Reviewerly/exHarmony huggingface dataset:
The GR2 Algorithm for Subgraph Isomorphism. A Study from Parallelism to Quantum Computing

In this paper is presented the GR2 Algorithm in the context of a study that encompassed elements of Parallel Programming and pruning techniques. Also there were executed circuits having 5, 10 and 15 qubits on quantum computers. For these circuits were used classical and quantum gates along with oracles and Grover’s Algorithm. The original contribution consists in highlighting the importance of Parallel Programming in classiscal computing as well as in quantum computing.
Estimation and Adaption of Indoor Ego Airflow Disturbance with Application to Quadrotor Trajectory Planning

It is ubiquitously accepted that during the autonomous navigation of the quadrotors, one of the most widely adopted unmanned aerial vehicles (UAVs), safety always has the highest priority. However, it is observed that the ego airflow disturbance can be a significant adverse factor during flights, causing potential safety issues, especially in narrow and confined indoor environments. Therefore, we propose a novel method to estimate and adapt indoor ego airflow disturbance of quadrotors, meanwhile applying it to trajectory planning. Firstly, the hover experiments for different quadrotors are conducted against the proximity effects. Then with the collected acceleration variance, the disturbances are modeled for the quadrotors according to the proposed formulation. The disturbance model is also verified under hover conditions in different reconstructed complex environments. Furthermore, the approximation of Hamilton-Jacobi reachability analysis is performed according to the estimated disturbances to facilitate the safe trajectory planning, which consists of kinodynamic path search as well as B-spline trajectory optimization. The whole planning framework is validated on multiple quadrotor platforms in different indoor environments.
UFuzzer: Lightweight Detection of PHP-Based Unrestricted File Upload Vulnerabilities Via Static-Fuzzing Co-Analysis

Unrestricted file upload vulnerabilities enable attackers to upload malicious scripts to a web server for later execution. We have built a system, namely UFuzzer, to effectively and automatically detect such vulnerabilities in PHP-based server-side web programs. Different from existing detection methods that use either static program analysis or fuzzing, UFuzzer integrates both (i.e., static-fuzzing co-analysis). Specifically, it leverages static program analysis to generate executable code templates that compactly and effectively summarize the vulnerability-relevant semantics of a server-side web application. UFuzzer then “fuzzes” these templates in a local, native PHP runtime environment for vulnerability detection. Compared to static-analysis-based methods, UFuzzer preserves the semantics of an analyzed program more effectively, resulting in higher detection performance. Different from fuzzing-based methods, UFuzzer exercises each generated code template locally, thereby reducing the analysis overhead and meanwhile eliminating the need of operating web services. Experiments using real-world data have demonstrated that UFuzzer outperforms existing methods in either efficiency, or accuracy, or both. In addition, it has detected 31 unknown vulnerable PHP scripts including 5 CVEs.
Pixel-Level Face Image Quality Assessment for Explainable Face Recognition

In this work, we introduce the concept of pixel-level face image quality that determines the utility of single pixels in a face image for recognition.We propose a training-free approach to assess the pixel-level qualities of a face image given an arbitrary face recognition network.To achieve this, a model-specific quality value of the input image is estimated and used to build a sample-specific quality regression model.Based on this model, quality-based gradients are back-propagated and co•nverted into pixel-level quality estimates.In the experiments, we qualitatively and quantitatively investigated the meaningfulness of our proposed pixel-level qualities based on real and artificial disturbances and by comparing the explanation maps on faces incompliant with the ICAO standards.In all scenarios, the results demonstrate that the proposed solution produces meaningful pixel-level qualities enhancing the interpretability of the face image and its quality.The code is publicly available.
Speed Planning for Autonomous Vehicles at Intersections

This paper studies the problem of speed planning for autonomous vehicles at intersections, which is of vital importance to the safety and efficiency of autonomous vehicles. Firstly, we formulate the problem in s -t graph. Then we design a numerical calculation method of speed planning with the help of s − t and v − t graph. With the subtle modification on Rapidly-exploring Random Tree (RRT) method, approximate optimal solution is obtained by exploring s − t space. We evaluate the proposed method with randomly generated scenarios, and the simulation results show that the autonomous vehicle controlled by the proposed method passes all of the test scenarios safely. Our main contributions are as follows: (1) RRT is modified to explore in s − t graph for speed planning. (2) The proposed st-RRT method could be carried out over 130 times per second. (3) The solution of the proposed method could reach approximately optimal concerning pass times, and even global optimal in some specific scenarios.
CUSTOM: Aspect-Oriented Product Summarization for E-Commerce

Product summarization aims to automatically generate product descriptions, which is of great commercial potential. Considering the customer preferences on different product aspects, it would benefit from generating aspect-oriented customized summaries. However, conventional systems typically focus on providing general product summaries, which may miss the opportunity to match products with customer interests. To address the problem, we propose CUSTOM, aspect-oriented product summarization for e-commerce, which generates diverse and controllable summaries towards different product aspects. To support the study of CUSTOM and further this line of research, we construct two Chinese datasets, i.e., SMARTPHONE and COMPUTER, including 76,279/49,280 short summaries for 12,118/11,497 real-world commercial products, respectively. Furthermore, we introduce EXT, an extraction-enhanced generation framework for CUSTOM, where two famous sequence-to-sequence models are implemented in this paper. We conduct extensive experiments on the two proposed datasets for CUSTOM and show results of two famous baseline models and EXT, which indicates that EXT can generate diverse, high-quality, and consistent summaries (https://github.com/JD-AI-Research-NLP/CUSTOM).
Integrating student and teacher insights to a school-based alcohol program through co-design

School-based programs may be more effective when an array of stakeholders, including users, are involved during the relevant stages of program co-creation-specifically during key development stages such as design, implementation and evaluation. How such programs can be operationally co-created and tested is less known and is therefore the purpose of this article. Two sequential co-design phases underpin this study. First, a co-design session with 20 health and physical education (HPE) teachers focussed on reviewing, testing and critically discussing initial prototype online modules for an alcohol education program. Teacher insights were assessed and incorporated, and the updated online modules were tested with secondary school students (n = 120) capturing their experience in a classroom setting. Insights from each group were analysed using thematic analysis. Teachers and students serve important roles in program co-creation. Teachers remain an underutilized stakeholder group whom are however critical in delivering important insights to enhance educational program design. Teachers demanded more relevance to the national curriculum, further alignment with curriculum assessment standards, and age appropriate content. Student feedback focussed largely on realistic content, personalization and gamification elements. This study represents an application of the seven-step co-design process and advances understanding of the 'fuzzy back-end' of the process, namely reflecting on feasibility of integrating teacher and student feedback and ideas. More specifically, how these key-yet distinct-stakeholder groups can be involved, and the merits of their involvement are discussed.There is much research existing for designing change programs across many contexts. Commonly, involving consumers and other related people in the design of these programs is recommended. However less is known about who to involve in the design of programs, what they can be expected to tell you when involved, and when in the design they should be involved. This study explores these questions when involving teachers and students in the design of a school based online education program. Focussing on alcohol education and moderation strategies, the findings of this study support that different stakeholders contribute their own unique insights, whether based on personal bias or based on the nature of their involvement as stakeholders (e.g. as teachers, as parents, as loyal consumers etc.). Furthermore, this study highlights the need for research to distinguish which recommendations to adopt between students and teachers, in a manner generalizable to other contexts and stakeholder groups. From the findings of the current study, the authors recommend multi-disciplinary teams to contrast and enhance a research teams’ perceptions regarding stakeholder insights.
Hitting the target: stopping active learning at the cost-based optimum

Abstract Active learning allows machine learning models to be trained using fewer labels while retaining similar performance to traditional supervised learning. An active learner selects the most informative data points, requests their labels, and retrains itself. While this approach is promising, it raises the question of how to determine when the model is ‘good enough’ without the additional labels required for traditional evaluation. Previously, different stopping criteria have been proposed aiming to identify the optimal stopping point. Yet, optimality can only be expressed as a domain-dependent trade-off between accuracy and the number of labels, and no criterion is superior in all applications. As a further complication, a comparison of criteria for a particular real-world application would require practitioners to collect additional labelled data they are aiming to avoid by using active learning in the first place. This work enables practitioners to employ active learning by providing actionable recommendations for which stopping criteria are best for a given real-world scenario. We contribute the first large-scale comparison of stopping criteria for pool-based active learning, using a cost measure to quantify the accuracy/label trade-off, public implementations of all stopping criteria we evaluate, and an open-source framework for evaluating stopping criteria. Our research enables practitioners to substantially reduce labelling costs by utilizing the stopping criterion which best suits their domain.
Hand Gesture Recognition Using CNN

This research work emphasizes the utilization of machine learning and convolution neural network (CNN) to recognize hand gesture lively, in spite of variations in hand sizes and spatial position in the image by providing our own personalized system inputs as a dataset representing the gestures according to the classes developed and to implement our model that will identify and classify the gesture into one of the defined categories. CNN utilizes three layers, where two are hidden layers and another one is convolution. The proposed model has been designed with three classes containing personalized gestures. The classes considered here are first-aid, food, and water. This model can be used for in-flight comfort facilities by travelers and also where there is a need for the use of these gestures.
An Active Learning Strategy for SVM-Based Captioning

Remote sensing (RS) image captioning (IC) is a novel technique introduced recently in the RS community to enrich the description of very high resolution (VHR) images. The goal of RSIC is to generate a sentence that summarizes the content of an image. In general, RSIC is developed in a supervised way where annotated samples are needed to train the system. However, obtaining annotated samples is costly and time consuming in particular when the labels are sentence descriptions that are very subjective. In order to cope with the problem of having large amounts of training samples in this work we propose an active learning solution for RSIC that selects the most important samples to label and include in the training. Experimental results performed on UCM caption dataset show the promising effectiveness of the proposed active learning strategy.
Contribution of the generalized nonunique inverses to the minimum-energy control theory: The inverse model control investigation

The innovative analytical approach to the minimum-energy design problem of the inverse model control (IMC) state-space structures is presented in this work. Following the recent papers, it should be concluded that the optimal behavior of the IMC strategy cannot longer be associated with the application of the well-known Moore–Penrose minimum-norm inverse. However, the minimum-energy IMC-oriented scheme has only be obtained through heuristic methods. Nevertheless, in the recent authors’ work, it has been proven for the first time that such an issue can be considered in an analytical manner. Yet, the obtained results have only been valid for the second-order state-space systems. Therefore, the motivation instance proposed in the manuscript, confirming the possibility of extending such paradigm to higher-order plants, will certainly contribute to the introduction of the new unified minimum-energy IMC theory canon. Since the nonunique σ and H inverses can successfully be employed in the robustification of the discussed control strategy, they can also be helpful in the case of our essential considerations. Thus, from now on the yet unexplored research area can now be investigated in the analytical manner, what has never been seen before in the modern IMC-originated control theory and practice. The predefined methodology clearly fills the gap in the analytical control design procedures and opens a new chapter in the knowledge related to the well-known and broadly accepted multivariable control canons.
Impact of Augmentation on Batik Classification using Convolution Neural Network and K-Neareast Neighbor

Augmentation is a technique to increase the amount of data artificially by making more variations of the image such as changing the position of the image, changing the size of the image to changing the color of the image. In this research, the Convolutional Neural Network and K-Nearest Neighbor algorithms were used as classification methods with batik objects. The batik used is limited to 4 classes, namely Kawung, Lunglungan, Megamendung and Parang batik. The data used are 1,443 batik images. After data augmentation, 6,300 images were obtained for each technique. There are 5 augmentation techniques used, namely Random Noise, Random Rotation, Grayscale, Horizontal Flip and Vertical Flip. In this research, we succeeded in increasing the accuracy of the Convolutional Neural Network and K-Nearest Neighbor algorithms using augmentation techniques. The Convolutional Neural Network algorithm increased an average accuracy of 6% of the five augmentation techniques. Meanwhile, the K-Nearest Neighbor algorithm can increase by more than 12%. The impact of data augmentation in this study is very good, as evidenced by the increase in accuracy in both algorithms. The best accuracy is obtained when using the Vertical Flip technique, the highest accuracy is obtained in both algorithms, namely 96.92% and 81.36% on the CNN and K-NN algorithms.
Software Implementation of Data Hiding in Vector Images

The work conducted a study of methods of concealing data into vector images, the features of their mathematical models, which make it possible to perform steganographic transformations for concealing and extracting information sequences. The purpose of the work was to analyze existing methods of concealing data into geometric elements of vector graphics, in particular the Bezier curves of the third order, the own programming implementation in the programming language JavaScript. Developed software implementation allows performing the operations of hiding and extracting messages in SVG files.
Geospatial Modelling and Trend Analysis of Coronavirus Outbreaks Using Sentiment Analysis and Intelligent Algorithms

The coronavirus became global pandemic soon after its spread engulfed more than hundred countries. With the increasing number of cases every day and no vaccine availability, the Lockdown and Social Distancing became new norm to contain the spread of Coronavirus. The unexpected times brought lots of different kinds of opinions and sentiments related to the coronavirus. Social media has recently emerged as a leading technique to disseminate news online. This Chapter focuses on collecting unstructured data from Twitter, present in tweets and extract opinions including sentiments of people from all over the world and applying machine learning model to test the validity of Sentiments. In addition to the Sentiment Analysis done using lexicon-based techniques, this research also provides the Geo-spatial analysis using Python, demonstrating the countries from where the outbreak and lockdown affected lives as well as the rising number of responses to the pandemic that were expressed country-wise via twitter.
Fake Review Identification Method Based on Topic Model and Att-BiLSTM

The review rating system provides valuable information to potential users, but it also encourages the creation of profit-driven fake reviews. Fake reviews and comments not only drive consumers to buy low-quality products or services, but also erode consumers' long-term confidence in review rating platforms. At present, two main reasons for the low detection accuracy of fake comments in recent studies are: (1) lack of feature learning of emotional intensity of text; (2) the inaccuracy of the identification of topic words in comments. To solve the above problems, we propose a novel identification method based on topic model and Att-BiLSTM mechanism. The proposed method calculates text affective and subjective values using TextBlob, incorporating the topic feature to train the classifier for fake review recognition. Comparative experiments show that the model effect is better than other models.
Enhancing Student Support via the Application of a Voice User Interface System

Within higher education, student support is essential to enhance student learning experiences in order to improve academic success. Key struggles that Computer Science students face during their studies at university include academic writing and programming. As such, many students find these topics challenging whereby even reducing interest in the subject. With the significant growth of technology during recent years to bridge the gap of human computer interaction with the real world, Voice User Interfaces (VUI) can potentially be used as solution within student support systems. This paper investigates the application of a VUI system aimed at providing student support and derives insights on user experience. As part of this study, a VUI prototype was designed and implemented to help Computer Science students in their studies by providing academic guides notably on academic writing, referencing, and programming. Evaluation involved the application of the User Experience Questionnaire (UEQ) to assess three constructs notably, response behavior, response quality and compensability.
Secure encryption key management as a SecaaS based on Chinese wall security policy

Cloud computing has become very popular and many mobile IT users are accessing business data and services without going through corporate networks. In this context the common security mechanism of most services and interactions is based on the encryption/signing keys, which themselves depend highly on the cryptographic key management system (CKMS) itself. Outsourcing CKMS to the cloud Computing as a trusted security as a service (CKMS_SecaaS) remains a real challenge, which we deal to the establishing of the trust between customers and service providers problems. To deal with this challenge we have proposed an approach that provides a CKMS_SecaaS based on the trusted platform module (TPM), which is the foundation for the trust, keys generation, and SecaaS authentication. In this paper, we propose an approach for keys security based on Chinese Wall Security Policy (CWSP) as a dynamic firewall mechanism, which it is for access and information flow control. We note that, the TPM with CKMS_SecaaS are considered a real shared environment, they host and manage a lot of objects (keys and related data), objects belong to different users groups, and they are considered sensitive data (encryption keys). Therefore, the CWSP is a very interesting candidates to our context, which it provides the creation of walls between companies’ objects based on an access control rules.
Investigation of independent reinforcement learning algorithms in multi-agent environments

Independent reinforcement learning algorithms have no theoretical guarantees for finding the best policy in multi-agent settings. However, in practice, prior works have reported good performance with independent algorithms in some domains and bad performance in others. Moreover, a comprehensive study of the strengths and weaknesses of independent algorithms is lacking in the literature. In this paper, we carry out an empirical comparison of the performance of independent algorithms on seven PettingZoo environments that span the three main categories of multi-agent environments, i.e., cooperative, competitive, and mixed. For the cooperative setting, we show that independent algorithms can perform on par with multi-agent algorithms in fully-observable environments, while adding recurrence improves the learning of independent algorithms in partially-observable environments. In the competitive setting, independent algorithms can perform on par or better than multi-agent algorithms, even in more challenging environments. We also show that agents trained via independent algorithms learn to perform well individually, but fail to learn to cooperate with allies and compete with enemies in mixed environments.
A Privacy-Preserving Pedestrian Dead Reckoning Framework Based on Differential Privacy

Pedestrian dead reckoning (PDR) is a widely used approach to estimate locations and trajectories. Accessing location-based services with trajectory data can bring convenience to people, but may also raise privacy concerns that need to be addressed. In this paper, a privacy-preserving pedestrian dead reckoning framework is proposed to protect a user’s trajectory privacy based on differential privacy. We introduce two metrics to quantify trajectory privacy and data utility. Our proposed privacy-preserving trajectory extraction algorithm consists of three mechanisms for the initial locations, stride lengths and directions. In addition, we design an adversary model based on particle filtering to evaluate the performance and demonstrate the effectiveness of our proposed framework with our collected sensor reading dataset.
Programmable Photonic Circuits using Silicon Photonic MEMS

We present a silicon photonics technology extended with low-power MEMS scalable to large circuits. This enables us to make photonic waveguide meshes that can be reconfigured using electronics and software.