Reviewerly/exHarmony huggingface dataset:
New Scheduling Algorithms for Improving Performance and Resource Utilization in Hadoop YARN Clusters

The MapReduce framework has become the defacto scheme for scalable semi-structured and un-structured data processing in recent years. The Hadoop ecosystem has evolved into its second generation, Hadoop YARN, which adopts fine-grained resource management schemes for job scheduling. Nowadays, fairness and efficiency are two main concerns in YARN resource management because resources in YARN are shared and contended by multiple applications. However, the current scheduling in YARN does not yield the optimal resource arrangement, unnecessarily causing idle resources and inefficient scheduling. It omits the dependency between tasks which is extremely crucial for the efficiency of resource utilization as well as heterogeneous job features in real application environments. We thus propose a new YARN scheduler which can effectively reduce the makespan (i.e., the total execution time) of a batch of MapReduce jobs in Hadoop YARN clusters by leveraging the information of requested resources, resource capacities and dependency between tasks. For accommodating heterogeneity in MapReduce jobs, we also extend our scheduler by further considering the job iteration information in the scheduling decisions. We implemented the new scheduling algorithm as a pluggable scheduler in YARN and evaluated it with a set of classic MapReduce benchmarks. The experimental results demonstrate that our YARN scheduler effectively reduces the makespans and improves resource utilizations.
In-Vehicle Cognitive Route Decision Using Fuzzy Modeling and Artificial Neural Network

The departments of transportation worldwide are facing various challenges despite introducing and incorporating various vehicular features. One of such challenges is to make vehicles autonomous, intelligent, and capable of self-learning to evolve their knowledge repository. In this paper, human cognition is proposed to be implemented in vehicles so that they can perform human-like decisions. Therefore, the process of vehicular route decision is debated cognitively in order to provide route information intelligently. The in-vehicle routes provided by the GPS are not optimal and lack on-demand user requirements. GPS connectivity issues, in certain conditions, make it difficult for vehicles to take real-time decisions. This leads to the idea of self-decision by the vehicle controller. We propose a cognitive framework for vehicles to make self-decisions that use cognitive memory for storing route experiences. The framework strengthens the existing in-vehicle route finding capability and its provision in a more realistic manner. The user is provided with all available route-related information that is required for the journey. In addition, the route episodes are learned, stored, and accessed inside the cognitive memory for an optimal route provision. The vehicle learns about the routes and matures with route-experience by itself with the passage of time. In simulations, fuzzy modeling is used to validate the impact of cognitive parameters over static/conventional parameters. Moreover, artificial neural networks are used to minimize the error rate in learning to achieve cognitive route decisions. The proposed in-vehicle cognitive framework outperforms the existing route provision system that is inadequate and provokes the user’s anxieties during driving. Besides, the proposed scheme gradually gets mature in delivering optimal as well as latest route-related information.
A parallel Fortran framework for neural networks and deep learning

This paper describes neural-fortran, a parallel Fortran framework for neural networks and deep learning. It features a simple interface to construct feed-forward neural networks of arbitrary structure and size, several activation functions, and stochastic gradient descent as the default optimization algorithm. Neural-fortran also leverages the Fortran 2018 standard collective subroutines to achieve data-based parallelism on shared- or distributed-memory machines. First, I describe the implementation of neural networks with Fortran derived types, whole-array arithmetic, and collective sum and broadcast operations to achieve parallelism. Second, I demonstrate the use of neural-fortran in an example of recognizing hand-written digits from images. Finally, I evaluate the computational performance in both serial and parallel modes. Ease of use and computational performance are similar to an existing popular machine learning framework, making neural-fortran a viable candidate for further development and use in production.
Interval prediction of wave energy characteristics using meta-cognitive interval type-2 fuzzy inference system

While significant efforts for online learning have been devoted to arrive at reliable predictions of crisp values, the problem of prediction interval (PI) in practical data is one of the underexplored areas in the existing literature. PI aims to produce upper and lower bound predictions which capture possible domain solution. This paper aims to extend a prominent meta-cognitive learning algorithm, namely meta-cognitive interval type-2 fuzzy inference system (McIT2FIS), to cope with the problem of prediction interval in real-time. McIT2FIS is constructed under interval type-2 fuzzy inference system and realizes the meta-cognitive learning theory featuring the basic three elements of human learning: what-to-learn, how-to-learn, when-to-learn. Unlike existing works in PI, McIT2FIS-PI works fully in the online mode and is capable of performing automatic knowledge acquisition from data streams. The efficacy of McIT2FIS-PI has been experimentally validated in a real-world wave characteristics prediction in Semakau Island, Singapore, where it is capable of delivering accurate short-term prediction intervals of wave parameters. The performance of McIT2FIS-PI is also compared with existing state-of-the-art fuzzy inference systems in benchmark problems where it attains competitive accuracy while retaining comparable complexity.
An instance-based learning recommendation algorithm of imbalance handling methods

Imbalance learning is a typical problem in domain of machine learning and data mining. Aiming to solve this problem, researchers have proposed lots of the state-of-art techniques, such as Over Sampling, Under Sampling, SMOTE, Cost sensitive, and so on. However, the most appropriate methods on different learning problems are diverse. Given an imbalance learning problem, we proposed an Instance-based Learning (IBL) recommendation algorithm to present the most appropriate imbalance handling method for it. First, the meta knowledge database is created by the binary relation 〈data characteristic measures-the rank of all candidate imbalance handling methods〉 of each data set. Afterwards, when a new data set comes, its characteristics will be extracted and compared with the example in the knowledge database, where the instance-based k-nearest neighbors algorithm is applied to identify the rank of all candidate imbalance handling methods for the new dataset. Finally, the most appropriate imbalance handling method will be derived through combining the recommended rank and individual bias. The experimental results on 80 public binary imbalance datasets confirm that the proposed recommendation algorithm can effectively present the most appropriate imbalance handling method for a given imbalance learning problem, with the hit rate of recommendation up to 95%.
Large-Scale Talent Flow Forecast with Dynamic Latent Factor Model?

The understanding of talent flow is critical for sharpening company talent strategy to keep competitiveness in the current fast-evolving environment. Existing studies on talent flow analysis generally rely on subjective surveys. However, without large-scale quantitative studies, there are limits to deliver fine-grained predictive business insights for better talent management. To this end, in this paper, we aim to introduce a big data-driven approach for predictive talent flow analysis. Specifically, we first construct a time-aware job transition tensor by mining the large-scale job transition records of digital resumes from online professional networks (OPNs), where each entry refers to a fine-grained talent flow rate of a specific job position between two companies. Then, we design a dynamic latent factor based Evolving Tensor Factorization (ETF) model for predicting the future talent flows. In particular, a novel evolving feature by jointly considering the influence of previous talent flows and global market is introduced for modeling the evolving nature of each company. Furthermore, to improve the predictive performance, we also integrate several representative attributes of companies as side information for regulating the model inference. Finally, we conduct extensive experiments on large-scale real-world data for evaluating the model performances. The experimental results clearly validate the effectiveness of our approach compared with state-of-the-art baselines in terms of talent flow forecast. Meanwhile, the results also reveal some interesting findings on the regularity of talent flows, e.g. Facebook becomes more and more attractive for the engineers from Google in 2016.
Deep Road Scene Understanding

Road scene understanding is a difficult task in autonomous driving. In this letter, we propose a novel deep encoder-decoder architecture for road scene understanding in an end-to-end manner. This core trainable understanding engine includes an encoder network, a decoder network with two streams, and a pixel-level fusion network with classification layer. The encoder network is composed of the front-end model of the classical convolution neural network, VGGNet. The decoder network with two streams includes multi-scale skip connection modules to reduce the down-scaling effect. Finally, a fusion network fuses the two-level information from the two streams of the decoder network for precise pixel-level classification. Additionally, the convolution layer is added to each skip connection module to increase the depth of the architecture. Our architecture achieves outstanding performance on the publicly available CamVid dataset and significantly outperforms previous architectures. This deep architecture is ideal for road scene understanding.
Automatic Persona Generation for Online Content Creators: Conceptual Rationale and a Research Agenda

As the quantity of social and online analytics data has drastically increased, a wide variety of methods are deployed to make sense of this data, typically via computational and algorithmic approaches. However, in many cases, these approaches trade one form of complexity for another by ignoring the principles of human cognitive processing. In this perspective manuscript, we propose an approach of employing Personas as an alternative form of making large volumes of online user analytics information useful to end users of the user and customer analytics, with results applicable in software development, business sectors, communication industry, and other domains where understanding online user behavior is deemed important. Toward this end, we have developed a system that automatically generates data-driven Personas from social media and online analytics data, capable of handling hundreds of millions of user interactions from tens of thousands of pieces of content on YouTube, Facebook and Google Analytics, while retaining the privacy of individual users of those channels. Our approach (1) identifies and prioritizes user segments by their online behavior, (2) associates the segments with demographic data, and (3) creates rich Persona profiles by dynamically adding characteristics, such as names, photos, and descriptive quotes. This chapter characterizes the currently open research problems in automatic Persona generation, such as de-aggregation of data, cross-platform data mapping, filtering of toxic comments, and choosing the right information content according to end-user needs. Addressing these problems requires the use of state-of-the-art techniques of computer and information science within one system and benefits greatly from inter-disciplinary collaboration. Overall, the research agenda set in this work aims at achieving the vision for automatic user profiling using diverse online and social media platforms and advanced data processing methods for the end goal of making complex analytics data more useful for human decision makers, especially those working with online content.
Managing Human Errors: Augmented Reality systems as a tool in the quality journey

Abstract The manufacturing industry is shifting, entering a new era with smart and connected devices. The fourth industrial revolution is promising increased growth and productivity by the Smart Factory and within the enabling technologies is Augmented Reality (AR). At the same time as the technology is introduced, errors in manufacturing are a problem which is affecting the productivity and the quality. This research aims to find the main causes of human errors in assembly lines and thereafter explores whether augmented reality is an appropriate tool to be used in order to address those issues. Based on a literature review that identified and characterized a preliminary set of root causes for human errors in assembly lines, these causes were empirically studied in an exercise that covered an in-depth case study in a multinational automotive company. Data in form of interviews and deviation- reports have been used to identify the causing factors and the result showed that the main causes of human errors are the amount of thinking, deciding and searching for information which affected the cognitive load of the operator and in result their performance. Several interviews with experts in augmented reality allowed to verify if AR technology would be feasible to solve or mitigate the found causes. Besides that, in repetitive manual assembly operations, AR is better used showing the process in order to train new operators. At the same time for experienced operators, AR should show information only when an error occurs and when there is a need of taking an active choice. Nevertheless, while theoretically able to managing human error when fully developed, the desired application makes the augmentation of visual objects redundant and increasingly complex for solving the identified causes of errors which questions the appropriateness of using AR systems. Furthermore, the empirical findings showed that for managing human errors, the main bottleneck of an AR system is the systems artificial intelligence capabilities.
A Hardware-Efficient Recognition Accelerator Using Haar-Like Feature and SVM Classifier

Significantly improved performance of the various learning algorithms has revived the interest in computer vision for recognition applications during the current decade.This paper reports a vision-based hardware recognition architecture combining the Haar-like feature extraction with the support vector machine (SVM) classification.To support an optimal tradeoff between resource requirements, processing speed, and recognition accuracy, a 12-bit fixed-point computation for block-based feature normalization and a recycling allocation of minimalized memory resources are proposed in this paper.Furthermore, an efficient scale generation of target objects for recognition is enabled by configurable windows with high size flexibility.Additionally, a parallel-partial SVM-classification architecture is developed for improving the recognition speed, by accumulating the partially completed SVM results for multiple windows in parallel.The proposed hardware architecture is verified with an Altera DE4 platform to achieve a high throughput rate of 216 and 70 f/s for XGA (1024×768) and HD (1920×1080) video resolutions, respectively.A recycled memory space of only 193 KB is sufficient for processing high-resolution images up to 2048×2048 pixels during online testing.Using the INRIA person dataset, 89.81% average precision and maximum accuracy of 96.93% for pedestrian recognition are realized.Furthermore, about 99.08% accuracy is achieved for two car recognition tasks using the UIUC dataset (side view of cars) and a frontal car dataset collected by ourselves at Hiroshima University with the proposed hardware-architecture framework.
Error modeling and extrinsic–intrinsic calibration for LiDAR-IMU system based on cone-cylinder features

The extrinsic and intrinsic calibration of light detection and ranging (LiDAR) and inertial measurement unit (IMU) system is an essential prerequisite for its using in robots navigation or localization tasks. However, the existing LiDAR-IMU calibration method usually based on either point or planar features and existed adjustment parameter correlation limitations, which great restrict the extrinsic–intrinsic calibration flexibility and versatility. For this reason, a novel calibration technique that incorporates cone and cylinder features is proposed to overcome these drawbacks. The basic principle of our proposed method is that, first of all, we establish the transformation relationship between LiDAR and IMU coordinate frame, the LiDAR-IMU system calibration parameters and cone-cylinder geometric constrained. Secondly, the LiDAR extrinsic parameters are calibrated by estimating the pose of each scanned point datasets lies on the cone-cylinder surface and then solving the cone-cylinder geometric constrained optimization problem. Thirdly, the restricted maximum likelihood estimation (RMLE) algorithm is used to solve the optimization of IMU intrinsic parameters calibration Finally, intensive experimental studies are conducted to check the validity of our proposed method, the experimental results are presented that validate the proposed method and demonstrate the overall performances of LiDAR-IMU system obviously improved compared to plane based calibration method.
Native Language Identification in Very Short Utterances Using Bidirectional Long Short-Term Memory Network

Native language identification (NLI) is the task of identifying the first language of a user based on their speech or written text in a second language. In this paper, we propose the use of spectrogram- and cochleagram-based features extracted from very short speech utterances (0.8 s on average) to infer the native language of an Urdu speaker. The bidirectional long short-term memory (BLSTM) neural networks are adopted for the classification of utterances among the native languages. A set of experiments is carried out for the network architecture search and the system's accuracy is evaluated on the validation data set. Overall accuracy of 74.81% and 71.61% is achieved using the Mel-frequency cepstral coefficients (MFCC) and Gammatone frequency cepstral coefficients (GFCC), respectively. Moreover, the optimized MFCC feature-based BLSTM network and GFCC feature-based BLSTM network are merged together to take advantage of both the feature sets. The experiments show that the performance of the merged network surpasses the individual BLSTM networks and accuracy of 75.69% is achieved on the evaluation data. The effect of test data duration is also analyzed (from 0.27 s to 1.5 s); in addition, it is observed that with very short duration as 0.4 s, an accuracy of over 50% can be achieved.
Urban factories: Industry insights and empirical evidence within manufacturing companies in German-speaking countries

Ongoing global megatrends such as urbanization, digitalization and the limitation of resources are leading to strong changes in the economy, affecting the manufacturing industry likewise. In the long-term focus, the manufacturing industry has to focus on urban areas for developing possible solutions to the challenges of the future. The resulting requirements for manufacturing companies in urban surroundings require a new way of thinking and acting for all involved parties. This paper presents the results of an empirical study in the manufacturing industry of German-speaking countries on their perception of urban factories. Within this descriptive-explorative study, more than 100 companies assessed logistical, technological, constructional and economic aspects of urban factories to analyze the present awareness of the interviewed focus groups from the mechanical and plant engineering, metal industry and the automotive industry. The applied questionnaire focuses on central statements regarding urban factories and urban production. The prior validation of the questionnaire relies on semi-structured in-depth interviews with experts, respectively city and company representatives, from the field of urban production. The companies' answers enabled the analysis of the current appraisement of urban factories and their perception within manufacturing companies. The main challenges and chances of urban factories as well as suitability characteristics for the successful and seamless integration of manufacturing sites within urban areas are explored. Derived from the results of this study, the attractiveness of urban value-added as well as the needs for raising the economic attractiveness of urban factories are investigated. Thereof, the responses from the interviews illustrate tendencies towards potential (partial) relocation of the companies' own production to urban areas or the restructuring of manufacturing systems for urban areas. Both theoretical and practical implications of the study as well as limitations and future directions for research are discussed.
Simulation of qubit quantum circuits via Pauli propagation

We present algorithms to estimate outcomes for qubit quantum circuits. Notably, these methods can simulate a Clifford circuit in linear time without ever writing down stabilizer states explicitly. These algorithms outperform previous noisy near-Clifford techniques for most circuits. We identify a large class of input states that can be efficiently simulated despite not being stabilizer states. The algorithms leverage probability distributions constructed from Bloch vectors, paralleling previously known algorithms that use the discrete Wigner function for qutrits.
Signature Verification Using Critical Segments for Securing Mobile Transactions

The explosive usage of mobile devices enables conducting electronic transactions involving direct signature on such devices. Thus, user signature verification becomes critical to ensure the success deployment of online transactions such as approving legal documents and authenticating financial transactions. Existing approaches mainly focus on user verification targeting the unlocking of mobile devices or performing continuous verification based on a user's behavioral traits. Few studies provide efficient real-time user signature verification. In this work, we propose a critical segment based online signature verification system to secure mobile transactions on multi-touch mobile devices. Our system identifies and exploits the segments which remain invariant within a user's signature to capture the intrinsic signing behavior embedded in each user's signature. Our system extracts useful features from a user's signature that describe both the geometric layout of the signature as well as behavioral and physiological characteristics in the user's signing process. Given the input signatures for user enrollment, our system further designs a quality score to identify the problematic signature sets to achieve robust user signature profile construction. Moreover, we develop the signature normalization and interpolation methods to achieve robust signature verification in the presence of signature geometric distortions caused by different writing sizes, orientations and locations on touch screens. Our experimental evaluation of 25 subjects over six months time period shows that our system is highly accurate in provide signature verification and robust to signature forging attacks.