Reviewerly/exHarmony huggingface dataset:
DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings

We propose DiffCSE, an unsupervised contrastive learning framework for learning sentence embeddings.DiffCSE learns sentence embeddings that are sensitive to the difference between the original sentence and an edited sentence, where the edited sentence is obtained by stochastically masking out the original sentence and then sampling from a masked language model.We show that DiffSCE is an instance of equivariant contrastive learning (Dangovski et al., 2021), which generalizes contrastive learning and learns representations that are insensitive to certain types of augmentations and sensitive to other "harmful" types of augmentations.Our experiments show that DiffCSE achieves state-of-the-art results among unsupervised sentence representation learning methods, outperforming unsupervised SimCSE 1 by 2.3 absolute points on semantic textual similarity tasks. 2
Suitability Of Influxdb Database For Iot Applications

Large amounts of data are generated every moment by connected objects creating Internet of Things (IoT). IoT isn’t about things; it’s about the data those things create and collect. Organizations rely on this data to provide better user experiences, to make smarter business decisions, and ultimately fuel their growth. However, none of this is possible without a reliable database that is able to handle the massive amounts of data generated by IoT devices. Relational databases are known for being flexible, easy to work with, and mature but they aren’t particularly known for is scale, which prompted the creation of NoSQL databases. Another thing to note is that IoT data is time-series in nature. In this paper we are discussed and compare about top five time-series database like InfluxDB, Kdb+, Graphite, Prometheus and RRDtool.
Multifractal analysis of temporal and spatial characteristics of earthquakes in Eurasian seismic belt

Abstract Seismic activity has complexity and randomness, and its temporal and spatial distribution has complexity, stage, level, and inheritance. The study of the temporal and spatial distribution characteristics of seismic activity is of great significance to the understanding of the law of seismic activity, such as the law that the time series of seismicity in the seismic belt is consistent with the complexity of geographical structure, the prediction of seismic risk, and other research related to earthquake. This article selects the seismic data catalog of the whole Eurasian seismic belt as the research object. Based on the characteristics of the seismic geological environment and tectonic environment characteristics, the multifractal analysis method is used for the seismic data of the seismic activity directory. The results show that the seismic activity of seismic zones has obvious multifractal structure of complex in time series and spatial scales, which can well reveal the seismic characteristics of seismic activity in time and space. In terms of time series, the study area <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msub> <m:mrow> <m:mi>D</m:mi> </m:mrow> <m:mrow> <m:mi mathvariant="normal">∞</m:mi> </m:mrow> </m:msub> </m:math> {D}_{{\rm{\infty }}} decreases significantly with time and energy before the occurrence of a large earthquake, and the time series of seismic activity in the study area is highly complex and highly correlated with the geological structure. Spatially, the spatial distribution of seismic intensity in the study area is infinite and sparse, showing the characteristics of infinite clustering. Therefore, it can reveal the basic rule of seismic activity effectively and lay a certain theoretical foundation for earthquake prevention and control in this seismic zone.
Generative and Discriminative Fuzzy Restricted Boltzmann Machine Learning for Text and Image Classification

The restricted Boltzmann machine (RBM) is an excellent generative learning model for feature extraction. By extending its parameters from real numbers to fuzzy ones, we have developed the fuzzy RBM (FRBM) which is demonstrated to possess better generative capability than RBM. In this paper, we first propose a generative model named Gaussian FRBM (GFRBM) to deal with real-valued inputs. Then, motivated by the fact that the discriminative variant of RBM can provide a self-contained framework for classification with competitive performance compared with some traditional classifiers, we establish the discriminative FRBM (DFRBM) and discriminative GFRBM (DGFRBM) that combine both the generative and discriminative facility by adding extra neurons next to the input units. Specifically, they can be trained into excellent stand-alone classifiers and retain outstanding generative capability simultaneously. The experimental results including text and image (both clean and noisy) classification indicate that DFRBM and DGFRBM outperform discriminative RBM models in terms of reconstruction and classification accuracy, and they behave more stable when encountering noisy data. Moreover, the proposed learning models show some promising advantages over other standard classifiers.
All-optical inhibitory dynamics in photonic neuron based on polarization mode competition in a VCSEL with an embedded saturable absorber

An all-optical spike inhibition scheme based on polarization-mode competition (PMC) in a vertical-cavity surface-emitting laser (VCSEL) with an embedded saturable absorber is proposed and investigated numerically. The inhibitory dynamics is characterized by spike amplitude and first-spike latency (FSL) for the first time, to the best of our knowledge. The effects of time differences between inhibitory and excitatory inputs, inputs strengths, bias current, as well as noise on the spike amplitude and FSL are examined. The results show that a spike can be triggered in the y-polarization mode by excitatory input and can be inhibited in the presence of inhibitory input due to PMC.
On-line prediction of ferrous ion concentration in goethite process based on self-adjusting structure RBF neural network

Outlet ferrous ion concentration is an essential indicator to manipulate the goethite process in the zinc hydrometallurgy plant. However, it cannot be measured on-line, which leads to the delay of this feedback information. In this study, a self-adjusting structure radial basis function neural network (SAS-RBFNN) is developed to predict the outlet ferrous ion concentration on-line. First, a supervised cluster algorithm is proposed to initialize the RBFNN. Then, the network structure is adjusted by the developed self-adjusting structure mechanism. This mechanism can merge or divide the hidden neurons according to the distance of the clusters to achieve the adaptability of the RBFNN. Finally, the connection weights are determined by the gradient-based algorithm. The convergence of the SAS-RBFNN is analyzed by the Lyapunov criterion. A simulation for a benchmark problem shows the effectiveness of the proposed network. The SAS-RBFNN is then applied to predict the outlet ferrous ion concentration in the goethite process. The results demonstrate that this network can provide a more accurate prediction than the mathematical model, even under the fluctuating production condition.
Reverse active learning based atrous DenseNet for pathological image classification

Due to the recent advances in deep learning, this model attracted researchers who have applied it to medical image analysis. However, pathological image analysis based on deep learning networks faces a number of challenges, such as the high resolution (gigapixel) of pathological images and the lack of annotation capabilities. To address these challenges, we propose a training strategy called deep-reverse active learning (DRAL) and atrous DenseNet (ADN) for pathological image classification. The proposed DRAL can improve the classification accuracy of widely used deep learning networks such as VGG-16 and ResNet by removing mislabeled patches in the training set. As the size of a cancer area varies widely in pathological images, the proposed ADN integrates the atrous convolutions with the dense block for multiscale feature extraction.The proposed DRAL and ADN are evaluated using the following three pathological datasets: BACH, CCG, and UCSB. The experiment results demonstrate the excellent performance of the proposed DRAL + ADN framework, achieving patch-level average classification accuracies (ACA) of 94.10%, 92.05% and 97.63% on the BACH, CCG, and UCSB validation sets, respectively.The DRAL + ADN framework is a potential candidate for boosting the performance of deep learning models for partially mislabeled training datasets.
Asking Clarification Questions in Knowledge-Based Question Answering

The ability to ask clarification questions is essential for knowledge-based question answering (KBQA) systems, especially for handling ambiguous phenomena.Despite its importance, clarification has not been well explored in current KBQA systems.Further progress requires supervised resources for training and evaluation, and powerful models for clarification-related text understanding and generation.In this paper, we construct a new clarification dataset, CLAQUA, with nearly 40K open-domain examples.The dataset supports three serial tasks: given a question, identify whether clarification is needed; if yes, generate a clarification question; then predict answers base on external user feedback.We provide representative baselines for these tasks and further introduce a coarse-to-fine model for clarification question generation.Experiments show that the proposed model achieves better performance than strong baselines.The further analysis demonstrates that our dataset brings new challenges and there still remain several unsolved problems, like reasonable automatic evaluation metrics for clarification question generation and powerful models for handling entity sparsity. 1* The work was done while Jingjing Xu and Yuechen Wang were interns in Microsoft Research, Asia.1 The dataset and code
Differentially Private High-Dimensional Data Publication in Internet of Things

Internet of Things and the related computing paradigms, such as cloud computing and fog computing, provide solutions for various applications and services with massive and high-dimensional data, while produces threatens on the personal privacy.Differential privacy is a promising privacy-preserving definition for various applications and is enforced by injecting random noise into each query result such that the adversary with arbitrary background knowledge cannot infer sensitive input from the noisy results.Nevertheless, existing differentially private mechanisms have poor utility and high computation complexity on high-dimensional data because the necessary noise in queries is proportional to the size of the data domain, which is exponential to the dimensionality.To address these issues, we develop a compressed sensing mechanism (CSM) that enforces differential privacy on the basis of the compressed sensing framework while providing accurate results to linear queries.We derive the utility guarantee of CSM theoretically.An extensive experimental evaluation on real-world datasets over multiple fields demonstrates that our proposed mechanism consistently outperforms several state-of-the-art mechanisms under differential privacy.
Virtual and Augmented Reality Applications to Support Data Analysis and Assessment of Science and Engineering

For decades, virtual reality (VR) and augmented reality (AR) technologies have remained absent from science and engineering workflows due to the custom-built nature of the hardware and software and oftentimes highly task-specific design. In addition to high cost, these characteristics create a critical entry barrier for most users to invest in VR and AR applications. Recent developments in both VR and AR technologies in the commercial sector have resulted in the availability of affordable consumer-grade and reliable VR and AR hardware and software tools. However, significant research is needed to adapt VR and AR technology use for data-intensive scientific and engineering applications such as network modeling research, human sciences research, and visualization of complex simulated environments. Our VR and AR applications provide examples of how scientists and engineers can use advanced visualization technologies to perform data analysis and assessment, thus transforming scientific discovery.
Natural language understanding approaches based on joint task of intent detection and slot filling for IoT voice interaction

Internet of Things (IoT) based voice interaction system, as a new artificial intelligence application, provides a new human–computer interaction mode. The more intelligent and efficient communication approach poses greater challenges to the semantic understanding module in the system. Facing with the complex and diverse interactive scenarios in practical applications, the academia and the industry urgently need more powerful Natural Language Understanding (NLU) methods as support. Intent Detection and Slot Filling joint task, as one of the core sub-tasks in NLU, has been widely used in different human–computer interaction scenarios. In the current era of deep learning, the joint task of Intent Detection and Slot Filling has also changed from previous rule-based methods to deep learning-based methods. It is an important problem to explore how to realize the models of these tasks to be refined and targeted designed, and to make the Intent Detection task better serve the improvement of precision of Slot Filling task by connecting the before and after tasks. It has great significance for building a more humanized IoT voice interaction system. In this study, we designed two joint models to realize Intent Detection and Slot Filling joint task. For the Intent Detection type task, one is based on BiGRU-Att-CapsuleNet (hybrid-based model) and the other is based on the RCNN model. Both methods use the BiGRU-CRF model for the Slot Filling type task. The hybrid-based model can enhance the semantic capture capability of a single model. And by combining specialized models built independently for each task to achieve a complete joint task, it can be better to achieve optimal performance on each task. This study also carried out detailed comparative experiments of tasks and joint tasks on multiple datasets. Experiments show that the joint models have achieved competitive results in 7 typical datasets included in multiple scenarios in English and Chinese compared with other models.
Median modified wiener filter for improving the image quality of gamma camera images

The filter technique was applied to noise images, as noise is the significant factor that cause poor image quality due to lower photon counting. The purpose of this study is to confirm that image quality can be improved using the median modified Wiener filter (MMWF) technique; this is achieved via a National Electrical Manufacturers Association International Electrotechnical Commission body phantom with four large spheres that are filled with the 99mTc radioisotope when evaluating the image quality. Conventional filters such as Wiener, Gaussian, and median filters were designed, and signal to noise ratio, coefficient of variation, and contrast to noise ratio were used as the evaluation parameters. The improvement in the image quality was in the following order, from the least to the highest improvement, in all cases: Wiener filter, Gaussian filter, median filter, and the MMWF technique. The results show that the image quality was improved from 20.6 to 65.5%, 7.4–40.3%, and 12.7–44.7% for the SNR, COV, and CNR values, respectively, when using the MMWF technique, compared with the use of conventional filters. In conclusion, our results demonstrated that the MMWF technique is useful for reducing the noise distribution in gamma camera images.
An Improved Method of Particle Swarm Optimization for Path Planning of Mobile Robot

The existing particle swarm optimization (PSO) algorithm has the disadvantages of application limitations and slow convergence speed when solving the problem of mobile robot path planning. This paper proposes an improved PSO integration scheme based on improved details, which integrates uniform distribution, exponential attenuation inertia weight, cubic spline interpolation function, and learning factor of enhanced control. Compared with other standard functions, our improved PSO (IPSO) can achieve better optimal results with less number of iteration steps than the different four path planning algorithms developed in the existing literature. IPSO makes the optimal path length with less than 20 iteration steps and reduces the path length and simulation time by 2.8% and 1.1 seconds, respectively.
Deep adaptive registration of multi-modal prostate images

Artificial intelligence, especially the deep learning paradigm, has posed a considerable impact on cancer imaging and interpretation. For instance, fusing transrectal ultrasound (TRUS) and magnetic resonance (MR) images to guide prostate cancer biopsy can significantly improve the diagnosis. However, multi-modal image registration is still challenging, even with the latest deep learning technology, as it requires large amounts of labeled transformations for network training. This paper aims to address this problem from two angles: (i) a new method of generating large amount of transformations following a targeted distribution to improve the network training and (ii) a coarse-to-fine multi-stage method to gradually map the distribution from source to target. We evaluate both innovations based on a multi-modal prostate image registration task, where a T2-weighted MR volume and a reconstructed 3D ultrasound volume are to be aligned. Our results demonstrate that the use of data generation can significantly reduce the registration error by up to 62%. Moreover, the multi-stage coarse-to-fine registration technique results in a mean surface registration error (SRE) of 3.66 mm (with the initial mean SRE of 9.42 mm), which is found to be significantly better than the one-step registration with a mean SRE of 4.08 mm.
Ethics and Good Practice in Computational Paralinguistics

With the advent of ‘heavy Artificial Intelligence’ – big data, deep learning, and ubiquitous use of the internet, ethical considerations are widely dealt with in public discussions and governmental bodies. Within Computational Paralinguistics with its manifold topics and possible applications (modelling of long-term, medium-term, and short-term traits and states such as personality, emotion, or speech pathology), we have not yet seen that many contributions. In this article, we try to set the scene by (1) giving a short overview of ethics and privacy, (2) describing the field of Computational Paralinguistics, its history and exemplary use cases, as well as (de-)anonymisation and peculiarities of speech and text data, and (3) proposing rules for good practice in the field, such as choosing the right performance measure, and accounting for representativity and interpretability.
Experimental wavelength-space division multiplexing of quantum key distribution with classical optical communication over multicore fiber

We demonstrate quantum key distribution (QKD) with classical signals in a seven-core fiber using dense wavelength division multiplexing. Quantum signals are transmitted in an outer core separately and intercore crosstalk (IC-XT) is the main impairment of them. In order to alleviate IC-XT, we propose a quantum-classical interleave scheme. Then the properties of IC-XT are analyzed based on the measurement results, which indicate counter-propagation is a better co-existence method than co-propagation. Finally, we perform QKD experiments in the presence of two classical channels with a channel spacing of 100 GHz between quantum channel and the nearest classical channels. The experiment results prove counter-propagation almost immune to IC-XT, which is consistent with our analysis. Also, the feasibility of the transmission over the range of metropolitan area networks is validated with our scheme.
Federated Learning Based on Dynamic Regularization

We propose a novel federated learning method for distributively training neural network models, where the server orchestrates cooperation between a subset of randomly chosen devices in each round. We view Federated Learning problem primarily from a communication perspective and allow more device level computations to save transmission costs. We point out a fundamental dilemma, in that the minima of the local-device level empirical loss are inconsistent with those of the global empirical loss. Different from recent prior works, that either attempt inexact minimization or utilize devices for parallelizing gradient computation, we propose a dynamic regularizer for each device at each round, so that in the limit the global and device solutions are aligned. We demonstrate both through empirical results on real and synthetic data as well as analytical results that our scheme leads to efficient training, in both convex and non-convex settings, while being fully agnostic to device heterogeneity and robust to large number of devices, partial participation and unbalanced data.
A novel social distancing analysis in urban public space: A new online spatio-temporal trajectory approach

Social distancing in public spaces plays a crucial role in controlling or slowing down the spread of coronavirus during the COVID-19 pandemic. Visual Social Distancing (VSD) offers an opportunity for real-time measuring and analysing the physical distance between pedestrians using surveillance videos in public spaces. It potentially provides new evidence for implementing effective prevention measures of the pandemic. The existing VSD methods developed in the literature are primarily based on frame-by-frame pedestrian detection, addressing the VSD problem from a static and local perspective. In this paper, we propose a new online multi-pedestrian tracking approach for spatio-temporal trajectory and its application to multi-scale social distancing measuring and analysis. Firstly, an online multi-pedestrian tracking method is proposed to obtain the trajectories of pedestrians in public spaces, based on hierarchical data association. Then, a new VSD method based on spatio-temporal trajectories is proposed. The proposed method not only considers the Euclidean distance between tracking objects frame-by-frame but also takes into account the discrete Fréchet distance between trajectories, hence forms a comprehensive solution from both static and dynamic, local and holistic perspectives. We evaluated the performance of the proposed tracking method using the public dataset MOT16 benchmark. We also collected our own pedestrian dataset “SCU-VSD” and designed a multi-scale VSD analysis scheme for benchmarking the performance of the social distancing monitoring in the crowd. Experiments have demonstrated that the proposed method achieved outstanding performance on the analysis of social distancing.
Evidential Reasoning With Hesitant Fuzzy Belief Structures for Human Activity Recognition

In the original belief function (BF) theory, a precise-valued belief structure has been widely used to represent uncertain information. However, this mentioned belief structure is difficult to effectively measure the specific hesitant situation, especially when decision makers have a set of possible values for the belief assignments of focal elements. In order to model the hesitant nature of the behavior of people to make a decision under uncertainty, we propose a hesitant fuzzy belief structure (HFBS) that is based on the BF theory and the recent hesitant fuzzy set theory. We also present the novel rule of combination of HFBS that is used and evaluated in a wearable human activity recognition (HAR) system coupled with an extreme learning machine. The evaluation of this new HFBS approach is done from two benchmark datasets. We clearly show its effectiveness and its superiority compared to various methods used classically for the wearable HAR.