Reviewerly/exHarmony huggingface dataset:
Developing the potential of visualization technologies in hybrid tuition

Research problem. The problem of the pedagogical technologies effectiveness for students’ distance learning has grown more acute in the extreme period of the world pandemic lockdowns. The analysis of the training quality and students’ estimations showed a decrease in the significance of vocational and oriented texts; weak association between educational material and the reality of the profession; vagueness of visual representation of study objects; misunderstanding of the processes studied; reluctance to read the texts of methodological materials; misunderstanding of presentation graphics; rapid fatigue and reduced concentration of attention in monologue lectures. The research focuses on the experience of using the developing potential of imaging technologies in hybrid training. Research aim: To analyze the experience of implementing various modern technologies for visualizing the content of vocational and oriented educational disciplines in hybrid university tuition; to identify and experimentally test the positive effects of the influence of visualization technologies in hybrid tuition. Research methods: Theoretical methods – the analysis of the developing potential of pedagogical technologies for visualizing objects and processes in domestic and foreign universities. Experimental methods included: case visualization; Flickr-illustration of presentations; static and dynamic animation; video and qualimetric analysis. Empirical methods included; statistical analysis of the students’ questionnaire results; learning products and the results of academic achievements. The study involved 103 students on 2-3 undergraduate courses. Conclusions and recommendations. The research provides conclusions about the significant developing potential of pedagogical technologies of visualization in the educational process of the university. The analysis of the educational results demonstrated the demand for visualization technologies. Most assessments of academic achievements and the students’ own predictions were above average. Case visualization as a variant of visualization technology provides a realistic presentation of real-life situations. Flickr-illustration of presentations is based on advanced Internet skills. The cooperative presentations by several students ensured the development of teamwork in a geographically distributed remote format. The animated images and dynamism of the objects revived lectures; reduced the fatigue; and the monotony of teachers’ and students’ distance meetings. This method contributed to the concentration of attention and the development of students’ professional intuition. It provides the ability to repeat and watch video recordings of laboratory at a convenient time for detailed monitoring of the process under study. Thus, visualization technologies in the modern digital version improve the quality of professional education both in full-time and in a distance format of training in the universities. Application: In the development of training courses on MOODLE platforms as well as other digital higher education educational environments.
Open-domain clarification question generation without question examples

An overarching goal of natural language processing is to enable machines to communicate seamlessly with humans.However, natural language can be ambiguous or unclear.In cases of uncertainty, humans engage in an interactive process known as repair: asking questions and seeking clarification until their uncertainty is resolved.We propose a framework for building a visually grounded questionasking model capable of producing polar (yesno) clarification questions to resolve misunderstandings in dialogue.Our model uses an expected information gain objective to derive informative questions from an off-the-shelf image captioner without requiring any supervised question-answer data.We demonstrate our model's ability to pose questions that improve communicative success in a goal-oriented 20 questions game with synthetic and human answerers.
High Dynamic Weak Signal Tracking Algorithm of a Beidou Vector Receiver Based on an Adaptive Square Root Cubature Kalman Filter

Compared with a scalar tracking receiver, the Beidou vector tracking receiver has the advantages of smaller tracking errors, fast loss-of-lock reacquisition, and high stability. However, in extremely challenging conditions, such as highly dynamic and weak signals, the loop will exhibit a high degree of nonlinearity, and observations with gross errors and large deviations will reduce the positioning accuracy and stability. In view of this situation, based on the concepts of cubature Kalman filtering and square root filtering, a square root cubature Kalman filtering (SRCKF) algorithm is given. Then, combining this algorithm with the concept of covariance matching based on an innovation sequence, an adaptive square root cubature Kalman filter (ASRCKF) algorithm is proposed. The algorithm was verified, and the tracking performance of the vector locking loop (VLL) realized by the algorithm was compared with the SRCKF VLL and the ASRCKF scalar locking loop (SLL). The simulation results show that, regardless of whether in a highly dynamic weak signal environment or in a general situation where the signal-to-noise ratio is higher than the tracking threshold, the tracking accuracy and stability of the ASRCKF VLL are higher than those of the SRCKF VLL and the ASRCKF SLL, the three-dimensional position error of the ASRCKF VLL does not exceed 36 m, and the three-dimensional velocity error does not exceed 3.5 m/s.
Macro-Financial Parameters Influencing Bitcoin Prices: Evidence from Symmetric and Asymmetric ARDL Models

Bitcoins are evolving as a modern class of investment assets and it is crucial for investors to manage their investment risk. This paper examines the impact of macroeconomic-financial indicators on Bitcoin price using symmetric and asymmetric version of autoregressive distributed lag (ARDL) models with structural breaks. The asymmetric long-run association ascertained between Bitcoin prices and the macroeconomic-financial indicators is evident. Our empirical results indicate that the Bitcoin cannot be used to hedge against the inflation, Federal funds rate, stock markets and commodity markets. We further find that Bitcoin can be regarded as a hedging device for the oil prices. Our findings have significant implications for market participants who consider including alternate investment assets in their portfolios.
In Search of The Perfect Coin: China’s Approach towards Cryptocurrency and Its Own Central Bank Digital Currency

Ever since Bitcoin was introduced in 2008, Central Banks and regulators have watched carefully and cautiously over the development of cryptocurrencies. This development took a significant leap in 2019 when Facebook and the People’s Bank of China almost simultaneously announced their Libra project and Digital Currency Electronic Payment (DCEP) respectively, instantaneously creating a rivalry. This paper anchors on China’s DCEP, examines its potential benefits and risks to monetary policies, transaction security, and customer protection, in comparison with conventional fiat currency and privately issued cryptocurrencies. The structural design of the DCEP is also reviewed to understand how these features guard against the issues identified. Overall, while making a few recommendations on constructing a fully prepared legal framework, this paper recognizes the DCEP as a promising step forward as it combines the security offered by blockchain and cryptography technology and the stability supported by the Central Bank.
Geological, Mineralogical and Geochemical Studies of Kolosh Formation, Dokan Area, Kurdistan Region, Iraq

The geology, mineralogy and geochemical of The Kolosh Formation in Dokan area, northern Iraq has been studied. The formation sequence includes gray and dark gray marl that alternate from clastic submarine sediments represented by turbid deposits resulted from the last stages of the collision movement between the continental plates. The geochemical study showed that the Kolosh Formation is mainly dominated by detrital sediments (Clay) with a dominance of kaolinite illite, and albite wıth low amounts of quartz. The analysis revealed that the Kolosh Formation is dominated by relatively marginal marine sedimentation where shelf bay facies was deposited with carbonate facies deposited as shallow marine. In contrast, SiO2 is strongly negatively correlated with CaO and MgO, this supports their derivation from terrigenous sources during the deposition of Kolosh sediments.
The Effect of Flow Mind Map on Writing Accuracy and Learning Motivation at Islamic Higher Education

The investigation attempted to explore the influence of flow mind map on writing accuracy and learning motivation at Islamic Higher Education. There were two variables: flow mind map as a predictor variable; writing accuracy and learning motivation as the outcome variables. The study involved L2 participants at higher education in Kalimantan. The participants was 37 students, consisting of two groups: experiment class and control class. A main effect of one way Anova was used to measure an effect of flow mind map on learners’ writing score and learning motivation. The finding revealed that the value of writing accuracy at F (1,36) = 44.861, SS 3591.045, MS= 3591.045, p= 0.000; and the value of learning motivation at F (1,36) = 40.925, SS 2006.600, MS= 2006.600, p= 0.000. The significance value was below 0.050, meaning there was a statistically difference in the mean of using flow mind map on learners’ writing accuracy and learning motivation. It was recommended that language instructor motivate learners during the learning process. Due to the limited number of sample size, the further investigations with broader scope and larger sample size were needed to validate the research findings.
Optimization of DC motor speed control based on fuzzy logic-PID controller

In this paper the PID controller and the Fuzzy Logic Controller (FLC) are used to control the speed of separately excited DC motors. The proportional, integral and derivate (KP, KI, KD) gains of the PID controller are adjusted according to Fuzzy Logic rules. The FLC cotroller is designed according to fuzzy rules so that the system is fundamentally robust. Twenty-five fuzzy rules for self-tuning of each parameter of the PID controller are considered. The FLC has two inputs; the first one is the motor speed error (the difference between the reference and actual speed) and the second one is a change in the speed error (speed error derivative). The output of the FLC, i.e. the parameters of the PID controller, are used to control the speed of the separately excited DC Motor. This study shows that the precisiom feature of the PID controllers and the flexibllity feature of the fuzzy controller are presented in the fuzzy self-tuning PID controller. The fuzzy self – tuning approach implemented on the conventional PID structure improved the dynamic and static response of the system. The salient features of both conventional and fuzzy self-tuning controller outputs are explored by simulation using MATLAB. The simulation results demonstrate that the proposed self-tuned PID controller i.plementd a good dynamic behavior of the DC motor i.e. perfect speed tracking with a settling time, minimum overshoot and minimum steady state errorws.
Unrolled Variational Bayesian Algorithm for Image Blind Deconvolution

In this paper, we introduce a variational Bayesian algorithm (VBA) for image blind deconvolution. Our VBA generic framework incorporates smoothness priors on the unknown blur/image and possible affine constraints (e.g., sum to one) on the blur kernel, integrating the VBA within a neural network paradigm following an unrolling methodology. The proposed architecture is trained in a supervised fashion, which allows us to optimally set two key hyperparameters of the VBA model and leads to further improvements in terms of resulting visual quality. Various experiments involving grayscale/color images and diverse kernel shapes, are performed. The numerical examples illustrate the high performance of our approach when compared to state-of-the-art techniques based on optimization, Bayesian estimation, or deep learning.
Gradient descent on infinitely wide neural networks: global convergence and generalization

Many supervised machine learning methods are naturally cast as optimization problems. For prediction models which are linear in their parameters, this often leads to convex problems for which many mathematical guarantees exist. Models which are nonlinear in their parameters such as neural networks lead to nonconvex optimization problems for which guarantees are harder to obtain. In this paper, we consider two-layer neural networks with homogeneous activation functions where the number of hidden neurons tends to infinity, and show how qualitative convergence guarantees may be derived.
E2Net: Excitative-Expansile Learning for Weakly Supervised Object Localization

Weakly supervised object localization (WSOL) has gained recent popularity, which seeks to train localizers with only image-level labels. However, due to relying heavily on classification objective for training, prevailing WSOL methods only localize discriminative parts of object, ignoring other useful information, such as the wings of a bird, and suffer from severe rotation variations. Moreover, learning object localization imposes CNNs to attend non-salient regions under weak supervision, which may negatively influence image classification results. To address these challenges, this paper proposes a novel end-to-end Excitation-Expansion network, coined as E$^2$Net, to localize entire objects with only image-level labels, which served as the base of most multimedia tasks. The proposed E$^2$Net consists of two key components: Maxout-Attention Excitation (MAE) and Orientation-Sensitive Expansion (OSE). Firstly, MAE module aims to activate non-discriminative localization features while simultaneously recovering discriminative classification cues. To this end, we couple erasing strategy with maxout learning efficiently to facilitate entire-object localization without hurting classification accuracy. Secondly, to address rotation variations, the proposed OSE module expands less salient object parts along with all possible orientations. Particularly, OSE module dynamically combines selective attention banks from various orientated expansions of receptive-field, which introduces additional multi-parallel localization heads. Extensive experiments on ILSVRC 2012 and CUB-200-2011 demonstrate that the proposed E$^2$Net outperforms the previous state-of-the-art WSOL methods and also significantly improves classification performance.
Estimation of the Uncertainty due to Each Step of Simulating the Photovoltaic Conversion under Real Operating Conditions

The simulation of photovoltaic installations is a major issue for their sizing, their smart grid operation, and their fault detection and diagnosis. In this article, we study in detail every step of the simulation chain, either from the global horizontal irradiance and the ambient temperature (i.e., 4 steps of simulation) or considering the global in-plane irradiance and the module operating temperature (i.e., 1 step of simulation). The average quality estimation of the models is made through the calculations of average annual error between estimations and measurements, from 2016 to 2020. We have shown that the most uncertain step is the conversion of the global irradiance in its diffuse and direct components (17.2%, 2 models tested). If the model goes up to the in-plane irradiance, the average annual error decreases to 5.3% (6 models tested). The photovoltaic module temperature calculation induces an error of less than 2Â°C (4 models tested with 2 configurations). Meanwhile, the photoelectrical conversion shows a 3.5% error, similar to the measurement uncertainties, considering as input, the modules temperature, and the in-plane irradiance. If the simulation goes from the global irradiance and the ambient temperature measured locally, the estimation leads to a 6.7% average annual error. If the local measurements are not available, we can use the closest meteorological station’s records (13 for our study), and the error becomes 12.1%. Finally, we can also use the satellite images that lead to a 15.2% error, for average per year. The impact of available input shows that modeling the DC photovoltaic production, using global horizontal irradiance and ambient temperature, gives rise to an error of 6.6% for local measurements, 12.1% for weather station measurements, and 15.2% for satellite images estimations. This article thus draws up a review of the existing models, allowing to calculate the DC production of a photovoltaic module, depending on the atmospheric conditions, and highlights the most precise or most critical steps, considering in situ and weather station ground-based measurements, and also estimation from satellite images.
Alternative Input Signals Ease Transfer in Multilingual Machine Translation

Recent work in multilingual machine translation (MMT) has focused on the potential of positive transfer between languages, particularly cases where higher-resourced languages can benefit lower-resourced ones.While training an MMT model, the supervision signals learned from one language pair can be transferred to the other via the tokens shared by multiple source languages.However, the transfer is inhibited when the token overlap among source languages is small, which manifests naturally when languages use different writing systems.In this paper, we tackle inhibited transfer by augmenting the training data with alternative signals that unify different writing systems, such as phonetic, romanized, and transliterated input.We test these signals on Indic and Turkic languages, two language families where the writing systems differ but languages still share common features.Our results indicate that a straightforward multisource self-ensemble -training a model on a mixture of various signals and ensembling the outputs of the same model fed with different signals during inference -outperforms strong ensemble baselines by 1.3 BLEU on both language families.Further, we find that incorporating alternative inputs via self-ensemble can be particularly effective in low-resource settings, leading to +5 BLEU when only 5% of the total training data is accessible.Finally, our analysis demonstrates that including alternative signals yields more consistency and translates named entities more accurately, which is crucial for increased factuality of automated systems.
Defocused binary fringe phase error modeling and compensation using depth-discrete Fourier series fitting

The binary defocus fringe projection is a widely adopted way to increase the speed of fringe projection profilemotry. However, the projected patterns may deviate from ideal ones at some depths. We propose a theoretical model and a corresponding compensation method to explain and calibrate the phase error of defocus-projected patterns. We first low-pass filter the projected patterns at different depths to obtain corresponding ideal ones. Then, we calibrate the model coefficients based on the errors between the original and ideal fringe patterns. The calibrated phase error model can be used to compensate the phase error at arbitrary depths within the calibration volume. Experiments are conducted to verify the feasibility and performance of the proposed method.
Using Mobile-Assisted Language Learning (MALL) to Teach English in Indonesian Context: Opportunities and Challenges

In the realm of education, information technology (IT), particularly mobile devices and applications, is becoming increasingly popular. Language learning has become one of the areas of education where mobile technology is being used. In Indonesia, Mobile-Assisted Language Learning has been implemented some areas, while it is acknowledged that some areas have not benefited as much as others. Some have questioned whether MALL might be used in their areas, while others have stated that it is unavoidable. The following are the research questions: 1) what are the advantages and disadvantages of using MALL? 2) How feasible is it to utilize MALL in an Indonesian context? The nature of this study is descriptive qualitative. The data collection methods used was library research. Data reduction, data display, and conclusion drawing were the approaches used to analyze the data. This research examines a variety of literature (including peer-reviewed studies) on the benefits and drawbacks of adding MALL into the framework of English as a Foreign Language (EFL) education. The application of these concepts in an Indonesian setting is then examined. Finally, to stay up with global digital learning, this article advocates that MALL be used for EFL teaching in Indonesia. It also suggests that more research be done for each mobile device and app in various parts of Indonesia, particularly in rural and remote places.
Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregation

We design a scalable algorithm to privately generate location heatmaps over decentralized data from millions of user devices. It aims to ensure differential privacy before data becomes visible to a service provider while maintaining high data accuracy and minimizing resource consumption on users’ devices. To achieve this, we revisit distributed differential privacy based on recent results in secure multiparty computation, and we design a scalable and adaptive distributed differential privacy approach for location analytics. Evaluation on public location datasets shows that this approach successfully generates metropolitan-scale heatmaps from millions of user samples with a worstcase client communication overhead that is significantly smaller than existing state-of-the-art private protocols of similar accuracy.
The Naturality of Natural Deduction (II): On Atomic Polymorphism and Generalized Propositional Connectives

Abstract In a previous paper (of which this is a prosecution) we investigated the extraction of proof-theoretic properties of natural deduction derivations from their impredicative translation into System F. Our key idea was to introduce an extended equational theory for System F codifying at a syntactic level some properties found in parametric models of polymorphic type theory. A different approach to extract proof-theoretic properties of natural deduction derivations was proposed in a recent series of papers on the basis of an embedding of intuitionistic propositional logic into a predicative fragment of System F, called atomic System F. In this paper we show that this approach finds a general explanation within our equational study of second-order natural deduction, and a clear semantic justification in terms of parametricity.
Emerging Research Topic Detection Using Filtered-LDA

Comparing two sets of documents to identify new topics is useful in many applications, like discovering trending topics from sets of scientific papers, emerging topic detection in microblogs, and interpreting sentiment variations in Twitter. In this paper, the main topic-modeling-based approaches to address this task are examined to identify limitations and necessary enhancements. To overcome these limitations, we introduce two separate frameworks to discover emerging topics through a filtered latent Dirichlet allocation (filtered-LDA) model. The model acts as a filter that identifies old topics from a timestamped set of documents, removes all documents that focus on old topics, and keeps documents that discuss new topics. Filtered-LDA also genuinely reduces the chance of using keywords from old topics to represent emerging topics. The final stage of the filter uses multiple topic visualization formats to improve human interpretability of the filtered topics, and it presents the most-representative document for each topic.