Reviewerly/exHarmony huggingface dataset:
Handling Arrays

In the previous chapter, we talked about how to deal with strings, numbers, and arrays inside PL/pgSQL of PostgreSQL. We covered some examples of using strings, numbers, and arrays. In this chapter, we will dive deep into arrays. We will cover array index and array length, use foreach to iterate over the arrays, and convert arrays to a set of rows. It will also include finding duplicate elements in an array, appending elements to an array, and merging two arrays. By the end of this chapter, you will have a better understanding of how to use arrays in the context of PL/pgSQL.
Dealing with Strings, Numbers, and Arrays

In the previous chapter, we talked about the different data types available in PL/pgSQL and how to use them with some examples. In this chapter, we will cover how to work with strings, numbers, and arrays inside PL/pgSQL of PostgreSQL. It will include examples of how to declare variables, perform operations, and manipulate data within these data types. Additionally, it will cover best practices for working with these data types to ensure efficient and effective code. By the end of this chapter, you will have a better understanding of how to work with strings, numbers, and arrays within the context of PL/pgSQL.
Usability Evaluation of Food Bank Data Visualizations using Eye-Tracking Technology

Effective resource allocation in hunger-relief agencies relies on data-driven decision-making. In recent years, major hunger-relief organizations, such as food banks, have increasingly adopted data visualizations as a fundamental tool for presenting and comprehending analytics. As this trend has gained momentum, the need for establishing a standardized approach for food banks to presenting visualizations has become evident. These visualizations must be efficiently interpreted by users to prevent potentially dire miscommunications. A usability evaluation was conducted on selected visualizations using eye-tracking technology in this study. Usability concerns and design recommendations were identified. Findings of this study have the potential to improve food bank operations through evidence-based decision making.
Introduction to PL/pgSQL

In this chapter, we will start with an introduction of PL/pgSQL, on what is PL/pgSQL and what are the key features of it. We will talk about some common use cases where PL/pgSQL is used. PL/pgSQL comes by default when you install the PostgreSQL server. However, we will provide the steps to install PL/pgSQL. We will explain how PL/pgSQL works with a simple flow diagram. We will show some basic examples of PL/pgSQL code blocks which are called anonymous and named code blocks.
Custom Operators

In the previous chapter, we talked about cursors and how to use them in PL/pgSQL with some examples. In this chapter, we will start with what are different types of built-in operators available in PostgreSQL. We will cover custom operators and their purpose. We will walk through the use cases to create custom operators with simple examples. We will also explain the advantages and disadvantages of having custom operators. By the end of this chapter, you will have a better understanding of custom operators and where to use them effectively.
Exploring the potential of human-centered design combined with narrative medicine theory for children-friendly healing environment design

Applying narrative medicine theory to the design of the healing environment is a kind of HCD thinking. This paper conducts proper user research for a children-friendly healing environment, considering children's cognitive level and stakeholders, utilises general inductive topic analysis to identify key concerns and position them as design possibilities. This paper then examines the objects' existing experience and summarises three children-friendly healing narrative design strategies based on narrative design theory. Prototypes are constructed and evaluated. This paper applies design thinking and narrative medicine theory to inventive environmental design, developing a system framework and suggestions for a children-friendly healing environment.
Triggers

In the previous chapter, we talked about the different types of exceptions available in PL/pgSQL and how to handle them with some examples. In this chapter, we will cover triggers in PostgreSQL. It will start with the introduction of triggers and how to create them using PL/pgSQL code with a simple example. We will then talk about the types of triggers with some use cases and real-world examples of where and when to use these triggers using PL/pgSQL functions. We will also cover the advantages and disadvantages of triggers.
Texture Classification Using CNN and Dimensionality Reduction Techniques

Texture classification has been a popular topic in recent years for researchers. Texture classification frequently employs Convolution Neural Networks (CNN) as the traditional handcrafted methods were not successful for the immense size of data. Handcrafted methods are tedious and costly to develop and highly dependent on one application. Convolution neural networks (CNN) have recently emerged as the most important feature extractor: CNN-based feature extraction methods actually outperforms other feature extraction techniques. In precedent works, this issue of Texture Classification was resolved by deep-learning techniques but the classification accurateness was 96–97 percent. Deep learning actually learn features in incremental manner. But in CNN we deals with millions of parameters. Training it with large and complex data models can be expensive. It also needs extensive hardware. So Deep Learning has very high Dimensionality. So. in my methodology i have used Dimensionality reduction technique after the Feature extraction. So that further reduce the multidimensional to fewer dimension in Visualization. So, In my work, I have proposed the hybrid CNN technique that increases the accuracy as well as that better visualization of the network.
LKPD SISTEM PEREDARAN DARAH BERBASIS PENDEKATAN SAINTIFIK

The circulatory system is closely related to human life. In this material, students are expected to know the parts of the heart organ related to activities with the frequency of the heartbeat. Characteristics of SMP/MTs class VIII students are able to use logic so that to study the material of the circulatory system, images that represent the existence of the circulatory system are needed, namely in the form of teaching materials using a scientific approach. The use of a scientific approach in the 2013 curriculum aims to create learning according to process standards. The purpose of this study was to describe the LKPD of the circulatory system based on a scientific approach based on its validity and practicality. The method used in this research is the ADDIE development model. The data of this study consisted of data on the validity and practicality of the developed LKPD. The results showed that the LKPD of the circulatory system based on a scientific approach was declared valid and practical.&#x0D; Keywords: LKPD, Scientific Approaach, Circulatory System
Pedestrian Trajectory Forecasting in Videos

Pedestrian trajectory forecasting is an important research problem in computer vision, especially in autonomous vehicles and autonomous mobile robots. Now-a-days, from augmented reality to simulation, from small mobile robots to self-driving cars, human motion modeling has become an active research area. Pedestrian trajectory forecasting can be seen as part of human motion modeling, where based on the past behaviour, one tries to predict what might be the future path. In this paper, we attempt to address this problem. We create a dataset and propose a pedestrian trajectory forecasting pipeline that senses the environment, detects and tracks pedestrians and forecasts the future trajectory of the pedestrians in the video. We propose an LSTM-based motion model to forecast the sequential information of the pedestrians for the next few frames. Our LSTM-based motion forecasting model is trained and tested on custom annotated surveillance datasets. Our experimental results show that our proposed framework is robust and accurate.
Diagnosis and Classification of Cancer Using Machine Learning Techniques

There are many types of cancers that are dangerous for human beings. Especially Breast cancer is one of the most common reasons for increasing death in women worldwide. Diagnosis of breast cancer at the initial stage can minimize the mortality rate significantly. It is possible using machine learning algorithms. A cancer patient can survive longer if the model is more accurate and precise. A smart framework is essential to detect and classify breast cancer at an early stage with high accuracy and precision. In this study, a model is implemented using machine learning techniques for the detection and classification of breast cancer. We have used a support vector classifier (SVC), Naive Bayes, logistic regression, and k-nearest neighbor algorithms for classification purposes. After obtaining results, model performance is analyzed based on different evaluation matrices. Sensitivity, selectivity, accuracy, precision, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$f1$</tex> -score is taken into consideration as the performance measures. It is detected that the support vector classifier accomplished the maximum accuracy (98%), precision, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$f1$</tex> -score. Python programming language and Scikit-learn library are used for this work.
Chatbot for Healthcare System Using NLP and Python

After the 20th century ended, people in the 21st century started to worry more about their own health and the health of their families. Realizing that “health is wealth,” they decide that their health is the one asset they should safeguard throughout their lives. It is significant that medication is essentially necessary for most individuals to lead happy lives. However, it may be challenging to schedule an appointment with a doctor for every issue that arises in our bodies. The goal is to use Python and JSON to build a healthcare bot that, prior contacting a physician, could recognize the illness and give accurate, basic information about that as well. This will essentially contribute to lowering healthcare expenses and expanding access to health records through a healthcare chatbot, both of which are very important. These AI systems were software applications which utilise Python as a driver and will basically be kept in a CV file to basically communicate with users, proving that this will basically help to cut medical expenses and enhance accessibility to medical information through medical chatbots, or so they think. The idea is to primarily use Python and JSON to create a clinical bots which can recognise a illness and often offer additional basic details well about disorder prior to actually visiting a physician. The bots warehouses the database's information in order to recognise the statement keyword phrases, to start making a query judgement, and to respond to the query. This again illustrates how well the concept essentially is to generate a healthcare chatbot. Through offering appropriate advice on healthy lifestyle, a healthcare Chatbot can help resolve this issue. Natural language processing underpins its operation of healthcare chatbots and enables users to communicate health-related problems.
Dynamic SQL

In the previous chapter, we talked about custom casting in PostgreSQL and how to define our own custom casting functions. We have also discussed the use cases for custom casting and how to create them accordingly. In this chapter, we will cover the core concepts of dynamic SQL within PostgreSQL's plpgsql environment. We will provide a comprehensive overview of the syntax and its practical implementation. Additionally, we will cover some real-world scenarios and share best practices for harnessing the power of dynamic SQL effectively. By the end of this chapter, readers will have gained a solid grasp of dynamic SQL's intricacies, enabling them to confidently utilize this feature in PostgreSQL for various use cases.
Manajemen Keamanan di UPT Perpustakaan Universitas Negeri Semarang

Library security is an important factor for the continuity of library administration, without good security management, the library cannot guarantee that its users feel safe and comfortable in utilizing the library, and eventually the library is abandoned by its users. This study aims to describe security management at the Library of Semarang State University (UNNES) which is still in the old UNNES Library building. The method used is descriptive qualitative with observation and interview data collection techniques. The validity test uses technical triangulation, and data analysis uses data reduction, data presentation, and drawing and conclusions. The results found were that the UNNES Library had implemented security features in the form of physical security and security management. Physical security includes placement architecture, building architecture, guards, equipment and exhibition security, while security management includes library material security, access security and surveillance cameras. Weaknesses found in the UNNES Library were that security policy procedures and plans had not been set forth in text form even though they had been implemented. Suggestions for UNNES Library is to be able to make security policy procedures and plans in the form of text that is approved by the leadership and can be a guideline for the library.
Co-design Hardware and Algorithm for Vector Search

Vector search has emerged as the foundation for large-scale information retrieval and machine learning systems, with search engines like Google and Bing processing tens of thousands of queries per second on petabyte-scale document datasets by evaluating vector similarities between encoded query texts and web documents. As performance demands for vector search systems surge, accelerated hardware offers a promising solution in the post-Moore's Law era. We introduce FANNS, an end-to-end and scalable vector search framework on FPGAs. Given a user-provided recall requirement on a dataset and a hardware resource budget, FANNS automatically co-designs hardware and algorithm, subsequently generating the corresponding accelerator. The framework also supports scale-out by incorporating a hardware TCP/IP stack in the accelerator. FANNS attains up to 23.0× and 37.2× speedup compared to FPGA and CPU baselines, respectively, and demonstrates superior scalability to GPUs, achieving 5.5× and 7.6× speedup in median and 95th percentile (P95) latency within an eight-accelerator configuration. The remarkable performance of FANNS lays a robust groundwork for future FPGA integration in data centers and AI supercomputers.
ADT-FSE: A New Encoder for SZ

SZ is a lossy floating-point data compressor that excels in compression ratio and throughput for high-performance computing (HPC), time series databases, and deep learning applications. However, SZ performs poorly for small chunks and has slow decompression. We pinpoint the Huffman tree in the quantization factor encoder as the bottleneck of SZ. In this paper, we propose ADT-FSE, a new quantization factor encoder for SZ. Based on the Gaussian distribution of quantization factors, we design an adaptive data transcoding (ADT) scheme to map quantization factors to codes for better compressibility, and then use finite state entropy (FSE) to compress the codes. Experiments show that ADT-FSE improves the quantization factor compression ratio, compression and decompression throughput by up to 5×, 2× and 8×, respectively, over the original SZ Huffman encoder. On average, SZ_ADT is over 2× faster than ZFP in decompression. Case studies of the TDengine time series database and HDF5 file store confirm that SZ_ADT significantly boosts user-perceived application performance. In addition, ADT-FSE makes the compression ratio prediction of SZ_ADT easy and accurate, and has the potential to dramatically reduce the area size of SZ hardware implementation.
FASDA: An FPGA-Aided, Scalable and Distributed Accelerator for Range-Limited Molecular Dynamics

Conducting long-timescale simulations of small molecules using Molecular Dynamics (MD) is crucial in drug design. However, traditional methods to accelerate the process, including ASICs or GPUs, have limitations. ASIC solutions are not always generally available, while GPU solutions may not scale when processing small molecules. FPGAs are both communication processors and accelerators, with tight coupling between these capabilities, and so could be used to address strong scaling in this domain.
Interference-aware Multiplexing for Deep Learning in GPU Clusters: A Middleware Approach

A common strategy for improving efficiency in training deep learning entails multiplexing tasks on a single GPU. To mitigate the interference caused by multiplexing, existing approaches primarily employ kernel-level solutions to regulate GPU kernel execution, or harness hardware-level techniques to explicitly restrict GPU streaming multiprocessors and memory. Nevertheless, none of them perform satisfactorily in optimizing the completion time of tasks.
Optimizing Direct Convolutions on ARM Multi-Cores

Convolution kernels are widely seen in deep learning workloads and are often responsible for performance bottlenecks. Recent research has demonstrated that a direct convolution approach can outperform the traditional convolution implementation based on tensor-to-matrix conversions. However, existing approaches for direct convolution still have room for performance improvement. We present nDirect, a new direct convolution approach that targets ARM-based multi-core CPUs commonly found in smartphones and HPC systems. nDirect is designed to be compatible with the data layout formats used by mainstream deep learning frameworks but offers new optimizations for the computational kernel, data packing, and parallelization. We evaluate nDirect by applying it to representative convolution kernels and demonstrating its performance on four distinct ARM multi-core CPU platforms. We compare nDirect against state-of-the-art convolution optimization techniques. Experimental results show that nDirect gives the best overall performance across evaluation scenarios and platforms.
DGAP: Efficient Dynamic Graph Analysis on Persistent Memory

Dynamic graphs, featuring continuously updated vertices and edges, have grown in importance for numerous real-world applications. To accommodate this, graph frameworks, particularly their internal data structures, must support both persistent graph updates and rapid graph analysis simultaneously, leading to complex designs to orchestrate 'fast but volatile' and 'persistent but slow' storage devices. Emerging persistent memory technologies, such as Optane DCPMM, offer a promising alternative to simplify the designs by providing data persistence, low latency, and high IOPS together. In light of this, we propose DGAP, a framework for efficient dynamic graph analysis on persistent memory. Unlike traditional dynamic graph frameworks, which combine multiple graph data structures (e.g., edge list or adjacency list) to achieve the required performance, DGAP utilizes a single mutable Compressed Sparse Row (CSR) graph structure with new designs for persistent memory to construct the framework. Specifically, DGAP introduces a per-section edge log to reduce write amplification on persistent memory; a per-thread undo log to enable high-performance, crash-consistent rebalancing operations; and a data placement schema to minimize in-place updates on persistent memory. Our extensive evaluation results demonstrate that DGAP can achieve up to 3.2× better graph update performance and up to 3.77× better graph analysis performance compared to state-of-the-art dynamic graph frameworks for persistent memory, such as XPGraph, LLAMA, and GraphOne.
High-Performance and Programmable Attentional Graph Neural Networks with Global Tensor Formulations

Graph attention models (A-GNNs), a type of Graph Neural Networks (GNNs), have been shown to be more powerful than simpler convolutional GNNs (C-GNNs). However, A-GNNs are more complex to program and difficult to scale. To address this, we develop a novel mathematical formulation, based on tensors that group all the feature vectors, targeting both training and inference of A-GNNs. The formulation enables straightforward adoption of communication-minimizing routines, it fosters optimizations such as vectorization, and it enables seamless integration with established linear algebra DSLs or libraries such as GraphBLAS. Our implementation uses a data redistribution scheme explicitly developed for sparse-dense tensor operations used heavily in GNNs, and fusing optimizations that further minimize memory usage and communication cost. We ensure theoretical asymptotic reductions in communicated data compared to the established message-passing GNN paradigm. Finally, we provide excellent scalability and speedups of even 4--5x over modern libraries such as Deep Graph Library.
A GPU Algorithm for Detecting Strongly Connected Components

Detecting strongly connected components (SCCs) is an important step in various graph computations. The fastest GPU and CPU implementations from the literature work well on graphs where most of the vertices belong to a single SCC and the vertex degrees follow a power-law distribution. However, these algorithms can be slow on the mesh graphs used in certain radiative transfer simulations, which have a nearly constant vertex degree and can have significant variability in the number and size of SCCs. We introduce ECL-SCC, an SCC detection algorithm that addresses these shortcomings. Our approach is GPU friendly and employs innovative techniques such as maximum ID propagation and edge removal. On an A100 GPU, ECL-SCC performs on par with the fastest prior GPU code on power-law graphs and outperforms it by 7.8× on mesh graphs. Moreover, ECL-SCC running on the GPU outperforms fast parallel CPU code by three orders of magnitude on meshes.
Similarity and Complementarity Attention-Based Graph Neural Networks for Mashup-Oriented Cloud API Recommendation

Mashups, which combine various web application programming interfaces (APIs) to implement some complex requirements, have grown to be a popular technique for developing service-oriented software. However, recommending suitable cloud APIs for mashup creation is challenging due to the rapidly increasing number of comparable APIs. Many existing mashup-oriented cloud API recommendations focus on functional similarity and ignore functional complementarity, which significantly impacts the accuracy of the recommendation results. Therefore, this paper proposed a feature representation and recommendation method for cloud APIs that fuses both similarity and complementarity. A heterogeneous information network of the cloud API ecosystem was constructed, and the neighbors, based on metapaths, were aggregated using a self-attention mechanism to generate the features of similarity and complementarity for the cloud APIs. Then, the mashup-related attention was utilized to fuse the two features, taking into consideration the varying preferences of different mashups towards similarity and complementarity features of cloud APIs. This fusion resulted in features that align cloud APIs with mashup requirements, which were employed to predict the probability of the mashup invoking a particular candidate cloud API. The proposed method was evaluated on a real dataset, and the results showed that it outperforms the baseline method and enhances the performance of mashup-oriented cloud API recommendations.