Reviewerly/exHarmony huggingface dataset:
Application of Keyword Dynamic Query Software in Relational Database based on Big Data

In order to improve the precision and recall of keywords dynamic query in relational database, a dynamic keyword query algorithm in relational database based on association characteristics data mining and semantic retrieval is proposed, and the query software is designed. In order to improve the query efficiency, reduce the time overhead of graph traverse in query process, the method of path index is used to establish the keywords dynamic query model of reverse search from the keyword node and forward search from potential root node, and extract the related characteristics of the keywords in the relational database. Through the semantic retrieval, the words, nodes, weights and Voronoi path information of the keywords are extracted, and the data graph of the keyword dynamic query is constructed according to the matching degree between the text content and the keyword in the query result, and the database optimization query is realized. Standard Linux development tools are used for query software development and design. The simulation results show that the proposed method can improve the precision and recall of the dynamic keyword query in the relational database, and the matching performance of is better and the real-time performance and accuracy of the database query are enhanced.
GF-2 Panchromatic and Multispectral Remote Sensing Image Registration Algorithm

Image registration is an important step in remote sensing image preprocessing. The accuracy, efficiency, and automatic degree of image registration will directly affect the results of subsequent image processing and analysis. Due to the slow registration speed and low registration accuracy of SIFT algorithm for GF-2 panchromatic and multispectral remote sensing images, SIFT algorithm is improved in this article to improve the efficiency of image registration algorithm. In this article, the strategy of information entropy meshes is introduced, and feature extraction is carried out only for the regions with large information entropy, so that the running time of feature extraction can be reduced. By introducing Canny operator to eliminate the unstable edge response points, the number of feature points can be further reduced, thus the computation amount of the algorithm can be also reduced. Due to the poor registration effect of SIFT algorithm for remote sensing images, a new gradient calculation method and feature description method are used in this article to enhance the robustness of feature descriptors. In the feature matching stage, this article proposes a two-layer matching strategy, which uses mutual Euclidean distance for initial matching, then uses FSC algorithm for fine matching, and finally realizes the registration of GF-2 panchromatic and multispectral remote sensing images. Experimental results show that the proposed algorithm can obtain more correct matching point pairs when the number of extracted feature points is small. Moreover, the registration accuracy and speed are both higher than the comparison algorithm. This article uses the remote sensing images of urban scenes dominated by plains and mountainous scenes with small terrain fluctuation respectively for experiments. The algorithm in this article can achieve registration accuracy better than 0.5 pixels, and in terms of time consumption, it is only about 70% of SIFT algorithm, and the computational efficiency is better than SIFT algorithm, which can meet the actual GF-2 Pan and MSI registration tasks.
An efficient algorithm for hiding sensitive-high utility itemsets

Privacy-preserving utility itemset mining is the process of hiding sensitive-high utility itemsets (SHUIs) appearing in original database such that they will not be discovered in the sanitized database. The purpose of SHUI hiding algorithm is to conceal the set of SHUIs while minimizing the side ef fects which caused by data distortion process. In this paper, a novel algorithm, named EHSHUI (An Efficient Algorithm for Hiding Sensitive-high utility Itemsets), is proposed to minimize the side effects of the sanitization process. The proposed algorithm includes three heuristic steps: (1) The transaction on which the SHUI achieves maximal utility among transactions containing it is specified as victim transaction; (2) The item that causes minimal impacts on non-SHUIs is selected as victim item; and (3) An exactly number of utility is calculated for reducing internal utility of victim item from victim transaction. This strategy exactly identifies item and transaction for data modification such that it minimizes the impacts on non-SHUIs, data distortions, and the time to access database. The experiment results illustrate that the proposed algorithm achieves higher performance and lower side effects than the state-of-the-art.
Activity Planning for Assistive Robots Using Chance-Constrained Stochastic Programming

In this article, we present a framework for planning an activity to be executed with the support of a robotic navigation assistant. The two main components are the activity and the motion planner. The activity planner composes a sequence of abstract activities, chosen from a given set, to synthesize a plan. Each activity is associated with a point of interest in the environment and with probabilistic parameters that depend on the plan, which are characterized by simulations in realistic scenarios. The low-level action to pass from an activity to the next is handled by the motion planner, which secures the physical feasibility of the chosen actions and their compatibility with the constraints posed by the user and the environment. Indeed, the final plan must respect the user constraints and optimise his/her satisfaction from the activity. We show a possible model for the problem as a chance constrained optimization along with an efficient technique to find high-quality solutions.
Explicit Domain Adaptation With Loosely Coupled Samples

Transfer learning is an important field of machine learning in general, and particularly in the context of fully autonomous driving, which needs to be solved simultaneously for many different domains, such as changing weather conditions and country-specific driving behaviors. Traditional transfer learning methods often focus on image data and are black-box models. In this work we propose a transfer learning framework, core of which is learning an explicit mapping between domains. Due to its interpretability, this is beneficial for safety-critical applications, like autonomous driving. We show its general applicability by considering image classification problems and then move on to time-series data, particularly predicting lane changes. In our evaluation we adapt a pre-trained model to a dataset exhibiting different driving and sensory characteristics.
Covid-19 Through the Lens of the Peer-Reviewed Literature

What can we learn about the coronavirus pandemic from text-mining the titles of journal papers? By Samuel R. Neal and Cindy Zheng.
Optimal Control of Wheeled Mobile Robots: From Simulation to Real World

We study the problem of taking simulations to the real world (RW) for autonomous robotic systems with dynamic uncertainties and unknown disturbances while maintaining the optimal performance and stability of the designed controller designed in simulation. In general, an optimal and robust controller that is designed through simulation often does not perform similarly when deployed in the RW. We focus on using simulations to generate an optimal control policy utilizing the Memetic algorithm (MA) iteratively. The simulation-to-RW performance and stability are realized by using an adaptive fuzzy system to learn the uncertain part of the dynamic model, disturbance and noises. We demonstrate experimentally that this method permits the development of optimal control design in simulations and integrates adaptive learning rules to enable precise and repetitive trajectory tracking for the wheeled mobile robot (WMR) with disturbances and uncertainties.
Federated CF: Privacy-Preserving Collaborative Filtering Cross Multiple Datasets

In the era of information exploration, collaborative filtering algorithms have been widely adopted to offer useful contents according the different preferences of the users. Unfortunately, due to the sparsity of the original rating data, different data owners are highly motivated to collaborate with each other to guarantee the prediction accuracy via CF algorithms. However, as the original rating data contains sensitive information of the users, strict privacy preserving requirements might hinder the collaboration of different data owners. Although there exist some works to address the privacy issues in CF, they do not consider the cases where the CF algorithms are executed based the integration of multiple datasets. Thus in this paper, we propose a privacy-preserving multiparty scheme for collaborative filtering, using mixed MPC protocols combined with Yao garbled circuit and additive secret sharing. And the accuracy and efficiency of the proposed scheme is verified under the real dataset.
Inferring Quality of Experience for Adaptive Video Streaming over HTTPS and QUIC

Nowadays, Internet traffic encryption is rapidly increasing due to privacy and security concerns. This is because of the massive usage of end-to-end security protocols over Internet such as HTTPS and QUIC. The encryption trend will continue to rapidly increase in the future, and this trend concerns video streaming applications as well. Network providers face a serious challenge in managing their networks due to such widespread deployment of end-to-end security protocols. These operators need to have a clear visibility into traffic on their networks to monitor and manage both quality of experience (QoE)-and-service (QoS) impairments in popular video streaming services, in the most effective and efficient manner. Moreover, so many factors that influence QoE need to be taken care of to get an acceptable user experience. Most of the existing solutions use the deep packet inspection to infer these factors from the encrypted traffic. However, these solutions are inefficient, most of the time, leading to low QoE inference accuracy. To bridge this gap, we propose a machine-learning based solution that leverages a random forest classifier for a better QoE inference accuracy. The proposed solution uses network-and-transport layer information to infer QoE factors such as startup delay and stall events. It helps the network providers to react quickly and in real time for any impairments in the QoE of the encrypted video traffic. We evaluate our solution using an HTTP adaptive streaming service (YouTube) that uses HTTPS and QUIC protocols. Our experimental results show that our solution achieves up to 91.1% classification accuracy for HTTPS and up to 87.3% for QUIC.
The Deep Quality-Value Family of Deep Reinforcement Learning Algorithms

We present a novel approach for learning an approximation of the optimal state-action value function (Q) in model-free Deep Reinforcement Learning (DRL). We propose to learn this approximation while simultaneously learning an approximation of the state-value function (V). We introduce two new DRL algorithms, called DQV-Learning and DQV-Max Learning, which follow this specific learning dynamic. In short, both algorithms use two neural networks for separately learning the V function and the Q function. We validate the effectiveness of this training scheme by thoroughly comparing our algorithms to DRL methods which only learn an approximation of the Q function, namely DQN and DDQN. Our results show that DQV and DQV-Max present several important benefits: they converge significantly faster, can achieve super-human performance on DRL testbeds on which DQN and DDQN failed to do so, and suffer less from the overestimation bias of the Q function.
Siamese-Based BiLSTM Network for Scratch Source Code Similarity Measuring

As a popular block-based programming language, Scratch attracts considerable attention in society and educational fields. Code similarity measuring is a major research direction in Scratch, which plays a significant role in clone detection and project recommendation. However, there are few studies focusing on it. In this paper, we propose a Siamese-Based bidirectional Long Short-Term Memory (BiLSTM) network to solve this problem. Specifically, a token-based code representation scheme is designed to abstract the blocks in Scratch. Then the obtained token stream is fed to a word embedding model for training. Next, we devise an improved Siamese-based BiLSTM model to measure the source code similarity. Finally, in order to evaluate the performance of proposed model, we construct a dataset from Scratch official website. The results show that it achieves more than 90% accuracy and recall. In addition, the proposed model is applied in the code cluster task, and reaches 95% accuracy.
RSA-CP-IDABE: A Secure Framework for Multi-User and Multi-Owner Cloud Environment

Cloud has become one of the most widely used technologies to store data due to its availability, flexibility, and low cost. At the same time, the security, integrity, and privacy of data that needs to be stored on the cloud is the primary threat for cloud deployment. However, the increase in cloud utilization often results in the creation of a multi-user cloud environment, which requires its owners to manage and monitor the data more effectively. The security of information faces an additional threat, which is related to the increasing number of users and owners who deal with the data stored on the cloud. Many researchers have developed several frameworks and algorithms to address the security issues of the cloud environment. In the present work, a novel algorithm is proposed with the integration of Ciphertext Policy-Identity Attribute-based Encryption (CP-IDABE) and the Rivest–Shamir–Adelman (RSA) algorithm for securing the cloud. Both the owners and users are provided with the public and distinct secret keys that are generated by the Automated Certificate Authority (ACA). The attribute policy differentiates between the user and owner for accessing the cloud data. The proposed RSA-CP-IDABE algorithm also prevents the Man in the Middle (MITM) attack effectively. The performance of the proposed algorithm is evaluated for its time used for encryption, decryption, and execution for varying sizes of data. The obtained results are compared with the existing framework to show its effectiveness. The proposed algorithm can be enhanced with the revocation of privileges in the future.
Formal Verification of Swerving Maneuvers for Car Collision Avoidance

Many road vehicle accidents are the result of collisions with foreign objects, and automatic collision avoidance is of critical interest to car manufacturers and their customers. Previous work on formally verifying collision avoidance maneuvers typically assumes point-shaped or circular-shaped vehicles for simplicity. In this paper, we formulate and formally verify sufficient conditions for the safety of a representative collision avoidance system for cars with a realistic geometrical shape. The collision avoidance system discussed here is designed to issue swerving advisories. We model the vehicle kinematics and control advisory as a hybrid program, allowing to model both discrete decisions of the system and continuous dynamics of the car. We formally verify the collision avoidance system by providing rigorous, computer-checked mathematical proofs of collision avoidance under well-defined, explicit sufficient conditions on vehicle kinematics and parameters. This formal verification provides a mathematical guarantee that the collision avoidance system can prevent the vehicle from collision under all possible scenarios as long as certain conditions hold true.We model the system using differential dynamic logic dL and use the automated theorem prover KeYmaera X for formal verification. This work employs a purely symbolic model, and can thus be extended to verify other types of collision avoidance systems exhibiting richer behavior.
An interactive box based on fuzzy logic, mobile apps, and light indicators to stimulate the visuomotor, visual perception and cognition skills in children from 3 to 6 years

To achieve a successful transition to formal schooling, children must develop a set of specific skills. These skills are closely related to three fundamental processes: motor coordination, executive function, and visuospatial abilities. Several studies have shown that visuomotor integration and mathematics exhibits ongoing reciprocity in kindergarten and first-grade children. For these reasons, in this paper, we describe an interactive box that uses fuzzy logic, mobile apps, and light indicators to stimulate the acquisition of these skills in children from 3 to 5 years. The interactive box was tested with 45 children with ages between 4 and 6 years. The results show that most of the children are totally agree with the box in different evaluation criteria.
Topological data analysis in digital marketing

Abstract The ubiquitous internet is a multipurpose platform for finding information, an avenue for social interaction, and a primary customer touch‐point as a marketplace to conduct e‐commerce. The digital footprints of browsers are a rich source of data to drive sales. We use clickstreams (clicks) to track the evolution of session‐level customer browsing for modeling. We apply Markov chains (MC) to calculate probabilities of page‐level transitions from which relevant topological features ( persistence diagrams ) are extracted to determine optimal points (URL pages) for marketing intervention. We use topological summaries ( silhouettes, landscapes ) to distinguish the buyers and nonbuyers to determine the likelihood of conversion of active user sessions. Separately, we model browsing patterns via Markov chain theory to predict users' propensity to buy within a session. Extensive analysis of data applied to a large commercial website demonstrates that the proposed approaches are useful predictors of user behavior and intent. Utilizing computational topology in digital marketing holds tremendous promise. We demonstrate the utility of topological data analysis combined with MC and present its merits and disadvantages.
Maximizing Risk-aware Last-Minute Inter-Datacenter Backup with Progressive Disasters

Network failure caused by disasters results in communication disruption and huge data loss in backbone Data Center (DC) networks. To minimize such losses, in this paper, we consider a risk-aware dynamic backup model that significantly reduces data-loss in a time-varying network hit by a progressive disaster. We propose a mixed-integer linear program (MILP) for risk-aware backup maximization over a dynamic network hit by a progressive disaster. Further, we perform extensive numerical simulations to show that our proposed MILP performs significantly well compared to existing algorithms which only address emergency backup in static networks hit by stationary disasters.
Peruvian undergraduate students’ usage of digital technology in academic context

This paper presents the results of an interview of first-year university students at a private face-to-face university in Lima city on how they use digital technologies in their social and academic lives. This study was positioned in the interpretive paradigm with qualitative research design. Semi-structured interview with 13 open-ended questions was selected as the means of data. Atlas.ti. were used to analysed and coded all the interviews. To provide a well-structured approach to handling a large data set, thematic analysis was employed to analyse the verbatim transcription as outlined by Braun and Clarke. Contrary to the assumption that young learners are seen as highly adept technology users, the findings show that social networks (Facebook and Instagram) and WhatsApp are the most important applications for the participants.
Exchangeable Input Representations for Reinforcement Learning

Poor sample efficiency is a major limitation of deep reinforcement learning in many domains. This work presents an attention-based method to project neural network inputs into an efficient representation space that is invariant under changes to input ordering. We show that our proposed representation results in an input space that is a factor of m! smaller for inputs of m objects. We also show that our method is able to represent inputs over variable numbers of objects. Our experiments demonstrate improvements in sample efficiency for policy gradient methods on a variety of tasks. We show that our representation allows us to solve problems that are otherwise intractable when using naïve approaches.
Conjunction Model Design for Intermittent Streamflow Forecasts: Extreme Learning Machine with Discrete Wavelet Transform

Forecasting intermittent streamflow is essential for water management and water quality, planning of water supply, hydropower and irrigation systems. This chapter proposes a novel intelligent data analytic method using extreme learning machines combined with discrete wavelet transform (ELM-DWT) to forecast intermittent streamflow. Daily streamflow data from Uzunkopru and Babaeski, Turkey are utilized. Model input combinations involving antecedent flow are considered. The results are compared with artificial neural network combined with discrete wavelet transform (ANN-DWT) and single ELM and ANN methods. Optimal input combinations are decided utilizing auto-correlation and partial auto-correlations. Models are evaluated based on the three measures, root mean square errors (RMSE), Nash-Sutcliffe efficiency (NSE) and coefficient of correlation (R). In both stations, the optimal ELM-DWT model with five previous flow values as inputs provided the best accuracy compared to other corresponding models. DWT considerably increased the accuracy of single models in forecasting intermittent streamflow of both stations. The RMSE of the best ELM and ANN models were decreased by 38 (61)% and 59 (69)% using the proposed model (ELM-DWT) for the Uzunkopru (Babaeski) stations, respectively. The chapter demonstrates the importance of conjunction model design for intermittent streamflow forecasting with extreme learning machines and discrete wavelet transform as potential intelligent modeling methods for natural hazard and other risk mitigation tools.
Creating a Virtual Reality Lab

Use of virtual reality (VR) has increased in higher education in recent years and is projected to continue to increase. At the same time, there is a growing emphasis for institutions of higher education to re-envision learning spaces and teaching strategies that are student-centered rather than faculty centered. Use of VR, by faculty, requires a new pedagogy of teaching as well as a willingness to explore the use of an unknown technology in delivering curriculum. Having access to a technology lab that uses VR can be a welcomed support for faculty. VR can be expensive and creating a VR lab may not seem doable in settings other than large, well-funded universities. This chapter describes a technology lab that was established at a small Midwest liberal arts university, funded by a student technology fee and created by a student-led technology committee.