Reviewerly/exHarmony huggingface dataset:
A Path Planning Method for Vehicle Overtaking Maneuver Using Sigmoid Functions

Geometric curves are mostly applied as guidance for lane-keeping and lane-changing motions. In this work, we explore the capabilities of using sigmoid functions in vehicle overtaking scenarios. Such curves are parameterized based on the current relative distance and speed concerning the preceding vehicle. Besides, the overtaking decision, i.e., whether to change lane or not, can be determined by using a piecewise function in the planning module. Then, model predictive control (MPC) is applied to track the generated overtaking path. Case studies including two scenarios, i.e., preceding vehicle driving at constant and varying speed, show that the proposed control framework is capable of self-decision making and handling overtaking maneuver effectively.
Time and Attribute Based Dual Access Control and Data Integrity Verifiable Scheme in Cloud Computing Applications

In the era of big data, cloud-based industrial applications can provide available and convenient data access for resource-constrained smart devices.Attribute-based encryption can be used to ensure data security while providing fine-grained data access.However, current attribute-based encryption schemes rarely consider the access control of time, and the integrity verification of data simultaneously.In response to the above two problems, we propose a time and attribute based dual access control and data integrity verifiable scheme in cloud computing applications (DCDV).Firstly, a hierarchical time tree is introduced in the attribute-based encryption technology by using of hierarchical identity-based encryption technology to set an effective access time period and a specified decryptable time period for the user's attributes key and encrypted data separately.The decryption operation can only be performed if the attribute set of user satisfies the data owner's access policy and the effective access time period of the user's attributes key completely covered the decryption time period set by the data owner.In this way, the data is dual controlled with time and attributes to solve the problem of privacy data leakage caused by private key leakage.Secondly, by using of the inverted index and Merkle hash tree, the data verification tree is designed.The data user can verify the integrity of the ciphertext data returned by the cloud server without decryption, which solves the problem that the cloud server may delete or modify the data.Finally, the security proof and efficiency analysis show that our scheme is secure and practical.
A new optimization algorithm for non-stationary time series prediction based on recurrent neural networks

Deep neural network with recurrent structure was proposed in the recent years and has been applied to time series forecasting. Many optimization algorithms are developed under the assumption of invariant and stationary data distributions, which is invalid for the non-stationary data. A novel optimization algorithm for modeling non-stationary time series is proposed in this paper. A moving window and exponential decay weights are used in this algorithm to eliminate the effects of the history gradients. The regret bound of the new algorithm is analyzed to ensure the convergency of the calculation. Simulations are done on short-term power load data sets, which are typically non-stationary. The results are superior to the existing optimization algorithms.
Crowd Analysis for Congestion Control Early Warning System on Foot Over Bridge

Crowds occur in a variety of situations like concerts, rallies, marathons, stadiums, railway stations, etc. Crowd analysis is essential from the point of view of safety and surveillance, abnormal behavior detection and thereby reducing the chance of a mishap. Generally, congestion in the crowd can lead to severe problems like a stampede. This congestion is due to increasing crowd count; thereby increasing the crowd density in regions and abnormal crowd motion. Most of the congestion control approaches follow a hardware-oriented approach. This paper proposes a software-oriented approach, Congestion Control Early Warning System (CCEWS), for congestion control with the help of object detection and object tracking technique. Object detection is performed by following the faster R-CNN architecture in which Google inception model is used as a pre-trained CNN model and with the help of proposed object tracking technique the crowd abnormality is analyzed. The proposed congestion control technique exhibits quite significant results on the proposed dataset made from the virtual simulation of FOB (foot over bridge) scenario.
Deep learning in Arabic sentiment analysis: An overview

Sentiment analysis became a very motivating area in both academic and industrial fields due to the exponential increase of the online published reviews and recommendations. To solve the problem of analysing and classifying those reviews and recommendations, several techniques have been proposed. Lately, deep neural networks showed promising outcomes in sentiment analysis. The growing number of Arab users on the Internet along with the increasing amount of published Arabic reviews and comments encouraged researchers to apply deep learning to analyse them. This article is a comprehensive overview of research works that utilised the deep learning approach for Arabic sentiment analysis.
Vision Based System for Banknote Recognition Using Different Machine Learning and Deep Learning Approach

Visually impaired people faced a problem in identifying and recognizing the different types of banknote due to some reasons. This problem draws researchers' attention to introduce an automated banknote recognition system that can be divided into a vision-based system and sensor-based system. The main aim of this study is to have deeper analysis on the effect of region and orientation on the performance of Machine Learning and Deep Learning respectively using Malaysian Ringgit banknotes (RM 1, RM 5, RM 10, RM 20, RM 50 and RM 100). In this project, two experiments conducted on two types of banknote image: different region and orientation captured by using handphone camera in a controlled environment. Feature extraction of the RGB values called RB, RG, and GB from banknote image with different region were used to the machine learning classification algorithms such as k-Nearest Neighbors (kNN) and Decision Tree Classifier (DTC), Support Vector Machine (SVM) and Bayesian Classifier (BC) for recognizing each class of banknote. Banknote image with different orientation was directly feed to AlexNet, a pre-trained model of Convolutional Neural Network (CNN), the most popular image processing structure of Deep Learning Neural Network. Ten-fold cross-validation was used to select the optimized kNN, DTC, SVM, and BC which was based on the smallest cross-validation loss. After that, the performance of kNN, DTC, SVM, BC and AlexNet model was presented in a confusion matrix. Both kNN and DTC achieved 99.7% accuracy but both SVM and BC perform better by succeeded to achieve 100% accuracy. It also can be concluded that AlexNet can only perform great in testing new data if only the data had previously been trained with similar orientation. Orientation does give effect to the performance of AlexNet model.
Measuring the reusability of software components using static analysis metrics and reuse rate information

Nowadays, the continuously evolving open-source community and the increasing demands of end users are forming a new software development paradigm; developers rely more on reusing components from online sources to minimize the time and cost of software development. An important challenge in this context is to evaluate the degree to which a software component is suitable for reuse, i.e. its reusability. Contemporary approaches assess reusability using static analysis metrics by relying on the help of experts, who usually set metric thresholds or provide ground truth values so that estimation models are built. However, even when expert help is available, it may still be subjective or case-specific. In this work, we refrain from expert-based solutions and employ the actual reuse rate of source code components as ground truth for building a reusability estimation model. We initially build a benchmark dataset, harnessing the power of online repositories to determine the number of reuse occurrences for each component in the dataset. Subsequently, we build a model based on static analysis metrics to assess reusability from five different properties: complexity, cohesion, coupling, inheritance, documentation and size. The evaluation of our methodology indicates that our system can effectively assess reusability as perceived by developers.
Investigating Random Undersampling and Feature Selection on Bioinformatics Big Data

This paper aims to address a key research issue regarding the ECBDL'14 bioinformatics big data competition. The ECBDL'14 dataset was the big data target in the competition, and it consisted of 631 attributes and about 32 million instances, of which about 98% belonged to the negative class. The ECBDL'14 competition dataset has recently been used in the literature to assess the effect of class imbalance on big data analytics. The contribution of our paper is two-fold. First, a survey of several literature works that utilized the EDBDL'14 dataset, either fully or partially, is presented. Second, compared to the Random Oversampling approach used by the winning algorithm for the competition, we utilize a Random Undersampling approach in conjunction with a Feature Selection approach. Through Random Undersampling, different class distributions were generated, ranging from slightly imbalanced to balanced. Prior to sampling, we perform Feature Selection by computing Feature Importance with the Random Forest learner within the Apache Spark framework. Subsequently, classification performance is computed for Random Forest, Logistic Regression, and Gradient-Boosted Trees in the same big data analytics framework. The key results of our study indicate that our proposed solution had a higher prediction performance (minimally) compared to that of the highest value of the winning algorithm. However, it is important to note that Random Undersampling, compared to Random Oversampling, imposes a lower computational burden and results in a faster training time, which is beneficial to data analytics. We conclude that our solution clearly outperforms the ECBLD'14 winning algorithm.
A Local Approach to Forward Model Learning: Results on the Game of Life Game

This paper investigates the effect of learning a forward model on the performance of a statistical forward planning agent. We transform Conway's Game of Life simulation into a single-player game where the objective can be either to preserve as much life as possible or to extinguish all life as quickly as possible. In order to learn the forward model of the game, we formulate the problem in a novel way that learns the local cell transition function by creating a set of supervised training data and predicting the next state of each cell in the grid based on its current state and immediate neighbours. Using this method we are able to harvest sufficient data to learn perfect forward models by observing only a few complete state transitions, using either a look-up table, a decision tree, or a neural network. In contrast, learning the complete state transition function is a much harder task and our initial efforts to do this using deep convolutional auto-encoders were less successful.We also investigate the effects of imperfect learned models on prediction errors and game-playing performance, and show that even models with significant errors can provide good performance.
Twitter Sentiment Analysis Based on US Presidential Election 2016

Twitter is among the most popular social networking Web sites today [], with approximately 317 million monthly active users (Quarter 3 2016). Of these, 67 million users are from the USA. Twitter being a micro-blogging platform is widely used by people to express their opinions. Approximately, 500 million tweets are posted in a day, which is around 6000 tweets per second. Assuming, even one-tenth of these tweets reflect an emotion that results in a lot of people-generated data, which can prove to be a treasure trove of information if studied carefully. We intend to perform sentimental analysis on Twitter data of the US Presidential Election 2016 and then overlay our findings with respect to the two main candidates: Hillary Clinton and Donald Trump with the actual election result, to be able to categorically state whether Twitter can be used as a proper indication of any election.
Searching the Latent Space of a Generative Adversarial Network to Generate DOOM Levels

In this work, following the same approach successfully applied to evolve Super Mario levels, we applied the CMA-ES to search the latent space of a GAN previously trained to generate DOOM levels. Combining a search algorithm with a model trained in a supervised setting, allows to take advantage from both these paradigms. From one hand, the GAN is able to generate contents exploiting the design patterns learned from all the examples it was trained from. On the other hand, the CMA-ES can effectively search this design space for specific contents that meet some given design objectives. In particular, we tested our approach evolving three very different type of levels: an arena level (i.e., few large areas), a labyrinth level (i.e., many corridors and small areas), and a complex level (i.e., a balanced mix of large and small areas). Our results show that the latent space of a GAN can be effectively searched by the CMA-ES to find DOOM levels that fit accurately the objectives but, at the same time, are also novel.
Automatic grading and hinting in open-ended text questions

Open-ended text questions provide better assessment of learner's knowledge, but analysing answers for this kind of questions, checking their correctness, and generating of detailed formative feedback about errors for the learner are more difficult and complex tasks than for closed-ended questions like multiple-choice. The analysis of answers for open-ended questions can be performed on different levels. Analysis on character level allows to find errors in characters' placement inside a word or a token; it is typically used to detect and correct typos, allowing to differ typos from actual errors in the learner's answer. The word-level or token-level analysis allows finding misplaced, extraneous, or missing words in the sentence. The semantic-level analysis is used to capture formally the meaning of the learner's answer and compare it with the meaning of the correct answer that can be provided in a natural or formal language. Some systems and approaches use analysis on several levels. The variability of the answers for open-ended questions significantly increases the complexity of the error search and formative feedback generation tasks. Different types of patterns including regular expressions and their use in questions with patterned answers are discussed. The types of formative feedback and modern approaches and their capabilities to generate feedback on different levels are discussed too. Statistical approaches or loosely defined template rules are inclined to false-positive grading. They are generally lowering the workload of creating questions, but provide low feedback. Approaches based on strictly-defined sets of correct answers perform better in providing hinting and answer-until-correct feedback. They are characterised by a higher workload of creating questions because of the need to account for every possible correct answer by the teacher and fewer types of detected errors. The optimal choice for creating automatised e-learning courses are template-based open-ended question systems like OntoPeFeGe, Preg, METEOR, and CorrectWriting which allows answer-until-correct feedback and are able to find and report various types of errors. This approach requires more time to create questions, but less time to manage the learning process in the courses once they are run.
Approaches in Intelligent Music Production

Music production technology has made few advancements over the past few decades. State-of-the-art approaches are based on traditional studio paradigms with new developments primarily focusing on digital modelling of analog equipment. Intelligent music production (IMP) is the approach of introducing some level of artificial intelligence into the space of music production, which has the ability to change the field considerably. There are a multitude of methods that intelligent systems can employ to analyse, interact with, and modify audio. Some systems interact and collaborate with human mix engineers, while others are purely black box autonomous systems, which are uninterpretable and challenging to work with. This article outlines a number of key decisions that need to be considered while producing an intelligent music production system, and identifies some of the assumptions and constraints of each of the various approaches. One of the key aspects to consider in any IMP system is how an individual will interact with the system, and to what extent they can consistently use any IMP tools. The other key aspects are how the target or goal of the system is created and defined, and the manner in which the system directly interacts with audio. The potential for IMP systems to produce new and interesting approaches for analysing and manipulating audio, both for the intended application and creative misappropriation, is considerable.
Blockchain-Based Mobility-as-a-Service

In this paper, we present a vision for a blockchain-based Mobility-as-a-Service (MaaS) as an application of edge computing. In current MaaS systems, a central MaaS operator plays a crucial role serving an intermediate layer which manages and controls the connections between transportation providers and passengers with several other features. Since the willingness of public and private transportation providers to connect to this layer is essential in the current realization of MaaS, in our vision, to eliminate this layer, a novel blockchain-based MaaS is proposed. The solution also improves trust and transparency for all stakeholders as well as eliminates the need to make commercial agreements with separate MaaS agents. From a technical perspective, the power of computing and resources are distributed to different transportation providers at the edge of the network providing trust in a decentralised way. The blockchain-based MaaS has the potential to emerge as the main component for a smart city transportation offering efficiency and reducing carbon dioxide emissions.
Quantum teleportation of photonic qudits using linear optics

One of the challenges of photon-based quantum teleportation is that both a source of entangled photons and an entangled basis measurement are required. For qubits, one can perform a probabilistic entangled basis measurement using linear optics, making the scheme efficient. However, for photonic qudits, an equivalent scheme remains difficult to devise. In this paper, we generalize the probabilistic photonic qubit teleportation protocol to qudits. The method relies on producing permutation entangled states nondeterministically which are superpositions of permutations of the spatial and qudit states of the photons. Our scheme nondeterministically teleports a photonic qudit using only entangled photon sources, linear optics, and photon detectors, and should be experimentally realizable for small qudit dimensions.
Context pyramidal network for stereo matching regularized by disparity gradients

Also after many years of research, stereo matching remains to be a challenging task in photogrammetry and computer vision. Recent work has achieved great progress by formulating dense stereo matching as a pixel-wise learning task to be resolved with a deep convolutional neural network (CNN). However, most estimation methods, including traditional and deep learning approaches, still have difficulty to handle real-world challenging scenarios, especially those including large depth discontinuity and low texture areas. To tackle these problems, we investigate a recently proposed end-to-end disparity learning network, DispNet (Mayer et al., 2015), and improve it to yield better results in these problematic areas. The improvements consist of three major contributions. First, we use dilated convolutions to develop a context pyramidal feature extraction module. A dilated convolution expands the receptive field of view when extracting features, and aggregates more contextual information, which allows our network to be more robust in weakly textured areas. Second, we construct the matching cost volume with patch-based correlation to handle larger disparities. We also modify the basic encoder-decoder module to regress detailed disparity images with full resolution. Third, instead of using post-processing steps to impose smoothness in the presence of depth discontinuities, we incorporate disparity gradient information as a gradient regularizer into the loss function to preserve local structure details in large depth discontinuity areas. We evaluate our model in terms of end-point-error on several challenging stereo datasets including Scene Flow, Sintel and KITTI. Experimental results demonstrate that our model decreases the estimation error compared with DispNet on most datasets (e.g. we obtain an improvement of 46% on Sintel) and estimates better structure-preserving disparity maps. Moreover, our proposal also achieves competitive performance compared to other methods.
Private Sequential Function Computation

In this paper, we introduce the problem of private sequential function computation, where a user wishes to compute a composition of a sequence of K linear functions, in a specific order, for an arbitrary input. The user does not run these computations locally, rather it exploits the existence of N noncolluding servers, each can compute any of the K functions on any given input. However, the user does not want to reveal any information about the desired order of computations to the servers. For this problem, we study the capacity, defined as the supremum of the number of desired computations, normalized by the number of computations done at the servers, subject to the privacy constraint. In particular, we show that the capacity satisfies (1- 1 N )/(1 - 1 max(K,N)) ≤ C ≤ 1. For the achievability, we show that the user can retrieve the desired order of computations, by choosing a proper order of inquiries among different servers, while keeping the order of computations for each server fixed, irrespective of the desired order of computations.