Reviewerly/exHarmony huggingface dataset:
PointTransformer: Encoding Human Local Features for Small Target Detection

The improvement of small target detection and obscuration handling is the key problem to be solved in the object detection task. In the field operation of chemical plant, due to the occlusion of construction workers and the long distance of surveillance shooting, it often leads to the phenomenon of missed detection. Most of the existing work uses multiple feature fusion strategies to extract different levels of features and then aggregate them into global features, which does not utilize local features and makes it difficult to improve the performance of small target detection. To address this issue, this paper introduces Point Transformer, a transformer encoder, as the core backbone of the object detection framework that first uses a priori information of human skeletal points to obtain local features and then uses both self-attention and cross-attention mechanisms to reconstruct the local features corresponding to each key point. In addition, since the target to be detected is highly correlated with the position of human skeletal points, to further boost Point Transformer's performance, a learnable positional encoding method is proposed by us to highlight the position characteristics of each skeletal point. The proposed model is evaluated on the dataset of field operation in a chemical plant. The results are significantly better than the classical algorithms. It also outperforms state-of-the-art by 12 percent of map points in the small target detection task.
A generalized linear joint trained framework for semi-supervised learning of sparse features

The elastic-net is among the most widely used types of regularization algorithms, commonly associated with the problem of supervised generalized linear model estimation via penalized maximum likelihood. Its nice properties originate from a combination of $\ell_1$ and $\ell_2$ norms, which endow this method with the ability to select variables taking into account the correlations between them. In the last few years, semi-supervised approaches, that use both labeled and unlabeled data, have become an important component in the statistical research. Despite this interest, however, few researches have investigated semi-supervised elastic-net extensions. This paper introduces a novel solution for semi-supervised learning of sparse features in the context of generalized linear model estimation: the generalized semi-supervised elastic-net (s2net), which extends the supervised elastic-net method, with a general mathematical formulation that covers, but is not limited to, both regression and classification problems. We develop a flexible and fast implementation for s2net in R, and its advantages are illustrated using both real and synthetic data sets.
Is Artificial Intelligence a Threat or a Benefit?

Artificial Intelligence AI refers to systems or machines that mimic human intelligence to perform tasks and can improve themselves based on the information they gather. Creating intelligent machines is not without concerns and ethical issues, including how safe they can be and how to ensure that they do not harm humans and other morally relevant organisms. and how they may differ from humans in ethical issues related to privacy and confidentiality. Also, as AI progress accelerates, more robots and autonomous systems will be created and replace human labour. On the other hand, AI has its advantages. It has many applications that lead humanity towards making this planet a better place to live. For every technology to survive, its advantages must outweigh its disadvantages. Most of the disadvantages of AIs are related to the ethics and concerns that AIs bring to human society. For many, this may sound intimidating. Some say it will save humanity, others say it will destroy us. Either way, if that happens, the world will change forever. This paper discusses about the details, Advantages and Disadvantages of AI.
Produce feldspar mineral map using Spectral Angle Mapper (SAM) technique by hyperspectral image analysis

The analysis of the hyperspectral image is an important method used in mineral detection. A map of the Feldspar mineral will produced for the study area, which lies to the west of the holy province of Karbala and limited in image that download from USGS website (type hyperspectral image from EO-1 satellite) , by using Spectral Angle Mapper (SAM) technique in the Erdas software, which is based on calculating the spectral reflection curve of any point on the image and the spectral reflection curve of feldspar mineral that is available in the spectral library of the software. For the purpose of verifying the results of the image analysis, ground truth ( field work ) was used by collecting 12 soil samples and finding the spectral curve for each sample by the spectral analysis device (ASD) and the result show the match between image and ground, where produced Feldspar mineral map.
Rancang Bangun Sistem Ujian Online SMP Berbasis Website dengan Menggunakan Metode SDLC pada SMPN 6 Kota Cirebon

SMP NEGERI 6 Cirebon City is a school that has an A accreditation score and gets the title as a National Standard School. However, there are still problems including when the exam is still visible cheating, the lack of types of questions and seating positions that allow students to work together, the values ??obtained from each lesson are still manually inputted from each class by each eye teacher. lessons, and there are teachers who feel tired to count and match the answers for each subject being tested. The method used is the Systems Development Life Cycle (SDLC), using the earliest SDLC approach used for software development, namely WaterFall. The results of the study indicate that this online-based exam system can help improve the effectiveness of online exams at SMP Negeri 6 Cirebon City for the better.
Sistem Informasi Akademik Berbasis Website untuk Meningkatkan Prestasi Belajar Siswa MAN 3 Cirebon di Masa Pandemi Covid-19

Parents need information about children's activities and learning outcomes in school. Academic information system is one way that information about the results of learning activities can be delivered more easily and quickly by the school and can be well received by parents as guardians of students. Therefore, an academic information system is designed that is able to provide information about student learning outcomes and activities so that parents can still supervise learning activities in schools. The system is expected to be able to be a material for evaluating children on the results of their learning in school so that children are able to become outstanding students. The purpose of this study is so that parents can find out the results of children's learning, namely grades and other learning activities in school. This research uses a qualitative approach where data collection is obtained from interviews, observations, and documentation. In addition, this system is designed using the waterfall method with stages of analysis, design or design, implementation, verification, and maintenance. The result of the research is the application of a web-based academic information system capable of being a means of parental supervision of the results of children's learning activities so that it has an impact on increasing student achievement.
Sistem Informasi Geografis Panduan Wisata Berbasis Android di Kota Cirebon

City guide is very important for tourists and nomads in cities that are a source of tourist destinations as well as a source of destination for immigrants both for education or for work, one of which is the city of Cirebon. Based on data collected from the Cirebon City Culture and Tourism Office, in 2019 there were 987,588 tourist visits in Cirebon City. The problem that occurs is that many of the immigrants or tourists are not informed about the places in the city of Cirebon. In this research, a tourist guide geographic information system will be made regarding the information and location of places in the city of Cirebon which can be accessed through their android devices. The method used in the development of this system is the waterfall method. The waterfall method was chosen because in the development process it has a clear work sequence and is also easy to manage because of the simple model. The stages of system development in the waterfall method are Requirements Analysis, System Design, Implementation, Testing and Maintenance. The design of the city guide information system was built using the Java programming language using the Android Studio Integrated Development Environment (IDE), SQL database, and using the Google Maps API feature. The result is an android-based city guide application that contains information and location of places in the city of Cirebon such as tourist attractions, hotels, shopping centers, health services and others. This system will make it easier for tourists because there are recommendations about destinations that can be visited while in the city of Cirebon. It is hoped that the system created can become an information that can support and facilitate the activities of immigrants or tourists while in the city of Cirebon.
Analisis Usabilitas Pengguna E-learning Menggunakan HCI di SMKAl-Musyawirin

Currently Al-Musyawirin Vocational School is implementing the web e-learning method by utilizing internet technology to attract students' interest in learning. However, the lack of student understanding in learning to use web e-learning and the unattractive e-learning design causes obstacles to online learning. Therefore, it is necessary to design an e-learning web learning system using HCI (Human Computer Interaction) so that it can be more optimal and not boring. The research method used for this research is the survey method from SUS consisting of 10 questions, each of which has 5 points with a Likert scale as a response. The results of this study obtained an SUS score of 77, which was obtained by the teacher and a score of 68.9 which was obtained by students at SMK Al-Musyawirin. On the Grade Scale, the two SUS scores can be seen to be in Grade C (okay) which indicates the score is classified as normal, neither above nor below the average.
RWYI: Reading What You Are Interested in with a Learning-Based Text Interactive System

As computer vision and human-computer interaction technology mature, vision-based auxiliary text reading has become the mainstream method to optimize the learning and reading experience. Most of the existing auxiliary text reading methods use scene text recognition combined with human gesture recognition to complete the task in multiple stages. However, these methods cannot accurately and effectively extract the textual information that readers are interested in complex and varied reading scenarios. To improve the text reading experience, we propose a human-centered fast auxiliary text reading method. It utilizes a hand-text hybrid object detection (HTD) model to instantly locate text of interest to readers, a font-consistent prior text image superresolution network (FCSRN) to recover low-resolution text images to enhance the accuracy of text recognition, and a convolutional recurrent neural network (CRNN) text recognition operator to obtain the content of the text, that is, interesting to readers. To verify the effectiveness of the proposed method, we tested the performance of the text localization module on a homemade HTD dataset and the performance of the FCSRN on the public text image superresolution dataset called TextZoom. Quantitative experiments on the overall performance of the fast auxiliary reading system, called reading what you are interested in (RWYI), were designed. The experiments indicate that the proposed method can meet the needs of human-computer interactive auxiliary reading in text reading scenarios and optimize the reading experience.
Optimization Design of Street Public Space Layout on Account of Internet of Things and Deep Learning

With the gradual improvement of material living standards, people have higher and higher requirements for the livability of modern cities. As an important component of urban construction, the optimal layout of street public space has gradually received more and more attention. In the development stage of the new era, it is very important to improve the image of the city by transforming the street construction, optimizing the urban public space, and building a place full of vitality. Implementing the people-oriented connotation and improving the green travel components in the city, such as encouraging walking and increasing bicycles, are of great significance for optimizing the street public space. This article studies the relevant content of the optimization design of street public space layout based on the Internet of Things and deep learning and expounds the solutions for the optimization design of street public space layout based on the Internet of Things and deep learning. Design research provides cutting-edge scientific theories and evidence. This paper uses data to prove that based on the Internet of Things and deep learning technology, the optimized design of street public space layout has increased the latter's recognition among residents by an average of 21.7%. The designed model has both space utilization and environmental protection. Very good results have been obtained.
Crıme Data Analysıs Usıng Machıne Learnıng Technıques

Individuals, authorities, and governments have all placed a high focus on reducing crime. This research work investigates a few data mining techniques and machine learning algorithms that are used to mine crime datasets and methods, or techniques utilized in crime data analysis, forecasting and prediction. Crime estimating is a way of trying to be mining out and decreasing the upcoming crimes. This leads in predicting the future crime that will have chances to happen. In addition, a formal introduction is made of crime in India. In today’s world, where crime is on the rise, it is critical to be able to predict forthcoming crimes with greater precision. India's official criminal code is the Indian Penal Code (IPC). It is a comprehensive code which focuses to cover all characteristic of criminal law. Here, the importance of data mining techniques and machine learning algorithms in resolving crime problems by uncovering hidden criminal trends cannot be overstated. As a result, the goal of this research project may be to examine and discuss alternative strategies for predicting crime and provides reasonable exploration of data mining techniques and machine learning algorithms for estimation and prediction of upcoming crime.
Malicious URL Detection Using Machine Learning Techniques

Cyber security is a very important requirement for users. With the rise in Internet usage in recent years, cyber security has become a serious concern for computer systems. When a user accesses a malicious Web site, it initiates a malicious behavior that has been pre-programmed. As a result, there are numerous methods for locating potentially hazardous URLs on the Internet. Traditionally, detection was based heavily on the usage of blacklists. Blacklists, on the other hand, are not exhaustive and cannot detect newly created harmful URLs. Recently, machine learning methods have received a lot of importance as a way to improve the majority of malicious URL detectors. The main goal of this research is to compile a list of significant features that can be utilized to detect and classify the majority of malicious URLs. To increase the effectiveness of classifiers for detecting malicious URLs, this study recommends utilizing host-based and lexical aspects of the URLs. Malicious and benign URLs were classified using machine learning classifiers such as AdaBoost and Random Forest algorithms. The experiment shows that Random Forest performs really well when checked using voting classifier on AdaBoost and Random Forest Algorithms. The Random Forest achieves about 99% accuracy.
Effects of neighborhood-based collaborative filtering parameters on their blockbuster bias performances

Collaborative filtering algorithms are efficient tools for providing recommendations with reasonable accuracy performances to individuals. However, the previous research has realized that these algorithms are undesirably biased towards blockbuster items. i.e., both popular and highly-liked items, in their recommendations, resulting in recommendation lists dominated by such blockbuster items. As one most prominent types of collaborative filtering approaches, neighborhood-based algorithms aim to produce recommendations based on neighborhoods constructed based on similarities between users or items. Therefore, the utilized similarity function and the size of the neighborhoods are critical parameters on their recommendation performances. This study considers three well-known similarity functions, i.e., Pearson, Cosine, and Mean Squared Difference, and varying neighborhood sizes and observes how they affect the algorithms’ blockbuster bias and accuracy performances. The extensive experiments conducted on two benchmark data collections conclude that as the size of neighborhoods decreases, these algorithms generally become more vulnerable to blockbuster bias while their accuracy increases. The experimental works also show that using the Cosine metric is superior to other similarity functions in producing recommendations where blockbuster bias is treated more; however, it leads to having unqualified recommendations in terms of predictive accuracy as they are usually conflicting goals.
Creating a System of IOE-PDPTA to Bridge Tourists and Poster Designers: An Application of IOE in Personalized Poster Design

Current applications of the Internet of Everything (IOE) tend to ignore the components of people, processes, and data. In the current study, we propose applying the IOE to design, the core of which comprises people, processes, and data. Evidence of the positive effects of the IOE on design indirectly confirms the value of these three components. The selected case study was poster designs advertising personalized travel. We reported the process of how the system was created, followed by an actual simulation with the personalized poster design for the tourism promotion for the city of Tainan, Taiwan. This study created an IOE framework and applied relevant algorithms to collect data to target design issues. Data were collected, converted, and cleaned through multiple physical objects, internet resources, and output keywords, which were then provided to three designers with various backgrounds and styles. Posters were designed based on the keywords identified. As the targeted audience, 124 respondents were recruited to participate in a survey on the finished posters. Analysis of this feedback demonstrated the validity of the proposed framework.
Review of Various Applications of Machine Learning

The usage of machine learning proposes an intelligent diagnostic study program that supports a web-based learning model aiming to develop students' ability to integrate information by allowing them to select study topics of interest, find information on those topics by searching online for related reading courseware and discussing what they have learned with their peers. The suggested learning program can effectively help students improve their knowledge while browsing online using the "webbased learning" approach, based on our test results. This study, on the other hand, proposes to use a machine-learning algorithm to anticipate future stock prices by combining open source libraries with pre-existing algorithms to help make this uncertain business model predictable. The result is entirely dependent on numbers and is predicted by many assumptions that may or may not occur in the real world, such as the forecast period. At the same time, the study also aims to provide a tool to anticipate accurate and timely traffic data. This fact has prompted us to pursue a solution to the problem of predicting traffic flow based on traffic data and models. Due to a large amount of available data for the transport system, it is difficult to accurately predict traffic flow.
Mitigating Security and Privacy issues in IoT Application using Blockchain: A Review

We live in a world where everything is connected via the Internet of Things (IoT).Despite this, IoT privacy remains a serious challenge, particularly due to IoT networks' vast scale and dispersed nature.Using protected solutions, such as incorporating blockchain technology into privacy-based services, is one approach to privacy-related concerns.Various Internet of Things security and authentication issues have been resolved by the decentralized nature of blockchain technology.This paper examines how blockchain technology mitigates the security and privacy concerns of IoT networks.In addition, we investigate the structure and uses of blockchain technology for recommender system privacy and trust management solutions.The limitations of adopting the blockchain technology also discussed.From the analysis of literature works, the blockchain technology could be able to circumvent IoT limitations such as data security and privacy.In addition, it may offer IoT customers distributed storage, transparency, trust, safe distributed IoT networks, and privacy and security assurance.
The Seeker: Network Element Manager Integrated with IPDB

Any country in the world constantly seeks the upgrade in every aspect of life and so is network industry.India is one of them as developing nation to constantly evolve in network industry although the evolution is considered to be moderate.With the growing amount of data produced every minute and the need of quick access of data at the user end in a network, the efficient yet exhaustive use of cloud resources has become more important than ever.Hence to reduce the time consumption and complication of the data collection when needed, the collaborative approach can be applied where in cloud gaffer along with network IMSI profile view including network element IP database manager can be developed known as The Seeker.The Seeker is a new gen software tool which is born with the collaborative approach in which cloud gaffer along with 5G/4G network IMSI profile view including network element IP database manager.There are micro services which require minimal resources, whereas there are other critical heavy resource usage projects which requires almost the entire cloud pot.Not only so, but there is also Generic Cloud which provides specific number of required resources and there are CBIS/NCIR cloud which provide another kind of resources.The variety of cloud products that any company serves to the user requires different types of hardware, input/output operation efficiency & geo-dependency to name a few.Thus, while deploying a cloud product or a service, finding out the right cloud for the use case becomes a time taking and hectic job.Even if we do so, many a times we find that someone else also using the same cloud resources and the job gets stuck.This tool will provide an one stop solution to procure that right cloud resources using OpenStack.Moreover, when the database is solid, the optimization of the resources becomes easier too!When it comes to Network Element Manager, it allows the user to obtain IP addresses that have not been allocated.The user must provide information about the cloud, IP type and vlan, IP in each.However, this is to automate the user request by processing the search into database and providing the users with required information.Moving on to latest Network IMSI profile view, in general network company uses shared data layer (SDL) as a backend of subscriber database.Imagine in a month how many requests will be raised regarding troubleshooting.Hence to achieve the solution for above problems THE SEEKER will have to be used.