Reviewerly/exHarmony huggingface dataset:
&amp;lt;i&amp;gt;Understanding and incorporating stakeholder perspectives in international engineering: a phrase mining analysis&amp;lt;/i&amp;gt;

<b><sc>Abstract.</sc></b> As water becomes increasingly scarce, it becomes more important to ensure its reliable access, especially in rural areas dependent on agriculture for both national food security and personal livelihoods. Too often the aid that is funneled to support these rural regions results in poor project sustainability and resiliency. With a variety of major actors in these project implementations, the actual definition of an achievable and successful project remains elusive. This research seeks to better understand how projects are evaluated from the perspectives of four main stakeholders: engineer, funder, government, and international NGO. We have used text mining analysis on a novel Stakeholder Evaluation Database (SED) to find representative phrases for each of the main stakeholders. Using cosine similarity, we compare top phrases across stakeholders and find evidence of differing priorities. While most stakeholder comparisons showed convergence, funders and international NGOs remain relatively dissimilar, a surprising result from stakeholders that often must interact closely throughout the project process. We also find a lack of available information on the opinions of the recipients of projects. This process is a first step in recognizing the differing priorities of stakeholders. Further refinement of the method seeks to include the recipient community perspectives, giving a voice to those most dependent upon and vulnerable to the success or failure of international engineering.
The Essence of Simulation and Artificial Intelligence

In response to the controversy over whether machines can think and the queries on artificial intelligence (AI), this paper clarifies that the essence of simulation and AI (i.e.simulation intelligence) should be pursuing assimilation (similartaxis) as well as keeping dissimilation to a prototype (natural intelligence).Both commonness and obvious otherness (individuality) must exist between them.This is a 'Both/And' thinking mode and not a simplified exclusive 'Either/Or' thinking.Based on four definitions and five hypotheses, some corollaries are educed logically to clarify the relation between machine intelligence and human mind and to answer the queries on AI.This paper points out that the 'Either/Or' thinking, such as 'either true intelligence or false' or 'either whole mind or nothing', resulted in some cognitive mistakes on AI and the mysticism had most deleterious consequences among the research of AI and cognitive science.
&amp;lt;i&amp;gt;An Application of Face Recognition based on Gaussian Processes&amp;lt;/i&amp;gt;

<b><sc>Abstract.</sc></b> In recent years, the rapid development of science and technology, as well as the improvement of hardware computing power, gives birth to the era of artificial intelligence. Among them, the development of face recognition technology is very hot, and its application involves many fields such as military, medical, security, games, etc. It also accelerates the change of people's lifestyle and the re-location and recognition of face ID. In this paper, we will make a summary of the application, development status and application processes of face recognition, and use a new face representation method to realize face recognition using Gaussian processes(Raquel et al.,2007), and make a prospect for the future.
Blockchain Technology To Support Italian Tax Process

The rapid spread of blockchain technology and all its applications has aroused great interest from the scientific world and is likely to change many business contexts in the coming years.The fiscal area represents a strategic sector for the public administration and the recent introduction of the electronic tax process by the Italian Ministry of Economy and Finance has made it possible to significantly improve the resolution of tax disputes.With the aim of overcoming some of its data security limits, in this work we propose a blockchain based solution designed to improve the conduct of the digital tax process by making it safer, faster and with lower transaction costs.In particular, the main improvements relate to two different dimensions of analysis, on the one hand a better level of privacy guaranteed by the introduction of trial files within a blockchain network, on the other hand the use of smart contracts ensures the enforceability of the court's decision and reduces transaction costs related to the conduct of the tax process.The specific model designed is based on a permissioned blockchain and an algorithm for reaching distributed consent, for which we describe in detail the principles of operation that allow the different entities involved to take part in the tax process.
MobileFAN: Transferring Deep Hidden Representation for Face Alignment

Facial landmark detection is a crucial prerequisite for many face analysis applications. Deep learning-based methods currently dominate the approach of addressing the facial landmark detection. However, such works generally introduce a large number of parameters, resulting in high memory cost. In this paper, we aim for a lightweight as well as effective solution to facial landmark detection. To this end, we propose an effective lightweight model, namely Mobile Face Alignment Network (MobileFAN), using a simple backbone MobileNetV2 as the encoder and three deconvolutional layers as the decoder. The proposed MobileFAN, with only 8% of the model size and lower computational cost, achieves superior or equivalent performance compared with state-of-the-art models. Moreover, by transferring the geometric structural information of a face graph from a large complex model to our proposed MobileFAN through feature-aligned distillation and feature-similarity distillation, the performance of MobileFAN is further improved in effectiveness and efficiency for face alignment. Extensive experiment results on three challenging facial landmark estimation benchmarks including COFW, 300W and WFLW show the superiority of our proposed MobileFAN against state-of-the-art methods.
ANALISIS TINGKAT KEMATANGAN SISTEM INFORMASI PENELUSURAN PERKARA (SIPP) DI PENGADILAN NEGERI JAKARTA UTARA MENGGUNAKAN KERANGKA KERJA COBIT 4.1

Pengadilan Negeri Jakarta Utara is a first level court that has a role in serving justice for the community. Along with the development of IT in its services Pengadilan Negeri Jakarta Utara uses the Case Search Information System (SIPP), a web-based application that is intended for administration and tracking of case data and information. In this study the author analyzes the system maturity level using the COBIT 4.1 framework. With the aim of evaluating the maturity level so that it can provide recommendations for improving service and system management that are more effective and efficient. The authors conducted research focusing on Internal Perspective business goals, namely Improve and maintain operations and staff productivity, based on the results of the SIPP analysis at Pengadilan Negeri Jakarta Utara was at a good level, at an average maturity level of 3.65. Agencies already have formal and written standard procedures that have been socialized and have a number of quantitative indicators or measures that are used as targets as well as performance objectives
THE USE OF TECHNOLOGY IN STEM EDUCATION. AN EMPIRICAL RESEARCH

In Romania, the latest results in PISA as well as national exams show a decrease in the interest of students in studying sciences, whilst the results are much below the OECD average.The present study has been carried out at national level by a research team from the University of Bucharest within the framework of the international Horizon 2020 project Scientix, coordinated by European SchoolNet.The study addresses the beliefs and attitudes of STEM teachers in the pre-university education on current practices, evolution and trends in the science-related skills of students.Two investigative techniques have been used: document analysis and an online standardized questionnaire-based survey.The questionnaire was developed by Friday Institute for Educational Innovation, NC State University.So far, the quantitative data was collected from 259 STEM teachers.The survey invited teachers to express their opinions related to their attitudes, values and beliefs on their teaching efficacy, development of the 21 st Century skills, educational leadership, as well as some other themes such as the extent to which teaching and teachers affect student learning, the amount and quality of use of ICT for learning in STEM classes, the frequency of using certain STEM instructional practices, awareness of STEM careers.This article's focus is on one out of the seven constructs -the use of technology in STEM classes.The construct is operationalised in eight sub-items.The discussion on the each of the constructs as well as on the relations between them is quite relevant in the light of the relationship between beliefs and practices, while the results are quite striking, with impact on tailored professional development approached.
Extract Method Refactoring by Successive Edge Contraction

Segmentation, a new approach based on successive edge contraction is introduced for extract method refactoring. It targets identification of distinct functionalities implemented within a method. Segmentation builds upon data and control dependencies among statements to extract functionalities from code by successive contraction of edges in the Structure Dependence Graph (SDG). Three edge contractions are explored, namely structural control edge contraction, exclusive data dependence edge contraction, and sequential data dependence edge contraction. The SDG is first constructed from the program, which is then collapsed into a segment graph that captures dependence between subtasks. An intermediate representation for data and control dependencies among statements keeps the technique language independent. The approach is evaluated on four case studies, including three from the open source domain, and the findings are reported.
Convolutional Neural Network with Median Layers for Denoising Salt-and-Pepper Contaminations

We propose a deep fully convolutional neural network with a new type of layer, named median layer, to restore images contaminated by the salt-and-pepper (s&p) noise. A median layer simply performs median filtering on all feature channels. By adding this kind of layer into some widely used fully convolutional deep neural networks, we develop an end-to-end network that removes the extremely high-level s&p noise without performing any non-trivial preprocessing tasks, which is different from all the existing literature in s&p noise removal. Experiments show that inserting median layers into a simple fully-convolutional network with the L2 loss significantly boosts the signal-to-noise ratio. Quantitative comparisons testify that our network outperforms the state-of-the-art methods with a limited amount of training data. The source code has been released for public evaluation and use (https://github.com/llmpass/medianDenoise).
Research Guides: Online Tutorials for Library Instruction: Avoiding Plagiarism

This guide describes the online tutorials offered through Brightspace by D2L, how to access them, and FAQs for students and faculty.
Pointers in Recursion: Exploring the Tropics

We translate the usual class of partial/primitive recursive functions to a pointer recursion framework, accessing actual input values via a pointer reading unit-cost function.These pointer recursive functions classes are proven equivalent to the usual partial/primitive recursive functions.Complexity-wise, this framework captures in a streamlined way most of the relevant sub-polynomial classes.Pointer recursion with the safe/normal tiering discipline of Bellantoni and Cook corresponds to polylogtime computation.We introduce a new, non-size increasing tiering discipline, called tropical tiering.Tropical tiering and pointer recursion, used with some of the most common recursion schemes, capture the classes logspace, logspace/polylogtime, ptime, and NC.Finally, in a fashion reminiscent of the safe recursive functions, tropical tiering is expressed directly in the syntax of the function algebras, yielding the tropical recursive function algebras.
HDR Image Synthesis Using Visual Brightness Mapping and Local Surround-Based Image Fusion

An HDR (High Dynamic Range) image synthesis is a method which is to photograph scenes with wide luminance range and to reproduce images close to real visual scenes on an LDR (Low Dynamic Range) display. In general, HDR images are reproduced by taking images with various camera exposures and using the tone synthesis of several images. In this paper, we propose an HDR image tone mapping method based on a visual brightness function using dual exposed images and a synthesis algorithm based on local surround. The proposed algorithm has improved boundary errors and color balance compared with existing methods. Also, it improves blurring and noise amplification due to image mixing.
LibGuides: LLC Student Jobs: Home

This guide offers information for students seeking employment in any of FSCJ's Library and Learning Commons locations.
Distributional Negative Sampling for Knowledge Base Completion

State-of-the-art approaches for Knowledge Base Completion (KBC) exploit deep neural networks trained with both false and true assertions: positive assertions are explicitly taken from the knowledge base, whereas negative ones are generated by random sampling of entities. In this paper, we argue that random sampling is not a good training strategy since it is highly likely to generate a huge number of nonsensical assertions during training, which does not provide relevant training signal to the system. Hence, it slows down the learning process and decreases accuracy. To address this issue, we propose an alternative approach called Distributional Negative Sampling that generates meaningful negative examples which are highly likely to be false. Our approach achieves a significant improvement in Mean Reciprocal Rank values amongst two different KBC algorithms in three standard academic benchmarks.
Span programs and quantum time complexity

Span programs are an important model of quantum computation due to their tight correspondence with quantum query complexity. For any decision problem $f$, the minimum complexity of a span program for $f$ is equal, up to a constant factor, to the quantum query complexity of $f$. Moreover, this correspondence is constructive. A span program for $f$ with complexity $C$ can be compiled into a bounded error quantum algorithm for $f$ with query complexity $O(C)$, and vice versa. In this work, we prove an analogous connection for quantum time complexity. In particular, we show how to convert a quantum algorithm for $f$ with time complexity $T$ into a span program for $f$ such that it compiles back into a quantum algorithm for $f$ with time complexity $\widetilde{O}(T)$. While the query complexity of quantum algorithms obtained from span programs is well-understood, it is not generally clear how to implement certain query-independent operations in a time-efficient manner. We show that for span programs derived from algorithms with a time-efficient implementation, we can preserve the time efficiency when implementing the span program. This means in particular that span programs not only fully capture quantum query complexity, but also quantum time complexity. One practical advantage of being able to convert quantum algorithms to span programs in a way that preserves time complexity is that span programs compose very nicely. We demonstrate this by improving Ambainis's variable-time quantum search result using our construction through a span program composition for the OR function.
Retrieval of Color Space Conversion Matrix via Convolutional Neural Network

The problem of proper identification of the color space associated with digital luma and chroma data has been widely reported by video processing professionals.The problem arises from confusing, and sometimes conflicting, statements regarding color space usage and description.Although standards allow for the carriage of descriptive metadata regarding color space, some applications do not require the presence of such metadata.Those standards typically recite assumptions on color space that should be followed in the absence of embedded color space descriptors.The unfortunate result of this approach is a state of confusion in the industry, and consequently the possibility of errors in rendering the output of decoded video and images.Our work represents the first known attempt to determine color space directly from luma/chroma pixel data, and provides an alternative to sole reliance on potentially missing or incorrect metadata, or weakly followed defaults.Although a color space is defined by many parameters, such as primary chromaticity, transfer characteristics and matrix coefficients, we chose to focus on determining which standard for matrix coefficients had been used to create a given luma/chroma image.We addressed the problem via deep convolutional neural networks (DCNNs), trained on millions of images.Our results are encouraging, and suggest that DCNNs can be used to solve this ill-posed problem.
Predicting Solar Insolation and Energy Harvest of PV Modules in Saudi Arabia

Recently, energy consumption has increased worldwide. In Saudi Arabia, total generation capacity reached about 74.3 GW in 2016. This increasing demand of electrical energy in addition to other factors such as environmental challenges being faced by the world and increasing oil prices have all led to the increasing use of Renewable Energy Resources (RERs). Consequently, Saudi Arabia aims to use Photovoltaic (PV) extensively in the generation of electricity in the near future, both in utility scale and residential scale. In this paper, the insolation and energy delivered from residential scale PV modules will be assessed in different cities within Saudi Arabia. Simulation tool will be used for this purpose.
Sistem Informasi Manajemen Instalasi Gawat Darurat BP-RB PKU Muhammadiyah Pakem, Sleman, Yogyakarta

BP-RB PKU Muhammadiyah Pakem is a medical center that is still relatively new and is seriously preparing hardware, software, and brain devices, especially in Emergency Installation (IGD). Patients who come to the emergency room need fast and appropriate assistance. At BP-RB PKU Muhammadiyah Pakem there is a patient registration process, medical records and calculation of payments still made manually so patients must wait for inspection or payment. This makes searching medical records and visiting data examining previous patients longer because they have to look for lines and sheets. The purpose of this study is to establish an Emergency Management Information System that can help health practitioners in the management process in the emergency unit to be faster and easier in finding patient medical records for previous visits and recording patients' medical records so that patients can be treated quickly and accurately . This study uses several steps including: Defining the main problem, Collecting data, Developing information systems, Defining the main problem, Collecting data, Developing information systems. Methods in system development include software requirements analysis, design, implementation, development. The method for building a web-based system using PHP (Hypertext Prepocessor) and database management systems with My SQL.Keywords: System, Manajemen, IGD
Bayesian Inference for Latent Chain Graphs

In this article we consider Bayesian inference for partially observed Andersson-Madigan-Perlman (AMP) Gaussian chain graph (CG) models. Such models are of particular interest in applications such as biological networks and financial time series. The model itself features a variety of constraints which make both prior modeling and computational inference challenging. We develop a framework for the aforementioned challenges, using a sequential Monte Carlo (SMC) method for statistical inference. Our approach is illustrated on both simulated data as well as real case studies from university graduation rates and a pharmacokinetics study.
Generating SQL Statements from Natural Language Queries: A Multitask Learning Approach (S)

NL2SQL advocates an idea of helping engineers and/or end users generate SQL statements from natural language queries.However, it still remains a strong challenge in improving its precision and scalability.This paper introduces MultiSQL, a multitask deep learning approach to performing NL2SQL.MultiSQL unifies the task representations and trains a model in parallel on multiple tasks, including NL2SQL, machine translation, etc.It employs a multitask question-answering network for jointly learning all tasks and transferring knowledge among tasks.We have evaluated MultiSQL on two query datasets:WikiSQL (an open sourced dataset) and CnSQL (a Chinese dataset we created).The evaluation results clearly show the effectiveness of MultiSQL.In particular, the accuracies achieved by MultiSQL approximate those achieved by the state-of-the-art NL2SQL methods on WikiSQL, and its accuracy is 78%, which is 17% higher than the "Chinese2English + NL2SQL" method on CnSQL.
A Method to Recommend Artifacts to New Tasks in Software Projects (S)

The software development workflow typically involves developers executing tasks and manipulating artifacts.When developers receive a new task they typically envision a task context with the artifacts they intend to manipulate based on their past experiences.Given software projects may last several months, accumulating a vast amount of tasks, artifacts and developers, envisioning this initial task context may be difficult and error-prone.Developers have to walk-through months of past experiences or examine the experience of other developers, select similar tasks and then define the initial context.This paper introduces a method that helps developers defining the initial task context by combining interaction information over artifacts with text information of tasks.First, the Method uses the Clustering technique to organize project tasks into similar groups by interaction in artifacts.Then, the Method uses the Natural Language Processing technique to associate a new task with groups of similar tasks by interaction.The evaluation shows that the clustering of similar tasks by interaction produces similar tasks assigned with artifacts that will be edited by new tasks.The association of new tasks with similar groups by interaction indicates correlation between textual similarity and interaction similarity.
Best Practices in Accelerating the Data Science Process in Python

The number of data science and big data projects is growing, and current software development approaches are challenged to support and contribute to the success and frequency of these projects.Much has been researched on how data science algorithm is used and the benefits of big data, but very little has been written about what best practices can be leveraged to accelerate and effectively deliver data science and big data projects.Big data characteristics such as volume, variety, velocity, and veracity complicate these projects.The proliferation of open-source technologies available to data scientists can also complicate the landscape.With the increase in data science and big data projects, organizations are struggling to deliver successfully.This paper addresses the data science and big data project process, the gaps in the process, best practices, and how these best practices are being applied in Python, one of the common data science open-source programming languages.