Reviewerly/exHarmony huggingface dataset:
Temporal Attention Feature Encoding for Video Captioning

In this paper, we propose a novel video captioning algorithm including a feature encoder (FENC) and a decoder architecture to provide more accurate and richer representation. Our network model incorporates feature temporal attention (FTA) to efficiently embed important events to a feature vector. In FTA, the proposed feature is given as the weighted fusion of the video features extracted from 3D CNN, and, therefore it allows a decoder to know when the feature is activated. In a decoder, similarly, a feature word attention (FWA) is used for weighting some elements of the encoded feature vector. The FWA determines which elements in the feature should be activated to generate the appropriate word. The training is further facilitated by a new loss function, reducing the variance of the frequencies of words. It is demonstrated with experimental results that the proposed algorithms outperforms the conventional algorithms in VATEX that is a recent large-scale dataset for long-term video sentence generation.
Eye Tracking Using a Smartphone Camera and Deep Learning

Tracking eye movements has been a central part in understanding attention and visual processing in the mind. Studying how the eyes move and what they fixate on during specific moments has been cons ...
Cloud optical depth retrieval via sky's infrared image for solar radiation prediction

Photovoltaic (PV) system is developed to harness solar energy as an alternative energy to reduce the dependency on fossil fuel energy. However, the output of the PV system is not stable due to the fluctuation of solar radiation. Hence, solar radiation prediction in advanced is needed to make sure the tap changer in PV system has enough time to respond. In this research, the cloud base temperature is identified from the sky’s thermal image. From the cloud base temperature, cloud optical depth (COD) is calculated. Artificial neural network (ANN) models are established by using different combinations of current solar radiation and COD to predict the solar radiation several minutes in advanced. R-squared value is used to measure the accuracy of the models. For prediction in advanced for every minute, with COD as input, always show the highest R-squared value. The highest R-squared value is 0.8899 for the prediction for 1 minute in advanced and dropped to 0.5415 as the minute of prediction in advanced increase to 5. This shows that the proposed methodology is suitable for prediction of solar radiation for short term in advanced.
On Revealing Shared Conceptualization Among Open Datasets

Openness and transparency initiatives are not only milestones of science progress but have also influenced various fields of organization and industry. Under this influence, varieties of government institutions worldwide have published a large number of datasets through open data portals. Government data covers diverse subjects and the scale of available data is growing every year. Published data is expected to be both accessible and discoverable. For these purposes, portals take advantage of metadata accompanying datasets. However, a part of metadata is often missing which decreases users' ability to obtain the desired information. As the scale of published datasets grows, this problem increases. An approach we describe in this paper is focused towards decreasing this problem by implementing knowledge structures and algorithms capable of proposing the best match for the category where an uncategorized dataset should belong to. By doing so, our aim is twofold: enrich datasets metadata by suggesting an appropriate category and increase its visibility and discoverability. Our approach relies on information regarding open datasets provided by users-dataset description contained within dataset tags. Since dataset tags express low consistency due to their origin, in this paper we will present a method of optimizing their usage through means of semantic similarity measures based on natural language processing mechanisms. Optimization is performed in terms of reducing the number of distinct tag values used for dataset description. Once optimized, dataset tags are used to reveal shared conceptualization originating from their usage by means of Formal Concept Analysis. We will demonstrate the advantage of our proposal by comparing concept lattices generated using Formal Concept Analysis before and after the optimization process and use generated structure as a knowledge base to categorize uncategorized open datasets. Finally, we will present a categorization mechanism based on the generated knowledge base that takes advantage of semantic similarity measures to propose a category suitable for an uncategorized dataset.
Overcoming barriers to the training of effective learned optimizers

In this work we focus on general-purpose learned optimizers capable of training a wide variety of problems with no user-specified hyperparameters. We introduce a new, neural network parameterized, hierarchical optimizer with access to additional features such as validation loss to enable automatic regularization. Most learned optimizers have been trained on only a single task, or a small number of tasks. We train our optimizers on thousands of tasks, making use of orders of magnitude more compute, resulting in optimizers that generalize better to unseen tasks. The learned optimizers not only perform well, but learn behaviors that are distinct from existing first order optimizers. For instance, they generate update steps that have implicit regularization and adapt as the problem hyperparameters (e.g. batch size) or architecture (e.g. neural network width) change. Finally, these learned optimizers show evidence of being useful for out of distribution tasks such as training themselves from scratch.
Rancangan Aplikasi Registrasi Dan Pengajian Klaim Nasabah Asuransi (Studi Kasus: PT Asuransi Etiqa Internasional Indonesia)

Today's technology users are already very numerous in number, even the existence of technology today cannot be separated from every human activity. The increasing use of technology today is evidenced by the large number of companies that have used technology to simplify all work done.But there are companies that have not used technology to the maximum to facilitate the work of their employees such as at PT. Asuransi Etiqa Internasional Indonesia, such as the process of filing a claim where the company claims staff must come to the location of the customer who submitted a claim for the survey and collected the data needed to submit a claim. This certainly makes the claim process ineffective and relatively slower. Therefore it is necessary to have a registration application and submission of customer claims in order to facilitate the ongoing claims process.The results obtained from this study are in the form of designing a registration application and submitting a customer claim that has several features, namely insurance customer registration and also filing a claim. The design of this application uses a prototype system development model because at PT. Asuransi Etiqa Internasional Indonesia does not have a previous systemapplication so it requires a design to create the application, and uses Balsamiq Wireframes for designing interfaces on desktop applications and Visio for designing the program flow.Keywords: Technology, Application for Registration and Submission of Customer Claims, Prototype, Balsamiq Wireframes, Visio
Distributed Associative Memory Network with Association Reinforcing Loss

Despite recent progress in memory augmented neural network research, associative memory networks with a single external memory still show limited performance on complex relational reasoning tasks. The main reason for this problem comes from the lossy representation of a content-based addressing memory and its insufficient associating performance for long temporal sequence data. To address these problems, here we introduce a novel Distributed Associative Memory architecture (DAM) with Association Reinforcing Loss (ARL) function which enhances the relation reasoning performance of memory augmented neural network. In this framework, instead of relying on a single large external memory, we form a set of multiple smaller associative memory blocks and update these sub-memory blocks simultaneously and independently with the content-based addressing mechanism. Based on DAM architecture, we can effectively retrieve complex relational information by integrating diverse representations distributed across multiple sub-memory blocks with an attention mechanism. Moreover, to further enhance the relation modeling performance of memory network, we propose ARL which assists a task's target objective while learning relational information exist in data. ARL enables the memory augmented neural network to reinforce an association between input data and task objective by reproducing stochastically sampled input data from stored memory contents. With this content reproducing task, it enriches the representations with relational information. In experiments, we apply our two main approaches to Differential Neural Computer (DNC), which is one of the representative content-based addressing memory model and achieves state-of-the-art performance on both memorization and relational reasoning tasks.
LibGuides: History Unfolded Newspaper Sprint 2021: How to Participate

Explains how to participate in the History Unfolded Newspaper Sprint at Snow College. This page explains how to participate in the Newspaper Sprint
Untangle: Critiquing Disentangled Recommendations

The core principle behind most collaborative filtering methods is to embed users and items in latent spaces, where individual dimensions are learned independently of any particular item attributes. It is thus difficult for users to control their recommendations based on particular aspects (critiquing). In this work, we propose Untangle: a recommendation model that gives users control over the recommendation list with respect to specific item attributes, (e.g.:less violent, funnier movies) that have a causal relationship in user preferences. Untangle uses a refined training procedure by training (i) a (partially) supervised β-VAE that disentangles the item representations and (ii) a second phase which optimized to generate recommendations for users. Untangle gives control on critiquing recommendations based on users preferences, without sacrificing on recommendation accuracy. Moreover only a tiny fraction of labeled items is needed to create disentangled preference representations over attributes.
Analyzing the feasibility and availability of solar PV system for stand-alone PV System in (J&K)

Over the years non-renewable energy sources are used for the generation of power, due to which the availability of these sources will be depleted in near future.  In order to preserve these sources, the power industry is focusing on use of renewable sources for power generation. Reasi is located in lower area of J&K and winter rain fall is very rare due to its frost nature. The availability of power in this area is limited and power demand is more.  To provide a balance between supply and power, the scarcity of power can be compensated by exploiting the solar energy in Reasi. This paper presents feasibility of solar PV system installation by analyzing the availability of solar energy  in rural area of Reasi.Keywords: PV system, Renewable energy, Non-renewable energy, Nasa larc
Implementering av diagnostiskt verktyg i en webbapplikation : Hur kan ett verktyg för att analysera diagnostiska data för felsökning byggas i en React webbapplikation?

Sweden produced 215 kilotons of electronic waste in 2016. One way to reduce this amount is to repair electronic devices when they fail instead of replacing them. The company Mavenoid has a troubles ...
SISTEM DAN PERAWATAN POMPA PEMADAM KEBAKARAN PADA KAPAL SS. BARAKUDADI DOCK PT. CITRA BAHARI SHIPYARD

Fire Pump Systems And Maintenance On Board SS. Barakuda In DockPT. Citra Bahari ShipyardFire extinguishing system is a very vital system on a ship, this system is useful to overcome the danger of that occurs on the ship. The extinguisher system can generally be divided into two, namely: - Passive management system, this system is in the form of class rules regarding the use of materials in areas at high risk of and also installation of fixes in areas that are at risk of fire. - The prevention system is active, this system is in the form of more active accident management, for example the placement of a light extinguisher in all angles and vulnerable areas that are at risk of fire. Basically the principle of blackouts is to break the fire triangle which consists of heat, oxygen, and fuel. So that by knowing this, it can be done with the selection of outage media according to the risk and class of the accident. Maintenance of a extinguisher pump is very important to deal with damage or unwanted things happen. Maintenance if done regularly and in accordance with the guidebook will maintain a good pump performance. Key words: Fire, Fire Prevention System, Maintenance.
Toward Synergism in Macro Action Ensembles

Macro actions have been demonstrated to be beneficial for the learning processes of an agent. A variety of techniques have been developed to construct more effective macro actions. However, they usually fail to provide an approach for combining macro actions to form a synergistic macro action ensemble. A synergistic macro action ensemble performs better than individual macro actions within it. Motivated by the recent advances of neural architecture search, we formulate the construction of a synergistic macro action ensemble as a sequential decision problem and evaluate the ensemble in a task. The formulation of sequential decision problem enables coherency in the macro actions to be considered. Also, our evaluation procedure takes synergism into account since the synergism among the macro action ensemble exhibits when jointly used by an agent. The experimental results show that our framework is able to discover synergistic macro action ensembles. We further perform experiments to validate the synergism property among the macro actions in an ensemble.
THE FEATURES OF COGNITIVE STYLES OF SOFTWARE ENGINEERS IN EDUCATIONAL ACTIVITIES

The article identifies and characterizesthe features of the manifestation ofcognitive styles of future softwareengineers in the educational process inhigher education institutions. Theanalysis of the scientific works devotedto features of cognitive styles in themodern environment is carried out. Theclassification of student’s cognitivestyles according to his individual style isoffered. It is proved that themanifestation of cognitive styles infuture software engineers occurs inthree areas of activity - cognitive,communicative and creative. Accordingto each direction the detailedcharacteristic of its display on thefollowing parameters is given in thework: theoretical, active, affective style.
Why Does Decentralized Training Outperform Synchronous Training In The Large Batch Setting

Distributed Deep Learning (DDL) is essential for large-scale Deep Learning (DL) training. Using a sufficiently large batch size is critical to achieving DDL runtime speedup. In a large batch setting, the learning rate must be increased to compensate for the reduced number of parameter updates. However, a large batch size may converge to sharp minima with poor generalization, and a large learning rate may harm convergence. Synchronous Stochastic Gradient Descent (SSGD) is the de facto DDL optimization method. Recently, Decentralized Parallel SGD (DPSGD) has been proven to achieve a similar convergence rate as SGD and to guarantee linear speedup for non-convex optimization problems. While there was anecdotal evidence that DPSGD outperforms SSGD in the large-batch setting, no systematic study has been conducted to explain why this is the case. Based on a detailed analysis of the DPSGD learning dynamics, we find that DPSGD introduces additional landscape-dependent noise, which has two benefits in the large-batch setting: 1) it automatically adjusts the learning rate to improve convergence; 2) it enhances weight space search by escaping local traps (e.g., saddle points) to find flat minima with better generalization. We conduct extensive studies over 12 state-of-the-art DL models/tasks and demonstrate that DPSGD consistently outperforms SSGD in the large batch setting; and DPSGD converges in cases where SSGD diverges for large learning rates. Our findings are consistent across different application domains, Computer Vision and Automatic Speech Recognition, and different neural network models, Convolutional Neural Networks and Long Short-Term Memory Recurrent Neural Networks.
Evaluation of a Multi-Atlas Segmentation System

This chapter evaluates a multi-atlas segmentation system using an online atlas selection approach to choose a subset of optimal atlases for multi-atlas segmentation. The atlas selection is performed in two phases. In the first phase, the correlation coefficient of the image content in a cubic region is used to rank the atlases in an atlas pool. A subset of atlases based on this ranking is selected, and deformable image registration is performed to generate deformed contours and deformed images. In the second phase, Kullback-Leibler divergence is used to measure the similarity of local intensity histograms between the new image and each of the deformed images, and the measurements are used to rank the previously selected atlases to further identify a subset of optimal atlases. The deformed contours from these optimal atlases are fused together using a modified simultaneous truth and performance level estimation algorithm to produce the final segmentation. The approach is validated with promising results using a public grand challenge dataset for thoracic auto-segmentation.
Database Security Request for Proposal: Defending both Internal and Public Facing Databases against Known Vulnerabilities

Open Web Application Security Project (OWASP) vulnerabilities are important for business leaders to know and understand. The issues are well known and understood, as are the mitigation and fix actions to harden database vulnerabilities. Database vulnerabilities remain persistent because of the proliferation of databases in many aspects of data storage and web applications, where the perceived value of the information in the database is high and the attack mechanism is easy. There are actually several database vulnerabilities, depending on the database employment, which expands the targets and, thus, the vulnerabilities. Individual databases holding organizational data have vulnerabilities, as do web application databases— with web application SQL injection being the most well-known vulnerability.
Autonomous Learning of Object-Centric Abstractions for High-Level Planning

We propose a method for autonomously learning an object-centric representation of a continuous and high-dimensional environment that is suitable for planning. Such representations can immediately be transferred between tasks that share the same types of objects, resulting in agents that require fewer samples to learn a model of a new task. We first demonstrate our approach on a simple domain where the agent learns a compact, lifted representation that generalises across objects. We then apply it to a series of Minecraft tasks to learn object-centric representations, including object types—directly from pixel data—that can be leveraged to solve new tasks quickly. The resulting learned representations enable the use of a task-level planner, resulting in an agent capable of forming complex, long-term plans with considerably fewer environment interactions.
VIDEO REPORT USING ANDROID APPLICATION FOR IMPROVING STUDENTS' INFORMATION LITERACY IN LEARNING SOLAR SYSTEM

This study aims to investigate the effect of making a video report in learning Solar System can improve the information literacy skill of 7th-grade students. This study was conducted in a private school in Bandung. This research is used weak experimental research used one group pre-test post-test design. A sample of 32 students was selected conveniently from one class available in the school. All of the samples are treated by making the video report in the moon phases model simple project. The data was collected via the pre-test and post-test administration and Questionnaire to strengthen the result of students’ information literacy skills. The video report product also assesses by using the video report rubric. The results were statistically analyzed by using SPSS software by employing a paired sample t-test. The result shows 0.55 which is categorized into middle based on the category in Normalized Gain. Results indicate that after applied the treatment to the students, there is an improvement of students’ Information Literacy skills after making the video report. Therefore, the result of students’ information literacy showed that there was a significant difference between the result of the post-test and the result of the standard average of the knowledge dimension. It is concluded that making a video report is useful to conduct as the learning strategy to improve students’ Information Literacy Skills.
Predictive modelling of burnout among Kazakhstani English teacher candidates

Introduction. In Kazakhstan, 55% of English pre-service teacher candidates do not plan to work in public schools after graduation. The current quantitative study, using statistical analysis, sought to determine the causes of this phenomenon. The aim of the present research was to determine the probability of early career burnout among future English teachers using predictive modeling. Methodology and research methods. To create a predictive model, the authors used the following statistical data analysis tools: factor analysis, ordinal regression, and the Сhi-square test. The selection of the above tools provides to predict the probability of work intention in a Kazakhstani public school for more than five years. This period acts as a proxy variable for predicting future teachers’ burnout rates. The study involved 160 pre-service teachers, who received State scholarships for free tuition, with an obligation to work in public schools after graduation. Results and scientific novelty. The data indicated that some respondents would only work for a period of less than five years, due to a sense of despair about their profession. The results demonstrate that pre-service teachers require better training. Furthermore, the authors found a correlation in teaching career expectations and the gender gap, where mature female participants were more likely to carry an intention to work for more than five years. Practical significance. The present study makes a contribution to further research aimed at preventing the shortage and turnover of teaching staff in the workplace by detecting early warning signs of burnout among prospective teachers. The authors identified the indicators of the risk group of novice teachers, who are more prone to burnout due to emotional exhaustion, low self-efficacy, low expectations, and prospects from a teaching career. The authors believe that effective career guidance provides an adequate selection of the most motivated applicants with high occupational expectations.