Reviewerly/exHarmony huggingface dataset:
AENAR: An aspect-aware explainable neural attentional recommender model for rating predication

Explainable rating predication becomes challenging with the largely growing number of information and items. Of particular interest is to capture users’ preferences for various items by using textual reviews to achieve accurate and interpretable recommendations. In this paper, we report an aspect-aware explainable neural attentional recommender model for rating predication (AENAR) and this model enables intelligent predication and recommendation by capturing the varying aspect attentions that users pay to different items. The experimental results based on six public datasets reveals that the designed model consistently outperforms five existing state-of-the-art alternatives. Furthermore, the designed attention network allows to highlight the context-aware information in textual reviews that unambiguously suggest users’ aspect-level preference for their desired items, improving the interpretability of the rating prediction. • Applied CNN to extract fine-grained textual feature information. • An attention mechanism designed for capturing the diverse user preferences. • Explainable model that can trace back to keywords in review via attention mechanism.
Estimation of water attenuation coefficient by imaging modeling of the backscattered light with the pulsed laser range-gated imaging system

Compared to conventional floodlighted imaging systems, a pulsed laser range-gated imaging system can get a real-time high performance underwater image when the distance of target is known. Otherwise, optical properties of the water, mainly the attenuation coefficient of the water, should be obtained to automatically set the parameters of the imaging system. Typically, special instrument is required to measure the attenuation coefficient of the water. In this work, a water attenuation coefficient estimation method is proposed merely with the pulsed laser range-gated imaging system. The imaging model of the backscattered light of the pulsed laser range-gated imaging system is built through the light propagation theory. As a result, the water attenuation coefficient is calculated by nonlinear estimation method. Experiments under different water conditions are designed and carried out to verify the proposed method. Results show that the relative errors between the estimation and the reference are about 1% for the water in the water tank and seawater pool, and about 4% for the towing boat tank, respectively.
Self-Supervised Deep Learning Framework for Anomaly Detection in Traffic Data

The current state of practice in traffic data quality control features rule-based data checking and validation processes, where the rules are subjective and insensitive to variation inherited with traffic data. In this paper, self-supervised deep learning approaches were explored to leverage the existence of multiple sources of traffic volume data, which permitted cross-checking of one data source against another for improved robustness. Two types of models were developed, aiming at detecting data anomalies at two distinct timescales. Particularly, a novel variational autoencoder (VAE)-based model was formulated for discerning data anomalies at the daily level and four recurrent model structures, including recurrent neural networks (RNN), gated recurrent units (GRU), long short-term memory (LSTM) units, and liquid time constant (LTC) networks, were evaluated for detecting anomalies in finer incremental timescales (i.e., 5-min intervals). The effectiveness of the proposed methods was demonstrated using two independent sources of traffic data from the Georgia Department of Transportation: (1) traffic counts collected by inductive loops as part of the statewide traffic count program, and (2) traffic volumes acquired by a video detection system as part of the Georgia 511, an advanced traveler information system in Georgia. Based on our experiments, the VAE-based model achieved a precision of 0.95, recall of 0.92, and F1 score of 0.94. Among the recurrent models, the fully connected LTC produced the lowest prediction error and achieved a precision of 0.82, recall of 0.88, and F1 score of 0.85.
Twin-nanofiber structure for a highly efficient single-photon collection

Optical nanofiber-based single-photon source has attracted considerable interest due to its property of seamless integration with a single-mode fiber. With nanostructure engraved in the nanofiber, the single-photon collection efficiency can be greatly boosted with enhanced interaction between the single quantum emitter and the guided light. However, the prerequisite nanofabrication processes introduce complexities and extra loss. Here, we demonstrate that by simply placing a quantum emitter in the gap of two parallel nanofibers, single-photon coupling efficiency may reach 54.2%. Our numerical simulation results indicate that photon coupling efficiency of such simple structure is insensitive to the discrepancy in nanofiber radii, which further reduces the difficulties in device fabrication.
Target Detection of Low-Altitude UAV Based on Improved YOLOv3 Network

Most existing methods are difficult to detect low-altitude and fast-moving drones. A low-altitude unmanned aerial vehicle (UAV) target detection method based on an improved YOLOv3 network is proposed. While keeping the basic framework of the original model unchanged, the YOLOv3 model is improved. That is, multiscale prediction is added to enhance the detection ability of small-target objects. In addition, the two-axis Pan/Tilt/Zoom (PTZ) camera is controlled based on proportional integral derivative (PID), so that the target tends to the center of the field of view. It is more conducive to accurate detection. Finally, experiments are carried out using real UAV datasets. The results show that the mean average precision (mAP), AP50, and AP75 are 25.12%, 39.75%, and 26.03%, respectively, which are better than other methods. Also, the frame rate is 21 frames·s−1, which meets the performance requirements.
New Results in Vague Incidence Graphs with Application

Vague incidence graph (VIG), belonging to the FG family has good capabilities when facing with problems that cannot be expressed by FGs. When an element membership is not clear, neutrality is a good option that can be well-supported by a VIG. The previous definitions limitations in connectivity concept have led us to offer new definitions in VIGs. Hence, in this paper, the VIG and its matrix form are proposed. Vague incidence subgraph (VISG) is defined with several properties. Incidence pairs, paths, and connectivities between pairs in VIGs are introduced. Likewise, different types of strong and cut pair in VIGs are examined with their properties. Universities are one of the most important centers for human education and play an important role in the development of the country. But the point to be very careful is that the employees of a university must do their job in the best possible way. Therefore, we have tried to identify the most effective person in a university according to its performance by presenting an application.
Learning meta-adversarial features via multi-stage adaptation network for robust visual object tracking

Visual tracking is a crucial research topic in computer vision, which aims to locate any object as precisely as possible over a sequence of image frames. However, the existing trackers often suffer from the object drifting problem due to the difficulty of adapting to complex environments. In this paper, we propose a novel multi-stage adaptation network (MAN), including the meta-adaptation, feature adaptation, and location adaptation sub-networks, to improve the adaptability and robustness of tracking. Specifically, the meta-adaptation sub-network takes advantage of meta-learning to enhance the generalization ability for the new tracking sequence. The feature adaptation sub-network exploits an adversarial attention mask module and a multi-level and multi-scale meta-classifier module for improving the robustness and discriminative ability. Moreover, the location adaptation sub-network can refine the tracking location to avoid the drifting problem. The three sub-networks can benefit from each other and are strategically integrated in a whole framework. Extensive experimental results demonstrate that the proposed tracker outperforms the state-of-the-art methods on several challenging datasets, including OTB50, OTB2013, OTB100, UAV123, UAV20L, NfS, LaSOT, VOT2016, and VOT2018.
Transformation of education through Big Data: digital twins case study

Abstract Being one of the most decisive social phenomena, education is forced to be transformed by digitalization. As information society emerges, new challenges arise in implementing such technologies as Big Data and Artificial Intelligence. The authors have reviewed the existing practice of introducing new technologies in education. Identifying possible ways to effectively integrate Big Data into education is the aim of authors’ research. The paper investigates the issues of introducing new academic disciplines and technologies, as well as the results of overall education system transformation on the case study of Digital Twins technology as integral part of Big Data technology implementation in Big Science.
Why Is It Not Solved Yet?

Autoscaling is a task of major importance in the cloud computing domain as it directly affects both operating costs and customer experience. Although there has been active research in this area for over ten years now, there is still a significant gap between the proposed methods in the literature and the deployed autoscalers in practice. Hence, many research autoscalers do not find their way into production deployments. This paper describes six core challenges that arise in production systems that are still not solved by most research autoscalers. We illustrate these problems through experiments in a realistic cloud environment with a real-world multi-service business application and show that commonly used autoscalers have various shortcomings. In addition, we analyze the behavior of overloaded services and show that these can be problematic for existing autoscalers. Generally, we analyze that these challenges are only insufficiently addressed in the literature and conclude that future scaling approaches should focus on the needs of production systems.
Comparative Evaluation of NLP-Based Approaches for Linking CAPEC Attack Patterns from CVE Vulnerability Information

Vulnerability and attack information must be collected to assess the severity of vulnerabilities and prioritize countermeasures against cyberattacks quickly and accurately. Common Vulnerabilities and Exposures is a dictionary that lists vulnerabilities and incidents, while Common Attack Pattern Enumeration and Classification is a dictionary of attack patterns. Direct identification of common attack pattern enumeration and classification from common vulnerabilities and exposures is difficult, as they are not always directly linked. Here, an approach to directly find common links between these dictionaries is proposed. Then, several patterns, which are combinations of similarity measures and popular algorithms such as term frequency–inverse document frequency, universal sentence encoder, and sentence BERT, are evaluated experimentally using the proposed approach. Specifically, two metrics, recall and mean reciprocal rank, are used to assess the traceability of the common attack pattern enumeration and classification identifiers associated with 61 identifiers for common vulnerabilities and exposures. The experiment confirms that the term frequency–inverse document frequency algorithm provides the best overall performance.
A vision for design in the era of collective computing

In this study, we envision engineering design activities for collective computing, an upcoming era of complex systems of massive social interaction through a wide variety of connected computing devices. A literature review reveals how collective computing, compared to the previous eras of personal and ubiquitous computing, may lead to new design tasks and design processes, as well as new roles for designers. Based on this review, new design activities for the collective computing era are envisioned, and further revised in an interview study with 24 informants. The result is a vision for design in the collective computing era, with actionable guidance for designers in terms of a coherent set of new design activities proposed in relation to advances in computing.
A self-adaptive level-based learning artificial bee colony algorithm for feature selection on high-dimensional classification

Feature selection is an important data preprocessing method in data mining and machine learning, yet it faces the challenge of “curse of dimensionality” when dealing with high-dimensional data. In this paper, a self-adaptive level-based learning artificial bee colony (SLLABC) algorithm is proposed for high-dimensional feature selection problem. The SLLABC algorithm includes three new mechanisms: (1) A novel level-based learning mechanism is introduced to accelerate the convergence of the basic artificial bee colony algorithm, which divides the population into several levels and the individuals on each level learn from the individuals on higher levels, especially, the individuals on the highest level learn from each other. (2) A self-adaptive method is proposed to keep the balance between exploration and exploitation abilities, which takes the diversity of population into account to determine the number of levels. The lower the diversity is, the fewer the levels are divided. (3) A new update mechanism is proposed to reduce the number of selected features. In this mechanism, if the error rate of an offspring is higher than or is equal to that of its parent but selects more features, then the offspring is discarded and the parent is retained, otherwise, the offspring replaces its parent. Further, we discuss and analyze the contribution of these novelties to the diversity of population and the performance of classification. Finally, the results, compared with 8 state-of-the-art algorithms on 12 high-dimensional datasets, confirm the competitive performance of the proposed SLLABC on both classification accuracy and the size of the feature subset.
Augmenting data with generative adversarial networks: An overview

Performance of neural networks greatly depends on quality, size and balance of training dataset. In a real environment datasets are rarely balanced and training deep models over such data is one of the main challenges of deep learning. In order to reduce this problem, methods and techniques are borrowed from the traditional machine learning. Conversely, generative adversarial networks (GAN) were created and developed, a relatively new type of generative models that are based on game theory and consist of two neural networks, a generator and a discriminator. The generator’s task is to create a sample from the input noise that is based on training data distribution and the discriminator should detect those samples as fake. This process goes through a finite number of iterations until the generator successfully fools the discriminator. When this occurs, sample becomes a part of new (augmented) dataset. Even though the original GAN creates unlabeled samples, variants that soon appeared removed that limitation. Generating artificial data through these networks appears to be a meaningful solution to the imbalance problem since it turned out that artificial samples created by GAN are difficult to differentiate from the real ones. In this manner, new samples of minority class could be created and dataset imbalance ratio lowered.
Machine learning model for umbilical cord classification using combination coiling index and texture feature based on 2-D Doppler ultrasound images

The umbilical cord is an organ that circulates oxygen and nutrition from mother to fetus during pregnancy. This study aims to classify the umbilical cord based on ultrasound images. The similarity of shape and coil between each class becomes a challenge. Therefore, it requires feature values that are relevant to the characteristics of these three classes. The condition of imbalanced data sets in this study is also an obstacle that causes the classifier's performance to degrade on minority classes. Therefore, this study proposes a machine learning model capable of properly dealing with imbalanced data sets and recognizing the umbilical cord class.Furthermore, this study proposes a new feature extraction method, namely, the umbilical coiling index (UCI), which directly adopts obstetricians' knowledge. The proposed model consists of five stages: image preprocessing, feature extraction, feature selection, oversampling data using SMOTE, and Classification. Machine learning method observations were carried out comprehensively on five based classifiers: Random Forest, KNN, Decision tree, SVM, Naïve Bayes, and Multiclassifier. The results showed that the Random forest and Multiclassifier methods provide the highest accuracy, precision, recall, and F-measure performance in imbalanced data sets.
UAV Path Planning Based on Multicritic-Delayed Deep Deterministic Policy Gradient

Deep deterministic policy gradient (DDPG) algorithm is a reinforcement learning method, which has been widely used in UAV path planning. However, the critic network of DDPG is frequently updated in the training process. It leads to an inevitable overestimation problem and increases the training computational complexity. Therefore, this paper presents a multicritic-delayed DDPG method for solving the UAV path planning. It uses multicritic networks and delayed learning methods to reduce the overestimation problem of DDPG and adds noise to improve the robustness in the real environment. Moreover, a UAV mission platform is built to train and evaluate the effectiveness and robustness of the proposed method. Simulation results show that the proposed algorithm has a higher convergence speed, a better convergence effect, and stability. It indicates that UAV can learn more knowledge from the complex environment.
Experimental quantum simulation of dynamic localization on curved photonic lattices

Dynamic localization, which originates from the phenomena of particle evolution suppression under an externally applied AC electric field, has been simulated by suppressed light evolution in periodically curved photonic arrays. However, experimental studies on their quantitative dynamic transport properties and application for quantum information processing are rare. Here we fabricate one-dimensional and hexagonal two-dimensional arrays both with sinusoidal curvatures. We successfully observe the suppressed single-photon evolution patterns, and for the first time, to the best of our knowledge, measure the variances to study their transport properties. For one-dimensional arrays, the measured variances match both the analytical electric-field calculation and the quantum walk Hamiltonian engineering approach. For hexagonal arrays as anisotropic effective couplings in four directions are mutually dependent, the analytical approach suffers, whereas quantum walk conveniently incorporates all anisotropic coupling coefficients in the Hamiltonian and solves its exponential as a whole, yielding consistent variances with our experimental results. Furthermore, we implement a nearly complete localization to show that it can preserve both the initial injection and the wave packet after some evolution, acting as a memory of a flexible time scale in integrated photonics. We demonstrate a useful quantum simulation of dynamic localization for studying their anisotropic transport properties and a promising application of dynamic localization as a building block for quantum information processing in integrated photonics.
A step towards meeting battery raw material demand: the geology and exploration of graphite deposits, examples from northern Norway

Abstract In northern Norway, we have identified more than 40 occurrences of graphite through airborne geophysical surveys and follow-up fieldwork. Out of these, 29 have been investigated in detail using ground-based geophysical methods. For 21 of these occurrences, in combination with surface sampling, it is possible to estimate approximately the grade and tonnage of graphite. The graphite deposits are concentrated in two areas: the island of Senja and the Lofoten–Vesterålen islands. The aggregated tonnage and average graphite grade are 8.1 Mt and 12.6% graphite, respectively. Data collected from 17 international deposits show similar averages of 30.7 Mt and 10.8% graphite. Thus, the Norwegian deposits show a similar span in tonnage and grade and are most likely typical for graphite deposits worldwide. Similarly, and common elsewhere, the Norwegian graphite deposits in a succession of high metamorphic grade supracrustal rocks, comprising meta-arenites, carbonates and garnet- and pyroxene-bearing gneisses of variable composition. In parts, this succession is migmatized. Two bench-scale ore-dressing trials show that the graphite can be upgraded to market quality. Our work demonstrates a successful approach for locating and evaluating graphite deposits by combining nearly all the methods used in economic geology investigations.
An online advertising intervention to increase adherence to stay-at-home-orders during the COVID-19 pandemic: An efficacy trial monitoring individual-level mobility data

The COVID-19 pandemic has led public health departments to issue several orders and recommendations to reduce COVID-19-related morbidity and mortality. However, for various reasons, including lack of ability to sufficiently monitor and influence behavior change, adherence to these health orders and recommendations has been suboptimal. Starting April 29, 2020, during the initial stay-at-home orders issued by various state governors, we conducted an intervention that sent online website and mobile application advertisements to people’s mobile phones to encourage them to adhere to stay-at-home orders. Adherence to stay-at-home orders was monitored using individual-level cell phone mobility data, from April 29, 2020 through May 10, 2020. Mobile devices across 5 regions in the United States were randomly-assigned to either receive advertisements from our research team advising them to stay at home to stay safe (intervention group) or standard advertisements from other advertisers (control group). Compared to control group devices that received only standard corporate advertisements (i.e., did not receive public health advertisements to stay at home), the (intervention group) devices that received public health advertisements to stay at home demonstrated objectively-measured increased adherence to stay at home (i.e., smaller radius of gyration, average travel distance, and larger stay-at-home ratios). Results suggest that 1) it is feasible to use mobility data to assess efficacy of an online advertising intervention, and 2) online advertisements are a potentially effective method for increasing adherence to government/public health stay-at-home orders.