Reviewerly/exHarmony huggingface dataset:
Analyzing Comments on Social Media with XG Boost Mechanism

This article looks at analyzing the sentiment of a tweet and the methods used to do it. This includes detecting emotions such as negative and positive, and various character traits such as emoticons, quotes, hashtags, mentions, etc. The Word2Vec model is used to improve performance and activate words in vectors. Bag of Words uses machine learning algorithms like Random Forest and Naive Bayes. Finally, the XG Boost model is used with advanced parameters to determine the most effective ranking model for sentiment analysis.
Retraction: Survey on video compression techniques for efficient transmission (J. Phys.: Conf. Ser. 1916 012211)

This article (and all articles in the proceedings volume relating to the same conference) has been retracted by IOP Publishing following an extensive investigation in line with the COPE guidelines. This investigation has uncovered evidence of systematic manipulation of the publication process and considerable citation manipulation. IOP Publishing respectfully requests that readers consider all work within this volume potentially unreliable, as the volume has not been through a credible peer review process. IOP Publishing regrets that our usual quality checks did not identify these issues before publication, and have since put additional measures in place to try to prevent these issues from reoccurring. IOP Publishing wishes to credit anonymous whistleblowers and the Problematic Paper Screener [1] for bringing some of the above issues to our attention, prompting us to investigate further. [1] Cabanac G, Labbé C and Magazinov A 2021 arXiv: 2107.06751v1 Retraction published: 23 February 2022
Social Network Images Vulnerability Detection Using Graph Neural Network (GNN)

The empirical study involves detecting vulnerable images in social media. Use a vulnerable image detection dataset with a 78% data accuracy rate using a graph neural network. The study investigates that sharing vulnerable pictures on social networks might cause cyberbullying or stalking victims leads to many stressful events. The inexcusable behavior by many people is encompassing in the digital world. This phenomenon leads to threats of violence, slander, or even leakage of personal images may be included. The study associates vulnerability detection with the pictures posted on social media. In this way, the people of the society will be able to identify the vulnerable images and help to build social awareness. The ability of GNN to model the dependence between nodes in a network allows for a breakthrough in graph analysis research. A graph autoencoder framework can help GNNs solve the network embedding challenge. The approach associate's construction of a custom dataset with two classes, involve Vulnerable and Nonvulnerable images. We declare image size and batch size. After creating the GNN model data is trained using 10 epochs and then plot the accuracy and loss curve. The significance of the accuracy rate reached a satisfactory level. For the statistical approach, a confusion matrix is constructed to compare the actual and prediction data this helps to understand the performance of the proposed model. Graph neural network is very effective to classify images and other things. The findings constructed a Graph Neural Networks (GNNs) is an effective framework for representation learning of graphs.
Sanjay: A Remote Vulnerability Scanning Framework

Many techniques are laid out that try to protect the web application from unauthorized access with the growing concern for web security. To uncover the possible inconsistencies that can damage the application, new approaches have been introduced. Vulnerability scanning is the most widely used technique. By the term vulnerability, we meant the possible device flaws that make it vulnerable to attack. Scanning these vulnerabilities in the system provides a means of identifying and developing new strategies to protect the system from the risk of damage. This paper focuses on the use of a remote vulnerability scanning framework- “SANJAY” which completely and comprehensively scans the given target most effectively and easily with the help of integrated tools like SLACK and VPS.
Developing material for English for Holiday program

An English program like English For Holiday (EFH), is concerned with helping children to learn English. In relation to English Education Department of Universitas Ahmad Dahlan, the department has employed an EFH program to help children with English learning during their school break. To accomplish the continuous improvement of the program, it needs research related to it, as these kinds of research provides meaningful data and product, which are important for the improvement of the program’s quality. Generally, the ultimate aim of the research is to contribute to the establishment of a better EFH program, especially of English Education Department, Universitas Ahmad Dahlan. Specifically, this research aims to develop syllabus and material for EFH program of EED UAD. This research started in May and was complete in November 2020 at English Education Department of Universitas Ahmad Dahlan (UAD). It utilized Research and Development (R&amp;D) type with ADDIE model initiated by McGriff (2000) which endeavors a development process encompassing five major stages i.e. analyze, design, develop, implement, and evaluation. The research results show that the EFH material is developed to fulfil the children’s needs of material for EFH program. The developed product offers an alternative solution to fill the children’s needs. Based on the evaluation, it is found out that the developed material is considered very good. Through this material, children participating in EFH program can learn English in an enjoyable way. Although the major target is to improve children’s speaking skill, through this material, children can learn and develop their other skills i.e. listening reading and writing as the developed material is written in an integrated way involving all four skills of English.
NEPG: Partitioning Large-Scale Power-Law Graphs

We propose Neighbor Expansion on power-law graph(NEPG), a distributed graph partitioning method based on a specific power-law graph that offers both good scalability and high partitioning quality. NEPG is based on a heuristic method, Neighbor Expansion, which constructs the different partitions and greedily expands from vertices selected randomly. NEPG improves the partitioning quality by selecting the vertices according to the properties of the power-law graph. We put forward theoretical proof that NEPG can reach the higher upper bound in partitioning quality. The empirical evaluation demonstrates that compared with the state-of-the-art distributed graph partitioning algorithms, NEPG significantly improved partitioning quality while reducing the graph construction time. The performance evaluation demonstrates that the time efficiency of the proposed method outperforms the existing algorithms.
GIS Based Index Overlay Method in Targeting Heavy Mineral Deposits, Southern Kerala Coast, India

Owing to the strategic importance in defence and the other industrial applications, the heavy minerals have attracted the attention of the geoscientists since long time. But they have been using mostly the traditional techniques for a long time for targeting the heavy mineral deposits. Later, the scientists have started employing modern techniques like scintillometer based field surveys, remote sensing and the laboratory based sedimentological and heavy mineral studies. But since the traditional techniques are more cumbersome and would be very difficult to cover the vast length of the Indian coasts of over 7500 km, faster and effective techniques are necessary. So, the information value method was accomplished in the present study to demonstrate the targeting of heavy minerals in parts of Kerala extending from 76° 41' - 08° 53' in NNW and 77° 13' - 08° 17' in SSE (1,811sqkm). In this study five geosystem maps, viz; Lithology, Lineament Frequency, Lineament Density, Lineament Intersection density and Geomorphology were prepared using the raw and digitally processed LANDSAT ETM 7 and IRS LISS IV FCC data sets. These geosystem maps were firstly prepared as vector GIS layers and then converted into raster maps using ARC GIS software with the pixel size of 100sq.m and the total pixels of 1,64,358. On the basis of the contribution of the above five main geosystem variables towards the heavy mineral potentials, weightages were assigned (Wi) to each of them. Similarly, depending upon the heavy mineral possibilities of the sub variables of the above five main geosystem variables, scores were assigned (Si) to each sub variables of the 5 main geosystem variables. Then, the Wi values were multiplied with the corresponding Si values of each of the sub variables of the 5 main geosystem variables and those were considered as final weightages (HMP-Heavy Mineral potentials) and assigned the same to the corresponding pixels of each sub variables of the five main variables. Followingly, the each WiSi weighted values of the 1,64,358 pixels of the lithology main geosystem variable were added with corresponding pixels of the remaining 4 main geosystem variables using ADD function menu of ARC GIS and integrated raster GIS database was generated with all the 1,64,358pixels having the cumulative WiSi values (ΣWiSi). Then, on the dynamic range of the ΣWiSi values, these pixels were classified into Very High, High, Moderate, Low and Very Low zones of heavy mineral concentrations. This was validated with heavy mineral weight percentage data derived from the field samples collected from the study area during survey. This study was basically undertaken to replicate it for the rapid appraisals of the probable heavy mineral target areas along the Indian coasts. However this can be replicated anywhere.
PFA: Performance and Fairness-Aware LLC Partitioning Method

In server for cluster, the number of running programs is increasing. The benefit of consolidating multiple programs is good for server utilization, but it also leads to the program’s performance degradation. Severe performance degradation can result in significant losses. Therefore, it is essential to divide the shared resource to support consolidation. Our experiment showed that even some shared resources such as CPU cores and memory had been divided, the performance of programs still drop down significantly compared to the program running alone, then we found out the primary reason was the contention for LLC. In this paper, we proposed the LLC partitioning method to improve the performance for consolidation programs. We classify the LLC usage type of the program by analyzing the LLC behavior, then allocate reasonable LLC ways according to the LLC usage type. Meanwhile, we monitor the program’s performance in real-time and allocate the LLC ways dynamically. The experiment found that compared with the default LLC allocation method, our method reduced the performance loss by an average of 6.73% and improved the fairness by 0.03. Compared with the CPA method, our method reduced the performance loss by an average of 4.86%.
Retraction: A survey on intrusion detection system and prerequisite demands in IoT networks (J. Phys.: Conf. Ser. 1916 012179)

This article (and all articles in the proceedings volume relating to the same conference) has been retracted by IOP Publishing following an extensive investigation in line with the COPE guidelines. This investigation has uncovered evidence of systematic manipulation of the publication process and considerable citation manipulation. IOP Publishing respectfully requests that readers consider all work within this volume potentially unreliable, as the volume has not been through a credible peer review process. IOP Publishing regrets that our usual quality checks did not identify these issues before publication, and have since put additional measures in place to try to prevent these issues from reoccurring. IOP Publishing wishes to credit anonymous whistleblowers and the Problematic Paper Screener [1] for bringing some of the above issues to our attention, prompting us to investigate further. [1] Cabanac G, Labbé C and Magazinov A 2021 arXiv: 2107.06751v1 Retraction published: 23 February 2022
Blockchain: A Security Solution for the IoT?

Whenever there are strong statements from people that the Internet of Things (“IoT”) causes more evil than good, I wonder if the individuals that belong to such school of thought are inadvertently dismissing the opportunity to continuously extend the capability of an intelligent platform that arguably solves some of the world's greatest problems, made possible using billions of integrated smart devices connectivity. It is my view that the IoT like every other great invention can generally be used for both good and bad reasons, however, careful consideration regarding it implementation and use needs to be put in place. Technological advancements have reached a point where almost everything is wired up or connected wirelessly to the internet and the IoT has created opportunities for people to leverage on the benefits that comes with the interconnectivity of smart devices to collect data and make intelligent decisions. As evident in recent technological innovations, the advent of the IoT has led to significant progress in the field of science and technology. Today, the world has seen the widespread adoption of IP-based networking, breakthroughs in artificial intelligence, development of robotics capability, and the growth of cloud computing. These technological developments and many others are bringing about significant revolutionary changes, suggesting the IoT is as transformative as the industrial revolution. As appealing as these advancements may seem, there are many questions challenging the overall security and privacy aspects of the IoT. This challenge is amplified by other considerations such as the rapid spread of similar IoT devices, the ability of some devices to automatically connect to other devices, and the possibility of deploying these devices in unsecure environments. Even though many security considerations have been put in place to guard against malicious activities targeted against the proper use of the IoT, reoccurring high-profile incidents where a single IoT device has been used to infiltrate attacks to larger network persists. Addressing the security concerns in an IoT environment could be made more effective by the implementation of a security model that has capabilities to negate any central attack to a larger IoT network even when a single point of entry is compromised. Although not without its own challenges, blockchain seems to have this capability, and has arguably drawn attention as the next generation technology that suits the end-to-end security requirements within an IoT environment.
AMP Task Effectiveness

This chapter details the process to analyze the AMP Task Effectiveness and the implementation of the results.
Multifunctional Rendering Swarm Robot

Multifunctional Rendering Swarm robot make a huge impact in this present busy world . This robot works perfectly as an surveillance robot as well as an personal assisting robot for household purposes and most of the work that carried on is swarm mechanism of the personal assisting robots. This have dual mechanism for moment such as mecanum wheel as well as Klann mechanism to the six legs to overcome the hard terrain landscapes in particular house surroundings , with and arm mechanism to pick and place of assigned objects. This robot can be controlled by voice as well as limited hand gestures to perform tasks which use text to speech and speech to text, Convolution Neural Networking (CNN) respectively . This robot have a 3600 night vision camera for surveillance at night times through OpenCV . This will be doing parallel jobs such as talking to you as an assistant , cleaning house , and many more . as the advancement in the world is not up-to the real time applications, this helps in interfacing future and present situations to get updated. As swarm robots, this will be updating its status to the fellow robots and assigning particular task to divide and do faster which depends on deep learning and artificial intelligence. All this can be acknowledged and examined by any nominee of the user who are at distant places in case of any emergency and respond as a speech modulation talk back , digital display available for commands display or any work been processed, time consuming smart work is been done through this robot. This is also equipped with the sanitizer for the people hygiene and safety. This robots can also be used in military, schools, software industries and many more industries.
Synthetic Face Image Generation Using Deep Learning

Designers often find it difficult in creating posters that best suit their purpose. Many posters are being removed from social platforms like Twitter, YouTube, and Facebook due to copyright claims, privacy issues, and so on. So, this paper aims to design a realistic face image generator that generates facial images of people who do not exist in the real world. This paper focuses on the concepts of the deep convolutional generative adversarial networks principle in this paper (DCGAN). These generated images can help designers in designing these posters. Generative adversarial networks (GANs) are computational frameworks that pit two neural networks against each other (thus the “adversarial”) to produce fresh data. They are commonly utilized in the creation of images, videos, and voices.
Graph Homomorphism Features: Why Not Sample?

Recent research in the domain of computed graph embeddings has shown that graph homomorphism numbers constitute expressive features that are well-suited for machine learning tasks such as graph classification. In this work-in-progress paper, we attempt to make this methodology scalable by obtaining additive approximations to graph homomorphism densities via a simple sampling algorithm. We show in experiments that these approximate homomorphism densities perform as well as homomorphism numbers on standard graph classification datasets. Moreover, we show that, unlike algorithms that compute homomorphism numbers, our sampling algorithm is highly scalable to larger graphs.
A Video-Based Uncertainty Technique for Human Action Recognition—A Deep Learning Approach

The Human action recognition (HAR) has diverse application in many research areas and also a great challenge in the ground of computer perception and deep learning. Deep learning is a method which uses complex models to solve tasks difficult to implement by hand, like in computer vision. The purpose of this research work is to design deep learning-based uncertainty techniques that recognize various action recognitions (running, walking) of different human in videos with better accuracy. Bayesian method used here to maintain a general structure to evaluate unpredictability of human actions. A well-calibrated prediction in regression tasks is our ultimate desire (provide a credible interval of 95%). A simple procedure has been suggested for that, when applied to Bayesian models. This model produces accuracy of 92% for running and 96% for walking.
Design and Research of ADHOC Based Wireless Network System

Fiasco watching is a champion the diverse maximum hard software in some distance flung uncommonly delegated frameworks as putting in established order based totally frameworks are neither feasible nor suitable in that conditions. In the direction of disaster, setbacks commonly take secures in packs in some nearby sheltered zones (as an instance, some university systems, and quick tents in a few terrific us of a regions, and so forth.). Thusly, the post catastrophe help assignments and route of benefits are regularly guard pushed. Thusly, dynamic after and engrossing sanctuary data (concerning open useful useful resource, required beneficial useful resource, reputation of setbacks and volunteers at the sheltered residence, and so on.) at a manage station is essential for great need examination from the piecewise positive points of view at every safe haven. Anyhow, in a regular fiasco circumstance, correspondence shape gets irritated snappy and the cell accessibility may be pitifully open in multiple areas of the catastrophe struck locale. As such, control correspondence of safe haven information to control station is restrained. The objective of this assignment is to have a examine using reducing aspect cellular cellular phone based keen framework for following and acclimatizing cozy houses' information and supporting the correspondence with manage station. In this splendid situation, the power of mitigation government/salvage car/police van with phones assume a easy element to make the specified ground- breaking correspondence established order. This text is an overview on the drift reputation and heading of studies on impromptu networking. We type the on-going exploration and diagram the actual challenges which want to be tackled before throughout the board association of the technology is practicable. The perspectives exhibited by way of manner of Perkins in [1] are applied as a motive, that is then supplemented with communicate and references to the latest publications. distinctive methodologies and conventions had been proposed to deal with in particular appointed structures management problems, and several institutionalization efforts are underneath route in the internet Engineering task pressure, just as scholarly and mechanical studies of difference projects.
CREATING COMMON DIGITAL SPACE OF EDUCATION SYSTEM IN THE SVERDLOVSK REGION ON THE EXAMPLE OF SECONDARY VOCATIONAL EDUCATION

The article describes the current state of the education system, an integral part of which is the digital transformation, that includes all forms of interaction between the participants of the educational process. The article presents the experience of creating a common digital space on the example of secondary vocational education in the Sverdlovsk Region.
Interpolation Problem on Outlier Contaminated Seismogram Using Extreme Learning Machine

In this work, we present a weighted l1 norm-based Extreme Learning Machine (ELM), namely, enhanced Regularized ELM (eRELM) for regression problems with outliers contaminated data. The enhancement includes the introduction of a weighting scheme based on l1 norm to a regularization term in the optimization problem. Seismogram is typically recorded with high frequency. Therefore, it requires a large amount of storage. Interpolation allows the seismogram to be stored with the same quality with a smaller number of samples. Our proposed method shows its robustness on interpolation of outlier contaminated seismogram outperforming other ELM based regression techniques by achieving 0.001245 of root mean squared error.