Reviewerly/exHarmony huggingface dataset:
Adapted Receptive Field Temporal Convolutional Networks with Bar-Shaped Structures Tailored to Industrial Process Operation Models

Recurrent neural networks (RNNs) have been predominately employed to deal with industrial process operation modeling problems that are hard to be described by first-principles approaches. However, the substantial amount of iterative calculations in RNNs dramatically increases the training difficulty and reduces the speed of the network convergence. Thus, RNNs are an increasingly ineffective method when handling a large amount of historical operation data with a long operating horizon. In contrast, convolutional neural networks (CNNs) are known for fast computation but have rarely been utilized to account for time series information. To adopt the benefit of CNNs to handle the industrial time series trend prediction, in this study, we propose an adapted receptive field temporal convolutional network with bar-shaped structures (BS-ARFTCN). Based on the temporal convolutional network (TCN), a reshaped CNN structure, BS-ARFTCN, replaces the convolution windows with the translation-only sliding bar-shaped windows tailored to the specific dimensionality and functionality. As a result, this network allows the states to be predicted by its historical information, specified by the adapted receptive field in the TCN, in a computationally efficient manner. In addition, the Tennessee Eastman Process is used to validate the performance of the BS-ARFTCN algorithm, in which the result is compared with that of the long short-term memory network and the RNN. The BS-ARFTCN algorithm is demonstrated to provide a more accurate prediction with less computational resources and training time.
Scalable Hyperparameter Selection for Latent Dirichlet Allocation

Latent Dirichlet allocation (LDA) is a heavily used Bayesian hierarchical model used in machine learning for modeling high-dimensional sparse count data, for example, text documents. As a Bayesian model, it incorporates a prior on a set of latent variables. The prior is indexed by some hyperparameters, which have a big impact on inference regarding the model. The ideal estimate of the hyperparameters is the empirical Bayes estimate which is, by definition, the maximizer of the marginal likelihood of the data with all the latent variables integrated out. This estimate cannot be obtained analytically. In practice, the hyperparameters are chosen either in an ad-hoc manner, or through some variants of the EM algorithm for which the theoretical basis is weak. We propose an MCMC-based fully Bayesian method for obtaining the empirical Bayes estimate of the hyperparameter. We compare our method with other existing approaches both on synthetic and real data. The comparative experiments demonstrate that the LDA model with hyperparameters specified by our method outperforms models with the hyperparameters estimated by other methods. Supplementary materials for this article are available online.
Assessment of Common Side Channel Countermeasures With Respect To Deep Learning Based Profiled Attacks

A recent line of research has investigated a new profiling technique based on deep learning as an alternative to the well-known template attack. The related published works have demonstrated that Deep Learning based Side-Channel Attacks (DL-SCA) are very efficient when targeting cryptographic implementations protected with the common side-channel countermeasures such as Boolean masking, jitter and random delays insertion. In this paper, we assess the efficiency of this new profiling attack when targeting other commonly involved countermeasures. First, we target a more complex masking scheme based on Shamir's secret sharing and prove that this new profiling approach is still performing well. Second, we conduct a security evaluation of two side-channel countermeasures (shuffling and 1-amongst- N) against DL-SCA. The simulation and practical experiments prove, as expected, that these countermeasures are also vulnerable to these profiling attacks.
Artificial Neural Network as Ensemble Technique Fuser for Improving Classification Accuracy

Ensemble learning is one of the highly accurate and robust learning approaches. In these approaches, different classifiers are used as an ensemble technique, and then the main questions that faces up is how to fuse the results of each individual classifier into a final decision. In this paper, we will propose a fuser based on an Artificial Neural Network to produce the final decision. our proposed approach has been experimentally validated on three public datasets (available in UCI repository). The first dataset is called Wisconsin Prognosis Breast Cancer (WPBC) dataset. This dataset has 35 attributes and 198 instances. The second dataset is called Breast Cancer Wisconsin (Original) DataSet. This dataset has 10 attributes and 699 instances. The third dataset is called Diabetic Retinopathy Debrecen DataSet. This dataset has 20 attributes with and 1151 instances. Our proposed approach gives higher accuracy in these datasets. The accuracy of our approach is 82.7% with the (WPBC) DataSet, 98.5% with the Breast Cancer Wisconsin (Original) DataSet and 75% with Diabetic Retinopathy Debrecen DataSet.
Data-Driven Solar Generation Forecast Considering Temporal Characteristics of Data

Compared to traditional generating resources, distributed Photovoltaic (PV) resources are highly intermittent in nature. Hence, ensuring reliability under high penetration of PV systems would require additional reserves, purchase of power from real-time markets, or renewable curtailments, all of which dramatically increase cost of power generation and transmission. Accurate PV forecasting can eliminate the need for these costly measures and is necessary for seamless grid operations with high levels of solar PV systems. PV forecasting, however, is challenged by the fact that PV generation behavior differs in different regions and under different weather patterns. Hence, it is critical to consider both the spatial and temporal characteristics of the data when developing a data-driven forecast model. This paper seeks to provide a comparison of different methods for forecasting solar irradiance, using data from the Syracuse area, with diverse weather patterns that challenge PV forecasting. For a large dataset, a new approach to forecasting based on Symbolic Aggregate Approximation is introduced which enables increasing the calculation speed and reducing the dimensions for accurate forecasting.
Information Matrix Fusion for Nonlinear, Asynchronous and Heterogeneous Systems

The Information Matrix Fusion (IMF) algorithm is extended from linear, homogeneous and synchronous systems to nonlinear, asynchronous (with arbitrary local tracker sampling times for full rate as well as reduced rate communication) and heterogeneous systems. In this case, the heterogeneous estimates from local trackers (LT) are in different state spaces with various dimensions and are related by a nonlinear relationship with no inverse transformation. The main application of these results is the fusion of tracks from radar and IR/EO (infrared/electrooptical) sensors, which estimate target states that are nonlinearly related and of different dimensions with nonlinear filters. Different from Track-to-track Fusion, the IMF does not require the cross-covariance between the estimation errors. The performance of the proposed algorithm is shown via simulation based on Monte-Carlo runs.
Comparison of the Scleral Search Coil and Video-Oculography Techniques for Three-Dimensional Eye Movement Measurement

The coil signals were processed by dual-phase detectors and three-dimensional eye position components were calculated using a custom algorithm. A related artifact was observed when the wire from the coil is close to either of the eye lids. Contact with the eyelid will limit mechanically the range of accurate measurement, causing saturation and distortion effects in the torsional component. Correct positioning of the coil is therefore of great importance, especially in subjects with droopy eye lids. The scleral search coil induction coils were fixed to the sled chassis, so that eye movements were measured relative to the sled. For this reason the Video-oculography camera was also fixed to the subject helmet, which in turn was fixed to the sled. This effectively minimises any discrepancy between the several systems due to head rotation.
Solving word sense disambiguation problem using combinatorial PSO

In natural language processing, the problem of finding the intended meaning or “sense” of a word which is activated by the use of that word in a particular context is generally known as word sense disambiguation (WSD) problem. The solution to this problem impacts many other fields of natural langua ge processing including sentiment analysis and machine translation. Here, WSD problem is modelled as a combinatorial optimization problem where the goal is to find a sequence of meanings or senses that maximizes the semantic meaning among the targeted words. In this work, an algorithm is proposed that uses a combinatorial version of particle swarm optimization algorithm for solving WSD problem. The test results show that the algorithm performs better than existing methods.
A Novel Grip Force Cognition Scheme for Robot-Assisted Minimally Invasive Surgery

Obtaining a grip force in robot-assisted minimally invasive surgery (RMIS) has always been a crucial research topic. A novel grip force cognition scheme is proposed in this article. We utilize the dynamic analysis of the cable-driven system to conduct feature engineering and add prior knowledge to the Gaussian process regression (GPR). Specifically, the feature and GPR model candidates are acquired by analyzing the dynamic characteristics first. After that, a wrapper searching method combined with exhaustive search (ES) and sequential backward selection (SBS) is proposed to determine the features and GPR model. The hyperparameters are optimized by differential evolution (DE) by minimizing the negative log marginal likelihood (NLML). Besides, the training set is extended by including several different objects to avoid the algorithm “learning” the object property. Our method is verified on a cable-driven platform to ensure the errors are all derived from the algorithm rather than the cable tension loss. Our method reduces the errors on our testing set by about 64% and outperforms several popular model-based and learning-based methods. This article illustrates the significance to integrate the system's characteristics when introducing machine learning techniques into robot systems.
Medical diagnosis and treatment is <i>NP-complete</i>

There is great interest in the pursuit of process-driven, algorithmic allocation of health-care resources, including computer-aided medical diagnosis and treatment (MDT). Little is understood regarding the computational complexity of MDT; however, and if determined, how relevant the complexity would be to automating MDT. We approach analysing the computational complexity of MDT in several ways: (1) proving that MDT is computationally intractable (NP-complete, NPC) by reducing the travelling salesperson (TSP) and set-cover (SETCOVER) problems to MDT, (2) showing, in contrast, an example of MDT in tractable form, and (3) showing that, leaving aside TSP and SETCOVER, there are computationally efficient, heuristic-driven search methods based on human diagnostician methods. Calculations show that the sparseness of actual symptom-treatments sets in the space of all possible sets is astronomical (e.g., 10−204,678). While the computational complexity of MDT is interesting theoretically, pragmatically uncovering actual human cognitive practices of MDT will accelerate the development of artificial intelligence to assist and eventually replace physicians.
Improving text simplification by corpus expansion with unsupervised learning

Automatic sentence simplification aims to reduce the complexity of vocabulary and expressions in a sentence while retaining its original meaning. We constructed a simplification model that does not require a parallel corpus using an unsupervised translation model. In order to learn simplification by unsupervised manner, we show that pseudo-corpus is constructed from the web corpus and that the corpus expansion contributes to output more simplified sentences. In addition, we confirm that it is possible to learn the operation of simplification by preparing large-scale pseudo data even if there is non-parallel corpus for simplification.
CNN inference simulator for accurate and efficient accelerator design

Tiling techniques and bit quantization are one of the popular optimization methods for designing convolutional neural networks (CNNs) inference accelerators as they provide extremely high efficiency for limited hardware resources. In this paper, we present a simulator for designing CNN accelerator using these optimization techniques. The simulator provides intermediate and final results that support development of the design strategy for accuracy-guaranteed bit-width and dynamic fixed point with user defined tiling factor.
Evaluating Usability of Permissioned Blockchain for Internet-of-Battlefield Things Security

Military technology is ever-evolving to increase the safety and security of soldiers on the field while integrating Internet-of-Things solutions to improve operational efficiency in mission oriented tasks in the battlefield. Centralized communication technology is the traditional network model used for battlefields and is vulnerable to denial of service attacks, therefore suffers performance hazards. They also lead to a central point of failure, due to which, a flexible model that is mobile, resilient, and effective for different scenarios must be proposed. Blockchain offers a distributed platform that allows multiple nodes to update a distributed ledger in a tamper-resistant manner. The decentralized nature of this system suggests that it can be an effective tool for battlefields in securing data communication among Internet-of-Battlefield Things (IoBT). In this paper, we integrate a permissioned blockchain, namely Hyperledger Sawtooth, in IoBT context and evaluate its performance with the goal of determining whether it has the potential to serve the performance needs of IoBT environment. Using different testing parameters, the metric data would help in suggesting the best parameter set, network configuration and blockchain usability views in IoBT context. We show that a blockchain-integrated IoBT platform has heavy dependency on the characteristics of the underlying network such as topology, link bandwidth, jitter, and other communication configurations, that can be tuned up to achieve optimal performance.
Stochastic Modeling and Optimal Control for Automated Overtaking

This paper proposes a solution to the overtaking problem where an automated vehicle tries to overtake a human-driven vehicle, which may not be moving at a constant velocity. Using reachability theory, we first provide a robust time-optimal control algorithm to guarantee that there is no collision throughout the overtaking process. Following the robust formulation, we provide a stochastic reachability formulation that allows a trade-off between the conservative overtaking time and the allowance of a small collision probability. To capture the stochasticity of a human driver's behavior, we propose a new martingale-based model where we classify the human driver as aggressive or nonaggressive. We show that if the human driver is nonaggressive, our stochastic time-optimal control algorithm can provide a shorter overtaking time than our robust algorithm, whereas if the human driver is aggressive, the stochastic algorithm will act on a collision probability of zero, which will match the robust algorithm. Finally, we detail a simulated example that illustrates the effectiveness of the proposed algorithms.
Research on the Architecture of Iot Middleware Platform Based on BeiDou Navigation Satellite System

This paper introduces the composition, function and characteristics of BeiDou satellite navigation and positioning system, analyzes the feasibility and unique advantages of the development of BeiDou Navigation Satellite System and the Internet of things middleware based on the characteristics of both positioning, communication and functions and the application of iot middleware platform. This paper introduces the existing application cases of BeiDou system, and expounds the bright prospect that the iot middleware platform and BeiDou system complement and promote each other.
Online Signature Profiling using Generative Adversarial Networks

A signature is an ability learned by humans from an elementary age. The skill to generate one's own exclusive signature along with imitating another writer's signature is a challenging and complex task. In real time scenarios like E-Commerce and M-Commerce payments, user verification based on online signatures constrain the verification framework needs to be trained extensively with huge samples, which unfeasible to obtain. Hence, as a solution, in this paper, we propose a first its of kind of attempt in which an intelligent framework tries to learn the online signatures of a writer using Deep Generative Adversarial Networks (DGANs). Thorough experimental analysis on three widely used datasets MCYT-100, SVC, SUSIG confirms the supremacy of the method and boost confidence in real time deployment of our framework in data centric applications like offline signature verification, forged document detection, etc.
Action Recognition in Sports Videos Using Stacked Auto Encoder and HOG3D Features

Sports analytics is an emerging area of research with applications to personalized training and entertainment. In this work, an approach for sports action recognition using stacked autoencoder and HOG3D features is presented. We demonstrate that actions in sports videos can be recognized by 2D interpretation of HOG3D features, extracted from the bounding box of the player as input to a deep learning model. The ability of a stacked autoencoder to learn the underlying global patterns associated with each action is used to recognize human actions. We demonstrate the efficacy of the proposed classification system for action recognition on ACASVA dataset.
An Efficient Framework for Object Tracking in Video Surveillance

Visual tracking using object-video frames has drawn a lot of attention after the emerging advances of computer vision applications, for example, controlling, navigation, event detection, object modeling, and automatic inspection. The process of locating the moving object or multiple objects over time in a given video sequence is known as video tracking. Applications of video tracking were human–computer interaction, video communication and video compression, augmented reality, medical imaging, traffic control, video editing, and surveillance. It is considered as a time-consuming process due to the amount of data that are contained in the video. A lot of techniques emerged in research in order to track the objects effectively, but there is a lack in accuracy. Since there is a lack in accuracy and other parameters related to processing techniques, the researchers involved in video tracking field face problems in tracking the targeted object due to the presence of shadow in the video data set. So it is necessary to eliminate the shadows of the targeted object in the video. Our proposed method is used to eliminate the shadow pixels and to get the result with better accuracy.
Hand Gesture Based Music Player Control in Vehicle

Music players are mostly used in vehicle which required interaction between human and music player system for access and control. It is reported that handling music player system in car while driving is one of the reason to distract drivers. This leads to severe accidents which are possible to life threatening. Nowadays many branded automobiles have adaptive human machine interaction system which is very expensive. To overcome the issue, hand gesture is the key to control music player system in vehicles. We developed hand gesture based music player control system in car using accelerometer sensor and PIC microcontroller. The setup is compact so that it can easily accommodate in music player location in car, less weighty and mainly affordable to general public. This system helps to access all functions of music player like change modes (i.e. FM/USB/Auxiliary/MP3 player), forward, rewind and play, stop and scan operations of songs playlist. The response time to all above functions is very quick as it uses RF trans-receivers. Our system show outperforming results about identifying accurate functions to its respective gestures and will not rely on post-processing techniques as in case of gesture recognition using image processing techniques.
Classification of causes of speech recognition errors using attention-based bidirectional long short-term memory and modulation spectrum

In this paper, we address the problem of classifying four common utterance characteristics related to the utterance speed, which cause speech recognition errors. We previously proposed bidirectional long short-term memory (BLSTM) as a classifier and the modulation spectrum as an acoustic feature. However, the performance of it is still insufficient, since BLSTM classified the utterance characteristics from the overall utterance, while most of the recognition errors resulted from utterance characteristics occur in only a small part of utterance. In this paper, we propose an approach to enhance classifier by using attention mechanism (attention-based BLSTM). Attention-based BLSTM enables the classifier to weight each frame according to its importance instead of directly measuring overall information from the speech. Furthermore, we investigate the correspondence of utterance characteristics to different modulation spectrum block lengths. To evaluate the performance of the proposed method, we conducted a classification experiment on Japanese conversational speeches with four different utterance characteristics: `fast', `slow', `filler', and `stutter'. As a result, the proposed method improved the F-score by 0.033-0.129 compared with the previously proposed method using BLSTM. This result confirms the effectiveness of attention-based BLSTM in classifying cause of errors based on utterance characteristics.
Let's build Bridges, not Walls: SPARQL Querying of TinkerPop Graph Databases with Sparql-Gremlin

This article presents sparql-gremlin, a tool to translate SPARQL queries to Gremlin pattern matching traversals. Currently, sparql-gremlin is a plugin of the Apache TinkerPop graph computing framework, thus the users can run queries expressed in the W3C SPARQL query language over a wide variety of graph data management systems, including both OLTP graph databases and OLAP graph processing frameworks. With sparql-gremlin, we perform the first step to bridge the query interoperability gap between the Semantic Web and Graph database communities. The plugin has received adoption from both academia and industry research in its short timespan.
Application of the Faster R-CNN Algorithm in Scene Recognition Function Design

The aim of smart tourism is to use information technology to improve the travel experience. The scene recognition proposed in this paper can enhance the self-guided tour experience, allowing visitors to understand the background and culture of the scenery better, thus achieving in-depth tourism and cultural tourism. Compared with other deep learning algorithms, the Faster R-CNN algorithm can improve the accuracy of scene recognition while ensuring speed. In this paper, the feasibility of the Faster R-CNN algorithm in the scene recognition function is verified by experiments, which has high theoretical value and commercial value.