Reviewerly/exHarmony huggingface dataset:
Data Hiding for Ensuring the Quality of the Host Image and the Security of the Message

Concealing a message in the APPn markers of a JPEG image not only protects the security of the message but also induces no change to the quality of the image. However, from the existing literature, whether it is a plaintext message or a ciphertext message hidden in APPn, it is easy for an attacker to identify the confidentiality of the hidden message, which is not conducive to the security of the message. Inspired by the natural language processing (NLP) and format-preserving encryption (FPE), this paper proposes a data hiding method, which is focused on the quality assurance of the host image and the concealment of the plain text having complete semantics based on the NLP and FPE. This method first uses the NLP and FPE to identify and encrypt the sensitive words in the plain text and then hides the ciphertext text with the plaintext style in the APPn of a JPEG image after replacing the plaintext words with the ciphertext words that have the plaintext style. The experimental results confirm that the structure, size, and quality of the host image do not show any changes before or after the data hiding and the recovered host image is also identical to its original appearance. In addition, the strategy that the semantic similarity is regulated autonomously by the user also makes it possible to obtain the ideal ciphertext words with very low similarity. Moreover, more than 80% of the ciphertext texts have reasonable semantics. Compared with the existing literature, our algorithm has a better performance.
Suspicious Object Detection At Public Places

Suspicious object detection is practically useful in areas of computer vision due to its application in automatic video surveillance systems for the detection of suspicious activities that might endanger public safety, especially in crowded places like airports, railway stations, shopping malls, movie theatres and the like. A suspicious object is defined as one that has been lying stationary at a certain place with unattended by human for an extended period of time. Such objects are usually inconspicuous common place objects that people often carry around including backpacks,suitcases and boxes.The present work is try to make a flexible and modular framework that can be used to experiment with several different methods for each stage of the overall task of detecting abandoned and removed objects in a video stream. Many existing methods have been implemented for each of these stages and integrated into the system in a way that makes it possible to switch between them in real time. This enables the user to observe and compare the performance of different methods of solving the same problem. We are using Netbeans to develop this system.
A Survey on Blockchain for Enabling Transparency in transactions of Government Direct Benefit Transfers (DBT)

Blockchain has a great potential in distributed shared peer to peer ledger like spreadsheets that record any transaction.A copy of ledger is shared between all stakeholders.Blockchain plays an important role for secure decentralization and brings more transparency to the system.Traditionally, within the society, people have created trust through intermediaries.They use these third party entities because they trust that they will store and protect their goods and send the right amount when they request it, and to the right person.In Government sector, there is a critical need to have more transparency in transactions and so this system has been designed as an effective mechanism to avoid the corruption.Blockchain technology provides the transparency so that actors present in this use case can track the flow of any transaction.Any transaction pertaining to DBT (Direct Benefit Transfer) is recorded.Blockchain replaces the need for intermediaries by redirecting the trust to decentralized systems.
A MODEL TO EXPLORE LEARNING PROCESSES IN LEARNER-GENERATED DIGITAL MEDIA (LGDM) ASSIGNMENTS

Appears in: INTED2019 Proceedings Publication year: 2019Pages: 29-35ISBN: 978-84-09-08619-1ISSN: 2340-1079doi: 10.21125/inted.2019.0015Conference name: 13th International Technology, Education and Development ConferenceDates: 11-13 March, 2019Location: Valencia, Spain
Penerapan Algoritma ACO untuk Penjadwalan Kuliah Pengganti pada Perguruan Tinggi (Studi Kasus: Program Studi Informatika, Universitas Multimedia Nusantara)

Students and scheduling are both essential parts in a higher educational institution. However, after schedules are arranged and students has agreed to them, there are some occasions that can occur beyond the control of the university or lecturer which require the courses to be cancelled and arranged for replacement course schedules. At Universitas Multimedia Nusantara, an agreement between lecturers and students manually every time to establish a replacement course. The agreement consists of a replacement date and time that will be registered to the division of BAAK UMN which then enter the new schedule to the system. In this study, Ant Colony Optimization algorithm is implemented for scheduling replacement courses to make it easier and less time consuming. The Ant Colony Optimization (ACO) algorithm is chosen because it is proven to be effective when implemented to many scheduling problems. Result shows that ACO could enhance the scheduling system in Universitas Multimedia Nusantara, which specifically tested on the Department of Informatics replacement course scheduling system. Furthermore, the newly built system has also been tested by several lecturers of Informatics UMN with a good level of perceived usefulness and perceived ease of use.&#x0D; Keywords—scheduling system, replacement course, Universitas Multimedia Nusantara, Ant Colony Optimization
THE HURDLES OF THE TEACHER IN THE PRACTICE OF TARGET LANGUAGE IN THE INDONESIAN EFL JUNIOR HIGH SCHOOL

In teaching learning process, the target language is an important thing which has been noticed by the teacher. The phenomenon about misunderstanding language is not only occurring in daily life with soft conversation but also in the formal education. In education event, when the teacher teaches students in the classroom, they use target language during activities. The aims of this study were to describe the teacher's hurdles in using target language at first grade of Junior high school and how the teacher delivered instruction by using target language at EFL class. This study used a descriptive qualitative approach. This study used a qualitative description. Five English teachers were involved. The researcher collected the data as interview, and observation. Referring to the finding of the study, based on result of interview found the teachers problems used target language in teaching learning process, such as; the low vocabulary of the students, the ability of English skill of the students, the situation in the classroom did not support the use of target language, the time was not effective. Based on the findings of classroom observation in classroom the teacher delivered used the target language in simple sentence on greeting, giving instruction, asking question, delivering materials and closing.
One Shot Learning In AI Innovation

Author(s): Ram, Natalie | Abstract: Modern algorithmic design far exceeds the limits of human cognition in many ways.Armed with large data sets, programmers promise that their algorithms can betterpredict which prisoners are most likely to recidivate and where future crimes arelikely to occur. Software designers further hope to use large data sets to uncoverrelationships between genes and disease that would take human researchers muchlonger to identify.
Textual Coherence Improvement of Extractive Document Summarization Using Greedy Approach and Word Vectors

There is a growing body of attention to importance of document summarization in most NLP tasks.So far, full coverage information, coherence of output sentences and lack of similar sentences (nonredundancy) are the main challenges faced to many experiments in compacted summaries.Although some research has been carried out on compact summaries, there have been few empirical investigations into coherence of output sentences.The aim of this essay is to explore a comprehensive and useful methodology to generate coherent summaries.The methodological approach taken in this study is a mixed method based on most likely n-grams and word2vec algorithm to convert separated sentences into numeric and normalized matrices.This paper attempts to extract statistical properties from numeric matrices.Using a greedy approach, the most relevant sentences to main document subject are selected and placed in the output summary.The proposed greedy method is our backbone algorithm, which utilizes a repeatable algorithm, maximizes two features of conceptual coherence and subject matter diversity in the summary.Suggested approach compares its result to similar model Q_Network and shows the superiority of its algorithm in confronting with long text document.
Recurrent Neural Network Language Model Training Using Natural Gradient

Recurrent neural network language models (RNNLMs) have become an increasing popular choice for state-of-the-art speech recognition systems. RNNLMs are normally trained by minimizing the cross entropy (CE) using the stochastic gradient descent (SGD) algorithm. However, the SGD method doesn't consider the correlation between parameters and therefore can lead to unstable and slow convergence in training. Second-order optimization methods provide a possible solution to this issue. However these methods are either computationally heavy or do not have competitive performance. In this paper, a novel optimization method - stochastic natural gradient based on minimum variance assumption (SNGM) is proposed for training RNNLMs. It allows the natural gradient method to operate at a comparable training efficiency to the SGD method. By modifying the gradient according to the local curvature of the KL-divergence between current and updated probabilistic distributions, the proposed SNGM approach is shown to outperform both the SGD and limited memory BFGS methods across three tasks: Penn Treebank, Switchboard conversational speech recognition and AMI meeting room transcription in terms of both perplexity and word error rate.
Analyzing Decision Support System for Determining Eligibility of Home Ownership Loan Using Fuzzy Mamdani Logical Method and Technology Acceptance Model in Banking

Every year, the number of people who want to own a house by credit increase, therefore the chances of increasing problem loans will be greater. Non-Performing Loan (NPL) is an indicator to assess the performance of bank functions. The higher the NPL value, the performance of the bank is considered worse. In order to maintain the caution of banks (creditors) in the provision of credit, the bank needs to assess and determine the criteria of debtors who can be given credit. So the bank can make decisions precisely and accurately. In this research, the Researcher will design decision support system with Mamdani fuzzy logic method with the resulted output in the form of "rejected" or "approved" decision. The accuracy rate in this study is 98%. 2% of the difference consists of 63.64% is a debtor with NPL credit status (problematic). After the system was designed in this study, the researcher conducted an analysis to see the user behavior when using the system. The method used is Technology Acceptance Model (TAM). The result of TAM analysis is the simultaneous relationship among of all variables on the acceptance of information technology (acceptance of IT) have a positive impact. However, a partial relationship between perceived usefulness and attitude toward using does not have a positive effect.
AN APPROACH TO DEVELOP A MOTION-SENSITIVE, LOCALLY MULTIPLAYER-HYBRID (MULTIPID) 3D VIDEO GAME

Playing video games has been gaining increased interest over the last two decades regardless of age, gender and any kind of status. Video games are generally controlled via frequently used input devices such as game controllers or mouse and keyboard. However, there are also newer technologies which can detect human motion through sensors. This technology enhances the game-play experience due to its interactivity level and therefore, such an experience embraces many players more than traditional controllers. The term “Hybrid” is used for both the multi-genre games and for games integrating conventional controllers together with new technology devices, e.g. motion-sensitive devices. In this paper, we use the term for the latter. Our aim is to provide the game developers a roadmap to generate a 3D hybrid game in order to enhance the game-play experience where we mainly control the game with keyboard but also integrate Kinect to sense human motion and solve interactive puzzles at the end of each level. Besides, we also suggest to enable the multiplayer mode for some interactive puzzles to challenge the players and observe their contentedness in terms of game-play. After this experience, we named this game model as MultipId game-model as it combines the features of both multiplayer and hybrid modes. We developed a game employing this model which is discussed in the Results section. We believe that increasing the number of MultipId games gives a competitive edge over existing single-player games employing only traditional controllers.
컨테이너 터미널 안전관리 개선방안에 관한 연구 - 위험성 평가 및 보우타이기법이용 -

It is time to study safety improvement on container terminals to maintain a high-quality service to customers’ productivity and safety. The data analyzed was of the past 3 years accidents data from a container terminal in the Busan port. I especially tried to found out the equipment that had the highest number of accidents and what the main causes were. This study suggests the top hazards identified in the container terminal using risk assessment, Moreover, it seeks control measurements to prevent hazards by bowtie methodology.
Doppler Data Association Scheme for Multi-Target Tracking in an Active Sonar System

In many wireless sensors, the target kinematic states include location and Doppler information that can be observed from a time series of range and velocity measurements. In this work, we present a tracking strategy for comprising target velocity components as part of the measurement supplement procedure and evaluate the advantages of the proposed scheme. Data association capability can be considered as the key performance for multi-target tracking in an active sonar system. Then, we proposed an enhanced Doppler data association (DDA) scheme which exploits target range and target velocity components for linear multi-target tracking. If the target velocity measurements are not incorporated into target kinematic state tracking, the linear filter bank for the combination of target velocity components can be implemented. Finally, a significant enhancement in the multi-target tracking capability provided by the proposed DDA scheme with the linear multi-target combined probabilistic data association method is demonstrated in a sonar underwater scenario.
Variation in voice quality within speakers

Little is known about the nature or extent of everyday variability in voice quality within a speaker or how this differs across speakers. Using a suite of measures that map between acoustics and perception of voice quality, this study elucidates which acoustic variables within speakers’ individual voice spaces best characterize speakers. Based on studies of faces and cognitive models of speaker recognition, we hypothesized that a few measures would be important across speakers, but that much intra-speaker variability would be idiosyncratic. By using principal component analysis, we tested this hypothesis against a set of multiple sentence productions from 100 native speakers of English (fifty females and fifty males), recorded over three days. Acoustic variables measured every 5 ms on vowels and approximants corresponded to F0, vowel quality, spectral noise, source spectral shape, and variability. Across speakers the balance between higher harmonic amplitudes and inharmonic energy in the voice accounted for the most variance (females = 20%/males = 22%). Vowel quality and its variability accounted for an additional 12%/12% of variance. Remaining variance appeared largely idiosyncratic, suggesting that the speaker-specific voice space is different for different people. Notably, F0 did not emerge from these analyses. Implications for voice recognition are discussed. [Work supported by NIH/NSF.]
Creating Color Image Signature Based On Laplacian Equation

Colour image retrieval, identification and recognition play an important role in vital computerized applications, so creating an image signature which can be used as a key to identify the image is an essential and important problem to be solved.Â In this paper a new method of image signature extraction will be proposed. The proposed method will be implemented and tested and the experimental results will be compared with other method results to prove the efficiency of the proposed method by mean of creating a unique signature which will require a minimum memory space and a smaller time to extract the signature.
Trading the Dream: Does Social Media Affect Investors’ Activity - The Story of Twitter, Telegram and Reddit

Bitcoin has received much attention in the media and by investors in recent years, although there remains scepticism and a lack of understanding of this cryptocurrency. I add to the literature on Bitcoin by studying How and if social media platforms such as Twitter, Telegram and Reddit are affecting Bitcoin’s prices and volume. Findings confirm that volumes of exchanged tweets may predict the fluctuations of Bitcoin’s prices and mainly Bitcoin volume. I also find that Reddit and Telegram have greater impact on Bitcoin volume rather than Twitter. Results indicate that information about the use of Twitter and other social media platforms can assist in tracking real world behavior and may even predict real financial markets trend.
Proposal of a Machine Learning Approach to Differentiate Mild and Alzheimer's Condition in MR Images Using Shape Changes in Corpus Callosum.

The brain ventricles are surrounded by periventricular structures that are affected by dementia which results in neurodegenerative disorder such as Alzheimer's Disease (AD). The change in morphology of these structures must effect the shape and volume of Corpus Callosum (CC). These alterations in morphology of CC are considered to be a significant image biomarker for the early diagnosis of Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD) subjects. Shape descriptors provide useful information about change in morphology of various brain structures during disease progression. In this work, Lattice Boltzmann criterion based hybrid level set method (LSM) is used to segment CC. Geometric and pseudo-Zernike moment measures are extracted from the segmented area of CC and are statistically analyzed using Statistical Package for Social Science (SPSS). The performance metric of significant moments is validated using machine learning algorithms. Results demonstrate that, hybrid level set is able to delineate CC and the segmented images are in high correlation with ground truth images. High accuracy value of 85.0% has been achieved using Multilayer Perceptron (MLP) classifier for Healthy Control (HC) versus AD subjects. Thus, moments are able to classify MCI from HC and AD subjects with high accuracy and hence the results are found to be clinically significant.
Managing a Mass Collections Review from Assessment to Deselection

Starting in 2017, Falvey Memorial Library at Villanova University initiated a long-overdue review of the monograph collection. This was partly to ensure a healthy collection with room for growth, but also to prepare for a renovation of the building—ideally completing the collection review by the summer of 2019. This presentation explores the ongoing process of organizing and enacting the weeding project. This includes using OCLC Online Computer Library Center, Inc. (OCLC)’s GreenGlass software to assist liaison librarians with selection, developing criteria for deselection, and incorporating faculty participation in the project without undermining it. The talk highlights the ongoing challenges related to this project, which hopefully encourages some discussion of the issues academic libraries encounter when reviewing and withdrawing volumes from their print collections.
Configuring Personal Data for a Quantified-Self Archive

An audience exists for personal information, including quantified-self data, beyond an individual's social network and social communities. In the era of big data, the research and policy arenas are two areas where up-to-date assemblages of personal information have market value. In this ongoing study, we examine the long-term value of small data [2], acknowledging that there is also a societal need and an audience for rich, personalized collections of digital self-tracking records. Using qualitative research methods, we interviewed 18 people to explore the nature of self-tracking data that exists as a byproduct of daily life, and their sense of why and how their data could be archived for posterity. In the process, the intellectual and design challenges of a digital quantified-self archive are explored.
Evading Deep Neural Network and Random Forest Classifiers by Generating Adversarial Samples

With recent advancements in computing technology, machine learning and neural networks are becoming more wide-spread in different applications or software, such as intrusion detection applications, antivirus software and so on. Therefore, data safety and privacy protection are increasingly reliant on these models. Deep Neural Networks (DNN) and Random Forests (RF) are two of the most widely-used, accurate classifiers which have been applied to malware detection. Although their effectiveness has been promising, the recent adversarial machine learning research raises the concerns on their robustness and resilience against being attacked or poisoned by adversarial samples. In this particular research, we evaluate the performance of two adversarial sample generation algorithms - Jacobian-based Saliency Map Attack (JSMA) and Fast Gradient Sign Method (FGSM) on poisoning the deep neural networks and random forests models for function call graph based malware detection. The returned results show that FGSM and JSMA gained high success rates by modifying the samples to pass through the trained DNN and RF models.
A literature review of studies on interactive 3D information visualization for the web

Web browsers provide a feature-rich, cross-platform, and easily accessible environment presenting the potential to reduce the costs and complexity of interactive 3D Information Visualization (I3DIV) applications involving multi-projection systems and diversified interaction devices. To better understand how these applications can be developed for web browsers, we conducted a Systematic Literature Review (SLR) composed by two searches for scientific articles, one regarding how multi-projection systems can be implemented using web browsers (which selected 10 articles), and another one regarding how interaction devices besides mouse and keyboard can be used in web browsers (which selected 26 articles); their results were then compiled under the mantle of multi-projected I3DIV applications for web browsers. Results show, among other things, that multi-projection systems for the web are most commonly implemented using client-server and decentralized graphic clusters, that diversified interaction devices are most commonly accessed in web browsers using an approach we called "device-server-browser", and that there is currently only one software resource that can be used to develop multi-projected I3DIV applications for web browsers, however it also has limitations regarding usage and, possibly, performance. We conclude then that there is a gap for a resource that addresses these subjects in a more appropriate way.