Reviewerly/exHarmony huggingface dataset:
Characterizing the Latent Classes in a Mixture IRT Model Using DIF

Mixture IRT models address the heterogeneity in a population by extracting latent classes and allowing item parameters to vary between latent classes. Once the latent classes are extracted, they need to be further examined to be characterized. Some approaches have been adopted in the literature for this purpose. These approaches examine either the examinee or the item characteristics conceptually or statistically. In this study, we propose a two-step procedure for characterizing the latent classes. First, a DIF analysis can be conducted to determine the items that function differentially between the latent classes using the latent class membership information. Then, the characteristics of the items with DIF can be further examined to use this information for characterizing the latent classes. We provided an empirical example to illustrate this procedure.
Adoption and Use of Ontologies in the Utility Sector: A Technological Mediation Perspective

Standardization is believed to be a driver for adoption and integration of digital practices. Beyond the standardization at the level of technology application lies potential for standardization in the higher-order formalized knowledge bases; the so-called ontologies. Ontologies need to abstract and integrate knowledge from disciplines and industry standards. From a philosophical perspective, ontologies can be seen as social constructs and part of the shared social context in which practitioners work. If they do not contain an adequate representation of a community’s shared reality, they may delimit sector-wide unified digital practices. Following the interpretive approach of technological mediation, the objective of this study is to explain why the development and use of a sector-wide domain ontology in construction are not always as effective and efficient as they could potentially be. Which obstacles hamper the creation of a sector-wide domain ontology and how can these be overcome? We apply the technical mediation perspective to assess how coexisting different standards complicate the development of a shared domain ontology. Additionally, a collaborative end user–based design approach is applied to address ontology development complexities. It is shown that a digital modeling standard mediates a specific view or model of reality by amplifying certain aspects of the world at the expense of other aspects. The heterogeneity of existing standards makes it difficult to develop a shared and sector-wide domain ontology because different utility owners model different representations of the world. A collaborative user-oriented development approach is a means to elicit these differences more adequately, and to develop shared conceptualizations about the domain of the utility sector. The domain ontology serves here as mediator between the different digital standards that capture the distinctive modeled realities of utility owners.
Practical Continuously Non-malleable Randomness Encoders in the Random Oracle Model

A randomness encoder is a generalization of encoding schemes with an efficient procedure for encoding uniformly random strings. In this paper we continue the study of randomness encoders that additionally have the property of being continuous non-malleable. The beautiful notion of non-malleability for encoding schemes, introduced by Dziembowski, Pietrzak and Wichs (ICS'10), states that tampering with the codeword can either keep the encoded message identical or produce an uncorrelated message. Continuous non-malleability extends the security notion to a setting where the adversary can tamper the codeword polynomially many times and where we assume a self-destruction mechanism in place in case of decoding errors. Our contributions are: (1) two practical constructions of continuous non-malleable randomness encoders in the random oracle model, and (2) a new compiler from continuous non-malleable randomness encoders to continuous non-malleable codes, and (3) a study of lower bounds for continuous non-malleability in the random oracle model.
An Emergent Design Approach to test the Quality of the Software System

The organizations involved in software production have an interest in ensuring that the software developed conforms to a practice accepted by industry, namely compliance with standards, excellent quality control, efficiency and timeliness. Therefore, emergent design is an appropriate solution for acquiring higher quality software at low cost but meeting the production deadline. Indeed, it aims to reduce technical debt, accidental complexity as well as hyper-genericity contained in a software development process. In this article, we propose an approach supporting the modeling of emergent design, which is a way to make it easy for a designer to test the design quality of an object-oriented system. Our strategy is to define the necessary steps for the realization of emergent design within the software development process. Therefore, emergent design elements that make up our model are redundancy, readability, testability, cohesion, coupling and refactoring. As a case study, we instantiate in the proposed model a UML class diagram to test its design quality in order to improve the diagram if necessary. In order to validate our approach, we evaluated the performance of the UML class diagram obtained after the realization of emergent design proposed in this paper using the CM-Builder’s technique. The result of the performance evaluation with a high score of 93.61% concerning the accuracy of the proposed approach allows us to deduce that the method presented in this paper is essential for software designers in order to achieve a modular and easy to maintain system.
Modeling dynamic target deformation in camera calibration

Most approaches to camera calibration rely on calibration targets of well-known geometry. During data acquisition, calibration target and camera system are typically moved w.r.t. each other, to allow image coverage and perspective versatility. We show that moving the target can lead to small temporary deformations of the target, which can introduce significant errors into the calibration result. While static inaccuracies of calibration targets have been addressed in previous works, to our knowledge, none of the existing approaches can capture time-varying, dynamic deformations. To achieve high-accuracy calibrations despite moving the target, we propose a way to explicitly model dynamic target deformations in camera calibration. This is achieved by using a low-dimensional deformation model with only few parameters per image, which can be optimized jointly with target poses and intrinsics. We demonstrate the effectiveness of modeling dynamic deformations using different calibration targets and show its significance in a structure-from-motion application.
ThinkTwice: A Two-Stage Method for Long-Text Machine Reading Comprehension

Long-text machine reading comprehension (LT-MRC) requires machine to answer questions based on a lengthy text. Despite transformer-based models achieve promising results, most of them are incapable of dealing with long sequences for their time-consuming. In general, a proper solution by sliding window splits the passage into equally spaced fragments, then predicts the answer based on each fragment separately without considering other contextual fragments. However, this approach suffers from lack of long-distance dependency, which severely damages the performance. To address this issue, we propose a two-stage method ThinkTwice for LT-MRC. ThinkTwice casts the process of LT-MRC into two main steps: 1) it firstly retrieves several fragments that the final answer is most likely to lie in; 2) then extracts the answer span from these fragments instead of from the lengthy document. We do experiments on NewsQA. The experimental results demonstrate that ThinkTwice can capture the most informative fragments from a long text. Meanwhile, ThinkTwice achieves considerable improvements compared to all existing baselines. All codes have been released at Github (https://github.com/Walle1493/ThinkTwice).
Detection and Diagnosis of Fault Using Light-Weighted Midori Blocks

Modified block ciphers increase the performance and lower the time duration for high-speed applications. High-speed applications are mainly required for wearable medical devices, and compact devices which are implantable in the human body. Various security algorithm suffers due to inclusion of malicious and natural faults. The proposed model modifies the Midori block as light-weighted nature. This proposed algorithm introduces lower latency and less hardware complexity. The proposed model adds diagnosis algorithm in different stages of the work flow. Diagnosis algorithm reduces the fault value to the minimum. The fault diagnosis systems implemented with nonlinear S-box layer and beat structures. The beat structure includes 64-bit and 128-bit Midori symmetric key ciphers. The designed systems are benchmarked on a field programmable gate array (FPGA) with injected faults. The proposed light-weighted block cipher blocks yields, good energy efficiency rate and implemented using VHDL code.
Isolation in Rust

Rust is the first practical programming language that has the potential to provide fine-grained isolation of untrusted computations at the language level. A combination of zero-overhead safety, i.e., safety without a managed runtime and garbage collection, and a unique ownership discipline enable isolation in systems with tight performance budgets, e.g., databases, network processing frameworks, browsers, and even operating system kernels.
The Solution of Xiaomi AI Lab to the 2021 Language and Intelligence Challenge: Multi-format Information Extraction Task

Information Extraction is a challenging task in Natural Language Processing. There are several formats of information extraction that researchers mainly focused on. Typically, relation extraction and event extraction are two type of information extraction tasks that could facilitate many related business. In this work, we will introduce our solutions for the 2021 Language and Intelligence Challenge. We propose our methods for the relation extraction and event extraction tasks, respectively. The event extraction task is further divided into sentence level and document level. In relation extraction task, we propose a knowledge-based relation extraction method; in event extraction task, we propose a hybrid method which utilize event types and event triggers separately to extract event roles and event arguments. Finally, our solution ranked the second in the private leaderboard.
Multi-Robot Motion Planning with Unlabeled Goals for Mobile Robots with Differential Constraints

This paper studies the multi-robot motion-planning problem with unlabeled goals where n robots have to reach m goals. The proposed approach also takes into account the underlying dynamics of each robot to produce dynamically-feasible trajectories that enable the robots to reach all the goals while avoiding collisions with the obstacles and each other. The approach leverages the idea of combining sampling-based motion planning with goal assignment and multi-agent search. In fact, the goal-assignment layer seeks to effectively utilize the robots based on estimated costs to reach the remaining goals. The multi-agent search provides nonconflicting paths over roadmap graphs, which then guide the sampling-based expansion of a motion tree. The goal assignments and multi-agent paths are frequently updated based on the progress made during the motion-tree expansion. Simulation experiments using an increasing number of robots with nonlinear dynamics demonstrate the efficiency of the approach.
Microwave Method for Determining the Content of Harmful Gases in Atmosphere

The article considers a new method for determining the content of harmful gases in the atmosphere, based on the measurement of the dielectric permittivity of the controlled medium. Information about the change in the dielectric permittivity of the air is obtained by measuring the phase progression of the microwave signal as it passes through the measuring line. A mathematical model is presented to account for the influence of weather conditions on the accuracy of measurements. The calculated values for the permittivity of the atmosphere under various meteorological conditions coincide with the experimental ones.
Translationese and Register Variation in English-To-Russian Professional Translation

This study explores the impact of register on the properties of translations. We compare sources, translations and non-translated reference texts to describe the linguistic specificity of translations common and unique between four registers. Our approach includes bottom-up identification of translationese effects that can be used to define translations in relation to contrastive properties of each register. The analysis is based on an extended set of features that reflect morphological, syntactic and text-level characteristics of translations. We also experiment with lexis-based features from n-gram language models estimated on large bodies of originally- authored texts from the included registers. Our parallel corpora are built from published English-to-Russian professional translations of general domain mass-media texts, popular-scientific books, fiction and analytical texts on political and economic news. The number of observations and the data sizes for parallel and reference components are comparable within each register and range from 166 (fiction) to 525 (media) text pairs; from 300,000 to 1 million tokens. Methodologically, the research relies on a series of supervised and unsupervised machine learning techniques, including those that facilitate visual data exploration. We learn a number of text classification models and study their performance to assess our hypotheses. Further on, we analyse the usefulness of the features for these classifications to detect the best translationese indicators in each register. The multivariate analysis via text classification is complemented by univariate statistical analysis which helps to explain the observed deviation of translated registers through a number of translationese effects and detect the features that contribute to them. Our results demonstrate that each register generates a unique form of translationese that can be only partially explained by cross-linguistic factors. Translated registers differ in the amount and type of prevalent translationese. The same translationese tendencies in different registers are manifested through different features. In particular, the notorious shining-through effect is more noticeable in general media texts and news commentary and is less prominent in fiction.
Distance Teaching in Educational Organizations under Pandemic Conditions

Higher education has to develop effective means of pedagogical communication and teaching patterns as a result of the social and economic changes brought about by the COVID-19 pandemic. Distance learning requires no face-to-face contact with the teacher. It is based on unconventional teaching patterns and uses modern means of informatization. The research objective was to develop a new system of didactic support in the context of a total distance learning format. The author analyzed the opinions of academics and students of two institutions of higher education. The research revealed some problems related to the use of traditional teaching methods in distance learning. The article introduces a new complex approach to the design of effective didactic support based on the psychological, pedagogical, and methodological features of distance learning. The paper also features various definitions of didactic support, its stages, and component, i.e. content, procedure, and motivation. The new approach was tested experimentally. It involved a rating system, which improved the motivational, emotional, and cognitive aspects of distance learning. A higher degree of formalization of information content also improved such indicators as universality, adaptability, multivariance, and multifunctionality.
Vizspectraldata: a WEB-Based Application for Hyperspectral Data Visualization

This paper presents VizSpectralData, a web based application that runs entirely in the front end and allows spectral data from csv files to be opened, visualized and processed. The system is presented together with the algorithms it implements using real data collected from several carbonate rock samples. It is an open source alternative for simple visualization and processing to proprietary softwares, it is developed in javascript, html and css. It has features to visualize the reflectance, continuum removed spectra, and the derivative of the spectra. It allows to import and export spectral libraries in CSV format.
Nonparametric estimation of highest density regions for COVID-19

Highest density regions refer to level sets containing points of relatively high density. Their estimation from a random sample, generated from the underlying density, allows to determine the clusters of the corresponding distribution. This task can be accomplished considering different nonparametric perspectives. From a practical point of view, reconstructing highest density regions can be interpreted as a way of determining hot-spots, a crucial task for understanding COVID-19 space-time evolution. In this work, we compare the behaviour of classical plug-in methods and a recently proposed hybrid algorithm for highest density regions estimation through an extensive simulation study. Both methodologies are applied to analyse a real data set about COVID-19 cases in the United States.
A Benchmark Study on the Use of WebRTC Technology for Multimedia IoT

The increase in Smart Things with video and audio features and multimedia traffic is expected to change the Internet of Things (IoT) architecture. Multimedia applications need high bandwidth and high CPU. WebRTC technology offers opportunities in this regard. In this study, the usability of WebRTC technology for multimedia applications on resource-constrained IoT devices is investigated. In this way, a benchmarking study is conducted to determine the requirements of IoT devices with audio and video capabilities. In the tests performed, the video quality and costs are investigated in low CPU and low bandwidth situations.
Cataloging dependency injection anti-patterns in software systems

Dependency Injection (DI) is a commonly applied mechanism to decouple classes from their dependencies in order to provide higher modularization. However, bad DI practices often lead to negative consequences, such as increasing coupling. Although white literature conjectures about the existence of DI anti-patterns, there is no evidence on their practical relevance, usefulness, and generality. The objective of this study is to propose and evaluate a catalog of DI anti-patterns and associated refactorings. We reviewed existing reported DI anti-patterns in order to analyze their completeness. The limitations found in literature motivated proposing a novel catalog of 12 DI anti-patterns. We developed a tool to statically analyze the occurrence level of the candidate DI anti-patterns in both open-source and industry projects. Next, we survey practitioners to assess their perception on the relevance, usefulness, and their willingness on refactoring anti-pattern instances of the catalog. Our static code analyzer tool showed a relative recall of 92.19% and high average precision. It revealed that at least 9 different DI anti-patterns appeared frequently in the analyzed projects. Besides, our survey confirmed the perceived relevance of the catalog and developers expressed their willingness to refactor instances of anti-patterns from source code. The catalog contains DI anti-patterns that occur in practice and that are perceived as useful. Sharing it with practitioners may help them to avoid such anti-patterns, thus improving source-code quality.
Improving Neural Network Robustness via Persistency of Excitation

Improving adversarial robustness of neural networks remains a major challenge. Fundamentally, training a neural network via gradient descent is a parameter estimation problem. In adaptive control, maintaining persistency of excitation (PoE) is integral to ensuring convergence of parameter estimates in dynamical systems to their true values. We show that parameter estimation with gradient descent can be modeled as a sampling of an adaptive linear time-varying continuous system. Leveraging this model, and with inspiration from Model-Reference Adaptive Control (MRAC), we prove a sufficient condition to constrain gradient descent updates to reference persistently excited trajectories converging to the true parameters. The sufficient condition is achieved when the learning rate is less than the inverse of the Lipschitz constant of the gradient of loss function. We provide an efficient technique for estimating the corresponding Lipschitz constant in practice using extreme value theory. Our results in both standard and adversarial training illustrate that networks trained with the PoE-motivated learning rate schedule have similar clean accuracy but are significantly more robust to adversarial attacks than models trained using current state-of-the-art heuristics.
Efficient Computation of Robustness of Convolutional Neural Networks

Validation of CNNs is extremely important, especially when they are used in safety-critical domains. In particular, in the latest years, the focus of validation has been put on assessing the robustness of CNNs, i.e., their ability to correctly classify perturbed input data. A way to measure robustness is to check the network accuracy over many datasets obtained by altering the input data in different ways, but this is time and resource-consuming. In this paper, we present ASAP, a method to efficiently compute the robustness of a CNN, exploiting a parabola-based approximation which allows to adaptively select only relevant alteration levels. The method is tested on two different benchmarks (MNIST and breast cancer classification). Moreover, we compare ASAP with other techniques based on uniform sampling, numerical integration, and random sampling.
Heterogeneous multi-parameter feature-level fusion for multi-source power sensing terminals: fusion mode, fusion framework and scene verification

At present, with the construction and advancement of domestic basic power facilities, the Internet of Things technology is slowly being effectively integrated with the traditional power grid. The effective integration of heterogeneous and multi-parameters generated by multi-source power sensing terminals is the realization of current power The key technology of the Internet of Things. At present, the integration of domestic power Internet of Things parameters is mainly based on the integration of homogeneous multi-parameters and main decision-making parameters. There are large technical gaps in the integration of heterogeneous and multi-dimensional multi-source parameters, which cannot meet the current power Internet of Things. The need for deep integration of multiple heterogeneous terminal parameters. This article analyzes the current status of multi-source power sensing terminal parameter fusion, analyzes the current power feature fusion method, fusion framework and fusion business scenarios, and finally verifies the effect of feature fusion through related scenarios.
Approximation of values of algebraic elements over the ring of power sums

Let ℚℰ ℤ be the set of power sums whose characteristic roots belong to ℤ and whose coefficients belong to ℚ, i.e. G:ℕ→ℚ satisfies