Reviewerly/exHarmony huggingface dataset:
RePQC: A 3.4-uJ/Op 48-kOPS Post-Quantum Crypto-Processor for Multiple-Mathematical Problems

Post-quantum cryptography (PQC) is investigated to replace the classical public cryptography algorithms, which would be completely broken by large-scale quantum computers. However, current PQC schemes have completely different mathematical foundations and parameter sets, which makes the implementation of unified PQC processor extremely challenging. To address this issue, an agile PQC processor, RePQC, is proposed in this work to support schemes on multiple mathematical problems. First, the hierarchical calculation framework, ranging from algorithm level, task level, and coefficient level, is proposed to achieve desirable flexibility and energy efficiency. Second, a hybrid processing element array is built to support arithmetic and logical operations simultaneously, while algorithm-hardware co-design is utilized in task-level schedulers to further improve the algorithm-oriented energy efficiency. Finally, parallelism exploration and algorithm-level computation transformation is further utilized to optimize the configuration on RePQC for higher throughput. Fabricated in a 28-nm process, RePQC achieves the energy efficiency of 3.4 uJ/Op and the throughput of 48 kOPS, which is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$23\times $ </tex-math></inline-formula> higher than the state-of-the-art work, respectively. To the best of our knowledge, RePQC is the first silicon-proven PQC processor for different mathematical problems.
Genesis and Prospects of Higher Insurance Education Development in Russia

The article discusses the history and stages of development of insurance education in Russia in comparison with the development of the insurance market in 1990–2020, determines the prospects and conditions for development of insurance education. The internal and external factors that influenced the direction and rate of the market of educational services development in the field of insurance are highlighted, the reasons for the creation and liquidation of specialized departments in Russian universities are analyzed. Based on the analysis of the Russian insurance market in 1990-2020, an understanding of the real and reported indicators of the insurance market is being formed, the differences in which caused at the first stage the creation of a significant number of insurance departments in Russian universities, not supported by a real need for specialists, and then the liquidation of the departments with the loss of the already established competencies for training insurance specialists. In the late 2010s, the modernization processes in the requirements for insurance organizations took place, the financial stability and solvency standards were being raised, mandatory actuarial valuation and new forms of financial reporting were being introduced that required specialized knowledge, basic and professional standards were being developed and adopted with the participation of self-regulatory organizations of insurers. All of that actualized the activities of specialized insurance departments based on the integration of professional requirements fixed in professional and basic standards, and educational standards, which should allow the implementation of economically successful undergraduate and master’s programs.The history of insurance departments in modern Russia may be of interest to researchers of the history of education in modern Russia, the origins and causes of the current state of the educational services market, periods of growth and decline in the number of active players. Based on the analysis of archival and current insurance development strategies, the article highlights the adopted basic and professional standards, prospects for the development of insurance departments, gives recommendations for interaction with professional participants of the insurance market. The revealed trends are also typical for other highly professional departments.
A novel calibration method for a uniaxial MEMS-based 3D reconstruction system

Fringe projection profilometry using a uniaxial MEMS micro-vibration mirror is becoming popular in three-dimensional (3D) reconstruction owing to the advantages of fast projection, small size, low cost, and no demand of focus optics. The calibration method is crucial and directly affects the accuracy of 3D reconstruction. In conventional phase-height calibration methods, there exists a problem of recalibration of system parameters if the maximum fringe frequency varies between the stages of calibration and reconstruction. In this paper, the fringe projection is realized by a MEMS mirror with a 1.15 kHz resonant frequency and a line laser. The voltage of line laser is modulated according to the scanning position, which is related to the vibration characteristics of MEMS mirror. Subsequently, the uniaxial MEMS-based 3D reconstruction system is constructed. We propose a novel calibration method for the uniaxial MEMS-based 3D reconstruction system. The proposed calibration method is derived from the scanning characteristics of a uniaxial vibration mirror and considers the camera distortion. The proposed method is free from the problem of recalibration and the limitations of installation. The experimental results show the proposed method can reconstruct the 3D shape of target in high resolution and verify the feasibility of the system.
Feature selection and cascade dimensionality reduction for self-supervised visual representation learning

Self-supervised visual representation learning focuses on capturing comprehensive features via exploiting the unlabeled datasets. However, existing contrastive learning based SSL frameworks are subjected to higher computational consumption and unsatisfactory performance. To handle these issues, we present a novel single-branch SSL method that incorporates an adaptive feature selection and activation module and a progressive cascade dimensionality reduction module, called APNet. Specifically, our method first fully exploits the unlabeled datasets and extracts intra- and inter-image information via introducing montage image. In addition, a novel adaptive feature selection and activation module is designed to generate the most comprehensive features. Besides, a progressive cascade dimensionality reduction module is proposed to capture the most representative features from latent vectors through cascade dimensionality increasing–decreasing operations. Extensive experiments have demonstrated the robustness and effectiveness of APNet. Specifically, APNet exceeds MoCo-v3 by 3.1% on the ImageNet-100 dataset, and consumes only half of the calculation. Code is available at https://github.com/AI-TYQ/APNet.
Practical reusable garbled circuits with parallel updates

The central contribution of the current paper is to establish a practical and effective reusable garbled circuit scheme with parallel updates, where each update is applied to the circuit in parallel. First of all, we present a strengthened notion of inner product functional encryption (IPE) called simulation-based security (SIM-security) for multi-instance and show that a secret-key IPE scheme is multi-instance SIM-secure. Then we put forward a reusable garbled circuit scheme based on the IPE scheme. The idea is a reduction from re-randomizable garbled circuits in which the distribution of the original garbled circuit is computationally indistinguishable from the re-randomized garbled circuit. Finally, we implemented reusable garbled circuits and validate the practicality of our reusable garbled circuit scheme. Experimental results demonstrate that our reusable garbled circuit scheme is suitable for a wide range of real-world scenarios.
Evaluating the usefulness of an online participative modelling tool when post-graduate engineers co-model enterprise operations

Digital participatory enterprise modelling (PEM) is an emerging knowledge area that may increase collaboration and understanding amongst team members in modelling enterprise operations, especially when team members are geographically dispersed. The COVID-19 pandemic emphasised the need to use participatory design practices when in-person face-to-face participation is not possible. Within a tertiary post-graduate engineering education context, this study uses an online approach to demonstrate the use of PEM to students. The main objective is to investigate whether an interactive modelling tool is useful to post-graduate engineering students when they also apply digital PEM within the context of their own enterprise. Using design science research to further evolve an existing story card method (SCM), we address a key concern that was identified during a previous design iteration of the SCM, namely that the previous modelling tool did not encourage active participation during modelling due to the latency of the tool. Although multiple participative modelling tools are available, we used a list of entry requirements to short-list two tools. We provide a comparative analysis of the two tools, motivating selection of a single tool that was used in combination with the SCM. We involved 36 participants in applying the SCM, of which 25 completed a survey to evaluate whether the tooling encouraged participative design. Using a demonstration case to illustrate the notion of participative design to the post-graduate participants, using the selected tool in combination with the SCM, we obtained positive feedback about the participative enterprise modelling tool that was used by post-graduate engineering students. The feedback also provides guidance towards our future teaching practices, encouraging participative online co-modelling, especially when post-graduate students conduct their studies remotely.
Linear and Nonlinear control of a Visual Servoing system with Pan-Tilt Structure

In the last few years, the problem of automatically tracking a moving object has become a topic of interest, being therefore the subject of several studies. This work aims at presenting two distinct control approaches to solve the tracking problem for a visual servoing system with Pan-Tilt structure. Linear and nonlinear (gain scheduling) Proportional-Derivatives controllers are implemented in an actual micro-controller (Arduino Mega). The experimental results show a satisfactory level of efficiency of the systems established through the adopted methods, both for step and ramp shaped input errors.
Moth Flame Optimization Based FCNN for Prediction of Bugs in Software

The software engineering technique makes it possible to create highquality software.One of the most significant qualities of good software is that it is devoid of bugs.One of the most time-consuming and costly software procedures is finding and fixing bugs.Although it is impossible to eradicate all bugs, it is feasible to reduce the number of bugs and their negative effects.To broaden the scope of bug prediction techniques and increase software quality, numerous causes of software problems must be identified, and successful bug prediction models must be implemented.This study employs a hybrid of Faster Convolution Neural Network and the Moth Flame Optimization (MFO) algorithm to forecast the number of bugs in software based on the program data itself, such as the line quantity in codes, methods characteristics, and other essential software aspects.Here, the MFO method is used to train the neural network to identify optimal weights.The proposed MFO-FCNN technique is compared with existing methods such as AdaBoost (AB), Random Forest (RF), K-Nearest Neighbour (KNN), K-Means Clustering (KMC), Support Vector Machine (SVM) and Bagging Classifier (BC) are examples of machine learning (ML) techniques.The assessment method revealed that machine learning techniques may be employed successfully and through a high level of accuracy.The obtained data revealed that the proposed strategy outperforms the traditional approach.
Using Contrastive Samples for Identifying and Leveraging Possible Causal Relationships in Reinforcement Learning

A significant challenge in reinforcement learning is quantifying the complex relationship between actions and long-term rewards. The effects may manifest themselves over a long sequence of state-action pairs, making them hard to pinpoint. In this paper, we propose a method to link transitions with significant deviations in state with unusually large variations in subsequent rewards. Such transitions are marked as possible causal effects, and the corresponding state-action pairs are added to a separate replay buffer. In addition, we include contrastive samples corresponding to transitions from a similar state but with differing actions. Including this Contrastive Experience Replay (CER) during training is shown to outperform standard value-based methods on 2D navigation tasks. We believe that CER can be useful for a broad class of learning tasks, including for any off-policy reinforcement learning algorithm.
Solving age-word problems using domain ontology and BERT

An age word problem (ageWP) typically involves sentences that express relationships between the age of the agents and asks for the age of one of them. Automatically solving ageWPs is a challenging task as we need to tackle temporal relationships between the agent’s ages, frame and solve the equations for the required unknowns. To the best of our knowledge, there exists only one ageWP dataset consisting of just 124 examples. The dataset is too small to employ a learning-based solver, mainly consisting of ageWPs with simple temporal relationships. To address this issue, in our earlier work, we designed a description-logic based ontology (ageWP-ont) for the domain of age word problems and utilized it to automatically generate a large number of ageWPs. Sentences in these ageWPs relate the ages of agents in a temporally complex manner. In this paper, we focus on solving these problems. We analyzed an existing learning-based solver of algebraic word-problems that uses a traditional machine learning approach and found that the solver can be adapted to our domain. But we found that this approach does not seem to perform well, perhaps due to the complex nature of the ageWPs. As we have the ontology of the domain on hand, we propose a new approach of utilizing it in the deep-learning based NLU component of the solution. We annotate parts of the ageWP sentences with class-names from ageWP-ont and train a BERT-based language model (LM) that learns to predict the instances for these classes in the given sentences. An RDF graph is populated with these values and serves as a concrete problem-specific instance of the ontology. The dataset for training the LM is automatically generated with the help of ageWP-ont. Finally, for the actual solving of a given ageWP, we make use of its RDF graph and employ Semantic Web Rule Language (SWRL) rules. We implemented the proposed system and achieved 68.8% accuracy. The work demonstrates that combining deep learning with ontologies can give impressive results.
Tutorial on understanding meta-learning for fast adaptation

Deep learning methods provide excellent performance when sufficiently large annotated data is available. But their performance degrades when the models are applied to different target domains with very different data distributions as compared to that of the source domain. The performance of such models can be improved by domain adaptation. In this tutorial, we discuss two domain adaptation techniques, i.e. fine-tuning and meta-learning, with a methodical analysis of meta-learning based adaptation. Meta-learning works in a manner similar to how humans take in a handful of examples and learn new skills very quickly and efficiently. Generally, data-level and parameter-level approaches are considered when solving a few-shot learning problem. Meta-learning is one of the parameter-level approaches. The tutorial is targeted toward those who wish to gain comprehensive insight and practical experience in applying meta-learning for fast adaptation. We show applications of these techniques through Jupyter notebooks, demonstrating how core concepts can be translated into empirical work. For those who are new to this area, we will provide a hands-on session and online pedagogical guide that can serve as the basis for fostering future research. We hope to convey a comprehensive understanding of recent advances and current state-of-the-art approaches to those who have prior knowledge of the areas. We would expect some basic experience in the execution of Jupyter notebooks and some basic knowledge of concepts related to machine learning as a prerequisite for participation.
Addressing Data Intrinsic Characteristics for Augmentation for Breast Cancer Classification

Breast cancer is the most frequently diagnosed cancer among females worldwide. The task of correctly diagnosing cancer using histopathology in its very earlier stages is a challenging and critical task. Most of the present machine learning techniques require a lot of data to analyze and predict a benign tumour in its early stages, and such data is not available readily. In this paper, we propose the idea of data augmentation of breast cancer tissue images by addressing data intrinsic characteristics. The aim is to detect the micro presence of the tumour cells and highlight it over multiple synthetic images for classifiers to predict benign tumours in very early stages with high accuracy. The initial experimental analysis highlights the proposed technique’s impact and significance in boosting the performance of standard classifier(s).
RetroKD : Leveraging Past States for Regularizing Targets in Teacher-Student Learning

Several recent works show that higher accuracy models may not be better teachers for every student, and hence, refer this problem as student-teacher “knowledge gap". Further, they propose techniques, which, in this paper, we discuss are constrained to certain pre-conditions: 1). Access to Teacher Model/Architecture 2). Retraining Teacher Model 3). Models in Addition to Teacher Model. Being well known that for a lot of settings, these conditions may not hold true challenges the applicability of such approaches. In this work, we propose RetroKD, which smoothes out the logits of a student network by leveraging students’ past state logits with the ones from the teacher. By doing so, we hypothesize that the present target will no longer be as hard as the teacher target and not as more uncomplicated as the past student target. Such regularization on learning the parameters alleviates the needs as required by other methods. Our extensive set of experiments comparing against the baselines for CIFAR 10, CIFAR 100, and TinyImageNet datasets and a theoretical study further help in supporting our claim. We performed crucial ablation studies such as hyperparameter sensitivity, the generalization study by showing the flatness on loss landscape and feature similarly with teacher network.
UDAAN - Machine Learning based Post-Editing tool for Document Translation

We introduce UDAAN, an open-source post-editing tool that can reduce manual editing efforts to quickly produce publishable-standard documents in several Indic languages. UDAAN has an end-to-end Machine Translation (MT) plus post-editing pipeline wherein users can upload a document to obtain raw MT output. Further, users can edit the raw translations using our tool. UDAAN offers several advantages: i. Domain-aware, vocabulary-based lexical constrained MT. ii. source-target and target-target lexicon suggestions for users. Replacements are based on the source and target texts’ lexicon alignment. iii. Translation suggestions are based on logs created during user interaction. iv. Source-target sentence alignment visualisation that reduces the cognitive load of users during editing. v. Translated outputs from our tool are available in multiple formats: docs, latex, and PDF. We also provide the facility to use around 100 in-domain dictionaries for lexicon-aware machine translation. Although we limit our experiments to English-to-Hindi translation, our tool is independent of the source and target languages. Experimental results based on the usage of the tools and users’ feedback show that our tool speeds up the translation time by approximately a factor of three compared to the baseline method of translating documents from scratch. Our tool is available for both Windows and Linux platforms. The tool is open-source under MIT license, and the source code can be accessed from our website, https://www.udaanproject.org. Demonstration and tutorial videos for various features of our tool can be accessed here. Our MT pipeline can be accessed at https://udaaniitb.aicte-india.org/udaan/translate/.
FloorGAN: Generative Network for Automated Floor Layout Generation

In this work, we propose a generative adversarial network, FloorGAN, to synthesize floor plans guided by user constraints. Our approach considers user inputs in the form of room types, and spatial relationships and generates layout designs that satisfy these requirements. We evaluate our approach on the dataset, RPLAN, consisting of 80,000 vector-graphics floor plans of residential buildings designed by professional architects. We perform both qualitative and quantitative analysis along three metrics - Realism, Diversity, and Compatibility to evaluate the generated layout designs. We compare our approach with the existing baselines and outperform on all these metrics. The layout designs generated by our approach are more realistic and of better quality.
Gradient Perturbation-based Efficient Deep Ensembles

Modern deep neural networks achieve excellent predictive performance due to their massive scale, flexible architecture design and availability of large training datasets. However, several applications additionally demand reliable estimates of model and predictive uncertainty that help in making robust predictions with limited training data, enabling out-of-distribution generalization, etc. Neural networks do not offer such uncertainly estimates out-of-the-box. Although Bayesian approaches to deep learning do provide a natural way to quantify model and predictive uncertainty by inferring the posterior distribution of the model weights and averaging the model’s predictions over the entire posterior distribution, standard Bayesian inference methods such as MCMC and variational inference are difficult to design and scale to massive networks. An appealing and popular alternative is to learn an ensemble of model weights (popularly known as deep ensembles) and averaging the model’s predictions over the ensemble. However, due to the need for multiple training runs, this approach also tends to be computationally expensive. In this work, we present PEG (Perturbed Ensemble via Gradient updates), a simple and efficient approach to constructing deep ensembles using gradients computed over validation data. Experiments show that PEG can not only create an ensemble provided a pre-trained model, but it can also further enrich pre-trained models that are deep ensembles themselves. On several benchmark datasets and architectures, PEG was found to perform favorably in comparison to state-of-the-art baselines in terms of predictive performance as well as other uncertainty quantification metrics.