Reviewerly/exHarmony huggingface dataset:
Automatic Speech Recognition for Tulu Language Using Gmm-Hmm and DNN-HMM Techniques

In this work a first Automatic Speech Recognition (ASR) for Tulu language is developed. Seven hours of speech database for Tulu is recorded from native speakers in natural conditions for read speech. Kaldi toolkit is employed to develop GMM-HMM and DNN-HMM based ASR systems. Different speech units are employed to build the system and a detailed set of experiments is carried out on the collected dataset. It was observed that because of the lesser data, monophone GMM-HMM models provide better word error rate (WER) when compared to triphone models. More data is required for the system to reach to better performance with triphones.
Higher Education in Hungary in the Time of the Pandemic

In the spring of 2020, the coronavirus pandemic presented the world with new challenges. In the first half of March, parallel to the spread of COVID-19, teaching and lecture halls, colleges and other facilities in universities and university campuses were emptied almost simultaneously. Almost overnight, all higher educational institutions switched from face-to-face teaching to online teaching, namely absentee or distance learning. In our study, we surveyed students (both Hungarian and English-speaking students) and higher education faculty at the end of the second semester of the 2019/2020 academic year to explore their experiences with online education during the pandemic. In our analysis, we explore some aspects of distance learning during the pandemic that we consider important, and we seek to compare the student and teacher views. The results show that the introduction of fully online teaching during the pandemic was relatively smooth, but this period was not without challenges and difficulties that manifested themselves in the learning and teaching process, in the availability of learning materials, in the digital skills of students, and in the work overload of teachers. In summary, however, the response of higher education to digitalisation today, besides its inevitability, is to prioritise the incorporation of past experiences in determining the focus of further development.
RETRACTED ARTICLE: Incremental Outlier Feature Clustering Algorithm in Blockchain Networks Based on Big Data Analysis

Outlier detection in data mining aims at identifying anomalous observations known as outliers. The standard communication network incremental outlier feature clustering technique has a low clustering impact, requires several iterations, and takes a long clustering time. A novel incremental outlier based on big data analysis is proposed to overcome these problems for communicating networks in a Blockchain environment. Through big data analysis, the Point feature clustering algorithm uses the kernel density estimation technique with Gaussian kernel function to estimate the incremental outlier density of the communication network in the Blockchain framework. It mines the incremental outlier and applies the empirical mode based on the outlier mined for optimal outcome. The decomposition technique is used to extract outlier features, give weights to the features, and calculate the outlier degree and clusters. According to the results, the communication network, based on the Blockchain environment, may be clustered for incremental outlier features, and then outliers are ideally detected optimally. The simulation of the proposed technique is performed on three types of datasets. The simulation to form a cluster with the proposed method takes around 10 s of clustering time. The simulation results reveal that the proposed incremental outlier feature clustering (OFC) technique has a better outlier feature clustering impact on Blockchain-based networks, and requires a fewer iterations due to low computational and space complexity than other existing clustering techniques.
Preserving Privacy of High-Dimensional Data by l-Diverse Constrained Slicing

In the modern world of digitalization, data growth, aggregation and sharing have escalated drastically. Users share huge amounts of data due to the widespread adoption of Internet-of-things (IoT) and cloud-based smart devices. Such data could have confidential attributes about various individuals. Therefore, privacy preservation has become an important concern. Many privacy-preserving data publication models have been proposed to ensure data sharing without privacy disclosures. However, publishing high-dimensional data with sufficient privacy is still a challenging task and very little focus has been given to propound optimal privacy solutions for high-dimensional data. In this paper, we propose a novel privacy-preserving model to anonymize high-dimensional data (prone to various privacy attacks including probabilistic, skewness, and gender-specific). Our proposed model is a combination of l-diversity along with constrained slicing and vertical division. The proposed model can protect the above-stated attacks with minimal information loss. The extensive experiments on real-world datasets advocate the outperformance of our proposed model among its counterparts.
Experimental Analysis of Data Storage and Integrity Management over Cloud Environment using Integrated Data Security Strategy

Cloud storage has led to a rise in the number of data owners who want to outsource their data. Before outsourcing, sensitive data should be secured to protect privacy. Various searchable encryption systems are available to assure the availability of the data they protect. Increasing numbers of customers want to keep their data on cloud servers as cloud computing continues to grow. The major goal of this system is to create a cloud server with robust encryption and decryption data logics and prevent registration centres, as well as various data owners and users, free of duplication. In order to assist more customers on processing their data in the public cloud, new security issues must be resolved. The suggested system must include a novel technique to deliver duplication-free data services across the cloud environment since the entire cloud has certain space maintenance issues. Using a third-party registration center to maintain data on a distant server is a major security concern in the cloud computing environment. This system uses a Message Digest Algorithm (MD5) and an Intelligent Data Hashing Algorithm (DHA) to process data using a 256-bit unbreakable encryption technique to provide the highest level of security.
A hybrid attention mechanism for blind automatic modulation classification

Abstract Recently, deep leaning has been making great progress in automatic modulation classification, just like its success in computer vision. However, radio signals with harsh impairments (oscillator drift, clock drift, noise) would significantly degrade the performance of the existing classifiers. To overcome the problem and explore the depth reason, a hybrid attention convolution network is proposed to enhance the capability of feature extraction. First, a spatial transformer network module with long short‐term memory is introduced to synchronize and normalize radio signals. Second, a channel attention module is constructed to weight and assemble feature maps, exploring global feature representations with more context‐relevant information. By combining these two modules, a relatively lightweight classifier with complex convolution layer for final classification is further researched through visualization. Moreover, different structures of attention module are compared and optimized in detail. Experimental result shows that our proposed hybrid model achieves the best performance among all compared models when SNR is upper than 7 dB, and it peaks at 93.448 at 0 dB, 2.7% higher than that of CLDNN and 97.560 at 20 dB, 8.2% higher than that of ResNet. And our model can be more efficient after a trade‐off between accuracy and model size.
Localization and tracking of closely-spaced human targets based on infrared sensors

In recent years, indoor human localization and tracking technology has been a research hotspot. However, the problem caused by closely-spaced targets is often avoided for target localization and tracking. As a common situation in real scene, it is of great research value and application prospect to find out ways to effectively solve the problem of accurately locating and tracking closely-spaced targets. Thermopile array (TPA) sensors, a kind of widely investigated infrared sensors, are often used to detect human targets, which are expected to solve the closely-spaced problem. This paper proposes a method for localization and tracking of indoor closely-spaced targets by using TPA sensors. Visual background extractor (ViBE) algorithm is used to preprocess the data and extract the infrared characteristic region of human body. Several algorithms such as connected components labelling algorithm, temperature peak method and k-means++ clustering algorithm are used to locate the closely-spaced targets. In the aspect of closely-spaced target tracking algorithm, the temperature summation of target’s infrared thermal map is used to assist the nearest neighbor data association algorithm for data association of closely-spaced targets. Kuhn-Munkres algorithm is used to match the location point and trajectory properly. The Kalman filter, the first-order particle filter and the second-order particle filter are compared and analyzed to further reduce the tracking error. Finally, the system is tested in the actual indoor scene. The experimental results show that the proposed system can achieve the goal of localization and tracking of closely-spaced targets.
Transfer Learning Based Breast Cancer Detection and Classification using Mammogram Images

Breast cancer can be considered a deadly disease affecting women over the globe. Since severity of breast cancer is high at advanced stages, early detection processes find useful to increase the survival rate. The recently presented medical imaging modalities and deep learning (DL) models pave the way to design effective breast cancer, classification models. In this view, this study develops a new deep transfer learning enabled breast cancer detection and classification (DTL-BCDC) model using mammogram images. The proposed DTL-BDCD technique mainly intends to identify the presence of breast cancer. Primarily, the DTL-BDCD model involves contrast enhancement using CLAHE technique and adaptive weighted segmentation (AWS) technique is used for determining the infected regions. Besides, Densely Connected Networks (DenseNet-169) model is employed for feature extraction and multilayer perceptron (MLP) is utilized for breast cancer classification. The design of DenseNet169 with MLP model for breast cancer detection shows the novelty of the work. In order to demonstrate the enhanced outcomes of the DTL-BDCD model, a wide range of simulations take place on benchmark datasets and the comparison study reported the betterment of the DTL-BDCD model over the recent approaches.
RS-PKE

Since more and more data from lightweight platforms like IoT devices or mobile apps are being outsourced to the cloud, the need to ensure privacy while retaining data usability is essential. In this paper, we design a framework where lightweight platforms like IoT devices can encrypt documents and generate document indexes using the public key before uploading the document to the cloud, and an admin can search and retrieve the top-k most relevant documents that match a specific keyword using the private key. In most existing searchable encryption that supports IoT, all the documents that match a queried keyword are returned to the admin. This is not practical as IoT devices continuously upload data. We formally name our framework asRanked Searchable Public-Key Encryption (RS-PKE). We also implemented a prototype of RS-PKE and tested it in the Amazon EC2 cloud using the RFC dataset. The comprehensive evaluation demonstrates that RS-PKE is efficient and secure for practical deployment.
Survey on Methods of Face Mask Detection System

In these intense COVID-multiple times, there is a need to recognize if an individual is wearing a face mask or not to ensure himself just as others around. Making a protected situation is at the head of everybody’s psyche as we get ready to open offices once more. Measures must be taken to secure those returning to work; to help keep them and their families liberated from hurt. New approaches are being organized each day to meet guidelines and HR strategies. Face Mask Detection logical empowers office proprietors to realize who enters their structures without a necessary face mask. Face Mask Detection System can be used at airports to detect non-cover inspectors. It can be used effectively on office premises to see if representatives are complying with occupational safety regulations. Emergency clinics can check whether its employees wear a mask during their working day or not. Numerous new calculations are being formulated utilizing convolutional models to make the calculation as exact as could reasonably be expected. These convolutional structures have made it conceivable to separate even the pixel subtleties. These frameworks can recognize people without mask utilizing their transferred pictures.
Integration and Sharing of College English Teaching Resources Using Cloud Computing Platform

Intelligent computing has been used in the field of education and teaching for a long time in other countries. China is currently conducting education cloud research, but it has not yet been applied to the sharing of instructional resources in universities. The creation of a university instructional resource sharing network based on intelligent computing will help to alleviate the uneven distribution of educational resources to some extent and achieve the goal of high-quality instructional resource sharing, which is critical for improving China’s tertiary education overall. The information age has arrived, thanks to the advancement of information technology and the growth of communication networks. The impact of instructional resources on English classrooms is growing day by day against the backdrop of digitization. The educational resource sharing service platform can effectively deal with the problems of excessive infrastructure investment and low utilization of educational resources with intelligent computing as the technical support. It is essential for promoting educational resource sharing as well as interregional tertiary education exchanges and collaboration. As a result of the research on intelligent computing and educational resource sharing service platform, this paper discusses specific problems such as platform module design, system inspection, and application form, in order to provide a reference for the establishment of educational resource sharing service platform in universities, based on the overall and functional requirements of the platform, a platform that allows teachers and students to access personalized digital resources on demand for reading, downloading, printing, and sharing.
Performance Evaluation of Blockchains Towards Sharing of Digital Twins

Citizens need to be involved to construct digital twins by sensing the urban environments using their IoT devices and sharing the sensed data among application providers. This "cooperative sensing" is one of the key solutions to achieve sustainable smart cities/super cities and offers improvement of the Quality of Life of citizens. In this view, blockchain and distributed ledger technology are collecting much attention to build a distributed data exchanging/sharing system for IoT data as well as cryptocurrency. However, the blockchain has a scalability issue in terms of large data volume and high-frequency transaction cases. In this paper, in order to reveal the comprehensive performance of blockchain when it is applied to sensor data sharing, we build a data-sharing system using Hyperledger Fabric which is one of the well-known blockchain platforms and evaluate the system performance by using a variety of sensing data, including images and 3D objects.
Securing Healthcare Data using Decentralized Approach

According to WHO, brain stroke seems to be the second most common cause overall, accounting for about eleven percent of all mortality. Data security and privacy are in great demand in the healthcare industry. Data is now kept in a centralized manner in present systems, with all data being stored in a single area. In such systems, there is a high possibility for an intruder or third party to access and change the data. In Healthcare, as the data is the most crucial factor, so if there are any small changes made by the intruder in the data, it may lead to provide false outcome. In this proposed system, the data are secured in a decentralized approach using IPFS (InterPlanetary File System) protocol and Block chain. The risk of data failures and outages can be reduced while improving security, performance, and privacy using this strategy. The required data will be collected from the IPFS network by using the hash value and it will be trained with the ANN (Artificial Neural Network) algorithm to get the final model.
Detecting Criminal Activities and Promoting Safety Using Deep Learning

Automation and autonomous systems are among the few powerhouses of innovation that drive entire domains towards advancing further in leaps and bounds. Great technological innovations can be attributed to tasks that are made easier and more perceptible by automation, and artificial intelligence is here to make these automated systems smart enough to perform their tasks with the power of decision-making, thereby greatly reducing human intervention in redundant processes. Our project follows the aforementioned ideals: building a product to minimize manual labor (both physical and mental) for tasks that can be seamlessly automated and processed while solving the main problem statement at hand. Currently, surveillance cameras play a vital role to ensure the safety of the people, yet they are plain video-providing entities with no smart decision making mechanisms of their own. Because of this growth of data composed from surveillance cameras, automated video streams have become a requisite for automatically detecting abnormal events. The main aim of the project focuses on promoting safety on campus by employing deep learning techniques to automate the task of monitoring and reporting crimes from the physical Closed-Circuit Television (CCTV), assigning the responsibility of detecting criminal activity to a framework that can identify patterns to differentiate them for smarter monitoring. The model in this paper that we propose was able to distinguish between certain crimes with a 0.94 and 0.95 precision for Assault and Abuse respectively.
Spoken language identification in unseen channel conditions using modified within-sample similarity loss

State-of-the-art spoken language identification (LID) systems use sophisticated training strategies to improve the robustness to unseen channel conditions in the real-world test samples. However, all these approaches require training samples from multiple channels with corresponding channel-labels, which is not available in many cases. Recent research in this regard has shown the possibility of learning a channel-invariant representation of the speech using an auxiliary loss function called within-sample similarity loss (WSSL), which does not require samples from multiple channels. Specifically, the WSSL encourages the LID network to ignore channel-specific contents in the speech by minimizing the similarities between two utterance-level embeddings of same sample. However, as WSSL approach operates at sample-level, it ignores the channel variations that may be present across different training samples within same dataset. In this work, we propose a modification to the WSSL approach to address this limitation. Specifically, along with the WSSL, the proposed modified WSSL (mWSSL) approach additionally considers the similarities with two global-level embeddings which represent the average channel-specific contents in a given mini-batch of training samples. The proposed modification allows the network to have a better view of the channel-specific contents in the training dataset, leading to improved performance in unseen channel conditions.
Parametric model‐based deinterleaving of radar signals with non‐ideal observations via maximum likelihood solution

In modern electromagnetic environments, as the number of radars continuously increasing, the intercepted interleaving signals become more complex and are easily affected by different non-ideal factors. However, the widely used deinterleaving methods based on the pulse repetition interval (PRI) are difficult to effectively deinterleave signals with complex PRI modulation and are very sensitive to the received signal quality. To solve the deinterleaving problem in these complex situations, this paper proposes a parametric model-based deinterleaving solution consisting of two cascaded stages. The proposed method does not require priors such as the number of sources and corresponding modulation types, which makes it more suitable for complex environments in non-cooperative scenarios. In addition, it can adapt to complex non-ideal situations, including parameter measurement noise, missing pulse and spurious pulse conditions. Simulation results show that the deinterleaving precision of the proposed method outperforms baseline methods in non-ideal conditions and achieves 99.33% accuracy.
Citizen science for studying earthquakes

Seismologist-citizen partnership helped understand the 2021 Haiti earthquake.
Privacy Protection Scheme of Medical Electronic Health Records Based on Blockchain and Asymmetric Encryption

Abstract The current privacy protection scheme of medical electronic health records ignores the security of role interaction, which leads to low security of medical electronic health record data and a long time for data encryption and decryption. This paper proposes a privacy protection method of medical electronic health records based on blockchain and asymmetric encryption. This paper uses blockchain technology and asymmetric encryption algorithm to establish an electronic health record system model, selects the alliance chain to control the permissions of alliance members, constructs the corresponding sub key, uses shared users for information interaction, obtains the file key through verification (so as to decrypt the data and obtain the shared data plain text), and realizes medical electronic health through the privacy and protection scheme process of health records. Experimental results show that the data security of the proposed method is as high as 92 %, and the encryption and decryption time is only 0.45 s, which can effectively improve the data security and shorten the data encryption and decryption time.
Cryptographically strong S-P boxes and their application in steganography

For the security of digital data, different cryptosystems have been proposed in recent years. The substitution boxes are used to generate confusion in the cryptosystems for hiding the secret data. In the literature, the techniques for designing substitution boxes have been proposed for the sake of confusion. Among these techniques, the chaotic substitution boxes have more tendency of generating diffusion as compared to confusion. In this article, a novel method is introduced for the construction of substitution permutation boxes to get the advantage of both confusion and diffusion. These substitution permutation boxes are based upon the action of symmetric group on Galois field. This particular group action would also provide mathematical justification of bits permutation. For application purpose, these boxes are applied for a robust steganography scheme. The algebraic and statistical analyses suggest that proposed boxes provide confusion and diffusion of the standard level. The standard analysis of steganography has revealed that proposed boxes are suitable for the techniques of secure communication.