Reviewerly/exHarmony huggingface dataset:
NP-PROV: Neural Processes with Position-Relevant-Only Variances

Neural Processes (NPs) families encode distributions over functions to a latent representation given a set of context data, and decode posterior mean and variance at unknown locations. Since mean and variance are derived from the same latent space, they may fail on out-of-domain tasks where fluctuations in function values amplify the model uncertainty. We present a new member named Neural Processes with Position-Relevant-Only Variances (NP-PROV). NP-PROV hypothesizes that a target point close to a context point has small uncertainty, regardless of the function value at that position. The resulting approach derives mean and variance from a function-value-related space and a position-related-only latent space separately. Our evaluation on synthetic and real-world datasets reveals that NP-PROV can achieve state-of-the-art likelihood while retaining a bounded variance when drifts exist in the function value.
Renormalization Approach to the Task of Determining the Number of Topics in Topic Modeling

Topic modeling is a widely used approach for clustering text documents, however, it possesses a set of parameters that must be determined by a user, for example, the number of topics. In this paper, we propose a novel approach for fast approximation of the optimal topic number that corresponds well to human judgment. Our method combines the renormalization theory and the Renyi entropy approach. The main advantage of this method is computational speed which is crucial when dealing with big data. We apply our method to Latent Dirichlet Allocation model with Gibbs sampling procedure and test our approach on two datasets in different languages. Numerical results and comparison of computational speed demonstrate a significant gain in time with respect to standard grid search methods.
Full-Text Retrieval System for Structured Data of University Archives

Through the research on the demand of archives utilization, it shows that the full-text retrieval strategy based on the original documents of university archives has some defects, such as bloated index library, slow retrieval speed and difficult to locate page numbers. By extracting abstract information from original documents, this paper expands the information quantity of archives structured data, and constructs a full-text retrieval system based on structured data using Lucene. Through theoretical analysis and simulation comparison, it's shown that the index library is greatly reduced, the retrieval speed is significantly improved, and there's only little change of the recall.
Block-level based Query Data Access Service Availability for Query Process System

Blockchain is a kind of apportioned database. It is a genuine kind of circulated database with especially exact geographies and limits to without issues inquiry current blockchains are to a great extent by the by underneath improvement, as well as accessible for fingers-on designers that is Querying. The Blockchain isn't constantly similar to a social database.Despite the way that databases store convoluted tables, a cryptocurrency Blockchain is regularly nothing more noteworthy than a huge rundown of advanced marks, exchange information and extraordinary qualities utilized for ascertaining mining trouble. A blockchain incorporates a chain of blocks, with each block highlighting the previous block inside the chain. A block may furthermore have a grouping of exchanges, and different records identified with those exchanges, for example, the sum, an advanced mark of the exchange initiator, and the arrangement with of the beneficiary, among various things. Despite the fact that, Data Access questioning capacity of a Blockchain database trust upon at the execution that BitcoinBlockchain can be questioned.
Provenance Location-based Authentication in Cloud Computing

Cloud computing will continue flourishing. Like any technology that is widely used and advancing with many benefits, it also comes with issues, some of which are unavoidable. Security is one of the major concerns for cloud computing. To protect user information leakages or system intrusion, the first simple step is to make sure that only legitimate users can access the computing resources. This makes user authentication an important safeguard. There are many approaches to authentication for users in the cloud environments. The password-based authentication is one such technique that is old but is still in use because it is simple. Unfortunately, it is also vulnerable for attack. To alleviate this drawback, recent research has applied provenance to authentication but most do not deal with location. Using a location as part of authentication increasingly becomes necessary, as mobile and wearable technology become a big part of our daily life. This paper presents a new approach to authentication on cloud computing environments. The approach uses data provenance as well as a location of user request. The paper describes the approach, illustrates its use in use case scenarios, and compares its performance with that of the machine-learning model. Using a data synthesized from simulated authentication system, it is shown that the decision tree model has an average of 83.5% accuracy while ours, by relying on theoretical ground, results in 100% accuracy.
On the relationship between network connectivity and group performance in small teams of humans: experiments in virtual reality

Abstract Optimizing group performance is one of the principal objectives that underlie human collaboration and prompts humans to share resources with each other. Connectivity between individuals determines how resources can be accessed and shared by the group members, yet, empirical knowledge on the relationship between the topology of the interconnecting network and group performance is scarce. To improve our understanding of this relationship, we created a game in virtual reality where small teams collaborated toward a shared goal. We conducted a series of experiments on 30 groups of three players, who played three rounds of the game, with different network topologies in each round. We hypothesized that higher network connectivity would enhance group performance due to two main factors: individuals’ ability to share resources and their arousal. We found that group performance was positively associated with the overall network connectivity, although registering a plateau effect that might be associated with topological features at the node level. Deeper analysis of the group dynamics revealed that group performance was modulated by the connectivity of high and low performers in the group. Our findings provide insight into the intricacies of group structures, toward the design of effective human teams.
Conceptualizing Technology-Enhanced Learning Constructs: A Journey of Seeking Knowledge Using Literature-Based Discovery

Successful implementation remains a challenge as technology-enhanced learning (TEL) continues to progress. Therefore, it is necessary to develop a TEL maturity model as an assessment method and improvement roadmap. Recalling the analogy standing on the shoulders of giants, it emphasizes that we should build a model on previously established knowledge in literatures. Nonetheless, this resolution is followed by several challenges. They include the scope of discussion, purposive review, subjectivity, interpretable result, and trustworthiness. Thus, we set out the research objective on determining TEL maturity constructs using a literature-based knowledge discovery. This study uses content analysis as a technique to process the literatures, covering 548 research papers from 12 repositories. As an approach to building constructs and resolve the challenges as mentioned earlier, this study discusses how to set a research direction and uses Leximancer to improve the trustworthiness for both the analysis process and results. We then interpret the results to build a TEL maturity model constructs.
Guidelines for practical navigation systems based on wide‐field‐integration of optic flow

Abstract This paper shows some guidelines to improve the estimation accuracy of a navigation system, which is based on wide‐field‐integration (WFI) of optic flow. Optic flow is a vector field of relative velocities obtained by photoreceptors in image sensors, and WFI of optic flow enables motion estimation robust by integrating wide range of optic flow. Since the system has several attractive features (small size, light weight, and low computation), it is applicable to an autonomous control system of micro air vehicles. However, due to some restrictions of real systems, WFI of optic flow theory should be applied while considering sensor arrangement for better estimation. In this paper, first an adequate number and adequate optical‐axes of image sensors are investigated. Then, a new estimation system combining a gyro sensor with WFI of optic flow is discussed by numerical simulations. Finally, the essential effect for the number of cameras are verified in experiments.
An Architecture for Real-Time Reasoning and Learning

This paper compares the various conceptions of “real-time” in the context of AI, as different ways of taking the processing time into consideration when problems are solved. An architecture of real-time reasoning and learning is introduced, which is one aspect of the AGI system NARS. The basic idea is to form problem-solving processes flexibly and dynamically at run time by using inference rules as building blocks and incrementally self-organizing the system’s beliefs and skills, under the restriction of time requirements of the tasks. NARS is designed under the Assumption of Insufficient Knowledge and Resources, which leads to an inherent ability to deal with varying situations in a timely manner.
Probabilistic Neural Network to Classify Image of Children’s Face with Down Syndrome

Down Syndrome is a condition of the physical and mental underdevelopment of a child due to chromosomal development abnormalities. This condition has different facial symptoms. Although Down syndrome has a unique feature on the face, they have a similar face with their parents and siblings, so it is quite complex to tell the difference. These symptoms contain specific information for facial recognition. In this research, the proposed method consists of five stages. The first stage is the input image. The second stage is pre-processing which composed of grayscale and CLAHE processes. The third stage is the image segmentation to bring up the special features of the image. The process continued with feature extraction that will generate an invariant moment. The final stage is the classification process to determine the type of face such as mosaic, trisomy 21 or without syndrome. The test result using the test data of 22 images showed that the system has an accuracy rate of 91% with the success percentage for recall and precision is at 88%.
Global Goods Software for the Immunization Cold Chain

This paper explores the challenge of taking Global Goods software to international scale. Global Goods software is non-commercial software designed to support global development goals. We argue that a fundamental challenge behind this type of software is the different roles of the global organizations that fund projects, the country leadership that controls implementation, and the actual users of the software. To address this, it necessary to have a design process that balances interests of stakeholders and a technical design that allows for modularity and extensibility. We present a case study of an application for country level management of the immunization cold chain that we have developed and contrast it with major Global Goods software systems such as DHIS2 and OpenMRS. The contributions of the work include the design of a pipeline for building a Global Goods application that is deployed across multiple countries, a collection of lessons learned during system design and implementation, and a comparison of extensibility strategies of different global goods applications.
Extreme Learning Machine Under Minimum Information Divergence Criterion

In recent years, extreme learning machine (ELM) and its improved algorithms have been successfully applied to various classification and regression tasks. In these algorithms, MSE criterion is commonly used to control training error. However, MSE criterion is not suitable to deal with outliers, which can exist in general regression or classification tasks. In this paper, a novel extreme learning machine under minimum information divergence criterion (ELM-MinID) is proposed to deal with the training set with noises. In minimum information divergence criterion, the Gaussian kernel function and Euclidean information divergence are utilized to substitute the mean square error (MSE) criterion to enhance the anti-noise ability of ELM. Experimental results on two synthetic datasets and eleven benchmark datasets show that this method is superior to traditional ELMs.
Recognition and Location of Persimmons Based on K-Means and Epipolar Constraint SIFT Matching Algorithm

Aiming at the requirements of intelligent persimmon picking operations, this paper designs a recognition and positioning system for persimmon picking robots. The system uses the H component under the HSV model to preprocess the image, uses K-means clustering algorithm for segmentation, and successfully extracts fruit targets after subsequent morphology and desiccation processing. In order to extract the three-dimensional coordinates of the fruit, the images obtained by the left and right cameras after calibration are matched using the SIFT algorithm. Finally, the distance is solved by the similar triangle principle. After testing, the sample image cluster center is 2, the number of iterations is 200, and the working range is 200mm ~ 800mm. The recognition success rate is about 90%, and the positioning error is ±15mm, which can fully meet the needs of picking robot operations.
Active and passive information behaviour of the professoriate: A descriptive comparative pattern analysis

Background: The study explored and analysed patterns in active and passive information behaviour of the professoriate in different information sources in teaching and research context. Exploring patterns in human information behaviour fills the knowledge gaps in this under-researched area, besides having practical significance. Objectives: The study explored the patterns in active and passive information behaviour in different information sources used by the professoriate in the social sciences and humanities in three federal universities in Nigeria. Methods: The study used a descriptive survey to explore the active and passive information behaviour of the professoriate. The sample consisted of 246 professors from the social sciences and humanities departments at three federal universities in Nigeria. Data were collected using an adapted questionnaire and analysed descriptively using the Statistical Package for Social Sciences. Results: The patterns that emerged across electronic resources, media, print sources, interpersonal and academic gathering, in three usage categories, showed both consistent and divergent results. A broad pattern in frequently-used sources revealed that the more active information seeking takes place in an information source, the more chances of passive encounters, and vice versa. This pattern is, however, consistent in electronic resources, print sources, and academic gathering, but differs in media and interpersonal sources. Media has more instances of passive encounters than active usage whilst in interpersonal sources, information encountered in active engagements with professional colleagues did not yield significant result. Conclusion: Exploring patterns in human information behaviour is still evolving, with the benefit of advancing a better understanding of active and passive information behaviour at a micro level.
Digital Prequalification for Nursing Trainees with Migration Background

As a profession with high demands on theoretical as well as practical medical knowledge, nursing is an area in which trainees and trainers can benefit strongly from digital learning technologies. This applies even more to trainees with migration background, as the demand on theoretical and practical expertise in nursing is accompanied by high requirements on technical as well as colloquial linguistic proficiency. At this point, digital learning and training can help profoundly by including multi-lingual functionality. At the same time, advanced technologies like virtual reality become more and more important. Consequently, factors like ease of use, comprehensibility, attractiveness, scalability and extensibility need consideration in equal measure in the design and implementation of a learning solution. In this paper, we look at the challenges, give some examples of existing solutions and outline our current project for a mutli-channel approach, funded by the German Ministry of Health.
Counting Crowds with Perspective Distortion Correction via Adaptive Learning

The goal of crowd counting is to estimate the number of people in the image. Presently, use regression to count people number became a mainstream method. It is worth noting that, with the development of convolutional neural networks (CNN), methods that are based on CNN have become a research hotspot. It is a more interesting topic that how to locate the site of the person in the image than simply predicting the number of people in the image. The perspective transformation present is still a challenge, because perspective distortion will cause differences in the size of the crowd in the image. To devote perspective distortion and locate the site of the person more accuracy, we design a novel framework named Adaptive Learning Network (CAL). We use the VGG as the backbone. After each pooling layer is output, we collect the 1/2, 1/4, 1/8, and 1/16 features of the original image and combine them with the weights learned by an adaptive learning branch. The object of our adaptive learning branch is each image in the datasets. By combining the output features of different sizes of each image, the challenge of drastic changes in the size of the image crowd due to perspective transformation is reduced. We conducted experiments on four population counting data sets (i.e., ShanghaiTech Part A, ShanghaiTech Part B, UCF_CC_50 and UCF-QNRF), and the results show that our model has a good performance.
Unlocking Oilfield Development using Blockchain

Abstract Many operators and service providers integrate oilfield data in the form of data hubs. Third party vendors often control these data hubs in the oil and gas industry, and data quality is often challenged. The digital gentrification of legacy wells and optimization of subsurface data management for exploration is at reach by applying smart technology ledgers, better known as blockchain. Blockchain technology provides a platform to keep all data linked. The data is smoothly shared among different groups like exploration, drilling, completion and production. Over time, some data might be lost of which the inheriting company might not be aware. If all the data is loaded onto the blockchain, it becomes easy to track. When we deal with subsurface analysis in offset well analysis, the data of all wells in a field (or a nearby field) readily become available on a blockchain database to assist in better decision-making. The data generated are stored in blockchain and can act as historical data in the future to better train models and assist in making informed decisions in both green and brown fields, as in subsurface analysis. For instance, a reservoir characterization project or a determination of landing zone progresses smoother when all historical data are available for review. Another opportunity for blockchain is in the field of legacy wells. When acquisition of wells happens, the inheriting company receives data, and some of which is still in traditional paper report format. Over time, inheriting companies might not be aware of the risk of losing 25 to 40% of these legacy data. If all this data is loaded onto the blockchain, it becomes easier to track the history of these legacy wells, and they can be better optimized. On production inventories, the immediate effect translates in the real time quantification of reported production and the inventories, which have an instantaneous effect on the price of commodities like WTI crude or Brent crude instead of waiting longer to correct estimated values. The carbon negative narrative also offers a unique opportunity to connect private investors with operator leadership. As capital is at a premium to sustain development campaigns in the Permian basin and as more incentives in the form of tax credits become available for CO2 injection, investors need a tool to sustain the trust where they deploy their capital. By generating smart contracts in blockchain as a medium to elevate trust and involve players immediately as they report volumes of CO2 injected, they have a first view at how many tax credit incentives they become eligible to claim. Application of Blockchain represents an opportunity for digital gentrification across upstream, midstream and downstream in the energy sector. It also acts as a tool to connect and accelerate environmental efforts. The quicker its adoption, the larger the benefits and profits become.
Moving for the Movement

By leveraging approaches from other disciplines, designers can expand the boundaries of interaction design to tackle complex socio-technical problems. To address the challenges of networked social justice movements, we developed a workshop for designers and social justice activists based in Viewpoints and Composition, a philosophy and set of techniques for the theatre. Building on other experience prototyping and somatic methods, the workshop leads participants through the design of a hypothetical internet-enabled social justice campaign, encouraging them to imagine the felt-experience of networked social justice movement building in a socio-spatial context. We conclude with insights from the workshop and plans to further develop these techniques.
Adaptive Thresholding Algorithms and Morphological to Improve the Quality of Takepan Sasak Image Readability

Abstract One of the cultural heritages in West Nusa Tenggara, especially Lombok, is takepan (lontar ancient manuscripts). Takepan written on leaf sheets of Lontar is known as the jejawan script (derived from ancient Javanese). local adaptive[1] adaptively setting thresholds for each pixel can behave better than the global method in degraded and damaged document images. Adaptive binarasi can also reduce noise when segmenting text from the background. The process of determining the threshold value works on blocks in the image. Images that have contrast and lighting variations that are very difficult to solve as background or background because there are a lot of pixels, this recommends using Local Adaptive Thresholding. From the research that has been done, it can be concluded that adaptive thresholding and morphology with dilation approach help maximize image readability with the highest PSNR value reaching 34,107 dB. In further research can be developed using feature extraction so that it can recognize the character of the letter in takepan.