Reviewerly/exHarmony huggingface dataset:
Ambiguity Resolution Between Constant Velocity and Coordinated Turn Models for Multimodel Target Tracking

Multimodel-based algorithms, such as the interacting multiple model, have been used in target estimation problems with different dynamics models. The combination of a constant velocity (CV) model and a coordinated turn (CT) model is widely used to track a maneuvering target. However, the distinction between the two models may not be distinct when the target is weakly maneuvering. In this letter, we propose a new method to resolve the ambiguity between CV and CT models that can provide a faster maneuver response and a higher estimation accuracy than traditional methods.
Attention based sequence-to-sequence framework for auto image caption generation

Caption generation using an encoder-decoder approach has recently been extensively studied and implemented in various domains, including image captioning and code captioning. In this research article, we propose one particular approach for completing a capture generation task using an “attention”-based sequence-to-sequence framework that, when combined with a conventional encoder-decoder model, generates captions in an attention-based manner. ResNet-152 is a Convolutional Neural Network (CNN) based encoder that generates a comprehensive representation of an input image while embedding that into a fixed size length vector. To predict the next sentence, the decoder uses LSTM, a Recurrent Neural Network (RNN), and an attention-based mechanism to concentrate attention on certain sections of an image selectively. Define a set of epochs to 69, which should be enough for training the model to generate informative descriptions, and the validation loss has reached its minimum limit and no longer decreases. We present the datasets as well as the evaluation metrics, as well as quantitative and qualitative analysis. Experiments on the MSCOCO and Flickr8k benchmark datasets illustrate the model’s efficacy in comparison to the baseline techniques. On MSCOCO, evaluation scores included BLEU-1 0.81, BLEU-2 0.61, BLEU-3 0.47, and 0.33 METEOR. For Flickr8k BLEU-1 0.68, BLEU-2 0.49, BLEU-3 0.41, METEOR 0.23, and 0.86 on SPICE. The proposed approach is comparable with several state-of-the-art methods in terms of standard evaluation metric, demonstrating that it can produce more accurate and richer captions.
Design and Development of Robotic Arm Movements and Body Frame for a Social Robot for Graduation Rites

The COVID-19 pandemic has brought crisis to people from around the world resulting to a transition from face-to-face classes to an online class in the academic sector. Graduation ceremonies also transition into an online ceremony where students passively attend the session. In this study, a Social Robot named “Gradbot” is developed to help the students participate actively in their online ceremonies. The Body frame was designed using Fusion360. The Gradbot is compose of the Arduino microcontroller, servo motors, Bluetooth module, mounted on a 2WD car chassis and was simulated using Tinkercad and MATLAB. This study also includes the investigation of the degrees of freedom, type of joints, workspace, and the cartesian product of the developed Gradbot.
A Three-Stage Curriculum Learning Framework with Hierarchical Label Smoothing for Fine-Grained Entity Typing

In this paper, we study the noisy labeling problem on the fine-grained entity typing (FET) task. Most existing methods propose to divide the training data into "clean" and "noisy" sets and use different strategies to deal with them during the training process. However, the "clean" samples used in these methods are not actually clean, some of them also contain noisy labels. To overcome this issue, we propose a three-stage curriculum learning framework with hierarchical label smoothing to train the FET model, which can use relatively clean data to train the model and prevent the model from overfitting to noisy labels. Experiments conducted on three widely used FET datasets show that our method achieves the new state-of-the-art performance. Our code is publicly available at https://github.com/xubodhu/NFETC-CLHLS .
Digest of Blockchain Technologies to Design System for Big Image Data Provenance and Security

Data quantities are rapidly increasing in many industry sectors due to the development of new sensors, mobile and cloud technologies, advancements in IoT and AI, and growth of social and entertainment media. Many applications (e.g., in finance, healthcare, government data) have strict information security requirements for simultaneous access, record updating, and validation in an immutable manner, which can be achieved with distributed ledger technology (DLT). In this paper we review and analyze sixty-three currently available blockchain (BC) systems and their components in context of the DLT for big image data storage, provenance tracking, replication, and sharing. We also elaborate the key BC system components and architecture for major information security concerns.
On semantically labelled syntax trees and the non-existence of certain Sahlqvist formulae

Abstract We elaborate on semantically labelled syntax trees that provide a method of proving the non-existence of modal formulae satisfying certain syntactic properties and defining a given class of frames and use them to show that there are classes of Kripke frames that are definable by both non-Sahlqvist and Sahlqvist formulae, but the latter requires more propositional variables.
Multi-behavior Graph Neural Networks for Session-based Recommendation

Session-based recommendation aims to predict user behaviors based on short-term anonymous sessions. Given the singleness of the training data of the existing session recommendation model, it is not easy to learn from a simple session to a complex transition pattern. To obtain complete session information and complex transitions of items, we propose a novel method, named Multi-behavior Graph Neural Networks for Session-based Recommendation (MBGNN). Specifically, this study uses multiple behavior sequences to construct a session graph, and input the session graph into the graph neural network to learn the complex transition patterns between items to obtain the global information about the session. Then, according to the different importance of the various sessions in the recommendation process, feature fusion is carried out to obtain a more detailed local representation. Finally, the learned information is input into the prediction layer to calculate the probability score of each item to make more accurate recommendations for users. Extensive experiments and comparisons were conducted on two real datasets. The experimental results show that our proposed model is superior to the state-of-the-art methods.
Answer-Agnostic Question Generation in Privacy Policy Domain using Sequence-to-Sequence and Transformer Models

This paper presents a transformer and sequence-to-sequence mapping approach, augmented using relevant named entities, towards generating questions for text understanding in the domain of policies (such as privacy agreements). To date, most work in question generation has used general-purpose benchmarks such as the SQuAD corpus; however, the application of question generation to document understanding has largely omitted use cases in the privacy policy domain. A privacy policy is a legal document that clearly divulges the policies of an organization regarding the gathering and usage of customer data. Reading and understanding privacy policies to gain a measure of the rationality of terms of service before accepting them is of critical importance to users. However, users frequently ignore these policies or base their decisions on insufficient understanding of the “fine print” due to the complex language and excessive length of policies. This work focuses on building a question generation system to assist reading comprehension of privacy policies. This work can be used to improve the quality of question-answering systems in this domain, which consequently can help users understand the privacy policies before agreeing to them. This paper uses existing deep learning models like T5, which is a transformer model, and several sequence-to-sequence models to generate questions. Since, existing named entity recognition (NER) tools do not work in this domain, we also created our own named entity labels to add auxiliary information to our models. Adding the auxiliary information improves the results over the baseline models giving us a promising future direction to further add some constraints, as a means to add more background knowledge to our models to generate better quality questions.
Randomized Pixel Selection for Concealing the AES Encrypted Text Message Inside a Video File

This paper proposes a method for information hiding inside the video by modifying the least significant bit (LSB) and then making the information hidden inside the random pixels by using the Mersenne Twister algorithm. Furthermore, to add an extra layer of security onto the hidden information, first, the plain text is subject to be encrypted using the AES algorithm by providing a 128-Bit key, the encrypted text in Hexadecimal format is converted into binary format. The Mersenne Twister algorithm implemented in Python is used for random selection of frames as well as for choosing the pixels for embedding our information into the LSB position. As an observational result, the final pixel value in the modified video file shows not much deviation from the original value. The results show a high PSNR value of 71.482 and a low MSE value of 0.0046. Even the security of the message is kept in mind as without knowing the secret key of the AES algorithm, it would take years to compute back the original message.
Intelligence in QED-Tutrix: Balancing the Interactions Between the Natural Intelligence of the User and the Artificial Intelligence of the Tutor Software

When considering the mathematics competences referential (Niss & Højgaard, 2019), whether from Québec Education Program, the Standards of the National Council of Teachers of Mathematics or some large international studies such as the Programme for International StudentStudent Assessment, reasoning, and mathematical proofs are always at the forefront. Although the notion of proof is limited, in elementary school, to mathematical reasoning and conviction, and in post-secondary education, to demonstration and written communication, the evolution of the treatment reserved for deductive reasoning in secondary school has the appearance, depending on the region, of an eternally shilly-shallying between valorization and marginalization.
Follow Me: Hierarchical Parallel Execution Synchronization in Behavior Trees

The Behavior Tree (BT) has gained considerable popularity in many areas such as robotics and artificial intelligence, due to its advantages of modularity, reactivity, reusability, readability, etc. In the classic BT, tasks under the Parallel node are assumed to be independent of each other and cannot be executed synchronously. To address this problem, we introduce hierarchy into parallel tasks by dividing parallel tasks into leader and follower tasks. In addition, we define the state value for each task node to achieve efficient synchronization between tasks. We also propose two new parallel operators to solve the synchronous execution of parallel tasks in BTs. We integrate our approach into a popular BT framework and evaluate it by multiple experiments. Results show that our approach improves the parallel execution in traditional BT models and has great practical significance.
Out-of-plane displacement measurement for an optical fiber image transmission element using optical flow

To realize the industrial in-situ deformation detection of an optical fiber image transmission element, a method for out-of-plane displacement detection based on the combination of optical flow and an image spherizing algorithm is presented. After capturing the 2D deformation images, the out-of-plane displacement can be extracted from the in-plane displacement estimated by the optical flow method, which means that the out-of-plane displacement information contained in a 2D image is fully used. The principle of the Brox optical flow method is introduced and the accuracy of the method is proven by comparing test results with the digital image correlation method. Experimental results show that the relative errors and the absolute errors of the measurement are less than 2% and 0.1 µm. Because the optical flow algorithm introduces a time parameter and the smoothness assumption, the new method is more appropriate for dynamic and microscale out-of-plane displacement measurement. Unlike conventional methods, it does not need phase unwrapping or frequency domain conversion during detection and has many attractive characteristics such as nondestructive testing, a simple arrangement, and low errors. This method has been applied to the industrial in-situ monitoring of an optical fiber image transmission element.
Traffic Sign Classification Based on Prototypes

Traffic signs classification is an indispensable task for intelligent vehicles. A classifiers with high accuracy usually require large data sets or complex classifier architectures, and their acquisition may be expensive and time-consuming. In order to solve this problem, a new method was proposed. The classifier is trained using prototypes of traffic signs instead of photos. First is the prototype of the task object, and then combines the background photos captured from the search engine to build the prototype data set. Then, after pre-training ConvNets on the existing database TSRD, the classifier was fine-tuned on the prototype data set. The classifier was tested on GTSRB-dataset and achieved an accuracy of 90.13%.
Progressive Transmission and Inference of Deep Learning Models

Modern image files are usually progressively transmitted and provide a preview before downloading the entire image for improved user experience to cope with a slow network connection. In this paper, with a similar goal, we propose a progressive transmission framework for deep learning models, especially to deal with the scenario where pre-trained deep learning models are transmitted from servers and executed at user devices (e.g., web browser or mobile). Our progressive transmission allows inferring approximate models in the middle of file delivery, and quickly provide an acceptable intermediate outputs. On the server-side, a deep learning model is divided and progressively transmitted to the user devices. Then, the divided pieces are progressively concatenated to construct approximate models on user devices. Experiments show that our method is computationally efficient without increasing the model size and total transmission time while preserving the model accuracy. We further demonstrate that our method can improve the user experience by providing the approximate models especially in a slow connection.
A Systematic Literature Review of Blockchain Technology for Identity Management

Thanks to identity management solutions, identities and operations such as access control have become increasingly common in real-world applications. The rapid growth of blockchain technology has a significant impact on various sectors like financial and healthcare institutions. Blockchain technology has attracted significant attention due to its distinctive characteristics, such as decentralization, trustworthiness, and immutability, therefore, it has recently been employed in identity management where the decentralized and distributed nature of blockchain technology eliminates the need for a trusted third party. Recently, endeavors have been made to implement blockchain-based identity management solutions that enable users to take ownership of their identities. Therefore, it is imperative to conduct an extensive review of the implementation of blockchain in identity management. This systematic review analyzes the state-of-the-art blockchain researches in the realm of identity management and provides a systematic analysis of the identified literature. After extracting relevant scientific papers, we divide the results into various categories. Finally, we highlight several research challenges and potential future research directions on blockchain in the context of identity management.
SIFT feature extraction applied in SVM classification

In this paper, we explore the image feature extraction approach called Scale-Invariant Feature Transform. We use the extracted features for training and testing of a classification technique, the Support Vector Machines. For the benchmarks, we train and test the classifier using features extracted from real image data. This model, we compare with a classification model trained using pixel values.
Using an Estimation of Distribution Algorithm to Achieve Multitasking Semantic Web Service Composition

Web service composition composes existing Web services to accommodate users’ requests for required functionalities with the best possible quality of services (QoS). Due to the computational complexity of this problem, evolutionary computation (EC) techniques have been employed to efficiently find composite services with near-optimal functional quality (i.e., quality of semantic matchmaking, QoSM for short) or nonfunctional quality (i.e., QoS) for each composition request individually. With a rapid increase in composition requests from a growing number of users, solving one composition request at a time can hardly meet the efficiency target anymore. Driven by the idea that the solutions obtained from solving one request can be highly useful for tackling other related requests, multitasking service composition approaches have been proposed to efficiently deal with multiple composition requests concurrently. However, existing attempts have not been effective in learning and sharing knowledge among solutions for multiple requests. In this article, we model the problem of collectively handling multiple service composition requests as a new multitasking service composition problem and propose a new permutation-based multifactorial evolutionary algorithm based on an estimation of distribution algorithm (EDA), named PMFEA-EDA, to effectively and efficiently solve this problem. In particular, we introduce a novel method for effective knowledge sharing across different service composition requests. For that, we develop a new sampling mechanism to increase the chance of identifying high-quality service compositions in both the single-tasking and multitasking contexts. Our experiment shows that our proposed approach, PMFEA-EDA, takes much less time than existing approaches that process each service request separately, and also outperforms them in terms of both QoSM and QoS.
Optimizing Social Visibility in OSNs With Anonymity Guarantees: Efficient Algorithms and Applications

Online social network (OSN) is an ideal venue to enhance one's visibility. This paper considers how a user (called requester) in an OSN selects a small number of users and invites them as new friends/followers so as to maximize her “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">social visibility</i> ”. More importantly, the requester has to do this under the anonymity setting, which means she is not allowed to know the neighborhood information of other users in the OSN. In this paper, we first develop a mathematical model to quantify the social visibility and formulate the problem of visibility maximization with anonymity guarantee, abbreviated as “VisMAX-A”. We prove that the VisMAX-A problem is NP-hard even with an “perfect query oracle” which knows users’ neighborhood information. Then we design an algorithmic framework named as “AdaExp,” which adaptively expands the requester's visible set in multiple rounds. In each round of the expansion, the algorithmic framework uses a query oracle with anonymity guarantee to select one user to increase the requester's visibility. We prove the theoretical guarantees of our algorithmic framework, which quantify how the query oracle may influence the solution guarantee. By using probabilistic data structures like the k-minimum values (KMV) sketch and online learning methods like multi-armed bandit (MAB), we design an efficient query oracle with anonymity guarantees to estimate the best candidate to increase requester's visibility. We also conduct experiments on real-world social networks and validate the effectiveness of our algorithms. Lastly, we demonstrate applications of our algorithm on real-world data and show that one can increase her social visibility significantly by only adding a few (e.g., one or two) friends/followers in the OSNs.
Cyber-Physical Systems in Decarbonisation

Digital technologies such as advanced metering, big data, machine learning, and the Internet of Things are receiving significant attention as they provide the potential to facilitate the decarbonisation of industry while requiring limited investments. The orchestration of these novel technologies, so-called cyber-physical systems (CPS), provides additional synergetic effects that increase efficiency of energy provision and industrial production, thereby optimising economic feasibility and environmental impact.
A Block PatchMatch-Based Energy-Resource Efficient Stereo Matching Processor on FPGA

This paper presents a field programmable gate array (FPGA)-based, high-performance and energy-resource efficient stereo matching processor. The proposed processor executes block-level PatchMatch-based stereo matching algorithm with a random search strategy to avoid estimation of all disparity levels. To take advantages of different block scales, a coarse-to-fine multi-scale propagation (MSP) scheme is proposed for label update. Based on that, a dedicated hardware architecture is further proposed to explore the benefit of the algorithm. Experimental results show that the proposed FPGA-based processor, running at 350MHz, achieves a peak performance of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1920\times 1080.165$ </tex-math></inline-formula> .7 frame per second (FPS) at 128 disparity levels with 3.35W power dissipation. The energy and resource efficiency of the proposed design outperforms state-of-the-art FPGA-based stereo matching processors. When disparity level increases to 256, the computing resource increment of the proposed design is much less than existing designs because random search instead of winner-takes-all (WTA) is utilized. Moreover, unlike existing dedicated stereo matching processors which output only disparity information, the proposed design is also capable of deriving plane slant. This information can be beneficial for follow-up tasks like 3D reconstruction.