Reviewerly/exHarmony huggingface dataset:
A Novel STAP Method with Enhanced Degrees of Freedom

In this paper, a new space-time adaptive processing (STAP) method based on improved nested arrays and pulses configurations is proposed.Specifically, we first decompose the sensor array into two uniform linear arrays (ULAs) plus a separate sensor, similarly for pulse trains.Then, the original received signals from the physical array and pulse trains are introduced into the virtual domain, where the virtual clutter plus noise covariance matrix (CNCM) estimation is performed.Since the system has more virtual sensors and pulses from the perspective of virtual domain, the degrees of freedom (DOF) capability is effectively enhanced to improve the angle and Doppler resolution of radar.With the spatial-temporal smoothing technique, the STAP filter is designed by reconstructing the CNCM and virtual signal steering vector.Simulation results validate the effectiveness and superiority of the proposed algorithm.
Noise Reduction in Images Using Autoencoders

Abstract: Ideally, the signals which are pure can exist only on paper. As there are some techniques for denoising the provided signal up to some degree, so procedure during that time it is important that such techniques must be reconcilable with the most of the devices. This article describes a for denoising with the help of an autoencoder using image processing technique and algorithms which are based on deep learning. With the aid of autoencoders, noise reduction is not accomplished using a conventional method in which the output signal is essentially the same signal that was used as an input previously. Here the main focus remains originality as the autoencoder follows a back propagation process It is one of the approaches that focuses on the techniques described in this article are interchangeable. i.e., Working for any signal and having, reliability, efficient Ness and compatibility with more devices.
De-noising of partial discharge ultrasonic signal of insulation bar in large motor based on GMC-wavelet

Abstract In view of the bad operation environment of large motor, which often suffers from various strong noise interference, the partial discharge ultrasonic signal is often annihilated, which makes it difficult to detect and analyse. A de-noising method based on generalized minimax concavity (GMC) and wavelet for partial discharge (PD) ultrasonic signal is proposed. GMC is used to enhance the sparsity of PD ultrasonic signal and eliminate the high-frequency noise signal at the same time. Then the residual high-frequency sparse noise and low-frequency noise of the former are de-noised again combined with wavelet. Finally, the signal is reconstructed to achieve the purpose of de-noising the original PD ultrasonic signal with noise. Compared with ℓ 1 -norm method, GMC method, wavelet method and ℓ 1 -norm-wavelet method, the simulation results show that based on time domain analysis, the de-noising effect of the proposed method is obviously better than the other four methods. The SNR and MSE of the former are better than those of the latter. In addition, the insulation bar discharge model of large motor is constructed to obtain the actual PD ultrasonic signal, which further verifies its effectiveness, and its de-noising effect is also better than the four methods. This method can not only enhance the sparsity of the target signal and improve the estimation accuracy, but also achieve the de-noising effect, while retaining the effective information of PD ultrasonic signal characteristics. This method can provide new ideas for other types of PD signal de-noising, and lay the foundation for later feature analysis.
Ensemble Extreme Learning Algorithms for Alzheimer’s Disease Detection

Alzheimer's disease has proven to be the major cause of dementia in adults, making its early detection an important research goal.We have used Ensemble ELMs (Extreme Learning Models) on the OASIS (Open Access Series of Imaging Studies) data set for Alzheimer's detection.We have explored various single layered light-weight ELM networks.This is an extension of the conference paper submitted on implementation of various ELMs to study the difference in the timing of execution for classification of Alzheimer's Disease (AD) Data.We have implemented various ensemble ELMs like Ridge, Bagging, Boosting and Negative correlation ELMs and a comparison of their performance on the same data set is provided.
Application of Artificial Intelligence to improve Customer Understanding: Transformer based Topic Modeling in practice

Since the past few years, so-called pre-trained Language Models (PLM) are considered state-of-the-art in the field of Natural Language Processing (NLP) and are thus experiencing widespread and successful application. In addition to traditional supervised Machine Learning (ML) tasks such as spam email or customer churn classification, this technology opens up advanced approaches to unsupervised learning and data analytics in general. One of particular interest is the automatic identification of latent topics within a large collection of texts, also known as Topic Modeling (TM). Such modelling approaches offer great potential, especially for industrial environments as well as the consumer goods market, to explore increasing amounts of data from diverse and constantly growing data sources. As a holistic concept, it can be utilized in a highly targeted and efficient manner for applications through an optimized combination of Artificial Intelligence (AI) and Cloud Computing (CC) systems.
Blockchain Enabled Architecture with Selective Consensus Mechanisms for IoT Based Saffron-Agri Value Chain

The Internet of Things (IoT) is the backbone behind numerous smart and automated applications in the modern era by providing seamless connectivity and information retrieval among the physical and virtual objects. IoT networks are resource constraint platforms hence prone to security and privacy challenges. Blockchain technology comes to the forefront to improvise the security, privacy and less dependency on the third party centralized servers. There exists a rich amount of work with numerous practical applications by fusing IoT and blockchain. In blockchain technology, the consensus mechanisms are considered to be the driving force in its implementation. In this paper, we propose a simplified blockchain based internet of things (BIoT) architecture for resource constrained IoT devices with selective consensus mechanisms based on the scale of IoT networks. We have selectively highlighted some of the important consensus algorithms which are favourable for the IoT networks. We have tailored the blockchain framework in a manner that suits to the resource constrained IoT networks. To evaluate our design, we implemented a prototype leveraging the blockchain and IoT network. The preliminary results suggest that the proposed system incorporating supply chain management of Saffron agri-value chain outperforms the existing systems. Furthermore, we have carried out a detailed case study on the cultivation and marketing strategies for maintaining the originality and transparency starting from farmer-to-consumer as saffron-Agri value chain.&#x0D;
Vendor-Agnostic Reconfiguration of Kubernetes Clusters in Cloud Federations

Kubernetes (K8s) defines standardized APIs for container-based cluster orchestration so it becomes possible for application managers to deploy their applications in a unified manner across different cloud providers. A practical problem is however feature incompatibility between different K8s vendors, who offer commercial K8s products based on the open-source K8s distribution. A large number of documented features in this open-source distribution are optional features that are turned off by default, but can be activated by setting specific combinations of parameters and plug-in components in configuration manifests for the K8s control plane and worker node agents. However, none of these configuration manifests are standardized, giving K8s vendors the freedom to hide the manifests behind a single, more restricted, and proprietary customization interface. Therefore some optional K8s features cannot be activated consistently across K8s vendors and applications that require these features cannot be run on those vendors. In this paper we present a unified, vendor-agnostic feature management approach that bypasses the proprietary customization interface of K8s vendors in order to consistently activate optional K8s features across a federation of clusters hosted by different Kubernetes vendors. We describe vendor-agnostic reconfiguration tactics that are already applied in industry and cover a wide range of optional K8s features. Based on these tactics, we design and implement an autonomic controller for declarative feature compatibility management across a cluster federation. We found that the features configured through our vendor-agnostic approach have no impact on application performance when compared with a cluster where the features are configured using the configuration manifests of the open-source K8s distribution. Moreover, the maximum time to complete reconfiguration of a single feature is within 100 seconds, which is 6 times faster than using proprietary customization interfaces of mainstream K8s vendors such as Google Kubernetes Engine. However, there is a non-negligible disruption to running applications when performing the reconfiguration; this disruption impact does not appear using the proprietary customization methods of the K8s vendors. Therefore, our approach is best applied in the following three use cases: (i) when starting up new K8s clusters, (ii) when optional K8s features of existing clusters must be activated as quickly as possibly and temporary disruption to running applications can be tolerated or (iii) when proprietary customization interfaces do not allow to activate the desired optional feature.
Automatic Generation of Literary Sentences in French

In this paper, we introduce a model for the automatic generation of literary sentences in French. It is based on algorithms that we have previously used to generate sentences in Spanish and Portuguese, and on a new corpus consisting of literary texts in French that we have constructed, called [FR]. Our automatic text generation algorithm combines language models, shallow parsing and deep learning, artificial neural networks. We have also proposed and implemented a manual evaluation protocol to assess the quality of the artificial sentences generated by our algorithm, by testing if they fulfill four simple criteria. We have obtained encouraging results from the evaluators for most of the desired features of our artificially generated sentences.
The Effect of Picture Teaching Media to the Ability of Grade 7th Students in English Vocabulary Mastery of SMP Yayasan Perguruan Keluarga (YPK) Pematangsiantar

English is an international language that use by many people in this world. Human used English to make communication in daily life. It can help human to make connection with other people, nation country in this world to get information. As we know in learning English there are four skills, they are: speaking, listening, writing, and reading. Vocabulary is also an essential skill for learning to read, speak, write and listen. Without sufficient vocabulary, people cannot communicate and express their feeling both in form of spoken and written effectively. Nowadays, the students must improve their vocabulary because in their school, they are not only learning vocabulary in English lesson, but also in other books, there are also some English vocabularies that must be known by students. In improving student vocabulary, the students need some method or technique. The used picture of media will be needed to help the students in teaching-learning vocabulary. Picture is one of visual material in teaching that well established. This researcher experience at learning practice program in SMP YPK Pematangsiantar, many of students were lack of vocabularies.The students had many problems in mastering English words and this situation made them to be lazy to study English. One of the strategies can be used to solve the problems in teaching English is using media. As what have been mentioned previously, the purpose of this research was to find out the effect by uding Picture media on students’ ability in english vocabulary at grade seventh SMP YPK. Researcher chose two classes as the sample that divided into two groups are VII-6 as Experimental Group and VII-4 as Control Group. From all the analyzed data, it is found that there was a significant using Picture media on students’ ability in English Vocabulary at grade seventh SMP YPK Pematangsiantar. It shows that the result of t-test was higher than t-table (5.02&gt;1.67), therefore the null hypothesis (Ho) is rejected and alternative hypothesis (Ha) was accepted.Based on the result of the research, it was concluded that by using Picture media on students’ ability in English Vocabulary is most effective.
INAV-Informative Net Asset Value

The project INAV- Informative Net Asset Value is used to reduce efforts used by the land buyers and land sellers by directing the land buyers directly with the land sellers who are interested to sell their properties without interfering with the mediators, who plays a role in between buyer and seller. The main aim of our research paper is to connect land or flat buyers and sellers from different place, by using this application they can find the location and the land for selling and also there will be the contact details of seller with that they can directly contact them. The customer can search the location of the land which they need using the google maps which is in build into the application. The project uses the Android Studio software using the java language and MySQL Server to store data.
Hybrid Cryptography: Secure File Storage on using AES, RC6 and Blow Fish Algorithm

In this era, cloud computing is used in various fields like industry, military, college, etc. for various services and storage of huge amount of data. Data stored in this cloud can be accessed or retrieved on the users request without direct access to the server computer. But the major concern regarding storage of data online that is on the cloud is the Security. This Security concern can be solved using various ways, the most commonly used techniques are cryptography and steganography. But sometimes a single technique or algorithm alone cannot provide high-level security. So we have introduces a new security mechanism that uses a combination of multiple cryptographic algorithms of symmetric key and steganography. In this proposed system, RC6 (Rivest Cipher 6), AES (Advanced Encryption Standard) and Blowfish algorithms are used to provide security to data. All the algorithms use 128-bit keys. LSB steganography technique is used to securely store the key information. Key information will contain the information regarding the encrypted part of the file, the algorithm and the key for the algorithm. File during encryption is split into three parts. These individual parts of the file will be encrypted using different encryption algorithm simultaneously with the help of multithreading technique. The key information is inserted into an image using the LSB technique. Our methodology guarantees better security and protection of customer data by storing encrypted data on a single cloud server, using AES, RC6 and Blowfish algorithm.
How Do University Students Get Relevant Information?

The web and learning have evolved parallel as technological changes have influenced teaching and learning processes. In this study, I intend to extend this parallel with two other dimensions, namely, human 1.0-3.0 and influencer 1.0-3.0. The concepts are closely related to how the online world became popular abroad and what their impact is on learning and education. Thus, the question, “what is the significance of social media, and of its latest, most popular actors, of the “work” of influencers (which can be interpreted as fake news) in the lives of students in higher education?”, is also a very pertinent issue to touch on. Its involvement in our lives is ever growing and very often influences our media literacy. This gives us even more reason to look into social media’s impact. However, our main goal is to find answers to the following questions:• What opportunities does the digital toolkit give to students? What kind of digital literacy do students think they need to thrive in the job market?• To what extent does the ICT literacy of pedagogical students differ from that of other students (lawyer, economics, doctor, technical)? What form of cognitive development is used for lifelong learning?• To what extent are students’ IT literacy influenced by cultural, material, and family capital?• How is information acquisition implemented in education? How conscious is the use of media among university students, and what is their critical attitude?• To what extent does online media penetrate the medium of formal-informal and non-formal learning? How does the influencer activity of professional opinion leaders help students to think critically and thoughtfully? The sample of the survey is made up of students from the University of Debrecen. From the results we can see, that university students behave differently in the online space, on social media platforms and on messengers than they would elsewhere, thus this affects how they get information. The current situation, the pandemic, clearly demonstrates that advanced digital competence is essential for a confident presence in the online space and advanced critical thinking. Problems of digital inequality and division have surfaced, and the constructed reality mediated by the media is becoming increasingly distorted. During this period, the relationship between the media and media consumers has changed greatly, and the interaction has intensified.
APPetite for Something New: The Katona József Library’s Mobile Application

The Katona József Library of Bács-Kiskun County has always placed great emphasis on reaching out to all age groups. In order to achieve our goal, we need to be informed and up-to-date with the latest trends and to be present on as many virtual platforms as possible. That’s why, at the beginning of 2021, a few enthusiastic librarians of our institution thought that a library mobile app could be the next important milestone in our continuous development and renewal. All this experimentation and testing has finally paid off: the Katona József Library’s mobile app is available on the Google Play Store from Autumn 2021. It’s no secret that our primary goal with our entirely self-developed, in-house app was to target young people, perhaps the hardest-to-reach age group for libraries. So, in addition to creating content specifically for teens (book reviews, games), we also wanted the look and feel of the app to be coherent and dynamic. Of course, we did not want the app to replace our library’s website, but to provide content that could be enjoyed on a smaller screen. As the application is easy to navigate and use, so that older people who are generally less familiar with the digital world will have no problems using it. For those who are a little apprehensive, one of our tutorials promoting the app will give them all the help they need to become a confident user. So what does our library app do? In addition to the Programme Guide, there is a dedicated menu with a regularly updated document guide. And with our ever-expanding thematic video selection, you can watch videos of our library programmes at any time. Of course, a library mobile app would be useless without a Catalogue menu, so webOPAC is also just a click away. With our Virtual Tour, which is unique among national library mobile apps, you can even take a look around our library from the comfort of your own home, sitting in your armchair. Games were also included in the app. We have thought of games for all ages, with four to four games for children, teens and adults. Puzzles are mainly related to the world of books, but there are also some puzzles on local history. We hope that our innovation will live up to our expectations and will appeal to a wider audience than just young people. Our aim is to ensure that our application remains a popular and constantly renewing service in the long term.
Multi-Lingual DALL-E Storytime

While recent advancements in artificial intelligence (AI) language models demonstrate cutting-edge performance when working with English texts, equivalent models do not exist in other languages or do not reach the same performance level. This undesired effect of AI advancements increases the gap between access to new technology from different populations across the world. This unsought bias mainly discriminates against individuals whose English skills are less developed, e.g., non-English speakers children. Following significant advancements in AI research in recent years, OpenAI has recently presented DALL-E: a powerful tool for creating images based on English text prompts. While DALL-E is a promising tool for many applications, its decreased performance when given input in a different language, limits its audience and deepens the gap between populations. An additional limitation of the current DALL-E model is that it only allows for the creation of a few images in response to a given input prompt, rather than a series of consecutive coherent frames that tell a story or describe a process that changes over time. Here, we present an easy-to-use automatic DALL-E storytelling framework that leverages the existing DALL-E model to enable fast and coherent visualizations of non-English songs and stories, pushing the limit of the one-step-at-a-time option DALL-E currently offers. We show that our framework is able to effectively visualize stories from non-English texts and portray the changes in the plot over time. It is also able to create a narrative and maintain interpretable changes in the description across frames. Additionally, our framework offers users the ability to specify constraints on the story elements, such as a specific location or context, and to maintain a consistent style throughout the visualization.
Langevin algorithms for Markovian Neural Networks and Deep Stochastic control

Stochastic Gradient Descent Langevin Dynamics (SGLD) algorithms, which add noise to the classic gradient descent, are known to improve the training of neural networks in some cases where the neural network is very deep. In this paper we study the possibilities of training acceleration for the numerical resolution of stochastic control problems through gradient descent, where the control is parametrized by a neural network. If the control is applied at many discretization times then solving the stochastic control problem reduces to minimizing the loss of a very deep neural network. We numerically show that Langevin algorithms improve the training on various stochastic control problems like hedging and resource management, and for different choices of gradient descent methods.