Reviewerly/exHarmony huggingface dataset:
Question-Answering and Recommendation System on Cooking Recipes

Question answering (QA), one of the important applications of Natural Language Processing (NLP) aims to take the user questions and returned to the user with the answers. An open domain QA system deals with a set of questions that can be of any domain. The other type of QA is close-domain where it deals with the questions under a specific domain e.g., agriculture, medicine, education, tourism, etc. Our cooking question answering system is an example of a closed domain QA system. Here, users can ask the cooking related questions and the system returns the actual answer to the user. In the present article, we have proposed different modules of a cooking QA system. In addition to dataset preparation, the development of a cooking ontology, the classification of questions as well as the extraction of candidate answers are also treated as other important aspects which are discussed in this paper in details. In the cooking QA system, automatic evaluation metrics are like precision, recall, f-score, and c@1 have been used for the evaluation of precise answers. Also, human evaluation is used on the basis of a rating scale. Moreover, the recommendation of recipes has also been attempted and the evaluation metrics show satisfactory performances of the systems.
Eye Tracking and the Museum Experience in Italy

Understanding museum audiences and evaluating their experiences have long been at the core of theoretical and empirical research, from several disciplinary angles. Yet, a very limited amount of studies has, to date, resorted to eye tracking as a tool to map museum visitors' gazes, their preferences and also their difficulties in museum fruition. Eye tracking offers an objective window into audience perception and reception of a variety of stimuli, allowing the researcher to overcome many of the biases involved in qualitative empirical studies. This article presents the methodology used for two experiments carried out with eye tracking and other analytical tools at two different museums. It also focuses on the results obtained by the mixed-method investigations, with a view to promoting more systematic research into the museum experience with portable eye tracking technology.
Gene Subset Selection for Transfer Learning using Bilevel Particle Swarm Optimization

In classification problems that involve multiple sources, data distributions may vary. Therefore, knowledge of dimensions that differ in the source and target data is important to reduce the distance between domains, allowing accurate transfer knowledge. Here, we present a novel method to identify (in)variant genes between source and target datasets and integrate such results to simultaneously reduce the variance between two distributions while optimizing the size and classification error of the selected subset. In particular, we use an evolutionary computation particle swarm optimization algorithm to implement such a bilevel multi-objective programming approach, allowing us to solve a gene subset selection problem.
CHARACTERISTICS OF WATER RING COMPRESSOR MACHINES VACUUM FISH PUMPING UNITS

The operation of the Samson KS910 pump under various conditions is investigated. The values of empirical constants in mathematical dependencies for calculating pump characteristics were found using the least squares method based on published experimental data. The results of calculating the productivity and power consumption of the Samson KS910 in the vacuum pump mode depending on the pressure in the working chamber at different values of the rotor speed and pressure in the working chamber are presented.
Single MR image super-resolution via mixed self-similarity attention network

The single-image super-resolution (SISR) network based on deep learning is dedicated to learning the mapping between low-resolution (LR) images and high-resolution (HR) images. The optimal parameters of these networks often require extensive training on large-scale external image databases. For medical magnetic resonance (MR) images, there is a lack of large data sets containing high-quality images. Some deep networks that perform well on natural images cannot be fully trained on MR images, which limits the super-resolution (SR) performance. In traditional methods, the non-local self-similarity has been verified as useful statistical prior information for image restoration. The inherent feature correlation not only exists between pixels, but some patches also tend to be repeated at different positions within and across scales of MR images. Therefore, in this paper, we propose a mixed self-similarity attention network (MSAN) to explore the long-range dependencies of different regions fully. In the feature map of the entire input MR image, the prior information of self-similarity is divided into two scales: point-similarity and patch-similarity. We use points and patches that are highly similar to the current area to restore a more detailed structural texture. The internal correlation items can be used as an essential supplement to the limited external training dataset. Besides, the large number of less informative background in MR images will interfere with practical self-similarity information. A dual attention mechanism combining first-order attention and second-order attention gives more weight to salient features and suppresses the activation of useless features. Comprehensive experiments demonstrate that the proposed achieves significantly superior results on MR images SR while outperforming state-of-the-art methods by a large margin quantitatively and visually.
FareQR: Fast and Reliable Screen-Camera Transfer System for Mobile Devices using QR Code

With the availability of mobile devices display and camera, Screen-Camera links for Visible Lite Communications (VLC) has attracted much more attention due to its convenience, infrastructure-free and security. In conventional Screen-camera link, the senders encode data bits into a barcode stream and receivers capture the barcode scream and decode the barcodes. But conventional Screen-camera communication systems all suffer from both CMOS rolling shutter and inter frame mixing problem when display rate is close to camera capture rate and this leads to a high block transfer error rate. In this paper, we propose a system called FareQR by adding an outline border to the barcode stream to help the receiver detect mixed frames and de-obfuscation each mixed frames into perfect QRCodes. We formulate the mixed frame de-obfuscation problem as a hard-decision-decoding task, and propose a Viterbi algorithm to resolve each block in the mixed areas. We test the FareQR and result demonstrate that our work can recover the mixed area and reduce the block transfer error rate.
Cognitive Load Measurement Based on EEG Signals

Measurement of cognitive load should be advantageous in designing an intelligent navigation system for the visually impaired people (VIPs) when navigating unfamiliar indoor environments. Electroencephalogram (EEG) can offer neurophysiological indicators of perceptive process indicated by changes in brain rhythmic activity. To support the cognitive load measurement by means of EEG signals, the complexity of the tasks of the VIPs during navigating unfamiliar indoor environments is quantified considering diverse factors of well-established signal processing and machine learning methods. This chapter describes the measurement of cognitive load based on EEG signals analysis with its existing literatures, background, scopes, features, and machine learning techniques.
An Image Processing Method: Edge-guided Directional Walk Mean Smoothing

Abstract Image denoising is an important computer vision task, it is also the basis of many other high-level vision tasks. Recently, various image denoising algorithms have proposed, such as BM3D, sparse representation and deep learning, that have achieved impressive performance in AWGN denoising or blind denoising tasks. However, when dealing with Dongba painting (A traditional painting from China’s ethnic minorities), there are problems that Dongba painting have blurred edges and faded colors, it is important to retain its original style when denoising. In this paper, we propose a novel image post-processing technique termed edge-guided directional walk mean smoothing (EDWMS), it can effectively calculate the mean of pixels along different directions then obtain denoised image with sharp edge. Experiments show that the proposed EDWMS outperforms compared algorithms in both edge sharpness and color saturation.
Dataset Meta-Learning from Kernel-Ridge Regression

One of the most fundamental aspects of any machine learning algorithm is the training data used by the algorithm. We introduce the novel concept of ϵ-approximation of datasets, obtaining datasets which are much smaller than or are significant corruptions of the original training data while maintaining similar performance. We introduce a meta-learning algorithm Kernel Inducing Points (KIP) for obtaining such remarkable datasets, drawing inspiration from recent developments in the correspondence between infinitely-wide neural networks and kernel ridge-regression (KRR). For KRR tasks, we demonstrate that KIP can compress datasets by one or two orders of magnitude, significantly improving previous dataset distillation and subset selection methods while obtaining state of the art results for MNIST and CIFAR10 classification. Furthermore, our KIP-learned datasets are transferable to the training of finite-width neural networks even beyond the lazy-training regime. Consequently, we obtain state of the art results for neural network dataset distillation with potential applications to privacy-preservation.
Effective preprocessing based neural machine translation for English to Telugu cross-language information retrieval

&lt;span id="docs-internal-guid-5b69f940-7fff-f443-1f09-a00e5e983714"&gt;&lt;span&gt;In cross-language information retrieval (CLIR), the neural machine translation (NMT) plays a vital role. CLIR retrieves the information written in a language which is different from the user's query language. In CLIR, the main concern is to translate the user query from the source language to the target language. NMT is useful for translating the data from one language to another. NMT has better accuracy for different languages like English to German and so-on. In this paper, NMT has applied for translating English to Indian languages, especially for Telugu. Besides NMT, an effort is also made to improve accuracy by applying effective preprocessing mechanism. The role of effective preprocessing in improving accuracy will be less but countable. Machine translation (MT) is a data-driven approach where parallel corpus will act as input in MT. NMT requires a massive amount of parallel corpus for performing the translation. Building an English - Telugu parallel corpus is costly because they are resource-poor languages. Different mechanisms are available for preparing the parallel corpus. The major issue in preparing parallel corpus is data replication that is handled during preprocessing. The other issue in machine translation is the out-of-vocabulary (OOV) problem. Earlier dictionaries are used to handle OOV problems. To overcome this problem the rare words are segmented into sequences of subwords during preprocessing. The parameters like accuracy, perplexity, cross-entropy and BLEU scores shows better translation quality for NMT with effective preprocessing.&lt;/span&gt;&lt;/span&gt;
Geocube: Towards the Multi-Source Geospatial Data Cube in Big Data Era

The big data is characterized by challenges on variety, volumes, velocity etc. Recent advocate of data cube in the Earth observation (EO) domain has shown great promise to provide analysis ready data for remote sensing applications. It is possible to develop a geospatial big data infrastructure layered on the data cube by incorporating a uniform analysis-ready multidimensional data structure and exploiting its usage in connecting EO data analytics and OLAP (Online Analytical Processing), thus enabling a multi-source geospatial data cube accommodating both EO and location-based social-economic data. The creation of such a geospatial data cube, named GeoCube, needs special attentions from geospatial perspective including the formalization of spatio-temporal dimensions, tiling along these dimensions for high performance geoprocessing, and processing of geospatial/EO queries against these dimensions. This will help develop a new framework for big geospatial data analytics while at the time keeping connections to the data cube in the business intelligence domain. The paper will highlight these issues and identify a research agenda for developing such a geospatial data cube.
Rethinking Coherence Modeling: Synthetic vs. Downstream Tasks

Although coherence modeling has come a long way in developing novel models, their evaluation on downstream applications for which they are purportedly developed has largely been neglected. With the advancements made by neural approaches in applications such as machine translation (MT), summarization and dialog systems, the need for coherence evaluation of these tasks is now more crucial than ever. However, coherence models are typically evaluated only on synthetic tasks, which may not be representative of their performance in downstream applications. To investigate how representative the synthetic tasks are of downstream use cases, we conduct experiments on benchmarking well-known traditional and neural coherence models on synthetic sentence ordering tasks, and contrast this with their performance on three downstream applications: coherence evaluation for MT and summarization, and next utterance prediction in retrieval-based dialog. Our results demonstrate a weak correlation between the model performances in the synthetic tasks and the downstream applications, {motivating alternate training and evaluation methods for coherence models.
Smart Digital Mammographic Screening System for Bulk Image Processing

Treating breast cancer is easier at early stages. However, proper diagnosis is essential for this purpose. Mammography helps in early detection of cancer cells. Existence of masses, calcification and mammogram are the evidences that help radiologists in early cancer identification. This paper proposes a smart digital mammographic screening system for processing images in large volumes irrespective of the nature of images. Watershed segmentation is performed based on appropriate selection of internal and external markers using multiple threshold extended maxima transformations in this technique. Distinguishing between healthy breast tissue and masses can be performed efficiently using a two-stage classifier. Extreme Learning Machine based single layer feed forward network along with Bayesian classifier is used for reducing false positive areas. Feature vector with features like texture and contrast are calculated using these approaches. Digital Mammography Screening database (DMS) is created with 100 mammographic images for the purpose of evaluation. Further, online databases like Breast Cancer Database (BCDB) and BreakHis are also used for analysis. Overall sensitivity of the datasets using the Bayesian classifier and Extreme Learning Machine is found to be 85% and 90% respectively.
multi-imbalance: Open Source Python Toolbox for Multi-class Imbalanced Classification

This paper presents multi-imbalance, an open-source Python library, which equips the constantly growing Python community with appropriate tools to deal with multi-class imbalanced problems. It follows the code conventions of sklearn package. It provides implementations of state-of-the-art binary decomposition techniques, ensembles, as well as both novel and classic re-sampling approaches for multi-class imbalanced classification. For demonstration and documentation, consult the project web page: www.cs.put.poznan.pl/mlango/multiimbalance.php.
Video Enhancement and Low-Resolution Facial Image Reconstruction for Crime Investigation

In video examination, image or video quality improvement is an emanant area of research. Images and videos acquired by the imaging sensors have rich and detailed information. The viewable representation of low-quality videos can be improved using video enhancement techniques. A video enhancement and reconstruction framework for facial images with low resolution are proposed in this paper. The video enhancement phase improves the appearance of images that are not sharply defined. In the reconstruction phase, the facial regions are selected from the high-quality video frames and the resolutions of these selected facial regions are increased. After reconstructing the facial regions, the aim is to authenticate and verify the human face through a face recognition network. A detailed analysis of the experiments is reported and is observed that the results obtained are significant.
System mapping simplex spaces: facilitating change in L2 educational contexts from a complexity theory perspective

Abstract The claim that educational contexts are dynamic, complex spaces is hardly a novel one. Yet, to date limited explicit treatment in the literature engages L2 classrooms as such. This paucity is partially explained by methodological concerns with investigating diverse and contextually dense places in ways that capture the richness of these environments. While no single method can mitigate this concern, system mapping can help researchers explore system dynamics through a mapping technique that diagrams system variables through the interpretive lens of relevant stakeholders. Stakeholders engage with variables relative to a guiding statement, question, or objective meaningful to the system’s development or maintenance, thus providing insights into interactions across and beyond the conventional teacher–student interface. System mapping is particularly useful for problem solving and developing in-depth understandings of relations across system components. After describing and expanding on system mapping and its uses in education research, this work moves through a sample study in the L2 writing context to demonstrate its utility.
Hybrid System Combination Framework for Uyghur–Chinese Machine Translation

Both the statistical machine translation (SMT) model and neural machine translation (NMT) model are the representative models in Uyghur–Chinese machine translation tasks with their own merits. Thus, it will be a promising direction to combine the advantages of them to further improve the translation performance. In this paper, we present a hybrid framework of developing a system combination for a Uyghur–Chinese machine translation task that works in three layers to achieve better translation results. In the first layer, we construct various machine translation systems including SMT and NMT. In the second layer, the outputs of multiple systems are combined to leverage the advantage of SMT and NMT models by using a multi-source-based system combination approach and the voting-based system combination approaches. Moreover, instead of selecting an individual system’s combined outputs as the final results, we transmit the outputs of the first layer and the second layer into the final layer to make a better prediction. Experiment results on the Uyghur–Chinese translation task show that the proposed framework can significantly outperform the baseline systems in terms of both the accuracy and fluency, which achieves a better performance by 1.75 BLEU points compared with the best individual system and by 0.66 BLEU points compared with the conventional system combination methods, respectively.
2D vs. 3D U-Net abdominal organ segmentation in CT data using organ bounds

We compare axial 2D U-Nets and their 3D counterparts for pixel/voxel-based segmentation of five abdominal organs in CT scans. For each organ, two competing CNNs are trained. They are evaluated by performing five-fold cross-validation on 80 3D images. In a two-step concept, the relevant area containing the organ is first extracted by detected bounding boxes and then passed as input to the organ-specific U-Net. Furthermore, a random regression forest approach for the automatic detection of bounding boxes is summarized from our previous work. The results show that the 2D U-Net is mostly on par with the 3D U-Net or even outperforms it. Especially for the kidneys, it is significantly better suited in this study.
Deciphering African Financial Development Interaction With Institutional Quality And Economic Growth Nexus

The paper scrutinized the correlation between financial development interaction with institutional quality and economic growth in Africa. The study adopted 30 different interactions. The study used the Augmented mean group estimation technique to estimate the model. Gross domestic savings/GDP and broad money/GDP positively influenced growth with the majority of interactions with institutional quality indicators. Credit to Private Sector/GDP interaction with Voice &amp; Accountability; and Political Stability has a higher impact on growth than any interaction variable. However, government effectiveness, regulatory quality, and corruption control are weak in Africa; even if interacted with financial development indicators, it mostly reduces economic growth. This study recommends that governments in Africa strengthen financial development indicators; Bank Deposit/GDP, Gross Domestic Savings/GDP and Credit to private sector/GDP, and institutional quality indicator political stability &amp; absence of violence since their interaction has proven to aid rapid economic growth.JEL Classification: E17, F62, F63How to Cite:Nketia, E. B., &amp; Kong, Y. (2021). Decipheting African Financial Development Interaction with Institutional Quality and Economic Growth Nexus. Etikonomi: Jurnal Ekonomi, 20(1), 23 – 44. https://doi.org/10.15408/etk.v20i1.16177.
Reward Poisoning in Reinforcement Learning: Attacks Against Unknown Learners in Unknown Environments.

We study black-box reward poisoning attacks against reinforcement learning (RL), in which an adversary aims to manipulate the rewards to mislead a sequence of RL agents with unknown algorithms to learn a nefarious policy in an environment unknown to the adversary a priori. That is, our attack makes minimum assumptions on the prior knowledge of the adversary: it has no initial knowledge of the environment or the learner, and neither does it observe the learner's internal mechanism except for its performed actions. We design a novel black-box attack, U2, that can provably achieve a near-matching performance to the state-of-the-art white-box attack, demonstrating the feasibility of reward poisoning even in the most challenging black-box setting.
Benefits of Using Mobile Apps as a Support for Mandarin Language Learning

Abstract Conventional face-to-face learning is not enough to keep up with the demands of today’s internet-based technology. Along with the emergence of the industrial revolution 4.0, the term education 4.0 also appears in the world of education, where conventional face-to-face learning is considered to be unable to fully support the latest learning. Mandarin is the second largest language after English, so mobile apps that support Mandarin learning are growing more and more diverse. Therefore, foreign language learners make mobile apps as supporting learning. This study uses a survey method to determine the use of mobile apps in learning Mandarin, data were collected from questionnaires distributed to 149 students of the Chinese Literature program. Respondents stated: Most of them downloaded 1-3 apps on their smartphones; the majority of mobile apps used are in the form of dictionaries and translators; there are relatively many mobile apps available, but only a few can help them learn effectively; they prefer mobile apps with material that is presented in an audiovisual manner; they use mobile apps to support classroom learning and independent learning; supporting self-learning at any time and any place is the main reason they use mobile apps in learning Mandarin.