Reviewerly/exHarmony huggingface dataset:
Qubit recycling and the path counting problem

Recently, it was shown that the qudits used in circuits of a convolutional form (e.g., Matrix Product State sand Multi-scale Entanglement Renormalization Ansatz) can be reset unitarily \href{https://doi.org/10.1103/PhysRevA.103.042613}{[Phys. Rev. A 103, 042613 (2021)]}, even without measurement. We analyze the fidelity of this protocol for a family of quantum circuits that interpolates between such circuits and local quantum circuits, averaged over Haar-random gates. We establish a connection between this problem and a counting of directed paths on a graph, which is determined by the shape of the quantum circuit. This connection leads to an exact expression for the fidelity of the protocol for the entire family that interpolates between convolutional circuit and random quantum circuit. For convolutional circuits of constant window size, the rate of convergence to unit fidelity is shown to be $\frac{q^2}{q^2+1}$, independent of the window size, where $q$ is the local qudit dimension. Since most applications of convolutional circuits use constant-sized windows, our result suggests that the unitary reset protocol will likely work well in such a regime. We also derive two extra results in the convolutional limit, which may be of an independent interest. First, we derive exact expressions for the correlations between reset qudits and show that it decays exponentially in the distance. Second, we derive an expression for the the fidelity in the presence of noise, expressed in terms of the quantities that define the property of the channel, such as the entanglement fidelity.
On the Privacy Risks of Deploying Recurrent Neural Networks in Machine Learning Models

We study the privacy implications of training recurrent neural networks (RNNs) with sensitive training datasets. Considering membership inference attacks (MIAs), which aim to infer whether or not specific data records have been used in training a given machine learning model, we provide empirical evidence that a neural network's architecture impacts its vulnerability to MIAs. In particular, we demonstrate that RNNs are subject to a higher attack accuracy than feed-forward neural network (FFNN) counterparts. Additionally, we study the effectiveness of two prominent mitigation methods for preempting MIAs, namely weight regularization and differential privacy. For the former, we empirically demonstrate that RNNs may only benefit from weight regularization marginally as opposed to FFNNs. For the latter, we find that enforcing differential privacy through either of the following two methods leads to a less favorable privacy-utility trade-off in RNNs than alternative FFNNs: (i) adding Gaussian noise to the gradients calculated during training as a part of the so-called DP-SGD algorithm and (ii) adding Gaussian noise to the trainable parameters as a part of a post-training mechanism that we propose. As a result, RNNs can also be less amenable to mitigation methods, bringing us to the conclusion that the privacy risks pertaining to the recurrent architecture are higher than the feed-forward counterparts.
Blind My - An Improved Cryptographic Protocol to Prevent Stalking in Apple's Find My Network

In 2020, Apple introduced the Find My protocol, which allows owners to crowdsource the location of their lost Apple devices even when the lost device has no active internet connection (e.g., Wi-Fi, Cellular). The Find My protocol is the basis for Apple's AirTag tracking tokens which were released later in 2021. In order to prevent malicious use of these tokens, Apple also implemented ``item safety alerts'' which can warn a person if they are being tracked by an AirTag without their knowledge. However, researchers have recently identified several shortcomings with these alerts that allow modified AirTags to track unsuspecting victims indefinitely without being detected. Making matters worse, while recognizing the observed malicious use of AirTags, news reports, Apple's press releases, and their intended anti-tracking improvements to the protocol do not consider the potential surreptitious use of the Find My network by custom built AirTag clones. In this work, we present an improved Find My protocol which effectively limits the capabilities of malicious AirTags and guarantees that they can be detected while tracking. We accomplish this by adding additional cryptographic verification into the protocol, which restricts tags to only using a bounded set of keys while tracking. In order to maintain - and exceed - the privacy guarantees of the current Find My protocol, we make use of specialized partial blind signatures. To demonstrate the practicality of this protocol, we implement it end-to-end using a programmable device with the same SoC (nRF52832) as in current AirTags. We also benchmark the cryptographic operations of our protocol and show that they require only modest overhead during the initial pairing procedure.
From Continual Learning to Causal Discovery in Robotics

Reconstructing accurate causal models of dynamic systems from time-series of sensor data is a key problem in many real-world scenarios. In this paper, we present an overview based on our experience about practical challenges that the causal analysis encounters when applied to autonomous robots and how Continual Learning~(CL) could help to overcome them. We propose a possible way to leverage the CL paradigm to make causal discovery feasible for robotics applications where the computational resources are limited, while at the same time exploiting the robot as an active agent that helps to increase the quality of the reconstructed causal models.
Earn While You Reveal: Private Set Intersection that Rewards Participants

In Private Set Intersection protocols (PSIs), a non-empty result always reveals something about the private input sets of the parties. Moreover, in various variants of PSI, not all parties necessarily receive or are interested in the result. Nevertheless, to date, the literature has assumed that those parties who do not receive or are not interested in the result still contribute their private input sets to the PSI for free, although doing so would cost them their privacy. In this work, for the first time, we propose a multi-party PSI, called "Anesidora", that rewards parties who contribute their private input sets to the protocol. Anesidora is efficient; it mainly relies on symmetric key primitives and its computation and communication complexities are linear with the number of parties and set cardinality. It remains secure even if the majority of parties are corrupted by active colluding adversaries.
Deep learning approach for interruption attacks detection in LEO satellite networks

The developments of satellite communication in network systems require strong and effective security plans. Attacks such as denial of service (DoS) can be detected through the use of machine learning techniques, especially under normal operational conditions. This work aims to provide an interruption detection strategy for Low Earth Orbit (\textsf{LEO}) satellite networks using deep learning algorithms. Both the training, and the testing of the proposed models are carried out with our own communication datasets, created by utilizing a satellite traffic (benign and malicious) that was generated using satellite networks simulation platforms, Omnet++ and Inet. We test different deep learning algorithms including Multi Layer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Gated Recurrent Units (GRU), and Long Short-term Memory (LSTM). Followed by a full analysis and investigation of detection rate in both binary classification, and multi-classes classification that includes different interruption categories such as Distributed DoS (DDoS), Network Jamming, and meteorological disturbances. Simulation results for both classification types surpassed 99.33% in terms of detection rate in scenarios of full network surveillance. However, in more realistic scenarios, the best-recorded performance was 96.12% for the detection of binary traffic and 94.35% for the detection of multi-class traffic with a false positive rate of 3.72%, using a hybrid model that combines MLP and GRU. This Deep Learning approach efficiency calls for the necessity of using machine learning methods to improve security and to give more awareness to search for solutions that facilitate data collection in LEO satellite networks.
Rethinking Voxelization and Classification for 3D Object Detection

The main challenge in 3D object detection from LiDAR point clouds is achieving real-time performance without affecting the reliability of the network. In other words, the detecting network must be confident enough about its predictions. In this paper, we present a solution to improve network inference speed and precision at the same time by implementing a fast dynamic voxelizer that works on fast pillar-based models in the same way a voxelizer works on slow voxel-based models. In addition, we propose a lightweight detection sub-head model for classifying predicted objects and filter out false detected objects that significantly improves model precision in a negligible time and computing cost. The developed code is publicly available at: https://github.com/YoushaaMurhij/RVCDet.
On the number of Diophantine $m$-tuples in finite fields

We use a new argument to improve the error term in the asymptotic formula for the number of Diophantine $m$-tuples in finite fields, which is due to A. Dujella and M.Kazalicki (2021) and N. Mani and S. Rubinstein-Salzedo (2021).
Quantum Multi-Agent Actor-Critic Neural Networks for Internet-Connected Multi-Robot Coordination in Smart Factory Management

As one of the latest fields of interest in both academia and industry, quantum computing has garnered significant attention. Among various topics in quantum computing, variational quantum circuits (VQC) have been noticed for their ability to carry out quantum deep reinforcement learning (QRL). This paper verifies the potential of QRL, which will be further realized by implementing quantum multi-agent reinforcement learning (QMARL) from QRL, especially for Internet-connected autonomous multi-robot control and coordination in smart factory applications. However, the extension is not straightforward due to the non-stationarity of classical MARL. To cope with this, the centralized training and decentralized execution (CTDE) QMARL framework is proposed under the Internet connection. A smart factory environment with the Internet of Things (IoT)-based multiple agents is used to show the efficacy of the proposed algorithm. The simulation corroborates that the proposed QMARL-based autonomous multi-robot control and coordination performs better than the other frameworks.
On Knuth's conjecture for back and forward arcs in Depth First Search in a random digraph with geometric outdegree distribution

Donald Knuth, in a draft of a coming volume of The Art of Computer Programming, has recently conjectured that in Depth-First Search of a random digraph with geometric outdegree distribution, the numbers of back and forward arcs have the same distribution. We show that this conjecture is equivalent to an equality between two generating functions defined by different recursions. Unfortunately, we have not been able so use this to prove the conjecture, which still is open, but we hope that this note will inspire others to succeed with the conjecture.
Analisis Spasial Perubahan Penggunaan Lahan Untuk Mitigasi Bahaya Lahan Kritis di Kabupaten Manokwari

Land degradation is the result of land use or utilization that exceeds the capacity of the land, which will cause critical land. The impacts that occur due to critical land include land degradation in the quality of soil properties, decreasing conservation and production functions, as well as affecting the social and economic life of the people who use the land. This study aimed to reduce the hazard of critical land, based on modeling changes in land cover/use using the Artificial Neural Network (ANN) method. The results of this study indicated that land classified as dangerous to become critical land includes sloping land with converted land cover/use. Reducing the risk of critical land hazard can be done by using land that is in accordance with the ability of the land, by following the rules of soil and water conservation, as well as the role of the government in conserving upstream areas that are oriented towards regional spatial patterns.
Model of learning through problem solving in geogebra activity on the topic „sum of the angles in a triangle“

The paper proposes a model of e-interactive mathematics learning in the GeoGebra Activity/Classroom cloud platform. Methodological development is related to solving educational problems and arriving at an idea for solving the tasks, through research with dynamic constructions. The model aims to master the elements of the research approach.
Augmented reality for crewed hydrographic surveys - How augmented reality can leverage water depth management in the port of Hamburg

For the process of hydrographic surveying, the skipper of the survey vessel needs a variety of real-time navigational and hydrographic information to navigate the vessel safely and precisely. The survey data is typically displayed on several monitors on board. As part of a research project, we are exploring the potential of augmented reality – a technology that can extend reality by overlaying it with computergenerated visualizations – to assist the skipper in hydrographic surveys. We report an overview of the technology itself, our proof of concept and several challenges we focus on to create a usable and valuable prototype system.
Malaysian Sign Language Recognition Using 3D Hand Pose Estimation

Sign languages are one of those mediums for hearing-impaired people. These languages transmit meaning by visual-manual treatment., or more simply, hand movement. Currently, there are only 95 sign language interpreters registered with the Malaysian Federation of the Deaf as of 2020, compared to 40,389 hearing-impaired individuals with disabilities registered with the welfare department which is a problem. Therefore, with the use of deep-learning technology., this paper proposes to alleviate the scarcity of Malaysian Sign Language interpreters for the benefit of hearing-impaired persons. The paper aims to test and report a sequenced 3D keypoint hand pose estimation model for Malaysian Sign Language Recognition and evaluate the implementation of action model in decoding basic poses of Malaysian Sign Language. According to the findings, the detecting of 3D keypoints and incorporating into LSTM models using deep learning machine learning platform and framework like TensorFlow and MediaPipe enables the detection of Malaysian sign language 3D hand posture estimation. The results demonstrated that 3D hand posture estimation may be utilised to estimate sign language in real time, providing for a better interpretation approach for the deaf community.
Predicting Cybersecurity Risk - A Methodology for Assessments

Defining an appropriate cybersecurity incident response model is a critical challenge that all companies face on a daily basis.However, there is not always an adequate answer. This is due to the lack of predictive models based on data (evidence). There is a significant investment in research to identify the main factors that can cause such incidents, always trying to have the most appropriate response and, consequently, enhancing response capacity and success. At the same time, several different methodologies assess the risk management and maturity level of organizations.There is, however, a gap in determining an organization's degree of proactive responsiveness to successfully adopt cybersecurity and an even more significant gap in assessing it from a risk management perspective. This paper proposes a model to evaluate this capacity, a model that intends to evaluate the methodological aspects of an organization and indicates the apparent gaps that can negatively impact the future of the organization in the management of cybersecurity incidents and presents a model that intends to be proactive.
The influence of different parameters on classical neural network fitting

Numerous network models can successfully fit some datasets since the advent of deep learning. The performance of the deep learning model continues to improve, mainly benefiting from the constantly added modules, such as convolution kernels. To quantitatively evaluate the role of these modules, it is essential to research how each module in the network topology affects the network's functionality by developing the standard model in this study. The paper takes LeNet, AlexNet, and VGG19 as examples, considering how each module affects prediction accuracy. In LeNet, AlexNet, or VGG19, different outcomes can be obtained by altering the linear layer of the net's dimension, the convolution kernel size, the dropout layer's percentage, or the pooling layer's deletion. This experiment is based on the CIFAR-10 dataset to test the original network. According to the experimental findings, the network's performance will also depend on the size of the convolution kernel, the dropout ratio, and whether the pooling layer is kept.
PENINGKATAN MINAT BACA AL-QUR’AN MELALUI STRATEGI KOMUNIKASI PERSUASIF

The purpose of this study was to find out how to increase youth reading interest in the Koran through persuasive communication strategies of the Al-Irsyad Mosque Youth Association (IRMA) Board. This type of research is field research with a qualitative approach and analyzed with descriptive qualitative. The data collection techniques that the researchers used in this study were observation, interviews and documentation. Data analysis techniques are data reduction, data display and drawing conclusions. The research results of Irma's members used a persuasive communication strategy covering three stages, namely strategy formulation, implementation, and strategy evaluation.
Pengaruh Fasilitas Perpustakaan terhadap Minat Baca Siswa di Perpustakaan MAN Curup Rejang Lebong

The purpose of this study was to determine library facilities, the level of interest in reading and the effect of limited library facilities on students' reading interest at MAN Curup Rejang Lebong. This study used a quantitative research approach which showed the effect of variable X on variable Y. The population in this study were 541 students of MAN Curup Rejang Lebong. The sample in this study was 54 people or 10%. Data collection techniques used are observation, questionnaires and documentation. While technical data analysis using descriptive statistical analysis and inferential statistical analysis techniques using Simple Linear Regression. With the results of the study showing that the library facilities in Curup Rejang Lebong MAN were in the good category with a percentage rate of 79.80 percent. The reading interest level of students at MAN Curup Rejang Lebong is in the high category with a percentage rate of 79.76 percent. This can be proven by the results of simple linear regression, namely the results obtained f count&gt; f table, namely 71.300&gt; 4.08, then Ho is rejected and Ha is accepted. So it can be concluded that there is an influence of library facilities on students' reading interest at MAN Curup Rejang Lebong.
How Textiles Open Up Data as a Material in and on the Body

A designerly journey into textiles and HCI leads to thinking about data as a material for a cybernetic future.
Image enhancement towards object detection via foreground salience guidance

Although the schemes based on deep learning applied to car autonomous driving show good performance in object detection un- der condition of normal weather, there are many difficulties in complex weather scenarios. When facing heavy fog interference, the general solution is to design two networks for image enhancement and object detection respectively, which means one dehazes the foggy image and the other detects the dehazed image. But this may bring two problems: 1. After the image is dehazed, it may cause some distortion of the image, especially the areas that are unlikely to be reconstructed due to severe haze; 2. For object detection, the interesting part of an image is the nearby area where the object is located. The rest background area that is far away from the object may not help. In response to the above two problems, in this work we proposed to use foggy images except whose area of object bounding box is clear as ground-truth in the training of image enhancement. In other words, only the key area around the object is clear in order to guide the network to perform image dehazing operations focusing on the target areas and finally improves the following object detection performance. By using U-net for image dehazing on the Foggy Cityscapes dataset and Faster R- CNN network with the same structure for object detection, the average accuracy mAP is increased by % compared to normal training, which demonstrates the effectiveness of our approach on foggy image object detection.
Accurate Latent Factor Analysis via Dynamic-Neighbor-cooperated Hierarchical Particle Swarm Optimizers

High-Dimensional and Incomplete (HDI) matrices, which usually contain a large amount of valuable latent information, can be well represented by a Latent Factor Analysis (LFA) model. The performance of an LFA model heavily rely on its optimization process. Thereby, some prior studies employ the Particle Swarm Optimization (PSO) to enhance an LFA model's optimization process. However, the particles within the swarm follow the static evolution paths and only share the global best information, which limits the particles' searching area to cause sub-optimum issue. To address this issue, this paper proposes a Dynamic-neighbor-cooperated Hierarchical PSO-enhanced LFA (DHPL) model with two-fold main ideas. First is the neighbor-cooperated strategy, which enhances the randomly chosen neighbor's velocity for particles' evolution. Second is the dynamic hyper-parameter tunning. Extensive experiments on two benchmark datasets are conducted to evaluate the proposed DHPL model. The results substantiate that DHPL achieves a higher accuracy without hyper-parameters tunning than the existing PSO-incorporated LFA models in representing an HDI matrix.
Daniel Y. (Danny) Abramovitch [People in Control]

Q. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">How did your education and early career lead to your initial and continuing interest in the control field?</b>
Chemical solution bottle with QR code as an innovation for learning media in the laboratory

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Isnanik Juni Fitriyah, Munzil Munzil, Yessi Affriyenni, Erti Hamimi; Chemical solution bottle with QR code as an innovation for learning media in the laboratory. AIP Conf. Proc. 10 January 2023; 2569 (1): 060017. https://doi.org/10.1063/5.0112134 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search