Reviewerly/exHarmony huggingface dataset:
Macro-economic Determinants of Recent Inflation in Ethiopia

Expansion is one of the major macro-economic issues which determine the better functioning of the economy.Additionally, it leads a multi-dimensional effect on the overall social, economic, financial, mechanical and political affairs of the country.To come with this problem, the study focuses on the identification and assessment of variables which determine the rate of inflation in Ethiopia using annual data from the year 1985 to 2018.The study used secondary time series data and applies econometric analysis to identify significant variables in determining inflation both in the long run and short run and descriptive methods of data analysis to identify the trend of inflation and money supply.The finding of this study shows that real interest rate and real effective exchange rate are significant in determining inflation both in the short run and long run.Broad money supply determines inflation only in the long run and gross domestic saving doesn't determine the rate of inflation both in the long run and short run.The monetary base of the country which is broad money supply has some dispersion or different impact in determining inflation.It has significant impact in the long run but became statistically insignificant in the short run since monetary policy has higher outside lag and needs longer time to affect inflation after increasing money supply.So it is advisable to the policy makers to find the right balances whether money supply permanently determines inflation rate or it is insignificance in both short run and long run models.
Integrating Light-Weight Cryptography with Diacritics Arabic Text Steganography Improved for Practical Security Applications

Cryptography and steganography are combined to provide practical data security. This paper proposes integrating light-weight cryptography with improved Arabic text steganography for optimizing security applications. It uses light-weight cryptography to cope with current limited device capabilities, to provide acceptable required security. The work tests hiding encrypted secret information within Arabic stego-cover texts, using all common diacritics found naturally in the Arabic language. The study considers different challenging situations and scenarios in order to evaluate security practicality. It further carries out simulations on some short texts from the Holy Quran, taking them as standard authentic texts, that are fixed and trusted, therefore providing realistic study feedback that is worth monitoring. Our improved approach features preferred capacity and security, surpassing the best previous diacritics stego approach, showing interesting potential results for attractive enlightening exploration to come.
Meta-GMVAE: Mixture of Gaussian VAE for Unsupervised Meta-Learning

Unsupervised learning aims to learn meaningful representations from unlabeled data which can captures its intrinsic structure, that can be transferred to downstream tasks. Meta-learning, whose objective is to learn to generalize across tasks such that the learned model can rapidly adapt to a novel task, shares the spirit of unsupervised learning in that the both seek to learn more effective and efficient learning procedure than learning from scratch. The fundamental difference of the two is that the most meta-learning approaches are supervised, assuming full access to the labels. However, acquiring labeled dataset for meta-training not only is costly as it requires human efforts in labeling but also limits its applications to pre-defined task distributions. In this paper, we propose a principled unsupervised meta-learning model, namely Meta-GMVAE, based on Variational Autoencoder (VAE) and set-level variational inference. Moreover, we introduce a mixture of Gaussian (GMM) prior, assuming that each modality represents each class-concept in a randomly sampled episode, which we optimize with Expectation-Maximization (EM). Then, the learned model can be used for downstream few-shot classification tasks, where we obtain task-specific parameters by performing semi-supervised EM on the latent representations of the support and query set, and predict labels of the query set by computing aggregated posteriors. We validate our model on Omniglot and Mini-ImageNet datasets by evaluating its performance on downstream few-shot classification tasks. The results show that our model obtain impressive performance gains over existing unsupervised meta-learning baselines, even outperforming supervised MAML on a certain setting.
Practical Challenges of Virtual Assistants and Voice Interfaces in Industrial Applications

Virtual assistant systems promise ubiquitous and straightforward access to information, applications, and physical appliances.Their foundation on intent-oriented queries and support of natural language makes them an ideal tool for human-centric applications.The general approach to build such systems and the main building blocks are well-understood and offered as off-the-shelf components.While there are prominent examples in the service sector, other sectors such as the manufacturing and process industries have nothing comparable.We investigate the practical challenges of building a virtual assistant using a representative and simplified case from of knowledge retrieval domain.A qualitative study reveals two significant obstacles: Firstly, while systems can be tuned to tackle the most relevant types of question tasks, user acceptance is lower than for comparable GUI-based systems and, secondly, a disproportional amount of effort to get all details and having a robust system.Overall, implementing a virtual assistant for an industrial application is technically feasible yet requires significant effort and understanding of the target audience.
Temporal Graph Super Resolution on Power Distribution Network Measurements

The applications of super-resolution (SR) technology in the field of image completion are successful. Nevertheless, industry applications demand not only image completion but also the topology and time-series completion. In this article, the SR technology on a topology graph is studied in the scenario of recovering measurements in power distribution systems for cost saving and security & stability improvement. The power flow and voltage magnitude measurements on feeders are reported at different frequencies. In this article, a new data completion method considering distribution system topology is proposed. Firstly, the graph convolutional neural network (GCN) is used for spatial-temporal convolution on a graph, and then the power system state estimation (SE) is used introducing the physical constraints. This method realizes the super-resolution of distribution system measurements, improves the state awareness of distribution systems. Hence, it helps to improve the efficiency of distribution network operation and to reduce equipment failures.
Any Port in a Storm: Cryptocurrency Safe-Havens during the COVID-19 Pandemic

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A Verifiable Steganography-Based Secret Image Sharing Scheme in 5G Networks

With the development and innovation of new techniques for 5G, 5G networks can provide extremely large capacity, robust integrity, high bandwidth, and low latency for multimedia image sharing and storage. However, it will surely exacerbate the privacy problems intrinsic to image transformation. Due to the high security and reliability requirements for storing and sharing sensitive images in the 5G network environment, verifiable steganography-based secret image sharing (SIS) is attracting increasing attention. The verifiable capability is necessary to ensure the correct image reconstruction. From the literature, efficient cheating verification, lossless reconstruction, low reconstruct complexity, and high-quality stego images without pixel expansion are summarized as the primary goals of proposing an effective steganography-based SIS scheme. Compared with the traditional underlying techniques for SIS, cellular automata (CA) and matrix projection have more strengths as well as some weaknesses. In this paper, we perform a complimentary of these two techniques to propose a verifiable secret image sharing scheme, where CA is used to enhance the security of the secret image, and matrix projection is used to generate shadows with a smaller size. From the steganography perspective, instead of the traditional least significant bits replacement method, matrix encoding is used in this paper to improve the embedding efficiency and stego image quality. Therefore, we can simultaneously achieve the above goals and achieve proactive and dynamic features based on matrix projection. Such features can make the proposed SIS scheme more applicable to flexible 5G networks. Finally, the security analysis illustrates that our scheme can effectively resist the collusion attack and detect the shadow tampering over the persistent adversary. The analyses for performance and comparative demonstrate that our scheme is a better performer among the recent schemes with the perspective of functionality, visual quality, embedding ratio, and computational efficiency. Therefore, our scheme further strengthens security for the images in 5G networks.
Facial Expression Recognition Using Transfer Learning on Deep Convolutional Network

A novel method is proposed for facial expression recognition.We have implemented two techniques for automatic facial expression recognition.First, we applied transfer learning to AlexNet, and VGG19 for classification.Second, we used AlexNet and Vgg19 for feature extraction and cascaded it with an SVM for classification.We achieved 86.11% accuracy with AlexNet and 94.44% with AlexNet-SVM cascade.We also achieved 94.44% accuracy with VGG19 and 86.11 with VGG19-SVM cascade.We used JAFFE Data Set to train our four models.Our system achieves an improvement in accuracy for JAFFE Data Set.
Sensing everyday activity: Parent perceptions and feasibility

Mobile and wearable sensors provide a unique opportunity to capture the daily activities and interactions that shape developmental trajectories, with potential to revolutionize the study of development (de Barbaro, 2019). However, developmental research employing sensors is still in its infancy, and parents’ comfort using these devices is uncertain. This exploratory report assesses parent willingness to participate in sensor studies via a nationally representative survey (N = 210) and live recruitment of a low-income, minority population for an ongoing study (N = 359). The survey allowed us to assess how protocol design influences acceptability, including various options for devices and datastream resolution, conditions of data sharing, and feedback. By contrast, our recruitment data provided insight into parents’ true willingness to participate in a sensor study, with a protocol including 72 h of continuous audio, motion, and physiological data. Our results indicate that parents are relatively conservative when considering participation in sensing studies. However, nearly 41 % of surveyed parents reported that they would be at least somewhat willing to participate in studies with audio or video recordings, 26 % were willing or extremely willing, and 14 % reported being extremely willing. These results roughly paralleled our recruitment results, where 58 % of parents indicated interest, 29 % of parents scheduled to participate, and 10 % ultimately participated. Additionally, 70 % of caregivers stated their reason for not participating in the study was due to barriers unrelated to sensing while about 25 % noted barriers due to either privacy concerns or the physical sensors themselves. Parents’ willingness to collect sensitive datastreams increased if data stayed within the household for individual use only, are shared anonymously with researchers, or if parents receive feedback from devices. Overall, our findings suggest that given the correct circumstances, mobile sensors are a feasible and promising tool for characterizing children’s daily interactions and their role in development.
Real Time Hand Gesture Recognition in Depth Image using CNN

Hand gestures can play a notable role in computer vision, and hand gesture-based methods can stand out in providing a native way of interaction.Deafness is a degree of loss such that a person is unable to understand speech, spoken language.Sign language declined the gap in spoken language.The hand gesture is analyzed identically to sign language presenting the naturalness of intercommunication for deaf people.Real-time hand gesture recognition has been proposed in our research.Our proposed CNN model architecture will remediate the communication barrier of deaf people.The proposed model has achieved an accuracy of 94.61% to recognize 11 several different gestures using depth images.
The Effect of Piracy Website Blocking on Consumer Behavior

In this study, we ask what drives the success or failure of various supply-side anti-piracy enforcement actions such as piracy website blocking. We do this in the context of three court-ordered events affecting consumers in the United Kingdom: We first
Net-DNF: Effective Deep Modeling of Tabular Data

A challenging open question in deep learning is how to handle tabular data. Unlike domains such as image and natural language processing, where deep architectures prevail, there is still no widely accepted neural architecture that dominates tabular data. As a step toward bridging this gap, we present Net-DNF a novel generic architecture whose inductive bias elicits models whose structure corresponds to logical Boolean formulas in disjunctive normal form (DNF) over affine soft-threshold decision terms. Net-DNFs also promote localized decisions that are taken over small subsets of the features. We present an extensive experiments showing that Net-DNFs significantly and consistently outperform fully connected networks over tabular data. With relatively few hyperparameters, Net-DNFs open the door to practical end-to-end handling of tabular data using neural networks. We present ablation studies, which justify the design choices of Net-DNF including the inductive bias elements, namely, Boolean formulation, locality, and feature selection.
Design of English Diagnostic Practice Sentence Repetition Recognition System Based on Matching Tree and Edge Computing

English reading ability is an important indicator to measure learners’ English ability. However, because reading ability cannot be directly observed, people usually take tests to judge the reading ability of learners. Therefore, it is very necessary to design a reasonable diagnostic practice sentence repetition recognition system to analyze and test, inform learners of the advantages and disadvantages in reading, and give corresponding countermeasures. In order to properly solve the problem of repeated recognition of English diagnostic sentences, we have developed a new recognition system combined with matching tree and edge computing technology. First, the matching tree algorithm is used for the repetitive diagnosis of English sentences. The algorithm has achieved good results in the repetitive diagnosis and matching. Secondly, an English diagnostic practice sentence repetition recognition system architecture is built through edge computing algorithms, which improves the efficiency of the English diagnostic system. Finally, through the simulation test of the English diagnostic practice system, the applicability of the established repeated recognition model is verified.
The Strategy of Enhancing Employee Reward Using TOPSIS Method as a Decision Support System

Giving rewards for good performance and achievement of tasks needs to be done as a form of recognition and appreciation by the organization/institution to employees, as well as being part of the process of achieving organizational goals. This study aims to develop a Decision Support System that uses the Technique for Order of Preference by Similarity (TOPSIS) method with the PHP programming language to select reward recipients at University. The data used came from 2 groups, namely educational staff (lecturers) and non-educative staff (employees). Determination criteria applied to the educative group are 10 things, namely: tenure, DP3 value, the value on the percentage of work attendance, value on the percentage of teaching attendance, value on lecturer functional position, value on research implementation, the value on implementation of community service, value on the results of the questionnaire by students, the value of employment status, and the value of sanctions. There are 5 determinant criteria used in the non-educative group, namely: tenure, DP3 value, percentage of work attendance, the value of employment status, and value of sanctions. The results of this study are in the form of an information system program as a decision-making tool for the process of selecting reward recipient employees.
Image Haze Removal Algorithm Based on Nonsubsampled Contourlet Transform

In order to avoid the noise diffusion and amplification caused by traditional dehazing algorithms, a single image haze removal algorithm based on nonsubsampled contourlet transform (HRNSCT) is proposed. The HRNSCT removes haze only from the low-frequency components and suppresses noise in the high-frequency components of hazy images, preventing noise amplification caused by traditional dehazing algorithms. First, the nonsubsampled contourlet transform (NSCT) is used to decompose each channel of a hazy and noisy color image into low-frequency sub-band and high-frequency direction sub-bands. Second, according to the low-frequency sub-bands of the three channels, the color attenuation prior and dark channel prior are combined to estimate the transmission map, and use the transmission map to dehaze the low frequency sub-bands. Then, to achieve the noise suppression and details enhancement of the dehazed image, the high-frequency direction sub-bands of the three channels are shrunk, and those shrunk sub-bands are enhanced according to the transmission map. Finally, the nonsubsampled contourlet inverse transform is performed on the dehazed low-frequency sub-bands and enhanced high-frequency sub-bands to reconstruct the dehazed and noise-suppressed image. The experimental results show that the HRNSCT provides excellent haze removal and noise suppression performance and prevents noise amplification during dehazing, making it well suited for removing haze from noisy images.
CHEF: Cross-modal Hierarchical Embeddings for Food Domain Retrieval

Despite the abundance of multi-modal data, such as image-text pairs, there has been little effort in understanding the individual entities and their different roles in the construction of these data instances. In this work, we endeavour to discover the entities and their corresponding importance in cooking recipes automatically as a visual-linguistic association problem. More specifically, we introduce a novel cross-modal learning framework to jointly model the latent representations of images and text in the food image-recipe association and retrieval tasks. This model allows one to discover complex functional and hierarchical relationships between images and text, and among textual parts of a recipe including title, ingredients and cooking instructions. Our experiments show that by making use of efficient tree-structured Long Short-Term Memory as the text encoder in our computational cross-modal retrieval framework, we are not only able to identify the main ingredients and cooking actions in the recipe descriptions without explicit supervision, but we can also learn more meaningful feature representations of food recipes, appropriate for challenging cross-modal retrieval and recipe adaption tasks.
Evaluation of teaching quality of computing method course based on improved BP neural network

Abstract Calculation method is a very important basic course of engineering, in the environment of engineering education, a large number of colleges and universities bring calculation methods into the category of professional compulsory courses. This course is not only theoretical, but also practical. Therefore, teachers are required to combine the two organically. The evaluation of teaching quality is of great practical significance to the cultivation of students and the optimization of curriculum. This paper establishes a more convenient and efficient teaching quality evaluation model of calculation method course by making corresponding evaluation index of calculation method course, and proves its excellence through experiments.
Critical Aspects of Person Counting and Density Estimation

Many scientific studies deal with person counting and density estimation from single images. Recently, convolutional neural networks (CNNs) have been applied for these tasks. Even though often better results are reported, it is often not clear where the improvements are resulting from, and if the proposed approaches would generalize. Thus, the main goal of this paper was to identify the critical aspects of these tasks and to show how these limit state-of-the-art approaches. Based on these findings, we show how to mitigate these limitations. To this end, we implemented a CNN-based baseline approach, which we extended to deal with identified problems. These include the discovery of bias in the reference data sets, ambiguity in ground truth generation, and mismatching of evaluation metrics w.r.t. the training loss function. The experimental results show that our modifications allow for significantly outperforming the baseline in terms of the accuracy of person counts and density estimation. In this way, we get a deeper understanding of CNN-based person density estimation beyond the network architecture. Furthermore, our insights would allow to advance the field of person density estimation in general by highlighting current limitations in the evaluation protocols.
Blockchain-Based Automated System for Identification and Storage of Networks

Network topology is one of the major factors in defining the behavior of a network. In the present scenario, the demand for network security has increased due to an increase in the possibility of attacks by malicious users. In this paper, a blockchain-based system is suggested for securely discovering and storing networks. Techniques such as cloud-based storage systems are not efficient and are lacking in trust, privacy, security, and data control. The blockchain-based technique suggested in this paper is capable of resolving these challenges. Experiments were performed using Mininet, Cisco Packet Tracer, and Ethereum blockchain with the network inference algorithm. This algorithm is capable of inferring the network topology even when only partial information regarding the network is available. The results obtained clearly show that the network is resistant to malicious users and various external attacks, making the network robust.
Zadeh's Centenary

This is the introductory paper in a special issue on fuzzy logic dedicated to the centenary of the birth of Lotfi A. Zadeh published by International Journal of Computers Communications &amp; Control (IJCCC). In 1965, Lotfi A. Zadeh published in the journal „Information and Control” the article titled „Fuzzy sets”, which today reaches over 117 thousand citations. The total sum of citations for all his papers is above 253 thousand. Based on the notion of fuzzy sets, fuzzy logic and the concept of soft computing emerged, bringing extremely important implications to the field of Artificial Intelligence (AI). In 2017, I published, whith F.G. Filip and M.J. Manolescu, a 42-page long paper in the IJCCC about the life and masterwork of Lotfi A. Zadeh, from which I will use some information in this material [15].