Reviewerly/exHarmony huggingface dataset:
Researching the Research: Applying Machine Learning Techniques to Dissertation Classification

This research examines industry-based dissertation research in a doctoral computing program through the lens of machine learning algorithms to determine if natural language processing-based categorization on abstracts alone is adequate for classification. This research categorizes dissertation by both their abstracts and by their full-text using the GraphLab Create library from Apple’s Turi to identify if abstract analysis is an adequate measure of content categorization, which we found was not. We also compare the dissertation categorizations using IBM’s Watson Discovery deep machine learning tool. Our research provides perspectives on the practicality of the manual classification of technical documents; and, it provides insights into the: (1) categories of academic work created by experienced fulltime working professionals in a Computing doctoral program, (2) viability and performance of automated categorization of the abstract analysis against the fulltext dissertation analysis, and (3) natual language processing versus human manual text classification abstraction.
Causal Query in Observational Data with Hidden Variables.

This paper discusses the problem of causal query in observational with hidden variables, with the aim of seeking the change of an outcome when manipulating a variable while given a set of plausible confounding variables which affect the manipulated variable and the outcome. Such an on data to estimate the causal effect of the manipulated variable is useful for validating an experiment design using historical or for exploring confounders when studying a new relationship. However, existing data-driven methods for causal effect estimation face some major challenges, including poor scalability with high dimensional data, low estimation accuracy due to heuristics used by the global causal structure learning algorithms, and the assumption of causal sufficiency when hidden variables are inevitable in data. In this paper, we develop a theorem for using local search to find a superset of the adjustment (or confounding) variables for causal effect estimation from observational under a realistic pretreatment assumption. The theorem ensures that the unbiased estimate of causal effect is included in the set of causal effects estimated by the superset of adjustment variables. Based on the developed theorem, we propose a data-driven algorithm for causal query. Experiments show that the proposed algorithm is faster and produces better causal effect estimation than an existing data-driven causal effect estimation method with hidden variables. The causal effects estimated by the proposed algorithm are as accurate as those by the state-of-the-art methods using domain knowledge.
A multidimensional framework to study accessible guidance in u-learning systems for disabled learners

As with normal learners, disabled learners should also benefit from technology and more specifically accessible technologies. The design and implementation of learning solutions adapted to the needs of these learners are the major challenges faced by situation-aware ubiquitous learning systems. The learning process guidance is one of the most important forms of adaptation. This is why our study will focus on guidance work and those dedicated to disabled learners. The aim of this paper is to give an overview of the state-of-art in situation-aware ubiquitous learning systems. To do so, the study adopts a classification framework based on four different views (awareness, adaptation, guidance and u-accessibility). The framework has been used six ubiquitous learning systems dealing with guidance and / or learning for disabled and/or non-disabled learners.
Fitness Landscape Analysis of Dimensionally-Aware Genetic Programming Featuring Feynman Equations

Genetic programming is an often-used technique for symbolic regression: finding symbolic expressions that match data from an unknown function. To make the symbolic regression more efficient, one can also use dimensionally-aware genetic programming that constrains the physical units of the equation. Nevertheless, there is no formal analysis of how much dimensionality awareness helps in the regression process. In this paper, we conduct a fitness landscape analysis of dimensionally-aware genetic programming search spaces on a subset of equations from Richard Feynman’s well-known lectures. We define an initialisation procedure and an accompanying set of neighbourhood operators for conducting the local search within the physical unit constraints. Our experiments show that the added information about the variable dimensionality can efficiently guide the search algorithm. Still, further analysis of the differences between the dimensionally-aware and standard genetic programming landscapes is needed to help in the design of efficient evolutionary operators to be used in a dimensionally-aware regression.
On modality bias in the TVQA dataset.

TVQA is a large scale video question answering (video-QA) dataset based on popularTV shows. The questions were specifically designed to require “both vision and languageunderstanding to answer”. In this work, we demonstrate an inherent bias in the datasettowards the textual subtitle modality. We infer said bias both directly and indirectly,notably finding that models trained with subtitles learn, on-average, to suppress videofeature contribution. Our results demonstrate that models trained on only the visualinformation can answer ∼45% of the questions, while using only the subtitles achieves∼68%. We find that a bilinear pooling based joint representation of modalities damagesmodel performance by 9% implying a reliance on modality specific information. Wealso show that TVQA fails to benefit from the RUBi modality bias reduction techniquepopularised in VQA. By simply improving text processing using BERT embeddings withthe simple model first proposed for TVQA, we achieve state-of-the-art results (72.13%)compared to the highly complex STAGE model (70.50%). We recommend a multimodalevaluation framework that can highlight biases in models and isolate visual and textualreliant subsets of data. Using this framework we propose subsets of TVQA that respondexclusively to either or both modalities in order to facilitate multimodal modelling asTVQA originally intended.
Optimization of Low-Cost Monitoring Systems for On-Site Earthquake Early-Warning of Critical Infrastructures

In the last years, monitoring systems based on low-cost and miniaturized sensors (MEMS) revealed as a very successful compromise between the availability of data and their quality. Also applications in the field of seismic and structural monitoring have been constantly increasing in term of number and variety of functions. Among these applications, the implementation of systems for earthquake early warning is a cutting-edge topic, mainly for its relevance for the society as millions of peoples in various regions of the world are exposed to high seismic hazard. This paper introduces the optimization of an already established seismic (and structural) monitoring system, that would make it suitable for the implementation of the earthquake early warning. In particular, the sampling code has been improved and a new triggering algorithm able to automatically detect the ground shaking due to the propagation of the seismic waves has been developed. The preliminary results indicate that the system is very flexible and easy to implement, and encourage to perform further developing steps.
Resource reduction for quantum error correction using quantum multiplexed photons

Quantum error correction codes require large numbers of physical resources, in terms of photons and qubits. Here, we show how we can drastically reduce the number of photons required in quantum communication through quantum multiplexing.
Semi-supervised Sequential Generative Models

We introduce a novel objective for training deep generative time-series models with discrete latent variables for which supervision is only sparsely available. This instance of semi-supervised learning is challenging for existing methods, because the exponential number of possible discrete latent configurations results in high variance gradient estimators. We first overcome this problem by extending the standard semi-supervised generative modeling objective with reweighted wake-sleep. However, we find that this approach still suffers when the frequency of available labels varies between training sequences. Finally, we introduce a unified objective inspired by teacher-forcing and show that this approach is robust to variable length supervision. We call the resulting method caffeinated wake-sleep (CWS) to emphasize its additional dependence on real data. We demonstrate its effectiveness with experiments on MNIST, handwriting, and fruit fly trajectory data.
Joint Hierarchical Semantic Clipping and Sentence Extraction for Document Summarization

Extractive document summarization aims to select a few sentences while preserving its main information on a given document, but the current extractive methods do not consider the sentence-information repeat problem especially for news document summarization. In view of the importance and redundancy of news text information, in this paper, we propose a neural extractive summarization approach with joint sentence semantic clipping and selection, which can effectively solve the problem of news text summary sentence repetition. Specifically, a hierarchical selective encoding network is constructed for both sentence-level and documentlevel document representations, and data containing important information is extracted on news text; a sentence extractor strategy is then adopted for joint scoring and redundant information clipping. This way, our model strikes a balance between important information extraction and redundant information filtering. Experimental results on both CNN/Daily Mail dataset and Court Public Opinion News dataset we built are presented to show the effectiveness of our proposed approach in terms of ROUGE metrics, especially for redundant information filtering.
Improving the Quality of Requirements in Middleware Requirements Specifications

The importance of middleware in the success of IT and enterprise systems cannot be over emphasized. Digital Transformation is increasing this importance exponentially. Despite such importance, the topic of requirements engineering in middleware has limited attention in the literature. The authors have observed in their experience that the challenges faced during the requirements elicitation for middleware projects result in poor Quality of Requirements (QoR). It is thus important to understand the characteristics, nature, structure and process of middleware requirements as well as these challenges better to be able to improve the QoR. Based on the issues faced due to unavailability of adequate data and people's time in many middleware projects in over twenty five years, the authors and their organization have developed and leveraged successfully a structured methodology to elicit and document the requirements for middleware with high quality by working around the limitations faced in such projects.
Towards the Specification and Verification of Legal Contracts

A contract is a legally binding agreement that expresses high-level requirements of parties in terms of obligations, powers and constraints. Parties' actions influence the status of a contract and shall comply with its clauses. Manual contract monitoring is very laborious in real markets, such as transactive energy, where plenty of complex contracts are running concurrently. Furthermore, liability, right and performance transition through run-time operations such as subcontracting, assignment and substitution complicate contract interpretation. Automation is needed to ensure that contracts respect desirable properties and to support monitoring of compliance and handling of violations. In this thesis research, I propose an innovative ontology that defines fundamental contractual notions (such as the ones mentioned above) and their relationships, on which is built a specification language, called Symboleo, that provides syntax and axiomatic semantics of contracts via first-order logic. Symboleo enables the development of advanced automation tools such as a compliance checker that monitors contracts at runtime, and a model checking verification method that analyzes liveness and safety properties of contracts. This paper reports on the problem domain, research method, current status, expected contributions, and main foreseen challenges.
WarningNet: A Deep Learning Platform for Early Warning of Task Failures under Input Perturbation for Reliable Autonomous Platforms

There is a growing interest in deploying complex deep neural networks (DNN) in autonomous systems to extract task-specific information from real-time sensor data and drive critical tasks. The perturbations in sensor data due to noise or environmental conditions can lead to errors in information extraction and degrade reliability of the entire autonomous systems. This paper presents a light-weight deep learning plat-form, WarningNet, that operates on sensor data to estimate potential task failures due to spatiotemporal input perturbations. Experimental results show that WarningNet can provide early warning of the performance degradation of different tasks within a fraction of the time required for the task to complete. As a case-study, we show that the early warning can be leveraged to improve the task reliability under adverse condition using on-demand input pre-processing.
LIBRARY SERVICES FROM HOME (LSFH)

Problem Statement: The author focuses on limited online library services, direct verbal communication, access to referrals, librarian resource conditions and diversity of LSFH support facilities. Purpose: This study aims to know the LSFH policy by the Library in deciding the spread of COVID-19 on IPDN Method: This study uses qualitative methods. The technique of collecting data observation, documentation, and online interviews of 10 respondents consisting of 5 LSFH librarians and 5 LSFH service recipients Result: The results showed that the e-Resources Service was the easiest to implement and improved. Scientific Publication Services had the greatest technical challenges due to constrained server and application access, while the Library Guidance Service was a challenge to the capability and quality of delivering a librarian Conclusion: The LSFH policy by IPDN Library runs quite well. The writer's suggestion to equalization of the capacity of librarians with sufficient bandwidth, librarians' capacity improvement, and improvement of applications and scientific publication server.
Evolutionary Algorithms in Intelligent Systems

Evolutionary algorithms and metaheuristics are widely used to provide efficient and effective approximate solutions to computationally difficult optimization problems [...]
S, R, and Data Science

Data science is increasingly important and challenging. It requires computational tools and programming environments that handle big data and difficult computations, while supporting creative, high-quality analysis. The R language and related software play a major role in computing for data science. R is featured in most programs for training in the field. R packages provide tools for a wide range of purposes and users. The description of a new technique, particularly from research in statistics, is frequently accompanied by an R package, greatly increasing the usefulness of the description. The history of R makes clear its connection to data science. R was consciously designed to replicate in open-source software the contents of the S software. S in turn was written by data analysis researchers at Bell Labs as part of the computing environment for research in data analysis and collaborations to apply that research, rather than as a separate project to create a programming language. The features of S and the design decisions made for it need to be understood in this broader context of supporting effective data analysis (which would now be called data science). These characteristics were all transferred to R and remain central to its effectiveness. Thus, R can be viewed as based historically on a domain-specific language for the domain of data science.
CIFAR-10 Image Classification Based on Convolutional Neural Network

Aiming at the problems of gradient diffusion and network redundancy caused by the degradation of network performance during the training of most convolutional neural image classification models, the ResNet neural network model was improved, and the data expansion technology was used to expand the data and use SGD to fine-tune the depth Convolutional network to avoid gradient dissipation.In the CIFAR-10 test set, experiments show that the test accuracy of CIFAR-10 in the model data set reaches 90.85%.Compared with other models, it improves the accuracy of image classification.And by manually observing and comparing the classification effects of the 10 types of objects of the model, the model can distinguish each object more accurately.
Student Satisfaction Towards the Use of F-Learn Services in Online Learning

This research aims to analyse the level of student satisfaction as the users of F-Learn service in online learning. The level of student satisfaction is measured by using the End User Computing Satisfaction (EUCS), model which includes five dimensions, namely: 1) content; 2) accuracy; 3) format; 4) ease of use; and 5) timeliness. The research method used was a survey with a quantitative descriptive approach. The sampling technique used was saturated sampling because the population used in this study was relatively small, namely 30 students as the users of the F-Learn service in online learning. The results show that the level of student satisfaction as users of the F-Learn service in all dimensions are at a good level. Meanwhile, the level of satisfaction in each dimension shows: 1) the content is at an acceptable level; 2) accuracy at the level is quite good; 3) format at an acceptable level, 4) ease of use at an acceptable level; and 5) timeliness at a good enough level. The findings of this study will be used as a measuring tool for the e-learning system (F-Learn) and as input for further development.
Blockchain-Based Chit Fund System: A Financial Inclusion Tool

Recent government surveys indicate the presence of a large populous, who do not have access to banking or credit services. The majority of these people reside in rural areas, where there is a lack of networking and banking infrastructure. Also, several restrictions are imposed by the government in order to be eligible for availing various services. The banks and other financial institutions are also challenged from a business standpoint to make these services available in the remote areas. In order to tackle this issue, a blockchain-based chit fund solution was designed in this paper. Here, the main motive is to provide financial inclusion for these indigent people. Besides that, with blockchain technology, an innovative system can be developed to help individuals to save or credit as per their need.
Instruction Extension of an Open Source RV32IMC Core for NTRU Cryptosystem

Post-quantum cryptography is one of the popular research topics today. The implementation of the proposed algorithms is also extremely important. In this study, the NTRU cryptosystem, which is one of the post-quantum cryptography candidate algorithms, is designed and run on an open source RV32IMC processor. New instruction candidates have been developed and integrated into the functional unit of the processor with a functional profiling method. The results show that NTRU cryptosystem can be accelerated by 32.3% with this simple method.
An Architecture for Argumentation-Based Epistemic Planning: A First Approach With Contextual Preferences

Argumentation-based planning systems present an interesting proposal for complex and dynamic domains where defeasible argumentation is used in the reasoning processes during the construction of plans by considering the available knowledge. In many real-world application scenarios, knowledge is provided with explicit priorities. Despite its importance, existing planning systems do not provide additional reasoning capacities of dynamically changing the preferences expressed by these priorities when a plan is being constructed. In this article, we present an argumentation and planning architecture, and propose a set of software engineering guidelines to analyze and design planning systems leveraging this capacity.
Construction of Econometric Model of Social Economic Loss Caused by Water Resources Imbalance Risk

Yang, H.; Xi, X.; Han, S.S., and Pang, Y.P., 2020. Construction of econometric model of social economic loss caused by water resources imbalance risk. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 730–733. Coconut Creek (Florida), ISSN 0749-0208.Due to the imbalance risk of water resources, resulting in social and economic losses, this paper puts forward and designs the measurement model of social and economic losses caused by the risk of water resources imbalance. Through water supply and water demand two aspects of water resources in the planning year are predicted to determine whether the supply and demand of water resources are balanced, so as to analyze the risk of water resources imbalance; select risk evaluation indicators, use entropy method to determine the weight of evaluation indicators, and divide the risk level of water resources imbalance, and build a fuzzy comprehensive evaluation model of water resources imbalance risk In the evaluation results, the data envelopment analysis method is used to calculate the redundancy of input-output indicators in the economic loss model of water resources security risk, and the construction of social and economic loss measurement model caused by water resources imbalance risk is completed. The experimental results show that the model can improve the total social economic benefits, reduce the socio-economic losses and improve the utilization rate of water resources under the risk of water resources imbalance.
Selection of Support Vector Candidates Using Relative Support Distance for Sustainability in Large-Scale Support Vector Machines

Support vector machines (SVMs) are a well-known classifier due to their superior classification performance. They are defined by a hyperplane, which separates two classes with the largest margin. In the computation of the hyperplane, however, it is necessary to solve a quadratic programming problem. The storage cost of a quadratic programming problem grows with the square of the number of training sample points, and the time complexity is proportional to the cube of the number in general. Thus, it is worth studying how to reduce the training time of SVMs without compromising the performance to prepare for sustainability in large-scale SVM problems. In this paper, we proposed a novel data reduction method for reducing the training time by combining decision trees and relative support distance. We applied a new concept, relative support distance, to select good support vector candidates in each partition generated by the decision trees. The selected support vector candidates improved the training speed for large-scale SVM problems. In experiments, we demonstrated that our approach significantly reduced the training time while maintaining good classification performance in comparison with existing approaches.