Reviewerly/exHarmony huggingface dataset:
Spin-orbit implementation of the Solovay-Kitaev decomposition of single-qubit channels

The Solovay-Kitaev theorem allows us to approximate any single-qubit gate to arbitrary accuracy with a finite sequence of fundamental operations from a universal set of gates. Inspired by this decomposition, we present a quantum channel simulator capable of implementing any completely positive trace-preserving map. Our realization consists of one ancillary qubit, encoded in the transverse mode of a laser beam (orbital degree of freedom), one qubit system, encoded in its polarization (spin), one spin-orbit CNOT gate and four single-qubit operations performed with prisms and polarization components. Our results describe the implementation of arbitrary single-qubit channels on the photon polarization using the transverse mode as the ancillary qubit.
Entropy guided adversarial model for weakly supervised object localization

Weakly Supervised Object Localization is challenging due to the lack of bounding box annotations. Previous works tend to generate a Class Activation Map (CAM) to localize the object. However, the CAM highlights only the most discirminative part of the object and does not highlight the whole object. To address this problem, we propose an Entropy Guided Adversarial model (EGA model) to perform better localization of objects. EGA model uses adversarial learning method to create adversarial examples, i.e., images where a perturbation is added. Treating adversarial examples as data augmentation regularize our model as well as detect more discriminative visual pattern on the CAM. We further apply the Shannon entropy on the generated CAM to guide the model during training. Minimizing the entropy loss forces the model to generate a high-confident CAM. The high-confident CAM detects the whole object while excludes the background. Extensive experiments show that EGA model improves classification and localization performances on state-of-the-art benchmarks. Ablation experiments also show that both the adversarial learning and the entropy loss contribute to the algorithm performance.
User Identity Linkage Across Social Networks by Heterogeneous Graph Attention Network Modeling

Today, social networks are becoming increasingly popular and indispensable, where users usually have multiple accounts. It is of considerable significance to conduct user identity linkage across social networks. We can comprehensively depict diversified characteristics of user behaviors, accurately model user profiles, conduct recommendations across social networks, and track cross social network user behaviors by user identity linkage. Existing works mainly focus on a specific type of user profile, user-generated content, and structural information. They have problems of weak data expression ability and ignored potential relationships, resulting in unsatisfactory performances of user identity linkage. Recently, graph neural networks have achieved excellent results in graph embedding, graph representation, and graph classification. As a graph has strong relationship expression ability, we propose a user identity linkage method based on a heterogeneous graph attention network mechanism (UIL-HGAN). Firstly, we represent user profiles, user-generated content, structural information, and their features in a heterogeneous graph. Secondly, we use multiple attention layers to aggregate user information. Finally, we use a multi-layer perceptron to predict user identity linkage. We conduct experiments on two real-world datasets: OSCHINA-Gitee and Facebook-Twitter. The results validate the effectiveness and advancement of UIL-HGAN by comparing different feature combinations and methods.
Asynchronous Coagent Networks

Coagent policy gradient algorithms (CPGAs) are reinforcement learning algorithms for training a class of stochastic neural networks called coagent networks. In this work, we prove that CPGAs converge to locally optimal policies. Additionally, we extend prior theory to encompass asynchronous and recurrent coagent networks. These extensions facilitate the straightforward design and analysis of hierarchical reinforcement learning algorithms like the option-critic, and eliminate the need for complex derivations of customized learning rules for these algorithms.
Intelligent Control and HCI for a Powered Wheelchair Using a Simple Expert System and Ultrasonic Sensors

Intelligent control and human computer interaction is investigated for a powered wheelchair using a simple expert system and ultrasonic sensors. The aim is to make driving easier. Signals from sensors and joysticks are interpreted. The interpreted signals are mixed so that the systems collaborate with the human driver to improve their control over direction and speed. Ultrasonic sensors identify hazards and the system suggests a safer speed and direction. Results are presented from drivers completing a series of timed routes using joysticks to control wheelchairs both with a microcomputer and sensors assisting them and without. Recently published systems are used to contrast and compare results. The new system described in this paper consistently performed better. An additional result appears to be that the amount of support from the microcomputer and sensors should be altered depending on surroundings and situations. The research is part of a bigger research project to improve mobility and enhance the quality of life of disabled powered wheelchair users by increasing their self-reliance and self-confidence.
The forensic discrimination of quartz sands from the Swan Coastal Plain, Western Australia

Soil is a valuable resource in many criminal cases. Used to link crime scenes, suspects, vehicles and tools, it has undergone a resurgence in recent years with multiple groups focusing on methods to allow for better differentiation. Many times the methods are focused on soil fractions, such as the clay or organic fractions. However, very sandy soils, such as the quartz sands of the Swan Coastal Plain, present a challenge to these methods. Clays and organic materials are very limited in these soils and hence, another characteristic needs to be added to the examination process. The authors here present a potential technique useful for the differentiation of soils where the soil texture is dominated by quartz sand. The quartz sand grains are used as a sampling medium, where the fine coatings (particles <20 μm in size) are recovered and analysed using X-ray diffraction (XRD). The peak intensities for a total of 7 commonly occurring minerals, converted to relative percentages, were used to compare 52 sub-surface soils from known locations, and a further 339 soils from various stratigraphies, based on an algorithm for examining similarity. Results indicate this technique allows a degree of discrimination sufficient for inclusion in a standard soil comparison protocol, specifically for cases where the traditional fractionated analyses are not appropriate.
Lightweight Functional Session Types

Row types provide an account of extensibility that combines well with parametric polymorphism and type inference.We discuss the integration of row types and session types in a concurrent functional programming language, and how row types can be used to describe extensibility in session-typed communication.On receiving a channel c, the function calc offers a choice of two behaviors, labeled Add and Neg on c.In the Add case, it then expects to read two values
Scrutinising COVID-19 laws: An early glimpse into the scrutiny work of federal parliamentary committees

Australia’s parliamentary model of rights protection depends in large part on the capacity of the federal Parliament to scrutinise the law-making activities of the Executive government. Emergency law-making undertaken in response to the COVID-19 pandemic has challenged the Australian Parliament’s capacity to provide meaningful scrutiny of proposed laws, particularly identifying and addressing the impact of emergency powers on the rights of individuals. In this context, the work of parliamentary committees has become increasingly important. Special committees, such as the Senate Select Committee on COVID-19, have been set up to provide oversight and review of Australia’s response to the pandemic. This article gives an early glimpse into the key features of the COVID-19 Committee and the way it may interact with other committees within the federal system to scrutinise the government's legislative response to the pandemic. It also offers some preliminary thoughts on the capacity of these committees to deliver meaningful rights scrutiny.
An Image Impulsive Noise Denoising Method Based on Salp Swarm Algorithm

Image noise denoising is a very important task in image processing.Aiming at the shortcomings of traditional median filtering to handle image impulse noise, an approach based on Salp Swarm Algorithm (SSA) to eliminate image impulse noise is presented in the paper.In this method, the improved extremum method is used to detect the position of impulse noise pixels, and then the Salp Swarm algorithm is used to find the optimal pixel value instead of the noise pixel to complete the denoising process of the image.Experimental results testfies that image impulse noise could be effectively filtered out through the proposed method and the manipulated image is clear and more detail could be revealed for human vision.
GeoNaija: Enhancing the Teaching and Learning of Geography through Mobile Applications

Recently, researchers have identified several challenges attendant to the teaching and learning of Geography in Nigerian secondary schools.Consequently, in order to address these issues we propose GeoNaija -an educational mobile application platform that aid the teaching and learning of Geography.For the development of the application, we employed the cyclical ever-evolving analysis, design, development, implementation, and evaluation (ADDIE) instructional design method.This method was chosen due to the fact that it is popular, easy to apply, allows for rapid prototyping, saves times and provides continual feedback.The newly developed mobile app will make the teaching and learning of Geography concepts easier, captivating and delightful for both the students and the teachers.The significance and value of the research was made clearly evident through a survey that elicited students' responses on questions relating to the app's presentation, visual, navigation and accessibility design.90 secondary students was used for the survey.The study affirmed that developed mobile application will positively change the perspectives of the students and help eliminate the gap between the conventional and other informal forms of education.
A nonlocal Weickert type PDE applied to multi-frame super-resolution

<p style='text-indent:20px;'>In this paper, we propose a nonlocal Weickert type PDE for the multiframe super-resolution task. The proposed PDE can not only preserve singularities and edges while smoothing, but also can keep safe the texture much better. This PDE is based on the nonlocal setting of the anisotropic diffusion behavior by constructing a nonlocal term of Weickert type, which is known by its coherence enhancing diffusion tensor properties. A mathematical study concerning the well-posedness of the nonlocal PDE is also investigated with an appropriate choice of the functional space. This PDE has demonstrated its efficiency by combining the diffusion process of Perona-Malik in the flat regions and the anisotropic diffusion of the Weickert model near strong edges, as well as the ability of the non-local term to preserve the texture. The elaborated experimental results give a great insight into the effectiveness of the proposed nonlocal PDE compared to some PDEs, visually and quantitatively.
Optimization of Geographic Information Systems for Forest Fire Risk Assessment

Forest fires are natural disasters defined as uncontrolled and unrestrained movements of fire across forested areas. They pose a permanent hazard of forest and forested land loss. The increasingly more frequent forest fires are often of great magnitudes, threatening not only forests but also crops, residential areas, and human lives. Forest fire fighting involves three crucial stages: prevention, detection, and extinguishment. This paper discusses the preventive measures against forest fires as well as methods of forest monitoring as a means of preserving forest biodiversity. Using modern information technology, such as geographic information systems (GIS), can improve strategic and tactical planning and actions aimed at fire prevention. The paper showcases the application of selected information system as forest fire protection in Serbia.
On the validity of the formal Edgeworth expansion for posterior densities

We consider a fundamental open problem in parametric Bayesian theory, namely the validity of the formal Edgeworth expansion of the posterior density.While the study of valid asymptotic expansions for posterior distributions constitutes a rich literature, the validity of the formal Edgeworth expansion has not been rigorously established.Several authors have claimed connections of various posterior expansions with the classical Edgeworth expansion, or have simply assumed its validity.Our main result settles this open problem.We also prove a lemma concerning the order of posterior cumulants which is of independent interest in Bayesian parametric theory.The most relevant literature is synthesized and compared to the newly-derived Edgeworth expansions.Numerical investigations illustrate that our expansion has the behavior expected of an Edgeworth expansion, and that it has better performance than the other existing expansion which was previously claimed to be of Edgeworth type.
Problems of the implementation of artificial intelligence in education

Л. Н. Духанина, А. А
State Sharding with Space-aware Representations

State sharding is a common solution to the scalability problem in blockchain systems, allowing nodes to hold a partial view of the system state. With such sharding, the processing of a transaction might not be completed locally within a node and require the involvement of multiple shards. Such cross-shards transactions have a high negative impact on system performance and are frequent with traditional state partition solutions which are often based on a simple mapping of data into shards. By locating together parts of the system state accessed by frequent transactions, the amount of cross-shard transactions can be reduced. On the other hand, the representation of such particular mappings can be memory intensive. In this paper, we study traffic-aware sharding that can be described in memory-efficient mappings. We first survey existing mapping schemes in common blockchains. We indicate the tradeoff between the size of the mapping of data to shards and the required transaction processing time and suggest algorithms for finding memory-light sharding of low cross-shard rate. We examine the efficiency of the solutions and the required frequency of sharding recomputation based on real transaction information of the Ethereum network.
Low-Error Data Recovery Based on Collaborative Filtering With Nonlinear Inequality Constraints for Manufacturing Processes

This study proposes a data recovery model where substituted values can be further limited by nonlinear and inequality constraints to approximate the ground truth. The objective is to generate substituted values for multifactors while considering their lower/upper bounds, data means, and nonlinearity at the same time. This is critical when data need to fall inside a nonlinear range, e.g., a partial hypersphere centered at a given mean. In view of such, this study proposes collaborative filtering with nonlinear inequality constraints to tackle the problem. The proposed method consists of three steps. First, the system finds class-dependent and box-bounded imputation basis factors for an incomplete data set. Class-dependent bases can reflect data domains well. Second, class-dependent imputation coefficients are located by the proposed nonnegative coefficient discovery with nonlinear inequality constraints. This step limits searching space and avoids generating substituted values out of range. Finally, constrained iterative projection pursuit is proposed for measuring the quality of recovered data by examining reconstruction residuals. By using both the nonlinear inequality constraints and the constrained iterative projection pursuit, the system can recover data while satisfying multifactor nonlinear coeffects required by manufacturers. Experimental results showed that the proposed method was capable of generating substituted values with lower root-mean-squared errors. In addition, errors were reduced by at least 10.06% on average, better than those of the baselines. Furthermore, the classification accuracy of the proposed method after data imputation was higher than that of the baselines. Such findings indicated that the proposed method could approximate the characteristics of data when missing values appeared. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work was motivated by the problem of missing values in industrial heterogeneous sensor readings. When data recovery is performed, the process should consider multifactor nonlinearity, lower/upper bounds, historical references, and the divergence between substituted values and references at the same time in order to reconstruct original sensor readings as many as possible. Existing approaches generally have solutions to linear or nonlinear equality constraints but not the aforementioned nonlinear inequality ones. This work designs a self-dictionary method—class-dependent and box-bounded imputation basis factors along with constrained iterative projection pursuit—for finding substituted values. Real industrial experiments were conducted based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {L}_{2}$ </tex-math></inline-formula> -norms. Future research will address the design for other norms.
pXRF Measurements on Soil Samples for the Exploration of an Antimony Deposit: Example from the Vendean Antimony District (France)

Mineral exploration is increasingly challenging in inhabited areas. To evaluate the potential of soil analysis by pXRF (portable X-ray fluorescence) as a low-footprint exploration technique, we revisited a historic Sb district in an agricultural area and performed shallow-soil sampling (Ah and B horizons) along profiles across known veins to capture the endogenic geochemical anomaly signals. Despite an expected bias between pXRF measurements and laboratory analyses, the former effectively located the Sb veins, especially when using their multi-element capabilities. Composition data processing (CoDa) and horizon-selective sampling significantly improved the method’s efficiency. On-site measurements allow dynamic sampling and mapping, helping with faster, cost-effective sample selection for further laboratory investigations. Based on this case study, where similar geochemical patterns were obtained for both horizons, application of an on-site approach to a humic horizon can increase survey efficiency and decrease impacts.
Encryption technology of voice transmission in mobile network based on 3DES-ECC algorithm

Abstract The traditional design of voice collector has poor anti attack ability, which makes the encryption effect of voice transmission poor. Therefore, taking the mobile network voice collector as the research object, 3des-ecc algorithm is applied to the information transmission encryption of the mobile network voice collector.An improved speech signal collector is designed, which combines 3DES and ECC algorithm to realize the encryption of speech transmission information. An improved voice signal collector is designed, which combines 3DES and ECC algorithm to realize the encryption of voice transmission information. In the process of encryption, 168-bit random key is generated first, and it is grouped according to 56 bits as 3DES key, and then the plaintext is encrypted by the key to generate ciphertext; the random key is encrypted by ECC public key of the receiver. The experimental results show that the encryption time of this method is less than 1 s, the data integrity is 93%, and the data loss rate is only 0.33%. It has better anti attack ability, fast encryption speed and good encryption effect.
Expertise and Uncertainty Processing with Nonlinear Scaling and Fuzzy Systems for Automation

Abstract Integration of domain expertise and uncertainty processing is increasingly important in automation solutions which rely on data analytics and artificial intelligence. We need a level to assess what is approximately correct. Uncertainties of the inputs are taken into account by using fuzzy numbers as the inputs of different fuzzy and parametric systems. Nonlinear scaling functions ( NSFs ) integrate these solutions and make them easier to tune. Fuzzy rule-based systems are represented with scaled fuzzy inputs. Membership functions ( MFs ) can be developed from NSFs and existing MFs can be used in developing NSFs . Fuzzy set systems and linguistic equation ( LE ) systems become consistent within the limits of detail. In recursive analysis, both meanings and interactions on all levels can be tuned together with genetic algorithms. In applications, the modular overall system consists of similar subsystems, which are normally used, with extensions to fuzzy. The compact fuzzy modules can be developed for specific tasks which are combined within Cyber Physical Systems ( CPS ). Uncertainty processing is embedded in the recursive analysis. The fuzzy extensions provide a feasible way for the sensitivity analysis of the solution.
Unsupervised Learning Facial Parameter Regressor for Action Unit Intensity Estimation via Differentiable Renderer

Facial action unit (AU) intensity is an index to describe all visually discernible facial movements. Most existing methods learn intensity estimator with limited AU data, while they lack of generalization ability out of the dataset. In this paper, we present a framework to predict the facial parameters (including identity parameters and AU parameters) based on a bone-driven face model (BDFM) under different views. The proposed framework consists of a feature extractor, a generator, and a facial parameter regressor. The regressor can fit the physical meaning parameters of the BDFM from a single face image with the help of the generator, which maps the facial parameters to the game-face images as a differentiable renderer. Besides, identity loss, loopback loss, and adversarial loss can improve the regressive results. Quantitative evaluations are performed on two public databases BP4D and DISFA, which demonstrates that the proposed method can achieve comparable or better performance than the state-of-the-art methods. What's more, the qualitative results also demonstrate the validity of our method in the wild.
Instance Adaptive Self-Training for Unsupervised Domain Adaptation

The divergence between labeled training data and unlabeled testing data is a significant challenge for recent deep learning models. Unsupervised domain adaptation (UDA) attempts to solve such a problem. Recent works show that self-training is a powerful approach to UDA. However, existing methods have difficulty in balancing scalability and performance. In this paper, we propose an instance adaptive self-training framework for UDA on the task of semantic segmentation. To effectively improve the quality of pseudo-labels, we develop a novel pseudo-label generation strategy with an instance adaptive selector. Besides, we propose the region-guided regularization to smooth the pseudo-label region and sharpen the non-pseudo-label region. Our method is so concise and efficient that it is easy to be generalized to other unsupervised domain adaptation methods. Experiments on 'GTA5 to Cityscapes' and 'SYNTHIA to Cityscapes' demonstrate the superior performance of our approach compared with the state-of-the-art methods.