Reviewerly/exHarmony huggingface dataset:
Handwritten Character Recognition Using Deep Convolutional Neural Networks

The automatic detection and recognition of characters in images are an important problem in various applications. The traditional shallow networks in machine learning have limitations in image classification due to their inability to effectively utilize the spatial relationships between the pixels of the image. But the incredible advances in deep learning methods and deep architecture in the recent years have opened doors to the possibility of employing these techniques. In this paper, a deep convolutional neural network (CNN) with minimal preprocessing for the effective classification of handwritten characters has been proposed. The application of this network yielded an accuracy of 99.50 and 94.66% on the test data of MNIST and EMNIST datasets, respectively.
The Impact of Cybersecurity on Healthcare

The advancement of technology in recent times has posed a number of serious threats to the integrity of the systems, networks, and programs utilized in different fields such as Healthcare organizations. Such threats have compounded with the development of cybersecurity, a move which involves protecting the systems from imminent threats that materialize in the form of digital attacks. Inadvertently, cybersecurity has become an important aspect of digital protection from ill-intentioned people who exploit the vulnerability of systems. This paper addresses the impact of cybersecurity in healthcare organizations. While on it, the paper will also present the various types of security threats in the industry and utilizing AES (Advanced Encryption Standard) as a protective measure for health-based organizations.
Never Underestimate Substitution Cipher with Diffusion

This work investigates whether a modified substitution cipher can have any cryptographic importance in today’s scenario. A classical substitution cipher is a confusion-only cipher. Due to the absence of a proper diffusion mechanism, this cipher is vulnerable to frequency attacks. We propose a block cipher model based on the substitution cipher in which we introduce a diffusion mechanism and multiple encryption rounds. The key indirectly used in the encipherment process is supplied to a deterministic random bit generator to shuffle a look-up table (LUT) using a uniform shuffling algorithm randomly. After studying the cryptographic properties of randomly generated LUTs, we perform a case study of an imaginary block cipher assumed to have a diffusion function equipped with a perfect diffusion property. Though, a randomly generated permutation may possess weaker cryptographic properties with a high probability. From the investigation, we find that with a robust diffusion mechanism, the proposed modified substitution cipher can have sufficient potentiality against several popularly known attacks.Ali, Md Rasid Roy Chowdhury, Dipanwita
An underground information visualization method and its deep shaft application

With the development and utilization of underground space, the emergence of visualization technology and the increasing amount of associated information, underground engineering information visualization has become increasingly relevant and important. However, at present, information visualization problems are commonly encountered during the process of visualization expression of underground engineering information. These problems include monotonous visualization form, color matching ignoring visual perception, and spatial occlusion reducing information readability. These issues not only reduce the visualization effect and the efficiency of information acquisition, but they are also associated with certain safety issues. In view of the problems that exist in underground engineering information visualization, the aim of this paper was to provide a theoretical basis applicable to cases in order to improve the visualization expression of underground spatial information. This paper conducted research from two perspectives: the visualization principle and the visualization method. The No. 1 shaft deep foundation pit of the Shanghai hard X-ray project was used as the engineering background in order to realize the visual display, analysis, and early warning of the engineering data of the foundation pit project.
Asterisk-Shaped Features for Tabular Data

Data often accumulates in tabular format with many attribute items, and prediction using machine learning adds value to data for business. However, studies on machine learning for tabular data only input attribute values, which reduces accuracy. Therefore, we propose an inference method that inputs attribute values and values from aggregated tabular data that has varying attribute values for each attribute item. In an experiment, we compared our proposed method with AutoGluon-Tabular using AutoML benchmark datasets. Our proposed method achieved the highest accuracy for 21 out of 39 datasets.
Experience with anomaly detection using ensemble models on streaming data at HIPA

Anomaly detection techniques are applied in many industry settings with great success. This paper presents experimental evidence of usefulness of these techniques in the High Intensity Proton Accelerator at the Paul Scherrer Institute. We present an anomaly detection model built as a combination of the Numenta NuPIC model and an LSTM autoregressor that works on streaming data from several beam diagnostic devices, automatically learning complex patterns from these signals after a relatively small adaptation time and small number of false positives. We show how this system could have alerted human experts of the failure of critical beam instrumentation tens of minutes in advance by finding anomalies preceding it, which stood unnoticed for several hours. We also present a full framework to exploit these models, which allows to monitor live data from the diagnostic devices, model results and diagnostic data and show historical data for easier model tuning.
Comparative Analysis of Different Data Replication Strategies in Cloud Environment

In this paper, we performed a comparative study of the different data replication strategies such as Adaptive Data Replication Strategy (ADRS), Dynamic Cost Aware Re-Replication and Rebalancing Strategy (DCR2S) and Efficient Placement Algorithm (EPA) in the cloud environment. The implementation of these three techniques is done in JAVA and the performance analysis is conducted to study the performance of those replication techniques by various parameters. The parameters used for the performance analysis of these three techniques are Load Variance, Response Time, Probability of File Availability, System Byte Effective Rate (SBER), Latency, and Fault Ratio. From the analysis, it is evaluated that by varying the number of file replicas, it shows deviations in the outcomes of these parameters. The comparative results were also analyzed.
Banjarese Chatbot Using Seq2Seq Model

The significant development of chatbot technology began with the implementation of the Seq2Seq (Sequence-to-Sequence) model which is based on two RNN (Recurrent Neural Network) architecture. Long-term training with vanilla RNN has the problem of vanishing or exploding gradient, so it is necessary to do research that focuses more on optimization steps for this problem. The optimization carried out on the Seq2Seq model is to apply a LSTM (Long-Short Term Memory) gate mechanism with a stable ability to understand long-term information. A GRU (Gated Recurrent Unit) is faster in training and an attention mechanism that can handle long sequence data. Also, gradient descent can minimize the error rate. A combination of gate types, gradient descent, and attention mechanisms needs to be done to produce a better conversational model. The test results of the conversational model performance were obtained by taking into account the metric scores in accuracy, precision, recall, F1, and loss by using 80% train data and 20% test data from 5000 lines of Banjarese conversation. In the 50th epoch, the best score was from testing the LSTM gate model with metric values of accuracy 64,67%, precision 85,20%, recall 62,49%, F1 72,09%, and loss 3,6073.
Smart Areca Nut Plucking Robot

with shortage of workers, areca cultivators are thinking that it’s hard to collect their harvest. Be that as it may, culling areca nuts need never again be a repetitive assignment. Areca nut tree climbing is a dangerous activity that requires talented workers. On the off chance that the person slips, it will be unsalvageable harm to him and as to his family. Shortage of workers is rising as one of the difficulties in cultivating. As of late, getting works who climb areca nut tree is a noteworthy test. Areca nut trees develop to a stature of around 60 to 70 feet. It is required to climb the areca nut tree around four to multiple times in a year for gathering. Just talented workers can complete these activities. In the undertaking a robot is planned through which a portion of the above challenges are tended to. This robot climbs the areca nut trees of differing breadth. In the venture, The robot moves as human ascensions the tree and a sensor is mounted on the robot. The robot moves the tree and sensor choose which areca nuts are matured and prepared to cut. So by this the client can control the cutting task.
Agile Project Management

This chapter presents the notion of agile project management and identifies the existing challenges in agile project management in the new millennium. It sets the scene for discussions presented by various authors too. In particular, the chapter identifies the experience and the related problems with agile project management. It also identifies the importance of adoption of agile project management and debates about the measure of agility.
Center heatmap attention for few-shot object detection

With the development of computer vision and deep learning, the convolutional neural network has been widely used in image processing such as object detection and semantic segmentation, and has achieved breakthrough achievements. However, when the training samples are insufficient, the conventional neural network usually has unsatisfactory robustness. In order to solve the problem, we improve the generalization performance of the few-shot detectors by focusing on the target center and can identify novel categories. The paper proposes a new attention mechanism based on the auxiliary circle feature map of the object center. By selecting an auxiliary circle feature map with the object center as the center of the circle and the minimum size in height and width as the diameter, adding it to the anchor-free CenterNet network as soft attention to promote network training. Several experiments on PASCAL VOC2007/2012 datasets show that the proposed method achieves the most advanced level in terms of the accuracy and standard deviation of few-shot object detection, which indicates the algorithm’s effectiveness.
Research on Energy Efficiency Optimization of Device-to-Device Network Based on Blockchain

In the device-to-device (D2D) network, a large number of applications directly perform computing and storage tasks on intelligent terminal devices, but it brings huge energy consumption pressure to the energy limited intelligent terminal. In order to improve the green energy-saving user experience of intelligent terminal, the blockchain technology is used to actively mobilize the redundant resources in D2D network, and a reasonable and flexible D2D network resource sharing management strategy is proposed to improve the network energy efficiency. In order to solve the problem of lack of trust foundation and incentive mechanism in collaborative computing between intelligent terminals in D2D network, it uses blockchain technology to provide a safe and reliable cooperation platform, adopts appropriate incentive mechanism to encourage intelligent terminals to participate in cooperation, realizes optimal pairing, and uses reputation value proving machine as consensus mechanism, which can avoid resource waste and energy consumption caused by mining tasks in traditional blockchain.
VLADIMIR SHYNKARUK'S PHILOSOPHICAL HUMANISM

Within Marxism, anthropologisation and humanization took place either under the influence of the ideas of early Marx or in view of social change. The latter was characteristic of Volodymyr Shinkaruk's philosophy. The proposed philosophical concept of creative scientific and technical work proved fruitful in post-industrial society. It is proved that Volodymyr Shinkaruk saw the anthropological principles of humanism in the personal pursuit of the future. This desire ensured the acceptance of values and accelerated progress in science, but could also be a reason for utopianism.
Extended variational inference for Dirichlet process mixture of Beta‐Liouville distributions for proportional data modeling

Bayesian estimation of parameters in the Dirichlet mixture process of the Beta-Liouville distribution (i.e., the infinite Beta-Liouville mixture model) has recently gained considerable attention due to its modeling capability for proportional data. However, applying the conventional variational inference (VI) framework cannot derive an analytically tractable solution since the variational objective function cannot be explicitly calculated. In this paper, we adopt the recently proposed extended VI framework to derive the closed-form solution by further lower bounding the original variational objective function in the VI framework. This method is capable of simultaneously determining the model's complexity and estimating the model's parameters. Moreover, due to the nature of Bayesian nonparametric approaches, it can also avoid the problems of underfitting and overfitting. Extensive experiments were conducted on both synthetic and real data, generated from two real-world challenging applications, namely, object detection and text categorization, and its superior performance and effectiveness of the proposed method have been demonstrated.
Scalable Generalized Multitarget Linear Regression With Output Dependence Estimation

Nowadays the phenomenon of Big Data is overwhelming our capacity to extract relevant knowledge through classical machine learning techniques. Multitarget regression has arisen in several interesting industrial and environmental application domains, such as ecological modeling and energy forecasting. However, standard multi-target regressors are not designed to perform well with such amounts of data. This paper proposes a scalable implementation for a multi-target linear regression algorithm with output dependence estimation for Big Data analytics in Apache Spark. Our experiments on large-scale datasets show an accurate analysis compared to standard implementation and order of training time reduction as the available number of working nodes in the processing cluster increases.
Putting RDF2vec in order

The RDF2vec method for creating node embeddings on knowledge graphs is based on word2vec, which, in turn, is agnostic towards the position of context words. In this paper, we argue that this might be a shortcoming when training RDF2vec, and show that using a word2vec variant which respects order yields considerable performance gains especially on tasks where entities of different classes are involved.
The Institutionalization of Crypto Assets and Decentralized Financial Markets

The interest in decentralized financial markets and cryptoassets is growing steadily, with by now an operationalized asset volume and assets under management (AUM) accounting towards a monetary equivalent of several billion USD. With growing public and investment interest, an intense academic debate surrounding the analysis of these alternative concepts in terms of financial market theory has started. In this context, the actual institutionalization of these new opportunities is a previously unaddressed field. This paper will deliver a first framework on the institutionalization process that cryptoassets and decentralized financial markets undergo within the current transition from experimental to institutional phases of the ecosystem with the help of selected literature and hypotheses derived from current trends in Decentralized Finance (DeFi). Furthermore, the paper will discuss the form of dependency of these new forms of financial markets, which are mostly based on the use of distributed ledger technologies (DLT), on institutional structures, outlining specific examples from a market organization perspective. The argumentation underlines the hypothesis that institutional components, such as decentralized governance features and proper incentive management systems, are key for widespread acceptance of alternative financial market concepts.
grlc: the git repository linked data API constructor.

RDF is a knowledge representation format (and an enabling technology for Linked Open Data) which has gained popularity over the years and it continues to be adopted in different domains such as life sciences and humanities.RDF data is typically represented as sets of triples, consisting of subject, predicate and object, and is usually stored in a triple store.SPARQL is one of the most commonly used query languages used to retrieve linked data from a triple store.However writing SPARQL queries is not a trivial task and requires some degree of expertise.
Confidence Indexing of Automated Detected Synsets: A Case Study on Contemporary Turkish Dictionary

In this study, a novel confidence indexing algorithm is proposed to minimize human labor in controlling the reliability of automatically extracted synsets from a non-machine-readable monolingual dictionary. Contemporary Turkish Dictionary of Turkish Language Association is used as the monolingual dictionary data. First, the synonym relations are extracted by traditional text processing methods from dictionary definitions and a graph is prepared in Lemma-Sense network architecture. After each synonym relation is labeled by a proper confidence index, synonym pairs with desired confidence indexes are analyzed to detect synsets with a spanning tree-based method. This approach can label synsets with one of three cumulative confidence levels (CL-1, CL-2, and CL-3). According to the confidence levels, synsets are compared with KeNet which is the only open access Turkish Wordnet. Consequently, while most matches with the synsets of KeNet is determined in CL-1 and CL-2 confidence levels, the synsets determined at CL-3 level reveal errors in the dictionary definitions. This novel approach does not find only the reliability of automatically detected synsets, but it can also point out errors of detected synsets from the dictionary.
User experience factors, a comparative study of cultural heritage interactive technologies in developing and developed countries

The cultural heritage (CH) sector has always been looking for preeminent ways to improve visitors’ interactions with their collections through interactive technologies such as applications and websites. However, economic inequality between developed and developing countries hinders the effective and widespread deployment of interactive technologies; therefore, there is a lack of understanding about how visitors interact with such applications in developing countries. Our research aims to understand the current user experience (UX) practices with CH interactive technologies in developing and developed countries and discuss how to improve the interactive application design for museums’ user experience in developing countries. We conducted two field surveys to examine the current UX practices with audio guides and websites at national CH museums in Vietnam and Australia. Additionally, short interviews with interactive service providers and museum interactive service managers confirm the current UX practices and help to fill the UX gaps in developing countries. Our work complements the wealth of knowledge about designing good UX in developed countries and concludes that UX requirements are likely similar between developing and developed countries.
A Multidimensional Analysis of Released COVID-19 Location-Based Mobile Applications

The spread of coronavirus disease (COVID-19) has triggered a series of responses worldwide ranging from traveling restrictions and shelter-in-place orders to lockdowns, contact tracing, social distancing, and other mitigation measures. To assist with contact tracing and ensure the safety of citizens, a significant number of mobile applications has been developed, utilizing geospatial information and proximity sensing. We perform a thorough research on seven digital databases (Appbrain, e-Health Hub, GDPRhub, “fs0c131y”, News Sites, Appstore, and Google Play), identifying a total of 160 apps regarding COVID-19 related to our research questions. The aim of this research is to identify the main categories of apps and analyze their functions based on a proposed framework of by mapping aspects that affect their functionalities regarding Services, Technology, Societal &amp; Business, and Legal aspects. As the world comes to the new normal, the utilization of these apps might become more essential for more mobile users and developers. The new encryption protocols that are established are also in favor of this argument. Future work can utilize our framework to further examine the development, design, and adoption of such mobile applications.
Cross-region building counting in satellite imagery using counting consistency

Estimating the number of buildings in any geographical region is a vital component of urban analysis, disaster management, and public policy decision. Deep learning methods for building localization and counting in satellite imagery, can serve as a viable and cheap alternative. However, these algorithms suffer performance degradation when applied to the regions on which they have not been trained. Current large datasets mostly cover the developed regions and collecting such datasets for every region is a costly, time-consuming, and difficult endeavor. In this paper, we propose an unsupervised domain adaptation method for counting buildings where we use a labeled source domain (developed regions) and adapt the trained model on an unlabeled target domain (developing regions). We initially align distribution maps across domains by aligning the output space distribution through adversarial loss. We then exploit counting consistency constraints, within-image count consistency, and across-image count consistency, to decrease the domain shift. Within-image consistency enforces that the building count in the whole image should be greater than or equal to the count in any of its sub-image. Across-image consistency constraint enforces that if an image contains considerably more buildings than the other image, then their sub-images shall also have the same order. These two constraints encourage the behavior to be consistent across and within the images, regardless of the scale. To evaluate the performance of our proposed approach, we collected and annotated a large-scale dataset consisting of challenging South Asian regions having higher building densities and irregular structures as compared to existing datasets. We perform extensive experiments to verify the efficacy of our approach and report improvements of approximately 7–20% over the competitive baseline methods. The dataset and code are available here: https://github.com/intelligentMachines-ITU/domain-Adaptive-Building-Counting .