Reviewerly/exHarmony huggingface dataset:
Temporal Quantum Memory and Non-Locality of Two Trapped Ions under the Effect of the Intrinsic Decoherence: Entropic Uncertainty, Trace Norm Nonlocality and Entanglement

The engineering properties of trapped ions and their capacity to engender numerous quantum information resources determine many aspects of quantum information processing. We devise a setup of coherent and even coherent fields acting on two trapped ions to generate quantum memory, non-locality, and entanglement. Various effects, such as intrinsic decoherence, Lamb–Dicke regime, and dipole–dipole interaction are investigated. The inter-coupling of trapped ions, as well as the generation and dynamics of correlations between them, are analyzed. Using quantum memory assisted entropic uncertainty, trace-norm measurement induced non-locality, and concurrence, we find that the coherent and even coherent fields successfully generate non-local correlations in trapped-ions, with the latter being more resourceful for the dynamics and preservation of the non-local correlations. Furthermore, we observe that the entropic uncertainty and the trace norm induced non-locality present symmetrical dynamics. The dipole–dipole interaction improves correlation’s generation, robustness, and entropic uncertainty suppression.
Modeling Hybrid Feature-Based Phishing Websites Detection Using Machine Learning Techniques

In this paper, we mainly present a machine learning based approach to detect real-time phishing websites by taking into account URL and hyperlink based hybrid features to achieve high accuracy without relying on any third-party systems. In phishing, the attackers typically try to deceive internet users by masking a webpage as an official genuine webpage to steal sensitive information such as usernames, passwords, social security numbers, credit card information, etc. Anti-phishing solutions like blacklist or whitelist, heuristic, and visual similarity based methods cannot detect zero-hour phishing attacks or brand-new websites. Moreover, earlier approaches are complex and unsuitable for real-time environments due to the dependency on third-party sources, such as a search engine. Hence, detecting recently developed phishing websites in a real-time environment is a great challenge in the domain of cybersecurity. To overcome these problems, this paper proposes a hybrid feature based anti-phishing strategy that extracts features from URL and hyperlink information of client-side only. We also develop a new dataset for the purpose of conducting experiments using popular machine learning classification techniques. Our experimental result shows that the proposed phishing detection approach is more effective having higher detection accuracy of 99.17% with the XG Boost technique than traditional approaches.
An Economic Approach to Size of a Renewable Energy Mix in Small Islands

The importance of renewable energy exploitation reduces the energy dependence on fossil fuels. Despite technological progress, in several remote areas and small islands the energy production is nowadays dominated by the utilization of fossil fuels. With new, increasingly stringent laws on polluting emissions and the need to lower production costs, it is necessary to exploit as many renewable sources as possible. In order to implement these considerations, it was decided to study renewable energy production. The study was carried out by estimating the energy production on a monthly and annual basis considering a mix of three plants, namely marine, solar, and wind. Simulations on wave production were carried out on a new device developed by the research team at the University of Palermo. In order to be able to perform these simulations, input climate data are required. These data are normally available in literature or obtainable by using specific GIS tools. As criterium, the Levelized Cost of Energy, normally applied to a single technology, is extended to the entire energy mix. Minimizing this parameter, the best solution is individuated, and capable of supplying 50% of the summer electrical load with renewable energy sources. The results carried out from a case study based in aeolian islands show that the solar production reaches 10.2%, the wind production reaches 45.47% and sea wave production reaches 3.04%. In this way, the diesel production decreases to 41.29%. This method can be easily applied for several small islands, estimating for several sites the ability to reduce the energy production from fossil fuels.
From photos to sketches - how humans and deep neural networks process objects across different levels of visual abstraction

Line drawings convey meaning with just a few strokes. Despite strong simplifications, humans can recognize objects depicted in such abstracted images without effort. To what degree do deep convolutional neural networks (CNNs) mirror this human ability to generalize to abstracted object images? While CNNs trained on natural images have been shown to exhibit poor classification performance on drawings, other work has demonstrated highly similar latent representations in the networks for abstracted and natural images. Here, we address these seemingly conflicting findings by analyzing the activation patterns of a CNN trained on natural images across a set of photographs, drawings, and sketches of the same objects and comparing them to human behavior. We find a highly similar representational structure across levels of visual abstraction in early and intermediate layers of the network. This similarity, however, does not translate to later stages in the network, resulting in low classification performance for drawings and sketches. We identified that texture bias in CNNs contributes to the dissimilar representational structure in late layers and the poor performance on drawings. Finally, by fine-tuning late network layers with object drawings, we show that performance can be largely restored, demonstrating the general utility of features learned on natural images in early and intermediate layers for the recognition of drawings. In conclusion, generalization to abstracted images, such as drawings, seems to be an emergent property of CNNs trained on natural images, which is, however, suppressed by domain-related biases that arise during later processing stages in the network.
An efficient learning-based method for underwater image enhancement

The Underwater image enhancement, which targets removing the noise, scattering effect, and the bluish, greenish, or yellowish tone from underwater images, has lately attracted much attention. Traditional methods generally assume some prior knowledge when performing constrained optimization. Recently, the deep neural network (DNN)-based methods have exhibited superior performance over the traditional methods. However, state-of-the-art DNN models generally cost a large number of parameters, which is unfriendly to practical implementation. By contrast, this paper proposes a simple yet effective convolution neural network (CNN)-based framework to enhance underwater images. Our method consists of two stages, CNN-based enhancement and YUV-based post-processing. In the first stage, a lightweight CNN network is developed to extract the latent features from the input image for enhancement. Specifically, our CNN cascades three residual groups which leverage channel attention blocks to strengthen the feature extraction capability. In the second stage, the output of our CNN model is transformed to the YUV color space where the luminance component is further corrected, improving the brightness of the whole image. Our model is verified on different underwater image datasets. Experimental results show that our method outperforms state-of-the-art methods both qualitatively and quantitatively. Moreover, in terms of computational complexity, our CNN model costs only 0.89M parameters which is only 21% and 81% of the existing method FUnIE-GAN and WaterNet, respectively.
CASMS: Combining clustering with attention semantic model for identifying security bug reports

Inappropriate public disclosure of security bug reports (SBRs) is likely to attract malicious attackers to invade software systems; hence being able to detect SBRs has become increasingly important for software maintenance. Due to the class imbalance problem that the number of non-security bug reports (NSBRs) exceeds the number of SBRs, insufficient training information, and weak performance robustness, the existing techniques for identifying SBRs are still less than desirable. This prompted us to overcome the challenges of the most advanced SBR detection methods. In this work, we propose the CASMS approach to efficiently alleviate the imbalance problem and predict bug reports. CASMS first converts bug reports into weighted word embeddings based on t f − i d f and w o r d 2 v e c techniques. Unlike the previous studies selecting the NSBRs that are the most dissimilar to SBRs, CASMS then automatically finds a certain number of diverse NSBRs via the Elbow method and k -means clustering algorithm. Finally, the selected NSBRs and all SBRs train an effective Attention CNN–BLSTM model to extract contextual and sequential information. The experimental results have shown that CASMS is superior to the three baselines (i.e., FARSEC, SMOTUNED, and LTRWES) in assessing the overall performance ( g -measure) and correctly identifying SBRs ( recall ), with improvements of 4.09%–24.26% and 10.33%–36.24%, respectively. The best results are easily obtained under the limited ratio ranges of the two-class training set (1:1 to 3:1), with around 20 experiments for each project. By evaluating the robustness of CASMS via the standard deviation indicator, CASMS is more stable than LTRWES. Overall, CASMS can alleviate the data imbalance problem and extract more semantic information to improve performance and robustness. Therefore, CASMS is recommended as a practical approach for identifying SBRs.
KR-GCN: Knowledge-Aware Reasoning with Graph Convolution Network for Explainable Recommendation

Incorporating knowledge graphs (KGs) into recommender systems to provide explainable recommendation has attracted much attention recently. The multi-hop paths in KGs can provide auxiliary facts for improving recommendation performance as well as explainability. However, existing studies may suffer from two major challenges: error propagation and weak explainability. Considering all paths between every user-item pair might involve irrelevant ones, which leads to error propagation of user preferences. Defining meta-paths might alleviate the error propagation, but the recommendation performance would heavily depend on the pre-defined meta-paths. Some recent methods based on graph convolution network (GCN) achieve better recommendation performance, but fail to provide explainability. To tackle the above problems, we propose a novel method named K nowledge-aware R easoning with G raph C onvolution N etwork (KR-GCN). Specifically, to alleviate the effect of error propagation, we design a transition-based method to determine the triple-level scores and utilize nucleus sampling to select triples within the paths between every user-item pair adaptively. To improve the recommendation performance and guarantee the diversity of explanations, user-item interactions and knowledge graphs are integrated into a heterogeneous graph, which is performed with the graph convolution network. A path-level self-attention mechanism is adopted to discriminate the contributions of different selected paths and predict the interaction probability, which improves the relevance of the final explanation. Extensive experiments conducted on three real-world datasets show that KR-GCN consistently outperforms several state-of-the-art baselines. And human evaluation proves the superiority of KR-GCN on explainability.
DRS-Net: A spatial–temporal affective computing model based on multichannel EEG data

Affective computing based on electroencephalography (EEG) is a promising field that highly integrates research and technology. A critical challenge is effectively extracting and integrating the temporal and spatial information to form a better representation for multichannel EEG data. Most existing studies use hand-selected features from each channel, which neglect high-dimensional dynamic temporal features and interplay of data from different electrodes. This study proposed a Dynamic Reservoir State Network (DRS-Net) to recognize the subject's emotional states. The novel end-to-end model constructs a dynamic reservoir state encoder to extract multi-channel EEG data's dynamic high dimension non-linear spatial–temporal information with high speed and low complexity. Then, a Long-Short Term Memory-dense decoder model is devised to detect emotional states. The effectiveness of the proposed DRS-Net model was evaluated on SEED, SEED-IV, and DEAP datasets. To validate the performance of the proposed method, we first combined the hand-selected features (differential entropy, power spectra density, fractal dimension, and statistics features) and classic machine learning classifiers methods (support vector machine, random forest, and k-nearest neighbor). Then, we compare them with the proposed method and other state-of-the-art deep learning methods. The experimental results generated by our method outperform all other methods in terms of accuracy and F1 score.
Technological Solutions for Sign Language Recognition: A Scoping Review of Research Trends, Challenges, and Opportunities

Sign languages are critical in conveying meaning by the use of a visual-manual modality and are the primary means of communication of the deaf and hard of hearing with their family members and with the society. With the advances in computer graphics, computer vision, neural networks, and the introduction of new powerful hardware, the research into sign languages has shown a new potential. Novel technologies can help people learn, communicate, interpret, translate, visualize, document, and develop various sign languages and their related skills. This paper reviews the technological advancements applied in sign language recognition, visualization, and synthesis. We defined multiple research questions to identify the underlying technological drivers that strive to improve the challenges in this domain. This study is designed in accordance with the PRISMA methodology. We searched for articles published between 2010 and 2021 in multiple digital libraries ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , Elsevier, Springer, IEEE, PubMed, and MDPI). To automate the initial steps of PRISMA for identifying potentially relevant articles, duplicate removal and basic screening, we utilized a Natural Language Processing toolkit. Then, we performed a synthesis of the existing body of knowledge and identified the different studies that achieved significant advancements in sign language recognition, visualization, and synthesis. The identified trends based on analysis of almost 2000 papers clearly show that technology developments, especially in image processing and deep learning, are driving new applications and tools that improve the various performance metrics in these sign language-related task. Finally, we identified which techniques and devices contribute to such results and what are the common threads and gaps that would open new research directions in the field.
A New Fast Ant Colony Optimization Algorithm: The Saltatory Evolution Ant Colony Optimization Algorithm

Various studies have shown that the ant colony optimization (ACO) algorithm has a good performance in approximating complex combinatorial optimization problems such as traveling salesman problem (TSP) for real-world applications. However, disadvantages such as long running time and easy stagnation still restrict its further wide application in many fields. In this study, a saltatory evolution ant colony optimization (SEACO) algorithm is proposed to increase the optimization speed. Different from the past research, this study innovatively starts from the perspective of near-optimal path identification and refines the domain knowledge of near-optimal path identification by quantitative analysis model using the pheromone matrix evolution data of the traditional ACO algorithm. Based on the domain knowledge, a near-optimal path prediction model is built to predict the evolutionary trend of the path pheromone matrix so as to fundamentally save the running time. Extensive experiment results on a traveling salesman problem library (TSPLIB) database demonstrate that the solution quality of the SEACO algorithm is better than that of the ACO algorithm, and it is more suitable for large-scale data sets within the specified time window. This means it can provide a promising direction to deal with the problem about slow optimization speed and low accuracy of the ACO algorithm.
Using deep learning to predict human decisions and using cognitive models to explain deep learning models

Deep neural networks (DNNs) models have the potential to provide new insights in the study of cognitive processes, such as human decision making, due to their high capacity and data-driven design. While these models may be able to go beyond theory-driven models in predicting human behaviour, their opaque nature limits their ability to explain how an operation is carried out, undermining their usefulness as a scientific tool. Here we suggest the use of a DNN model as an exploratory tool to identify predictable and consistent human behaviour, and using explicit, theory-driven models, to characterise the high-capacity model. To demonstrate our approach, we trained an exploratory DNN model to predict human decisions in a four-armed bandit task. We found that this model was more accurate than two explicit models, a reward-oriented model geared towards choosing the most rewarding option, and a reward-oblivious model that was trained to predict human decisions without information about rewards. Using experimental simulations, we were able to characterise the exploratory model using the explicit models. We found that the exploratory model converged with the reward-oriented model's predictions when one option was clearly better than the others, but that it predicted pattern-based explorations akin to the reward-oblivious model's predictions. These results suggest that predictable decision patterns that are not solely reward-oriented may contribute to human decisions. Importantly, we demonstrate how theory-driven cognitive models can be used to characterise the operation of DNNs, making DNNs a useful explanatory tool in scientific investigation.
Key phrase aware transformer for abstractive summarization

Abstractive summarization aims to generate a concise summary covering salient content from single or multiple text documents. Many recent abstractive summarization methods are built on the transformer model to capture long-range dependencies in the input text and achieve parallelization. In the transformer encoder, calculating attention weights is a crucial step for encoding input documents. Input documents usually contain some key phrases conveying salient information, and it is important to encode these phrases completely. However, existing transformer-based summarization works did not consider key phrases in input when determining attention weights. Consequently, some of the tokens within key phrases only receive small attention weights, which is not conducive to encoding the semantic information of input documents. In this paper, we introduce some prior knowledge of key phrases into the transformer-based summarization model and guide the model to encode key phrases. For the contextual representation of each token in the key phrase, we assume the tokens within the same key phrase make larger contributions compared with other tokens in the input sequence. Based on this assumption, we propose the Key Phrase Aware Transformer (KPAT), a model with the highlighting mechanism in the encoder to assign greater attention weights for tokens within key phrases. Specifically, we first extract key phrases from the input document and score the phrases’ importance. Then we build the block diagonal highlighting matrix to indicate these phrases’ importance scores and positions. To combine self-attention weights with key phrases’ importance scores, we design two structures of highlighting attention for each head and the multi-head highlighting attention. Experimental results on two datasets (Multi-News and PubMed) from different summarization tasks and domains show that our KPAT model significantly outperforms advanced summarization baselines. We conduct more experiments to analyze the impact of each part of our model on the summarization performance and verify the effectiveness of our proposed highlighting mechanism.
The Impact of Reading Strategy Instruction on Reading Comprehension, Strategy Use, Motivation, and Self-Efficacy in Chinese University EFL Students

This study investigated the effect of explicit reading strategy instruction on reading comprehension, reading strategy use, reading motivation, and reading self-efficacy in Chinese university EFL learners. A total of 117 first-year university students were randomly assigned to either the experimental group or the control group. Students in the experimental group received a 16-week reading strategy training embedded into their English reading classes. The data were collected through five major instruments: a reading comprehension test, a reading strategy questionnaire, a reading motivation questionnaire, a reading self-efficacy questionnaire, and a semi-structured interview. Independent-samples t-test results showed that there was a significant difference in reading comprehension between the experimental group and the control group after the reading strategy instruction, suggesting that students who received reading strategy instruction made significant improvement in their reading comprehension. ANCOVA analysis of pre- and post-questionnaires results showed that there were no significant changes in reading strategy use, reading motivation, and reading self-efficacy at the end of the strategy instruction. Furthermore, interview data, showed that experimental group students held very positive attitudes toward the reading strategy training. Interview results further suggested that lack of significant changes in strategy use, motivation, and self-efficacy at the end of strategy training could be explained by a dynamic interplay of individual and contextual factors.
Informative pairs mining based adaptive metric learning for adversarial domain adaptation

Adversarial domain adaptation has made remarkable in promoting feature transferability, while recent work reveals that there exists an unexpected degradation of feature discrimination during the procedure of learning transferable features. This paper proposes an informative pairs mining based adaptive metric learning (IPM-AML), where a novel two-triplet-sampling strategy is advanced to select informative positive pairs from the same classes and informative negative pairs from different classes, and a metric loss imposed with special weights is further utilized to adaptively pay more attention to those more informative pairs which can adaptively improve discrimination. Then, we incorporate IPM-AML into popular conditional domain adversarial network (CDAN) to learn feature representation that is transferable and discriminative desirably (IPM-AML-CDAN). To ensure the reliability of pseudo target labels in the whole training process, we select more confident target ones whose predicted scores are higher than a given threshold T, and also provide theoretical validation for this simple threshold strategy. Extensive experiment results on four cross-domain benchmarks validate that IPM-AML-CDAN can achieve competitive results compared with state-of-the-art approaches.