Reviewerly/exHarmony huggingface dataset:
CRPGAN: Learning image-to-image translation of two unpaired images by cross-attention mechanism and parallelization strategy

Unsupervised image-to-image translation (UI2I) tasks aim to find a mapping between the source and the target domains from unpaired training data. Previous methods can not effectively capture the differences between the source and the target domain on different scales and often leads to poor quality of the generated images, noise, distortion, and other conditions that do not match human vision perception, and has high time complexity. To address this problem, we propose a multi-scale training structure and a progressive growth generator method to solve UI2I task. Our method refines the generated images from global structures to local details by adding new convolution blocks continuously and shares the network parameters in different scales and also in the same scale of network. Finally, we propose a new Cross-CBAM mechanism (CRCBAM), which uses a multi-layer spatial attention and channel attention cross structure to generate more refined style images. Experiments on our collected Opera Face, and other open datasets Summer↔Winter, Horse↔Zebra, Photo↔Van Gogh, show that the proposed algorithm is superior to other state-of-art algorithms.
Investigating Factors Impacting Customer Acceptance of Artificial Intelligence Chatbot: Banking Sector of Kuwait

The Purpose: This study investigates the role of Artificial Intelligence- chatbot (AI chatbot) quality and AI chatbot users across various banking needs and its impact on customer acceptance of AI chatbots through the mediating role of perceived usefulness and ease of use. Design/methodology/approach – This quantitative study uses a cross-sectional time dimension. The questionnaire of this study was developed using multiple academic sources. Partial least square structural equation modeling was used to analyze the data, and the SmartPLS 4 software was used for the calculation. Finding - The findings indicated a significant positive direct relationship between AI chatbot quality and acceptance of AI chatbot (path coefficient of 0.138 and p-value of 0.022). At the same time, the direct relationship between the AI-chatbot user and the acceptance of the AI chatbot was insignificant (path coefficient = 0.0.096, and p-value = 0.246). While the results of the indirect relationship reveal that perceived usefulness and ease of use partially mediated the relationship between AI chatbot quality and acceptance of AI chatbots. The perceived usefulness and ease of use fully mediated the relationship between AI chatbot users and acceptance of the AI chatbot. Originality/value – The results of this study developed a framework for banking and other customer-oriented businesses in understanding and developing AI chatbots to address customer needs.
Making and Using AI in the Library: Creating a BERT Model at the National Library of Sweden

How can novel AI techniques be made and put to use in the library?Combining methods from data and library science, this article focuses on Natural Language Processing technologies, especially in national libraries.It explains how the National Library of Sweden's collections enabled the development of a new BERT language model for Swedish.It also outlines specific use cases for the model in the context of academic libraries, detailing strategies for how such a model could make digital collections available for new forms of research, from automated classification to enhanced searchability and improved OCR cohesion.Highlighting the potential for cross-fertilizing AI with libraries, the conclusion suggests that while AI may transform the workings of the library, libraries can also play a key role in the future development of AI.
Unveiling the present status of open access repositories: a comparative analysis of India and China

Purpose Open access repository is an essential element of an organization's strategy for enhancing the visibility and accessibility of its intellectual output to a global audience. Owing to its importance, the study aims to explore the current status of open access repositories in India and China by analyzing the different characteristic features of repositories. Design/methodology/approach The data for the study is collected from OpenDoar which is labeled as a quality assured repository directory across the globe. The country-wise contribution of Asian repositories is extracted from OpenDoar using various filtration options available in the repository. Further, the URL of every Indian and Chinese repository was manually accessed to gather the following metadata: Repository Type, Software Usage, Repository Interface Language, Year of Development, Subject Coverage, Content Coverage, and the utilization of Web 2.0 tools by repositories. Findings The findings of the study highlights that among the Asian countries, India is at 4th rank while China is at 5th rank in terms of repository count. The study depicts that India has shown more promising growth than China. However, both the countries mainly focused on institutional repositories while disciplinary, aggregated, and governmental repositories are very few in number, therefore building such repositories is the need of an hour. Dspace as the preferred software and English as a dominant interface language occupy the prominent places in the repositories of both countries. Moreover, the repositories of both countries have embraced web 2.0 tools like RSS 1.0, RSS 2.0 and Atom with little presence of social media tools. Research limitations/implications The study has limitations, and results should be interpreted with caution. The comparison between the two countries is based on only one data source, i.e. OpenDoar. However, there is a possibility that future studies can take various repository directories as a data source that will give a clear picture of comparison. Originality/value The study can be beneficial to the policymakers and the administrators of these two regions as it will provide them a vivid picture of the diffrent characteristic features of their repositories so that they can formulate better policies that will be helpful to foster green open access.
Impact of Jigsaw Cooperative Learning Strategy on academic achievement and opinion among Nursing Students

Introduction: Employing innovative teaching strategies is urgently needed for nursing education systems. Objective: This study aimed to determine the effectiveness of the jigsaw cooperative learning strategy on nursing students’ academic achievement and opinions. Materials and Methods: This research is a randomized controlled trial. The study sample consisted of 160 nursing students randomly selected and assigned to the intervention (80 students in 8 groups of 10 students each) and the control group (80 students in another 8 groups of 10 students each). The study data were collected at two time points after the intervention. A self-administered questionnaire was used to collect data about students’ academic achievement and opinions regarding the jigsaw cooperative learning strategy (only for the intervention group). The Chi-squared test and repeated measure analysis of variance (ANOVA) were used for data analysis. Results: The mean age of the experimental group was 20.8±0.74 years, while that of the control groups was 20.7±0.81 years. There is a significant difference in mean scores of academic achievements between the control and intervention groups (P=0.001) over time based on repeated measure ANOVA, and a significant difference between the two groups over time group effect by using repeated measure ANOVA (P=0.001). The paired t-test showed a significant difference between students’ academic performance in time I and time II (P&lt;0.05). In the control group, the descriptive statistics show that students’ academic achievement (time I) was slightly higher in the posttest (time I) and dropped slightly in time II. The paired t-test showed a nonsignificant difference between students’ academic performance in time II and time I. Conclusion: The results indicated the substantial need for using novel nursing education models in nursing education to enhance learning outcomes in training settings.
Spatially Multiplexed Quantum Entropy Source With Single LD for 100-Gbps Random Numbers and Beyond

Fast quantum random number (RN) generation is an essential technology for cryptographic applications requiring unpredictable RNs. This letter reports a spatially multiplexed quantum entropy source (QES) with a single laser diode (LD), utilizing parallel homodyne measurement of vacuum fluctuation, for an aggregated RN generation rate of 100 Gbit/s and beyond. The QES uses a high-power distributed feedback LD and a low-loss 1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 16 silica planar lightwave circuit splitter and achieves eight-channel parallel operation with a broad bandwidth of 4 GHz each. A four-channel aggregated RN generation rate of 4 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 25 Gbit/s and further scalability up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8\times25$ </tex-math></inline-formula> Gbit/s with eight channels are demonstrated via offline post-processing.
High-Accuracy and Low-Latency Tracker for UAVs Monitoring Tibetan Antelopes

As the habitat areas of Tibetan antelopes usually exhibit poaching and unpredictable risks, combining target recognition and tracking with intelligent Unmanned Aerial Vehicle (UAV) technology is necessary to obtain the real-time location of injured Tibetan antelopes to better protect and rescue them. (1) Background: The most common way to track an object is to detect each frame of it, and it is not necessary to run the object tracker and classifier at the same rate, because the speed for them to change class is slower than objects move. Especially in the edge reasoning scene, UAV real-time monitoring requires to seek a balance between the frame rate, latency, and accuracy. (2) Methods: A backtracking tracker is proposed to recognize Tibetan antelopes which generates motion vectors through stored optical flow, achieving faster target detection. The lightweight You Only Look Once X (YOLOX) is selected as the baseline model to reduce the dependence on hardware configuration and calculation cost while ensuring detection accuracy. Region-of-Interest (ROI)-to-centroid tracking technology is employed to reduce the processing cost of motion interpolation, and the overall processing frame rate is smoothed by pre-calculating the motions of different objects recognized. The On-Line Object Tracking (OLOT) system with adaptive search area selection is adopted to dynamically adjust the frame rate to reduce energy waste. (3) Results: using YOLOX to trace back in the native Darkenet can reduce latency by 3.75 times, and the latency is only 2.82 ms after about 10 frame hops, with the accuracy being higher than YOLOv3. Compared with traditional algorithms, the proposed algorithm can reduce the tracking latency of UAVs by 50%. By running and comparing in the onboard computer, although the proposed tracker is inferior to KCF in FPS, it is significantly higher than other trackers and is obviously superior to KCF in accuracy. (4) Conclusion: A UAV equipped with the proposed tracker effectively reduces reasoning latency in monitoring Tibetan antelopes, achieving high recognition accuracy. Therefore, it is expected to help better protection of Tibetan antelopes.
VDDL: A Deep Learning-Based Vulnerability Detection Model for Smart Contracts

With the widespread use of smart contracts in various fields, the research on smart contract vulnerability detection has increased yearly. Most of the previous research work is based on symbol detection and comparison with expert-defined error patterns to analyze the possible vulnerabilities of smart contracts. The accuracy and performance of such methods are generally low. In response to this problem, this paper proposes an efficient smart contract vulnerability detection model VDDL (Vulnerability Detection Based on Deep Learning). VDDL uses a multi-layer bidirectional Transformer architecture as the model architecture, involving a multi-head attention mechanism and a masking mechanism. A multi-head attention mechanism is applied in the encoder-decoder layer. The masking mechanism randomly masks the input tokens and combined with the context to predict the masked tokens, a deep bidirectional representation for training is realized. Furthermore, VDDL incorporates CodeBERT, a large bimodal pre-trained model for natural and programming languages, to improve training performance. We collected 47,038 smart contracts as datasets for model training and testing. In the end, the accuracy of VDDL reached 92.35%, the recall reached 81.43%, and the F1-score reached 86.38%, which can efficiently detect possible loopholes in smart contracts.
Discriminative Radial Domain Adaptation

Domain adaptation methods reduce domain shift typically by learning domain-invariant features. Most existing methods are built on distribution matching, e.g., adversarial domain adaptation, which tends to corrupt feature discriminability. In this paper, we propose Discriminative Radial Domain Adaptation (DRDR) which bridges source and target domains via a shared radial structure. It's motivated by the observation that as the model is trained to be progressively discriminative, features of different categories expand outwards in different directions, forming a radial structure. We show that transferring such an inherently discriminative structure would enable to enhance feature transferability and discriminability simultaneously. Specifically, we represent each domain with a global anchor and each category a local anchor to form a radial structure and reduce domain shift via structure matching. It consists of two parts, namely isometric transformation to align the structure globally and local refinement to match each category. To enhance the discriminability of the structure, we further encourage samples to cluster close to the corresponding local anchors based on optimal-transport assignment. Extensively experimenting on multiple benchmarks, our method is shown to consistently outperforms state-of-the-art approaches on varied tasks, including the typical unsupervised domain adaptation, multi-source domain adaptation, domain-agnostic learning, and domain generalization.
Multimodal Vision Transformers with Forced Attention for Behavior Analysis

Human behavior understanding requires looking at minute details in the large context of a scene containing multiple input modalities. It is necessary as it allows the design of more human-like machines. While transformer approaches have shown great improvements, they face multiple challenges such as lack of data or background noise. To tackle these, we introduce the Forced Attention (FAt) Transformer which utilize forced attention with a modified backbone for input encoding and a use of additional inputs. In addition to improving the performance on different tasks and inputs, the modification requires less time and memory resources. We provide a model for a generalised feature extraction for tasks concerning social signals and behavior analysis. Our focus is on understanding behavior in videos where people are interacting with each other or talking into the camera which simulates the first person point of view in social interaction. FAt Transformers are applied to two downstream tasks: personality recognition and body language recognition. We achieve state-of-the-art results for Udiva v0.5, First Impressions v2 and MPII Group Interaction datasets. We further provide an extensive ablation study of the proposed architecture.
Strong simulation of linear optical processes

In this paper, we provide an algorithm and general framework for the simulation of photons passing through linear optical interferometers. Given n photons at the input of an m-mode interferometer, our algorithm computes the probabilities of all possible output states with time complexity O(n(n+m−1m−1)), linear in the number of output states (n+m−1m−1). It outperforms the permanent-based method by an exponential factor, and for the restricted problem of computing the probability for one given output it improves the time complexity over the state-of-the-art for the permanent of matrices with multiple rows or columns, with a tradeoff in the memory usage. Our algorithm also has additional versatility by virtue of its use of memorisation – the storing of intermediate results – which is advantageous in situations where several input states may be of interest. Additionally it allows for hybrid simulations, in which outputs are sampled from output states whose probability exceeds a given threshold, or from a restricted set of states. We consider a concrete, optimised implementation, and we benchmark the efficiency of our approach compared to existing tools.
Academic writing and AI: Day-1 experiment

To check the ability of AI to identify precise and detailed scientific information, I experiment with how accurate AI recognizes scientific terms, their origins, meanings, and usages, and whether the accuracy increases over time
Distributed Motion Planning for Multiple Quadrotors in Presence of Wind Gusts

This work demonstrates distributed motion planning for multi-rotor unmanned aerial vehicle in a windy outdoor environment. The motion planning is modeled as a receding horizon mixed integer nonlinear programming (RH-MINLP) problem. Each quadrotor solves an RH-MINLP to generate its time-optimal speed profile along a minimum snap spline path while satisfying constraints on kinematics, dynamics, communication connectivity, and collision avoidance. The presence of wind disturbances causes the motion planner to continuously regenerate new motion plans, thereby significantly increasing the computational time and possibly leading to safety violations. Control Barrier Functions (CBFs) are used for assist in collision avoidance in the face of wind disturbances while alleviating the need to recalculate the motion plans continually. The RH-MINLPs are solved using a novel combination of heuristic and optimal methods, namely Simulated Annealing and interior-point methods, respectively, to handle discrete variables and nonlinearities in real-time feasibly. The framework is validated in simulations featuring up to 50 quadrotors and Hardware-in-the-loop (HWIL) experiments, followed by outdoor field tests featuring up to 6 DJI M100 quadrotors. Results demonstrate (1) fast online motion planning for outdoor communication-centric multi-quadrotor operations and (2) the utility of CBFs in providing effective motion plans.
Transformer-Based Neural Network Machine Translation Model for the Kurdish Sorani Dialect

The transformer model is one of the most recently developed models for translating texts into another language. The model uses the principle of attention mechanism, surpassing previous models, such as sequence-to-sequence, in terms of performance. It performed well with highly resourced English, French, and German languages. Using the model architecture, we investigate training the modified version of the model in a low-resourced language such as the Kurdish language. This paper presents the first-ever transformer-based neural machine translation model for the Kurdish language by utilizing vocabulary dictionary units that share vocabulary across the dataset. For this purpose, we combine all the existing parallel corpora of Kurdish – English by building a large corpus and training it on the proposed transformer model. The outcome indicated that the suggested transformer model works well with Kurdish texts by scoring (0.45) on bilingual evaluation understudy (BLEU). According to the BLEU standard, the score indicates a high-quality translation.
A super-resolution network using channel attention retention for pathology images

Image super-resolution (SR) significantly improves the quality of low-resolution images, and is widely used for image reconstruction in various fields. Although the existing SR methods have achieved distinguished results in objective metrics, most methods focus on real-world images and employ large and complex network structures, which are inefficient for medical diagnosis scenarios. To address the aforementioned issues, the distinction between pathology images and real-world images was investigated, and an SR Network with a wider and deeper attention module called Channel Attention Retention is proposed to obtain SR images with enhanced high-frequency features. This network captures contextual information within and across blocks via residual skips and balances the performance and efficiency by controlling the number of blocks. Meanwhile, a new linear loss was introduced to optimize the network. To evaluate the work and compare multiple SR works, a benchmark dataset bcSR was created, which forces a model training on wider and more critical regions. The results show that the proposed model outperforms state-of-the-art methods in both performance and efficiency, and the newly created dataset significantly improves the reconstruction quality of all compared models. Moreover, image classification experiments demonstrate that the suggested network improves the performance of downstream tasks in medical diagnosis scenarios. The proposed network and dataset provide effective priors for the SR task of pathology images, which significantly improves the diagnosis of relevant medical staff. The source code and the dataset are available on https://github.com/MoyangSensei/CARN-Pytorch .
Path Planning of Multiple Unmanned Aerial Vehicles Covering Multiple Regions Based on Minimum Consumption Ratio

Investigating the path planning of multiple unmanned aerial vehicles (UAVs) covering multiple regions, this work proposes an effective heuristic method of region coverage path planning to reduce the complexity of the problem. The proposed method decomposes the solution process into two stages. First of all, the two most important parameters affecting the performance of UAV missions were considered, namely, the flying speed and the scan width. According to these two parameters of UAVs, a new multi-regional allocation scheme based on the minimum consumption ratio was proposed. With this allocation scheme, the coverage task allocation and path pre-planning of UAVs were obtained. Then, the UAVs’ trajectory routes were optimized based on the dynamic planning algorithm to reduce the time consumption of UAVs on the transfer path between regions. The method was evaluated with numerical experiments. The results showed that the proposed method can effectively solve the path planning problem of multiple UAVs covering multiple regions. Compared with an advanced algorithm, the time consumption for homogeneous and heterogenous UAV performance was reduced by 5.1% and 3%, respectively.