Reviewerly/exHarmony huggingface dataset:
Öğretmen Adaylarının Basılı ve E-Kitap Kullanımına İlişkin Görüşleri

Today, especially the young generation's interest in technology has accelerated the shift from printed books to e-books by affecting reading habits.It is possible to use the e-book as a tool in education and teaching, in which reading is generally carried out through printed books, but e-books are being used with the developing technology.Teachers' mastery of modern technologies and their knowledge and attitudes about reading e-books are very important in terms of guiding students.Therefore, the opinions and preferences of the pre-service teachers regarding both printed and e-books were determined and the results and suggestions regarding the use of e-books were reached.The study is descriptive research and it was carried out using a scanning model.As a data collection tool, "Determining Pre-Service Teachers' Views on Printed and E-Book Usage" questionnaire was used by the researchers and frequency and cross-table analysis were used in SPSS 20.0 package program.Content analysis was used for openended questions in the questionnaire.To determine the reliability of the study, another field expert was also evaluated by coding the research data, and the reliability calculated with the formula of Miles and Huberman was found .89.According to the findings obtained from the study, it was understood that the pre-service teachers described materials such as pdf and word on the internet as e-books and did not know exactly what the e-book was.Therefore, e-books are not sufficiently recognized and used by preservice teachers; it is concluded that printed books are preferred more.
Anonymized User Linkage Under Differential Privacy

This work develops a user linkage method for anonymized social networks under differential privacy. The existing works have primarily focused on multi-dimensional features of user profile (e.g. user name, gender, ID, etc.) in general, but overlooked the special issue of privacy security. As such, the existing methods can be suboptimal for identifying users across anonymized social networks under differential privacy. In this paper, we try to study the problem of combining differential privacy with anonymized user linkage problem simultaneously. To the best of our knowledge, none of the existing works has paid special attention to connect these two separate research problems. To tackle the challenges, we first propose a Hierarchy Differential Privacy-based Generator (HDP) to generate a social publishing graph that can well preserve differential privacy protection in the original social graph by performing anonymized user linkage purposely. We then apply the proposed AUL, short for Anonymized User Linkage, to identify users across anonymized social networks via probabilistic generative models in a semi-supervised manner. We conduct extensive experiments on several real datasets covering the tasks of social publishing graph generation and anonymized user linkage. Both quantitative results and qualitative analysis verify the effectiveness and rationality of our HDP+AUL framework.
Image distortion correction method in a nonuniform temperature field by using Runge–Kutta ray tracing

To solve the imaging distortion problem in a nonuniform temperature field for visual measurement, this paper studies the Runge–Kutta ray tracing algorithm. First, the light ray emitted by the spatial light point will be abstracted by the proposed method. Then, the ray trajectory in a 3D nonuniform temperature gradient environment will be reproduced, and the ray distortion can be corrected. Finally, the distortion is compared with the actual image distortion obtained through the corner detection method. The experimental results show that the correction method for the distortion image caused by nonuniform temperature gradient heat source interference is feasible by using the Runge–Kutta ray tracing algorithm, which can realize the error compensation for image distortion and obtain an accurate corrected image.
Shadow: Exploiting the Power of Choice for Efficient Shuffling in MapReduce

How to reduce the costly cross-rack data transferring is challenging in improving the performance of MapReduce platforms. Previous schemes mainly exploit the data locality in the Map phase to reduce the cross-rack communications. However, the Map locality based schemes may lead to highly skewed distribution of Map tasks across racks in the platform, resulting in serious load imbalance among different cross-rack links during Shuffling. Recent research results show that the slow Shuffling is the root cause of the MapReduce performance degradation. Very limited work has been done for speeding up the Shuffle phase. A notable scheme leverages the principle of the power of choice to balance the network loads on different cross-rack links during Shuffling for a specific type of sampling applications, where processing a random subset of the large-scale data collection is sufficient to derive the final result. The scheme launches a few additional tasks to offer more choices for task selection during Shuffling. However, such a scheme is designed for sampling applications and not applicable to general applications, where all the input data instead of a random subset is processed. In this work, we observe that with high Map locality, the network is mainly saturated in Shuffling but relatively free in the Map phase. A little sacrifice in Map locality may greatly accelerate Shuffling. Based on this, we propose a novel scheme called Shadow for Shuffle-constrained general applications, which strikes a trade-off between Map locality and Shuffling load balance. Specifically, Shadow iteratively chooses an original Map task from the most heavily loaded rack and creates a duplicated task for it on the most lightly loaded rack. During processing, Shadow makes a choice between an original task and its replica by efficiently pre-estimating the job execution time. We conduct extensive experiments to evaluate the Shadow design. Results show that Shadow greatly reduces the cross-rack skewness by 36.6 percent and the job execution time by 26 percent compared to existing schemes.
Privacy Protection of Social Networks Based on Classified Attribute Encryption

With the rapid development of social networks, privacy has also attracted attention. Based on this problem, a privacy protection scheme for social networks based on classified attribute encryption (PPSSN) is proposed for the data owner and attribute management server to manage user permissions; the approach reduces data owner overhead and also avoids use of a property management server to limit access user collusion attacks. To balance the privacy and security of data publication, this scheme classifies users and designs access control for different users and different privileges. In addition, this paper also introduces a good friend data cache mechanism to improve and optimize the original scheme to reduce the cost of decryption. The efficiency and system overhead of the proposed scheme are compared and analyzed based on experiments. The experiments show that the proposed scheme improves query efficiency, reduces system cost, and enhances privacy security.
Identifying Phishing Through Web Content and Addressed Bar-Based Features

Phishing which can also be called spoofing is mainly used to explain an approach being used by Internet scammers or cybercriminals to lure a genuine Internet user into revealing vital, confidential and classified information with the intention of using information gathered against them. Through this form of vulnerability, cybercriminals use the information obtained to gain access into personal information to rob individual of valuables ranging. Since Internet users have increased gloabally, the number of people accessing email, social media and only transaction has increased accordingly. The upsurge in the number of Internet user has therefore enhanced the nefarious activities of the cybercriminals. Verification and checking of address bar features and content of web were adopted in handling phishing detection in this work. Efforts were made to properly study various features of websites considered as phishing as well as those that match the research work. To verify the efficiency of the system, a dataset comprising of phishing websites was downloaded from the popular phishing sites. To start with, a total number of 110 Universal Resource Locators (URLs) considered to be phishing were verified where the system was able to detect 88 websites considered to be phishing while only twenty two (22) URLs were detected as non-phishing websites. With this result, the efficiency and accuracy level of the system is put at 80%.
Fuzzy Support Vector Machine with Imbalanced Regulator and its Application in Stroke Classification

As a typical cardiovascular disease, stroke is the world's second most deadly disease with the characteristics of high mortality, incidence, recurrence rate and disability rate. To improve the stroke predicting accuracy is a necessary measure for reducing the happening of stroke. As a non-invasive and portable method? Transcranial Doppler (TCD) can provide valuable information in diagnosis of cerebrovascular disease by measuring the blood flow speed in intracranial arteries. How to improve diagnosis accuracy performance based on TCD data set is a necessary demand for realizing cerebrovascular disease TCD intelligent diagnosis system. But TCD data set often presents an imbalanced phenomenon for less patients' data over all examiners. This imbalanced phenomenon easily makes a lower predicting accuracy of cerebrovascular patients. In this paper, an improved fuzzy support vector machine model is proposed by combining class center distance and information entropy. The model efficiently utilizes the advantages of class center distance and information entropy. Moreover, the imbalanced fuzzy support vector machine is used by combining an adaptive imbalanced regulator. The different influence of the imbalance regulating factor value on the classification effect were discussed in detail for finding a suitable regulating factor value. A practical clinical imbalanced TCD dataset is used by collecting clinical TCD data for identify the performance of the proposed method. The experimental results showed that the proposed method can get a better classification performance with higher accuracies and G-means over imbalanced TCD dataset than other benchmark methods.
A Novel Recommendation Algorithm Considering Average Similarity and User-based Collaborative Filtering

This paper attempts to improve the accuracy of traditional collaborative filtering recommendation algorithms.To solve the sparsity of the scoring matrix, the author designed a novel collaborative filtering recommendation algorithm based on average similarity (AS) and user-based collaborative filtering (USF).The proposed algorithm was subjected to parallelization programming on MapReduce, followed by the analysis on the parallelization of the algorithm.Next, the proposed algorithm was verified through experiments with varied ratios.The experiments show that our algorithm can compensate for the sparseness of the scoring matrix in traditional algorithms, and output accurate recommendation results.The research findings shed important new light on solving recommendation problems in the ear of big data.
RGB-D-based Hand Gesture Recognition for Letters Expression

Hand Gesture Recognition (HGR) is a system to translate the hand gestures express to literature, which is a natural way of communication between deaf-mutes and non-disabled people. However, due to the complexity of relative positions of fingers, hands sizes, and environmental illumination, the hand gesture recognition is difficult. In this paper, a Fully Connected Neural Network (FCNN) algorithm for RGB-D sensor based HGR is proposed. We firstly build datasets of fingers joints and the center coordinates of hands in 3 dimensions. Then we normalize the samples to eliminate the natural difference of hands. Finally, the data are classified using a 3 layers FCNN. Totally 13,000 data of 26 hand gestures are collected. We randomly select 80% of these data for training and 20% of them for testing. According to the experiments, the average recognition accuracy is 94.73%.
A Tool for Automation of Knowledge Management Diagnostics in Software Development Companies

Knowledge Management (KM) is responsible for using the intellectual resources of an organization. KM principles are both determining and effective factors for the software product quality. Several KM process have been proposed in the literature. However, for a KM initiative to be successful, it is important to consider the current state of KM activities in a company. In this context, the objective of this work is to measure KM activities in software engineering companies by means of a tool that automates the process of KM diagnosis. This paper presents the Software Engineering KNOWledge management diagnosis (SEKNOW) tool, its main functionalities, the potential users profile and examples of use. In addition, we show how the architecture was implemented in order to make the tool extensible to multiple diagnostics and how the companies can employ the tool to fetch data to their systems. Finally, we present similar tools and compare their functionalities.
Penerapan Model Rapid Application Development pada Aplikasi Helpdesk Trouble Ticket PT. Satkomindo Mediyasa

PT. Satkomindo Mediyasa is a limited liability company engaged in providing satellite-based telecommunications infrastructure networks (Very Small Aperture Terminal / VSAT). In an effort to service customer VSAT network connections, PT. Satkomindo Mediyasa often gets various reports of complaints from its customers. However, the complaints report management process that has been running so far is still not effective, causing the handling of customer complaints to run slowly and require a long time. In formulating the solution to the problem, it is proposed a helpdesk trouble ticket information system to help PT. Satkomindo Mediyasa in managing complaints reports is faster and more effective. Information systems were developed using the PHP and MySQL programming languages. The system development model used is Rapid Application Development (RAD) by being modeled through Unified Modeling Language (UML) diagrams. The results of this study produce a trouble ticket helpdesk information system that helps PT. Satkomindo Mediyasa in managing customer complaints reports is faster and more effective.
Attentional Neural Network for Emotion Detection in Conversations with Speaker Influence Awareness

Emotion detection in conversations has become a very important and challenging task. Most of previous studies do not distinguish different speakers in a dialogue and fail to characterize inter-speaker dependencies. In this paper, we propose Speaker Influence-aware Neural Network model (SINN) to predict the emotion of the last utterance in a conversation, which explicitly models the self and inter-speaker influences of historical utterances with GRUs and hierarchical attention matching network. Moreover, the empathy phenomenon is also considered by an emotion state tracking component in SINN. Finally, the target utterance representation is enhanced by speaker influence aware context modeling, where the attention mechanism is used to extract the most relevant features for emotion classification. Experiment results on DailyDialog dataset confirm that our model consistently outperforms the state-of-the-art methods.
Siamese Feature Pyramid Network for Visual Tracking

Visual tracking is an important technology of robot-assisted surgery in 5G-health. Recently, discriminative correlation filter (DCF) methods utilizing in-network feature hierarchy in convolutional neural networks (CNNs) have made state-of-art results in visual tracking. However, their models are complex, which can not run in real-time. Different from DCF methods, SiamFC (Siamese Fully Convolutional) can operate at 86 frames-per-second, while it doesn't leverage the in-network feature hierarchy. Inspired by the high speed of SiamFC and in-network feature hierarchy in CNNs, a Siamese model based on feature pyramid network is proposed to improve tracking performance. The proposed tracking algorithm can not only benefit from fine-grained spatial details in low level features, but also the semantic information in high level features. Besides, a group of ablation experiments are conducted. Without the bells and whistles, the performance improvements are visible compared to SiamFC.
Performance Comparison of Asymmetric Cryptography (Case study- Mail message)

Encryption is a well known technology for protecting sensitive data. ElGamal encryption and RSA algorithm is made before storing mails to mail server. ElGamal decryption and RSA decryption is made after retrieving mails from mail server. This system is implemented to secure mail server system for local government’s important mail messages. These algorithms consume a considerable amount of time and resources such as memory, CPU time, and computation time to encrypt and decrypt data. In this paper, the results are comparison of these algorithms in term of encryption time, decryption time, and memory usage over variable file sizes. After processing, it is proven that RSA performs a faster encryption process than ElGamal. However, ElGamal decryption process is faster than RSA. This system is also expressed comparison of storage Size between RSA and ElGamal. Both of these algorithms are cryptographic public-key algorithms but have functions in different ways. This system is using C# programming language and SQL Server to store mail messages.
Developing and Implementing a Cybersecurity Course for Middle School

Investing in raising a generation with more security-aware minds is vital in preventing many of the security incidents that threaten individuals, organizations, businesses and nations today. In addition, there is a high need for well-trained cybersecurity professionals to protect our networks, computer systems, and infrastructure. Early exposure to basic cybersecurity concerns and concepts is key to developing awareness, piquing student interest, and laying foundation for more complex skill development. In this work, authors present their work developing a middle school cybersecurity curriculum for U.S. grade 8 students. This includes standards, objectives, and lessons for implementation within a year-long business and computer science course. The authors also share their experience and results from piloting the curriculum with nearly sixty grade 8 students in two classes at a Title I middle school in Columbus, GA during the 2017–2018 school year. A comparison of pre and post test results on the content show a significant growth in mastery of key cybersecurity concepts. In addition, artifacts from the course indicate an increased student interest in cybersecurity.
Extending self-orthogonal codes

In this short note we give an exact count for the number of self-dual codes over a finite field $\mathbb F_q$ of odd characteristic containing a given self-orthogonal code. This generalizes an analogous result of MacWilliams, Sloane, and Thompson over the field $\mathbb F_2$ to arbitrary odd finite fields $\mathbb F_q$.
Learning Spatial-Channel Attention for Visual Tracking

Convolutional neural networks have an advantage of strong representation and has been widely applied to visual tracking. However, simply deepening network in pursuit of boosting performance is inappropriate for tracking because of its speed requirement. We leverage spatial attention and channel attention to enhance features of objects without much extra computational cost. The fused spatial-channel attention enables network to extract discriminative and robust features of targets or background. Furthermore, we propose inter-instance loss to make our tracker be aware of not only target-background classification but also instances classification across multi-domains. Extensive experiments on Object Tracking Benchmark (OTB) show that the proposed tracker obtains an Area-Under-Curve (AUC) score of 66.8% on OTB2015, outperforming most of the state-of-art trackers.
Facial recognition techniques using SVM: A comparative analysis

This paper presents a literary review of facial recognition in 2D, which plays an important role in the life of the human being in terms of safety, work activity, etc. The focus is on the results obtained by some researchers with the application of feature extraction techniques, pattern classifiers, databases and their respective percentage of efficiency obtained. The objective is to determine efficient techniques that allow an optimal 2D facial recognition process, based on the quality of databases, feature extractors and pattern classifiers.
MEDIA EDUCATION TECHNOLOGIES IN DEVELOPING STUDENTS' PROFESSIONAL COMPETENCE

The article concerns new media education technologies social networks-Knyhospalah didactics, Wikididactics networks (blogs and socia -blog---s Internet technologies dealing with using Wikipedia articles in the education process, among them first --In Ukrainian higher education, media education technologies, self-elaborated media education techniques, social networks-based -proved appropriate in the professional training.
Automated Composition, Analysis and Deployment of IoT Applications

Building IoT applications of added-value from a set of available devices with minimal human intervention is one of the main challenges facing the IoT. This is a difficult task that requires models for specifying objects, in addition to user-friendly and reliable composition techniques which in turn prevent the design of erroneous applications. In this work, we tackle this problem by first describing IoT applications using abstract models obtained from existing models of concrete devices. Then, we propose automated techniques for building compositions of devices using a repository of available devices, and an abstract goal of what the user expects from such compositions. Since the number of possible solutions can be quite high, we use both filtering and ranking techniques to provide the most pertinent solutions to users. The provided solutions satisfy the given goal and may be analysed with respect to properties such as deadlock-freeness or unmatched send messages. Finally, the application can be deployed using existing execution engines.
Image Forgery Detection Based on Deep Transfer Learning

The recent digital revolution has sparked a growing interest in applying convolutional neural networks (CNNs) and deep learning to the field of image forensics. The proposed methods aim to train algorithms for solving a range of predetermined tasks. However, training a model that has been randomly initialized requires extensive time for computation as well as an enormous pool of training data to draw from. Moreover, such a model needs to be developed and redeveloped from the ground up if there are any alterations to the feature-space distribution. In addressing these problems, the present paper proposes a novel approach to training image forgery detection models. The method applies prior knowledge that has been transferred to the new model from previous steganalysis models. Additionally, because CNN models generally perform badly when transferred to other databases, transfer learning accomplished through knowledge transfer allows the model to be easily trained for other databases. The various models are then evaluated using image forgery techniques such as shearing, rotating, and scaling images. The experimental results, which show an image manipulation detection has validation accuracy of over 94.89%, indicate that the proposed transfer learning approach successfully accelerates CNN model convergence but does not improve image quality.