Reviewerly/exHarmony huggingface dataset:
Language-guided Semantic Mapping and Mobile Manipulation in Partially Observable Environments.

Recent advances in data-driven models for grounded language understanding have enabled robots to interpret increasingly complex instructions. Two fundamental limitations of these methods are that most require a full model of the environment to be known a priori, and they attempt to reason over a world representation that is flat and unnecessarily detailed, which limits scalability. Recent semantic mapping methods address partial observability by exploiting language as a sensor to infer a distribution over topological, metric and semantic properties of the environment. However, maintaining a distribution over highly detailed maps that can support grounding of diverse instructions is computationally expensive and hinders real-time human-robot collaboration. We propose a novel framework that learns to adapt perception according to the task in order to maintain compact distributions over semantic maps. Experiments with a mobile manipulator demonstrate more efficient instruction following in a priori unknown environments.
“Internet + Internship Management”: College Internship Management Optimization

Internship is a core step in talent training in colleges and is related to knowledge increase and quality development of students. In teaching practice, colleges should consider how to give better play to the teachers' roles, how to solve the problems of ineffective communication and mismanagement in internships. “Internet+ higher education”, “big data + education management” and other new concepts emerge, providing new inspirations for optimization of internships. This paper analyzes the “Internet + internship management” model from the perspective of building an internship management network platform, relying on the internship management platform to focus on student professional growth, teacher-student communication and student employment, and formulating a scientific incentive system.
A Comprehensive Survey on Optimization Techniques in Image Processing

This paper proposes an analysis on various methods of optimization with requisition to image processing. The intent of the paper is to make available widespread evaluation of the optimization methods along with the applicability of different optimization methods. Traditional techniques in general are unsuccessful to resolve such large scale problems particularly with nonlinear objective functions. Some optimization techniques frequently fail to resolve optimization problems that have many local optima. To prevail over these problems, there is a need to extend further powerful optimization techniques. The modern techniques are used to solve linear, nonlinear, differential and non-differential optimization problems.This literature review compiles different work presented on optimization and concludes with the most accurate and appropriate method for optimization.
Knowledge-Based Fusion for Image Tampering Localization

In this paper we introduce a fusion framework for image tampering localization, that moves towards overcoming the limitation of available tools by allowing a synergistic analysis and multiperspective refinement of the final forensic report. The framework is designed to combine multiple state-of-the-art techniques by exploiting their complementarities so as to produce a single refined tampering localization output map. Extensive evaluation experiments of state-of-the-art methods on diverse datasets have resulted in a modular framework design where candidate methods go through a multi-criterion selection process to become part of the framework. Currently, this includes a set of five passive tampering localization methods for splicing localization on JPEG images. Our experimental findings on two different benchmark datasets showcase that the fused output achieves high performance and advanced interpretability by managing to leverage the correctly localized outputs of individual methods, and even detecting cases that were missed by all individual methods.
Scaling Mentoring Support with Distributed Artificial Intelligence

Mentoring is the activity when an experienced person (the mentor) supports a less knowledgeable person (the mentee), in order to achieve the learning goal. In a perfect world, the mentor would be always available when the mentee needs it. However, in the real world higher education institutions work with limited resources. For this, we need to carefully design socio-technical infrastructures for scaling mentoring processes with the help of distributed artificial intelligence. Our approach allows universities to quickly set up a necessary data processing environment to support both mentors and mentees. The presented framework is based on open source standards and technologies. This will help leveraging the approach, despite the organizational and pedagogical challenges. The deployed infrastructure is already used by several universities.
Learn2Link: Linking the Social and Academic Profiles of Researchers

People have presence across different information networks on the social web. The problem of user identity linking, is the task of establishing a connection between accounts of the same user across different networks. Solving this problem is useful for: personalized recommendations, cross platform data enrichment and verifying online information among others. In this paper, we propose a deep learning based approach that jointly models heterogeneous data: text content, network structure as well as profile names and images, in order to solve the user identity linking problem. We perform experiments on a real world problem of connecting the social profile (Twitter) and academic profile (DBLP) of researchers. Experimental results show that our joint model achieves a 97% F1 score outperforming state-of-the-art results that consider profile, content or network features only.
Development of a Recurrent Neural Network Model for English to Yorùbá Machine Translation

This research developed a recurrent neural network model for English to Yoruba machine translation.Parallel corpus was obtained from the English and Yoruba bible corpus.The developed model was tested and evaluated using both manual and automatic evaluation techniques.Results from manual evaluation by ten human evaluators show that the system is adequate and fluent.Also, results from automatic evaluation shows that the developed model has decent and good translation as well as higher accuracy because it has better correlation with human judgment.
BotSlayer: DIY Real-Time Influence Campaign Detection

BotSlayer is an application that helps track and detect potential manipulation of information spreading on Twitter. It can be used by journalists, corporations, political candidates, and civil society organizations to discover online coordinated campaigns in real time. BotSlayer uses an anomaly detection algorithm to flag hashtags, links, accounts, phrases, and media that are trending and amplified in a coordinated fashion by likely bots. A Web dashboard lets users explore the tweets and accounts associated with suspicious campaigns, visualize their spread, and search related content on multiple search engines and social media platforms. BotSlayer is easily installed and configured in the cloud. It will aid in the study and early detection of social media manipulation phenomena.
FINANCIAL DEVELOPMENT IN DEVELOPING COUNTRIES

What are the economic, political, institutional, socio-cultural, and geographical determinants of financial development in developing countries? This paper uses the two-way fixed effects (with clustered standard errors) and annual panel data from 1980 to 2018 for 69 developing countries in sub-Saharan Africa, Middle East and North Africa, East and South Asia, Latin America, and the Caribbean to address this question. The principal component analysis is employed to construct a financial development index based on three financial development indicators. This study builds on the previous studies by introducing new potential determinants of financial development such as the perception of corruption, and by exploring important quadratic and interaction effects. The results show that national income, trade openness, indices of political stability and Polity2 (a democracy score), perception of corruption, the predominant religion in the countries, and geographical factors such as territorial access to the sea explain the differences in the levels of financial development across countries and regions. A rise in national income leads to a higher level of financial development and countries with a high perceived level of corruption have a lower level of financial development. There is strong evidence of threshold effects as trade openness has a diminishing marginal effect on financial development while the auxiliary growth regressions show that financial development has an increasing marginal effect on national income. Of the five regions studied, East and South Asia and sub-Saharan Africa have the highest and lowest levels of financial development, respectively. Also, fuel-exporting countries, least developed countries, and landlocked countries tend to have lower levels of financial development. These results have relevant policy implications for developing countries in their continued efforts to achieve better financial development and ultimately, sustainable economic development.
Sequence Generation with Mixed Representations

Tokenization is the first step of many natural language processing (NLP) tasks and plays an important role for neural NLP models. Tokenization methods such as byte-pair encoding and SentencePiece, which can greatly reduce the large vocabulary size and deal with out-of-vocabulary words, have shown to be effective and are widely adopted for sequence generation tasks. While various tokenization methods exist, there is no common acknowledgement which one is the best. In this work, we propose to leverage the mixed representations from different tokenizers for sequence generation tasks, which can take the advantages of each individual tokenization method. Specifically, we introduce a new model architecture to incorporate mixed representations and a co-teaching algorithm to better utilize the diversity of different tokenization methods. Our approach achieves significant improvements on neural machine translation tasks with six language pairs, as well as an abstractive summarization task.
MC-Saar-Instruct: a Platform for Minecraft Instruction Giving Agents

We present a comprehensive platform to run human-computer experiments where an agent instructs a human in Minecraft, a 3D blocksworld environment. This platform enables comparisons between different agents by matching users to agents. It performs extensive logging and takes care of all boilerplate, allowing to easily incorporate new agents to evaluate them. Our environment is prepared to evaluate any kind of instruction giving system, recording the interaction and all actions of the user. We provide example architects, a Wizard-of-Oz architect and set-up scripts to automatically download, build and start the platform.
Diachronic Topics in New High German Poetry

Statistical topic models are increasingly and popularly used by Digital Humanities scholars to perform distant reading tasks on literary data. It allows us to estimate what people talk about. Especially Latent Dirichlet Allocation (LDA) has shown its usefulness, as it is unsupervised, robust, easy to use, scalable, and it offers interpretable results. In a preliminary study, we apply LDA to a corpus of New High German poetry (textgrid, with 51k poems, 8m token), and use the distribution of topics over documents for a classification of poems into time periods and for authorship attribution.
Synthesizing strategies under expected and exceptional environment behaviors

We consider an agent that operates with two models of the environment: one that captures expected behaviors and one that captures additional exceptional behaviors. We study the problem of synthesizing agent strategies that enforce a goal against environments operating as expected while also making a best effort against exceptional environment behaviors. We formalize these concepts in the context of linear-temporal logic, and give an algorithm for solving this problem. We also show that there is no trade-off between enforcing the goal under the expected environment specification and making a best-effort for it under the exceptional one.
A Novel Fused Random Pooling Method for Convolutional Neural Network to Improve Image Classification Accuracy

A convolutional neural network (CNN) is an artificial neural network type used in image recognition and processing. The layers of CNN comprise of an input layer, an output layer, and a hidden layer that contains multiple convolutional layers, pooling layers, and fully connected layers. However, CNN faces issues such as overfitting, which occurs when a model learns the detail and noise in training data to the extent that it negatively affects the model's performance on new data. In this study, a new technique of pooling called fused random pooling replaces the deterministic pooling of CNNs based on a random approach in choosing activation to create better pooled feature maps, thereby decreasing overfitting by performing regularization technique and hyper-parameter tuning by defining the network's best parameters such as the number of layers, filter size, and optimization algorithms. Decrease in the training error and increase in accuracy have been realized in the CIFAR-10, CIFAR-100, and Street View House Numbers (SVHN) datasets as compared to studies in various CNN pooling methods. In the CIFAR-10, the test error and accuracy attained 4.11 percent and 92.37 percent, respectively. In CIFAR-100, a 17.76 percent training error and 69.19 percent accuracy was realized. In the SVHN dataset, fused random pooling achieved a training error of 3.10 percent and accuracy of 96.90 percent. Therefore, fused random pooling proved to be efficient in terms of the decrease in training error and increase in accuracy.
Integrating VR-Enabled BIM in Building Design Studios, Architectural Engineering Program, UAEU: A Pilot Study

The Architecture, Engineering and Construction (AEC) industry is increasingly adopting Building Information Modeling (BIM) as a tool aiming at reducing errors and achieving efficient use of resources. Empowering BIM with virtual reality (VR) helps detecting changes and clashes easily and thus correct them before construction begins. Recently, VR-enabled BIM has been rapidly recognized and implemented in the AEC education due to its benefits of providing an engaging and immersive environment. With the aim to recognize its potentials and challenges, this research piloted a process of integrating the VR-enabled BIM as a teaching and learning tool in one of the conventionally delivered building design studios at the UAEU. The output of the research revealed the effectiveness of the tool in significantly enhancing the students' understanding of their building architectural, structural and constructional designs. Some challenges have been identified related to cost, availability, training, and other technical issues. Such difficulties should be overcome before entirely integrating the VR-enabled BIM as a teaching and learning tool in all building design relevant courses.
One size does not fit all: external driver of the cryptocurrency world

Purpose Considering the different motivation for the creation of each of these cryptocurrencies, the purpose of this paper is to examine whether there is a dominant external factor in the cryptocurrency world. Using a novel two-step time and frequency independent methodology, the authors examine a large scope of cryptocurrencies and external factors within the same period, and analytical framework. Design/methodology/approach The examined cryptocurrencies are Bitcoin, Ethereum, Ripple, Litecoin, Monero and Dash. In total, 18 external factors from 5 factor families are selected based on the mining motivation of these cryptocurrencies. The study first examines discrete wavelet transform-based (WTB) correlations, reduce the dimension and focuson relevant pairs. Selected pairs are further examined by wavelet coherence to capture the intermittent nature of the relationships allowing the most needed “Flexibility of frequency and time domains”. Findings Each coin appears to operate as a unique character with the exception of Bitcoin and Litecoin. There is no prominent external driver. The cryptocurrency market is not a clear substitute for a specific factor or market. Two-step WTB filtered wavelet coherence analysis help us to analyze a large number of factor without the loss of focus. The co-movements within the cryptocurrencies spillover from Ethereum to altcoins and later to Bitcoin. Originality/value The study presents one of the first examples of two-step WTB filtered wavelet coherence analysis. The methodology suggests an approach for simultaneous examination of large number of variables. The scope of the study provides a rather holistic view of the co-movements of external factors and major cryptocurrencies.
Land Record Maintenance Using Blockchain

Storing data in real time along with keeping it secure is the biggest challenge in industry today. Many land issues arise because there is no database which is protected for tracking the real-time changes in data. Also, land records currently are registered in paper format. This kind of data is thus vulnerable to any changes and maybe destroyed by natural or man-made disasters. The emerging blockchain technology is a boon to store any information in real time and is immune to any changes. In this paper, we propose a solution in the form of distributed app (DApp) which uses the idea of blockchain as distributed database, smart contracts using ethereum platform and Polyline API from Google to mark the land boundaries. Smart contracts allow the performance of credible transactions by using sophisticated cryptography and without interference from third parties. These transactions are traceable and irreversible. Proponents of smart contracts claim that many kinds of contractual clauses may be made partially or fully self-enforcing. In this case, along with the self-verifiable clauses, involving banking parties can perform additional monetary checking. A user can sell or transfer a property that he owns or may buy a new land plot open for sale in desired geographical area. This solution allows maintaining land records easily and in real time without having a single point of failure for the database system. Removal of third-party interventions such as brokers from the process of land title transfer between old and new owners makes the process more transparent and cheaper.
Harmonization Sometimes Harms

In this paper we argue that harmonization is not the preferred way to produce a gold standard in all cases. Neither does a majority vote based harmonization produce an appropriate gold standard centroid, nor would a mere centroid be a good basis for training a system that reproduces prototypical user reactions given some understanding task. We discuss these claims in the context of sentiment inference.
Asynchronous Gibbs Sampling.

Gibbs sampling is a Markov Chain Monte Carlo (MCMC) method often used in Bayesian learning. MCMC methods can be difficult to deploy on parallel and distributed systems due to their inherently sequential nature. We study asynchronous Gibbs sampling, which achieves parallelism by simply ignoring sequential requirements. This method has been shown to produce good empirical results for some hierarchical models, and is popular in the topic modeling community, but was also shown to diverge for other targets. We introduce a theoretical framework for analyzing asynchronous Gibbs sampling and other extensions of MCMC that do not possess the Markov property. We prove that asynchronous Gibbs can be modified so that it converges under appropriate regularity conditions -- we call this the exact asynchronous Gibbs algorithm. We study asynchronous Gibbs on a set of examples by comparing the exact and approximate algorithms, including two where it works well, and one where it fails dramatically. We conclude with a set of heuristics to describe settings where the algorithm can be effectively used.
Measuring acceleration fields via regularized digital image correlation

Abstract Digital image correlation (DIC) is a full-field measurement technique. In instantaneous approaches (i.e., registering two images), DIC only gives access to displacement (or velocity) fields. Consequently, acceleration fields are not one of the primary measured variables. To evaluate acceleration fields, a regularization scheme has to be used. The latter may be either heuristic or mechanically motivated. The key idea of the paper is to use spatiotemporal analyses in order to explicitly measure acceleration fields. Various regularization schemes will be assessed, and their relative merits will be studied when performing uncertainty quantifications. Pyrotechnic cutting simulations will provide a set of artificial pictures to be studied in order to validate the new implementations. This analysis enables the measurement performances to be evaluated for the new implementations.
Use of XR technologies to bridge the gap between Higher Education and Continuing Education

This paper describes an initiative that brings together the University of South-Eastern Norway (USN), its student representative body Student Democracy (SDSN), and the Dresser-Rand AS (D-R) engineering and manufacturing company (now called Siemens Oil and Gas) with the objective of creating modular 1-ECTS eXtended Reality (XR) learning packages (LP) created to build a continuum between the shop floor and the classroom. These LPs will not only be offered as part of standard USN bachelor and master programs, but also as continuing education for D-R employees. XR and other cloud technologies are used to create innovative education and training content, and at the same time to help close the gap between the classroom and the shopfloor, by enabling USN academic staff to interact live with company trainees during on-the-job training, and by enabling industry experts to interact live with students in USN classrooms.
Low-level multiscale image segmentation and a benchmark for its evaluation

In this paper, we present a segmentation algorithm to detect low-level structure present in images. The algorithm is designed to partition a given image into regions, corresponding to image structures, regardless of their shapes, sizes, and levels of interior homogeneity. We model a region as a connected set of pixels that is surrounded by ramp edge discontinuities where the magnitude of these discontinuities is large compared to the variation inside the region. Each region is associated with a scale that depends upon the fraction of the strong and weak parts of its boundary. Traversing through the range of all possible scales, we obtain all regions present in the image. Regions strictly merge as the scale increases; hence a tree is formed where the root node corresponds to the whole image, nodes closer to the root along a path correspond to larger regions and those further from the root capture smaller regions representing embedded details. To evaluate the accuracy and precision of our algorithm, as well as to compare it to the existing algorithms, we present a new benchmark dataset for low-level image segmentation. We provide evaluation methods for both boundary-based and region-based performance of algorithms. We also annotate different parts of the images that are difficult to segment with the types of segmentation challenges they pose. This enables our benchmark to give an account of which algorithm fails where. We show that our proposed algorithm performs better than the widely used low-level segmentation algorithms on this benchmark.