Reviewerly/exHarmony huggingface dataset:
Implementation of negation handling techniques using modified syntactic rule in Indonesian sentiment analysis

Hotels are one of the supporting facilities for tourism in Indonesia. The development of hotels is an essential part of the tourism sector. Hotel development can be performed by knowing public opinion obtained from user reviews. However, direct comment analysis, by reading reviews one by one, becomes difficult due to the vast numbers involved. This problem can be overcome by implementing sentiment analysis. In practice, sentiment analysis has critical issues that can affect performance, i.e., several words have different meanings in different sentence contexts, and the existence of the negation words can invert the meaning of an entire text. Therefore, this research proposes the implementation of negation handling techniques using modified syntactic rules in Naive Bayes based sentiment analysis. The proposed modified syntactic rules of negation handling techniques have been shown to improve sentiment analysis performance by 3.3%; compared to sentiment analysis without negation handling.
Visual Cognitive Mechanism Guided Video Shot Segmentation

Shot segmentation of video sequences is one of the key technologies in video information processing, especially video retrieval. Traditional shot segmentation methods have low detection rate for the gradient shot and the abrupt shot, especially in a single scene. To deal with this problem, this paper proposes a video segmentation method based on visual cognition mechanism. This method proposes a block granularity color histogram to strengthen the visual salient area, and a highlight measure to describe the difference between the front and back frames. This brings great improvements to the accuracy of detecting shot switching in a single scene. In addition, based on the brightness visual perception in video, the difference between adjacent multi-frames in the sliding window is used to capture the brightness change for the gradient shots. Comparing with traditional methods, the proposed algorithm achieves better segmentation effect and has higher precision and recall rate.
CRA: Enabling Data-Intensive Applications in Containerized Environments

Today, a modern data center hosts a wide variety of applications comprising batch, interactive, machine learning, and streaming applications. In this paper, we factor out the commonalities in a large majority of these applications, into a generic dataflow layer called Common Runtime for Applications (CRA). In parallel, another trend, with containerization technologies (e.g., Docker), has taken a serious hold on cloud-scale data centers, with direct implications on building next generation of data center applications. Container orchestrators (e.g., Kubernetes) have made deployment a lot easy, and they solve many infrastructure level problems, e.g., service discovery, auto-restart, and replication. For best in class performance, there is a need to marry the next generation applications with containerization technologies. To that end, CRA leverages and builds upon the containerization and resource orchestration capabilities of Kubernetes/Docker, and makes it easy to build a wide range of cloud-edge applications on top. To the best of our knowledge, we are the first to present a cloud native runtime for building data center applications. We show the efficiency of CRA through various micro-benchmarking experiments.
Nonparametric Mixture of Sparse Regressions on Spatio-Temporal Data -- An Application to Climate Prediction

Climate prediction is a very challenging problem. Many institutes around the world try to predict climate variables by building climate models called General Circulation Models (GCMs), which are based on mathematical equations that describe the physical processes. The prediction abilities of different GCMs may vary dramatically across different regions and time. Motivated by the need of identifying which GCMs are more useful for a particular region and time, we introduce a clustering model combining Dirichlet Process (DP) mixture of sparse linear regression with Markov Random Fields (MRFs). This model incorporates DP to automatically determine the number of clusters, imposes MRF constraints to guarantee spatio-temporal smoothness, and selects a subset of GCMs that are useful for prediction within each spatio-temporal cluster with a spike-and-slab prior. We derive an effective Gibbs sampling method for this model. Experimental results are provided for both synthetic and real-world climate data.
Tensorizing Restricted Boltzmann Machine

Restricted Boltzmann machine (RBM) is a famous model for feature extraction and can be used as an initializer for neural networks. When applying the classic RBM to multidimensional data such as 2D/3D tensors, one needs to vectorize such as high-order data. Vectorizing will result in dimensional disaster and valuable spatial information loss. As RBM is a model with fully connected layers, it requires a large amount of memory. Therefore, it is difficult to use RBM with high-order data on low-end devices. In this article, to utilize classic RBM on tensorial data directly, we propose a new tensorial RBM model parameterized by the tensor train format (TTRBM). In this model, both visible and hidden variables are in tensorial form, which are connected by a parameter matrix in tensor train format. The biggest advantage of the proposed model is that TTRBM can obtain comparable performance compared with the classic RBM with much fewer model parameters and faster training process. To demonstrate the advantages of TTRBM, we conduct three real-world applications, face reconstruction, handwritten digit recognition, and image super-resolution in the experiments.
An Online Password Guessing Method Based on Big Data

Password authentication is the most widely used authentication method in information systems. The traditional proactive password detection method is generally implemented by counting password length, character class number and computing password information entropy to improve password security. However, passwords that pass proactive password detection do not represent that they are secure. In this paper, based on the research of the characteristics of password distribution under big data, we propose an online password guessing method, which collects a dataset of guessing passwords composed of weak passwords, high frequency passwords and personal information related passwords. It is used to guess the 13k password dataset leaked in China's largest ticketing website, China Railways 12306 website. The experimental results show that even if our guess object has passed the strict proactive password detection, we can construct a guessing password dataset contain only 100 passwords, and effectively guess 4.84% of the passwords.
BETA

To cope with diverse network conditions, HTTP Adaptive Streaming (HAS) enables video players to dynamically change the video quality throughout the video stream. However, effective adaptation that minimizes stalls and start-up time while maximizing quality and stability remains elusive, especially when available bandwidth is variable or multiple players compete for the bottleneck capacity. Conventional approach to adaptation is to make a decision on the next video segment quality based on hysteresis of prior throughput measurements. This approach is not robust to bandwidth fluctuation at small time scales, which can consequently lead to stalls, bandwidth waste, and unstable quality, mainly due to the inability to mitigate significant bandwidth reduction during the segment download. We propose BETA- Bandwidth-Efficient Temporal Adaptation, an agile approach that allows HAS players to refine the quality level within video segments on the fly, according to the actual bandwidth conditions experienced while downloading each segment. We define a new HAS-oriented transmission order of video frames within segments that facilitates decodability of partial frames and paves the way for changing the paradigm from discrete to continuous bitrate ladders for HAS. BETA can work with any adaptation algorithm or HAS player to significantly improve robustness and efficiency in dynamic network environments and for low-latency streams, as well as to dramatically reduce content storage and encoding infrastructure requirements. Our evaluation using the real player implementation shows that BETA improves video quality by up to 20%, reduces number of stalls by 20%-100% in nearly 80% of cases, and cuts down wasted bandwidth by 22%-100%.
Towards an Automatic Q&amp;A Generation for Online Courses - A Pipeline Based Approach

Personalization of online courses is one of the challenges of the 21st century. Although different methodologies for personalization in educational contexts are already existing, there is a bottleneck: personalization by context is always limited to existing learning material; creation of those is a time-consuming task. In this paper we introduce a pipeline to generate questions and valid answers based on educational texts, limited to factual questions for given sentences. We combined NLP technologies with an efficient methodology that is normally used in bioinformatics and adjusted it to generate Q&A-pairs. Instructors can suggest corrections in natural language. Our system generates questions and corresponding answers based on sentences of which 70% make sense.
System Analysis and the Task of Ontological Modeling of Multimodal Transportation

Abstract The basic principles of synthesis and processing of information, laid down by Tim B. Lee and determined at the level of schemes, models, syntax and ontology dictionaries, have been further developed in the following study. Detailed study and formalization of specific areas of knowledge based on conceptual schemes in the basis of ontological engineering constitutes today the basis for the generation of modern knowledge and is the key to reducing the production of information debris. A comparative analysis of tools for constructing, comparing (analyzing) ontologies has shown the expediency of using the Protégé system. With the help of this tool, the authors implemented an instance of an ontological model of the domain of “multimodal transport” with a view to its subsequent application in the construction of appropriate multi-agent models.
The Method of Predicting Retention Threshold Voltage Distribution for NAND Flash Memory Based on Back-Propagation Neural Network

A new retention threshold voltage (Vth) distributions prediction method for 3-D TLC and QLC NAND flash memories based on back-propagation neural network (BpNN) is proposed. The data pre-processing and post-processing techniques are developed to improve the prediction accuracy of BpNN model. Based on our proposed BpNN model, the predicted V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sub> distributions after different data retention times (t) and program/erase (P/E) cycles have good agreement with the measurements, which can be used for read voltage optimization (RVO) not only hard-decision decoding algorithm but also soft-decision decoding algorithm. Especially, this BpNN model can be embedded into the SSD controller and help in improving the reliability of NAND flash memory.
Research on classification and recognition of badminton batting action based on machine learning

At present, the application of artificial intelligence in the identification and classification of sports technology is still relatively small, and it is difficult to effectively improve the training and competition quality of athletes. Based on this, this study takes badminton as an example for an alysis. Moreover, based on the complexity and multi-deformation of this motion, this study uses machine learning as the basic algorithm to design a real-time classification algorithm for badminton action. At the same time, this paper improves the traditional algorithm, designs an improved training model, and verifies the effectiveness of the design algorithm by experimental method. In addition, this paper constructs a feature statistics and pace training system with the support of machine learning algorithms through statistical analysis and statistical badminton technical features and realizes the intelligentization of badminton batting action classification and recognition. Finally, this paper designs a comparative test for system functional testing. The system test shows that the system can effectively improve the action classification and recognition effect and can provide theoretical reference for subsequent related research.
Active Stereo Vision with High Resolution on an FPGA

We present a novel FPGA based active stereo vision system, tailored for the use in a mobile 3D stereo camera. For the generation of a single 3D map the matching algorithm is based on a correlation approach, where multiple stereo image pairs instead of a single one are processed to guarantee an improved depth resolution. To efficiently handle the large amounts of incoming image data we adapt the algorithm to the underlying FPGA structures, e.g. by making use of pipelining and parallelization.Experiments demonstrate that our approach provides high-quality 3D maps at least three times more energy-efficient (5.5 fps/W) than comparable approaches executed on CPU and GPU platforms. Implemented on a Xilinx Zynq-7030 SoC our system provides a computation speed of 12.2 fps, at a resolution of 1.3 megapixel and a 128 pixel disparity search space. As such it outperforms the currently best passive stereo systems of the Middlebury Stereo Evaluation in terms of speed and accuracy. The presented approach is therefore well suited for mobile applications, that require a highly accurate and energy-efficient active stereo vision system.
Fast Superpixel Segmentation with Deep Features

In this paper, we propose a superpixel segmentation method which utilizes extracted deep features along with the combination of color and position information of the pixels. It is observed that the results can be improved significantly using better initial seed points. Therefore, we incorporated a one-step k-means clustering to calculate the positions of the initial seed points and applied the active search method to ensure that each pixel belongs to the right seed. The proposed method was also compared to other state-of-the-art methods quantitatively and qualitatively, and was found to produce promising results that adhere to the object boundaries better than others.
Evolving Roguelike Dungeons With Deluged Novelty Search Local Competition

With the increasing demand for differentiated virtual environments, the need for dynamic content generation arises, speeding up development, reducing productions costs, and increasing the product shelf life. In general, procedurally generated world offer a greater range of details in the environment and experience customization without sacrificing performance, but this is only guaranteed when the content obtained satisfies the user in quality and diversity. Thus, the use of algorithms that are able to guarantee the quality of the generated experience without lacking diversity is required. This paper presents a technique that can be used to develop virtual environments both in its production, providing the developer with different design possibilities to be developed and refined, as well as execution, building unique simulations while interfacing with the user. Therefore, the technique developed makes use of morphological niches to ensure diversity of solutions, producing results that provide the best solutions within each group, surpassing in quality of the existing techniques, such as novelty search and local competition.
Functional gradient approach to probabilistic minimax active learning

Many active learning methods select informative and representative examples by employing a parametric approach. However, there is very limited research pursuing a functional non-parametric approach to informative and representative active learning. The present study proposes a general functional approach to active learning based on a minimax objective function. Using this general algorithm, the current paper presents a specific algorithm based on a simple class of functions. Experiments show that the proposed method is efficient in selecting examples. It is interesting that the resulting algorithm can be interpreted from a spectral filtering perspective. This establishes a relationship between active learning, boosting, and spectral filtering and opens up new avenues for developing even better active learning algorithms.
EEG Classification using Semi Supervised Learning

The major challenge in the current brain-computer interface research is the accurate classification of time-varying electroencephalographic (EEG) signals.The labeled EEG samples are usually scarce, while the unlabeled samples are available in large quantities and easy to collect in real applications.Semisupervised learning (SSL) methods can utilize both labeled and unlabeled data to improve performance over supervised approaches.However, it has been reported that the unlabeled data may undermine the performance of SSL in some cases.This study proposes a three stages technique for automatic detection of epileptic seizure in EEG signals.In practical application of pattern recognition, there are often diverse features extracted from raw data which needs to be recognized.Proposed method is based on time series signal, spectral analysis and recurrent neural networks (RNNs).Decision making was performed in three stages:(i)feature extraction using Welch method power spectrum density estimation (PSD) (ii)dimensionality reduction using statistics over extracted features and time series signal samples (iii)EEG classification using recurrent neural networks.This study shows that Welch method power spectrum density estimation is an appropriate feature which well represents EEG signals.We achieved higher classification accuracy (specificity, sensitivity, classification accuracy) in comparison with other researches to classify EEG signals exactly 100% in this study.To improve the safety of SSL, we proposed a new safety-control mechanism by analyzing the differences between unlabeled data analysis in supervised and semi-supervised learning.We then develop and implement a safe classification method based on the semi-supervised extreme learning machine (SS-ELM).Following this approach, the Wasserstein distance is used to measure the similarities between the predictions obtained from ELM and SS-ELM algorithms, and a different risk degree is thereby calculated for each unlabeled data instance.A risk-based regularization term is then constructed and embedded into the objective function of the SS-ELM.Extensive experiments were conducted using benchmark and EEG datasets to evaluate the effectiveness of the proposed method.Experimental results show that the performance of the new algorithm is comparable to SS-ELM and superior to ELM on average.It is thereby shown that the proposed method is safe and efficient for the classification of EEG signals.
Part of speech tagging for Twitter conversations using Conditional Random Fields model

Part-of-Speech Tagging is the technology of assigning the appropriate parts-of-speech to a word. Part-of-speech tagging is very useful in information retrieval, information extraction, and speech processing. This research presents a part-of-speech tagging, especially for twitter text data. The process of part-of-speech tagging for twitter conversation is a difficult task. Several approaches have been made to develop an accurate tagging system but most of them are relevant to news text data and web contents. Therefore, this research intends to develop a part-of- speech tagger model for twitter speech. using CRF toolkit. The system was developed for nearly 1000 twitter conversations employing Conditional Random Field stochastic model. The data for twitter speech was downloaded from the internet. A POS-tagged text corpus, template file and CoNLL file for both training and testing database were prepared accordingly. The training was carried out for both the unigram model as well as the bigram model. The performance of the system over these models was obtained through this examination which showed a significant efficiency, calculated from the number of correctly tagged words and the total number of words.
Characterizing Compatible View Updates in Syntactic Bidirectionalization

Given a function that takes a source data and returns a view, bidirectionalization aims at producing automatically a new function that takes a modified view and returns the corresponding, modified source. In this paper, we consider simple first-order functional programs specified by (conditional) term rewrite systems. Then, we present a bidirectionalization technique based on the injectivization and inversion transformations from [24]. We also prove a number of relevant properties which ensure that changes in both the source and the view are correctly propagated and that no undesirable side-effects are introduced. Furthermore, we introduce the use of narrowing—an extension of rewriting that replaces matching with unification—to precisely characterize compatible (also called in-place) view updates so that the resulting bidirectional transformations are well defined. Finally, we discuss some directions for dealing with view updates that are not compatible.
Identifying Unexpected Behaviors of Agent-Based Models Through Spatial Plots and Heat Maps

Verification and validation (V&V) techniques commonly require modelers to collect and statistically analyze large amounts of data which require specific methods for ordering, filtering, or converting data points. Modelers need simple, intuitive, and efficient techniques for gaining insight into unexpected behaviors to help in determining if these behaviors are errors or if they are artifacts resulting from the model’s specifications. We present an approach to begin addressing this need by applying heat maps and spatial plots to visually observe unexpected behaviors within agent-based models. Our approach requires the modeler to specify hypotheses about expected model behavior. Agent level outputs of interest are then used to create graphical displays to visually test the hypotheses. Visual identification of unexpected behaviors can direct focus for additional V&V efforts and inform the selection process of follow-on V&V techniques. We apply our approach to a model of obesity.
Transferring of Continuous Variable Squeezed States in 20 km Fiber

Transferring of a real quantum state in a long-distance channel is an important task in the development of quantum information networks. For greatly suppressing the relative phase fluctuations between the signal beam and the corresponding local oscillator beam, the usual method is to transfer them with time-division and polarization-division multiplexing through the same fiber. But the nonclassical states of light are very sensitive to the channel loss and extra noise, this multiplexing method must bring the extra loss to the quantum state, which may result in the vanishing of its quantum property. Here, we propose and realize a suitable time multiplexing method for the transferring and measurement of nonclassical states. Only the local oscillator beam is chopped into a sequence of light pulses and transmitted through fiber with continuous orthogonal-polarized signal beam. Finally, when the local oscillator pulses are properly time delayed compared to the signal beam, the quantum state can be measured in the time sequences without the influence of extra noise in the fiber. Our work provides a feasible scheme to transfer a quantum state in relative long distance and construct a practical quantum information network in metropolitan region.