Reviewerly/exHarmony huggingface dataset:
Learning Dense Representations of Phrases at Scale

Open-domain question answering can be reformulated as a phrase retrieval problem, without the need for processing documents on-demand during inference (Seo et al., 2019).However, current phrase retrieval models heavily depend on sparse representations and still underperform retriever-reader approaches.In this work, we show for the first time that we can learn dense representations of phrases alone that achieve much stronger performance in opendomain QA.We present an effective method to learn phrase representations from the supervision of reading comprehension tasks, coupled with novel negative sampling methods.We also propose a query-side fine-tuning strategy, which can support transfer learning and reduce the discrepancy between training and inference.On five popular open-domain QA datasets, our model DensePhrases improves over previous phrase retrieval models by 15%-25% absolute accuracy and matches the performance of state-of-the-art retriever-reader models.Our model is easy to parallelize due to pure dense representations and processes more than 10 questions per second on CPUs.Finally, we directly use our pre-indexed dense phrase representations for two slot filling tasks, showing the promise of utilizing DensePhrases as a dense knowledge base for downstream tasks.
A Survey of Self-Sovereign Identity Ecosystem

Self-sovereign identity is the next evolution of identity management models. This survey takes a journey through the origin of identity, defining digital identity and progressive iterations of digital identity models leading up to self-sovereign identity. It then states the relevant research initiatives, platforms, projects, and regulatory frameworks, as well as the building blocks including decentralized identifiers, verifiable credentials, distributed ledger, and various privacy engineering protocols. Finally, the survey provides an overview of the key challenges and research opportunities around self-sovereign identity.
see: An R Package for Visualizing Statistical Models

The see package is embedded in the easystats ecosystem, a collection of R packages that operate in synergy to provide a consistent and intuitive syntax when working with statistical models in the R programming language (R Core Team, 2021).Most easystats packages return comprehensive numeric summaries of model parameters and performance.The see package complements these numeric summaries with a host of functions and tools to produce a range of publication-ready visualizations for model parameters, predictions, and performance diagnostics.As a core pillar of easystats, the see package helps users to utilize visualization for more informative, communicable, and well-rounded scientific reporting.
Why and How Savitzky–Golay Filters Should Be Replaced

Savitzky-Golay (SG) filtering, based on local least-squares fitting of the data by polynomials, is a popular method for smoothing data and calculations of derivatives of noisy data. At frequencies above the cutoff, SG filters have poor noise suppression; this unnecessarily reduces the signal-to-noise ratio, especially when calculating derivatives of the data. In addition, SG filtering near the boundaries of the data range is prone to artifacts, which are especially strong when using SG filters for calculating derivatives of the data. We show how these disadvantages can be avoided while keeping the advantageous properties of SG filters. We present two classes of finite impulse response (FIR) filters with substantially improved frequency response: (i) SG filters with fitting weights in the shape of a window function and (ii) convolution kernels based on the sinc function with a Gaussian-like window function and additional corrections for improving the frequency response in the passband (modified sinc kernel). Compared with standard SG filters, the only price to pay for the improvement is a moderate increase in the kernel size. Smoothing at the boundaries of the data can be improved with a non-FIR method, the Whittaker-Henderson smoother, or by linear extrapolation of the data, followed by convolution with a modified sinc kernel, and we show that the latter is preferable in most cases. We provide computer programs and equations for the smoothing parameters of these smoothers when used as plug-in replacements for SG filters and describe how to choose smoothing parameters to preserve peak heights in spectra.
The impact of a blockchain platform on trust in established relationships: a case study of wine supply chains

Purpose This paper examines the impact of a blockchain platform on the role and importance of trust in established buyer-supplier relationships. Design/methodology/approach A literature review provides insight into trust development in supply chains. Research uses a case study of two wine supply chains: the producers, importers, logistics companies and UK Government agencies. Semi-structured interviews determine how trust and trustworthiness develop in buyer-supplier relationships and the impact of a blockchain-based technology proof of concept on supply chain trust. Findings A blockchain-based platform introduces common trusted data, reducing data duplication and improving supply chain visibility. The platform supports trust building between parties but does not replace the requirements for organisations to establish a position of trust. Contrary to literature claims for blockchain trustless disintermediation, new intermediaries are introduced who need to be trusted. Research limitations/implications The case study presents challenges specific to UK customs borders, and research needs to be repeated in different contexts to establish if findings are generalisable. Practical implications A blockchain-based platform can improve supply chain efficiency and trust development but does not remove the need for trust and trust-building processes. Blockchain platform providers need to build a position of trust with all participants. Originality/value Case study research shows how blockchain facilitates but does not remove trust, trustworthiness and trust relationships in established supply chains. The reduction in information asymmetry and improved supply chain visibility provided by blockchain does not change the importance of trust in established buyer-supplier relationships or the trust-based policy of the UK Government at the customs border.
Augmentation-Free Self-Supervised Learning on Graphs

Inspired by the recent success of self-supervised methods applied on images, self-supervised learning on graph structured data has seen rapid growth especially centered on augmentation-based contrastive methods. However, we argue that without carefully designed augmentation techniques, augmentations on graphs may behave arbitrarily in that the underlying semantics of graphs can drastically change. As a consequence, the performance of existing augmentation-based methods is highly dependent on the choice of augmentation scheme, i.e., augmentation hyperparameters and combinations of augmentation. In this paper, we propose a novel augmentation-free self-supervised learning framework for graphs, named AFGRL. Specifically, we generate an alternative view of a graph by discovering nodes that share the local structural information and the global semantics with the graph. Extensive experiments towards various node-level tasks, i.e., node classification, clustering, and similarity search on various real-world datasets demonstrate the superiority of AFGRL. The source code for AFGRL is available at https://github.com/Namkyeong/AFGRL.
k-means as a variational EM approximation of Gaussian mixture models

We show that k-means (Lloyd’s algorithm) is obtained as a special case when truncated variational EM approximations are applied to Gaussian mixture models (GMM) with isotropic Gaussians. In contrast to the standard way to relate k-means and GMMs, the provided derivation shows that it is not required to consider Gaussians with small variances or the limit case of zero variances. There are a number of consequences that directly follow from our approach: (A) k-means can be shown to increase a free energy (a.k.a. ELBO) associated with truncated distributions and this free energy can directly be reformulated in terms of the k-means objective; (B) k-means generalizations can directly be derived by considering the 2nd closest, 3rd closest etc. cluster in addition to just the closest one; and (C) the embedding of k-means into a free energy framework allows for theoretical interpretations of other k-means generalizations in the literature. In general, truncated variational EM provides a natural and rigorous quantitative link between k-means-like clustering and GMM clustering algorithms which may be very relevant for future theoretical and empirical studies.
Are we done with object recognition? The iCub robot’s perspective

We report on an extensive study of the benefits and limitations of current deep learning approaches to object recognition in robot vision scenarios, introducing a novel dataset used for our investigation. To avoid the biases in currently available datasets, we consider a natural human–robot interaction setting to design a data-acquisition protocol for visual object recognition on the iCub humanoid robot. Analyzing the performance of off-the-shelf models trained off-line on large-scale image retrieval datasets, we show the necessity for knowledge transfer. We evaluate different ways in which this last step can be done, and identify the major bottlenecks affecting robotic scenarios. By studying both object categorization and identification problems, we highlight key differences between object recognition in robotics applications and in image retrieval tasks, for which the considered deep learning approaches have been originally designed. In a nutshell, our results confirm the remarkable improvements yield by deep learning in this setting, while pointing to specific open challenges that need be addressed for seamless deployment in robotics.
State estimation under non-Gaussian Lévy and time-correlated additive sensor noises: A modified Tobit Kalman filtering approach

The Tobit Kalman filter (TKF) is a powerful tool in solving the state estimation problem for linear systems with censored measurements. This paper is concerned with the Tobit Kalman filtering problem for discrete time-varying systems subject to non-Gaussian Lévy and time-correlated additive measurement noises. By referencing to the measurement differencing method, the time-correlation of the measurement noises is transformed into the cross-correlation between the equivalent measurement noise and the process noise. Then, by resorting to the Lévy-Ito theorem, the non-Gaussian Lévy measurement noises are transformed into equivalent Gaussian noises with unknown covariances. Based on the transformed Gaussian measurement noises, a modified recursive TKF is designed where the unknown noise covariances are carefully calculated. Simulation results are provided to illustrate the effectiveness of the proposed filter.
Cascaded Cavities Boost the Indistinguishability of Imperfect Quantum Emitters

Recently, Grange et al. [Phys. Rev. Lett. 114, 193601 (2015)PRLTAO0031-900710.1103/PhysRevLett.114.193601] showed the possibility of single-photon generation with a high indistinguishability from a quantum emitter despite strong pure dephasing, by "funneling" emission into a photonic cavity. Here, we show that a cascaded two-cavity system can further improve the photon characteristics and greatly reduce the Q factor requirement to levels achievable with present-day technology. Our approach leverages recent advances in nanocavities with an ultrasmall mode volume and does not require ultrafast excitation of the emitter. These results were obtained by numerical and closed-form analytical models with strong emitter dephasing, representing room-temperature quantum emitters.
Entanglement-enhanced optical gyroscope

Abstract Fiber optic gyroscopes (FOG) based on the Sagnac effect are a valuable tool in sensing and navigation and enable accurate measurements in applications ranging from spacecraft and aircraft to self-driving vehicles such as autonomous cars. As with any classical optical sensors, the ultimate performance of these devices is bounded by the shot-noise limit (SNL). Quantum-enhanced interferometry allows us to overcome this limit using non-classical states of light. Here, we report on an entangled-photon gyroscope that uses path-entangled NOON-states ( N = 2) to provide super-resolution and phase sensitivity beyond the shot-noise limit.
FA-RPN: Floating Region Proposals for Face Detection

We propose a novel approach for generating region proposals for performing face detection. Instead of classifying anchor boxes using features from a pixel in the convolutional feature map, we adopt a pooling-based approach for generating region proposals. However, pooling hundreds of thousands of anchors which are evaluated for generating proposals becomes a computational bottleneck during inference. To this end, an efficient anchor placement strategy for reducing the number of anchor-boxes is proposed. We then show that proposals generated by our network (Floating Anchor Region Proposal Network, FA-RPN) are better than RPN for generating region proposals for face detection. We discuss several beneficial features of FA-RPN proposals (which can be enabled without re-training) like iterative refinement, placement of fractional anchors and changing size/shape of anchors. Our face detector based on FA-RPN obtains 89.4% mAP with a ResNet-50 backbone on the WIDER dataset.
A HOG-SVM Based Fall Detection IoT System for Elderly Persons Using Deep Sensor

The population of elderly persons continues to grow at a high rate, and fall accidents in elderly persons have become a major public health problem. Highly developed IoT technology and machine learning enable the use of multimedia devices in a wide variety of elderly person's protection areas. In this paper, a HOG-SVM based fall detection IoT system for elderly persons is proposed. To ensure privacy and in order to be robust to changes of the light intensity, deep sensor is employed instead of RGB camera to get the binary images of elderly persons. The persons are detected and tracked by Microsoft Kinect SDK, and the unwanted noise is reduced by noise reduction algorithm. After obtaining the denoised binary images, the features of persons are extracted by histogram of oriented gradient and the image classification is performed for judging the fall status by the liner support vector machine. If a fall is detected, the IoT system sends alert to the hospital or family members. This study builds a data set which includes 3500 images, and the experimental results show that the proposed method outperforms related works in terms of accuracy.
Diversity-Sensitive Conditional Generative Adversarial Networks

We propose a simple yet highly effective method that addresses the mode-collapse problem in the Conditional Generative Adversarial Network (cGAN). Although conditional distributions are multi-modal (i.e., having many modes) in practice, most cGAN approaches tend to learn an overly simplified distribution where an input is always mapped to a single output regardless of variations in latent code. To address such issue, we propose to explicitly regularize the generator to produce diverse outputs depending on latent codes. The proposed regularization is simple, general, and can be easily integrated into most conditional GAN objectives. Additionally, explicit regularization on generator allows our method to control a balance between visual quality and diversity. We demonstrate the effectiveness of our method on three conditional generation tasks: image-to-image translation, image inpainting, and future video prediction. We show that simple addition of our regularization to existing models leads to surprisingly diverse generations, substantially outperforming the previous approaches for multi-modal conditional generation specifically designed in each individual task.
Genomic Data Compression

Recently, there has been growing interest in genome sequencing, driven by advances in sequencing technology, in terms of both efficiency and affordability. These developments have allowed many to envision whole-genome sequencing as an invaluable tool for both personalized medical care and public health. As a result, increasingly large and ubiquitous genomic data sets are being generated. This poses a significant challenge for the storage and transmission of these data. Already, it is more expensive to store genomic data for a decade than it is to obtain the data in the first place. This situation calls for efficient representations of genomic information. In this review, we emphasize the need for designing specialized compressors tailored to genomic data and describe the main solutions already proposed. We also give general guidelines for storing these data and conclude with our thoughts on the future of genomic formats and compressors.
Application Research of an Innovative Online Education Model in Big Data Environment

Online education is a network-based approach to teaching. It is a method of content dissemination and rapid learning through the application of UGC and Internet technology.Compared with traditional school education, online learning can obtain more resources, more autonomy, and no longer limited time and space for learning.Through the questionnaire, this paper finds that learners in the online education model still have some shortcomings in the learning process.For example, the learning process is not durable.Therefore, this paper uses neural network classification algorithm to analyze the related factors that affect the learning behavior of online education students.And propose corresponding control strategies for different influencing factors.By constructing a learning process control strategy model for large educational data, to help learners improve their learning efficiency, help the online education model break through the bottleneck, the online education industry has maintained rapid development.Finally, through the comparative analysis of the improved online education model and the traditional online education model, finding an improved online education model can better improve students' interest in learning.Provided a reference for the development of online education,It also provides a reference for the transformation and upgrading of traditional education to online education.
DuerQuiz

In talent recruitment, the job interview aims at selecting the right candidates for the right jobs through assessing their skills and experiences in relation to the job positions. While tremendous efforts have been made in improving job interviews, a long-standing challenge is how to design appropriate interview questions for comprehensively assessing the competencies that may be deemed relevant and representative for person-job fit. To this end, in this research, we focus on the development of a personalized question recommender system, namely DuerQuiz, for enhancing the job interview assessment. DuerQuiz is a fully deployed system, in which a knowledge graph of job skills, Skill-Graph, has been built for comprehensively modeling the relevant competencies that should be assessed in the job interview. Specifically, we first develop a novel skill entity extraction approach based on a bidirectional Long Short-Term Memory (LSTM) with a Conditional Random Field (CRF) layer (LSTM-CRF) neural network enhanced with adapted gate mechanism. In particular, to improve the reliability of extracted skill entities, we design a label propagation method based on more than 10 billion click-through data from the large-scale Baidu query logs. Furthermore, we discover the hypernym-hyponym relations between skill entities and construct the Skill-Graph by leveraging the classifier trained with extensive contextual features. Finally, we design a personalized question recommendation algorithm based on the Skill-Graph for improving the efficiency and effectiveness of job interview assessment. Extensive experiments on real-world recruitment data clearly validate the effectiveness of DuerQuiz, which had been deployed for generating written exercises in the 2018 Baidu campus recruitment event and received remarkable performances in terms of efficiency and effectiveness for selecting outstanding talents compared with a traditional non-personalized human-only assessment approach.
Bilingual Lexicon Induction through Unsupervised Machine Translation

A recent research line has obtained strong results on bilingual lexicon induction by aligning independently trained word embeddings in two languages and using the resulting crosslingual embeddings to induce word translation pairs through nearest neighbor or related retrieval methods.In this paper, we propose an alternative approach to this problem that builds on the recent work on unsupervised machine translation.This way, instead of directly inducing a bilingual lexicon from cross-lingual embeddings, we use them to build a phrasetable, combine it with a language model, and use the resulting machine translation system to generate a synthetic parallel corpus, from which we extract the bilingual lexicon using statistical word alignment techniques.As such, our method can work with any word embedding and cross-lingual mapping technique, and it does not require any additional resource besides the monolingual corpus used to train the embeddings.When evaluated on the exact same cross-lingual embeddings, our proposed method obtains an average improvement of 6 accuracy points over nearest neighbor and 4 points over CSLS retrieval, establishing a new state-of-the-art in the standard MUSE dataset.
Combining RSA and audio steganography on personal computers for enhancing security

This paper proposed an enhanced system for securing sensitive text data on personal computer benefitting from the combination of both techniques: cryptography and steganography. The system security is generated by involving RSA cryptography followed by audio-based steganography as two sequential layers to ensure the best possible security gaining the advantages from both. The study modeled the system and implemented it to be tested to explore the relation between security, capacity, and data dependency. The experimentations covered securing data within 15 differently sized audios showing interesting results. The results gave enhancement in capacity versus security trade-off, allowing the user and application to be the decision maker in the choice. The work showed the possibility of accepting security of 1-LSB, 2-LSB, and 3-LSB methods and their reasonable effect on the cover. The study also shows that using 3-LSB is giving acceptable security benefitting extra capacity more than 1-LSB and 2-LSB found in the literature.
Speech Technology Progress Based on New Machine Learning Paradigm

Speech technologies have been developed for decades as a typical signal processing area, while the last decade has brought a huge progress based on new machine learning paradigms. Owing not only to their intrinsic complexity but also to their relation with cognitive sciences, speech technologies are now viewed as a prime example of interdisciplinary knowledge area. This review article on speech signal analysis and processing, corresponding machine learning algorithms, and applied computational intelligence aims to give an insight into several fields, covering speech production and auditory perception, cognitive aspects of speech communication and language understanding, both speech recognition and text-to-speech synthesis in more details, and consequently the main directions in development of spoken dialogue systems. Additionally, the article discusses the concepts and recent advances in speech signal compression, coding, and transmission, including cognitive speech coding. To conclude, the main intention of this article is to highlight recent achievements and challenges based on new machine learning paradigms that, over the last decade, had an immense impact in the field of speech signal processing.