Reviewerly/exHarmony huggingface dataset:
Towards Data-Driven Capability Interface

In computing, interface is the notion used for exposing the business logic of a software component for consumption. The interface of a component is deliberately defined separately from the component’s implementation to define entry points, and at the same time prevent access to the component’s internal resources and logic. Another advantage is that replacing the implementation of one component with another that has a same interface enables continuous consumption because how a component internally meets the requirements of the interface is irrelevant to its consumer. This paper investigates the possibilities to introduce the notion of interface in capability-oriented IS engineering. Capability Driven Development (CDD) is an example of a methodological approach for configuring dynamic, context aware, re-deployable business capabilities on top of existing enterprise information systems to enable continuous delivery of business for varying situational contexts. CDD relies on capability as the central component that integrates other elements of organizational design such as goals, KPIs, context information, processes, resources, and software services. These elements produce and use lot of different data, internal as well as external. In order to facilitate the uptake and use of capabilities, most of the necessary data should be made available for the use by the consumers of the capability. In this study, we provide an initial view on how the data interface of the capability component should be defined. The proposal is illustrated on the service concerning a regional roads maintenance.
Computing endomorphism rings and Frobenius matrices of Drinfeld modules

Let Fq[T] be the polynomial ring over a finite field Fq. We study the endomorphism rings of Drinfeld Fq[T]-modules of arbitrary rank over finite fields. We compare the endomorphism rings to their subrings generated by the Frobenius endomorphism and deduce from this a refinement of a reciprocity law for division fields of Drinfeld modules proved in our earlier paper. We then use these results to give an efficient algorithm for computing the endomorphism rings and discuss some interesting examples produced by our algorithm.
40 Years Journey of Function Point Analysis: Against Real-time and Multimedia Applications

For various project of software development, the size, effort, and cost are essential aspects for planning schedules and control software development to achieve the preferred outcome. These aspects require a method to measure software accurately and reliably. Function Point Analysis is used as a standard method for that purpose. It is now 40 years old since Albrecht founded it in 1979. Bringing its advantages and some of the drawbacks from earlier versions, this method is now faced with current software development needs, namely, the real-time and the multimedia applications. Broadly translated our findings indicate that current software development does not only focus on system functionality, but non-functional factors also begin to influence the value of a system for the users. Therefore, in 40 years journey of Function Point Analysis, this method is not adequate for current software development, especially in real-time and multimedia applications because of the inability to measure non-functional factors.
Coupling meteorological variables with Moderate Resolution Imaging Spectroradiometer atmospheric products for estimating global solar radiation

Global solar radiation is a crucial variable for scientific researches and solar energy application, while it is measured at very few sites mainly due to the technical and fiscal obstacles. Developing robust and accurate models for estimating global solar radiation had been being a focus for many studies. This study was conducted to develop integrated models combining Moderate Resolution Imaging Spectroradiometer atmospheric products and meteorological variables. 43 empirical models based on the meteorological variables were collected. A total of 645 integrated models incorporating atmospheric constituents into the empirical models were developed. The researched models were evaluated and compared at Chongqing in Three Gorges Reservoir Area, China. The results showed that the integrated models outperformed the empirical models. The best integrated model had the root mean square error of 0.817 MJ m−2 and relative root mean square error of 8.11%. On average, the integrated models had the root mean square error of 1.071 MJ m−2, 15.6% smaller than the empirical models. The results suggest that coupling Moderate Resolution Imaging Spectroradiometer atmospheric products with meteorological variables can enhance the performance of the conventional empirical models, which may provide a promising alternative to generate global solar radiation data with better accuracy.
Using Distillation to Improve Network Performance after Pruning and Quantization

As the complexity of processing issues increases, deep neural networks require more computing and storage resources. At the same time, the researchers found that the deep neural network contains a lot of redundancy, causing unnecessary waste, and the network model needs to be further optimized. Based on the above ideas, researchers have turned their attention to building more compact and efficient models in recent years, so that deep neural networks can be better deployed on nodes with limited resources to enhance their intelligence. At present, the deep neural network model compression method have weight pruning, weight quantization, and knowledge distillation and so on, these three methods have their own characteristics, which are independent of each other and can be self-contained, and can be further optimized by effective combination. This paper will construct a deep neural network model compression framework based on weight pruning, weight quantization and knowledge distillation. Firstly, the model will be double coarse-grained compression with pruning and quantization, then the original network will be used as the teacher network to guide the compressed student network. Training is performed to improve the accuracy of the student network, thereby further accelerating and compressing the model to make the loss of accuracy smaller. The experimental results show that the combination of three algorithms can compress 80% FLOPs and reduce the accuracy by only 1%.
The Semantic Prosody of “Youyu”: Evidence from Corpora

Semantic prosody provides a new perspective to identify the affective meaning of a word. Based on two comparable corpora and an online parallel corpus, this paper explores the semantic prosody of a Chinese functional word ‘youyu’ (‘because’). The statistics in the Chinese corpus, TorCH2014, indicate that “youyu” has two colligations that exhibit obvious negative semantic prosodies. The evidence of its English equivalents in the English corpus and the parallel corpus also proves that the semantic prosody of “youyu” is negative. This study shows that the combination of comparable corpora and parallel corpus provides a powerful tool for language research.
Decision Support for Project Risk Management Using the Information-Semantic Field

This paper presents a topic of risk management during implementation of major industry projects in atomic energetics, including international market based on information and semantic field usage. The information risks register for atomic energetics international projects is presented. The analysis of information and semantic field as part of a research of impact on Hungary's Paks-2 NPP implementation is performed. Project risks are evaluated, and economic efficiency is determined for two scenarios - with information risk and without it. Main micro-segmentation principle and risks mitigation indications are defined. Proposed scheme of decision support system. Author acknowledges support from the MEPhI Academic Excellence Project (Contract No. 02.a03.21.0005).
Puzzle game solving with breadth first search algorithm

Abstract Today’s games have been played by many people from young to old age. There are many types of games, one of which is a puzzle game. Puzzles, which are brain teasers that challenge the skills of their players, never seem to lose their popularity and are never consumed by age. Puzzle is one type of game that is enough to squeeze the brain to solve it. The solution that can be used to simplify problem solving is to apply a search algorithm that is used to check the initial state to the final state and provide the most optimal solution for completing the puzzle, the purpose of this study were using algorithm used to solve these problems using the Breadth First Search algorithm. The use of the Breadth First Search algorithm in solving puzzle games can make it easier for users to get the best solution in the form of completion steps and also the possibility of solving various conditions based on the puzzle conditions that you want to solve.
Investigating the Establishment of Architecture Principles for Supporting Large-Scale Agile Transformations

The widespread use of agile methods shows a fundamental shift in the way organizations try to cope with unpredictable competitive environments. In large-scale agile settings, multiple development activities need to be coordinated to achieve desirable enterprise-wide effects and agility. A powerful instrument to effectively guide and steer large-scale agile endeavors is the formulation and usage of architecture principles. Despite their raison d'être to guide large organizational transformations, extant studies on how principles can be used to support large-scale agile transformations are still lacking. Against this backdrop, we present a multiple-case study involving five German companies that aims to shed light on the establishment of architecture principles to support large-scale agile transformations. Based on our results from sixteen semi-structured interviews, we present current practices as well as challenges faced by organizations during the application of architecture principles. In addition, we show a set of principles used to support large-scale agile transformations.
Robust Motion Blur Kernel Estimation by Kernel Continuity Prior

The accurate kernel estimation is key to the blind motion deblurring. Many previous methods depend on the image regularization to recover strong edges in the observed image for kernel estimation. However, the estimated kernel will be degraded when recovered strong edges are less accurate, especially in images full of small-scale edges. Different from previous methods, we focus on the kernel regularization. Inspired by the fact that the blur kernel is highly related to the continuous camera motion trajectory during the image capturing, we propose to encourage the continuity of the kernel through a kernel prior. The proposed prior measures the continuity of each element in the kernel and generates a continuity map. By encouraging the sparsity of the map using L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> norm, discontinuous kernel elements are suppressed. Since the model with the proposed prior is non-convex and non-linear, an approximation method is proposed to minimize the cost function efficiently. Numerous experimental results show that our method outperforms state-of-the-art methods on both the normal and challenging cases. Moreover, the proposed prior is able to further improve the performance of existing MAP-based methods.
PRIME: Block-Wise Missingness Handling for Multi-modalities in Intelligent Tutoring Systems

Block-wise missingness in multimodal data poses a challenging barrier for the analysis over it, which is quite common in practical scenarios such as the multimedia intelligent tutoring systems (ITSs). In this work, we collected data from 194 undergraduates via a biology ITS which involves three modalities: student-system logfiles, facial expressions, and eye tracking. However, only 32 out of the 194 students had all three modalities and 83% of them were missing the facial expression data, eye tracking data, or both. To handle such a block-wise missing problem, we propose a Progressively Refined Imputation for Multi-modalities by auto-Encoder (PRIME), which trains the model based on single, pairwise, and entire modalities for imputation in a progressive manner, and therefore enables us to maximally utilize all the available data. We have evaluated PRIME against single-modality log-only (without missingness handling) and five state-of-the-art missing data handling methods on one important yet challenging student modeling task: to predict students’ learning gains. Our results show that using multimodal data as a result of missing data handling yields better prediction performance than using logfiles only, and PRIME outperforms other baseline methods for both learning gain prediction and data reconstruction tasks.
Sketch Fewer to Recognize More by Learning a Co-Regularized Sparse Representation

Categorizing free-hand human sketches has profound implications in applications such as human computer interaction and image retrieval. The task is non-trivial due to the iconic nature of sketches, signified by large variances in both appearance and structure when compared with photographs. Despite recent advances made by deep learning methods, the requirement of a large training set is commonly imposed making them impractical for real-world applications where training sketches are cumbersome to obtain - sketches have to be hand-drawn one by one other than crawled freely on the Internet. In this work, we aim to delve further into the data scarcity problem of sketch-related research, by proposing a few-shot sketch classification framework. The model is based on a co-regularized embedding algorithm where common/shareable parts of learned human sketches are exploited, thereby can embed query sketch into a co-regularized sparse representation space for few-shot classification. A new dataset of 8,000 part-level annotated sketches of 100 categories is also proposed to facilitate future research. Experiment shows that our approach can achieve an 5-way one-shot classification accuracy of 85%, and 20-way one-shot at 51%.
Estimation Of Global Solar Radiation by Using ANN and ANFIS

Solar energy has the greatest potential among the energy sources available in the world and it is clean and universal energy source. The first parameter that should be determined carefully when planning systems based on solar energy is the solar radiation value. The solar radiation values obtained from ground observations can be estimated with various software models. Especially, estimation models designed using emerging computer technology have ease of use and accuracy. In this study, the well-known artificial intelligence techniques such as artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are used to estimate solar radiation. The analysis of solar radiation requires complex, lengthy and time consuming procedures and artificial intelligence techniques such as ANN and ANFIS eliminate great effort and time. A system that measures atmospheric data such as light, temperature and humidity using sensors is designed and for this study, measurements were made for over a month. The atmospheric data obtained from the climatic conditions of the province of Karaman and the surface solar radiation values measured using the Pyranometer are utilized to construct these models. As a result of testing the constructed models, the ANFIS model is found to be more successful than ANN.
Slang-Based Text Sentiment Analysis in Instagram

A large amount of user-generated content on social media has led to the pursuitAly, Elton Shah of quickly and accurately mining through data and gathering useful insights. Text sentiment analysis has become a necessary tool in classifying user opinions within Web generated content. Due to the various ways, opinions can be conveyed, performing text sentiment analysis in specific domains becomes a difficult task. With an even greater degree of difficultyVan der Haar, Dustin Terence added when slang or colloquialisms are used. There is a great deal of research into investigating various classifiers in a traditional natural language processing setting each with their own merits and demerits. In this paper, we present a slang-based dictionary classifier with the objective of determining the sentiment of Instagram comments within the context of fashion, or more specifically sports shoes, and compare it with the performance of other classifiers such as a Naive Bayes, J48, lexicon and random forest. The dataset used for the benchmark was created from popular fashion Instagram accounts. Overall, the random forest classifier yields the best results with an accuracy of 88%, precision of 84% and a recall of 88%.
Effective and Robust Detection of Adversarial Examples via Benford-Fourier Coefficients

Adversarial examples have been well known as a serious threat to deep neural networks (DNNs). In this work, we study the detection of adversarial examples, based on the assumption that the output and internal responses of one DNN model for both adversarial and benign examples follow the generalized Gaussian distribution (GGD), but with different parameters (i.e., shape factor, mean, and variance). GGD is a general distribution family to cover many popular distributions (e.g., Laplacian, Gaussian, or uniform). It is more likely to approximate the intrinsic distributions of internal responses than any specific distribution. Besides, since the shape factor is more robust to different databases rather than the other two parameters, we propose to construct discriminative features via the shape factor for adversarial detection, employing the magnitude of Benford-Fourier coefficients (MBF), which can be easily estimated using responses. Finally, a support vector machine is trained as the adversarial detector through leveraging the MBF features. Extensive experiments in terms of image classification demonstrate that the proposed detector is much more effective and robust on detecting adversarial examples of different crafting methods and different sources, compared to state-of-the-art adversarial detection methods.
Large-Scale Information Extraction from Emails with Data Constraints

Email is the most frequently used web application for communication and collaboration due to its easy access, fast interactions, and convenient management. More than 60% of the email traffic constitutes business to consumer (B2C) emails (e.g., flight reservations, payment reminder, order confirmations, etc.). Most of these emails are generated by filling a template with user or transaction specific values from databases. In this paper we describe various algorithms related to extracting important information from these emails. Unlike web pages, emails are personal and due to privacy and legal considerations, no other human except the receiver can view them. Thus, adapting extraction techniques used for web pages, such as HTML wrapper-based techniques, have privacy and scalability challenges. We describe end-to-end information extraction system for emails—data collection, anonymization, classification, building the information extraction models, deployment, and monitoring. To handle the privacy and scalability issues, we focus on algorithms which can work with minimum human annotated samples for building classifier and extraction techniques. Similarly, we present algorithms to minimize samples for human inspection to detect precision and recall gaps in the extraction pipeline.
Generation of Photovoltaic Output Power Forecast Using Artificial Neural Networks

An artificial neural network (ANN) model is used for forecasting the power provided by photovoltaic solar panels using feed forward neural network (FFNN) of a photovoltaic installation located in the city of Mohammedia (Morocco). One year of hourly data on solar irradiance, ambient temperature and output PV power were available for this study. For this, different combinations of inputs with different numbers of hidden neurons were considered. To evaluate this model several statistic parameters were used such as the coefficient of correlation (R), the Root Mean Squared Error (RMSE) and the Mean Absolute Error (MAE). The results of this model tested on unknown data showed that the model works well, with regression coefficients lying between 99.6% and 99.8% for sunny days and between 93% and 96% for cloudy days.
Served on a Silver Platter working Towards an Academic Research Data Concierge Service

&#x0D; &#x0D; &#x0D; This article will discuss the development of a research concierge service aimed at supporting the unique data needs of researchers at each stage of the research lifecycle. Although based on work at one institution, a North American academic 4-year research university, the presented strategies are transferable to many different types of institutions. The concept behind this service emerged when staff members from the Office of Research, Information Technology, and University Libraries embarked on an endeavour, by way of a newly formed collaborative working group, to identify disparate research support services around campus. Steps will be suggested to assess and align existing support services as well as uncover gaps in service within an institution. A review of organisational models from other institutions with complementary services will also be included. The article will also examine the challenges faced by our institution in creating this collaborative group and the new service approach.&#x0D; &#x0D; &#x0D;
Transforming Ground and Flight Testing through Digital Engineering

The ongoing digital revolution is transforming all aspects of product development, delivery, operation, and support. Driven by cyber-physical technologies such as the Internet of Things (IoT), Cloud Computing, Big Data Analytics, Digital Engineering, and Digital Manufacturing it enables transforming current document-centric processes to a digital, connected, model-based systems engineering environment involving all the advances of the new cyber-physical world. Concepts for transforming ground and flight testing of aeronautical systems employing digital engineering principles and practices are introduced. A value metric for testing based on the quantification and planned mitigation of uncertainty of technical performance measures (TPM) is introduced. Traditional model validation by comparing model output with experimental data is transformed into an improved predictive capability using an integrated validation and calibration process for a digital surrogate response surface model using all heterogeneous data available. Approaches to using quantified margins and uncertainties for TPMs derived from calibrated digital surrogates to project the best way to master risk and reduce the time to achieve an operational capability are presented. Concepts for digital engineering-enabled innovative approaches to air vehicles development and operation are outlined.
Outsourcing IOTA proof‐of‐work to volunteer public devices

Abstract Distributed ledger technologies will play an important role in the communication and data integrity of internet of things (IoT) devices in the future. IOTA is a public distributed ledger that can serve as a scalable, secure and feeless settlement layer for IoT transactions. It enables micro‐transactions for smart devices and offers a highly scalable architecture for network consensus and overcomes the inefficiencies of existing distributed ledgers. Like most mining‐based ledgers, IOTA also require solving a computational puzzle for every request to the ledger to avoid unwanted (ie, spam) content added to the ledger. We propose a system architecture that consumes the available computational resources of public volunteer devices for solving the expensive computational puzzles. We show that our proposed architecture can integrate with the global data and network layer of the IOTA ledger and acts as a middleware for providing computational resources from public volunteer devices to the IOTA network.