Reviewerly/exHarmony huggingface dataset:
Accident Prediction Model Using Environmental Sensors for Industrial Internet of Things

We present an accident prediction model using environmental sensors for industrial Internet of Things (IIoT), with the aim of preventing various accidents that occur at construction sites.The model is expressed as association rules generated by analyzing data collected from environmental sensors that periodically measure the changes in their surrounding environment.To develop the prediction model, we conduct the following three steps: preprocessing, association rule generation, and visualization.In the preprocessing step, the continuous value within the dataset is converted into the categorical value.In the association rule generation step, the association rules used for the prediction model are generated to represent the relationship between the accident types and causes.Finally, in the visualization step, the generated association rules are visualized in the form of a matrix plot and network graph.To demonstrate the accident prediction model, we performed an experimental implementation using open-source R. The results show that the generated association rules enable the prediction of various accidents including heatstroke, asphyxiation, collapse, and fire on the basis of the environmental factors of the construction site.
Significance Tests for Neural Networks

We develop a pivotal test to assess the statistical significance of the feature variables in a single-layer feedforward neural network regression model. We propose a gradient-based test statistic and study its asymptotics using nonparametric techniques. Under technical conditions, the limiting distribution is given by a mixture of chi-square distributions. The tests enable one to discern the impact of individual variables on the prediction of a neural network. The test statistic can be used to rank variables according to their influence. Simulation results illustrate the computational efficiency and the performance of the test. An empirical application to house price valuation highlights the behavior of the test using actual data.
Focusing Attention Network for Answer Ranking

Answer ranking is an important task in Community Question Answering (CQA), by which “Good” answers should be ranked in the front of “Bad” or “Potentially Useful” answers. The state of the art is the attention-based classification framework that learns the mapping between the questions and the answers. However, we observe that existing attention-based methods perform poorly on complicated question-answer pairs. One major reason is that existing methods cannot get accurate alignments between questions and answers for such pairs. We call the phenomenon “attention divergence”. In this paper, we propose a new attention mechanism, called Focusing Attention Network(FAN), which can automatically draw back the divergent attention by adding the semantic, and metadata features. Our Model can focus on the most important part of the sentence and therefore improve the answer ranking performance. Experimental results on the CQA dataset of SemEval-2016 and SemEval-2017 demonstrate that our method respectively attains 79.38 and 88.72 on MAP and outperforms the Top-1 system in the shared task by 0.19 and 0.29.
Generalized Bayesian Canonical Correlation Analysis with Missing Modalities

Multi-modal learning aims to build models that can relate information from multiple modalities. One challenge of multi-modal learning is the prediction of a target modality based on a set of multiple modalities. However, there are two challenges associated with the goal: Firstly, collecting a large, complete dataset containing all required modalities is difficult; some of the modalities can be missing. Secondly, the features of modalities are likely to be high dimensional and noisy. To deal with these challenges, we propose a method called Generalized Bayesian Canonical Correlation Analysis with Missing Modalities. This method can utilize the incomplete sets of modalities. By including them in the likelihood function during training, it can estimate the relationships among the non-missing modalities and the feature space in the non-missing modality accurately. In addition, this method can work well on high dimensional and noisy features of modalities. This is because, by a probabilistic model based on the prior knowledge, it is strong against outliers and can reduce the amount of data necessary for the model learning even if features of modalities are high dimensional. Experiments with artificial and real data demonstrate our method outperforms conventional methods.
“If We Only Knew How You Feel”—A Comparative Study of Automated vs. Manual Classification of Opinions of Customers on Digital Media

The Web development has drastically changed the human interaction and communication, leading to an exponential growth of data generated by users in various digital media.This mass of data provides opportunities for understanding people's opinions about products, services, processes, events, political movements, and organizational strategies.In this context, it becomes important for companies to be able to assess customer satisfaction about their products or services.One of the ways to evaluate customer sentiment is the use of Sentiment Analysis, also known as Opinion Mining.This research aims to compare the efficiency of an automatic classifier based on dictionary with the classification by human jurors in a set of comments made by customers in Portuguese language.The data consist of opinions of service users of one of the largest Brazilian online employment agencies.The performance evaluation of the classification models was done using Kappa index and a Confusion Matrix.As the main finding, it is noteworthy that the agreement between the classifier and the human jurors came to moderate, with better performance for the dictionary-based classifier.This result was considered satisfactory, considering that the Sentiment Analysis in Portuguese language is a complex task and demands more research and development.
Obtaining conclusive information from incomplete experimental quantum tomography

We demonstrate that incomplete quantum tomography can give conclusive information in experimental realizations. We divide the state space into a union of multiple disjoint subsets and determine conclusively to which of the subsets a system, prepared in completely unknown state, belongs. In other words, we construct and solve membership problems. Our membership problems are partitions of the state space into a union of four disjoint sets formed by fixing two maximally entangled reference states and boundary values of a fidelity function ``radius'' between the reference states and the unknown preparation. We study the necessary and sufficient conditions of the measurements that solve these membership problems conclusively. We construct and experimentally implement such informationally incomplete measurement on two-photon polarization states with combined one-qubit measurements, and we solve the membership problem in example cases.
Sparse evolutionary Deep Learning with over one million artificial neurons on commodity hardware

Artificial Neural Networks (ANNs) have emerged as hot topics in the research community. Despite the success of ANNs, it is challenging to train and deploy modern ANNs on commodity hardware due to the ever-increasing model size and the unprecedented growth in the data volumes. Particularly for microarray data, the very-high dimensionality and the small number of samples make it difficult for machine learning techniques to handle. Furthermore, specialized hardware such as Graphics Processing Unit (GPU) is expensive. Sparse neural networks are the leading approaches to address these challenges. However, off-the-shelf sparsity inducing techniques either operate from a pre-trained model or enforce the sparse structure via binary masks. The training efficiency of sparse neural networks cannot be obtained practically. In this paper, we introduce a technique allowing us to train truly sparse neural networks with fixed parameter count throughout training. Our experimental results demonstrate that our method can be applied directly to handle high dimensional data, while achieving higher accuracy than the traditional two phases approaches. Moreover, we have been able to create truly sparse MultiLayer Perceptrons (MLPs) models with over one million neurons and to train them on a typical laptop without GPU, this being way beyond what is possible with any state-of-the-art techniques.
Nasogastric Tube Dislodgment Detection in Rehabilitation Patients Based on Fog Computing with Warning Sensors and Fuzzy Petri Net

The use of nasogastric (NG) tubes in acute, critical, and long-term care may lead to mechanical, infectious, and metabolic complications.NG intubation is a risk factor for aspiration and complications of organ injury.Mechanical complications include deliberate selfextubation and accidental extubation, both of which comprise unplanned extubation and occur in >35% of cases in rehabilitation rooms.Therefore, we intend to propose a digital warning tool to detect NG tube dislodgment over several days or weeks for a continuous insertion of the NG tube.On the basis of fog computing, integrating dexter-to-sinister light-controlled sensors and fuzzy Petri net (FPN) was performed to achieve the proposed assistant tool.The proposed intelligent algorithm can also be easily implemented using a high-level programming language (Language C/C++) in an embedded system.The experimental results demonstrated the feasibility of the algorithm under normal conditions and partial and NG two-tube dislodgments.
Means and covariances of photon numbers in multimode Gaussian states

In the field of continuous variables and particularly of Gaussian states an important role is played by the statistics of photon numbers. This issue has been considered in pioneering works for the one- and two-mode cases, but a systematic approach seems not to be available. In this paper, the most general multimode mixed Gaussian state, which is generated starting from a multimode thermal state and processed by the most general Gaussian unitary, is considered. In the $N$-mode the photon numbers are represented by $N$ random variables, one for each mode, and the target is the evaluation of the means and of the covariances. The means describe statistically the amount of photon numbers in each mode, while the covariances give the correlations between the photon numbers in the different modes. For both means and covariances a closed-form result is obtained, expressed in simple and compact formulas. The main tools are provided by the representation of Gaussian unitaries (given by a cascade of an $N$-mode squeeze operator, an $N$-mode rotation operator, and a parallel set of $N$ single-mode displacement operators) and by the corresponding Bogoliubov transformations.
Never Forget: Balancing Exploration and Exploitation via Learning Optical Flow

Exploration bonus derived from the novelty of the states in an environment has become a popular approach to motivate exploration for deep reinforcement learning agents in the past few years. Recent methods such as curiosity-driven exploration usually estimate the novelty of new observations by the prediction errors of their system dynamics models. Due to the capacity limitation of the models and difficulty of performing next-frame prediction, however, these methods typically fail to balance between exploration and exploitation in high-dimensional observation tasks, resulting in the agents forgetting the visited paths and exploring those states repeatedly. Such inefficient exploration behavior causes significant performance drops, especially in large environments with sparse reward signals. In this paper, we propose to introduce the concept of optical flow estimation from the field of computer vision to deal with the above issue. We propose to employ optical flow estimation errors to examine the novelty of new observations, such that agents are able to memorize and understand the visited states in a more comprehensive fashion. We compare our method against the previous approaches in a number of experimental experiments. Our results indicate that the proposed method appears to deliver superior and long-lasting performance than the previous methods. We further provide a set of comprehensive ablative analysis of the proposed method, and investigate the impact of optical flow estimation on the learning curves of the DRL agents.
High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributions

A novel machine learning optimization process coined Restrictive Federated Model Selection (RFMS) is proposed under the scenario, for example, when data from healthcare units can not leave the site it is situated on and it is forbidden to carry out training algorithms on remote data sites due to either technical or privacy and trust concerns. To carry out a clinical research under this scenario, an analyst could train a machine learning model only on local data site, but it is still possible to execute a statistical query at a certain cost in the form of sending a machine learning model to some of the remote data sites and get the performance measures as feedback, maybe due to prediction being usually much cheaper. Compared to federated learning, which is optimizing the model parameters directly by carrying out training across all data sites, RFMS trains model parameters only on one local data site but optimizes hyper-parameters across other data sites jointly since hyper-parameters play an important role in machine learning performance. The aim is to get a Pareto optimal model with respective to both local and remote unseen prediction losses, which could generalize well across data sites. In this work, we specifically consider high dimensional data with shifted distributions over data sites. As an initial investigation, Bayesian Optimization especially multi-objective Bayesian Optimization is used to guide an adaptive hyper-parameter optimization process to select models under the RFMS scenario. Empirical results show that solely using the local data site to tune hyper-parameters generalizes poorly across data sites, compared to methods that utilize the local and remote performances. Furthermore, in terms of dominated hypervolumes, multi-objective Bayesian Optimization algorithms show increased performance across multiple data sites among other candidates.
FastFusionNet: New State-of-the-Art for DAWNBench SQuAD

In this technical report, we introduce FastFusionNet, an efficient variant of FusionNet [12]. FusionNet is a high performing reading comprehension architecture, which was designed primarily for maximum retrieval accuracy with less regard towards computational requirements. For FastFusionNets we remove the expensive CoVe layers [21] and substitute the BiLSTMs with far more efficient SRU layers [19]. The resulting architecture obtains state-of-the-art results on DAWNBench [5] while achieving the lowest training and inference time on SQuAD [25] to-date. The code is available at https://github.com/felixgwu/FastFusionNet.
Corn quality identification using image processing with k-nearest neighbor classifier based on color and texture features

Corn is food crop commodity that is widely used, including as raw material for animal feed. Determination of corn quality at the farm level is often associated with drying time. This method has weaknesses, namely low efficiency, objectivity and level of consistency and also can lead to conflicts between traders and farmers. This study aims to identify the quality of corn using digital image processing based on color and texture features. This research uses Pertiwi-3 and Pertiwi-6 corn varieties. The corn quality identification system uses 7 features input (hue, saturation, value, contrast, correlation, energy, homogeneity) and KNN algorithm as classifiers. The number of image data used are 500 images with a test ratio of 70: 30. This research is able to classify the quality of corn into 10 quality categories. The highest accuracy is obtained at 90.00% when the k value (the nearest neighbor) is 5 and the distance calculation method is Cityblock.
Flux capacitors for JavaScript deloreans

Interactive visualizations have become an effective and pervasive mode of allowing users to explore the data in a visual, fluid, and immersive manner. While modern web, mobile, touch, and gesturedriven next-generation interfaces such as Leap Motion allow for highly interactive experiences, they pose unique and unprecedented workloads to the underlying data platform. Usually, these visualizations do not need precise results for most queries generated during an interaction, and the users require the intermediate results as feedback only to guide them towards their goal query. We present a middleware component - Flux Capacitor, that insulates the backend from bursty and query-intensive workloads. Flux Capacitor uses prefetching and caching strategies devised by exploiting the inherent physics-metaphor of UI widgets such as friction and inertia in range sliders, and typical characteristics of user-interaction. This enables low interaction response times while intelligently trading off accuracy
Line attractors of coupled ring neural networks with block circulant weight matrix

The continuous attractors of neural networks with symmetric connection weights have been studied widely. However, most of the matrices are asymmetric. In this paper, the model is coupled by some subnetworks. The connection weight matrix of each subnetwork is a circulant matrix, so the whole connection matrix is a block circulant matrix which is asymmetric. The conditions that guarantee this model with block circulant connection weight matrix to possess continuous attractor is obtained. Furthermore, the continuous attractor is a one-dimensional line attractor.
A Survey on Rice Crop Yield Prediction in India Using Improved Classification Technique

&lt;p&gt;India is an agricultural country. Agriculture is the important contributor to the Indian economy. There are many classification techniques like Support Vector Machine(SVM), LADTree, Natve Bayes, Bayesnet, K Nearest Neighbour(KNN), Locally Weighted Learning(LWL) on rice crop production datasets. They have some drawbacks like low accuracy and more errors. To achieve more significant result, To increase classification accuracy and reducing classification errors, our research uses classification method Bayesnet based adaboost will be proposed in work. Rice crop yield depend on environment's parameters like Rainfall, minimum temperature, average temperature, Maximum temperature, Vapour Pressure, potential evapotranspiration, reference crop evapotranspiration, cloud cover, wet day frequency for the kharif season. our dataset containing these environmental parameters for accurate prediction of Rice crop yield.&lt;/p&gt;
A Fine-Grained Sparse Accelerator for Multi-Precision DNN

Neural Networks (NNs) have made a significant breakthrough in many fields, while they also pose a great challenge to hardware platforms since the state-of-the-art neural networks are both communicational- and computational-intensive. Researchers proposed model compression algorithms using sparsification and quantization, along with specific hardware architecture designs, to accelerate various applications. However, the irregularity of memory access caused by the sparsity severely damages the regularity of intensive computation loops. Therefore, the architecture design for sparse neural networks is crucial to better software and hardware co-design for neural network applications. To face these challenges, this paper first analyzes the computation patterns of different NN structures and unify them into the form of sparse matrix-vector multiplication, sparse matrix-matrix multiplication, and element-wise multiplication. On the basis of the EIE which supports only the fully-connected network and recurrent neural network (RNN), we expand it to support the convolution neural network (CNN) using the input vector transform unit. This paper designs a multi-precision multiplier with supporting datapath, which makes the proposed architecture have a better acceleration effect in the low-bit quantization with the same hardware architecture. The proposed accelerator architecture can achieve the equivalent performance and energy efficiency up to 574.2 GOPS, 42.8 GOPS/W for CNN and 110.4 GOPS, 8.24 GOPS/W for RNN under 4-bit quantization on Xilinx XCKU115 FPGA running at 200MHz. And it is the state-of-the-art accelerator supporting CNN-RNN-based models like the long-term recurrent convolutional network with 571.1 GOPS performance and 42.6 GOPS/W energy efficiency under 4-bit data format.
Fast Object Localization via Sensitivity Analysis

Deep Convolutional Neural Networks (CNNs) have been repeatedly shown to perform well on image classification tasks, successfully recognizing a broad array of objects when given sufficient training data. Methods for object localization, however, are still in need of substantial improvement. In this paper, we offer a fundamentally different approach to the localization of recognized objects in images. Our method is predicated on the idea that a deep CNN capable of recognizing an object must implicitly contain knowledge about object location in its connection weights. We provide a simple method to interpret classifier weights in the context of individual classified images. This method involves the calculation of the derivative of network generated activation patterns, such as the activation of output class label units, with regard to each input pixel, performing a sensitivity analysis that identifies the pixels that, in a local sense, have the greatest influence on internal representations and object recognition. These derivatives can be efficiently computed using a single backward pass through the deep CNN classifier, producing a sensitivity map of the image. We demonstrate that a simple linear mapping can be learned from sensitivity maps to bounding box coordinates, localizing the recognized object. Our experimental results, using real-world data sets for which ground truth localization information is known, reveal competitive accuracy from our fast technique.
California’s exposure to volcanic hazards

Cover.California map showing the location of volcanoes ranked as moderate to very high threat according to the U.S. Geological Survey's (USGS) 2018 national volcanic threat assessment.California's volcanic
Developing BacaBicara: An Indonesian Lipreading System as an Independent Communication Learning for the Deaf and Hard-of-Hearing

Deaf and hard-of-hearing people have limitations in communication, espe-cially on aspects of language, intelligence, and social adjustment. To com-municate, deaf people use sign language or lipreading. For normal people, it is very difficult to use sign language. They have to memorize many hand signs. Therefore, lipreading is a necessary for communication between nor-mal and deaf people. In Indonesia, there is still few education media for deaf people to learn lipreading. To overcome this challenge, we develop a lipread-ing educational media to help deaf and hard-of-hearing to learn Bahasa In-donesia, called BacaBicara. User-Centered Design (UCD) is implemented to design the application and to analyze the constraints and conceptual models for the needs of users. This conceptual model uses the picture, lipreading video, text, and sign language to help the users understand the contents. The High fidelity prototype was implemented for evaluating usability testing. Based on the evaluation of the application, the results show that the proto-type matches the usability goals and the user experience.