Reviewerly/exHarmony huggingface dataset:
The ‘Impact Opportunity’ for Academic Libraries through Grey Literature

This paper proposes a new role for academic libraries as part of a wider ‘research practice’ activity for research institutions, incorporating support, training and expertise in relation to scholarly communication and research impact. The role libraries hold within research institutions is changing as the world shifts towards a digital and increasingly open future. This requires a rethink of the types of services and skill sets that are appropriate for an academic library to encompass. The increased focus of institutions and funders on the societal impact of research offers an opportunity for academic libraries to further integrate their work into the open research agenda. Libraries can draw on what is now over a decade of experience introducing open access, institutional repositories and research data management service to their academic communities to inform the development of impact services.An immediate service that libraries can offer is assisting with the identification of, and sometimes deposit into the institutional repository of, works that are sitting outside the peer reviewed literature – grey literature. This material needs to be collected for the purposes of demonstrating outcomes and pathways to impact. This paper describes the need to consider item classifications within digital repositories. If this new service is considered an option into the future, libraries themselves and potentially research offices will need to look not just at workflows but also item classifications within systems to ensure they encompass this broader collection of works.
Digital technology in modern education: risks and resources

Abstract The article discusses new digital opportunities in the educational process. It is concerned with the analysis of the characteristics of the modern didactics of vocational education in the context of the application of digital technologies in the educational process of secondary and higher education institutions. On the basis of the interviews conducted with the students, the risks of introducing digital technologies in the educational process are justified, ways of using the potential of digital technologies and changing the essence of the work of the pedagogue in the digital environment are proposed.
Hyperspectral Characteristics of Oil Sand, Part 1: Prediction of Processability and Froth Quality from Measurements of Ore

This study is the first of two companion papers using hyperspectral data to generate predictive models of oil sand ore and froth characteristics as a potential new means for process control. In Alberta, Canada, shallow oil sands deposits are accessed by surface mining and crushed ore is transported to a processing plant for extraction of bitumen using flotation processes. The ore displays considerable variability in clay, bitumen, and fines which affects their behavior in flotation units. Using oil sand ore spanning a range of bitumen and fines characteristics, flotation experiments were performed to generate froth in a batch extractor to determine ore processability (e.g., separation performance) and froth characteristics (color, bitumen, solids). From hyperspectral observations of ore, models can predict the %bitumen content and %fines (particle passing at 44 and 3.9 µm) of ore but the models with highest r2 (&gt;0.96) predict the solids/bitumen of froth and the processability of ore. Spectral observations collected on ore upstream of the separation vessels could therefore offer a first order assessment of froth quality for an ore blend before the ore enters the plant. These models could also potentially be used to monitor and control the performance of the blending process as another means to control the performance of the flotation process.
Predictive K-means with Local Models

Supervised classification can be effective for prediction but sometimes weak on interpretability or explainability (XAI). Clustering, on the other hand, tends to isolate categories or profiles that can be meaningful but there is no guarantee that they are useful for labels prediction. Predictive clustering seeks to obtain the best of the two worlds. Starting from labeled data, it looks for clusters that are as pure as possible with regards to the class labels. One technique consists in tweaking a clustering algorithm so that data points sharing the same label tend to aggregate together. With distance-based algorithms, such as k-means, a solution is to modify the distance used by the algorithm so that it incorporates information about the labels of the data points. In this paper, we propose another method which relies on a change of representation guided by class densities and then carries out clustering in this new representation space. We present two new algorithms using this technique and show on a variety of data sets that they are competitive for prediction performance with pure supervised classifiers while offering interpretability of the clusters discovered.
Aided Inertial Estimation of Wing Shape

Advanced large-wing-span aircraft result in more structural flexibility and the potential for instability or poor handling qualities. These shortcomings call for stability augmentation systems that entail active structural control. Consequently, the in-flight estimation of wing shape is beneficial for the control of very flexible aircraft. This paper proposes a new methodology for estimating flexible structural states based on extended Kalman filtering by exploiting ideas employed in aided inertial navigation systems. High-bandwidth-rate gyro angular velocities at different wing stations are integrated to provide a short-term standalone inertial shape estimation solution, and additional low-bandwidth aiding sensors are then employed to bound diverging estimation errors. The proposed filter implementation does not require a flight dynamics model of the aircraft, facilitates the often tedious Kalman filtering tuning process, and allows for accurate estimation under large and nonlinear wing deflections. To illustrate the approach, the technique is verified by means of simulations using sighting devices as aiding sensors, and an observability study is conducted. In contrast to previous work in the literature based on stereo vision, a sensor configuration that provides fully observable state estimation is found using only one camera and multiple rate gyros for Kalman filtering update and prediction phases, respectively.
Near-deterministic weak-value metrology via collective non-linearity

Abstract Weak-value amplification employs postselection to enhance the measurement of small parameters of interest. The amplification comes at the expense of reduced success probability, hindering the utility of this technique as a tool for practical metrology. Following other quantum technologies that display a quantum advantage, we formalize a quantum advantage in the success probability and present a scheme based on non-linear collective Hamiltonians that shows a super-extensive growth in success probability while simultaneously displaying an extensive growth in the weak value. We propose an experimental implementation of our scheme.
On completely homogeneous c-spaces

<h2>Abstract</h2> In this paper we determine completely homogeneous c-spaces. Various properties of completely homogeneous c-spaces are discussed. Relation between completely homogeneous c-space and hereditary homogeneous c-space is studied. Completely homogeneous connective spaces are also characterised.
Dynamic Range Compression and Detail Enhancement Algorithm Combined with PE for High Dynamic Range Infrared Images

To address the problem of noise suppression and contrast enhancement in enhancing the details of potential or small targets in infrared images with a high dynamic range,a dynamic range and detail enhancement algorithm based on a guided filter image is proposed.The base layer is compressed by a plateau-histogram equalization algorithm.The detail layer is denoised by a median filter first,and then the potential and small target details are enhanced by nonlinear mapping.Finally,the image is integrated according to a certain weight to obtain the enhanced details.Based on the subjective and objective experimental results,compared with algorithms based on mapping,histogram equalization,and a bilateral filter,this algorithm can improve the contrast of the target scene of the infrared image in the process of dynamic range reduction.The proposed algorithm can highlight the texture features and obtain a good detail enhancement effect.
Enhanced-visual-cryptography-based optical information hiding system

Recent years, with the rapid development of information technology, the information security has received more and more attention. A variety of encryption methods to protect the information have been reported. Visual cryptography is one of the encryption methods, which has highly security because of its threshold feature. And the cryptographic information can be explained by a naked eye in the decryption process. In the application of visual cryptography, however, each shared image is limited to transparency films and overlapping on computer. In our previous work, we proposed the scheme of invisible visual cryptography and developed the visual-cryptography-based optical hiding system (VCOH), which transformed the conventional visual cryptography shares into diffraction optical elements (DOEs). It not only increases the application range of visual cryptography, but also enhances security. In this paper, we propose an optical information hiding system based on the extended visual cryptography, which inherits the concept of invisible visual cryptography. In contrast to our previous work, the method proposed in this work can hide a meaningful image instead of text messages. Meanwhile, the capacity and imperceptibility of the method are greatly increased. The hiding process of the system contains two steps. Firstly, the secret image is converted into meaningful shares through the extended visual cryptography algorithm. Secondly, the meaningful shares are able to hide in phase-keys through an iterative phase retrieval algorithm, such as Gerchberg-Saxton algorithm and Yang-Gu iterative algorithm. Then the phase-keys can be made into diffraction optical elements (DOEs) to store and transport in a physical way. In the decryption process, DOEs are illuminated with the laser beam to reconstruct the meaningful shares. The secret image can be explained by the direct overlapping of the reconstructed shares without any optical or cryptographic knowledge. The simulation and optical experimental results show that the proposed method has good performance of security and validate the feasibility of the proposed method. Besides, in this paper the robustness and security issues are also analyzed. This system has a high security because of its indistinguishability under adaptive chosen ciphertext attack (IND-CCA2) security. Additionally, this system is relatively less robust than the VCOH because it shares meaningful images with highly complex and detailed structures.
The ADAPT Centre’s neural MT systems for the WAT 2020 document-level translation task

In this paper we describe the ADAPT Centre’s (Team ID: adapt-dcu) submissions to the WAT 2020 document-level Business Scene Dialogue (BSD) translation task. We only considered translating from Japanese to English for this task and secured the third position in the competition as per the rankings of the MT systems based on the human evaluation scores. The machine translation (MT) systems that we built for this task are state-of-the-art Trans- former models. In order to improve the translation quality of our neural MT (NMT) systems, we made use of both in-domain and out-of- domain data for training. We applied various data augmentation techniques for fine-tuning the model parameters. This paper outlines the experiments we carried out for this task and reports the MT systems’ performance on the evaluation test set.
Network with Sub-networks: Layer-wise Detachable Neural Network

We introduce network with sub-networks, a neural network which its weight layers could be detached into sub-neural networks during inference. To develop weights and biases which could be inserted in both base and sub-neural networks, firstly, the parameters are copied from sub-model to base-model. Each model is forward-propagated separately. Gradients from a pair of networks are averaged and, used to update both networks. Our base model achieves the test-accuracy which is comparable to the regularly trained models, while the model maintains the ability to detach weight layers.
On the Challenges of Open World Recognition Under Shifting Visual Domains

Robotic visual systems operating in the wild must act in unconstrained scenarios, under different environmental conditions while facing a variety of semantic concepts, including unknown ones. To this end, recent works tried to empower visual object recognition methods with the capability to i) detect unseen concepts and ii) extended their knowledge over time, as images of new semantic classes arrive. This setting, called Open World Recognition (OWR), has the goal to produce systems capable of breaking the semantic limits present in the initial training set. However, this training set imposes to the system not only its own semantic limits, but also environmental ones, due to its bias toward certain acquisition conditions that do not necessarily reflect the high variability of the real-world. This discrepancy between training and test distribution is called domain-shift. This work investigates whether OWR algorithms are effective under domain-shift, presenting the first benchmark setup for assessing fairly the performances of OWR algorithms, with and without domain-shift. We then use this benchmark to conduct analyses in various scenarios, showing how existing OWR algorithms indeed suffer a severe performance degradation when train and test distributions differ. Our analysis shows that this degradation is only slightly mitigated by coupling OWR with domain generalization techniques, indicating that the mere plug-and-play of existing algorithms is not enough to recognize new and unknown categories in unseen domains. Our results clearly point toward open issues and future research directions, that need to be investigated for building robot visual systems able to function reliably under these challenging yet very real conditions. Code available at https://github.com/DarioFontanel/OWR-VisualDomains
Characterization of delamination failure of two-dimensional horseshoe stretchable interconnects using digital image correlation

The purpose of this paper is to achieve efficient detection and prediction of delamination failure of stretchable interconnects by using digital image correlation (DIC) methods. In order to achieve this goal, a visual experiment system and a delamination failure detection and prediction method are designed, and the mechanical characteristics of two-dimensional horseshoe-shaped interconnect are experimentally studied. Firstly, the evolution law of von Mises strain on the surface of the sample was analyzed, and then the time and location of the failure were determined by measuring and calculating the strain at the failure site, and the site that was about to fail was predicted based on this criterion. The experimental results show that von Mises strain field has a distribution feature closely related to the two-dimensional horseshoe geometry, and the boundaries of each strain region are obvious. When a 2 μm layer thickness sample was stretched 3.2 mm at 50 °C, the sample delaminates at 60 pixels on the horizontal reference line, based on the 0.0187 strain difference at the failure point, the location where the failure is about to occur is successfully predicted.
Evaluation of Multiple Approaches to Estimate Daily Solar Radiation for Input to Crop Process Models

Daily solar radiation is a critical input for estimating plant growth and development, yet this variable is infrequently measured compared to other climate variables. This study evaluates the sensitivity of simulated maize and soybean production from the CERES-Maize and CROPGRO-Soybean modules of the Decision Support System for Agrotechnology Transfer (DSSAT) to daily solar radiation estimates obtained from traditional (stochastic, empirical, and mechanistic models) and non-traditional (satellite estimation, reanalysis datasets, and regional climate model simulations) approaches, using as an example radiation estimates for Hancock, Wisconsin, USA. When compared to observations, radiation estimates obtained from empirical and mechanistic models and a satellite-based dataset generally had smaller biases than other approaches. Daily solar radiation estimates from a reanalysis dataset and regional climate model simulations overestimate incoming daily solar radiation. When the radiation estimates were used as an input to CERES-Maize, no significant differences were found for maize yield obtained from the different radiation estimates compared to yield from observed radiation, even though differences were found in the daily values of leaf area index, crop evapotranspiration, and crop dry weight (biomass). In contrast, significant differences were found in simulated soybean yield from CROPGRO-Soybean for the majority of the radiation estimates.
Blockchain Technology in Healthcare Industry

Blockchain is one of the most growing technologies that is playing a vital role in the professional world today. Blockchain is the technology that is going to revolutionize many industries in the future including healthcare. It is simply defined as a decentralized, distributed ledger that records the provenance of a digital asset. Blockchain is used as a backbone for many industries such as cryptocurrencies, bitcoin, and many more. Although its capability and application have to be extended far beyond. The objective of blockchain is to allow digital information to be recorded and distributed, but not edited. In the last couple of years many industries finding new ways to implement blockchain technology with a wide range of domains. And this sudden increase in technology also provided many new application opportunities, including financial services, smart contracts, energy trading, supply chain, healthcare, etc. In this paper, we know briefly about blockchain technologies and some of their applications. We also show how blockchain is going to revolutionize the healthcare industry in the future.
A parallel evolutionary multiple-try metropolis Markov chain Monte Carlo algorithm for sampling spatial partitions

We develop an Evolutionary Markov Chain Monte Carlo (EMCMC) algorithm for sampling spatial partitions that lie within a large and complex spatial state space. Our algorithm combines the advantages of evolutionary algorithms (EAs) as optimization heuristics for state space traversal and the theoretical convergence properties of Markov Chain Monte Carlo algorithms for sampling from unknown distributions. Local optimality information that is identified via a directed search by our optimization heuristic is used to adaptively update a Markov chain in a promising direction within the framework of a Multiple-Try Metropolis Markov Chain model that incorporates a generalized Metropolis-Hasting ratio. We further expand the reach of our EMCMC algorithm by harnessing the computational power afforded by massively parallel architecture through the integration of a parallel EA framework that guides Markov chains running in parallel.
Optimal Entanglement Witnesses: A Scalable Data-Driven Approach

Multipartite entanglement is the key resource allowing quantum devices to outperform their classical counterparts, and entanglement certification is fundamental to assess any quantum advantage. The only scalable certification scheme relies on entanglement witnessing, typically effective only for special entangled states. Here we focus on finite sets of measurements on quantum states (hereafter called quantum data); and we propose an approach which, given a particular spatial partitioning of the system of interest, can effectively ascertain whether or not the data set is compatible with a separable state. When compatibility is disproved, the approach produces the optimal entanglement witness for the quantum data at hand. Our approach is based on mapping separable states onto equilibrium classical field theories on a lattice; and on mapping the compatibility problem onto an inverse statistical problem, whose solution is reached in polynomial time whenever the classical field theory does not describe a glassy system. Our results pave the way for systematic entanglement certification in quantum devices, optimized with respect to the accessible observables.
Online deep learning based on auto-encoder

Online learning is an important technical means for sketching massive real-time and high-speed data. Although this direction has attracted intensive attention, most of the literature in this area ignore the following three issues: (1) they think little of the underlying abstract hierarchical latent information existing in examples, even if extracting these abstract hierarchical latent representations is useful to better predict the class labels of examples; (2) the idea of preassigned model on unseen datapoints is not suitable for modeling streaming data with evolving probability distribution. This challenge is referred as "model flexibility". And so, with this in minds, the online deep learning model we need to design should have a variable underlying structure; (3) moreover, it is of utmost importance to fusion these abstract hierarchical latent representations to achieve better classification performance, and we should give different weights to different levels of implicit representation information when dealing with the data streaming where the data distribution changes. To address these issues, we propose a two-phase Online Deep Learning based on Auto-Encoder (ODLAE). Based on auto-encoder, considering reconstruction loss, we extract abstract hierarchical latent representations of instances; Based on predictive loss, we devise two fusion strategies: the output-level fusion strategy, which is obtained by fusing the classification results of encoder's each hidden layer; and feature-level fusion strategy, which is leveraged self-attention mechanism to fusion the every hidden layer's output. Finally, in order to improve the robustness of the algorithm, we also try to utilize the denoising auto-encoder to yield hierarchical latent representations. Experimental results on different datasets are presented to verify the validity of our proposed algorithm (ODLAE) outperforms several baselines.
Refining Yolov4 for Vehicle Detection

Real-time vehicle detection is a technology employed in applications like selfdriving cars, traffic camera surveillance. Every year we see better and updated stateof-the-art (SOTA) object detectors, but as those are trained on general-purpose datasets (like MS COCO), we miss out on targeted model improvements for vehicular data. The aim of this paper is to improve the newly released, YOLOv4 detector, specifically, for vehicle tracking applications using some existing methods such as optimising anchor box predictions by using k-means clustering. We also carefully hand-pick and verify some key techniques mentioned in the original paper, to optimise YOLOv4 as per the requirements of our dataset (UA-DETRAC).Our fine-tuned model is also compared with the existing models on a number of performance metrics such as - precision, recall, F1 score, mean average precision, and the average IoU. Our experimental results show that the SOTA model which already has real-time object detection capabilities can be further improved for highly targeted use cases. We urge the readers to expand the scope of the paper (and the original model) to other specific situations as well.