Reviewerly/exHarmony huggingface dataset:
Dual Priors Network for RGB-D Salient Object Detection

Although the detection accuracy of RGB-D salient object detection by deep learning has met most of the task requirements, it is still a challenge to predict complete salient objects under the guidance of poor depth maps. In this paper, edge prior and depth prior are utilized to guide the network to detect salient objects, and a novel dual priors network called DPNet is proposed for RGB-D SOD. DPNet utilizes edge priors to compensate for the inadequate guidance of low-quality depth maps. Specially, the input RGB and depth maps are encoded by ResNet-50 backbone. To fuse these information effectively, multi-modal feature fusion module downsamples high-resolution features to enhance low-resolution features with its rich semantic information. Then the initial salient masks are decoded by a coarse-grained mask decoder. In addition, edge prior serves as label and is captured by an edge aware module. Finally, the fine-grained salient masks are obtained by fusing the initial salient masks and the salient edges. The experimental results on six benchmarks indicate that the proposed method outperforms ten state-of-the-art methods in six evaluation metrics.
Entity Set Co-Expansion in StackOverflow

Given a few seed entities of a certain type (e.g., Software or Programming Language), entity set expansion aims to discover an extensive set of entities that share the same type as the seeds. Entity set expansion in software-related domains such as StackOverflow can benefit many downstream tasks (e.g., software knowledge graph construction) and facilitate better IT operations and service management. Meanwhile, existing approaches are less concerned with two problems: (1) How to deal with multiple types of seed entities simultaneously? (2) How to leverage the power of pre-trained language models (PLMs)? Being aware of these two problems, in this paper, we study the entity set co-expansion task in StackOverflow, which extracts Library, OS, Application, and Language entities from StackOverflow question-answer threads. During the co-expansion process, we use PLMs to derive embeddings of candidate entities for calculating similarities between entities. Experimental results show that our proposed SECoExpan framework outperforms previous approaches significantly.
Heterogeneous Randomized Response for Differential Privacy in Graph Neural Networks

Graph neural networks (GNNs) are susceptible to privacy inference attacks (PIAS) given their ability to learn joint representation from features and edges among nodes in graph data. To prevent privacy leakages in GNNs, we propose a novel heterogeneous randomized response (HeteroRR) mechanism to protect nodes’ features and edges against PIAS under differential privacy (DP) guarantees, without an undue cost of data and model utility in training GNNs. Our idea is to balance the importance and sensitivity of nodes’ features and edges in redistributing the privacy budgets since some features and edges are more sensitive or important to the model utility than others. As a result, we derive significantly better randomization probabilities and tighter error bounds at both levels of nodes’ features and edges departing from existing approaches, thus enabling us to maintain high data utility for training GNNs. An extensive theoretical and empirical analysis using benchmark datasets shows that HeteroRR significantly outperforms various baselines in terms of model utility under rigorous privacy protection for both nodes’ features and edges. That enables us to defend PIAs in DP-preserving GNNs effectively.
Factor Detection Task of Cyberbullying Using the Deep Learning Model

Cyberbullying is an important issue in recent years because the opinion of everyone can be easily circulated and discussed in the era of social media and Internet celebrity. However, this communication of convenience on the Internet exacerbates the diffusion of malicious comments and makes cyberbullying easier to happen. Cyberbullying leads to psychological trauma or suicide to victims. Cyberbullying belongs to a complex problem with many different factors which is quite difficult to detect. Most studies have focused on whether a comment contains cyberbullying behaviors or key words in an article or a sentence. Their research has not considered cyberbullying factors for each comment, and cannot explain what kind of reason to make the cyberbullying in the real world. Therefore, this paper proposes annotation rules to define six factors of cyberbullying, and these factors can help with follow-up analysis of cyberbullying patterns. The Chinese factors of cyberbullying corpus is collected from the largest forum, and manually annotated by the three experts. The Chinese factors of cyberbullying corpus will be used to train deep learning models to detect six factors of cyberbullying on each comment. In addition, the paper analyzes the cyberbullying factors on other larger data using the proposed model.
Unified Temporal Erasable Itemset Mining with a Lower-Bound Strategy

Erasable itemset mining has been a valuable mining problem for manufacturers. It can extract less profitable materials from a product dataset and provide managers with good decision-making and a trade-off between cost and profit. However, the traditional erasable itemset mining methods seldom consider the time factor. For time-sensitive industries such as agro-processors, the time range is important in determining which materials are less profitable. Hong et al. first proposed the concept of temporal erasable itemset mining and seven lifespan options. They also proposed a unified temporal erasable (UTE) mining algorithm for getting incomplete temporal erasable itemsets. Howerever, the UTE algorithm does not satisfy the property of downward closure. In this work, we propose an improved algorithm to improve the performance of the UTE algorithm based on a lower-bound strategy and satisfying the property of downward closure. The proposed algorithm uses a hash table to store information that will be reused during the mining process to avoid scanning a dataset multiple times. The designed lower-bound strategy can preserve the downward closure property, narrowing the search space of candidate itemsets. In numerical experiments, we compare the performance using several metrics between the proposed method and the previous work. From the results of experiments, our proposed method outperforms the existing method on various metrics, such as execution time and the number of candidate erasable itemsets.
Scalable Learning with Incremental Probabilistic PCA

Incremental class learning is the classification problem of learning a model where instances from new object classes are added sequentially, and it is desired that the model be retrained only on the new classes with minimal training on the old classes. One major problem facing class incremental learning is catastrophic forgetting, where the updated model forgets the old classes and focuses only on the new classes. This paper proposes a simple and novel incremental class learning method that uses a self-supervised pretrained feature extractor to obtain meaningful features and trains Probabilistic PCA models on the extracted features for each class separately. The Mahalanobis distance is used to obtain the classification result, and an equivalent equation is derived to make the approach computationally affordable. Experiments on standard and large datasets show that the proposed approach outperforms existing state of the art incremental learning methods by a large margin. The fact that the model is trained on each class separately makes it applicable to training on very large datasets such as the whole ImageNet with more than 10,000 classes.
Noise‐tolerate and adaptive coefficient zeroing neural network for solving dynamic matrix square root

Abstract The solving of dynamic matrix square root (DMSR) problems is frequently encountered in many scientific and engineering fields. Although the original zeroing neural network is powerful for solving the DMSR, it cannot vanish the influence of the noise perturbations, and its constant‐coefficient design scheme cannot accelerate the convergence speed. Therefore, a noise‐tolerate and adaptive coefficient zeroing neural network (NTACZNN) is raised to enhance the robust noise immunity performance and accelerate the convergence speed simultaneously. Then, the global convergence and robustness of the proposed NTACZNN are theoretically analysed under an ideal environment and noise‐perturbed circumstances. Furthermore, some illustrative simulation examples are designed and performed in order to substantiate the efficacy and advantage of the NTACZNN for the DMSR problem solution. Compared with some existing ZNNs, the proposed NTACZNN possesses advanced performance in terms of noise tolerance, solution accuracy, and convergence rate.
Fast and optimal branch-and-bound planner for the grid-based coverage path planning problem based on an admissible heuristic function

This paper introduces an optimal algorithm for solving the discrete grid-based coverage path planning (CPP) problem. This problem consists in finding a path that covers a given region completely. First, we propose a CPP-solving baseline algorithm based on the iterative deepening depth-first search (ID-DFS) approach. Then, we introduce two branch-and-bound strategies (Loop detection and an Admissible heuristic function) to improve the results of our baseline algorithm. We evaluate the performance of our planner using six types of benchmark grids considered in this study: Coast-like, Random links, Random walk, Simple-shapes, Labyrinth and Wide-Labyrinth grids. We are first to consider these types of grids in the context of CPP. All of them find their practical applications in real-world CPP problems from a variety of fields. The obtained results suggest that the proposed branch-and-bound algorithm solves the problem optimally (i.e., the exact solution is found in each case) orders of magnitude faster than an exhaustive search CPP planner. To the best of our knowledge, no general CPP-solving exact algorithms, apart from an exhaustive search planner, have been proposed in the literature.
Breast Cancer Prediction and Control Using BiLSTM and Two-Dimensional Convolutional Neural Network

Breast cancer has a devastating effect on women. Different strategies of breast cancer classification exist with minimal work done on the prediction of the occurrence of the disease in potential carriers. In this study, a breast cancer predictive system has been developed using bidirectional long short-term memory (BiLSTM) for feature extraction and learning while the two-dimensional convolutional neural network (CNN) was used for breast cancer classification. Histopathological images were used for cancer prediction. Python was used as the programming language for implementing the system. The model was tested using datasets from The Cancer Imaging Archive (TCIA) repository. An accuracy level of 98.8% (higher than the most recent existing model) was achieved for the prediction of the future occurrence of breast cancer based on the tests on the dataset. The application of the model using live data from women can help in the prediction and control of the occurrence of breast cancer amongst women.
AI-Readiness

As AI adoption in the Maltese islands is in its infancy, there is minimal literature to which one can refer to when researching the implementation of AI solutions in the Maltese public administration. In this regard, a report published by Capgemini Consulting, analysing the AI readiness in numerous countries of which the majority are European, ranked Malta the 19th out of 35 countries (Tinholt in Artificial intelligence readiness and performance benchmark, 2018 [1]).
Speeding up Smartphone-Based Dew Computing: In Vivo Experiments Setup Via an Evolutionary Algorithm

Dew computing aims to minimize the dependency on remote clouds by exploiting nearby nodes for solving non-trivial computational tasks, e.g., AI inferences. Nowadays, smartphones are good candidates for computing nodes; hence, smartphone clusters have been proposed to accomplish this task and load balancing is frequently a subject of research. Using the same real-i.e., in vivo-testbeds to evaluate different load balancing strategies based on energy utilization is challenging and time consuming. In principle, test repetition requires a platform to control battery charging periods between repetitions. Our Motrol hard-soft device has such a capability; however, it lacks a mechanism to assure and reduce the time in which all smartphone batteries reach the level required by the next test. We propose an evolutionary algorithm to execute smartphone battery (dis)charging plans to minimize test preparation time. Charging plans proposed by the algorithm include charging at different speeds, which is achieved by charging at maximum speed while exercising energy hungry components (the CPU and screen). To evaluate the algorithm, we use various charging/discharging battery traces of real smartphones and we compare the time-taken for our method to collectively prepare a set of smartphones versus that of individually (dis)charging all smartphones at maximum speed.
Improving a Library FAQ: Assessment and Reflection of the First Year’s Use

ABSTRACTABSTRACTIn 2020, the Leonard Lief Library created a searchable online knowledge base (FAQs) as a complement to virtual reference during the library’s pandemic-related closure. One year of search query data was used to assess the online knowledge base. This paper discusses the assessment’s findings and planned improvements to the FAQs. A content analysis of user queries revealed what users are seeking in the knowledge base. The study examined the actions taken by users after conducting a search to determine the knowledge base’s success rate. The knowledge base was successful in answering user questions almost half of the time. The top three query categories were access, non-library, and instructional. The frequency of access-related queries was expected, due to COVID-19 library building closure. The prevalence of questions about other campus units was unexpected. This finding suggests a perception of the academic library as a source of campus information and supports including this type of information in the library FAQs. This study adds to the body of assessment research within reference services, an area not well represented in the LIS literature. The methodology employed provides a model for assessment of online FAQs that can be adopted by other libraries.KEYWORDS: Knowledge baseFAQassessmentonline referencequery analyzer AcknowledgmentsThe authors would like to thank John DeLooper, Web Services-Online Learning Librarian, our colleague in the Leonard Lief Library at Lehman College for his work on creating and customizing the knowledge base’'s landing page.Disclosure statementWe have no known conflict of interest to disclose.
Lightweight image super-resolution via overlapping back-projection feedback network for embedded devices

Super-resolution (SR) technology is widely used in embedded devices because it can improve image quality. However, to achieve improved performance, SR networks usually take a massive memory because of their large number of parameters. They are not applicable for embedded devices with low power consumption. In this work, we propose an overlapping back-projection feedback network (LOBFN) for image SR, which is a lightweight network designed for embedded devices. First, a back-projection feedback block (PFB) and recursive concatenation are used to learn the hierarchical representations of the network. Second, an overlapping back-projection suitable for lightweight network is proposed to minimize the reconstruction errors. Finally, a fusion attention module (FAM) is proposed to perceive information-rich features. The final experiments proved that the proposed LOBFN significantly improved the SR performance of lightweight networks.
ATICVis: A Visual Analytics System for Asymmetric Transformer Models Interpretation and Comparison

In recent years, natural language processing (NLP) technology has made great progress. Models based on transformers have performed well in various natural language processing problems. However, a natural language task can be carried out by multiple different models with slightly different architectures, such as different numbers of layers and attention heads. In addition to quantitative indicators such as the basis for selecting models, many users also consider the language understanding ability of the model and the computing resources it requires. However, comparing and deeply analyzing two transformer-based models with different numbers of layers and attention heads are not easy because it lacks the inherent one-to-one match between models, so comparing models with different architectures is a crucial and challenging task when users train, select, or improve models for their NLP tasks. In this paper, we develop a visual analysis system to help machine learning experts deeply interpret and compare the pros and cons of asymmetric transformer-based models when the models are applied to a user’s target NLP task. We propose metrics to evaluate the similarity between layers or attention heads to help users to identify valuable layers and attention head combinations to compare. Our visual tool provides an interactive overview-to-detail framework for users to explore when and why models behave differently. In the use cases, users use our visual tool to find out and explain why a large model does not significantly outperform a small model and understand the linguistic features captured by layers and attention heads. The use cases and user feedback show that our tool can help people gain insight and facilitate model comparison tasks.
Multi-features fusion for short-term photovoltaic power prediction

In recent years, in order to achieve the goal of "carbon peaking and carbon neutralization", many countries have focused on the development of clean energy, and the prediction of photovoltaic power generation has become a hot research topic.However, many traditional methods only use meteorological factors such as temperature and irradiance as the features of photovoltaic power generation, and they rarely consider the multi-features fusion methods for power prediction.This paper first preprocesses abnormal data points and missing values in the data from 18 power stations in Northwest China, and then carries out correlation analysis to screen out 8 meteorological features as the most relevant to power generation.Next, the historical generating power and 8 meteorological features are fused in different ways to construct three types of experimental datasets.Finally, traditional time series prediction methods, such as Recurrent Neural Network (RNN), Convolution Neural Network (CNN) combined with eXtreme Gradient Boosting (XGBoost), are applied to study the impact of different feature fusion methods on power prediction.The results show that the prediction accuracy of Long Short-Term Memory (LSTM), stacked Long Short-Term Memory (stacked LSTM), Bi-directional LSTM (Bi-LSTM), Temporal Convolutional Network (TCN), and XGBoost algorithms can be greatly improved by the method of integrating historical generation power and meteorological features.Therefore, the feature fusion based photovoltaic power prediction method proposed in this paper is of great significance to the development of the photovoltaic power generation industry.
Modeling of value-based and meaningful choice situation for trainees in digital pedagogy

This paragraph is devoted to the peculiarities of creating value-based and meaningful choice situations for students in the conditions of digital pedagogy. As part of the study, a didactic model was developed in the pedagogical process, aimed at increasing the motivation of students by creating situations of sense choice using information and communication technologies (ICT). The proposed model of value-based and meaningful choice situations for students includes: purpose, principles, content, technologies, methods and teaching techniques. These components in the complex regulate emotional and sensitive as well as motivational and value-based manifestations of students in situations of choice. The diverse content is integrated into students’ personal sense, and technologies and methods directly “incorporate” the student’s sense development process.
The α-interval valued fuzzy sets defined on α-interval valued set

In this study, ?-interval valued set is defined whose elements are closed sub-intervals including ? of unit interval that is I = [0, 1]. With different order relation on this set, the properties of ?-interval valued set are examined. By the help of this order relation, it is shown that ?-interval valued set is complete lattice. Negation function on ?-interval valued set is given in order to study the theoretical properties of this set. By means of discussions on ?-interval valued set, the fundamental features of ?-interval valued set are studied. By the help of ?-interval valued set, ?-interval valued fuzzy sets are defined. The fundamental algebraic properties of these sets are examined. The level subsets of ?-interval valued fuzzy sets are defined to give the relations between ?-interval valued sets and crisp sets. With the help of this definition, some propositions and examples are given.
Outlier Detection of Crowdsourcing Trajectory Data Based on Spatial and Temporal Characterization

As an emerging type of spatio-temporal big data based on positioning technology and navigation devices, vehicle-based crowdsourcing data has become a valuable trajectory data resource. However, crowdsourcing trajectory data has been collected by non-professionals and with multiple measurement terminals, resulting in certain errors in data collection. In these cases, to minimize the impact of outliers and obtain relatively accurate trajectory data, it is crucial to detect and clean outliers. This paper proposes an efficient crowdsourcing trajectory outlier detection (CTOD) method that detects outliers from the trajectory sequence data in both spatial view and temporal view. Specifically, we first use the adaptive spatial clustering algorithm based on the Delaunay triangulation (ASCDT) algorithm to remove the location offset points in the trajectory sequence. After that, based on the most basic attributes of the trajectory points, a 6-dimensional movement feature vector is constructed for each point as an input. The feature-rich trajectory sequence data is reconstructed using the proposed temporal convolutional network autoencoder (TCN-AE), and the Squeeze-and-Excitation (SE) channel attention mechanism is introduced. Finally, the effectiveness of the CTOD method is experimentally verified.