Reviewerly/exHarmony huggingface dataset:
Banking Reforms and Deposit Money Banks Profitability in Nigeria

The study carried out an investigation into how banking reforms have enhanced or inhibited Deposit Money Banks (DMB’s) profitability in Nigeria. The data set comprising 12 of the 22 banks that have national and international authorisation was annual data that covered the period 2006 to 2018 and were analysed by adopting a dynamic GMM econometric methodology for the derived panel data. The conclusions from the study was that bank credit risk exposure, the inflation rate, and the exchange rate were significant indicators of profitability of DMB’S in Nigeria. The estimated results show that when bank credit risk exposure rise by 10%, the return on equity will drop by about 9%. It further shows that when inflation rises by 10%, return on equity will decrease by about 1.4% and that when the exchange rate appreciates by 10%, return on equity will rise by 0.06%. The result allows us to conclude that the banking reforms may have reduced the credit risk exposure of DMB’s thereby increasing their profitability, whilst enhancing the benefits of recapitalisation of deposit money banks. The reforms seem to have enhanced market concentration as envisaged by the structure conduct performance (SCP) hypothesis as well as efficiency as postulated by the efficiency structural hypotheses .The CBN should intensify efforts at tackling the exposure of DMB’s to exchange rate volatility and spikes in the inflation rate due to increasing national indebtedness, falling foreign reserves and the falling value of the naira due drop in revenue from crude oil sales.
Interaction art using Video Synthesis Technology

Media art, which is a combination of media technology and art, is making a lot of progress in combination with AI, IoT and VR. This paper aims to meet people's needs by creating a video that simulates the dance moves of an object that users admire by using media art that features interactive interactions between users and works. The project proposed a universal image synthesis system that minimizes equipment constraints by utilizing a deep running-based Skeleton estimation system and one of the deep-running neural network structures, rather than a Kinect-based Skeleton image. The results of the experiment showed that the images implemented through the deep learning system were successful in generating the same results as the user did when they actually danced through inference and synthesis of motion that they did not actually behave.
DCR-Net: A Deep Co-Interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classification

In dialog system, dialog act recognition and sentiment classification are two correlative tasks to capture speakers intentions, where dialog act and sentiment can indicate the explicit and the implicit intentions separately. Most of the existing systems either treat them as separate tasks or just jointly model the two tasks by sharing parameters in an implicit way without explicitly modeling mutual interaction and relation. To address this problem, we propose a Deep Co-Interactive Relation Network (DCR-Net) to explicitly consider the cross-impact and model the interaction between the two tasks by introducing a co-interactive relation layer. In addition, the proposed relation layer can be stacked to gradually capture mutual knowledge with multiple steps of interaction. Especially, we thoroughly study different relation layers and their effects. Experimental results on two public datasets (Mastodon and Dailydialog) show that our model outperforms the state-of-the-art joint model by 4.3% and 3.4% in terms of F1 score on dialog act recognition task, 5.7% and 12.4% on sentiment classification respectively. Comprehensive analysis empirically verifies the effectiveness of explicitly modeling the relation between the two tasks and the multi-steps interaction mechanism. Finally, we employ the Bidirectional Encoder Representation from Transformer (BERT) in our framework, which can further boost our performance in both tasks.
Towards Document Image Quality Assessment: A Text Line Based Framework and A Synthetic Text Line Image Dataset

Since the low quality of document images will greatly undermine the chances of success in automatic text recognition and analysis, it is necessary to assess the quality of document images uploaded in online business process, so as to reject those images of low quality. In this paper, we attempt to achieve document image quality assessment and our contributions are twofold. Firstly, since document image quality assessment is more interested in text, we propose a text line based framework to estimate document image quality, which is composed of three stages: text line detection, text line quality prediction, and overall quality assessment. Text line detection aims to find potential text lines with a detector. In the text line quality prediction stage, the quality score is computed for each text line with a CNN-based prediction model. The overall quality of document images is finally assessed with the ensemble of all text line quality. Secondly, to train the prediction model, a large-scale dataset, comprising 52,094 text line images, is synthesized with diverse attributes. For each text line image, a quality label is computed with a piece-wise function. To demonstrate the effectiveness of the proposed framework, comprehensive experiments are evaluated on two popular document image quality assessment benchmarks. Our framework significantly outperforms the state-of-the-art methods by large margins on the large and complicated dataset.
Assessing the Hindrances and Prospects of Women’s Entrepreneurship Development: The Case of Hosaena Town, Hadya Zone, SNNP, Ethiopia

Women's entrepreneurship is considered to have a noteworthy part in economic development.The issue of women empowerment has been important for Governments and other non-governmental organizations.Both primary and secondary data was collected.The result of the study indicates that women entrepreneurs are exposed to various obstacles in the area of economy such as: limited access to loan and finance, difficulties in accessing markets, the lack of own land and inheritance, and difficulties in accessing raw materials.The main difficulties identified by women entrepreneurs in the area of legal and administrative functions in the survey are: high interest rates, high taxes, difficulties in getting a loan, insufficient support from Governmental and Financial institutions, and the lack of possibility to influence policy making.In relation to social and cultural factors, results from the survey are ambiguous.The government should create gender-neutral laws and policies to create a stimulating environment for potential women entrepreneurs.Therefore, government should offer different forms of support to women entrepreneurs such as: tax incentives and grants for starting a business.Additional effort should be directed to increase the quality of existing support to entrepreneurship development as well to designing new programs that would better alleviate the obstacles identified in this study.
Reduplication in Lamkang

Photograph of a poster presentation about reduplication in Lamkang at the ICOLSI-38 conference at IIT Guwahati, Assam, India
Penerapan Aplikasi Mobile Untuk Mencari dan Memberikan Pertolongan Terhadap Kerusakan Pada Kendaraan Berdasarkan Lokasi Terdekat

For motor vehicles and cars, each year always causes an increase in the number of motor vehicle problems such as leaky tires, running out of gas or breaking down and the number of problems always increases. Where when these problems occur drivers always have difficulty in finding the nearest workshop or help. To seek help for problems with vehicles in the city of Padang, through mobile devices becomes very important considering vehicle users who find it difficult to find help. In this study the author tries to resolve the above problems by creating an Android-based system that makes it easy for motorists and cars to find help with the problems they are experiencing later. In the study of this mobile application the author will utilize GPS on existing smartphones so that they can search for other people who are around the location to make it easier to find the nearest aid provider. The study was conducted from the stages of needs analysis, design, implementation, until testing.
Generalized Spacing-Statistics and a New Family of Non-Parametric Tests

Random divisions of an interval arise in various context, including statistics, physics, and geometric analysis. For testing the uniformity of a random partition of the unit interval $[0,1]$ into $k$ disjoint subintervals of size $(S_k[1],\ldots,S_k[k])$, Greenwood (1946) suggested using the squared $\ell_2$-norm of this size vector as a test statistic, prompting a number of subsequent studies. Despite much progress on understanding its power and asymptotic properties, attempts to find its exact distribution have succeeded so far for only small values of $k$. Here, we develop an efficient method to compute the distribution of the Greenwood statistic and more general spacing-statistics for an arbitrary value of $k$. Specifically, we consider random divisions of $\{1,2,\dots,n\}$ into $k$ subsets of consecutive integers and study $\|S_{n,k}\|^p_{p,w}$, the $p$th power of the weighted $\ell_p$-norm of the subset size vector $S_{n,k}=(S_{n,k}[1],\ldots,S_{n,k}[k])$ for arbitrary weights $w=(w_1,\ldots,w_k)$. We present an exact and quickly computable formula for its moments, as well as a simple algorithm to accurately reconstruct a probability distribution using the moment sequence. We also study various scaling limits, one of which corresponds to the Greenwood statistic in the case of $p=2$ and $w=(1,\ldots,1)$, and this connection allows us to obtain information about regularity, monotonicity and local behavior of its distribution. Lastly, we devise a new family of non-parametric tests using $\|S_{n,k}\|^p_{p,w}$ and demonstrate that they exhibit substantially improved power for a large class of alternatives, compared to existing popular methods such as the Kolmogorov-Smirnov, Cramer-von Mises, and Mann-Whitney/Wilcoxon rank-sum tests.
Efficient Knowledge Graph Validation via Cross-Graph Representation Learning

Recent advances in information extraction have motivated the automatic construction of huge Knowledge Graphs (KGs) by mining from large-scale text corpus. However, noisy facts are unavoidably introduced into KGs that could be caused by automatic extraction. To validate the correctness of facts (i.e., triplets) inside a KG, one possible approach is to map the triplets into vector representations by capturing the semantic meanings of facts. Although many representation learning approaches have been developed for knowledge graphs, these methods are not effective for validation. They usually assume that facts are correct, and thus may overfit noisy facts and fail to detect such facts. Towards effective KG validation, we propose to leverage an external human-curated KG as auxiliary information source to help detect the errors in a target KG. The external KG is built upon human-curated knowledge repositories and tends to have high precision. On the other hand, although the target KG built by information extraction from texts has low precision, it can cover new or domain-specific facts that are not in any human-curated repositories. To tackle this challenging task, we propose a cross-graph representation learning framework, i.e., CrossVal, which can leverage an external KG to validate the facts in the target KG efficiently. This is achieved by embedding triplets based on their semantic meanings, drawing cross-KG negative samples and estimating a confidence score for each triplet based on its degree of correctness. We evaluate the proposed framework on datasets across different domains. Experimental results show that the proposed framework achieves the best performance compared with the state-of-the-art methods on large-scale KGs.
Virtual Reality

The diversity of learners within education is neither linear nor constant. Educators are challenged to be responsive and understanding when encouraging learners to construct meaning while adhering to stringent standards. The objective of this study is to integrate science standards into authentic learning experiences, created in both a traditional teaching method and virtual reality (VR) platform, for 8th grade middle school students in Lafayette, Louisiana. The authentic experiences were based on oral histories of the residents of Isle de Jean Charles, Louisiana, who have lost 98% of their ancestral homeland since 1955. These experiences were then tied to the National Science Standards (8-MS-ESS1-4, 8-MS-ESS2-2, and 8-MS-ESS3-1). The students were split into two groups and given either a PowerPoint or VR experience, both having the same content. The researchers tracked engagement, focus, interest, and how important the students thought the content was. Using an experimental approach, the researchers also gave a pre- and posttest to determine if the VR experience resulted in better academic learning than a regular, PowerPoint-based lecture. The students were also asked to comment on their experience of the PowerPoint versus the VR and describe what their experience.
Visually Aware Skip-Gram for Image Based Recommendations

The visual appearance of a product significantly influences purchase decisions on e-commerce websites. We propose a novel framework VASG (Visually Aware Skip-Gram) for learning user and product representations in a common latent space using product image features. Our model is an amalgamation of the Skip-Gram architecture and a deep neural network based Decoder. Here the Skip-Gram attempts to capture user preference by optimizing user-product co-occurrence in a Heterogeneous Information Network while the Decoder simultaneously learns a mapping to transform product image features to the Skip-Gram embedding space. This architecture is jointly optimized in an end-to-end, multitask fashion. The proposed framework enables us to make personalized recommendations for cold-start products which have no purchase history. Experiments conducted on large real-world datasets show that the learned embeddings can generate effective recommendations using nearest neighbour searches.
BUT-FIT at SemEval-2020 Task 4: Multilingual commonsense

This paper describes work of the BUT-FIT's team at SemEval 2020 Task 4 - Commonsense Validation and Explanation. We participated in all three subtasks. In subtasks A and B, our submissions are based on pretrained language representation models (namely ALBERT) and data augmentation. We experimented with solving the task for another language, Czech, by means of multilingual models and machine translated dataset, or translated model inputs. We show that with a strong machine translation system, our system can be used in another language with a small accuracy loss. In subtask C, our submission, which is based on pretrained sequence-to-sequence model (BART), ranked 1st in BLEU score ranking, however, we show that the correlation between BLEU and human evaluation, in which our submission ended up 4th, is low. We analyse the metrics used in the evaluation and we propose an additional score based on model from subtask B, which correlates well with our manual ranking, as well as reranking method based on the same principle. We performed an error and dataset analysis for all subtasks and we present our findings.
Employ the Principle Components in the Detection of Feedback

Abstract In linear dynamic feedback models, the relationship between two input series input and output shows that the approach is a propose to discover a feedback in linear dynamic system through examining autocorrelation function and partial autocorrelation function for principle components by using few tests of time series identification and exact using Ljung-Box test depending on Simulation approach to show the efficiency of the suggested approach and application on linear and nonlinear models within and without feedback, The obtained result is good and encourage.
Defense Against Adversarial Examples Using Quality Recovery for Image Classification

Adversarial examples can be used to exploit vulnerabilities in neural networks and threaten their sensitive applications. Adversarial attacks are evolving daily, and are rapidly rendering defense methods that assume specific attacks obsolete. This paper proposes a new defense method that does not assume a specific adversarial attack, and shows that it can be used efficiently to protect a network from a variety of adversarial attacks. Adversarial perturbations are small values; consequently, an image quality recovery method is considered to be an effective way to remove adversarial perturbations because such a method often includes a smoothing effect. The proposed method, called the denoising-based perturbation removal network (DPRNet), aims to eliminate perturbations generated by an adversarial attack for image classification tasks. DPRNet is an encoder–decoder network that excludes adversarial images during training and can reconstruct a correct image from an adversarial image. To optimize DPRNet’s parameters for eliminating adversarial perturbations, we also propose a new perturbation removal loss (PRloss) metric, which consists of a reconstructed loss and a Kullback–Leibler divergence loss that expresses the class probability distribution difference between an original image and a reconstructed image. To remove adversarial perturbation, the proposed network is trained using various types of distorted images considering the proposed PRloss metric. Thus, DPRNet eliminates image perturbations, allowing the images to be classified easily. We evaluate the proposed method using the MNIST, CIFAR-10, SVHN, and Caltech 101 datasets and show that the proposed defense method invalidates 99.8%, 95.1%, 98.7%, and 96.0% of the adversarial images that are generated by several adversarial attacks in the MNIST, CIFAR-10, SVHN, and Caltech 101 datasets, respectively.
BENCHMARKING BIO-INSPIRED COMPUTATION ALGORITHMS AS WRAPPERS FOR FEATURE SELECTION

Reducing the number of features when applying machine learning algorithms may be beneficial not only from the standpoint of computational cost but also of overall quality.Wrapper-based procedures are widely utilised to achieve this.The choice of the wrapper is of utmost importance.Bio-inspired computation algorithms represent a viable choice and are widely adopted.Due to the sheer number of available algorithms, this choice could prove to be somewhat difficult, especially since not all are made equally.The aim of this paper is to explore several optimisers on diverse datasets representing classification problems in order to evaluate their performance and suitability for the task of feature selection.
Mathematical Abilities Development of Elementary School Children in Extracurricular Activities

The article highlights the relevance of the problem of improving the elementary school children’s mathematical education. The analysis of psychological and pedagogical literature on the research problem is performed. The author defines the psychological and pedagogical foundations for elementary school children’s quantitative skills development, and also notes the main forms and types of extracurricular activities in mathematics. The study presents after-school math program, aimed at elementary school children’s quantitative skills development.
Cultural Interference in English Speaking Class

The local culture can give people an advantage in understanding and developing intercultural knowledge and skills. It is also useful for achieving a successful life in this challenging global world. In order to understand people it is important to understand their values and culture. Indonesia consists of various ethnicities, which consequently affect people's culture as a whole nation. This research was done at one of universities in Makassar, State Islamic University of Alauddin. It comprised 15 respondents from various ethnic backgrounds such as: Makassarese, Buginese, Javanese, and others. The interview used was developed on the basis of Hofstede's work on values. The study showed that respondents showed five cultural dimensions in speaking which indicated high on Power Distance, Collectivism, Masculinity; and low on Uncertainty Avoidance, and Short and Long Term Orientation.
Innovative Growth Strategy in the US, Europe and Japan

The Korean government is implementing innovative growth policies such as not only a super-connected intelligence project, smart city, smart factories for innovative SMEs, and start-up ecosystem projects, but also policies to innovate regulations and create an innovative infrastructure. The purpose of this study is to suggest the policy implications for the Korean government by analyzing innovative growth policies of major countries such as the United States, Germany, France, and Japan. Against this backdrop, this study focused on a comparative analysis of fourth industrial revolution areas currently being pursued by the Korean government: smart factories, start-up ecosystem, and the fintech industry.
Method for detecting and counteracting Virus Detection in BMP images

The aim of the article is to develop a method for protecting modern systems against attacks using BMP image files and HID attacks. This article describes the features of BMP format images. The method of injecting computer viruses in BMP image and attacks to overcome the means of protection. HID attacks and the possibility of combining these attacks are also considered. Features of functioning of modern means of protection IDS, IPS, antiviruses, firewalls and their shortcomings are presented. Such attacks are possible due to the fact that security tools will only analyze executable files, DLLs, Word documents, Java applets. Most of the protection tools simply do not pay attention to images or another secure file type. Because they believe, that there is no reason to spend the processor cycle on image analysis. HID devices are perceived by security tools as a simple interface between a computer and a user, therefore they are trusted. The article suggests methods for detecting viruses in BMP image files based on checking reserved fields that should be zero, matching the real file size with the value in the file header, matching the pixel size specified in the header with real. The article also offers a method to counteract HID attacks based on analysis of text input speed. Developed programs demonstrate the effectiveness of protection against the considered attacks.
Penerapan Model Kooperatif TGT Untuk Meningkatkan Keterampilan Sosial dan Hasil Belajar IPS Kelas VIII E SMPN 1 Randudongkal

&lt;p class="Abstrakabstract"&gt;This research aims: (1) to improve social skill and learning outcomes of social studies of the eighth graders exactly Class E of SMPN 1 Randudongkal using TGT cooperative learning model with variations of the game. This research is Classroom Action Research that uses the model of Kemmis and Taggart. The Techniques of Collecting data used observation, interviews, test, field notes and documentation. The result of the research indicated an improvement of social skills and learning outcomes in social studies after the writer applied cooperative learning model TGT with variations of the game. The writer can proved there is the improvement of social skills. Before the action was done, the average of social skills was 46.88, after Cycle 1, there was the improvement in 72.66, in the end of Cycle 2, the average of social skills became 80.78. The improvement of the students’ learning outcomes can also be proved by the classical completeness percentage. The classical completeness percentage was 40.62% before the action. It was be 78.12% in the end of Cycle 1 and in the end of Cycle 2 improved to become 87.50%.&lt;/p&gt;