Reviewerly/exHarmony huggingface dataset:
Benchmarking entanglement-based QKD protocols in noisy channels

We address the efficiency and robustness of quantum key distribution (QKD) based on entangled states in conditions of noisy quantum channels. We consider schemes based on either source or detection station located in the middle or the channel, the latter configuration allowing realization of measurement-device independent QKD. In our study we focus both on discrete-variable (DV) QKD protocols utilizing direct photodetection as well as continuous-variable (CV) ones, which are based on the homodyne detection. Equivalently parametrizing the channel excess noise for both DV and CV protocols, we use robustness of an ideal CV QKD protocol as a benchmark for the entanglement-based DV QKD. We show that DV protocols typically overcome the CV ones in both source- and detector-in-the-middle setup configurations. However, this advantage can be undermined by the practical aspects on the DV protocols. Considering quantum-dot type of the DV sources of entanglement, we show the levels of pair generation and collection efficiencies needed to overcome any CV protocols. The requirements are generally compatible with the existing technologies but may require experimental efforts to achieve. Our results are essential for the practical entanglement-based QKD protocols with modern efficient sources, aimed at potential scalability in the quantum networks.
Bio-inspired enhancement for optical detection of drones using convolutional neural networks

Threats posed by drones urge defence sectors worldwide to develop drone detection systems. Visible-light and infrared cameras complement other sensors in detecting and identifying drones. Application of Convolutional Neural Networks (CNNs), such as the You Only Look Once (YOLO) algorithm, are known to help detect drones in video footage captured by the cameras quickly, and to robustly differentiate drones from other flying objects such as birds, thus avoiding false positives. However, using still video frames for training the CNN may lead to low drone-background contrast when it is flying in front of clutter, and omission of useful temporal data such as the flight trajectory. This deteriorates the drone detection performance, especially when the distance to the target increases. This work proposes to pre-process the video frames using a Bio-Inspired Vision (BIV) model of insects, and to concatenate the pre-processed video frame with the still frame as input for the CNN. The BIV model uses information from preceding frames to enhance the moving target-to-background contrast and embody the target’s recent trajectory in the input frames. An open benchmark dataset containing infrared videos of small drones (&lt; 25 kg) and other flying objects is used to train and test the proposed methodology. Results show that, at a high sensor-to-target distance, the YOLO algorithms trained on BIV-processed frames and concatenation of the BIV-processed frames with still frames increase the Average Precision (AP) to 0.92 and 0.88, respectively, compared to 0.83 when it is trained on still frames alone.
Investigating U.S. and German pre-service teachers’ beliefs regarding digital technology

ABSTRACTBased on different educational policies around the world, pre-service teachers are expected to use digital technology in their future teaching in school. However, to do this successfully, they need knowledge, skills and appropriate beliefs regarding utilising digital technology in learning scenarios. Thus, this study explores pre-service teachers’ beliefs regarding current digital technologies in their learning and future teaching. 232 pre-service teachers from Germany and the US participated in the comparative study and responded to the Digital Technologies Survey. The results show that overall pre-service teachers’ beliefs regarding digital technology in learning and teaching are on a moderate level. However, significant differences were seen between pre-service teachers from Germany and the US. For US pre-service teachers, digital technology seems more important and helpful for their current learning and future teaching than for German pre-service teachers. The same can be seen for self-assessed competence. The results and further implications are discussed.KEYWORDS: Beliefsdigitisation of educationICT enhanced teaching and learninghigher educationtechnology in education and training Disclosure statementNo potential conflict of interest was reported by the author(s).
Center point-based feature representation for tracking

Center points are commonly the results of anchor-free object detectors. Starting from this initial representation, a regression scheme is utilized to determine a target point set to capture object properties such as enclosing bounding boxes and further attributes such as class labels. When only trained for the detection tasks, the encoded center point feature representations are not well suited for tracking objects since the embedded features are not stable over time. To tackle this problem, we present an approach of joint detection and feature embedding for multiple object tracking. The proposed approach applies an anchor-free detection model to pairs of images to extract single-point feature representations. To generate temporal stable features which are suitable for track association across short time intervals, auxiliary losses are applied to reduce the distance of tracked identities in the embedded feature space. The abilities of the presented approach are demonstrated on real-world data reflecting prototypical object tracking scenarios.
One-shot logo detection for large video datasets and live camera surveillance in criminal investigations

Logos on clothing are sometimes one of the crucial clues to find a suspect in surveillance video. Automatic logo detection is important during investigations to perform the search as quickly as possible. This can be done immediately after an incident on live camera streams or retrospectively on large video datasets from criminal investigations for forensic purposes. It is common to train an object detector with many examples on a logo dataset to perform logo detection. To obtain good performance, the logo dataset must be large. However, it is time-consuming and difficult to obtain a large training set with realistic annotated images. In this paper, we propose a novel approach for logo detection that requires only one logo image (or a few images) to train a deep neural network. The approach consists of two main steps: data generation and logo detection. In the first step, the logo image is artificially blended in a person re-identification dataset to generate an anonymized synthetic dataset with logos on clothing. Various augmentation steps appeared to be necessary to reach a good performance. In the second step, an object detector is trained on the synthetic dataset, subsequently providing detections on recorded images, video files, and live streams. The results consist of a quantitative assessment based on an ablation study of the augmentation steps and a qualitative assessment from end users that tested the tool.
Efficient visual information indexation for supporting actionable intelligence and knowledge generation

Visual indexing, or the ability to search and analyze visual media such as images and videos, is important for law enforcement agencies because it can speed up criminal investigations. As more and more visual media is created and shared online, the ability to effectively search and analyze this data becomes increasingly important for investigators to do their job effectively. The major challenges for video captioning include accurately recognizing the objects and activities in the image, understanding their relationships and context, generating natural and descriptive language, and ensuring the captions are relevant and useful. Near real-time processing is also required in order to facilitate agile forensic decision making and prompt triage, hand-over and reduction of the amount of data to be processed by investigators or subsequent processing tools. This paper presents a captioning-driven efficient video analytic which is able to extract accurate descriptions of images and videos files. The proposed approach includes a temporal segmentation technique providing the most relevant frames. Subsequently, an image captioning approach has been specialized to describe visual media related to counter-terrorism and cybercrime for each relevant frame. Our proposed method achieves high consistency and correlation with human summary on SumMe dataset, outperforming previous similar methods.
The ‘Other Talk’: a co-design study of board games for cross-generational communication of life-and-death issues

This study presents the collaborative design experience of developing a cross-generational communication game about life-and-death issues. The research activities involved two groups of end-users from different generations, who were tasked with working together to build tools that would encourage a deeper communication between them. To overcome limited knowledge on serious game design and encourage participation, this study utilises an experimental approach that involves a series of workshop activities following a design circle. The results indicate that engagement among cross-generational participants can be enhanced by involving a small, mixed group and utilising structural design materials in the co-design process. The groups developed a collaborative mode of task-oriented and ability-based division of labour to develop the game. This approach also stimulated individuals to take on multiple roles, that is, users, testers, informants, and design partners, and shift between roles as the group dynamic, supporting materials, and design stages evolve. Additionally, this collaborative process encourages participants to reflect on their role as future end-users when communicating sensitive issues to others.
Investigation of anomalies in federal budget revenues

Studies of abnormal values play an important role in understanding the generating processes and are one of the key ones in financial analysis and forecasting. This article is devoted to the study of anomalies in federal budget revenues. Based on statistical data for the period from 2008 to 2022, abnormal values of budget revenues in different time periods were revealed using statistical models and machine learning methods, their advantages and disadvantages are shown. Practical recommendations on the use of machine learning methods to identify anomalies in the field of public finance are given.
New methods for explainable variational autoencoders

A new deep learning algorithm for performing anomaly detection and multi-class classification with explainability using counterfactuals is described. The system is a Variational Autoencoder (VAE) with a modified loss function and new methods for counterfactual identification. An additional hinge-loss term is added to VAE training. This enables convenient synthetic data generation and allows straightforward construction of multi-class counterfactuals. Counterfactuals are synthetic data generated to explain system decisions by answering the question: “If this data was not anomalous or was in another class, what modifications would need to be made?” To determine counterfactuals, a path is determined through the embedding space via adversarial attack-like techniques to minimize reconstruction error, with the restriction of minimally altering the number of columns changed. Large changes are allowed, unlike adversarial attack approaches, so changes are isolated and easily visible. Anomaly detection is performed by modifying a result to lower its anomalousness. Classification changes are performed by modifying the data to another class. Multi-class classification is performed on the embedding space of the VAE via an attached linear support vector machine (SVM). By adding the hinge-loss term to the VAE embedding training as well as the SVM, the embedding is modified to prefer class separation without being informed of the specific class labels. This causes the classes in the embedding space to be separated by hyperplanes, making counterfactual generation convenient and SVM classification accurate. Accuracy is shown to be comparable to other deep learners. Approaches to accommodating the image and time-series data are discussed.
Adaptive dynamic windowing approach based on risk degree function

Aiming at the problem that the dynamic window approach (DWA) has low planning efficiency and it is difficult to avoid fast-moving obstacles, an improved adaptive DWA based on risk degree function is proposed in this paper. The risk function is designed and introduced into the evaluation function of the traditional DWA to evaluate the risk of collision between dynamic obstacles and the robot, so that the robot can effectively avoid faster obstacles. Then, according to the fuzzy control principle, the adaptive weight coefficient is designed to improve the evaluation function, so that the mobile robot can move to the target point more efficiently. The simulation results show that compared with the traditional DWA, the adaptive DWA based on risk degree function reduces the time of completing the planning task by about 8%, and the path length after the completion of the planning by about 8%, it indicates that the improved algorithm has higher efficiency. After 50 repeated experiments, using the adaptive DWA based on risk degree function successfully avoids all obstacles to complete the planning task, which shows that this algorithm has higher security.
Privacy-preserving federated learning with various computer-vision tasks for security applications

Federated learning (FL) is a hot research topic enabling training on databases of multiple organizations while preserving the privacy of people whose personal data is stored in the databases. FL supports the sharing of trained machine-learning (ML) models between different organizations without sharing personal data. This is important for many security applications – including video surveillance and document authentication – because access to more data leads to better performance. Over the last years, many papers proposed FL frameworks, but most lack at least one of the following aspects: open-source availability, flexibility in decentralized topology, flexibility in using ML frameworks (e.g., PyTorch), real deployment (not only simulation), and results on multiple computer-vision (CV) tasks. In this paper, we give an overview of existing FL frameworks to assess these aspects. Furthermore, we implemented various CV tasks in a federated way and describe the implementation in the paper. This does not only include a small-scale image classification task, but also more challenging CV tasks, such as object detection, semantic segmentation, and person re-identification. Experiments were performed and the results show that models that are trained with privacy-preserving FL perform much better than the baseline with access to only a subset of the data and reach performance close to the upper limit with access to all data.
Inertial measurement data from loose clothing worn on the lower body during everyday activities

Abstract Embedding sensors into clothing is promising as a way for people to wear multiple sensors easily, for applications such as long-term activity monitoring. To our knowledge, this is the first published dataset collected from sensors in loose clothing. 6 Inertial Measurement Units (IMUs) were configured as a ‘sensor string’ and attached to casual trousers such that there were three sensors on each leg near the waist, thigh, and ankle/lower-shank. Participants also wore an Actigraph accelerometer on their dominant wrist. The dataset consists of 15 participant-days worth of data collected from 5 healthy adults (age range: 28–48 years, 3 males and 2 females). Each participant wore the clothes with sensors for between 1 and 4 days for 5–8 hours per day. Each day, data were collected while participants completed a fixed circuit of activities (with a video ground truth) as well as during free day-to-day activities (with a diary). This dataset can be used to analyse human movements, transitional movements, and postural changes based on a range of features.
Efficient storage and retrieval of high-quality face images for effective face recognition

Recent developments in chip technology have led to the integration of smart Artificial Intelligence (AI) capabilities in surveillance cameras such as vehicle and pedestrian detection, license plate recognition, and face detection (FD). FD cameras are being widely deployed in various public locations to identify wanted individuals. However, the large size of identity databases and the need for personal information security make it necessary to perform face recognition (FR) on servers rather than on cameras. On the other hand, more FD cameras and detected faces lead to extensive power consumption and computational power demand for real-time FR along with large storage devices to store face images. While FD cameras track detected faces and select the best quality face for FR, multiple faces belonging to the same identities might be sent to the servers due to track algorithms or reentering of identity to the scene. Therefore, we propose a method for finding similar faces belonging to the same identities and removing low-quality faces to keep only the highest-quality face in the server for storage and FR. We utilize facial embedding vectors, obtained from aligned faces using an FR model, and store them in a database along with face image information such as face capture time, camera IP, and face quality score. If a face has similar faces in the database, the highest-quality face is kept in the database and others are removed. As a result, our proposed method eliminates extra face images and keeps the highest-quality face images for high performance, efficient FR, and effective storage.
Optimizing for an arbitrary Schrödinger cat state

We derive a set of functionals for optimization towards an arbitrary cat state and demonstrate their application by optimizing the dynamics of a Kerr-nonlinear Hamiltonian with two-photon driving. The versatility of our framework allows us to adapt our functional towards optimization of maximally entangled cat states, applying it to a Jaynes-Cummings model. We identify the strategy of the obtained control fields and determine the quantum speed limit as a function of the cat state's excitation. Finally, we extend our optimization functionals to open quantum system dynamics and apply it to the Jaynes-Cummings model with decay on the oscillator. For strong dissipation and large cat radii, we find a change in the control strategy compared to the case without dissipation. Our results highlight the power of optimal control with functionals specifically crafted for complex physical tasks and the versatility of the quantum optimal control toolbox for practical applications in the quantum technologies.
RMAU-Net: Breast Tumor Segmentation Network Based on Residual Depthwise Separable Convolution and Multiscale Channel Attention Gates

Breast cancer is one of the most common female diseases, posing a great threat to women’s health, and breast ultrasound imaging is a common method for breast cancer diagnosis. In recent years, U-Net and its variants have dominated the medical image segmentation field with their excellent performance. However, the existing U-type segmentation networks have the following problems: (1) the design of the feature extractor is complicated, and the calculation difficulty is increased; (2) the skip connection operation simply combines the features of the encoder and the decoder, without considering both spatial and channel dimensions; (3) during the downsampling phase, the pooling operation results in the loss of feature information. To address the above deficiencies, this paper proposes a breast tumor segmentation network, RMAU-Net, that combines residual depthwise separable convolution and a multi-scale channel attention gate. Specifically, we designed the RDw block, which has a simple structure and a larger sensory field, to overcome the localization problem of convolutional operations. Meanwhile, the MCAG module is designed to correct the low-level features in both spatial and channel dimensions and assist the high-level features to recover the up-sampling and pinpoint non-regular breast tumor features. In addition, this paper used the Patch Merging operation instead of the pooling method to prevent the loss of breast ultrasound image information. Experiments were conducted on two breast ultrasound datasets, Dataset B and BUSI, and the results show that the method in this paper has superior segmentation performance and better generalization.
Asymmetric one-sided semi-device-independent steerability of quantum discordant states

Superlocality and superunsteerability provide operational characterization of quantum correlations in certain local and unsteerable states, respectively. Such quantum correlated states have a nonzero quantum discord. A two-way nonzero quantum discord is necessary for quantum correlations pointed out by superlocality. On the other hand, in this work, we demonstrate that a two-way nonzero quantum discord is not necessary to demonstrate superunsteerability. To this end, we demonstrate superunsteerability for one-way quantum discordant states. This in turn implies the existence of one-way superunsteerability and also the presence of superunsteerability without superlocality. Superunsteerability for nonzero quantum discord states implies the occurrence of steerability in a one-sided semi-device-independent way. Just like one-way steerability occurs for certain Bell-local states in a one-sided device-independent way, our result shows that one-way steerability can also occur for certain nonsuperlocal states but in a one-sided semi-device-independent way.
YOLOv7-3D: A Monocular 3D Traffic Object Detection Method from a Roadside Perspective

Current autonomous driving systems predominantly focus on 3D object perception from the vehicle’s perspective. However, the single-camera 3D object detection algorithm in the roadside monitoring scenario provides stereo perception of traffic objects, offering more accurate collection and analysis of traffic information to ensure reliable support for urban traffic safety. In this paper, we propose the YOLOv7-3D algorithm specifically designed for single-camera 3D object detection from a roadside viewpoint. Our approach utilizes various information, including 2D bounding boxes, projected corner keypoints, and offset vectors relative to the center of the 2D bounding boxes, to enhance the accuracy of 3D object bounding box detection. Additionally, we introduce a 5-layer feature pyramid network (FPN) structure and a multi-scale spatial attention mechanism to improve feature saliency for objects of different scales, thereby enhancing the detection accuracy of the network. Experimental results demonstrate that our YOLOv7-3D network achieved significantly higher detection accuracy on the Rope3D dataset while reducing computational complexity by 60%.