Reviewerly/exHarmony huggingface dataset:
Space infrastructures for Quantum-Safe secure communications: technologies, roadmap and initiatives from an industrial standpoint

Quantum Key Distribution (QKD) is currently the only known technique that is able to guarantee information-theoretic secure communications.While many countries have already started developing use-cases based mostly on terrestrial links, the high losses that afflict fiber-based channels make them unsuitable for very-long-distance communications.Taking the quantum networks to a national and global scale thus requires the use of satellite-based quantum communications, as the attenuation experienced by photons in space communications is order of magnitudes lower than the one that characterizes terrestrial networks.Thales Alenia Space in Italy (TAS-I) is positioning itself as a key actor in the development of the technology needed to perform space quantum communications.In this work, we present an overview of the initiatives that are currently being carried out by TAS-I, and define a roadmap whose steps are planned to take the current laboratory experimentation towards the development of a fully operational QKD constellation meant to provide secret keys on a global scale.We also describe a possible implementation of an Italian demonstrator mission based on a LEO satellite that acts as a trusted node to distribute shared keys among a certain number of ground stations; we study the effects of some major system choices and assess the overall achievable performance in terms of Secret Key Volume (SKV).
Effective matching of patients to clinical trials using entity extraction and neural re-ranking

Clinical trials (CTs) often fail due to inadequate patient recruitment. Finding eligible patients involves comparing the patient's information with the CT eligibility criteria. Automated patient matching offers the promise of improving the process, yet the main difficulties of CT retrieval lie in the semantic complexity of matching unstructured patient descriptions with semi-structured, multi-field CT documents and in capturing the meaning of negation coming from the eligibility criteria.This paper tackles the challenges of CT retrieval by presenting an approach that addresses the patient-to-trials paradigm. Our approach involves two key components in a pipeline-based model: (i) a data enrichment technique for enhancing both queries and documents during the first retrieval stage, and (ii) a novel re-ranking schema that uses a Transformer network in a setup adapted to this task by leveraging the structure of the CT documents.We use named entity recognition and negation detection in both patient description and the eligibility section of CTs. We further classify patient descriptions and CT eligibility criteria into current, past, and family medical conditions. This extracted information is used to boost the importance of disease and drug mentions in both query and index for lexical retrieval. Furthermore, we propose a two-step training schema for the Transformer network used to re-rank the results from the lexical retrieval. The first step focuses on matching patient information with the descriptive sections of trials, while the second step aims to determine eligibility by matching patient information with the criteria section.Our findings indicate that the inclusion criteria section of the CT has a great influence on the relevance score in lexical models, and that the enrichment techniques for queries and documents improve the retrieval of relevant trials. The re-ranking strategy, based on our training schema, consistently enhances CT retrieval and shows improved performance by 15% in terms of precision at retrieving eligible trials.The results of our experiments suggest the benefit of making use of extracted entities. Moreover, our proposed re-ranking schema shows promising effectiveness compared to larger neural models, even with limited training data. These findings offer valuable insights for improving methods for retrieval of clinical documents.
Dual-downlink quantum key distribution with entangled photons: prospects for daylight operation

In recent years, quantum key distribution (QKD) has seen the first proof-of-concept demonstrations from space.Next on the agenda towards a full-blown global quantum internet is to address the more practical aspects, such as efficiency, flexibility, and accessibility of QKD services.One of the main challenges that remains to be solved in this regard is to enable operation in the presence of daylight noise.Here, we present a complete framework for modelling daylight QKD from an orbiting satellite.We include the effects of atmospheric turbulence and adaptive optics correction at the receivers.We consider single-and multi-mode fibre coupling as a means of spatial filtering, for which we derived simple formulas for estimating coupling efficiencies of signal as well as noise.Using our framework, we identify the most critical system parameters for daylight operation and discuss the choice of signal wavelength and detection technology.Finally, we provide simulation results for various parameter combinations in a hypothetical daylight QKD between Berlin and Munich via a satellite in a low Earth orbit.The results show a clear advantage of 800 nm signal wavelength over 1550 nm with the currently available technology.Moreover, we show the relevance of single-mode fibre coupling and the importance of detectors with low timing jitters.We anticipate our work will provide valuable insight and tools to aid the future feasibility studies of daylight QKD in dual-downlink configurations.
Pelaksanan Ujian Kompetensi IT dalam meningkatkan kualitas lulusan di SMKN Suak Tapeh Banyuasin

Vocational High School (SMK) is a formal education unit that organizes vocational education where the orientation produces graduate students who are ready to work. One area of expertise that is in great demand by students is computer and network engineering (TKJ), where this area of expertise teaches how to make students skilled and competent in the field of computer and network engineering. In this era of digital transformation, computer networks are part of human needs so there are many job opportunities in this field. The purpose of this service and socialization is to increase the understanding and knowledge of SMK students in the field of TKJ in conducting education and preparing themselves to enter the world of work. The partners of this service activity are students of SMKN Suak Tapeh Banyuasin where this activity is carried out in the Computer Laboratory Computer and Network Engineering Study Program. While the method of activity used in this service is planning, directing, guiding and teaching network practicum and conducting competency exams that must be followed before completing education at school. Through this service activity occurs increasing understanding from SMK students majoring in TKJ regarding the areas of expertise they take so as to improve the quality of graduates of SMK N Suak Tapeh and also as a provision to face the world of work. In this era of digital transformation, computer networks are part of human needs so there are many job opportunities in this field. The purpose of this service and socialization is to increase the understanding and knowledge of SMK students in the field of TKJ in conducting education and preparing themselves to enter the world of work. These service partners are students of SMKN Suak Tapeh Banyuasin
Modelling concurrent IM/DD key distribution and data transmission over an optical LEO-to-ground link

We propose and analyze numerically concurrent intensity modulation/direct detection (IM/DD) optical key distribution (OKD) combined with conventional data transmission over a LEO-to-ground optical link. The idea is validated by numerical simulations which indicate that in a realistic scenario it is possible to generate simultaneously up to 200 Mbits of a secret key and downlink transmit up to 140 Gbits of data during a single nearly-zenithal pass of a LEO satellite. Such lengths of the secret key are three orders of magnitude higher in comparison to typical quantum key distribution systems, although they are generated under different security assumptions. The simplicity and robustness of IM/DD OKD protocols could make them an attractive and cost-effective option to provide security in the physical layer of space communication systems.
Generative Artificial Intelligence and Remote Sensing: A perspective on the past and the future [Perspectives]

The first phase of 2023 has been marked with an explosion of interest around generative AI systems, which generate content. This type of machine learning promises to enable the creation of synthetic data and outputs in many different modalities. OpenAI’s ChatGPT has certainly taken the world by storm and opened discourse on how the technology should be used.
Keyword Search of Incomplete Graph Based on Kernel Principal Component Analysis

In the keyword search problem, the incomplete graph situation is a unavoidable role. Aiming at the incomplete graph situation that is not fully considered in the existing methods, a keyword search method for incomplete graphs based on kernel principal component analysis is proposed. First, the dimension reduction of the feature vector is carried out through the kernel function; then, the keyword feature vector is extracted through KPCA; finally, all the feature vectors are sorted to obtain the final keyword search result. The experimental results show that the proposed method achieves high accuracy in keyword search with less running time; compared with other algorithms, the proposed method has better performance in terms of recall and precision.
Detection Technology and Safety Hazard Analysis of Lightning Devices in Gas Stations

This article uses the lightning voltage pulse formula and rolling ball method to calculate and analyze the protection range and lightning voltage pulse waveform characteristics of the lightning arrester. This article analyzes the technical points of lightning protection installation and construction in the business hall, canopy, distribution room, refueling area, storage tank area and other areas of the station building from the aspects of direct lightning protection and induction lightning protection. It proposes the precautions for lightning protection device detection in each area of the gas station, emphasizes the daily management of lightning protection safety hazards, and ensures the stable operation of the gas station.
Image Stitching Based on Optimized SIFT Algorithm

The article proposes an optimized SIFT algorithm to address the issue of uneven feature point distribution in the image stitching process, which leads to long feature point matching time and potential excessive overlap in the final panoramic image. After the feature points are extracted, the region of the feature points is divided by the image segmentation algorithm, make the number of feature points in the region less than the threshold value and then the feature points are matched. Additionally, the RANSAC algorithm is applied to eliminate incorrect matches. Experimental results show that the improved algorithm has significant advantages over the original method in terms of feature point matching time, accuracy, and final image quality.
Collaborative Search Planning of UAV Swarms Based on Deep Reinforcement Learning

In a multi-agent environment, efficient collaborative search among multiple unmanned aerial vehicles (UAVs) is crucial for area search and path planning. Therefore, it is important to consider the collaborative learning among UAVs when designing collaborative strategies. This paper analyzes the problems of existing value-decomposition-based multi-agent reinforcement learning algorithms in multi-UAV area search and path planning, namely the underestimation of optimal joint actions, which leads to suboptimal policy generation. Subsequently, a new non-monotonicity value-decomposition algorithm is proposed based on the addition of a masked highway connection strategy. This algorithm suppresses the optimization of certain action pairs and focuses more on the optimal joint actions, thus better optimizing the objective and recovering the value of optimal joint actions. Multiple simulation experiments demonstrate that the proposed algorithm can achieve improved performance in environments involving UAV swarms and enhance the collaborative effectiveness of UAV swarms in search tasks.
Merged Intersection Over Union: A Metric and a Loss for Aerial Images in Transmission Lines

With the development of information technology and new energy, efficient power inspection is crucial for the safe and stable operation of the power grid. In recent years, State Grid of China has vigorously promoted intelligent transmission line inspection, gradually replacing the initial manual inspection of transmission lines with Unmanned Aerial Vehicle (UAV) inspection. The UAV inspection mainly relies on UAV high-altitude collection of transmission line images, and uses object detection (OD) and recognition algorithms to automatically determine various device defects in the transmission line. Due to the current mainstream OD methods using IoU to measure the degree of overlap between the predicted box and the real box, IoU is not only unable to measure scenes where the object boxes do not coincide, but also extremely sensitive to small-scale object position changes. If we continue to use IoU to measure high-altitude transmission line defect objects with large scale span, the OD algorithm model cannot achieve better performance. In order to alleviate this problem, we propose a new evaluation metric method, which is the Merged Intersection Over Union (MIoU). MIoU is a plug and play component that can be embedded into label assignment and loss function of any anchor-based detector to replace commonly used IoU metric. We conducted experiments on the MIoU performance in the transmission line component fault dataset, and a large number of experiments showed that using our proposed metric model improved 6.8% AP compared to the benchmark model, and significantly improved the recall rate of the model on certain key defect classes.
Super-Resolution Algorithm Based on Transformer+CNN

CNN and Transformer have their own excellent performance in image super-resolution, but these methods are difficult to be applied to the field of image SR alone due to the challenges of balancing model performance and complexity. To address these issues, we propose a Transformer+CNN-based algorithmic model for image super-resolution. First, a channel attention module is added to the model framework to model the correlation between channels of the feature maps; second, different training strategies are used to fine-tune the parameters at the end of the network base training; finally, a new loss function is introduced to optimize the parameters in the fine-tuning phase. The experimental results show that the method is more effective than the excellent algorithms in recent years.
A Feature Defusion Network for Surface Anomaly Detection of Industrial Products

In this paper, we propose a feature defusion network for industrial product surface anomaly detection. This method addresses two prominent issues in industrial product surface anomaly detection tasks: the presence of complex textures on the surface of the product to be inspected and the scarcity of anomaly samples. In our network, two variational autoencoders are used as the main branch network, and the similarity loss is applied to separate the texture features and structural features of the sample data in the latent space. Then, an external anomaly classifier is used to detect the structural features. We conducted a set of comparative experiments to validate the effectiveness of this method in anomaly detection tasks of this kind, and the results show that this method can meet the detection requirements of industrial production very well.
Continuous speech recognition based on DCNN-LSTM

In traditional speech recognition, the MFCC algorithm is commonly used as the feature extraction method, but this method is filtered by an artificially designed band-pass filter, which will cause information loss in the input signal. In this paper, the spectrogram is used as the extraction feature input to the network for training. This feature contains more original speech information and avoids the additional loss of information processing caused by the extraction algorithm. This paper models the acoustic model based on the neural convolutional network, proposes an acoustic model of the deep convolutional long short-term memory network (DCNN-LSTM), and explores the recognition effect of the network under three different feature inputs. Among the three different input features, the acoustic model trained by MFCC as the input feature has poor recognition effect, and the word error rate reaches 45.96 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ; Spectrogram as the input feature can significantly reduce the word error rate to 28.41%; after data enhancement SpeAugment has the best effect as a feature input, and can reduce the word error rate to 23.50% after improving the network model.
A Method for Detecting Temperature Anomalies of Disc Suspension Insulators on Power Transmission Lines

Coupled with the extensive application of infrared thermal imaging technology in intelligent fault diagnosis of insulators on the transmission line, current image processing algorithms struggle to effectively identify the abnormal hot spots in infrared images of insulators. To cope with this problem, this paper proposes a lightweight model based on the ShuffleNet V2 network, which is based on the key point detection algorithm of the Gaussian heat map and embedded with efficient channel attention (ECA) mechanism to accurately locate and identify the iron cap area in the infrared image of insulators. After detecting the position of the insulator's iron cap, Hough straight line detection algorithm is used to combine the attitude of the insulator string, and then extract the thermal information of the insulator iron cap area. The model takes floating-point operations per second (FLOPs), memory consumption, model size, average precision (object keypoint similarity)-AP (OKS), and inference time as evaluation indexes. The experimental results demonstrate that the FLOPs size of the model is 4.65×107, the model size is 11.72 M, and the average precision reaches 93.39%. The memory consumption is 22.98 M and the inference time is 1.60 s when deployed and tested in Raspberry Pi. In comparison with the existing methods, the model achieves a sound balance between detection precision and inference velocity, meets the needs of edge equipment deployment, and provides a new solution for improving the inspection efficiency of the transmission line system.
Noncontact Intelligent Vibrometer Based on Laser Triangulation

Effective vibration measurement is crucial for monitoring the health of mechanical systems and preventing failure. Current methodologies face limitations such as mass loading, signal attenuation, and environmental sensitivity. This paper presents a novel noncontact laser triangulation vibrometer (LTV) based on the laser triangulation principle for vibration measurements. The LTV offers rapid sampling, high precision, compact design, and easy integration, addressing the challenges faced by existing techniques. Validation experiments, including low-frequency tests on a piezoelectric actuator and high-frequency tests on a speaker diaphragm, demonstrate the LTV's high accuracy, repeatability, and adaptability in vibration measurement applications. Results show that the LTV can measure vibrations up to 45 kHz, and can achieve a low residual nonlinearity of ± 0.0121% F.S. and a low noise RMS of 0.549 μm. The proposed LTV provides a high-performance alternative to traditional vibration measurement methods for monitoring machinery health, diagnosing faults, and reducing maintenance costs.
Heterogeneous Knowledge Graph Personalized Recommendation Algorithm

In recent years, the use of knowledge graph and deep learning for recommendation has been widely studied and applied, but most recommendation models are incomplete in the representation of heterogeneous information items. Aiming at the extraction of high-order representations of user heterogeneous features and project features, a personalized recommendation model based on heterogeneous knowledge graph (HKGR) is proposed. The propagation of heterogeneous neural networks is used to model user preferences and project characteristics to improve recommendation performance. The accuracy of model recommendation is improved, and the ability of the model to cope with data sparse scenarios is enhanced. The experimental results show that the key components in this method are compared with the co-field baseline method, which is conducive to improving the recommended performance.
Detection of Prostate Capsule Based on Attentional Mechanism and Feature Fusion

The insufficient feature extraction ability of the network will lead to the decrease of the detection precision of the object detection. A new attention mechanism feature fusion network based on channels was proposed to solve that. We added Primary Component Analysis Squeeze-and-Excitation Networks (PCA-SENet) block to the backbone network VGG16 to increase the feature extraction ability, down-sampling and lateral connections were utilized to finish low-level and high-level feature fusion, new model called PCA-SENet Neighbour Single Shot Multibox (PNSSD) was proposed. The results showed that the proposed method had the mAP for prostate capsule image detection reached 82.02%, attention mechanism and feature fusion are helpful to improve the detection precision of object detection network.
Automating Code Review

Code reviews are popular in both industrial and open source projects. The benefits of code reviews are widely recognized and include better code quality and lower likelihood of introducing bugs. However, code review comes at the cost of spending developers' time on reviewing their teammates' code. The goal of this research is to investigate the possibility of using Deep Learning (DL) to automate specific code review tasks. We started by training vanilla Transformer models to learn code changes performed by developers during real code review activities. This gives the models the possibility to automatically (i) revise the code submitted for review without any input from the reviewer; and (ii) implement changes required to address a specific reviewer's comment. While the preliminary results were encouraging, in this first work we tested DL models in rather simple code review scenarios, substantially simplifying the targeted problem. This was also due to the choices we made when designing both the technique and the experiments. Thus, in a subsequent work, we exploited a pre-trained Text- To- Text-Transfer-Transformer (T5) to overcome some of these limitations and experiment DL models for code review automation in more realistic and challenging scenarios. The achieved results show the improvements brought by T5 both in terms of applicability (i.e., scenarios in which it can be applied) and performance. Despite this, we are still far from performance levels making these techniques deployable in practice, thus calling for additional research in this area, as we discuss in our future work agenda.