Reviewerly/exHarmony huggingface dataset:
Visual Classification of Data Sets with the Assistance of Experts in the Problems of Intelligent Agents Learning for Incompletely Automated Control Systems

The paper proposes a method of data classification, which allows replacing nodes, with which operators or experts interact, with their intelligent software analogues in incompletely automated control systems. The classification is based on the algorithm of interactive interaction between an expert and the system, in which the offered visual images of the corresponding vectors from the data set and the reference ones are compared. Using various methods of visualizing data vectors and stricter selection requirements, a method is described that allows to classify all the vectors from the presented data set. The experiment on the visual classification of data on the example of diagnosing an ultrasonic flow meter is also described. The results obtained are compared with other classification methods.
Comprehensive defect-detection method for a small-sized curved optical lens

During quality-assurance procedures in the mass production of small-sized curved optical lenses, fine defects are usually detected via manual observation, which is not recommended owing to the associated drawbacks of high error rate, low efficiency, and nonamenability to quantitative analysis. To address this concern, this paper presents a comprehensive defect-detection system based on transmitted fringe deflectometry, dark-field illumination, and light transmission. Experimental results obtained in this study reveal that the proposed method demonstrates efficient and accurate detection of several microdefects occurring in small-sized optical lenses, thereby providing valuable insights into the optimization of parameters concerning the mass production of optical lenses. The proposed system can be applied to the actual mass production of small-sized curved optical lenses.
Performance of Inter-vehicular Distance Estimation: Pose from Orthography and Triangle Similarity

This paper reports the comparison of two methods of distance measuring using a digital camera and computing resources namely: Pose from Orthographic Projection and Triangle Similarity. Both methods incorporating a computer vision algorithm to properly functioned. Specifically, this report describes the utilization of such methods for vehicular application, for example, the inter-vehicular distance estimation, hence, to improve safety driving moreover to support the developing Advanced Driver-Assistance Systems ADAS. Each method constructed in an algorithm wrote in Python running in a Raspberry Pi computer equipped with a suitable camera. To process the incoming images, an OpenCV library was tasked to conduct a classification to distinguish vehicle-like features in an image frame from the rest of the universe. Haar cascade classifier was chosen to perform the image features classification. The algorithm then annotates and marks the classified features as a candidate for a vehicle-like object. The classifier was trained by a precompiled dataset. Both methods compared for the best performance on three distance measurement: 10, 15, and 20 meters. With experiment setup, the best distance to measure was 15 meters with small error to the ground truth.
Load balancing under $d$-thinning

In the classical balls-and-bins model, $m$ balls are allocated into $n$ bins one by one uniformly at random. In this note, we consider the $d$-thinning variant of this model, in which the process is regulated in an on-line fashion as follows. For each ball, after a random bin has been selected, an overseer may decide, based on all previous history, whether to accept this bin or not. However, one of every $d$ consecutive suggested bins must be accepted. The maximum load of this setting is the number of balls in the most loaded bin. We show that after $\Theta (n)$ balls have been allocated, the least maximum load achievable with high probability is $(d+o(1))\sqrt [d]{\frac{d\log n} {\log \log n}}$. This should be compared with the related $d$-choice setting, in which the optimal maximum load achievable with high probability is $\frac{\log \log n} {\log d}+O(1)$.
A Novel Collaborative Optimization Framework for Web Video Event Mining Based on the Combination of Inaccurate Visual Similarity Detection Information and Sparse Textual Information

The high speed and low latency of 5G mobile network have accelerated the speed and amount of information transmission. Web video is likely to become the main mode of news production and dissemination in the future for its richer information and more convenient dissemination, which will subvert the traditional mode of event mining. Therefore, event mining based on web videos has become a new research hotspot. However, web videos are vulnerable to video editing, lighting, shooting perspective and shooting angle, and other factors, resulting in the inaccurate visual similarity detection problem. Generally speaking, effectively integrating humungous volumes of cross-model information would give a great help. However, web videos are described with few terms, and thus sparse text information becomes a challenge for cross-model information combination. To address this issue, this paper proposes a new collaborative optimization framework with the combination of inaccurate visual similarity detection information and sparse textual information. This framework is composed of three steps. After obtaining the statistics of the distribution characteristics of each word in all Near-Duplicate Keyframes (NDKs), the high-level semantic cross-correlation between NDKs is first mined with the help of textual features, forming a new set of semantic relevant NDKs with different visual expressions. Next, textual distribution features are enriched through finding more semantically related words by the new NDK set with various forms of visual expressions, solving the sparse distribution problem for each word in all NDKs. Finally, Multiple Correspondence Analysis (MCA) is used to mine the events. Experimental results with a large number of real data demonstrate that the proposed model outperforms the existing methods for web video event mining.
Application of an improved version of McDiarmid inequality in finite-key-length decoy-state quantum key distribution

Abstract In practical decoy-state quantum key distribution, the raw key length is finite. Thus, deviation of the estimated single photon yield and single photon error rate from their respective true values due to finite sample size can seriously lower the provably secure key rate R . Current method to obtain a lower bound of R follows an indirect path by first bounding the yields and error rates both conditioned on the type of decoy used. These bounds are then used to deduce the single photon yield and error rate, which in turn are used to calculate a lower bound of the key rate R . Here we report an improved version of McDiarmid inequality in statistics and show how use it to directly compute a lower bound of R via the so-called centering sequence. A novelty in this work is the optimization of the bound through the freedom of choosing possible centering sequences. The provably secure key rate of realistic 100 km long quantum channel obtained by our method is at least twice that of the state-of-the-art procedure when the raw key length ℓ raw is ≈10 5 –10 6 . In fact, our method can improve the key rate significantly over a wide range of raw key length from about 10 5 to 10 11 . More importantly, it is achieved by pure theoretical analysis without altering the experimental setup or the post-processing method. In a boarder context, this work introduces powerful concentration inequality techniques in statistics to tackle physics problem beyond straightforward statistical data analysis especially when the data are correlated so that tools like the central limit theorem are not applicable.
Berry-phase-based quantum gates assisted by transitionless quantum driving

We propose a novel method for constructing geometric quantum gates using three- or two-level systems, in which a controllable variable, the detuning between the driving frequency and the atomic energy spacing, is introduced to realize geometric transformations. In particular, we have two instantaneous eigenstates with opposite eigenvalues constituting a closed loop in the parameter space. The accumulated dynamical phase is then exactly cancelled when the loop is completed, which is beyond the traditional parallel-transport restriction. We apply the transitionless quantum driving method to enhance the speed and fidelity of geometric gates. Gate fidelity in the presence of decoherence is also estimated.
Fourier compression: A customization method for quantum control protocols

Quantum optimal control (QOC) is the field devoted to the production of external control protocols that actively guide quantum dynamics. Solutions to QOC problems were shown to constitute continuous submanifolds of control space. A solution navigation method exploiting this property to achieve secondary features in the control protocols was proposed [Larocca, Calzetta, and Wisniacki, Phys. Rev. A 101, 023410 (2020)]. Here, we present a navigation-powered protocol postprocessing mechanism allowing naturally inflexible optimization algorithms to produce user-customized solutions, for example, in terms of a Fourier decomposition. We test the performance of the method in two inherently different models: the two-level Landau-Zener system and the quantum harmonic oscillator.
Cross-Modal Retrieval for CPSS Data

Data generated and collected from Cyber-Physical-Social Systems (CPSS) that usually in the forms of image, audio, video, and text, are complex and heterogeneous. How to deal with the cross-modal retrieval problem for heterogeneous CPSS data has drawn considerable interests recently. The hashing based methods have been widely studied in building bilateral semantic associations of binary codes for cross-model retrieval. However, most existing methods discard binary constraints and learn linear projections as hashing functions. Moreover, none of them consider the cross-modal retrieval application in the scenario of Cloud-Fog-Edge computing. Therefore, how to learn more compact and discriminative binary codes with discrete constraints and nonlinear hashing functions for CPSS data in the Cloud-Fog-Edge architecture is still an open problem. In this paper, we propose a nonlinear discrete cross-modal hashing (NDCMH) method based on concise binary classification for CPSS data which fully investigates the nonlinear relationship embedding, discrete optimization as well as the hashing functions learning. Different from previous methods, our work presents a concise but promising cross-modal hashing method that builds a direct connection between original CPSS data and binary codes, which can alleviate the impact of large quantization loss. Furthermore, we execute the cross-modal retrieval service at cloud and fog. Specifically, hashing functions are deployed at the fog plane to reduce the amount data transfer and storage need on the cloud. Extensive experiments carried out on typical CPSS datasets demonstrate that the proposed NDCMH significantly outperforms other state-of-the-art methods.
Light dressing of a diatomic superconducting artificial molecule

An artificial molecule comprised of two artificial atoms made of strongly coupled superconducting transmons is investigated under irradiation by intense light. The measured spectral features are very complex, but the authors show that they can be modeled and well explained in terms of dressed states of qubits.
Convolutional neural network weights regularization via orthogonalization

Regularization methods play an important role in artificial neural networks training, improving generalization performance and preventing them from overfitting. In this paper, we introduce a new regularization method, based on the orthogonalization of convolutional layer filters. Proposed method is easy to implement and it has plug-and-play compatibility with modern training approaches, without any changes or adaptations on their part. Experiments with MNIST and CIFAR10 datasets showed that the effectiveness of the suggested method depends on number of filters in the layer, and maximum increase in quality is achieved for architectures with small number of parameters, which is important for training fast and lightweight neural networks.
Implementation of Low-Cost Mobile Robot for Rescue Challenges

One of the biggest challenges in today’s world in the field of robotics is rescue robotics. This paper aims in the design and implementation of mobile robot for the search and rescue operations in natural calamities such as earthquakes. These rescue robots reduce the response time as compared to humans and help in getting information to the rescue teams using sensors. The main issues concerned with the present rescue robots are modularity, mobility, durability, and robustness. The robot is designed considering all the required parameters in SOLIDWORKS CAD and simulated in Rviz with the control interface as Robot Operating System (ROS). The robot is designed in such a way that it can do all the mobility tasks like climbing stairs, moving on uneven terrains, step fields, sand, and gravel, as well as exploring tasks like finding the injured victims and hazardous signs.
Quantum frequency estimation with conditional states of continuously monitored independent dephasing channels

We discuss the problem of estimating a frequency via N-qubit probes undergoing independent dephasing channels that can be continuously monitored via homodyne or photo-detection. We derive the corresponding analytical solutions for the conditional states, for generic initial states and for arbitrary efficiency of the continuous monitoring. For the detection strategies considered, we show that: i) in the case of perfect continuous detection, the quantum Fisher information (QFI) of the conditional states is equal to the one obtained in the noiseless dynamics; ii) for smaller detection efficiencies, the QFI of the conditional state is equal to the QFI of a state undergoing the (unconditional) dephasing dynamics, but with an effectively reduced noise parameter.
Conditional spectroscopy via nonstationary optical homodyne quantum state tomography

Continuous variable quantum state tomography is one of the most powerful techniques to study the properties of light fields in quantum optics. However, the need for a fixed phase reference has so far prevented widespread usage in other fields such as semiconductor spectroscopy. Here, we introduce non-stationary quantum state tomography, which adapts the technique to the special requirements of ultrafast spectroscopy. In detail, we gain access to the amplitude and phase of light fields with a temporal resolution of about 100\,fs without the need for a fixed phase reference. Further, we show how our technique allows us to perform conditional studies of stochastic dynamics that are inaccessible experimentally by conventional means, and demonstrate the capabilities experimentally by monitoring the stochastic dynamics of a thermal light field on the sub-ps scale. Finally, we discuss differences and similarities to more standard Hanbury Brown-Twiss photon correlation experiments, which may be considered as the discrete variable analogues of our technique.
Effect of Stage Volume Ratio on Audience Acoustics in Concert Halls

This report proposes the stage volume ratio (Vo/V) as an acoustic design factor for concert halls and presents an investigation of the dependence of the acoustic parameters of an auditorium on the stage volume through computer simulation. Firstly, the ratio of the stage volume to the total volume of a concert hall was defined as Vo/V through case studies of existing concert halls. It was verified using a simple model that the stage acoustic parameter STEarly and audience acoustic parameters G and C80 decreased, whereas, the reverberation time and early decay time increased with increasing Vo/V. Secondly, a computer simulation was performed for nine existing concert halls, while changing Vo/V from −20% to 30%. The room acoustic parameters exhibited the same patterns as suggested by the simple model. Vo/V significantly affected the bass ratio and bass index. A comparison of the effects of Vo/V and the sound absorption coefficient in nine concert halls revealed that Vo/V contributed approximately 15% to the reverberation and affected the bass characteristics more substantially. Thus, Vo/V is a critical design factor when determining the warmth of the audience acoustics. The study results could be used as a basis for acoustic design in the future.
On the Hadamard product of degenerate subvarieties

We consider generic degenerate subvarieties $X\_i \subset \mathbb P^n$. We determine an integer $N$, depending on the varieties, and for $n geq N$ we compute dimension and degree formulas for the Hadamard product of the varieties $X\_i$. Moreover, if the varieties $X\_i$ are smooth, their Hadamard product is smooth too. For $n < N$, if the $X\_i$ are generically $d\_i$-parameterized, the dimension and degree formulas still hold. However, the Hadamard product can be singular and we give a lower bound for the dimension of the singular locus.
A Novel Changing Athlete Body Real-Time Visual Tracking Algorithm Based on Distractor-Aware SiamRPN and HOG-SVM

Athlete detection in sports videos is a challenging task due to the dynamic and cluttered background. Distractor-aware SiamRPN (DaSiamRPN) has a simple network structure and can be utilized to perform long-term tracking of large data sets. However, similarly to the Siamese network, the tracking results heavily rely on the given position in the initial frame. Hence, there is a lack of solutions for some complex tracking scenarios, such as running and changing of bodies of athletes, especially in the stage from squatting to standing to running. The Haar feature-based cascade classifier is involved to catch the key frame, representing the video frame of the most dramatic changes of the athletes. DaSiamRPN is implemented as the tracking method. In each frame after the key frame, a detection window is given based on the bounding box generated by the DaSiamRPN tracker. In the new detection window, a fusion method (HOG-SVM) combining features of Histograms of Oriented Gradients (HOG) and a linear Support-Vector Machine (SVM) is proposed for detecting the athlete, and the tracking results are updated in real-time by fusing the tracking results of DaSiamRPN and HOG-SVM. Our proposed method has reached a stable and accurate tracking effect in testing on men’s 100 m video sequences and has realized real-time operation.
Central Bank Digital Currency: Benefits and Drawbacks

Prompted by technological advances and a decline in cash usage, many Central Banks are investigating whether it would be possible to issue a digital complement to cash, a so-called Central Bank Digital Currency (CBDC). Despite ongoing research and occasional pilots, Central Banks have shied away from introducing a CBDC for public use. Even though CBDCs would have the potential to counteract some of the problems that could arise for the payment system in the future when the use of cash is rapidly declining, they also present significant risks for financial stability.This article contributes to the discussion by setting out a CBDC framework and formulating broad design principles for CBDC in line with the central bank´s function as Lender of last Resort (LOLR). The attributes and functionality of a CBDC are highly determinative of the architectural design and technical solution chosen, particularly in the context of LOLR. Therefore, we argue in favour of a solid coin for e-emergency liquidity assistance, available 24 hours a day and seven days per week, anonymous, interest-bearing and unlimited, to prevent bank runs and restore financial stability in times of financial distress.
DeepRoom: 3D Room Layout and Pose Estimation from a Single Image

Though many deep learning approaches have significantly boosted the accuracy for room layout estimation, the existing methods follow the long-established traditional pipeline. They replace the front-end model with CNN and still rely heavily on post-processing for layout reasoning. In this paper, we propose a geometry-aware framework with pure deep networks to estimate the 2D as well as 3D layout in a row. We decouple the task of layout estimation into two stages, first estimating the 2D layout representation and then the parameters for 3D cuboid layout. Moreover, with such a two-stage formulation, the outputs of deep networks are explainable and also extensible to other training signals jointly and separately. Our experiments demonstrate that the proposed framework can provide not only competitive 2D layout estimation but also 3D room layout estimation in real time without post-processing.
Hardware-Assisted QR Code Generation Using Fault-Tolerant TRNG

True random number generator (TRNG) is used to generate a purely random sequence in key generation. Real-world applications use key bits or strings as a passcode to secure systems. The security of a system depends on a robust design and the ambiguity of the keys that are used so that they are unpredictable. In this proposal, TRNGs are designed using reversible gates and fault-tolerant circuits. So the chance of the TRNG hardware produces faulty output is avoided. The inputs for this system are obtained from CPU usage. The generated true random number sequence is used in generating QR codes due to the uniqueness of the generated sequence. The proposed TRNG design highlights the effectiveness of using reversible fault-tolerant gates for TRNG application over the conventional logic implementation and reversible gate design in terms of power, area, and randomness. The proposed design in 90 nm technology consumes only 25.26 µW of power.
Data hiding in partially encrypted HEVC video

In this study, an efficient scheme for hiding data directly in partially encrypted versions of high efficiency video coding (HEVC) videos is proposed. The content owner uses stream cipher to selectively encrypt some HEVC-CABAC bin strings in a format-compliant manner. Then, the data hider embeds the secret message into the encrypted HEVC videos using the specific coefficient modification technique. Consequently, it can be used in third-party computing environments (more generally, cloud computing). For security and privacy purposes, service providers cannot access the visual content of the host video. As the coefficient is only slightly modified, the quality of the decrypted video is satisfactory. The encrypted and marked bitstreams meet the requirements of format compatibility, and have the same bit rate. At the receiving end, data extraction can be performed in the encrypted domain or decrypted domain that can be adapted to different application scenarios. Several standard video sequences with different resolutions and contents have been used for experimental evaluation.