Reviewerly/exHarmony huggingface dataset:
Nonclassicality induced by nonstationary squeezed reservoirs

We study nonclassical effects in the dynamics of an open quantum system. The model involves a harmonic oscillator coupled to a reservoir of noninteracting harmonic oscillators. Different system-bath interaction schemes as well as reservoir states are considered. Particularly, the squeezed reservoirs coupled to the system through single and two quanta exchange processes are put in the spotlight. We investigate the quantumness conveyed to the system through the bath by computing a nonclassicality measure for different bath properties. The measure of nonclassicality is calculated for projective measurements both in the number state basis and in a basis formed by a set of coherent states. Our results show that in both bases the measure exhibits characteristic features for each bath state and the form of its interaction with the system. Some of those features are independent from the measurement scheme (number or coherent) and, thus, emergent from the reservoir. This allows for fingerprinting and identifying the environmental effects by tracking a given probe with appropriate measurements. Hence, it may prove useful for distinguishing different sources of decoherence.
Quick-look method and system for multi-domain space science experiment data

Abstract Space science experiment data contains huge application value. It is important to design methods and systems to have a quick look at the process of data processing. An integration framework is proposed in this paper. Several basic visual components are designed to support the quick look requirements of different types of data. The dynamic configuration mechanism of the visual view can effectively improve the scalability and user experience friendliness of the system. The method of the interactive layout of the front-end interface can make the visual effect of the page richer. It also supports the self-adaptation of the display screen with different resolutions. The dynamic registration mechanism for visual views ensures the synchronization of data display in multiple identical visual views. Finally, a quick-look system is implemented, which achieves the integration and quick look of multi-domain space science experiment data. The validity of the proposed system is verified by the practical application.
Power Insulator Defect Detection Based on Multi-scale Dense Adaptive Sensing

Abstract To solve the problem that the insulator image taken by an unmanned aerial vehicle (UAV) in the process of power inspection cannot be accurately identified, located, and detected, a power insulator defect detection method based on multi-scale dense adaptive sensing is proposed. Firstly, a dense connection architecture based on multi-scale is proposed to improve the feature extraction ability of the algorithm for insulators. Secondly, an adaptive multi-scale weight transfer module is proposed to improve further the perception ability of the algorithm for insulator defect features and input multi-branch feature images. Finally, a multi-branch detection unit is proposed, and the parallel attention structure is used to improve the detection effect of the network on different scale branch insulator images. The experimental results in the existing insulator defect data set show that the proposed network can accurately identify and locate insulator defects, with an average detection accuracy of 95.6%, superior to the comparison algorithm in accuracy and speed.
The Integral Points on the Elliptic Curve y <sup>2</sup> = 7 qx(x <sup>2</sup> + 128)

Abstract The positive integer points of elliptic curves are very important in the theory of numbers and arithmetic algebra; it has a wide range of applications in cryptography and other fields. The main purpose of this paper is to apply elementary methods, the properties of congruence and Legendre symbols, to study the elliptic curve y 2 = 7 qx ( x 2 + 128)and proved that the elliptic curve has at most three integer points when q = 5(mod8) is an odd prime number.
A Fast Gap Detection Algorithm Based on Machine Vision

Abstract In order to be able to quickly locate the position of the gap of an industrial device in the industrial inspection process, a fast gap detection algorithm based on machine vision is proposed by using the feature of a high gray level difference between the image gap and the background in machine vision. First, nonlinear equalization preprocessing is adopted, and then multiple one-dimensional functions are composed based on the sum of the gray values of the horizontal axis as well as the differentiation, and then the proximity position averaging operation is done after obtaining the positional information through the extreme value and the most value operation. The algorithm transforms the two-dimensional image into a combination of multiple one-dimensional functions with related features, and finally accurate position information is obtained by analysing and fusing the one-dimensional functions. Multiple discriminative bases are used for the same data source to ensure the accuracy of the results. Experiments show that the method proposed is faster and less error-prone, has higher accuracy and detection efficiency, and meets the needs of industrial real-time detection, compared with the traditional Hough transform algorithm and the LSD detection algorithm.
LIS e disabilità comunicativa: un caso clinico L’uso dei segni della LIS per favorire l’autonomia comunicativa di un soggetto con problemi fono-articolatori

This paper reports the result of a longitudinal case study on a girl suffering from a rare genetic illness that prevents her from communicating through speaking. We set three major goals. First, to investigate the specific signs that are used by the girl for communication purposes. Second, to analyse their ‘home signs’ and finally, to increase her vocabulary readapting specific signs from the Italian Sign Language to her articulatory restrictions, teaching her 12 new signs connected to words of her everyday language. At the end of the programme, the goals were almost thoroughly achieved and the girl remembered correctly seven signs.
Complex Access Scenarios (CAS) Service Request

A complex access scenario (CAS) is an access event-modeling scheme that focuses on contextual and situational awareness addressing the service concerns arising for complex systems. The CAS is structured in the form of a service request establishing the following criteria: (1) satisfies classification for complexity, such as properties, characteristics, conditions, etc.; (2) concatenates the access criteria to the complexity class; otherwise, downgrades the class to complicated; and (3) confirms that the request is system generated. The significance of this work involves maturing the request analysis process within CAS to identify an effective knowledge representation scope for how to develop schemas that are applicable to the associations described in given operating environments. The scope of this work is associating CAS to human-computer interaction (HCI), where the human-in-the-loop element of this use case begins to play a factor in the complexity of the system service request. Our method surveys HCI usability factors to identify CAS service requests applicable to usage scenarios in the HCI space that address system complexity. The goal is that with increased CAS comprehension and application, networking components and their specific utilization are used effectively and securely in complex computing operating environments, which still suffer from ambiguous workload requirements. The purpose of this work and future iterations that follow are to place more emphasis on the CAS topic by demonstrating how CAS addresses access scenarios specific to complex computing environments like HCI.
Opportunities and challenges in advanced communication technologies in the environment: a case of forest management communications

Advanced information communication technologies (ICT) offer significant benefits to applied communications, including potential cost reductions, service improvements, and expanded natural resource education in an era of ever-decreasing budgets.Limited guidance exists about how to design and implement virtual reality and augmented reality experiences in environmental spaces but clearly it takes time, expertise, equipment, and iterative feedback with experts and users.Our 18-month collaborative development process used best practices, stakeholder involvement, and multimedia teams to create and test augmented reality and virtual reality visitor education about forest management.Based on this effort, we share challenges, opportunities, and ideas to implement ICT in applied environmental education.
Generating ICS vulnerability playbooks with open standards

Abstract Organizations face attacks on industrial control systems (ICS) as vulnerabilities are pervasive. However, patching vulnerable systems by simply updating to the newest version is often not an option and shifts focus to workarounds. Beyond pure patching, workarounds specify other remediation measures (e.g., firewall or VPN configuration) that must be taken due to system availability requirements, complexity, or heterogeneous devices. In this paper, we introduce vulnerability playbooks based on open standards. Pushing the envelope of cybersecurity playbooks—steps organizations should follow when responding to cybersecurity incidents reactively—for ICS vulnerability management offers organizations a more transparent, repeatable process and faster, possibly automated actions. We have designed a process model to collect and transform security advisories in Common Security Advisory Framework (CSAF) format and generate Collaborative Automated Course of Action Operations (CACAO) playbooks based on listed remediation advice. With a proof of concept, we demonstrate that structured CSAF documents can be seamlessly transformed into CACAO playbooks. For our industrial use case, we must also use unstructured security advice highlighting quality differences (compared to CSAF). Our generated 79 standard conformant CACAO playbooks with 485 identified actions hint at imbalanced advice toward patching. Preferably, vendors should include detailed technical remediation advice, provide APIs, and go beyond patching recommendations in their security advisories. Subscribers should structure their assets and use machine learning to normalize, generate, and prioritize CACAO playbooks. With CSAF and CACAO, we see two open standards for handling vulnerabilities.
On the Influence of Data Imbalance on Supervised Gaussian Mixture Models

Imbalanced data present a pervasive challenge in many real-world applications of statistical and machine learning, where the instances of one class significantly outnumber those of the other. This paper examines the impact of class imbalance on the performance of Gaussian mixture models in classification tasks and establishes the need for a strategy to reduce the adverse effects of imbalanced data on the accuracy and reliability of classification outcomes. We explore various strategies to address this problem, including cost-sensitive learning, threshold adjustments, and sampling-based techniques. Through extensive experiments on synthetic and real-world datasets, we evaluate the effectiveness of these methods. Our findings emphasize the need for effective mitigation strategies for class imbalance in supervised Gaussian mixtures, offering valuable insights for practitioners and researchers in improving classification outcomes.
Robust and Refined Salient Object Detection Based on Diffusion Model

Salient object detection (SOD) networks are vulnerable to adversarial attacks. As adversarial training is computationally expensive for SOD, existing defense methods instead adopt a noise-against-noise strategy that disrupts adversarial perturbation and restores the image either in input or feature space. However, their limited learning capacity and the need for network modifications limit their applicability. In recent years, the popular diffusion model coincides with the existing defense idea and exhibits excellent purification performance, but there still remains an accuracy gap between the saliency results generated from the purified images and the benign images. In this paper, we propose a Robust and Refined (RoRe) SOD defense framework based on the diffusion model to simultaneously achieve adversarial robustness as well as improved accuracy for benign and purified images. Our proposed RoRe defense consists of three modules: purification, adversarial detection, and refinement. The purification module leverages the powerful generation capability of the diffusion model to purify perturbed input images to achieve robustness. The adversarial detection module utilizes the guidance classifier in the diffusion model for multi-step voting classification. By combining this classifier with a similarity condition, precise adversarial detection can be achieved, providing the possibility of regaining the original accuracy for benign images. The refinement module uses a simple and effective UNet to enhance the accuracy of purified images. The experiments demonstrate that RoRe achieves superior robustness over state-of-the-art methods while maintaining high accuracy for benign images. Moreover, RoRe shows good results against backward pass differentiable approximation (BPDA) attacks.
Applying Blockchain Technology in Smart Contracts for Construction Payment: A Proposed Solution

The construction industry has been facing the challenging problem of delayed payments and disputes over payment between owners and contractors. While multiple studies have been conducted to address this issue and reduce the delays caused by slow payments, the results have not been optimal. In this study, we propose a new process that leverages smart contract technology in Blockchain to solve the problem of slow payment in construction projects and post-contract disputes. This process aims to minimize the risks associated with project stoppage or prolonged duration, ensuring project continuity and safeguarding the rights of all parties involved. Specifically, we utilize smart contracts to facilitate automated payment transactions between owners and contractors. The smart contracts also act as a third-party monitor to ensure that both parties fulfill their obligations in the project. Furthermore, Blockchain technology is employed to record all project lifecycle transactions in a chronological order, enabling efficient management and retrieval of project-related data. Overall, our proposed approach offers a novel solution to the challenges posed by delayed payments and disputes in the construction industry, enhancing transparency, and promoting trust among project stakeholders.
Quantum space-time marginal problem: global causal structure from local causal information

Abstract Spatial and temporal quantum correlations can be unified in the framework of the pseudo-density operators (PDOs), and quantum causality between the involved events in an experiment is encoded in the corresponding PDO. We study the relationship between local causal information and global causal structure. A space-time marginal problem is proposed to infer global causal structures from given marginal causal structures where causal structures are represented by the reduced PDOs; we show that there almost always exists a solution in this case. By imposing the corresponding constraints on this solution set, we could obtain the required solutions for special classes of marginal problems, like a positive semidefinite marginal problem, separable marginal problem, etc. We introduce a space-time entropy and propose a method to determine the global causal structure based on the maximum entropy principle. The notion of quantum pseudo-channel (QPC) is also introduced and we demonstrate that the QPC marginal problem can be solved by transforming it into a PDO marginal problem via the channel-state duality.
Monte Carlo approach to the evaluation of the security of device-independent quantum key distribution

Abstract We present a generic study on the information-theoretic security of multi-setting device-independent quantum key distribution (DIQKD) protocols, i.e. ones that involve more than two measurements (or inputs) for each party to perform, and yield dichotomic results (or outputs). The approach we develop, when applied in protocols with either symmetric or asymmetric Bell experiments, yields nontrivial upper bounds on the secure key rates, along with the detection efficiencies required upon the measuring devices. The results imply that increasing the number of measurements may lower the detection efficiency required by the security criterion. The improvement, however, depends on (i) the choice of multi-setting Bell inequalities chosen to be tested in a protocol, and (ii) either a symmetric or asymmetric Bell experiment is considered. Our results serve as an advance toward the quest for evaluating security and reducing efficiency requirement of applying DIQKD in scenarios without heralding.
An Investigation into Augmented Reality’s Ability to Assist the Preconstruction Phase of Building Projects

This study investigated how augmented reality can support architectural, engineering, and construction (AEC) professionals during the pre-construction phase of building projects. Existing literature acknowledged the need for more comprehensive research on AEC professionals’ opinions about digital technologies, especially augmented reality (AR). To address this gap, researchers conducted semi-structured interviews to gain deeper insights into the current applications of AR in the industry, its various uses in pre-construction, and future recommendations. The study yielded three main findings. Firstly, it identified several AR applications that could be specifically applied to the AEC industry. Secondly, AEC professionals disclosed various reasons for adopting AR in their work, including its assistance in defect analysis, the design stage, and communication improvement. Finally, AEC professionals provided several future recommendations, but they argued that widespread AR adoption would require a cultural transformation among industry practitioners. These findings largely aligned with existing literature on AR usage in the AEC industry. The data gathered from the interviews could be valuable to diverse professionals within the AEC industry, particularly AEC companies contemplating investments in AR technology. The insights gained from this research shed light on the potential benefits and challenges of integrating AR into the pre-construction process of building projects.
SinMPI: Novel View Synthesis from a Single Image with Expanded Multiplane Images

Single-image novel view synthesis is a challenging and ongoing problem that aims to generate an infinite number of consistent views from a single input image. Although significant efforts have been made to advance the quality of generated novel views, less attention has been paid to the expansion of the underlying scene representation, which is crucial to the generation of realistic novel view images. This paper proposes SinMPI, a novel method that uses an expanded multiplane image (MPI) as the 3D scene representation to significantly expand the perspective range of MPI and generate high-quality novel views from a large multiplane space. The key idea of our method is to use Stable Diffusion [Rombach et al. 2021] to generate out-of-view contents, project all scene contents into an expanded multiplane image according to depths predicted by monocular depth estimators, and then optimize the multiplane image under the supervision of pseudo multi-view data generated by a depth-aware warping and inpainting module. Both qualitative and quantitative experiments have been conducted to validate the superiority of our method to the state of the art. Our code and data are available at https://github.com/TrickyGo/SinMPI.
Hand Pose Estimation with Mems-Ultrasonic Sensors

Hand tracking is an important aspect of human-computer interaction and has a wide range of applications in extended reality devices. However, current hand motion capture methods suffer from various limitations. For instance, visual hand pose estimation is susceptible to self-occlusion and changes in lighting conditions, while IMU-based tracking gloves experience significant drift and are not resistant to external magnetic field interference. To address these issues, we propose a novel and low-cost hand-tracking glove that utilizes several MEMS-ultrasonic sensors attached to the fingers, to measure the distance matrix among the sensors. Our lightweight deep network then reconstructs the hand pose from the distance matrix. Our experimental results demonstrate that this approach is both accurate, size-agnostic, and robust to external interference. We also show the design logic for the sensor selection, sensor configurations, circuit diagram, as well as model architecture.
Fusing Monocular Images and Sparse IMU Signals for Real-time Human Motion Capture

Either RGB images or inertial signals have been used for the task of motion capture (mocap), but combining them together is a new and interesting topic. We believe that the combination is complementary and able to solve the inherent difficulties of using one modality input, including occlusions, extreme lighting/texture, and out-of-view for visual mocap and global drifts for inertial mocap. To this end, we propose a method that fuses monocular images and sparse IMUs for real-time human motion capture. Our method contains a dual coordinate strategy to fully explore the IMU signals with different goals in motion capture. To be specific, besides one branch transforming the IMU signals to the camera coordinate system to combine with the image information, there is another branch to learn from the IMU signals in the body root coordinate system to better estimate body poses. Furthermore, a hidden state feedback mechanism is proposed for both two branches to compensate for their own drawbacks in extreme input cases. Thus our method can easily switch between the two kinds of signals or combine them in different cases to achieve a robust mocap. Quantitative and qualitative results demonstrate that by delicately designing the fusion method, our technique significantly outperforms the state-of-the-art vision, IMU, and combined methods on both global orientation and local pose estimation. Our codes are available for research at https://shaohua-pan.github.io/robustcap-page/.
Intrinsic Harmonization for Illumination-Aware Image Compositing

Despite significant advancements in network-based image harmonization techniques, there still exists a domain disparity between typical training pairs and real-world composites encountered during inference. Most existing methods are trained to reverse global edits made on segmented image regions, which fail to accurately capture the lighting inconsistencies between the foreground and background found in composited images. In this work, we introduce a self-supervised illumination harmonization approach formulated in the intrinsic image domain. First, we estimate a simple global lighting model from mid-level vision representations to generate a rough shading for the foreground region. A network then refines this inferred shading to generate a harmonious re-shading that aligns with the background scene. In order to match the color appearance of the foreground and background, we utilize ideas from prior harmonization approaches to perform parameterized image edits in the albedo domain. To validate the effectiveness of our approach, we present results from challenging real-world composites and conduct a user study to objectively measure the enhanced realism achieved compared to state-of-the-art harmonization methods.