Reviewerly/exHarmony huggingface dataset:
GraphCA: Learning from Graph Counterfactual Augmentation for Knowledge Tracing

With the popularity of online learning in educational settings, knowledge tracing (KT) plays an increasingly significant role. The task of KT is to help students learn more effectively by predicting their next mastery of knowledge based on their historical exercise sequences. Nowadays, many related works have emerged in this field, such as Bayesian knowledge tracing and deep knowledge tracing methods. Despite the progress that has been made in KT, existing techniques still have the following limitations: 1) Previous studies address KT by only exploring the observational sparsity data distribution, and the counterfactual data distribution has been largely ignored. 2) Current works designed for KT only consider either the entity relationships between questions and concepts, or the relations between two concepts, and none of them investigates the relations among students, questions, and concepts, simultaneously, leading to inaccurate student modeling. To address the above limitations, we propose a graph counterfactual augmentation method for knowledge tracing. Concretely, to consider the multiple relationships among different entities, we first uniform students, questions, and concepts in graphs, and then leverage a heterogeneous graph convolutional network to conduct representation learning. To model the counterfactual world, we conduct counterfactual transformations on students' learning graphs by changing the corresponding treatments and then exploit the counterfactual outcomes in a contrastive learning framework. We conduct extensive experiments on three real-world datasets, and the experimental results demonstrate the superiority of our proposed GraphCA method compared with several state-of-the-art baselines.
Safe Trajectory Sampling in Model-Based Reinforcement Learning

Model-based reinforcement learning aims to learn a policy to solve a target task by leveraging a learned dynamics model. This approach, paired with principled handling of uncertainty allows for data-efficient policy learning in robotics. However, the physical environment has feasibility and safety constraints that need to be incorporated into the policy before it is safe to execute on a real robot. In this work, we study how to enforce the aforementioned constraints in the context of model-based reinforcement learning with probabilistic dynamics models. In particular, we investigate how trajectories sampled from the learned dynamics model can be used on a real robot, while fulfilling user-specified safety requirements. We present a model-based reinforcement learning approach using Gaussian processes where safety constraints are taken into account without simplifying Gaussian assumptions on the predictive state distributions. We evaluate the proposed approach on different continuous control tasks with varying complexity and demonstrate how our safe trajectory-sampling approach can be directly used on a real robot without violating safety constraints.
Discovering Runtime Requirements from User Interactions: Ideas and Preliminary Studies

Precisely understanding and capturing runtime requirements is the basis for the success of self-adaptive systems. Runtime goal models are an effective way to represent such runtime requirements on top of traditional design-time goal models. However, due to the increasingly complex environments and unpredictable stakeholder behaviors, the runtime requirements are challenging to explore at the design time fully and are usually captured manually. In this paper, we present ongoing work on automatically mining runtime requirements from runtime user interactions. Specifically, our approach takes design-time goal models as input, which are first associated with user operation sequences. Then, we mine runtime behavior patterns from the operation sequences, shedding light on the runtime requirements. We have demonstrated our approach with a real system, which is an online data-flow diagram (DFD) modeling platform and has been used for more than three years. By applying our approach, a runtime goal model for this system has been automatically established, reasonably showing modelers' runtime requirements when modeling DFDs.
DETR Based Prohibited Item Detection in X-ray Security Checking Images

Autonomous and intelligent security checking has always been the research hotspot in industry and academia. Intelligent analysis of X-ray security checking images based on computer vision is an important means to promote the implementation of intelligent security checking technology due to its portability, easy maintenance, and low cost. We noticed that DETR-based object detection algorithms exhibit strong performance on various datasets. Compared with the object detection algorithms of the CNN structure, DETR uses the Transformer-based feature extraction structure to achieve effective feature aggregation through global attention. In this paper, we explore in depth the application of the DETR algorithm to the detection of prohibited items in X-ray security checking images. Some CNN based object detection models, such as YOLO, SSD et al., have taken into the comparison. The simulation results based on SIXray dataset have demonstrated the state-of-the-art performance of DETR model in prohibited item detection in X-ray images.
Guided Sampling-Based Motion Planning with Dynamics in Unknown Environments

Despite recent progress improving the efficiency and quality of motion planning, planning collision-free and dynamically-feasible trajectories in partially-mapped environments remains challenging, since constantly replanning as unseen obstacles are revealed during navigation both incurs significant computational expense and can introduce problematic oscillatory behavior. To improve the quality of motion planning in partial maps, this paper develops a framework that augments sampling-based motion planning to leverage a high-level discrete layer and prior solutions to guide motion-tree expansion during replanning, affording both (i) faster planning and (ii) improved solution coherence. Our framework shows significant improvements in runtime and solution distance when compared with other sampling-based motion planners.
Guarding Your Social Circle: Strategies to Protect Key Connections and Edge Importance

With the growing use of social networks and the consequent rise in the sharing of personal information online, privacy has become a major concern, leading to an increased demand for efficient anonymization techniques. This research proposes innovative methods for hiding information in weighted social network graphs. We provide a topology-modification technique for precisely hiding important nodes in the user network. We also present a differential privacy-based method to safeguard edge weights while taking weight topology correlations into account. With accuracy rates of 87.04% and 94.73%, respectively, our approaches are highly effective in connections to significant nodes and concealing edge weights, respectively. Our methods, which safeguard data integrity to a larger extent than earlier techniques, alter the original graph as little as possible (only 12% of the original edges are changed), as measured by well-known graph metrics. Experiment results on the extracted Instagram posts show that our solutions outperform current approaches in terms of privacy and preserved usefulness.
Handling outliers in bankruptcy prediction models based on logistic regression

The primary tool for managing bank default risk is the credit rating of potential customers. The focus of the present study is on the logistic regression method used to construct 95% of the lender scorecards. The aim of the research is to determine how much the treatment of outliers improves the classification accuracy of the models when using a method that is highly sensitive to outliers, and which method of treating outliers results in the highest classification accuracy. Furthermore, what criteria should be used to determine the cut-off value of the models for a sample that does not contain solvent and insolvent businesses in equal proportions. The analysis was carried out on a sample of 1677 construction companies. The results show that the treatment of outliers significantly improves the predictive ability of the models, while the replacement of outliers with the closest non-outlier proved to be the most effective for treating outliers. When determining the cut-off, it is inappropriate to use the value that results in the highest classification accuracy, as this may lead to an increase in the proportion of first-order errors. The optimisation of this value may depend on the degree of credit risk taken by a given financial institution in its portfolio of loans.
Traveling-wave parametric amplifier–induced qubit dephasing: analysis and mitigation

The mitigation of dephasing poses a significant challenge in improving the performance of error-prone superconducting quantum computing systems. Here, we investigate the dephasing of a transmon qubit in a dispersive readout regime using a Josephson travelling-wave parametric amplifier as the preamplifier. Our findings reveal that the potent pump leakage from the preamplifier may lead to severe dephasing. This issue could be attributed to a mixture of measurement-induced dephasing, ac Stark effect, and heating. We show that pulse-mode readout is a promising measurement scheme to mitigate qubit dephasing while minimizing the need for bulky circulators. Our work provides key insights into mitigating decoherence from microwave-pumped preamplifiers, which will be critical for advancing large-scale quantum computers.
CineScale2: a dataset of cinematic camera features in movies

The position and orientation of the camera in relation to the subject(s) in a movie scene, namely camera "level" and camera "angle", are essential features in the film-making process due to their influence on the viewer's perception of the scene. We provide a database containing camera feature annotations on camera angle and camera level, for about 25,000 image frames. Frames are sampled from a wide range of movies, freely available images, and shots from cinematographic websites, and are annotated on the following five categories - Overhead, High, Neutral, Low, and Dutch - for what concerns camera angle, and on six different classes of camera level: Aerial, Eye, Shoulder, Hip, Knee, and Ground level. This dataset is an extension of the Cinescale dataset [1], which contains movie frames and related annotations regarding shot scale. The CineScale2 database enables AI-driven interpretation of shot scale data and opens to a large set of research activities related to the automatic visual analysis of cinematic material, such as movie stylistic analysis, video recommendation, and media psychology. To these purposes, we also provide the model and the code for building a Convolutional Neural Network (CNN) architecture for automated camera feature recognition. All the material is provided on the the project website; video frames can be also provided upon requests to authors, for research purposes under fair use.
Image encryption scheme based on optical chaos and DNA Rubik’s cube algorithm

Abstract In order to improve the complexity of optical chaotic sequence output and the encryption efficiency of bit plane encryption, an image encryption algorithm based on optical chaos and DNA Rubik’s cube scrambling is proposed. An external disturbance structure of light injection is proposed to improve the complexity of the chaotic sequence of light output from the slave laser by the external disturbance generated by the mutual injection between the driving laser and the main laser. Meanwhile, in the image algorithm, in addition to Rubik’s cube scrambling at the pixel level, this paper proposes a bit plane encryption method based on DNA plane Rubik’s cube scrambling, which reduces the amount of encrypted data and further improves the security of image encryption by using encryption selection of different DNA encoding modes. The scrambled images are diffused by DNA calculation and optical chaos sequence at the bit-level and pixel level respectively. Through the quantitative analysis of the security simulation of the proposed algorithm, the results show that the algorithm guarantees a certain encryption efficiency under the condition of good security, the key space is 10 64 , the information entropy of each test ciphertext image is close to the theoretical maximum, and the test indexes of plaintext sensitivity and key sensitivity are close to ideal values. For differential attacks, chosen plaintext attacks, known plaintext attacks, exhaustive attacks, cropping attacks, and noise attacks have good robustness.
Detecting entanglement harnessing Lindblad structure

Abstract The problem of entanglement detection is a long standing problem in quantum information theory. One of the primary procedures of detecting entanglement is to find the suitable positive but non-completely positive maps. Here we try to give a generic prescription to construct a positive map that can be useful for such scenarios. We study a class of positive maps arising from Lindblad structures. We show that two famous positive maps viz. transposition, reduction map and Choi map can be obtained as a special case of a class of positive maps having Lindblad structure. Generalizing the transposition map to a one parameter family we have used it to detect genuine multipartite entanglement. Finally being motivated by the negativity of entanglement, we have defined a similar measure for genuine multipartite entanglement.
Time-aware Fourier optics: modeling implications and device performance predictions

The theory of Fourier Optics uses scalar diffraction models which provide simple, computationally efficient, and reasonably accurate tools for designing, optimizing and interpreting the performance of a wide range of devices and observed optical phenomena. Conventional approaches assume the time-separability of the relevant wave phenomena interpreted in terms of partial differential equations. Since light propagates in a time-dependent manner, certain mathematical formalisms must be injected to assure the validity of these methods. Moreover, in the case of two-dimensional propagation, the actual accuracy of the model suffers on account of the inherent time-dependent and diffusive nature of planar wave propagation. To resolve these imprecisions, we propose a “Time-Aware” approach to Fourier Optics and scalar diffraction, which closely matches the predictions of Fourier Optics for slowly changing input signals but accounts for a time-based error in integrated photonic devices with high clock speed.
DEED: DEep Evidential Doctor

As Deep Neural Networks (DNN) make their way into safety-critical decision processes, it becomes imperative to have robust and reliable uncertainty estimates for their predictions for both in-distribution and out-of-distribution (OOD) examples. This is particularly important in real-life high-risk settings such as healthcare, where OOD examples (e.g., patients with previously unseen or rare labels, i.e., diagnoses) are frequent, and an incorrect clinical decision might put human life in danger, in addition to having severe ethical and financial costs. While evidential uncertainty estimates for deep learning have been studied for multi-class problems, research in multi-label settings remains untapped. In this paper, we propose a DEep Evidential Doctor (DEED), which is a novel deterministic approach to estimate multi-label targets along with uncertainty. We achieve this by placing evidential priors over the original likelihood functions and directly estimating the parameters of the evidential distribution using a novel loss function. Additionally, we build a redundancy layer (particularly for high uncertainty and OOD examples) to minimize the risk associated with erroneous decisions based on dubious predictions. We achieve this by learning the mapping between the evidential space and a continuous semantic label embedding space via a recurrent decoder. Thereby inferring, even in the case of OOD examples, reasonably close predictions to avoid catastrophic consequences. We demonstrate the effectiveness of DEED on a digit classification task based on a modified multi-label MNIST dataset, and further evaluate it on a diagnosis prediction task from a real-life electronic health record dataset. We highlight that in terms of prediction scores, our approach is on par with the existing state-of-the-art having a clear advantage of generating reliable, memory and time-efficient uncertainty estimates with minimal changes to any multi-label DNN classifier.
Comparing novel and conventional methods for optical system alignment using deflectometry

This paper compares two different ways of aligning optical systems using deflectometry: a novel method incorporating the sine condition test and deflectometry, and conventional multi-field points measuring deflectometry. The study aims to provide experimental evidence that the novel method is effective and has advantages over the conventional method. The experiment was carried out using a singlet as the unit-under-test with visible deflectometry composed of a camera and a monitor. In the conventional approach, the camera is moved to multiple field points to measure transmitted wavefront aberrations. For the accurate measurement of the aberrations, accurate knowledge of the camera positions is desired for conventional deflectometry. In the novel approach, the camera is fixed at an on-axis field point, while the monitor is moved to at least two positions in the longitudinal direction. Even if the camera is fixed, the new method can derive the linear behavior of aberrations over the field as it measures pupil mapping error and slope mapping error instead of wavefront error directly. Also, it is insensitive to positional errors in the monitor. This is made possible by discovering the complementary aspects of the sine condition test and deflectometry. We emphasize that the new alignment method is not performing two independent tests separately. The test can be done via one test setup requiring a camera and a monitor equivalent to a deflectometry system. The results demonstrate the benefits of the novel alignment method, which eliminates the need for accurate control of the test instrument movement during the alignment process.
Optomechanical analysis of the alignment tolerance of threaded lens mounts

Threaded mounts are one of the most common interfaces between optical systems and commercial-off-the-shelf cameras. Popular examples include the established C-mount, as well as the newer TFL-mount which accommodates for larger sensor formats such as the APS-C detector. In all cases, the thread is used to adjust for focus by clocking the optical system with respect to a fixed camera assembly or vice versa. For this reason, the alignment between the datum axis of the optical system and the array detector plane inside the camera depends on both the allowances and tolerances of the thread interface, and on the manufacturing tolerances of the mount components. To highlight how the stack up of these tolerances can affect image quality of an optical system, we first perform an inverse sensitivity analysis to determine the detector alignment specification as a function of system F/#, field of view, and chief ray angle. We then calculate the misalignment contributions of the thread between the optical system and the lock ring that sets the camera axial position for best focus. This optomechanical analysis allows us to determine if thread mounts are appropriate for the specifications of the optical system under consideration and to specify the tolerances of the thread interface when this is the case.
Marielle Franco: a quantum victim of the Bolsonaro-19

Who ordered the feminicide of Rio de Janeiro councilwoman Marielle Franco on 14 March 2018? Our response draws on feminist language criminology in an era of misogyny; and it admits the hypothesis of a quantum causality (or imputation by remote symbiosis), which serves as explanatory model, for instance, of the intellectual authorship of crimes executed by radical members of Latin American political sects (Bolsonarism, Kirchnerism, and so on). Such connatural followers are so psychically entangled with their charismatic leader that they begin to act on the same frequency as him, even spontaneously eliminating eventual threats.
TrustNetFL: Enhancing Federated Learning with Trusted Client Aggregation for Improved Security

Federated Learning (FL) has emerged as a promising approach for training machine learning models across individual devices while preserving data privacy. However, FL faces many challenges, specifically a vulnerability to adversarial attacks due to its strict adherence to ensuring individual client model and data privacy. To mitigate these issues, dynamic clipping techniques have been proposed which dynamically adjust the gradient clipping threshold during model aggregation. However, current iterations depend on specific and often intensive calculations to determine a clipping threshold which can lead to an over fitting to a specific dataset or attacker model. In this paper, we focus on improving the limitations of existing FL and dynamic clipping approaches by introducing a novel method that incorporates a group of trusted users during the aggregation of client models for a global update. By identifying and utilizing a network of trusted users, our defense method TrustNetFL enhances the robustness of model aggregation against malicious updates. This method not only maintains the model's performance but also improves its resistance to adversarial influences. We demonstrate the effectiveness of our defense through extensive experiments thus showcasing its superiority and simplicity in achieving enhanced model security in FL settings.
AirFC: Designing Fully Connected Layers for Neural Networks with Wireless Signals

This paper proposes and experimentally validates a new paradigm for computing with wireless signals over-the-air (OTA). It demonstrates the first fully connected (FC) neural network (NN) constructed entirely using channel propagation and signal interference principles. Our design is based on architecting the desired linear operation of an FC layer through the superposition of signals emitted from multiple transmitters and received at a single receiver, similar to multiple input single output (MISO) systems. Our design takes into account several practical considerations, such as the impact of multiple subcarriers, the number of transmit antennas, and the changing wireless channel. The key outcome of our work is developing a principled methodology that transforms a given trained digital FC NN into its OTA equivalent. This novel computational paradigm, which we call AirFC, allows us to run NN tasks without compute-specific hardware during tests. We validate our design using 9 time-synchronized software-defined radios (SDRs) available on the ORBIT testbed, emulating a 16 antenna array. We use the MNIST dataset as input to our wireless FC NN and demonstrate classification with 92.61% accuracy, which proves that our NN with OTA FC layers performs similar to the conventional, all-digital version with an accuracy decrease of only 0.73%.