Reviewerly/exHarmony huggingface dataset:
MULTI-UAV Task Allocation Based on Improved Genetic Algorithm

The path length of multiple unmanned aerial vehicle (multi-UAV) has a certain impact on the task allocation of multi-UAV.In order to improve the efficiency of multi-UAV and reduce the loss of multi-UAV during the process of performing tasks, this paper takes the path length as one of the influencing factors of the evaluation function.The UAV path length, UAV performance, and task characteristics are taken as the influencing factors of multi-UAV task allocation evaluation function.In addition, in order to improve the efficiency of genetic algorithm (GA) in solving multi-UAV task allocation problem, this paper proposes a fusion genetic algorithm based on improved simulated annealing (ISAFGA).In order to improve the population diversity of GA, the second selection operation of GA is carried out and the improved simulated annealing algorithm (SA) is used in the second selection operation.The threshold is set to improve the acceptance criteria of new solutions in SA, and then the promotion of secondary selection operation on population diversity is improved.The simulation results showed that the improved algorithm could improve the diversity of the population and improve the global search ability, and verified the effectiveness of the improved algorithm.
Transformation and digital literacy: Systematic literature mapping

Abstract The advancements of technology have allowed digital transformation to reach all productive sectors, including the education sector and its members. This transformation is linked to emerging technologies, the digitalization of processes and resources, and the demand for users to upgrade to the latest technological updates. This research aims to analyze digital transformation and media literacy publications that impact higher education. Its purpose is to identify the types of research and topics they address and explore the scope of digital transformation in higher education institutions. The systematic mapping method was used to analyze 298 articles published in two databases, Scopus and Web of Science (WoS). Inclusion and exclusion criteria were applied to select the articles that could be included in this research. The results show that the largest proportion of articles were found in Scopus, and used both qualitative and quantitative empirical research methods, followed by theoretical-conceptual methods and, to a lesser extent, mixed methods. Likewise, the publications originated in five continents, and the Journal of Adolescent and Adult Literacy had the largest number of publications, with 14. Forty-two percent of the studies were classified in the strategy category, with the most mentioned topic being digital pedagogies. This research provides a perspective on digital transformation studies in higher education institutions and their internalization approaches. This research may be of value to trainers, students, decision-makers, and researchers interested in transformation, educommunication, and educational innovation.
Single Image Dehazing Using Wavelet-Based Haze-Lines and Denoising

Haze reduces the contrast of an image and causes the loss in colors, which has a negative effect on the subsequent object detection; therefore, single image dehazing is a challenging visual task.In addition, defects exist in previous existing dehazing approaches: Pixel-based dehazing approaches are likely to result in insufficient information to estimate the transmission, whereas patch-based ones are prone to generate shadows.They both also tend to induce color deviations.Therefore, this study proposes a novel method based on multi-scale wavelet and non-local dehazing.A hazy image is first decomposed into a low-frequency and three high-frequency sub-images by wavelet transform.Non-local dehazing and wavelet denoising are then employed on the low-frequency and high-frequency sub-images to remove the haze and noise, respectively.Finally, a haze-free image is obtained from the reconstruction of sub-images.The proposed method focuses on the dehazing and denoising on the low-frequency and high-frequency images respectively, through which the details on the image can be well preserved.Experimental results indicate that the proposed method is superior to the state-of-the-art approaches in both quantitative and qualitative evaluation on the synthetic and real-world image datasets.
Pattern2Vec: Representation of clickstream data sequences for learning user navigational behavior

Summary Word embedding approaches represent data sequences to handle their contextual meaning in the NLP tasks. Nowadays, there is an emerging need to understand the user behavior patterns over navigational clickstream data. However, representing the URL data sequences utilizing existing embedding approaches to cluster users' behavior with unsupervised machine learning tasks is a challenging task. This study introduces the Patter2Vec embedding approach using a representation vector to construct contextual, precise, and interpretable clusters over the hidden and popular navigational patterns. To test the usability of the proposed representation in clustering tasks, we conduct an experimental study, which indicates that Pattern2Vec outperforms existing embedding approaches.
A Novel Multivariate Time-Series Anomaly Detection Approach Using an Unsupervised Deep Neural Network

With the development of hardware technology, we can collect increasingly reliable time series data, in which time series anomaly detection is an important task to find problems in time and avoid risks. It is not easy to establish a multivariate time series anomaly detection system, because the collected data not only have different attributes, scales, and characteristic information but also have horizontal and vertical connections among these data collected by various sensors. In addition, there is no clear boundary regarding whether the data are abnormal, and there is currently no unified definition of multidimensional time-series anomalies. Recently, deep learning methods have shown outstanding advantages in the processing of multidimensional time series. In this paper, we propose a definition of point anomalies in multivariate time series and an unsupervised deep learning method, the multilayer convolutional recurrent autoencoded anomaly detector (MCRAAD), which is used to detect anomalies in multivariate time series. We calculate the feature matrix sequence through the data in the sliding window, extract the characteristics of the feature matrix sequence by a multilayer convolutional encoder, obtain the time relations in the feature matrix by using several ConvLSTM units, and finally reconstruct the feature matrix sequence with the convolutional decoder to predict the self-feature matrix. In addition, we propose a threshold setting method to assist with the determination of anomalies. Finally, we test our model on synthetic datasets and a real dataset of house monitoring. The results of experiments show that our method is superior to these basic models in detecting ability and robustness. This model also provides an effective method for multivariate time-series anomaly detection in real life.
Developing an intelligent virtual reality interactive system based on the ADDIE model for learning pour-over coffee brewing

This study aims to use the analysis, design, development, implementation, and evaluation (ADDIE) model to develop an intelligent virtual reality (VR) interactive system for learning pour-over coffee brewing. The operation of pour-over coffee brewing is complex. An intelligent system is a concrete actuator that realizes artificial intelligence (AI) decisions. When learners face the challenge of learning complex skills, an intelligent system can actively analyze their learning behaviors and provide corresponding intelligent feedback to correct them. Furthermore, head-mounted-display VR can structure an immersive environment to improve the learning experience and knowledge transfer. This research integrates these two techniques into an intelligent VR interactive system and utilizes the ADDIE model to enable the system to achieve the ideal educational framework. The participants were 103 students who experienced and evaluated this system. The results indicated that the intelligent VR interactive system matched the participants' learning requirements and enhanced their learning performance. In addition, the learners proposed some suggestions to optimize the system, such as adding detailed content to the intelligent feedback and note function. Overall, this system increases the effectiveness of learning. The study suggests that instructors can use an intelligent VR interactive system to strengthen learners’ manipulative ability and knowledge.
Holographic mixed-reality neuronavigation with a head-mounted device: technical feasibility and clinical application

OBJECTIVE The authors aimed to evaluate the technical feasibility of a mixed-reality neuronavigation (MRN) system with a wearable head-mounted device (HMD) and to determine its clinical application and accuracy. METHODS A semiautomatic registration MRN system on HoloLens smart glasses was developed and tested for accuracy and feasibility. Thirty-seven patients with intracranial lesions were prospectively identified. For each patient, multimodal imaging–based holograms of lesions, markers, and surrounding eloquent structures were created and then imported to the MRN HMD. After a point-based registration, the holograms were projected onto the patient's head and observed through the HMD. The contour of the holograms was compared with standard neuronavigation (SN). The projection of the lesion boundaries perceived by the neurosurgeon on the patient's scalp was then marked with MRN and SN. The distance between the two contours generated by MRN and SN was measured so that the accuracy of MRN could be assessed. RESULTS MRN localization was achieved in all patients. The mean additional time required for MRN was 36.3 ± 6.3 minutes, in which the mean registration time was 2.6 ± 0.9 minutes. A trend toward a shorter time required for preparation was observed with the increase of neurosurgeon experience with the MRN system. The overall median deviation was 4.1 mm (IQR 3.0 mm–4.7 mm), and 81.1% of the lesions localized by MRN were found to be highly consistent with SN (deviation &lt; 5.0 mm). There was a significant difference between the supine position and the prone position (3.7 ± 1.1 mm vs 5.4 ± 0.9 mm, p = 0.001). The magnitudes of deviation vectors did not correlate with lesion volume (p = 0.126) or depth (p = 0.128). There was no significant difference in additional operating time between different operators (37.4 ± 4.8 minutes vs 34.6 ± 4.8 minutes, p = 0.237) or in localization deviation (3.7 ± 1.0 mm vs 4.6 ± 1.5 mm, p = 0.070). CONCLUSIONS This study provided a complete set of a clinically applicable workflow on an easy-to-use MRN system using a wearable HMD, and has shown its technical feasibility and accuracy. Further development is required to improve the accuracy and clinical efficacy of this system.
Secure Elliptic Curve Crypto-Processor for Real-Time IoT Applications

Cybersecurity is a critical issue for Real-Time IoT applications since high performance and low latencies are required, along with security requirements to protect the large number of attack surfaces to which IoT devices are exposed. Elliptic Curve Cryptography (ECC) is largely adopted in an IoT context to provide security services such as key-exchange and digital signature. For Real-Time IoT applications, hardware acceleration for ECC-based algorithms can be mandatory to meet low-latency and low-power/energy requirements. In this paper, we propose a fast and configurable hardware accelerator for NIST P-256/-521 elliptic curves, developed in the context of the European Processor Initiative. The proposed architecture supports the most used cryptography schemes based on ECC such as Elliptic Curve Digital Signature Algorithm (ECDSA), Elliptic Curve Integrated Encryption Scheme (ECIES), Elliptic Curve Diffie-Hellman (ECDH) and Elliptic Curve Menezes-Qu-Vanstone (ECMQV). A modified version of Double-And-Add-Always algorithm for Point Multiplication has been proposed, which allows the execution of Point Addition and Doubling operations concurrently and implements countermeasures against power and timing attacks. A simulated approach to extract power traces has been used to assess the effectiveness of the proposed algorithm compared to classical algorithms for Point Multiplication. A constant-time version of the Shamir’s Trick has been adopted to speed-up the Double-Point Multiplication and modular inversion is executed using Fermat’s Little Theorem, reusing the internal modular multipliers. The accelerator has been verified on a Xilinx ZCU106 development board and synthesized on both 45 nm and 7 nm Standard-Cell technologies.
Deep Joint Source Channel Coding for Wireless Image Transmission with OFDM

We present a deep learning based joint source channel coding (JSCC) scheme for wireless image transmission over multipath fading channels with non-linear signal clipping. The proposed encoder and decoder use convolutional neural networks (CNN) and directly map the source images to complex-valued baseband samples for orthogonal frequency division multiplexing (OFDM) transmission. The proposed model-driven machine learning approach eliminates the need for separate source and channel coding while integrating an OFDM datapath to cope with multipath fading channels. The end-to-end JSCC communication system combines trainable CNN layers with non-trainable but differentiable layers representing the multipath channel model and OFDM signal processing blocks. Our results show that injecting domain expert knowledge by incorporating OFDM baseband processing blocks into the machine learning framework significantly enhances the overall performance compared to an unstructured CNN. Our method outperforms conventional schemes that employ state-of-the-art but separate source and channel coding such as BPG and LDPC with OFDM. Moreover, our method is shown to be robust against non-linear signal clipping in OFDM for various channel conditions that do not match the model parameter used during the training.
A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions

Traditional recommendation systems are faced with two long-standing obstacles, namely data sparsity and cold-start problems, which promote the emergence and development of Cross-Domain Recommendation (CDR). The core idea of CDR is to leverage information collected from other domains to alleviate the two problems in one domain. Since the early 2010s, many efforts have been engaged for cross-domain recommendation. Recently, with the development of deep learning and neural networks, a large number of methods have emerged. However, there is a limited number of systematic surveys on CDR, especially regarding the latest proposed methods as well as the recommendation scenarios and recommendation tasks they address. In this survey article, we first proposed a two-level taxonomy of cross-domain recommendation that classifies different recommendation scenarios and recommendation tasks. We then introduce and summarize existing cross-domain recommendation approaches under different recommendation scenarios in a structured manner. We also organize datasets commonly used. We conclude this survey by providing several potential research directions about this field.
VDSimilar: Vulnerability detection based on code similarity of vulnerabilities and patches

Vulnerability detection using machine learning is a hot topic in improving software security. However, existing works formulate detection as a classification problem, which requires a large set of labelled data while capturing semantical and syntactic similarity. In this work, we argue that similarity in the view of vulnerability is the key in detecting vulnerabilities. We prepare a relatively smaller data set composed of both vulnerabilities and associated patches, and attempt to realize security similarity from (i) the similarity between pair of vulnerabilities and (ii) the difference between a pair of vulnerability and patch. To achieve this, we setup the detection model using the Siamese network cooperated with BiLSTM and Attention to deal with source code, Attention network to improve the detection accuracy. On a data set of 876 vulnerabilities and patches of OpenSSL and Linux, the proposed model (VDSimilar) achieves about 97.17% in AUC value of OpenSSL (where the Attention network contributes 1.21% than BiLSTM in Siamese), which is more outstanding than the most advanced methods based on deep learning.
3D-CNN based Dynamic Gesture Recognition for Indian Sign Language Modeling

Hand gestures used in Indian Sign Language (ISL) are static and dynamic in the time domain. The Indian Sign Language is available as a standard but is still not very common among peoples. In this paper, we have used a 3-dimensional convolutional based Convolution Neural Network to model the most utilized gestures of the Indian community. The trained model can provide a natural language output corresponding to the signs of the ISL. This in turn will help in reducing the problems faced while communicating with deaf and dumb peoples. Moreover, these dynamic gestures can be used in medical, industrial and various other fields. We took 20 gestures from standard Indian Sign Language (ISL) and trained our model on the dataset made by replicating the actions of those gestures. Ten subjects volunteered to make the dataset in distinct backgrounds, light conditions and orientations. Network model used produced good results in terms of accuracy, precision, recall and f1-scores.
Combining the outputs of various k-nearest neighbor anomaly detectors to form a robust ensemble model for high-dimensional geochemical anomaly detection

Machine learning techniques provide useful methods for high-dimensional geochemical anomaly detection for mineral exploration targeting. However, the instability of the machine learning models often leads to the uncertainty of high-dimensional geochemical anomaly detection result. Combining various individual models to form an adaptive ensemble anomaly detector is a feasible way to enhance the robustness of machine learning anomaly detectors. In this study, the average method, maximization method, average of maximum (AOM) method, and maximum of average (MOA) method were adopted to combine the outputs of various k -nearest neighbor (KNN) anomaly detectors to improve the robustness of the KNN models in the high-dimensional geochemical anomaly detection in the Baishan district (Jilin Province, China). The effectiveness of the four combination algorithms for high-dimensional geochemical anomaly detection was evaluated by comparing the ensemble models obtained by using the four combination algorithms with the single KNN model, Gaussian mixture model (GMM), one-class support vector machine (OCSVM), and isolation forest (IForest) in the case study. It is found that the four ensemble models (a) perform similarly well in high-dimensional geochemical anomaly detection, and (b) perform better than the single KNN model, GMM, OCSVM, and IForest in high-dimensional geochemical anomaly detection. Therefore, the average method, maximization method, AOM method, and MOA method are potentially useful algorithms for combining the outputs of various KNN models to form robust ensemble models for high-dimensional geochemical anomaly detection. • A KNN model is used to detect high-dimensional geochemical anomalies. • Robust high-dimensional geochemical anomaly detection models are formed by combining the outputs of various KNN models. • The robust models are compared with KNN, GMM, OCSVM and IForest.
How Am I Doing?: Evaluating Conversational Search Systems Offline

As conversational agents like Siri and Alexa gain in popularity and use, conversation is becoming a more and more important mode of interaction for search. Conversational search shares some features with traditional search, but differs in some important respects: conversational search systems are less likely to return ranked lists of results (a SERP), more likely to involve iterated interactions, and more likely to feature longer, well-formed user queries in the form of natural language questions. Because of these differences, traditional methods for search evaluation (such as the Cranfield paradigm) do not translate easily to conversational search. In this work, we propose a framework for offline evaluation of conversational search, which includes a methodology for creating test collections with relevance judgments, an evaluation measure based on a user interaction model, and an approach to collecting user interaction data to train the model. The framework is based on the idea of “subtopics”, often used to model novelty and diversity in search and recommendation, and the user model is similar to the geometric browsing model introduced by RBP and used in ERR. As far as we know, this is the first work to combine these ideas into a comprehensive framework for offline evaluation of conversational search.
Collaborative Unsupervised Visual Representation Learning from Decentralized Data

Unsupervised representation learning has achieved outstanding performances using centralized data available on the Internet. However, the increasing awareness of privacy protection limits sharing of decentralized unlabeled image data that grows explosively in multiple parties (e.g., mobile phones and cameras). As such, a natural problem is how to leverage these data to learn visual representations for downstream tasks while preserving data privacy. To address this problem, we propose a novel federated unsupervised learning framework, FedU. In this framework, each party trains models from unlabeled data independently using contrastive learning with an online network and a target network. Then, a central server aggregates trained models and updates clients’ models with the aggregated model. It preserves data privacy as each party only has access to its raw data. Decentralized data among multiple parties are normally non-independent and identically distributed (non-IID), leading to performance degradation. To tackle this challenge, we propose two simple but effective methods: 1) We design the communication protocol to upload only the encoders of online networks for server aggregation and update them with the aggregated encoder; 2) We introduce a new module to dynamically decide how to update predictors based on the divergence caused by non-IID. The predictor is the other component of the online network. Extensive experiments and ablations demonstrate the effectiveness and significance of FedU. It outperforms training with only one party by over 5% and other methods by over 14% in linear and semi-supervised evaluation on non-IID data.