Reviewerly/exHarmony huggingface dataset:
Architecture agnostic algorithm for reconfigurable optical interferometer programming

We develop the learning algorithm to build an architecture agnostic model of a reconfigurable optical interferometer. A procedure of programming a unitary transformation of optical modes of an interferometer either follows an analytical expression yielding a unitary matrix given a set of phase shifts or requires an optimization routine if an analytic decomposition does not exist. Our algorithm adopts a supervised learning strategy which matches a model of an interferometer to a training set populated by samples produced by a device under study. A simple optimization routine uses the trained model to output phase shifts corresponding to a desired unitary transformation of the interferometer with a given architecture. Our result provides the recipe for efficient tuning of interferometers even without rigorous analytical description which opens opportunity to explore new architectures of the interferometric circuits.
Constructing saturating sets in projective spaces using subgeometries

A $$\varrho $$ -saturating set of $$\text {PG}(N,q)$$ is a point set $${\mathcal {S}}$$ such that any point of $$\text {PG}(N,q)$$ lies in a subspace of dimension at most $$\varrho $$ spanned by points of $${\mathcal {S}}$$ . It is generally known that a $$\varrho $$ -saturating set of $$\text {PG}(N,q)$$ has size at least $$c\cdot \varrho \,q^\frac{N-\varrho }{\varrho +1}$$ , with $$c>\frac{1}{3}$$ a constant. Our main result is the discovery of a $$\varrho $$ -saturating set of size roughly $$\frac{(\varrho +1)(\varrho +2)}{2}q^\frac{N-\varrho }{\varrho +1}$$ if $$q=(q')^{\varrho +1}$$ , with $$q'$$ an arbitrary prime power. The existence of such a set improves most known upper bounds on the smallest possible size of $$\varrho $$ -saturating sets if $$\varrho <\frac{2N-1}{3}$$ . As saturating sets have a one-to-one correspondence to linear covering codes, this result improves existing upper bounds on the length and covering density of such codes. To prove that this construction is a $$\varrho $$ -saturating set, we observe that the affine parts of $$q'$$ -subgeometries of $$\text {PG}(N,q)$$ having a hyperplane in common, behave as certain lines of $$\text {AG}\big (\varrho +1,(q')^N\big )$$ . More precisely, these affine lines are the lines of the linear representation of a $$q'$$ -subgeometry $$\text {PG}(\varrho ,q')$$ embedded in $$\text {PG}\big (\varrho +1,(q')^N\big )$$ .
Influence of the humanitarian environment on improving the quality of training of specialists in a technical university

The article is devoted to the consideration of the theoretical aspects of the influence of the organization of the humanitarian environment in a technical educational institution on improving the quality of training specialists, increasing their competitiveness in the labor market. The analysis of special literature and generalization of different points of view on the subject of research act as research methods. The humanitarian environment represents a certain content orientation of the educational system of curricula and work programs of disciplines that include the unity of material and spiritual values that are responsible for the formation of students’ personality. Each teacher considers this problem in his own way, so the points of view have their own specifics. The humanitarian environment of the university includes a specially organized training system in which a set of specially selected methods and technologies is used to «nurture» a competitive specialist with a set of not only professional, but also personal competencies. The training of specialists in a technical university has its own characteristics related to the further professional implementation of graduates. Students at the stage of training must master special competencies, develop personal qualities, be motivated to study disciplines and master new types of activities. The organization of the humanitarian environment in the organization should be implemented at all levels: university, faculty, at each specific lesson in the study of disciplines, both provided for in the curriculum and extracurricular according to the plan of educational work. For this purpose, it is necessary to maintain and control the interaction of students with all components of the educational environment of a higher educational institution using methods such as: humanitarian technologies, mentoring, organizing a positive psychological climate in the institution, introducing additional educational programs, using active and innovative teaching methods.
Implementing a Scalable and Elastic Computing Environment Based on Cloud Containers

In this article we look at the potential of cloud containers and we provide some guidelines for companies and organisations that are starting to look at how to migrate their legacy infrastructure to something modern, reliable and scalable. We propose an architecture that has an excellent relationship between the cost of implementation and the benefits it can bring, based on the "Pilot Light" topology. The services are reconfigured inside small docker containers and the workload is balanced using load balancers that allow horizontal autoscaling techniques to be exploited in the future. By generating additional containers and utilizing the possibilities given by load balancers, companies and network systems experts may model and calibrate infrastructures based on the projected number of users. Containers offer the opportunity to expand the infrastructure and increase processing capacity in a very short time. The proposed approach results in an easily maintainable and fault-tolerant system that could help and simplify the work in particular of small and medium-sized organisations.
Maximum Correntropy High-Order Extended Kalman Filter

In this paper, a novel maximum correntropy high-order extended Kalman filter (H-MCEKF) is proposed for a class of nonlinear non-Gaussian systems presented by polynomial form. All high-order polynomial terms in the state model are defined as implicit variables and regarded as parameter variables; the original state model is equivalently formulated into a pseudo-linear form with original variables and parameter variables; the dynamic relationship between each implicit variable and all variables is modeled, then an augmented linear state model appears by combing with pseudo-linear state model; similarly, the nonlinear measurement model can be equivalently rewritten into linear form; once again, the statistical characteristics of non-Gaussian modeling error are described by mean value and variance based on their finite samples; combing original measurement model with predicted value regarded as added state measurement, a cost function to solve the state estimation based on maximum correntropy criterion (MCC) is constructed; on the basis of this cost function, the state estimation problem can be equivalently converted into a recursive solution problem in the form of Kalman filter, in which the filter gain matrix is solved by numerical iteration though its fixed-point equation; illustration examples are presented to demonstrate the effectiveness of the new algorithm.
Node-wise Localization of Graph Neural Networks

Graph neural networks (GNNs) emerge as a powerful family of representation learning models on graphs. To derive node representations, they utilize a global model that recursively aggregates information from the neighboring nodes. However, different nodes reside at different parts of the graph in different local contexts, making their distributions vary across the graph. Ideally, how a node receives its neighborhood information should be a function of its local context, to diverge from the global GNN model shared by all nodes. To utilize node locality without overfitting, we propose a node-wise localization of GNNs by accounting for both global and local aspects of the graph. Globally, all nodes on the graph depend on an underlying global GNN to encode the general patterns across the graph; locally, each node is localized into a unique model as a function of the global model and its local context. Finally, we conduct extensive experiments on four benchmark graphs, and consistently obtain promising performance surpassing the state-of-the-art GNNs.
Constructing Emotional Consensus and Utilizing Unpaired Data for Empathetic Dialogue Generation

Researches on dialogue empathy aim to endow an agent with the capacity of accurate understanding and proper responding for emotions.Existing models for empathetic dialogue generation focus on the emotion flow in one direction, that is, from the context to response.We argue that conducting an empathetic conversation is a bidirectional process, where empathy occurs when the emotions of two interlocutors could converge on the same point, i.e., reaching an emotion consensus.Besides, we also find that the empathetic dialogue corpus is extremely limited, which further restricts the model performance.To address the above issues, we propose a dual-generative model, Dual-Emp, to simultaneously construct the emotion consensus and utilize some external unpaired data.Specifically, our model integrates a forward dialogue model, a backward dialogue model, and a discrete latent variable representing the emotion consensus into a unified architecture.Then, to alleviate the constraint of paired data, we extract unpaired emotional data from open-domain conversations and employ Dual-Emp to produce pseudo paired empathetic samples, which is more efficient and low-cost than the human annotation.Automatic and human evaluations demonstrate that our method outperforms competitive baselines in producing coherent and empathetic responses.
Quantum diffusion map for nonlinear dimensionality reduction

Inspired by random walk on graphs, diffusion map (DM) is a class of unsupervised machine learning that offers automatic identification of low-dimensional data structure hidden in a high-dimensional dataset. In recent years, among its many applications, DM has been successfully applied to discover relevant order parameters in many-body systems, enabling automatic classification of quantum phases of matter. However, classical DM algorithm is computationally prohibitive for a large dataset, and any reduction of the time complexity would be desirable. With a quantum computational speedup in mind, we propose a quantum algorithm for DM, termed quantum diffusion map (qDM). Our qDM takes as an input $N$ classical data vectors, performs an eigen-decomposition of the Markov transition matrix in time $O(\log^3 N)$, and classically constructs the diffusion map via the readout (tomography) of the eigenvectors, giving a total expected runtime proportional to $N^2 \text{polylog}\, N$. Lastly, quantum subroutines in qDM for constructing a Markov transition matrix, and for analyzing its spectral properties can also be useful for other random walk-based algorithms.
A Context-Aware Neural Embedding for Function-Level Vulnerability Detection

Exploitable vulnerabilities in software systems are major security concerns. To date, machine learning (ML) based solutions have been proposed to automate and accelerate the detection of vulnerabilities. Most ML techniques aim to isolate a unit of source code, be it a line or a function, as being vulnerable. We argue that a code segment is vulnerable if it exists in certain semantic contexts, such as the control flow and data flow; therefore, it is important for the detection to be context aware. In this paper, we evaluate the performance of mainstream word embedding techniques in the scenario of software vulnerability detection. Based on the evaluation, we propose a supervised framework leveraging pre-trained context-aware embeddings from language models (ELMo) to capture deep contextual representations, further summarized by a bidirectional long short-term memory (Bi-LSTM) layer for learning long-range code dependency. The framework takes directly a source code function as an input and produces corresponding function embeddings, which can be treated as feature sets for conventional ML classifiers. Experimental results showed that the proposed framework yielded the best performance in its downstream detection tasks. Using the feature representations generated by our framework, random forest and support vector machine outperformed four baseline systems on our data sets, demonstrating that the framework incorporated with ELMo can effectively capture the vulnerable data flow patterns and facilitate the vulnerability detection task.
SentNoB: A Dataset for Analysing Sentiment on Noisy Bangla Texts

In this paper, we propose an annotated sentiment analysis dataset made of informally written Bangla texts.This dataset comprises public comments on news and videos collected from social media covering 13 different domains, including politics, education, and agriculture.These comments are labeled with one of the polarity labels, namely positive, negative, and neutral.One significant characteristic of the dataset is that each of the comments is noisy in terms of the mix of dialects and grammatical incorrectness.Our experiments to develop a benchmark classification system show that hand-crafted lexical features provide superior performance than neural network and pretrained language models.We have made the dataset and accompanying models presented in this paper publicly available at https://git.io/JuuNB.
Optimal Control of Families of Quantum Gates

Quantum optimal control (QOC) enables the realization of accurate operations, such as quantum gates, and supports the development of quantum technologies. To date, many QOC frameworks have been developed, but those remain only naturally suited to optimize a single targeted operation at a time. We extend this concept to optimal control with a continuous family of targets, and demonstrate that an optimization based on neural networks can find families of time-dependent Hamiltonians realizing desired classes of quantum gates in minimal time.
Precision Fibre Angle Inspection for Carbon Fibre Composite Structures Using Polarisation Vision

This paper evaluates the precision of polarisation imaging technology for the inspection of carbon fibre composite components. Specifically, it assesses the feasibility of the technology for fibre orientation measurements based on the premise that light is polarised by reflection from such anisotropically conductive surfaces. A recently commercialised Sony IMX250MZR sensor is used for data capture by using various lighting conditions. The paper shows that it is possible to obtain sub-degree accuracy for cured and dry woven and unidirectional materials in ideal conditions, which comprised dark field illumination. Indeed, in ideal conditions, the average relative angles can be measured to an accuracy of 0.1–0.2°. The results also demonstrate a precision of the order 1° for more general illumination, such as dome illumination and ambient lighting, for certain material type/lens combinations. However, it is also shown that the precision varies considerably depending on illumination, lens choice and material type, with some results having errors above 2°. Finally, a feasibility study into the inspection of three-dimensional components suggests that only limited application is possible for non-planar regions without further research. Nevertheless, the observed phenomena for such components are, at least, qualitatively understood based on physics theory.
Privacy Attacks for Automatic Speech Recognition Acoustic Models in A Federated Learning Framework

This paper investigates methods to effectively retrieve speaker information from the personalized speaker adapted neural network acoustic models (AMs) in automatic speech recognition (ASR). This problem is especially important in the context of federated learning of ASR acoustic models where a global model is learnt on the server based on the updates received from multiple clients. We propose an approach to analyze information in neural network AMs based on a neural network footprint on the so-called Indicator dataset. Using this method, we develop two attack models that aim to infer speaker identity from the updated personalized models without access to the actual users' speech data. Experiments on the TED-LIUM 3 corpus demonstrate that the proposed approaches are very effective and can provide equal error rate (EER) of 1-2%.
Coordinate-Free Circumnavigation of a Moving Target Via a PD-Like Controller

This article proposes a coordinate-free controller for a nonholonomic vehicle to circumnavigate a fully- actuated moving target by using range-only measurements. If the range rate is available, our proportional derivative (PD) like controller has a simple structure as the standard PD controller, except the design of an additive constant bias and a saturation function in the error feedback. We show that if the target is stationary, the vehicle asymptotically encloses the target with a predefined radius at an exponential convergence rate, i.e., an exact circumnavigation pattern can be completed. For a moving target, the circumnavigation error converges to a small region whose size is shown proportional to the maneuverability of the target, e.g., the maximum linear speed and acceleration. Moreover, we design a second-order sliding mode (SOSM) filter to estimate the range rate and show that the SOSM filter can recover the range rate in a finite time. Finally, the effectiveness and advantages of our controller are validated via both numerical simulations and real experiments.
Digitalization of Learning Media through Digital Book Development Using the Flipbook Application

The development and advancement of information and communication technology that penetrates various fields of human life had enormous potential to be used, therefore it was important to integrate thoroughly between technology into the learning process. This study aims to develop a flipbook-based digital as an instructional media that was suitable for the learning activities. This research approach was research and development (RnD) with the ADDIE as a development model which consists of Analysis, Design, Development, Implementation, Evaluation. However, this research only focuses on knowing the feasibility level of the flipbook. The subjects of this study were media experts (2 people), material experts (2 people), subject teachers (4 people), and 44 high school class XI Science students. The research instrument used a Likert scale of 1 - 5 to find out the opinions of each validator of the flipbook as instructional media. The results of assessment from each validator will be analyzed descriptively using a percentage to determine the level of feasibility. The results showed that the flipbook-based digital media was included in the category of "feasible" to be used as an instructional learning media for biology class in high school, this was indicated by the results of the validation by each of the validators who obtained a “feasible” result.
EVA Improved

Homomorphic encryption (HE), especially the CKKS scheme, can be extremely challenging to use. The EVA language and compiler (Dathathri et al., PLDI 2020) was an attempt at addressing this challenge. EVA allows a developer to express their encrypted computation in a simple form with a Python-integrated language called PyEVA. It then compiles the program into an executable form by inserting operations such as relinearization and rescaling, applying optimizations, and choosing encryption parameters with the objective of minimizing execution time. Compiled programs can be executed with a parallelizing back-end against a library of HE primitives.
SeqAttack: On Adversarial Attacks for Named Entity Recognition

Named Entity Recognition is a fundamental task in information extraction and is an essential element for various Natural Language Processing pipelines.Adversarial attacks have been shown to greatly affect the performance of text classification systems but knowledge about their effectiveness against named entity recognition models is limited.This paper investigates the effectiveness and portability of adversarial attacks from text classification to named entity recognition and the ability of adversarial training to counteract these attacks.We find that character-level and word-level attacks are the most effective, but adversarial training can grant significant protection at little to no expense of standard performance.Alongside our results, we also release SeqAttack, a framework to conduct adversarial attacks against token classification models (used in this work for named entity recognition) and a companion web application to inspect and cherry pick adversarial examples.
A Sequential Graph Neural Network for Short Text Classification

Short text classification is an important problem of natural language processing (NLP), and graph neural networks (GNNs) have been successfully used to solve different NLP problems. However, few studies employ GNN for short text classification, and most of the existing graph-based models ignore sequential information (e.g., word orders) in each document. In this work, we propose an improved sequence-based feature propagation scheme, which fully uses word representation and document-level word interaction and overcomes the limitations of textual features in short texts. On this basis, we utilize this propagation scheme to construct a lightweight model, sequential GNN (SGNN), and its extended model, ESGNN. Specifically, we build individual graphs for each document in the short text corpus based on word co-occurrence and use a bidirectional long short-term memory network (Bi-LSTM) to extract the sequential features of each document; therefore, word nodes in the document graph retain contextual information. Furthermore, two different simplified graph convolutional networks (GCNs) are used to learn word representations based on their local structures. Finally, word nodes combined with sequential information and local information are incorporated as the document representation. Extensive experiments on seven benchmark datasets demonstrate the effectiveness of our method.
Reactive Navigation of an Unmanned Aerial Vehicle With Perception-Based Obstacle Avoidance Constraints

In this article we propose a reactive constrained navigation scheme, with embedded obstacles avoidance for an Unmanned Aerial Vehicle (UAV), for enabling navigation in obstacle-dense environments. The proposed navigation architecture is based on Nonlinear Model Predictive Control (NMPC), and utilizes an on-board 2D LiDAR to detect obstacles and translate online the key geometric information of the environment into parametric constraints for the NMPC that constrain the available position-space for the UAV. This article focuses also on the real-world implementation and experimental validation of the proposed reactive navigation scheme, and it is applied in multiple challenging laboratory experiments, where we also conduct comparisons with relevant methods of reactive obstacle avoidance. The solver utilized in the proposed approach is the Optimization Engine (OpEn) and the Proximal Averaged Newton for Optimal Control (PANOC) algorithm, where a penalty method is applied to properly consider obstacles and input constraints during the navigation task. The proposed novel scheme allows for fast solutions, while using limited on-board computational power, that is a required feature for the overall closed loop performance of an UAV and is applied in multiple real-time scenarios. The combination of built-in obstacle avoidance and real-time applicability makes the proposed reactive constrained navigation scheme an elegant framework for UAVs that is able to perform fast nonlinear control, local path-planning and obstacle avoidance, all embedded in the control layer.
Linked Gaussian Process Emulation for Systems of Computer Models Using Matérn Kernels and Adaptive Design

Linked Gaussian Process Emulation for Systems of Computer Models Using Matérn Kernels and Adaptive Design