Reviewerly/exHarmony huggingface dataset:
Semantics-driven attentive few-shot learning over clean and noisy samples

Over the last couple of years, few-shot learning (FSL) has attracted significant attention towards minimizing the dependency on labeled training examples. An inherent difficulty in FSL is handling ambiguities resulting from having too few training samples per class. To tackle this fundamental challenge in FSL, we aim to train meta-learner models that can leverage prior semantic knowledge about novel classes to guide the classifier synthesis process. In particular, we propose semantically-conditioned feature attention and sample attention mechanisms that estimate the importance of representation dimensions and training instances. We also study the problem of sample noise in FSL, towards utilizing meta-learners in more realistic and imperfect settings. Our experimental results demonstrate the effectiveness of the proposed semantic FSL model with and without sample noise.
Enhancing Accuracy of Solar Power Forecasting by Input data Preprocessing and Competitive Model Selection Methods

This paper compares various prediction models and preprocessing methods based on data from the Kaggle competition "AMS 2013-2014 Solar Energy Prediction Contest". Four predictive models are used: Linear Regression (LR), Random Forest (RF), Gradient Boost Machine (GBM), and Extreme Gradient Boost (XGBOST). The forecasting accuracy of these four prediction models was compared by changing the preprocessing methods. There are four preprocessing methods proposed in this paper. First, training data is designed by averaging closest four points using the weighted average. Furthermore, training data is designed by averaging points within a circle using the weighted average. Second, various prediction intervals are tested. Third, we propose a data selection method by analyzing the correlation of each parameter. Fourth, the data interpolation is tested. Forecasting accuracy is measured by the mean absolute error.
Recursive LSTM for the Classification of Named Entity Recognition for Hindi Language

NER assumes a key part in Information Extraction from reports (for example email), conversational information, and so forth. Many tongue handling applications, for example, data recovery, question responding to, and machine interpretation, depend on NER. It tends to be challenging to determine the ambiguities of lexical components utilized in a text arrangement. There is too much work has been already done in English language but there is a need to improve accuracy for the NER in Hindi language. In this research researcher are minimize chances of misclassification by using different classifier namely location, name, weather etc. BiLSTM Development of a NER framework for Indian languages is a similarly troublesome task. In this paper, Researcher have done the different research to contrast the aftereffects of NER and typical implanting and quick text implanting layers to examinations the exhibition of word installing with various bunch sizes to prepare the profound learning models. In this paper, Researcher have done the different examinations to contrast the consequences of NER and typical implanting and quick text installing layers to investigations the presentation of word inserting with various group sizes to prepare the profound learning models. The value of the precision of proposed system architecture is 76.13% which is way more than other system architectures. Also, the value of recall and F1-score of proposed system architecture is 71.49 and 74.26 respectively. So, by comparing proposed system architecture with existing SpaCy, CoreNLP and NLTK it is easy to conclude that proposed system architecture is reliable in all the sense.
Efficient Proofs of Knowledge for Threshold Relations

Recently, there has been great interest towards constructing efficient zero-knowledge proofs for practical languages. In this work, we focus on proofs for threshold relations, in which the prover is required to prove knowledge of witnesses for k out of $$\ell $$ statements. The main contribution of our work is an efficient and modular transformation that starting from a large class of $$\varSigma $$ -protocols and a corresponding threshold relation $$\mathcal {R}_\mathsf {k,\ell }$$ , provides an efficient $$\varSigma $$ -protocol for $$\mathcal {R}_\mathsf {k,\ell }$$ with improved communication complexity w.r.t. prior results. Our transformation preserves statistical/perfect honest-verifier zero knowledge.
Image Quality Classification for Automated Visual Evaluation of Cervical Precancer

Image quality control is a critical element in the process of data collection and cleaning. Both manual and automated analyses alike are adversely impacted by bad quality data. There are several factors that can degrade image quality and, correspondingly, there are many approaches to mitigate their negative impact. In this paper, we address image quality control toward our goal of improving the performance of automated visual evaluation (AVE) for cervical precancer screening. Specifically, we report efforts made toward classifying images into four quality categories (“unusable”, “unsatisfactory”, “limited”, and “evaluable”) and improving the quality classification performance by automatically identifying mislabeled and overly ambiguous images. The proposed new deep learning ensemble framework is an integration of several networks that consists of three main components: cervix detection, mislabel identification, and quality classification. We evaluated our method using a large dataset that comprises 87,420 images obtained from 14,183 patients through several cervical cancer studies conducted by different providers using different imaging devices in different geographic regions worldwide. The proposed ensemble approach achieved higher performance than the baseline approaches.
Texture Image Analysis for Larger Lattice Structure using Orthogonal Polynomial framework

An Orthogonal Polynomial Framework using 3 x 3 mathematical model has been proposed and attempted for the textureanalysis by L.Ganesan and P.Bhattacharyya during 1990. They proposed this frame work which was unified to address both edgeand texture detection. Subsequently, this work has been extended for different applications by them and by different authors overa period of time. Now the Orthogonal Polynomial Framework has been shown effective for larger grid size of (5 x 5) or (7 x 7) orhigher, to analyze textured surfaces. The image region (5 x 5) under consideration is evaluated to be textured or untextured usinga statistical approach. Once the image region is concluded to be textured, it is proposed to be described by a local descriptor,called pro5num, computed by a simple coding scheme on the individual pixels based on their computed significant variances. Thehistogram of all the pro5nums computed over the entire image, called pro5spectrum, is considered to be the global descriptor.The novelty of this scheme is that it can be used for discriminating the region under consideration is micro or macro texture,based on the range of values in the global descriptor. This method works fine for many standard texture images. The works usingthe proposed descriptors for many texture analysis problems with (5 x5) including higher grid size and applications are underprogress
360TourGuiding: Towards Virtual Reality Training for Tour Guiding

Tour guiding plays an important role in turning sightseeing tours into memorable experiences. Tour guides, especially inexperienced ones, must practice intensively to perfect their craft. It is key that guides acquire knowledge about sights, in-situ presentation skills, and perfection ability to interact with and engage tourists. Therefore, tour-guide education requires on-site training at the place of interest including live tourist audiences. However, for modest budgets, such setups are costly and tourism students have to practice tour guiding at home or in simulated class-room setups. It has become a challenge for students to adequately prepare themselves for jobs in terms of relevant knowledge and skills. To tackle this problem, we propose 360TourGuiding, a VR system enabling its users to practice tour guiding with 360 travel videos plus the attendance of remote audiences participating through their mobile and personal device. This paper reports on the concept, on our design, current implementation, and on a pilot study with the current 360TourGuiding prototype. Based on qualitative feedback gained through the pilot study, we discuss possible system improvements, future system updates, and plans for empirical evaluation.
sMolBoxes: Dataflow Model for Molecular Dynamics Exploration

We present sMolBoxes, a dataflow representation for the exploration and analysis of long molecular dynamics (MD) simulations. When MD simulations reach millions of snapshots, a frame-by-frame observation is not feasible anymore. Thus, biochemists rely to a large extent only on quantitative analysis of geometric and physico-chemical properties. However, the usage of abstract methods to study inherently spatial data hinders the exploration and poses a considerable workload. sMolBoxes link quantitative analysis of a user-defined set of properties with interactive 3D visualizations. They enable visual explanations of molecular behaviors, which lead to an efficient discovery of biochemically significant parts of the MD simulation. sMolBoxes follow a node-based model for flexible definition, combination, and immediate evaluation of properties to be investigated. Progressive analytics enable fluid switching between multiple properties, which facilitates hypothesis generation. Each sMolBox provides quick insight to an observed property or function, available in more detail in the bigBox View. The case study illustrates that even with relatively few sMolBoxes, it is possible to express complex analyses tasks, and their use in exploratory analysis is perceived as more efficient than traditional scripting-based methods.
Of shadows and gaps in spatial search

Spatial search occurs in a connected graph if a continuous-time quantum walk on the adjacency matrix of the graph, suitably scaled, plus a rank-one perturbation induced by any vertex will unitarily map the principal eigenvector of the graph to the characteristic vector of the vertex. This phenomenon is a natural continuous-time analogue of Grover search. The spatial search is said to be optimal if it occurs with constant fidelity and in time inversely proportional to the shadow of the target vertex on the principal eigenvector. Extending a result of Chakraborty \etal ({\em Physical Review A}, {\bf 102}:032214, 2020), we prove a simpler characterization of optimal spatial search. Based on this characterization, we observe that some families of distance-regular graphs, such as Hamming and Grassmann graphs, have optimal spatial search. We also show a matching lower bound on time for spatial search with constant fidelity, which extends a bound due to Farhi and Gutmann for perfect fidelity. Our elementary proofs employ standard tools, such as Weyl inequalities and Cauchy determinant formula.
Nexus of Advanced Technology Platforms for Strengthening Cyber-Defense Capabilities

The spectrum of current threat vectors is far more complex now than ever before. The current threat vectors are kinetic, asymmetric, dual-use, and hybrid, which renders it difficult to assess and, even, distinguish from a routine event. The threats not only jeopardize the security posture but also cyber defense capabilities, the very line of defense that is designed to protect against such threats. Countering threat vectors in multiple domains, viz., social, physical, and informational, is a major challenge and requires technology augmentation to assess, act and thwart such persistent and pervasive threats. The use of artificial intelligence (AI), machine learning (ML), data analytics (DA), and deep learning (DL) provides tools that enhance cyber defense capabilities. In addition, certain initiatives on decision support systems to enhance resiliency, such as heuristics expert elicitations and analysis, and also capacity building through technology accelerator networks are discussed to enhance capacity and capability in support of cyber-defense. The use of technology platforms can present certain challenges, which are described.
Contact tracing as an essential prevention tool for the spreading of COVID-19 among healthcare workers.

Healthcare workers, since the beginning of the COVID-19 pandemic, have been universally recognized as especially susceptible, with infection risks higher than the general population. Therefore, it is important to apply infection prevention and control measures in healthcare institutions to limit infections associated with the provision of healthcare services. In this contest, contact tracing, in fact, is an essential public health measure to fight the COVID-19 pandemic, with active case finding and testing, and physical distancing. Globally, new weekly cases and deaths are decreased, but WHO emphasizes that cases and deaths should be interpreted with caution because several countries have been progressively changing COVID-19 testing strategies, resulting in lower overall numbers of tests performed and consequently lower numbers of cases detected. Health personnel, therefore, work at a high-risk department and this has been associated with a 2.13 times higher risk of COVID-19 compared to the general departments of any hospital and, if the healthcare workers had inability to continue working and if there was the possibility of hospitals turning into sources of infection, it could be an interruption in healthcare services. A coordination with the system of surveillance is important that remains in the hospital in order to fight COVID-19 and help restructure the response in the light of detected transmission and guarantee safe hospital care.
Histology segmentation using active learning on regions of interest in oral cavity squamous cell carcinoma

In digital pathology, deep learning has been shown to have a wide range of applications, from cancer grading to segmenting structures like glomeruli. One of the main hurdles for digital pathology to be truly effective is the size of the dataset needed for generalization to address the spectrum of possible morphologies. Small datasets limit classifiers' ability to generalize. Yet, when we move to larger datasets of whole slide images (WSIs) of tissue, these datasets may cause network bottlenecks as each WSI at its original magnification can be upwards of 100 000 by 100 000 pixels, and over a gigabyte in file size. Compounding this problem, high quality pathologist annotations are difficult to obtain, as the volume of necessary annotations to create a classifier that can generalize would be extremely costly in terms of pathologist-hours. In this work, we use Active Learning (AL), a process for iterative interactive training, to create a modified U-net classifier on the region of interest (ROI) scale. We then compare this to Random Learning (RL), where images for addition to the dataset for retraining are randomly selected. Our hypothesis is that AL shows benefits for generating segmentation results versus randomly selecting images to annotate. We show that after 3 iterations, that AL, with an average Dice coefficient of 0.461, outperforms RL, with an average Dice Coefficient of 0.375, by 0.086.
A Comparison of Spatiotemporal Visualizations for 3D Urban Analytics

Recent technological innovations have led to an increase in the availability of 3D urban data, such as shadow, noise, solar potential, and earthquake simulations. These spatiotemporal datasets create opportunities for new visualizations to engage experts from different domains to study the dynamic behavior of urban spaces in this under explored dimension. However, designing 3D spatiotemporal urban visualizations is challenging, as it requires visual strategies to support analysis of time-varying data referent to the city geometry. Although different visual strategies have been used in 3D urban visual analytics, the question of how effective these visual designs are at supporting spatiotemporal analysis on building surfaces remains open. To investigate this, in this paper we first contribute a series of analytical tasks elicited after interviews with practitioners from three urban domains. We also contribute a quantitative user study comparing the effectiveness of four representative visual designs used to visualize 3D spatiotemporal urban data: spatial juxtaposition, temporal juxtaposition, linked view, and embedded view. Participants performed a series of tasks that required them to identify extreme values on building surfaces over time. Tasks varied in granularity for both space and time dimensions. Our results demonstrate that participants were more accurate using plot-based visualizations (linked view, embedded view) but faster using color-coded visualizations (spatial juxtaposition, temporal juxtaposition). Our results also show that, with increasing task complexity, plot-based visualizations perform better in preserving efficiency (time, accuracy) compared to color-coded visualizations. Based on our findings, we present a set of takeaways with design recommendations for 3D spatiotemporal urban visualizations for researchers and practitioners. Lastly, we report on a series of interviews with four practitioners, and their feedback and suggestions for further work on the visualizations to support 3D spatiotemporal urban data analysis.
A Comparative Survey of Consensus Algorithms Based on Proof of Work

Blockchain is the foundation of cryptocurrencies and many other industries such as healthcare, supply logistics, and so on. It is a system of distributed ledger that is now attracting lot of research attention. Peer-to-peer and cryptography technologies are essential components of blockchain, as are consensus procedures that ensure blockchain systems’ transparency, decentralization, and security. The Proof of Work (PoW) consensus protocol is now adopted by most blockchain systems, although other variations are available. We examine PoW and its six variants and analyze their pros, cons, scalability, maintenance cost, block generation time, transaction cost, energy consumption, validator selection criteria, mining profitability, and 51% attack in this study.
Trusted Threat Intelligence Sharing in Practice and Performance Benchmarking through the Hyperledger Fabric Platform

Historically, threat information sharing has relied on manual modelling and centralised network systems, which can be inefficient, insecure, and prone to errors. Alternatively, private blockchains are now widely used to address these issues and improve overall organisational security. An organisation's vulnerabilities to attacks might change over time. It is utterly important to find a balance among a current threat, the potential countermeasures, their consequences and costs, and the estimation of the overall risk that this provides to the organisation. For enhancing organisational security and automation, applying threat intelligence technology is critical for detecting, classifying, analysing, and sharing new cyberattack tactics. Trusted partner organisations can then share newly identified threats to improve their defensive capabilities against unknown attacks. On this basis, organisations can help reduce the risk of a cyberattack by providing access to past and current cybersecurity events through blockchain smart contracts and the Interplanetary File System (IPFS). The suggested combination of technologies can make organisational systems more reliable and secure, improving system automation and data quality. This paper outlines a privacy-preserving mechanism for threat information sharing in a trusted way. It proposes a reliable and secure architecture for data automation, quality, and traceability based on the Hyperledger Fabric private-permissioned distributed ledger technology and the MITRE ATT&CK threat intelligence framework. This methodology can also be applied to combat intellectual property theft and industrial espionage.
Chinese relation extraction based on characters and words fusion

Chinese relation extraction is designed to extract the relation between a given entity pair from unstructured text. Neither existing character input-based nor word-input methods can take full advantage of the semantic information in the data. Although studies have explored this, there are still problems with the lack of semantic information and the low speed of reasoning. To address the issues, we propose FCW-BERT: Fuse Characters and Words-BERT for Chinese RE, which add word information in a flat structure as input, and enhance the interaction between characters and words with a relative position encoding. Furthermore, we utilize TextCNN for further local feature extraction, experiments on two real-world datasets in distinct domains show consistent and significant superiority and robustness of our model, as compared with other baselines.
On the Privacy Risks of Cell-Based NAS Architectures

Existing studies on neural architecture search (NAS) mainly focus on efficiently and effectively searching for network architectures with better performance. Little progress has been made to systematically understand if the NAS-searched architectures are robust to privacy attacks while abundant work has already shown that human-designed architectures are prone to privacy attacks. In this paper, we fill this gap and systematically measure the privacy risks of NAS architectures. Leveraging the insights from our measurement study, we further explore the cell patterns of cell-based NAS architectures and evaluate how the cell patterns affect the privacy risks of NAS-searched architectures. Through extensive experiments, we shed light on how to design robust NAS architectures against privacy attacks, and also offer a general methodology to understand the hidden correlation between the NAS-searched architectures and other privacy risks.
Automatic Meta-Path Discovery for Effective Graph-Based Recommendation

Heterogeneous Information Networks (HINs) are labeled graphs that depict relationships among different types of entities (e.g., users, movies and directors). For HINs,meta-path-based recommenders (MPRs) utilize meta-paths (i.e., abstract paths consisting of node and link types) to predict user preference, and have attracted a lot of attention due to their explainability and performance. We observe that the performance of MPRs is highly sensitive to the meta-paths they use, but existing works manually select the meta-paths from many possible ones. Thus, to discover effective meta-paths automatically, we propose the Reinforcement learning-based Meta-path Selection (RMS) framework. Specifically, we define a vector encoding for meta-paths and design a policy network to extend meta-paths. The policy network is trained based on the results of downstream recommendation tasks and an early stopping approximation strategy is proposed to speed up training. (RMS) is a general model, and it can work with all existing MPRs. We also propose a new MPR called RMS-HRec, which uses an attention mechanism to aggregate information from the meta-paths. We conduct extensive experiments on real datasets. Compared with the manually selected meta-paths, the meta-paths identified by (RMS) consistently improve recommendation quality. Moreover, RMS-HRec outperforms state-of-the-art recommender systems by an average of 7% in hit ratio. The codes and datasets are available on https://github.com/Stevenn9981/RMS-HRec.