Reviewerly/exHarmony huggingface dataset:
RacketStore

Online app search optimization (ASO) platforms that provide bulk installs and fake reviews for paying app developers in order to fraudulently boost their search rank in app stores, were shown to employ diverse and complex strategies that successfully evade state-of-the-art detection methods. In this paper we introduce RacketStore, a platform to collect data from Android devices of participating ASO providers and regular users, on their interactions with apps which they install from the Google Play Store. We present measurements from a study of 943 installs of RacketStore on 803 unique devices controlled by ASO providers and regular users, that consists of 58,362,249 data snapshots collected from these devices, the 12,341 apps installed on them and their 110,511,637 Google Play reviews. We reveal significant differences between ASO providers and regular users in terms of the number and types of user accounts registered on their devices, the number of apps they review, and the intervals between the installation times of apps and their review times. We leverage these insights to introduce features that model the usage of apps and devices, and show that they can train supervised learning algorithms to detect paid app installs and fake reviews with an F1-measure of 99.72% (AUC above 0.99), and detect devices controlled by ASO providers with an F1-measure of 95.29% (AUC = 0.95). We discuss the costs associated with evading detection by our classifiers and also the potential for app stores to use our approach to detect ASO work with privacy.
머신러닝을 이용한 드론의 고장진단에 관한 연구

The Fourth Industrial Revolution has led to the development of drones for commercial and private applications. Therefore, the malfunction of drones has become a prominent problem. Failure mode and effect analysis was used in this study to analyze the primary cause of drone failure, and blade breakage was observed to have the highest frequency of failure. This was tested using a vibration sensor placed on drones along the breakage length of the blades. The data exhibited a significant increase in vibration within the drone body for blade fracture length. Principal component analysis was used to reduce the data dimension and classify the state with machine learning algorithms such as support vector machine, k-nearest neighbor, Gaussian naive Bayes, and random forest. The performance of machine learning was higher than 0.95 for the four algorithms in terms of accuracy, precision, recall, and f1-score. A follow-up study on failure prediction will be conducted based on the results of fault diagnosis.
Exploitation of Meta-Heuristic Search Methods with Bio-Inspired Algorithms for Optimal Feature Selection

It is very difficult and crucial to achieve the selection of optimal features, particularly for the classification task. Because the conventional method of identifying features that function independently has resulted in the selection of unrelated features, the consistency of the classification's accuracy has been degraded. The objective of this article is to optimize Meta-heuristic algorithms, particularly Tabu Search (TS) and Harmony Search (HS), using the capabilities of bioinspired search algorithms in conjunction with the wrapper. The essential stages are to idealize the TS and HS combination with appropriate bio-search methods, and to incorporate the creation of various feature subsets. The following step is to do a subset evaluation to confirm the optimum feature set. The evaluation criteria are based on the number of features utilized and the classification accuracy. To be tested, eight (8) comparison datasets of different sizes were carefully chosen. Extensive testing has indicated that the ideal combination of the chosen bio-search algorithm and meta-heuristics algorithms, especially TS and HS, promises to offer a better optimum solution (i.e. fewer features with greater classification accuracy) for the selected datasets. As a consequence of this research, the ability of bio-inspired algorithms with wrapper/filtered to select and identify characteristics would enhance the efficiency of TS and HS.
How Can Organizations Prevent Cyber Attacks Using Proper Cloud Computing Security?

Cloud Computing is a collection of services and products delivered over a network or the Internet. The intruder no longer has to attack the user’s entire infrastructure. They can point out applications that override the apps anyone makes in the cloud and use simple bandwidth attacks to make them unavailable to that service. There are various security attacks such as Denial-of-Service (DoS) attacks, Malware injection attacks, Side-channel attacks, Man-in-the-middle, and Authentication attacks. Secure HTTP is an excellent example of a DoS attack. A machine-learning algorithm such as Support Vector Machine (SVM), Naive Bayes, Decision tree, Logistic regression, and Ensemble methods can detect this attack in the cloud Cloud Computing is a flexible technology that provides reliable and tremendous resources that are needed for users who want low infrastructure costs. Although the cloud has many advantages, it faces many challenges such as exposure, network dependence, downtime, vendor access, and limited control.
Optimization Based Undersampling for Imbalanced Classes

The classification methods consider the probability of predicting the majority class to be high when the number of class observations is different.To address this problem, there are some methods such as resampling methods in the literature.Undersampling, one of the resampling methods, creates balance by removing data from the majority class.This study aims to compare different optimization methods to determine the most suitable observations to be taken from the majority class while undersampling.Firstly, a simple simulation study was conducted and graphs were used to analyze the discrepancy between the resampled datasets.Then, different classifier models were constructed for different imbalanced data sets.In these models, random undersampling, undersampling with genetic algorithm, undersampling with differential evolution algorithm, undersampling with an artificial bee colony, and under-sampling with particle herd optimization were compared.The results were given rank numbers differing depending on the Sağlam et al. ( 2021) ADYU J SCI, 11(2), 385-409 386 classifiers and data sets and a general mean rank was obtained.As a result, when undersampling, artificial bee colony was seen to perform better than other methods of optimization.
Formation of the Creative Teacher’s Personality in the Context of Modern Education

In the context of the modernization of contemporary education, pedagogical research is often devoted to an assessment of the competence-based approach. The competence-based approach in education focuses on: 1) achieving a sufficiently high level of knowledge, experience, and awareness for carrying out activities and communicating in various fields, 2) differentiating informational, social, communicative, pedagogical, and other types of competence, 3) serving as a basis for the restructuring the educational process and overcoming a one-sided orientation to the subject’s education. Under the conditions of modern creativity, the ability to create should be considered as one of the key competencies of a modern teacher. The creative approach focuses on the formation of creative individuality, the development of a style of creative activity, non-standard solutions of pedagogical problems, and the ability to innovate. In this article, we consider the main definitions of creativity, the pedagogy of creativity, pedagogical creativity and the experience of different techniques of using creativity in the pedagogical process of secondary schools and universities.
Learning Subspaces of Different Dimensions

We introduce a Bayesian model for inferring mixtures of subspaces of different dimensions. The model allows flexible and efficient learning of a density supported in an ambient space which in fact can concentrate around some lower-dimensional space. The key challenge in such a mixture model is specification of prior distributions over subspaces of different dimensions. We address this challenge by embedding subspaces or Grassmann manifolds into a sphere of relatively low dimension and specifying priors on the sphere. We provide an efficient sampling algorithm for the posterior distribution of the model parameters. We illustrate that a simple extension of our mixture of subspaces model can be applied to topic modeling. The utility of our approach is demonstrated with applications to real and simulated data.
Flick - Indigenous short video application

In the advent of Musical.ly and Dubsmash, short video applications and lip-syncing technology have become a trend in recent years. Since the entry of TikTok the short video market space is booming. Hundreds of thousands of hours were spent daily on short video apps and the demand for a localized indigenous short video app is growing. Keeping the Indian audience in mind and having a more straightforward feed, we aim to create a TikTok clone titled Flick. Some of the key features would be localized-feed based on user demography and widespread video visibility. The Flick app allows users to create short videos, upload, share, like and download. App-link: https://play.google.com/store/apps/details?id=com.nishanth.flick
Development and application of the semantic annotation framework for digital images

Purpose The purpose of this paper is to semantically annotate the content of digital images with the use of Semantic Web technologies and thus facilitate retrieval, integration and knowledge discovery. Design/Methodology/Approach After a review and comparison of the existing semantic annotation models for images and a deep analysis of the characteristics of the content of images, a multi-dimensional and hierarchical general semantic annotation framework for digital images was proposed. On this basis, taking histories images, advertising images and biomedical images as examples, by integrating the characteristics of images in these specific domains with related domain knowledge, the general semantic annotation framework for digital images was customized to form a domain annotation ontology for the images in a specific domain. The application of semantic annotation of digital images, such as semantic retrieval, visual analysis and semantic reuse, were also explored. Findings The results showed that the semantic annotation framework for digital images constructed in this paper provided a solution for the semantic organization of the content of images. On this basis, deep knowledge services such as semantic retrieval, visual analysis can be provided. Originality/Value The semantic annotation framework for digital images can reveal the fine-grained semantics in a multi-dimensional and hierarchical way, which can thus meet the demand for enrichment and retrieval of digital images.
Bone Marrow Cell Segmentation Based on Improved U-Net

Automatic segmentation of bone marrow cells plays an important role in the diagnosis of many blood diseases such as anemia and leukemia. Due to the complex morphology and wide variety of bone marrow cells, their segmentation is still a challenging task. To improve the accuracy of bone marrow cell segmentation, we propose an end-to-end U-shaped network based on the pyramid residual convolution and the attention mechanism. Specifically, the standard convolution and dilated convolution are combined as its feature encoder, which designs a pyramid residual convolution block to extract multi-scale features. Then, the attention gating mechanism is introduced into each skip connection module for fusing the shallow and deep information. Finally, the proposed method combines convolution and deconvolution for feature decoding to achieve the final segmentation of bone marrow cells. Experiments with quantitative and qualitative comparisons are carried out on a self-built bone marrow smear dataset. Experimental results demonstrate the superiority of the proposed method over several state-of-the-art methods.
Deep Categorization of Blood Cells u sing Depthwise Convolutions

Modern-day computation has become indispensable in the healthcare industry. From medical image processing to cost reduction, Artificial Intelligence has proved its significance in solving complex healthcare problems. One of the primary areas in which it can be of greater use in hematology. Categorization of white-blood cells is imperative to pre-identify abnormalities. Through this paper, we collected image samples for 4 major White Blood cell groups, which are Neutrophils, Lymphocytes, Monocytes, and Eosinophils. The aim of this research is to put forward an intelligent system that efficiently alleviates the stringent requirement of a cytological study. The proposed system classifies 4 white-blood-cell types based on their morphological variation. With the experimental modulations that we chose to integrate, the presented model attained an accuracy of 97%.
Use of Classification Approaches for Takri Text Challenges

Takri is a low-resource class of scripts, used in north-west India which include states of J&K, H.P., Punjab, and Uttarakhand. This class of script has almost 13 scripts, identified in the whole region of North-west India. The paper focuses on classifying the various challenges in the script. The challenges identified are classified for effectiveness using ML classifiers and their performance is evaluated for accuracy. We believe that this classification of challenges will further aid the researchers of NLP community in solving them more efficiently.
Balancing Innovation and Protection: Understanding the Landscape for DeFi Regulation

This paper explores the current landscape of crypto regulation as well as the potential consequences of future innovations in the blockchain and cryptocurrency space. Part II discusses the birth of blockchain and cryptocurrency before presenting the array of regulations currently or soon to be enacted at the international and domestic levels. Part III analyzes why the increasingly decentralized nature of blockchains threatens the pursuit of justice and security. Part IV concludes with thoughts on future research in this area.
Algorithmic Correspondence for Relevance Logics, Bunched Implication Logics, and Relation Algebras via an Implementation of the Algorithm PEARL

The non-deterministic algorithmic procedure PEARL (acronym for ‘Propositional variables Elimination Algorithm for Relevance Logic’) has been recently developed for computing first-order equivalents of formulas of the language of relevance logics \(\mathcal {L}_R\) in terms of the standard Routley-Meyer relational semantics. It succeeds on a large class of axioms of relevance logics, including all so called inductive formulas. In the present work we re-interpret PEARL from an algebraic perspective, with its rewrite rules seen as manipulating quasi-inequalities interpreted over Urquhart’s relevant algebras, and report on its recent Python implementation. We also show that all formulae on which PEARL succeeds are canonical, i.e., preserved under canonical extensions of relevant algebras. This generalizes the “canonicity via correspondence” result in []. We also indicate that with minor modifications PEARL can also be applied to bunched implication algebras and relation algebras.
Remote education of pedagogy students during the Covid-19 pandemic

The article presents the issues of online education of future teachers. The article consists of two parts - the first one provides theoretical and synthetic summary of the reports published so far on distance learning during the Covid-19 pandemic. The second part presents the results of the author's own research, conducted with the help of the author's online survey, devoted to the issues of pedagogy students’ experiences related to remote education. The study has shown that remote learning requires changes in order to increase the efficiency and motivation of students who, without appropriate professional practice, will not be able to efficiently perform the tasks and functions entrusted to them in the future, once they take up employment in the profession.
EMRBchain: A Blockchain-Based Electronic Medical Record Sharing Platform

In the current electronic medical record (EMR) system, there still exist many problems such as centralization, poor security and privacy protection. In order to solve these problems, we design an EMR sharing system EMRBchain and integrate blockchain technology into EMR for its tamper proofing and strong security. And with the combination of the symmetric encryption algorithm AES and the asymmetric encryption algorithm ECC, it will be helpful to implement the secure storage and sharing of EMRs. In this paper, we present a virtual identifier generation algorithm that converts the patient’s identity into a unique and distinct identifier, which will protect the privacy of patients and support EMR search. Finally, to overcome data redundancy in traditional blockchain, we adopt the second order storage method to make it compatible with the existing EMR systems.
Public Key Based Searchable Encryption with Fine-Grained Sender Permission Control

Public key encryption with keyword searched (PEKS) is a promising cryptographic primitive that realizes keyword search in the ciphertext. Since it can provide flexible access to encrypted data, PEKS has been widely used in various fields, such as Cloud Computing and Internet of Things. Until now, many PEKS schemes with fine-grained access control have been proposed to satisfy the requirements of data sharing. However, most previous work only considered the control of data receiver and ignored the control of data sender. In practice, the malicious data sender might correctly generate ciphertexts containing useless information, which in turn increases the computational burden and communication load for the data receiver.To address the above problem, we introduce the concept of PEKS with fine-grained sender permission control, named SCPEKS. In SCPEKS, only those ciphertexts containing matching keywords and of which data sender attributes satisfy the authorized receiver policy will be returned to the data receiver. Also, we present a detailed construction of SCPEKS and prove that the instance achieves ciphertext indistinguishability and unforgeability. Moreover, comparisons with other related schemes suggest that the proposed scheme achieves flexible bidirectional access control at the expense of a slightly higher computation and communication cost.
Facial feature transfer based on self-recognition style-encoding face editing network

This paper proposes a face attribute transfer system based on a deep convolutional network and a generative adversarial network, Self-recognition Style-encoding Face Editing Network(SSFEN). The network only needs a face source image to edit the facial features of the image. The whole network consists of two modules: self-recognition style-encoding network and multi-styles transfer network.The self-recognition style-encoding network aims to learn the style features of all faces in the dataset according to the input initial face image and features, and through further analysis and processing of the deep convolutional network, it outputs the facial feature coding of the original image. The multi-styles transfer network draws on the idea of generating a adversarial network, and only trains one generator to complete the style transfer in multiple fields, and adopts the generator to complete the editing of each field of a single face source image. In the joint training of the self-recognition style-encoding network and the multi-styles transfer network, we mainly apply the multi-label and multi-class discrimination loss, the adversarial attribute loss, the style domain recognition loss and the cyclic loss of the target domain. In addition, the style domain isolation loss is proposed to reduce the mutual influence between various target domains when the face is edited in a single target domain, which increases the accuracy of facial feature editing.
The impact of training and development on knowledge creation embedded in interpersonal trust between colleagues during remote work in the it sector

Purpose – Knowledge creation is a key activity for organizations in current economics, linked to successful business strategy. Particularly in pandemic situations, it is necessary to create new solutions and to adapt to changing situations. This paper focuses on how to strengthen an organization’s capacity to create knowledge through employee training and development. The purpose of this paper is to study the effect of training and development on knowledge creation moderated by interpersonal trust in colleagues for remote working situations. Design/methodology/approach – The research began with an in-depth literature review on knowledge creation to create a set of constructs. Based on the identified constructs, research hypotheses were derived. A sample of 220 employees form IT organizations has been used to gather data through a standardized questionnaire and test the hypotheses through simple moderation model. The data was collected during the first wave of the COVID-19 pandemic. Findings – The results indicate that training and development influence positively knowledge creation in remote setting. Additionally the results indicate that interpersonal trust in colleagues moderates these relationship. Moreover, the findings highlight that interpersonal trust is critically important for knowledge creation, especially when there are low levels of development and training in the organization. Originality/value – From a theoretical perspective, the study contributes to literature considering a training and development and its effect on knowledge creation. This investigation, based on the theory of social constructivism, emphasizes the importance of interpersonal trust as a platform for facilitating the knowledge creation based on employees' competences. In fact, a few studies have considered the impact of training and development on other variables, such as productivity, but never on knowledge creation. From a managerial perspective, the research suggests managers to implement and involve practices strengthening training and development as well as interpersonal trust within business. The promotion of interpersonal trust can have a positive effect on the creation of knowledge. © The Authors, 2021. All Rights Reserved.
Developing a Virtual Reality-Aided Learning Space for Secondary Education: The Service Design Approach

Virtual Reality (VR) has re-gained its popularity in recent years. Educational applications are an important agenda among the recent developments. Most related studies focused on the VR content design for course delivery. The physical learning space, where the VR devices and its related equipment are installed, is also a critical factor in developing such type of courses. However, current design of VR-aided learning spaces is still lack of a design strategy that is systematic, user-centered, and service-driven. This project investigates the potential of using Service Design (SD) methods to design a teaching-oriented VR Laboratory for secondary education. We had completed a full SD development cycle that included the four stages, namely Exploration, Creation, Reflection, and Implementation. The SD approach ensured a user-centered design process and considered the interests of multiple stakeholders. The proposed design was then physically implemented at a local school. Till the writing of this paper, the space has served more than 2,000 students. Judging by the regular use and the feedbacks collected from the semi-structured interview, we find the user-centered and co-creative characteristics of SD serve well to designing such a learning space. It is expected that the work can provide practical insights to the related design practitioners by demonstrating how the proposed deign approach facilitate the integration of emerging technologies such as VR within an educational environment.