Reviewerly/exHarmony huggingface dataset:
SPMAC: Secure and privacy-preserving multi-authority access control for fog-enabled IoT cloud storage

The fog computing paradigm extends the storage, network and computing facilities of the Internet of Things (IoT) to the edge of the network and provides increased convenience in real life. However, IoT still faces great security challenges, such as unauthorized access, collusion attacks between users, cloud attack, and privacy leaks. Most recently, Xiong et al. proposed a scheme to attempt to solve these issues (Xiong et al., 2020). However, we found that their scheme still could not resist the collusion attacks between non-revoked users and revoked users. This study performs the security analysis of SEM-ACSIT and proposes a secure and privacy-preserving multi-authority access control scheme named SPMAC. The proposed scheme can realize flexible user and attribute revocation. The proposed scheme achieves forward security and backward security. For the resource-limited devices, This scheme csn achieve lightweight computing using online–offline encryption technique, outsourced decryption method, and fog computing. It is worth noting that the scheme proposes a ciphertext update check algorithm, allowing users to confirm whether the ciphertext from the untrusted cloud and the user secret key have been updated. The security analysis shows that the proposed scheme can resist multiple collusion attacks, such as revoked and non-revoked users attack, cloud-users attack, and cloud attack. In addition, we realize the hidden access policy to protect users’ privacy. Finally, the results of simulation experiments indicate that the proposed scheme has great practical significance in the access control of IoT.
Lightweight marine biological target detection algorithm based on YOLOv5

Underwater object detection currently faces many challenges, such as the large number of parameters in existing object detection models, slow inference speed, blurring of underwater images, and aggregation of small targets, making it difficult to conduct efficient underwater object detection. This paper proposes a lightweight underwater object detection algorithm based on YOLOv5.The method uses depth-wise separable convolution instead of ordinary convolution to reduce the number of parameters and computational complexity. A C3 module based on Ghost convolution is designed to further compress the model size and improve the computational speed. In the feature extraction stage, a RepVgg module based on structural reparameterization is used to convert the multi -branch structure into a single-branch structure in the inference stage, improving the feature extraction ability of the model and increasing the inference speed. A Rep-ECA module is designed to embed the efficient channel attention module ECANet into the RepVGG module, selecting more effective channel information and improving the model’s feature extraction ability for small objects in blurred images, thereby improving detection precision. Experimental results show that in the URPC underwater object detection dataset, the proposed algorithm has a 39% lower model parameter count compared to the original model, a 42% reduction in computational complexity. The model can achieve a frame rate of 85 on a single Nvidia GTX 1080ti GPU, which is a 24% improvement over the original model, while mAP reaches 85.1%, a 1.1% improvement over the original model. The algorithm can improve the detection precision and achieve lightweight, which lays a foundation for the deployment of underwater equipment.
Quantum projections on conceptual subspaces

One of the main challenges of cognitive science is to explain the representation of conceptual knowledge and the mechanisms involved in evaluating the similarities between these representations. Theories that attempt to explain this phenomenon should account for the fact that conceptual knowledge is not static. In line with this thinking, many studies suggest that the representation of a concept changes depending on context. Traditionally, concepts have been studied as vectors within a geometric space, sometimes called Semantic-Vector Space Models (S-VSMs). However, S-VSMs have certain limitations in emulating human biases or context effects when the similarity of concepts is judged. Such limitations are related to the use of a classical geometric approach that represents a concept as a point in space. Recently, some theories have proposed the use of sequential projections of subspaces based on Quantum Probability Theory (Busemeyer and Bruza, 2012; Pothos et al., 2013). They argue that this theoretical approach may facilitate accounting for human similarity biases and context effects in a more natural way. More specifically, Pothos and Busemeyer (2011) proposed the Quantum Similarity Model (QSM) to determine expectation in conceptual spaces in a non-monotonic logic frame. To the best of our knowledge, previous data-driven studies have used the QSM subspaces in a unidimensional way. In this paper, we present a data-driven method to generate these conceptual subspaces in a multidimensional manner using a traditional S-VSM. We present an illustration of the method taking Tversky's classical examples to explain the effects of Asymmetry, Triangular Inequality, and the Diagnosticity by means of sequential projections of those conceptual subspaces.
ELSA: Secure Aggregation for Federated Learning with Malicious Actors

Federated learning (FL) is an increasingly popular approach for machine learning (ML) in cases where the training dataset is highly distributed. Clients perform local training on their datasets and the updates are then aggregated into the global model. Existing protocols for aggregation are either inefficient, or don’t consider the case of malicious actors in the system. This is a major barrier in making FL an ideal solution for privacy-sensitive ML applications. We present Elsa, a secure aggregation protocol for FL, which breaks this barrier - it is efficient and addresses the existence of malicious actors at the core of its design. Similar to prior work on Prio and Prio+, Elsa provides a novel secure aggregation protocol built out of distributed trust across two servers that keeps individual client updates private as long as one server is honest, defends against malicious clients, and is efficient end-to-end. Compared to prior works, the distinguishing theme in Elsa is that instead of the servers generating cryptographic correlations interactively, the clients act as untrusted dealers of these correlations without compromising the protocol’s security. This leads to a much faster protocol while also achieving stronger security at that efficiency compared to prior work. We introduce new techniques that retain privacy even when a server is malicious at a small added cost of 7-25% in runtime with negligible increase in communication over the case of semi-honest server. Our work improves end-to-end runtime over prior work with similar security guarantees by big margins - single-aggregator RoFL by up to 305x (for the models we consider), and distributed trust Prio by up to 8x.
DSMISR: Differential Siamese Multi-scale Attention Network for Iris Image Super Resolution

Iris images sampled in relaxed acquisition conditions usually have poor high frequency details, which severely affects the accuracy of iris recognition. Super resolution, as a kind of prior knowledge, can enhance the texture details for sequent recognition, but there are few relevant studies. In this work, we propose a learning-based super resolution neural network, i.e., Differential Siameses Multi-scale attention network for Iris super resolution (DSMISR). We develop a unique differential siamese multi-scale attention (DSMA) block, which extracts features via siamese structure but segments them into different scales. In this way, the module can generate multi-scale information in a consistent manifold, and utilize the differential information to improve the final performance. DSMA block simultaneously accelerates the information dissemination in the shifted window scheme and recovers more high frequency information. With this novel module, we reduced the computing cost by up to 25% compared to SwinIR. Extensive results show that DSMISR gets better PSNR and SSIM scores and decreases the ERR in iris recognition system effectively.
Incorporating Failure Knowledge into Design Decisions for IoT Systems: A Controlled Experiment on Novices

Internet of Things (IoT) systems allow software to directly interact with the physical world. Recent IoT failures can be attributed to recurring software design flaws, suggesting IoT software engineers may not be learning from past failures. We examine the use of failure stories to improve IoT system designs.We conducted an experiment to evaluate the influence of failure-related learning treatments on design decisions. Our experiment used a between-subjects comparison of novices (computer engineering students) completing a design questionnaire. There were three treatments: a control group (N=7); a group considering a set of design guidelines (N=8); and a group considering failure stories (proposed treatment, N=6). We measured their design decisions and their design rationales. All subjects made comparable decisions. Their rationales varied by treatment: subjects treated with guidelines and failure stories made greater use of criticality as a rationale, while subjects exposed to failure stories more frequently used safety as a rationale. Building on these findings, we suggest several research directions toward a failure-aware IoT engineering process.
A Cooperative Perception System Robust to Localization Errors

Cooperative perception is challenging for safety-critical autonomous driving applications. The errors in the shared position and pose cause an inaccurate relative transform estimation and disrupt the robust mapping of the Ego vehicle. We propose a distributed object-level cooperative perception system called OptiMatch, in which the detected 3D bounding boxes and local state information are shared between the connected vehicles. To correct the noisy relative transform, the local measurements of both connected vehicles (bounding boxes) are utilized, and an optimal transport theory-based algorithm is developed to filter out those objects jointly detected by the vehicles along with their correspondence, constructing an associated co-visible set. A correction transform is estimated from the matched object pairs and further applied to the noisy relative transform, followed by global fusion and dynamic mapping. Experiment results show that robust performance is achieved for different levels of location and heading errors, and the proposed framework outperforms the state-of-the-art benchmark fusion schemes, including early, late, and intermediate fusion, on average precision by a large margin when location and/or heading errors occur.
On the simultaneous scattering of two photons by a single two-level atom

Abstract The interaction of light with a single two-level emitter is the most fundamental process in quantum optics, and is key to many quantum applications. As a distinctive feature, two photons are never detected simultaneously in the light scattered by the emitter. This is commonly interpreted by saying that a single two-level quantum emitter can only absorb and emit single photons. However, it has been theoretically proposed that the photon anticorrelations can be thought of as arising from quantum interference between two possible two-photon scattering amplitudes, which one refers to as coherent and incoherent. This picture is in stark contrast to the aforementioned one, in that it assumes that the atom has two different mechanisms at its disposal to scatter two photons at the same time. Here we experimentally validate the interference picture by showing that, when spectrally rejecting only the coherent component of the fluorescence light of a single two-level atom, the remaining light consists of photon pairs that have been simultaneously scattered by the atom. Our results offer fundamental insights into the quantum-mechanical interaction between light and matter and open up novel approaches for the generation of highly non-classical light fields enabling, for example, Fourier-limited photon-pair sources that approach the theoretical limit in brightness.
Federated recognition mechanism based on enhanced temporal-spatial learning using mobile edge sensors for firefighters

Abstract Background Interest in Human Action Recognition (HAR), which encompasses both household and industrial settings, is growing. HAR describes a computer system’s capacity to accurately recognize and evaluate human activities and behaviors, akin to what humans call perception. Real-time federated activity identification architecture is suggested in this work to monitor smartphone user behavior. The main aim is to decrease accidents happening in an indoor environment and assure the security of older individuals in an indoor setting. The idea lends itself to a multitude of uses, including monitoring the elderly, entertainment, and spying. Results In this paper, we present a new smartphone sensor-based human motion awareness federated recognition scheme using a temporal-spatial weighted BILSTM-CNN framework. We verify new federated recognition based on temporal-spatial data better than existing machine learning schemes in terms of activity recognition accuracy. Several methods and strategies in the literature have been used to attain higher HAR accuracy. In particular, six categories of typical everyday human activities are highlighted, including walking, jumping, standing, moving from one level to another, and picking up items. Conclusion Smartphone-based sensors are utilized to detect the motion activities carried out by elderly people based on the raw inertial measurement unit (IMU) data. Then, weighted bidirectional long short-term memory (BILSTM) networks are for learning about temporal motion features; they are swiftly followed by single-dimensional convolutional neural networks (CNN), which are built for reasoning about spatial structure features. Additionally, the awareness mechanism highlights the data segments to choose discriminative contextual data. Finally, a sizeable dataset of HDL activity datasets is gathered for model validation and training. The results confirm that the proposed ML framework performs 18.7% better in terms of accuracy, 27.9% for the case of precision, and 0.24.1% when evaluating based on the F1-score for client 1. Similarly, for client 2 and client 3, the performance betterment in terms of accuracy is 18.4% and 10.1%, respectively.
Who (and with whom) uses more emoji? Exploring individual, relational, and motivational characteristics driving emoji use

Emoji use, despite being pervasive in digital communication, is often dependent on individual characteristics (e.g., gender, age, personality), relational dimensions (e.g., intimacy with specific others), motives for using them (e.g., because emojis allow in better expressing emotions), and context in which they are used (e.g., emoji use is considered more appropriate with closer interlocutors, such as friends). However, research is yet to examine if and how these variables associate to emoji use frequency, when considered together. In a correlational study (N = 444), we explored the relative contribution of individual characteristics, perceived relatedness to others, and motives to explain the frequency of emoji use with different interlocutors. Hierarchical linear regressions showed that being younger, scoring higher on conscientiousness, and resorting to emoji for personal contact were correlates of emoji use frequency with closer interlocutors (e.g., family and friends). In contrast, being older, scoring lower on agreeableness, and perceiving more relational intimacy were correlates of emoji use frequency with more distant interlocutors (e.g., supervisors and doctors). Overall, results highlight the need to account for multiple variables at different levels to examine emoji use patterns in digital communication.
Viewpoint Generation Using Feature-Based Constrained Spaces for Robot Vision Systems

The efficient computation of viewpoints while considering various system and process constraints is a common challenge that any robot vision system is confronted with when trying to execute a vision task. Although fundamental research has provided solid and sound solutions for tackling this problem, a holistic framework that poses its formal description, considers the heterogeneity of robot vision systems, and offers an integrated solution remains unaddressed. Hence, this publication outlines the generation of viewpoints as a geometrical problem and introduces a generalized theoretical framework based on Feature-Based Constrained Spaces (C-spaces) as the backbone for solving it. A C-space can be understood as the topological space that a viewpoint constraint spans, where the sensor can be positioned for acquiring a feature while fulfilling the constraint. The present study demonstrates that many viewpoint constraints can be efficiently formulated as C-spaces, providing geometric, deterministic, and closed solutions. The introduced C-spaces are characterized based on generic domain and viewpoint constraints models to ease the transferability of the present framework to different applications and robot vision systems. The effectiveness and efficiency of the concepts introduced are verified on a simulation-based scenario and validated on a real robot vision system comprising two different sensors.
Dynamic Fall Detection Using Graph-Based Spatial Temporal Convolution and Attention Network

The prevention of falls has become crucial in the modern healthcare domain and in society for improving ageing and supporting the daily activities of older people. Falling is mainly related to age and health problems such as muscle, cardiovascular, and locomotive syndrome weakness, etc. Among elderly people, the number of falls is increasing every year, and they can become life-threatening if detected too late. Most of the time, ageing people consume prescription medication after a fall and, in the Japanese community, the prevention of suicide attempts due to taking an overdose is urgent. Many researchers have been working to develop fall detection systems to observe and notify about falls in real-time using handcrafted features and machine learning approaches. Existing methods may face difficulties in achieving a satisfactory performance, such as limited robustness and generality, high computational complexity, light illuminations, data orientation, and camera view issues. We proposed a graph-based spatial-temporal convolutional and attention neural network (GSTCAN) with an attention model to overcome the current challenges and develop an advanced medical technology system. The spatial-temporal convolutional system has recently proven the power of its efficiency and effectiveness in various fields such as human activity recognition and text recognition tasks. In the procedure, we first calculated the motion along the consecutive frame, then constructed a graph and applied a graph-based spatial and temporal convolutional neural network to extract spatial and temporal contextual relationships among the joints. Then, an attention module selected channel-wise effective features. In the same procedure, we repeat it six times as a GSTCAN and then fed the spatial-temporal features to the network. Finally, we applied a softmax function as a classifier and achieved high accuracies of 99.93%, 99.74%, and 99.12% for ImViA, UR-Fall, and FDD datasets, respectively. The high-performance accuracy with three datasets proved the proposed system’s superiority, efficiency, and generality.
CLeBPI: Contrastive Learning for Bug Priority Inference

Automated bug priority inference (BPI) can reduce the time overhead of bug triagers for priority assignments, improving the efficiency of software maintenance. There are two orthogonal lines for this task, i.e., traditional machine learning based (TML-based) and neural network based (NN-based) approaches. Although these approaches achieve competitive performance, our observation finds that existing approaches face the following two issues: 1) TML-based approaches require much manual feature engineering and cannot learn the semantic information of bug reports; 2) Both TML-based and NN-based approaches cannot effectively address the label imbalance problem because they are difficult to distinguish the semantic difference between bug reports with different priorities. We propose CLeBPI (Contrastive Learning for Bug Priority Inference), which leverages pre-trained language model and contrastive learning to tackle the above-mentioned two issues. Specifically, CLeBPI is first pre-trained on a large-scale bug report corpus in a self-supervised way, thus it can automatically learn contextual representations of bug reports without manual feature engineering. Afterward, it is further pre-trained by a contrastive learning objective, which enables it to distinguish semantic differences between bug reports, learning more precise contextual representations for each bug report. When finishing pre-training, we can connect a classification layer to CLeBPI and fine-tune it for BPI in a supervised way. We choose four baseline approaches and conduct comparison experiments on a public dataset. The experimental results show that CLeBPI outperforms all baseline approaches by 23.86%–77.80% in terms of weighted average F1-score, showing its effectiveness. This paper propose CLeBPI, a pre-trained model combining contrastive learning that can automatically predict bug priority. Experimental results show that It achieves new result in BPI and can effectively alleviate label imbalance problem.
InfraDet3D: Multi-Modal 3D Object Detection based on Roadside Infrastructure Camera and LiDAR Sensors

Current multi-modal object detection approaches focus on the vehicle domain and are limited in the perception range and the processing capabilities. Roadside sensor units (RSUs) introduce a new domain for perception systems and leverage altitude to observe traffic. Cameras and LiDARs mounted on gantry bridges increase the perception range and produce a full digital twin of the traffic. In this work, we introduce InfraDet3D, a multi-modal 3D object detector for roadside infrastructure sensors. We fuse two LiDARs using early fusion and further incorporate detections from monocular cameras to increase the robustness and to detect small objects. Our monocular 3D detection module uses HD maps to ground object yaw hypotheses, improving the final perception results. The perception framework is deployed on a real-world intersection that is part of the A9 Test Stretch in Munich, Germany. We perform several ablation studies and experiments and show that fusing two LiDARs with two cameras leads to an improvement of +1.90 mAP compared to a camera-only solution. We evaluate our results on the A9 infrastructure dataset and achieve 68.48 mAP on the test set. The dataset and code will be available at https://a9-dataset.com to allow the research community to further improve the perception results and make autonomous driving safer.