Reviewerly/exHarmony huggingface dataset:
ParsingPhrase: Parsing-based automated quality phrase mining

Phrases represent independent semantics in natural language but usually have indeterminate lengths and different combinations. So, extracting meaningful phrases from unstructured texts will substantially reduce semantic ambiguity and lay the foundation for downstream natural language tasks. Most existing research obtains candidate phrases by N-grams, which includes meaningless word sequences and degrades algorithm performance. In this paper, we propose a novel phrase-mining algorithm, called ParsingPhrase, which effectively extracts combination phrases from text and improves phrase quality by syntactic features. It consists of three stages. Firstly, all sentences in texts are represented as parsing trees by PCFG (Probabilistic Context-Free Grammar). We propose PBMP (Parsing-Based Phrase mining) to obtain candidate phrases from those parsing trees. Then, we introduce a new phrase evaluation indicator, called Significance, that relies on the role of phrases to measure their importance. We integrate the Significance with conventional evaluation indexes for a more reasonable phrase evaluation. Finally, we optimize the phrase quality again by exploiting the optimal phrase composition features of sentences. To the best of our knowledge, it is the first work to employ parsing for combination phrase mining and evaluation, meanwhile offering a solution for syntactic disambiguation. Experiments on three real corpora demonstrate that the ParsingPhrase exceeds state-of-the-art baselines, is 7% higher in the candidate phrase conversion rate, and is 6% better in terms of Precision.
Incomplete mixed data-driven outlier detection based on local–global neighborhood information

Outlier detection is a crucial task for identifying unexpected patterns, errors, and behaviors; therefore, maximizing the valuable information obtained from ubiquitous, incomplete, redundant, noisy, and mixed data poses a great challenge. To achieve efficient graph-based outlier detection, we enhance the connectivity between similar objects and weaken the connectivity between heterogeneous objects. The network structure proposed in this paper is called “an incomplete local and global neighborhood information (ILGNI) network.” In this network, incomplete mixed data can be exploited considering two aspects; single-attribute local information and multi-attribute global information. Specifically, we initially utilize unsupervised attribute reduction methods to improve data quality. Then, from the perspective of local and global information, we use the level of similarity of objects to design strong-neighborhood and weak-similarity relations to deal with incomplete data. On this basis, the topology reconstruction of a high number of fine-grained neighborhood information networks can be realized. Finally, the outlier scores are calculated considering the stationary distribution of the Markov random walk model over the ILGNI network. The experiments conducted on four real-telecom fraud datasets demonstrate that the proposed algorithm can achieve enhanced outlier detection performance with low time complexity. In addition, the proposed method can effectively mine the information obtained from incomplete data and has high applicability to feature-related and feature-independent datasets.
aeroBERT-Classifier: Classification of Aerospace Requirements Using BERT

The system complexity that characterizes current systems warrants an integrated and comprehensive approach to system design and development. This need has brought about a paradigm shift towards Model-Based Systems Engineering (MBSE) approaches to system design and a departure from traditional document-centric methods. While MBSE shows great promise, the ambiguities and inconsistencies present in Natural Language (NL) requirements hinder their conversion to models directly. The field of Natural Language Processing (NLP) has demonstrated great potential in facilitating the conversion of NL requirements into a semi-machine-readable format that enables their standardization and use in a model-based environment. A first step towards standardizing requirements consists of classifying them according to the type (design, functional, performance, etc.) they represent. To that end, a language model capable of classifying requirements needs to be fine-tuned on labeled aerospace requirements. This paper presents an open-source, annotated aerospace requirements corpus (the first of its kind) developed for the purpose of this effort that includes three types of requirements, namely design, functional, and performance requirements. This paper further describes the use of the aforementioned corpus to fine-tune BERT to obtain the aeroBERT-Classifier: a new language model for classifying aerospace requirements into design, functional, or performance requirements. Finally, this paper provides a comparison between aeroBERT-Classifier and other text classification models such as GPT-2, Bidirectional Long Short-Term Memory (Bi-LSTM), and bart-large-mnli. In particular, it shows the superior performance of aeroBERT-Classifier on classifying aerospace requirements over existing models, and this is despite the fact that the model was fine-tuned using a small labeled dataset.
OdeBERT: One-stage Deep-supervised Early-exiting BERT for Fast Inference in User Intent Classification

User intent classification is a vital task for analyzing users’ essential requirements from the users’ input query in information retrieval systems, question answering systems, and dialogue systems. Pre-trained language model Bidirectional Encoder Representation from Transformers (BERT) has been widely applied to the user intent classification task. However, BERT is compute intensive and time-consuming during inference and usually causes latency in real-time applications. To improve the inference efficiency of BERT for the user intent classification task, this article proposes a new network named one-stage deep-supervised early-exiting BERT as one-stage deep-supervised early-exiting BERT (OdeBERT). In addition, a deep supervision strategy is developed to incorporate the network with internal classifiers by one-stage joint training to improve the learning process of classifiers by extracting discriminative category features. Experiments are conducted on publicly available datasets, including ECDT, SNIPS, and FDQuestion. The results show that the OdeBERT can speed up original BERT 12 times faster at most with the same performance, outperforming state-of-the-art baseline methods.
Large-Scale Competitive Learning-Based Salp Swarm for Global Optimization and Solving Constrained Mechanical and Engineering Design Problems

The Competitive Swarm Optimizer (CSO) has emerged as a prominent technique for solving intricate optimization problems by updating only half of the population in each iteration. Despite its effectiveness, the CSO algorithm often exhibits a slow convergence rate and a tendency to become trapped in local optimal solutions, as is common among metaheuristic algorithms. To address these challenges, this paper proposes a hybrid approach combining the CSO with the Salp Swarm algorithm (SSA), CL-SSA, to increase the convergence rate and enhance search space exploration. The proposed approach involves a two-step process. In the first step, a pairwise competition mechanism is introduced to segregate the solutions into winners and losers. The winning population is updated through strong exploitation using the SSA algorithm. In the second step, non-winning solutions learn from the winners, achieving a balance between exploration and exploitation. The performance of the CL-SSA is evaluated on various benchmark functions, including the CEC2017 benchmark with dimensions 50 and 100, the CEC2008lsgo benchmark with dimensions 200, 500 and 1000, as well as a set of seven well-known constrained design challenges in various engineering domains defined in the CEC2020 conference. The CL-SSA is compared to other metaheuristics and advanced algorithms, and its results are analyzed through statistical tests such as the Friedman and Wilcoxon rank-sum tests. The statistical analysis demonstrates that the CL-SSA algorithm exhibits improved exploitation, exploration, and convergence patterns compared to other algorithms, including SSA and CSO, as well as popular algorithms. Furthermore, the proposed hybrid approach performs better in solving most test functions.
Quo Vadis Business Simulation Games in the 21st Century?

Business simulation games have become popular in higher education and business environments. The paper aims to identify the primary research trends and topics of business simulation games research using a systematic and automated literature review with the motivation of research (learning driven and domain driven). Based on these findings, the future development of business simulation games research projected papers that research business simulation games were extracted from Scopus. Second, the research timeline, main publication venues and citation trends have been analysed. Third, the most frequent words, phrases, and topics were extracted using text mining. Results indicate that the research on business simulation games has stagnated, with the most cited papers published in the 2000s. There is a balance between learning-driven and domain driven-research, while technology-driven research is scarce, indicating that the technology used for business simulation games is mature. We project that the research on business simulation games needs to be directed in the area of new technologies that could improve communication with and among the users (virtual reality, augmented reality, simulation games) and technologies that could improve the reasoning and decision-making complexity in business simulation games (artificial intelligence).
Recent Developments in Game-Theory Approaches for the Detection and Defense against Advanced Persistent Threats (APTs): A Systematic Review

Cybersecurity has become a prominent issue in regard to ensuring information privacy and integrity in the internet age particularly with the rise of interconnected devices. However, advanced persistent threats (APTs) pose a significant danger to the current contemporary way of life, and effective APT detection and defense are vital. Game theory is one of the most sought-after approaches adopted against APTs, providing a framework for understanding and analyzing the strategic interactions between attackers and defenders. However, what are the most recent developments in game theory frameworks against APTs, and what approaches and contexts are applied in game theory frameworks to address APTs? In this systematic literature review, 48 articles published between 2017 and 2022 in various journals were extracted and analyzed according to PRISMA procedures and our formulated research questions. This review found that game-theory approaches have been optimized for the defensive performance of security measures and implemented to anticipate and prepare for countermeasures. Many have been designed as part of incentive-compatible and welfare-maximizing contracts and then applied to cyber–physical systems, social networks, and transportation systems, among others. The trends indicate that game theory provides the means to analyze and understand complex security scenarios based on technological advances, changes in the threat landscape, and the emergence of new trends in cyber-crime. In this study, new opportunities and challenges against APTs are outlined, such as the ways in which tactics and techniques to bypass defenses are likely to evolve in order to evade detection, and we focused on specific industries and sectors of high interest or value (e.g., healthcare, finance, critical infrastructure, and the government).
Blockchain-Enabled Internet of Vehicles Applications

Internet of Vehicles (IoV) is a network that connects vehicles and everything. IoV shares traffic data by connecting vehicles with the surrounding environment, which brings huge potential to people’s life. However, a large number of connections and data sharing will seriously consume vehicle resources during the interaction. In addition, how to build a safe and reliable connection to ensure vehicle safety is also an issue to consider. To solve the above problems, researchers introduce blockchains into IoV to build a safe and reliable vehicle network relying on the distributed account structure, immutable, transparent and security features of blockchains. We have investigated the application of blockchains in IoV in recent years, and have summarized and compared these studies according to their purposes. On this basis, we also point out the future trends and opportunities.
A Vehicle Recognition Model Based on Improved YOLOv5

The rapid development of the automobile industry has made life easier for people, but traffic accidents have increased in frequency in recent years, making vehicle safety particularly important. This paper proposes an improved YOLOv5s algorithm for vehicle identification and detection to reduce vehicle driving safety issues based on this problem. In order to solve the problems of a disappearing model training gradient in the YOLOv5s algorithm, difficulty in recognizing small objects and poor recognition accuracy caused by the boundary frame regression function, it is necessary to implement a new function. These aspects have been enhanced in this article. On the basis of the traditional YOLOv5s algorithm, the ELU activation function is used to replace the original activation function. The attention mechanism module is then added to the YOLOv5s algorithm’s backbone network to improve the feature extraction of small and medium-sized objects. The CIoU Loss function replaces the original regression function of YOLOv5s, thereby enhancing the convergence rate and measurement precision of the loss function. In this paper, the constructed dataset is utilized to conduct pertinent experiments. The experimental results demonstrate that, compared to the previous algorithm, the mAP of the enhanced YOLOv5s is 3.1% higher, the convergence rate is 0.8% higher, and the loss is 2.5% lower.
LSTM-Based Projectile Trajectory Estimation in a GNSS-Denied Environment

This paper presents a deep learning approach to estimate a projectile trajectory in a GNSS-denied environment. For this purpose, Long-Short-Term-Memories (LSTMs) are trained on projectile fire simulations. The network inputs are the embedded Inertial Measurement Unit (IMU) data, the magnetic field reference, flight parameters specific to the projectile and a time vector. This paper focuses on the influence of LSTM input data pre-processing, i.e., normalization and navigation frame rotation, leading to rescale 3D projectile data over similar variation ranges. In addition, the effect of the sensor error model on the estimation accuracy is analyzed. LSTM estimates are compared to a classical Dead-Reckoning algorithm, and the estimation accuracy is evaluated via multiple error criteria and the position errors at the impact point. Results, presented for a finned projectile, clearly show the Artificial Intelligence (AI) contribution, especially for the projectile position and velocity estimations. Indeed, the LSTM estimation errors are reduced compared to a classical navigation algorithm as well as to GNSS-guided finned projectiles.
Personalized Movie Recommendations Based on a Multi-Feature Attention Mechanism with Neural Networks

With the rapid growth of the Internet, a wealth of movie resources are readily available on the major search engines. Still, it is unlikely that users will be able to find precisely the movies they are more interested in any time soon. Traditional recommendation algorithms, such as collaborative filtering recommendation algorithms only use the user’s rating information of the movie, without using the attribute information of the user and the movie, which has the problem of inaccurate recommendations. In order to achieve personalized accurate movie recommendations, a movie recommendation algorithm based on a multi-feature attention mechanism with deep neural networks and convolutional neural networks is proposed. In order to make the predicted movie ratings more accurate, user attribute information and movie attribute information are added, user network and movie network are presented to learn user features and movie features, respectively, and a feature attention mechanism is proposed so that different parts contribute differently to movie ratings. Text features are also extracted using convolutional neural networks, in which an attention mechanism is added to make the extracted text features more accurate, and finally, personalized movie accurate recommendations are achieved. The experimental results verify the effectiveness of the algorithm. The user attribute features and movie attribute features have a good effect on the rating, the feature attention mechanism makes the features distinguish the degree of importance to the rating, and the convolutional neural network adding the attention mechanism makes the extracted text features more effective and achieves high accuracy in MSE, MAE, MAPE, R2, and RMSE indexes.
An Effective Task Sampling Strategy Based on Category Generation for Fine-Grained Few-Shot Object Recognition

The recognition of fine-grained objects is crucial for future remote sensing applications, but this task is faced with the few-shot problem due to limited labeled data. In addition, the existing few-shot learning methods do not consider the unique characteristics of remote sensing objects, i.e., the complex backgrounds and the difficulty of extracting fine-grained features, leading to suboptimal performance. In this study, we developed an improved task sampling strategy for few-shot learning that optimizes the target distribution. The proposed approach incorporates broad category information, where each sample is assigned both a broad and fine category label and converts the target task distribution into a fine-grained distribution. This ensures that the model focuses on extracting fine-grained features for the corresponding broad category. We also introduce a category generation method that ensures the same number of fine-grained categories in each task to improve the model accuracy. The experimental results demonstrate that the proposed strategy outperforms the existing object recognition methods. We believe that this strategy has the potential to be applied to fine-grained few-shot object recognition, thus contributing to the development of high-precision remote sensing applications.
Magic Leap 1 versus Microsoft HoloLens 2 for the Visualization of 3D Content Obtained from Radiological Images

The adoption of extended reality solutions is growing rapidly in the healthcare world. Augmented reality (AR) and virtual reality (VR) interfaces can bring advantages in various medical-health sectors; it is thus not surprising that the medical MR market is among the fastest-growing ones. The present study reports on a comparison between two of the most popular MR head-mounted displays, Magic Leap 1 and Microsoft HoloLens 2, for the visualization of 3D medical imaging data. We evaluate the functionalities and performance of both devices through a user-study in which surgeons and residents assessed the visualization of 3D computer-generated anatomical models. The digital content is obtained through a dedicated medical imaging suite (Verima imaging suite) developed by the Italian start-up company (Witapp s.r.l.). According to our performance analysis in terms of frame rate, there are no significant differences between the two devices. The surgical staff expressed a clear preference for Magic Leap 1, particularly for the better visualization quality and the ease of interaction with the 3D virtual content. Nonetheless, even though the results of the questionnaire were slightly more positive for Magic Leap 1, the spatial understanding of the 3D anatomical model in terms of depth relations and spatial arrangement was positively evaluated for both devices.
LSTM-BEND: Predicting the Trajectories of Basketball

The capacity to comprehend and anticipate the movements of dynamic objects deployed in sports contexts is a need for autonomous systems. A basketball game is an example of one such ambiance, with its dynamic and intricate motions fueled by numerous social interactions. One such crucial part of such systems is to forecast future basketball positions to determine whether or not the ball will hit or miss the basket based on those predictions. This is a difficult task as basketball motion depends on several factors, including the stroke applied by the player on the ball, environmental factors like topology and geometry, etc., and the fact that the subject of analyzing the trajectory of the basketball is one which is interesting interested. In this letter, we concentrate on predicting basketball motion trajectories in actual basketball tournaments, and the objective is to forecast basketball's future locations based on its past positions. Therefore, this letter investigates whether long short-term memory models can accurately anticipate the motion trajectories of basketballs in light of their performance in sequence prediction challenges. In particular, a long-short-term memory network-integrated relation network-based bimodal exponential normal distribution process is suggested.
Learning to Bound: A Generative Cramér-Rao Bound

The Cramér-Rao bound (CRB), a well-known lower bound on the performance of any unbiased parameter estimator, has been used to study a wide variety of problems. However, to obtain the CRB, requires an analytical expression for the likelihood of the measurements given the parameters, or equivalently a precise and explicit statistical model for the data. In many applications, such a model is not available. Instead, this work introduces a novel approach to approximate the CRB using data-driven methods, which removes the requirement for an analytical statistical model. This approach is based on the recent success of deep generative models in modeling complex, high-dimensional distributions. Using a learned normalizing flow model, we model the distribution of the measurements and obtain an approximation of the CRB, which we call Generative Cramér-Rao Bound (GCRB). Numerical experiments on simple problems validate this approach, and experiments on two image processing tasks of image denoising and edge detection with a learned camera noise model demonstrate its power and benefits.
Learning to Learn Evolutionary Algorithm: A Learnable Differential Evolution

Research on evolutionary optimization has flourished for several decades. Now it has come to a turning point. With the advancement of artificial intelligence, especially deep learning and reinforcement learning, it is becoming appealing to rethink the design and development of evolutionary algorithm (EA). From our perspective, a new-generation EA should be <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">learned</i> rather than manually designed, based on learning from optimization experiences (such as obtained from optimizing a family of optimization problems), the deep understanding of the roles of recombination operators, and the usage of experiences extracted through history optimization trajectories, so as to intelligently decide the control parameters that can adapt to the problem landscape changes. This learning can be conducted by strongly coupling with reinforcement learning since an evolutionary search procedure can be modeled as a Markov Decision Process (MDP). In this paper we propose a framework for automatic learning of EA, and present an exemplar study on learning a differential evolution (DE). Experimental results show that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">learned</i> adaptive DE is very competitive to some recent EAs on a commonly-used test suite, which indicates that the proposed learning framework has a great potential for the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">automatic</i> design of promising EAs.