Reviewerly/exHarmony huggingface dataset:
Total Style Transfer with a Single Feed-Forward Network

The development of recent image style transfer methods allows the quick transformation of an input content image into an arbitrary style. However, these methods have a limitation that the scale-across style pattern of a style image cannot be fully transferred into a content image. In this paper, we propose a new style transfer method, named total style transfer, that resolves this limitation by utilizing intra/inter-scale statistics of multi-scaled feature maps without losing the merits of the existing methods. First, we use a more general feature transform layer that employs intra/inter-scale statistics of multi-scaled feature maps and transforms the multi-scaled style of a content image into that of a style image. Secondly, we generate a multi-scaled stylized image by using only a single decoder network with skip-connections, in which multi-scaled features are merged. Finally, we optimize the style loss for the decoder network in the intra/inter-scale statistics of image style. Our improved total style transfer can generate a stylized image with a scale-across style pattern from a pair of content and style images in one forwarding pass. Our method achieved less memory consumption and faster feed-forwarding speed compared with the recent cascade scheme and the lowest style loss among the recent style transfer methods.
Relationship between Sciences and Education Lecturers’ Perceived Use of Mobile Technologies for Instruction

Mobile technologies enable both education and science lecturers to provide the educational materials to their students on mobile technologies. But many science and education lectures educators were likely not using mobile technology to its maximum potential. The objectives were to examine the relationship between science and education lecturers and the effect of specialisation in the use of mobile technologies for instruction. The study was a descriptive research of survey type. 160 respondents were randomly selected for this study from the faculty of science and education. Frequency, percentage, multiple regression, and ANOVA were used for the analysis. The findings established that a positive relationship exists between science and education lecturers in the use of mobile technologies for instruction. It was recommended that lecturers should be encouraged to explore different ways in which mobile technologies can be used in teaching and learning.
Venetian Drawing Conversations

This one-day workshop invites designers, researchers and practitioners whose work might involve design, to collectively speculate about designed artefacts and technologies through the creation of drawing conversations: visual dialogues resulting from the merging of drawings created by different people. The workshop aims to use drawing as an activity for collaborative engagement with ambiguity, interpretation and mutual learning. Through drawing activities, we aim to join in Venice's rich creative traditions, and develop speculative visualisations in order to find common grounds between the diverse research interests of our organisers and participants.
Grouping of Village Status in West Java Province Using the Manhattan, Euclidean and Chebyshev Methods on the K-Mean Algorithm

The Ministry of Villages, Development of Disadvantaged Areas and Transmigration (Ministry of Village PDTT) is a ministry within the Indonesian Government in charge of rural and rural development, empowerment of rural communities, accelerated development of disadvantaged areas, and transmigration. Village Potential Data for 2014 (Podes 2014) in West Java Province is data issued by the Central Statistics Agency in collaboration with the Ministry of Village PDTT which is in unsupervised data format, consists of 5319 village data. The Podes 2014 data in West Java Province were made based on the level of village development (village specific) in Indonesia, by making the village as the unit of analysis. Base on the Regulation of the Minister of Villages, Disadvantaged Areas and Transmigration of the Republic of Indonesia number 2 of 2016 concerning the village development index, the Village is classified into 5 village status, namely Very Disadvantaged Village, Disadvantaged Village, Developing Village, Advanced Village and Independent Village based on the ability to manage and increase the potential of social, economic and ecological resources. Village status is in fact inseparable from village development that is under government funding support. However, village development funds have not been distributed effectively and accurately according to the conditions and potential of the village due to the lack of clear information about the status of the village. Therefore, the information regarding the villages priority in term of which villages needs more funding and attention from the government is still lacking. Data mining is a method that can be used to group objects in a data into classes that have the same criteria (clustering). One of the algorithms that can be used for the clustering process is the k-means algorithm. Data grouping using k-means is done by calculating the closest distance from data to a centroid point. In this study, different types of distance calculation in the K-means algorithm are compared. Those types are Manhattan, Euclidean and Chebyshev. Validation tests have been carried out using the execution time and Davies Bouldin index. From this test, the data Village Potential 2014 in West Java province have grouped all the 5 status of the village with the obtained number of villages for each cluster is a cluster village Extremely Backward many as 694 villages, cluster Villages 567 villages, cluster village Evolving as much as 1440 villages, the cluster with Desa Maju1557 villages and the cluster Independent Village for 1061 villages. For distance calculation, Chebyshev has the most efficient accumulation time of 1 second compared to Euclidean 1.6 seconds and Manhattan 2.4 seconds. Meanwhile, the Euclidean method has the value, Davies Index most optimal which is 0.886 compared to the Manhattan method 0.926 and Chebyshev 0.990.
How well do practicing radiologists interpret the results of CAD technology? A quantitative characterization

Many studies have shown that using a computer-aided detection (CAD) system does not significantly improve diagnostic accuracy in radiology, possibly because radiologists fail to interpret the CAD results properly. We tested this possibility using screening mammography as an illustrative example. We carried out two experiments, one using 28 practicing radiologists, and a second one using 25 non-professional subjects. During each trial, subjects were shown the following four pieces of information necessary for evaluating the actual probability of cancer in a given unseen mammogram: the binary decision of the CAD system as to whether the mammogram was positive for cancer, the true-positive and false-positive rates of the system, and the prevalence of breast cancer in the relevant patient population. Based only on this information, the subjects had to estimate the probability that the unseen mammogram in question was positive for cancer. Additionally, the non-professional subjects also had to decide, based on the same information, whether to recall the patients for additional testing. Both groups of subjects similarly (and significantly) overestimated the cancer probability regardless of the categorical CAD decision, suggesting that this effect is not peculiar to either group. The misestimations were not fully attributable to causes well-known in other contexts, such as base rate neglect or inverse fallacy. Non-professional subjects tended to recall the patients at high rates, even when the actual probably of cancer was at or near zero. Moreover, the recall rates closely reflected the subjects' estimations of cancer probability. Together, our results show that subjects interpret CAD system output poorly when only the probabilistic information about the underlying decision parameters is available to them. Our results also highlight the need for making the output of CAD systems more readily interpretable, and for providing training and assistance to radiologists in evaluating the output.
Low-complexity signal detection networks based on Gauss-Seidel iterative method for massive MIMO systems

Abstract In massive multiple-input multiple-output (MIMO) systems with single- antenna user equipment (SAUE) or multiple-antenna user equipment (MAUE), with the increase of the number of received antennas at base station, the complexity of traditional detectors is also increasing. In order to reduce the high complexity of parallel running of the traditional Gauss-Seidel iterative method, this paper proposes a model-driven deep learning detector network, namely Block Gauss-Seidel Network (BGS-Net), which is based on the Gauss-Seidel iterative method. We reduce complexity by converting a large matrix inversion to small matrix inversions. In order to improve the symbol error ratio (SER) of BGS-Net under MAUE system, we propose Improved BGS-Net. The simulation results show that, compared with the existing model-driven algorithms, BGS-Net has lower complexity and similar the detection performance; good robustness, and its performance is less affected by changes in the number of antennas; Improved BGS-Net can improve the detection performance of BGS-Net.
Distance Education vs. Face-to-Face Education

The article deals with the experience of distance education during the COVID-19 pandemic. The author used on-line questionnaires to describe distance learning and proved that formal distance education cannot be carried out without the teacher as a transmitter of information. In fact, the teacher has as many as fourteen different functions in the academic process. The author used this list to analyze face-to-face, distance synchronous, and distance asynchronous education modes. Asynchronous distance education proved to be able to provide most of these functions. Distance education per se separates the teacher and the student both in space and time. As a result, distance education is impossible if the curriculum involves training with special tools or body work. Since one of these elements is always present, formal education cannot be exclusively remote. Distance education is fundamentally different from full-time face-to-face education. For it to be effective, it cannot be implemented as full-time education. The research creates practical grounds for designing effective methods of distance education in the future.
Automatic Image Caption Generation Using Deep Learning

Abstract Image captioning is an interesting and challenging task with applications in diverse domains such as image retrieval, organizing and locating images of users’ interest etc. It has huge potential for replacing manual caption generation for images and is especially suitable for large scale image data. Recently, deep neural network based methods have achieved great success in the field of computer vision, machine translation and language generation. In this paper, we propose an encoder-decoder based model that is capable of generating grammatically correct captions for images. This model makes use of VGG16 Hybrid Places 1365 as encoder and LSTM as decoder. To ensure the complete ground truth accuracy, the model is trained on the labelled Flickr8k and MSCOCO Captions datasets. Further, the model is evaluated using all standard metrics such as BLEU, METEOR, GLEU and ROUGE L. Experimental results indicate that the proposed model obtained a BLEU-1 score 0.6666, METEOR score 0.5060 and GLEU score 0.2469 on Flickr8k dataset and BLEU-1 score 0.7350, METEOR score 0.4768 and GLEU score 0.2798 on the MSCOCO Captions dataset. Thus, the proposed method achieved a significant performance as compared to the state-of-art approaches. To evaluate the efficacy of the model further, we also show the results of a caption generation from live sample images that reinforces the validity of the proposed approach.
Construction of the Evaluation Index System of Physical Education Teaching in Colleges and Universities Based on Scientific Knowledge Graph

The purpose of physical education (PE) teaching in general institutions of higher education is to enhance students’ fitness and mental quality, which in turn improves students’ physiological ability and sports skills and finally enables students to develop healthy lifestyle. The conventional assessment of students’ PE instruction only focuses on the evaluation of their body condition and sports skills, ignoring the cultivation of their mental and physical development and healthy living habits and evaluating students with a relatively single evaluation standard, lacking the cultivation of “teaching to suit the individual.” At present, the evaluation method of instructional quality of PE teachers in China’s institutions of higher education is relatively single and dominated by qualitative methods, which cannot be applied to instructional innovation. The evaluation of physical education teaching quality plays an important role in building a complete and effective teaching process, which plays an important role in judging whether the formulation of physical education teaching objectives and principles is reasonable, and the evaluation of students’ learning behavior and learning effect. This article takes the relevant theory of scientific knowledge graph as the guiding idea, based on the current situation of PE teaching assessment in institutions of higher education, and employs experimental research methods to conduct empirical studies on 10 universities in Jilin Province. The empirical findings suggest that students’ satisfaction with the new evaluation method is 83.68 points on average, and the accuracy rate is 84.54 points on average; teachers’ satisfaction with the new evaluation method is 86.12 points on average, and the accuracy rate is 88.4 points on average.
UNIQUE LARGE-SCALE RESEARCH FACILITIES "SEISMIC INFRASOUND ARRAY FOR MONITORING ARCTIC CRYOLITOZONE AND CONTINUOUS SEISMIC MONITORING OF THE RUSSIAN FEDERATION, NEIGHBOURING TERRITORIES AND THE WORLD"

The description of the Unique Large-Scale Research Facilities "Seismic infrasound array for monitoring Arctic cryolithozone and continuous seismic monitoring of the Russian Federation, neighbouring territories and the world" (URF SIA MAC), its hardware and software components are presented. Methods and principle areas of research carried out using the URF are outlined. The URF SIA MAC includes 364 seismic stations throughout Russia and three infrasound arrays. The data obtained at the URF serve to provide domestic fundamental and applied research in the field of Earth Sciences (seismology, geophysics, geodynamics, geoecology) and to study catastrophic events of a natural induced nature. The modern URF data archive includes digital seismic records since 1993. In addition, there is a significant amount of analogue records (since 1904) available for further digitization. The URF SIA MAC provides seismological data to many Russian scientific, research and design organizations. The article presents the main results of the Federal Research Centre Geophysical Survey of the Russian Academy of Sciences obtained in recent years using the URF data.
Powershap: A Power-full Shapley Feature Selection Method

Feature selection is a crucial step in developing robust and powerful machine learning models. Feature selection techniques can be divided into two categories: filter and wrapper methods. While wrapper methods commonly result in strong predictive performances, they suffer from a large computational complexity and therefore take a significant amount of time to complete, especially when dealing with high-dimensional feature sets. Alternatively, filter methods are considerably faster, but suffer from several other disadvantages, such as (i) requiring a threshold value, (ii) not taking into account intercorrelation between features, and (iii) ignoring feature interactions with the model. To this end, we present powershap, a novel wrapper feature selection method, which leverages statistical hypothesis testing and power calculations in combination with Shapley values for quick and intuitive feature selection. Powershap is built on the core assumption that an informative feature will have a larger impact on the prediction compared to a known random feature. Benchmarks and simulations show that powershap outperforms other filter methods with predictive performances on par with wrapper methods while being significantly faster, often even reaching half or a third of the execution time. As such, powershap provides a competitive and quick algorithm that can be used by various models in different domains. Furthermore, powershap is implemented as a plug-and-play and open-source sklearn component, enabling easy integration in conventional data science pipelines. User experience is even further enhanced by also providing an automatic mode that automatically tunes the hyper-parameters of the powershap algorithm, allowing to use the algorithm without any configuration needed.
Lexical Acquisition during Dialogues through Implicit Confirmation

We have been addressing the problem of acquiring attributes of unknown terms through dialogues and previously proposed an approach using the implicit confirmation process. It is crucial for dialogue systems to ask questions that do not diminish the user’s willingness to talk. In this paper, we conducted a user study to investigate user impression for several question types, including explicit and implicit, to acquire lexical knowledge. We clarified the order among the types and found that repeating the same question type annoys the user and degrades user impression even when the content of the questions is correct. We also propose a method for determining whether an estimated attribute is correct, which is included in an implicit question. The method exploits multiple-user responses to implicit questions about the attribute of the same unknown term. Experimental results revealed that the proposed method exhibited a higher precision rate for determining the correctly estimated attributes than when only single-user responses were considered.
HEA-D: A Hybrid Evolutionary Algorithm for Diversified Top-k Weight Clique Search Problem

The diversified top-k weight clique (DTKWC) search problem is an important generalization of the diversified top-k clique (DTKC) search problem with extensive applications, which extends the DTKC search problem by taking into account the weight of vertices. In this paper, we formulate DTKWC search problem using mixed integer linear program constraints and propose an efficient hybrid evolutionary algorithm (HEA-D) that combines a clique-based crossover operator and an effective simulated annealing-based local optimization procedure to find high-quality local optima. The experimental results show that HEA-D performs much better than the existing methods on two representative real-world benchmarks.
A direct computational interpretation of second-order arithmetic via update recursion

Second-order arithmetic has two kinds of computational interpretations: via Spector’s bar recursion of via Girard’s polymorphic lambda-calculus. Bar recursion interprets the negative translation of the axiom of choice which, combined with an interpretation of the negative translation of the excluded middle, gives a computational interpretation of the negative translation of the axiom scheme of comprehension. It is then possible to instantiate universally quantified sets with arbitrary formulas (second-order elimination). On the other hand, polymorphic lambda-calculus interprets directly second-order elimination by means of polymorphic types. The present work aims at bridging the gap between these two interpretations by interpreting directly second-order elimination through update recursion, which is a variant of bar recursion.
Women’s Contributions to Biomedical Healthcare in Ghana: A Focus on Obuasi

From economic, through politics to domestic support, women have been the major engineers of valuable roles towards the development of every culture. Historically, their impacts in medicine and healthcare in general have been evident across time and space. Prior to European influx and the modernization of healthcare in Ghana, women delivered such roles that simulate that of modern midwives, nurses, herbalists and priestesses. Although, denied access to formal education in the colonial days, because of cultural reasons, women have risen to occupy central stages in biomedical services. Regardless of their numerical strength and contributions towards the provision of healthcare, they have been neglected and marginalized both within the society and by scholars. Significantly, the place of Obuasi, in particular, within the literature on women’s contribution to healthcare delivery has received little attention. Dwelling on a qualitative research approach grounded in both primary and secondary data, the current study attempted a prime discourse on the contribution of women in the biomedical spheres using the Obuasi community as a case study. The current study has revealed that women as nurses and midwives work toward reducing child mortality and improvement of maternal health. Also, we have analyzed the challenges women face within the biomedical sphere as nurses and midwives.
Emotional Feature Extraction from Texts by Support Vector Machine with Local Multiple Kernel Learning

SVM), emotional textEmotional analysis in texts is one of the difficult problems in text feature extraction.Semantic information is not unique for a large number of text features, which increases the difference in feature weight.Previous studies had dimensional disasters, loss of feature information, and a weak generalization ability in text feature extraction.To solve these problems, we first analyzed the advantages of support vector machines (SVMs) by multiple kernel learning (MKL).Then, an algorithm with local multiple kernel learning (LMKL) was proposed for a threshold model to select the locally optimal kernel function.It helped understand which text feature distinguishes emotions more effectively.Next, we analyzed the features of the local multiple kernel learning algorithm and discussed its generalization ability.The effectiveness of the method in this study was verified through comparison with other methods.The method reduced the feature dimensionality of the sample data set.Since the features with a weak classification ability were reduced, the accuracy of the classification was improved with increased efficiency.
A Computational Literature Analysis of Conversational AI Research with a Focus on the Coaching Domain

We conduct a computational analysis of the literature on Conversational AI. We identify the trend based on all publications until the year 2020. We then concentrate on the publications for the last five years between 2016 and 2020 to find out the top ten venues and top three journals where research on Conversational AI has been published. Further, using the Latent Dirichlet Allocation (LDA) topic modeling technique, we discover nine important topics discussed in Conversational AI literature and specifically two topics related to the area of coaching. Finally, we detect the key authors who have contributed significantly to Conversational AI research and area(s) related to coaching. We determine the key authors' areas of expertise and how the knowledge is distributed across different regions. Our findings show an increasing trend and thus, an interest in Conversational AI research, predominantly from the authors in Europe.