Reviewerly/exHarmony huggingface dataset:
An Efficient Online/Offline Signcryption Scheme for Internet of Things in Smart Home

The delivery of unified intelligent services is accomplished through a networked environment comprised of a wide array of electronic devices. Through the use of Internet of Things (IoT) technology, smart homes collect data from their surroundings and use it to improve their tenants’ lives. Remote control, real-time monitoring, and a fire alarm are all characteristics of smart home security. Since smart homes hold personally identifying information about their residents, security is critical to ensure their reliability and prevent data breaches. In this paper, a certificateless online/offline signcryption (COOS) technique for IoT-enabled smart homes is proposed. The proposed solution takes advantage of a resource-constrained smart home device–friendly algorithm known as the Hyperelliptic Curve Cryptosystem (HCC). The suggested approach satisfies the security requirements of unforgeability, confidentiality, resistance to replay attacks, and non-repudiation. The complexity analysis in terms of communicational and computational costs demonstrates the efficiency of the proposed scheme. Finally, we validate the security against Man-In-The-Middle-Attack (MITM) and anti-reply attacks using Automated Validation of Internet Security Protocols and Applications (AVISPA). The data imply that the recommended course of action is safe.
Analysis on Contribution of Cryptography and Steganography in Protecting Information in Diverse Environments

Information is a key parameter in any type of application that must be treated with the utmost care at all times when it is used for any purpose. Security measures for this information are critical when dealing with anonymous or illegitimate users of web-based services and applications. Information security can be dealt with any techniques, which are specifically protected with a great extent of algorithms that are available under network security. Cryptography is the most succsessful technique used for protecting the information to any domain specific application, which contains huge information. This research work has analyzed various algorithms of cryptography and steganography for securing the data. Finally, this research work has analyzed the aforementioned strategies by combining steganography along with cryptography for obtaining good results in securing the sensitive information.
Isolated spoken word recognition using packed-MFCC on padded-voice signal for unscripted languages

Voice-based applications like Alexa, Siri and Google Assistant have become very common these days. These voice-operated devices are based on scripted languages, which have their own set of alphabets, phonemes and grammar, whereas languages of oral tradition do not have all these. Because of the fundamental difference between languages of scripted and unscripted nature, techniques used for languages like English are found unfit to be used for languages of oral tradition. In this paper, an isolated-spoken word recognition system for unscripted languages is modelled. Model uses a packed-Mel frequency cepstral coefficients (MFCC) feature over padded-voice with support vector machine (SVM) as classifier. The model is tested and compared against various other statistical features with different classifiers like K-nearest neighbour (KNN) and stochastic gradient descent (SGD). SVM is found best in terms of recognition accuracy for data set of language Kurukh, spoken by Oraon community, having 8,900 samples.
Variational Learning for the Inverted Beta-Liouville Mixture Model and Its Application to Text Categorization

The finite invert Beta-Liouville mixture model (IBLMM) has recently gained some attention due to its positive data modeling capability.Under the conventional variational inference (VI) framework, the analytically tractable solution to the optimization of the variational posterior distribution cannot be obtained, since the variational object function involves evaluation of intractable moments.With the recently proposed extended variational inference (EVI) framework, a new function is proposed to replace the original variational object function in order to avoid intractable moment computation, so that the analytically tractable solution of the IBLMM can be derived in an effective way.The good performance of the proposed approach is demonstrated by experiments with both synthesized data and a real-world application namely text categorization.
A Generalized Net Model with Intuitionistic Fuzzy Assessments of the Process of Cardiopulmonary Resuscitation

In the paper a Generalized Net (GN, [5, 6]) model of the Cardiopulmonary Resuscitation (CPR) process is presented. The CPR is a collection of diagnostic and therapeutic actions performed to restore suspended basic vital functions (respiration, cardiac activity, functions of the central nervous system) during cardiac arrest. The developed GN model is a set of transitions that present the sequence of actions of the CPR process with its three interconnected stages: From the input places of the GN model enter tokens that present the initial status of the person who needs from CPR. The new and enriched knowledge about the process of cardiopulmonary resuscitation of the output of the model is obtained. In the research, intuitionistic fuzzy assessments $$\left\langle {\mu ,\nu } \right\rangle$$ which estimate the execution time of the CPR process are suggested.
Data-Driven Automotive Development: Federated Reinforcement Learning for Calibration and Control

The importance of data-driven methods in automotive development continuously increases. In this area, reinforcement learning methods show great potential, but the required data from system interaction can be expensive to produce during the traditional development process. In the automotive industry, data collection is additionally constrained by privacy aspects with regard to intellectual property interests or customer data. Suitable reinforcement learning approaches need to overcome these challenges for effective and efficient learning. One possible solution is the utilization of federated learning that enables learning on distributed data through model aggregation. Therefore, we investigate the federated reinforcement learning methodology and propose a concept for a continuous automotive development process. The concept contributes separated training loops for the development and for the field operation. Furthermore, we present a customization and verification procedure within the aggregation step. The approach is exemplary shown for an electric motor current control.
Evaluating Hash-Based Post-Quantum Signature in Smart IoT Devices for Authentication

The Quantum computing era has changed the idea of solving hard problems efficiently through its working mechanisms and algorithms. After the invention of Shor’s and Grover’s algorithms, existing public key cryptography will not be considered a quantum-safe cryptographic mechanism. Post-quantum cryptography is a promising way to replace vulnerable public key cryptographic primitives. This paper exhibits the implementation of hash-based post-quantum digital signatures on raspberry pi 0, comparing the performance achieved in raspberry pi 3, used in many popular IoT devices.
PKCS #10 Certificate-Signing Request (CSR)

There is another cryptographic structure similar to the X.509 certificate called the PKCS #10 Certificate Signing Request (CSR). This is used to submit a public key and information to construct a Subject Distinguished Name to a Registration Authority (RA) to request creation of an X.509 digital certificate. The RA must validate all information submitted in the CSR before passing the request on to the Certification Authority (CA), who actually creates the certificate and digitally signs it. To prevent fraud, the CSR is digitally signed using the private key associated with the submitted public key. Once the CSR has been submitted, there is no need to keep it around (you can actually create a new CSR from any existing digital certificate for renewal or replacement purposes).
The Bitcoin Dilemma -The Inevitability of Increasing Energy Consumption in the Cryptocurrency Mining Arms Race

I derive the Bitcoin Dilemma which challenges the industry assertion that Proof-of-Work cryptocurrency mining energy consumption will fall with increasingly energy efficient miners. I show for the first time within a well-specified economic model, and verify with econometrics, that recent runups in mining electricity consumption has a simple and inevitable explanation. For a coin with inelastic supply and increasing demand, accelerating electricity demand is consistent with economic theory. I also demonstrate that improvements in mining efficiency, or decreases in electricity costs as sustainable energy is diverted to this industry, exacerbates acceleration of energy consumption because of a Prisoner’s Dilemma arms-race-to-the-bottom.
Multi-scale Cross-Modal Transformer Network for RGB-D Object Detection

RGB-D object detection is a fundamental yet challenging task due to the inherent difference between the RGB and Depth information. In this paper, we propose a Multi-scale Cross-modal Transformer Network (MCTNet) consisting of two well-designed components: the Multi-modal Feature Pyramid module (MFP), and the Cross-Modal Transformer (CMTrans). Specially, we introduce the MFP to enrich the high-level semantic features with geometric information and enhance low-level geometric clues with semantic features, which is demonstrated facilitating the further cross-modal feature fusion. Furthermore, we develop the CMTrans to effectively exploit the long-range attention between the enhanced RGB and depth features, enabling the network to focus on regions of interest. Extensive experiments show our MCTNet surpasses state-of-the-art detectors by 1.6% mAP on SUN RGB-D and 1.0% mAP on NYU Depth v2, which demonstrates the effectiveness of the proposed method.
CQL Grammars for Lexical and Semantic Information Extraction for Portuguese and Italian

The anchorage to real data is one of the main parameters that guarantees the quality and the coverage of lexical resources, especially in the context of specialized domains. Thus, lexicon extraction from corpora is a consensual method for building lexical resources. However, given that data validation by experts in specialized contexts is a necessary step, the automatic screening of data becomes fundamental to maximize the informational value of the interaction with experts. In this paper we present and discuss a hybrid methodology, combining linguistic and statistical approaches, focusing on the extraction of specialized lexical units and salient semantic information using CQL grammars. The proposed method involves several steps, from frequency information analyses, concordances and collocations extraction to manual revision and expert validation and encompasses the construction and application of knowledge-based patterns CQL grammars. We present two CQL grammars for lexical and semantic information extraction developed for Portuguese and Italian and evaluate results from its application to specialized corpora on Public Art domain, demonstrating the value of this method for lexicon and semantic information extraction from large data.
An Overview of Deep Learning in Medical Imaging

With every passing moment, technology offers us more effective and efficient methods of transform ing and improving the analytics of the medical world. Fields of AI like Machine learning and Deep learning have renewed the diagnostic and treatment processes by making them automated and more capable and proficient. However, there is an immense need for understanding Deep Learning models like ANNs, CNNs, RNNs, AEs, and many more being used in the field of healthcare for classification (Computer-aided diagnosis), detection (Computer-aided detection), segmentation, registration of medical images. The purpose of this study is to do a comparative analysis of commonly used deep learning models that are being used in today's modern world for health care. This paper uses conceptual research methodology to compare the pros and cons of some of the widely used Deep Learning models which will help the readers in selecting the most appropriate model as per their requirements.
A Deep Learning-Based Face Mask Detection

The unprecedented spread of coronavirus disease 2019 has resulted in a global pandemic. During the lockdown time of the COVID-19 pandemic, face mask detection is the popular topic in machine learning paradigm. Face mask detection uses deep learning algorithms to determine whether or not a person is wearing a mask. Face detection and recognition have become a highly prevalent challenge in image processing and computer vision domain. Usually, the face detection is achieved with the help of facial landmarks with the help of the popular library OpenCV for real-time computer vision. There are several convolution network methods available to build a model as accurate as possible. Convolutional neural networks are commonly employed for tasks like image processing, recognition, and detection, which demand greater computational power and time to train any model or program from the initial concept. This paper uses the concept of pre-trained network and advanced techniques like fine-tuning and feature extraction with the base pre-trained network model. Transfer learning technique (fine-tuning) with the base model of a pre-trained network makes the program problem much faster, easier, and feasible rather than training any model from the initial stage.
Fostering Judiciary Applications with New Fine-Tuned Models for Legal Named Entity Recognition in Portuguese

Artificial Intelligence applied to Law is getting attention in the community, both from the Judiciary and the lawyers, due to possible gains in procedural celerity and the automation of repetitive tasks, among other use cases. Many of its benefits can be derived from Natural Language Processing since legal proceedings are textual document-based. Named Entity Recognition is the NLP task of identifying and classifying named entities in unstructured text to help achieve these goals. In recent years, transformers emerged as the new state-of-the-art architecture for some tasks in NLP systems. NLP resources in Portuguese, such as models and datasets, are needed to allow Portuguese speaking countries to benefit from this new NLP level. This paper presents the first fine-tuned BERT models trained exclusively on Brazilian Portuguese for Legal NER. The models achieved new state-of-the-art on LeNER-Br dataset, a Portuguese NER corpus for the legal domain. We also built a prototype application for Judiciary users to evaluate how the model performed with authentic law documents. The results showed that the models were able to extract information with good quality. Both the models and the prototype application are publicly available for the community.
A semi-supervised clustering-based classification model for classifying imbalanced data streams in the presence of scarcely labelled data

Data streams are potentially infinite in length, fast changing and scarcely labelled. It is practically impossible to label all the observed instances. Online frameworks for classifying data streams are generally supervised in nature assuming the availability of labelled data and hence cannot be used for data streams. Semi-supervised learning (SSL) addresses this problem of scarcely labelled data by using large amount of unlabelled data together with labelled data to build classifiers. Data streams may also suffer from the problem of imbalanced data. Previous works in learning from data streams have analysed problems of imbalanced data. But to the best of our knowledge no work has applied semi-supervised learning approaches for classifying imbalanced data streams so far. This paper proposes a model using a semi-supervised clustering technique to classify an imbalanced data stream in the presence of scarcely labelled data. The results prove that the model outperforms many state-of-the-art techniques.
Artificial Intelligence Used in Accident Detection Using YOLO

Computer-based accident detection carried out using video surveillance which has become advantageous but a difficult job. Recent framework has been proposed to detect accidents of the vehicles. This framework utilizes YOLO for detection of vehicle accidents with better efficiency and would avoid false alarms. In this framework, detection is done based on speed and tangent of vehicle with respect to another vehicle. The purpose of this framework is for better accuracy and speed alert generation and avoiding false alarm due to various parameters that include daylight, dark visibility, weather conditions like rainstorm, hail, etc. When compared to other methodologies, YOLO has performed far better results in accident detection using the CCTV surveillance videos. This framework was relatively used for development in vehicle accident detection in present day–to-day life.
Online Digital Workshop: An Opportunity for Digital Learning in Kenya

Institutions wishing to introduce e-learning into their educational system need to consider the necessary resources and evaluate the levels of technological literacy of their teaching staff. The teachers’ preparation in the field of digital skills is essential to help students to profit their learning period. This requires previous training to allow teachers to achieve an appropriate level in terms of digital competence. This need is especially emphasized in a developing country like Kenya, where digital training for qualified teachers is still emerging. In this paper, we present the work done in a rural area of Homa Bay County (Kenya) and how that face-to-face work has needed to turn into e-learning due to the COVID-19 pandemic. Moreover, we describe the transition from temporary informal remote work to a formal approach, which will use a platform as a training, improvement, and reference plan to ensure the level of digital competence needed by the Kenyan teachers. The proposed solution is based on the Common Digital Competence Framework for Teachers, a Spanish adaptation an of the European frameworks DigComp and DigCompEdu, which allow establishing the criteria to identify digital competence needs and how to prepare digital activities for teachers.
Blockchain Technology Efficiently Managing Information and Cyber Security

Any individual who utilises public services is appropriately stressed that, in spite of organisations' earnest attempts to secure their frameworks, criminals may still access government databases and steal or manipulate records. This chapter is an attempt to analyse blockchain technology and whether this technology is effective in supporting cybersecurity. Existing research in blockchain technology tends to focus on finding what this technology is and how it can be implemented for data protection. However, there is no special study on the effectiveness of this technology, regardless of whether it actually protects population data. Therefore, the purpose of this chapter is to outline the effectiveness of blockchain technology in protecting data and the factors that may affect the effectiveness of their solutions.
Using LSTM Models on Accelerometer Data to Improve Accuracy of Tap Strap 2 Wearable Keyboard

This paper proposes the implementation of three different long short-term memory (LSTM) recurrent neural network (RNN) models to improve the accuracy of the input readings of the Tap Strap 2, a Bluetooth wearable keyboard device. The Tap Strap 2 was found in the previous study to have an undesirably low level of accuracy when it came to outputting the correct characters upon interpreting data from the built-in sensors. In response to this, raw accelerometer data was obtained from the device and used to train an LSTM model. This model would be used to not only determine which features correspond to which characters, but also would use contextual information from past characters to determine which character was most likely pressed whenever the input seems ambiguous. This paper first provides an description of the LSTM RNN used in this experiment. It then evaluates the effectiveness of the models in reducing the accuracy problems of the Tap Strap 2.
Creating Custom Widgets

While most development tasks can be solved with buttons, text editing widgets, and other components provided by PyQt, you may at some point find yourself in a situation where no single widget provides you with the tools or functionality that you need. You might even find yourself needing to use a widget you made in other GUIs and therefore need a way to easily import your custom-made widget into other applications. Thankfully, PyQt allows developers to build and import their own widgets for solving new and unforeseen tasks.
Collaborative Target Search for Multiple UAVs Based on Collaborative Particle Swarm Optimization Genetic Algorithm

In order to solve the problem of poor target and low efficiency of multi-UAV search in uncertain environment, this paper proposes a multi-UAV collaborative path search algorithm based on collaborative search and particle swarm optimization. First, a regional raster map environment and a search probability map model are established, then a rolling prediction method is adopted, and a collaborative particle swarm genetic algorithm is used to generate a prediction path. Finally, the optimal prediction search path is calculated by the fitness function. The path satisfies the minimum turning radius constraint of the UAV, while avoiding the threat area and strengthening the search for key areas. The UAV moves a waypoint along the optimal prediction search path, and enters the next search path. Before each waypoint path search, it updates the raster map probability according to Bayesian formula until the end of the task. Simulation results verify the effectiveness of the proposed algorithm.
The Sentiment Analysis and Sentiment Orientation Prediction for Hotel Based on BERT-BiLSTM Model

With development of social network, online comments have influenced various areas especially for the hotel industry. However, how to utilize text resource and even guide business decision with it becomes a problem to be solved. This paper proposes a compound model which combines BERT with BiLSTM respectively as upstream and downstream task to be trained. Then, classification results of the star hotel online reviews predicted by BERT-BiLSTM model are used to construct Sentiment Index. Finally, it can be concluded that the sentiment orientation is relevant with sales and it makes sense to predict sentiment orientation and sales. The contribution of this work is that we predict hotel sales based on the relationship between Sentiment Index and sales.
Generalized Net Model of Biometric Authentication System Based on Palm Geometry and Palm Vein Matching

The introduction of technological safeguards is more important than ever, but with the increase in networks, devices, connections and applications, this it is extremely complicated. For many people, biometric security is the answer - the technology that can serve and support all industries. The biometric systems are large-scale technology implementations that have to provide capabilities in time. The aim of the current investigation is to construct a Generalized net (GN) model of biometric authentication system based on palm geometry and palm vein matching. It presents the idea of reading biometric data through a censor and camera. In the former version, via a censor placed in the housing of the device, the palm of the user is scanned. In the latter, the data is collected via a scan of the user’s veins with the camera of the device. Both variants are run simultaneously. The aims are to ease the use of smart devices, to increase the security and guarantee the protection of personal data.