Reviewerly/exHarmony huggingface dataset:
A Novel Traffic Management Protocol Design for Avoiding Data Loss through Artificial Intelligence

Multiple sensors are installed and wirelessly networked in Wireless Sensor Networks, which are occurrence communication information available instantly and securely of many sensors nodes. This enables data retrieval from surveillance devices. Whenever an event occurs in a wireless sensor network, the information related with that event must be transmitted to the data collection node and  due to high data traffic, the sink node serves as the network's barrier. Loss of data occurs as a consequence of congestion, and it might be vital data. Artificial Intelligence enabled Neural Networks Design (NN) based Congestion Controller method is presented to attain this goal. The Wireless Sensor Network communication is controlled by the waveform convolution operation, which activates the proposed model. Using this present methodology of AINNCCM method, the up-stream traﬃc ratio is measured to prevent overcrowding in the future and the downstream traffic figure is projected, with all three aspects working together to improve Quality of Service (QoS) by enhancing the ratio of Packet-Loss, throughput ratio and Overall Network . This AINNCCM method avoids congestion issues and network traffic by using AI logic and also improving the overall network Quality-of-Service.
Artificial Intelligence in Oncology- Technologies being used and scope in India

Cancer is a top cause of mortality in the world. Late detection and remissions are the biggest threats facing patients. Early detection of the same and a prediction of the prognosis and recurrence is one of the best chances we have against the fight with the disease. It has been found that artificial intelligence (AI) could be more accurate than even multivariate and statistical analysis which are themselves more accurate than empirical analysis. There has been a recent surge in literature pertaining to the use of machine learning and deep learning models for the diagnosis and prognosis of cancers. This article comprises a look at the AI technologies being used with respect to three of the most common cancers in the world - breast cancer, lung cancer, and gastric cancer. It also further delves the status of AI for Oncology in developing nations like India and the opportunities and the challenges facing an wide adoption of AI in such nations.
Perancangan Aplikasi Biografi Tokoh Penerima Penghargaan Nobel Dengan Menerapkan Algoritma Horspool Boyer Moore

The Nobel Prize recipient is very important to know because it makes it easier for the public to see a list of several figures and even the information presented because this award is considered the highest award, while a biography can be said to be a story or information about the life of a figure who has received the highest award so that the public can Know about the character who received the Nobel Prize. String matching uses the Horspool Boyer Moore algorithm to match the pattern to be searched, so that the match in the pattern selection can be seen. It is hoped that Boyer Moore's Horspool Algorithm can complete the match in the search for the award recipient. So that it can help the public in the search for figures who receive the Nobel Prize.
Data-Efficient Training of High-Resolution Images in Medical Domain

The ability of Graphical Processor Units (GPUs) to quickly train dataand compute-intensive deep networks has led to rapid advancements across diverse domains such as robotics, medical imaging and autonomous driving.However, memory constraints with GPU-based training for memory-intensive deep networks have forced researchers to adopt various workarounds: 1) resize the input image, 2) divide input image into smaller patches, or use smaller batch-sizes in order to fit both the model and batch training data into GPU memory.While these alternatives perform well when dealing with natural images, they suffer from 1) loss of highresolution information, 2) loss of global context and 3) sub-optimal batch sizes.Such issues will likely to become more pressing for domains like medical imaging, where data is scarce and images are often of very high resolution with subtle features.Therefore, in this paper, we demonstrate that training can be made more data-efficient by using a distributed training setup with high-resolution images and larger effective batch sizes, with batches being distributed across multiple nodes.The distributed GPU training framework, which partitions the data and only shares model parameters across different GPUs, gets around the memory constraints of single GPU training.We conduct a study in which experiments are performed for different image resolutions (ranging from 112 × 112 to 1024 × 1024) and different number of images per class to determine the effect of image resolutions on network performance.We illustrate our findings on two medical imaging datasets namely, SD-198 skin-lesion and NIH Chest X-rays.
High-speed Search for Optimal Operation Parameters of Air Conditioning System in Data Center by Using Regression Prediction and Deep Reinforcement Learning on CFD Simulation

Demand for data centers has been increasing year by year, and so has the urgency to reduce their power consumption. In particular, air conditioning power consumption is still in strong demand for reduction. For this purpose, setting the optimum operation parameters of air conditioning is crucial for creating desired environmental state of data center. In this paper, we propose a procedure to search operation parameters such as operating conditions of air conditioning and transmittance of meshes installed in the aisles in a data center. Specifically, the proposed procedure consists of regression prediction by deep neural network (DNN) and search by means of deep reinforcement learning (DRL). Regression prediction by DNN using Computational Fluid Dynamics (CFD), based on the airflow distribution results of low mesh number, determines that of high mesh number in about 20 seconds. As a result, we established a search procedure for optimal operation parameters that realize the desired environmental state in less than 4 hours for a data center that accommodates around 500 servers. The demand of the server is also taken into account when dynamically controlling the operation parameters. These results indicate that the proposed procedure exhibits promising potential for a practical tool not only for designing a data center but also for efficiently managing environmental state control in case of module expansion and contraction, as well as real-time operation.
Computational Intelligence Techniques for Recommendation System in Big Data

The quantity of the information in the domain of e-commerce and mobile commerce is growing every day. The growing data is heterogeneous and unstructured that can be called as the Big Data. This big data can be analyzed and used to create recommendations. When users buy products or do any kind of shopping, they tend to like similar kind of items. This purchasing behaviour forms a pattern which can be analyzed to unveil new range of products suiting their tastes. Recommender systems or recommendation systems generate and maintain profiles of the customers on which they use profile exploitation techniques like information filtering methods, user profile item matching techniques, and profile adaption to forecast whether a client would be in the vein of a particular set of products or not. Computational intelligence approaches increase recommendation accuracy while still addressing the problems like scalability, cold start, etc. The most commonly used algorithms for personalized recommendations are collaborative filtering (CF) and content-based filtering. The main problem of CF or content-based filtering is its scalability, i.e., calculation cost exists far above the ground if the dataset is hefty. These techniques used previously for recommender system do not work successfully on big data. Hence, more advanced techniques for handling big data are required to be incorporated. In this chapter, the basics of the big data and recommendation system are discussed followed by the discussion of the techniques to filter the big data in order to get the desired output. The evaluation of the recommendation system is done on the basis of different parameters along with the comparison between the content-based filtering and CF. Few case studies are also covered in the chapter for better understanding of recommendation systems followed by proposing a framework for the book recommendation system. The chapter supplies a wide-ranging clarification of existing recommender systems with Big Data implementation by implementing the movie recommendation system.
Towards Enabling Deep Learning-Based Question-Answering for 3D Lidar Point Clouds

Remote sensing lidar point cloud dataset embeds inherent 3D topological, topographical and complex geometrical information which possess immense potential in applications involving machine-understandable 3D perception. The lidar point clouds are unstructured, unlike images, and hence are challenging to process. In our work, we are exploring the possibility of deep learning-based question-answering on the lidar 3D point clouds. We are proposing a deep CNN-RNN parallel architecture to learn lidar point cloud features and word embedding from the questions and fuse them to form a feature mapping for generating answers. We have restricted our experiments for the urban domain and present preliminary results of binary question-answering (yes/no) using the urban lidar point clouds based on the perplexity, edit distance, evaluation loss, and sequence accuracy as the performance metrics. Our proposed hypothesis of lidar question-answering is the first attempt, to the best of our knowledge, and we envisage that our novel work could be a foundation in using lidar point clouds for enhanced 3D perception in an urban environment. We envisage that our proposed lidar question-answering could be extended for machine comprehension-based applications such as rendering lidar scene descriptions and content-based 3D scene retrieval.
Range of web Crawling from HTTP Parse and HTML Requests as Static Digreph and Web Pages

This paper presents the Shark calculation utilizing the refined form of calculation, PageRank look and Shark look, one of the primary energetic web calculations. Shark search has a comprehensive database mapping system that makes it easy for users to customize online maps to suit their needs. Preparatory tests appear significant improvements over the initial calculation of page rank. Recently, the internet has grown a lot. By using different hypertext connections, users can find their tools. This Internet use led to web crawlers being invented. Web-based crawlers are full text search engines that support web browsers. Such web crawlers can be used for further study. For example, the broken information can be utilized in complex systems to discover lost connections, population identification. In this paper we discussed web crew members: their design, styles and different challenges when the Web crawlers are used by search engines.
HPI Future SOC Lab – Proceedings 2017

The “HPI Future SOC Lab” is a cooperation of the Hasso Plattner Institute (HPI) and industry partners. Its mission is to enable and promote exchange and interaction between the research community and the industry partners. The HPI Future SOC Lab provides researchers with free of charge access to a complete infrastructure of state of the art hard and software. This infrastructure includes components, which might be too expensive for an ordinary research environment, such as servers with up to 64 cores and 2 TB main memory. The offerings address researchers particularly from but not limited to the areas of computer science and business information systems. Main areas of research include cloud computing, parallelization, and In-Memory technologies. This technical report presents results of research projects executed in 2017. Selected projects have presented their results on April 25th and November 15th 2017 at the Future SOC Lab Day events.
Training and Education during the Pandemic Crisis

The COVID-19 pandemic is deeply redesigning the approach and practices in the field of training targeting lawenforcement agencies (LEAs). The effects are interesting and multifaceted and are expected to persist on themedium and long term. In fact, a new room for debating on positive and negative experiences, advantages anddifficulties, emerging needs and requirements, pioneering ideas and ground-breaking initiatives has opened. The H2020 ANITA project wants to contribute to this debate by providing details about how it has managed to reorganise its training activities from in-person to remote sessions, the perception and feedback from the participating LEAs and the criteria it is using to design curricula for public and private stakeholders on online illegal trafficking.It is emerging that digitalised and remote trainings addressed to law enforcement agencies require both a newand innovative didactic concept and a ground-breaking learning paradigm. In-person lessons are essential fortheir wide-ranging benefits, but the COVID-19 pandemic is showing how distance learning could be also revolutionary and powerful. However, the equilibrium among the three main components represented by educating, discussing and exchanging should be established. In this framework, the hypothesis on if and how innovative knowledge-based and user-centred technologies – like the ANITA platform, which is investigation-based – could be used also to further build capacities, training and curricula for LEAs is explored. These tools are likely to have a great potential for generating contents to be used for multi-stakeholders training and capacity building, mutual understanding and exchange of information and practices.
Enhanced Long Short-Term Memory (ELSTM) Model for Sentiment Analysis

Sentiment analysis is used to embed an extensive collection of reviews and predicts people's opinion towards a particular topic, which is helpful for decision-makers. Machine learning and deep learning are standard techniques, which make the process of sentiment analysis simpler and popular. In this research, deep learning is used to analyze the sentiments of people. It has an ability to perform automatic feature extraction, which provides better performance, a more vibrant appearance, and more reliable results than conventional feature-based techniques. Traditional approaches were based on complicated manual feature extractions that were not able to provide reliable results. Therefore, the presented study aimed to improve the performance of the deep learning approach by combining automatic feature extraction with manual feature extraction techniques. The enhanced ELSTM model is proposed with hyper-parameter tuning in previous Long Short-Term Memory (LSTM) to get better results. Based on the results, a novel model of sentiment analysis and novel algorithm are proposed to set the benchmark in the field of textual classification and to describe the procedure of the developed model, respectively. The results of the ELSTM model are presented by training and testing accuracy curve. Finally, a comparative study confirms the best performance of the proposed ELSTM model.
Multi-Level Feature Fusion Based Transfer Learning for Person Re-Identification

Most of the currently known methods treat person re-identification task as classification problem and used commonly neural networks. However, these methods used only high-level convolutional feature or to express the feature representation of pedestrians. Moreover, the current data sets for person reidentification is relatively small. Under the limitation of the number of training set, deep convolutional networks are difficult to train adequately. Therefore, it is very worthwhile to introduce auxiliary data sets to help training. In order to solve this problem, this paper propose a novel method of deep transfer learning, and combines the comparison model with the classification model and multi-level fusion of the convolution features on the basis of transfer learning. In a multi-layers convolutional network, the characteristics of each layer of network are the dimensionality reduction of the previous layer of results, but the information of multi-level features is not only inclusive, but also has certain complementarity. We can using the information gap of different layers of convolutional neural networks to extract a better feature expression. Finally, the algorithm proposed in this paper is fully tested on four data sets (VIPeR, CUHK01, GRID and PRID450S). The obtained re-identification results prove the effectiveness of the algorithm.
A Study on the Improvement Direction of the EBSe English Textbook Program.

This study aims to analyze the current use and efficacy of the EBSe English Textbook program to facilitate the continued development and improvement of the program. The survey respondents were students, parents, and teachers who have used the EBSe English Textbook program for teaching and learning English. This survey sought to gain feedback on three main categories: current usage, satisfaction and efficacy, and suggestions for improvements to the EBSe English Textbook program. A total of 1,947 responses to the survey were collected. The analysis of the survey data reveals that many students, parents, and teachers used the EBSe English Textbook program. In addition, most of users who have used the program were highly satisfied with the effectiveness of the EBSe English Textbook program. The efficacy of the program was shown to be positive in the following four major objectives: showing complementary effect on school English education, forming a regular habit of English learning, increasing interest in learning of English, and reducing the cost of private education. But EBSe program was still insufficient to solve the problem of private education expenses, and there were many requests for improvement and update. Respondents showed great interest in the EBSe English Textbook program and suggested several areas of improvement. Based on these findings, we suggest further improvement and development of the EBSe English Textbook program to facilitate an brighter future for the program.
A semantic web services discovery approach integrating multiple similarity measures and k-means clustering

Web Service (WS) discovery is an essential task for implementing complex applications in a Service Oriented Architecture (SOA), such as selecting, composing, and providing services. This task is limited semantically in the incorporation of the customer’s request and the web services. Furthermore, applying suitable similarity methods for the increasing number of WSs is more relevant for efficient web service discovery. To overcome these limitations, we propose a new approach for web service discovery integrating multiple similarity measures and K-Means clustering. The approach enables more accurate services appropriate to the customer's request by calculating different similarity scores between the customer's request and the web services. The global semantic similarity is determined by applying K-Means clustering using the obtained similarity scores. The experimental results demonstrated that the proposed semantic web service discovery approach outperforms the state-of-the approaches in terms of precision (98%), recall (95%), and F-measure (96%). The proposed approach is efficiently designed to support and facilitate the selection and composition of web services phases in complex applications.
Visualizing Data, Dynamics, and Risk

Objective: Develop an awareness of the variety of both simple and more nuanced data visualization tactics and tools available. Appreciate how to develop visual renderings that best meet the needs of data examination and storytelling for the audience in question.
A Translator for Indian sign boards to English using Tesseract and seq2seq model

Language translator for Indian language to English have been developed and it have proven to a challenging domain due to large combination of character in india scripts such as Tamil, Kannada and Hindi. In this paper we propose a system which captures Indian printed character and translates it into English, we have discussed the various method and machine learning model that was used to build this system with a accuracy of 87%.In addition the paper also includes the project .A Generic sensor Frame for the webOSplatform.A centralized daemon to control and access all the sensors that are connected to the webOS device.
7. Quantum inspired simulated annealing technique for automatic clustering

Clustering is a popular data mining tool whose aim is to divide a data set into a number of groups or clusters. The aim of this work is to develop a quantum inspired algorithm which is capable of finding the optimal number of clusters in an image data set automatically. The article aims to attention on the quantum inspired automatic clustering technique based on ameta-heuristic algorithm named simulated annealing. The quality of this clustering algorithm has been measured by two separate fitness functions named DB index and I index. A comparison has been made between the quantum inspired algorithm with its classical counterparts on the basis of the mean of the fitness, standard deviation, standard error and computational time. Finally, the proof of the superiority of the proposed technique over its classical counterparts has been made by the statistical superiority test named t-test. The proposed technique has been used to cluster four publicly available real life image data sets and four Berkeley image data sets of different dimensions.
An Analysis on the Students’ Speaking Skill in Online Class

Analysis of students 'speaking skills in this study aims to determine how students' speaking skills using WhatsApp. The Students have difficulty expressing ideas of the students having difficulty speaking. This research was conducted in class X MIPA and IIS in 2019/2020. As a research subject, online learning involved 40 class X students. The design and method used in this study was descriptive research. The author uses the test as an instrument in this study. The results of this study indicate that the mean score of the students is 70.3 which indicates that the students' speaking skills are good. Students can speak English and understand what is being said, Students also feel confident when speaking English. Meanwhile 11 or 25.1% of students did not like learning English, especially speaking skills. Students did not speak English and did not understand what was said. This means that the speaking ability of students in this class is good.
60 Years of Pride, Dedication on Display at MAF During Hurricane Ida

On Sunday, Aug. 29, 2021 – 16 years to the day of Hurricane Katrina, one of the most devastating storms to make landfall along the Louisiana/Mississippi Gulf Coast – emergency crews at NASA’s Michoud Assembly Facility in New Orleans found themselves in familiar territory.
Ein künstliches Gedächtnis in N Schritten

62 |00:00:00 Start00:01:16 RNN-Architekturen00:02:29 Many-to-Many00:02:51 One-to-Many00:03:06 Many-to-One00:03:16 Long-Short-Term Memory Networks00:03:36 LSTM Zelle00:04:01 Input, Forget und Output Gates00:04:23 Gestapelte LSTM-Netze