Reviewerly/exHarmony huggingface dataset:
Fusing hybrid attentive network with self-supervised dual-channel heterogeneous graph for knowledge tracing

Recently the large-scale influence of Covid-19 promoted the fast development of intelligent tutoring systems (ITS). As a major task of ITS, Knowledge Tracing (KT) aims to capture a student’s dynamic knowledge state based on his historical response sequences and provide personalized learning assistance to him. However, most existing KT methods have encountered the data sparsity problem. In real scenarios, an online tutoring system usually has an extensive collection of questions while each student can only interact with a limited number of questions. As a result, the records of some questions could be extremely sparse, which degrades the performance of traditional KT models. To resolve this issue, we propose a Dual-channel Heterogeneous Graph Network (DHGN) to learn informative representations of questions from students’ records by capturing both the high-order heterogeneous and local relations. As the supervised learning manner applied in previous methods is incapable of exploiting unobserved relations between questions, we innovatively integrate a self-supervised framework into the KT task and employ contrastive learning via the two channels of DHGN, supplementing as an auxiliary task to improve the KT performance. Moreover, we adopt the attention mechanism, which has achieved impressive performance in natural language processing tasks, to effectively capture students’ knowledge state. But the standard attention network is inapplicable to the KT task because the current knowledge state of a student usually shows strong dependency on his recently interactive questions, unlike the situation of language processing tasks, which focus more on the long-term dependency. To avoid the inefficiency of standard attention networks in the KT task, we further devise a novel Hybrid Attentive Network (HAN), which produces both the global attention and the hierarchical local attention to model the long-term and short-term intents, respectively. Then, by the gating network, a student’s long-term and short-term intents are combined for efficient prediction. We conduct extensive experiments on several real-world datasets. Experimental results demonstrate that our proposed methods achieve significant performance improvement compared to existing state-of-the-art baselines, which validates the effectiveness of the proposed dual-channel heterogeneous graph framework and hybrid attentive network.
Few-shot website fingerprinting attack with cluster adaptation

Website fingerprinting attacks can destroy users' privacy on the Internet, even when the communication is through an anonymous system, such as Tor. Deep learning-based website fingerprinting attacks have made great progress in anonymous traffic classification, and obtained high classification accuracy. However, deep website fingerprinting attacks require a large amount of training data with annotations. With the version update of anonymous network systems or network condition changing, previously collected traffic traces cannot be used for the classification of the newly collected traces, and recollecting traffic traces is time-consuming and labor-intensive. To address the above problems, this paper proposes Cluster Website Fingerprinting Attack (CWFA) for the few-shot website fingerprinting attack, which is based on the clustering assumption: samples belonging to the same cluster have the same category. CWFA utilizes deep neural networks to extract the trace features, and aligns the labeled category centers with unlabeled target cluster centers in the feature space. CWFA can preserve the category-level structure and facilitates the classification of the newly collected traffic traces. We conduct extensive experiments on public datasets in both closed-world and open-world scenarios, and we test the performance of the defended traces. Compared with the state-of-the-art WF methods, experiment results demonstrate the effectiveness and superiority of our proposed method.
A game theory-based fusion algorithm for autonomous navigation of smart ships

Recent technological advancements facilitate the autonomous navigation of smart ships. Modern navigation systems and a range of various sensors provide real-time data on ship movements. Nevertheless, the disturbance of the marine environment and the occasional failure of the sensors affect the accuracy and reliability of such data. Hence, it is essential to develop methods that accurately and reliably estimate the ship’s motions. This paper proposes a data fusion method based on game theory and investigates its accuracy and reliability. In this method, we consider a model-driven discrete-time Kalman fusion, and a data-driven adaptive weighted fusion as the game objects estimating the ship’s position and its heading in real-time. We then design the corresponding game strategy based on local and historical estimations. Following the designed game strategy, the local estimations are then converted to the final fusion results. To verify the validity and effectiveness of the proposed method, we carry out extensive simulations and model experiments. The results confirm the accuracy of the estimations provided by this method and demonstrate its fault-tolerance performance. It is also shown that the proposed method meets the actual engineering requirements in real-time.
A Fully Hybrid Algorithm for Deadline Constrained Workflow Scheduling in Clouds

With the migration of more and more workflows to clouds, the workflow scheduling in clouds (WSC) becomes a critical problem. Although many algorithms have been presented for WSC, there is still room and need for improvement. This paper formulates WSC as a constrained optimization problem that optimizes workflow execution cost within a workflow deadline constraint and proposes a fully hybrid workflow scheduling algorithm, called HPCP-PSO to solve it. Unlike previous works, HPCP-PSO is based on the repeated and alternated execution of two different methods, namely, the heuristic IaaS Cloud Partial Critical Paths (IC-PCP) and meta-heuristic Particle Swarm Optimization (PSO). Moreover, HPCP-PSO incorporates with two novel designs: 1) a new solution encoding strategy not only to sufficiently embody the elasticity of cloud resources, but also to reflect the scheduling relationship between assigned and unassigned tasks; 2) a solution repair strategy on each infeasible lease process to utilize a user-defined deadline more effectively and enhance the solution efficiency of the algorithm. Extensive experiments are conducted on four real-world scientific workflows and the results show that compared with IC-PCP, PSO, and HGSA, the proposed algorithm outperforms them on average by 35.83%, 70.53%, and 87.71% in terms of workflow execution cost.
On Intelligent Mathematics underpinning Contemporary Abstract Sciences and Autonomous AI

It is recognized that the kernels of human knowledge and theories of abstract sciences have been archived in mathematical forms with indispensable generality and abstractedness [1]–[8]. Intelligent Mathematics (IM) [8]–[14] is contemporary mathematics for manipulating complex mathematical entities in the brain and intelligence systems known as hyperstructures ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{H}$</tex> ) for formalizing rigorous expressions, inferences, and computational intelligence generation. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{H}$</tex> [8] are nonnumerical abstract entities beyond those of real numbers ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{R}$</tex> ) and bits ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{B}$</tex> ). Therefore, IM becomes highly demanded across modern Abstract Sciences [15]–[17] as counterparts of classic entity-oriented Concrete Sciences.
Towards Formal XAI: Formally Approximate Minimal Explanations of Neural Networks

Abstract With the rapid growth of machine learning, deep neural networks (DNNs) are now being used in numerous domains. Unfortunately, DNNs are “black-boxes”, and cannot be interpreted by humans, which is a substantial concern in safety-critical systems. To mitigate this issue, researchers have begun working on explainable AI (XAI) methods, which can identify a subset of input features that are the cause of a DNN’s decision for a given input. Most existing techniques are heuristic, and cannot guarantee the correctness of the explanation provided. In contrast, recent and exciting attempts have shown that formal methods can be used to generate provably correct explanations. Although these methods are sound, the computational complexity of the underlying verification problem limits their scalability; and the explanations they produce might sometimes be overly complex. Here, we propose a novel approach to tackle these limitations. We (i) suggest an efficient, verification-based method for finding minimal explanations , which constitute a provable approximation of the global, minimum explanation; (ii) show how DNN verification can assist in calculating lower and upper bounds on the optimal explanation; (iii) propose heuristics that significantly improve the scalability of the verification process; and (iv) suggest the use of bundles , which allows us to arrive at more succinct and interpretable explanations. Our evaluation shows that our approach significantly outperforms state-of-the-art techniques, and produces explanations that are more useful to humans. We thus regard this work as a step toward leveraging verification technology in producing DNNs that are more reliable and comprehensible.
Smell &amp; Paste: Low-Fidelity Prototyping for Olfactory Experiences

Low-fidelity prototyping is so foundational to Human-Computer Interaction, appearing in most early design phases. So, how do experts prototype olfactory experiences? We interviewed eight experts and found that they do not because no process supports this. Thus, we engineered Smell & Paste, a low-fidelity prototyping toolkit. Designers assemble olfactory proofs-of-concept by pasting scratch-and-sniff stickers onto a paper tape. Then, they test the interaction by advancing the tape in our 3D-printed (or cardboard) cassette, which releases the smells via scratching. Our toolkit uses commodity materials; keeps iterations quick, approachable, and cheap; and circumvents electronics, programming, and chemical handling. We evaluated Smell & Paste in two studies. We found that the toolkit was approachable to people of any technical background and that novices and experts appropriated and extended the toolkit, making it personalized. Novices produced prototypes quickly, and experts were excited about the kit's technical affordances and integrating it into their practice.
Hybrid grey wolf and improved particle swarm optimization with adaptive intertial weight-based multi-dimensional learning strategy for load balancing in cloud environments

The popularization of Internet applications and rapid advent of information technologies have invited increased number of developers and companies to focus on the area of Cloud Computing (CC). The most significant issues and challenges in the domain of CC include Load Balancing (LB), scheduling of task executions and managing resource allocation. In particular, LB being the process of distributing computing resources and workloads has attracted maximum attention as it is the predominant issue in CC. In this paper, Hybrid Grey Wolf and Improved Particle Swarm Optimization Algorithm with Adaptive Intertial Weight-based multi-dimensional Learning Strategy (HGWIPSOA)-based LB scheme is proposed for enhancing precision and rapidness in task scheduling and assignment of resources to Virtual Machines (VMs) in cloud environments. In the proposed scheme, initially, Grey Wolf Optimization Algorithm (GWOA) is incorporated into Particle Swarm Optimisation (PSO) for considering the highest fitness particle as alpha wolf search agent, such that the objective of allocating tasks to VMs is attained effectively and efficiently. It then integrates chaos, Adaptive Inertial Weight (AIW) and Dimensional Learning (DL) into PSO specifically to prevent premature convergence and achieve better convergence speed and global search ability depending on the best experience determined by particles for effective LB. The simulation experiments of the proposed HGWIPSOA mechanism confirm better results by offering 21.32 % improved throughput, 19.84 % reduced makespan, 24.98 % minimized degree of imbalance, 18.74 % reduced latency and 27.92 % reduced execution time independent of the number of tasks in the cloud environment on par with benchmarked LB schemes.
Soft skills required from software professionals in New Zealand

Soft skills (e.g., communication) significantly contribute to software project success. We aim to understand (a) what are relevant soft skills in software engineering, (b) how soft skills relate to characteristics of hiring organizations, and (c) how reliably we can automatically identify soft skills in job adverts to support their continuous analysis. We focus on soft skills required by organizations in New Zealand, a country with a small but growing software sector characterized by a skills shortage, reliance on offshoring, and embedded in a bi-cultural context. We manually analyzed 530 job adverts from New Zealand's largest portal for technology-related positions. We identified soft skills following an inductive approach, i.e., without pre-defined soft skills. We complemented the manual analysis with an automated analysis using Flexiterm (an approach for term recognition). We found explicit references to soft skills in 82% of adverts. Adverts from recruitment agencies (compared to hiring companies) included fewer soft skills. We identified 17 soft skills and proposed a contextualized software engineering description. Communication-related skills are most in demand. Soft skills related to broader human or societal values (e.g., empathy, cultural awareness) or distributed development are not common. Soft skills do not depend on company size or core business and domain of companies, or whether a company operates globally. Automatically identifying soft skills in adverts is error-prone. Employers explicitly ask for soft skills. Our findings support previous studies that highlight the importance of communication. On the other hand, identified soft skills only partially overlap with those reported in other skills classifications. Characteristics specific to New Zealand do not impact the demand for soft skills. Our findings benefit researchers in human aspects of software engineering and to those responsible for staff, curricula and professional development.
An accelerating convolutional neural networks via a 2D entropy based-adaptive filter search method for image recognition

The success of CNNs for various vision tasks has been accompanied by a significant increase in required FLOPs and parameter quantities, which has impeded the deployment of CNNs on devices with limited computing resources and power budgets. Network pruning, which compresses and accelerates CNN models, is an effective solution to this issue. Some studies have considered pruning as a special case of neural network search (NAS) in recent years. However, existing techniques are often computationally complex or prone to sub-optimal pruning results. As such, this paper proposes a novel acceleration method via a 2D Entropy based-Adaptive Filter Search (2EAFS). The importance of corresponding filters, measured by utilizing the amount of information contained in feature maps, is employed as a theoretical guide to simplify the complex exhaustive search process. Information entropy is then normalized layer by layer and the resulting value is used to calculate a layer-wise importance score in a single step. Additionally, a sparse constraint equation is constructed based on the negative correlation between filter pruning rates and the importance of convolutional layers. The Nelder–Mead search algorithm is then adopted to quickly and adaptively determine the optimal pruning architecture. Finally, importance weights are inherited using the pruning rate and 2D entropy and model performance are restored through fine-tuning. Extensive experiments conducted with the CIFAR-10/100, ILSVRC-2012, NWPU-RESISC45 and CUB-200-2011 datasets showed this approach achieved considerable accuracy increases, with significant reductions in FLOPs and required parameters that surpassed current state-of-the-art methods by a wide margin. For example, 2EAFS achieved a 44.1% reduction in FLOPs over ResNet-50, with only a 0.53% Top-5 accuracy decrease for ILSVRC-2012.
Recursive state estimation for state-saturated systems with two groups of measurements: Handling delayed and degraded sensors

In this paper, the recursive fusion estimation problem is investigated for a class of state-saturated systems with two groups of sensors, one with instantaneous measurements and the other with delayed measurements. The phenomena of sensor gain degradations and sensor measurement delays are regulated by a number of mutually independent random variables that are uniformly distributed over known intervals. First, an equivalent model to the original measurement system is constructed by reorganizing the instantaneous and delayed measurements. Then, by turning to a constrained variance method, we construct an upper bound of the estimation error covariance by solving two Riccati-like recursive equations whose dimension is the same as that of the original system. Subsequently, the estimator gain matrix is computed through minimizing the constructed upper bound, and the boundedness of the acquired upper bound is also discussed. Finally, we provide a simulation example to verify the usefulness of our designed fusion estimation algorithm.
Application of Artificial Intelligence Model Solar Radiation Prediction for Renewable Energy Systems

Solar power is an excellent alternative power source that can significantly cut our dependency on nonrenewable and destructive fossil fuels. Solar radiation (SR) can be predicted with great precision, and it may be possible to drastically minimize the impact cost associated with the development of solar energy. To successfully implement solar power, all projects using solar energy must have access to reliable sun radiation data. However, the deployment, administration, and performance of photovoltaic or thermal systems may be severely impacted by the lack of access to and the ambiguity of this data. Methods for estimating and predicting solar radiation can help solve these problems. Prediction techniques can be put to use in the real world to, for example, keep the power grid functioning smoothly and ensure that the supply of electricity exactly matches the demand at all times. Recently developed forecasting methods include the deep learning convolutional neural networks combined with long short-term memory (CNN-LSTM) model. This study provides a comprehensive examination of meteorological data, along with the CNN-LSTM methods, in order to design and train the most accurate SR forecasting artificial neural network model possible. Weather data was collected from a NASA meteorological station that included details such as the current temperature, the relative humidity, and the speed of the wind. This research revealed that SR is highly correlated with both temperature and radiation. Furthermore, the findings demonstrated that the CNN-LSTM algorithm outperformed the other algorithm-trained models, as evidenced by the performance score of the respective models, with a maximum coefficient determination (R²) &gt; 95% and a minimum mean square error (MSE) of 0.000987 at the testing step. In comparison with different existing artificial intelligence models, the CNN-LSTM model outperformed the other models. These scenarios demonstrated that a basic implementation of CNN-LSTM can be used to supplement conventional methods for predicting SR, provide possibilities to monitor radiation at a low cost, and encourage the adoption of data-driven management.
Review of GrabCut in Image Processing

As an image-segmentation method based on graph theory, GrabCut has attracted more and more researchers to pay attention to this new method because of its advantages of simple operation and excellent segmentation. In order to clarify the research status of GrabCut, we begin with the original GrabCut model, review the improved algorithms that are new or important based on GrabCut in recent years, and classify them in terms of pre-processing based on superpixel, saliency map, energy function modification, non-interactive improvement and some other improved algorithms. The application status of GrabCut in various fields is also reviewed. We also experiment with some classical improved algorithms, including GrabCut, LazySnapping, OneCut, Saliency Cuts, DenseCut and Deep GrabCut, and objectively analyze the experimental results using five evaluation indicators to verify the performance of GrabCut. Finally, some existing problems are pointed out and we also propose some future work.
KHGCN: Knowledge-Enhanced Recommendation with Hierarchical Graph Capsule Network

Knowledge graphs as external information has become one of the mainstream directions of current recommendation systems. Various knowledge-graph-representation methods have been proposed to promote the development of knowledge graphs in related fields. Knowledge-graph-embedding methods can learn entity information and complex relationships between the entities in knowledge graphs. Furthermore, recently proposed graph neural networks can learn higher-order representations of entities and relationships in knowledge graphs. Therefore, the complete presentation in the knowledge graph enriches the item information and alleviates the cold start of the recommendation process and too-sparse data. However, the knowledge graph's entire entity and relation representation in personalized recommendation tasks will introduce unnecessary noise information for different users. To learn the entity-relationship presentation in the knowledge graph while effectively removing noise information, we innovatively propose a model named knowledge-enhanced hierarchical graph capsule network (KHGCN), which can extract node embeddings in graphs while learning the hierarchical structure of graphs. Our model eliminates noisy entities and relationship representations in the knowledge graph by the entity disentangling for the recommendation and introduces the attentive mechanism to strengthen the knowledge-graph aggregation. Our model learns the presentation of entity relationships by an original graph capsule network. The capsule neural networks represent the structured information between the entities more completely. We validate the proposed model on real-world datasets, and the validation results demonstrate the model's effectiveness.