image, the image segmentation technique will be used to create segments in the image that will make it easy to analyze and process the image for further computations.
Learning Diverse Models for End-to-End Ensemble Tracking

In visual tracking, how to effectively model the target appearance using limited prior information remains an open problem. In this paper, we leverage an ensemble of diverse models to learn manifold representations for robust object tracking. The proposed ensemble framework includes a shared backbone network for efficient feature extraction and multiple head networks for independent predictions. Trained by the shared data within an identical structure, the mutually correlated head models heavily hinder the potential of ensemble learning. To shrink the representational overlaps among multiple models while encouraging the diversity of individual predictions, we propose the model diversity and response diversity regularization terms during training. By fusing these distinctive prediction results via a fusion module, the tracking variance caused by the distractor objects can be largely restrained. Our whole framework is end-to-end trained in a data-driven manner, avoiding the heuristic designs of multiple base models and fusion strategies. The proposed method achieves state-of-the-art results on seven challenging benchmarks while operating in real-time.
Detecting Trojan Attacks on Deep Neural Networks

Machine learning and Artificial Intelligent techniques are the most used techniques. It gives opportunity to online sharing market where sharing and adopting model is being popular. It gives attackers many new opportunities. Deep neural network is the most used approached for artificial techniques. In this paper we are presenting a Proof of Concept method to detect Trojan attacks on the Deep Neural Network. Deploying trojan models can be dangerous in normal human lives (Application like Automated vehicle). First inverse the neuron network to create general trojan triggers, and then retrain the model with external datasets to inject Trojan trigger to the model. The malicious behaviors are only activated with the trojan trigger Input. In attack, original datasets are not required to train the model. In practice, usually datasets are not shared due to privacy or copyright concerns. We use five different applications to demonstrate the attack, and perform an analysis on the factors that affect the attack. The behavior of a trojan modification can be triggered without affecting the test accuracy for normal input datasets. After generating the trojan trigger and performing an attack. It's applying SHAP as defense against such attacks. SHAP is known for its unique explanation for model predictions.
Bayesian gaussian finite mixture model

Abstract In data analysis it is common to assume that data are generated from one population. However, due to some underlying factors, data might come from several sources that should be considered as several sub-populations. Partitioning methods such as k-means clustering, as well as hierarchical clustering, are two commonly used methods for identifying data grouping. The grouping is done based on distance between observations. We discuss alternative means of data grouping, based on data distribution, known as Finite Mixture Model. Parameter estimation will be done using the Bayesian approach. Incorporation of prior distribution on the parameter of interest will complete data information from the sample, resulting in updated information in the form of posterior distribution. Gaussian distribution is assumed for the sampling model. Markov Chain Monte Carlo with Gibbs Sampling algorithm is implemented for sampling from the posterior distribution. Data on wave sensitivity on monkeys’ eyes were used to illustrate the method.
Camera Pose Estimation using CNN

Estimating camera pose is a significant process, which assures the success of the 3D modeling performance. This research presents a camera pose estimation using convolutional neural network (CNN) to transfer learning from pre-trained deep learning VGG19 model in order to extract features from a single image using several datasets captured in indoor and outdoor environments with diverse perspectives and photographic styles. Due to the large dimensions of the extracted features, Latent Semantic Analysis (LSA) are introduced prior to the CNN input. Then, the CNN is trained to predict the camera views and translations. The prediction performance is measured in terms of average mean square errors and compared to the reference techniques. As a result, the regression estimation of the proposed CNN model outperforms the others with average 0.24 degrees rotation error and 0.26 m. translation errors.
Contrastive Learning in Frequency Domain for Non-I.I.D. Image Classification

Non-I.I.D. image classification is an important research topic for both academic and industrial communities. However, it is a very challenging task, as it violates the famous hypothesis of independent and identically distributed (I.I.D.) in conventional machine learning, and the classifier minimizing empirical errors on training images does not perform well on testing images. In this work, we propose a novel model called Contrastive Learning in Frequency Domain (CLFD) to learn invariant representations for Non-I.I.D. image classification. In CLFD, model learning includes two steps: contrastive learning in the frequency domain for pre-training, and image classification with fine-tuning. In the first pre-training step, anchor, positive and negative images are transformed by Discrete Cosine Transform (DCT) and then projected into vector space. This step is to obtain stable invariant features by minimizing the contrastive loss. In the step of image classification with fine-tuning, the features from ResNet are mapped into the label space by a simple fully connected layer, and the classification loss is utilized to fine-tune the parameters in the ResNet. Extensive experiments conducted on public NICO dataset demonstrate the effectiveness of the proposed CLFD, which outperforms the state-of-the-art methods.
A hybrid approach for Maximum Entropy clustering using Salp Swarm Algorithm

To address the problem that Maximum Entropy Clustering Algorithm(MEC) is sensitive to the initial clustering centers, we propose a hybrid approach for Maximum Entropy Clustering using Salp Swarm Algorithm (MEC-SSA). First, there are m data samples that are randomly selected as salp populations to get the optimal initial cluster centers. Secondly, the optimal initial clustering centers of MEC are obtained by SSA using DB index as its fitness function. Finally, we can get the better cluster centers by MEC approach. MEC-SSA can alleviate the sensitivity of MEC for initial clustering centers. Further experiments conducted in UCI data show that SSA helps to improve the performance of MEC.
A Secure Experimentation Sandbox for the Design and Execution of Trusted and Secure Analytics in the Aviation Domain

The undergoing digital transformation of the aviation industry is driven by the rise of cyber-physical systems and sensors and their massive deployment in airplanes, the proliferation of autonomous drones and next-level interfaces in the airports, connected aircrafts-airports-aviation ecosystems and is acknowledged as one of the most significant step-function changes in the aviation history. The aviation industry as well as the industries that benefit and are highly dependent or linked to it (e.g. tourism, health, security, transport, public administration) are ripe for innovation in the form of Big Data analytics. Leveraging Big Data requires the effective and efficient analysis of huge amounts of unstructured data that are harnessed and processed towards revealing trends, unseen patterns, hidden correlations, and new information, and towards immediately extracting knowledgeable information that can enable prediction and decision making. Conceptually, the big data lifecycle can be divided into three main phases: i) the data acquisition, ii) the data storage and iii) the data analytics. For each phase, the number of available big data technologies and tools that exploit these technologies is constantly growing, while at the same time the existing tools are rapidly evolving and empowered with new features. However, the Big Data era comes with new challenges and one of the crucial challenges faced nowadays is how to effectively handle information security while managing massive and rapidly evolving data from heterogeneous data sources. While multiple technologies and techniques have emerged, there is a need to find a balance between multiple security requirements, privacy obligations, system performance and rapid dynamic analysis on diverse large data sets. The current paper aims to introduce the ICARUS Secure Experimentation Sandbox of the ICARUS platform. The ICARUS platform aims to provide a big data-enabled platform that aspires to become an “one-stop shop” for aviation data and intelligence marketplace that provides a trusted and secure “sandboxed” analytics workspace, allowing the exploration, curation, integration and deep analysis of original, synthesized and derivative data characterized by different velocity, variety and volume in a trusted and fair manner. Towards this end, a Secure Experimentation Sandbox has been designed and integrated in the holistic ICARUS platform offering, that enables the provisioning of a sophisticated environment that can completely guarantee the safety and confidentiality of data, allowing to any interested party to utilize the platform to conduct analytical experiments in closed-lab conditions.
Research on Education Performance Evaluation Based on Characteristic Index Vector Analysis

Abstract The research on the performance of cultivating innovative and entrepreneurial talents in higher vocational colleges is to combine the teaching theory of innovation and entrepreneurship education with the practice of university parks, form new thinking of innovation and entrepreneurship education, and cultivate a number of innovative and entrepreneurial talents in the new era. This paper proposes the design of teaching performance model based on data decision. With the method of characteristic index information sampling, the sample data of educational resource service performance are obtained. Combined with the optimal strategy to track the human resource management and service performance of colleges and universities, the cost function of education index performance management is constructed. Based on the characteristic index query and optimization control of education performance management, the performance prediction is carried out, and the design of human resource service performance model based on characteristic index vector is realized.
Experimental Identification of Relationship between Leakage Trace SNR and Correlation Coefficient in Differential Power Analysis

A purpose of this work is providing a SCA resistance evaluation method that can associate the evaluation result with design criteria of countermeasure circuits at the system level. A method evaluating the SCA resistance of cryptographic circuits based on the signal-to-noise ratio (SNR) of the side-channel trace is examined. Here, it is assumed that SNR is used as SCA resistance design targets and the targets are determined based on analytically derived relationship between the SNR and correlation coefficients calculated in SCA. This work has experimentally verified that the SNR and correlation coefficients calculated from measured side-channel traces satisfy the relationship. A printed circuit board (PCB) with a field-programmable gate array (FPGA) implementing an advanced encryption standard (AES) circuit was used as an equipment under test, and the core power supply voltage fluctuation of the FPGA was measured on the PCB as side-channel trace. The SNR is calculated using a signal component obtained from the trace when encrypting a random plaintext set and a noise component obtained from the trace when encrypting a constant plaintext set. The correlation coefficient is calculated based on the correlation power analysis. The SNR was changed in the range of 20 dB by changing measurement conditions, and the relationship was examined. As a result, the SNRs and the correlation coefficients of the measured trace obeyed the analytical relational expression.
Specificity of the Spanish-Language Social Network Discourse

The paper aims to reveal linguistic peculiarities of the Spanish-language social network discourse by the material of Facebook, Instagram and Twitter. The author describes peculiarities of the Spanish-language social network discourse at all the language levels (orthographic, lexical, grammatical), provides a comprehensive linguistic analysis of the Spanish-language Internet communication in the three most popular social networks, identifies the basic characteristics of this type of discourse, which constitutes scientific originality of the study. The following conclusions are justified: social network discourse is characterized by a tendency to linguistic economy and by deliberate violation of orthographic and grammatical norms to simplify communication.
A modified supervised learning rule for training a photonic spiking neural network to recognize digital patterns

A modified supervised learning rule which is suitable for training photonic spiking neural networks (SNN) is proposed for the first time. The proposed learning rule is independent of the time intervals between actual spike and desired spike or between presynaptic spike and postsynaptic spike. Based on the proposed supervised learning rule, 10 digital images are learned in photonic neural network which consists of 30 presynaptic neurons and 10 postsynaptic neurons. Presynaptic and postsynaptic neurons are photonic neurons based on vertical-cavity surface-emitting lasers with an embedded saturable absorber (VCSEL-SA). The results show that 10 digital images are recognized correctly in photonic SNN after enough training. Additionally, the effects of learning rate, the jitters of learning rate, initial weights distribution of SNN and bias current of postsynaptic neurons (VCSELs-SA) on the recognized error are examined carefully based on the proposed learning rule. To the best of our knowledge, such modified supervised learning rule has not yet been reported, which would contribute to training photonic neural networks, and hence is interesting for neuromorphic photonic systems and pattern recognition.
Intelligent Anti-virus Mask Based on Gas Detection Sensor

Abstract This article developed an intelligent anti-virus mask, which is composed of MQ-135 sensor, single-chip microcomputer, valve mechanism and so on. When there is no poisonous gas, the valve of the canister of the mask is opened to breathe normally; when there is poisonous gas, the valve of the canister of the mask is closed and an alarm is issued, and air enters and exits through the canister. Experiments show that the gas mask can accurately detect the concentration of alcohol gas volatilized by alcohol diluents of 30%, 50% and 80% concentration, and trigger the mask to perform alarm and protective actions in a timely and accurate manner. The experiment verifies the safety and versatility of the smart gas mask, indicating that the product has high engineering application value and practical promotion significance.
Computer Vision Based Framework For Detecting Phishing Webpages

One of the most dangerous threats on the internet nowadays is phishing attacks. This type of attack can lead to data breaches, and with it to image and financial loss in a company. The most common technique to exploit this type of attack is by sending emails to the target users to trick them to send their credentials to the attacker servers. If the user clicks on the link from the email, then good detection is needed to protect the user credentials. Many papers presented Computer Vision as a good detection technique, but we will explain why this solution can generate lots of false positives in some important environments. This paper focuses on challenges of the Computer Vision detection technique and proposes a combination of multiple techniques together with Computer Vision technique in order to solve the challenges we have shown. We also will present a methodology to detect phishing attacks that will work with the proposed combination techniques.
Cluster approach to analysis of publication titles

Abstract Text analysis is a promising field of study with many unsolved problems. First of all, most methods are labor and time consuming. We want to pay special attention to patents. The most important thing in analyzing patents as a reflection of a company’s research activities is not to be late. Technology is emerging very quickly. So speed of response to changes in the world of scientific research is very important now. Therefore, we propose an alternative method of patent analysis based on clustering. Its main advantage is that it does not require different train/test datasets and it could be applied immediately. In this article, we compare different clustering algorithms, because the quality of the conclusions depends on it.
Information and Communication Technology of Getting Future Masters Ready for Scientific Communication

The article explains the necessity to improve the quality of information and communication technologies used to get future masters of technical specialties ready for professional communication in the scientific community. Specific features of information and communication technologies are identified. Based on the information and system approaches, an information and communication technology has been developed to get future masters of technical specialties ready for real and virtual scientific communication. The information model of the technology consists of a set of nodes, communication channels and cycles and serves as part of a complex which is used to study the educational process as a system able to perceive, store and transmit information. The construction of the information model helps the educator to monitor the circulating information and regulate the process. The competitive advantages of the information and communication technology have been identified. The results of testing the technology are presented. The implementation of the models together with the pedagogical conditions according to the step-by-step algorithm shows positive dynamics in levels of future masters' readiness for scientific communication.
Objects distance measurement in augmented reality for providing better user experience

Abstract In this paper we present an algorithm for providing better user experience in AR applications based on HMD (Head Mounted Display), pair of cameras and Deep Learning semantic segmentation. The user can view the scene, captured by the cameras using HMD along with augmented information regarding distances to all detected objects. The cameras are attached to the HMD and must be located as close as possible each other. Moreover one of the cameras is shifted at a known distance closer to the scene with respect to the other camera. Based on the traditional pinhole camera model and estimating the size of the projection of a given object in pixel coordinate systems in both of the cameras, we are able to calculate the distance from the cameras to the object. For location and calculation of the size of projected objects we used a semantic segmentation based on deep learning algorithm.
A fixed-points based framework for compliance of behavioural contracts

We study compliance relations between behavioural contracts in a syntax independent setting based on Labelled Transition Systems. We introduce a fixed-point based family of compliance relations, and show that many compliance relations appearing in literature belong to this family. We then study fix-compliance in the context of synchronous and asynchronous session contracts.