Reviewerly/exHarmony huggingface dataset:
Privacy Preservation for On-Chain Data in the Permission less Blockchain using Symmetric Key Encryption and Smart Contract

Blockchain technology provides several suitable characteristics such as immutability, decentralization and verifiable ledger. It records the transactions in a decentralized way and can be integrated into several fields like eHealth, e-Government and smart cities etc. However, blockchain has several privacy and security issues, one of them is the on-chain data privacy. To deal with this issue we provide a privacy-preserving solution for permission less blockchain to empower the user to take control of transaction data in the open ledger. This work focuses on designing and developing the peer-to-peer system using symmetric cryptography and ethereum smart contract. In this scheme, we create smart contracts for the interaction of the data provider, data consumer, and access control list. Data providers register authorized users in the access control list. Data consumers can check their validity in the access control list. After successful validation, data consumers can request the security key from data providers to access secret information. Based on successful validation, a smart contract that is created between the data provider and data consumer is executed to send a key to the data consumer for accessing the secret information. The smart contracts of this proposed model are modeled in solidity, and the performance of the contracts is assessed in the Ropsten test network.
Block-chain Technology for Food supply chains

The technology Block-chain is most emerging and widely used in almost all the applications like supply chain management systems, healthcare, banking industries, business, IT sectors, government organizations, agriculture etc. The food products are more sensitive, and it is very hard to trace the origin, buyers, sellers, farmers with the existing centralized method. The usage of Block-chain technology is proposed and experimented in this work along with the features of decentralization, increased security, immutability, and tamper proof for supply chains. The proposed work uses the smart contract on Ethereum for food supply chain management systems, which avoids falsifying information, corrupting database, external attacks. The analysis of food supply chains is shown in the experimental results.
JRL-YOLO: A Novel Jump-Join Repetitious Learning Structure for Real-Time Dangerous Object Detection

Campus security incidents occur from time to time, which seriously affect the public security. In recent years, the rapid development of artificial intelligence has brought technical support for campus intelligent security. In order to quickly recognize and locate dangerous targets on campus, an improved YOLOv3-Tiny model is proposed for dangerous target detection. Since the biggest advantage of this model is that it can achieve higher precision with very fewer parameters than YOLOv3-Tiny, it is one of the Tinier-YOLO models. In this paper, the dangerous targets include dangerous objects and dangerous actions. The main contributions of this work include the following: firstly, the detection of dangerous objects and dangerous actions is integrated into one model, and the model can achieve higher accuracy with fewer parameters. Secondly, to solve the problem of insufficient YOLOv3-Tiny target detection, a jump-join repetitious learning (JRL) structure is proposed, combined with the spatial pyramid pooling (SPP), which serves as the new backbone network of YOLOv3-Tiny and can accelerate the speed of feature extraction while integrating features of different scales. Finally, the soft-NMS and DIoU-NMS algorithm are combined to effectively reduce the missing detection when two targets are too close. Experimental tests on self-made datasets of dangerous targets show that the average MAP value of the JRL-YOLO algorithm is 85.03%, which increases by 3.22 percent compared with YOLOv3-Tiny. On the VOC2007 dataset, the proposed method has a 9.29 percent increase in detection accuracy compared to that using YOLOv3-Tiny and a 2.38 percent increase compared to that employing YOLOv4-Tiny, respectively. These results all evidence the great improvement in detection accuracy brought by the proposed method. Moreover, when testing the dataset of dangerous targets, the model size of JRL-YOLO is 5.84 M, which is about one-fifth of the size of YOLOv3-Tiny (33.1 M) and one-third of the size of YOLOv4-Tiny (22.4 M), separately.
Handwritten keyword spotting using deep neural networks and certainty prediction

Word spotting using deep Convolutional Neural Networks (CNN) has recently obtained significant results in handwritten documents retrieval application. In this paper, we propose a novel word spotting method based on Monte-Carlo dropout CNN to compute the certainty of extracted features that can be used in both query-by-example (QBE) and query-by-string (QBS) word spotting scenarios. In the QBE and during the training, an adaptable certainty threshold is assigned for the words of each class. Cosine distance between the predicted certainty of the query image and the retrieval set is compared with the certainty threshold of each class in the matching step. For the QBS, the query class is compared to the class of the retrieval set obtained by the certainty prediction. We evaluated our proposed method on four public handwritten databases. Experimental results showed that the accuracy achieved in both QBE and QBS scenarios outperforms the state-of-the-art methods.
A hybrid cryptography technique for data storage on cloud computing

Cloud computing security has received great attention by researchers in the past few years due to the importance of the data used and the increasing use of cloud computing, which has become the provision of many services and in various fields. So, data Security is a major issue in storing data on clouds. Cryptography techniques are among the most important ways to provide data security on cloud. This paper presents a data protection model where data is encrypted using a hybrid cryptographic algorithm which is composed of Advanced Encryption Standard (AES), Blowfish and Message-Digest algorithm (MD5). Thus, this model provides speed and robust data encryption.
Montgomery-friendly primes and applications to cryptography

This paper deals with Montgomery-friendly primes designed for the modular reduction algorithm of Montgomery. These numbers are scattered in the literature and their properties are partially exploited. We exhibit a large family of Montgomery-friendly primes which give rise to efficient modular reduction algorithms. We develop two main outcomes. The first one is dedicated directly to cryptography, in particular for isogeny-based approaches and more generally to elliptic curves cryptography (ECC). We suggest more appropriate finite fields and curves in terms of complexity for the recommended security levels, for both isogeny-based cryptography and ECC. The second issue is mainly arithmetic (even if its main use is cryptography), and we propose families of alternative RNS bases. We show that, for dedicated architectures with word operators, we can reach, for a same or better complexity, larger RNS bases with Montgomery-friendly pairwise co-primes than the RNS bases generally used in the literature with pseudo-Mersenne numbers.
Unsupervised Approach to Detect Extreme Sentiments on Social Networks

Online Social Network (OSN) platforms enable people freedom of expression to share their ideas, views, and emotions that could be negative or positive. Previous studies have investigated the user's sentiments on such platforms to study people's behavior for different scenarios and purposes. The mechanism to collect information on public views attracted researchers by analyzing data from social networks and automatically classifying the polarity of public opinion(s) due to the use of concise language in posts as tweets. In this paper, we propose an unsupervised approach for the automatic detection of people's extreme sentiments on social networks. The approach is based on two steps: 1) We automatically build a standard lexicon consisting of extreme sentiments terms having high extreme positive and negative polarity, and extend that same lexicon with word embedding method [1]; 2) To validate the lexicon, using an unsupervised approach for automatic detection of extreme sentiments. We further evaluated our system's performance on five different social networks and media datasets. This final task shows that, in these datasets, posts that were previously classified as negatives or positives are indeed extremely negatives or positives in numerous cases.
Multi-view clustering with interactive mechanism

Existing multi-view clustering methods either seek to directly learn a consistent spectral embedding, or to learn a consistent graph. This work presents a novel model, called Multi-view Clustering with Interactive Mechanism (MCIM). Using the interactive mechanism, the uniform graph and spectral embedding can be learned alternatively and promote to each other. Furthermore, we perform spectral embedding learning on Grassmann manifold via an implicitly weighted-learning scheme and reveal the clustering result via graph learning. To solve the proposed model, we propose an efficient optimization method and provide the corresponding convergence analysis. The experimental results on real image datasets demonstrate the superiorities of MCIM compared to several SOTA methods.
Transformers aftermath

Attention, particularly self-attention, is a standard in current NLP literature, but to achieve meaningful models, attention is not enough.
Quantum Circuits – An Application in Qiskit-Python

For many practical applications, Quantum computers offer major speed-ups over traditional machines. The production of quantum chips has recently made tremendous strides, with the quantity of Qubits increasing and fidelity increasing. The smallest unit of information is the binary digit i.e. bit in general computing. In Quantum Computing the term Qubit (Quantum Bit) is used for the same purpose. In order to differentiate between a classical bit and a Qubit, Bra-Ket or Dirac notation is used. So, the Qubits are represented as |0〉 and |1〉 and are often read as Ket 0 for zero state and Ket 1 for one state. In this paper mainly programming and application sides of Quantum Computing are focused. For that, Qiskit module and its tools are used in Python programming language. General circuit optimization scheme is given. The Quantum circuits with the concept of Quantum Gates implemented are stronger compared to the optimizing algorithm and with faster execution time. In addition, for prime factorization, access to IBM's Quantum computer is carried out successfully.
Embedding API dependency graph for neural code generation

The problem of code generation from textual program descriptions has long been viewed as a grand challenge in software engineering. In recent years, many deep learning based approaches have been proposed, which can generate a sequence of code from a sequence of textual program description. However, the existing approaches ignore the global relationships among API methods, which are important for understanding the usage of APIs. In this paper, we propose to model the dependencies among API methods as an API dependency graph (ADG) and incorporate the graph embedding into a sequence-to-sequence (Seq2Seq) model. In addition to the existing encoder-decoder structure, a new module named “embedder” is introduced. In this way, the decoder can utilize both global structural dependencies and textual program description to predict the target code. We conduct extensive code generation experiments on three public datasets and in two programming languages (Python and Java). Our proposed approach, called ADG-Seq2Seq, yields significant improvements over existing state-of-the-art methods and maintains its performance as the length of the target code increases. Extensive ablation tests show that the proposed ADG embedding is effective and outperforms the baselines.
Efficient Twitter Data Cleansing Model for Data Analysis of the Pandemic Tweets

Twitter data generally tends to be unstructured and often very noisy, cluttered/disorganized, and clothed in informal language. In this paper, we propose an intelligent Twitter data cleansing model that can solve data quality problems associated with twitter text. This model can correct a wide variety of anomalies from slangs, typos, Elongated (repeated Characters), transposition, Concatenated words, complex spelling mistakes as unorthodox use of acronyms, manifold forms of abbreviations of same words, and word boundary errors. The effects of whole range of tasks of Twitter Data Cleansing Model (TDCM) on the performance of sentiment classification utilizing feature models and three common classifiers have been investigated and evaluated. We conducted our experiments on two sets of pandemics twitter datasets: COVID-19 and Dengue datasets. The primary objective of this paper is to both increase the accuracy and the quality of twitter data and to purify and cleanse twitter data for further analysis. The experiment results seem to indicate that the accuracy of sentiment classification increases once the data quality problems associated with the Twitter text are solved. In COVID-19 twitter dataset, the best performance obtained using Random forest classifier after cleansing the data in terms of accuracy, recall, and f1-score are found to be at 84.7%, 88.5%, and 86.3% respectively. However, the best performance in terms of precision at 84.5% was observed using SVM classifier when compared to that obtained with other classifiers. Further, in the Dengue twitter dataset, the best performance for cleansing data in terms of accuracy, precision and f1-score are observed to be 81.7%, 83.7% and 88.6% respectively using Random forest classifier. The best performance in terms of recall, however, is 94.9% and was obtained using SVM classifier when compared with those obtained with other classifiers.
Facial Authentication System by Combining of Feature Extraction using Raspberry Pi

This work provides the mixture of two main component analysis (PCA) and linear discrimination analysis (LDA) feature extraction using Euclidean distance classifications for upgrade the efficiency on face-based real-time authentication method. Combining the extraction of functions that measure LDA and PCA values so that the matching scores become acquired. The matching scores represent sums extraction values from both functions. Distinctive values from face images could be improved also by combined extractor. Measure the distance to find a shortest distance between some of the template images by using Euclidean distance for test images. The output of a system authenticates for personal identity dependent from each user's face image. Results are obtained from the 11-user image indicate that perhaps the combined extractor offers a greatest result compared to the performance of the single extraction feature. The current technique provides greater average performance than just a single extractor, however on the facial authentication device may overcome low performance. The real-time system will be more compatible and flexible with existing service conditions, so improved results will be needed.
Multiple Human Association and Tracking from Egocentric and Complementary Top Views

Crowded scene surveillance can significantly benefit from combining egocentric-view and its complementary top-view cameras. A typical setting is an egocentric-view camera, e.g., a wearable camera on the ground capturing rich local details, and a top-view camera, e.g., a drone-mounted one from high altitude providing a global picture of the scene. To collaboratively analyze such complementary-view videos, an important task is to associate and track multiple people across views and over time, which is challenging and differs from classical human tracking, since we need to not only track multiple subjects in each video, but also identify the same subjects across the two complementary views. This paper formulates it as a constrained mixed integer programming problem, wherein a major challenge is how to effectively measure subjects similarity over time in each video and across two views. Although appearance and motion consistencies well apply to over-time association, they are not good at connecting two highly different complementary views. To this end, we present a spatial distribution based approach to reliable cross-view subject association. We also build a dataset to benchmark this new challenging task. Extensive experiments verify the effectiveness of our method.
Supervised deep convolutional generative adversarial networks

Generative adversarial networks (GANs) are one of the most important generative network models. Using real samples, the GAN generates fake samples from the noise given as input to the network. This popular network model, which has recently emerged and consists of several variants, has different applications in many areas. Some of the studies have been implemented by applying GANs to real-world problems. Another part is aimed at improving the performance of GANs or eliminating the disadvantages observed over time. One of these studies is DCGAN. The importance of DCGAN is that it contributes significantly to balancing GAN training with its convolutional architecture. GAN and naturally DCGAN have an unsupervised network structure. While the network is informed that the samples given as input are real or fake, the category label information is not given to the network. In the present study, a method is proposed, which enables creating a supervised network structure when using multi-categories data set with DCGAN structure. The proposed method ensures that noise can be given a category label and this generated category label information can be used in the output layer. This method, which is easily applicable and effective, is named as Supervised DCGAN (SDCGAN).
Mixture-Based Probabilistic Graphical Models for the Label Ranking Problem

The goal of the Label Ranking (LR) problem is to learn preference models that predict the preferred ranking of class labels for a given unlabeled instance. Different well-known machine learning algorithms have been adapted to deal with the LR problem. In particular, fine-tuned instance-based algorithms (e.g., k-nearest neighbors) and model-based algorithms (e.g., decision trees) have performed remarkably well in tackling the LR problem. Probabilistic Graphical Models (PGMs, e.g., Bayesian networks) have not been considered to deal with this problem because of the difficulty of modeling permutations in that framework. In this paper, we propose a Hidden Naive Bayes classifier (HNB) to cope with the LR problem. By introducing a hidden variable, we can design a hybrid Bayesian network in which several types of distributions can be combined: multinomial for discrete variables, Gaussian for numerical variables, and Mallows for permutations. We consider two kinds of probabilistic models: one based on a Naive Bayes graphical structure (where only univariate probability distributions are estimated for each state of the hidden variable) and another where we allow interactions among the predictive attributes (using a multivariate Gaussian distribution for the parameter estimation). The experimental evaluation shows that our proposals are competitive with the start-of-the-art algorithms in both accuracy and in CPU time requirements.
Data-Driven Object Vehicle Estimation by Radar Accuracy Modeling with Weighted Interpolation

For accurate object vehicle estimation using radar, there are two fundamental problems: measurement uncertainties in calculating an object’s position with a virtual polygon box and latency due to commercial radar tracking algorithms. We present a data-driven object vehicle estimation scheme to solve measurement uncertainty and latency problems in radar systems. A radar accuracy model and latency coordination are proposed to reduce the tracking error. We first design data-driven radar accuracy models to improve the accuracy of estimation determined by the object vehicle’s position. The proposed model solves the measurement uncertainty problem within a feasible set for error covariance. The latency coordination is developed by analyzing the position error according to the relative velocity. The position error by latency is stored in a feasible set for relative velocity, and the solution is calculated from the given relative velocity. Removing the measurement uncertainty and latency of the radar system allows for a weighted interpolation to be applied to estimate the position of the object vehicle. Our method is tested by a scenario-based estimation experiment to validate the usefulness of the proposed data-driven object vehicle estimation scheme. We confirm that the proposed estimation method produces improved performance over the conventional radar estimation and previous methods.
Distributed Design of Robust Kalman Filters Over Corrupted Channels

We study distributed filtering for a class of uncertain systems over corrupted communication channels. We propose a distributed robust Kalman filter with stochastic gains, through which upper bounds of the conditional mean square estimation errors are calculated online. We present a robust collective observability condition, under which the mean square error of the distributed filter is proved to be uniformly upper bounded if the network is strongly connected. For better performance, we modify the filer by introducing a switching fusion scheme based on a sliding window. It provides a smaller upper bound of the conditional mean square error. Numerical simulations are provided to validate the theoretical results and show that the filter scales to large networks.
A Methodical Overview on Detection, Identification and Proactive Prevention of Phishing Websites

Detecting and finding some phishing websites in real-time for a day now is really a dynamic and nuanced topic involving several variables and requirements. Fuzzy logic strategies may be an important method in detecting and testing phishing websites due to the ambiguities involved in the detection. Instead of exact principles, Fuzzy logic provides a more intuitive way of dealing with quality variables. An approach to fuzziness resolution and an open and intelligent phishing website detection model will be proposed in the Phishing website assessment. This approach is based on smooth logic and machine learning algorithms that define various factors on the phishing website. A total of 30 characteristics or features and phishing website attributes can be used for phishing detection with high accuracy. A real-time phishing dataset is used which is downloaded from the UCI machine learning repository.
Multi-lingual Indian Text Detector for Mobile Devices

Detection of text in natural scene images is a challenging problem owing to its significant variations in the appearance of the texts as well as the background. The task becomes even more difficult for Indian texts, due to the presence of multiple languages and scripts. Most of the recent efficient schemes use very deep CNN which requires large memory and computation. In this paper, we proposed a multi-lingual text detection system based on the compressed versions of an object detection framework called YOLO (You Only Look Once) [13] namely, YOLO v3-Tiny and YOLO v4-Tiny. The aspect ratios of the anchor boxes are calculated using K-means clustering on the ground truth values, so that it can accurately detect words of varying lengths. The text detector has been evaluated on the IndicSceneText2017 [11] data set which consists of three Indian languages along with English. Experimental results prove the efficiency of the proposed scheme, for use in embedded systems and mobile devices due to its fast inference speed and small model size.