Reviewerly/exHarmony huggingface dataset:
Ensemble Approach for Twitter Sentiment Analysis

Due to enlargement of social network and online marketing websites.The Blogs and reviews of the user are acquired from these websites.And these become useful for analysis and Decision making for various types of products, marketing and movie etc. with the extent of the usefulness of social Reviews.It is to be needed carefully analysis of that data.There are various techniques and methods are available that can accurately analyses the social information and provides greater accuracy for the analysis.But one of the major issues available with the social media data is that data is unstructured and noisy.It is to be required to solve this problem.So here in this paper a framework is proposed that includes latest data preprocessing techniques instead of noise removal like stemming, Lemmatization and Tokenization.After Pre-Processing of data ensemble methods is applied that increase the accuracy of previous classification algorithms.This method is inherent from bagging concept.First apply Decision Tree, Kneighbor and Naive Bayes classifier that not provide batter accuracy after that boosting concept is applied with the help of AdaBoost method that improves the accuracy of previous classical classifiers.At last our proposed ensemble method ExtraTree classifier is applied that inherent from bagging concept.Here we use the Extra Tree classifier that take the various sample are taken from training set and various random trees are created.It is also called as extremely randomized tree that provides extreme refined view.So that, it is to be conveying that The ExtraTree classifier of bagging ensemble method outperforms than all other techniques that are previously applied in this paper.with using some novel preprocessing techniques data that produced is more refined and that provides clean and pure base for the implementation of ensemble techniques.And also contributes in improving the accuracy of the applied methods.
Many Faces of Feature Importance: Comparing Built-in and Post-hoc Feature Importance in Text Classification

Feature importance is commonly used to explain machine predictions.While feature importance can be derived from a machine learning model with a variety of methods, the consistency of feature importance via different methods remains understudied.In this work, we systematically compare feature importance from built-in mechanisms in a model such as attention values and post-hoc methods that approximate model behavior such as LIME.Using text classification as a testbed, we find that 1) no matter which method we use, important features from traditional models such as SVM and XGBoost are more similar with each other, than with deep learning models; 2) posthoc methods tend to generate more similar important features for two models than built-in methods.We further demonstrate how such similarity varies across instances.Notably, important features do not always resemble each other better when two models agree on the predicted label than when they disagree.One of favorite places to eat on the King W side, simple and relatively quick.I typically always get the chicken burrito and the small is enough for me for dinner.Ingredients are always fresh and watch out for the hot sauce cause it's skull scratching hot.Seating is limited so be prepared to take your burrito outside or you can even eat at Metro Hall Park.methods models SVM ( 2) XGBoost LSTM with attention BERT built-in sauce, seating, park, prepared, even, always, can, fresh, quick, favorite is, can, quick, fresh, at, to, always, even, favorite, and me, be, relatively, enough, always, fresh, ingredients, prepared, quick, favorite ., ingredients, relatively, quick, places, enough, dinner, typically, me, i LIME the, dinner, be, quick, and, even, you, always, fresh, favorite you, to, fresh, quick, at, can, even, always, and, favorite dinner, ingredients, typically, fresh, places, cause, quick, and, favorite, always one, watch, to, enough, limited, cause, and, fresh, hot, favorite
Latency-Aware Adaptive Video Summarization for Mobile Edge Clouds

With the technological advances in wireless multimedia domain, these videos made by mobile edge devices dominate network traffics. The video summarization technology enables users to understand the storyline of a video before a client requests the complete video content. Summarizing a video on edge devices and transmitting the summary between them requires a user-oriented and adaptive solution due to the limited capability and the dynamic wireless links of edge devices. Therefore, it is beneficial to improve the user's viewing experience and the bandwidth utilization ratio if we generate and transmit a video summary based on network connections and the user's tolerant latency. Unfortunately, previous summarization approaches are incapable of adjusting the summary size adapted to the varying network bandwidth and the user's attitude towards latency. To timely and flexibly deal with mobile videos, we first formulate the video summarization optimization problem with the elastic number of selected representative segments and the outlier detection within a bounded time budget. Furthermore, we develop an online greedy algorithm called the Elastic Video Summarization Algorithm (EVS) to solve the NP hard problem. We analyze the properties associated with EVS and further design an improved EVS-II to reduce computation complexity. Finally, the experimental results demonstrate that our proposed algorithms outperform other existing researches in fitting network bandwidth and detecting outliers.
Optimal working point in digitized quantum annealing

We present a study of the digitized quantum annealing protocol proposed by R. Barends et al. [Nature (London) 534, 222 (2016)]. Our analysis, performed on the benchmark case of a transverse Ising chain problem, shows that the algorithm has a well-defined optimal working point for the annealing time ${\ensuremath{\tau}}_{\mathrm{P}}^{\mathrm{opt}}$, scaling as ${\ensuremath{\tau}}_{\mathrm{P}}^{\mathrm{opt}}\ensuremath{\sim}\mathrm{P}$, where $\mathrm{P}$ is the number of digital Trotter steps, beyond which the residual energy error shoots up toward the value characteristic of the maximally disordered state. We present an analytical analysis for the translationally invariant transverse Ising chain case, but our numerical evidence suggests that this scenario is more general, surviving, for instance, the presence of disorder.
FastJoin: A Skewness-Aware Distributed Stream Join System

In the bigdata era, many applications are required to perform quick and accurate join operations on large-scale realtime data streams, such as stock trading and online advertisement analysis. To achieve high throughput and low latency, distributed stream join systems explore efficient stream partitioning strategies to execute the complex stream join procedure in parallel. Existing systems mainly deploy two kinds of partitioning strategies, i.e., random partitioning and hash partitioning. Random partitioning strategy partitions one data stream uniformly while broadcasting all the tuples of the other data stream. This simple strategy may incur lots of unnecessary computations for low-selectivity stream join. Hash partitioning strategy maps all the tuples of the two data streams according to their attributes for joining. However, hash partitioning strategy suffers from a serious load imbalance problem caused by the skew distribution of the attributes, which is common in real-world data. The skewed load may seriously affect the system performance. In this paper, we carefully model the load skewness problem in distributed join systems. We explore the key tuples which lead to the heavy load skewness, and propose an efficient key selection algorithm, GreedyFit to find out these key tuples. We design a lightweight tuple migration strategy to solve the load imbalance problem in real-time and implement a new distributed stream join system, FastJoin. Experimental results using real-world data show that FastJoin can significantly improve the system performance in terms of throughput and latency compared to the state-of-the-art stream join systems.
Solving Optimal Camera Placement Problems in IoT Using LH-RPSO

With the increasing need for public security and intelligent life and the development of Internet of Things (IoT), the structure and application of vision sensor network are becoming more and more complex.It is no longer a system with simple static monitoring, but a complex system that can be used for intelligent processing, such as target localization, identification, tracking and so on.In order to accomplish various tasks efficiently, it is important to determine the deployment plan of camera network in advance.Many researches discretize the optimal camera placement problem into a binary integer programming (BIP) problem, which is NP-hard, and put forward some approximate solutions including greedy heuristics, semidefinite programming, simulated annealing, etc.In practice, however, camera parameters include both continuous values (location and orientation) and discrete values (camera type).To get a much more accurate result, we do not discretize the continuous camera parameters any more, on the contrary, we handle the continuous values in continuous domain directly.Meanwhile, a Latin Hypercube based Resampling Particle Swarm Optimization (LH-RPSO) algorithm is proposed to effectively solve the problem.To validate the proposed algorithm, we compared it with standard Particle Swarm Optimization (PSO) and Resampling Particle Swarm Optimization (RPSO).Simulation results for an outdoor planar regions illustrated the efficiency of the proposed algorithm.
Fast Trajectory Planning for Off-Road Autonomous Driving with a Spatiotemporal Tunnel and Numerical Optimal Control Approach

This paper proposes a unified and fast trajectory planning method for an autonomous vehicle with avoidance of static obstacles in the off-road scenarios. Nominally, an optimal control problem should be used to cover generic off-road planning cases. However, the scale of the formulated optimal control problem is intractable, because the ego vehicle has to avoid collision with every obstacle at every moment. To address this issue, we aim to discard the redundant collision-avoidance constraints, because the vehicle may not really have chances to collide with all of the obstacles at every moment. Concretely, with the help of a reference trajectory derived by hybrid* algorithm, we develop a spatiotemporal tunnel-based strategy such that the ego vehicle is restricted to move within a sequence of tunnels during different periods of the entire dynamic process. Through this, the collision-avoidance constraints are replaced by the within-tunnel constraints, thereby making the scale of the optimal control problem completely irrelevant to the complexity of the environment.
Adaptive FH-SVM for Imbalanced Classification

Support vector machines (SVMs), powerful learning methods, have been popular among machine learning researches due to their strong performance on both classification and regression problems. However, traditional SVM making use of Hinge Loss cannot deal with class imbalance problems, because it applies the same weight of loss to each class. Recently, Focal Loss has been widely used for deep learning to address the imbalanced datasets. The significant effectiveness of Focal loss attracts the attention in many fields, such as object detection, semantic segmentation. Inspired by Focal loss, we reconstructed Hinge Loss with the scaling factor of Focal loss, called FH Loss, which not only deals with the class imbalance problems but also preserve the distinctive property of Hinge loss. Owing to the difficulty of the trade-off between positive and negative accuracy in imbalanced classification, FH loss pays more attention on minority class and misclassified instances to improve the accuracy of each class, further to reduce the influence of imbalance. In addition, due to the difficulty of solving SVM with FH loss, we propose an improved model with modified FH loss, called Adaptive FH-SVM. The algorithm solves the optimization problem iteratively and adaptively updates the FH loss of each instance. Experimental results on 31 binary imbalanced datasets demonstrate the effectiveness of our proposed method.
Large-Scale Semi-Supervised Training in Deep Learning Acoustic Model for ASR

This study investigated large-scale semi-supervised training (SST) to improve acoustic models for automatic speech recognition. The conventional self-training, the recently proposed committee-based SST using heterogeneous neural networks and the lattice-based SST were examined and compared. The large-scale SST was studied in deep neural network acoustic modeling with respect to the automatic transcription quality, the importance data filtering, the training data quantity and other data attributes of a large quantity of multi-genre unsupervised live data. We found that the SST behavior on large-scale ASR tasks was very different from the behavior obtained on small-scale SST: 1) big data can tolerate a certain degree of mislabeling in the automatic transcription for SST. It is possible to achieve further performance gains with more unsupervised fresh data, and even the automatic transcriptions have a certain degree of errors; 2) the audio attributes, transcription quality and importance of the fresh data are more important than the increased data quantity for large-scale SST; and 3) there are large differences in performance gains on different recognition tasks, such that the benefits highly depend on the selected data attributes of unsupervised data and the data scale of the baseline ASR system. Furthermore, we proposed a novel utterance filtering approach based on active learning to improve the data selection in large-scale SST. The experimental results showed that the SST with the proposed data filtering yields a 2-11% relative word error rate reduction on five multi-genre recognition tasks, even with the baseline acoustic model that was already well trained on a 10000-hr supervised dataset.
A Two-Stage Model for Chinese Grammatical Error Correction

Chinese grammatical error correction (GEC) is more challenging than English GEC due to its language characteristics.In this paper, a two-stage model was proposed to solve the Chinese GEC problem.The model consists of two components: a spelling check model and a GEC model.The spelling check model based on language model focuses on correcting spelling errors, while the GEC model based on neural sequence-to-sequence (seq2seq) model focuses on correcting grammatical errors.In addition, two generative methods allow the seq2seq model to correct an erroneous sentence incrementally with repeated inference steps.Furthermore, only one seq2seq model is used for grammatical correction rather than ensemble multiple models, which greatly speeds up the generation of final results and saves computing resources.The two-stage model achieves 31.01F 0.5 on NLPCC 2018 test set, significantly outperforms all prior approaches on this task.
A Method of Ontology Integration for Designing Intelligent Problem Solvers

Nowadays, designing knowledge-based systems which involve knowledge from different domains requires deep research of methods and techniques for knowledge integration, and ontology integration has become the foundation for many recent knowledge integration methods. To meet the requirements of real-world applications, methods of ontology integration need to be studied and developed. In this paper, an ontology model used as the knowledge kernel is presented, consisting of concepts, relationships between concepts, and inference rules. Additionally, this kernel is also added to other knowledge, such as knowledge of operators and functions, to form an integrated knowledge-based system. The mechanism of this integration method works upon the integration of the knowledge components in the ontology structure. Besides this, problems and the reasoning method to solve them on the integrated knowledge domain are also studied. Many related problems in the integrated knowledge domain and the reasoning method for solving them are also studied. Such an integrated model can represent the real-world knowledge domain about operators and functions with high accuracy and effectiveness. The ontology model can also be applied to build knowledge bases for intelligent problem solvers (IPS) in many mathematical courses in college, such as linear algebra and graph theory. These IPSs have great potential in helping students perform better in those college courses.
A Dependency Structure Annotation for Modality

This paper presents an annotation scheme for modality that employs a dependency structure.Events and sources (here, conceivers) are represented as nodes and epistemic strength relations characterize the edges.The epistemic strength values are largely based on Saurí and Pustejovsky's (2009) FactBank, while the dependency structure mirrors Zhang and Xue's (2018b) approach to temporal relations.Six documents containing 377 events have been annotated by two expert annotators with high levels of agreement.
A Randomly Guided Firefly Algorithm Based on Elitist Strategy and its Applications

Firefly algorithm (FA) is one of the swarm intelligence algorithms, which is proposed by Yang in 2008. The standard FA has some disadvantages, such as high computational time complexity, slow convergence speed and so on. The main reason is that FA employs a full attracted model, which makes the oscillation of each firefly during its movement. To overcome these disadvantages, based on elitist strategy, a randomly guided firefly algorithm (ERaFA) is proposed. In this algorithm, for improving the convergence speed, an elitist attraction model is developed based on random selection from elite fireflies, which can lead the firefly to a right direction. To deal with the possible failure of the elite guidance, opposite learning strategy is adopted. Meanwhile, to strengthen the local search ability of our algorithm, and help our algorithm jump out a local optimum position, a new mechanism is proposed, which is similar to the crossover operator in GA. The performance of ERaFA is evaluated by some well-known test functions and applied to solve three constrained engineering problems. The results show that ERaFA is superior to FA and some other state-of-the-art algorithms in terms of the convergence speed and robustness.
Adversarial Distillation for Learning with Privileged Provisions

Knowledge distillation aims to train a student (model) for accurate inference in a resource-constrained environment. Traditionally, the student is trained by a high-capacity teacher (model) whose training is resource-intensive. The student trained this way is suboptimal because it is difficult to learn the real data distribution from the teacher. To address this issue, we propose to train the student against a discriminator in a minimax game. Such a minimax game has an issue that it can take an excessively long time for the training to converge. To address this issue, we propose adversarial distillation consisting of a student, a teacher, and a discriminator. The discriminator is now a multi-class classifier that distinguishes among the real data, the student, and the teacher. The student and the teacher aim to fool the discriminator via adversarial losses, while they learn from each other via distillation losses. By optimizing the adversarial and the distillation losses simultaneously, the student and the teacher can learn the real data distribution. To accelerate the training, we propose to obtain low-variance gradient updates from the discriminator using a Gumbel-Softmax trick. We conduct extensive experiments to demonstrate the superiority of the proposed adversarial distillation under both accuracy and training speed.
Graph-based Multi-Vehicle Overtaking Strategy for Autonomous Vehicles

The paper proposes an advanced graph-based optimal solution to overtaking scenarios for autonomous vehicles. The advantage of the method is that it is possible to consider several human-driven vehicles in the environment of autonomous vehicles. There is a probability-based approach in the background of the graph-based route selection optimization, with which the motions of the human-driven vehicles are predicted. The result of the method is the road and the velocity profile of the autonomous vehicle, with which emergencies and even collisions can be avoided. The efficiency of the optimization algorithm is illustrated through a simulation scenario.
Selfish herd optimizer with levy-flight distribution strategy for global optimization problem

In recent years, research on stochastic optimization algorithms has received more and more attention from researchers, especially bio-inspired optimization algorithms. Selfish herd optimizer (SHO) is a novel bio-inspired optimization algorithm. It has features that are easy to understand and implement. However, its global search ability is insufficient and precision needs to be further improved. Therefore, we add levy-flight distribution strategy to improve its global search ability and precision. Our main contribution is to use selfish herd optimizer with levy-flight distribution strategy (LFSHO) to solve function and engineering example optimization problem. From experiment results, we can see that LFSHO has more advantages than other algorithms, according to precision, convergence speed and standard variance. It can conclude that LFSHO is a new method for solving function optimization problem and engineering example optimization problem.
A Binary Multi-Scale Quantum Harmonic Oscillator Algorithm for 0–1 Knapsack Problem With Genetic Operator

The 0-1 knapsack problem is a typical discrete combinatorial optimization problem with numerous applications.In this paper, a binary multi-scale quantum harmonic oscillator algorithm (BMQHOA) with genetic operator is proposed for solving 0-1 knapsack problem.The framework of BMQHOA is consisted of three nested phases including energy level stablization, energy level decline and scale adjustment.In BMQHOA, the number of different bits between solutions is defined as the distance between solutions to map the continuous search space into the discrete search space.Repair operator with greedy strategy is adopted in BMQHOA to guarantee the knapsack capacity constraint.The current best solution is used to perform a random mutation with the origin solutions, making solutions evolve towards the current optimal solution.The performance of BMQHOA is evaluated on two low-dimensional and three high-dimensional KP01 data sets, and computational results are compared with several state-of-art 0-1 knapsack algorithms.Experimental results demonstrate that the proposed BMQHOA can get the best solutions of most knapsack data sets, and performs well on convergence and stability.