Reviewerly/exHarmony huggingface dataset:
Think Rationally about What You See: Continuous Rationale Extraction for Relation Extraction

Relation extraction (RE) aims to extract potential relations according to the context of two entities, thus, deriving rational contexts from sentences plays an important role. Previous works either focus on how to leverage the entity information (e.g., entity types, entity verbalization) to inference relations, but ignore context-focused content, or use counterfactual thinking to remove the model's bias of potential relations in entities, but the relation reasoning process will still be hindered by irrelevant content. Therefore, how to preserve relevant content and remove noisy segments from sentences is a crucial task. In addition, retained content needs to be fluent enough to maintain semantic coherence and interpretability. In this work, we propose a novel rationale extraction framework named RE2, which leverages two continuity and sparsity factors to obtain relevant and coherent rationales from sentences. To solve the problem that the gold rationales are not labeled, RE2 applies an optimizable binary mask to each token in the sentence, and adjust the rationales that need to be selected according to the relation label. Experiments on four datasets show that RE2 surpasses baselines.
Maritime Ship Detection Method for Satellite Images Based on Multiscale Feature Fusion

Remote sensing ship recognition technology is one of the important research areas for achieving ocean security monitoring. However, maritime ship targets features such as multi-scale, arbitrary direction, and dense arrangement, and their imaging is often influenced by factors such as sea fog and sea surface background. It is challenging for fast and accurate detection of remote sensing ships. We propose a new ship detection algorithm YOLO-Remote Sensing Ship Detection (YOLO-RSSD) based on YOLOv5, which effectively improves the accuracy of ship detection while ensuring detection speed. It is attributed to our proposed four optimization measures in this model: First, the K-means++ algorithm is introduced in the data preprocessing part to improve the clustering effect and obtain candidate box sizes suitable for multi-scale ship dataset. Then, an improved BiFPN structure is embeded in the feature fusion part to achieve cross-layer multi-scale weighted feature fusion. Thirdly, the original bounding box regression loss function is replaced with the EIoU loss, which is effective for accelerating the convergence and improving the regression accuracy of predicted boxes. Finally, a channel attention mechanism is introduced in the convolutional unit to enhance the model's ability to capture ship features. Experimental results show that the YOLO-RSSD model achieves a detection accuracy of 96.1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> for remote sensing ships, which is 4.3 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> higher than the original YOLOv5 network. In addition, YOLO-RSSD performs good robustness and generalization ability. This means that our method has high practical value and provides a new solution for the analysis and application of remote sensing images.
Artificial Intelligence in Software Project Management

The success of a software project highly depends on how well the project is managed. This includes crucial activities such as estimating the effort required to develop the software project, creating a software project schedule including allocation of human resources, managing project risks, monitoring progress, etc. Inadequate handling of such activities can thus lead to serious consequences to software companies. However, software project management is also a very challenging task that involves multiple business and human factors, and conflicting objectives. These challenges are exacerbated when dealing with medium- to large-size software projects. Under such circumstances, artificial intelligence has the potential to play a significant role in supporting software project managers, enabling them to make more informed management decisions. This chapter discusses how artificial intelligence techniques can support software project managers with two key software project management activities: software project scheduling and software effort estimation.
Recommendations for Developing Immersive Virtual Reality Serious Game for Autism: Insights From a Systematic Literature Review

The use of serious games for the treatment of people with autism is currently considered a promising approach due to its positive effects on promoting learning through playful and motivating experiences.In recent years, increased research has focused on serious games utilizing Immersive Virtual Reality (IVR) technologies, such as large-scale projection-based systems and head-mounted displays.The high level of immersion provided by IVR has been found to benefit learning outcomes, as it reduces environmental distractions and helps individuals focus on learning tasks while also addressing social anxiety.Researchers have conducted significant work in this field over the past decade, yielding promising results.However, the development of these learning interventions comes with methodological challenges and issues, especially in how to conduct the development process and design IVR-based serious games for the learning of people with autism.Based on these premises, this systematic review thoroughly analyzes the literature on developing IVR-based serious games for individuals with autism, discussing inherent shortcomings and reflecting on them.Then, twenty IVR-based serious games for people with autism developed between 2009 and mid-2021 are selected and analyzed, focusing on the people engaged in the development process, the design methodology adopted, and the serious game design framework employed.From this analysis, a set of recommendations are proposed to support anyone interested in developing IVR-based serious games for people with autism.In addition, the gaps left unsolved in the autism literature are highlighted, upon which a research agenda is grounded.
Blockchain Technology to Support Agri-Food Supply Chains: A Comprehensive Review

Blockchain technology is a distributed ledger that guarantees the immutability of data and asset tracking.Its characteristics can result in an improvement to traditional agri-food Supply Chains.Blockchain-based traceability systems let producers improve their visibility and consumers trust the products they buy and where they come from.Actual literature lacks an exhaustive analysis of the benefits and drawbacks of Blockchain applied to the agri-food domain.This paper aims to overcome this issue: to understand the state-of-the-art related to the application of Blockchain in the agri-food sector, we screened 183 papers and performed a literature review answering six Research Questions.The work takes into consideration the advantages and disadvantages of implementing such paradigm, with an analysis on the role of industries.The main findings are related to research directions: they regard the lack of training both for industries and stakeholders, the involvement of additional technologies (i.e., Big Data, Edge Computing), and the absence of supporting tools for developers.
A Personal Privacy Data Protection Scheme for Encryption and Revocation of High-Dimensional Attribute Domains

With the frequent occurrence of private data breaches, it is now more necessary than ever to address how to protect private data. The combination of Ciphertext-Policy Attribute-Based Encryption (CP-ABE) and blockchain typically enables secure storage and sharing of data. However, in high-dimensional attribute domains, that is, the number of attributes is large, these schemes have issues such as low security of data protection, high computational overhead, and high cost of attribute revocation. This paper proposes a personal privacy data protection scheme for encryption and revocation of high-dimensional attribute domains to address these issues. The proposed scheme is made up of three components. Firstly, Fast High-dimensional Attribute Domain-based Message Encryption (HAD-FME) is proposed to improve data security and reduce computational cost. Secondly, an Attribute Revocation Mechanism Based on Sentry Mode (SM-ARM) is designed in combination with smart contracts. Lastly, a Blockchain-based Model for Personal Privacy Data Protection (BC-PPDP) is proposed by integrating HAD-FME with SM-ARM. The security analysis results show that HAD-FME proposed in this paper is secure under the DLIN assumption, and the attribute revocation satisfies both forward and backward security. Experiments show that HAD-FME has higher computational efficiency than existing schemes in the high-dimensional attribute domains, SM-ARM has lower revocation cost than existing attribute revocation mechanisms, and smart contracts and blockchain work well.
DeepQPrep: Neural Network Augmented Search for Quantum State Preparation

There is an increasing interest in the area of quantum computing but developing quantum algorithms is difficult.Neural Network augmented search algorithms have proven quite successful for general search problems (like program generation) but current approaches to quantum program generation make very restricted use of them.In this paper we present DeepQPrep, a Neural Network based approach to generate quantum circuits for state preparation; a common yet expensive task needed in many applications of quantum computing.We illustrate that Neural Network augmented search algorithms have significant promise for automated quantum program generation; DeepQPrep generated programs were able to solve 99% and 76.9% of 20000 previously unseen state prepartion tasks in a supervised machine learning context within two different application scenarios.The circuits produced by DeepQPrep are also shallower (on average) than their ground truth counterparts.We also compare DeepQPrep to IBM Qiskit's approach to state preparation and illustrate that even when constrained, DeepQPrep generates significantly shallower circuits despite Qiskit solving more of the state preparation tasks.Based on our results, we argue that neural network augmented search approaches exhibit significant promise for generalised approaches to quantum program induction warranting further study in more complex scenarios.
Uncertainty-Aware Source-Free Domain Adaptive Semantic Segmentation

Source-Free Domain Adaptation (SFDA) is becoming topical to address the challenge of distribution shift between training and deployment data, while also relaxing the requirement of source data availability during target domain adaptation. In this paper, we focus on SFDA for semantic segmentation, in which pseudo labeling based target domain self-training is a common solution. However, pseudo labels generated by the source models are particularly unreliable on the target domain data due to the domain shift issue. Therefore, we propose to use Bayesian Neural Network (BNN) to improve the target self-training by better estimating and exploiting pseudo-label uncertainty. With the uncertainty estimation of BNNs, we introduce two novel self-training based components: Uncertainty-aware Online Teacher-Student Learning (UOTSL) and Uncertainty-aware FeatureMix (UFM). Extensive experiments on two popular benchmarks, GTA5 → Cityscapes and SYNTHIA → Cityscapes, show the superiority of our proposed method with mIoU gains of 3.6% and 5.7% over the state-of-the-art respectively.
Open zero-shot learning via asymmetric VAE with dissimilarity space

Generalized Zero-Shot Learning (GZSL) aims to classify samples from seen and unseen classes using class-level features. Although existing GZSL methods have achieved remarkable progress, GZSL assumes a closed-set scenario, where both the seen and unseen classes are pre-defined. This assumption does not satisfy the complexity and variability of real-world scenarios. To address this, a new paradigm called Open Zero-Shot Learning (OZSL) has emerged, which aims to identify seen and unseen classes while rejecting the unknown ones where no semantic and visual features are available. Due to the absence of semantic features for unknown classes, the conventional methods adopted in GZSL are no longer applicable. Our research discovered that dissimilarities within the semantic space could be transferred to the visual space, achieving a shared dissimilarity space. Building upon this idea, we propose an asymmetric Variational Autoencoder (VAE) architecture. The encoders compute the dissimilarities within features and learn the latent distribution of dissimilarities, while the decoders synthesize features for unseen and unknown classes conditioned on original features and dissimilarities. We evaluate the proposed method on benchmark datasets, including AWA, CUB, SUN, and FLO, and achieve state-of-the-art OZSL performance.
Integrating Vision Transformer-Based Bilinear Pooling and Attention Network Fusion of RGB and Skeleton Features for Human Action Recognition

Abstract In this paper, we propose VT-BPAN, a novel approach that combines the capabilities of Vision Transformer (VT), bilinear pooling, and attention network fusion for effective human action recognition (HAR). The proposed methodology significantly enhances the accuracy of activity recognition through the following advancements: (1) The introduction of an effective two-stream feature pooling and fusion mechanism that combines RGB frames and skeleton data to augment the spatial–temporal feature representation. (2) The development of a spatial lightweight vision transformer that mitigates computational costs. The evaluation of this framework encompasses three widely employed video action datasets, demonstrating that the proposed approach achieves performance on par with state-of-the-art methods.
Child sexual abuse material on the darknet

Abstract By routing traffic through a random combination of servers worldwide, the darknet obfuscates the identity of its users, making it an attractive medium for journalists, dissidents, and individuals committing crimes. Since 2008, access to the darknet has been facilitated by the The Onion Router (TOR) browser, bringing the darknet within reach of an increasingly wider audience. Tens of thousands of darknet forums serve the criminal needs of millions of users each day and hundreds of these darknet forums are especially dedicated to the exchange of child sexual abuse materials (CSAM). Practitioners who work with men with sexual offences may therefore face individuals whose sexual offences occurred partly or wholly in the darknet. In the current review article, we summarize both the scientific literature and evidence obtained from CSAM forum “take-downs,” to describe the organization of darknet CSAM forums and the activities of their members. These forums report large and international memberships of individuals who, much like mainstream social media, interact online on a regular basis, creating large, online communities in which like-minded individuals can socialize and barter CSAM with minimal risk of discovery. Not all forum members contribute equally to the community, and especially administrators appear indispensable for the proper functioning of the CSAM forum. Implications for future research and law enforcement are discussed.
Batch Active Learning for Multispectral and Hyperspectral Image Segmentation Using Similarity Graphs

Abstract Graph learning, when used as a semi-supervised learning (SSL) method, performs well for classification tasks with a low label rate. We provide a graph-based batch active learning pipeline for pixel/patch neighborhood multi- or hyperspectral image segmentation. Our batch active learning approach selects a collection of unlabeled pixels that satisfy a graph local maximum constraint for the active learning acquisition function that determines the relative importance of each pixel to the classification. This work builds on recent advances in the design of novel active learning acquisition functions (e.g., the Model Change approach in arXiv:2110.07739) while adding important further developments including patch-neighborhood image analysis and batch active learning methods to further increase the accuracy and greatly increase the computational efficiency of these methods. In addition to improvements in the accuracy, our approach can greatly reduce the number of labeled pixels needed to achieve the same level of the accuracy based on randomly selected labeled pixels.
Dual Reversible Data Hiding in Encrypted Halftone Images Using Matrix Encoding

Data hiding and reversible data hiding research has primarily focused on grayscale and color images, because binary and halftone images are prone to visual distortion caused by a small number of errors in pixel representation. As a result, reversible data hiding is more useful than halftone-based data hiding. This study proposes an investigation of encrypted halftone images based on dual reversible data hiding, which improves the reversibility and security of the image by utilizing a dual cover image. Since halftone images are adequately compressed, they are beneficial in low-channel-bandwidth environments. Hamming code (HC) (7,4) is applied to each block of the halftone image to hide the secret data, and two halftone images are recorded and sent to different receivers at the end of the embedding process. Recipients can use the proposed method and the two marked images to extract the message and recover the cover halftone image. The proposed data hiding method can enhance the quality of the decrypted image by appropriately increasing the block size, and conversely, sufficiently large amounts of data can be hidden by reducing the block size. The experimental results provide evidence of the effectiveness of the proposed method in terms of both image quality and the embedding rate.
Research on Illegal Mobile Device Identification Based on Radio Frequency Fingerprint Feature

Internet of Things (IoT) technology is widely used in new power systems, and it also provides many new modes for network attacks. Illegal terminal device identification is also a significant topic in the field of wireless authentication technology. Some kinds of power network equipment are located in sparsely populated areas and rely on IoT terminals for real-time monitoring. Attackers use illegal terminals to connect power IoT devices for production monitoring and to carry out network attacks, which may cause serious damage, such as power data theft and misoperation of power network equipment. Radio frequency fingerprint (RFF) can extract hardware features from different devices, and is widely used for device identification and authentication. The area over which power network equipment placed is vast, and there are many wireless communication devices and terminals. It is difficult to identify illegal devices through commonly used network management techniques, thus making it difficult to distinguish between the mobile terminals of employees and illegal terminals in general spectrum screening. In response to the above situation, this paper uses the characteristics of the squared spectrum of random access preamble signals to extract hardware device features, proposes an illegal device identification algorithm based on Gaussian distribution theory, and evaluates its performance. The experimental results show that, when the signal-to-noise ratio (SNR) is greater than 15 dB, the average recognition result is greater than 92%. In addition, the algorithm has low computational complexity and high engineering application value.
Path Planning for Perpendicular Parking of Large Articulated Vehicles Based on Qualitative Kinematics and Geometric Methods

Since large articulated vehicles have uncertainties in trailer articulation angle as well as dynamic complexity, it is not easy to accurately establish a reliable motion plan. In this paper, two geometric path plans constructed based on the empirical rules of driving experts are presented so that articulated vehicles can automatically perform perpendicular parking on a reverse path. By analyzing the empirical parking methods of professional drivers, these path plans were constructed by appropriately combining several standardized simple basic motions to facilitate implementation in real vehicles. In addition, the path plans included appropriate complementary motions to effectively respond to uncertainties arising from articulation angles, etc. The complementary motions developed in this study are based on the results of qualitative analysis on the behavior of articulated vehicles. The usefulness of the proposed articulated vehicle parking method has been proven through hundreds of experimental tests using a scaled model automated vehicle.
Big data: an optimized approach for cluster initialization

Abstract The k-means, one of the most widely used clustering algorithm, is not only faster in computation but also produces comparatively better clusters. However, it has two major downsides, first it is sensitive to initialize k value and secondly, especially for larger datasets, the number of iterations could be very large, making it computationally hard. In order to address these issues, we proposed a scalable and cost-effective algorithm, called R-k-means, which provides an optimized solution for better clustering large scale high-dimensional datasets. The algorithm first selects O(R) initial points then reselect O(l) better initial points, using distance probability from dataset. These points are then again clustered into k initial points. An empirical study in a controlled environment was conducted using both simulated and real datasets. Experimental results showed that the proposed approach outperformed as compared to the previous approaches when the size of data increases with increasing number of dimensions.
Deep semantic image compression via cooperative network pruning

Incorporating semantic analysis into image compression can significantly reduce the repetitive computation of fundamental semantic analysis in downstream applications such as semantic image retrieval. In this paper, we tackle the semantic image compression task, which embeds semantics in the compressed bitstream. An intuitive solution to this task is joint multi-task training, which generally results in the trade-off of one task to accommodate the other. We thus provide an alternative pilot solution: given a pair of pre-trained teacher networks that specialize in image compression and semantic inference respectively, we first fuse both models to acquire an ensemble model and then leverage cooperative network pruning and retraining to condense the knowledge. Various experiments on five benchmark datasets validate that the proposed method achieves on par and in many cases better performance than the teachers yet comes in a more compact size, and outperforms its multi-task learning and knowledge distillation counterparts.