Reviewerly/exHarmony huggingface dataset:
Dynamic Knowledge Distillation for Pre-trained Language Models

Knowledge distillation (KD) has been proved effective for compressing large-scale pretrained language models.However, existing methods conduct KD statically, e.g., the student model aligns its output distribution to that of a selected teacher model on the pre-defined training dataset.In this paper, we explore whether a dynamic knowledge distillation that empowers the student to adjust the learning procedure according to its competency, regarding the student performance and learning efficiency.We explore the dynamical adjustments on three aspects: teacher model adoption, data selection, and KD objective adaptation.Experimental results show that (1) proper selection of teacher model can boost the performance of student model; (2) conducting KD with 10% informative instances achieves comparable performance while greatly accelerates the training; (3) the student performance can be boosted by adjusting the supervision contribution of different alignment objective.We find dynamic knowledge distillation is promising and provide discussions on potential future directions towards more efficient KD methods. 1
Using Augmented Reality Technology in Higher Education Institutions

One way to increase the effectiveness of a mobile-oriented learning environment is to use augmented reality technology, which enables the integration of real and virtual learning tools using mobile Internet devices. The purpose of the article is to theoretically substantiate the use of augmented reality technology in physics classes. The article describes and substantiates the relevance of the application of augmented reality technology, considers possible uses of this technology in the educational process, in particular in terms of mobile distance learning. Examples of mobile applications aimed at practice-oriented learning using augmented reality technology are described. Examples of real educational systems that allow to study the disciplines of physical orientation on the basis of this technology are considered. It was found that augmented reality technologies stimulate the educational process and provide the opportunity to implement knowledge in both humanities and natural sciences. This technology expands the educational process towards clarity and motivates the student to further study the material. Visual education is the best thing that can be offered to modern students, which will contribute to the development of scientific and technological progress. The introduction of augmented reality technology makes it possible to improve the quality of education by motivating students to self-study, increasing audience interest in educational material, developing a desire to use modern interactive technical capabilities and technologies, replacing textbooks and laboratory equipment with multimedia computer models. distance learning.
Semiconductor Quantum Computing: Toward a CMOS quantum computer on chip

Quantum computing is experiencing the transition from a scientific to an engineering field with the promise to revolutionize an extensive range of applications demanding high-performance computing. Many implementation approaches have been pursued for quantum computing systems, where currently the main streams can be identified based on superconducting, photonic, trapped-ion, and semiconductor qubits. Semiconductor-based quantum computing, specifically using CMOS technologies, is promising as it provides potential for the integration of qubits with their control and readout circuits on a single chip. This paves the way for the realization of a large-scale quantum computing system for solving practical problems. In this paper, we present an overview and future perspective of CMOS quantum computing, exploring developed semiconductor qubit structures, quantum gates, as well as control and readout circuits, with a focus on the promises and challenges of CMOS implementation.
Learning Sampling Distributions Using Local 3D Workspace Decompositions for Motion Planning in High Dimensions

Earlier work has shown that reusing experience from prior motion planning problems can improve the efficiency of similar, future motion planning queries. However, for robots with many degrees-of-freedom, these methods exhibit poor generalization across different environments and often require large datasets that are impractical to gather. We present SPARK and FLAME , two experience-based frameworks for sampling-based planning applicable to complex manipulators in 3 D environments. Both combine samplers associated with features from a workspace decomposition into a global biased sampling distribution. SPARK decomposes the environment based on exact geometry while FLAME is more general, and uses an octree-based decomposition obtained from sensor data. We demonstrate the effectiveness of SPARK and FLAME on a Fetch robot tasked with challenging pick-and-place manipulation problems. Our approaches can be trained incrementally and significantly improve performance with only a handful of examples, generalizing better over diverse tasks and environments as compared to prior approaches.
Resolution Learning in Deep Convolutional Networks Using Scale-Space Theory

Resolution in deep convolutional neural networks (CNNs) is typically bounded by the receptive field size through filter sizes, and subsampling layers or strided convolutions on feature maps. The optimal resolution may vary significantly depending on the dataset. Modern CNNs hard-code their resolution hyper-parameters in the network architecture which makes tuning such hyper-parameters cumbersome. We propose to do away with hard-coded resolution hyper-parameters and aim to learn the appropriate resolution from data. We use scale-space theory to obtain a self-similar parametrization of filters and make use of the N-Jet: a truncated Taylor series to approximate a filter by a learned combination of Gaussian derivative filters. The parameter σ of the Gaussian basis controls both the amount of detail the filter encodes and the spatial extent of the filter. Since σ is a continuous parameter, we can optimize it with respect to the loss. The proposed N-Jet layer achieves comparable performance when used in state-of-the art architectures, while learning the correct resolution in each layer automatically. We evaluate our N-Jet layer on both classification and segmentation, and we show that learning σ is especially beneficial when dealing with inputs at multiple sizes.
Fast User Adaptation for Human Motion Prediction in Physical Human–Robot Interaction

Accurate prediction of human movements is required to enhance the efficiency of physical human-robot interaction. Behavioral differences across various users are crucial factors that limit the prediction of human motion. Although recent neural network-based modeling methods have improved their prediction accuracy, most did not consider an effective adaptations to different users, thereby employing the same model parameters for all users. To deal with this insufficiently addressed challenge, we introduce a meta-learning framework to facilitate the rapid adaptation of the model to unseen users. In this study, we propose a model structure and a meta-learning algorithm specialized to enable fast user adaptation in predicting human movements in cooperative situations with robots. The proposed prediction model comprises shared and adaptive parameters, each addressing the user's general and individual movements. Using only a small amount of data from an individual user, the adaptive parameters are adjusted to enable user-specific prediction through a two-step process: initialization via a separate network and adaptation via a few gradient steps. Regarding the motion dataset that has 20 users collaborating with a robotic device, the proposed method outperforms existing meta-learning and non-meta-learning baselines in predicting the movements of unseen users.
Higher education innovatics: management of innovation in the sphere of higher education and science

The purpose of the article is the development of the concept and fundamentals of the innovatics of higher education, further development, and improvement of higher education innovatics, the definition of directions, and management of innovations in the field of higher education and science. Higher education innovatics, proposed by the authors, studies and summarizes theoretical issues and practices of implementing and managing innovation activities of higher educational institutions, scientific institutions, organizations and management bodies associated with the higher education system. This article analyzes innovative changes in the higher education system caused by the environment of academic capitalism. Among them: basic issues of the organizational theory of higher education innovatics; analysis of the direction and impact of innovation processes in the field of higher education and science on the objects and subjects of innovative transformations; the role of the state in the management of innovation activities in the field of higher education and science; types of innovations in the higher education system that can lead to innovative changes; innovations of economic and market type in higher education; innovations of organizational type in the higher education sphere; innovations of educational and pedagogical type in the system of higher education; means and tools for innovation management in education; problems of the forced or emergency distance learning, etc.Innovative activity in the field of higher education and science leads to serious innovative transformations both in the entire field of higher education and science, and in its subjects – universities, research institutes, and related organizations and institutions.
Synthia: multidimensional synthetic data generation in Python

2016;Patki et al., 2016). As such, copula-based data generation models -probabilistic models that allow for the statistical properties of observed data to be modelled in terms of individual behavior and (inter-)dependencies (Joe, 2014) -have shown potential in several applications such as finance, data science, and meteorology (Kamthe et al.,
Honey or Poison? Solving the Trigger Curse in Few-shot Event Detection via Causal Intervention

Event detection has long been troubled by the trigger curse: overfitting the trigger will harm the generalization ability while underfitting it will hurt the detection performance.This problem is even more severe in few-shot scenario.In this paper, we identify and solve the trigger curse problem in few-shot event detection (FSED) from a causal view.By formulating FSED with a structural causal model (SCM), we found that the trigger is a confounder of the context and the result, which makes previous FSED methods much easier to overfit triggers.To resolve this problem, we propose to intervene on the context via backdoor adjustment during training.Experiments show that our method significantly improves the FSED on ACE05, MAVEN and KBP17 datasets.
YOLOV3 Based Ship Detection in Visible and Infrared Images

Ship detection is one of the most important researches in the field of navigation safety and marine environment monitoring. Synthetic aperture radar (SAR) imagery has been used as a promising data source for monitoring maritime activities. However, the resolution of SAR images is limited, and it cannot effectively detect densely distributed and small ships, especially near harbors. In order to effectively manage ships during the day and night, this research uses visible and infrared images for ship detection. In this study, an improved architecture based on You only look once version 3 (Yolov3) is proposed for ship detection. Yolov3 provided multi-scale feature extraction to enhance the recognition of small targets. In addition, the study also considered the influence of Yolov3 parameters on ship detection, including the input image size, the number of filters in convolution layers and the detection scales. The experiment is based on a data set containing six types of ships, and a total of 5, 513 visible and infrared images from the harbors in northern Taiwan. The experimental results show that when the model parameters are selected as: 352x352 input size, the scale of large target deleted and the convolution filters reduced by 30%, Yolov3 has better ship detection performance and computational efficiency. Compared with the original Yolov3 with 87.9% mean average precision (mAP) and 87.0 billion floating point operations per second (BFLOPs), the proposed architecture can achieve 89.1 % mAP and 24.3 BFLOPs.
MESH-BASED GRAPH CONVOLUTIONAL NEURAL NETWORKS FOR MODELING MATERIALS WITH MICROSTRUCTURE

Predicting the evolution of a representative sample of a material with microstructure is a fundamental problem in homogenization. In this work we propose a graph convolutional neural network that utilizes the discretized representation of the initial microstructure directly, without segmentation or clustering. Compared to feature-based and pixel-based convolutional neural network models, the proposed method has a number of advantages: (a) it is deep in that it does not require featurization but can benefit from it, (b) it has a simple implementation with standard convolutional filters and layers, (c) it works natively on unstructured and structured grid data without interpolation (unlike pixel-based convolutional neural networks), and (d) it preserves rotational invariance like other graph-based convolutional neural networks. We demonstrate the performance of the proposed network and compare it to traditional pixel-based convolution neural network models and feature-based graph convolutional neural networks on multiple large datasets.
PAC-Bayes Unleashed: Generalisation Bounds with Unbounded Losses

We present new PAC-Bayesian generalisation bounds for learning problems with unbounded loss functions. This extends the relevance and applicability of the PAC-Bayes learning framework, where most of the existing literature focuses on supervised learning problems with a bounded loss function (typically assumed to take values in the interval [0;1]). In order to relax this classical assumption, we propose to allow the range of the loss to depend on each predictor. This relaxation is captured by our new notion of HYPothesis-dependent rangE (HYPE). Based on this, we derive a novel PAC-Bayesian generalisation bound for unbounded loss functions, and we instantiate it on a linear regression problem. To make our theory usable by the largest audience possible, we include discussions on actual computation, practicality and limitations of our assumptions.
iBlock: An Intelligent Decentralised Blockchain-based Pandemic Detection and Assisting System

The recent COVID-19 outbreak highlighted the requirement for a more sophisticated healthcare system and real-time data analytics in the pandemic mitigation process. Moreover, real-time data plays a crucial role in the detection and alerting process. Combining smart healthcare systems with accurate real-time information about medical service availability, vaccination, and how the pandemic is spreading can directly affect the quality of life and economy. The existing architecture models are become inadequate in handling the pandemic mitigation process using real-time data. The present models are server-centric and controlled by a single party, where the management of confidentiality, integrity, and availability (CIA) of data is doubtful. Therefore, a decentralised user-centric model is necessary, where the CIA of user data is assured. In this paper, we have suggested a decentralized blockchain-based pandemic detection and assistance system (iBlock). The iBlock uses robust technologies like hybrid computing and IPFS to support system functionality. A pseudo-anonymous personal identity is introduced using H-PCS and cryptography for anonymous data sharing. The distributed data management module guarantees data CIA, security, and privacy using cryptography mechanisms. Furthermore, it delivers useful intelligent information in the form of suggestions and alerts to assist the users. Finally, the iBlock reduces stress on healthcare infrastructure and workers by providing accurate predictions and early warnings using AI/ML.
Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).
RPTCN: Resource Prediction for High-dynamic Workloads in Clouds based on Deep Learning

Resource management is challenging in clouds due to the dynamics and sharing characteristics. The crucial problem is how to allocate resources accurately and satisfy demands of workloads timely. The traditional solution is to use historical data to predict future resource usage. Although these resource prediction methods can predict the periodicity, they can not accurately predict mutation points due to the high dynamics and uncertainty of resource usage. To tackle this issue, in this paper we propose a resource usage prediction method - RPTCN, which is based on a deep learning method - temporal convolutional networks (TCNs) in cloud systems. We add a fully connected layer and attention mechanism to TCNs to improve the prediction accuracy. In order to explore the relationship between the usage of different resources in the temporal dimension, we use correlation analysis to screen performance indicators as multidimensional feature input for prediction. Finally, we evaluate the performance of this method on Alibaba trace v2018. Evaluations show that RPTCN improves the overall MAE and MSE by 6.50%~89.03% and 0.41%~68.82% respectively compared to baselines in dynamic and long-term prediction of resource usage. Moreover, the convergence and generalization of RPTCN are also better than the baselines.
AToM: Active topology monitoring for the bitcoin peer-to-peer network

Abstract Over the past decade, the Bitcoin P2P network protocol has become a reference model for all modern cryptocurrencies. While nodes in this network are known, the connections among them are kept hidden, as it is commonly believed that this helps protect from deanonymization and low-level attacks. However, adversaries can bypass this limitation by inferring connections through side channels. At the same time, the lack of topology information hinders the analysis of the network, which is essential to improve efficiency and security. In this paper, we thoroughly review network-level attacks and empirically show that topology obfuscation is not an effective countermeasure. We then argue that the benefits of an open topology potentially outweigh its risks, and propose a protocol to reliably infer and monitor connections among reachable nodes of the Bitcoin network. We formally analyze our protocol and experimentally evaluate its accuracy in both trusted and untrusted settings. Results show our system has a low impact on the network, and has precision and recall are over 90% with up to 20% of malicious nodes in the network.
Modular specification and verification of closures in Rust

Closures are a language feature supported by many mainstream languages, combining the ability to package up references to code blocks with the possibility of capturing state from the environment of the closure's declaration. Closures are powerful, but complicate understanding and formal reasoning, especially when closure invocations may mutate objects reachable from the captured state or from closure arguments. This paper presents a novel technique for the modular specification and verification of closure-manipulating code in Rust. Our technique combines Rust's type system guarantees and novel specification features to enable formal verification of rich functional properties. It encodes higher-order concerns into a first-order logic, which enables automation via SMT solvers. Our technique is implemented as an extension of the deductive verifier Prusti, with which we have successfully verified many common idioms of closure usage.
Visible Watermark Removal via Self-calibrated Localization and Background Refinement

Superimposing visible watermarks on images provides a powerful weapon to cope with the copyright issue. Watermark removal techniques, which can strengthen the robustness of visible watermarks in an adversarial way, have attracted increasing research interest. Modern watermark removal methods perform watermark localization and background restoration simultaneously, which could be viewed as a multi-task learning problem. However, existing approaches suffer from incomplete detected watermark and degraded texture quality of restored background. Therefore, we design a two-stage multi-task network to address the above issues. The coarse stage consists of a watermark branch and a background branch, in which the watermark branch self-calibrates the roughly estimated mask and passes the calibrated mask to background branch to reconstruct the watermarked area. In the refinement stage, we integrate multi-level features to improve the texture quality of watermarked area. Extensive experiments on two datasets demonstrate the effectiveness of our proposed method.
The Body Electric: A NIME designed through and with the somatic experience of singing

This paper presents the soma design process of creating Body Electric: a novel interface for the capture and use of biofeedback signals and physiological changes generated in the body by breathing, during singing.This NIME design is grounded in the performer's experience of, and relationship to, their body and their voice.We show that NIME design using principles from soma design can offer creative opportunities in developing novel sensing mechanisms, which can in turn inform composition and further elicit curious engagements between performer and artefact, disrupting notions of performer-led control.As contributions, this work 1) offers an example of NIME design for situated living, feeling, performing bodies, and 2) presents the rich potential of soma design as a path for designing in this context.
A Novel Plain-Text Related Image Encryption Algorithm Based on LB Compound Chaotic Map

Chaos systems have been widely used in image encryption algorithms. In this article, we introduce an LB (Logistic-Baker) compound chaotic map that can greatly improve the complexity of original Logistic map and Baker map, as well as the generated sequences have pseudo-randomness. Furthermore, based on the LB compound chaotic map, an image encryption algorithm is proposed. To resist the differential attack, and enhance the sensitivity of plain-text, the parameters of this algorithm are plain-text related. In this algorithm, the compound chaotic function is influenced by the plain-text image; thus, the specific form of this chaotic map, and its dynamics will be different when encrypting different images. Numerical experiment results indicate that the effect of this novel plain-text related image encryption scheme is excellent, as well as can be competitive with other corresponding algorithms.