Reviewerly/exHarmony huggingface dataset:
DEVELOPMENT OF ART PERFORMANCE TICKETS INFORMATION SYSTEM AT PUBLIC HIGH SCHOOL

The development of technology and increasing human needs are two things that influence each other. As one example is the need to purchase art performance ticket using the internet. At present, most printed tickets allow misuse by irresponsible parties, but it also takes longer to get tickets. Therefore a solution is needed to purchase tickets with the website. In addition to making it easy for buyers and organizers, tickets will be accepted quickly. E-Ticketing is a way to document the sales process of a business activity without having to physically issue valuable documents or paper tickets. A website-based ticket art purchase application is needed to make purchasing process easier. The making of this application uses HTML as client-side development, PHP as server-side development, and MySQL as a database. With this application it is expected to help the organizers and buyers in making it easier to get tickets. In the testing phase this application starts from creating an account to getting a ticket going well. Then from the conclusions of the application made with this application, the buyer will get the convenience of getting tickets and buying tickets for events held at school by opening the website that has been provided.
Agile Scrum Adoption of the Application Development Projects of Company C

Company C has incurred 31 percent of delayed projects for more than three months due to the method of applying the Agile practices. The study assesses which among the Agile practices should Company C improve on to meet its project timeliness. Company C was evaluated against other IT companies, namely Company A, B and D based on how in-depth the Agile practices were applied in the company using one-way ANOVA to test the difference of means of the companies on each Agile practice. The Agile practices that Company C excels and needs improvement were obtained. A binary logistic bootstrap regression is performed to determine which Agile practices positively affect in improving the project timeliness of Company C. Based on the identified best implementation of the Agile Scrum practice in Company C from one-way ANOVA, and the positive and negative coefficients in regression, the practices that should be maintained and improved were obtained for the formulation of the Agile framework of Company C. To improve project completion rate, Company C should improve on the following practices: increasing frequency of evaluating backlog items at least every other sprint, limiting stand-up meeting to 15 minutes, creating separate implementation task, conducting sprint planning with key persons, consistent iteration length, more frequent iteration demos, performance of unit testing, understanding architectural design and implemented by the team, performing pair-programming on key portions, collaborating more frequently of team members and using of formal written document as supplement for informal communication should be implemented.
Patch-based Generative Adversarial Network for Single Image Haze Removal

Image haze removal is widely used in various fields and is an important link to improve the ability of image recognition under special circumstances. In order to improve the image haze removal level, this paper proposes a single image haze removal method based on network image translation (Pix2Pix). Relying on the encoder-decoder model and the Markov Discriminator (PatchGAN), it is possible to maintain a high resolution and texture structure of the image while defogging. Finally, experiments on synthetic prove that this algorithm is better than others in objective indicators of PSNR and SSIM.
Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning.

Learning the underlying patterns in data goes beyond instance-based generalization to external knowledge represented in structured graphs or networks. Deep learning that primarily constitutes neural computing stream in AI has shown significant advances in probabilistically learning latent patterns using a multi-layered network of computational nodes (i.e., neurons/hidden units). Structured knowledge that underlies symbolic computing approaches and often supports reasoning, has also seen significant growth in recent years, in the form of broad-based (e.g., DBPedia, Yago) and domain, industry or application specific knowledge graphs. A common substrate with careful integration of the two will raise opportunities to develop neuro-symbolic learning approaches for AI, where conceptual and probabilistic representations are combined. As the incorporation of external knowledge will aid in supervising the learning of features for the model, deep infusion of representational knowledge from knowledge graphs within hidden layers will further enhance the learning process. Although much work remains, we believe that knowledge graphs will play an increasing role in developing hybrid neuro-symbolic intelligent systems (bottom-up deep learning with top-down symbolic computing) as well as in building explainable AI systems for which knowledge graphs will provide scaffolding for punctuating neural computing. In this position paper, we describe our motivation for such a neuro-symbolic approach and framework that combines knowledge graph and neural networks.
A Social Media-Machine Learning Approach to Detect Public Perception of Transportation Systems

Social sensors such as Facebook and Twitter are used by the public to express their perceptions and circumstances in various aspects of their lives. People publish tweets and post Facebook messages related to transportation systems to report what they have seen on the road, especially when an unusual or unsafe situation (e.g., pothole, traffic crash) occurs. Some messages directly refer to a public agency’s account (e.g., State Department of Transportation), expecting the public agency to respond to these posts. Many posts contain real-time urgent incident information (e.g., broken traffic lights, flooding) or other inquiries, comments, requests, suggestions, and complaints about transportation systems. However, many of such posts fail to get a timely response from related agencies, perhaps because public agencies do not have an efficient way to identify the public’s perception of the transportation system in their jurisdiction. The objective of this study was to develop a novel approach to efficiently monitor and detect critical real-time information from public awareness. This study used tweets for demonstration. A keyword-extraction process using the Term Frequency method was implemented to gather valid transportation terms in public tweets. A machine learning-based model was trained to identify transportation-related tweets from the live stream. The model training started with manually identifying relevant tweets (historical data). The results showed that, for identifying transportation-related posts, the trained machine learning model’s overall performance was 0.848. Furthermore, this trained model has been applied in a live streaming data collection process, and 81.86% of the total collected tweets were manually identified as relevant tweets. Hence, this framework can be employed by public agencies to identify important public posts related to transportation in a timely and proactive manner.
Comparative Study of Some Supervised Machine Learning Algorithms for Information Retrieval

The volume and quality of online data has increased tremendously.Retrieval of such data relies so much on efficient methods.In recent times, information retrieval looks to the intelligence-based and inductive learning methods, such as genetic algorithm, neural networks and machine learning.Researchers however, have leverage on these newer techniques in order to enhance the retrieval capabilities and information processing of current information storage and retrieval systems.These methods provide various degrees of accuracy.But how effective are these methods and which of them is better suited for the information retrieval task?This paper investigates the efficiency of the selected algorithms: Artificial Neural Network, Support vector machine, and Genetic Algorithm, on designing the model for efficient and intelligent information retrieval.The selected algorithms were critically studied in line with the available matching models for information retrieval.Models like the Vector space model, Binary model, probabilistic models, Inverted Index, Latent semantic Analysis and the Latent Semantic Index models were respectively examined.The result from the experimentation from the respective algorithms shows that the neural network, in combination with Genetic algorithm or alone, performs better.However, it takes more time to execute.
Evaluation of Nephrology Dataset Through Deep Learning Technique

Multiple parameters need to be analyzed for making diagnosis in patients with renal disorders. An automatic system that would analyze these parameters and provide diagnosis without the help of a specialist would decrease the burden on already overburdened clinicians. Deep learning has already made a huge impact on medical fields like cancer diagnosis, precision medicine, and can be applied to build this automated system which can diagnose renal disorders. This study has been carried out using the data of nephrology related diseases extracted from a standard based healthcare dataset based on openEHR standard. A feed forward neural network has been used for the creation of the model that would predict the kidney disease present in the patient based on several parameters (such as abdominal ultrasonography, enrolled for transplantation, vascular access, HbsAg, venous fistula amount, urea reduction rate, HIC antibodies and HIV). An accuracy of 98.7% has been achieved through the experiments.
Integrated Development Environment with Interactive Scatter Plot for Examining Statistical Modeling

The development of a statistical modeling program requires example data to observe and verify the behavior of the program. Such example data are either taken from an existing dataset or synthesized using commands. Programmers may want to directly design an arbitrary dataset or modify it interactively, but it is difficult to do so in current development environments. We therefore propose combining a code editor with an interactive scatter plot editor to efficiently understand the behavior of statistical modeling algorithms. The user interactively creates and modifies the dataset on the scatter plot editor, while the system continuously executes the code in the editor, taking the data as input, and shows the result in the editor. This paper presents the design rationale behind the system and introduces several usage scenarios.
PNG Data Detector for DECA

DECA, a novel file carving application, is an example of digital forensic tools that rely heavily on accurately detecting the type of data fragments stored in the disk blocks. This work is an attempt to create a method of detection and classification of PNG data types for DECA which originally only identifies and extracts JPEG data. The PNG file format was examined in order to implement the PNG data detector that was integrated into DECA. We then examined the results of decision-theoretic file carving, implemented in DECA, combined with the implemented PNG data detector.
An Evaluation of Knowledge Graph Embeddings for Autonomous Driving Data: Experience and Practice.

The autonomous driving (AD) industry is exploring the use of knowledge graphs (KGs) to manage the vast amount of heterogeneous data generated from vehicular sensors. The various types of equipped sensors include video, LIDAR and RADAR. Scene understanding is an important topic in AD which requires consideration of various aspects of a scene, such as detected objects, events, time and location. Recent work on knowledge graph embeddings (KGEs) - an approach that facilitates neuro-symbolic fusion - has shown to improve the predictive performance of machine learning models. With the expectation that neuro-symbolic fusion through KGEs will improve scene understanding, this research explores the generation and evaluation of KGEs for autonomous driving data. We also present an investigation of the relationship between the level of informational detail in a KG and the quality of its derivative embeddings. By systematically evaluating KGEs along four dimensions -- i.e. quality metrics, KG informational detail, algorithms, and datasets -- we show that (1) higher levels of informational detail in KGs lead to higher quality embeddings, (2) type and relation semantics are better captured by the semantic transitional distance-based TransE algorithm, and (3) some metrics, such as coherence measure, may not be suitable for intrinsically evaluating KGEs in this domain. Additionally, we also present an (early) investigation of the usefulness of KGEs for two use-cases in the AD domain.
Towards an Extension of the Linking of the Open Dutch WordNet with Dutch Lexicographic Resources

This extended abstract presents on-going work consisting in interlinking and merging the Open Dutch WordNet and generic lexicographic resources for Dutch, focusing for now on the Dutch and English versions of Wiktionary and using the Algemeen Nederlands Woordenboek as a quality checking instance. As the Open Dutch WordNet is already equipped with a relevant number of complex lexical units, we are aiming at expanding it and proposing a new representational framework for the encoding of the interlinked and integrated data. The longer term goal of the work is to investigate if and on how senses can be restricted to particular morphological variations of Dutch lexical entries, and how to represent this information in a Linguistic Linked Open Data compliant format.
(Trans)forming Knowledge and Design Concepts in the Design Workshop

This chapter deals with the transition from research to design within the Body Games project—an interdisciplinary design project focused on the development of a digital playground for children. It describes how various forms of knowledge and field material are turned into design concepts at a particular workshop through the creation, circulation, combination, and transformation of knowledge pieces. The design team was not simply on a joint mission to save children from obesity. The obesity framework served to combine different research, funding, and business interests within a single framework, and was thus as much an instrument in the service of the Body Games project as the other way around. At the very end of the design workshop we were left with a single design vision or strategy, fixed in a single drawing. Like a rite of passage the design workshop facilitated the transition from research to design.
Constructing Arabic Language Resources from Google N-gram Dataset

Abstract The Google N-gram dataset contains millions of books in 22 different categories. Therefore, it is used widely as a language resource in natural language processing. The Arabic language has several resources, but most of them are small in size. As with any research, if training resources are large, then the validity of any research is higher. To build a new language resource manually by humans take years to accomplish, but using automatic methods can be done in a few days. Google N-gram dataset does not support the Arabic language yet. This will cause a challenge for researchers who use this dataset for extracting Arabic text. In this paper, an extraction method that can produce a lexicon and N-gram corpus from the English Google N-gram dataset is presented. The steps to build these recourses and to overcome Google N-gram dataset challenges are explained. The experiment results show the success of the proposed method in the automatic construction of the resources in a short time. This method will be useful to researchers in several research fields.
Distributed Fusion State Estimation Based on Coded Measurements

The problem of distributed fusion state estimation for a spatially array of sensors in Network Control System (NCS) is studied. The measurement of each sensor is transmitted through a lossy chain, and the dropped packet model of each chain is independent and identically distributed. To reduce the requirement of communication bandwidth and the impact of packet loss, a compressed coding scheme is proposed, which combines the measurement of each sensor to make the transmitted information a scalar. Then, the local estimation and the local estimation error covariance in the case of packet loss are obtained. Then, the linear unbiased estimation method is used for distributed fusion state estimation. Finally, the effectiveness of the proposed scheme is confirmed by simulation.
Cast: A Context-Aware Collaborative Storytelling Platform

The process of creating stories with others is fun and engaging for participants, but often labor-intensive. This is especially true for the central organizer, who is usually needed to provide scaffolds that guide participant contributions and enforce a cohesive story structure. To make this task easier for the organizer and participants, we propose the use of context-awareness to scaffold participant contributions in a collaborative storytelling experience. We present Cast, a platform that allows an organizer to concisely define a story script that is opportunistically deployed to users through a context-aware mobile app. Leveraging user contexts to define their roles within a story provides users with scaffolds that promote cohesive narrative development, and automating this process prevents burdening the organizer. Results from a pilot study show that creating stories with Cast is engaging yet low-effort for participants, showcasing the potential for context-awareness to make collaborative storytelling easier without sacrificing enjoyability.
Sampling techniques for Arabic Sentiment Classification

Over the last decade, the web 2.0 has been shifting the web to turn it into an opinion platform. This results in a large amount of raw data that overwhelms human capacity to extract valuable knowledge without assistance of machines. In real world applications, sentiment analysis faces imbalanced data problem. To tackle this problem, sampling techniques have been proposed. In this paper, we focus on studying the performance of these techniques on Imbalanced Data of Arabic Sentiment. We then conduct a comparative evaluation using Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF) as classification algorithms.
DIM: Adaptively Combining User Interests Mined at Different Stages Based on Deformable Interest Model

User interest mining is widely used in the fields of personalized search and personalized recommendation. Traditional methods ignore the formation of user interest which is a process that evolves over time. This leads to the inability to accurately describe the distribution of user interest. In this paper, we propose the interest tracking model (ITM). To add the timing, ITM uses Dirichlet distribution and multinomial distribution to describe the evolutional process of interest topics and frequent patterns, which well adapts to the evolution of user interest hidden in short texts between different time slices. In addition, it is well known that user interest is composed of long-term interest and situational interest including short-term interest and social hot topics. State-of-the-art methods simply regard the users’ long-term interest as the users’ final interest, which makes those unable to completely describe the user interest distribution. To solve this problem, we propose the deformable interest model (DIM) which designs an objective function to combine users’ long-term interest and situational interest and more comprehensively and accurately mine user interest. Furthermore, we present the degree of deformation which measures the subinterest's degree of influence on final interest and propose in DIM the influence real-time update mechanism. The mechanism adaptively updates the degree of deformation through the linear iteration and reduces the degree of dependence of the interest model on training sets. We present results via a dataset consisting of Flickr users and their uploaded information in three months, a dataset consisting of Twitter users and their tweets in three months, and a dataset consisting of Instagram users and their uploaded information in three months, showing that the perplexity is reduced to 0.378, the average accuracy is increased to 94%, and the average NMI is increased to 0.20, which prove better interest prediction.
Efficient, QoE aware delivery of 360° videos on VR headsets over mobile links

Virtual reality headsets have been in a great demand in the past years due to the immersive experience it can now provide to the users. An immersive experience of high quality, however, requires a high bandwidth connection. The latest generation of these headsets support wireless connectivity and rely on a battery to power the headset. With advancements in 5G technology, soon these headsets would be able to connect to the network directly. To provide a similar immersive and uninterrupted experience to the users, the headsets would need to take into account the intermittent behavior of the wireless channel.
WEB Protection Scheme Based on A Cloud Computing Platform

The management of the cloud computing platform adopts web interface, the web is open and easy to use, which makes the security problems of the cloud computing platform be increased prominently. The web security problems have a variety of attack forms, the main-stream attack methods include SQL injection, cross-site scripting (XSS), and HTTP header injection attacks, and so on. For solving these security problems, this paper designs a reason-able security protection scheme based on some defense methods. For SQL injection attack and XSS attack, the web protection scheme based on the cloud computing platform is designed and implemented in this paper, and proposes a typical defense method for the SQL injection attacks, designs the SQL injection filters and conducts the verification. For the XSS attacks, design a reasonable and feasible defense scheme to achieve the main defense functions, and study the characteristics of other web attacks, and briefly give the corresponding defense methods. This paper also designs the experimental environments, the experimental results show that the proposed protection schemes are effective to the corresponding attack methods.
Facial Expression Recognition of Animated Human Characters

Recognition of animated facial expression is an important and a fundamental task for automatically implementing analysis and evaluation of animation movie products. In this work, an improved algorithm was proposed for recognizing facial expression of animated human characters based on the Local Binary Pattern (LBP) and Support Vector Machine (SVM). The proposed method was tested on an animated facial expression database which contained human characters from top rated animations. The experimental results showed that the recognition accuracy of facial expression was significantly improved compared with other methods.