Reviewerly/exHarmony huggingface dataset:
A Comparison of Apriori Algorithm and Apriorib with Hashing Algorithm

Data mining helps us in finding a pattern or some information that can help us relate different items even in a very big dataset. Association mining rules is one of the best applications of data mining that helps us in finding frequent itemsets [1]. Apriori algorithm is an algorithm that helps us find these similarities between different items using concepts of lift, support and confidence. This paper discusses about apriori algorithm, how it works, what are its advantages, disadvantages and how it can be made better and efficient with the help of data structure hash map [4].
The Mediating Effects of Career Motivation in the Relationship Between University Student’Core Competencies and Career Preparation Behaviors

The purpose of this study is twofold: to investigate the role of career motivation as a mediator in the relationship between core competencies and career behavior; and to gain a better understanding on career education in university. Data was collected from 230 university students enrolled at K University. SPSS 26.0 was used to examine the correlation between each variable, and multiple regression analysis was conducted to analyze the effect of core competencies on career preparation behavior. The Baron and Kenny method (specifically, three-stage mediating regression analysis) was conducted to verify the mediating effect of career motivation. Model 4 (among the models presented in PROCESS Macro 4.0) was further used to confirm the significance of the indirect effect between the independent variable and the dependent variable. The results showed that core competencies had a significant positive effect on career preparation behavior. The mediating effect of career motivation is significant in the relationship between core competencies and career preparation behaviors. Results are discussed along with implications of the present study and suggestions for the effective career support for university students.
Semantic Keywords Extraction from Paper Abstract in the Domain of Educational Big Data to support Topic Clustering

Keywords are the list of valuable words present in a paragraph, that help in quickly understanding the context of the paragraph. These keywords hold the generic and overall meaning of the paragraph. Extraction of valid and meaningful keywords from scientific documents became one of the hot topics for researchers. Such research not only facilitates better comprehension of articles but also explores the scientific manner of understanding big repositories of scientific documents. In this study, we propose Semantic keyword extraction by adding a new feature that includes domain-specific grammar rules and deduction of adjectives. Our algorithm incorporates frequencies of keywords that are appearing repeatedly. The proposed frame-work extracts the keywords from the scientific paper abstract to support topic clustering. Such topic clustering benefits the new researchers to easily and quickly find their research topic in the concerned field of educational big data. We have selected the educational big dataset that includes 1028 published research papers regarding education learning, education management, students’ information system, etc. For evaluating the results and performance of a Semantic Keyword Extractor, we have used a general dataset. The proposed keyword extractor gives a precision of 76.8% which outperforms other keywords extractors. In our research, our proposed framework classified scientific papers into 3 meaningful groups by using an unsupervised machine learning clustering technique called k-means.
A brief history of the Geological Survey of Israel

The Geological Survey of Israel (GSI) was founded in 1948 with the establishment of the State of Israel. Over the years, changes of the research directions were dictated by national needs as the population grew from less than 1 million to more than 9 million in 2020 in a small country of only 22,000 km2 with highly diverse geological features. This was associated with a change in the structure of the survey and establishing it as a key player in sustainable development and effective utilization of natural resources. It became possible by the upgrade of the staff levels and the research facilities after the survey was recognized as an independent research institute by the government. The achievement of the GSI as an applied science organization in response to the country’s ever-developing challenges is going hand in hand with becoming an international geo-scientific recognized organization.
User Comprehension of Complexity Design Graph Reports

Decision makers spend significant time and effort interpreting information derived from large multidimensional databases; data are usually represented by several dashboard diagrams. The Complexity Design (CoDe) methodology provides a technique modeling graphical reports on data extracted by a data warehouse, where the charts composing the dashboard diagrams are integrated with a visual representation of the logical relationships among them. The generated visualizations (CoDe Graphs) are automatically obtained by connecting dashboard diagrams through graphical conceptual links. After analyzing the state of the art regarding the evaluation of graphical representation comprehensibility, we propose a classification of those evaluation approaches and evaluate the comprehensibility of CoDe Graphs concerning dashboard reports through a controlled experiment, involving 47 participants. Results show that CoDe Graphs reduce participants effort while improving effectiveness and efficiency in comprehension tasks.
Comparing Bag of features Techniques for Image Retrieval

Bag of Feature (BOF) is a customizable technique that provides a free hand on the type of feature selection for creating visual-word vocabulary that fits better for the various image retrieval applications and this is the main advantage of using this technique to create a customizable feature mix of local and global features. One of the two prominent techniques for creating Bag of features in image processing and computer vision is through grid formation and custom extractor. Therefore, selection of type of feature used for Bag creation is very basic and critical one as whole process of Retrieval depends upon it. Feature Color is the well known one to be used most. In this paper L*a*b color space is used for creating one of the feature Bag as L*a*b Colorbag. While two other KAZEbag and SURFbag are created using different descriptor methods namely SURF and Kaze. For unfolding different hidden patters out of collected visual words some machine learning intelligence is adopted like support Vector Machine is used here for clustering and creating cluster centers. MATLAB R2021a is used for all the implementation. Along with the Dataset of Natural images with ten categories is used for evaluation. All the three created Bags are compared on the different parameters such as number of features extracted, strongest feature metric, word frequency and average precision.
Object representation enhancement for self‐supervised colocalization

Self-supervised colocalization is to localize common objects in the data set containing only one superclass without using human-annotated labels. Existing methods achieve impressive results by employing self-supervised pretext learning. However, a common limitation still exists. They either tend to overextend activations to the background, or they tend to activate the most discriminative object part. To alleviate this problem, we propose an object representation enhancement model to weaken background distraction and to mine complementary object regions during the object representation learning. Specifically, we first propose an Object-aware Representation Enhancement (ORE) module to estimate an object mask for each input image, guiding the model to disregard the background content and focus on the foreground object. The ORE module and the subsequent self-supervised learning can mutually reinforce each other. Then we propose a Masked Self-supervised Learning branch and design a masked attention consistency objective to induce the model to activate complementary parts of the object effectively. Extensive experiments on four fine-grained data sets demonstrate the superiority of the proposed model.
Cloud Computing based Medical Assistance &amp; Pill Reminder

This is an innovative System for any client generally focusing on the matured populace as a clinical aide. This paper proposes an application which is an integration of many important medical implementations into a single source so it can work within and communicate within the same application. The client or anybody benefit of the client can enter the medication updates, for example, tablet tone, name, amount and when it ought to be taken with an update. The update assists the client with investigating the subtleties of tablets that the individual in question needs to take and will likewise stand up the subtleties. Here a specialist himself can enter the subtleties for the patients helping that person to remind the client. The System additionally assists the client with clicking image of drugs
Data Visualization Using Augmented Reality for Education: A Systematic Review

Current education systems use data visualization to present the data in a more comprehensible format. Augmented data visualization is an extended version to present the data in a 2D or 3D form in our field of vision. This study conducted a systematic literature review to identify the current state of the art research in augmented reality and potential future research. Research paid especial attention towards the effective use of augmented reality for data visualization to offer a better pedagogical experience. A total of 39 studies have been filtered between 2017 to 2021 from two recognized databases, IEEE Xplore and ScienceDirect. Three research questions are designed for further analysis. Finally, the paper concludes with a future projection and uncovers research gaps that need to be addressed.
Framework for Live Migration of FPGA based ECB-mode AES-128 accelerator

Virtual Machines in cloud datacenters are live migrated for assuring service availability in the event of errors and maintenance. FPGA based hardware accelerators must also be live migrated for the same reasons. AES is widely accepted encryption standard and is therefore commonly used for security provision. In this paper, we propose a novel live migration technique for migration of FPGA accelerated ECB mode AES-128 encryption algorithm using the concept of pipelining which gracefully migrates the application to the destination. The proposed methodology is explained with the help of outer-round only pipelined design of AES-128. Experiments show that the proposed method can reduce downtime nearly to zero, ensuring the service’s availability at maximum.
Generating Interpretable Data-Based Explanations for Fairness Debugging using Gopher

Machine learning (ML) models, while increasingly being used to make life-altering decisions, are known to reinforce systemic bias and discrimination. Consequently, practitioners and model developers need tools to facilitate debugging for bias in ML models. We introduce Gopher, a system that generates compact, interpretable and causal explanations for ML model bias. Gopher identifies the top-k coherent subsets of the training data that are root causes for model bias by quantifying the extent to which removing or updating a subset can resolve the bias. We describe the architecture of Gopher and will walk the audience through real-world use cases to highlight how Gopher generates explanations that enable data scientists to understand how subsets of the training data contribute to the bias of a machine learning (ML) model. Gopher is available as open-source software; The code and the demonstration video are available at https://gopher-sys.github.io/.
Mask R-CNN-Oriented Pottery Display and Identification System

Traditional pottery identification methods are time consuming and costly. In order to cater to more pottery industry needs, we propose a Mask R-CNN-based pottery identification method to build an automatic pottery identification system. We first improve the loss function of Mask R-CNN by using generalized intersection over union loss function, through the pattern of migration learning, to compensate for the disadvantage of a small collective amount of pottery data. For different types of pottery, we use the mask algorithm to enhance the features of the outer contour of the pottery. In addition, we use the minimum external matrix algorithm to accurately extract the outer contour bit pose features of pottery to improve the model’s accuracy in recognizing the outer contour of pottery. To meet the testing conditions of pottery, with the support of potters and archaeologists, we make our pottery data set according to pottery categories. The experimental results prove that our method performs best in the comprehensive recognition accuracy of pottery, with the recognition accuracy above 90%. The recognition accuracy is also the best in pottery color decoration and grain decoration, and the grain recognition accuracy stays above 87%, which is better than other pottery recognition methods.
Scalable Implementation of Array of 8-bit-Based RSA With Large Key Size

Since 1977, many cryptographic attacks have been applied to RSA (Rivest Shamir Adleman). Researchers have proposed security solutions against these attacks, such as padding into the data to be encrypted and using keys of significant length from 1024 bits to 4096 bits. Therefore, to implement RSA., we need an arithmetic unit working with a big integer like in Python and Maple. The maximal length of almost programming platforms doesn't exceed 64 bits. This article presents a scalable algorithm of the RSA cryptosystem with a large key size based on arrays of 8 bits. Therefore, keys, message to be encrypted, and encrypted data are arrays. Each array case is of type unsigned integer 8 bit. However, it codes only 7 bits; the 8 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> bit is used for the carrying of an arithmetic operation. The proposed arithmetic unit allows subtraction, division, and multiplication of large numbers, and it is scalable with all platforms like C/C++, Java, and PHP. The proposed algorithm is implemented on MATLAB environment. Simulation tests are verified with Python, where we find that the proposed implementation generates RSA keys, encrypts, and decrypts data with 0% failure.
Design of Autonomous UAV Guidance System Using Monte Carlo Tree Search

In this paper, we propose an online path-planning algorithm using a variation of Monte Carlo tree search (MCTS) for navigating intelligently unmanned aerial vehicles (UAVs) to track mobile ground targets, called the P-UAV algorithm. The proposed algorithm employs a non-myopic method applied to a partially observable Markov decision process (POMDP) model, accounting for long-term decision making. Our algorithm integrates a heuristic technique to efficiently generate paths. The algorithm yields to parallel processing methods to significantly enhance its computational performance, making it suitable for realtime implementation. Simulation experiments show that our path-planning algorithm is efficient and achieves good exploration-exploitation tradeoff in finding a near-optimal solution despite the very large search space.
On-site Impression Grasping System Using SNS Location Information and Sentiment Analysis

In the recent years, information from such as Social Networking Service (SNS) is overflowing. It draws a great attention from research community in efficiently collecting and analyzing what is being shared in real time. Extracting topics in SNS and analyzing the emotional expression related to those topics are one of the means to know the trends of social interest. In the conventional sentiment analysis, the sentiment of a sentence is estimated on a word-by-word basis, which tends to give undesired results. In many cases, such as negative auxiliary verbs, adverbs, and adjectives related to emotional words are ignored in configurating the emotional expressions. In this research, a method of natural language processing (NLP) in sentiment analysis is performed phrase by phrase by combining the results of active text analysis with the location information. We propose a method to grasp the social impression on an event by improving the phrase level sentiment analysis combining to the location information.
A pragmatic approach to recover access time of Apriori algorithm by applying intersection on CSS for redefining FIS through matrix implementation in textual data

Nowadays data analytics OLAP (online Analytical Processing) is mostly accepted domain of current researchers and the concept of data mining serves better for the same. There are so many data mining methodologies defined for data analytics. Mining Association rule is widely used in data mining methods for data categorization. Apriori Algorithm is popular method for defining n-element. Frequent item set form k number of huge transactional data set online transaction processing (OLTP) using Association Mining rule (AMR). In this paper, researchers executed original Apriori on transactional data set containing 35039 number of transactions, divided into three data sets DS-1 to DS-3 with 20039, 12000, 5000 number of transactions with variable length with minimum support of 30%, 60% and 80% respectively. Researchers carried out experimental work and compared results of Apriori Algorithm with our proposed algorithm (enhanced version of Apriori algorithm) on the same perimeter and state improvement with 11%, 30% and 27% of Rate of Improvements in DS-1 to Ds-3 respectively for 30% minimum support. Our proposed algorithm is working far much better then Apriori algorithm at each parameter which was included to conclude the results.
Prediksi Tingkat Kepuasan Pelayanan Online Menggunakan Metode Algoritma C4.5

Online services are services that are carried out with the help of internet technology. This service allows various types of business to be completed easily without having to meet face-to-face with officers. Everything can run quickly and in real time. Especially during the Covid-19 pandemic as it is affecting the world, including Indonesia. Services that are carried out online are increasingly being carried out. Because it can help people to register online, so there is no need to meet face to face. make it easier for the public to register for the upcoming Adminduk and find out the increase or decrease in face-to-face services due to the pandemic. The data that is processed in this online service from 2019 to 2020 is about data about people who do not understand online services, negative community responses, positive community expectations. The method used is the C4.5 algorithm, which is one of the algorithms used to perform classification or segmentation or grouping and is predictive in nature. The advantages of this method are that it can produce a decision tree that is easy to interpret, has an acceptable level of accuracy, is efficient in handling type attributes. discrete and can handle both discrete and numeric type attributes. The results of this study can predict the value or level of community satisfaction in 2021 quickly and accurately. Therefore, this study can be used as a reference to determine the level of satisfaction in the population and civil registration office of Padang City. The method used is Prediction with C4.5 Algorithm using 14 attributes. The data used in this study is the data from the questionnaire research through the Disdukcapil database of 50 population data which has been presented in a csv file. The analysis in this study uses software tools Rapid Miner version 5.3000. The result of this research is to get 3 rules. So that this research can be used as a reference in predicting the level of online service satisfaction very well
Explainable AI: Foundations, Applications, Opportunities for Data Management Research

Algorithmic decision-making systems are successfully being adopted in a wide range of domains for diverse tasks. While the potential benefits of algorithmic decision-making are many, the importance of trusting these systems has only recently attracted attention. There is growing concern that these systems are complex, opaque and non-intuitive, and hence are difficult to trust. There has been a recent resurgence of interest in explainable artificial intelligence (XAI) that aims to reduce the opacity of a model by explaining its behavior, its predictions or both, thus allowing humans to scrutinize and trust the model. A host of technical advances have been made and several explanation methods have been proposed in recent years that address the problem of model explainability and transparency. In this tutorial, we will present these novel explanation approaches, characterize their strengths and limitations, position existing work with respect to the database (DB) community, and enumerate opportunities for data management research in the context of XAI.
Mind the Whisper: Enriching Collocated Social Interactions in Public Places through Audio Narratives

The quality of social interaction has great importance for psychological and physiological health. Previous research indicates that smartphones have adverse effects on collocated social interactions. Most HCI works addressed this issue by restricting smartphone use during social interactions. Diverging from previous work, we designed WHISPER, an audio narrative box that aims to enrich collocated social interactions without restricting mobile technology use. We conducted a user study in a café environment with 21 participants to understand how users react to WHISPER and how it would influence their social interactions. In this paper, we present the result of this study and discuss four implications for technologies designed to enhance collocated social interactions (Respectfulness, Balanced Ambiguity, Adaptability, and Being Targeted) and two implications for research touching upon the HCI work on Design for Behavior Change and Collocated Interactions (Designing responsible interventions for accommodating unintended outcomes and Quantifying the quality of social interactions).
George Bernard Dantzig

George Dantzig introduced the world to the power of optimization, creating trillions of dollars of value and saving countless years of life across the globe. Linear programs and Dantzig's many other contributions to optimization have driven enormous increases in productivity throughout the global economy. Linear programming has also become a vital tool in advancing artificial intelligence and machine learning.
Compact Walks: Taming Knowledge-Graph Embeddings with Domain- and Task-Specific Pathways

Knowledge-graph (KG) embeddings have emerged as a promise in addressing challenges faced by modern biomedical research, including the growing gap between therapeutic needs and available treatments. The popularity of KG embeddings in graph analytics is on the rise, due at least partially to the presumed semanticity of the learned embeddings. Unfortunately, the ability of a node neighborhood picked up by an embedding to capture the node's semantics may depend on the characteristics of the data. One of the reasons for this problem is that KG nodes can be promiscuous, that is, associated with a number of different relationships that are not unique or indicative of the properties of the nodes.