Reviewerly/exHarmony huggingface dataset:
Effect of Internal Marketing on Organizational Silence Mediated by Organizational Culture: the Case of Ferdowsi University Employees

The present research explored the effect of internal marketing on organizational silence mediated by organizational culture among the employees of Ferdowsi University of Mashhad. The present applied research used descriptive data to conduct a survey. The research population was the employees of Ferdowsi University of Mashhad, 968 in total. The data were collected in 2018. Using Cochran’s formula, the required sample size was estimated at 275, and the sampling method was convenient. For data collection, three questionnaires were used: the internal marketing questionnaire (Pournabi, 2015), organizational silence (Mirkamali, Poorkarimi, &amp; Karami, 2017) and organizational culture (Denison, 2000). For descriptive statistics, frequencies, percentages and standard deviations were used. For inferential statistics, first the normality of data was checked through Kolmogorov-Smirnov test. The skewness and kurtosis were also checked. To test the conceptual model, to analyze observations and to explore the concurrent effects of variables within the model, structural equation modelling (SEM) was used. The required analyses were run via SPSS22 and SmartPLS at the significance level of .05. To test convergent and discriminant validity, average variance extracted (AVE) was used and to test the reliability of questionnaires, Cronbach’s alpha test of internal consistency was run (internal marketing=97%, organizational silence=94% and organizational culture=95%) along with the PLS approach. The research findings revealed a statistically significant negative correlation between internal marketing and organizational silence. A statistically significant positive correlation was estimated between internal marketing and organizational culture. A statistically significant negative correlation was found between organizational culture and organizational silence. Organizational culture mediated the correlation between internal marketing and organizational silence.
Exploring the limits of joint control: the case of COVID-19 digital proximity tracing solutions

JIPITEC 12 (2021) 3 - Referring to the judgment of the CJEU in Fashion-ID, some scholars have anticipated that, “at this rate everyone will be a [joint] controller of personal data”. This contribution follows this arguably provocative, but not entirely implausible, line of thinking. In the first part of the article, we highlight the ambiguities inherent to the concept of “joint control” and confront them with those pertaining to the notion of “identifiability”. In the second part, we investigate the effects of the broad legal test for joint control on the role of the individual user of BLE-based COVID-19 digital proximity tracing solutions. This offers the possibility to examine, at a theoretical level, whether the impact of the broad notion of joint control differs depending on the architecture of the system (i.e. centralized or decentralized). We found out that the strict application of the joint controllership test could lead to unexpected and, most likely, unintended results. First, an app user could, in theory, qualify as a joint controller with a national health authority regardless of the protocol’s architecture. Second, an actor could, again in theory, be considered as a joint controller of data that is not personal from that actor’s perspective.
Providing a Secure Cloud Storage by Using Attribute Based Temporary Key Word Search Scheme

The cloud providers are not fully trusted in the accept of temporary keyword search on confidential data. Hence this is the main focus of this research, it is necessary to outsource data in the encrypted format. In the attribute-based keyword search scheme the authorized users generate some tokens which were in encrypted format and send them to cloud for the search operation. These tokens can be used to extract all the cipher texts which are generated at any time and contain the search token which were generated by authorized users. Since this may lead to some information leakage, a new cryptographic primitive is introduced which is more secure to propose a scheme in which the search tokens can only extract the cipher texts generated in a specified time interval and that cryptographic primitive is called key-policy attribute-based temporary keyword search (KPABTKS) which provide this property. To evaluate the security, we have to prove that the proposed scheme achieves the keyword secrecy property and is secure against selectively chosen keyword attack (SCKA) both in the random oracle model and Decisional Bilinear Diffie-Hellman (DBDH) assumption. And at last the research will show the complexity of the encryption algorithm is linear with respect to the number of the involved attributes.
Predictions of Citations of a Scholarly Paper

Abstract: Bibliometrics is a statistical analysis of written publications such as books or articles. A bibliographic citationis a reference to a book, article, web page, or other published item. Thus citations are useful for identifying the progress ofthe particular work and measuring the quality of the research article. The cited papers are downloaded using the crawler. Fromthe downloaded article, identify article relation by analyzing the citation context of the article. So first extract the citation context from the article. Citation context are classifies based on cue phrases of Simon tufel. Next, identify the relation of unlabeled article by word embedding. After labeling all articles identifythe perspective behind the citation of the article. In this project, citation relation is identified based on cue phrases of Simon tufel finally article impact is quantified based on the citation network formed from citation analysis. Index Terms: bibliometrics, citation, word embedding, article
PENGARUH PENERAPAN METODE RESITASI YANG DIBANTU OLEH GAMBAR BROSUR PARIWISATA DALAM TEKS DESKRIPSI PENGAJARAN

The objective of the research was to find out the effect of using Recitation Method in speaking descriptive text. The scope of the research is focused on speaking skill. The researcher limited the discussion on speaking descriptive text by applying Recitation Method. This research was conducted at Madrasah Aliyah Muhammadiyah 1 Medan Jl. Mandala By Pass No.140, Bantan, Medan Tembung, Kota Medan, Sumatera Utara 20226. The reason for choosing this school because it was practical for collecting the data and a similar research had never been conducted in this school. In this school, most of the student have problems to express their idea orally, so they confuse to speak.Based on the data analysis and the findings, the conclusions were there is any significant improvement on the ten grade accomodation student's descriptive speaking before and after the treatment Madrasah Aliyah Muhammadiyah 1 Medan. There is any significant difference on the tengrade accomodation student's descriptive speaking taught by using tourism brochures and those who are not at Madrasah Aliyah Muhammadiyah 1 Medan. Keywords : Recitation Method, tourism brochures pictures, speaking descriptive text.
The Estimation Method of Regression Model and Its Comparisons

Machine learning is one of the current development trends of computer technology, and regression algorithm is an important algorithm in machine learning. For the accuracy of the algorithm, it is essential to select good evaluation indicators. This paper proposes a curve for evaluating the regression equation, which has similar characteristics to the Receiver-Operating Curve. This article describes the calculation and drawing method of the curve. Compared with other regression equation evaluation indicators, this curve can more intuitively reflect the sensitivity and specificity of the model. The area under the curve can be used to compare the performance between different models, and it has stronger robustness. However, it can be further optimized in terms of accuracy and time cost, and it may be combined with other evaluation indicators in the future to improve its accuracy.
Detection of Adversarial DDoS Attacks Using Generative Adversarial Networks with Dual Discriminators

DDoS (Distributed Denial of Service) has become a pressing and challenging threat to the security and integrity of computer networks and information systems.The detection of DDoS attacks is essential before any mitigation approaches can be taken.AI (Artificial Intelligence) and ML (Machine Learning) have been applied to the detection of DDoS attacks with satisfactory achievement.However, new types of attacks emerge as the technology for DDoS attacks keep evolving.This study investigates the impact of a new sort of DDoS attackadversarial DDoS attack.We synthesize attacking traffic using Wasserstein Generative Adversarial Networks with Gradient Penalty (GP-WGAN).Experiment results reveal that the synthesized traffic can penetrate the systems, including Random Forest, k-Nearest Neighbor, and Multi-Layer Perceptron, without being detected.This observation is an alarming and pessimistic wake-up call implying the urgent need for countermeasures to adversarial DDoS attacks.To this problem, we propose an adversarial GAN Intrusion Detection System (AG-IDS) featuring dual discriminators.The additional discriminator is designed to capture adversarial DDoS traffic.As indicated in the experimental results, the proposed AG-IDS can be an effective solution to adversarial DDoS attacks.
The Blended Instruction on Cloud via an Interactive Augmented Reality Technology Model to Enhance Digital Literacy

The objectives of the study were 1) Synthesize the conceptual framework of blended instruction on the cloud via an interactive augmented reality technology model to enhance digital literacy, 2) Design the blended instruction on the cloud via an interactive augmented reality technology model, 3) Develop the blended instruction on the cloud via an interactive augmented reality technology model, and 4) Study the suitability assessment of the blended instruction on the cloud via an interactive augmented reality technology model. The proposed model develops digital literacy skills, one of the most important skills for learners in the 21st century that contributes to the learning society in the digital world. The samples group used in the study were nine experts in higher education. Then analyzing the data obtained from the assessment, using mathematic mean and standard deviation. Results of the assessment found the following. 1) The developed teaching and learning model consisted of four components: inputs, blended instruction on cloud processes, outcomes, and feedback. 2) The blended instruction on the cloud processes consists of 3 steps: the preparation, teaching and learning, presentation and summary of the learning results. 3) The assessment of the suitability of the developed teaching and learning model was at the highest appropriate. 4) The suitability assessment in the developed teaching and learning model was at the highest appropriate.
LT-OCF: Learnable-Time ODE-based Collaborative Filtering

Collaborative filtering (CF) is a long-standing problem of recommender systems. Many novel methods have been proposed, ranging from classical matrix factorization to recent graph convolutional network-based approaches. After recent fierce debates, researchers started to focus on linear graph convolutional networks (GCNs) with a layer combination, which show state-of-the-art accuracy in many datasets. In this work, we extend them based on neural ordinary differential equations (NODEs), because the linear GCN concept can be interpreted as a differential equation, and present the method of Learnable-Time ODE-based Collaborative Filtering (LT-OCF). The main novelty in our method is that after redesigning linear GCNs on top of the NODE regime, i) we learn the optimal architecture rather than relying on manually designed ones, ii) we learn smooth ODE solutions that are considered suitable for CF, and iii) we test with various ODE solvers that internally build a diverse set of neural network connections. We also present a novel training method specialized to our method. In our experiments with three benchmark datasets, Gowalla, Yelp2018, and Amazon-Book, our method consistently shows better accuracy than existing methods, e.g., a recall of 0.0411 by LightGCN vs. 0.0442 by LT-OCF and an NDCG of 0.0315 by LightGCN vs. 0.0341 by LT-OCF in Amazon-Book. One more important discovery in our experiments that is worth mentioning is that our best accuracy was achieved by dense connections rather than linear connections.
Secure Data Exchange Based on Wireless Sensor Network for Environmental Monitoring Using Dynamical Attributed Based Encryption

The basics Internet of things based on Wireless Sensor Network (WSN) has grown rapidly in Industrial Revolution 4.0.Many researchers use the Wireless Sensor Network technology for obtaining the sensor data and stored in the Data Center.The collected data through WSN from an environment was sent to a Data Center.In the Data Center, all the sensor data can be accessed by everyone using their devices.In this case, the user can get the data with a smartphone, laptop, and personal computer through the HTTP Protocol.Data Center without protection from illegal access is extremely dangerous.All the sensor data stored in the data center can be intercepted, tracked, and even changed by a user without access.There is required a security mechanism for protecting all of the data stored in the Data Center.Ciphertext Policy Attributed-Based Encryption can be deployed as a security mechanism for protecting all the sensor data in the Environmental Monitoring.The security mechanism protected Data Center with encryption, decryption, verification, and revocation access for all the users.The only user with the access right can get the sensor data from environmental monitoring.Our security mechanism offers revocation and verification with a dynamic attribute for each user with a lower computational time.We used the CP-ABE with Dynamical attribute and timestamp digital signature based on Elliptic Curve Digital Signature Algorithm (ECDSA) 384 bits.
An Efficient Activity Discovery for Geriatric Care through Dynamic Segmentation Technique

The geriatric care becomes an issue that demands more attention. The increase in the geriatric population, living independently, needs care and monitoring of the day long activities of these people in a remote way becomes a challenge to the researcher. Activity monitoring could be done remotely through the analysis of the sensory data whereas the sensors are placed in strategic locations within the residence. We offer a dynamic segmentation technique that in turn discovers the activities online. The existing activity monitoring is mostly based on scripted data. But it becomes un-realistic to prepare the training data set through repeated execution of a specific activity by a geriatric person. Thus, the paper renders a discovery approach that will be able to identify the activities without any prior learning. The claims are substantiated by the experimental findings. The algorithm is executed on benchmark data set ARUBA to establish the claims of the said technique.
Selection of Priorities in the System of Adaptive Ecological Monitoring During Combat

The paper presents approaches to the development of priorities in the system of adaptive environmental monitoring of areas of hostilities and military exercises. The peculiarities of forming an interval scale for indices and indicators of disturbances of ecosystems that are under the influence of military-man-made load are also given.
중급 한국어 학습자의 상호작용능력 발달 연구 -시간차에 따른 조율 활동을 중심으로-

This study was conducted to examine how the aspect of alignment activities in conversations between intermediate Korean language learners changed over a 10-week period. Most listeners expressed themselves relatively simple in the beginning but became increasingly complex over time. Listeners increasingly expressed agreement or empathy with speakers, building a cooperative relationship. Moreover, when conflicting opinions emerged, listeners increasingly attempted to mitigate the negative results of this conflict by expressing empathy and adding validating comments about the speakers’ opinions. However, after 10 weeks, listeners would still discontinue conversations or avoid particular subjectss when there were conflicting opinions. Listeners increasingly recomposed, summarized, or revised the speakers’ words over time. They also provided additional information or added specific explanations about what speakers said to achieve mutual understanding and contributed to conversations based on their own experiences. Listeners increasingly provided the uncompleted parts of speakers’ sentences over time. However, they would still interrupt speakers because they were unable to know when the speakers had fully expressed their thoughts. (Yonsei University)
State-Of-The-Art Techniques On Medical Image Analysis Using Deep Learning

Digital imaging systems are currently being used in medical diagnosis andresearch. Digital images refer to the collection of pixels or picture elements that vary interms of brightness and color. Medical images, specifically refers to the images generatedfrom modalities like CT (Computerized Tomography), Ultrasound and MRI (MagneticRes- onance Imaging). The main notion behind the study of medical images is to equip thequality of imaging. The recent innovations greatly dealt with recognition of imagesthrough Deep Learning methodologies which is the subset of Machine Learning. It learnsautomatically from the machine by practice without the need of programming explicitly.The medicinal imaging system includes five major processing steps such as acquisition,augmentation, segmentation, feature extrac- tion/retrieval and categorization. Throughdeep learning process, pre- diction can be done easily for analysis purpose. In this paper,the tech- nologically advanced methods that are being currently in use in medical imagingand the different phases of analysis process, structural design of deep learning, along withthe challenges are discussed.
Hybrid Multisource Feature Fusion for the Text Clustering

The text clustering technique is an unsupervised text mining method which are used to partition a huge amount of text documents into groups. It has been reported that text clustering algorithms are hard to achieve better performance than supervised methods and their clustering performance is highly dependent on the picked text features. Currently, there are many different types of text feature generation algorithms, each of which extracts text features from some specific aspects, such as VSM and distributed word embedding, thus seeking a new way of obtaining features as complete as possible from the corpus is the key to enhance the clustering effects. In this paper, we present a hybrid multisource feature fusion (HMFF) framework comprising three components, feature representation of multimodel, mutual similarity matrices and feature fusion, in which we construct mutual similarity matrices for each feature source and fuse discriminative features from mutual similarity matrices by reducing dimensionality to generate HMFF features, then k-means clustering algorithm could be configured to partition input samples into groups. The experimental tests show our HMFF framework outperforms other recently published algorithms on 7 of 11 public benchmark datasets and has the leading performance on the rest 4 benchmark datasets as well. At last, we compare HMFF framework with those competitors on a COVID-19 dataset from the wild with the unknown cluster count, which shows the clusters generated by HMFF framework partition those similar samples much closer.
ENHANCED TWITTER SENTIMENT ANALYSIS USING HYBRID CLASSIFICATION METHODS AND RESULT ANALYSIS

The utilization of social media, in the course of recent years, has heightened hugely.Social media has framed a stage for the accessibility of plentiful information. A great many individuals express their discernments through social media. Sentiment Analysis (SA) of such perspectives and discernments is considerable to gauge public opinion on a particular/explicit topic of concern. Twitter is a microblogging webpage wherein clients can post updates (tweets) to companions (supporters).This paper proposes an instrument for extricating the suppositions from the tweets posted on Twitter. Tweets can be delegated positive, nonpartisan or negative. This model ends up being profoundly compelling and exact in the investigation of sentiments. This paper presents a crossover approach of utilizing Subterranean insect Settlement Enhancement and Molecule Multitude Improvement with classifiers. For each tweet, pre-handling will be finished by performing different cycles, for example tokenization; expulsion of stop-words, stemming etc. Besides, highlights are removed by the Uni-gram include extraction procedure. Grouping is completed by a Help Vector machine (SVM) classifier with Subterranean insect State Enhancement and Molecule Multitude Improvement to advance the arrangement execution. For solving ACSO, this work presents a hybrid classification based on ant colony optimization (ACO) and particle swarm optimization (PSO). In addition, the ACO framework includes a pheromone disruption method for dealing with pheromone stagnation. The execution showed that the new half breed slant arrangement had the option to further develop the precision execution. The productivity of the proposed framework was approved on the Creep Tweet dataset.
StyleAugment: Learning Texture De-biased Representations by Style Augmentation without Pre-defined Textures

Recent powerful vision classifiers are biased towards textures, while shape information is overlooked by the models. A simple attempt by augmenting training images using the artistic style transfer method, called Stylized ImageNet, can reduce the texture bias. However, Stylized ImageNet approach has two drawbacks in fidelity and diversity. First, the generated images show low image quality due to the significant semantic gap betweeen natural images and artistic paintings. Also, Stylized ImageNet training samples are pre-computed before training, resulting in showing the lack of diversity for each sample. We propose a StyleAugment by augmenting styles from the mini-batch. StyleAugment does not rely on the pre-defined style references, but generates augmented images on-the-fly by natural images in the mini-batch for the references. Hence, StyleAugment let the model observe abundant confounding cues for each image by on-the-fly the augmentation strategy, while the augmented images are more realistic than artistic style transferred images. We validate the effectiveness of StyleAugment in the ImageNet dataset with robustness benchmarks, such as texture de-biased accuracy, corruption robustness, natural adversarial samples, and occlusion robustness. StyleAugment shows better generalization performances than previous unsupervised de-biasing methods and state-of-the-art data augmentation methods in our experiments.