Reviewerly/exHarmony huggingface dataset:
Target detection method based on improved YOLOv3

With the continuous development of the field of computer vision and the need for the accuracy and timeliness of model recognition in practical applications, YOLOv3 draws on YOLOvl and YOLOv2. Although there are not many innovations, it maintains the speed advantage of the YOLO family and the detection accuracy is improved, especially for the detection ability of small objects [1]. However, in the three-layer feature network structure of YOLOv3, the correlation between the layers is not strong, which may lead to the situation that the information of the input image cannot be used efficiently, thus affecting the final accuracy. In this regard, this paper mainly studies the improvement method for the YOLOv3 feature pyramid network, and there are many improvement methods for this network. This paper uses residual linking and attention mechanism methods. The fusion judgment of features is realized by introducing the CBAM attention module and adding residual links in the same layer and residual links across layers. Finally, we use the VOC07+12 dataset [2] to obtain an accuracy of 79.5% under the condition of IoU=0.75 after 150 iterations, which is an improvement compared to the 73.6% accuracy of the original model.
Color-to-Gray Conversion Based on Boundary Points

Color-to-gray conversion is popularly used in black and white printing and other image processing tasks. In this paper, we propose an adaptive linear weighting algorithm based on the boundary points to realize decolorization. First, we develop a new algorithm for the boundary points detection of the color image by Sobel operator. Secondly, the linear parametric model with a discrete searching algorithm is employed to get the optimal grayscale image, which is aiming at maximally preserving the boundary points of the color image. Finally, we linearly stretch all the gray values of the optimal grayscale image into all the grayscale intensities. Experimental results show that the proposed algorithm obtains a good performance compared with several state-of-the-art methods both qualitatively and quantitatively.
Masked Face Detection with Illumination Awareness

Mask mandate has been applied in many countries in the last two years as a simple but effective way to limit the Covid-19 transmission. Besides the guidance from authorities regarding mask use in public, numerous vision-based approaches have been developed to aid with the monitoring of face mask wearing. Despite promising results have been obtained, several challenges in vision-based masked face detection still remain, primarily due to the insufficient of a quality dataset covering adequate variations in lighting conditions, object scales, mask types, or occlusion levels. In this paper, we investigate the effectiveness of a lightweight masked face detection system under different lighting conditions and the possibility of enhancing its performance with the employment of an image enhancement algorithm and an illumination awareness classifier. A dataset of human subjects with and without face masks in different lighting conditions is first introduced. An illumination awareness classifier is then trained on the collected dataset, the labeling of which is processed automatically based on the difference in detection accuracy when an image enhancement algorithm is taken into account. Experimental results have shown that the combination of the masked face detection system with the illumination awareness and an image enhancement algorithm can boost the system performance to up to 8.6%, 7.4%, and 8.5% in terms of Accuracy, F1-score, and AP-M, respectively.
A Federated Learning Framework for IoT: Application to Industry 4.0

Predictive maintenance aims to anticipate indus-trial equipment failures in order to allow early scheduling of corrective actions. Such a maintenance approach is based on a detailed analysis that takes into account the technical and contextual characteristics of the target industrial equipment. However, this analysis requires a significant period of time to collect a representative quantity of data to learn a predictive model. Federated learning (FL in short) is a promising approach that allows several participants to build collaboratively a global predictive model. This approach has been widely explored in generic loT applications and large scale architectures. However, the implementation of FL in actual environments requires to consider several issues to adapt to existing loT architectures, including the management/orchestration of the federated tasks and handling the limitations of computational resources. Indeed, most of the current research focus on the aggregation of heavy deep learning algorithms. In this paper, we propose an architecture for FL in the context of loT based on the classical 3-layer architecture standardized by ETSI <sup>1</sup><sup>1</sup>https://www.etsi.org/. We consider new features for performing federated tasks (training, aggregation and man-agement of each participant). We also propose a stacking-based aggregation method to build the global model in a cost-efficient way. We evaluate finally the performance and effectiveness of this approach on real use-case scenarios. The comparison with other models trained in a centralized way highlights the benefit of our approach.
SPRITE: A Scalable Privacy-Preserving and Verifiable Collaborative Learning for Industrial IoT

Recently collaborative learning is widely applied to model sensitive data generated in Industrial loT (1IoT). It enables a large number of devices to collectively train a global model by collaborating with a server while keeping the datasets on their respective premises. However, existing approaches are limited by high overheads and may also suffer from falsified aggregated results returned by a malicious server. Hence, we propose a Scal-able, Privacy-preserving and veRIfiable collaboraTive lEarning (SPRITE) algorithm to train linear and logistic regression models for IloT. We aim to reduce burden from resource-constrained IloT devices and trust dependence on cloud by introducing fog as a middleware. SPRITE employs threshold secret sharing to guarantee privacy-preservation and robustness to IloT device dropout whereas verifiable additive homomorphic secret sharing to ensure verifiability during model aggregation. We prove the security of SPRITE in an honest-but-curious setting where the cloud is untrustworthy. We validate SPRITE to be scalable and lightweight through theoretical overhead analysis and extensive testbed experimentation on an IloT use-case with two real-world industrial datasets. For a large-scale industrial setup, SPRITE records 65% and 55% improved performance over its competitor for linear and logistic regressions respectively while reducing communication overhead for an IloT device by 90%.
Pairing IoT Devices with Spatial Keys

Secure Device-to-Device (D2D) communication is important for the Internet-of-Things (IoT) devices. Key agreement between devices is the important first step in building a secure D2D channel. Due to the lack of third-party certification, key agreement for IoT devices has to rely on the untrusted channel between them, which makes the process vulnerable to attacks such as eavesdropping, jamming, and predictable channel attack. We solve this problem with a novel key agreement method named EchoKey, where two nearby devices can generate symmetric key independently, leveraging the ambient sound signal that can be locally collected by devices. The intuition of this idea is that the propagation delays of the ambient sounds and its echoes will carry fine-grained information about the spatial context of the receiving device, which can be transformed to a special key for that device. So, nearby devices which have similar spatial context will generate similar keys. We implement a prototype of EchoKey and evaluate its performance in resisting different attacks. The results tell that, with EchoKey, the key agreement process is even undetectable by an attacker which is only 50cm away from the pairing devices, and is thus resistant to all attacks mentioned above.
Role of Artificial Intelligence in Reshaping Retail

Artificial intelligence is a rapidly growing field of study and is reshaping retailing in important ways. In order to survive and compete in an ever-changing and diversified customer market, retailers need to more agile and innovative in terms of their value chain by adopting new technologies. AI has been considered as the one of the earmarked transformative technology among of the new technologies impacting the retail industry. The study provides insights on artificial intelligence making its way in retail industry especially in retail value chain and how retailers take benefit of AI in order to understand the customer better as customer became more empowered by smart devices which have entirely changed their expectation. This conceptual paper also discuss framework for understanding AI, significant innovations done in retails which helped them to evolve and the future of AI in retailing based on secondary data and concludes with broad suggestions for future research into AI.
Blockchain-Based Secure Menger's Authentication for Industrial IOT

Industrial Internet of Things (I-IoT) is depicted as the foremost promising evolutionary approaches for promoting industrial manufacturing condition and productivity. When the process of manufacturing is more complex, the complete needs to be provided under the administrative IoT where the devices from diverse environment intends to work over the same task which provides higher privacy and security among the devices during communication process. Some prevailing authentication approaches outcomes in providing third-party security and deals with the key management issues. Therefore, privacy and security needs to be given higher concentration as it remains unsolved. In this research, a novel and an efficient security mechanism known as Menger's authentication assisted with block chain is anticipated to establish trust mechanism among various domains. Here, security is adopted with authentication process. The menger's authentication mechanism is provided to maintain device privacy that realizes authentication among the connected devices. However, the key distribution among the connected parties' remains negotiated with proper communication channel. Here, the simulation is done in MATLAB environment and the outcomes show better outcomes during the authentication process.
Advance Eye Blink Sensor Controller Based Wheel Chair

These days one can perceive that physically challenged people are glimpsed in a great deals of activities more or less the same as normal human beings. But to uplift them in various activities and to perform their daily activities one should always stand with them. Empirically, a person always being behind them will not be possible. This paper crips at restoring flairs and performance of daily activities by physically crippled people without holding others hand by executing a eye blink sensor wheel chair. As the wheel chair is blocked out with Eye blink technology, for the movement of the physically disabled people, it grips less power of the operator to run without any strain. This system is designed in such manner that it counts the number of eye blinks and based on that it will move forward, Left and Right direction. Thus by implementing the eye blink sensor wheel chair in the life's of physically disabled people reduces the physical and mental strain.
Security System using Open CV based Facial Recognition

In this article, an ongoing face-acknowledgement-based security framework for homes and workplaces has been introduced. It has been created utilizing a Local Binary Pattern Histogram (LBPH) calculation. Facial Recognition has been adequately used to foster frameworks with the end goal of safety in workplaces and homes, just as monitoring crimes through ID by these reconnaissance frameworks. Variations in posture and points of human faces, like light, have been a significant limiting factor for most face recognition calculations; however, the LBPH calculation accounts for both of these variations. This paper features the benefit of this calculation over the others and furthermore shares the reasons behind leaning toward its OpenCV execution over MATLAB.
Digital Signature with Message Security Process

This is the time of internet, and we are communicating our confidential data over internet in daily life. So, it is necessary to check the authenticity in communication to stop non-repudiation, of the sender. We are using the digital signature for stopping the non-repudiation. There are many versions of digital signature are available in the market. But in every algorithm, we are sending the original message and the digest message to the receiver. Hence, there is no security applied on the original message. In this paper we are proposed an algorithm which can secure the original and its integrity. In this paper we are using the RSA algorithm as the encryption and decryption algorithm, and SHA256 algorithm for making the hash.
Improvement to Recommendation system using Hybrid techniques

Currently, recommendation systems are a common tool for providing individualized recommendations and item information to users. For personalization in the recommendation system, there are a variety of strategies that can be used. To improve system performance and offset the shortcomings of individual recommendation strategies, a hybrid recommender system integrates two or even more recommendation techniques. The demand to summarize all of the knowledge on actual methods and algorithms utilized in hybrid recommended systems necessitates the need for a systematic review in the domain. These materials will be employed to aid in the development of an auto-switching hybrid recommender system. In the content-based filtering technique, the algorithm is based on the contents of items and the collaborative filtering technique algorithm combines the relationship between user and item. Both of the approaches of recommendation system are suffers from some limitations, this is a big issue to predict better recommendations to the user. Hybrid systems are introduced to overcome the main limitations of both techniques. These systems are made with a combination of content-based and collaborative filtering techniques and have advantages of both techniques. With the use of hybrid systems, the quality of recommendations is improved. Hybrid recommendation systems use previous data of a user to find his/her interest and then they target the set of an adjacent user which is similar with that user and according to adjacent user recommend things to the user. Hybrid systems offer the items that share the common things that a user rated highly (Content-based filtering) and make suggestions by comparing the interest of a similar user (Collaborative filtering).
A Novel Framework For Data Visualization Via Power System Monitoring Control And Protection Based On Wireless Communication

The importance of Power System Measurement in the design, Making preparations and operating electrical power transmission networks are becoming more common. Based on an energy management system, this study discusses and recommends data visualisation for real-time power system operation. Because real-time power system operation involves the processing of a massive volume of online data, operators require simple data visualisation to extract meaningful information from the large data. In particular, in an emergency situation, data visualisation can aid operators in dealing with the problem swiftly and efficiently. As a result, the goal of this research is to enhance output displays of real-time power system functioning by displaying on-line huge data. Through this research, we will be able to provide a better visualisation approach for real-time power system operation, with highly legible output displays and comprehensible information. Volatility in power transmission and distribution compels power grid management to rely more heavily on communications networks to monitor and run their systems. Malevolent actors will have a larger assault surface as a result of the increased communication. Indeed, cyber attacks on power grids have historically resulted, widespread blackouts. In this research, we look at the internet architecture underlying power grids so order to uncover the fundamental issues power grid cyber security.
Application of cryptographic methods to blockchain technology to increase data reliability

Controlling technological processes using digital technologies affects minimizing costs, both in terms of time and economy, and consequently dramatically increases labor productivity. Optimization of automation of controlled processes based on digital technologies is one of the main directions of development of information technologies. Increasing the dependability of the data of controlled mechanisms is one of the main directions of research today. In the modern era of information and globalization, a sharp growth in the set of data leads to the evolution of processes for storing, processing, and transferring data in network technologies. As a result, the problem of data reliability in these networked systems is growing. Today, improving data reliability based on information security is an urgent problem. Blockchain technology, which appeared at the beginning of this century, represents a new approach to securing data in networked systems. The priority of blockchain technology is the presence of a special cryptographic chain in its structure. This study addressed the issue of improving cryptographic techniques and cryptographic strength analysis based on blockchain mechanisms. The modern RSA6 cryptographic method has been improved based on the blockchain engine and the D_ RSA6 algorithm has been developed. The crypto security of the D_RSA6 algorithm has been analyzed and experimented with. It is proved that the developed algorithm D_RSA6 is the optimal method for increasing the reliability of network systems data. The algorithm, based on the integration of blockchain technology with cryptographic methods, leads to a 2-fold increase in cryptocurrency tolerance. This allows you to increase the reliability of the data.
Evaluation of Block-Chain Transaction Accuracy using Neural Network Model

Block chain is known as distributed ledger technology (DLT). A distributed ledger, near real-time updates, chronological and time-stamped, cryptographically sealed, and programmable contracts are the core aspects of Block chain Technology. Brain cells, known as biological neurons, are used to implement deep learning in Neural Networks. A consequence of this is the implementation of deep learning by means of neural networks with several hidden layers. A lot of the recent work in financial transactions has focused on the intersection between block chain and artificial intelligence (AI). Artificial neural networks of this type are included. AI can help to lower the carbon impact of block chain technology. The cost and energy consumption of mining operations will be reduced significantly if AI machines replace human miners.
Face Mask Detection Using OpenCv and Machine Learning

In this pandemic corona virus put a huge impact on our daily life and It also put a huge impact on global trade. Protect face has become a new trend and It has become normal this day. Now these day or in future many people will required to were mask in order to protect themselves and also to protect nearby people and also to the surrounding. Face detection has become important in protecting worldwide population. So in this project we are making AI machine to recognize a people who is wearing a mask or not. It will helps us to protect the environment from spreading the virus. To build this project we need the help machine learning, deep learning and neural network which will help us to achieve the aim of this project. The tolls we required to achieve this project is jupyter notebook and we have to install the numpy, opencv, tensorflow and numpy and learning tool. This approach will help us to detect the person is wear mask in the picture as well as in the picture. It will also recognize and identify a moving face and mask.
Ensemble Classifier with Hybrid Feature Transformation for High Dimensional Data in Healthcare

In recent years, the growth of high-dimensional data applications has posed a significant challenge to data analytics, particularly high-dimensional data classification. To solve this problem, efficient feature selection and extraction approaches are paramount to the success of classification analyses. The dimensionality problem of high-dimensional data, which comprises a large number of variables and complicated data matrices, is a big barrier in the health care research domains. We propose an Ensemble Classifier with Hybrid Feature Transformation for handling extremely complex dimensional data. This is proven to be efficient for selecting and extracting features from a very large dataset in a classification task. The proposed approach has been applied to the classification of an unbalanced dataset of gene expression RNA-Seq data in five types of cancer. In the hybrid feature transformation approach, the original datasets are transformed in two stages. The first phase selects the highest score features from the original dataset, which are then sent to the second phase of feature transformation. As part of the second phase, features are transformed into a new set of features. Finally, a dataset with six features is applied to the ensemble classifier which achieves 99.96&#x0025; accuracy by averaging the probability systems of the groups of classifiers. The findings clearly show that the proposed approach outperforms the existing methods for high-dimensional data classification.
Towards Bilingual Word Embedding Models for Engineering

Word embeddings represent the semantic meanings of words in high-dimensional vector space. Because of this capability, word embeddings could be used in a wide range of Natural Language Processing (NLP) tasks. While domain-specific monolingual word embeddings are common in literature, domain-specific bilingual word embeddings are uncommon. In general, large text corpora are required for training high quality word embeddings. Furthermore, training domain-specific word embeddings necessitates the use of source texts from the relevant domain. To train bilingual domain-specific word embeddings, the domain-specific texts must also be available in two different languages. In this paper, we use a large dataset of engineering-related articles in German and English to train bilingual engineering-specific word embedding models using different approaches. We will evaluate our trained models, identify the most promising approach, and demonstrate that the best performing one is very capable of representing semantic relationships between engineering-specific words and mapping languages in a shared vector space. Moreover, we show that the additional use of an engineering-specific learning dictionary can improve the quality of bilingual engineering-specific word embeddings.