Reviewerly/exHarmony huggingface dataset:
Preserving Privacy of Data in Distributed Systems Using Homomorphic Encryption

Distributed systems like cloud platforms are being used widely in recent times. However, such platforms face a major issue of data storage on cloud in terms of security. If data on the cloud is not encrypted, it can be accessed by unauthorized members. This violates the confidentiality of the data. If data is stored in an encrypted format, every authorized party member will have to decrypt the data first in order to perform operations on it and then encrypt it back to upload it on the distributed platform. This has to be done every time, and every member performs operations on data. Needles to say, it complicates the entire procedure of sharing data and a lot of time is wasted in encrypting and decrypting data even for small operations like searching and sorting. To avoid these complications, this paper suggests a way to store data on cloud by homomorphically encrypting it. Homomorphic encryption allows user to compute on encrypted data without the need of decrypting it. In this paper, elliptic curve cryptography (ECC) is used for homomorphic encryption of data. The size of the ciphertext generated by ECC is smaller than the ciphertext generated by other encryption schemes. As cloud is mostly used for storing databases, this paper further employs searching and sorting techniques on the encrypted data.
Industrial solution of film and television art based on game engine under artificial intelligence

With the rapid development of computer technology, higher requirements are put forward for the design of game software. As a typical example, artificial intelligence technology is one of the main technologies in the development and application of game software in recent years, which can meet the requirements of realtime characteristics and bring more rich experience for game players. This paper mainly discusses the application of AI technology from game engine, character movement and path planning.
Negotiating borders through feminisms

column Share on Negotiating borders through feminisms Author: Maryam Mustafa Lahore University of Management Sciences Lahore University of Management SciencesView Profile Authors Info & Claims InteractionsVolume 27Issue 6November - December 2020 pp 49–51https://doi.org/10.1145/3427099Published:02 November 2020Publication History 1citation623DownloadsMetricsTotal Citations1Total Downloads623Last 12 Months133Last 6 weeks11 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A least square matching optimization method of low altitude remote sensing images based on self-adaptive patch

Abstract This paper presents a novel matching optimization method based on self-adaptive template. The proposed method is designed to be effective for enhancing the accuracy of stereo matching. In order to improve the similarity of the initial matching windows and fully exploit the pixels around the corresponding image points, a self-adaptive patch is introduced instead of a constant patch. Then, an error equation is built to compute the optimal point according to space geometry relationship and epipolar line constraint. At last, a least square adjustment method is used to calculate the coordinate of the corresponding 3D point in the Object Space Coordinate System. Comparison studies and experimental results prove the high accuracy of the proposed algorithm in low-altitude remote sensing image point cloud optimization.
Improvements of Computer Assisted Virtual Environment (CAVE)

Virtual environment has become a common part of our everyday lives. From the dashboard projected into the car windshield to smartphones, gaming industry or even training courses. Pushing the limits of virtual reality further has long history and has escalated into the creation of virtual environment called CAVE. This paper presents creation of custom-made solution of computer assisted virtual environment - CAVE. Usage is supported with specific case studies and considers future challenges of this technology.
Unsupervised Monocular Depth Learning with Integrated Intrinsics and Spatio-Temporal Constraints

Monocular depth inference has gained tremendous attention from researchers in recent years and remains as a promising replacement for expensive time-of-flight sensors, but issues with scale acquisition and implementation overhead still plague these systems. To this end, this work presents an unsupervised learning framework that is able to predict at-scale depth maps and egomotion, in addition to camera intrinsics, from a sequence of monocular images via a single network. Our method incorporates both spatial and temporal geometric constraints to resolve depth and pose scale factors, which are enforced within the supervisory reconstruction loss functions at training time. Only unlabeled stereo sequences are required for training the weights of our single-network architecture, which reduces overall implementation overhead as compared to previous methods. Our results demonstrate strong performance when compared to the current state-of-the-art on multiple sequences of the KITTI driving dataset and can provide faster training times with its reduced network complexity.
Research on Application of Computer Vision Assist Technology in High-precision UAV Navigation and Positioning

Traditional UAV human-computer interaction requires specialized equipment and professional training. Convenient and novel interaction methods are often more popular. Using ordinary cameras, the UAV gesture control system based on computer vision and deep learning is studied. Under this background, the paper proposes a new method for the navigation information extraction of UAV autonomous landing terminal based on monocular vision/INS integrated navigation; it only needs to detect two feature points on the runway and then combine the attitude of the inertial navigation system Angle information, you can directly calculate the position vector of the aircraft relative to the airport; because the straight line has obvious observability, for the feature points that are difficult to extract, you can detect the two runway edge lines and a landing threshold line, and then take the intersection of the threshold line and the edge of the runway is used as the feature point; the algorithm is deduced in detail based on the geometric principle of imaging. Finally, the landing experiment shows that the extraction and conversion of the method is feasible.
Online Mathematics Education Outreach: Designing for Global, Open Access

In support of the mission to ‘increase interest, enjoyment, confidence, and ability in mathematics and computer science among learners and educators in Canada and internationally’, the University of Waterloo’s Centre for Education in Mathematics and Computing (CEMC), working in collaboration with local education technology provider DigitalEd, made its mathematics content globally accessible to serve as an online complement to mathematics educators, in addition to providing a source of online resources for independent study and upskilling of non-registered learnersIn September 2019, the full library of resources was launched and promoted globally, grouped into courses based on the topics covered in the Canadian curriculum for grades 7 to 12, along with special topics in financial literacy. As of April 2020, the resources had been accessed by over 250,000 users across 213 countries, with over 1.7M exercises attempted. Peak usage saw over 300,000 pageviews in a day, reaching over 40,000 unique users across 86 countries, with over 99.9% uptime, utilizing dynamically scaled cloud instances to handle unexpected loads. Continuous system monitoring was employed as a supplement to automated alerts and reliability engineering tools in order to maintain system performance and uptimeThis paper provides an overview of the considerations and principles followed in the planning and design of mathematics learning resources, structured as year-long courses in secondary school mathematics, and provides a summary of the requirements for the infrastructure put in place to enable continuous, scalable, global open access. It concludes with an overview of recommendations for localization as a means to increase adoption in regions where English is not the primary educational language.
Continuous Summative Assessment Sessions as a Motivational Tool for STEM students: a Case Study

While using a classical approach to summative assessment that was based on mid-term exams, it was observed that overall results for each consecutive generation of STEM students at the end of the semester failed to improve. To promote a deep learning approach among STEM students, that can be achieved through a continuous approach to learning, activities for continuous summative assessment were introduced. Students have been given the opportunity to take the test for each of the parts of the course twice in two week period with the best of the two results used for grading. A comparison of the obtained results and results of the previous generations of students was made and it showed that students that participated in the experiment achieved significantly better overall results and understanding of the course materials at the end of the semester. In order to expand the experiment into the online environment, a model for an Education Recommender System with assessment capabilities for STEM education was proposed. Based on the proposed model of the system, the prototype of the system was built, and online summative assessment sessions based on the offline experiment were designed for the next cycle of the research.
Turn-Level Recurrence Self-attention for Joint Dialogue Action Prediction and Response Generation

In task-oriented dialogue systems, semantically controlled natural language generation is the procedure to generate responses based on current context information. Seq2seq models are widely used to generate dialogue responses and achieve favorable performance. Nevertheless, how to effectively obtain the dialogue’s key information from history remains to be a critical problem. To overcome this problem, we propose a Turn-level Recurrence Self-Attention (TRSA) encoder, which effectively obtains progressive structural relationship in turn-level from conversation history. Moreover, we propose a novel model to predict dialogue actions and generate dialogue responses jointly, which is different from the separate training model used in previous studies. Experiments demonstrate that our model alleviates the problem of inaccurate attention in dialogue history and improves the degree of dialogue completion significantly. In the large-scale MultiWOZ dataset, we improve the performance by 3.9% of inform rate and 3.4% of success rate, which is significantly higher than the state-of-the-art.
Determining Sentiment of Tweets Using First Bosnian Lexicon and (AnA)-Affirmative and Non-affirmative Words

The paper presents a model for sentiment tagging of Twitter messages based on (AnA)—affirmative and non-affirmative words and tagged sentiment dictionary/lexicon. Lexicon was used in combination with intensifiers, where intensifier (AnAword), that stand next to the word, produce more powerful (more positive or more negative) sentiment value of word—or the whole tweet. The result of the study was creating the first Bosnian sentiment lexicon that can be used for sentiment analysis of the Bosnian language. Besides that, the paper presents a more powerful way to measure sentimental value by using AnAwords. By using confusion matrix model’s overall accuracy was counted for both positive and negative sentiment value. Good overall evaluation of model was presented with huge values 0.9075 and 0.9004 for positive and negative sentiment value, respectively.
Performance Analysis of Neo4j and MySQL Databases using Public Policies Decision Making Data

Currently, the development of data has increased rapidly, Solutions are needed to be able to manage data efficiently, one that can be offered is to utilize the database. The biggest decision in selecting a database is to select between SQL or NoSQL. MySQL is a database that uses SQL as a query language, consists of tables that store data in the form of columns and rows, then the new format database NoSQL, appeared, it is suitable for handling large amounts of data in a variety of formats. Neo4j is one of NoSQL that is widely used, it is a graph database which provides an easy way to visualize data by storing data in the form of nodes that are connected by edges. In this paper, we compared the performance of MySQL and Neo4j databases in terms of memory usage and execution time, also we presented the flexibility of the databases using P. The results show that MySQL has a faster execution time than Neo4j, although, both these databases have the same time complexity. It is also known that Neo4j has a higher memory usage than MySQL. But Neo4j has better flexibility than MySQL.
Information Hiding Techniques for Cryptography and Steganography

Cyber-attack is a category of attack that uses malicious code for intentionally altering the computer data, exploiting computer systems and networks, which might result in disruptive ramification and even compromise the data. Cyber-attack can impel to cybercrimes like information and identity theft. In computing context, the term security means cyber security. The main aim of cyber security is to protect the systems information, communication channels and sending information from unauthorized users. With the increasing volume and sophistication of cyber-attacks, user’s personal information must be protected. This paper presents a review of various data hiding techniques that are used to provide security to the information that is being shared over unsecure channel.
A Comparative Study of Four Genetic Algorithm-Based Crossover Operators for Solving Travelling Salesman Problem

Crossover operators have a significant effect on the performance of genetic algorithm (GA). In this chapter, we present a comparative study among four genetic algorithm-based crossover operators which are random cross mapping crossover (RCMX), edge recombination crossover (ERX), partially mapped crossover (PMX), and modified cycle crossover (CX2) in the context of travelling salesman problem (TSP). This study explores the effectiveness of these four crossover operators by applying them on ten different datasets.
Designing for Play and Appropriation in Museum Experiences involving Tangible Interactions and Digital Technologies

GLAM institutions have increasingly incorporated visitor experiences into their space as a way to position themselves in the "experience economy", and approach the ideals of "immersive heritage" and sensory museuology. The ludic turn - i.e. the introduction of play in the GLAM space - has been identified as one of the key turns that assists GLAM institutions to move towards those ideals. However, play, and the appropriation it bring in the space, can be disruptive. Following a research-through-design method, the focus of this Ph.D. research is to explore how to design experiences that use tangible interactions and digital technologies that facilitate play and appropriation in the GLAM space, along with providing insights on ways that this appropriation can be used to redesign the experience. To explore that topic, an empirical study along with four design experiments are conducted in the space of the Danish Architecture Centre.
An Ontological Analysis of the Notion of Treatment

Treatments are entities of central importance in many practices and applications, including both medical and technical ones. Treatments exhibit a number of intricate characteristics that give rise to practical as well as theoretical modelling challenges. One issue is that treatments can be viewed as endurants as well as events, the latter ones being temporally extended and having both a completed life history and a planned life for their future. There is also a normative relationship between a treatment viewed as an endurant and its life history. Other issues include various abstraction levels of treatments and their divisions into subtreatments. We address these issues by proposing an ontologically grounded modelling pattern of treatments based on an ontological analysis of the event notion in UFO. We also use this analysis to suggest visualizations of treatments.
A Novel Reinforcement-Learning-Based Approach to Scientific Workflow Scheduling

Recently, the Cloud Computing paradigm is becoming increasingly popular in supporting large-scale and complex workflow applications. The workflow scheduling problem, which refers to finding the most suitable resource for each task of the workflow to meet user defined quality of service (QoS), attracts considerable research attention. Multi-objective optimization algorithms in workflow scheduling have many limitations, e.g., the encoding schemes in most existing heuristic-based scheduling algorithms require prior experts' knowledge and thus they can be ineffective when scheduling workflows upon dynamic cloud infrastructures with real-time. To address this problem, we propose a novel Reinforcement-Learning-Based algorithm to multi-workflow scheduling over IaaS clouds. The proposed algorithm aims at optimizing make-span and dwell time and is to achieve a unique set of correlated equilibrium solution. In the experiment, our algorithm is evaluated for famous scientific workflow templates and real-world industrial IaaS cloud platforms by a simulation process and we compare our algorithm to the current state-of-the-art heuristic algorithms, e.g., NSGA-II, MOPSO, GTBGA. The result shows that our algorithm performs better than compared algorithm.
Research on Adaptive Comprehensive Learning Artificial Bee Colony Algorithm and Its Application in Constant Pressure Water Supply

Abstract The constant pressure water supply control system is nonlinear and hysteretic, which makes the control problem difficult to solve. In order to achieve accurate closed-loop control of water supply pressure, this paper designs ACLABC-PID controller based on improved Artificial Bee Colony algorithm. First of all, in view of the slow convergence speed and easy to fall into the local optimum of the ABC algorithm, an adaptive comprehensive learning strategy of the ABC algorithm (ACLABC) is proposed. The algorithm adopts the learning strategy of learning from other excellent individuals and global optimal individuals, enriches the diversity of the population, adaptively and gradually reduces the search area, and effectively eliminates the defects of easily falling into local extremum and slow convergence in the later stage. ACLABC algorithm takes both local development and global exploration into account, which improves the convergence speed and accuracy of the algorithm. Then, the ACLABC algorithm is applied to the PID control parameter optimization of constant pressure water supply, and compared with the ABC-PID controller and the critical proportion method PID controller, the simulation results show that the ACLABC-PID controller significantly improves the dynamic and steady performance of the system.
Data association framework based on biconnected gated recurrent unit network for multiple object tracking

In the tracking-by-detection scheme of multiple object tracking (MOT), the data association process in which existing tracking data and new detections are matched over time is very important. A framework is proposed to solve the data association problem in MOT in scenarios where there are potential target interactions and occlusions in crowded environments. This framework consists of an input layer and an association layer. The input layer is an end-to-end feature-map extraction model that incorporates a simplified Siamese convolutional neural network, which effectively distinguishes similar objects based on their appearance and motion. The association layer is composed of a bidirectional gated recurrent unit network with three layers of fully connected networks (FCNs), whose outputs are fed into the FCNs and transformed into an association matrix that reflects the matching scores between the detections and existing tracks. The matrix is then used to minimize the loss of the framework. The experimental results show that the proposed framework demonstrates outstanding performance for MOT, with its accuracy and precision in MOT reaching values as high as 26.1% and 71.2%, respectively.
A Novel Approach for Ensemble Feature Selection Using Clustering with Automatic Threshold

Feature Selection (FS) is the core part of data processing pipeline. Use of ensemble in FS is a relatively new approach aiming at producing more diversity in feature dataset, which provides better performance as well as more robust and accurate result. An aggregation step combined the output of each FS method and generate the Single feature Subset. In this paper, a novel ensemble method for FS “EFSCAT” is proposed which rank all the features and then cluster the most related features. To reduce the size of ranking an automatic threshold in every ranker is being introduced. This added thresholding step will improve the computational efficiency because it cutoff low-ranking features which were initially ranked by Ranker. Mean-shift clustering is then use to combined the results of each ranker. The process of aggregation will become very time efficient. “EFSCAT” will make the classification more robust and stable.
Study of Cost-Sensitive Learning Methods on Imbalanced Datasets

Imbalanced data having skewed class distribution is very common in many real-world applications. The imbalance class problem affects the performance of the system. In this paper, cost-sensitive learning approaches are used with three different types of datasets which are yeast 4, yeast 5, and yeast 6 to deal with the class-imbalanced problem. The performance metrics accuracy, TPR, TNR, and G-Mean are taken as evaluation measures of the learning classifiers. Boosting ensemble technique is an attractive method to tackle the class imbalance problem where the rare instances are the class of interest. Cost-sensitive boosting with improved C5.0/See5 as base classifier (CSAdaC2IC5.0) is used to solve the imbalance problem. Finally, the proposed method has given the best values for the training datasets using the performance metrics that are accuracy, TPR, and G-Mean.
Forensic analysis of Windows 10 Sandbox

With each Windows operating system Microsoft introduces new features to its users. Newly added features present a challenge to digital forensics examiners as they are not analyzed or tested enough. One of the latest features, introduced in Windows 10 version 1909 is Windows Sandbox; a lightweight, temporary, environment for running untrusted applications. Because of the temporary nature of the Sandbox and insufficient documentation, digital forensic examiners are facing new challenges when examining this newly added feature which can be used to hide different illegal activities. Throughout this paper, the focus will be on analyzing different Windows artifacts and event logs, with various tools, left behind as a result of the user interaction with the Sandbox feature on a clear virtual environment. Additionally, the setup of testing environment will be explained, the results of testing and interpretation of the findings will be presented, as well as open-source tools used for the analysis.