Reviewerly/exHarmony huggingface dataset:
Sarcasm Detection Methods in Deep Learning: Literature Review

Sarcasm is rapidly becoming prevalent in all forms of communication today. Each person has a different way of exploiting and understanding sarcasm. It is difficult for even humans to interpret sarcastic texts, as it depends on a lot of things like perspective, context and tone. This makes it a challenging task to train a machine to distinguish sarcastic text from non-sarcastic text. As there are no concrete rules upon which a model to detect sarcasm can be built, we must resort to promising and upcoming techniques to do so. In this paper, we have reviewed the works done in sarcasm detection using deep learning in combination with the natural language processing techniques. We have also proposed an outline of our own system based on current gaps in literature and challenges in the field.
Result Tracking-on-Cloud: A Development of Mobile Result Management System

Assessment results are something private and confidential to most students. One may feel uncomfortable if his or her result is shown publicly. In most academic institutions, students’ results are displayed on a printed paper that contains the assessment of the whole class and the paper is usually posted on the notice board outside of the lecturer’s office. Students need to present personally at the lecturer’s office to check their results. Most of the time, students' names would be hidden on the paper but, students are still able to see each other’s results. Indirectly, the students’ results are not private though the lecturer does not intend to share them. In order to improve the privacy level of result management, a mobile application was programmed. This mobile application is an innovative tool that embedding the use of technology to assist students and educators in this era of Industrial Revolution 4.0. In this paper, the proposed mobile application/ app named as Result Tracking-on-Cloud (RTC) was presented. The users concluded that it is energy and time saving as they can check their results without present physically to the lecturer's office. At the same time, the privacy of students' result is secured.
Character Segmentation and Recognition of Indian Devanagari Script

The paper presents Devanagari Character Segmentation and Recognition using neural networks. The hybrid features extraction technique which is the combination of geometric and statistical features is implemented. The geometric feature extraction technique uses directional features of Skeletonized Character image, whereas the statistical feature technique uses distribution of pixel density and Euclid features of the skeletonized character image. For classification, SVM (Support Vector Machine) and MLP (Multi Layer Perceptron) are used as classifiers. The Support Vector Machine has more accuracy as compare to MLP.
KOMPARASI ALGORITMA DECISION TREE, NAIVE BAYES DAN K-NEAREST NEIGHBOR UNTUK MEMPREDIKSI MAHASISWA LULUS TEPAT WAKTU

Private Universities (PTS) compete so tight in providing performance in producing quality graduates. In addition, the number of universities in Indonesia which counts a lot both PTN and PTS makes the higher competition between universities as well. So the university strives to improve quality and provide the best education for service recipients, namely students, where one of the problems if there are some students who are late graduating or not on time so that it becomes an obstacle to the progress of the college. Prediction of students graduating on time is needed by university management in determining preventive policies related to early prevention of Drop Out (DO) cases. This prediction aims to determine the academic factors that influence the period of study and build the best prediction model with Data Mining techniques. There are 11 attributes used for Data Mining Classification, namely NPM, Gender, Age, Department, Class, Occupation, Semester 1 Achievement Index, Semester 2 Achievement Index, Semester 3 Achievement Index, Semester 4 Achievement Index and Information as result attributes. From the results of evaluations and validations that have been carried out using the RapidMiner tools the accuracy of the Decision Tree (C4.5) method is 98.04% in the 3rd test. The accuracy of the Naïve Bayes Method is 96.00% in the 4th test. And the accuracy of the K-Nearest Neighbor Method (K-NN) of 90.00% in the second test.
Detachable Module for Semi-automating a Conventional Wheelchair

Wheelchairs assist people with disabilities to become productive members of their communities. About 650 million people of the global population, have disabilities. Studies show that around 65 million of these require a wheelchair. The main purpose of the project is to design and develop a module that can be attached to any conventional wheelchair model in order to make it self-propelling. There are a wide variety of wheelchairs that are available in the market. However, the maneuvering of these wheelchairs by the patient himself and for an outsider is difficult and usually results in further injuries. Thus, considering these aspects we introduce this new design concept for a wheelchair which has a much simpler structure compared to the complex versions currently available in market. The module is designed to reduce the effort of wheelchair users during manual propulsion. It will be entirely electric and is a new concept of transportation for the disabled. The module was designed in Autodesk Fusion 360 and force analysis was carried out in ANSYS Workbench.
Research on Distributed Vertical Frequent Pattern Mining Method Based on Metadata Integration

Abstract In order to better integrate the information industry into people’s life and work, and even in social development, how to mine data has become a hot issue. Metadata is a kind of data about data. Mining metadata helps data application and storage. Therefore, it is very important to find an efficient and intelligent data mining method. This paper introduces the metadata and its integration technology. On this basis, it introduces the distributed vertical frequent mode, and introduces its use in the process of mining metadata, providing a new working idea for the staff engaged in related industries.
Retrospective analysis tools of local area network traffic

The article considers the problem of detecting a time-distributed network attack. Reportson the state of information space security from Kaspersky Lab and IBM Security are analyzed, aswell as stages of a retrospective analysis of network traffic based on research in the field of computer forensics are identified. Assumptions were made about possible problems at each stageand solutions were proposed. Existing network traffic analysis systems used to solve the existingproblem are investigated, a comparative description is made and their limitations are revealed
A Novel Steganography Algorithm using Edge Detection and MPC Algorithm

With the rapid development of the Internet, preserving the security of confidential data has become a challenging issue. An effective method to this end is to apply steganography techniques. In this paper, we propose an efficient steganography algorithm which applies edge detection and MPC algorithm for data concealment in digital images. The proposed edge detection scheme partitions the given image, namely cover image, into blocks. Next, it identifies the edge blocks based on the variance of their corner pixels. Embedding the confidential data in sharp edges causes less distortion in comparison to the smooth areas. To diminish the imposed distortion by data embedding in edge blocks, we employ LSB and MPC algorithms. In the proposed scheme, the blocks are split into some groups firstly. Next, a full tree is constructed per group using the LSBs of its pixels. This tree is converted into another full tree in some rounds. The resultant tree is used to modify the considered LSBs. After the accomplishment of the data embedding process, the final image, which is called stego image, is derived. According to the experimental results, the proposed algorithm improves PSNR with at least 5.4 compared to the previous schemes.
Application of Mobile Teaching Mode Based on MosoTeach: Benefits and Challenges

With the development of mobile Internet and the popularity of smart devices, mobile teaching is considered to be a trend of future teaching. As one of teaching softwares based on mobile terminal, MosoTeach enjoys great popularity in China. With the information collected through literature research as well as authors’ experiences of applying MosoTeach in classroom teaching, the present paper introduces a mobile teaching mode based on MosoTeach, and addresses functions of MosoTeach, the application of mobile teaching mode based on MosoTeach, and its benefits and challenges. Three benefits are pointed out, which are diversification, personalization and liberalization of learning; timely, all-sided and formative assessment system; and the convenience of collecting data and doing statistic analysis. Meanwhile, there are also some arguments about this mode such as the efficiency of participation, balancing between activities in and out of the classroom, balancing between face-to-face interaction and interaction through MosoTeach as well as the popularization in poverty-stricken areas, and some suggestions are put forward to solve these problems. Teachers should plan and organize the teaching materials well, and differentiate activities according to different phases in which those activities are carried out and keep balance between face-to-face interaction and interaction through MosoTeach.
Research on Extracting Named Entities in Software Engineering Field from Wiki Webpage

Extracting entity concepts from wiki pages is a common way of entity recognition. The common methods for named entity recognition are based on Conditional Random Field (CRF) and rules, such as Harvesting Domain Specic Knowledge Graph from Content of Webpages (HDSKG). However, the features of entity concepts and term phrases in the field of software engineering are not fully considered in HDSKG. To solve the problem, we propose a more efficient algorithm. We first use the webpage title to construct the domain dictionary, and then design the regular rules according to the entity concept features in the software engineering field. Next, the domain dictionary is used to improve the NP chunks in the chunking process. The experimental results show that compared with HDSKG, the proposed algorithm has a significant improvement in the number of entities, accuracy, precision and recall rate.
Multimedia Image Processing Lab Experiment/Simulation

Image Processing in Multimedia Applications treats a number of critical topics in multimedia systems, with respect to image and video processing techniques and their implementations. These techniques include the Image and video compression techniques and standards, and Image and video indexing and retrieval techniques. Image Processing is an important tool to develop a Multimedia system design.
VIS Capstone Address: Visualizing Temporality and Chronologies for the Humanities

The charts, graphs, network diagrams, and other graphics generally grouped under the rubric “information visualizations” were almost all developed to serve the empirical, statistical, or social sciences. They use explicit quantities, standard metrics, and unambiguous visual elements to create clear, concise, legible messages. The terms on which visualizations are assessed tends to value these properties-and to be premised on the idea that the best graphics are those that “show the data”-as if data had inherent form. The difficulty with this entire set of assumptions is that they do not establish foundations for visualizing complex historical events, narrative discourse, or human experience as it is expressed in documents and artifacts where “data” are not self-evident or standard. The humanities, in adopting visualizations from empirically based fields, has failed to develop methods that engage with the interpretative aspects of its own discourse. Using three case studies-one from news narratives, one from mixed historical chronologies, one from imagined crisis scenarios-this paper argues for the development of visualizations capable of showing variable timescales, comparative chronology, and partial and unfolding temporalities. The basic components of these systems are non-linear, affective, and unable to be standardized in ways that trouble empirical approaches but serve interpretative understandings of experience and its representation in cultural expressions. The questions of how such systems might be designed and where they might have useful applications will be raised to generate discussion.
Is more automation always better An empirical study of customers' willingness to use autonomous vehicle functions

Sometime, many (maybe all) vehicles on our streets will drive autonomously - or at least have autonomous functions. However, in the short run, consumers' preferences regarding the automation of pivotal vehicle functions are not entirely clear. This paper accordingly investigates consumers' willingness to use three levels of automation (none, partial, and full) of potentially autonomous vehicle functions (safety, parking prediction, and remote diagnostics). The results show that consumers' willingness to use autonomous vehicle functions is generally the highest for moderately autonomous functions and that the willingness to use these functions decreases above a certain level of autonomy. This paper also finds that this effect is moderated by gender and depends on individual involvement level with respect to autonomous vehicle functions, that is, highly involved consumers are more likely to appreciate autonomous vehicle functions compared with low-involved consumers.
Visual Analysis of Multivariate Time Series of Static and Mobile Sensors

The mini-challenge 2 of VAST Challenge 2019 asks the participants to make sense of the radiation conditions in St. Himark using radiation readings from the both stationary monitors and mobile sensors, particularly, to detect and monitor a bunch of contaminated cars running in the city. This paper presents our visual analysis solution to detect and localize these cars using various visualization techniques, including small multiples, distribution histogram, and animation. As a result, we detected most of these cars, characterize their behavior during the given time period and give suspected locations of these cars at the end. We also developed a visual analysis interface using Tableau to help users evaluate the uncertainty of sensor readings, make sense of radiation changes in different area, and make future plans to deploy more sensors.
Generalized Neural Policies for Relational MDPs

A Relational Markov Decision Process (RMDP) is a first-order representation to express all instances of a single probabilistic planning domain with possibly unbounded number of objects. Early work in RMDPs outputs generalized (instance-independent) first-order policies or value functions as a means to solve all instances of a domain at once. Unfortunately, this line of work met with limited success due to inherent limitations of the representation space used in such policies or value functions. Can neural models provide the missing link by easily representing more complex generalized policies, thus making them effective on all instances of a given domain? We present the first neural approach for solving RMDPs, expressed in the probabilistic planning language of RDDL. Our solution first converts an RDDL instance into a ground DBN. We then extract a graph structure from the DBN. We train a relational neural model that computes an embedding for each node in the graph and also scores each ground action as a function over the first-order action variable and object embeddings on which the action is applied. In essence, this represents a neural generalized policy for the whole domain. Given a new test problem of the same domain, we can compute all node embeddings using trained parameters and score each ground action to choose the best action using a single forward pass without any retraining. Our experiments on nine RDDL domains from IPPC demonstrate that neural generalized policies are significantly better than random and sometimes even more effective than training a state-of-the-art deep reactive policy from scratch.
Movie Summarization based on Indonesian Subtitles with Restricted Boltzmann Machine

A summary of a movie provides a concise description of the movie both concretely and abstractly. Automatic movie summarization can be applied to get a movie summary. In this study, we propose a movie summarization method on Indonesian subtitles with five stages, namely: preprocessing, feature extraction, feature enhancement, sentence ranking, and summary generation. The preprocessing consist of document segmentation, paragraph segmentation, case folding, normalization, stopword removal, and part-of-speech tagging. Twelve intrinsic features, namely: number of thematic words, sentence position, sentence length, sentence position relative to paragraph, number of proper noun, number of numerals, number of verbs, number of noun, term frequency-inverse sentence frequency, sentence to centroid similarity, bi-gram key phrase list, and tri-gram key phrase list are extracted and enhanced with Restricted Boltzmann Machine (RBM) to improve the quality of the summary. Results indicate that the summary produced is understandable and has the name of the main character in the provided text. Compare to movie summarization without RBM, RBM improves the average quality of the summary of 6% (F1-score). Questionnaire results also show that the movie summarization developed in this study produces a summary that is suitable for the provided subtitles.
Experimental extraction of nonlocal weak values for demonstrating the failure of a product rule

We experimentally demonstrate an alternative method for measuring nonlocal weak values in linear optics, avoiding the use of second-order interaction. The method is based on the concept of modular values. The paths of two photons, initialized in hyperentangled states, are adopted as the meter with the polarization acting as the system. The modular values are read out through the reconstructed final states of the meter. The weak value of nonlocal observables is given through its connection to the modular value. Comparing the weak values of local and nonlocal observables, we demonstrate the failure of product rules for an entangled system. Our results significantly simplify the task of measuring nonlocal weak values and will play an important role in the application of weak measurement.
Investigating the Compositional Structure of Deep Neural Networks

The current understanding of deep neural networks can only partially explain how input structure, network parameters and optimization algorithms jointly contribute to achieve the strong generalization power that is typically observed in many real-world applications. In order to improve the comprehension and interpretability of deep neural networks, we here introduce a novel theoretical framework based on the compositional structure of piecewise linear activation functions. By defining a direct acyclic graph representing the composition of activation patterns through the network layers, it is possible to characterize the instances of the input data with respect to both the predicted label and the specific (linear) transformation used to perform predictions. Preliminary tests on the MNIST dataset show that our method can group input instances with regard to their similarity in the internal representation of the neural network, providing an intuitive measure of input complexity.
A Visual Object Tracking Algorithm Based on Dynamics Pattern and Convolutional Feature

Deep visual feature-based method has demonstrated impressive performance in visual tracking attributing to its powerful capability of visual feature representation. However, in some complex environments such as dramatic change of appearance, illumination variation and rotation, the extracted deep visual feature is insufficient for accurately characterizing the target. To solve this problem, we present an integrated tracking framework which combines a Long Short-Term Memory (LSTM) network and a Convolutional Neural Network (CNN). Firstly, the LSTM extracted dynamics feature of target on time sequence, resulting the state of target at present time step. With that state, the accurate preprocessed bounding box was obtained. Then, deep convolutional feature of the target was extracted using a CNN, based on the processed bounding box. Finally, the position of the target was determined based on the score of the feature. During tracking stage, in order to improve the adaptation of the network, the parameters of the network were updated using samples of the target captured while successful tracking. The experiment shows that the proposed method achieves outstanding tracking performance and robustness in cases of partial occlusion, out-of-view, motion blur and fast motion.
An Analysis on the Contents Associated with Physical Activity in Korea Teacher’s Manual Nuri Curriculum for Age of 3~5

The purpose of this study was to analyze and reveal problems in the physical activity contents in the Teacher’s Manual Nuri Curriculum (TMNC) according to various categories and to suggest directions about healthy physical activity for Korea preschoolers. In pursuit of this study, we examined the frequency of Activities in connection with the Nuri Curriculum (ANC) and Activities in connection with Type of Activity (ATA). We then examined the frequency of Activities of Physical & Exercise & Health. Next, we examined the contents of ATA in Physical & Exercise & Health. We found that the activity contents of Physical & Exercise & Health related to physical activity were a lower percentage rather than four other domains (Communication, Art Experience, Social Relationship, and Nature Exploration) in the Nuri Curriculum. Second, the rate of contents related with fundamental movement skills was lower in the activities of Physical & Exercise & Health. Third, physical activities, games, and outdoor activities were a higher percentage in Physical Exercise & Health. These results indicated that the Nuri curriculum should consider adding contents related to physical education. Although this study indicated that the contents of ATA in Physical & Exercise & Health should include higher physical activities, we will examine this using scientific equipment about the physical activity level in TMNC.
Choosing a Suitable Public Cryptosystem for Internet of Things

The Internet of Things (IoT) has ushered in numerous devices in many areas in our life and in industries. It could comprise devices with sensors to gather and broadcast data over the internet. As the devices are IP-based and the media are shared, any user in the network can have an access to the communication contents. The only way to impose access control in the sensor networks is through cryptography. A key is applied to encrypt the communication to prevent from unauthorized access to the network. Choosing a suitable key management scheme is very important in sensor networks as it should satisfy the constraints of the sensors. There are two indispensable public cryptosystems available in the literatures-RSA and Elliptic curve cryptography (ECC). ECC gives strong resistance to cryptanalytic attacks. So, it is used with smaller key sizes than RSA (Valenta, L., et al., 2018. In Search of CurveSwap: Measuring Elliptic Curve Implementations in the Wild. 2018 IEEE European Symposium on Security and Privacy (EuroS&amp;P) , April; IEEE. pp.384–398). The most prettiness of using elliptic curve cryptography over other cryptosystems (i.e., RSA) is that it provides same security strength for a lesser key without breaching the system, thereby consuming less resources and ameliorating performances and fast data throughput of the devices. To choose a suitable public cryptosystem for use in IoT devices like sensor networks, elliptic curve cryptography and RSA are comparatively analyzed in this paper.